The Royal Pharmaceutical Society and the British Pharmaceutical Students’ Association have called for all overseas candidates to sit the March 2021 registration assessment in their home countries.

The government implied wholesalers had more personal protective equipment in stock than was the case during the first wave of the COVID-19 pandemic, the Healthcare Distribution Association has said.

The Pharmaceutical Services Negotiating Committee is in talks with the government over potential plans to distribute COVID-19 treatments in primary care.

Community pharmacies should be reimbursed for their additional costs during the COVID-19 pandemic “as soon as possible”, the prime minister has told The Pharmaceutical Journal.

The General Pharmaceutical Council has said it is “double, treble, quadruple-checking” for any “flexibility” that would allow all overseas candidates to sit the March 2021 registration assessment exam in their countries of residence.

Starting COVID-19 patients on prophylactic anticoagulation within 24 hours of being admitted to hospital has been linked to a reduced risk of mortality.

The General Pharmaceutical Council has said most candidates living in countries with a two-hour or more time difference from the UK will be able to apply to sit the registration assessment at home.

A new type of drug that targets chemotherapy directly to cancer cells reduces the risk of death from the most common type of bladder cancer by 30%, a phase III trial in the New England Journal of Medicine has suggested.

Pharmacy negotiators have discussed proposals to take “a patient registration-based approach” to the community pharmacy contractual framework.

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When university and hospital trusts were called to the UK parliament last year to answer questions on why they were not following the rules on reporting results, we saw how effective the questioning from politicians was. Those of you who watched the parliamentary session saw the pressure the university representatives were put under. Because the politicians asked […]

“Children are not small adults.” We invoke this saying, in a vague and hand-wavy manner, whenever we talk about the need to study drugs in pediatric populations. It’s an interesting idea, but it really cries out for further elaboration. If they’re not small adults, what are they? Are pediatric efficacy and safety totally uncorrelated with adult efficacy and safety? Or are children actually kind of like small adults in certain important ways?

Pediatric post-marketing studies have been completed for over 200 compounds in the years since BPCA (2002, offering a reward of 6 months extra market exclusivity/patent life to any drug conducting requested pediatric studies) and PREA (2007, giving FDA power to require pediatric studies) were enacted. I think it is fair to say that at this point, it would be nice to have some sort of comprehensive idea of how FDA views the risks associated with treating children with medications tested only on adults. Are they in general less efficacious? More? Is PK in children predictable from adult studies a reasonable percentage of the time, or does it need to be recharacterized with every drug?

Essentially, my point is that BPCA/PREA is a pretty crude tool: it is both too broad in setting what is basically a single standard for all new adult medications, and too vague as to what exactly that standard is.

In fact, a 2008 published review from FDA staffers and a 2012 Institute of Medicine report both show one clear trend: in a significant majority of cases, pediatric studies resulted in validating the adult medication in children, mostly with predictable dose and formulation adjustments (77 of 108 compounds (71%) in the FDA review, and 27 of 45 (60%) in the IOM review, had label changes that simply reflected that use of the drug was acceptable in younger patients).

So, it seems, most of the time, children are in fact not terribly unlike small adults.

But it’s also true that the percentages of studies that show lack of efficacy, or bring to light a new safety issue with the drug’s use in children, is well above zero. There is some extremely important information here.

To paraphrase John Wanamaker: we know that half our PREA studies are a waste of time; we just don’t know which half.

This would seem to me to be the highest regulatory priority – to be able to predict which new drugs will work as expected in children, and which may truly require further study. After a couple hundred compounds have gone through this process, we really ought to be better positioned to understand how certain pharmacological properties might increase or decrease the risks of drugs behaving differently than expected in children. Unfortunately, neither the FDA nor the IOM papers venture any hypotheses about this – both end up providing long lists of examples of certain points, but not providing any explanatory mechanisms that might enable us to engage in some predictive risk assessment.

While FDASIA did not advance PREA in terms of more rigorously defining the scope of pediatric requirements (or, better yet, requiring FDA to do so), it did address one lingering concern by requiring that FDA publish non-compliance letters for sponsors that do not meet their commitments. (PREA, like FDAAA, is a bit plagued by lingering suspicions that it’s widely ignored by industry.)

The first batch of letters and responses has been published, and it offers some early insights into the problems engendered by the nebulous nature of PREA and its implementation.

These examples, unfortunately, are still a bit opaque – we will need to wait on the FDA responses to the sponsors to see if some of the counter-claims are deemed credible. In addition, there are a few references to prior deferral requests, but the details of the request (and rationales for the subsequent FDA denials) do not appear to be publicly available. You can read FDA’s take on the new postings on their blog, or in the predictably excellent coverage from Alec Gaffney at RAPS.

Looking through the first 4 drugs publicly identified for noncompliance, the clear trend is that there is no trend. All these PREA requirements have been missed for dramatically different reasons.

Here’s a quick rundown of the drugs at issue – and, more interestingly, the sponsor responses:

1. Renvela - Genzyme (full response)

Genzyme appears to be laying responsibility for the delay firmly at FDA’s feet here, basically claiming that FDA continued to pile on new requirements over time:

Genzyme’s correspondence with the FDA regarding pediatric plans and design of this study began in 2006 and included a face to face meeting with FDA in May 2009. Genzyme submitted 8 revisions of the pediatric study design based on feedback from FDA including that received in 4 General Advice Letters. The Advice Letter dated February 17, 2011  contained further recommendations on the study design, yet still required the final clinical study report  by December 31, 2011.

This highlights one of PREA’s real problems: the requirements as specified in most drug approval letters are not specific enough to fully dictate the study protocol. Instead, there is a lot of back and forth between the sponsor and FDA, and it seems that FDA does not always fully account for their own contribution to delays in getting studies started.

2. Hectorol - Genzyme (full response)

In this one, Genzyme blames the FDA not for too much feedback, but for none at all:

On December 22, 2010, Genzyme submitted a revised pediatric development plan (Serial No. 212) which was intended to address FDA feedback and concerns that had been received to date. This submission included proposed protocol HECT05310. [...] At this time, Genzyme has not received feedback from the FDA on the protocol included in the December 22, 2010 submission.

If this is true, it appears extremely embarrassing for FDA. Have they really not provided feedback in over 2.5 years, and yet still sending noncompliance letters to the sponsor? It will be very interesting to see an FDA response to this.

3. Cleviprex – The Medicines Company (full response)

This is the only case where the pharma company appears to be clearly trying to game the system a bit. According to their response:

Recognizing that, due to circumstances beyond the company’s control, the pediatric assessment could not be completed by the due date, The Medicines Company notified FDA in September 2010, and sought an extension. At that time, it was FDA’s view that no extensions were available. Following the passage of FDASIA, which specifically authorizes deferral extensions, the company again sought a deferral extension in December 2012. 

So, after hearing that they had to move forward in 2010, the company promptly waited 2 years to ask for another extension. During that time, the letter seems to imply that they did not try to move the study forward at all, preferring to roll the dice and wait for changing laws to help them get out from under the obligation.

4. Twinject/Adrenaclick – Amedra (full response)

The details of this one are heavily redacted, but it may also be a bit of gamesmanship from the sponsor. After purchasing the injectors, Amedra asked for a deferral. When the deferral was denied, they simply asked for the requirements to be waived altogether. That seems backwards, but perhaps there's a good reason for that.

---

The articles identified through the search had to match the corresponding trial in terms of the information registered at ClinicalTrials.gov (i.e., same objective, same sample size, same primary outcome, same location, same responsible party, same trial phase, and same sponsor) and had to present results for the primary outcome. 

So it appears that a reviewed had to score the journal article as an exact match on 8 criteria in order for the trial to be considered the same. That could easily lead to exclusion of journal articles on the basis of very insubstantial differences. The authors provide no detail on this; and again, that would be easy to verify if the study dataset was published. 

The reason I harp on this, and worry about the matching methodology, is that two of the authors of this study were also involved in a methodologically opaque and flawed study about clinical trial results posted in the JCO. In that study, as well, the authors appeared to use an incorrect methodology to identify published clinical trials. When I pointed the issues out, the corresponding author merely reiterated what was already (insufficiently) in the paper's Methodology section.

I find it strange beyond belief, and more than a little hypocritical, that researchers would use a public, taxpayer-funded database as the basis of their studies, and yet refuse to provide their data for public review. There are no technological or logistical issues preventing this kind of sharing, and there is an obvious ethical point in favor of transparency.

But if the authors are reasonably close to correct in their results, I'm not sure what to make of this study. 

The Nature article covering this study contend that

[T]he [ClinicalTrials.gov] database was never meant to replace journal publications, which often contain longer descriptions of methods and results and are the basis for big reviews of research on a given drug.

I suppose that some journal articles have better methodology sections, although this is far from universally true (and, like this study here, these methods are often quite opaquely described and don't support replication). As for results, I don't believe that's the case. In this study, the opposite was true: ClinicalTrial.gov results were generally more complete than journal results. And I have no idea why the registry wouldn't surpass journals as a more reliable and complete source of information for "big reviews".

Perhaps it is a function of my love of getting my hands dirty digging into the data, but if we are witnessing a turning point where journal articles take a distant back seat to the ClinicalTrials.gov registry, I'm enthused. ClinicalTrials.gov is public, free, and contains structured data; journal articles are expensive, unparsable, and generally written in painfully unclear language. To me, there's really no contest. 

Carolina Riveros, Agnes Dechartres, Elodie Perrodeau, Romana Haneef, Isabelle Boutron, & Philippe Ravaud (2013). Timing and Completeness of Trial Results Posted at ClinicalTrials.gov and Published in Journals PLoS Medicine DOI: 10.1371/journal.pmed.1001566

On this date 349 years ago, Samuel Pepys relates in his famous diary a remarkable story about an upcoming medical experiment. As far as I can tell, this is the first written description of a paid research subject.

According to his account, the man (who he describes as “a little frantic”) was to be paid to undergo a blood transfusion from a sheep. It was hypothesized that the blood of this calm and docile animal would help to calm the man.

Some interesting things to note about this experiment:

• Equipoise. There is explicit disagreement about what effect the experimental treatment will have: according to Pepys, "some think it may have a good effect upon him as a frantic man by cooling his blood, others that it will not have any effect at all".
• Results published. An account of the experiment was published just two weeks later in the journal Philosophical Transactions. 
• Medical Privacy. In this subsequent write-up, the research subject is identified as Arthur Coga, a former Cambridge divinity student. According to at least one account, being publicly identified had a bad effect on Coga, as people who had heard of him allegedly succeeded in getting him to spend his stipend on drink (though no sources are provided to confirm this story).
• Patient Reported Outcome. Coga was apparently chosen because, although mentally ill, he was still considered educated enough to give an accurate description of the treatment effect. 

Depending on your perspective, this may also be a very early account of the placebo effect, or a classic case of ignoring the patient’s experience. Because even though his report was positive, the clinicians remained skeptical. From the journal article:

The Man after this operation, as well as in it, found himself very well, and hath given in his own Narrative under his own hand, enlarging more upon the benefit, he thinks, he hath received by it, than we think fit to own as yet.

…and in fact, a subsequent diary entry from Pepys mentions meeting Coga, with similarly mixed impressions: “he finds himself much better since, and as a new man, but he is cracked a little in his head”.

The amount Coga was paid for his participation? Twenty shillings – at the time, that was exactly one Guinea.

[Image credit: Wellcome Images]

The decade following passage of FDAAA has been one of easing standards for drug approvals in the US, most notably with the advent of “breakthrough” designation created by FDASIA in 2012 and the 21st Century Cures Act in 2016.

Although, as of this writing, there is no nominee for FDA Commissioner, it appears to be safe to say that the current administration intends to accelerate the pace of deregulation, mostly through further lowering of approval requirements. In fact, some of the leading contenders for the position are on record as supporting a return to pre-Kefauver-Harris days, when drug efficacy was not even considered for approval.

Build a better mouse model, and pharma will beat a path to your door - no laws needed.

In this context, it is at least refreshing to read a proposal to increase efficacy standards. This comes from two bioethicists at McGill University, who make the somewhat-startling case for a higher degree of efficacy evaluation before a drug begins any testing in humans.

We contend that a lack of emphasis on evidence for the efficacy of drug candidates is all too common in decisions about whether an experimental medicine can be tested in humans. We call for infrastructure, resources and better methods to rigorously evaluate the clinical promise of new interventions before testing them on humans for the first time.

The author propose some sort of centralized clearinghouse to evaluate efficacy more rigorously. It is unclear what they envision this new multispecialty review body’s standards for green-lighting a drug to enter human testing. Instead they propose three questions:

• What is the likelihood that the drug will prove clinically useful?
• Assume the drug works in humans. What is the likelihood of observing the preclinical results?
• Assume the drug does not work in humans. What is the likelihood of observing the preclinical results?

These seem like reasonable questions, I suppose – and are likely questions that are already being asked of preclinical data. They certainly do not rise to the level of providing a clear standard for regulatory approval, though perhaps it’s a reasonable place to start.

The most obvious counterargument here is one that the authors curiously don’t pick up on at all: if we had the ability to accurately (or even semiaccurately) predict efficacy preclinically, pharma sponsors would already be doing it. The comment notes: “More-thorough assessments of clinical potential before trials begin could lower failure rates and drug-development costs.” And it’s hard not to agree: every pharmaceutical company would love to have even an incrementally-better sense of whether their early pipeline drugs will be shown to work as hoped.

The authors note

Commercial interests cannot be trusted to ensure that human trials are launched only when the case for clinical potential is robust. We believe that many FIH studies are launched on the basis of flimsy, underscrutinized evidence.

However, they do not produce any evidence that industry is in any way deliberately underperforming their preclinical work, merely that preclinical efficacy is often difficult to reproduce and is poorly correlated with drug performance in humans.

Pharmaceutical companies have many times more candidate compounds than they can possibly afford to put into clinical trials. Figuring out how to lower failure rates – or at least the total cost of failure - is a prominent industry obsession, and efficacy remains the largest source of late-stage trial failure. This quest to “fail faster” has resulted in larger and more expensive phase 2 trials, and even to increased efficacy testing in some phase 1 trials. And we do this not because of regulatory pressure, but because of hopes that these efforts will save overall costs. So it seems beyond probable that companies would immediately invest more in preclinical efficacy testing, if such testing could be shown to have any real predictive power. But generally speaking, it does not.

As a general rule, we don’t need regulations that are firmly aligned with market incentives, we need regulations if and when we think those incentives might run counter to the general good. In this case, there are already incredibly strong market incentives to improve preclinical assessments. Where companies have attempted to do something with limited success, it would seem quixotic to think that regulatory fiat will accomplish more.

(One further point. The authors try to link the need for preclinical efficacy testing to the 2016 Bial tragedy. This seems incredibly tenuous: the authors speculate that perhaps trial participants would not have been harmed and killed if Bial had been required to produce more evidence of BIA102474’s clinical efficacy before embarking on their phase 1 trials. But that would have been entirely coincidental in this case: if the drug had in fact more evidence of therapeutic promise, the tragedy still would have happened, because it had nothing at all to do with the drug’s efficacy.

This is to some extent a minor nitpick, since the argument in favor of earlier efficacy testing does not depend on a link to Bial. However, I bring it up because a) the authors dedicate the first four paragraphs of their comment to the link, and b) there appears to be a minor trend of using the death and injuries of that trial to justify an array of otherwise-unrelated initiatives. This seems like a trend we should discourage.)

[Update 2/23: I posted this last night, not realizing that only a few hours earlier, John LaMattina had published on this same article. His take is similar to mine, in that he is suspicious of the idea that pharmaceutical companies would knowingly push ineffective drugs up their pipeline.]

Kimmelman, J., & Federico, C. (2017). Consider drug efficacy before first-in-human trials Nature, 542 (7639), 25-27 DOI: 10.1038/542025a

A person has tested positive in British Columbia, Canadian health officials said, though the results must be sent to another lab for confirmation.

NPR's Leila Fadel talks with actor Patrick Dempsey about his efforts to raise money for cancer treatment and prevention.

Sudan's civil war has displaced 10 million citizens. Here are profiles of two young people from the most vulnerable groups: an unaccompanied minor caring for twin brothers, a woman who was raped.

The National Institutes of Health, the crown jewel of biomedical research in the U.S., could face big changes under the new Trump administration, some fueled by pandemic-era criticisms of the agency.

Journalist Annie Lowrey has a rare disease that causes a near-constant itch that doesn't respond to most treatments. She likens the itchiness to a car alarm: "You can't stop thinking about it."

Nearly every day since she was a child, Alex Leow, a psychiatrist and computer scientist at the University of Illinois Chicago, has played the piano. Some days she plays well, and other days her tempo lags and her fingers hit the wrong keys. Over the years, she noticed a pattern: How well she plays depends on her mood. A bad mood or lack of sleep almost always leads to sluggish, mistake-prone music.

In 2015, Leow realized that a similar pattern might be true for typing. She wondered if she could help people with psychiatric conditions track their moods by collecting data about their typing style from their phones. She decided to turn her idea into an app.

After conducting a pilot study, in 2018 Leow launched BiAffect, a research app that aims to understand mood-related symptoms of bipolar disorder through keyboard dynamics and sensor data from users’ smartphones. Now in use by more than 2,700 people who have volunteered their data to the project, the app tracks typing speed and accuracy by swapping the phone’s onscreen keyboard with its own nearly identical one.

The software then generates feedback for users, such as a graph displaying hourly keyboard activity. Researchers get access to the donated data from users’ phones, which they use to develop and test machine learning algorithms that interpret data for clinical use. One of the things Leow’s team has observed: When people are manic—a state of being overly excited that accompanies bipolar disorder—they type “ferociously fast,” says Leow.

Compared to a healthy user [top], a person experiencing symptoms of bipolar disorder [middle] or depression [bottom] may use their phone more than usual and late at night. BiAffect measures phone usage and orientation to help track those symptoms. BiAffect

BiAffect is one of the few mental-health apps that take a passive approach to collecting data from a phone to make inferences about users’ mental states. (Leow suspects that fewer than a dozen are currently available to consumers.) These apps run in the background on smartphones, collecting different sets of data not only on typing but also on the user’s movements, screen time, call and text frequency, and GPS location to monitor social activity and sleep patterns. If an app detects an abrupt change in behavior, indicating a potentially hazardous shift in mental state, it could be set up to alert the user, a caretaker, or a physician.

Such apps can’t legally claim to treat or diagnose disease, at least in the United States. Nevertheless, many researchers and people with mental illness have been using them as tools to track signs of depression, schizophrenia, anxiety, and bipolar disorder. “There’s tremendous, immediate clinical value in helping people feel better today by integrating these signals into mental-health care,” says John Torous, director of digital psychiatry at Beth Israel Deaconess Medical Center, in Boston. Globally, one in 8 people live with a mental illness, including 40 million with bipolar disorder.

These apps differ from most of the more than 10,000 mental-health and mood apps available, which typically ask users to actively log how they’re feeling, help users connect to providers, or encourage mindfulness. The popular apps Daylio and Moodnotes, for example, require journaling or rating symptoms. This approach requires more of the user’s time and may make these apps less appealing for long-term use. A 2019 study found that among 22 mood-tracking apps, the median user-retention rate was just 6.1 percent at 30 days of use.

App developers are trying to avoid the pitfalls of previous smartphone-psychiatry startups, some of which oversold their capabilities before validating their technologies.

But despite years of research on passive mental-health apps, their success is far from guaranteed. App developers are trying to avoid the pitfalls of previous smartphone psychiatry startups, some of which oversold their capabilities before validating their technologies. For example, Mindstrong was an early startup with an app that tracked taps, swipes, and keystrokes to identify digital biomarkers of cognitive function. The company raised US $160 million in funding from investors, including $100 million in 2020 alone, and went bankrupt in February 2023.

Mindstrong may have folded because the company was operating on a different timeline from the research, according to an analysis by the health-care news website Stat. The slow, methodical pace of science did not match the startup’s need to return profits to its investors quickly, the report found. Mindstrong also struggled to figure out the marketplace and find enough customers willing to pay for the service. “We were first out of the blocks trying to figure this out,” says Thomas Insel, a psychiatrist who cofounded Mindstrong.

Now that the field has completed a “hype cycle,” Torous says, app developers are focused on conducting the research needed to prove their apps can actually help people. “We’re beginning to put the burden of proof more on those developers and startups, as well as academic teams,” he says. Passive mental-health apps need to prove they can reliably parse the data they’re collecting, while also addressing serious privacy concerns.

Passive sensing catches mood swings early

A crucial component of managing psychiatric illness is tracking changes in mental states that can lead to more severe episodes of the disease. Bipolar disorder, for example, causes intense swings in mood, from extreme highs during periods of mania to extreme lows during periods of depression. Between 30 and 50 percent of people with bipolar disorder will attempt suicide at least once in their lives. Catching early signs of a mood swing can enable people to take countermeasures or seek help before things get bad.

But detecting those changes early is hard, especially for people with mental illness. Observations by other people, such as family members, can be subjective, and doctor and counselor sessions are too infrequent.

That’s where apps come in. Algorithms can be trained to spot subtle deviations from a person’s normal routine that might indicate a change in mood—an objective measure based on data, like a diabetic tracking blood sugar. “The ability to think objectively about my own thinking is really key,” says retired U.S. major general Gregg Martin, who has bipolar disorder and is an advisor for BiAffect.

The data from passive sensing apps could also be useful to doctors who want to see objective data on their patients in between office visits, or for people transitioning from inpatient to outpatient settings. These apps are “providing a service that doesn’t exist,” says Colin Depp, a clinical psychologist and professor at the University of California, San Diego. Providers can’t observe their patients around the clock, he says, but smartphone data can help close the gap.

Depp and his team have developed an app that uses GPS data and microphone-based sensing to determine the frequency of conversations and make inferences about a person’s social interactions and isolation. The app also tracks “location entropy,” a metric of how much a user moves around outside of routine locations. When someone is depressed and mostly stays home, location entropy decreases.

Depp’s team initially developed the app, called CBT2go, as a way to test the effectiveness of cognitive behavioral therapy in between therapy sessions. The app can now intervene in real time with people experiencing depressive or psychotic symptoms. This feature helps people identify when they feel lonely or agitated so they can apply coping skills they’ve learned in therapy. “When people walk out of the therapist’s office or log off, then they kind of forget all that,” Depp says.

Another passive mental-health-app developer, Ellipsis Health in San Francisco, uses software that takes voice samples collected during telehealth calls to gauge a person’s level of depression, anxiety, and stress symptoms. For each set of symptoms, deep-learning models analyze the person’s words, rhythms, and inflections to generate a score. The scores indicate the severity of the person’s mental distress, and are based on the same scales used in standard clinical evaluations, says Michael Aratow, cofounder and chief medical officer at Ellipsis.

Aratow says the software works for people of all demographics, without needing to first capture baseline measures of an individual’s voice and speech patterns. “We’ve trained the models in the most difficult use cases,” he says. The company offers its platform, including an app for collecting the voice data, through health-care providers, health systems, and employers; it’s not directly available to consumers.

In the case of BiAffect, the app can be downloaded for free by the public. Leow and her team are using the app as a research tool in clinical trials sponsored by the U.S. National Institutes of Health. These studies aim to validate whether the app can reliably monitor mood disorders, and determine whether it could also track suicide risk in menstruating women and cognition in people with multiple sclerosis.

BiAffect’s software tracks behaviors like hitting the backspace key frequently, which suggests more errors, and an increase in typing “@” symbols and hashtags, which suggest more social media use. The app combines this typing data with information from the phone’s accelerometer to determine how the user is oriented and moving—for example, whether the user is likely lying down in bed—which yields more clues about mood.

Ellipsis Health analyzes audio captured during telehealth visits to assign scores for depression, anxiety, and stress.Ellipsis Health

The makers of BiAffect and Ellipsis Health don’t claim their apps can treat or diagnose disease. If app developers want to make those claims and sell their product in the United States, they would first have to get regulatory approval from the U.S. Food and Drug Administration. Getting that approval requires rigorous and large-scale clinical trials that most app makers don’t have the resources to conduct.

Digital-health software depends on quality clinical data

The sensing techniques upon which passive apps rely—measuring typing dynamics, movement, voice acoustics, and the like—are well established. But the algorithms used to analyze the data collected by the sensors are still being honed and validated. That process will require considerably more high-quality research among real patient populations.

Greg Mably

For example, clinical studies that include control or placebo groups are crucial and have been lacking in the past. Without control groups, companies can say their technology is effective “compared to nothing,” says Torous at Beth Israel.

Torous and his team aim to build software that is backed by this kind of quality evidence. With participants’ consent, their app, called mindLAMP, passively collects data from their screen time and their phone’s GPS and accelerometer for research use. It’s also customizable for different diseases, including schizophrenia and bipolar disorder. “It’s a great starting point. But to bring it into the medical context, there’s a lot of important steps that we’re now in the middle of,” says Torous. Those steps include conducting clinical trials with control groups and testing the technology in different patient populations, he says.

How the data is collected can make a big difference in the quality of the research. For example, the rate of sampling—how often a data point is collected—matters and must be calibrated for the behavior being studied. What’s more, data pulled from real-world environments tends to be “dirty,” with inaccuracies collected by faulty sensors or inconsistencies in how phone sensors initially process data. It takes more work to make sense of this data, says Casey Bennett, an assistant professor and chair of health informatics at DePaul University, in Chicago, who uses BiAffect data in his research.

One approach to addressing errors is to integrate multiple sources of data to fill in the gaps—like combining accelerometer and typing data. In another approach, the BiAffect team is working to correlate real-world information with cleaner lab data collected in a controlled environment where researchers can more easily tell when errors are introduced.

Who participates in the studies matters too. If participants are limited to a particular geographic area or demographic, it’s unclear whether the results can be applied to the broader population. For example, a night-shift worker will have different activity patterns from those with nine-to-five jobs, and a city dweller may have a different lifestyle from residents of rural areas.

After the research is done, app developers must figure out a way to integrate their products into real-world medical contexts. One looming question is when and how to intervene when a change in mood is detected. These apps should always be used in concert with a professional and not as a replacement for one, says Torous. Otherwise, the app’s assessments could be dangerous and distressing to users, he says.

When mood tracking feels like surveillance

No matter how well these passive mood-tracking apps work, gaining trust from potential users may be the biggest stumbling block. Mood tracking could easily feel like surveillance. That’s particularly true for people with bipolar or psychotic disorders, where paranoia is part of the illness.

Keris Myrick, a mental-health advocate, says she finds passive mental-health apps “both cool and creepy.” Myrick, who is vice president of partnerships and innovation at the mental-health-advocacy organization Inseparable, has used a range of apps to support her mental health as a person with schizophrenia. But when she tested one passive sensing app, she opted to use a dummy phone. “I didn’t feel safe with an app company having access to all of that information on my personal phone,” Myrick says. While she was curious to see if her subjective experience matched the app’s objective measurements, the creepiness factor prevented her from using the app enough to find out.

Keris Myrick, a mental-health advocate, says she finds passive mental-health apps “both cool and creepy.”

Beyond users’ perception, maintaining true digital privacy is crucial. “Digital footprints are pretty sticky these days,” says Katie Shilton, an associate professor at the University of Maryland focused on social-data science. It’s important to be transparent about who has access to personal information and what they can do with it, she says.

“Once a diagnosis is established, once you are labeled as something, that can affect algorithms in other places in your life,” Shilton says. She cites the misuse of personal data in the Cambridge Analytica scandal, in which the consulting firm collected information from Facebook to target political advertising. Without strong privacy policies, companies producing mental-health apps could similarly sell user data—and they may be particularly motivated to do so if an app is free to use.

Conversations about regulating mental-health apps have been ongoing for over a decade, but a Wild West–style lack of regulation persists in the United States, says Bennett of DePaul University. For example, there aren’t yet protections in place to keep insurance companies or employers from penalizing users based on data collected. “If there aren’t legal protections, somebody is going to take this technology and use it for nefarious purposes,” he says.

Some of these concerns may be mediated by confining all the analysis to a user’s phone, rather than collecting data in a central repository. But decisions about privacy policies and data structures are still up to individual app developers.

Leow and the BiAffect team are currently working on a new internal version of their app that incorporates natural-language processing and generative AI extensions to analyze users’ speech. The team is considering commercializing this new version in the future, but only following extensive work with industry partners to ensure strict privacy safeguards are in place. “I really see this as something that people could eventually use,” Leow says. But she acknowledges that researchers’ goals don’t always align with the desires of the people who might use these tools. “It is so important to think about what the users actually want.”

This article appears in the July 2024 print issue as “The Shrink in Your Pocket.”

Elemind, a 5-year-old startup based in Cambridge, Mass., today unveiled a US $349 wearable for neuromodulation, the company’s first product. According to cofounder and CEO Meredith Perry, the technology tracks the oscillation of brain waves using electroencephalography (EEG) sensors that detect the electrical activity of the brain and then influence those oscillations using bursts of sound delivered via bone conduction.

Elemind’s first application for this wearable aims to suppress alpha waves to help induce sleep. There are other wearables on the market that monitor brain waves and, through biofeedback, encourage users to actively modify their alpha patterns. Elemind’s headband appears to be the first device to use sound to directly influence the brain waves of a passive user.

In a clinical trial, says Perry [no relation to author], 76 percent of subjects fell asleep more quickly. Those who did see a difference averaged 48 percent less time to progress from awake to asleep. The results were similar to those of comparable trials of pharmaceutical sleep aids, Perry indicated.

“For me,” Perry said, “it cuts through my rumination, quiets my thinking. It’s like noise cancellation for the brain.”

I briefly tested Elemind’s headband in May. I found it comfortable, with a thick cushioned band that sits across the forehead connected to a stretchy elastic loop to keep it in place. In the band are multiple EEG electrodes, a processor, a three-axis accelerometer, a rechargeable lithium-polymer battery, and custom electronics that gather the brain’s electrical signals, estimate their phase, and generate pink noise through a bone-conduction speaker. The whole thing weighs about 60 grams—about as much as a small kiwi fruit.

My test conditions were far from optimal for sleep: early afternoon, a fairly bright conference room, a beanbag chair as bed, and a vent blowing. And my test lasted just 4 minutes. I can say that I didn’t find the little bursts of pink noise (white noise without the higher frequencies) unpleasant. And since I often wear an eye mask, feeling fabric on my face wasn’t disturbing. It wasn’t the time or place to try for sound sleep, but I—and the others in the room—noted that after 2 minutes I was yawning like crazy.

How Elemind tweaks brain waves

What was going on in my brain? Briefly, different brain states are associated with different frequencies of waves. Someone who is relaxed with eyes closed but not asleep produces alpha waves at around 10 hertz. As they drift off to sleep, the alpha waves are supplanted by theta waves, at around 5 Hz. Eventually, the delta waves of deep sleep show up at around 1 Hz.

Ryan Neely, Elemind’s vice president of science and research, explains: “As soon as you put the headband on,” he says, “the EEG system starts running. It uses straightforward signal processing with bandpass filtering to isolate the activity in the 8- to 12-Hz frequency range—the alpha band.”

“Then,” Neely continues, “our algorithm looks at the filtered signal to identify the phase of each oscillation and determines when to generate bursts of pink noise.”

To help a user fall asleep more quickly [top], bursts of pink noise are timed to generate a brain response that is out of phase with alpha waves and so suppresses them. To enhance deep sleep [bottom], the pink noise is timed to generate a brain response that is in phase with delta waves.Source: Elemind

These auditory stimuli, he explains, create ripples in the waves coming from the brain. Elemind’s system tries to align these ripples with a particular phase in the wave. Because there is a gap between the stimulus and the evoked response, Elemind tested its system on 21 people and calculated the average delay, taking that into account when determining when to trigger a sound.

To induce sleep, Elemind’s headband targets the trough in the alpha wave, the point at which the brain is most excitable, Neely says.

“You can think of the alpha rhythm as a gate for communication between different areas of the brain,” he says. “By interfering with that communication, that coordination between different brain areas, you can disrupt patterns, like the ruminations that keep you awake.”

With these alpha waves suppressed, Neely says, the slower oscillations, like the theta waves of light sleep, take over.

Elemind doesn’t plan to stop there. The company plans to add an algorithm that addresses delta waves, the low-frequency 0.5- to 2-Hz waves characteristic of deep sleep. Here, Elemind’s technology will attempt to amplify this pattern with the intent of improving sleep quality.

Is this safe? Yes, Neely says, because auditory stimulation is self-limiting. “Your brain waves have a natural space they can occupy,” he explains, “and this stimulation just moved it within that natural space, unlike deep-brain stimulation, which can move the brain activity outside natural parameters.”

Going beyond sleep to sedation, memory, and mental health

Applications may eventually go beyond inducing and enhancing sleep. Researchers at the University of Washington and McGill University have completed a clinical study to determine if Elemind’s technology can be used to increase the pain threshold of subjects undergoing sedation. The results are being prepared for peer review.

Elemind is also working with a team involving researchers at McGill and the Leuven Brain Institute to determine if the technology can enhance memory consolidation in deep sleep and perhaps have some usefulness for people with mild cognitive impairment and other memory disorders.

Neely would love to see more applications investigated in the future.

“Inverse alpha stimulation [enhancing instead of suppressing the signal] could increase arousal,” he says. “That’s something I’d love to look into. And looking into mental-health treatment would be interesting, because phase coupling between the different brain regions appears to be an important factor in depression and anxiety disorders.”

Perry, who previously founded the wireless power startup UBeam, cofounded Elemind with four university professors with expertise in neuroscience, optogenetics, biomedical engineering, and artificial intelligence. The company has $12 million in funding to date and currently has 13 employees.

Preorders at $349 start today for beta units, and Elemind expects to start general sales later this year. The company will offer customers an optional membership at $7 to $13 monthly that will allow cloud storage of sleep data and access to new apps as they are released.

Last month when Google introduced its new AI search tool, called AI Overviews, the company seemed confident that it had tested the tool sufficiently, noting in the announcement that “people have already used AI Overviews billions of times through our experiment in Search Labs.” The tool doesn’t just return links to Web pages, as in a typical Google search, but returns an answer that it has generated based on various sources, which it links to below the answer. But immediately after the launch users began posting examples of extremely wrong answers, including a pizza recipe that included glue and the interesting fact that a dog has played in the NBA.

Renée DiResta has been tracking online misinformation for many years as the technical research manager at Stanford’s Internet Observatory.

While the pizza recipe is unlikely to convince anyone to squeeze on the Elmer’s, not all of AI Overview’s extremely wrong answers are so obvious—and some have the potential to be quite harmful. Renée DiResta has been tracking online misinformation for many years as the technical research manager at Stanford’s Internet Observatory and has a new book out about the online propagandists who “turn lies into reality.” She has studied the spread of medical misinformation via social media, so IEEE Spectrum spoke to her about whether AI search is likely to bring an onslaught of erroneous medical advice to unwary users.

I know you’ve been tracking disinformation on the Web for many years. Do you expect the introduction of AI-augmented search tools like Google’s AI Overviews to make the situation worse or better?

Renée DiResta: It’s a really interesting question. There are a couple of policies that Google has had in place for a long time that appear to be in tension with what’s coming out of AI-generated search. That’s made me feel like part of this is Google trying to keep up with where the market has gone. There’s been an incredible acceleration in the release of generative AI tools, and we are seeing Big Tech incumbents trying to make sure that they stay competitive. I think that’s one of the things that’s happening here.

We have long known that hallucinations are a thing that happens with large language models. That’s not new. It’s the deployment of them in a search capacity that I think has been rushed and ill-considered because people expect search engines to give them authoritative information. That’s the expectation you have on search, whereas you might not have that expectation on social media.

There are plenty of examples of comically poor results from AI search, things like how many rocks we should eat per day [a response that was drawn for an Onion article]. But I’m wondering if we should be worried about more serious medical misinformation. I came across one blog post about Google’s AI Overviews responses about stem-cell treatments. The problem there seemed to be that the AI search tool was sourcing its answers from disreputable clinics that were offering unproven treatments. Have you seen other examples of that kind of thing?

DiResta: I have. It’s returning information synthesized from the data that it’s trained on. The problem is that it does not seem to be adhering to the same standards that have long gone into how Google thinks about returning search results for health information. So what I mean by that is Google has, for upwards of 10 years at this point, had a search policy called Your Money or Your Life. Are you familiar with that?

I don’t think so.

DiResta: Your Money or Your Life acknowledges that for queries related to finance and health, Google has a responsibility to hold search results to a very high standard of care, and it’s paramount to get the information correct. People are coming to Google with sensitive questions and they’re looking for information to make materially impactful decisions about their lives. They’re not there for entertainment when they’re asking a question about how to respond to a new cancer diagnosis, for example, or what sort of retirement plan they should be subscribing to. So you don’t want content farms and random Reddit posts and garbage to be the results that are returned. You want to have reputable search results.

That framework of Your Money or Your Life has informed Google’s work on these high-stakes topics for quite some time. And that’s why I think it’s disturbing for people to see the AI-generated search results regurgitating clearly wrong health information from low-quality sites that perhaps happened to be in the training data.

So it seems like AI overviews is not following that same policy—or that’s what it appears like from the outside?

DiResta: That’s how it appears from the outside. I don’t know how they’re thinking about it internally. But those screenshots you’re seeing—a lot of these instances are being traced back to an isolated social media post or a clinic that’s disreputable but exists—are out there on the Internet. It’s not simply making things up. But it’s also not returning what we would consider to be a high-quality result in formulating its response.

I saw that Google responded to some of the problems with a blog post saying that it is aware of these poor results and it’s trying to make improvements. And I can read you the one bullet point that addressed health. It said, “For topics like news and health, we already have strong guardrails in place. In the case of health, we launched additional triggering refinements to enhance our quality protections.” Do you know what that means?

DiResta: That blog posts is an explanation that [AI Overviews] isn’t simply hallucinating—the fact that it’s pointing to URLs is supposed to be a guardrail because that enables the user to go and follow the result to its source. This is a good thing. They should be including those sources for transparency and so that outsiders can review them. However, it is also a fair bit of onus to put on the audience, given the trust that Google has built up over time by returning high-quality results in its health information search rankings.

I know one topic that you’ve tracked over the years has been disinformation about vaccine safety. Have you seen any evidence of that kind of disinformation making its way into AI search?

DiResta: I haven’t, though I imagine outside research teams are now testing results to see what appears. Vaccines have been so much a focus of the conversation around health misinformation for quite some time, I imagine that Google has had people looking specifically at that topic in internal reviews, whereas some of these other topics might be less in the forefront of the minds of the quality teams that are tasked with checking if there are bad results being returned.

What do you think Google’s next moves should be to prevent medical misinformation in AI search?

DiResta: Google has a perfectly good policy to pursue. Your Money or Your Life is a solid ethical guideline to incorporate into this manifestation of the future of search. So it’s not that I think there’s a new and novel ethical grounding that needs to happen. I think it’s more ensuring that the ethical grounding that exists remains foundational to the new AI search tools.

In the United States alone, more than 100,000 people currently need a lifesaving organ transplant. Instead of waiting for donors, one way to solve this crisis in the future is to assemble replacement organs with bio-printing—3D printing that uses inks containing living cells. Scientists in Israel have found that origami techniques could help fold sensors into bio-printed materials to help determine whether they are behaving safely and properly.

Although bio-printing something as complex as a human organ is still a distant possibility, there are a host of near-term applications for the technique. For example, in drug research, scientists can bio-print living, three-dimensional tissues with which to examine the effects of various compounds.

Ideally, researchers would like to embed sensors within bio-printed items to keep track of how well they are behaving. However, the three-dimensional nature of bio-printed objects makes it difficult to lodge sensors within them in a way that can monitor every part of the structures.

“It will, hopefully in the future, allow us to monitor and assess 3D biostructures before we would like to transplant them.” —Ben Maoz, Tel Aviv University

Now scientists have developed a 3D platform inspired by origami that can help embed sensors in bio-printed objects in precise locations. “It will, hopefully in the future, allow us to monitor and assess 3D biostructures before we would like to transplant them,” says Ben Maoz, a professor of biomedical engineering at Tel Aviv University in Israel.

The new platform is a silicone rubber device that can fold around a bio-printed structure. The prototype holds a commercial array of 3D electrodes to capture electrical signals. It also possesses other electrodes that can measure electrical resistance, which can reveal how permeable cells are to various medications. A custom 3D software model can tailor the design of the origami and all the electrodes so that the sensors can be placed in specific locations in the bio-printed object.

The scientists tested their device on bio-printed clumps of brain cells. The research team also grew a layer of cells onto the origami that mimicked the blood-brain barrier, a cell layer that protects the brain from undesirable substances that the body’s blood might be carrying. By folding this combination of origami and cells onto the bio-printed structures, Maoz and his colleagues were able to monitor neural activity within the brain cells and see how their synthetic blood-brain barrier might interfere with medications intended to treat brain diseases.

Maoz says the new device can incorporate many types of sensors beyond electrodes, such as temperature or acidity sensors. It can also incorporate flowing liquid to supply oxygen and nutrients to cells, the researchers note.

Currently, this device “will mainly be used for research and not for clinical use,” Maoz says. Still, it could “significantly contribute to drug development—assessing drugs that are relevant to the brain.”

The researchers say they can use their origami device with any type of 3D tissue. For example, Maoz says they can use it on bio-printed structures made from patient cells “to help with personalized medicine and drug development.”

The origami platform could also help embed devices that can modify bio-printed objects. For instance, many artificially grown tissues function better if they are placed under the kinds of physical stresses they might normally experience within the body, and the origami platform could integrate gadgets that can exert such mechanical forces on bio-printed structures. “This can assist in accelerating tissue maturation, which might be relevant to clinical applications,” Maoz says.

The scientists detailed their findings in the 26 June issue of Advanced Science.

Tech startups are stepping up to meet the needs of 60 million people worldwide who use opioids, representing about 1 percent of the world’s adult population. In the United States, deaths involving synthetic opioids have risen 1,040 percent from 2013 to 2019. The COVID-19 pandemic and continued prevalence of fentanyl have since worsened the toll, with an estimated 81,083 fatal overdoses in 2023 alone.

Innovations include biometric monitoring systems that help doctors determine proper medication dosages, nerve stimulators that relieve withdrawal symptoms, wearable and ingestible systems that watch for signs of an overdose, and autonomous drug delivery systems that could prevent overdose deaths.

Helping Patients Get the Dosage They Need

For decades, opioid blockers and other medications that suppress cravings have been the primary treatment tool for opioid addiction. However, despite its clinical dominance, this approach remains underutilized. In the United States, only about 22 percent of the 2.5 million adults with opioid use disorder receive medication-assisted therapy such as methadone, Suboxone, and similar drugs.

Determining patients’ ideal dosage during the early stages of treatment is crucial for keeping them in recovery programs. The shift from heroin to potent synthetic opioids, like fentanyl, has complicated this process, as the typical recommended medication doses can be too low for those with a high fentanyl tolerance.

A North Carolina-based startup is developing a predictive algorithm to help clinicians tailor these protocols and track real-time progress with biometric data. OpiAID, which is currently working with 1,000 patients across three clinical sites, recently launched a research pilot with virtual treatment provider Bicycle Health. Patients taking Suboxone will wear a Samsung Galaxy Watch6 to measure their heart rate, body movements, and skin temperature. OpiAID CEO David Reeser says clinicians can derive unique stress indications from this data, particularly during withdrawal. (He declined to share specifics on how the algorithm works.)

“Identifying stress biometrically plays a role in how resilient someone will be,” Reeser adds. “For instance, poor heart rate variability during sleep could indicate that a patient may be more susceptible that day. In the presence of measurable amounts of withdrawal, the potential for relapse on illicit medications may be more likely.”

Nerve Stimulators Provide Opioid Withdrawal Relief

While OpiAID’s software solution relies on monitoring patients, electrical nerve stimulation devices take direct action. These behind-the-ear wearables distribute electrodes at nerve endings around the ear and send electrical pulses to block pain signals and relieve withdrawal symptoms like anxiety and nausea.

The U.S. Food and Drug Administration (FDA) has cleared several nerve stimulator devices, such as DyAnsys’ Drug Relief, which periodically administers low-level electrical pulses to the ear’s cranial nerves. Others include Spark Biomedical’s Sparrow system and NET Recovery’s NETNeuro device.

Masimo’s behind-the-ear Bridge device costs US $595 for treatment providers.Masimo

Similarly, Masimo’s Bridge relieves withdrawal symptoms by stimulating the brain and spinal cord via electrodes. The device is intended to help patients initiating, transitioning into, or tapering off medication-assisted treatment. In a clinical trial, Bridge reduced symptom severity by 85 percent in the first hour and 97 percent by the fifth day. A Masimo spokesperson said the company’s typical customers are treatment providers and correctional facilities, though it’s also seeing interest from emergency room physicians.

Devices Monitor Blood Oxygen to Prevent Overdose Deaths

In 2023, the FDA cleared Masimo’s Opioid Halo device to monitor blood oxygen levels and alert emergency contacts if it detects opioid-induced respiratory depression, the leading cause of overdose deaths. The product includes a pulse oximeter cable and disposable sensors connected to a mobile app.

Opioid Halo utilizes Masimo’s signal extraction technology, first developed in the 1990s, which improves upon conventional oxygen monitoring techniques by filtering out artifacts caused by blood movement. Masimo employs four signal-processing engines to distinguish the true signal from noise that can lead to false alarms; for example, they distinguish between arterial blood and low-oxygen venous blood.

Masimo’s Opioid Halo system is available over-the-counter without a prescription. Masimo

Opioid Halo is available over-the-counter for US $250. A spokesperson says sales have continued to show promise as more healthcare providers recommend it to high-risk patients.

An Ingestible Sensor to Watch Over Patients

Last year, in a first-in-human clinical study, doctors used an ingestible sensor to monitor vital signs from patients’ stomachs. Researchers analyzed the breathing patterns and heart rates of 10 sleep study patients at West Virginia University. Some participants had episodes of central sleep apnea, which can be a proxy for opioid-induced respiratory depression. The capsule transmitted this data wirelessly to external equipment linked to the cloud.

Celero’s Rescue-Rx capsule would reside in a user’s stomach for one week.Benjamin Pless/Celero Systems

“To our knowledge, this is the first time anyone has demonstrated the ability to accurately monitor human cardiac and respiratory signals from an ingestible device,” says Benjamin Pless, one of the study’s co-authors. “This was done using very low-power circuitry including a radio, microprocessor, and accelerometer along with software for distinguishing various physiological signals.”

Pless and colleagues from MIT and Harvard Medical School started Celero Systems to commercialize a modified version of that capsule, one that will also release an opioid antagonist after detecting respiratory depression. Pless, Celero’s CEO, says the team has successfully demonstrated the delivery of nalmefene, an opioid antagonist similar to Narcan, to rapidly reverse overdoses.

Celero’s next step is integrating the vitals-monitoring feature for human trials. The company’s final device, Rescue-Rx, is intended to stay in the stomach for one week before passing naturally. Pless says Rescue-Rx’s ingestible format will make the therapy cheaper and more accessible than wearable autoinjectors or implants.

Celero’s capsule can detect vital signs from within the stomach. www.youtube.com

Autonomous Delivery of Overdose Medication

Rescue-Rx isn’t the only autonomous drug-delivery project under development. A recent IEEE Transactions on Biomedical Circuits and Systems paper introduced a wrist-worn near-infrared spectroscopy sensor to detect low blood oxygen levels related to an overdose.

Purdue University biomedical engineering professor Hugh Lee and graduate student Juan Mesa, who both co-authored the study, say that while additional human experiments are necessary, the findings represent a valuable tool in counteracting the epidemic. “Our wearable device consistently detected low-oxygenation events, triggered alarms, and activated the circuitry designed to release the antidote through the implantable capsule,” they wrote in an email.

Lee and Purdue colleagues founded Rescue Biomedical to commercialize the A2D2 system, which includes a wristband and an implanted naloxone capsule that releases the drug if oxygen levels drop below 90 percent. Next, the team will evaluate the closed-loop system in mice.

This story was updated on 27 August 2024 to correct the name of Masimo’s Opioid Halo device.

Blood pressure is one of the critical vital signs for health, but standard practice can only capture a snapshot, using a pressure cuff to squeeze arteries. Continuous readings are available, but only by inserting a transducer directly into an artery via a needle and catheter. Thanks to researchers at Caltech, however, it may soon be possible to measure blood pressure continuously at just about any part of the body.

In a paper published in July in PNAS Nexus, the researchers describe their resonance sonomanometry (RSM) approach to reading blood pressure. This new technology uses ultrasound to measure the dimensions of artery walls. It also uses sound waves to find resonant frequencies that can reveal the pressure within those walls via arterial wall tension. This information is sufficient to calculate the absolute pressure within the artery at any moment, without the need for calibration.

This last factor is important, as other non-invasive approaches only provide relative changes in blood pressure. They require periodic calibration using readings from a traditional pressure cuff. The RSM technology eliminates the need for calibration, making continuous readings more reliable.

How resonance sonomanometry works

The researchers’ RSM system uses an ultrasound transducer to measure the dimensions of the artery. It also transmits sound waves at different frequencies. The vibrations cause the arterial walls to move in and out in response, creating a distinct pattern of motion. When the resonant frequency is transmitted, the top and bottom of the artery will move in and out in unison.

This resonant frequency can be used to determine the tension of the artery walls. The tension in the walls is directly correlated with the fluid pressure of the blood within the artery. As a result, the blood pressure can be calculated at any instant based on the dimensions of the artery and its resonant frequency.

The researchers have validated this approach with both mockups and human subjects. They first tested the technology on an arterial model that used a thin-walled rubber tubing and a syringe to vary the pressure. They tested this mockup using multiple pressures and tubing of different diameters.

The researchers then took measurements with human subjects at their carotid arteries (located in the neck), using a standard pressure cuff to take intermittent measurements. The RSM technology was successful, and subsequently was also demonstrated on axillary (shoulder), brachial (arm), and femoral (leg) arteries. The readings were so clear that the researchers mention that they might even be able to detect blood pressure changes related to respiration and its impact on thoracic pressure.

Unlike traditional pressure cuff approaches, RSM provides data during the entire heartbeat cycle, and not just the systolic and diastolic extremes (In other words, the two numbers you receive during a traditional blood pressure measurement). And the fact that RSM works with different-sized arteries means that it should be applicable across different body sizes and types. Using ultrasound also eliminates possible complications such as skin coloration that can affect light-based devices.

The researchers tested their ultrasound-based blood pressure approach on subjects’ carotid arteries.Esperto Medical

“I’m a big fan of continuous monitoring; a yearly blood pressure reading in the doctor’s office is insufficient for decision making,” says Nick van Terheyden, M.D., the digital health leader with Iodine Software, a company providing machine learning technologies to improve healthcare insights. “A new approach based on good old rules of math and physics is an exciting development.”

The Caltech researchers have created a spinoff company, Esperto Medical, to develop a commercial product using RSM technology. The company has created a transducer module that is smaller than a deck of cards, making it practical to incorporate into a wearable armband. They hope to miniaturize the hardware to the point that it could be incorporated into a wrist-worn device. According to Raymond Jimenez, Esperto Medical’s chief technology officer, “this technology poses the potential to unlock accurate, calibration-free [blood pressure measurements] everywhere—in the clinic, at the gym, and even at home.”

It appears that there’s a significant market for such a product. “92 percent of consumers who intend to buy a wearable device are willing to pay extra for a health-related feature, and blood pressure ranks first among such features,” says Elizabeth Parks, the president of Internet of Things consulting firm Parks Associates.

In the future, rather than relying on arm-squeezing blood pressure cuffs, smart watches may be able to directly monitor blood pressure throughout the day, just as they already do for heart rate and other vital signs.

For the first time, scientists have created a flexible programmable chip that is not made of silicon. The new ultralow-power 32-bit microprocessor from U.K.-based Pragmatic Semiconductor and its colleagues can operate while bent, and can run machine learning workloads. The microchip’s open-source RISC-V architecture suggests it might cost less than a dollar, putting it in a position to power wearable healthcare electronics, smart package labels, and other inexpensive items, its inventors add.

For example, “we can develop an ECG patch that has flexible electrodes attached to the chest and a flexible microprocessor connected to flexible electrodes to classify arrhythmia conditions by processing the ECG data from a patient,” says Emre Ozer, senior director of processor development at Pragmatic, a flexible chip manufacturer in Cambridge, England. Detecting normal heart rhythms versus an arrhythmia “is a machine learning task that can run in software in the flexible microprocessor,” he says.

Flexible electronics have the potential for any application requiring interactions with soft materials, such as devices worn on or implanted within the body. Those applications could include on-skin computers, soft robotics, and brain-machine interfaces. But, conventional electronics are made of rigid materials such as silicon.

Open-source, Flexible, and Fast Enough

Pragmatic sought to create a flexible microchip that cost significantly less to make than a silicon processor. The new device, named Flex-RV, is a 32-bit microprocessor based on the metal-oxide semiconductor indium gallium zinc oxide (IGZO).

Attempts to create flexible devices from silicon require special packaging for the brittle microchips to protect them from the mechanical stresses of bending and stretching. In contrast, pliable thin-film transistors made from IGZO can be made directly at low temperatures onto flexible plastics, leading to lower costs.

The new microchip is based on the RISC-V instruction set. (RISC stands for reduced instruction set computer.) First introduced in 2010, RISC-V aims to enable smaller, lower-power, better-performing processors by slimming down the core set of instructions they can execute.

“Our end goal is to democratize computing by developing a license-free microprocessor,” Ozer says.

RISC-V’s is both free and open-source, letting chip designer dodge the costly licensing fees associated with proprietary architectures such as x86 and Arm. In addition, proprietary architectures offer limited opportunities to customize them, as adding new instructions is generally restricted. In contrast, RISC-V encourages such changes.

A bent Flex-RV microprocessor runs a program to print ‘Hello World’. Pragmatic Semiconductor

“We chose the Serv designed by Olof Kindgren... as the open source 32-bit RISC-V CPU when we designed Flex-RV,” Ozer says. “Serv is the smallest RISC-V processor in the open-source community.”

Other processors have been built using flexible semiconductors, such as Pragmatic’s 32-bit PlasticARM and an ultracheap microcontroller designed by engineers in Illinois. Unlike these earlier devices, Flex-RV is programmable and can run compiled programs written in high-level languages such as C. In addition, the open-source nature of RISC-V also let the researchers equip Flex-RV with a programmable machine learning hardware accelerator, enabling artificial intelligence applications.

Each Flex-RV microprocessor has a 17.5 square millimeter core and roughly 12,600 logic gates. The research team found Flex-RV could run as fast as 60 kilohertz while consuming less than 6 milliwatts of power.

All previous flexible non-silicon microprocessors were tested solely on the wafers they were made on. In contrast, Flex-RV was tested on flexible printed circuit boards, which let the researchers see how well it operated when flexed. The Pragmatic team found that Flex-RV could still execute programs correctly when bent to a curve with a radius of 3 millimeters. Performance varied between a 4.3 percent slowdown to a 2.3 percent speedup depending on the way it was bent. “Further research is needed to understand how bending conditions such as direction, orientation and angle impact performance at macro and micro scales,” Ozer says.

Silicon microchips can run at gigahertz speeds, much faster than Flex-RV, but that shouldn’t be a problem, according to Ozer. “Many sensors—for example, temperature, pressure, odor, humidity, pH, and so on—in the flexible electronics world typically operate very slowly at hertz or kilohertz regimes,” he says. “These sensors are used in smart packaging, labels and wearable healthcare electronics, which are the emerging applications for which flexible microprocessors will be useful. Running the microprocessor at 60 kHz would be more than enough to meet the requirements of these applications.”

Ozer and his team suggest each Flex-RV might cost less than a dollar. Although Ozer did not want to say how much less than a dollar it might cost, he says they are confident such low costs are possible “thanks to low-cost flexible chip fabrication technology by Pragmatic and a license-free RISC-V technology.”

The scientists detailed their findings online 25 September in the journal Nature.

Neuralink’s visual prosthesis Blindsight has been designated a breakthrough device by the U.S. Food and Drug Administration, which potentially sets the technology on a fast track to approval.

In confirming the news, an FDA spokesperson emphasized that the designation does not mean that Blindsight is yet considered safe or effective. Technologies in the program have potential to improve the current standard of care and are novel compared to what’s available on the market, but the devices still have to go through full clinical trials before seeking FDA approval.

Still, the announcement is a sign that Neuralink is moving closer to testing Blindsight in human patients. The company is recruiting people with vision loss for studies in the United States, Canada, and the United Kingdom.

Visual prostheses work by capturing visual information with a video camera, typically attached to glasses or a headset. Then a processor converts the data to an electrical signal that can be relayed to the nervous system. Retinal implants have been a common approach, with electrodes feeding the signal to nerves in the retina, at the back of the eye, from where it travels on to the brain. But Blindsight uses a brain implant to send the signal directly to neurons in the visual cortex.

In recent years, other companies developing artificial vision prosthetics have reached clinical research trials or beyond, only to struggle financially, leaving patients without support. Some of these technologies live on with new backing: Second Sight’s Orion cortical implant project is now in a clinical trial with Cortigent, and Pixium Vision’s Prima system is now owned by Science, with ex-Neuralink founder Max Hodak at the helm. No company has yet commercialized a visual prosthetic that uses a brain implant.

Elon Musk’s Claims About Blindsight

Very little information about Blindsight is publicly available. As of this writing, there is no official Blindsight page on the Neuralink website, and Neuralink did not respond to requests for comment. It’s also unclear how exactly Blindsight relates to a brain-computer interface that Neuralink has already implanted in two people with paralysis, who use their devices to control computer cursors.

Experts who spoke with IEEE Spectrum felt that, if judged against the strong claims made by Neuralink’s billionaire co-founder Elon Musk, Blindsight will almost certainly disappoint. However, some were still open to the possibility that Neuralink could successfully bring a device to market that can help people with vision loss, albeit with less dramatic effects on their sense of sight. While Musk’s personal fortune could help Blindsight weather difficulties that would end other projects, experts did not feel it was a guarantee of success.

After Neuralink announced on X (formerly Twitter) that Blindsight had received the breakthrough device designation, Musk wrote:

The Blindsight device from Neuralink will enable even those who have lost both eyes and their optic nerve to see.

Provided the visual cortex is intact, it will even enable those who have been blind from birth to see for the first time.

To set expectations correctly, the vision will be at first be [sic] low resolution, like Atari graphics, but eventually it has the potential be [sic] better than natural vision and enable you to see in infrared, ultraviolet or even radar wavelengths, like Geordi La Forge.

Musk included a picture of La Forge, a character from the science-fiction franchise Star Trek who wears a vision-enhancing visor.

Experts Puncture the Blindsight Hype

“[Musk] will build the best cortical implant we can build with current technology. It will not produce anything like normal vision. [Yet] it might produce vision that can transform the lives of blind people,” said Ione Fine, a computational neuroscientist at the University of Washington, who has written about the potential limitations of cortical implants, given the complexity of the human visual system. Fine previously worked for the company Second Sight.

A successful visual prosthetic might more realistically be thought of as assistive technology than a cure for blindness. “At best, we’re talking about something that’s augmentative to a cane and a guide dog; not something that replaces a cane and a guide dog,” said Philip Troyk, a biomedical engineer at the Illinois Institute of Technology.

Restoring natural vision is beyond the reach of today’s technology. But among Musks recent claims, Troyk says that a form of infrared sensing is plausible and has already been tested with one of his patients, who used it for help locating people within a room. That patient has a 400-electrode device implanted in the visual cortex as part of a collaborative research effort called the Intracortical Visual Prosthesis Project (ICVP). By comparison, Blindsight may have more than 1,000 electrodes, if it’s a similar device to Neuralink’s brain-computer interface.

Experts say they’d like more information about Neuralink’s visual prosthetic. “I’m leery about the fact that they are very superficial in their description of the devices,” said Gislin Dagnelie, a vision scientist at Johns Hopkins University who has been involved in multiple clinical trials for vision prosthetics, including a Second Sight retinal implant, and who is currently collaborating on the ICVP. “There’s no clear evaluation or pre-clinical work that has been published,” says Dagnelie. “It’s all based on: ‘Trust us, we’re Neuralink.’”

In the short term, too much hype could mislead clinical trial participants. It could also degrade interest in small but meaningful advancements in visual prosthetics. “Some of the [Neuralink] technology is exciting, and has potential,” said Troyk. “The way the messaging is being done detracts from that, potentially.”

Emteq Labs wants eyewear to be the next frontier of wearable health technology.

The Brighton, England-based company introduced today its emotion-sensing eyewear, Sense. The glasses contain nine optical sensors distributed across the rims that detect subtle changes in facial expression with more than 93 percent accuracy when paired with Emteq’s current software. “If your face moves, we can capture it,” says Steen Strand, whose appointment as Emteq’s new CEO was also announced today. With that detailed data, “you can really start to decode all kinds of things.” The continuous data could help people uncover patterns in their behavior and mood, similar to an activity or sleep tracker.

Emteq is now aiming to take its tech out of laboratory settings with real-world applications. The company is currently producing a small number of Sense glasses, and they’ll be available to commercial partners in December.

The announcement comes just weeks after Meta and Snap each unveiled augmented reality glasses that remain in development. These glasses are “far from ready,” says Strand, who led the augmented reality eyewear division while working at Snap from 2018 to 2022. “In the meantime, we can serve up lightweight eyewear that we believe can deliver some really cool health benefits.”

Fly Vision Vectors

While current augmented reality (AR) headsets have large battery packs to power the devices, glasses require a lightweight design. “Every little bit of power, every bit of weight, becomes critically important,” says Strand. The current version of Sense weighs 62 grams, slightly heavier than the Ray-Ban Meta smart glasses, which weigh in at about 50 grams.

Because of the weight constraints, Emteq couldn’t use the power-hungry cameras typically used in headsets. With cameras, motion is detected by looking at how pixels change between consecutive images. The method is effective, but captures a lot of redundant information and uses more power. The eyewear’s engineers instead opted for optical sensors that efficiently capture vectors when points on the face move due to the underlying muscles. These sensors were inspired by the efficiency of fly vision. “Flies are incredibly efficient at measuring motion,” says Emteq founder and CSO Charles Nduka. “That’s why you can’t swat the bloody things. They have a very high sample rate internally.”

Sense glasses can capture data as often as 6,000 times per second. The vector-based approach also adds a third dimension to a typical camera’s 2D view of pixels in a single plane.

These sensors look for activation of facial muscles, and the area around the eyes is an ideal spot. While it’s easy to suppress or force a smile, the upper half of our face tends to have more involuntary responses, explains Nduka, who also works as a plastic surgeon in the United Kingdom. However, the glasses can also collect information about the mouth by monitoring the cheek muscles that control jaw movements, conveniently located near the lower rim of a pair of glasses. The data collected is then transmitted from the glasses to pass through Emteq’s algorithms in order to translate the vector data into usable information.

In addition to interpreting facial expressions, Sense can be used to track food intake, an application discovered by accident when one of Emteq’s developers was wearing the glasses while eating breakfast. By monitoring jaw movement, the glasses detect when a user chews and how quickly they eat. Meanwhile, a downward-facing camera takes a photo to log the food, and uses a large language model to determine what’s in the photo, effectively making food logging a passive activity. Currently, Emteq is using an instance of OpenAI’s GPT-4 large language model to accomplish this, but the company has plans to create their own algorithm in the future. Other applications, including monitoring physical activity and posture, are also in development.

One Platform, Many Uses

Nduka believes Emteq’s glasses represent a “fundamental technology,” similar to how the accelerometer is used for a host of applications in smartphones, including managing screen orientation, tracking activity, and even revealing infrastructure damage.

Similarly, Emteq has chosen to develop the technology as a general facial data platform for a range of uses. “If we went deep on just one, it means that all the other opportunities that can be helped—especially some of those rarer use cases—they’d all be delayed,” says Nduka. For example, Nduka is passionate about developing a tool to help those with facial paralysis. But a specialized device for those patients would have high unit costs and be unaffordable for the target user. Allowing more companies to use Emteq’s intellectual property and algorithms will bring down cost.

In this buckshot approach, the general target for Sense’s potential use cases is health applications. “If you look at the history of wearables, health has been the primary driver,” says Strand. The same may be true for eyewear, and he says there’s potential for diet and emotional data to be “the next pillar of health” after sleep and physical activity.

How the data is delivered is still to be determined. In some applications, it could be used to provide real-time feedback—for instance, vibrating to remind the user to slow down eating. Or, it could be used by health professionals only to collect a week’s worth of at-home data for patients with mental health conditions, which Nduka notes largely lack objective measures. (As a medical device for treatment of diagnosed conditions, Sense would have to go through a more intensive regulatory process.) While some users are hungry for more data, others may require a “much more gentle, qualitative approach,” says Strand. Emteq plans to work with expert providers to appropriately package information for users.

Interpreting the data must be done with care, says Vivian Genaro Motti, an associate professor at George Mason University who leads the Human-Centric Design Lab. What expressions mean may vary based on cultural and demographic factors, and “we need to take into account that people sometimes respond to emotions in different ways,” Motti says. With little regulation of wearable devices, she says it’s also important to ensure privacy and protect user data. But Motti raises these concerns because there is a promising potential for the device. “If this is widespread, it’s important that we think carefully about the implications.”

Privacy is also a concern to Edward Savonov, a professor of electrical and computer engineering at the University of Alabama, who developed a similar device for dietary tracking in his lab. Having a camera mounted on Emteq’s glasses could pose issues, both for the privacy of those around a user and a user’s own personal information. Many people eat in front of their computer or cell phone, so sensitive data may be in view.

For technology like Sense to be adopted, Sazonov says questions about usability and privacy concerns must first be answered. “Eyewear-based technology has potential for a great future—if we get it right.”

Over the past 20 years, technological advances have enabled inventors to go from strength to strength. And yet, according to the legendary inventor Dean Kamen, innovation has stalled. Kamen made a name for himself with inventions including the first portable insulin pump for diabetics, an advanced wheelchair that can climb steps, and the Segway mobility device. Here, he talks about his plan for enabling innovators.

How has inventing changed since you started in the 1990s?

Dean Kamen: Kids all over the world can now be inventing in the world of synthetic biology the way we played with Tinkertoys and Erector Sets and Lego. I used to put pins and smelly formaldehyde in frogs in high school. Today in high school, kids will do experiments that would have won you the Nobel Prize in Medicine 40 years ago. But none of those kids are likely in any short time to be on the market with a pharmaceutical that will have global impact. Today, while invention is getting easier and easier, I think there are some aspects of innovation that have gotten much more difficult.

Can you explain the difference?

Kamen: Most people think those two words mean the same thing. Invention is coming up with an idea or a thing or a process that has never been done that way before. [Thanks to] more access to technology and 3D printers and simulation programs and virtual ways to make things, the threshold to be able to create something new and different has dramatically lowered.

Historically, inventions were only the starting point to get to innovation. And I’ll define an innovation as something that reached a scale where it impacted a piece of the world, or transformed it: the wheel, steam, electricity, Internet. Getting an invention to the scale it needs to be to become an innovation has gotten easier—if it’s software. But if it’s sophisticated technology that requires mechanical or physical structure in a very competitive world? It’s getting harder and harder to do due to competition, due to global regulatory environments.

[For example,] in proteomics [the study of proteins] and genomics and biomedical engineering, the invention part is, believe it or not, getting a little easier because we know so much, because there are development platforms now to do it. But getting a biotech product cleared by the Food and Drug Administration is getting more expensive and time consuming, and the risks involved are making the investment community much more likely to invest in the next version of Angry Birds than curing cancer.

A lot of ink has been spilled about how AI is changing inventing. Why hasn’t that helped?

Kamen: AI is an incredibly valuable tool. As long as the value you’re looking for is to be able to collect massive amounts of data and being able to process that data effectively. That’s very different than what a lot of people believe, which is that AI is inventing and creating from whole cloth new and different ideas.

How are you using AI to help with innovation?

Kamen: Every medical school has incredibly brilliant professors and grad students with petri dishes. “Look, I can make nephrons. We can grow people a new kidney. They won’t need dialysis.” But they only have petri dishes full of the stuff. And the scale they need is hundreds and hundreds of liters.

I started a not-for-profit called ARMI—the Advanced Regenerative Manufacturing Institute—to help make it practical to manufacture human cells, tissues, and organs. We are using artificial intelligence to speed up our development processes and eliminate going down frustratingly long and expensive [dead-end] paths. We figure out how to bring tissue manufacturing to scale. We build the bioreactors, sensor technologies, robotics, and controls. We’re going to put them together and create an industry that can manufacture hundreds of thousands of replacement kidneys, livers, pancreases, lungs, blood, bone, you name it.

So ARMI’s purpose is to help would-be innovators?

Kamen: We are not going to make a product. We’re not even going to make a whole company. We’re going to create baseline core technologies that will enable all sorts of products and companies to emerge to create an entire new industry. It will be an innovation in health care that will lower costs because cures are much cheaper than chronic treatments. We have to break down the barriers so that these fantastic inventions can become global innovations.

This article appears in the November 2024 print issue as “The Inventor’s Inventor.”

Manu Prakash spoke with IEEE Spectrum shortly after returning to Stanford University from a month aboard a research vessel off the coast of California, where he was testing tools to monitor oceanic carbon sequestration. The associate professor conducts fieldwork around the world to better understand the problems he’s working on, as well as the communities that will be using his inventions.

Prakash develops imaging instruments and diagnostic tools, often for use in global health and environmental sciences. His devices typically cost radically less than conventional equipment—he aims for reductions of two or more orders of magnitude. Whether he’s working on pocketable microscopes, mosquito or plankton monitors, or an autonomous malaria diagnostic platform, Prakash always includes cost and access as key aspects of his engineering. He calls this philosophy “frugal science.”

Why should we think about science frugally?

Manu Prakash: To me, when we are trying to ask and solve problems and puzzles, it becomes important: In whose hands are we putting these solutions? A frugal approach to solving the problem is the difference between 1 percent of the population or billions of people having access to that solution.

Lack of access creates these kinds of barriers in people’s minds, where they think they can or cannot approach a kind of problem. It’s important that we as scientists or just citizens of this world create an environment that feels that anybody has a chance to make important inventions and discoveries if they put their heart to it. The entrance to all that is dependent on tools, but those tools are just inaccessible.

How did you first encounter the idea of “frugal science”?

Prakash: I grew up in India and lived with very little access to things. And I got my Ph.D. at MIT. I was thinking about this stark difference in worlds that I had seen and lived in, so when I started my lab, it was almost a commitment to [asking]: What does it mean when we make access one of the critical dimensions of exploration? So, I think a lot of the work I do is primarily driven by curiosity, but access brings another layer of intellectual curiosity.

How do you identify a problem that might benefit from frugal science?

Prakash: Frankly, it’s hard to find a problem that would not benefit from access. The question to ask is “Where are the neglected problems that we as a society have failed to tackle?” We do a lot of work in diagnostics. A lot [of our solutions] beat the conventional methods that are neither cost effective nor any good. It’s not about cutting corners; it’s about deeply understanding the problem—better solutions at a fraction of the cost. It does require invention. For that order of magnitude change, you really have to start fresh.

Where does your involvement with an invention end?

Prakash: Inventions are part of our soul. Your involvement never ends. I just designed the 415th version of Foldscope [a low-cost “origami” microscope]. People only know it as version 3. We created Foldscope a long time ago; then I realized that nobody was going to provide access to it. So we went back and invented the manufacturing process for Foldscope to scale it. We made the first 100,000 Foldscopes in the lab, which led to millions of Foldscopes being deployed.

So it’s continuous. If people are scared of this, they should never invent anything [laughs], because once you invent something, it’s a lifelong project. You don’t put it aside; the project doesn’t put you aside. You can try to, but that’s not really possible if your heart is in it. You always see problems. Nothing is ever perfect. That can be ever consuming. It’s hard. I don’t want to minimize this process in any way or form.

In the spirit of the Halloween season, IEEE Spectrum presents a pair of stories that—although grounded in scientific truth rather than the macabre—were no less harrowing for those who lived them. In today’s installment, Robert Langer had to push back against his field’s conventional wisdom to pioneer a drug-delivery mechanism vital to modern medicine.

Nicknamed the Edison of Medicine, Robert Langer is one of the world’s most-cited researchers, with over 1,600 published papers, 1,400 patents, and a top-dog role as one of MIT’s nine prestigious Institute Professors. Langer pioneered the now-ubiquitous drug delivery systems used in modern cancer treatments and vaccines, indirectly saving countless lives throughout his 50-year career.

But, much like Edison and other inventors, Langer’s big ideas were initially met with skepticism from the scientific establishment.

He came up in the 1970s as a chemical engineering postdoc working in the lab of Dr. Judah Folkman, a pediatric surgeon at the Boston Children’s Hospital. Langer was tasked with solving what many believed was an impossible problem—isolating angiogenesis inhibitors to halt cancer growth. Folkman’s vision of stopping tumors from forming their own self-sustaining blood vessels was compelling enough, but few believed it possible.

Langer encountered both practical and social challenges before his first breakthrough. One day, a lab technician accidentally spilled six months’ worth of samples onto the floor, forcing him to repeat the painstaking process of dialyzing extracts. Those months of additional work steered Langer’s development of novel microspheres that could deliver large molecules of medicine directly to tumors.

In the 1970s, Langer developed these tiny microspheres to release large molecules through solid materials, a groundbreaking proof-of-concept for drug delivery.Robert Langer

Langer then submitted the discovery to prestigious journals and was invited to speak at a conference in Michigan in 1976. He practiced the 20-minute presentation for weeks, hoping for positive feedback from respected materials scientists. But when he stepped off the podium, a group approached him and said bluntly, “We don’t believe anything you just said.” They insisted that macromolecules were simply too large to pass through solid materials, and his choice of organic solvents would destroy many inputs. Conventional wisdom said so.

Nature published Langer’s paper three months later, demonstrating for the first time that non-inflammatory polymers could enable the sustained release of proteins and other macromolecules. The same year, Science published his isolation mechanism to restrict tumor growth.

Langer and Folkman’s research paved the way for modern drug delivery.MIT and Boston Children’s Hospital

Even with impressive publications, Langer still struggled to secure funding for his work in controlling macromolecule delivery, isolating the first angiogenesis inhibitors, and testing their behavior. His first two grant proposals were rejected on the same day, a devastating blow for a young academic. The reviewers doubted his experience as “just an engineer” who knew nothing about cancer or biology. One colleague tried to cheer him up, saying, “It’s probably good those grants were rejected early in your career. Since you’re not supporting any graduate students, you don’t have to let anyone go.” Langer thought the colleague was probably right, but the rejections still stung.

His patent applications, filed alongside Folkman at the Boston Children’s Hospital, were rejected five years in a row. After all, it’s difficult to prove you’ve got something good if you’re the only one doing it. Langer remembers feeling disappointed but not crushed entirely. Eventually, other scientists cited his findings and expanded upon them, giving Langer and Folkman the validation needed for intellectual property development. As of this writing, the pair’s two studies from 1976 have been cited nearly 2,000 times.

As the head of MIT’s Langer Lab, he often shares these same stories of rejection with early-career students and researchers. He leads a team of over 100 undergrads, grad students, postdoctoral fellows, and visiting scientists, all finding new ways to deliver genetically engineered proteins, DNA, and RNA, among other research areas. Langer’s reputation is further bolstered by the many successful companies he co-founded or advised, like mRNA leader Moderna, which rose to prominence after developing its widely used COVID-19 vaccine.

Langer sometimes thinks back to those early days—the shattered samples, the cold rejections, and the criticism from senior scientists. He maintains that “Conventional wisdom isn’t always correct, and it’s important to never give up—(almost) regardless of what others say.”

Three ways health plans can engage, connect with, and delight their pregnant members to nurture goodwill, earn long-term trust, and foster loyal relationships that last.

The post Pregnant and Empowered: Why Trust is the Latest Form of Member Engagement appeared first on MedCity News.

Navigating the regulatory and ethical requirements of different medical data providers across many different countries, as well as safeguarding patient privacy, is a mammoth task that requires extra resources and expertise.  

The post AI is Revolutionizing Healthcare, But Are We Ready for the Ethical Challenges?  appeared first on MedCity News.

CDMO Avid Bioservices is being acquired by the private equity firms GHO Capital Partners and Ampersand Capital Partners. Avid specializes in manufacturing biologic products for companies at all stages of development.

The post Private Equity Is Picking Up Biologics CDMO Avid Bioservices in $1.1B Acquisition appeared first on MedCity News.

The vast majority of older adults want to age at home. To support that goal, doctors should encourage them to consider their care options — long before they need assistance.

The post Through Early Discussions About Elder Care, Doctors Can Empower Seniors to Age in Place appeared first on MedCity News.

While startups in highly regulated industries like healthcare and finance are almost certain to face heightened scrutiny, there are controllable factors that can offset these challenges.

The post The Startup Economy is Turbulent. Here’s How Founders Can Recognize and Avoid Common Pitfalls appeared first on MedCity News.

AbbVie schizophrenia drug candidate emraclidine failed to beat a placebo in two Phase 2 clinical trials. The drug, once projected to compete with Bristol Myers Squibb’s Cobenfy, is from AbbVie’s $8.7 billion acquisition of Cerevel Therapeutics.

The post AbbVie Drug Expected to Rival Bristol Myers’s New Schizophrenia Med Flunks Phase 2 Test appeared first on MedCity News.

As radiopharmaceuticals enter a new phase, industry leaders must rethink external services and internal capabilities to master the complexities of delivering advanced therapies.

The post Unlocking the Future of Radioligand Therapy: From Discovery to Delivering at Scale appeared first on MedCity News.

The Pew Charitable Trusts joined dozens of research, health care, and nonprofit stakeholders in urging President-elect Joe Biden to prioritize and strengthen the national response to antibiotic resistance.

In 2018, opioid overdoses in the United States caused one death every 11 minutes, resulting in nearly 47,000 fatalities. The most effective treatments for opioid use disorder (OUD) are three medications approved by the Food and Drug Administration (FDA): methadone, buprenorphine, and naltrexone.

Few correctional facilities in the United States have treatment programs for individuals with opioid use disorder (OUD), despite clear evidence that certain medications reduce the risk of overdose and death. Even in facilities where treatment is available, the COVID-19 pandemic has complicated efforts to provide such care.

Between 1999 and 2014, opioid use disorder (OUD) among pregnant women more than quadrupled, risking the health of the women—before and after giving birth—and their infants. As states grapple with COVID-19’s exacerbation of the opioid crisis, several are taking innovative steps to address the needs of high-risk groups, including low-income, postpartum patients with OUD.