Opinion: Marty Makary misunderstood something fundamental about the FDA

On Tuesday, Food and Drug Administration Commissioner Marty Makary resigned from his position, leaving a vacancy at the top of the agency to match the turnover already challenging its drug and biologics divisions.

It was just a year ago that Makary joined Health and Human Services Secretary Robert F. Kennedy Jr. and National Institutes of Health Director Jay Bhattacharya on the X platform to triumphantly announce in a 58-second video that the Covid-19 vaccine would no longer be routinely recommended for healthy pregnant women and children. The video exuded the elation of Covid response skeptics at long last grabbing the reins of power.

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Preclinical and Emerging Clinical Evidence Supporting the Translational Potential of GLP-1 Receptor Agonists for Treating Psychostimulant and Nicotine Use Disorders

Psychostimulant use disorder (PSUD) and nicotine use disorder (NUD) affect tens of millions of people worldwide, yet no clinically-approved medications exist for PSUD and existing NUD treatments have limited long-term efficacy. Recent preclinical evidence suggests that glucagon-like peptide-1 receptor (GLP-1R) agonists could be repurposed for treating PSUD and NUD. This review summarizes the current literature investigating the efficacy of GLP-1R agonists to reduce psychostimulant and nicotine-mediated behaviors in animal models of PSUD and NUD, respectively.

Psychiatric risk implications from behavioral and neural effects of adolescent exposure to environmental insecticides: a systematic review of rodent studies

Adolescence is a sensitive neurodevelopmental period marked by remodeling of brain circuits that support cognitive development and emotion and behavior regulation. These maturation processes heighten psychiatric vulnerability to environmental exposures, including to toxicants such as insecticides. Epidemiological studies show widespread adolescent insecticide exposure and increasingly link this with psychiatric outcomes, yet underlying neural mechanisms remain poorly understood. Preclinical studies can clarify these associations and identify insecticide-induced mechanisms that may disrupt neurodevelopment and produce consequent long-term behavioral outcomes.

Continuous Theta-Burst Stimulation Improves Long-Term Outcomes in Alcohol Use Disorder by Modulating a Craving-Related Dynamic Network

Alcohol use disorder (AUD) is a chronic, relapsing disorder characterized by craving. While continuous theta-burst stimulation (cTBS) shows promise for AUD, its long-term effects on drinking reduction and underlying mechanisms remain unclear. We evaluated the 12-month efficacy of right dorsolateral prefrontal cortex (DLPFC) cTBS for reducing alcohol consumption and identified associated neural and molecular mechanisms.

Musk v. Altman week 3: Musk and Altman traded blows over each other’s credibility. Now the jury will pick a side.

In the final week of the Musk v. Altman trial, lawyers traded blows over Elon Musk’s and OpenAI CEO Sam Altman’s credibility. Altman was grilled on his alleged history of lying and self-dealing involving companies that do business with OpenAI. But he fired back, painting Musk as a power-seeker who wanted to control the development of artificial general intelligence (AGI)—powerful AI that can compete with humans on most cognitive tasks. 

As evidence of their commitment to AI safety, OpenAI brought out a golden trophy of a donkey’s ass that was gifted to an employee after he was called a “jackass” for standing up to Musk’s plans to race toward AGI. 

Lawyers for both sides also presented their closing arguments, floating unflattering mugshot-style photos of Musk and Altman next to each other on a giant screen. Musk’s lawyer Steven Molo argued that Altman and OpenAI president Greg Brockman broke their promise to use money Musk donated to maintain OpenAI as a nonprofit that develops AI for the benefit of humanity. Instead, they created a for-profit subsidiary that made them extraordinarily wealthy.

OpenAI’s lawyer Sarah Eddy argued that Altman and Brockman never promised to keep OpenAI a nonprofit. She added that even though it’s been restructured, OpenAI remains a nonprofit dedicated to developing AI safely.

She claimed that Musk sued too late—and that his real motive is to sabotage a competitor to his own AI company, xAI, which he launched in 2023. 

Musk is asking the court to unwind the 2025 restructuring that converted OpenAI’s for-profit subsidiary into a public benefit corporation and to remove Altman and Brockman from their roles. He is also seeking as much as $134 billion in damages from OpenAI and Microsoft, to be awarded to OpenAI’s nonprofit. 

The jury will begin deliberating on Monday and deliver an advisory verdict as soon as next week. The jury verdict is not binding on the judge, who will decide the case.

If the judge rules in Musk’s favor, it could upend OpenAI’s race toward an IPO at a valuation approaching $1 trillion. Meanwhile, xAI is expected to go public as a part of Musk’s rocket company SpaceX as early as June, at a target valuation of $1.75 trillion.

Musk the power-seeker, Altman the liar.

In the first week of the trial, Musk said he was suing to save OpenAI’s mission to build AI safely for the benefit of humanity. This week, Altman denied Musk was a paladin of AI safety and painted him as a power-seeker who wanted to control OpenAI. 

Altman told the jury that in 2017, when Musk and other cofounders were discussing creating a for-profit arm, they asked Musk what would happen to his control over such an entity if he died. “Maybe the control of OpenAI should pass to my children,” Musk said, according to Altman.

Musk’s lawyer shot back, grilling Altman on his alleged history of lying. He pointed out that OpenAI’s former executives Ilya Sutskever and Mira Murati, and former board members Helen Toner and Tasha McCauley, all testified that Altman had lied to them. In 2023, Altman was briefly fired as CEO over the alleged behavior.

Molo also pressed Altman about his personal investments in startups that do business with OpenAI. Altman testified that he tried to steer OpenAI to buying power from the nuclear energy company Helion Energy, a third of which he owns.

(Last Friday, the US House oversight committee launched an investigation into Altman’s potential conflicts of interest. Attorneys general from more than a half-dozen states called for the Securities and Exchange Commission to review them.)

During his closing statement, Molo put Altman’s credibility on the stand again. “Imagine that you’re on a hike, and you come upon one of those wooden bridges that you see on a trail, and it’s over a gorge,” he said. “A woman standing by the entry to the bridge says, ‘Don’t worry—the bridge is built on Sam Altman’s version of the truth.’ Would you walk across that bridge?”

Altman, who sat behind his lawyers, looked up uneasily every time his name was mentioned. 

During her closing argument, Eddy fired back. Musk “never cared about the nonprofit structure,” she said. “What he cared about was winning.” 

Musk, though, was absent. Despite the judge’s order that he remain available, he flew to China with President Trump.

Did Altman promise to keep OpenAI a nonprofit?

During her closing argument, Eddy argued that no testimony or evidence showed any conditions on Musk’s donations, or any promises made by Altman and Brockman to keep the company a nonprofit. “No commitments or promises were made. No restrictions were placed on Mr. Musk’s donations,” she said.

Eddy added that it was evident Musk wasn’t truly committed to keeping OpenAI a nonprofit. She noted that in 2017, he tried to create a for-profit subsidiary and fought a bitter battle with Altman and Brockman to have control over it.

“I was not opposed to there being a small for-profit that provides funding to the nonprofit,” Musk told the jury earlier in the trial, “as long as the tail didn’t wag the dog.” 

Eddy then argued that Musk sued too late, filing in 2024 after the statutes of limitations on his claims ran out. In 2019, OpenAI created a for-profit subsidiary, under which employees and investors received a capped return on their investment. 

But Musk testified that he discovered OpenAI had abandoned its nonprofit mission only in 2022, when Microsoft was preparing to invest $10 billion in OpenAI—a deal that closed in 2023. “I was disturbed to see OpenAI with a $20B valuation,” he texted Altman after reading the news. “This is a bait and switch.”

Musk told the jury that the $20 billion valuation made him realize “the for-profit is the tail wagging the dog.” 

“The 2023 deal was different,” Molo hammered home during his closing argument.

Is OpenAI still a nonprofit committed to its mission?

A central question raised in the last week of trial was whether OpenAI remains a nonprofit committed to developing AGI safely for the benefit of humanity. Eddy, the OpenAI lawyer, argued that the nonprofit still controls the for-profit and seeks to “help AGI turn out well for humanity.” “The OpenAI nonprofit is the best-resourced nonprofit in the world,” thanks to the for-profit, she added.

Molo countered that while the OpenAI’s nonprofit nominally controls the company, it does not do so in practice. OpenAI’s nonprofit and for-profit are controlled by the same people—seven of the nonprofit’s eight board members are on the for-profit’s board. The nonprofit hired employees only a month before the trial started and does work only in grant-making rather than AI research. 

Molo played a video interview of Altman saying that the nonprofit board’s failure to fire him in 2023 was “its own kind of governance failure.”

“We’re left with this nonprofit that doesn’t have any voice,” Jill Horwitz, a law professor at Northwestern University who studies nonprofits, told MIT Technology Review. “It doesn’t have much money, and OpenAI doesn’t think it has any obligation to fund it. It barely has a staff,” she says. “It’s unclear how on earth the nonprofit is supposed to exercise its duties and control the entire company.” 

Civil society groups and policymakers have spoken out against OpenAI’s restructuring over the years. So has Musk, although his own stake in the AI race makes him a dubious champion for the public interest. 

“The public interest in the nonprofit loses, no matter who wins or loses this trial,” says Horwitz.

Jackass for AI safety

Despite US District Judge Yvonne Gonzalez Rogers’s warning during the first week that this trial was not about AI safety, the issue stole the show again. Throughout the trial, the lawyers from both sides traded barbs over the safety track records of ChatGPT (which has allegedly caused teen suicides) and Grok (which has flooded X with porn).  

On the last day of testimony, OpenAI’s lawyer Bradley Wilson handed the judge a small golden trophy of a donkey’s ass, inscribed: “Never stop being a jackass for safety.” 

The trophy belonged to Joshua Achiam, OpenAI’s chief futurist. He testified that he’d warned, when Musk announced in 2018 that he was leaving OpenAI to race toward building AGI, that speed could compromise safety. Musk snapped and called him a “jackass,” said Achiam. His colleagues, including Dario Amodei, now CEO of Anthropic, gave him the trophy to enshrine the diss.

“I don’t want it,” said the judge.
The shenanigans spilled out into the street too. In front of the Oakland courthouse, a protester paraded around wearing a costume of Musk holding a bag of ketamine and driving a Cybertruck. Another held a photo of Sam Altman and a poster reading, “Stop AGI or we’re all gonna die.”

Factors Influencing the Initiation and Continued Engagement of Digital Mental Health Tools Among Adults: Theory of Planned Behavior–Informed Systematic Review

Background: Digital mental health tools (DMHTs) offer scalable support, but engagement varies. Understanding the shapes of initiation and ongoing use is essential for effective design and implementation. Objective: This study aims to synthesize determinants of adults’ initiation and engagement with DMHTs, organized through two lenses: (1) psychological factors aligned with the theory of planned behavior (TPB) and (2) design and access features. Methods: A systematic search of 9 databases (June 2025) identified qualitative and mixed methods primary studies reporting end-users’ experiences with DMHTs. Studies were screened and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Quality appraisal used quality assessment with diverse studies (QuADS). Data were synthesized using a framework-guided thematic approach, mapping findings to TPB constructs and complementary design and access domains. Results: A total of 22 studies met inclusion criteria. Findings clustered into 2 interdependent domains. TPB constructs explained how beliefs, social expectations, and perceived control shaped decisions to start and persist with DMHTs. Design and access features frequently acted through these same pathways, especially by altering perceived behavioral control (PBC), with cost, connectivity, device constraints, and time flexibility affecting feasibility, with content design and privacy shaping perceived value and trust. Perceived fit (goals, cultural or linguistic relevance, and routine alignment) consistently influenced both initiation and continuation. Several features operated bidirectionally; depending on context, the same feature could facilitate or hinder engagement. Conclusions: Engagement with DMHTs is jointly determined by users’ beliefs and the design and access conditions within which tools are offered. Implementation should pursue a dual strategy, strengthening willingness to seek support (addressing attitudes, norms, and perceived control) while engineering low-effort, trustworthy, and context-appropriate experiences. Priorities include equity-focused policies (data costs, devices, and connectivity), transparent data practices, co-design with diverse communities, and consistent, theory-informed outcome measures.
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STAT+: FDA drug center head Tracy Beth Høeg leaves as agency faces leadership vacuum

WASHINGTON — The acting director of the Food and Drug Administration’s drug center, Tracy Beth Høeg, is leaving the agency, according to an email obtained by STAT. The news comes days after Commissioner Marty Makary handed in his resignation. 

Michael Davis, currently the center’s deputy director, will take over as acting director. The FDA and Høeg did not immediately respond to requests for comment. Reuters and Bloomberg first reported the news. 

Katherine Szarama, the acting director of the Center for Biologics Evaluation and Research, is also leaving the agency. Karim Mikhail, who joined the FDA in 2025 as a senior adviser in the office of the commissioner, will replace her. He was previously CEO of the pharmaceutical company Amarin. 

Continue to STAT+ to read the full story…

Reducing Intrusive Trauma Memories Using a Brief Mental Imagery Competing Task Intervention: Case Series of Trauma-Exposed Women in Iceland

Background: There is a need for scalable and simple interventions for trauma-exposed people. In this case series, we built on our previous case study and case series findings and further explored the use and potential effectiveness of a brief novel intervention to reduce the number of past intrusive memories of trauma. The imagery competing task intervention consists of a memory reminder and the visuospatial task Tetris played with mental rotation, targeting 1 intrusive memory at a time. Here, we test remote delivery of the intervention, including guidance from researchers without specialist mental health training, in a sample of women in Iceland with current intrusive memories from trauma. Objective: In a case series of trauma-exposed women, we aimed to explore whether this brief novel intervention reduces the number of established intrusive memories (primary outcome) and improves general functioning and symptom reduction in posttraumatic stress, depression, and anxiety (secondary outcomes). The acceptability of the intervention along with adaptations, that is, delivery by psychology students without specialist mental health training and digital delivery, was explored. Methods: Participants (N=8) monitored the number of intrusive memories from an index trauma (occurring 3‐16 years previously) in a daily diary at baseline, during the intervention, and postintervention at 1-month and 3-month follow-ups. The intervention was delivered digitally with guidance from clinical psychologists or psychology students. A repeated AB design was used (“A”: preintervention baseline, “B”: intervention phase). Intrusions were targeted one by one, creating repetitions of an AB design (ie, length of baseline “A” and intervention “B” varied for each memory). Results: The number of intrusive memories reduced for all participants from the baseline phase compared with the intervention phase, although the reduction was minimal for 2 participants (6.3%‐93%). The number of intrusive memories continued to reduce for 6 out of 8 participants (58%‐100% reduction at 1-month follow-up; 72%‐100% reduction at 3-month follow-up). Symptoms of posttraumatic stress, depression, and anxiety were reduced for most participants postintervention and continued to decrease during the follow-up periods. Functioning was improved for 7 of the 8 participants from baseline to postintervention and continued to improve at the follow-up assessments for 3 participants. The intervention delivered digitally and partly by students was perceived to be an acceptable way to reduce the frequency of intrusive memories by all participants (mean rating 9.5 out of 10). Conclusions: Data from this case series of traumatized women provide preliminary evidence for the effectiveness of this novel brief intervention in reducing intrusive memories of trauma occurring several years ago and in improving functioning and reducing core symptom burden. This study will inform a randomized controlled trial of this novel intervention, which may have considerable implications for large-scale clinical management of traumatized populations. Trial Registration: ClinicalTrials.gov NCT04209283; https://clinicaltrials.gov/study/NCT04209283 International Registered Report Identifier (IRRID): RR2-10.2196/29873
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Large Language Models and Their Applications in Mental Health: Scoping Review

Background: Large language models (LLMs) are poised to transform mental health care, offering advanced capabilities in diagnosis, prognosis, and decision support. Since their inception, numerous mental health-focused LLMs have emerged in the scientific literature, reflecting the growing interest in leveraging these models across various clinical applications. With a broad range of models available, diverse optimization strategies, and multiple use cases, reviewing the current landscape is critical to understanding where future impact lies. Objective: This study aimed to conduct a scoping review investigating the use of LLMs in mental health across diagnostic, prognostic, and decision support tasks. Methods: We screened 3121 papers from PubMed, Scopus, and Web of Science for studies published between January 2023 and October 2025, using terms related to LLM and mental health. After removing duplicates, 2 reviewers (MCL and WWBG) independently screened the studies, with a third (JJK) to resolve conflicting opinions. We extracted and synthesized information on the models, use cases, datasets, and adaptation methods from selected papers. Results: In total, 41 papers were selected. Many studies included evaluations on OpenAI’s GPT series applications: GPT-4 (24 studies, 58.5%) and GPT-3.5 (16 studies, 39%). Others included Bidirectional Encoder Representations from Transformers-derived models (9 studies, 22%), LLaMA (8 studies, 19.5%), and RoBERTa-derived models (6 studies, 14.6%). While all studies initially applied out-of-the-box LLMs, several adapted them through few-shot learning or fine-tuning to better align with specific research goals. The most common use case was in diagnostics (31 studies, 75.6%), while the most common target condition was depression (11 studies, 26.8%). While many studies reported superior performance of LLMs, only a minority of studies (13 studies, 31.7%) validated LLM performance against clinician assessments using real patient data, with the majority relying on proxy outcomes such as clinical vignettes, examination questions, or social media posts. Conclusions: Despite rapid growth and diversity of LLM applications in mental health, the field remains nascent and exploratory. Future developments must emphasize consistent model adaptation procedures to ensure safety and clinical workflow alignment. Models must also be evaluated on robust evaluation criteria by using standardized protocols and real clinical outcome measures.
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