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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Personalized Pharmaco-Lifestyle Interventions for Severe Mental Illnesses (LIFETRAIN)

Conditions: Severe Mental Illness; Depression / Major Depressive Disorder; Bipolar Disorder (BD); Schizophrenia

Interventions: Drug: Semaglutide (SEMA); Behavioral: Exercise module; Behavioral: Anti-inflammatory diet module; Behavioral: Sleep intervention module; Behavioral: Social prescribing module; Device: Closed-loop transcranial alternating current stimulation (CL-tACS); Behavioral: Structured lifestyle psychoeducation; Device: Sham CL-tACS

Sponsors: Ludwig-Maximilians – University of Munich

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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.

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.

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.”

Evaluating Crowdsourced Data Collection for Carceral Death Surveillance: Pilot Study Using Amazon Mechanical Turk

Background: People who are incarcerated face significantly higher health risks than the general population, yet deaths in custody remain underreported and poorly monitored by public health systems. Although the federal Death in Custody Reporting Act requires reporting of all deaths in correctional facilities to the US Department of Justice, reporting has been inconsistent, delayed, and often publicly inaccessible. Consequently, researchers have turned to press releases issued by correctional agencies as one of the few timely sources of information on deaths in custody. However, these press releases vary widely in content and structure, making standardized data extraction difficult. Crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) may offer a faster, low-cost method for gathering data, but their utility in this setting remains untested. Objective: This pilot study evaluated whether MTurk could be used to extract structured information from press releases about deaths in custody. Methods: We selected 144 press releases describing deaths between 2000 and 2023 from state prison systems and Immigration and Customs Enforcement. Each press release was assigned to 3 MTurk crowd workers (who were required to be English speaking and located in the United States), resulting in 432 individual responses. Workers were informed in advance that the task involved reviewing sensitive content related to deaths in custody. Crowd workers completed a 16-question form aligned with Death in Custody Reporting Act variables, including age, race and ethnicity, date of death, and facility location. Data quality was assessed using strict concordance (all 3 responses matched), 2-way concordance (2 of 3 responses matched), and qualitative review of common errors. Task completion time was also recorded. Sampling included complete subsets of selected press releases and a stratified subset from systems with more complex reporting formats. Results: All 144 entries were completed within 48 hours. However, agreement across crowd workers was low: strict concordance was 14.2% (20/144) for age, 12.3% (18/144) for race or ethnicity, and 11.4% (16/144) for date of birth. Qualitative review identified frequent errors, missing data, and inattentive or automated responses. Crowd workers often misinterpreted system-specific terminology or copied placeholders instead of extracting information from the source. The low agreement indicated that this baseline MTurk configuration produced insufficient data quality for more resource-intensive use. Conclusions: MTurk enabled rapid task completion but produced low-quality results when applied to extracting structured data from carceral press releases. These findings suggest that general crowdsourcing platforms are poorly suited to complex data abstraction tasks without additional training or oversight. With improved task design or support from artificial intelligence tools, crowdsourcing may help address gaps in the surveillance of deaths in custody. Long-term improvements will require consistent, transparent, and standardized reporting practices across correctional institutions.
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