Markers of neuroinflammation in the CSF of patients with difficult to treat psychiatric disease

IntroductionThe immune system is recognized as participating in the pathophysiology of psychiatric disease and there is renewed interest in identifying biomarkers of this immune activation. MethodsWe measured serum and cerebral spinal fluid (CSF) autoantibodies with other routine and novel markers of neuroinflammation, including CSF cytokines in patients with atypical psychiatric presentations of both psychotic and mood disorders (n=35). Their markers were compared with cohorts of non-inflammatory neurological disease (NIND) controls (n=18), patients with central nervous system (CNS) viral infection (n=22) and autoimmune encephalitis (AE; n=40). ResultsThe most common autoantibody detected in the serum of patients with psychiatric disease were anti-nuclear antibodies followed by thyroid autoantibodies. Few atypical psychiatric patients had abnormal conventional CSF markers of neuroinflammation (pleocytosis, oligoclonal bands, abnormal CSF IgG: albumin ratio). Further analysis of CSF revealed elevation of ITAC/CXCL11 in the psychiatric cohort. TARC/CCL17 was lower in the psychiatric cohort compared to other groups in a random-effects multinomial model, despite no significant differences on univariate analysis. When the values of CSF cytokines were examined in individual patients, six patients (17%) had at least one CSF cytokine greater than four standard deviations above the mean of the NIND cohort group. Extensive serological evaluation revised the diagnoses of six (17%) of our psychiatric group, and these patients’ showed improvement with immunosuppression. ConclusionOur results suggest a subset of people with atypical psychiatric disease may have a predominant immune contribution. This highlights the need for reevaluation and further consideration of differential diagnosis where patient presentations are not clinically typical, do not respond to conventional psychotropic treatment, or if other risk factors for autoimmunity are present.

When the market asks no price: AI chatbot interaction as psychic market disruption

The rapid adoption of AI chatbots for emotional support and quasi-therapeutic interaction raises questions that existing clinical and regulatory frameworks are not equipped to address. This Perspective applies the psychic arbitrage framework—which reconceptualizes defense mechanisms as energy-conversion operations on internal psychic markets—to analyze the specific transactional distortions produced by chatbot interaction. The framework identifies four dysfunctions: liquidity illusion (apparent emotional processing without genuine containment), market-making blockage (narcissistic reflection replacing transformative engagement), closure of arbitrage circuits (path-dependent externalization displacing autonomous elaboration), and repertoire degradation (progressive intolerance of costly but productive transactions). The article proposes that chatbots trained via Reinforcement Learning from Human Feedback (RLHF) produce a functional analogue of the Dark Triad profile—narcissistic mirroring, Machiavellian retention, and psychopathic detachment—as systematic architectural output regularities, not personality attribution. Preliminary converging evidence from mechanistic interpretability, formal reward-learning analysis, clinical reports, and user behavior studies is broadly consistent with these predictions but does not yet demonstrate direct long-term causal effects. The framework offers clinicians a transactional vocabulary for assessing AI-related risk and generates falsifiable predictions for future research.

Distinct sleep-disordered breathing phenotypes in elderly patients with depressive disorder: links to hypoxemia severity and inflammatory burden

ObjectiveTo identify sleep-disordered breathing phenotypes in older adults with depressive disorder and obstructive sleep apnea-hypopnea syndrome (OSAHS) and to evaluate their associations with systemic inflammation.MethodsElderly patients with depressive disorder and OSAHS were consecutively enrolled from January to December 2025. A Gower distance matrix was constructed and phenotypes were derived using partitioning around medoids (PAM; k-medoids), with k selected based on silhouette, elbow criteria, and clinical interpretability. Blood samples were collected the morning after PSG to measure serum high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α).ResultsAmong 198 participants, k = 2 was selected based on internal validity metrics (silhouette and elbow) and clinical interpretability. Compared with the lower-hypoxia/less-severe OSAHS phenotype (Cluster 1, n = 92), the high-hypoxia/severe OSAHS phenotype (Cluster 2, n = 106) had higher BMI, HAMD-17, and ESS, and more severe AHI/ODI/TS90 with a lower LSaO2. The high-hypoxia/severe OSAHS phenotype also showed higher hs-CRP, IL-6, IL-1β, TNF-α, WBC, neutrophils, and NLR. The inflammatory burden score was higher in the high-hypoxia/severe OSAHS phenotype (β = 1.10 SD unadjusted; β = 1.67 SD adjusted for age, sex, BMI, comorbidity, smoking, drinking, education, and MoCA; β = 1.45 SD further adjusted for HAMD-17 and ESS; all P < 0.001). In men (n = 135), PAM clustering similarly identified two phenotypes differentiated mainly by AHI/ODI, with selective elevations in IL-1β and neutrophil counts.ConclusionsThe high-hypoxia/severe OSAHS phenotype in older adults with depressive disorder is independently associated with a higher systemic inflammatory burden.

Federal appeals court blocks mailing of abortion pill mifepristone

A federal appeals court has restricted access to one of the most common means of abortion in the U.S. by blocking the mailing of mifepristone prescriptions.

Friday’s unanimous ruling from a three-judge panel of the New Orleans-based 5th U.S. Circuit Court of Appeals is requiring that the abortion pill be distributed only in person and at clinics, overruling regulations set by the federal Food and Drug Administration.

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Opinion: What do medical students think about their education?

Below is a lightly edited, AI-generated transcript of the “First Opinion Podcast” interview with Tiffany Onyejiaka and Lauren Rice. Be sure to sign up for the weekly “First Opinion Podcast” on Apple PodcastsSpotify, or wherever you get your podcasts. Get alerts about each new episode by signing up for the “First Opinion Podcast” newsletter. And don’t forget to sign up for the First Opinion newsletter, delivered every Sunday.

Torie Bosch: Amid the rise of the Make America Healthy Again movement, medical school has become something of a battleground. Health secretary Robert F. Kennedy Jr. and others have argued that future doctors need to better understand nutrition and preventive care. But what do medical students themselves think about that claim?

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Opinion: STAT readers on MAHA activists, perimenopause, and diversity in medical school

First Opinion is STAT’s platform for interesting, illuminating, and provocative articles about the life sciences writ large, written by biotech insiders, health care workers, researchers, and others.

To encourage robust, good-faith discussion about issues raised in First Opinion essays, STAT publishes selected Letters to the Editor received in response to them. You can submit a Letter to the Editor here, or find the submission form at the end of any First Opinion essay.

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Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI’s models

In the first week of the landmark trial between Elon Musk and OpenAI, Musk took the stand in a crisp black suit and tie and argued that OpenAI CEO Sam Altman and president Greg Brockman had deceived him into bankrolling the company. Along the way, he warned that AI could destroy us all and sat through revelations that he had poached OpenAI employees for his own companies. He even confessed, to some audible gasps in the courtroom, that his own AI company, xAI, which makes the chatbot Grok, uses OpenAI’s models to train its own. 

The federal courthouse in Oakland, California, was packed with armies of lawyers carrying boxes of exhibits, journalists typing away at their laptops, and a handful of concerned OpenAI employees. Outside, protesters lined the streets, carrying signs urging people to quit ChatGPT, boycott Tesla, or both. Musk looked calm and comfortable, slipping in the occasional quip in his distinct South African accent. But he also was full of remorse. 

“I was a fool who provided them free funding to create a startup,” Musk told the jury. He said when he cofounded OpenAI in 2015 with Altman and Brockman, he was donating to a nonprofit developing AI for the benefit of humanity, not to make the executives rich. “I gave them $38 million of essentially free funding, which they then used to create what would become an $800 billion company,” he said.

Musk is asking the court to remove Altman and Brockman from their roles and to unwind the restructuring that allowed OpenAI to operate a for-profit subsidiary. The outcome of the trial 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.

This week’s testimony revolved around a central question of the trial: why Musk is suing OpenAI. Musk argued he was trying to save OpenAI’s mission to develop AI safely by restoring the company to its original nonprofit structure. OpenAI’s lawyer, William Savitt, who once represented Musk and his electric-car company Tesla, countered that Musk was “never committed to OpenAI being a nonprofit” and instead was suing to undermine his competitor. 

Who is the steward of AI safety?

During his direct examination early in the week, Musk painted himself as a longtime advocate of AI safety. He said he cofounded OpenAI to create a “counterbalance to Google,” which was leading the AI race at the time. He said that when he asked Google cofounder Larry Page what happens if AI tries to wipe out humanity, Page told him, “That will be fine as long as artificial intelligence survives.” 

“The worst-case scenario is a Terminator situation where AI kills us all,” Musk later told the jury.

Savitt stood at the lectern and argued that Musk was not a “paladin of safety and regulation.” As he cross-examined Musk in his sharp, surgical cadence, Savitt pointed out that xAI sued the state of Colorado in April over an AI law designed to prevent algorithmic discrimination. 

Musk’s lawyer, Steven Molo, sprang to his feet to object. He asked the judge if he, too, could weigh in on ChatGPT’s safety record. 

The lawyers then entered a heated debate about who was the true guardian of AI safety. 

The sparring continued the next morning. “We all could die as a result of artificial intelligence!” said Molo, suggesting that OpenAI could not be trusted to build AI safely.

“Despite these risks, your client is creating a company that’s in the exact space,” Judge Yvonne Gonzalez Rogers said sternly, referring to xAI. “I suspect there’s plenty of people who don’t want to put the future of humanity in Mr. Musk’s hands.”

When the lawyers began talking over each other, the judge snapped. “This is not a trial on whether or not artificial intelligence has damaged humanity,” she said. 

When did Musk think he was being duped?

As Savitt continued to cross-examine Musk, he pressed on the idea that Musk had never been committed to keeping OpenAI a nonprofit. He also claimed that Musk waited too long to sue OpenAI, filing after the statute of limitations ran out. 

Musk explained why he sued in 2024 rather than earlier, describing “three phases” in his views of OpenAI. In phase one, he was “enthusiastically supportive” of the company.” In phase two, “I started to lose confidence that they were telling me the truth,” he said. In phase three, “I’m sure they’re looting the nonprofit.” 

In 2017, Musk and other OpenAI cofounders discussed creating a for-profit subsidiary to raise enough capital to build artificial general intelligence—powerful AI that can compete with humans on most cognitive tasks. Musk wanted a majority interest in the subsidiary and the right to choose a majority of the board members. He also pitched having Tesla acquire OpenAI. (He left OpenAI in 2018.)

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

But it was only in late 2022, Musk testified, that he “lost trust in Altman” and his commitment to keeping the company a nonprofit. The key moment came, he said, when he learned that Microsoft would invest $10 billion in OpenAI. 

“I texted Sam Altman, ‘What the hell is going on? This is a bait and switch,’” he told the jury. Microsoft would give $10 billion only if it expected “a very big financial return,” he said.

Is Musk just trying to kill competition?

But Savitt argued that Musk was really suing to undermine OpenAI as a competitor to his empire of tech companies. While he was on the board of OpenAI, Musk was also running Tesla and his brain-implant company, Neuralink. He founded xAI in 2023.

Savitt pulled up an email that Musk had sent to a Tesla vice president in 2017 after hiring Andrej Karpathy, a founding member of OpenAI, to work at Tesla.“The OpenAI guys are gonna want to kill me. But it had to be done,” he wrote.

When asked about it, Musk was flustered. He claimed Karpathy had already decided to leave OpenAI when he recruited him to work at Tesla. “I believe it’s a free world,” he said.

Savitt pulled up another email that Musk sent to a cofounder at Neuralink in 2017. He wrote that they could “hire independently or directly from OpenAI.” When pressed about it, he sounded frazzled. “It’s a free country,” he said. “I can’t restrict their ability to hire people from other companies.” 

Savitt also pointed out that Tesla, SpaceX, Neuralink, and X were socially beneficial for-profit companies, like OpenAI. He stressed that xAI was also a closed-source, for-profit company.

But Musk claimed that xAI was not a real competitor to OpenAI. “We’re not currently tracking to reach AGI first,” he told the jury. 

In fact, Musk admitted that xAI uses OpenAI’s technology. In response to Savitt’s relentless questioning, he said xAI “partly” distills OpenAI’s models. Some people in the courtroom gasped. 

Distillation is a technique where a smaller AI model is trained to mimic the behavior of larger, more capable models, so it can run faster and more cheaply while performing nearly as well. But OpenAI and other AI companies have pushed back against the practice. In February, OpenAI accused the Chinese AI company DeepSeek of distilling its AI models. In August 2025, Wired reported that Anthropic had blocked OpenAI’s access to Claude for violating the company’s terms of service, which prohibit, among other things, reverse-engineering its services and building competing products. 

“It is standard practice to use other AIs to validate your AI,” argued Musk.

Next week, Stuart Russell, a computer scientist at UC Berkeley, will testify about AI safety. Brockman, who has been taking notes during Musk’s testimony, will also testify.

This story is part of MIT Technology Review’s ongoing coverage of the Musk v. Altman trial. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting.

Applicable Scenarios, Desired Features, and Risks of AI Psychotherapists in Depression Treatment From the Patient’s Perspective: Exploratory Qualitative Study

Background: Depression is a pervasive global mental health issue, yet access to trained professionals remains severely limited. With the rapid advancement of artificial intelligence (AI), digital tools are increasingly seen as a viable way to address this shortage. However, questions remain about how digital platforms for mental health care can be effectively designed. Objective: This study aimed to investigate, from an end user’s (patient’s) perspective, the potential use scenarios, desired features, and perceived risks of AI psychotherapists in depression treatment, providing design guidelines for their development. Methods: A grounded theory approach was applied to analyze qualitative responses from 452 individuals recruited via Amazon Mechanical Turk. Data were collected through a scenario-based online survey on AI-assisted depression treatment administered between March 2023 and May 2023. Participants responded to 3 open-ended questions regarding the potential use of AI in treating depression, the characteristics expected from an AI psychotherapist, and the associated perceived risks, along with demographic, control, and contextual measures. The open-ended responses were inductively coded into themes, with intercoder reliability established (Cohen κ=0.80). In addition, variations in themes were further examined across participant profiles, including social stigma, current depression severity, trust in an AI psychotherapist, and privacy awareness. Results: Participants envisioned AI psychotherapists across 5 primary scenarios: diagnosis, treatment, consultation, self-management, and companionship. Key desired features include professionalism, warmth, precision care, empathy, remote services, active listener, personalization, flexible treatment options, patience, trustworthiness, and basic treatment alternative, while critical concerns include diagnostic inaccuracy, treatment errors, privacy breach, lack of human interaction, technical malfunctions, and lack of emotional engagement. Based on these findings, a general MoSCoW (must have, should have, could have, and won’t have) prioritization framework was proposed to serve as a conceptual starting point for future AI system design and empirical validation in mental health care. Notably, feature prioritization varied across user profiles: individuals with higher stigma placed greater emphasis on privacy protection, those with more severe depression prioritized precision care and timely access, low-trust users de-emphasized remote services, and privacy-sensitive individuals showed reduced preference for features requiring extensive data disclosure. These patterns highlight the need for context-sensitive design. Conclusions: This study provides a patient-centered framework for designing AI psychotherapists and complements the existing literature by highlighting the importance of balancing clinical effectiveness with relational considerations. The findings offer actionable guidelines for designing AI mental health care tools that are aligned with user expectations and sensitive to individual differences.
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OTX-202 Smartphone App to Reduce Suicidal Ideation Among High-Risk Transition-Age Youth: Open-Label, Single-Arm, Phase 1 Clinical Trial

<strong>Background:</strong> The transition from adolescence to adulthood (18 to 25 years) is associated with an increased risk of suicidal ideation and behaviors. Suicide-focused cognitive behavioral therapies (CBTs) have been shown to significantly reduce suicidal ideation and behaviors but are not widely available to high-risk individuals. Digital therapeutics could improve access to these treatments. <strong>Objective:</strong> This study aimed to evaluate the acceptability, safety, and potential efficacy of OTX-202 among transition-age youth (18 to 25 years) receiving mental health care outside an inpatient hospital setting. <strong>Methods:</strong> In this phase 1 single-arm clinical trial, 59 transition-age youth with recent suicidal ideation or suicide attempts used OTX-202, a smartphone app designed to deliver suicide-focused CBT, concurrently with usual outpatient mental health care. After baseline, eligible patients completed 12 weekly assessments of suicidal ideation, depression, and anxiety. <strong>Results:</strong> From baseline to week 12, participants reported statistically significant, large reductions in suicidal ideation (mean difference –5.1, 95% CI –6.5 to –3.7; <i>d</i>=0.95). In total, 3 (5.1%; 95% CI 0%-11.2%) participants reported suicide attempts. Reductions in suicidal ideation and suicide attempt rates were consistent with results from previously published randomized clinical trials of suicide-focused CBTs. Participants rated OTX-202 in the 97th percentile of usability and completed a mean of 9.0 (SD 3.5) of 12 app modules, supporting the app’s acceptability. There were no patient deaths, device-related events, or severe adverse events, supporting the app’s safety. <strong>Conclusions:</strong> Results support the safety, acceptability, and potential efficacy of OTX-202 for reducing suicide risk among transition-age youth. <strong>Trial Registration:</strong> ClinicalTrials.gov NCT06008132; https://clinicaltrials.gov/study/NCT06008132