<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
BackgroundAlthough music listening and transcranial direct current stimulation (tDCS) alone have certain effects in the treatment of insomnia, the sleep regulatory effects and neural mechanisms of the combined treatment in patients with insomnia disorder (ID) are unclear. This study aimed to investigate the efficacy of combined bifrontal-tDCS (F3: anode, F4: cathode) with music listening in patients with ID using functional near-infrared spectroscopy (fNIRS).Methods76 ID patients were randomly divided into an intervention group (n=38) and a control group (n=38), and received 4 weeks of a total of 20 sessions of music + tDCS therapy and music + sham tDCS therapy (30-second stimulation with fade-in/fade-out to mimic somatic sensations), respectively. The Pittsburgh Sleep Quality Index Scale (PSQI), Self-rating Depression Scale (SDS), Self-rating Anxiety Scale (SAS), and Perceived Stress Scale (PSS-14) were compared between the two groups before and after treatment. Oxy-haemoglobin (HbO2) concentration and functional connectivity (FC) were assessed during the verbal fluency task using fNIRS.ResultsCompared with the control group, the PSQI total score (mean difference: -2.57 points, 95% CI: -4.43 to -0.71, p = 0.001), PSQI sub-scores except “sleep disturbance and daytime dysfunction”, SDS and SAS scores of the intervention group improved significantly after treatment. It was observed by fNIRS that the HbO2 concentration in the medial prefrontal cortex (mPFC), left dorsolateral prefrontal cortex (DLPFC), right ventrolateral prefrontal cortex, and right superior frontal cortex (SFC) increased significantly after treatment in the intervention group but was not superior to the control group. In addition, the FC enhancement of left SFC-left DLPFC and left SFC-mPFC after treatment was significantly better in the intervention group than in the control group, and the PSQI improvement was positively correlated with the FC enhancement of channel-averaged and left SFC-right DLPFC.ConclusionsCombining bifrontal-tDCS with music listening is more helpful in improving sleep quality and prefrontal functional connectivity in ID patients compared with music listening alone. For ID patients, music electrical stimulation headphones may be a safe, effective, and convenient new treatment strategy.Clinical trial registrationhttps://www.chictr.org.cn/, identifier ChiCTR2400086233.
Preclinical and phase 1/2 trial data show that anti-CD38 monoclonal antibody treatment restores platelet counts in patients with immune thrombocytopenia by increasing nicotinamide adenine dinucleotide (NAD+) levels, and low-dose oral treatment with the NAD+ precursor nicotinamide mononucleotide can similarly increase platelet counts without serious adverse effects.
I’ve been waiting for health secretary Robert F. Kennedy Jr. to do something big on oversight of what I call pop peptides, like BPC-157 and GHK-Cu. He had long signaled that he was going to free such peptides from what he saw as a past, misguided FDA that had banned them in 2023.
It’s finally happened — and the way it went down shook me up a bit.
For a few years, a loophole in compounding rules had allowed specialty pharmacies to make and market these peptides. It effectively meant that substances nominated for compounding — even unproven drugs — could be made and marketed by qualified pharmacies while the FDA pondered the nominations. But in 2023, the Food and Drug Administration rightly moved peptides to a no-compounding-allowed status called Category 2 due to concerns about safety and lack of clinical trial data. Now Kennedy is working to undo that with major risks to the public.
WASHINGTON — The Food and Drug Administration on Tuesday announced efforts to make clinical trials more efficient, starting by reviewing data in real time from trials conducted by AstraZeneca and Amgen.
The agency also asked the public to weigh in on a potential pilot program to work with companies that use AI to enhance safety monitoring and medication dose selections, identify safety signals, and improve patient recruitment in clinical trials.
AstraZeneca is conducting a Phase 2 trial of its combination therapy for patients with an aggressive form of lymphoma. The trial will take place at the University of Texas MD Anderson Cancer Center and the University of Pennsylvania. Amgen is conducting a Phase 1b trial of its treatment for small cell lung carcinoma. The trials will rely on a real-time data platform built by Paradigm Health.
Why does it take a new drug 10 years, on average, to come to market? Part of the reason lies in the dead time in the process.
Historically, trials have required tedious tabulations and repeated application submissions between phases, which is why 45% of the time from a Phase 1 trial until final submission is spent without any ongoing clinical trial in progress — idle time in the system.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Elon Musk and Sam Altman are going to court over OpenAI’s future
Elon Musk and OpenAI CEO Sam Altman head to trial this week in a case with sweeping consequences. Ahead of OpenAI’s IPO, the court could rule on whether the company can exist as a for-profit enterprise. It could even oust its leadership.
Musk, an OpenAI co-founder, claims he was deceived into bankrolling the firm under false pretenses. He’s seeking $134 billion in damages, the removal of Altman and president Greg Brockman, and the company’s restoration to a non-profit.
In a celebrated South Park episode, a community of gnomes sneak out at night to steal underpants. Why? The gnomes present their pitch deck. “Phase 1: Collect underpants. Phase 2: ? Phase 3: Profit.” It’s a business plan that captures the current state of AI.
Companies have built the tech (Step 1) and promised transformation (Step 3). But how they get there is still a big question mark. Read about the potential paths forward.
—Will Douglas Heaven
This story originally appeared in The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.
Welcome to the era of weaponized deepfakes
For years, experts have warned that deepfakes could be deployed in malicious ways. These dangers are now here.
Cheap, accessible models now produce weaponized deepfakes—from sexually explicit images to political propaganda—that look startlingly real. They’re already inciting violence, changing minds, and sowing mistrust, with women and marginalized groups disproportionately affected.
Weaponized deepfakes are on our list of the 10 Things That Matter in AI Right Now, MIT Technology Review’s guide to what’s really worth your attention in the busy, buzzy world of AI.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 OpenAI has ended its exclusive partnership with Microsoft The new deal allows OpenAI to court rivals such as Amazon. (Reuters $) + Microsoft will still license OpenAI’s tech, but no longer exclusively. (NYT $) + OpenAI is missing key growth targets ahead of its IPO. (WSJ $)
2 Google has signed a classified AI deal with the Pentagon It permits AI use for “any lawful government purpose.” (The Information $) + Over 600 Google workers had called for a block on the deal. (QZ) + AI firms are set to train military versions of their models on classified data. (MIT Technology Review)
3 The EU has told Google to open Android to AI rivals It wants to end Gemini’s built-in advantage. (Ars Technica) + Google calls the move an “unwarranted intervention.” (WSJ $) + A final decision is expected by the end of July. (Reuters $)
4 OpenAI is reportedly developing an AI-first smartphone It would replace apps with agents. (TechCrunch) + Qualcomm and MediaTek may be developing its processors. (Gizmodo)
5 A brain implant for depression is moving into human testing The FDA has approved a human study of the device. (Wired $) + BCIs have thus far struggled to reach the market. (MIT Technology Review)
6 A populist backlash against AI is gaining momentum in rural America From Indiana to Idaho, voters are pushing back against the technology. (NYT $) + Anti-AI protests are expanding worldwide. (MIT Technology Review)
7 DeepSeek has priced its new model 97% below OpenAI’s GPT-5.5 It aims to attract more enterprises, developers, and agent-based users. (SCMP) + Here are three reasons why DeepSeek V4 matters. (MIT Technology Review)
8 AI now generates a third of new websites A study found it’s making the web more cheery and less verbose. (404 Media)
9 Top talent is leaving Big Tech to launch their own AI startups Meta, Google, and OpenAI are facing a brain drain. (CNBC)
10 Taylor Swift is trademarking her voice and image The Grammy winner has been the target of numerous deepfakes. (NBC News) + A growing number of celebrities are fighting AI with trademarks. (BBC)
Quote of the day
“The reality is people don’t like him.”
—Judge Yvonne Gonzalez Rogers reacts to prospective jurors confessing their negative views of Elon Musk ahead of his legal battle with Sam Altman, The Verge reports.
One More Thing
How covid conspiracy theories led to an alarming resurgence in AIDS denialism
When Joe Rogan falsely declared that “party drugs” were an “important factor in AIDS,” several million people were listening. He also asserted that AZT, the earliest drug used to treat AIDS, killed people “quicker” than the disease itself—another claim that has been disproven.
Such comments illustrate an unmistakable resurgence in AIDS denialism: a false collection of theories arguing either that HIV does not cause AIDS or that there is no such thing as HIV at all. By the dawn of the millennium, these claims had largely fallen out of favour. That changed when the coronavirus arrived.
BackgroundNeck pain correlates with multiscale brain abnormalities, but cross-scale mechanisms of acupuncture analgesia are unclear. This study aimed to: (1) Explore differential modulation of thalamic functional networks by verum vs. sham acupuncture; (2) Examine associations between functional connectivity changes and micro gene expression to unravel its multiscale mechanisms.MethodsA total of 130 participants were initially enrolled, and 100 eligible neck pain patients were randomized 1:1 to the verum (n = 50) or sham (n = 50) acupuncture groups. Finally, 49 patients in each group were included for the final analysis due to one case of exclusion in each group, with treatment administered twice a week for 2 weeks. Visual Analog Scale (VAS), resting-state functional magnetic resonance imaging (fMRI), and Allen Human Brain Atlas (AHBA) transcriptome data were analyzed via Partial Least Squares (PLS) regression.ResultsBoth groups showed reduced post-treatment VAS (p < 0.001), with the verum group exhibiting a superior effect (Z = −6.877, p < 0.001). Neuroimaging revealed that verum acupuncture (VA) specifically induced significant decreases in functional connectivity (FC) between the right thalamus and left anterior cingulate cortex (T = −4.498) as well as between the right thalamus and right Rolandic operculum (T = −4.532, voxel-level p < 0.01, cluster-level p < 0.05), an effect absent in the sham acupuncture group (SA). Gene- FC association analysis indicated that PLS2 component explained 39.83% of FC variance (Pspin: permutation test p < 0.05), with weight genes showing significant spatial correlation to connectivity changes (r = 0.445, Pspin = 0.0011). A total of 809 genes were enriched in the innate immune response and phosphorylation regulation pathways, whereas 1,222 genes were enriched in the GABA-ergic synapse and synaptic membrane-related pathways.ConclusionVA relieves pain via modulating thalamus-anterior cingulate cortex networks, involving immune-inflammation and neural inhibition, providing first multi-scale validation integrating neuroimaging and transcriptomics.Clinical trial registrationThis trial was registered with the International Traditional Medicine Clinical Trial Registry (registration number: ITMCTR2023000001) prior to participant enrollment.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
In February, I picked up a flyer at an anti-AI march in London. I can’t say for sure whether or not its writers meant to riff on South Park’s underpants gnomes. But if they did, they nailed it: “Step 1: Grow a digital super mind,” it read. “Step 2: ? Step 3: ?”
Produced by Pause AI, an international activist group that co-organized the protest, it ended with this plea to the reader: “Pause AI until we know what the hell Step 2 is.”
In the South Park episode “Gnomes,” which first aired in 1998, Kenny, Kyle, Cartman, and Stan discover a community of gnomes that sneak out at night to steal underpants from dressers. Why? The gnomes present their pitch deck. “Phase 1: Collect underpants. Phase 2: ? Phase 3: Profit.”
The gnomes’ business plan has since become one of the greats among internet memes, used to satirize everything from startup strategies to policy proposals. Memelord in chief Elon Musk once invoked it in a talk about how he planned to fund a mission to Mars. Right now, it captures the state of AI. Companies have built the tech (Step 1) and promised transformation (Step 3). How they get there is still a big question mark.
As far as Pause AI is concerned, Step 2 must involve some kind of regulation. But exactly what it will call for and who will enforce it are up for debate.
AI boosters, on the other hand, are convinced that Step 3 is salvation and tend to glaze over the middle bit. They see us racing toward sunny uplands on the back of an “economically transformative technology,” as OpenAI’s chief scientist, Jakub Pachocki, put it to me a few weeks ago. They know where they want to go—more or less: It’s hazy up there and still some way off. But everyone’s taking a different route. Will they all make it? Will anyone?
For every big claim about the future, there is a more sober assessment of how the rubber meets the road—one that quells the hype. Consider two recent studies. One, from Anthropic, predicted what types of jobs are going to be most affected by LLMs. (A takeaway: Managers, architects, and people in the media should prepare for change; groundskeepers, construction workers, and those in hospitality, not so much.) But their predictions are really just guesses, based on what kinds of tasks LLMs seem to be good at rather than how they really perform in the workplace.
Another study, put out in February by researchers at Mercor, an AI hiring startup, tested several AI agents powered by top-tier models from OpenAI, Anthropic, and Google DeepMind on 480 workplace tasks frequently carried out by human bankers, consultants, and lawyers. Every agent they tested failed to complete most of its duties.
Why is there such wide disagreement? There are a number of factors. For a start, it’s crucial to consider who is making the claims (and why). Anthropic has skin in the game. What’s more, most of the people telling us that something big is about to happen have reached that conclusion largely on the basis of how fast AI coding tools are getting. But not all tasks can be hacked with coding. Other studies have found that LLMs are bad at making strategic judgment calls, for example.
What’s more, when they’re deployed, the tools aren’t just dropped into a cleanroom. They need to work in places contaminated with people and existing workflows. And sometimes adding AI will make things worse. Sure, maybe those workflows need to be torn up and refashioned around the new technology for it to achieve transformative status, but that will take time (and guts).
That big hole? It’s right where Step 2 should be. The lack of agreement on exactly what’s about to happen—and how—creates an information vacuum that gets filled by the latest wild claim of the week, evidence be damned. We’re so unmoored from any real understanding of what’s coming and how it will be deployed that a single social media post can (and does) shake markets.
We need fewer guesses and more evidence. But that’s going to require transparency from the model makers, coordination between researchers and businesses, and new ways to evaluate this technology that tell us what really happens when it’s rolled out in the real world.
The tech industry (and with it the world’s economy) rests on the held-out promise that AI really will be transformative. But that is not yet a sure bet. Next time you hear bold claims about the future, remember that most businesses are still figuring out what to do with their underpants.
