<![CDATA[New US patent backs Denovo’s ANK3 biomarker guiding DB104 for treatment‑resistant depression, highlighting promising efficacy in selected patients.]]>
<![CDATA[Lifestyle medicine pillars help reshape depression and anxiety treatment in psychiatry.]]>
Acupoint temperature as a biomarker: infrared thermography in the diagnosis of adolescents with major depressive disorder
BackgroundThe prevalence of adolescent major depressive disorder (MDD) is rising; however, diagnosis relies on subjective measures due to a lack of objective biomarkers. This study explored infrared thermography (IRT) as a non-invasive tool to quantify thermal radiation characteristics of acupoints in adolescents with MDD. The objective was to establish diagnostic models based on acupoint temperature-derived biomarkers.MethodsA prospective, multi-center observational study enrolled 108 participants (65 adolescents with MDD and 43 healthy controls [HCs]). We first examined correlations between acupoint temperatures and depression severity using Pearson analysis. Multiple linear and binary logistic regression models were developed to diagnose MDD and assess severity. The diagnostic model for MDD was visualized as a nomogram and validated using Receiver Operating Characteristic (ROC) curves, Hosmer-Lemeshow tests, calibration plots, and decision curve analysis (DCA). Internal validation was performed using the bootstrap method.ResultsAmong 27 acupoints analyzed, adolescents with MDD exhibited altered acupoint temperatures at Taiyang (EX-HN5), Quchi (LI11), Yanggu (SI5), and Waiqiu (GB36). Subsequent Pearson correlation analysis revealed negative correlations between the infrared relative temperatures of Taiyang (EX-HN5), Quchi (LI11), and Waiqiu (GB36) and depression severity (P = 0.001, r = -0.319; P = 0.022, r = -0.229; P = 0.001, r = -0.325) and a weak positive correlation between the infrared relative temperature of Yanggu (SI5) and depression severity (P = 0.043, r = 0.202). Building on these findings, two diagnostic models were developed: a linear regression model for depression severity of adolescents (Y = 52.25-9.52*TEX-HN5-13.07*TGB36) and a logistic regression model for adolescents with MDD diagnosis (P = ex/(1+ex), x = 0.22-1.14*TEX-HN5+0.45*TSI5-2.19*TGB36). The nomogram-based model demonstrated good calibration (Hosmer-Lemeshow P = 0.855), discrimination (AUC = 0.785, 95%CI: 0.693 – 0.876), and clinical utility. Internal validation using the bootstrap method produced a C-index of 0.752 (95% CI: 0.617 – 0.877), further confirming the model’s robustness.ConclusionsIn conclusion, acupoint temperature-based models show promising efficacy for the objective and non-invasive diagnosis and severity quantification of adolescents with MDD, offering valuable tools for early clinical intervention. Future studies should validate these findings across diverse populations and integrate multi-modal biomarkers to enhance diagnostic precision.Clinical Trial RegistrationClinicalTrials.gov, identifier NCT06750640.
Prevalence of Cognitive Distortion Markers in a Suicide Prevention Chat Service: Mixed Methods Study
Background: Suicide helplines increasingly employ chat services to aid those in urgent need, but the wording and structure of text-driven exchanges may affect their effectiveness. Objective: Given the association of cognitive distortions with depression and anxiety, this study investigated their prevalence in the language of individuals seeking help from the Dutch 113 suicide helpline. Methods: We observed the prevalence of cognitive distortions for both help seekers and counselors in a large volume of chat sessions (N=71,148) of the Dutch 113 suicide chat helpline using natural language processing. The results were compared to 2 large collections of online text data from Dutch social media and web content. Results: We found that nearly all types of cognitive distortions are more prevalent in the language of help seekers compared to the control group of helpline counselors. Distortions of the personalizing, emotional reasoning, and mental filtering types were, respectively, 20.22, 7.87, and 4.53 times more prevalent among help seekers, revealing a distinct pattern of thought and language among individuals affected by suicidality. Conclusions: Our results raise the prospect of improving the effectiveness of online therapeutic interventions that target cognitive distortions through lexical analysis that detects the cognitive and lexical markers of suicidality.
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The Download: Musk and Altman’s legal showdown, and AI’s profit problem
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.
Find out how the trial could upend the global AI race.
—Michelle Kim
The missing step between hype and 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.
Experts fear that they’re cratering trust and critical thinking. Here’s why they’re alarmed.
—Eileen Guo
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.
Follow the digital path from Covid skepticism to the return of a deadly conspiracy theory.
—Anna Merlan
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ Explore the planets from your laptop with this live sky map.
+ This marathon DJ set from Daphni is an incredible journey through electronic music.
+ NASA’s stunning Artemis II wallpapers bring a high-res piece of deep space to your phone.
+ This fascinating GPS explainer breaks down how your phone figures out exactly where you are.
Quality, reliability, and transparency of late-life depression videos on Chinese social media: a cross-sectional study of Douyin, Rednotes, and BiliBili
BackgroundLate-life depression is common in older adults and is often under-recognized. Short-video platforms have become a major source of mental health information. However, content quality and transparency remain uncertain.MethodsWe conducted a cross-sectional assessment of highly viewed videos on late-life depression on three Chinese platforms. We searched each platform using the keyword in Chinese “Late-life depression”. We selected the top 200 videos by view count on Douyin, Rednotes (Xiaohongshu), and BiliBili. After exclusions, 562 videos were included (Douyin, n=188; Rednotes, n=188; BiliBili, n=186). Two medically trained raters scored videos using the Global Quality Score (GQS), modified DISCERN (mDISCERN), and JAMA benchmark criteria. We also coded content categories and creator types. We assessed platform differences using non-parametric tests. We examined associations between a limited engagement proxy, defined as the comment-to-view ratio, and quality scores using Spearman correlation.ResultsVideo duration differed across platforms (p<0.001). Engagement indicators were higher on Douyin and Rednotes than on BiliBili. Symptoms were the most common topic on all platforms. Prevention and intervention ranked second on Douyin and Rednotes. On BiliBili, causes and case-based analysis were also common. Overall quality was moderate. Mean GQS ranged from 2.96 to 3.05. Transparency was limited. Mean JAMA ranged from 1.91 to 2.04. Reliability was slightly higher on BiliBili based on mDISCERN. Creator type was strongly associated with scores. Expert and institutional videos scored higher than general and marketing-oriented accounts. Correlations between visible audience interaction and quality were weak.ConclusionHighly viewed late-life depression videos on major Chinese platforms show moderate quality and limited transparency. Exposure does not reliably signal higher-quality information. Platforms and health authorities should strengthen source disclosure and promote evidence-based content from qualified creators.
Safety and preliminary efficacy of Aurora: a pilot, non-randomized clinical trial of a culturally adapted digital cognitive behavioral therapy intervention for anxiety and depression in Mexico
Background/objectiveAnxiety and depressive disorders are leading causes of disability worldwide, and access to evidence-based psychological treatment remains limited in many middle-income countries. Digital cognitive–behavioral therapy (CBT) interventions have emerged as scalable tools to address this treatment gap, yet few have undergone clinical evaluation in Latin American populations. This study aimed to assess the safety and preliminary efficacy of Aurora, a Spanish-language, culturally adapted digital CBT program, when used as an adjunct to pharmacotherapy in adults with generalized anxiety disorder.MethodsIn a multicenter, open-label, non-randomized pilot study, 34 adults diagnosed with generalized anxiety disorder receiving stable pharmacological treatment were assigned through pragmatic, convenience-based allocation either to an experimental group (Aurora plus medication; n = 24) or to a control group receiving medication alone (n = 10). The sample had a mean age of 39.85 ± 12.88 years, with a predominance of women (22/34). Participants were followed for 12 weeks with assessments at baseline and weeks 4, 8, and 12. Clinical outcomes included anxiety severity measured by the Generalized Anxiety Disorder-7 (GAD-7), pathological worry assessed by the Penn State Worry Questionnaire (PSWQ), and depressive symptoms evaluated using the Patient Health Questionnaire-9 (PHQ-9). Safety was monitored through structured adverse-event reporting. Statistical analyses included linear mixed-effects models for longitudinal outcomes, ordinal logistic regression for severity transitions, and negative binomial regression and Fisher’s exact test for adverse events, with false discovery rate correction applied where appropriate.ResultsAurora demonstrated a favorable safety profile, with no serious adverse events and comparable adverse-event incidence between groups under structured clinical monitoring at weeks 4, 8, and 12. Anxiety symptoms (GAD-7) showed a significant effect of time (F3,96 = 169.65; p < 0.001), indicating reductions across both groups. Pathological worry (PSWQ) demonstrated significant group (F1,31.12 = 6.96; p = 0.013) and group × time interaction effects (F3,93.4 = 7.86; p < 0.001), with greater reductions in the Aurora group, particularly at weeks 8 and 12. At week 12, ordinal analyses indicated higher odds of lower worry severity in the intervention group (β = 2.53; p = 0.004; OR = 12.5). Depressive symptoms decreased similarly in both groups. Positive effect increased progressively across intervention modules, and module-embedded cognitive measures of anxiety and depression showed significant reductions over time.ConclusionThis pilot study provides preliminary, hypothesis-generating evidence that a culturally adapted digital CBT intervention can be safely integrated with pharmacotherapy and may be associated with enhanced improvements in anxiety-related outcomes, particularly pathological worry, in a Mexican clinical population. However, the non-randomized design, small sample size, and baseline imbalances limit causal inference and generalizability, and findings should be interpreted with caution. Larger randomized controlled trials are needed to confirm efficacy, determine long-term clinical impact, and guide the implementation of digital therapeutics in Latin American mental health systems.
Human Ventral Tegmental Local Field Potentials in Treatment-Resistant Depression and Obsessive-Compulsive Disorder
The ventral tegmental area (VTA) is a key node within the limbic circuitry. Through dense dopaminergic, glutamatergic, and GABAergic projections, the VTA forms reciprocal loops with prefrontal and limbic cortices that are consistently implicated in major depressive disorder (MDD) and obsessive–compulsive disorder (OCD) (1,2). Decades of animal research have established the VTA as a central hub for motivational drive and reward prediction error signaling (3,4). Despite its presumed critical role in mental disorders, direct electrophysiological recordings from the human VTA have so far remained absent.
Multimodal Depression Detection Through Conversational Interactions with an Emotion-Aware Social Robot: Pilot Study
Background: Depression affects more than 300 million people worldwide and is a leading contributor to the global disease burden. Traditional diagnostic methods, such as structured clinical interviews, are reliable but impractical for frequent or large-scale screening. Self-report tools like the Patient Health Questionnaire-8 (PHQ-8) require disclosure and clinician oversight, limiting accessibility. Recent artificial intelligence–based approaches leverage multimodal behavioral cues (linguistic, acoustic, and visual) for automated depression detection but remain constrained by limited adaptability, scarce annotated data, weak emotional expression in real-world settings, and the high computational cost of deployment of socially assistive robots (SARs). Objective: This study introduces Depression Social Assistant Robot (DEPRESAR)-Fusion, a lightweight multimodal depression detection framework designed for natural interactions with emotion-aware SARs. The objective of this study was to enhance detection accuracy in everyday conversations while addressing the challenges of data scarcity, weak emotional cues, and computational efficiency. Methods: DEPRESAR-Fusion integrates acoustic, linguistic, and visual features with an emotion-aware response module powered by large language models to adapt conversational strategies dynamically. To stimulate richer emotional expression, participants were exposed to emotionally evocative videos before SAR interactions. To overcome data scarcity, we augmented training with (1) public depression-related social media corpora and (2) synthetic samples generated via large language models. The proposed multimodal fusion architecture was evaluated on benchmark clinical datasets for both binary depression classification and PHQ-8 regression tasks. Performance was compared against prior multimodal baselines using root mean square error, mean absolute error, and standard classification metrics. Results: Participants who viewed emotional stimuli before interacting with SARs exhibited significantly higher emotional expressiveness, leading to improved model performance. Regression tasks showed lower root mean square error and mean absolute error, while classification tasks achieved significantly higher accuracy than the nonstimulus condition. DEPRESAR-Fusion outperformed prior multimodal baselines across multiple benchmark datasets, achieving state-of-the-art performance in both binary classification and PHQ-8 regression. The system maintained a lightweight architecture suitable for real-time deployment on SARs. Conclusions: DEPRESAR-Fusion demonstrates that integrating emotion induction, data augmentation, and lightweight multimodal fusion can enable accurate and scalable depression detection in naturalistic SAR interactions. By bridging the gap between structured clinical assessments and everyday conversations, this approach highlights the potential of SAR-based systems as nonintrusive, artificial intelligence–driven tools for proactive mental health support.
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Enhancing Sleep and Mental Health: Longitudinal, Observational, Real-World Study From a Digital Mental Health Platform
Background: Poor sleep is closely linked to mental health challenges and workplace burnout. Mental health and workplace stressors can impair sleep, while good sleep quality supports cognitive and emotional resources to cope with daily challenges. Despite positive outcomes of maintaining good sleep, many people struggle to get enough restorative sleep at night. Given the bidirectional relationship between sleep and mental health, evidence-based digital mental health solutions may offer an accessible and scalable approach to improving sleep quality. Objective: This study examines whether engagement with an employer-sponsored, multimodal digital mental health platform is associated with improvements in sleep quality over time, and whether changes in sleep quality are associated with concurrent changes in mental health and burnout outcomes. Methods: This 12-month prospective, observational study followed working adults who were newly registered to an employer-sponsored digital mental health platform (Modern Health). The platform leveraged technology (mobile and web) to connect employees with comprehensive provider-led and self-guided care through therapy, coaching, on-demand digital resources, and group psychoeducational sessions. Participants [N=578; 61.1% (n=353) women; mean age 33.88, SD 8.73 years; 40.3% (n=233) people of color] completed measures of self-rated sleep quality, depression, anxiety, and burnout (exhaustion, cynicism, and professional efficacy) at baseline and after 3 and 12 months of accessing the platform. Upon registering for the platform, participants were given an initial care recommendation, but could flexibly engage in any combination of services. Participants in this study engaged with at least one care modality, including therapy, coaching, psychoeducation sessions, and self-guided mental health resources. We examined perceived sleep quality and associations with other study variables at baseline, changes in perceived sleep quality over time, and whether changes in sleep quality correlated with concurrent changes in mental health and burnout. Results: At baseline, 42% (243/578) reported poor sleep quality and were more likely to have higher levels of depression, anxiety, and burnout. A generalized linear mixed-effects model showed that each additional month of platform access was related to an increased odds of having good sleep quality by 3.7% (=.02). Linear mixed-effects models found that higher sleep quality over time was associated with lower depression, anxiety, exhaustion, cynicism, and efficacy (all <.001). Among participants reporting poor sleep quality at baseline, 44% (62/141) reported good sleep quality at 12 months. Within this subgroup, paired sample tests showed significant reductions in depression (−48.3%) and anxiety (−38.3%), and increased cynicism, burnout, though cynicism levels remained below the cutoff for high burnout (23.9%; all <.01). Conclusions: Use of an employer-sponsored digital mental health platform was associated with meaningful improvements in self-reported sleep quality over 12 months. These gains were associated with significant reductions in depression, anxiety, and burnout symptoms, highlighting broader well-being benefits of comprehensive mental health care.
