<![CDATA[New youth-focused substance use guidelines spotlight prevention, brain development, and coordinated mental health support to improve outcomes early.]]>
<![CDATA[“Each person the right to tell their own story in their own way”: An Italian-Canadian psychiatrist traces family journeys across 3 countries, revealing migration’s lessons on identity, belonging, and resilience.]]>
STAT+: Trump goes soft on insurance, and a medical underwriting chart
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We watched the Artemis II astronauts splash down safely last week. A reminder that legitimately amazing things can still happen. Parachute your thoughts here: bob.herman@statnews.com.
Tough talk, soft stance
A few months ago, President Trump confidently said he would be meeting with the country’s largest health insurance companies to pressure them to lower their premiums. The message was just that — a message to give the appearance that Trump officials were willing to crack down on health insurers, which have been at the center of Americans’ disdain of the health care system for decades.
Why opinion on AI is so divided
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In an industry that doesn’t stand still, Stanford’s AI Index, an annual roundup of key results and trends, is a chance to take a breath. (It’s a marathon, not a sprint, after all.)
This year’s report, which dropped today, is full of striking stats. A lot of the value comes from having numbers to back up gut feelings you might already have, such as the sense that the US is gunning harder for AI than everyone else: It hosts 5,427 data centers (and counting). That’s more than 10 times as many as any other country.
There’s also a reminder that the hardware supply chain the AI industry relies on has some major choke points. Here’s perhaps the most remarkable fact: “A single company, TSMC, fabricates almost every leading AI chip, making the global AI hardware supply chain dependent on one foundry in Taiwan.” One foundry! That’s just wild.
But the main takeaway I have from the 2026 AI Index is that the state of AI right now is shot through with inconsistencies. As my colleague Michelle Kim put it today in her piece about the report: “If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock.” (The Stanford report notes that Google DeepMind’s top reasoning model, Gemini Deep Think, scored a gold medal in the International Math Olympiad but is unable to read analog clocks half the time.)
Michelle does a great job covering the report’s highlights. But I wanted to dwell on a question that I can’t shake. Why is it so hard to know exactly what’s going on in AI right now?
The widest gap seems to be between experts and non-experts. “AI experts and the general public view the technology’s trajectory very differently,” the authors of the AI Index write. “Assessing AI’s impact on jobs, 73% of U.S. experts are positive, compared with only 23% of the public, a 50 percentage point gap. Similar divides emerge with respect to the economy and medical care.”
That’s a huge gap. What’s going on? What do experts know that the public doesn’t? (“Experts” here means US-based researchers who took part in AI conferences in 2023 and 2024.)
I suspect part of what’s going on is that experts and non-experts base their views on very different experiences. “The degree to which you are awed by AI is perfectly correlated with how much you use AI to code,” a software developer posted on X the other day. Maybe that’s tongue-in-cheek, but there’s definitely something to it.
The latest models from the top labs are now better than ever at producing code. Because technical tasks like coding have right or wrong results, it is easier to train models to do them, compared with tasks that are more open-ended. What’s more, models that can code are proving to be profitable, so model makers are throwing resources at improving them.
This means that people who use those tools for coding or other technical work are experiencing this technology at its best. Outside of those use cases, you get more of a mixed bag. LLMs still make dumb mistakes. This phenomenon has become known as the “jagged frontier”: Models are very good at doing some things and less good at others.
The influential AI researcher Andrej Karpathy also had some thoughts. “Judging by my [timeline] there is a growing gap in understanding of AI capability,” he wrote in reply to that X post. He noted that power users (read: people who use LLMs for coding, math, or research) not only keep up to date with the latest models but will often pay $200 a month for the best versions. “The recent improvements in these domains as of this year have been nothing short of staggering,” he continued.
Because LLMs are still improving fast, someone who pays to use Claude Code will in effect be using a different technology from someone who tried using the free version of Claude to plan a wedding six months ago. Those two groups are speaking past each other.
Where does that leave us? I think there are two realities. Yes, AI is far better than a lot of people realize. And yes, it is still pretty bad at a lot of stuff that a lot of people care about (and it may stay that way). Anyone making bets about the future on either side should bear that in mind.
Neural Mechanism Underlying Sensory Behavior Revealed in C. elegans
Animal behavior reflects a complex interplay between an animal’s brain and its sensory surroundings. In a new study published in Nature Neuroscience titled, “Neural sequences underlying directed turning in Caenorhabditis elegans,” researchers from Massachusetts Institute of Technology (MIT) have shown how neuron circuits within C. elegans nematode worms respond to odors and generate movement as they pursue favorable versus unfavorable smells. The results inform understanding of the basic principles of the sensory nervous system for therapeutic applications.
“Across the animal kingdom, there are just so many remarkable behaviors,” said Steven Flavell, PhD, associate professor at the Picower Institute at MIT, Howard Hughes Medical Institute (HHMI) investigator, and corresponding author of the study. “With modern neuroscience tools, we are finally gaining the ability to map their mechanistic underpinnings.”
Whether moving toward a food source or away from a predator, animals must integrate sensory stimuli to navigate to favorable locations. The neural circuits for navigation are tasked with generating directed movement while simultaneously integrating sensory input to update behavior. Understanding how neural circuits select, execute and adapt sensory-guided navigation behaviors uncovers basic principles of how nervous systems are organized to integrate sensory information and control behavior.
In C. elegans, the authors identified error-correcting turns during navigation and used whole-brain calcium imaging and cell-specific perturbations to determine their neural underpinnings. Defined neurons activated in a stereotyped order during each turn. Distinct neurons in this sequence respond to the spatial distribution of attractive and aversive olfactory cues, anticipate upcoming turn directions and drive movement, linking key features of this sensorimotor behavior across time.
“One thing that really excited us about this study is that we were able to see what a sensorimotor arc looks like at the scale of a whole nervous system: all the bits and pieces, from responses to the sensory cue until the behavioral response is implemented,” Flavell said.
The electrical activity of more than 100 neurons was tracked during sensory movement. Notably, C. elegans only have 302 neurons total. Instead of random movements, the worms executed turns with advantageous timing and at well-chosen angles.
The activity of SAA neurons was crucial for integrating odor detection with planned movement and predicted the direction of upcoming turns. Several neurons showed different activity patterns depending on the location of odors were and whether the worm was moving forward or in reverse.
Additionally, the neuromodulator, tyramine, was essential for turning and shifting gears. When the worms moved in reverse, tyramine from the neuron RIM enabled other neurons in the sequence to change their activity appropriately to execute the turns. In several experiments, the scientists knocked out RIM tyramine, which disrupted the navigation behaviors and the sequence of neural activity.
The post Neural Mechanism Underlying Sensory Behavior Revealed in <i>C. elegans</i> appeared first on GEN – Genetic Engineering and Biotechnology News.
<![CDATA[Exercise boosts recovery in depression and psychosis, and improves cognition and quality of life; clinicians can now use the 5A model to make movement a routine psychiatric tool.]]>
<![CDATA[Artemis II astronauts spark a psychiatrist’s look at “joy trains,” music, and meaning—practical ways teams rekindle joy and prevent burnout.]]>
A novel behavioral paradigm using mice to study predictive postural control
Postural control circuitry performs the essential function of maintaining balance and body position in response to perturbations that are either self-generated (e.g., reaching to pick up an object) or externally delivered (e.g., being pushed by another person). Human studies have shown that anticipation of predictable postural disturbances can modulate such responses. This indicates that postural control could involve higher-level neural structures associated with predictive functions, rather than being purely reactive. However, the underlying neural circuitry remains largely unknown. To enable studies of predictive postural control circuits, we developed a novel experimental paradigm for mice. In this paradigm, modeled after studies in humans and rats, a dynamic platform generated reproducible translational perturbations. While mice stood on their hind legs atop a perch to receive water rewards, they experienced backward translations that were either unpredictable or preceded by an auditory cue. To validate the paradigm, we investigated the effect of the auditory cue on postural responses to perturbations across multiple days in three mice. These preliminary results serve to validate a new postural control experimental paradigm, opening the door to the types of neural recordings and circuit manipulations that are currently possible in mice.
From peripheral initiation to central integration: a narrative review of the antihypertensive mechanisms of acupuncture in regulating autonomic nervous system homeostasis
Essential Hypertension (EH) is one of the most prevalent chronic cardiovascular diseases, imposing a significant burden on healthcare systems worldwide due to its high rates of disability and mortality. Long-term elevation of blood pressure leads to multi-organ damage in the heart, brain, and kidneys, resulting in severe complications such as coronary heart disease, stroke, and chronic kidney disease. Current treatment for hypertension primarily relies on pharmacological interventions. Although antihypertensive drugs have achieved notable success in controlling blood pressure, challenges remain, including poor long-term medication adherence, side effects, and inadequate blood pressure control in some patients with resistant hypertension. In parallel, acupuncture, a key modality of traditional Chinese medicine, has demonstrated unique advantages in hypertension management in recent years. Characterized by its holistic regulatory effects and minimal side effects, acupuncture is recognized by the World Health Organization as a recommended complementary and alternative therapy for hypertension, although its precise mechanisms remain incompletely understood. This review aims to summarize the “peripheral-central synergy” antihypertensive mechanism of acupuncture in regulating autonomic nervous system (ANS) homeostasis. Studies indicate that acupuncture primarily modulates autonomic homeostasis through the following pathways: (1) activating peripheral nerve fibers to convert physical stimulation into complex bioelectrical signals; (2) regulating synaptic neurotransmitter release and the expression of related membrane receptors; (3) modulating the synaptic microenvironment; (4) regulating the NTS-CVLM-RVLM neural circuit; and (5) modulating the HPA axis neuro-endocrine circuit. Through in-depth analysis, this review elucidates the multi-level and multi-dimensional impact of acupuncture therapy on primary hypertension, providing stronger evidence and a theoretical foundation for its clinical application.
Acute aerobic exercise modulates resting-state EEG microstate dynamics in individuals with internet addiction
Internet addiction (IA) is associated with impaired cognitive control and altered large-scale brain dynamics. Electroencephalogram (EEG) microstates provide a sensitive index of rapid neural network organization; however, whether acute aerobic exercise can modulate abnormal microstate dynamics in individuals with IA remains unclear. Forty young adults [IA: n = 20; healthy controls (HC): n = 20] completed resting-state EEG recordings before and after a single 30-min bout of moderate-intensity aerobic cycling. Microstate duration, occurrence, contribution, and transition probabilities were analyzed using mixed-design repeated-measures with time (pre/post) and Microstate (A–D) as within-subject factors and group (IA/HC) as a between-subject factor. Spearman correlations examined associations between exercise-induced microstate changes and IA severity. For microstate duration, the IA group exhibited longer duration of Microstate A than HC group at baseline (t = 2.47, p = 0.018). IA group showed reduced Microstate D occurrence at baseline compared with HC (t = 4.23, p < 0.001), followed by a significant post-exercise increase (t = −4.23, p = 0.001), eliminating group differences. Microstate contribution showed a significant Time × Group interaction [F(1, 38) = 4.68, p = 0.037, η2 = 0.110], with Microstate D contribution increasing selectively in the IA group (t = −3.71, p = 0.001). Changes in Microstate D occurrence were negatively correlated with IA severity (ρ = −0.55, p = 0.012). A single session of aerobic exercise rapidly normalizes aberrant microstate dynamics in IA, particularly within Microstate D, highlighting exercise as an effective acute neuromodulatory intervention.