Imported News – Page 86

July 6, 2026

Unlocking Microbiome Function with Anaerobic Workflows and Metabolic Phenotyping

Over the last decade, the microbiome has shifted from a scientific curiosity to one of the most promising frontiers in biology and medicine. Whether it’s gut microbes that influence the host’s metabolism, an oral microbe that prevents cavities, or a microbial consortium that improves immunotherapy response in cancer—each discovery brings growing excitement.

As the excitement has grown, so too has the realization that identifying microbes is only part of the story. Preserving microbial viability and physiological relevance—particularly with regard to anaerobes and microaerophiles—throughout collection, transport, and cultivation is a critical prerequisite for accurately measuring microbial behavior.

The field is also shifting from descriptive microbiome research towards mechanistic understanding—using metabolic phenotyping, which directly measures microbial activity including nutrient and substrate utilization, cell growth, stress response, and more. Because only when we truly understand how microbes behave can we begin to shape them into powerful tools for healing.

Next generation sequencing (NGS) has transformed our ability to identify which species are present in a microbial community (“who’s there”) and what they may potentially be capable of. However, gene presence does not guarantee gene expression or necessarily reflect real-world activity (“what the microbes are doing”). Two strains may carry similar metabolic genes yet behave very differently under gutrelevant conditions.

These are some of the important functional questions sequencing alone can’t answer:

What metabolic pathways are actually active?

How do the microbes adapt to various environments and interact?

What is the optimal environment to produce critical metabolites?

As the field pushes toward developing live biotherapeutic products, evidence-backed probiotics, and next-gen biomarkers, understanding these functional traits is essential.

This eBook explores how researchers can move beyond correlation and towards mechanism in microbiome research—from preserving physiologically relevant microbial communities to directly measuring microbial function through phenotyping.

The post Unlocking Microbiome Function with Anaerobic Workflows and Metabolic Phenotyping appeared first on GEN – Genetic Engineering and Biotechnology News.

July 6, 2026

<![CDATA[Expert explains why Alzheimer disease remains complex, how biomarkers and imaging track amyloid, and what new anti-tau and anti-inflammatory drugs emerge.]]>

July 6, 2026

STAT+: Vertex acquires Crinetics Pharmaceuticals for $10 billion as biotech M&A booms

Vertex Pharmaceuticals will spend $10 billion to acquire Crinetics Pharmaceuticals and its drug for a rare endocrine disorder, the companies announced Monday.

Through the deal, Vertex will pick up Crinetics’ commercial drug, Palsonify, which was launched last year and which treats a rare endocrine disorder called acromegaly, as well as other drug candidates that have blockbuster potential if approved. The company is also in the late stages of developing a therapy for congenital adrenal hyperplasia.

The $10 billion price tag for Crinetics amounts to roughly $85 per share of the company’s stock. Following the news, Crinetics shares rose 101% in after-hours trading.

Continue to STAT+ to read the full story…

July 6, 2026

Why Long, Uninterrupted Sitting May Matter for Cancer Prevention

Public health advice has long emphasized exercise: move more, meet weekly activity targets, reduce time spent sitting. But a growing body of evidence suggests that the pattern of inactivity may also matter. Sitting for long, uninterrupted periods may not carry the same health implications as the same amount of sedentary time broken up by movement.

A new study published in PLOS Medicine adds cancer outcomes to that discussion. Researchers analyzed accelerometer data from 91,292 UK Biobank participants who wore wrist activity monitors for seven days and were then followed for a median of more than 12 years. The study found that prolonged sedentary behavior was associated with higher risk of cancer mortality and cancer incidence, while interrupted sedentary behavior showed the opposite pattern.

Beyond “sit less, move more”

Sedentary behavior includes waking activities such as sitting, reclining, or lying down that require very low energy expenditure. Most guidelines and public health messages focus on total sedentary time. That is understandable: total sitting time is easy to communicate and has been repeatedly linked to poor cardiometabolic outcomes.

But the new study asks a more precise question: does it matter whether sedentary time happens in long blocks or is regularly interrupted?

The researchers defined prolonged sedentary behavior as bouts lasting at least 30 minutes in which at least 90% of the time was sedentary. Interrupted sedentary behavior included shorter bouts or sedentary periods broken up by movement. This distinction allowed the team to examine not only how much time people spent inactive, but how that inactivity was distributed throughout the day.

Long, unbroken sedentary bouts linked to higher risk

Each additional hour per day of prolonged sedentary behavior was associated with a 9% higher risk of cancer death. Prolonged sedentary time was also associated with higher overall cancer incidence, obesity-related cancers, and type 2 diabetes-related cancers.

Interrupted sedentary behavior showed an inverse association across the same outcomes. In other words, sedentary time that was broken up by activity was not linked to the same pattern of risk as long, uninterrupted bouts.

The authors summarize the implication clearly: “These findings suggest that not only the total amount of sedentary time, but also how sedentary time is accumulated, may be important for cancer risk.”

This matters because sedentary behavior is widespread and often built into modern work, transport, and leisure routines. For many people, the practical question is not whether they can avoid sitting altogether, but whether they can interrupt long sitting periods often enough to reduce risk.

Light movement should not be ignored

The study also used substitution models to estimate what might happen if sedentary time were replaced with different types of activity. Replacing one hour per day of prolonged sedentary behavior with light physical activity was associated with a 12% lower risk of cancer death. Replacing 30 minutes per day with moderate physical activity was also associated with lower cancer mortality risk.

That finding is important because light activity is more achievable for many people than structured exercise. Standing up, walking slowly, doing household tasks, or taking brief movement breaks may not feel like “exercise,” but they may still interrupt metabolically harmful patterns of prolonged inactivity.

As the authors note, “light movement shouldn’t be ignored.”

The biological rationale is plausible. Experimental studies have shown that breaking up sitting with short activity bouts can improve glucose and insulin responses compared with uninterrupted sitting. Prolonged sedentary time may also contribute to low-grade inflammation, impaired immune function, insulin dysregulation, and ectopic fat accumulation—pathways that have all been discussed in relation to cancer risk.

A public health signal, not proof of causation

The study has important strengths. It used device-measured activity rather than self-reported sitting time, reducing recall bias. It also followed participants for more than a decade and included multiple cancer outcomes.

Still, the findings should be interpreted carefully. This was an observational study, so it cannot prove that prolonged sitting directly causes cancer or cancer death. UK Biobank participants are also known to be healthier and more physically active than the general UK population, which may limit generalizability. Activity was measured over only seven days, and the monitors could not capture the context of sedentary behavior, such as whether someone was sitting at work, watching television, driving, or resting because of underlying illness.

The authors also note that residual confounding cannot be ruled out. People who sit for long uninterrupted periods may differ from others in ways that are difficult to fully measure, including occupation, frailty, health status, or broader lifestyle patterns.

Rethinking cancer prevention in everyday terms

Cancer prevention is often framed around smoking, alcohol, diet, body weight, vaccination, screening, and exercise. Sedentary behavior is increasingly part of that picture, but this study suggests the message may need to become more specific.

The goal may not only be to reduce total sitting time, but to avoid accumulating that time in long, uninterrupted blocks. For public health, that is a potentially actionable target: movement breaks at work, walk-and-talk meetings, standing transitions, household activity, or brief light-intensity movement after long periods of sitting.

The study does not suggest that movement breaks replace established cancer prevention measures. Nor does it mean that every hour of sitting carries the same risk for every person. But it does support a more nuanced view of inactivity: the body may respond differently to sedentary time that is regularly interrupted than to sedentary time accumulated in prolonged bouts.

As activity monitors become more common and prevention science becomes more personalized, cancer prevention advice may eventually move beyond broad exercise targets toward daily behavior patterns. For now, the message is simple enough: long sitting spells may be worth breaking, even with light movement.

The post Why Long, Uninterrupted Sitting May Matter for Cancer Prevention appeared first on Inside Precision Medicine.

July 6, 2026

Structured Large Language Model Workflows for Motivational Interviewing in Health Behavior Change: Proof-of-Concept Study

Background: Motivational interviewing (MI) is an effective approach for supporting health behaviorchange, but face-to-face delivery is resource-intensive and difficult to scale. Rule-based conversational agents (CAs) can improve access; however, their scripted interactions and limited language flexibility constrain MI delivery. While large language models (LLMs) are increasingly being used for MI coaching, their conversational fidelity and quality compared with human coaches and rule-based CAs remain understudied. Objective: This study aimed to describe the development of an LLM-based CA, Artificially Intelligent Motivational Interviewing (Aimi), orchestrated with structured workflows, and to evaluate its feasibility, conversational fidelity, and user perceptions during MI coaching interactions. Methods: We developed Aimi using structured LLM workflows designed to enhance MI fidelity. We conducted a within-participants study, where 18 adults interacted with (1) Aimi, (2) a novice MI-trained human coach, and (3) a rule-based CA during live text-based role-play coaching sessions. Transcripts were independently evaluated by an MI expert using the Motivational Interviewing Skill Code, Version 2.0 (MISC-2), to assess MI competency and fidelity. Participants completed a user experience questionnaire to provide general feedback and to assess session alliance, dialogue relevance, empathy, engagement, linguistic quality, and perceived motivation to change. Feedback from users was thematically summarized and categorized under strengths and weaknesses for each approach. Results: Aimi achieved fidelity scores comparable to those of the novice human coach and higher than those of the rule-based CA on summary metrics, including higher reflection-to-question ratios (median 0.84, IQR 0.62-0.92 vs 0.62, IQR 0.42-0.74 vs 0.25, IQR 0.17-0.38), more complex reflections (median 66.67%, IQR 46.97%-76.92% vs 50%, IQR 34.38%-61.88% vs 0.00%, IQR 0%-50%), and greater elicitation of client change talk (median 90.83%, IQR 85.89%-100% vs 73.21%, IQR 63.10%-83.19% vs 66.67%, IQR 57.86%-81.94%). User experience ratings showed no significant differences across conditions. User feedback revealed distinct strengths and limitations across the coaching interactions. Participants described Aimi’s interactions as personalized, fluid, and adaptive, though sometimes overly reflective and lengthy. The novice human coach was viewed as empathetic and supportive but slow to respond, whereas the rule-based coach was viewed as efficient and structured yet limited in depth and personalization. Conclusions: This study demonstrates the technical feasibility of structured LLM-workflows for MI coaching and their capacity to maintain conversational fidelity comparable to that of a novice MI-trained human coach. Given the role-play paradigm, single-rater coding, and small convenience sample, these comparative findings should be interpreted as exploratory. Our findings serve as a foundational baseline for the development of scalable behavior change interventions in clinical settings.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/9dab1300891971b49cd39d5057f5240f" />

July 6, 2026

Obamacare rolls shrank dramatically in many states over the past year, new federal data shows

NEW YORK — States across the country saw steep drops in the number of people covered by the Affordable Care Act over the past year, with Ohio and Oklahoma each losing nearly one-third of enrollees, according to new federal data that provides the first complete 50-state breakdown of sharp enrollment declines following the January expiration of enhanced subsidies.

The data, posted in late June by the Trump administration and first reported on by The Associated Press, reveals how changes in each state’s insured population led to around 2.6 million fewer Americans having Obamacare plans in February compared with the same time last year.

Read the rest…

July 6, 2026

Structural Biology Reveals New Drug Targets for Rare Neuromuscular Disease

Researchers at the University of California San Diego have uncovered the structural mechanisms that cause congenital myasthenic syndromes (CMS), revealing mutation-specific treatment strategies and identifying an existing antidepressant as a promising candidate for drug repurposing.

The study, published in Nature, explains how disease-causing mutations alter the human acetylcholine receptor (AChR), the protein responsible for converting nerve signals into muscle contraction. The findings also provide a framework for developing precision medicines tailored to the specific mutation carried by individual patients.

CMS is a rare inherited group of neuromuscular disorders that typically appears at birth or early childhood. Mutations in the AChR impair communication between nerves and muscles, leading to muscle weakness, difficulty walking, breathing problems, and, in severe cases, paralysis or death. Although more than 50 disease-causing mutations have been identified, the molecular mechanisms underlying these disorders have remained largely unknown.

To address this question, the investigators determined 12 high-resolution structures of representative mutant AChRs with and without therapeutic compounds. By combining these structural data with functional studies, they identified common mechanisms that explain the two major forms of CMS.

Fast-channel CMS is caused by loss-of-function mutations that reduce receptor opening and weaken neuromuscular transmission. The researchers discovered a previously unknown allosteric binding pocket that can be targeted by positive allosteric modulators (PAMs), compounds that enhance receptor activity without directly activating the channel.

Notably, different PAMs restored receptor function in different patient mutations rather than producing a universal effect. The authors write that this “mutation-specific activity underscores the capability and necessity of tailoring therapies to individual patient genotypes.”

The study also showed that the newly identified drug-binding pocket is largely absent until a PAM binds, creating an opportunity to design more effective compounds that stabilize receptor function. The authors conclude that these findings point to “a new class of allosteric drugs” with potential applications not only in fast-channel CMS but also in disorders such as myasthenia gravis, in which acetylcholine receptor function is compromised.

The team also investigated slow-channel CMS, a gain-of-function disorder in which receptors remain open too long, causing excessive calcium influx and progressive damage at the neuromuscular junction.

Structural analyses revealed that the current therapies quinidine and fluoxetine act through a shared mechanism, blocking the receptor pore despite their distinct chemical structures.

The investigators also evaluated reboxetine, an antidepressant already approved in several countries. Unlike the existing drugs, reboxetine selectively inhibited abnormal receptor activity across multiple slow-channel mutations while largely sparing normal receptor function.

The authors believe that reboxetine’s ability to suppress pathological receptor activity, together with its established clinical safety profile, “positions it as a potential candidate for slow-channel CMS therapy,” although they caution that its known adverse effects warrant careful evaluation in future clinical studies.

Beyond identifying therapeutic opportunities, the study establishes unifying principles of CMS pathogenesis. The researchers found that fast-channel mutations weaken the coupling between acetylcholine binding and channel opening, whereas slow-channel mutations stabilize an abnormally widened, desensitized-like pore. These shared structural mechanisms explain why genetically diverse mutations produce similar clinical symptoms while requiring different therapeutic strategies.

The work also revises understanding of receptor biology. The authors write that their findings “overturn the traditional view of the β subunit as merely a structural scaffold,” instead demonstrating that it plays “a central role in the gating cycle.”

Overall, the researchers conclude that their integrated structural and functional analyses define how CMS mutations disrupt receptor gating, reveal the molecular basis of current and candidate therapies, and identify new druggable sites for intervention. They conclude that the work provides “a roadmap towards the development of safer and more effective, mutation-specific treatments for both fast-channel and slow-channel CMS.”

The post Structural Biology Reveals New Drug Targets for Rare Neuromuscular Disease appeared first on Inside Precision Medicine.

July 6, 2026

<![CDATA[Open-label phase 2 trial tests IV Briumvi alongside antipsychotics for treatment-resistant schizophrenia, targeting B-cell depletion to improve symptoms and assess safety.]]>

July 6, 2026

Mining Association Rules From a Multimodal Dataset of a Digital Therapeutics Application for Sleep Improvement Through a Healthy Lifestyle: Quantitative Study

Background: The demand for sleep interventions is high and steadily growing. Digital therapeutics (DTx) can help individuals improve their sleep remotely, over an extended period, and with less effort from medical professionals. Obstructive sleep apnea (OSA), one of the most prevalent and consequential sleep disorders, can be treated with health-supporting behavior changes, such as physical exercise and weight loss, and, therefore, acts as a promising application for DTx. Objective: The study aimed to analyze a digital intervention from both medical and technological perspectives by moving beyond clinical markers and exploring more deeply how the DTx application was used. This study aimed to propose a novel way in which association rules can function as an exploratory tool to analyze the sleep, behavior, and engagement of participants with the DTx application on a day-to-day level. Methods: A lifestyle intervention study (N=192) targeted at adults with mild-to-moderate OSA aimed to reduce their OSA severity using a DTx application and an exercise program over a study period of 12 weeks. The participants’ OSA severity was assessed through polysomnography at the beginning and at the end of the study period, and the participants tracked their sleep with a digital sleep diary and a smartwatch over the course of the entire study. The DTx application provided data on when and how the participants pursued the proposed lifestyle interventions. These heterogeneous data sources were combined into one multimodal dataset, which was explored through descriptive statistics. Ultimately, the data were turned into a transaction-based format, and association rules were derived using the Apriori algorithm. Results: Analyzing the participants’ interaction with the application revealed the lifestyle interventions they pursued and how their behavior and sleep patterns changed over time. The Apriori algorithm generated a set of association rules with lift and confidence scores that were significantly higher than those for the co-occurrence of items through random chance. The rules show co-occurrence of missions and items from the sleep diary, as well as items derived from the watch measurements. Conclusions: The study showed the richness of the various data sources provided by a digital intervention using wearables and how they can be used to get an in-depth understanding of the study. The generated association rules showed the presence of significant co-occurrences across the different data modalities and highlighted their effectiveness as an exploratory tool for multimodal health data.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/4d2142852de4ffaa3f5207e94eb7234d" />

July 6, 2026

Your family’s $300 stake in OpenAI

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

OpenAI CEO Sam Altman’s oft-discussed promise that Americans will share in the wealth AI creates was in the news again last week. On Thursday, the Financial Times reported that Altman is in talks with President Trump about giving the US government a 5% stake in OpenAI.

In some ways, Altman’s plan is old news. He wrote about a more radical version of this back in 2021, proposing that all companies above a certain valuation (not just AI companies) pay 2.5% of their market value each year into a fund that sends Americans annual disbursements. In April this year, OpenAI described a narrower proposal that closely resembles what Altman is reportedly discussing with Trump now. And the notion has broad political appeal: Senator Bernie Sanders has proposed giving Americans a 50% stake in top AI companies.

What’s the logic here? For would-be recipients, it’s twofold. First, AI learns directly from human-generated work—books, movies, art—but AI companies generally never pay the authors of that work. A free equity stake could serve as a form of belated compensation. Second, the payout could mitigate the widespread anxiety that AI will cause a collapse of the labor market (even if economists disagree) by providing a safety net.

How large a safety net is up for debate. Details of OpenAI’s latest proposal are sparse, but let’s say the government were to distribute this equity stake directly to Americans. After its funding round in March the company was valued at $852 billion, making a 5% stake in OpenAI worth about $42.6 billion today (the company is reportedly delaying its IPO until it can reach a $1 trillion evaluation, a tall order given that it’s spending heavily on data centers and still has not turned a profit). Distributing that $42.6 billion equally among the roughly 133 million American households would give each about $320 in equity. But if it were to operate like other wealth funds, the government would not give equity directly to Americans but rather let the fund grow and then share a portion of the returns with everyone, perhaps delivering a bigger payout, if and when AI companies can ever start sustainably turning a profit.

If this dividend does materialize, what’s in it for tech companies? Altman might hope the promise of payouts could help swing public opinion a bit more back toward AI companies. (A majority of Americans don’t trust companies to use AI responsibly and oppose construction of data centers in their area, and half are more concerned than excited about the increased creep of AI into their daily lives.)

But the bigger prize for OpenAI might be that the Trump administration loves making tech deals—like its equity stake in Intel and its share of Nvidia’s sales to China, among others. Staying on the administration’s good side is pretty essential for AI companies right now (just ask Anthropic). It could mean not having your models deemed a supply chain risk, or getting more help from the White House in stopping your rivals from China.

My main takeaway is that these plans currently function more as a story than a policy. Altman has been talking about some version of this idea for five years and reportedly pitched it to President Trump soon after he took office, yet there is still little indication that a concrete plan is taking shape. The more ambitious proposal from Sanders is even less likely to gain traction.

But what these plans do reveal is just how up for debate the future of AI still is. Altman drew inspiration for his plan from the Alaska Permanent Fund, which was set up in the 1970s to give Alaskans a share in oil profits. The idea was based on two premises: that oil is a shared resource, and that eventually it will run out. Altman seems happy to concede the first claim about AI. But he’d balk at the second, having promised that AI will generate extraordinary wealth for decades to come. Whether Americans ever receive a check is beside the point; the proposal’s real purpose may be to convince them that the AI boom will be large enough to share.