Background: Despite the notable proliferation of smoking cessation mobile apps, to date, no validated, Spanish-language, culturally tailored mobile intervention exists for Spanish speakers in the United States. Objective: The aim of this study was to conduct formative research to inform the adaptation of an evidence-based smoking cessation intervention developed for Spanish-speaking Hispanic and Latino individuals from a printed format into a mobile app. Methods: Guided by a user-centered approach and in collaboration with product design industry experts, wireframes were developed to present the app’s layout and functionality. Focus groups were conducted over Zoom (Zoom Communications) with Spanish-speaking individuals who currently smoke to assess their previous mobile app experience, attitudes toward mobile apps, and feedback on app architecture and design. Two independent reviewers (RB in collaboration with another member from the qualitative core) trained in qualitative methods coded the focus group data using a thematic analysis approach and identified emerging themes. Results: The app wireframes included 4 navigation buttons on the home screen to organize and deliver evidence-based intervention content—Home (), Learn (), My Coach (), and Profile (). Different wireframe designs were generated in distinct color palettes. Data saturation was reached after three focus groups. Participants were 54% (7/13) women, had a mean age of 56 (SD 14.9) years, 39% (5/13) had an education ≤high school, and 31% (4/13) were married or cohabitating. All participants smoked daily, a mean of 14 (SD 7.8) cigarettes per day, for 32 (SD 16.9) years, and 54% (7/13) smoked ≤30 minutes of waking. Participants reported using social media, news, shopping, and gaming apps, but few used mobile health apps. Salient barriers for app use included worries regarding privacy breaches and fears about misinformation. Desired features included community-building elements, personalization, reward badges, knowledge checks, and audiovisual presentation of content within the app. Participants disliked having a countdown to quit date, preferring an “I quit” button to initiate monitoring progress. They also viewed sharing progress with support networks as a source of unwanted pressure, although a few saw it as motivational. Overall, participants liked the app design and indicated willingness to use it. Conclusions: This formative research provides critical insights into preferences related to the development of culturally tailored mobile smoking cessation interventions for Spanish-speaking individuals. Key findings highlighted enthusiasm for a smoking cessation app and the importance of including features that foster social connection and allow for personalization.
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Primary Care Physicians’ Interactions With a Novel Web-Based Active Learning Tool (The Community Fracture Capture Learning Hub): Qualitative Analysis
Background: Osteoporosis poses a significant global health burden and is responsible for over 8.9 million fragility fractures annually. Despite evidence-based guidelines and treatment, a substantial care gap persists, with only a low percentage of fracture patients receiving guideline-concordant care. Primary care physicians (PCPs) are pivotal in community-based fracture prevention but face challenges in translating knowledge into practice. While hospital-based fracture liaison services are effective, their reach is limited, necessitating scalable alternatives. Virtual communities of practice and web-based learning tools offer promising avenues for PCP professional education; however, their application in osteoporosis management remains underexplored. The Community Fracture Capture (CFC) Learning Hub was developed as an interactive, case-based platform to address these gaps by enhancing PCPs’ knowledge, confidence, and engagement in osteoporosis care. Objective: The study aimed to conduct a qualitative evaluation of PCPs’ interactions with the CFC Learning Hub, focusing on barriers and facilitators of the online learning tool and exploring PCP perceptions of the program. Methods: A qualitative analysis was performed using data from 55 PCPs across four 6-week cycles of the CFC Learning Hub (May 2022-October 2023). Data sources included discussion forum comments and responses to open-ended questions in end-of-cycle evaluations. Relational content analysis was used, with 2 researchers independently coding data using semantic and latent approaches. Themes were identified through iterative discussions and validated against existing literature. Results: Four themes emerged from PCP interactions: (1) user experience–guided platform design, where participants emphasized intuitive navigation, minimized fragmented sections, and clarity of interface as critical for engagement; (2) learning-supportive course structure, highlighting the importance of explicit links between case studies and foundational knowledge, weekly summaries, and quizzes aligned with content; (3) learners’ different styles and preferences, with diverging needs for synchronous vs asynchronous learning, didactic sessions, and peer-to-peer interactions; and (4) program content, where participants requested expanded topics and postprogram refreshers. Conclusions: The CFC Learning Hub demonstrated efficacy as a specialist- and peer-to-peer–supported online learning model for PCPs, addressing osteoporosis care gaps through user-centered design, adaptable content delivery, and collaborative moderation. Key successes included resolving usability issues iteratively and accommodating diverse learning preferences. These findings underscore the potential of the Hub to enhance primary care professional education and fracture prevention. The study advocates for broader adoption of the platform to bridge osteoporosis care disparities.
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Automated Safety Testing and Reporting Application for Conversational Safety Monitoring of Generative AI Tools for Mental Health: Development and Validation Study
<strong>Background:</strong> Artificial intelligence (AI)–based conversational tools are rapidly expanding within mental health care as a means of increasing access and scalability. At the same time, these systems introduce distinct safety risks arising from both user disclosures (eg, self-harm ideation) and inappropriate or inadequate AI responses. <strong>Objective:</strong> This study aimed to develop and evaluate the Automated Safety Testing and Reporting Application (ASTRA), an external system intended to identify clinically relevant risk behaviors across entire AI-mediated mental health conversations. <strong>Methods:</strong> ASTRA was tested on a dataset of 100 synthetic therapeutic conversations written by licensed clinicians to reflect risk behaviors and harmful responses between users and AI tools. Conversations varied in length and included both subtle and overt risk behavior examples across 8 predefined categories. Human coder consensus ratings served as the reference standard. ASTRA’s classifications were evaluated across 2 prompt iterations using standard diagnostic performance metrics and agreement statistics. <strong>Results:</strong> ASTRA demonstrated consistently high concordance with expert human ratings across all categories. Accuracy exceeded 0.90 for all risk behavior categories examined, with specificity uniformly high and sensitivity varying by category (range 0.55-1.00). Agreement beyond chance was substantial to almost perfect between ASTRA and human raters (κ=0.65-1.00). Detection of user self-harm indicators was particularly accurate, even in conversations where risk was expressed subtly. <strong>Conclusions:</strong> In this initial validation study, ASTRA reliably identified multiple forms of mental health–related risk behaviors at the conversation level. These findings support the feasibility of independent safety monitoring systems as a complement to AI tools used in mental health contexts and underscore the need for further evaluation using larger and real-world datasets.
Digitally Delivered Cognitive Behavioral Interventions for Alcohol and Other Drug Use: Meta-Analysis Across Consumption and Psychosocial Outcomes
<strong>Background:</strong> Cognitive behaviorally based interventions have broad appeal and potential for impact when treating adult alcohol and other drug use. Digitally delivered cognitive behaviorally based interventions (dCBIs) may offer this impact with the benefit of increased accessibility. Although prior reviews have indicated the benefits of dCBIs on substance use outcomes, the extension to psychosocial functioning outcomes is unknown. <strong>Objective:</strong> This meta-analysis provides an overview of dCBI effects across a range of functional end points. <strong>Methods:</strong> A literature search was conducted through October 2024. All primary and secondary reports of clinical trials of dCBI were obtained, and all available study end points were eligible for meta-analysis. Descriptive data were extracted and categorized into 1 of 13 different outcome types (eg, abstinence, quantity, cognitive, and quality of life) and into 2 broader outcome classes (ie, consumption and psychosocial). Robust variance estimation was used to conduct hypothesis tests on random effects pooled estimates with outcome class and comparison type as the primary subgroup variables of interest. <strong>Results:</strong> The study sample included 65 randomized trials (<i>K</i>=110 publications; 753 effect sizes) of dCBI for adult alcohol and other drug use. With respect to efficacy, dCBI as a stand-alone treatment in contrast to a minimal treatment control showed positive and statistically significant effects for consumption (<i>g</i>=0.27; <i>P<</i>.001; <i>I</i><sup>2</sup>=85.1%; <i>k</i>=31; <i>k<sub>es</sub></i>=134) and psychosocial (<i>g</i>=0.16; <i>P</i>=.008; <i>I</i><sup>2</sup>=75.2%; <i>k</i>=16; <i>k<sub>es</sub></i>=60) outcomes. As an addition to usual care, efficacy was demonstrated for consumption (<i>g</i>=0.23; <i>P<</i>.001; <i>I</i><sup>2</sup>=9.8%; <i>k</i>=20; <i>k<sub>es</sub></i>=65), but not psychosocial functioning. Efficacy compared to another digital or in-person intervention or cognitive behaviorally based intervention delivered by a therapist was not observed. Within the dCBI condition, large effect sizes were observed for both outcome classes (ie, 60%-80% of participants showed improvement relative to baseline), and effect size magnitude and statistical heterogeneity varied by the type of outcome examined. <strong>Conclusions:</strong> These results show a benefit for dCBI as a stand-alone therapy and an addition to usual care. Importantly, stand-alone effects were observed for both consumption and some psychosocial outcomes. This study is the first to offer a comprehensive look at dCBI intervention effects across a range of functional end points.
STAT+: Virginia governor vetoes legislation to create an advisory panel to lower the cost of prescription drugs
Virginia Gov. Abigail Spanberger has vetoed closely watched legislation to create an advisory panel to lower prescription drug costs, a setback to attempts by lawmakers across the United States to address the rising cost of medicines.
Unlike affordability boards in other states, the Virginia panel would have used Medicare as a benchmark. Rather than start from scratch to identify drugs considered expensive, each year the panel would have targeted the same drugs chosen by Medicare for price negotiations. The board would have also set upper payment limits to create a ceiling on what would be paid.
By doing so, Virginia would have leapfrogged plans by other states that are at varying stages of establishing affordability boards. Of the other nine states that have boards, none is eyeing all of the same drugs chosen annually by Medicare and only four have the authority to set upper payment limits.
STAT+: This spine surgery usually costs $1,400. Under No Surprises Act arbitration? $34,000
When health insurers contract with providers, they agree on prices for all kinds of procedures. For a lumbar laminectomy, a common spine surgery for ailments like herniated discs or arthritis, the median price is $1,400.
Out-of-network providers, those that don’t contract with health insurers, are getting 24 times that amount for the same surgery at the median — nearly $34,000 — through the No Surprises Act’s arbitration process. Some are even getting north of $100,000.
The lumbar laminectomy is just one example of the extraordinarily high awards being doled out under the flawed system created by the 2020 law. The law has successfully protected patients from unexpected bills, but it’s also been a major boon for providers. They’re not only securing massive sums when they win, but they’re also prevailing in over 80% of disputes.
How AI helped treat a newborn’s ultra rare disease. ‘It was almost like a light switch.’
In the first, tenuous weeks of her life, Jorie Kraus and her parents faced her possible death repeatedly. Muscles throughout her tiny body simply didn’t work properly. Her heart. Her legs. Her larynx. Even the involuntary action of breathing was labored, and constantly faltering.
In those panicked days, through a haze of terrible news and incomprehensible instructions, something incredible happened: A long-shot attempt to discover the root cause of her problems identified a widely available, yet previously unknown, treatment.
Roundtables: Inside the Musk v. Altman Trial
Listen to the session or watch below
Elon Musk lost his suit against OpenAI, in which he alleged CEO Sam Altman and President Greg Brockman had deceived him over the company’s non-profit status.
Watch as AI reporter and attorney Michelle Kim, who covered the trial for MIT Technology Review, joins in conversation with editor in chief Mat Honan to go behind the scenes of the trial and the implications for the AI race.
Speakers: Mat Honan, Editor in Chief, and Michelle Kim, AI Reporter
Recorded on May 19, 2026
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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
- Musk v. Altman week 2: OpenAI fires back, and Shivon Zilis reveals that Musk tried to poach Sam Altman
- Musk v. Altman week 3: Musk and Altman traded blows over each other’s credibility. Now the jury will pick a side.
- Here’s why Elon Musk lost his suit against OpenAI
Kraig Biocraft Labs Creates Immortalized Silk Gland Cell Line
Kraig Biocraft Laboratories reports that company scientists created an immortalized silk gland cell line which Kraig officials say could form the foundation for a next-generation biotech platform with potential applications in biopharmaceutical manufacturing, therapeutic peptides, biologically active proteins, and advanced biomaterials.
This development significantly expands the potential commercial reach of the company’s core technologies beyond recombinant spider silk fibers and textiles, according to Kim Thompson, founder and CEO.
“This scientific achievement opens the potential for entirely new markets,” notes Thompson. “While our research team is expanding our portfolio and creating exciting new opportunities, management remains focused on the ongoing expansion of recombinant spider silk production and commercialization.”
The company’s research team successfully isolated and established immortalized silk gland cells that demonstrate strong proliferative capacity, stable serial passaging, and robust long-term viability in vitro, he adds. Early testing has shown exceptionally strong recombinant protein expression and production capabilities, positioning the platform as a promising candidate for scalable industrial bioprocessing and recombinant protein manufacturing, continues Thompson.
“The potential applications for this technology are extraordinarily broad,” maintains Xiaoli Zhang, PhD, Kraig Labs’ CSO. “We believe these immortalized silk gland cells could become the basis for a highly versatile biotechnology platform capable of supporting future work in therapeutics, vaccines, recombinant proteins, and next-generation biomaterials.”
The immortalized cell line has also reportedly demonstrated adaptability toward suspension culture systems, which are critical for large-scale industrial manufacturing and modern bioprocessing. This capability could allow the platform to integrate with conventional bioprocessing infrastructure and support more efficient, scalable, and cost-effective production systems, points out Zhang.
The post Kraig Biocraft Labs Creates Immortalized Silk Gland Cell Line appeared first on GEN – Genetic Engineering and Biotechnology News.
Smart Wristband Detects Cardiac Arrest with 92% Sensitivity in Early Clinical Study
A wearable smart wristband that continuously measures blood flow at the wrist may be able to detect cardiac arrest and automatically trigger emergency alerts, according to findings from the DETECT-1b study published in the journal Circulation: Arrhythmia and Electrophysiology. Researchers at Radboud University Medical Center found that the technology detected shockable cardiac arrest events with 92% sensitivity during controlled clinical procedures in patients with ventricular arrhythmias. The findings are an early indication that monitoring the condition via a wearable device technology could eventually help reduce delays in providing care for out-of-hospital cardiac arrest by automatically alerting emergency medical systems or nearby responders.
“Our findings are important because many out-of-hospital cardiac arrests are unwitnessed. A smart technology wristband capable of automatically detecting cardiac arrest and triggering an alert could function as a digital witness,” said senior author Judith Bonnes, MD, PhD, a cardiologist at Radboud University Medical Center in the Netherlands. “With the device automatically notifying emergency services or nearby trained responders, help could arrive sooner, which may significantly improve survival chances.”
The DETECT-1b study was designed to evaluate a medically certified smart wristband that uses a light-based sensing method called photoplethysmography (PPG) that is capable of measuring blood flow changes in the wrist. The wearable device, called CardioWatch, continuously monitored pulse-related blood flow signals during procedures in which dangerous heart rhythms were intentionally induced under controlled conditions in people known to have abnormal heart rhythms.
The research is a follow-on of the DETECT-1a study. “In the DETECT-1a, we developed a wrist-derived photoplethysmography (PPG) algorithm for cardiac arrest detection using patient data from induced short-lasting circulatory arrests, achieving 98% sensitivity,” the researchers wrote.
DETECT-1b was developed as a prospective multicenter validation study involving adults undergoing ventricular tachycardia ablation or subcutaneous implantable cardioverter-defibrillator implantation. During these procedures, pulseless ventricular tachycardia or ventricular fibrillation was induced briefly to test cardiac function and device response. While this was occurring, the researchers recorded electrocardiogram and invasive blood pressure measurements as reference standards while patients wore the CardioWatch.
The study enrolled 49 adults in the Netherlands. Participants had a median age of 66 years, 84% were men, and many had existing cardiovascular disease. Seven participants had a prior history of cardiac arrest, 69% had experienced myocardial infarction, and approximately half had moderately or severely reduced left ventricular function.
For this study, the researchers evaluated 125.5 hours of data from the wristbands of the participant and identified 59 shockable cardiac arrest events among 26 patients. Fifty of the identified events were pulseless ventricular tachycardia and nine involved ventricular fibrillation. The algorithm detected 92% of all cardiac arrest events overall, including 100% of ventricular fibrillation episodes and 90% of pulseless ventricular tachycardia events.
“This is the first external validation in patients of a wearable-based cardiac arrest detection model, demonstrating that wrist-derived PPG reliably detects shockable cardiac arrest, with 100% sensitivity for VF,” the researcher wrote.
The algorithm developed to detect these events works by continuously monitoring the amplitude of PPG signals collected at the wrist. When signal amplitude decreases, the embedded algorithm evaluates signal quality to determine whether pulse-related peaks remain detectable. If pulse signals disappear, the algorithm triggers a cardiac arrest alarm.
The researchers also uncovered a number of false-positive alerts. Thirty-three alerts occurred in the absence of pulseless ventricular tachycardia or ventricular fibrillation. Nevertheless, of the 33 alert, 24 occurred during hemodynamically tolerated ventricular tachycardia and were considered clinically relevant. In total, nine of the alerts from 125.5 hours of monitoring were classified as false positives.
Lead study author Roos Edgar, a technical physician at Radboud, noted that the technology differs from prior cardiac arrest detection methods because the wrist-based design allows continuous monitoring during routine daily life. While many commercially available smart watches contain similar PPG sensors, they are not equipped to detect cardiac arrest.
The technology has the potential to be used for patients at elevated risk for cardiac arrest, including patients with ventricular arrhythmias or implantable cardioverter-defibrillators. The researchers said future deployment could also involve directly integrating the wristband’s alerts with emergency dispatch systems.
“The goal is to connect the wristband to emergency dispatch centers and volunteer responder networks in the Netherlands so that nearby rescuers and ambulance services can be alerted immediately when cardiac arrest is detected,” Bonnes said.
Future studies are expected to evaluate how the system performs during everyday activities, exercise and sleep, conditions that may create additional signal noise and complicate detection.
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