Tag: News Approved

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STAT+: FDA approves Sanofi diabetes drug for children with stage 3 diabetes

WASHINGTON — The Food and Drug Administration on Friday approved teplizumab, a type 1 diabetes drug developed by Sanofi, for children aged 8 and older with stage 3 diabetes. 

The drug was selected to go through a speedy review program launched last year by former FDA Commissioner Marty Makary, but the agency missed its goal date of April 21 to deliver a decision. 

STAT previously reported that Sanofi asked to pull its drug out of the program after former top drug regulator Tracy Beth Høeg disagreed with a staff decision to approve the drug. It’s rare for a center director, and particularly a political appointee like Høeg, to get involved in individual scientific reviews. 

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WHO director-general is profoundly concerned after visit to Ebola outbreak area

The director-general of the World Health Organization is “really worried” about the Ebola outbreak in the Democratic Republic of the Congo and Uganda, already the third largest on record. 

In an exclusive interview with STAT, Tedros Adhanom Ghebreyesus described the conditions he saw after returning from his second visit to the affected area since the outbreak was declared on May 15, and designated a public health emergency of international concern on May 17. Already there have been at least 708 confirmed cases combined in the two countries, 141 of whom have died. 

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Opinion: ‘I’m pretty much all in’: An interview with a woman starting medical residency at almost 73

Below is a lightly edited, AI-generated transcript of the “First Opinion Podcast” interview with Dawn Zuidgeest-Craft. Be sure to sign up for the weekly “First Opinion Podcast” on Apple Podcasts, Spotify, or wherever you get your podcasts. Get alerts about each new episode by signing up for the “First Opinion Podcast” newsletter. And don’t forget to sign up for the First Opinion newsletter, delivered every Sunday.

Torie Bosch: So I get a surprising number of ideas for First Opinion by watching TikTok. It’s for work, I swear. Recently, I came across a video of a woman proudly sharing the fact that her mother, age 72, had just completed medical school and matched into residency. I had to talk to the septuagenarian to find out more about going to medical school at an age when most people have already retired. And much to my delight, she agreed.

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Using a Virtual Reality CAVE–Based Mindfulness Intervention to Promote Mental Well-Being in Adolescents With Anxiety Symptoms: Pre-Post Mixed Methods Pilot Study

Background: Adolescent anxiety is a growing public health concern associated with significant social and emotional impairment. Mindfulness-based interventions (MBIs) have shown promise in reducing anxiety and improving well-being; however, engagement remains challenging. Virtual reality (VR)–based delivery may enhance immersion and attention, potentially addressing barriers of traditional mindfulness formats. Evidence on VR-based mindfulness interventions for adolescents, particularly in Hong Kong, remains limited. Objective: This study aimed to evaluate the feasibility and acceptability of a VR-MBI delivered via a CAVE, an enclosed VR environment with three projected walls displaying immersive natural scenes and ambient sounds, for adolescents with mild-to-moderate anxiety symptoms in Hong Kong. Secondary aims were to explore preliminary effects on psychological outcomes and physiological stress regulation and to identify facilitators and barriers to engagement. Methods: A mixed methods, single-group pre-post study was conducted with adolescents experiencing mild-to-moderate anxiety symptoms, recruited from secondary schools and youth service organizations in Hong Kong. Participants completed an 8-week group-based VR-MBI. Feasibility and acceptability were assessed using recruitment, attendance, retention, homework practice frequency, dropouts, and adverse events. Psychological outcomes were measured using the Depression Anxiety Stress Scale–21 and the Mindful Attention Awareness Scale. Heart rate variability indices, including the standard deviation of normal-to-normal intervals and root-mean-square of successive differences, were collected at baseline and postintervention using a wearable device. Focus group interviews explored participants’ experiences. Paired-sample tests and Wilcoxon signed rank tests examined pre-post changes, and qualitative data were analyzed using thematic analysis, with findings integrated through triangulation. Results: A total of 42 participants (mean age 14.88, SD 1.90 years; 20/42, 47.6% female; 22/42, 52.4% male) enrolled and completed both assessments. Attendance was high, with 73.8% (31/42) of participants attending at least 80% (8/10) sessions, and participants engaged in regular homework practice. No dropouts or adverse events were reported. No significant pre-post changes were observed in self-reported distress, anxiety, depression, stress, or trait mindfulness (all >.05). However, significant improvements were observed in both heart rate variability indices, standard deviation of normal-to-normal intervals (mean difference 17.6 ms, 95% CI −33.88 to −1.32; =.04; Cohen =0.38) and root-mean-square of successive differences (mean difference 20.20 ms, 95% CI −38.76 to −1.65; =.03; Cohen =0.39), which may suggest preliminary enhancements in physiological stress regulation. Qualitative findings suggested perceived benefits in emotional regulation, stress reduction, focus, and sleep, with the immersive environment and group-based format identified as key facilitators. Conclusions: The CAVE-based VR-MBI was feasible and acceptable for adolescents with mild-to-moderate anxiety symptoms in Hong Kong. Despite no significant changes in self-reported outcomes, physiological improvements and positive qualitative feedback suggest early benefits not captured by self-report measures. These findings support further investigation of using controlled designs and longer follow-up periods.
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Breaking Barriers in Student Mental Health Care With AI-Enhanced Group Cognitive Behavioral Therapy: Pilot Feasibility Study

Background: University students experience elevated psychological distress, with limited access to mental health services. While cognitive behavioral therapy (CBT) demonstrates efficacy for anxiety and depression, treatment gaps persist due to access barriers and insufficient between-session support. Large language model (LLM) chatbots could improve and scale CBT delivery. However, the scientific evaluation of chatbot-enhanced protocols is just emerging. Objective: This pilot study aimed to assess the feasibility, acceptability, and preliminary efficacy of an LLM-based ChatBot as an adjunct to group Unified Protocol (UP) therapy for between-session support in university students with subclinical anxiety and depression symptoms. Methods: A single-arm feasibility trial recruited university students aged 18 years and older with moderate subclinical symptoms (Social Phobia Inventory: 21‐40, Patient Health Questionnaire-9: 5‐14, or Generalized Anxiety Disorder-7: 5‐14), excluding those with current psychiatric disorders, suicidal ideation, or psychotropic medication use. The intervention comprised 4 weekly group UP counseling sessions complemented by an adjunctive Claude 3.7-Sonnet LLM ChatBot programmed with UP-based therapeutic prompts for between-session support rather than a stand-alone therapeutic agent. Primary feasibility outcomes included treatment adherence, chatbot engagement metrics, and system usability (System Usability Scale). Secondary outcomes assessed changes in generalized anxiety (Generalized Anxiety Disorder-7 Scale), social anxiety (Social Phobia Inventory), depression (Patient Health Questionnaire-9), and well-being (Short Warwick-Edinburgh Mental Wellbeing Scale) using paired tests. Qualitative feedback was collected through focus group interviews and analyzed using thematic analysis. Results: Of 72 screened participants, 37 met eligibility criteria and 19 initiated treatment (mean age 22.06, SD 1.78 years; 70.6% female). Retention was high with 17 completers (10.5% dropout rate). Among completers, 94.1% (16/17) attended ≥3 group sessions. The engagement with the CBT ChatBot was substantial: participants were active on a median of 23 days during the 34-day study period and exchanged a median of 15 messages in total. System usability was rated as excellent (mean 84.94, SD 10.98 out of 100). Pre-to-post comparisons revealed significant improvements in generalized anxiety (mean change −3.00, SD 3.46; =3.01, =.004; Cohen =0.71) and mental well-being (mean change +2.29, SD 3.65; =−2.17, =.02; Cohen =0.69). Social anxiety and depression showed nonsignificant trends toward improvement. Qualitative feedback highlighted the CBT ChatBot’s accessibility and nonjudgmental support while noting limitations in personalization. No adverse events or inappropriate chatbot interactions occurred. Conclusions: Augmenting a group UP therapy with an LLM ChatBot demonstrated high feasibility, acceptability, and preliminary efficacy signals for university students with subclinical symptoms. The hybrid intervention package achieved strong retention and engagement while maintaining safety. These findings support progression to a randomized controlled trial to definitively evaluate this technology-enhanced approach for expanding access to evidence-based mental health interventions.
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Passive Smart Home Monitoring for Delirium-Relevant Anomaly Detection in People Living With Dementia: Proof-of-Concept Study

<strong>Background:</strong> Delirium superimposed on dementia is associated with poor outcomes yet remains underdetected in home settings. Current detection relies on face-to-face clinical assessment (eg, the Confusion Assessment Method criteria), which is rarely applied outside hospitals. <strong>Objective:</strong> This proof-of-concept study developed a theory-driven framework for detecting delirium-consistent anomalous patterns in home-dwelling people with dementia, using passive smart home sensor data. <strong>Methods:</strong> The Technology Integrated Health Management dataset, an open access resource comprising a clinically derived cohort of older adults (aged 50 years) with a confirmed diagnosis of dementia or mild cognitive impairment, was used. The analysis included 13 patients who had at least 50% valid data for at least one 10-day analysis window, with data collected between April 1, 2019, and June 30, 2019. Individualized anomaly detection algorithms, including Isolation Forest and Long Short-Term Memory models, were applied to identify delirium-related anomalies within each participant. Predictor features consisted of theory-driven digital markers approximating key Confusion Assessment Method criteria, including agitation, disrupted sleep-wake cycles, and disorientation (indexed by activity entropy), along with clinically relevant indicators, such as physiological instability (early warning scores) and urinary tract infections. <strong>Results:</strong> Using matched thresholds, the Isolation Forest identified 77 anomalies (anomaly rate: 15.65%), and the Long Short-Term Memory model identified 78 anomalies (anomaly rate: 15.85%), with anomalies typically occurring in short temporal clusters; agreement between methods ranged from 0% to 40% across individuals. Feature importance analyses indicated that activity entropy, sleep quality, and early warning scores were the most influential features, with stronger interfeature correlations observed during anomaly periods than during nonanomaly periods. <strong>Conclusions:</strong> This study demonstrates the technical feasibility of detecting delirium-related anomalies through passive smart home monitoring. While lacking ground truth validation, the approach shows promise for early intervention in community settings. Future validation studies with clinically confirmed delirium labels are essential. <strong>Trial Registration:</strong>
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Prevalence and Predictors of Self-Reported Adverse Experiences in Digital Meditation Training: 2 Randomized Controlled Trials

Background: Digital meditation-based interventions (MBIs) reach vast global audiences with millions of active users, yet concerns persist about the frequency and nature of adverse experiences (ie, AExs) occurring during meditation training. Some researchers have argued that AExs are substantially underdetected and reflect iatrogenic harm caused by meditation (ie, adverse effects [AEfs]). Others contend that these experiences largely reflect common stressors that would be experienced without meditation. These competing perspectives underscore the need for further research, particularly in the context of digital MBIs, the most widely used form of meditation training. Objective: This study examined the prevalence, predictors, and subjective evaluations of AExs during a digital MBI and tested whether reported experiences may be caused by meditation practice via comparisons between meditation-exposed and nonexposed participants. Methods: Data were drawn from 2 trials of the Healthy Minds Program. Exploratory study 1 (n=315) consisted of a sample of distressed US undergraduate students to estimate the prevalence of AExs and identify baseline predictors. Preregistered confirmatory study 2 (n=594) sampled distressed US adults from all 50 states to replicate findings from study 1 and to examine participants’ subjective evaluations of AExs. Study 2 additionally compared AEx rates between participants who did and did not complete guided meditations to assess whether AExs could be caused by meditation exposure. Study 3 (n=87) used qualitative methods to analyze study 1 participants’ responses to an open-ended question regarding their strategies for coping with AExs. Results: In studies 1 and 2, 27.9% (88/315) and 10.1% (40/396) of participants, respectively, reported at least one AEx during the study period, with 6.7% (21/315) and 3% (12/396) reporting functional impairment, largely aligning with previous research. Critically, in study 2, rates of AExs did not significantly differ between participants who did and did not complete guided meditations, suggesting that these experiences were not caused by meditation practice. Higher baseline depression, anxiety, loneliness, experiential avoidance, and perceived barriers to meditation predicted more frequent AExs. In studies 1 and 2, 89.8% (79/88) and 90% (36/40) of participants who reported AExs, respectively, indicated that they were glad to have learned to meditate. Qualitative analyses showed that participants used diverse coping strategies, often using skills learned through the Healthy Minds Program. Conclusions: AExs were relatively common but occurred at comparable rates among participants who did and did not meditate, challenging claims that such experiences were caused by meditation practice in distressed individuals. Although a small subset of participants reported some degree of functional impairment, most evaluated their AExs as tolerable and described their overall MBI experience as positive. Together, these findings highlight the importance of distinguishing AExs that likely reflect epiphenomena of preexisting distress or symptoms from iatrogenic harm attributable to MBIs. Trial Registration: Study 1: ClinicalTrials.gov NCT04741529; https://clinicaltrials.gov/study/NCT04741529; Study 2: ClinicalTrials.gov NCT06282523; https://clinicaltrials.gov/study/NCT06282523
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STAT+: Trump administration revisits policy to close Medicare drug price negotiation loophole

WASHINGTON — The Trump administration on Friday proposed to change a policy that is designed to prevent drugmakers from avoiding Medicare price negotiation by adding active ingredients to drugs. 

The policy is part of an annual proposed rule that establishes the process that the Centers for Medicare and Medicaid Services uses to choose the next 20 drugs and biologics for price negotiation. Those drugs will be announced by Feb. 1, 2027, and their negotiated prices will take effect in 2029. The administration also considered a similar policy last year but put off a decision to study it further.

Medicare must wait seven to 11 years after a product is approved by the Food and Drug Administration before it can negotiate its price, depending on the type of medicine. Biologics that are typically administered in doctor offices get more time than drugs taken orally. 

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Distribution of bladder afferent activity across the sacral roots in sheep shows marked individual variation: implications for neuroprosthesis design

ObjectiveImplantable sacral anterior root stimulators enable bladder emptying after spinal cord injury but do not prevent reflex incontinence. A closed-loop neuroprosthesis that detects and inhibits reflex bladder contractions could address this, but first, reliable detection of bladder fullness from the sacral roots. Further, the distribution of afferent bladder activity between sacral roots, and the relationship between efferent and afferent activity within each root, remains unclear and must be clarified to guide implant design.MethodsElectrode books were implanted on the S1–S3 extra-dural sacral roots bilaterally in six terminally anesthetized sheep. Afferent electroneurogram (ENG) was recorded concurrently from all implanted roots during filling cystometries and correlated with bladder pressure. Each root was individually electrically stimulated and the bladder pressure response recorded. Post-mortem morphometric analysis determined fiber size distribution in each root.ResultsOverall, S2 ENG activity showed the highest correlation with bladder pressure, and electrical stimulation of S2 and S3 produced the greatest increases in bladder pressure. Fiber size distribution did not correlate with either ENG activity or bladder pressure response. Significant variation was identified between individual sheep, but notably, in four of six sheep, a single sacral root had both the highest ENG correlation to bladder pressure and the greatest bladder response to stimulation.SignificanceThis study demonstrates reliable recording of bladder afferents from sacral roots using clinically applicable electrodes. It provides the first systematic recording of bladder ENG concurrently across three pairs of sacral roots in multiple animals, and the first characterization of signal distribution between roots. Significant individual variation is identified, impacting the design of future implantable sacral neuroprostheses for bladder control.