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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Long-Read DNA Test Could Replace 15 Existing Tests for Rare Diseases

Researchers in the Netherlands have developed a DNA test for rare diseases that can provide much more comprehensive results than standard diagnostics in a shorter amount of time. A study published today in the New England Journal of Medicine reports that this new approach could replace 15 other genetic tests with a single analysis while increasing the number of patients who successfully receive a diagnosis. 

Taken together, all rare diseases affect approximately 400 million people worldwide. Of the more than 7,000 rare diseases that have been identified so far, about 80% are caused by genetic mutations. Obtaining a diagnosis can be critical for people suspected of having a rare disease, offering them perspective for the future, better guidance and treatment, and less uncertainty. However, these patients often have to undergo multiple rounds of testing and wait years before receiving a definitive answer. 

The new test is based on long-read genome sequencing, a technology that can read significantly longer stretches of DNA before assembling them into a complete genome. While conventional genomic tests typically read fragments around 300 nucleotides long, long-read sequencing can analyze stretches of up to 20,000 nucleotides at a time. The longer reads make it easier to accurately assemble the full genome, providing a more complete picture of the patient’s genetic makeup. 

“Thanks to long reads, we obtain an even more complete view of DNA and can detect complex and hard-to-find abnormalities. We then link these to specific conditions,” says Alexander Hoischen, PhD, professor of genomic technologies at Radboud University Medical Center. “In this way, our knowledge grows and we can make more diagnoses.”

In addition, the test can detect epigenetic modifications in the genome that affect gene function without altering the underlying DNA sequence. Although these modifications can be responsible for some rare disorders, conventional testing methods are currently unable to detect them. 

“With current diagnostics, this requires additional specialized tests, but with long reads we capture these modifications as a bonus—two in one,” explains Christian Gilissen, PhD, professor of genome bioinformatics at Radboud University Medical Center.

Earlier this year, the technology was used as part of the National Undiagnosed Hackathon, where over 140 experts across the Netherlands came together to search for a diagnosis for 33 families. Long-read sequencing was used to map their DNA in detail, leading to five new confirmed diagnoses within two days as well as strong suspicion of a diagnosis for another eight families. 

As the number of rare disease diagnoses continues to rise, this new test could make the diagnostic process much faster and more efficient. Long-read sequencing could also help researchers identify and investigate the genetic drivers of rare conditions, many of which remain largely understudied. 

Based on these findings, Lisenka Vissers, PhD, professor of translational genomics at Radboud University Medical Center, calls for the technology to be adopted worldwide as the first choice diagnostic approach when testing patients suspected to have a rare genetic disorder. 

The post Long-Read DNA Test Could Replace 15 Existing Tests for Rare Diseases appeared first on Inside Precision Medicine.

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 PodcastsSpotify, 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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<![CDATA[Alixorexton boosts wakefulness in type 1 narcolepsy, shows cognitive and fatigue gains, and hints at ADHD and neurodegenerative uses.]]>
<![CDATA[Let’s take a look at the next generation of pharmacotherapies for mood and anxiety disorders. ]]>
<![CDATA[Experts unpack why most psychiatric care relies on off‑label meds, how neuroscience guides safe polypharmacy, and what could reshape FDA approvals.]]>
<![CDATA[Once-nightly regular-release lithium cuts kidney risk, maintains bipolar control, improves adherence, and guides safer serum levels.]]>

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.

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>