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Received — 11 October 2025 ⏭ Favourites

For the first time, scientists pinpoint brain cells linked to depression

✇Latest Science News -- ScienceDaily
11 October 2025 at 13:56
Scientists identified two types of brain cells, neurons and microglia, that are altered in people with depression. Through genomic mapping of post-mortem brain tissue, they found major differences in gene activity affecting mood and inflammation. The findings reinforce that depression has a clear biological foundation and open new doors for treatment development.

Prevalence of Dropout and Influencing Factors in Digital Psychosocial Intervention Trials for Adult Illicit Substance Users: Systematic Review and Meta-Analysis

✇Journal of Medical Internet Research
By: Jiayi Li · Xinyi Liu · Xiayu Du · Tingni Mi · Zhihong Ren
11 October 2025 at 05:45
Background: Globally, the number of illegal drug users is rising, posing mental and physical health challenges and increasing societal burdens. Despite a significant need for treatment, only about 10% of these individuals receive it worldwide, often with poor adherence. Traditional treatments, while effective, suffer from high dropout rates due to limitations. The COVID-19 pandemic has spurred the growth of digital interventions like apps and online platforms, offering flexibility and cost-effectiveness that better meet patient needs and improve engagement. However, addressing the persistently high dropout rates in these online treatments is crucial and necessitates further research. Objective: This study aimed to estimate dropout rates among adults with illicit drug use participating in digital psychosocial intervention trials, and to identify factors associated with attrition. Methods: We conducted a systematic search of five major databases for English-language randomized trials published up to January 27, 2025. A total of 40 studies (80 arms; 9,563 participants) reporting 46 dropout rate estimates were included. A random-effects model was used to calculate pooled dropout rates, with meta-regression and subgroup analyses exploring potential moderators. The study was registered on PROSPERO (CRD42024534389). Results: At post-test, the pooled dropout rate in the intervention group across 17 studies was 22.4% (95% CI: 12.4%–37.2%). Dropout was significantly associated with education level, employment status, baseline clinical diagnosis, intervention frequency, and initial medication use. During the longest follow-up (29 studies), the dropout rate was 27.9% (95% CI: 18.8%–39.3%), with marital status, recruitment source, medication frequency, and intervention modality as significant predictors. Control group dropout rates were 25.9% and 28.3%, both higher than those in the intervention group. Conclusions: This meta-analysis revealed substantial dropout among adults with illicit drug use receiving digital psychosocial interventions. Targeted modifications to intervention design may improve engagement and long-term retention. Clinical Trial: The study was registered on PROSPERO (CRD42024534389).

Combined Immersive and Nonimmersive Virtual Reality With Mirror Therapy for Patients With Stroke: Systematic Review and Meta-Analysis of Randomized Controlled Trials

✇Journal of Medical Internet Research
By: Chaoran Gao · Yuan Chen · Yixin Wei · Yitong Qiu · Huiyan Song · Chenfan Gui · Qiang Gao
11 October 2025 at 04:15
Background: Stroke frequently leads to various functional impairments. Both virtual reality (VR) and mirror therapy (MT) have shown efficacy in stroke rehabilitation. In recent years, the combination of these two approaches has emerged as a potential treatment for stroke patients. Objective: This systematic review and meta-analysis aim to evaluate the efficacy of combined immersive and non-immersive VR with MT in stroke rehabilitation. Methods: Five electronic databases were systematically searched for relevant articles published up to Jan. 2025. Randomized controlled trials (RCTs) that investigated combination treatment of VR and MT for participants with stroke were included. A grey literature search was also conducted. The risk of bias and the certainty of the evidence were assessed using the Cochrane collaboration’s tool and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) guideline, respectively. Results: A total of 475 participants from 14 RCTs were included, of which 7 were eligible for meta-analysis. Meta-analysis revealed significant improvements in upper extremity (UE) motor function and hand dexterity, as evidenced by Fugl-Meyer assessment of upper extremity (FMA-UE) (MD 3.50, 95% CI 1.47 to 5.53; P=0.0007), manual function test (MFT) (MD 2.15, 95% CI 1.22 to 3.09; P6 months or not) revealed significant differences in the FMA-UE outcome. However, the pooled FMA-UE improvement did not consistently exceed the established minimal clinically important difference (MCID; 4.25–7.25), indicating that while statistically significant, the clinical meaningfulness of the observed effect remains uncertain. Narrative evidence also suggested potential benefits for lower extremity function, dynamic balance, and quality of life, though these findings were not meta-analyzed and should be interpreted with caution. Conclusions: Moderate-quality evidence supports VR-MT as a promising nonpharmacological intervention to improve upper extremity function and hand dexterity in stroke rehabilitation. While the intervention demonstrates statistically significant effects, it does not reach the minimum clinically important difference for the FMA-UE outcome. Preliminary descriptive evidence indicates possible advantages for lower extremity function, balance, and quality of life. Clinical Trial: PROSPERO CRD42024572150

Mentalizing Without a Mind: Psychotherapeutic Potential of Generative AI

✇Journal of Medical Internet Research
By: Karen Yirmiya · Peter Fonagy
10 October 2025 at 22:30
This paper explores the integration of generative artificial intelligence (AI) into psychotherapeutic practice through the lens of mentalization theory, with a particular focus on epistemic trust—a critical relational mechanism that facilitates psychological change. We critically examine AI’s capability to replicate core therapeutic components, such as empathy, embodied mentalizing, biobehavioral synchrony, and reciprocal mentalizing. Although current AI systems, especially large language models, demonstrate significant potential in simulating emotional responsiveness, cognitive empathy, and therapeutic dialogue, fundamental limitations persist. AI’s inherent lack of genuine emotional presence, reciprocal intentionality, and affective commitment constrains its ability to foster authentic epistemic trust and meaningful therapeutic relationships. Additionally, we outline significant risks, notably for individuals with complex trauma or relational vulnerabilities, highlighting concerns regarding pseudo-empathy, mistaking phenomenal experience for objective reality (psychic equivalence), fruitless ungrounded pursuit of social understanding (hypermentalization), and epistemic exploitation of individuals in whom artificial understanding by AI triggers excessive credulity. Nonetheless, we propose ethically informed pathways for integrating AI to enhance clinical practice, therapist training, and client care, particularly in augmenting human capacities within group and adjunctive therapy contexts. Paradoxically, AI could support psychotherapists in improving their capacity to mentalize, improve their understanding of their clients, and provide such understanding within the moral constraints that normally govern their work. This paper calls for careful ethical regulation similar to that limiting genetic manipulation, interdisciplinary research, and clinician involvement in shaping future AI-based psychotherapeutic models, emphasizing that AI’s role should complement rather than replace the irreplaceable relational core of psychotherapy.
  • ✇Latest Science News -- ScienceDaily
  • Scientists unlock nature’s secret to a cancer-fighting molecule
    Researchers have cracked the code behind how plants make mitraphylline, a rare cancer-fighting molecule. Their discovery of two critical enzymes explains how nature builds complex spiro-shaped compounds. The work paves the way for sustainable, lab-based production of valuable natural medicines. Supported by international collaborations, the findings spotlight plants as powerful natural chemists.
     
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Scientists unlock nature’s secret to a cancer-fighting molecule

✇Latest Science News -- ScienceDaily
9 October 2025 at 15:32
Researchers have cracked the code behind how plants make mitraphylline, a rare cancer-fighting molecule. Their discovery of two critical enzymes explains how nature builds complex spiro-shaped compounds. The work paves the way for sustainable, lab-based production of valuable natural medicines. Supported by international collaborations, the findings spotlight plants as powerful natural chemists.
  • ✇STAT
  • STAT+: Sarepta to seek approval for gene therapy in rare form of muscular dystrophy Jason Mast
    An experimental gene therapy from Sarepta Therapeutics increased levels of the gene missing in an ultra-rare form of muscular dystrophy, according to data the company presented Friday. The company has said it plans to file for approval in the disease, known as limb-girdle muscular dystrophy (LGMD) 2E. That would make it the first approved treatment in LGMD, a broad collection of highly rare diseases that can deprive patients of the ability to walk and in some cases shorten life. But it is lik
     
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STAT+: Sarepta to seek approval for gene therapy in rare form of muscular dystrophy

✇STAT
By: Jason Mast
11 October 2025 at 05:56

An experimental gene therapy from Sarepta Therapeutics increased levels of the gene missing in an ultra-rare form of muscular dystrophy, according to data the company presented Friday.

The company has said it plans to file for approval in the disease, known as limb-girdle muscular dystrophy (LGMD) 2E. That would make it the first approved treatment in LGMD, a broad collection of highly rare diseases that can deprive patients of the ability to walk and in some cases shorten life. But it is likely to face a significant uphill battle. 

The LGMD 2E therapy relies on the same gene-ferrying virus that Sarepta uses in its other treatments, including its approved gene therapy for Duchenne muscular dystrophy, Elevidys, and experimental gene therapies for several other LGMD subtypes. 

Continue to STAT+ to read the full story…

© Charles Krupa/AP

Automated AI based identification of autism spectrum disorder from home videos

✇npj Digital Medicine
By: Dong Yeong Kim · Ryemi Do · Youmin Shin · Hewoen Sim · Hanna Kim · Sungchul Cho · Geonhee Lee · Seyeon Park · Boa Jang · Hyojeong Lim · Sungji Ha · Jaeeun Yu · Hangnyoung Choi · Junghan Lee · Min-Hyeon Park · Ayeong Cho · Chan-Mo Yang · Dongho Lee · Heejeong Yoo · Yoojeong Lee · Guiyoung Bong · Johanna Inhyang Kim · Haneul Sung · Hyo-Won Kim · Eunji Jung · Seungwon Chung · Jung-Woo Son · Jae Hyun Yoo · Sekye Jeon · Jinseong Jang · You Bin Lim · Jeeyoung Chun · Wooseok Choi · Sooyeon Lee · Sohyun Park · Jisung Ahn · Chae Rim Lee · Keun-Ah Cheon · Young-Gon Kim · Bung-Nyun Kim
10 October 2025 at 08:00

npj Digital Medicine, Published online: 10 October 2025; doi:10.1038/s41746-025-01993-5

Automated AI based identification of autism spectrum disorder from home videos

DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics

✇Most Recent Articles: Clinical Epigenetics
By: Erfan Aref-Eshghi · Arash B. Abadi · Mohammad-Erfan Farhadieh · Amirreza Hooshmand · Fatemeh Ghasemi · Leila Youssefian · Hassan Vahidnezhad · Taylor Martin Kerrins · Xiaonan Zhao · Mahdi Akbarzadeh · Hakon Hakonarson and Amir Hossein Saeidian
10 October 2025 at 08:00
DNA methylation is an epigenetic modification that regulates gene expression by adding methyl groups to DNA, affecting cellular function and disease development. Machine learning, a subset of artificial intell...
Received — 10 October 2025 ⏭ Favourites

李强签署国务院令，公布《生物医学新技术临床研究和临床转化应用管理条例》

✇36氪
10 October 2025 at 17:04
国务院总理李强日前签署国务院令，公布《生物医学新技术临床研究和临床转化应用管理条例》（以下简称《条例》），自2026年5月1日起施行。《条例》旨在规范生物医学新技术临床研究和临床转化应用，促进医学科学技术进步和创新，保障医疗质量安全，维护人的尊严和健康。（新华社）

A human pan-disease blood atlas of the circulating proteome

✇AAAS: Table of Contents
By: María Bueno Álvez · Sofia Bergström · Josefin Kenrick · Emil Johansson · Mikael Åberg · Murat Akyildiz · Ozlem Altay · Hilda Sköld · Konstantinos Antonopoulos · Emmanouil Apostolakis · Yasin Hasan Balcioglu · Anna Bergström · Göran Bergström · Sophia Björkander · Suzanne Egyhazi Brage · Petter Brodin · Lynn Butler · Sara Cajander · Hanna Danielsson · Murat Dayangac · Gizem Dinler-Doganay · Levent Doğanay · Gunilla Enblad · Malin Enblad · Linn Fagerberg · Sara Falck-Jones · Anna Färnert · Mattias Forsberg · Laura Gonzalez · Anders Gummesson · Karin Gunnarsson · Iva Gunnarsson · Ulf Gyllensten · Göran Hesselager · Andreas Hober · Martin Höglund · Marie Holmqvist · Begum Horuluoglu · Rebecka Hultgren · Maria Jesus Iglesias · Helena Janols · Fredric Johansson · Anette Johnsson · Lars Klareskog · David Kotol · Inger Kull · Marika Kvarnström · Maximilian Julius Lautenbach · Ulrika Liljedahl · Henrik Lindman · Cecilia Lindskog · Miklos Lipcsey · Ingrid E Lundberg · Adil Mardinoglu · Erik Melén · Lingqi Meng · Anne-Sophie Merritt · Jan Mulder · Mai Thi-Huyen
9 October 2025 at 14:02
Science, Ahead of Print.
Received — 9 October 2025 ⏭ Favourites

Enhancing ECG Classification in Cardiac Diagnostics: A Novel Approach Using Adaptive Focal Cross-Entropy Loss Function

✇IEEE Journal of Biomedical and Health Informatics - new TOC
7 May 2025 at 21:16
Heart disease is the leading cause of mortality globally. Electrocardiograms (ECGs) are standard instruments for the examination of heart conditions, but traditional analysis is time-consuming and prone to errors. Novel advances in artificial intelligence have improved ECG classification. However, some limitations remain, such as poor interpretability, computational cost, and class imbalance. This study proposes a novel deep learning algorithm based on Depthwise Separable Residual Attention called DRA-ECG and a customized Adaptive Focal Cross-Entropy (AFCE) loss function for cardiac condition classification. This proposed methodology leverages the Continuous Wavelet Transform (CWT) method to transform 1D raw ECG signals into 2D scalograms to enhance feature representation and training. The proposed customized AFCE loss function incorporated into the DRA-ECG model addresses the class imbalance problem and boost the performance of the model. More so, this study incorporates edge feature detection as a preprocessing technique to denoise and enhance the trainable features of the 2D scalograms for optimal feature representation. The proposed DRA-ECG model achieves a high accuracy of 98.17%, recall of 95.78%, F1-score of 95.82%, and precision of 95.89%. This study shows that the results achieved by the proposed DRA-ECG surpass the current state-of-the-art and existing research works, concerning classification performance and generalization ability in ECG classification, which underlines the effectiveness of the novel AFCE loss function for ensuring high-classification accuracy and robustness. The proposed novel methodology enhances heart disease classification and provides a robust and reliable solution for medical diagnosis, addressing the major drawbacks of existing models.

Automated Depression Detection From Text and Audio: A Systematic Review

✇IEEE Journal of Biomedical and Health Informatics - new TOC
16 May 2025 at 21:18
Depression is a prevalent mental health disorder that presents significant challenges for timely diagnosis and intervention. Automated Depression Detection (ADD) systems using text and audio offer scalable mental health assessment solutions. This review systematically evaluates 65 studies published between 2018 and 2024, focusing on ADD methods that utilize machine learning models with multimodal data. We examine key methodologies, including data augmentation, multimodal fusion, and feature extraction, along with state-of-the-art ADD systems. The review emphasizes the need for culturally adaptable, high-quality datasets and interpretable models for clinical use. We also identify gaps in longitudinal data and real-world applications. Future research should focus on developing clinically integrated, cross-cultural ADD systems that are interpretable, scalable, and robust. The findings of this review contribute to the research field by providing a comprehensive overview of existing methodologies, identifying gaps in the current literature, and offering insights for future advancements in depression detection using speech and text analysis.
  • ✇STAT
  • STAT+: Digital health M&A picks up, driven by AI and private equity Mario Aguilar
    Earlier this year, Tom Stanis was puzzling through what was next for his startup Story Health, which helps providers care for people with heart failure. The company had some big-name customers and plans to expand, but it last raised money in 2022. Stanis saw two options: shake more cash out of a stingy venture capital market, or sell. Armed with $275 million in fresh funding and a built-in customer base, artificial intelligence company Innovaccer made the answer easy. It gobbled up Story Heal
     
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STAT+: Digital health M&A picks up, driven by AI and private equity

✇STAT
By: Mario Aguilar
8 October 2025 at 16:30

Earlier this year, Tom Stanis was puzzling through what was next for his startup Story Health, which helps providers care for people with heart failure. The company had some big-name customers and plans to expand, but it last raised money in 2022. Stanis saw two options: shake more cash out of a stingy venture capital market, or sell.

Armed with $275 million in fresh funding and a built-in customer base, artificial intelligence company Innovaccer made the answer easy. It gobbled up Story Health for an undisclosed mix of equity and cash in September. 

Story Health is the fourth Innovaccer acquisition in about a year as it aims to become the default AI platform for health systems. CEO Abhinav Shashank plans to rapidly expand and to “accelerate that development through M&A,” he told STAT.

Innovaccer’s shopping spree is just one example of a trend playing out in digital health: big, well-funded companies with momentum are snapping up smaller players.

Continue to STAT+ to read the full story…

© Adobe

  • ✇STAT
  • Opinion: STAT+: 5 things to consider to bring ambient digital scribes to clinical research Elise Felicione
    About a year ago, I logged into MyChart the day before my annual wellness visit and saw a new consent form. My doctor wanted to record our visit so artificial intelligence could create notes and update my medical record. I agreed. Just hours after my visit ended, a comprehensive, accurate visit summary appeared in MyChart. As a career clinical trialist, I immediately wondered: What if this tool were used for trial visits? Could it enable a breakthrough in research or patient engagement? How w
     
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Opinion: STAT+: 5 things to consider to bring ambient digital scribes to clinical research

✇STAT
By: Elise Felicione
8 October 2025 at 16:30

About a year ago, I logged into MyChart the day before my annual wellness visit and saw a new consent form. My doctor wanted to record our visit so artificial intelligence could create notes and update my medical record. I agreed. Just hours after my visit ended, a comprehensive, accurate visit summary appeared in MyChart.

As a career clinical trialist, I immediately wondered: What if this tool were used for trial visits? Could it enable a breakthrough in research or patient engagement? How will clinical research adapt to what could be a “new normal” in medical documentation?

Driven by a crisis of clinician burnout, ambient digital scribes (ADS) like the one my doctor used are being rapidly adopted. Studies suggest these digital scribes can reduce documentation burden, improve clinician efficiency, and potentially enhance the patient experience. By digitizing conversations in real time, these scribes open possibilities beyond routine care, particularly in clinical research and trials.

Continue to STAT+ to read the full story…

© Adobe

Presenting Artificial Intelligence Predictions Based on Electronic Medical Records to Clinicians in Hospitals: A Systematic Review

✇IEEE Journal of Biomedical and Health Informatics - new TOC
21 May 2025 at 21:17
Our objective was to investigate how artificial intelligence (AI) predictions calculated on structured hospital data are presented to clinicians. We performed a systematic review of 5 databases and 9 other reviews, identifying 31 studies on 21 implemented clinical AI systems. We report current approaches to presenting AI predictions to clinicians, whether and how interaction on the user interface (UI) is used, how UIs have been evaluated and the extent to which clinicians have been involved in UI design and testing. The results indicate variation across systems in presentation content and styles, evaluation methods, and interaction approaches. Half of the systems implemented a co-design approach to UI development. Our findings provide valuable insights for future AI-based clinical decision support system designers, clinical AI researchers and healthcare organisations seeking to implement clinical AI solutions.

Comparing Text-Based Clinical Risk Prediction in Critical Care: A Note-Specific Hierarchical Network and Large Language Models

✇IEEE Journal of Biomedical and Health Informatics - new TOC
27 May 2025 at 21:16
Clinical predictive analysis is a crucial task with numerous applications and has been extensively studied using machine learning approaches. Clinical notes, a vital data source, have been employed to develop natural language processing (NLP) models for risk prediction in healthcare with robust performance. However, clinical notes vary considerably in text composition–written by diverse healthcare providers for different purposes–and the impact of these variations on NLP modeling is also underexplored. It also remains uncertain whether the recent Large Language Models (LLMs) with instruction-following capabilities can effectively handle the risk prediction task out-of-the-box, especially when using routinely collected clinical notes instead of polished text. We address these two important research questions in the context of in-hospital mortality prediction within the critical care setting. Specifically, we propose a supervised hierarchical network with note-specific modules to account for variations across different note categories, and provide a detailed comparison with strong supervised baselines and LLMs. We benchmark 34 instruction-following LLMs based on zero-shot, few-shot, and chain-of-thought prompting with diverse prompt templates. Our results demonstrate that the note-specific network delivers improved risk prediction performance compared to established supervised baselines from both measurement-based and text-based modeling. In contrast, LLMs consistently underperform on this critical task, despite their remarkable performances in other domains. This highlights important limitations and raises caution regarding the use of LLMs for risk assessment in the critical setting. Additionally, we show that the proposed model can be leveraged to select informative clinical notes to enhance the training of other models.
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