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

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

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

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.
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  • 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
     

STAT+: Sarepta to seek approval for gene therapy in rare form of muscular dystrophy

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

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

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...
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  • 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
     

STAT+: Digital health M&A picks up, driven by AI and private equity

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…

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Efficient and accurate search in petabase-scale sequence repositories

Nature, Published online: 08 October 2025; doi:10.1038/s41586-025-09603-w

MetaGraph enables scalable indexing of large sets of DNA, RNA or protein sequences using annotated de Bruijn graphs.

Stop treating code like an afterthought: record, share and value it

Nature, Published online: 07 October 2025; doi:10.1038/d41586-025-03196-0

Scientists, research institutions, funders, libraries and publishers must all improve software practices.

HALO: hierarchical causal modeling for single cell multi-omics data

Nat Commun. 2025 Oct 7;16(1):8892. doi: 10.1038/s41467-025-63921-1.

ABSTRACT

Though open chromatin may promote active transcription, gene expression responses may not be directly coordinated with changes in chromatin accessibility. Most existing methods for single-cell multi-omics data focus only on learning stationary, shared information among these modalities, overlooking modality-specific information delineating cellular states and dynamics resulting from causal relations among modalities. To address this, the epigenome-transcriptome relationship can be characterized in relation to time as coupled (changing dependently) or decoupled (changing independently). We propose the framework HALO, adopting a causal approach to model these temporal causal relations on two levels. On the representation level, HALO factorizes these two modalities into both coupled and decoupled latent representations, revealing their dynamic interplay. On the individual gene level, HALO matches gene-peak pairs and characterizes their changes over time. HALO discovers analogous biological functions between modalities, distinguishes epigenetic factors for lineage specification, and identifies temporal cis-regulation interactions relevant to cellular differentiation and human diseases.

PMID:41057364 | PMC:PMC12504611 | DOI:10.1038/s41467-025-63921-1

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