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PubMed2026

Early infantile developmental and epileptic encephalopathy: clinical spectrum, diagnosis, outcomes, and evolving treatment strategies.

Early infantile developmental and epileptic encephalopathy (EIDEE) is among the most severe epilepsy syndromes, with onset before three months of age and an estimated incidence of approximately 10 per 100,000 live births. The 2022 International League Against Epilepsy classification unified the historically distinct Ohtahara syndrome and early myoclonic encephalopathy under a single diagnostic framework defined by frequent drug-resistant tonic and/or myoclonic seizures, an abnormal neurological examination, and an abnormal interictal electroencephalogram-most characteristically a burst-suppression pattern. This narrative review synthesizes the clinical, electrophysiological, neuroimaging, genetic, and therapeutic literature within the EIDEE framework. The clinical phenotype is characterized by central hypotonia, postnatal microcephaly, cortical visual impairment, and age-dependent syndromic evolution toward infantile epileptic spasms syndrome or Lennox-Gastaut syndrome in the majority of patients. Electroencephalography remains essential for syndromic classification, while systematic metabolic screening and early trio whole-exome or whole-genome sequencing are central to the etiologic workup, achieving diagnostic yields of 60-65%. The most commonly identified genetic causes include STXBP1, KCNQ2, and SCN2A variants. Outcomes are poor overall and strongly etiology-dependent: vitamin-responsive disorders carry a substantially more favorable prognosis, whereas mortality reaches 25% in genetic cohorts. Genotype-guided pharmacotherapy is now applicable to a clinically meaningful subset of patients, with sodium channel blockers, potassium channel openers, and emerging antisense oligonucleotide therapies representing important therapeutic advances. Gene therapy trials are underway but have encountered early safety signals, underscoring the vulnerability of this population. Critical unmet needs include earlier molecular diagnosis, precision therapies targeting developmental outcomes beyond seizure control, and prospective international registries to characterize the long-term natural history of EIDEE.

Humans↗

PubMed2026

Near and distance vergence facility provides complementary clinical information in concussion-related convergence insufficiency.

PURPOSE: To compare near and distance vergence facility testing in adolescents and young adults with concussion-related convergence insufficiency and evaluate changes following office-based vergence/accommodative therapy (OBVAM). METHODS: This secondary analysis of the CONCUSS randomized clinical trial evaluated vergence facility at near (40 cm) and distance (4 m) using a 12Δ base out/3Δ base in prism flipper. Participants aged 11-25 years with concussion-related convergence insufficiency were randomized to immediate or 6 weeks delayed OBVAM. Vergence facility was assessed at baseline, outcome time 1 assessment (after 12 therapy sessions for the immediate group and 6 weeks of watchful waiting for the delayed group), and outcome time 2 assessment (after both groups completed 16 therapy sessions). Agreement between near and distance vergence facility classifications was evaluated, and treatment-related changes were compared between groups. RESULTS: Of the 106 enrolled participants, 102 completed all study visits. At baseline, the near and distance vergence facility classifications demonstrated substantial discordance. Among 101 participants with both measures available, 49 demonstrated reduced distance vergence facility despite normal near vergence facility, whereas only two showed the opposite pattern (Cohen's κ = 0.12; p < 0.0001). Vergence facility improved following therapy in both treatment groups, with larger early improvements in the immediate-treatment group. CONCLUSIONS: Near and distance vergence facility testing provided complementary rather than interchangeable clinical information in adolescents and young adults with concussion-related convergence insufficiency. Both measures improved following vergence/accommodative therapy, supporting consideration of both testing distances in clinical assessment.

Humans↗

PubMed2026

Assessing the accuracy and efficiency of an electronic platform for managing childhood illnesses in rural China: A cluster randomized controlled trial.

OBJECTIVES: The Integrated Management of Childhood Illness (IMCI) faces challenges in capacity building and quality control. This trial aims to assess an electronic IMCI (eIMCI) platform in improving the effectiveness and efficiency in disease classification and management by community health workers (CHWs). DESIGN: Cluster randomized controlled trial. SETTING: Rural western China. PARTICIPANTS: 24 CHWs and 72 ill children aged 2 months to 5 years (3 children per CHW). CHWs were randomly assigned to intervention or control groups. INTERVENTIONS: The intervention CHWs received online training and performed disease management using the eIMCI platform featuring integrated training modules and decision-support tools. The control group received traditional face-to-face training and used paper-based IMCI protocols. MAIN OUTCOME MEASURES: Proportion of children correctly diagnosed or classified by CHWs, as determined by a pediatric specialist. Relative risk (RR) between groups was estimated using Poisson Generalized Linear Mixed Models incorporating a random intercept for CHW to account for clustering of children within individual CHWs and adjusting for key covariates at both the CHW and child levels. RESULTS: The intervention group (13 CHWs, 39 children) had a higher rate of correct classification (64.1%) compared to the control group (11 CHWs, 33 children) (39.4%, P = .056). Multivariable regression analysis confirmed this (RR = 2.1, 95% CI: 1.5-3.1; P < .001). No significant difference was found in correct treatment rates (38.5% vs. 27.3%, P = .316). Online training reduced time and costs by approximately 80%, though with a slight decrease in post-training evaluation scores. CONCLUSIONS: The eIMCI platform shows potential in enhancing IMCI implementation and significantly reducing the training burden in resource-limited settings. Trial registration: Chinese Clinical Trial Registry: ChiCTR2100042533, https://www.chictr.org.cn/showproj.html?proj=119995.

Humans↗

PubMed2026

Preoperative Proximal Migration of the Radial Head as an Independent Predictor of Suboptimal Outcomes After Osteochondral Autograft Transplantation for Capitellar Osteochondritis Dissecans: A Retrospective Cohort Study.

BACKGROUND: Osteochondral autograft transplantation (OAT) is widely performed for capitellar osteochondritis dissecans (OCD). However, preoperative predictors of suboptimal postoperative outcomes remain unclear. PURPOSE/HYPOTHESIS: The authors aimed to evaluate clinical outcomes after OAT for capitellar OCD and identify preoperative risk factors associated with suboptimal outcomes. They hypothesized that radiographic indicators of disease severity, including preoperative proximal migration of the radial head, lesion size, and lateral wall disruption, would be associated with suboptimal postoperative clinical outcomes. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: The records of adolescent athletes who underwent OAT for capitellar OCD with a minimum 2-year follow-up were retrospectively reviewed. Clinical outcomes included elbow range of motion (ROM) and Timmerman-Andrews (T-A) score. A suboptimal outcome was defined as a postoperative T-A score <160. Preoperative radiographs were used to measure proximal migration of the radial head relative to the coronoid process, hypertrophy of the radial head, OCD lesion area, and a 5-grade lateral wall disruption classification; measurement reliability was assessed. Multivariate logistic regression analysis was performed to identify independent predictors of a suboptimal outcome, and receiver operating characteristic (ROC) curve analysis was used to determine the optimal cutoff value for proximal migration. RESULTS: A total of 69 elbows (mean age, 13.6 years; mean follow-up, 48 months) were included. ROM and T-A scores improved significantly after OAT, and all athletes returned to any sports. Of these, 50 elbows (72%) achieved good outcomes, whereas 19 (28%) had suboptimal outcomes. Preoperative proximal migration was significantly greater in the suboptimal outcome group compared with the good outcome group (mean, 2.1 ± 2.3 vs 0.5 ± 1.7 mm; P = .002), as were lesion area (mean, 70 ± 16 vs 58 ± 20 mm2; P = .02) and lateral wall disruption grade (median, 5 vs 3; P = .01). On multivariate analysis, proximal migration of the radial head was the only independent predictor of a suboptimal outcome (adjusted OR, 1.47 per 1-mm increase; 95% CI, 1.03-2.09; P = .033). ROC analysis showed an area under the curve of 0.72 with an optimal cutoff of 2.2 mm (sensitivity, 56%; specificity, 84%). CONCLUSION: OAT resulted in significant clinical improvement in adolescents with capitellar OCD; however, 28% of patients were classified as having suboptimal outcomes. Preoperative proximal migration of the radial head is an independent predictor of a suboptimal postoperative outcome. A value >2.2 mm may indicate advanced radiocapitellar incongruity, a condition in which OAT may be less effective.

Humans↗

PubMed2026

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

Humans↗

PubMed2026

Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.

BACKGROUND: Early and accurate diagnosis of systemic lupus erythematosus (SLE) and its organ involvement is essential. Previous reviews of machine learning (ML) in SLE combined heterogeneous tasks and validation strategies and may have overinterpreted model performance. OBJECTIVE: This study evaluated the diagnostic performance of ML and deep learning (DL) models for 3 clinically distinct SLE-related tasks: SLE classification or diagnosis, lupus nephritis (LN) diagnosis, and neuropsychiatric systemic lupus erythematosus (NPSLE) discrimination. We also assessed methodological quality and certainty of evidence. METHODS: PubMed, Embase, Cochrane Library, Web of Science, and IEEE Xplore were searched from January 2014 to April 2026. Eligible peer-reviewed diagnostic accuracy studies developed or validated ML or DL models for 1 of the 3 prespecified tasks, used an accepted reference standard, and provided data for a 2×2 contingency table. Bivariate random-effects meta-analyses with the Hartung-Knapp-Sidik-Jonkman adjustment were used to pool sensitivity and specificity. We reported 95% prediction intervals (PIs), assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies for Artificial Intelligence tool (QUADAS-AI; Viknesh Sounderajah [Imperial College London]), and evaluated certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation framework for diagnostic test accuracy. RESULTS: Twenty-nine studies were included: 17 for SLE classification, 5 for LN diagnosis, and 7 for NPSLE discrimination. In the primary task-stratified analysis, pooled sensitivity was 0.91 (95% CI 0.86-0.94; 95% PI 0.56-0.99), and pooled specificity was 0.94 (95% CI 0.91-0.96; 95% PI 0.69-0.99), with low heterogeneity (I²=23.9% and 22.9%, respectively). DL models showed a sensitivity of 0.93 and specificity of 0.95, compared with 0.88 and 0.94 for traditional ML models. Certainty of evidence was high for most analyses but low for LN diagnosis because of inconsistency and imprecision. All studies were retrospective, and only 9 of 29 (31%) performed independent external validation. Overall risk of bias was high or unclear in 22 of 29 (75.9%) studies. No study reported model calibration, decision-curve analysis, or net clinical benefit. CONCLUSIONS: ML models showed promising diagnostic accuracy across 3 distinct SLE-related tasks, but wide PIs, limited external validation, and pervasive risk of bias restrict conclusions about real-world generalizability. Prospective multicenter studies with standardized tasks and reference standards, independent external validation, and formal assessment of calibration and clinical utility are required before clinical implementation.

Humans↗

PubMed2026

A Meta-learning-driven strategy for adulteration detection in sweet potato starch and vermicelli using Raman spectroscopy.

To address the widespread adulteration of sweet potato starch and its vermicelli with cheaper starches and overcome conventional supervised learning's dependency on large labeled datasets, this study developed a few-shot discrimination method integrating Raman spectroscopy with meta-learning. We constructed a meta-learning framework using cassava- and wheat-adulterated sweet potato starch as the source domain for training, with potato-adulterated sweet potato starch and cassava-adulterated sweet potato vermicelli as two target domains for testing. Raman spectra showed high consistency between sweet potato vermicelli and its raw starch, laying the foundation for cross-domain detection. Testing yielded comprehensive classification accuracies of 95.33% and 98.00% for the two target domains, significantly outperforming SVM, RF, and CNN (max. 85.24%). This approach effectively identifies subtle starch variety differences in complex adulteration, providing novel food quality inspection solutions and verifying the feasibility of raw material-to-finished product cross-domain detection.

Ipomoea batatas↗

PubMed2026

Machine learning-assisted Mn-N-C nanozyme colorimetric sensor array for trace-level detection of biogenic amines in meat.

Accurate detection of biogenic amines (BAs) in meat remains challenging due to their high structural similarity and co-occurrence. Herein, an Mn-N-C nanozyme was synthesized via a metal-organic framework confined pyrolysis strategy, possessing excellent oxidase (OXD)- and peroxidase (POD)-like activities. The dual enzyme-like activity showed Km values of 0.1584 mM (OXD) and 0.1498 mM (POD), respectively, in detection system. Leveraging these properties, a colorimetric sensor array was constructed, enabling the detection of four representative BAs within a concentration range of 2-10 ppm with 100% classification accuracy. In addition, a concentration independent recognition model based on an artificial neural network was developed to address signal nonlinearity interference in meat. The integrated system achieved accurate trace-level identification of BAs in perishable fish, pork, and chicken, demonstrating its applicability for early-stage BAs monitoring and quality deterioration warning during storage and transportation.

Biogenic Amines↗

PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗

PubMed2026

AI-enabled viral genomics: from virus discovery to host prediction and emerging variant forecasting.

The rapid expansion of metagenomic sequencing has generated vast repositories of viral sequence data that far outpace our capacity to interpret them using conventional approaches. Highly divergent sequences, sparse functional annotation, and taxonomically uneven sampling present fundamental challenges for reference-dependent methods, which lose sensitivity precisely for novel and understudied viruses with high public health relevance. Artificial intelligence (AI) provides a new avenue to address these challenges by enabling predictive inference from viral genomes and proteins while reducing dependence on sequence similarity. In this Review, we discuss representative advances in AI for virus discovery, taxonomic classification and functional annotation, prediction of host range and zoonotic potential, and efforts toward forecasting emerging variants. These advances are transforming viral genomics from a largely descriptive discipline into one with increasing predictive capability. We also critically assess the major challenges that constrain current approaches, including the availability of high-quality and representative datasets, rigorous model evaluation, biological interpretability and responsible governance for increasingly capable AI models.

Artificial Intelligence↗

PubMed2026

Safety, tolerability, and efficacy of alixorexton, a selective orexin 2 receptor agonist for narcolepsy type 1 (Vibrance-1): a randomised, double-blind, placebo-controlled, phase 2 trial.

BACKGROUND: The clinical potential of orexin 2 receptor (OX2R) agonism for improving measures of wakefulness and cataplexy in patients with narcolepsy type 1 has been described in a phase 2 study. Here, we aimed to evaluate the safety, tolerability, and efficacy of alixorexton, another oral OX2R agonist, in narcolepsy type 1. METHODS: In this randomised, double-blind, placebo-controlled, phase 2 trial, adult participants (aged 18-70 years) with narcolepsy type 1 were recruited from 46 hospitals and private research centres across the USA, Europe, and Australia. Participants were centrally randomly assigned (1:1:1:1) in blocks of four via an interactive response technology system stratified by region and baseline weekly cataplexy rate (WCR) to receive 4 mg, 6 mg, or 8 mg tablets of alixorexton or placebo once daily for 6 weeks, followed by an optional 7-week open-label extension. Participants had narcolepsy type 1, diagnosed per the International Classification of Sleep Disorders, Third Edition, and confirmed by overnight polysomnography and the Multiple Sleep Latency Test or cerebrospinal hypocretin-1 concentrations. The sponsor, assessors, investigators, and participants were masked during the randomised double-blind treatment period. Efficacy and safety assessments were conducted in participants who received at least one dose of study drug. The primary endpoint was change from baseline to week 6 in mean sleep latency (MSL) on the Maintenance of Wakefulness Test (MWT). Safety endpoints included treatment-emergent adverse events. This trial was registered with ClinicalTrials.gov (NCT06358950) and is completed. FINDINGS: Between May 24, 2024, and May 5, 2025, 153 individuals were screened and 92 participants were randomly assigned to receive alixorexton 4 mg (n=23), 6 mg (n=22), 8 mg (n=24), or placebo (n=23). Mean age was 33·5 years (SD 12·1), 57 (62%) were women, and 35 (38%) were men. At week 6, the observed MSL on the MWT was 2·3 min (SD 2·7) for placebo, 24·0 min (8·7) for alixorexton 4 mg, 25·9 min (9·4) for 6 mg, and 28·2 min (11·4) for 8 mg. Alixorexton improved MSL on the MWT, with a least-squares mean placebo-corrected change from baseline of 22·2 min (95% CI 17·2-27·2) for alixorexton 4 mg, 24·1 min (19·0-29·1) for 6 mg, and 26·0 min (21·0-31·0) for 8 mg (adjusted p=0·0099 for 4 mg, adjusted p<0·0001 for 6 mg and 8 mg). Treatment-emergent adverse events occurring in at least 5% of participants given alixorexton and more frequently than those given placebo up to week 6 were pollakiuria (38 [55%]), insomnia (19 [28%]), salivary hypersecretion (17 [25%]), micturition urgency (ten [14%]), blurred vision (ten [14%]), and hyperhidrosis (five [7%]). INTERPRETATION: In this phase 2 trial, once-daily oral alixorexton provided clinically meaningful improvements at 6 weeks for participants with narcolepsy type 1, including in wakefulness, excessive daytime sleepiness, and cataplexy. The treatment was generally well tolerated, with adverse events consistent with the known on-target effects of OX2R agonists. Together, these findings support the further phase 3 evaluation of alixorexton as a potential therapeutic option for people with narcolepsy type 1. FUNDING: Alkermes.

Humans↗

PubMed2026

Variations in the viral hepatitis C prevalence and treatment status by material hardship and cumulative risk among people who inject drugs.

People who inject drugs (PWID) are disproportionately impacted by viral hepatitis C (HCV). Among PWID, homelessness, poverty, and material insecurity elevate infectious disease risk. We hypothesized a positive association between material hardship (difficulty accessing basic needs) and lifetime diagnosis of HCV, and a negative association between cumulative risk (material hardship and years since first injection) and HCV treatment. From 2021-22, we conducted a survey among community-recruited PWID that included items on HCV outcomes, material hardship (a sum score of usually (4) to never (1) having difficulty finding food, clothing, shelter, restrooms, and showers in the past 3 months), and years since injection drug use initiation. We developed a latent variable, cumulative risk, by combining individual scores of material hardship indicators and years since first injection drug use. Among our sample of PWID (n = 471), 246 (53%) participants reported lifetime HCV diagnosis and 27% of those who tested positive for HCV reported ever or current treatment (n = 67). In modified-Poisson regression, a one-unit increase in material hardship score was associated with a 3% (OR: 1.03, 95% CI: 1.01%, 1.05%) increase in odds of HCV diagnosis. In latent modeling, among PWID testing positive for HCV, a one-unit increase in cumulative risk score was associated with a 0.28 (OR: -0.28, 95% CI: -0.50, -0.06), decrease in the Z-score odds of receiving HCV treatment. Findings emphasize structural interventions to strengthen material security and co-delivering basic needs with health services to improve HCV-related outcomes among PWID.

HCV↗

PubMed2026

Molecular Diagnostics for WHO Priority Bacterial Pathogens: A Bibliometric Mapping of Diagnostic Platforms, Resistance Markers, and Antimicrobial Resistance Research Trends.

Antimicrobial resistance (AMR) constrains effective treatment and carries implications for infection control, surveillance, and public health. The World Health Organization (WHO) priority bacterial pathogen framework has intensified the need for diagnostic innovation by redefining research priorities around organisms combining high disease burden with complex resistance profiles. Molecular diagnostics have accordingly moved beyond culture-based workflows, integrating rapid pathogen identification, resistance-marker detection, genomic surveillance, and clinical decision support. The present study conducted a bibliometric mapping of the literature on WHO priority pathogens. Rather than addressing resistance at a general level or a single pathogen or technology, it integrates priority pathogens, molecular platforms, and resistance markers within a single framework, tracing their joint thematic and temporal evolution along an explicit pathogen-platform-marker axis. Scopus-indexed articles and reviews (2000-2025) were retrieved, yielding 1746 publications after screening adapted from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Analyses used Bibliometrix/Biblioshiny, R, and VOSviewer. The literature expanded markedly after 2018, led by China and the United States. Methicillin-resistant Staphylococcus aureus (MRSA), Mycobacterium tuberculosis, Enterococcus faecium, and the Enterobacterales-carbapenemase axis constituted the principal thematic cores, whereas conventional polymerase chain reaction (PCR)/nucleic acid amplification testing (NAAT) and whole-genome sequencing were the dominant platforms. Overall, the field has evolved from pathogen detection into an AMR-centered translational domain encompassing resistance prediction, genomic epidemiology, surveillance, and clinical decision support. Diagnostic development, stewardship, and surveillance depend on hybrid workflows coupling rapid marker-targeted assays with genome-based characterization, delivering actionable resistance within clinically meaningful timeframes, and extending coverage to underrepresented pathogens and platforms.

Humans↗

PubMed2026

Strategies for mosaic variant calling in brain disorders.

The human brain is a genomic mosaic, where postzygotic mutations arising from embryogenesis to senescence drive diverse neurodevelopmental and neurodegenerative diseases. Because of numerous sequencing artifacts at ultralow variant allele frequencies (VAFs), detecting these variants remains a significant analytical challenge. This review focuses on single-nucleotide variants and small indels, summarizing current strategies for aligning sampling methods, including bulk, laser capture microdissection, and single-cell genomics, with the expected clonal architecture of the brain. It emphasizes that mosaic detection sensitivity is fundamentally constrained by sequencing depth, since even the most advanced algorithms cannot identify variants not physically represented in the sequencing library. The review further recommends the selection of variant calling algorithms based on validated VAF detection performance, matching tools like MuTect2 and MosaicForecast to their optimal performance ranges. Furthermore, we discuss how multitissue sampling, as emphasized by the SMaHT project, addresses the matched-control dilemma and supports accurate variant classification via cross-tissue VAF gradients. Integrating these established pipelines with multiomics modalities, including transcriptomic and epigenetic data, could advance the field toward a functional understanding of how the somatic genome impacts human brain health and disease.

Humans↗

PubMed2026

Relationships Between Risky Substance Use and Irritable Bowel Syndrome (IBS) Severity, Mental Health, and Smoking-Related Processes in Adults with IBS Who Smoke.

Objective: Irritable Bowel Syndrome (IBS) is a debilitating chronic pain condition that presents a major and growing public health burden. Combustible cigarette smoking plays a role in the maintenance of IBS, and the co-occurrence of these conditions warrants greater focus. Moreover, given that smoking co-occurs with other substance use behaviors, the impact of these comorbid behaviors on physical and mental health-related outcomes among individuals with IBS who smoke is warranted. The goal of the present cross-sectional study was to assess relationships of risky alcohol, cannabis, and opioid use with IBS severity, anxiety and depression, and smoking-related processes among adults with IBS who smoke. Methods: In total, 263 adults who met Rome IV criteria for IBS and who smoked combustible cigarettes were included in the study. Hierarchical regression analyses were conducted to examine the influence of risky substance uses on IBS symptom severity, IBS-related quality of life, anxiety, depression, perceived barriers for smoking cessation, and cigarette dependence. Results: Results indicated that opioid use was statistically related to all criterion variables, whereas alcohol use was specifically associated with depression and anxiety. Cannabis use showed a statistically significant association only with anxiety symptoms. Conclusions: The present investigation is among the first to explore the role of risky substance use in terms of a wide array of IBS, mental health, and smoking processes among adults with IBS who smoke. The findings suggest that substance use should be a focal point of screening and intervention for the IBS population to optimize outcomes beyond the reach of medical therapies.

Irritable bowel syndrome↗

PubMed2026

Intentions to use different modalities of long-acting HIV PrEP among men who have sex with men and transgender and gender diverse persons in the Netherlands.

Insight into intentions to use long-acting HIV pre-exposure prophylaxis (PrEP) is essential for successful implementation. We assessed intention to use three hypothetical long-acting PrEP modalities (oral, intramuscular and subdermal) among HIV-negative men who have sex with men (MSM) and transgender and gender diverse persons in Amsterdam, recruited through the Amsterdam Cohort Studies (ACS-participants) and social media (survey-participants). Intention was measured per modality using a 7-point Likert scale. We modeled the probability of high intention to use long-acting PrEP using relative risk regression. Of 1258 participants [1231 (97.9%) MSM; 557 ACS and 701 survey; median age 40 years (IQR 32-50)], 76% had ever used oral short-acting PrEP. Intention to use was highest for monthly oral long-acting PrEP [ACS: median 4.5 (IQR 2-6), 38% high intention; survey: median 7 (IQR 6-7), 81% high intention], followed by 2-monthly intramuscular PrEP [ACS: median 3 (IQR 2-5), 19% high intention; survey: median 5 (IQR 3-7), 47% high intention], and implants [ACS: median 2 (IQR 1-4), 9% high intention; survey: median 4 (IQR 2-7), 37% high intention]. PrEP-experienced participants had higher intention to use oral long-acting PrEP and injectables than PrEP-naïve participants. Offering multiple PrEP modalities may improve the acceptability of HIV prevention strategies and increase PrEP uptake and retention in PrEP care.

Humans↗

PubMed2026

Association between SGLT2 inhibitors and reporting of phimosis/paraphimosis: a comparative pharmacovigilance analysis of the WHO database.

PURPOSE: Recent data have discussed occurrence of phimosis with Sodium-glucose co-transporter-2 (SGLT2) inhibitors. However, the potential risk among the different SGLT2 inhibitors is unknown. METHODS: Using Individual Case Safety Reports (ICSRs) registered in the WHO pharmacovigilance database (01/01/2000-30/06/2025), comparisons between the different SGLT2 inhibitors and versus other drugs used in diabetes (DUD) were performed. Results are shown as Reporting Odds Ratios (ROR). RESULTS: Among 11 342 810 ICSRs, 227 were phimosis/paraphimosis with SGLT2 inhibitors, mainly between 45 and 64 years. The higher ROR value was found with empagliflozin followed by dapagliflozin and canagliflozin. ROR for SGLT2 inhibitors was higher that of all other DUD [34.72 (25.86-46.62)]. The reporting risk of phimosis/paraphimosis with SGLT2 inhibitors was also higher than that of each pharmacological class of DUD. CONCLUSION: The results suggest an association between SGLT2 inhibitors use and phimosis ICSRs. Empagliflozin had the higher reporting risk.

Humans↗

PubMed2026

Adherence and efficacy of the 0 - 7 - 21-day versus the 0 - 1 - 6-month hepatitis B vaccination schedules among people who use drugs: a two-year randomized controlled trial.

BACKGROUND: To compare the adherence and efficacy between the 0 - 7 - 21-day and the 0 - 1 - 6-month hepatitis B virus (HBV) vaccination schedules among people who use drugs (PWUD) in China. RESEARCH DESIGN AND METHODS: A randomized controlled trial was conducted in 1261 HBV-susceptible PWUD from compulsory isolated detoxification centers (CIDCs) and methadone maintenance treatment (MMT) clinics in Xi'an. A 20 µg per-dose vaccine was used. HBV surface antibody (anti-HBs), surface antigen, and core antibody were tested at months 7, 15, and 22 after the first dose. RESULTS: Third-dose coverage was significantly higher in the 0 - 7 - 21-day group (74.40%) than in the 0 - 1 - 6-month group (51.58%, p < 0.001), mainly driven by participants from CIDCs (77.75% vs. 45.69%). Anti-HBs positive rates at months 7, 15, and 22 among participants who completed all three doses were significantly higher for the 0 - 1 - 6-month schedule (90.71%, 76.82%, and 67.35%) than for the 0 - 7 - 21-day schedule (74.23%, 49.40%, and 40.95%; all p < 0.001). HBV infection incidence was similar between schedules, but significantly different between vaccinees and non-vaccinees (p = 0.018). CONCLUSIONS: The 0 - 7 - 21-day schedule substantially enhances three-dose completion in PWUD, but induces a notably weaker anti-HBs response and persistence. Schedules should be selected based on the management models for PWUD and their individual characteristics. CLINICAL TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR1900022403).

Humans↗