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PubMed2026

Integration of single-cell transcriptomics and genomic mutation analysis identifies an immunotherapy-resistant tumor subcluster and validates ARNTL2 as a malignant driver in lung adenocarcinoma.

BACKGROUND: Immunotherapy resistance in lung adenocarcinoma (LUAD) remains a critical clinical challenge, and the mechanisms underlying resistance-associated intratumoral heterogeneity are poorly characterized. METHODS: We performed single-cell RNA sequencing of LUAD patients receiving neoadjuvant immunotherapy (responders vs. non-responders), integrating inferCNV, GSVA, and differential expression analyses. Cluster-specific genes were validated across seven independent cohorts (TCGA-LUAD, GSE13213, GSE26939, GSE29016, GSE30219, GSE31210, GSE42127). A multi-algorithm machine learning framework was used to construct a prognostic model, and the immune microenvironment was characterized using TCIA scoring, seven infiltration algorithms, and ESTIMATE. ARNTL2 function was assessed by CCK-8 and Transwell assays in A549 and H1299 cells. RESULTS: Non-responders showed significant enrichment of epithelial cells, depletion of cytotoxic T/NK cells, and elevated copy number variation burden versus responders (p < 0.0001). A resistance-enriched malignant subcluster (Cluster 2) exhibited hyperproliferative and metabolic reprogramming signatures with upregulated KRT17, S100A2, and CST6, which showed tumor-specific overexpression, adverse prognostic value, and genomic amplification across cohorts. CoxBoost combined with survivalSVM achieved optimal predictive performance (C-index = 0.686), yielding robust risk stratification (HR: 2.54-10.51, all p < 0.05). Low-risk patients showed greater immune infiltration and higher TCIA immunophenoscores. ARNTL2 was an independent prognostic factor (HR: 2.07-4.64) strongly correlated with risk score (r = 0.69), and its knockdown suppressed proliferation and invasion in both LUAD cell lines (all p < 0.05). CONCLUSION: This study identifies a resistance-associated malignant subcluster in LUAD, constructs a validated CoxBoost + survivalSVM prognostic model with robust immune stratification, and establishes ARNTL2 as a core oncogenic driver and therapeutic target.

ARNTL2↗

PubMed2026

Ensemble DNA methylation clock demonstrates Immune-metabolic aging signatures associated with mortality.

Aging is a multifactorial process that is best described in terms of the progressive acquisition of multiple layers of phenotypic changes, such as epigenetic modifications, inflammation, and metabolic dysregulation. DNA methylation clocks have been extensively used to construct epigenetic clocks based on the DNAm profiles that can be used to estimate biological age and predict age-associated outcomes. Nevertheless, the vast majority of clocks constructed so far have been based on linear models, which are unlikely to fully account for the heterogeneity and non-linearity of survival-related DNAm signatures. In this work, we constructed a heterogeneous stacked ensemble survival model based on DNAm data obtained from the Framingham Heart Study. We first identified 190 CpG loci using elastic net Cox regression and subsequently constructed a survival prediction model based on the fusion of five complementary survival models by means of a neural network meta-learner. The prediction power of the survival model was evaluated in an external validation cohort, where we observed strong performance for predicting all-cause mortality that significantly exceeded PhenoAge and was statistically comparable to GrimAge. These performance estimates were derived in cohorts of European ancestry and externally validated in postmenopausal women aged 50-79 years, and should therefore be interpreted as applicable only to demographically similar populations.

Humans↗

PubMed2026

Single-center experience with isolated male epispadias: Outcomes of Thiersch-Duplay and modified Cantwell-Ransley repairs by anatomical subtype.

BACKGROUND: Isolated male epispadias (IME) is a rare congenital malformation. Surgical repair aims to improve urinary function, correct penile curvature, reconstruct the urethra and glans, and preserve future sexual function. Because available series are small, the influence of anatomical subtype and operative technique on outcome remains incompletely defined. OBJECTIVE: To report single-center outcomes of Thiersch-Duplay and modified Cantwell-Ransley repairs for IME, with attention to anatomical subtype, complications, and age-appropriate continence outcomes. METHODS: We retrospectively reviewed boys with IME who underwent primary urethral reconstruction in my hospital, Capital Medical University, from May 2005 to June 2024. Data were locked on 30 June 2024. Primary outcomes were postoperative complications graded by the Clavien-Dindo system and urinary continence at last follow-up in patients aged 5 years or older. Secondary outcomes included improvement of preoperative incontinence, ICIQ score, subsequent bladder neck reconstruction, penile appearance/residual curvature when documented, and patient/parent-reported sexual function. RESULTS: Sixty-seven patients were included: 35 underwent modified Cantwell-Ransley repair and 32 underwent Thiersch-Duplay repair. The cohort included 26 glanular (38.8%), 23 penile (34.3%), and 18 penopubic (26.9%) cases. Median age at surgery was 28 months in both groups. Median age at last follow-up was 92.8 months (IQR 63.9-108.4) after modified Cantwell-Ransley repair and 137.9 months (IQR 77.9-172.6) after Thiersch-Duplay repair (P = 0.011). Procedure distribution differed by meatal location (P = 0.036), although penopubic cases were treated with both procedures. Total complications occurred in 5/35 and 5/32 patients, respectively. Formal continence analysis included 26 modified Cantwell-Ransley patients and 27 Thiersch-Duplay patients aged 5 years or older. Postoperative urinary incontinence persisted in 14/26 (53.8%) and 12/27 (44.4%), respectively. Among age-eligible patients with preoperative incontinence, any improvement was documented in 13/20 (65.0%) and 14/17 (82.4%), and complete remission occurred in 6/20 (30.0%) and 5/17 (29.4%), respectively. Three patients, all with penopubic epispadias treated with Thiersch-Duplay repair, subsequently underwent bladder neck reconstruction for persistent incontinence. Erectile function data were available in 37/67 patients (55.2%). CONCLUSIONS: In this large single-center retrospective cohort, Thiersch-Duplay and modified Cantwell-Ransley repairs had comparable overall complication rates. Continence and reoperation patterns were strongly influenced by anatomical subtype, with penopubic epispadias representing the highest-risk group. These findings support individualized, anatomy-conscious operative planning and prospective evaluation of standardized selection criteria, rather than a single prescriptive algorithm. CLINICAL/TRANSLATIONAL IMPLICATION: This series supports standardized reporting of anatomical subtype, age-appropriate continence outcomes, and graded complications when counseling families and comparing outcomes across centers. LEVEL OF EVIDENCE: Level III.

Humans↗

PubMed2026

Trade-offs in avian parental care: a review of theory and meta-analysis of brood size manipulations.

The selective forces shaping parental care have been studied for over 50 years. While theoretical and experimental work has yielded qualitative progress, the large body of empirical work testing predictions about parental investment based on life-history trade-offs has yet to be synthesized. We first provide an overview of the core life-history theory exploring how selection might shape parental care. We then conduct a systematic review and meta-analysis on studies that experimentally manipulated brood size in birds, a widely used experimental approach to manipulate parental investment. We extracted 313 estimates from 62 studies representing 31 species of birds from 19 different families and tested key predictions on trade-offs in parental care derived from theory. Our analysis provides strong support for some predictions about life-history trade-offs in parental care, but weak or equivocal support for others. Specifically, we found that overall, avian parents respond to brood size manipulations as predicted by life-history theory: they increased care in response to brood enlargement, and decreased care in response to brood reductions. Furthermore, for the same relative manipulation size, responses to brood reductions were greater than responses to brood enlargements. This finding is consistent with predictions derived from life-history theory based on some types of non-linear utility curves. However, many predictions derived from theory are not well supported by our comparative analysis. Species' life-history traits such as clutch size (a measure of current reproduction), adult survival, and broods per year (two measures of future reproduction), explained little, if any, among-species variation in response to brood size manipulations. Several factors may explain this. We highlight that brood size manipulations may affect more than just perception of the value of current reproduction, such as altering parents' perception of predation risk. Importantly, these unintended consequences could lead to asymmetric responses like those we observed. Other common experimental approaches - such as hormone manipulations, altering a partner's effort, and food supplementation - often affect multiple traits or fitness components simultaneously, or may involve cues that poorly match the evolved mechanisms guiding parental behaviour. Our review of both theory and experimental approaches suggests that there are multiple opportunities for more precise experiments. We offer several recommendations for effective designs. One is improved understanding of the biology underlying the functions relating to costs and benefits, with careful consideration of not only how the manipulation will affect only one of those, but also the mechanisms that might alter how parents perceive the manipulation. We also emphasize general principles, such as assessing alternative hypotheses and devising multiple independent tests. Armed with these recommendations, we believe there are new opportunities to increase the strength of inference achieved from studies aimed at understanding the trade-offs affecting the evolution of parental care.

Animals↗

PubMed2026

Mechanistic Insights Into the Association Between Gut Microbiota Diversity and Atherosclerosis, Acute Coronary Syndrome, and Peripheral Arterial Disease Progression.

BACKGROUND: The gut microbiome has emerged as a potential contributor to cardiovascular diseases (CVDs), including atherosclerosis, acute coronary syndrome (ACS), and peripheral arterial disease (PAD). While observational studies link dysbiosis to CVD, causal relationships remain uncertain. METHODS: This narrative review synthesizes evidence from human observational studies, clinical interventions, and experimental models to distinguish association from mechanistic plausibility and clinical causality. Literature was searched through July 2026 in PubMed/MEDLINE, Web of Science, and Scopus. RESULTS: Microbial metabolites-including trimethylamine N-oxide (TMAO), short-chain fatty acids (SCFAs), bile acids, and lipopolysaccharide (LPS)-modulate endothelial function, immune cell programming, platelet activity, and plaque stability through receptor-mediated signaling and epigenetic regulation. SCFAs demonstrate potentially protective effects via GPCR and HDAC pathways, while TMAO is associated with atherothrombotic risk. However, much mechanistic evidence derives from preclinical studies. Heterogeneity from diet, geography, host characteristics, renal function, and medications substantially influences microbiota-CVD associations. CONCLUSION: The gut-vascular connection is biologically plausible, but definitive clinical causality remains unproven. Microbiome-directed therapies (dietary modulation, pre/pro/synbiotics, targeted metabolite inhibition) are investigational. Prospective, standardized, adequately powered human studies with clinically meaningful outcomes are essential before routine cardiovascular application.

Gastrointestinal Microbiome↗

PubMed2026

Generation of spCAS9 expressing human mesenchymal stem cell line to study gene function during osteoblast differentiation.

Human bone marrow-derived stromal cells (hMSCs) are a great resource for studying how genes influence cell fate and differentiation into various cell types like osteoblasts, adipocytes, and chondrocytes, among other cell types. However, genetic manipulation of primary hMSCs has been challenging due to their short lifespan and cellular senescence after limited passaging. Their low and unstable transfection efficiency also complicates gene delivery or inactivation, hindering long-term functional studies. The limited lifespan has been effectively solved by immortalizing hMSCs with telomerase reverse transcriptase (hMSCs-TERT). The use of these cells is ideal for functional studies of osteoblast and adipocyte differentiation through genetic manipulation, providing a stable and reliable model. Here, we have engineered a stable CAS9 expressing hMSC-TERT cell line (hMSC-TERTCAS9) via lentiviral transduction. The constitutive expression of spCas9 enables efficient and reproducible gene editing. We demonstrate the potential of these hMSC-TERTCAS9 cells for generating gene disruptions using plasmid delivery of guide RNAs as a fast and efficient strategy for targeted genome editing. The edited cells can be sorted and expanded as single cells to obtain homogenous clonal cell lines with mono- as well as bi-allelic gene deletions, a crucial step for producing reliable experimental results. We further validate this cell line as a powerful tool for studying gene function during hMSC proliferation and differentiation, providing 3 distinct examples of its utility. Through the generation of indels, single-cell sorting, and clonal selection, we have efficiently inactivated the vitamin D receptor and created both larger (256 nucleotides) gene disruptions in Forkhead box protein O1 and precise removals of a small genomic sequence (73 nucleotides) coding for microRNA MIR675. This novel hMSC-TERTCAS9 cell line represents a significant advancement, offering a stable, efficient, and versatile platform for advanced genetic studies, high-throughput screening, and the creation of reliable cellular disease models.

CRISPR-Cas9↗

PubMed2026

3D epigenomic remodelling mediated by Foxa1 drives gemcitabine resistance in pancreatic cancer.

Gemcitabine remains a cornerstone treatment for pancreatic ductal adenocarcinoma (PDAC), yet the emergence of resistance constitutes a major clinical challenge with poorly understood epigenomic mechanisms. Here, we identified the pioneer transcription factor Foxa1 as a master regulator of gemcitabine resistance through multi-omics analysis. Mechanistically, Foxa1 drives widespread super-enhancer (SE) reprogramming and 3D genome remodelling in resistant cells, which coordinately activates the expression of key resistance genes, notably Rrm1 and Cdadc1. This is accompanied by increased chromatin accessibility, elevated H3K27ac enrichment at SEs, and enhanced Foxa1 binding at regulatory elements. Moreover, post-translational stabilization of Foxa1 via USP7-mediated deubiquitination sustains this epigenomic program. Genetic ablation of Foxa1 or specific SE regions near Rrm1 resensitizes resistant cells to gemcitabine. Building upon this mechanism, we demonstrate that bromodomain and extraterminal (BET) inhibitors, which disrupt SE function, potently reverse resistance. Notably, the clinical-stage BET inhibitor AZD5153, in combination with gemcitabine, achieves robust tumor suppression and overcomes resistance in cell-derived xenograft (CDX) models by dismantling the Foxa1-mediated resistant transcriptome and reinvigorating drug sensitivity. Our findings establish Foxa1-orchestrated enhancer reprogramming as a fundamental mechanism of gemcitabine resistance and unveil a promising epigenetic therapy to restore treatment efficacy in PDAC.

Hepatocyte Nuclear Factor 3-alpha↗

PubMed2026

Factors associated with additional intervention requirement following ESWL in pediatric patients with urolithiasis.

OBJECTIVE: To identify predictors of additional intervention following extracorporeal shock wave lithotripsy (ESWL) in pediatric patients and to develop a clinically applicable predictive model. MATERIALS AND METHODS: This retrospective cohort study included 647 pediatric patients who underwent ESWL between 2015 and 2025. Demographic, clinical, and radiological variables were analyzed. Univariable and multivariable logistic regression analyses were performed to identify independent predictors of additional intervention. Model performance was evaluated using receiver operating characteristic curve analysis. RESULTS: Additional intervention was required in 65 patients (10.0%). On multivariable analysis, stone size 10-20 mm (OR: 3.04, p = 0.003), moderate (OR: 2.16, p = 0.049) and severe hydronephrosis (OR: 6.05, p < 0.001), and multiple stones (OR: 3.52, p = 0.030) were identified as independent risk factors. Increasing age (OR: 0.84, p = 0.026), history of urolithiasis (OR: 0.41, p = 0.006), and lower calyx location (OR: 0.14, p = 0.034) were associated with a reduced risk. The model demonstrated good discriminative performance (AUC: 0.794), with a sensitivity of 72% and specificity of 75%. Internal validation using bootstrap resampling demonstrated stable model performance, yielding a corrected AUC of 0.732. CONCLUSION: Stone burden, hydronephrosis severity, and stone multiplicity are key determinants of additional intervention after ESWL in pediatric patients. The proposed model shows good predictive performance and may support individualized risk stratification and clinical decision-making.

Humans↗

PubMed2026

The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.

Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST + AI for prediction model studies. Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST + AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection. AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.

Humans↗

PubMed2026

Family-Wise Error Rate Control in Clinical Trials With Overlapping Populations.

We consider clinical trials with multiple, overlapping patient populations that test multiple treatment policies specifically tailored to these populations. Such designs may lead to multiplicity issues, as false statements will affect several populations. For type I error control, often the family-wise error rate (FWER) is controlled, which is the probability to reject at least one true null hypothesis. If the joint distribution of the test statistics is known, the FWER level can be exhausted by determining critical values or adjusted-levels. The adjustment is typically done under the common ANOVA assumptions. However, the performed tests are then only valid under the rather strong assumption of homogeneous null effects, that is, when the null hypothesis applies to all subpopulations and their intersections. We show that under cancelling null effects, when heterogeneous effects cancel out in some or all subpopulations, this procedure does not provide FWER control. We also suggest different alternatives and compare them in terms of FWER control and their power.

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

Bioprospecting microbial genomes to expand the biocatalytic toolbox of rubber oxygenases.

A set of rubber oxygenases was discovered through phylogenetic analysis and AI-based structural modeling of complexes of the putative enzymes with a substrate mimicking cis-1,4-polyisoprene. Sixteen candidate proteins were selected from thermophilic microorganisms, all sequence-related to the Latex clearing protein from Streptomyces sp. K30 (LcpK30). Sequence truncation and solubility tags were then evaluated to enhance protein expression, with the SUMO tag proving to be the most effective. Including LcpK30, nine heme-containing oxygenases were successfully expressed in E. coli NEB 10-beta cells, purified (35-157 mg L-1 yield) and characterized. Steady-state kinetics revealed significant rubber latex-degrading properties for six of them, with the truncated SUMO-fused LcpK30 (SUMO-LcpK30T) showing activity in agreement with literature. Notably, the catalytic efficiencies of all the expressed homologs lay within one order of magnitude and the oxygenase from Thermomonospora echinospora was found to be particularly promising in terms of activity, especially at high latex concentrations (more than 1% w/v). The analysis of reaction mixtures by both HPLC and HPLC-MS confirmed the oxidation of cis-1,4-polyisoprene to form the expected isoprenoid oligomers (n = 2-12), whose distribution was consistent with the usual endo-type cleavage pattern in all but one case. This bioprospecting effort afforded a platform of new rubber-degrading enzymes with diverse efficiencies and product profiles, capable of adapting to targeted applications.

Oxygenases↗

PubMed2026

Three-dimensional source apportionment and quantitative characterization of horizontal and vertical transport fluxes of O3 and its precursors in the Beijing-Tianjin-Hebei region, China.

Persistent surface ozone (O3) pollution in the Beijing-Tianjin-Hebei (BTH) region is driven by coupled precursor emissions and multi-scale transport, yet its altitude-dependent transport and source contributions remain insufficiently quantified. Here we integrated the Weather Research and Forecasting and the Comprehensive Air Quality Model with Extensions with the Ozone Source Apportionment Technology and a quantitative transport-flux framework to characterize three-dimensional source apportionment and horizontal/vertical fluxes of O3, Volatile Organic Compounds‌ (VOCs), and Nitrogen Oxides (NOx) across dynamic meteorological scenarios. Simulations showed that VOCs and NOx were dominated by local emissions near the surface (73.61 %-82.18 %), whereas surface O3 was primarily controlled by regional transport, with local contributions of only 11.01 %-13.75 %. Notably, the transport dominance further strengthened with altitude, exceeding 93 % at 1.8 km. Industrial and transportation emissions together contributed more than 75 % of precursor emissions and account for approximately 80 % of O3 formation, while favorable/unfavorable meteorological years modulated long-range transport efficiency and the vertical distribution of contributions. Horizontal flux analysis highlighted three major pathways (Northwest-Southeast, Southeast-Northwest, and Southwest-Northeast), with Shijiazhuang serving as a critical pollutant "sink" across altitude layers. Vertical fluxes revealed an altitude transition near 600 m: net downward transport dominated below 600 m, whereas enhanced summer convection promoted upward transport above 600 m. These results support altitude-dependent, scenario-specific strategies for coordinated regional O3 mitigation in the BTH region.

Ozone↗

PubMed2025

Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.

BACKGROUND: Emergency radiographic interpretation for fractures is prone to missed or misdiagnoses. Artificial intelligence (AI) is expected to become a powerful tool to assist clinicians in fracture detection. PURPOSE: A systematic review and meta-analysis was performed to assess whether AI improves clinicians' ability to detect fractures on radiographs. MATERIALS AND METHODS: A literature search was conducted in PubMed, Web of Science, and Cochrane Library for studies published between January 1, 2010, and October 10, 2025. A meta-analysis of diagnostic accuracy studies was performed using a Summary Receiver Operating Characteristic (SROC) curve. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Subgroup analysis and meta-regression were conducted to explore potential sources of heterogeneity. RESULTS: A total of 26 studies were included . The pooled sensitivity of clinicians increased from 77% (95% CI: 72-81) to 87% (95% CI: 83-90) with AI assistance, while the pooled specificity improved from 88% (95% CI: 85-90) to 92% (95% CI: 89-94). The corresponding AUC values were 0.90 (95% CI: 0.87-0.92) before and 0.95 (95% CI: 0.93-0.97) after AI assistance. Eight studies were rated as high risk of bias. Subgroup analysis and meta-regression identified potential sources of heterogeneity, including fracture location, AI model type, high risk of bias, and reference standards. CONCLUSION: AI assistance significantly improves clinicians' diagnostic performance in detecting fractures on radiographs for extremity and trunk fractures.

Humans↗

PubMed2026

Ketoacidosis with SGLT2 inhibitors in routine clinical practice of type 2 diabetes: Scandinavian cohort and nested case-control study.

BACKGROUND: SGLT2 inhibitors increase the risk of ketoacidosis, but data from routine clinical practice are scarce. We used nationwide registers with the aim of assessing the incidence, risk factors, and prognosis of ketoacidosis during SGLT2 inhibitor treatment in patients with type 2 diabetes. METHODS: In this cohort and nested case-control study we used data from three Scandinavian countries. In a cohort of SGLT2 inhibitor-treatment episodes among patients with type 2 diabetes aged at least 18 years in Sweden, Denmark, and Norway, we estimated ketoacidosis incidence. Using a nested case-control design (matched on age, sex, region of birth, calendar time, and time since treatment initiation), we assessed risk factors and precipitating or co-occurring events. Changes in diabetes medications were evaluated. FINDINGS: The study period was Jan 1, 2013, to Dec 31, 2021, in Denmark and Sweden, and Jan 1, 2013, to Dec 31, 2022, in Norway. We included 322 597 treatment episodes among 282 282 patients with type 2 diabetes (mean age 63 years, 116 521 [36·1%] of 322 597 women). During a median (IQR) follow-up of 1·3 (0·7-2·8) years, 1452 ketoacidosis events occurred (incidence 2·43 per 1000 person-years). Although highest shortly after initiation, risk persisted throughout follow-up. Strong risk factors included high HbA1c (≥83 vs ≤52 mmol/mol: odds ratio [OR] 15·37 [95% CI 11·50-20·53]), malnutrition (OR 10·54 [7·32-15·18]), previous ketoacidosis (OR 10·40 [7·09-15·24]), low BMI (<20 kg/m2vs 20 to <25 kg/m2: OR 9·98 [5·68-17·53]), and recent hypoglycaemia (OR 5·22 [2·60-10·49]). Infection was the most common precipitating or co-occurring event (454 [31·8%] of 1428 vs 862 [6·1%] of 14 233 for ketoacidosis cases vs controls; OR 7·60 [6·62-8·71]). The strongest associations were observed for alcohol intoxication, acute renal events, acute abdomen, stroke, and major surgery, although associations for some transient exposures, particularly milder conditions, might have been overestimated because of under-registration among controls. Exploratory analyses suggested that the observed associations were largely general to patients with type 2 diabetes rather than specific to SGLT2 inhibitor use. At 1 year after ketoacidosis, 211 (25·1%) of 842 remained on SGLT2 inhibitors and insulin use increased from 269 (31·9%) of 842 to 615 (73·0%) of 842. INTERPRETATION: Ketoacidosis risk with SGLT2 inhibitors varies greatly by patient characteristics and is not confined to early in treatment. Risk should be assessed throughout treatment, and patients should be instructed to pause treatment during acute illness and stress. FUNDING: Region Stockholm, Swedish Society of Medicine, Karolinska Institutet.

Journal Article↗

PubMed2026

Predicting training outcomes for developmental dyslexia from EEG data.

Developmental dyslexia (DD) is characterised by lower-than-average reading abilities and is diagnosed in approximately 10% of individuals. The societal barriers may limit professional fulfilment and psychological wellbeing of individuals with DD, calling for the development of effective interventions to counteract them. As DD is associated with challenges in both phonological and visuo-attentional domains, different longitudinal training approaches were developed to strengthen them. However, they require a considerable amount of personal, social and economic resources and the outcomes may vary depending on individual differences in behavioural and neurophysiological functionality. Hence, predicting training outcomes might help in developing personalised treatment protocols and optimising the use of resources. In the present work we applied machine learning to resting-state EEG to predict longitudinal training outcomes in adults with DD enrolled in a randomized clinical trial. In particular, one group received a visuo-attentional training combined with transcranial alternating current stimulation (tACS), another group received visuo-attentional training with sham/placebo stimulation, and the third group received a phonological training with sham/placebo stimulation. The improvement in text reading speed was associated with spectral power in low-beta and individual frequencies in the alpha (IAF) and beta (IBF) bands, while the improvement in pseudoword reading was associated with IBF. The findings highlight the potential of capturing neural markers of treatment responsiveness in DD. Future studies should focus on the generalisability of predictive models to real-world settings, while investigating whether specific EEG markers predict responsiveness to distinct remediation protocols, thus supporting the development of personalised interventions.

Humans↗

PubMed2026

Enhancement of secondary organic aerosol formation from isoprene photooxidation by ammonia.

Ammonia (NH3) can participate in atmospheric secondary organic aerosol (SOA) formation by reacting with organic acids and carbonyl compounds in particle phase, but its influence on the gas phase chemistry remains unclear. This study performed a series of smog chamber experiments to investigate the influence of NH3 on the formation of SOA from isoprene photooxidation by OH radicals. Both gas and particle phase products were measured with a series of state-of- art instruments including a nitrate ion chemical ionization mass spectrometer (nitrate-CIMS) and high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS). Our results showed that in the presence of NH3 SOA in the chamber significantly increased, along with an enhanced oxidation of isoprene. CIMS analysis further showed that NH3 in the chamber homogeneously reacts not only with gas-phase organic acids but also with gaseous low volatility oxygenated organic molecules (OOMs) to generate extremely low volatility and ultralow volatility NH3-OOMs clusters. Quantum chemical calculation showed that NH3 can spontaneously interact with OOMs to form NH3-OOMs clusters by forming hydrogen bonds with RCOOH, R-OOH, and R-OH. These clusters can promote new particles formation and particle growth through nucleation and condensation, directly enhancing the isoprene SOA production with a contribution of 78% to the enhanced SOA. Moreover, the formation of NH3-OOMs clusters also results in more isoprene consumed by OH radicals, indirectly increasing the SOA production with a contribution of 22 % to the enhanced SOA. Our work for the first time clarified a synergetic effect of NH3 on isoprene SOA formation, which should be accounted for by models.

Aerosols↗

PubMed2026

Active Site Assembly by SMG5 as a Mechanism for SMG6 Endonuclease Licencing in Nonsense-mediated mRNA Decay.

Nonsense-mediated mRNA decay (NMD) is a conserved eukaryotic surveillance pathway that eliminates transcripts containing premature termination codons (PTCs). Substantial progress has been made in defining the transcript features that mark aberrant translation termination for NMD activation, yet key mechanistic steps remain incompletely understood - including how recruitment of the central NMD factor UPF1 is coupled to the downstream effector phase in which targeted mRNAs are nucleolytically degraded. In metazoans, NMD employs an endonucleolytic route mediated by SMG6, a PIN-domain nuclease, alongside SMG5 and SMG7, which act downstream of PTC recognition. SMG5 has recently been proposed to licence SMG6 activity, yet the molecular basis of this licencing has remained elusive. Here, we combine AlphaFold structural predictions with biochemical assays to investigate interactions among human SMG5, SMG6, and SMG7. Structural models predict a high-confidence interface between SMG5 and SMG6 PIN domains that forms a composite active site: a conserved SMG5 aspartate (D893) complements the SMG6 acidic triad to reinstate the canonical tetrad required for PIN-domain catalysis. In vitro, SMG6 alone exhibits weak endonucleolytic activity, which is enhanced ∼10-fold by the SMG5 PIN domain. Mutational analyses confirm that conserved residues from both proteins are essential for this composite configuration. Our findings reveal that the SMG5 PIN domain, previously considered catalytically inert, plays a critical role in activating SMG6 by completing its active site. This work provides mechanistic insight into the SMG5-dependent licencing step and uncovers a composite PIN nuclease architecture at the heart of the metazoan NMD effector phase.

Nonsense Mediated mRNA Decay↗