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PubMed2026

Outcomes and Associated Prognostic Factors for Orthograde Canal Obturation Using Ortho MTA III: A Randomised Prospective Clinical Trial.

AIM: To prospectively compare treatment outcomes for orthograde canal obturation using Ortho MTA III (OMTA) with the continuous wave of compaction (CWC) using gutta-percha (GP) and AH Plus sealer, and to identify associated predictive factors. METHODOLOGY: Informed consent was obtained (110 patients), and single- or two-rooted permanent teeth (n = 120) diagnosed with pulp necrosis (or previously treated) and asymptomatic apical periodontitis or chronic apical abscess (periapical index, PAI ≥ 3) were randomly assigned to two groups (n = 60/group). The canals were prepared to a minimal apical size #40 (ISO) based on their initial file size, disinfected and obturated by either CWC or OMTA using an enhanced disinfection protocol (GP disinfected, new gloves after each intraoperative radiograph and before starting obturation). Clinical and periapical radiographic examinations were conducted by two calibrated, independent endodontists during follow-up periods of at least 12 months. Success rates and associated predictive factors (tooth-, operator- and patient-related) were analysed statistically using binary and multiple logistic regression (p < 0.05). RESULTS: The median recall period was 30 months (14-48 months), and 104 teeth were finally analysed (recall rate: 86.67%). No significant differences in success rate were observed between the groups (p > 0.05) under both loose (OMTA: 88.24%, CWC: 83.02%) and strict criteria (OMTA: 64.71%, CWC: 58.49%). Multivariate analysis revealed that age (OR = 5.735, 95% CI: 1.286-25.577, p = 0.022), periapical lesion size (OR = 6.596, 95% CI: 1.397-31.138, p = 0.017) and PAI score (OR = 2.081, 95% CI: 1.047-4.136, p = 0.036) were significant predictors of treatment failure. CONCLUSIONS: Orthograde obturation of infected canals with Ortho MTA III demonstrated comparable success and treatment outcomes to those filled with GP and sealer by CWC, supporting its potential as a clinically viable alternative for the obturation of infected root canals. TRIAL REGISTRATION: cris.nih.go.kr registration number: KCT00099939.

Humans↗

PubMed2026

Analysis of end-stage renal disease mediated by cuproptosis-related genes.

OBJECTIVE: The complex pathophysiological mechanism of end-stage renal disease (ESRD) has not been fully understood. Cuproptosis is a newly discovered type of programmed cell death. Therefore, this study attempts to clarify the relationship between cuproptosis-related genes (CRGs) and the phenotype of ESRD. MATERIALS AND METHODS: The National Center for Biological Information Gene Expression Omnibus database was applied to obtain the GSE37171 dataset comprising whole-genome microarray analysis of peripheral blood samples. A 3 : 1 case-control design was employed with 75 ESRD patients and 20 healthy controls who were frequency-matched for age, sex, and ethnicity. Based on differentially expressed genes (DEGs) and genes related to cuproptosis, CRGs were identified. Thereafter, we explored two different subpopulations based on the cuproptosis gene and analyzed their expression and immune infiltration. Genes specific to the CRG cluster were identified through the weighted gene co-expression network analysis algorithm, and the best prediction model was determined and verified by four machine learning methods. RESULTS: The study identified 14 differentially expressed CRGs, among which ATP7B, SLC31A1, LIAS, LIPT1, DLD, MTF1, CDKN2A, DBT, and DLST had relatively high expression levels in the ESRD samples. Compared with the control group, expression levels of FDX1, DLAT, PDHA1, PDHB, and GLS were significantly lower in the ESRD group, and CRGs played a key role in the regulation of immune infiltration in ESRD. Two cuproptosis-related molecular clusters were identified in the ESRD samples. Cluster2 was more correlated with the immune infiltration of ESRD. By analyzing the intersection points between CRG cluster and key genes of ESRD, a total of 888 specific DEGs were identified. Functional differences related to specific DEGs were further explored using gene set variation analysis. Five significant genes (SMC5, USP47, USP53, AGA, and DMXL1) were identified by the support vector machine model as key predictors for ESRD disease risk, achieving an area under the curve (AUC) of 1.00 in internal validation. However, external validation in independent cohorts is required prior to clinical application. Individual gene analysis showed an AUC > 0.81 in discriminating ESRD patients from healthy controls, and the expression of all 5 genes in ESRD patients was significantly lower than in the control group. CONCLUSION: This study clarified the relationship between CRGs and the phenotype of ESRD, analyzed their specific roles in the immune microenvironment, and obtained a predictive model, providing new insights for the study of its potential therapeutic targets.

Humans↗

PubMed2026

Factors Associated With Accelerated Fracture Healing in Patients With Traumatic Brain Injury and Extremity Comminuted Fractures: A Retrospective Case-Control Study.

OBJECTIVE: Although traumatic brain injury (TBI) has been clinically associated with accelerated bone healing, the factors that determine which patients experience this phenomenon remain poorly defined, and previous findings are conflicting. This study aimed to investigate the clinical factors associated with accelerated fracture healing in patients with TBI combined with comminuted fractures of the limbs, so as to provide an evidence-based foundation for elucidating the clinical phenomenon of TBI-promoted fracture healing. METHODS: A retrospective case-control study design was employed. Patients between January 2020 and April 2024 with concurrent diagnoses of TBI and comminuted fractures were included. Based on radiographic findings and RUST/mRUST scores, patients were divided into an accelerated healing group (AHG) and a normal/delayed healing group (NDHG). Clinical data including demographics (age, sex, BMI), TBI characteristics (injury site, GCS score), admission laboratory indices (blood count, coagulation function, inflammatory markers), and fracture site/local soft tissue conditions, as well as functional outcomes assessed by the Short Musculoskeletal Function Assessment (SMFA) questionnaire at final follow-up were collected. Univariate analysis and multivariate logistic regression analysis were used to identify independent factors influencing accelerated fracture healing. Receiver operating characteristic (ROC) curves were plotted to evaluate their predictive value. RESULTS: A total of 119 patients were included, with 69 in the AHG and 50 in the NDHG. Significant differences were observed between the two groups in terms of age, BMI, GCS score, and platelet count (p < 0.05). Univariate analysis showed that age, BMI, GCS score, red blood cell count, and platelet count were associated with accelerated fracture healing (p < 0.20). Multivariate logistic regression analysis indicated that younger age (OR = 0.875, 95% CI: 0.821-0.934) and lower GCS score (indicating more severe TBI; OR = 0.490, 95% CI: 0.339-0.707) were independent predictors of accelerated fracture healing. ROC curve analysis showed that the area under the curve (AUC) for age and GCS score in predicting accelerated healing were 0.893 and 0.851, respectively. CONCLUSIONS: In patients with TBI combined with comminuted fractures, younger age and greater TBI severity (lower GCS score) are independent predictors of accelerated fracture healing. These findings assist clinicians in the early identification of patients with high healing potential to optimize treatment strategies, facilitate the early identification of high-risk patients, and provide clinical clues for further exploration of the molecular mechanisms underlying neurohumoral regulation of bone regeneration.

Humans↗

PubMed2026

Community-tailored One Health educational intervention to enhance knowledge and practices for zoonotic disease prevention in rural Thailand: A protocol for a prospective cluster randomised controlled Trial in Chanthaburi, Thailand (Saan Suk trial).

BACKGROUND: Zoonotic infectious disease risk arises at human-animal-environment interfaces where pathogen spillover can occur. Rural communities living in biodiverse settings may experience frequent contact with wildlife and shared environments through livelihoods, food practices, and economic activities. Reducing spillover risk and strengthening pandemic prevention requires both structural and individual-level change. Community-based interventions that promote awareness, risk perception, self-efficacy, pro-environmental behaviour, and safe coexistence with wildlife may support prevention by shifting behavioural determinants of zoonotic disease risk. The Saan Suk intervention was co-developed with rural communities in Thailand using a Human-Centred Design approach and is grounded in the Health Belief Model and One Health principles. The intervention is intended to be feasible, acceptable, and deliverable through Thailand's established Village Health Volunteer (VHV) system. METHODS: This protocol describes a parallel-arm, cluster-randomised controlled superiority trial that will be conducted during July - October 2026, in Chanthaburi Province, Thailand. 24 villages will be equally randomised to the Saan Suk intervention or the current practice (control). In intervention villages, trained VHVs will deliver, once a week over four weeks, a multimodal One Health educational intervention designed to improve knowledge of zoonotic spillover, promote protective behaviours, reduce risky wildlife-related contacts, and support respectful coexistence with wildlife. Trained outcome assessment teams will conduct structured interviews with 42 adult participants per village, yielding a total sample size of 1,008 participants. The sample size was calculated for the primary outcome, accounting for clustering, with 90% power to detect a medium effect size (6 points on the 0-100 knowledge scale) at a significance level of 0.05, accounting for a design effect with an ICC of 0.028. The primary outcome is knowledge of zoonotic spillover, transmission pathways, risk factors, protective and risky behaviours, and safe coexistence with wildlife. Secondary outcomes include attitudes, self-efficacy, preventive and risky behaviours, and reported contacts with major local reservoir hosts. A structured questionnaire was developed, expert-reviewed, and piloted for the outcome assessment. Outcomes will be analysed using mixed-effects regression models with random effects for village and adjustment for relevant pre-specified confounders. Primary analyses will follow the intention-to-treat principle. DISCUSSION: This trial will evaluate whether a co-designed, VHV-delivered One Health educational programme can improve knowledge of zoonotic disease prevention and behavioural determinants in rural communities living in close contact with wildlife and shared ecosystems. If effective and feasible, Saan Suk could inform integration into routine VHV training and community-based zoonotic disease and pandemic prevention strategies. TRIAL REGISTRATION: The Saan Suk trial is registered with the German Clinical Trials Register (DRKS). Registration ID: DRKS00038582; date of registration: 11 May 2026.

Zoonoses↗

PubMed2026

Emerging Principles in Spatial Functional Genomics.

Spatial transcriptomic and proteomic atlases have enabled mapping of gene programs within intact tissues, but these measurements remain largely descriptive and do not define the mechanisms controlling tissue biology. Pooled CRISPR screening provides scalable causal interrogation of gene function but remains largely confined to dissociated systems that lack spatial context. In vivo spatial functional genomics (SFG) bridges these approaches by integrating genetic perturbations with in situ transcriptomic and proteomic readouts to measure gene function within intact tissue ecosystems. By preserving spatial organization, SFG enables interpretation of perturbations through effects on cell-cell interactions, diffusible signals, multicellular niches, and tissue architecture. Here, we outline key design axes of SFG: perturbation strategy, barcoding strategy, and phenotypic readout. We discuss computational challenges, including spatial autocorrelation, neighborhood dependence, and context-aware null modeling, and highlight how SFG reveals non-cell-autonomous, architecture-dependent mechanisms of gene function, advancing toward predictive models of tissue organization and gene function.

Genomics↗

PubMed2026

Risk Factors for Long-Term Health-Related Quality-of-Life and Mental Health Outcomes in Traumatic Brain Injury: A Systematic Review and Meta-Analysis.

Traumatic brain injury (TBI) often leads to long-term disability, including persistent mental health issues and lower health-related quality of life (HRQoL). Early interventions can improve recovery, but because resources limit routine monitoring of all patients, trauma care remains largely symptom-driven. The combination of long-term disability and limited capacity for routine follow-up highlights the need for risk-stratified follow-up care and reliable evidence on early prognostic factors. However, the existing literature is sparse and methodologically heterogeneous, limiting the clinical applicability of findings. We therefore conducted a systematic review and meta-analysis to identify early risk factors for poorer long-term mental health and HRQoL outcomes. A systematic search of seven electronic databases identified studies of adult patients with TBI, with outcomes assessed at least 6 months postdischarge. Two authors independently screened the studies, assessed the risk of bias, and extracted the data. We pooled effect estimates using a random-effects meta-analysis and calculated 95% prediction intervals. A narrative synthesis was applied when meta-analysis was not feasible. The review was registered with PROSPERO (CRD42024576912) and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Of the 8,104 articles screened, 64 studies met the inclusion criteria (n = 334,672). Most studies (58%) had a low risk of bias. Female sex, socioeconomic disadvantage, psychiatric history, assaultive-related injuries, and previous TBI were consistently associated with worse long-term outcomes. Across meta-analyses, assault-related injuries more than doubled the odds of post-traumatic stress disorder (odds ratio [OR] = 2.72; 95% confidence interval [CI]: 2.01-3.66, I2 = 0%). Higher odds were also observed among females (OR = 1.33; 95% CI: 1.11-1.59, I2 = 0%), individuals with prior TBI (OR = 1.56; 95% CI: 1.07-2.27, I2 = 0%), and those with psychiatric history (OR = 2.38; 95% CI: 1.83-3.10, I2 = 48%). We found that female sex (OR = 1.72; 95% CI: 1.38-2.16, I2 = 58%), prior TBI (OR = 1.52; 95% CI: 1.25-1.85, I2 = 0%), and psychiatric history (OR = 3.25; 95%CI: 1.86-5.69, I2 = 98%) were associated with higher odds of depression. Furthermore, higher pooled anxiety scores were observed in females and in individuals with a psychiatric history. The study identified several readily available factors present before or at discharge that are associated with poor long-term HRQoL and mental health outcomes. Leveraging these factors in follow-up protocols, prediction modeling, and clinical decision support systems may facilitate risk-stratified postdischarge care for TBI patients.

Humans↗

PubMed2026

Effect of Semaglutide on the Inflammatory Biomarker High-Sensitivity CRP in Patients With Established Cardiovascular Disease and Overweight or Obesity in SELECT: A Prespecified Secondary Analysis.

BACKGROUND: In SELECT (Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity), among 17 604 patients with known atherosclerotic cardiovascular disease and overweight or obesity, but not diabetes, randomization to the glucagon-like peptide-1 receptor agonist semaglutide significantly reduced the primary outcome of major adverse cardiovascular events (MACE; cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke) compared with placebo (mean follow-up, 39.8 months). Inflammation, as indicated by plasma hsCRP (high-sensitivity C-reactive protein) level, is implicated as a biomarker predicting cardiovascular risk in obesity and atherosclerotic cardiovascular disease. SELECT provides a unique opportunity to study the relationship among hsCRP, obesity, weight loss, and MACE outcomes in semaglutide versus placebo groups. METHODS: In this prespecified SELECT substudy, we evaluated whether baseline hsCRP levels predicted MACE risk and examined the relationships between changes in hsCRP levels and time to first MACE, baseline body weight, weight loss, and other clinical measures among treatment groups over time (104, 208 weeks) using multiple approaches, including Cox modeling. RESULTS: Baseline hsCRP level, which was similar in the semaglutide (geometric mean 1.96 mg/L) and placebo (geometric mean 1.91 mg/L) groups, was prognostic of future MACE. The risk of MACE increased across baseline hsCRP level <2, 2-<10, and ≥10 mg/L subgroups, including significant associations with cardiovascular and all-cause death. Semaglutide reduced hsCRP levels (-37.8% [104 weeks]) and risk of MACE across all hsCRP subgroups. Greater reductions in ratio-to-baseline hsCRP with semaglutide were associated with greater weight loss, but preceded major weight loss, evident by 4 and 8 weeks, and occurred among those without weight loss. Semaglutide-associated changes in hsCRP were independent of low-density lipoprotein cholesterol levels, statin use, and atherosclerotic cardiovascular disease entry criteria. hsCRP reductions were found to be prognostic of decreased risk of MACE. Modeling suggests decreased inflammation as contributing in part to the benefits seen with semaglutide in SELECT. CONCLUSIONS: In SELECT, hsCRP data at baseline and in response to treatment with semaglutide support inflammation as a potential prognostic factor associated with cardiovascular risk in these generally well-treated patients with atherosclerotic cardiovascular disease and overweight or obesity but not diabetes. These findings suggest that the MACE reduction observed with semaglutide versus placebo in SELECT may have partially involved a decrease in inflammation. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03574597.

Humans↗

PubMed2026

Discovery and characterization of multifunctional bioactive peptides from Alaska Pollock (Gadus chalcogrammus) milt: hybrid in silico, in vitro, and proteomic approaches.

The growing demand for multifunctional bioactive peptides has sparked interest in underutilized marine by-products as sustainable bioresources. This study explored Alaska Pollock (Gadus chalcogrammus) milt protein as a novel source of peptides with anti-inflammatory, anti-hypertensive, and anti-diabetic effects. Protein composition was analyzed via LC-MS, followed by in silico digestion and bioactivity prediction. Molecular docking identified peptides targeting DPP-IV, α-glucosidase, ACE, GLP-1 receptor, COX-2, MuRF1, and the 20S proteasome. Among the candidates, a promising peptide (CLPPH) was synthesized and validated in vitro, demonstrating inhibitory effects on nitric oxide production, DPP-IV, ACE, and α-glucosidase. These results highlight CLPPH's potential as a multifunctional bioactive peptide and support the valorization of Alaska Pollock milt as a sustainable source for functional foods and nutraceutical applications.

Animals↗

PubMed2026

Genetic overlap between estimated glomerular filtration rate and cardiovascular disease identifies potential targets for cardiorenal syndrome.

Heart and kidney diseases frequently coexist, but the genetic basis of this relationship remains unclear. We analyzed genetic data from large-scale studies to investigate how kidney function (estimated glomerular filtration rate, eGFR) and six common cardiovascular diseases share genetic risk factors. Using MiXeR method, and conjunctional false discovery rate (conjFDR) to identify overlapping genetic regions, we found 478 shared genomic loci between eGFR and cardiovascular diseases. These shared genes are involved in tissue development and structure. We also identified 29 genes that could be targeted by existing medications approved by the US Food and Drug Administration, such as PRKAG2, PDE1A, and IGF1R. Among these, genetically predicted higher level of IGF1R expression is associated with a higher eGFR, which reflects good kidney function and is protective against cardiorenal diseases, such as atrial fibrillation, and myocardial infarction. These findings reveal genetic overlap between kidney function and cardiovascular diseases, highlighting potential targets for understanding and treating cardiorenal syndrome.

Humans↗

PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗

PubMed2026

O'nyong-nyong virus adaptive mutations in non-structural protein 1 and 3 enhance RNA replication and overcome FHL1 requirement.

Arthritogenic alphaviruses, like o'nyong-nyong virus (ONNV), cause debilitating musculoskeletal diseases and are geographically expanding. To predict their emergence, we seek to better understand evolutionary mechanisms that enable changes in virus tropism. Here, we identify adaptive mutations in the ONNV non-structural proteins (nsPs) that arose during cellular serial passaging and enabled ONNV to infect non-permissive Lunet cells. Using shotgun proteomics, we show that this human hepatoma cell line lacks the four-and-a-half-LIM domain protein 1 (FHL1), an essential host factor in ONNV RNA replication. Individual single nucleotide mutations in the nsP1 ring-aperture membrane-binding and oligomerization domain, the nsP3 macrodomain, and the nsP3 opal stop codon overcome FHL1 deficiency in Lunet cells by enhanced RNA replication. These findings demonstrate how subtle genomic changes in nsPs can profoundly influence alphavirus replication and tropism.

LIM Domain Proteins↗

PubMed2026

Single-nucleus transcriptomics reveals cell type-specific remodeling and epilepsy-associated microglia.

Temporal lobe epilepsy (TLE) is the most common acquired epilepsy, causing refractory seizures and cognitive deficits. We performed single-nucleus RNA sequencing on hippocampal tissue from mice 3 and 6 weeks following pilocarpine-induced status epilepticus, a robust model of TLE. Epilepsy samples showed reductions in Cck and Lamp5-Lhx6 interneuron subclusters, alongside increases in Cajal-Retzius cells, dentate granule (DG) cell precursors, and a mature DG cell subcluster. Among glia, an astrocyte subcluster and a markedly expanded microglia sublcuster were increased. We term this microglia population epilepsy-associated microglia (EAM). The transcriptomic profile of EAM overlaps with microglia described in models of Alzheimer's disease and traumatic brain injury, including enrichment of Myo1e and Igf1. EAM display amoeboid morphology, can be found in clumps around pyramidal and granule cell body layers, and exhibit enlarged vesicles and mitochondria. Cell-cell interaction analysis predicts DG cells as their primary interaction partners. This dataset defines transcriptomic programs underlying key cellular alterations in TLE, enabling mechanistic dissection of epileptogenesis.

TLE↗

PubMed2026

Safe and Stable Germline Transmission of MSTN Mutations in Cattle.

With the global population expected to reach 10 billion by 2050, sustainable livestock production is critical. Gene editing of the myostatin (MSTN) gene represents a promising strategy to enhance muscle growth in cattle. In this study, MSTN-mutated founder (F0) cows were used to generate F1 offspring via ovum pick-up, in vitro fertilization, and embryo transfer. Four F1 calves were born, all confirmed to be heterozygous for the MSTN mutation. Long-term monitoring showed normal growth and no visible health abnormalities. Whole-genome sequencing identified SNPs, INDELs, and structural variants, most with minimal predicted functional effects. Proteomic profiling of Longissimus dorsi muscle quantified 2947 proteins, revealing only subtle expression differences between MSTN-mutated and wild-type cattle. These results demonstrate stable inheritance and confirm that MSTN editing does not disrupt genome integrity or protein expression. Overall, our findings support the safety and utility of MSTN gene editing to improve livestock productivity for future food security.

Animals↗

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

Activity shapes large herbivores' ecological influences.

The ecological effects of large herbivores are shaped by their spatial and temporal patterns of activity (i.e. where, when and how intensely they use specific locations). When large herbivores' ecological influences are perceived to be undesirable, the traditional approach has been to reduce their population size. This numbers-first logic assumes that ecological effects scale primarily with abundance. We argue that this framing provides an incomplete understanding of large herbivores' ecological impacts. Using African elephants (Loxodonta africana) as a well-documented case study, we show that ecological effects on plants, animals and ecosystem processes correlate more with spatio-temporal patterns of activity than with population size. In large, open systems characterized by strong gradients of water availability, forage quality, shade and risk, elephants concentrate into predictable hotspots while relaxing activity elsewhere, generating localized impacts and opportunities for recovery. By contrast, in small, fenced or fragmented landscapes, where movements are constrained, and gradients are weak, spatial self-regulation breaks down, producing homogenized use and widespread ecological effects. We contend that understanding where, when and under what constraints herbivores use space provides a more general and mechanistic basis for interpreting ecological influence than abundance alone, with implications that extend beyond elephants to large herbivores globally.

Animals↗

PubMed2026

Wedge tarsectomy using patient specific instrumentation for complex multiplanar foot deformity Reconstruction: A prospective case series.

BACKGROUND: Bony correction in complex cavovarus deformities is often multiplanar. We examine our results following wedge tarsectomy (WT) using patient-specific instrumentation (PSI). METHODS: This single-centre, prospective case series evaluated noncorrectable cavovarus feet undergoing PSI-guided WT. Accuracy of PSI guides/plans, operative duration, and adjunctive procedures were recorded. Weightbearing CT (WBCT) measurements and PROM scores were recorded preoperatively and postoperatively, with 1 year follow-up. Data was then statistically analysed. RESULTS: Eleven patients were included. Planned correction was achieved (two required minor intraoperative adjustments to the initial osteotomy and nine required adjunctive procedures). Mean operative time was 135 min. Postoperative improvements were significant radiologically and in MOxFW walking distance. All fused by 3 months, with no significant complications. CONCLUSION: PSI-guided wedge tarsectomy safely achieves predictable multiplanar corrections. Our unit's experience has been excellent, with improvement in patients' walking, particularly with larger deformity corrections. LEVEL OF EVIDENCE: Level IV, prospective case series.

Humans↗

PubMed2026

Association between anaemia and osteoporosis: a systematic review and meta-analysis.

BACKGROUND: Osteoporosis significantly impacts global morbidity. Recent evidence suggests anaemia may contribute to osteoporosis risk. This systematic review and meta-analysis investigates this association. METHODS: PubMed, Scopus, EBSCO, and ScienceDirect were searched for papers. Studies with definition of anaemia and assessing osteoporosis outcomes were included. Meta-analysis utilized random-effects models (DerSimonian-Laird method), and study quality was assessed via Newcastle-Ottawa Scale (NOS). Analyses were performed using R Studio. RESULT: Eighteen studies (861,540 participants) were analyzed. Anaemia significantly increased osteoporosis risk in univariate analysis (OR 1.62; 95% CI 1.33-1.98; p < 0.001), despite high heterogeneity (I2 = 92.7%). The results remain significant in studies that reported multivariate analysis (OR 2.01; 95% CI 1.26-3.21; p = 0.004). Sensitivity analyses confirmed the robustness of our result. CONCLUSION: Anaemia significantly associated with osteoporosis, emphasizing the need for targeted screening in anaemic individuals. Further studies should consider incorporating anaemia into osteoporosis and fracture prediction tools.

Humans↗

PubMed2026

Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis.

BACKGROUND: Obstructive sleep apnea (OSA) is highly prevalent but remains substantially underdiagnosed. Polysomnography (PSG) is the reference standard, but its cost and limited availability constrain large-scale case identification. AI-based screening tools may support risk stratification and referral prioritization, but their diagnostic accuracy across apnea-hypopnea index (AHI) thresholds remains uncertain. OBJECTIVE: This review aimed to systematically evaluate the diagnostic accuracy of AI-based OSA screening tools at AHI thresholds of ≥5, ≥15, and ≥30 events/hour, with emphasis on models using non-PSG-derived inputs. METHODS: PubMed, Embase, Scopus, and Web of Science were searched for studies published from January 1, 2016, to May 3, 2026. Eligible studies included adults evaluated for suspected OSA or recruited from population-based cohorts, assessed AI-based models intended or interpretable for OSA screening, risk prediction, or screening-oriented severity classification, used PSG as the reference standard, and reported sufficient data to construct or reconstruct 2×2 contingency tables. Diagnostic accuracy was synthesized separately by AHI threshold and input source using bivariate random-effects models, with 95% CIs and prediction intervals (PIs). Risk of bias and certainty of evidence were assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) and GRADE (Grading of Recommendations Assessment, Development, and Evaluation), respectively. RESULTS: A total of 60 studies were included, of which 47 contributed data to the meta-analysis. At AHI thresholds of ≥5, ≥15, and ≥30 events/hour, pooled sensitivities were 0.94 (95% CI 0.92-0.96; 95% PI 0.71-0.99), 0.87 (95% CI 0.84-0.89; 95% PI 0.66-0.96), and 0.83 (95% CI 0.79-0.87; 95% PI 0.61-0.94), respectively; the corresponding specificities were 0.77 (95% CI 0.69-0.84; 95% PI 0.30-0.96), 0.81 (95% CI 0.75-0.85; 95% PI 0.39-0.96), and 0.91 (95% CI 0.87-0.94; 95% PI 0.55-0.99), respectively. The corresponding areas under the summary receiver operating characteristic curves were 0.943, 0.907, and 0.920. For non-PSG-derived tools, sensitivities were 0.92, 0.85, and 0.81, and specificities were 0.70, 0.74, and 0.85 at the 3 thresholds, respectively. For PSG-derived models, sensitivities were 0.96, 0.90, and 0.85, and specificities were 0.82, 0.88, and 0.96, respectively. Exploratory subgroup analyses suggested performance variation across selected study and model characteristics, including region, algorithmic framework, data source, and validation method. CONCLUSIONS: AI-based tools showed generally favorable screening performance for OSA across clinically relevant AHI thresholds, although wide PIs suggest variable performance across future comparable populations and settings. By synthesizing diagnostic accuracy across 3 AHI thresholds and distinguishing non-PSG-derived from PSG-derived models, this review extends previous broad or modality-specific reviews and offers a clinically interpretable, pathway-specific basis for linking model performance to intended use. The findings may clarify potential roles for non-PSG-derived tools in front-end screening and referral prioritization and for PSG-derived models in reduced-channel assessment and sleep-laboratory workflow support. Given substantial heterogeneity, limited external validation, and low or very low certainty of evidence, prospective validation is needed before routine implementation.

Humans↗