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PubMed2026

Diagnostic criteria and severity assessment for syndesmosis injury using magnetic resonance imaging: A systematic review.

High ankle sprains involving syndesmosis injury present challenges in both diagnosis and severity assessment. Magnetic resonance imaging is widely regarded as the preferred modality for evaluating syndesmosis injury and related structural damage. This systematic review primarily examined the diagnostic utility of magnetic resonance imaging. Secondarily, it explores grading and prognostics of syndesmosis injuries with magnetic resonance imaging and identified possible imaging parameters predictive of injury severity. A comprehensive search of MEDLINE, Embase, CINAHL Complete, and Scopus was performed through February 12, 2025, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Peer-reviewed human studies in English that used magnetic resonance imaging to assess syndesmosis injury were included. Excluded were review articles, case reports, abstract-only studies, and biomechanical or cadaveric investigations. Twenty-seven studies comprising 1931 ankles met inclusion criteria. Magnetic resonance imaging demonstrated high diagnostic accuracy for complete tears of the anterior and posterior inferior tibiofibular ligaments. Ancillary signs such as the ring-of-fire edema pattern, distal tibiofibular joint effusion, and widening of the distal joint space exhibited high specificity with variable sensitivity and may assist in grading injury severity. Magnetic resonance imaging in chronic syndesmosis injury primarily detects fibrotic scarring and post-injury changes. Evidence gaps remain regarding the parameters that best determine injury severity and indicate early surgical intervention in competitive athletes. Consolidating multiple magnetic resonance imaging findings into standardized diagnostic criteria may improve reliability and clinical decision-making.

Humans↗
PubMed2026

Modelling peak microbial pollution events caused by combined sewer overflows in a source-to-sea system.

Predicting peak microbial pollution events in downstream coastal bathing waters caused by combined sewer overflows (CSOs) is essential for protecting public health. In urban areas, wastewater effluents, CSOs, and surface runoff can contribute to elevated microorganism loads to downstream waters. These pressures are likely to be intensified by growing population density and more frequent heavy rainfalls due to climate change. This study developed a process-based model to simulate Escherichia coli (E. coli) emissions, transport, and fate from the initial sources to coastal beaches. A three-year retrospective simulation (2017-2019) shows that E. coli concentrations in CSO discharges varied widely across the catchment (4.6 - 7.3 (log10 CFU 100 ml-1)). 99th percentile E. coli concentrations (4.0 (log10 CFU 100 ml-1)) at the inland water outlet were dominated by local CSO emissions, whereas 90th percentile E. coli concentrations (3.6 (log10 CFU 100 ml-1)) reflected cumulative upstream contributions from both CSO and effluent emissions. With the simulation accuracy of 89%, the model reliably reproduced the E. coli dynamics on the downstream beach and showed strong performance in representing peak concentrations based on Complementary Cumulative Distribution Function (CCDF) analysis. The process-based model enables quantitative tracking of source contributions and identification of pollution hotspots, providing support for mitigation measures. The study lays down a source-to-sea modelling framework for representing pollution transport across the aquatic continuum and provides a transferable tool for microbial pollution forecasting and climate adaptation planning.

Climate projection↗
PubMed2026

Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.

PURPOSE: To synthesizes evidence on artificial intelligence (AI) performance in neuroblastoma (NB) diagnosis, risk stratification, prognosis, and genomic characterization. MATERIALS AND METHODS: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines (PROSPERO: CRD42024539475) across five databases. Meta-analyses used random-effects models with logit-transformed Area Under the Curve (AUCs) and cluster-robust standard errors. AI models were classified as Machine Learning Models (MLM) or Hybrid Nomograms (HN) based on their construction methodology. RESULTS: Of 3,742 articles identified, 53 were included. MLMs demonstrated higher point estimates than radiologists in differential diagnosis (AUC: 0.87 vs. 0.83), though this difference was not statistically significant and carried substantial uncertainty. HNs achieved stronger performance in risk stratification (AUC: 0.87). AI-derived nomograms (AUC: 0.9) and gene signatures (AUC: 0.8) outperformed conventional prognostic markers descriptively. Chemotherapy response prediction remained below clinical utility thresholds across all model types. Only 33.9% of models reported calibration and 24.5% underwent external validation. CONCLUSIONS: AI demonstrates proof-of-concept across multiple NB clinical domains. However, clinical adoption remains premature given persistent gaps in external validation, calibration, dataset size, and pediatric-specific model development. Future studies should test these models prospectively in multicenter pediatric cohorts, ideally through COG or SIOPEN, using shared definitions for diagnosis, risk group, treatment response, and survival outcomes.

Humans↗
PubMed2026

Penalized Cumulative Probability Model for a Continuous Outcome Subject to Detection Limits.

Mixed-type outcome data occur when the outcome variable's distribution is a mixture of both continuous and discrete ordinal variables. Such mixed-type outcomes are common in biomedical, psychological, and the health sciences, particularly for variables having either a detection or quantitation limit. When interest lies in identifying a combination of genomic features associated with a mixed-type outcome, any method used would require a variable selection strategy for high-dimensional data. Unfortunately, few variable selection methods exist for modeling a mixed-type outcome when the covariate space is high dimensional. This study develops a high-dimensional penalized cumulative probability model (CPM), to allow for the identification of genomic features associated with mixed-type outcome of interest. We demonstrated how such model may be estimated using the iterative penalization procedure-the generalized monotone incremental forward stagewise (GMIFS) algorithm. The Model-X knockoffs procedure was combined with the estimation algorithm to control the false discovery rates (FDR) when performing variable selection. Through extensive simulation studies, our penalized CPM was shown to outperform alternative methods in terms of controlled variable selection performance by achieving high statistical power with the FDR being controlled at the target level. We demonstrate the utility of our method by applying it to predict estimated glomeruli filtration rate (eGFR) in kidney transplant recipients at 24 months post-transplant using baseline gene expression data as predictors. Our CPM model identified five genes associated with this mixed-type outcome which have important links to renal disease, which may provide prognostic guidance for kidney transplantation recipients.

Models, Statistical↗
PubMed2026

A systematic review of macaque brain stimulation: Trends and future directions.

Neurostimulation techniques can powerfully modulate neural circuit activity and provide causal insights into the relationship between brain function and behavior. Macaque monkeys have long been a key animal model for brain stimulation studies. While stimulating the macaque brain with one or a few electrodes has already taught us much about brain function and dysfunction, recent technological advances promise a future with more precise stimulation using many more electrodes. However, such possibilities also increase the number of choices an experimenter has when designing their study. We can learn from a rich past, but a comprehensive overview of which brain regions have been studied and with what stimulation parameters is lacking. Here, we present a PRISMA-compliant systematic review of 734 macaque brain stimulation studies using electrical and/or optogenetic stimulation. We find a striking bias in which brain areas have traditionally been stimulated: a mere 10 brain regions account for half of all studies, with the remainder of studies investigating approximately 150 other areas. Across studies, stimulation frequency robustly predicted direct behavioral effects independent of brain region, while amplitude did not. Future studies could more systematically explore less studied regions through lower stimulation frequencies (e.g., 20-50 Hz) alongside established ranges (∼200 Hz). Tools such as fMRI or optical imaging can capture neural circuit engagement evoked by these frequencies, even when behavioral effects are absent or remain subtle. Our synthesis offers a guide towards the next steps in high-channel-count, high-precision stimulation approaches.

Animals↗
PubMed2026

Complement component C4 and neuroimaging in psychiatry: A systematic review.

INTRODUCTION: Genomic, transcriptomic, and proteomic studies suggest that the complement system contributes to the pathophysiology of various psychiatric disorders partly through neurodevelopmental effects linked to C4A protein levels variations. We conducted a systematic review to characterize how brain micro- and macrostructure and connectivity vary with proxies of in vivo brain C4A protein levels in both psychiatric and general-population cohorts. METHODS: We used Medline, Web of Science, and Embase, and included all studies published before April 14, 2025. Inclusion criteria were: (1) inclusion of healthy controls and/or individuals with psychiatric disorders assessed according to recognized diagnostic manuals (DSM or ICD); (2) use of MRI-based neuroimaging; and (3) use of genomic, transcriptomic and/or proteomic approaches as proxies of in vivo brain C4A proteins levels. RESULTS: From 317 identified articles, 11 were included. Associations between C4A levels and brain structure were heterogeneous across regions. Only the mOFC, dlPFC, and entorhinal cortex were implicated in more than one study. Findings for the mOFC and dlPFC varied by the type of metrics and clinical status, whereas higher C4A levels were more consistently associated with smaller entorhinal cortex surface area and cortical thickness in pediatric, middle-aged, and older general-population cohorts. In addition, one study found higher genetically predicted C4A expression to be associated with higher TSPO levels. CONCLUSION: The limited number of available studies and their methodological heterogeneity make synthesis challenging. However, biological hypotheses such as excessive synaptic pruning or broader inflammatory effects on the brain may provide plausible explanatory frameworks for the reported associations.

Humans↗
PubMed2026

The global potential of freshwater microbes for plastic degradation.

Plastic pollution is becoming increasingly severe on a global scale, and the potential for biodegradation as a treatment method that is environmentally friendly merits greater attention. A significant number of genes that associated the degradation of plastic (PDAGs) have been identified, however, the distribution of these genes among microorganisms in global inland waters remains to be elucidated. A global-scale meta-analysis was conducted, incorporating approximately 1000 metagenome datasets of inland waters across seven continents. A total of 13,109 metagenome-assembled genomes (MAGs) were obtained by means of metagenomics binning, and 22,621 PDAGs were identified from these. Among these recognized PDAGs, phenylacetaldehyde dehydrogenase (PAD) was the most dominant (n = 16,664), followed by catalase (n = 5931). The predominant hosts for PAD and catalase were identified as Gamma-proteobacteria and Bacteroidia, respectively. The largest number of both PAD and catalase was found in MAGs from North America, while the average gene number in single MAG was highest in MAGs from Oceania. In accordance with the prediction of traits, PDAG-carrying MAGs from Europe demonstrated the fastest growth rate and the lowest optimal growth rate. Furthermore, 25 styrene monooxygenase (StyA) enzymes were identified, which were found to cluster into two distinct groups hosted by Alpha-proteobacteria and Gamma-proteobacteria, respectively. Moreover, 11 MAGs were observed to possess the complete pathway of polystyrene degradation. These results explored the potential of inland water microorganisms as a biological resource for plastic degradation and provided valuable microbial reference information that can be used to develop biological treatment technologies for mitigating plastics.

Plastics↗
PubMed2025

Patient Ethnicity and Staff Use of Restraints and Restrictive Practice in Inpatient Psychiatric Services: A Systematic Review.

Restrictive practices such as restraints, seclusion, and forced medication are only intended to be used when the threat is at a level whereby an individual is likely to inflict harm on themselves or another individual. Demographic variations, including ethnicity, may be associated with the use of these practices. However, there is no systematic review on patient ethnicity specifically. The review therefore aimed to establish whether a patient's ethnic identity was associated with staff use of restrictive practices in inpatient psychiatric services. The systematic review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Four databases were searched (PsycINFO, Medline, Embase, and CINAHL). Methodological quality was assessed using the Critical Appraisal Skills Program Checklists. Fifteen studies met the inclusion criteria. A variety of ethnicities were identified within the studies. These were driven by the location of the study. Seclusion (14 studies), forced medication (4), and physical restraint (4) were explored. There were mixed findings, with ethnicity shown to predict restrictive practices in studies having larger participant numbers, longer follow-up periods and less methodological bias. It remains unclear whether ethnicity is a genuinely independent predictor of restraint and coercive practices or interacts with other risk factors. Staff working in inpatient settings should be aware of how unconscious biases might affect clinical practice. Recruiting a diverse workforce from minority ethnic groups into inpatient psychiatric services would be a positive step. However, support for these staff members is important, and all staff should be equipped to respond to ethnic diversity. Future research should explore beyond patient-level factors.

Humans↗
PubMed2026

Prevalence and cardiometabolic impact of mild autonomous cortisol secretion in primary aldosteronism: a systematic review and meta-analysis.

Many primary aldosteronism (PA) patients harbor concomitant mild autonomous cortisol secretion (MACS), termed "Connshing syndrome." The prevalence and cardiometabolic impact of this co-secretion have not been quantitatively synthesized. To determine the pooled prevalence of MACS in PA and evaluate its association with cardiovascular (CV) events, type 2 diabetes mellitus (T2DM), and obesity. PubMed, Embase, Cochrane, Web of Science, and Scopus through January 2026. Following PRISMA 2020 and MOOSE guidelines (PROSPERO: CRD420261334965), studies reporting MACS prevalence [post-1 mg dexamethasone suppression test (DST) cortisol ≥1.8 μg/dl] in PA were included. Prevalence was pooled using random-effects models. Odds ratios (ORs) were calculated for cardiometabolic outcomes. Certainty was evaluated using GRADE. Fourteen studies (2356 patients) were included. Pooled MACS prevalence was 26.2% [95% confidence interval (CI): 24.1-28.4; I2 = 24.2%; prediction interval: 21.6-31.4%], consistent across European (27.0%) and East Asian (25.2%) cohorts ( P = 0.62). MACS + PA patients had higher odds of CV events (OR 1.60; 95% CI: 1.07-2.38; P = 0.021) and T2DM (OR 1.37; 95% CI: 1.00-1.86; P = 0.048), but not obesity (OR 1.10; P = 0.411). Sensitivity analyses confirmed robustness. GRADE certainty was moderate for prevalence and low for CV events. Approximately one in four PA patients has MACS, associated with a 60% higher CV event risk. These findings support routine DST screening in PA and have implications for perioperative management and cardiometabolic risk stratification.

Humans↗
PubMed2026

Evaluating the pathogenic significance of unique chromosomal variants in craniosynostosis using patient-derived induced pluripotent stem cells and mouse modelling.

PURPOSE: Unravelling causal links between unique structural/copy-number variants (SV/CNV) and associated phenotypes is essential for correct genetic counselling. We investigated two families in which patients with craniosynostosis had SV/CNV potentially dysregulating a fibroblast growth factor (FGF)-encoding gene; a 730 kb dup(4)(q21.21) including FGF5; and a complex 568 kb interspersed 13q12.11 duplication, located 841 kb from FGF9. METHODS: We combined bioinformatic predictions of altered topologically-associating domain (TAD) structure, with experimental analysis (RNA- and ATAC- [assay for transposase-accessible chromatin] sequencing) of patient induced pluripotent stem cell lines (iPSCs) differentiated to neural crest (NCC) and osteoprogenitor (OPC) identities. For the dup(4)(q21.21) we generated a mouse bearing an equivalent rearrangement using CRISPR-Cas9 targeting. RESULTS: TAD analysis suggested potential dysregulation of the FGF5/FGF9 gene by bringing it into a novel genomic milieu. The RNA- and ATAC-seq assays demonstrated FGF5/FGF9 upregulation (2.7-18x) and local opening of chromatin, in 3/4 cell lines. For the dup(4)(q21.21), a causal role was supported by the mouse model, whereas interpretation of the 13q12.11 SV is confounded by a co-existing FOXP2 pathogenic variant. CONCLUSION: Patient iPSC-differentiated NCC and OPC lines, combined with TAD-based modelling to generate testable functional hypotheses, provide valuable functional evidence when evaluating causation of unique SV/CNV in craniosynostosis.

copy-number variant↗
PubMed2026

Assessment of temporomandibular joint space changes after orthognathic surgery in skeletal malocclusion patients: a systematic review.

PURPOSE: To interpret postoperative changes in temporomandibular joint (TMJ) joint space dimensions and condylar position following orthognathic surgery in patients with skeletal malocclusions, and to determine whether reported alterations represent clinically meaningful displacement or physiological adaptive remodeling. MATERIALS AND METHODS: A comprehensive search of PubMed, SCOPUS, Web of Science, EBSCOhost, and Cochrane Library was performed to assess pre- and postoperative TMJ changes using three-dimensional imaging. Joint spaces including anterior (AJS), superior (SJS), and posterior (PJS) and condylar morphology were evaluated. Methodological quality was appraised using the Joanna Briggs Institute (JBI) checklist. Due to methodological and clinical heterogeneity, findings were synthesized narratively with attention to malocclusion type and surgical movement. RESULTS: A total of 16 studies consisting 628 patients undergoing BSSO, Le Fort I osteotomy, vertical ramus osteotomy, or bimaxillary surgery were included. Most studies reported minor, adaptive postoperative changes in AJS, SJS, and PJS. Class II patients showed more consistent increases in AJS/SJS, whereas Class III patients demonstrated variable posterior or anterior remodelling depending on surgical movement. Volumetric analyses revealed region-specific adaptations without significant condylar displacement. Postoperative temporomandibular disorder symptoms were infrequent, and no consistent evidence supported detrimental TMJ effects attributable to surgery. CONCLUSION: Postoperative TMJ joint space changes after orthognathic surgery primarily represent physiological adaptive remodeling rather than pathological condylar displacement, with reported variability driven by malocclusion type, surgical movement, fixation method, and imaging protocol. Recognizing these predictable patterns is essential to prevent overinterpretation of postoperative imaging and to improve clinical assessment through standardized three-dimensional and long-term evaluation strategies.

Humans↗
PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗
PubMed2026

Longitudinal whole-genome analysis of bluetongue virus identifies conserved serotype-specific genomes and distinct genomic constellations within a Colorado sheep flock (2021-2023).

Bluetongue virus (BTV) is a segmented double-stranded RNA virus of ruminants transmitted by Culicoides spp. biting midges. Although the genome consists of ten segments, classification into serotypes is primarily based on genome segment 2. However, reassortment among genomic segments is a major driver of BTV evolution and diversity. This study used longitudinal whole-genome sequencing to characterize BTV genomes collected from 2021 to 2023 within a single sheep flock in Colorado, where multiple serotypes co-circulate. Whole-genome sequences were generated from fourteen blood samples representing four serotypes: BTV-6, -11, -13, and -17. Longitudinal sampling identified multiple BTV serotypes within individual sheep across consecutive years. Tanglegram analysis comparing segment phylogenies to the segment 2 tree demonstrated incongruent topologies across all genomic segments, suggestive of reassortment or the circulation of distinct genomic constellations. Nucleotide-level comparisons revealed high sequence homology among same-serotype samples from the same year, while the greatest genetic divergence was observed among BTV-17 genomes collected in different years. Additionally, all BTV-13 genomes contained a previously undescribed nonsynonymous substitution in segment 10 predicted to extend the encoded protein by three amino acids. Together, these findings demonstrate that highly conserved BTV genomes and distinct genomic constellations can be detected at the flock level across multiple years. This longitudinal whole-genome approach reveals the genetic complexity of endemic BTV populations, including novel variants and genomic patterns consistent with reassortment that are lost with conventional serotyped-based approaches, highlighting the need to integrate whole-genome characterization into endemic BTV monitoring programs.

Animals↗
PubMed2026

GSTT1 promotes stemness and FGFR inhibitor sensitivity in pancreatic cancer through regulation of CD133 (PROM1).

Pancreatic ductal adenocarcinoma (PDA) is among the deadliest malignancies, driven by metastatic progression and profound cellular heterogeneity. We previously identified glutathione S-transferase theta 1 (GSTT1) as a regulator of a slow-cycling, highly metastatic tumor cell population, suggesting that GSTT1High cells may possess stem-like properties. Here, we define the functional and molecular features of this subpopulation in metastatic PDA. Using a mCherry-tagged Gstt1 reporter system in metastatic murine PDAC cells, we enriched for Gstt1High cells and observed increased tumor sphere formation, accompanied by upregulation of stemness-associated genes including PROM1 (CD133) and activation of Wnt and FGF signaling pathways. In human PDA models, CD133HighGSTT1High cells exhibited enhanced tumor sphere initiation and expansion compared to other populations, defining a maximal stem-like state. Notably, sensitivity to FGFR inhibitors was observed only under tumor sphere conditions, highlighting a context-dependent therapeutic vulnerability. Mechanistically, FGFR3 expression correlated with GSTT1 and CD133 levels, and FGF signaling was required to sustain this state. GSTT1 knockdown reduced CD133 protein levels, impaired tumor sphere formation, and altered sensitivity to FGFR inhibition. These findings were largely recapitulated in patient-derived PDA organoids, where GSTT1 and PROM1 co-expression predicted increased tumor sphere formation and enhanced response to the multi-kinase inhibitor Nintedanib. Together, these results identify a GSTT1HighCD133High stem-like subpopulation in metastatic PDA and identify an FGFR-dependent signaling axis that sustains this state, representing a potential therapeutic vulnerability.

AC133 Antigen↗
PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗
PubMed2025

Mindfulness and Sex Education for Sexual Dysfunction in Breast Cancer Survivors: Mediators and Moderators of Treatment Outcome.

Mindfulness-based cognitive therapy (MBCT) and supportive-expressive sex education therapy (STEP) are effective group treatments for sexual dysfunction after breast cancer (BrCa). We explored mediators and moderators of outcomes following the 8-week groups. BrCa survivors (n = 116, mean age = 49.9 ± 9.5) were randomized to group and completed measures before, immediately after, and 6 months after treatment. Mediators assessed were changes in depression, chronic pain acceptance, pain catastrophizing, and trait mindfulness. Potential moderators included age, treatment expectations, baseline mental health, cancer treatment duration, use of chemotherapy, and adjuvant endocrine therapy. Longitudinal mediation and moderation were assessed using linear mixed models. Increases in pain acceptance mediated improvements in sexual desire and reductions in both sexual distress and vaginal pain. Decreases in pain catastrophizing mediated improvements in sexual distress. Higher expectations for treatment led to greater reductions in sexual distress. Those with low baseline anxiety showed greater improvements in desire and distress. Low baseline depression predicted greater improvements in desire, but only in the STEP arm. Older STEP participants improved significantly more than younger STEP participants. Cancer-related treatment variables, and the impact of adjuvant endocrine therapy, had differential effects on outcomes based on the treatment arm of the study. In conclusion, treatments aimed at improving pain acceptance and pain catastrophizing are likely to promote improvements in sexual health among BrCa survivors, and factoring in patients' expectations about treatment improvements, depression and anxiety, age, duration of cancer treatment, chemotherapy, and adjuvant hormonal therapy may help to guide treatment recommendations for sexual dysfunction.

Humans↗
PubMed2026

Serum Albumin on Admission: A Prognostic Marker of Morbidity and Mortality in Burns? A Systematic Review and Meta-Analysis.

Albumin is essential for maintaining oncotic pressure and vascular integrity. In burn injuries, increased capillary permeability leads to hypoalbuminemia, which is a recognized marker of poor outcomes in critical illness. However, its prognostic value in acute burn care remains underexplored. This study evaluated the prognostic value of admission serum albumin in predicting mortality, acute kidney injury (AKI), hospital and intensive care unit length of stay, ventilatory requirements, sepsis, and pulmonary infection in patients with burn injuries. A systematic search of PubMed, Scopus, Cochrane Library, Web of Science, MEDLINE, and Embase was conducted. Of 5587 studies screened, 19 were included in the systematic review and 9 in the meta-analysis. Statistical analysis was performed using RStudio, with pooled outcomes reported as odds ratios (ORs), standardized mean differences, and hierarchical summary receiver operating characteristic curves. Heterogeneity was assessed using Cochran's Q, I2, and tau2. Hypoalbuminemia on admission was significantly associated with increased mortality during admission (OR 9.51; 95% CI, 3.04-29.78; I2 49.3%). Admission hypoalbuminemia was also associated with an increased risk of AKI (OR 2.83; 95% CI, 2.49-3.22; I2 0%). Evidence for other outcomes was limited and heterogeneous. Admission serum albumin appears to be a valuable prognostic marker in patients with burn injuries, particularly for mortality and AKI. Further research is required to support its integration into burn-specific risk models, characterize albumin trends within the first 24 h postinjury, and establish optimal cut-off values.

Humans↗
PubMed2026

Integrative modeling of the genome structure and dynamics in fission yeast.

Genome organization in the nucleus is highly structured and dynamic. Recent advances in genomic technology have enabled the measurement of genome-wide architecture and locus-specific motion, yielding contact maps and live-cell trajectories. However, these outcomes are derived from different modalities and are not directly comparable, with their quantitative integration being a key challenge. Here we establish a genome-wide live-cell imaging platform in fission yeast Schizosaccharomyces pombe, tracking 131 chromosomal loci, along with the spindle pole body (SPB) and nucleolus, to construct a quantitative map of locus dynamics. By integrating these dynamics with contact data through polymer modeling of Hi-C data, we build a physics-based "digital twin" of the S. pombe genome consistent with the spatiotemporal dynamics of interphase chromatin. We validate it against genome-wide mobility patterns and known architectural features, including centromere and telomere clustering. The model also identifies distinct dynamical regimes: centromere- and telomere-proximal loci relax within [Formula: see text]150 s, whereas the remaining loci relax within [Formula: see text]70 s. We measure semiperiodic dynamics of SPB motion, including a characteristic peak near 225 s and [Formula: see text] fluctuations. We use the model with SPB-directed forcing to show how these low-frequency components propagate through the genome to drive genome-wide chromatin displacements. Together, this predictive physics-based modeling framework integrates genome structure and dynamics to reveal how nuclear mechanical driving forces shape chromosome motion, linking mechanically driven chromatin responses to genome maintenance and regulation.

Schizosaccharomyces↗