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PubMed2026

Assessing the threat of Bacillus cereus: From toxin characterization to modern detection strategies.

Bacillus cereus is a spore-forming pathogen responsible for both diarrheal and emetic foodborne illnesses worldwide. Its significance in food safety has received growing attention. Recent advances, including the discovery of novel virulence factors and the development of emerging detection technologies, have provided new insights into its pathogenic mechanisms and surveillance strategies. This review critically examines the global burden of B. cereus infections, and molecular mechanisms of its major virulence factors, and the performance characteristics of current detection knowledge gaps such as the viable-but-non-culturable state and regulatory blind spots for emetic toxins, and discuss unresolved challenges in clinical management. By integrating epidemiological, microbiological, and technological perspectives with critical lens, this review aims to provide a valuable reference for future research and food safety practices.

Bacillus cereus↗

PubMed2026

Integrated assessment of biocontrol potential and genome analysis of endophytic Bacillus velezensis MGL-B1 against mango stem-end rot.

Mango stem-end rot is a globally significant postharvest disease that severely threatens the mango industry, primarily caused by Botryosphaeria dothidea. However, information on biocontrol agents targeting this pathogen in mango remains limited. In this study, we isolated and identified a strain of Bacillus velezensis MGL-B1 from mango leaf tissues for the first time, which exhibited broad-spectrum antifungal activity. Both in vitro and in vivo assays demonstrated that MGL-B1 effectively inhibited the growth of B. dothidea, with an in vivo biocontrol efficacy reaching 83.72 ± 5.10%, comparable to that of the commonly used chemical fungicide thiabendazole. Further mechanistic analysis revealed that MGL-B1 acts by directly disrupting the integrity of the pathogen's mycelial cell membrane. In addition, its released volatile organic compounds (VOCs) also displayed significant antifungal activity, with components such as 2-nonanone, 2-nonanol, and phenylethyl alcohol being confirmed to exert antifungal effects in in vitro fumigation assays. qPCR analysis showed that MGL-B1 treatment significantly upregulated the transcriptional levels of genes involved in plant-pathogen interaction, phenylpropanoid biosynthesis, and antioxidant defense pathways in mango fruits, with upregulation folds of 16.32, 37.19, and 75.93, respectively; meanwhile, the expression of browning-related genes such as polyphenol oxidase (PPO) was markedly suppressed. Whole-genome sequencing further revealed 14 biosynthetic gene clusters for antimicrobial compounds, including five unknown gene clusters. Collectively, B. velezensis MGL-B1 represents a promising biocandidate strain with multiple antifungal mechanisms and excellent control efficacy, providing a valuable resource for green and sustainable management of mango diseases.

Mangifera↗

PubMed2026

DNM1L depletion leads to accelerated heteroplasmy shifting of m.10191C allele through ATG7-dependent pathways.

Nucleotide composition bias in mitochondrial DNA (mtDNA) makes the heavy strand prone to form a DNA secondary structure called a guanine quadruplex (G4). This secondary structure has been shown to inhibit polymerase processivity in vitro. We previously identified pathogenic mtDNA variants that lead to increased G4-forming propensity, including a T to C mutation at m.10191 (m.10191 T > C) that causes Leigh syndrome. Cells treated with G4 binding agent (G4BA) berberine show a reduction in m.10191C pathogenic heteroplasmy levels. To help better understand the underlying mechanism behind berberine-induced heteroplasmy shift, we examined the relationship between mitochondrial fission and berberine-mediated shift. Here we show that knockdown of the fission factor DNM1L leads to an accelerated heteroplasmy shift towards the healthy mtDNA allele, lowering m.10191C by 10% in 3 weeks, compared to the 5 weeks required for berberine alone. The specific mechanism involves ATG7, as knockdown of ATG7 is able to partially delay this accelerated heteroplasmy shift. Taken together, we show that DNM1L knockdown is able to accelerate berberine-induced m.10191C heteroplasmy shifting through an autophagy-related mechanism.

Humans↗

PubMed2026

Feeding the disease: The impact of nutritional supplementation on Nosema (Vairimorpha) infection in honey bees (Apis mellifera).

Honey bees (Apis mellifera) experience variable colony losses across regions and years, with infectious diseases representing a key component of colony health challenges. Among the most prevalent pathogens are the microsporidian parasites Nosema apis and Nosema ceranae, whose impacts on host survival and transmission vary widely depending on context. While nutritional supplementation is commonly used to support honey bee health, its effects on Nosema infection outcomes remain unclear. Here, we experimentally tested whether dietary enrichment alters survival and infection intensity following exposure to a mixed Nosema inoculum. Newly emerged worker bees were challenged with Nosema spores and maintained on either a basic sucrose diet or the same diet supplemented with a commercial pollen substitute. Dietary enrichment significantly increased both mortality risk and infection intensity in Nosema-infected bees, while having no detectable effect on survival in uninfected controls. These results indicate that supplementation can, counter intuitively, exacerbate nosemosis by promoting parasite replication rather than enhancing host resistance. Our findings highlight the importance of distinguishing nutritional effects on host tolerance versus resistance, and caution that interventions intended to improve bee nutrition may inadvertently increase pathogen production and transmission potential under certain conditions.

Animals↗

PubMed2026

Ancient DNA and Human Physiology.

Ancient DNA (aDNA) enables the reconstruction of chronologically sampled genomes from ancient humans, animals, plants, pathogens, and microorganisms, as well as environmental DNA, providing a record of biological changes through time. Improvements in short and degraded DNA extraction methods and low-cost sequencing now enable the generation of broad, cross-regional datasets that expand evolutionary analyses from past population demography to biological mechanisms. By tracking temporal shifts of allele frequencies, integrating functional genomics resources (e.g., gene expression, chromatin structure variation), modeling population demography to separate selection from genetic drift, and aligning genetic changes with archaeological, cultural, and climatic data, aDNA has the potential to link sequence variation to physiological function within their temporal and environmental contexts. In this review, we summarize illustrative case studies from aDNA research spanning complex traits, dietary adaptations, and responses to pathogens and other environmental changes, showing how human biology has evolved under multiple selective pressures through time. These dated signals help triage experimental work and expose mechanisms that are rare or absent in living cohorts. Although some challenges remain, such as geographic and temporal sampling disparities, limitations in data resolution and variant detection, and genotype-phenotype uncertainties, rapid methodological progress and stronger ethical frameworks are expanding what can be inferred, making aDNA a promising tool for refining physiological pathways, their timing, and their drivers.

Humans↗

PubMed2026

Enrichment of Lysobacter in a long-term organically managed agricultural field with low soilborne disease incidence.

Disease-suppressive soils, in which soilborne pathogens are naturally suppressed, offer a promising model for sustainable crop protection, particularly in organic farming systems where chemical disease control options are limited. Although disease suppression in these soils is considered to rely on biological control, the underlying mechanisms remain poorly understood. In this study, we investigated soil from a long-term organically managed field in Shiga Prefecture, Japan, where soilborne disease incidence has remained consistently low, to identify bacterial community features potentially associated with this field. The 16S rRNA gene amplicon sequencing indicated that this soil harbored a bacterial community distinct from those of nearby agricultural soils. Following the application of organic compounds, the genus Lysobacter, a taxon with known antagonistic activity against plant pathogens, was markedly enriched in response to proteinaceous organic inputs. This enrichment was consistent across sampling times and specific to certain proteinaceous organic inputs, whereas minimal effects were observed on chitin, N-acetyl-d-glucosamine, or cysteine. Broader soil surveys indicated that Lysobacter enrichment was not strictly associated with whether soils had been managed under organic or conventional farming practices. Stepwise multiple regression analysis identified 10 co-occurring bacterial genera that were strongly associated with Lysobacter abundance. These findings highlight condition-dependent Lysobacter enrichment as a characteristic microbial response to proteinaceous organic amendments in this low-disease-incidence field and provide microbial insights that may inform microbiome-based strategies for sustainable soil management.

Lysobacter↗

PubMed2026

Inactivation of Aspergillus flavus spores by dielectric barrier discharge cold plasma: Kinetics, physiological properties and proteomic analysis.

A. flavus, as a pathogen, poses a grave threat to both human and livestock health, significantly influencing agricultural production as well. This study aimed to investigate the inactivation effect and mechanism of dielectric barrier discharge cold plasma (DBD-CP) on A. flavus spores. The results exhibited that DBD-CP effectively inactivated A. flavus spores by the Weibull + Tail model. Furthermore, the physiological and proteomic analysis revealed that DBD-CP destructed cell wall and membrane integrity, causing cellular protein leakage and increasing membrane penetration of ROS generated from DBD-CP. Although intracellular ROS was excessively accumulated, the protein levels and activities of SOD and CAT were decreased, indicating that intracellular redox homeostasis was disrupted by DBD-CP. Subsequently, DBD-CP treatment induced cellular protein oxidation and changed protein structures, resulting in unstable protein structures. Meanwhile, protein synthesis and degradation in A. flavus spores were disturbed by inhibiting ribosome biogenesis, initiation process and NEDD8-mediated UPS, which did not compensate for the loss of protein caused by oxidative damage and leakage, leading to A. flavus spore inactivation. Besides, DBD-CP could attenuate A. flavus virulence by downregulating hydrolytic enzymes and CFEM-related proteins. This study provides novel insight into the inactivation mechanism of DBD-CP against A. flavus spores, which establishes a basis for the application of DBD-CP in controlling pathogenic fungi contamination in grains and crops, promoting the development of DBD-CP in food and agricultural decontamination.

Spores, Fungal↗

PubMed2026

Clinical and endocrine correlates of genetic etiologies in severe hypospadias: Study from 34 patients.

OBJECTIVE: Hypospadias is a prevalent congenital anomaly (0.3%-1.0%); however, severe hypospadias (defined as proximal cases with the meatus at the penoscrotal junction, scrotum, or perineum) is a rare and clinically challenging entity with a multifactorial etiology. This study aimed to characterize the interrelationships among the clinical, endocrine, and genetic profiles in children with severe hypospadias. MATERIALS AND METHODS: We conducted a comprehensive analysis of 34 male patients with severe hypospadias. Preoperative hormone levels were measured using two methods: chemiluminescent immunoassay for luteinizing hormone and follicle-stimulating hormone, and liquid chromatography-tandem mass spectrometry for testosterone (T), dihydrotestosterone (DHT), dehydroepiandrosterone (DHEA), 17α-hydroxyprogesterone (17α-OHP), and other steroids. Genetic analysis was conducted via whole exome sequencing. RESULTS: The diagnostic yield of clinically relevant genetic variants (including pathogenic and likely pathogenic, and variants of uncertain significance) in our cohort was 41.2% (14/34) of patients. Patients carrying these variants exhibited a more complex phenotypic profile compared to non-carriers, including a significantly higher rate of patients with ≥3 associated malformations and a greater prevalence of cryptorchidism. Furthermore, the group with clinically relevant variants showed selective elevations in adrenal-derived precursors, specifically 17α-OHP and DHEA. Correlation analysis revealed significant positive associations of both 17α-OHP levels and the T/DHT ratio with the number of associated malformations. CONCLUSION: This study reveals significant genetic heterogeneity in patients with severe hypospadias. Those carrying genetic variants was associated with more severe clinical phenotypes, while certain endocrine variations, including the elevation of adrenal-derived hormones, were also observed in this cohort.

Humans↗

PubMed2026

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

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

Artificial Intelligence↗

PubMed2026

Corrigendum to "The impact of diverse ancestry on polygenic risk score-based prediction models for psychiatric and neurodevelopmental disorders" [Psychiatry Research 2026 Oct:364:117322].

Explore the source record for details and available documents.

Published Erratum↗

PubMed2026

Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.

BACKGROUND: COVID-19 has been closely associated with coagulation abnormalities. However, existing biomarkers, including D-dimer and fibrin degradation products (FDP), exhibit limited accuracy in stratifying disease severity and predicting long-term clinical outcomes. OBJECTIVES: This study aimed to use proteomic analysis to identify plasma biomarkers associated with COVID-19 severity and prognosis, and validate their predictive utility for mortality and thromboembolic complications. METHODS: Plasma proteomic profiles were analyzed across three COVID-19 severity classes. Differential expression analysis and functional analysis were performed. Clustering analysis was used to identify proteins correlated with disease severity. Candidate biomarkers were validated in an independent cohort. Predictive performance of the biomarkers for mortality, sepsis and venous thromboembolism was evaluated using bootstrap-corrected ROC analyses and multivariable regression analyses. RESULTS: Proteomic analysis revealed progressive involvement of the coagulation and complement pathway with increasing disease severity. SERPINA1 and CD59 were identified as candidate biomarkers and exhibited significantly higher plasma levels in severe cases. Bootstrap-corrected ROC analyses demonstrated strong predictive performance: SERPINA1 achieved AUCs of 0.775 and 0.924 for 30-day and 12-month mortality, and CD59 achieved AUCs of 0.720 for sepsis; the combined model further improved prediction of 12-month mortality (AUC 0.946) and sepsis (AUC 0.904), outperforming D-dimer and FDP. Multivariable regression confirmed their independent prognostic value. CONCLUSION: This exploratory study identifies SERPINA1 and CD59 as candidate prognostic biomarkers in COVID-19, highlighting the role of coagulation and complement-related pathways in disease severity and warranting further prospective validation.

Humans↗

PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗

PubMed2026

Predictive value of anal sphincter electromyography for sacral neuromodulation test-phase outcomes.

BACKGROUND: Sacral neuromodulation (SNM) is an established therapy for refractory pelvic organ dysfunction. Anal sphincter electromyography (EMG) is commonly used preoperatively to assess sacral and peripheral nerve integrity. However, the prognostic significance of chronic neurogenic EMG changes for SNM outcomes remains unclear. OBJECTIVE: To evaluate whether chronic neurogenic changes on preoperative anal sphincter EMG predict the outcome of the SNM test phase. METHODS: We retrospectively analysed 62 consecutive patients with bladder and/or bowel dysfunction or pelvic pain who were candidates for SNM treatment and who underwent preoperative anal sphincter EMG. EMG findings were classified as normal or showing chronic neurogenic changes. SNM test-phase success was defined as a ≥50% improvement of symptoms at 24 days. Outcomes were compared between EMG groups. RESULTS: Of the 62 patients (49 women, 13 men), 30 (48%) had normal EMG findings and 32 (52%) showed chronic neurogenic changes. Overall, the SNM test phase was successful in 47 patients (76%). Success rates were similar in patients with normal EMG (72%) and neurogenic EMG changes (79%), with no statistically significant difference (p = 0.878). Sex-stratified analyses revealed no significant association between EMG findings and test-phase success in women or men. CONCLUSIONS: Chronic neurogenic changes on anal sphincter EMG do not predict SNM test-phase outcomes. These findings suggest that abnormal sphincter EMG results should not be used as a standalone criterion to exclude patients from SNM therapy. SIGNIFICANCE: Signs of neurogenic damage on anal sphincter EMG are not an indicator of reduced neuromodulatory capacity or diminished clinical response to SNM.

Humans↗

PubMed2026

Association of lipoprotein-associated phospholipase A2 with recurrence risk and its predictive value in large artery atherosclerotic stroke.

OBJECTIVE: To investigate the association of lipoprotein-associated phospholipase A2 (Lp-PLA2) with large artery atherosclerotic (LAA) stroke and its predictive value for recurrence. METHODS: We consecutively enrolled 412 acute LAA stroke patients. Using a cutoff of 200 ng/mL, patients were divided into high and low Lp-PLA2 groups, and into recurrence and non‑recurrence groups based on 1‑year follow‑up. Baseline characteristics, lipid profiles, National Institutes of Health Stroke Scale (NIHSS) scores, and vascular stenosis degree were compared. Binary logistic regression and Receiver Operating Characteristic (ROC) analysis were used to identify independent risk factors and evaluate predictive value. RESULTS: The high Lp-PLA2 group had significantly higher low-density lipoprotein cholesterol (LDL-C), small dense low-density lipoprotein cholesterol (sdLDL-C), prevalence of severe stenosis (≥70%), and proportion of NIHSS > 15 (all P < 0.05). The recurrence group showed elevated Lp-PLA2, higher LDL‑C and sdLDL-C, more severe neurological deficits, and more severe stenosis (all P < 0.001). Multivariable regression identified elevated Lp-PLA2 (per 10 ng/mL: OR = 1.139, 95% CI: 1.089-1.191), moderate (OR = 3.145) and severe (OR = 11.663) neurological deficits, and severe stenosis (OR = 9.390) as independent risk factors for recurrence (all P < 0.05). The Area Under the Curve (AUC) of Lp-PLA2 was 0.75 (95% CI: 0.69-0.82), with an optimal cutoff of 208.95 ng/mL. CONCLUSION: Elevated Lp-PLA2 is associated with adverse lipid profiles, more severe neurological deficits, and greater vascular stenosis in LAA stroke patients, and independently predicts 1‑year recurrence. Lp-PLA2 shows moderate predictive value, supporting its potential for risk stratification.

Humans↗

PubMed2026

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. METHODS: A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. RESULTS: Univariate analysis showed significant differences (P < 0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR = 9.752), smoking (OR = 5.171), AIP (OR = 6.691), TyG index (OR = 4.393), HALP score (OR = 2.831), and ≥ 2 comorbidities (OR = 3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC = 0.884, accuracy = 0.829, sensitivity = 0.812, specificity = 0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. CONCLUSION: Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.

Humans↗

PubMed2026

Risk prediction models for blood transfusion in patients undergoing total hip and knee arthroplasty: a systematic review and meta-analysis.

OBJECTIVE: To systematically review and evaluate published risk prediction models for perioperative blood transfusion in patients undergoing total hip or knee arthroplasty (THA/TKA). METHODS: We systematically searched PubMed, Web of Science, the Cochrane Library, and Embase from inception to May 31, 2025. Two researchers independently screened the literature, extracted data, and assessed the risk of bias and applicability using the Prediction model Risk Of Bias Assessment Tool (PROBAST). The area under the receiver operating characteristic curve (AUC) values were pooled via a meta-analysis using Stata 18.0. RESULTS: d Fourteen studies containing 36 prediction models were included. The incidence of blood transfusion among THA/TKA patients ranged from 3.2% to 30.8%. Preoperative hemoglobin (Hb) level, tranexamic acid (TXA) use, operative duration, intraoperative blood loss, and age were the most frequently incorporated predictors. Model sensitivity ranged from 58% to 94.5%, and specificity ranged from 71.3% to 94%. Meta-analysis showed that the pooled AUC value of the 13 validated models was 0.87 (95% CI: 0.85-0.90), suggesting good discriminatory performance. All models were rated as having a high risk of bias. The applicability of four studies was rated as unclear. CONCLUSION: Although the included studies demonstrated promising discriminative ability of prediction models for blood transfusion in THA/TKA, all were assessed as having a high risk of bias using the PROBAST tool. Therefore, future research should prioritize the development of models with larger sample sizes, rigorous study designs, and multicenter external validation.

Humans↗

PubMed2026

Assessment of the Potential of Different Anthropometric Indices in Predicting the Risk of Diabetes and Associated Co-morbidities.

Diabetes, a chronic disorder, is showing a rapidly increasing trend globally. India holds the second position in the global diabetes epidemic. The present investigation is an assessment of different anthropometric measurements and their association with type 2 diabetes to determine their diagnostic potential for diabetes as well as its co-morbidities. In this cross-sectional study, we have measured anthropometric parameters and blood biomarkers in subjects with diabetes. We have presented the comparisons of cost- and time-effective anthropometric variable with costly and time-dependent biochemical variables in control and diabetic groups (n = 233/group). Correlations between anthropometric variables and biochemical measurements, as well as the diagnostic utility of anthropometric variables for diabetes, were evaluated. The diagnostic utility of anthropometric variables for diabetes was assessed through receiver operating characteristic (ROC) curves. Neck circumference, sagittal abdominal diameter (SAD), skinfold thickness, and body roundness index (BRI) displayed high specificity and diagnostic utility for diabetes, emphasizing their potential in predicting diabetes and the further development of metabolic syndrome. The study highlights the importance of cost- and time-effective anthropometric assessments in diabetes risk evaluation and calls for further research to elucidate this intricate relationship and develop personalized management strategies.

Humans↗