Search Results (6,480)

Background: Hip fractures are common in the elderly and often require ICU admission post-surgery due to high ASA scores and comorbidities. Length of hospital stay after ICU is a crucial indicator affecting patient recovery, complication rates, and healthcare costs. This study aimed to develop and validate a machine learning-based model to predict the factors associated with extended hospital stay (>7 days from surgery to discharge) in hip fracture patients requiring postoperative ICU care. The findings could help clinicians optimize ICU bed utilization and improve patient management strategies. Methods: In this retrospective single-centre cohort study conducted in a tertiary ICU in Turkey (2017–2024), 366 ICU-admitted hip fracture patients were analysed. Conventional statistical analyses were performed using SPSS 29, including Mann–Whitney U and chi-squared tests. To identify independent predictors associated with extended hospital stay, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied for variable selection, followed by multivariate binary logistic regression analysis. In addition, machine learning models (binary logistic regression, random forest (RF), extreme gradient boosting (XGBoost) and decision tree (DT)) were trained to predict the likelihood of extended hospital stay, defined as the total number of days from the date of surgery until hospital discharge, including both ICU and subsequent ward stay. Model performance was evaluated using AUROC, F1 score, accuracy, precision, recall, and Brier score. SHAP (SHapley Additive exPlanations) values were used to interpret feature contributions in the XGBoost model. Results: The XGBoost model showed the best performance, except for precision. The XGBoost model gave an AUROC of 0.80, precision of 0.67, recall of 0.92, F1 score of 0.78, accuracy of 0.71 and Brier score of 0.18. According to SHAP analysis, time from fracture to surgery, hypoalbuminaemia and ASA score were the variables that most affected the length of stay of hospitalisation. Conclusions: The developed machine learning model successfully classified hip fracture patients into short and extended hospital stay groups following postoperative intensive care. This classification model has the potential to aid in patient flow management, resource allocation, and clinical decision support. External validation will further strengthen its applicability across different settings. Full article

The composition, architecture, and plant traits of temperate steppe communities are intricately associated with environmental factors. However, most studies primarily focus on aboveground observations, often overlooking the critical role of belowground root systems. Here we conducted a field survey at a large-regional scale to investigate the composition of temperate steppe communities and plant root traits. Cluster analysis, correspondence analysis and Pearson correlation coefficient matrix method were employed to classify vegetation associations based on plant community composition and root traits. The principal driving and limiting factors shaping plant root communities were systematically investigated. The results showed that the temperate steppe was categorized into three community subtypes: meadow steppe, typical steppe, and desert steppe, comprising five plant groups and thirteen plant associations. The RLFS analysis, based on belowground architectural and functional traits, demonstrated a spatial gradient differentiation with three ecological adaptations: tufted herbs, rhizome herbs, and non-tufted or rhizome herbs. Key environmental driving factors for meadow steppe included precipitation, soil carbon, nitrogen, and phosphorus content, while the average growing-season temperature as a limiting factor. The environmental driving factors for the typical steppe were not apparent, and the limiting factor was water. For the desert steppe, the environmental driving factors were altitude and average growing-season temperature. These findings reveal notable spatial heterogeneity and a distinct distribution pattern in community composition and vegetation classification based on belowground root traits in the Inner Mongolia steppes. Full article

Myelin oligodendrocyte glycoprotein antibody–associated disease (MOGAD) is an inflammatory demyelinating disorder of the central nervous system characterised by heterogeneous clinical and radiological presentations. Accurate interpretation of serum anti–myelin oligodendrocyte glycoprotein (anti-MOG) antibody titres is critical to improve diagnostic precision and prognostic assessment. This single-centre retrospective study evaluated 19 patients diagnosed with MOGAD in 2023, all of whom were seropositive for anti-MOG IgG, as confirmed by live cell-based assays (CBAs) using full-length human MOG and IgG1-specific secondary antibodies. Antibody quantification combined a ratiometric semi-quantitative fluorescence index with classical endpoint dilution titres, enabling classification into low, medium, and high titre groups. Stratification revealed titre-dependent phenotypic heterogeneity: high-titre patients were older at onset and predominantly presented with optic neuritis, often bilateral, and encephalic involvement, whereas low-titre patients more frequently exhibited spinal cord syndromes, cerebellar or brainstem symptoms, and a higher prevalence of cerebrospinal fluid-restricted oligoclonal bands. Semi-quantitative fluorescence ratios correlated consistently with endpoint titres, and exponential decay analysis demonstrated slower signal loss in high-titre sera, confirming assay reliability. No significant association emerged between titre level and monophasic versus relapsing disease course. Anti-MOG antibody titres could serve not only as a diagnostic biomarker but also to capture clinically relevant immunopathological diversity, supporting a titre-stratified approach to diagnosis and early prognostication. Incorporating semi-quantitative metrics alongside clinical and imaging features may refine the diagnostic algorithm and prevent misclassification of atypical presentations. Full article

Seasonally dry tropical forests (SDTFs) are shaped by strong climatic and edaphic constraints, including pronounced rainfall seasonality, extended dry periods, and shallow karst soils with limited water retention. Understanding how tree species respond to these pressures is crucial for predicting ecosystem resilience under climate change. In the Yucatán Peninsula, we characterized sixteen tree species along a spatial and seasonal precipitation gradient, quantifying wood density, predawn and midday water potential, saturated and relative water content, and specific leaf area. Across sites, diameter classes, and seasons, we measured ≈4 individuals per species (n = 319), ensuring replication despite natural heterogeneity. Using a principal component analysis (PCA) based on individual-level data collected during the dry season, we identified five functional groups spanning a continuum from conservative hard-wood species, with high hydraulic safety and access to deep water sources, to acquisitive light-wood species that rely on stem water storage and drought avoidance. Intermediate-density species diverged into subgroups that employed contrasting strategies such as anisohydric tolerance, high leaf area efficiency, or strict stomatal regulation to maintain performance during the dry season. Functional traits were strongly associated with precipitation regimes, with wood density emerging as a key predictor of water storage capacity and specific leaf area responding plastically to spatial and seasonal variability. These findings refine functional group classifications in heterogeneous karst landscapes and highlight the value of trait-based approaches for predicting drought resilience and informing restoration strategies under climate change. Full article

Crop canopy height is a key structural indicator that is strongly associated with crop development, biomass accumulation, and crop health. To overcome the limitations of time-consuming and labor-intensive traditional field measurements, Unmanned Aerial Vehicle (UAV)-based Light Detection and Ranging (LiDAR) offers an efficient alternative by capturing three-dimensional point cloud data (PCD). In this study, UAV-LiDAR data were acquired using a DJI Matrice 600 Pro equipped with a 16-channel LiDAR system. Three canopy height estimation methodological approaches were evaluated across three crop types: corn, soybean, and winter wheat. Specifically, this study assessed machine learning regression modeling, ground point classification techniques, percentile-based method and a newly proposed Dual-Range Averaging (DRA) method to identify the most effective method while ensuring practicality and reproducibility. The best-performing method for corn was Support Vector Regression (SVR) with a linear kernel (R² = 0.95, RMSE = 0.137 m). For soybean, the DRA method yielded the highest accuracy (R² = 0.93, RMSE = 0.032 m). For winter wheat, the PointCNN deep learning model demonstrated the best performance (R² = 0.93, RMSE = 0.046 m). These results highlight the effectiveness of integrating UAV-LiDAR data with optimized processing methods for accurate and widely applicable crop height estimation in support of precision agriculture practices. Full article

Background: Coronary collateral circulation (CCC) plays a crucial protective role in patients with chronic total occlusion (CTO), mitigating ischemia and improving long-term outcomes. However, the degree of collateral vessel development varies substantially among individuals. Systemic inflammatory and nutritional status may influence this variability. The Naples Prognostic Score (NPS) is a composite index reflecting these parameters, yet its relationship with CCC remains incompletely defined. Methods: We retrospectively analyzed 324 patients with angiographically confirmed CTO at Selçuk University Faculty of Medicine between 2014 and 2025. Coronary collaterals were graded using the Rentrop classification, and patients were categorized as having poor (grades 0–1) or good (grades 2–3) collaterals. The NPS was calculated using serum albumin, cholesterol, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. Baseline clinical and laboratory data were compared between groups. Univariate and multiple binary logistic regression analyses were performed to identify independent predictors of collateral development. Results: Of the 324 patients, 208 (64.2%) had poor and 116 (35.8%) had good collateral circulation. Patients with good collaterals had higher body mass index, HDL Cholesterol (HDL-C), and triglyceride levels, and significantly lower NPS values compared with those with poor collaterals (p < 0.05 for all). In multiple binary logistic regression analysis, HDL-C (OR 1.035; 95% CI 1.008–1.063; p = 0.011) and NPS (OR 0.226; 95% CI 0.130–0.393; p < 0.001) emerged as independent predictors of well-developed collaterals. Conclusions: Both NPS and HDL-C are independently associated with the degree of coronary collateral circulation in CTO patients. These findings highlight the interplay between systemic inflammation, nutritional status, lipid metabolism, and vascular adaptation. As simple and routinely available measures, NPS and HDL-C may serve as practical tools for risk stratification and identifying patients at risk of inadequate collateral formation. Prospective studies with functional assessments of collateral flow are warranted to confirm these associations and explore potential therapeutic interventions. Full article

Background: Early and accurate differentiation between melanomas and benign nevi is essential for making proper clinical decisions. This study aimed to identify clinical, morphological, and histopathological variables most strongly associated with melanoma, using both statistical and machine learning approaches. Methods: This study evaluated 184 melanocytic lesions using clinical, morphological, and histopathological parameters. Univariable analyses were performed in XLStat statistical software, version 2014.5.03, while multivariable machine learning models were developed in Jamovi (version 2.4). Five supervised algorithms (random forest, partial least squares, elastic net regression, conditional inference trees, and k-nearest neighbors) were compared using repeated cross-validation, with performance evaluated by accuracy, Kappa, sensitivity, specificity, F1 score, and calibration. Results: Univariable analysis identified significant differences between melanomas and nevi in age, horizontal diameter, gender, lesion location, and selected histopathological features (cytological and extracellular matrix changes, epidermal interactions). However, several associations weakened in multivariable analysis due to collinearity and overlapping effects. Using glmnet, the most influential independent predictors were cytological changes, horizontal diameter, epidermal interactions, and extracellular matrix features, alongside age, gender, and lesion location. The model achieved high discrimination (AUC = 0.97, 95% CI: 0.93–0.99) and accuracy (training: 95.3%; test: 92.6%), confirming robustness. Conclusions: Structured demographic, morphological, and histopathological data—particularly age, lesion size, cytological and extracellular matrix changes, and epidermal interactions—can effectively support classification of melanocytic lesions. Machine learning approaches (the glmnet model in our study) provide a reliable framework to evaluate such predictors and offer practical diagnostic support in dermatopathology. Full article

Decoding motor imagery (MI) of lower-limb movements from electroencephalography (EEG) signals remains a challenge due to the involvement of deep cortical regions, limiting the applicability of Brain–Computer Interfaces (BCIs). This study proposes a novel protocol that combines passive pedaling (PP) as sensory priming with MI at different speeds (30, 45, and 60 rpm) to improve EEG-based classification. Ten healthy participants performed PP followed by MI tasks while EEG data were recorded. An increase in spectral relative power around Cz associated with both PP and MI was observed, varying with speed and suggesting that PP may enhance cortical engagement during MI. Furthermore, our classification strategy, based on Convolutional Neural Networks (CNNs), achieved an accuracy of 0.87–0.89 across four classes (three speeds and rest). This performance was also compared with the standard Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA), which achieved an accuracy of 0.67–0.76. These results demonstrate the feasibility of multiclass decoding of imagined pedaling velocities and lay the groundwork for speed-adaptive BCIs, supporting future personalized and user-centered neurorehabilitation interventions. Full article

The morphogenesis of the primordial gut relies on signaling pathways such as Wnt, FGF, Notch, Hedgehog, and Hippo. Reciprocal crosstalk between the endoderm and mesoderm is integrated into the signaling pathways, resulting in craniocaudal patterning. These pathways are also involved in adult intestinal homeostasis including cell proliferation and specification of cell fate. Perturbations in this process can cause growth disturbances manifesting as adenomas, serrated lesions, and cancer. Significant differences have been observed between right and left colon cancers in the hindgut, and between the jejunoileum, appendix, and right colon in the midgut. The question is to what extent the embryology of the mid- and hindgut contributes to differences in the underlying tumor biology. This review examines the precursor lesions and consensus molecular subtypes (CMS) of colorectal cancer (CRC) to highlight the significance of embryology and tumor microenvironment (TME) in CRC. The three main precursor lesions, i.e., adenomas, serrated lesions, and inflammatory bowel disease-associated dysplasia, are linked to the CMS classification, which is based on transcriptomic profiling and clinical features. Both embryologic and micro-environmental underpinnings of the mid- and hindgut contribute to the differences in the tumors arising from them, and they may do so by recapitulating embryonic signaling cascades. This manifests in the range of CRC CMS and histologic cancer subtypes and in tumors that show multidirectional differentiation, the so-called stem cell carcinomas. Emerging evidence shows the limitations of CMS particularly in patients on systemic therapy who develop drug resistance. The focus is thus transitioning from CMS to specific components of the TME. Full article

Endometrial carcinoma (EC) is the most common malignancy of the female reproductive tract, with increasing incidence driven by aging populations and obesity. While molecular classification has improved diagnostic precision, the identification of clinically relevant metabolic biomarkers remains incomplete, and targeted therapies are not yet standardized. In this study, we investigated metabolic alterations in four EC cell lines (AN3-CA, EFE-184, HEC-1B and MFE-296) compared to non-malignant controls under normoxic and stress conditions (hypoxia and lactic acidosis) to identify metabolomic differences with potential clinical relevance. Untargeted gas chromatography–mass spectrometry (GC/MS) and targeted liquid chromatography–mass spectrometry (LC/MS) profiling revealed two distinct metabolic subtypes of EC. Cells of metabolic subtype 1 (AN3-CA and EFE-184) exhibited high biosynthetic and energy demands, enhanced cholesterol and hexosyl-ceramides synthesis and increased RNA stability, consistent with classical cancer-associated metabolic reprogramming. Cells of metabolic subtype 2 (HEC-1B and MFE-296) displayed a phospholipid-dominant metabolic profile and greater hypoxia tolerance, suggesting enhanced tumor aggressiveness and metastatic potential. Key metabolic findings were validated via real-time quantitative PCR. This study identifies and characterizes distinct metabolic subtypes of EC within the investigated cancer cell lines, thereby contributing to a better understanding of tumor heterogeneity. The results provide a basis for potential diagnostic differentiation based on specific metabolic profiles and may support the identification of novel therapeutic targets. Further validation in three-dimensional culture models and ultimately patient-derived samples is required to assess clinical relevance and integration with current molecular classifications. Full article

Organismal biosensing leverages the olfactory acuity of living systems to detect volatile organic compounds (VOCs) associated with cancer, offering a low-cost and non-invasive complement to conventional diagnostics. Early studies demonstrate its feasibility across diverse platforms. In C. elegans, chemotaxis assays on urine samples achieved sensitivities of 87–96% and specificities of 90–95% in case–control cohorts (n up to 242), while calcium imaging of AWC neurons distinguished breast cancer urine with ~97% accuracy in a small pilot cohort (n ≈ 40). Trained canines have identified prostate cancer from urine with sensitivities of ~71% and specificities of 70–76% (n ≈ 50), and AI-augmented canine breath platforms have reported accuracies of ~94–95% across ~1400 participants. Insects such as locusts and honeybees enable ultrafast neural decoding of VOCs, achieving 82–100% classification accuracy within 250 ms in pilot studies (n ≈ 20–30). Collectively, these platforms validate the principle that organismal behavior and neural activity encode cancer-related VOC signatures. However, limitations remain, including small cohorts, methodological heterogeneity, and reliance on binary outputs. This review proposes a Dual-Pathway Framework, where Pathway 1 leverages validated indices (e.g., the Chemotaxis Index) for high-throughput screening, and Pathway 2 applies machine learning to high-dimensional behavioral vectors for cancer subtyping, staging, and monitoring. By integrating these approaches, organismal biosensing could evolve from proof-of-concept assays into clinically scalable precision diagnostics. Full article

As with other non-coding RNAs (ncRNAs), the aberrant expression of long non-coding RNAs (lncRNAs) can be associated with different forms of cancers, including breast cancer (BC). Various lncRNAs may either promote or suppress cell proliferation, metastasis, and other related cancer signaling pathways by interacting with other cellular machinery, thus affecting the expression of BC-related genes. However, lncRNAs are characterized by features that are unlike protein-coding genes, which pose unique challenges when it comes to their study and utility. They are highly diverse and may display contradictory functions depending on factors like the BC subtype, isoform diversity, epigenetic regulation, subcellular localization, interactions with various molecular partners, and the tumor microenvironment (TME), which contributes to the intratumoral heterogeneity and phenotypic plasticity. While lncRNAs have potential clinical utility, their functional heterogeneity coupled with a current paucity of knowledge of their functions present challenges for clinical translation. Strategies to address this heterogeneity include improving classification systems, employing CRISPR/Cas tools for functional studies, utilizing single-cell and spatial sequencing technologies, and prioritizing robust targets for therapeutic development. A comprehensive understanding of the lncRNA functional heterogeneity and context-dependent behavior is crucial for advancing BC research and precision medicine. This review discusses the sources of lncRNA heterogeneity, their implications in BC biology, and approaches to resolve knowledge gaps in order to harness lncRNAs for clinical applications. Full article

Background/Objectives: Incomplete spinal cord injury (iSCI) represents a significant challenge in neurorehabilitation, with conventional limitations including recovery plateaus and declining patient motivation. Virtual reality (VR) and augmented reality (AR) have emerged as promising technologies to supplement traditional therapy through gamification and multisensory feedback. This systematic review and meta-analysis evaluates the effectiveness of VR and AR interventions for improving balance and locomotor function in patients with incomplete spinal cord injury. Methods: A systematic review was conducted following PRISMA guidelines, with searches in PubMed, Scopus, Web of Science, Science Direct, and Google Scholar. Randomized controlled trials and high-quality controlled studies evaluating VR/AR interventions in patients with iSCI (American Spinal Injury Association Impairment Scale [AIS] classifications B, C, or D) for a minimum of 3 weeks were included. A random-effects meta-analysis (Standardized Mean Difference, SMD; 95% Confidence Interval, CI) was conducted for the balance outcome. Results: Eight studies were included (n = 142 participants). The meta-analysis for balance (k = 5 studies) revealed a statistically significant improvement with a large effect size (SMD = 1.21, 95% CI: 0.04–2.38, p = 0.046). For locomotor function, a quantitative meta-analysis was not feasible due to a limited number of methodologically homogeneous studies; a qualitative synthesis of this evidence remained inconclusive. Substantial heterogeneity was observed in the balance analysis (I² = 81.5%). No serious adverse events related to VR/AR interventions were reported. Conclusions: VR/AR interventions show potential as an effective adjunctive therapy for improving balance in patients with iSCI, though the benefit should be interpreted with caution due to considerable variability between studies. The current evidence for locomotor function improvements is insufficient to draw conclusions, highlighting a critical need for more focused research. Substantial heterogeneity indicates that effectiveness may vary according to specific intervention characteristics, populations, and methodologies. Larger multicenter studies with standardized protocols are required to establish evidence-based clinical guidelines. Full article

Background: Pulmonary lobectomy remains the gold standard for early-stage non-small cell lung cancer, with the primary goal of complete tumor removal. Postoperative imaging is critical for evaluating recovery and identifying complications, yet systematic descriptions of radiologic patterns after lobectomy are limited. Methods: We conducted a retrospective analysis of 125 patients who underwent pulmonary lobectomy between 2019 and 2024 at a tertiary thoracic surgery center. Preoperative and postoperative imaging findings were coded and compared using a standardized classification system. Modalities included chest radiography, thoracic CT, ultrasound, PET-CT and MRI. Results: Postoperative imaging demonstrated a clear reduction in pathological findings. Emphysema decreased from 29.6% to 21.6%, pleural effusion from 12.8% to 3.2%, atelectasis/pleural thickening from 15.2% to 8.8%, and ground-glass infiltrates from 12.0% to 8.0%. The proportion of patients without abnormalities increased from 18.5% to 24.8%. Chest radiography (92%) and CT (89.6%) were the most frequently employed modalities. Patients treated with VATS lobectomy showed slightly fewer postoperative abnormalities compared with those undergoing open surgery. Conclusions: Pulmonary lobectomy is associated with measurable radiologic improvement, reflecting favorable structural recovery. Routine imaging follow-up, particularly chest radiography, remains essential for early detection of complications and guiding postoperative care. However, the retrospective single-center design and limited generalizability represent important limitations that should be considered when interpreting these findings. Full article

Background/Objectives: Alcohol consumption can increase the risk of dyslipidemia, thereby elevating the risk of cardiovascular diseases. However, the relationship between alcohol consumption patterns and dyslipidemia remains controversial. Based on large-scale cross-sectional data from the Chinese population, this study aims to investigate the correlations between various alcohol consumption behaviors and dyslipidemia among adult residents in China. Methods: Our analysis utilized data from the 2015–2017 China Nutrition and Health Surveillance project, which provides a large, nationally representative sample (N = 52,471). We employed a binary logistic regression model specifically designed for complex sampling frameworks. This model was utilized to assess the relationship between various alcohol consumption behaviors (including daily alcohol intake levels and drinking frequency) and the incidence of hypercholesterolemia, hypertriglyceridemia, low levels of high-density lipoprotein cholesterol (low HDL-C), and elevated levels of low-density lipoprotein cholesterol (high LDL-C). Drinking behaviors were classified into three distinct categories for analysis: China classification (never, moderate, excessive), WHO classification (never, low-risk, medium-risk, high-risk), and drinking frequency (never, <1, 1–3, 4–6, ≥7 times/week). Results: Compared with never drinkers, the risk of hypercholesterolemia was significantly higher in men who were excessive drinkers (aOR = 1.39, 95%CI: 1.24–1.57), medium-risk drinkers (aOR = 1.24, 95%CI 1.01–1.53), high-risk drinkers (aOR = 1.67, 95%CI: 1.4–1.95), and those who drank more than once a week (aOR range: 1.27–1.65), and there was no such association in women (p > 0.05). Compared with never drinkers, the risk of hypertriglyceridemia was higher in male drinkers with excessive drinking (aOR = 1.35, 95%CI: 1.24–1.47), medium-risk drinking (aOR = 1.29, 95%: 1.11–1.50), high-risk drinking (aOR = 1.52, 95%CI: 1.3–1.71), and a drinking frequency more than 1 time/week (aOR range: 1.22–1.38), while in women, it was moderate drinking (aOR = 0.85, 95%CI 0.77–0.94), low-risk drinking (aOR = 0.86, 95%CI 0.78–0.94), and a drinking frequency of more than once a week (aOR = 0.74, 95%CI 0.63–0.87) that reduced the occurrence of hypertriglyceridemia. Compared with non-drinkers, men with any drinking status had a lower risk of low HDL-C (aOR range: 0.38–0.90) and a similar association was also observed in women (aOR range: 0.26–0.84). Compared with never drinkers, male excessive drinkers (aOR = 0.86, 95%CI: 0.77–0.97), medium-risk drinkers (aOR = 0.80, 95%CI:0.65–0.99), high-risk drinkers (aOR = 0.83, 95%CI: 0.70–0.97), and those with a drinking frequency of 1–3 times/week (aOR = 0.89, 95%: 0.79–0.99) had a lower risk of high LDL-C, and there was no such association in women (p > 0.05). Conclusions: Significant gender differences were observed in the effects of alcohol consumption on lipid profiles. Men who were excessive drinkers, medium-risk drinkers, high-risk drinkers, and those who drank more than once a week had a higher risk of hypercholesterolemia and hypertriglyceridemia, but a lower risk of low HDL-C and high LDL-C. In women, moderate drinking was associated with a reduced risk of hypertriglyceridemia. Any alcohol consumption and drinking frequency more than 1 time/week were associated with a lower risk of low HDL-C in women. No significant association was found between alcohol consumption and hypercholesterolemia or high LDL-C in women. Full article