Search Results (5,587)

Intestinal dysbiosis has been linked to metabolic disorders, including insulin resistance and type 2 diabetes mellitus (T2DM). T2DM typically follows a prediabetic stage, during which insulin resistance develops. During the early stages of T2DM, its development can be corrected, thus potentially preventing or delaying the onset of the disease. This secondary, exploratory, cross-sectional comparison study aimed to contrast the gut microbiome of individuals with elevated fasting blood glucose to that of individuals with glucose levels within the normal range. This study involved 65 older adults (ages 76–83 years) enrolled from the randomized controlled trial entitled the “Generation 100 Study”, all of whom consented to provide their gut microbiome samples. We employed a high-throughput sequencing of the bacterial 16S rRNA gene to obtain metagenomic microbial profiles for all participants. These profiles were then correlated with clinical measures. Overall, microbial alpha diversity was significantly reduced in the high glucose group. We have also observed distinct patterns of microbial beta diversity between high and normal glucose groups. At the phylum level, we found that Synergistes, Elusimicobia, Euryarchaeota, Verrucomicrobia, and Proteobacteria were all significantly decreased in participants with high blood glucose. Additionally, P. copri (ASV 909561) was significantly elevated (10-fold increase) in the high glucose groups, suggesting that it may serve as an early T2DM marker. In contrast to prior reports on the Fusobacterium genus, we found that it was significantly increased in the normal glucose group, with a significant 151-fold increase compared to the high glucose group. Directly linking gut microbiota profiles with clinical indicators such as fasting blood glucose and T2DM diagnosis allows the identification of specific microbial features associated with glucose dysregulation, providing preliminary population-level evidence to guide future translational research. Our results indicate significant changes in the microbiome that may provide valuable insights for early intervention in pre-diabetic states. Full article

Quiescence is a pivotal state for all living organisms and cells. However, recent research indicates a lack of uniformity among quiescent cells. That is, even if the primary feature of quiescence—the ability to restart divisions—is maintained, quiescent cells within populations exhibit variation in their cellular architecture and characteristics. While it is known that the process of entry into quiescence is influenced by a combination of nutrient starvation and temporal factors, the underlying mechanisms remain to be fully elucidated. In this study, we compare the transcriptomes of known homogenous fractions of dense quiescent yeast isolated from populations of different ecological histories. These populations have undergone experimental enrichment of certain types of quiescent cells during cycles of growth and starvation for 300 generations. Transcriptome analysis revealed discrepancies in terms of characteristics associated mainly with energy turnover processes, biosynthesis, and cell wall maintenance. The results of this study suggest that quiescent cells possess the capacity to adapt their transcriptome activity in accordance with their evolutionary history. Full article

Cellular mechanical properties are critical indicators of cellular state and promising disease biomarkers. This study introduces a novel microhand system, featuring chopstick-like plate-shaped end-effectors, designed for stable and high-precision single-cell mechanical characterization. First, we automated the force sensor calibration to overcome the inefficiency and unreliability of conventional manual methods. To validate the system’s sensitivity, we precisely quantified the mechanical contributions of subcellular components, such as the actin cytoskeleton and chromatin, by measuring stiffness reductions after treatment with Cytochalasin D and Trichostatin A, respectively. Notably, when applied to a cellular model of Hutchinson–Gilford progeria syndrome, we successfully captured disease-induced mechanical alterations. A distinct population of high-stiffness cells was detected in progerin-overexpressing cells, a feature not observed in the control groups. Furthermore, by controlling the indentation speed and depth, the mechanical properties of the cytoplasm and nucleus could be distinctly evaluated. These results demonstrate that our microhand system is a highly sensitive and robust platform, capable of detecting subtle, disease-related changes and elucidating the contributions of specific subcellular structures to cell mechanics. Full article

Background: The introduction of the “trace” category in the Xpert MTB/RIF Ultra assay has significantly improved the sensitivity of molecular tuberculosis diagnostics. While it enhances sensitivity, especially in paucibacillary and extrapulmonary cases, its specificity remains debatable, making its interpretation outside select populations a topic of clinical uncertainty. Objectives: This study evaluates the diagnostic and clinical significance of trace-positive results obtained with the Xpert MTB/RIF Ultra assay in the context of a high-incidence TB setting, examining their association with clinical, imaging, and microbiological findings. Methods: A retrospective analysis was conducted on 65 samples with trace-positive Xpert Ultra results, collected over a six-year period from 59 distinct patients in a general hospital in Romania. Correlations were assessed with microscopy, culture, clinical features, imaging, treatment initiation, and prior TB history. A composite reference standard was used for diagnostic accuracy evaluation. Results: Of the 65 trace-positive samples, 29 (44.6%) were culture-positive and 5 (7.7%) were smear-positive. A high proportion of patients, 56 (94.9%), presented with TB-compatible symptoms, and 47 (79.6% of those with imaging) had highly suggestive radiological findings. Based on the composite reference standard, 47 patients (79.7%) were ultimately diagnosed with active TB. Anti-TB treatment was initiated in 44 patients (74.5%). Trace positivity was observed across various specimen types, including sputum, pleural fluid, and cerebrospinal fluid. Conclusions: In high TB burden environments, trace-positive Xpert Ultra results frequently reflect true disease when interpreted within the appropriate clinical and imaging framework. Our findings indicate that, in regions with high tuberculosis incidence such as Romania, trace-positive Xpert Ultra results may contribute meaningfully to clinical decision-making when interpreted alongside clinical and radiological findings, in alignment with current WHO guidance. Full article

Fluorescent proteins find application as biocompatible, genetically encoded labels for visualization of living organisms tissues. Green fluorescent proteins (GFPs) are the most diverse, but proteins with red fluorescence have advantages, such as lower phototoxicity and better penetration into biological tissues. A promising approach is to obtain red fluorescent proteins (RFPs) from GFPs by introducing mutations that stabilize the oxidized chromophore state with an extended conjugated π-system. However, to date this remains a non-trivial task and experimental developments are carried out mainly by random mutagenesis. Development of descriptors obtained in molecular modeling can rationalize this field. Herein, we rely on experimental data on the AzamiGreen fluorescent protein and its variants that are oxidized to the red form. We perform classical molecular dynamics (MD) and combined quantum mechanics/molecular mechanics (QM/MM) simulations to determine structural and dynamic features that govern oxidation. We demonstrate that the red state is predominantly stabilized by interactions of polar lysine residues with chromophore oxygen atoms. Dynamic network analysis demonstrates that in red fluorescent proteins the chromophore motions are correlated with the movement of surrounding protein side chains to a higher extent than in green variants. The presence of different resonance forms of the chromophore determines the fluorescence band maximum value: a decrease in the phenolate form population leads to the red shift. Full article

Skilled Birth Attendance (SBA) is essential for reducing maternal and neonatal mortality, yet access remains limited in many low- and middle-income countries. This study used machine learning to predict SBA use among Ugandan women and identify key influencing factors. We analyzed data from the 2016 Uganda Demographic and Health Survey, focusing on women aged 15 to 49 who had given birth in the preceding five years. After preparing and selecting relevant features, six tree-based models (decision tree, random forest, gradient boosting, XGBoost, LightGBM, CatBoost) and logistic regression were applied. Class imbalance was addressed using cost-sensitive learning, and hyperparameters were tuned via Bayesian optimization. XGBoost performed best (F1-score: 0.52; recall: 0.73; AUC: 0.75). SHapley Additive Explanations (SHAP) were used to interpret model predictions. Key predictors of SBA use included education level, antenatal care visits, region (especially Northern Uganda), perceived distance to a healthcare facility, and urban or rural residence. The results demonstrate the value of interpretable machine learning for identifying at-risk populations and guiding targeted maternal health interventions in Uganda. Full article

Background/Objectives: Prognostic assessment in endometrial cancer (EC) is based on clinical and pathological features such as histological type, FIGO stage, tumor grade, LVSI, P53 status, and hormone receptor expression. Recent molecular research has distinguished four EC subtypes, with MMR status (pMMR vs. dMMR) providing clinically relevant stratification due to its predictive value for immunotherapy. The present study aims to compare dMMR and pMMR tumors in terms of the prevalence of adverse histopathological prognostic factors. Methods: This retrospective study included 179 patients with endometrioid endometrial carcinoma (EEC) treated at the authors’ institution (1 January 2023–31 August 2025). Patients were classified by MMR status (pMMR vs. dMMR) based on immunohistochemistry, and clinicopathological variables, including FIGO stage, myometrial invasion depth, tumor grade, LVSI, ER/PR expression, and P53 status, were analyzed. Normality was assessed using the Shapiro–Wilk test. Categorical variables were tested with chi-square or Fisher’s exact tests, reporting odds ratios with 95% CI, while continuous variables were compared using the Mann–Whitney test and presented as median (IQR) with the Hodges–Lehmann difference and 95% CI. Multivariable logistic regression with Wald tests was performed. Results: dMMR tumors accounted for 29.05% of all cases. Patients in the dMMR group were significantly more likely to present with FIGO stage III/IV disease (p = 0.036) and to exhibit LVSI (p = 0.008). No differences were observed between the groups with respect to tumor grade, estrogen receptor positivity, progesterone receptor positivity, or the prevalence of deep myometrial invasion. The most frequent pattern of protein loss in the dMMR population was concurrent loss of MLH1 and PMS2. Conclusions: In the studied population, dMMR tumors more frequently exhibited adverse prognostic features of EC, such as advanced stage of disease and lymphovascular space invasion. This suggests the potential for effective immunotherapy in this patient group. Full article

Background: This study introduces the Adaptive Graph Fusion Network, an interpretable graph-based learning framework developed for large-scale prediction of intensive care outcomes. The proposed model dynamically constructs patient similarity networks through a density-aware kernel that adjusts neighborhood size based on local data distribution, thereby representing both frequent and rare clinical patterns. Methods: To characterize physiological evolution over time, the framework integrates a short-horizon convolutional encoder that captures acute variations in vital signs and laboratory results with a long-horizon recurrent memory unit that models gradual temporal trends. The approach was trained and internally validated on the publicly available eICU Collaborative Research Database, which includes more than 200,000 admissions from 208 hospitals across the United States. Results: The model achieved a mean area under the receiver operating characteristic curve of 0.91 across six critical outcomes, with in-hospital mortality reaching 0.96, outperforming logistic regression, temporal long short-term memory networks, and calibrated Transformer-based architectures. Feature attribution analysis using SHAP and temporal contribution mapping identified lactate trajectories, creatinine fluctuations, and vasopressor administration as dominant determinants of risk, consistent with established clinical understanding while revealing additional temporal dependencies overlooked by existing scoring systems. Conclusions: These findings demonstrate that adaptive graph construction combined with multi-horizon temporal reasoning improves predictive reliability and interpretability in heterogeneous intensive care populations, offering a transparent and reproducible foundation for future research in clinical machine learning. Full article

Non-suicidal self-injury (NSSI) has become an increasingly salient mental health concern among adolescents, and it commonly co-occurs with depression, anxiety, borderline personality disorder, substance use, and childhood maltreatment, forming a complex psychological risk structure. Despite a growing body of literature, a systematic understanding of the structural links between NSSI and psychiatric comorbidities remains limited. This study uses bibliometric and visualization methods to map the developmental trajectory and knowledge structure of the field and to identify research hotspots and frontiers. Drawing on the Web of Science Core Collection, we screened 1562 papers published between 2005 and 2024 on adolescent NSSI and comorbid psychological problems. Using CiteSpace 6.3.R1, VOSviewer 1.6.20, and R 4.3.3, we constructed knowledge graphs from keyword co-occurrence, clustering, burst-term detection, and co-citation analyses. The results show an explosive growth of research in recent years. Hotspots center on comorbidity mechanisms of mood disorders, the impact of childhood trauma, and advances in dynamic assessment. Research has evolved from describing behavioral features toward integrative mechanisms, with five current emphases: risk factor modeling, diagnostic standard optimization, cultural sensitivity, stratified intervention strategies, and psychological risks in special populations. With big data and AI applications, the field is moving toward dynamic prediction and precision intervention. Future work should strengthen cross-cultural comparisons, refine comorbidity network theory, and develop biomarker-informed differentiated interventions to advance both theory and clinical practice. Full article

Purpose: To characterize, using clustering analysis, the OCT morphological and clinical phenotypes of diabetic macular edema (DME) in a very large population (>2000 DME eyes) using standardized and validated OCT-based biomarkers. Methods: A cross-sectional study was conducted on OCT scans collected from 2355 eyes of 1688 patients with DME and performed during real-world clinical practice. OCT scans were automatically analyzed by a software able to automatically quantify OCT key biomarkers: intraretinal fluid (IRF), subretinal fluid (SRF), hyperreflective retinal foci (I-HRF), and external limiting membrane (ELM) and ellipsoid zone (EZ) interruption. Clustering analysis was performed using the above-mentioned biomarkers, including the distribution of IRF across the three ETDRS rings. Results: The overall population was predominantly composed of type 2 diabetes patients (89%), with a mean diabetes duration of 15.6 ± 10.7 years and mean best corrected visual acuity (BCVA) of 63 ± 18 ETDRS letters. Multivariate clustering identified four morphological phenotypes with distinct patterns of fluid distribution associated with different I-HRF counts, SRF volume, and percentages of ELM/EZ integrity (p < 0.0001). Conclusions: This large OCT analysis identified distinct morphological subtypes of DME, confirming the clinical relevance of key imaging biomarkers. The distribution and severity of DME features differ among clusters, supporting the importance of OCT-based phenotyping in tailoring treatment strategies and understanding disease evolution. Full article

The accurate monitoring of termite swarming—a key indicator of dispersal and population growth—is essential for early warning systems that mitigate infestation risks in agricultural and forestry environments. Automated detection and counting systems have become a viable alternative to labor-intensive and time-consuming manual inspection methods. However, detecting and counting such small and fast-moving targets as swarming termites poses a significant challenge. This study proposes the YOLOv11-ST algorithm and a novel counting algorithm to address this challenge. By incorporating the Fourier-domain parameter decomposition and dynamic modulation mechanism of the FDConv module, along with the LRSA attention mechanism that enhances local feature interaction, the feature extraction capability for swarming termites is improved, enabling more accurate detection. The SPPF-DW module was designed to replace the original network’s SPPF module, enhancing the feature capture capability for small targets. In comparative evaluations with other baseline models, YOLOv11-ST demonstrated superior performance, achieving a Recall of 87.32% and a mAP50 of 93.21%. This represents an improvement of 2.1% and 2.02%, respectively, over the original YOLOv11. The proposed counting algorithm achieved an average counting accuracy of 91.2%. These research findings offer both theoretical and technical support for the development of a detection and counting system for swarming termites. Full article

The mental eminence (chin) is a uniquely human anatomical feature with critical relevance across multiple domains of biomedical and anthropological research. This transdisciplinary review aims to synthesize current knowledge regarding its morphology, population variability, evolutionary origin, and surgical relevance. A comprehensive scoping review aims to map how the mental eminence has been defined and evaluated in anthropological, forensic research, identifying the main methodological approaches, anatomical landmarks, and sources of morphological variability, as well as the reliability and applicability of current assessment methods in clinical–forensic contexts. The search strategy was performed in October 2025. The authors initially identified 3125 records, and 26 studies were finally included and assessed for qualitative analysis. Moreover, the analysis integrates data from osteological collections, radiographic imaging, and modern morphometric studies. The mental eminence exhibits significant variability across human populations, with pronounced sexual dimorphism and evolutionary distinction from non-human primates. Its emergence in Homo sapiens is a key taxonomic trait. Clinically, the chin serves as a landmark in surgical procedures involving genioplasty, trauma reconstruction, and dental implantology. Recent advances in imaging and biometrics have refined its analysis in both anthropological and diagnostic contexts. Though often overlooked, mental eminence plays a vital role in craniofacial morphology and human evolution. Its study bridges osteology, anthropology, and surgery, offering insight into both phylogenetic development and applied anatomical practice. A multidisciplinary understanding of this structure enhances its diagnostic and therapeutic utility. Full article

Feature selection aims to identify a relevant subset of features from the original feature set to enhance the performance of machine learning models, which is crucial for improvig model accuracy. However, this task is highly challenging due to the enormous search space, often requiring the use of meta-heuristic algorithms to efficiently identify near-optimal feature subsets. This paper proposes an improved algorithm based on Northern Goshawk Optimization (NGO), called Elite-guided Hybrid Northern Goshawk Optimization (EH-NGO), for feature selection tasks. The algorithm incorporates an elite-guided strategy within the NGO framework, leveraging information from elite individuals to direct the population’s evolutionary trajectory. To further enhance population diversity and prevent premature convergence, a vertical crossover mutation strategy is adopted, which randomly selects two different dimensions of an individual for arithmetic crossover to generate new solutions, thereby improving the algorithm’s global exploration capability. Additionally, a boundary control strategy based on the global best solution is introduced to reduce ineffective searches and accelerate convergence. Experiments conducted on 30 benchmark functions from the CEC2017 and CEC2022 test set demonstrate the superiority of EH-NGO in global optimization, outperforming eight compared state-of-the-art algorithms. Furthermore, a novel feature selection method based on EH-NGO is proposed and validated on 22 datasets of varying scales. Experimental results show that the proposed method can effectively select feature subsets that contribute to improved classification performance. Full article

Background and Objectives: Malignant germ cell tumors (GCTs) are rare but clinically significant neoplasms arising in gonadal and extragonadal sites. Malignant GCTs, divided into seminomatous and non-seminomatous subtypes, show diverse biological behavior. Although molecular studies have advanced understanding of their origins and genetic features, little is known about metastatic patterns due to their rarity and generally favorable outcomes. This study aimed to describe metastatic patterns of malignant GCTs across primary sites and histologic subtypes using population-based database. Materials and Methods: Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) program for patients diagnosed with malignant GCTs between 2010 and 2022. Cases were stratified by primary site (testis, ovary, mediastinum), age group (<8 years vs. ≥8 years), and histologic subtype. Metastatic patterns were assessed using both overall and organotropic metastasis rates, and differences between groups were evaluated descriptively using appropriate statistical tests. Results: A total of 32,015 malignant GCTs were identified, comprising 93.0% testicular, 5.6% ovarian, and 1.4% mediastinal tumors. In patients aged ≥8 years, ovarian tumors tended to show generally lower lymph node and distant metastasis rates. In contrast, mediastinal tumors appeared to have the highest distant metastasis rates. Organotropic analysis suggested distinct subtype- and site-specific differences. For seminoma/dysgerminoma, the organotropic metastasis pattern was generally consistent across different primary sites, whereas the other subtypes showed variable organotropic metastasis rates depending on the primary site. Conclusions: The metastatic patterns of GCTs appear to differ by histologic subtype and primary site. These findings suggest that both subtype and site of origin should be considered when assessing metastatic risk and may provide a framework for improved risk stratification in clinical practice. Full article

Depressive symptoms are increasingly common in middle-aged and older adults and have become a major public health problem. People may experience transitions across different underlying states due to symptom variability over a course of many years. And risk factors may have different impact on different symptom states. However, existing research rarely considers the identification of important factors related to symptom conversion. The purpose of this study was to examine the risk associated with transitioning between various stages of depressive symptoms and their influencing factors, utilizing a multi-state model with a simultaneous feature selection method. We used the four waves of data from the China Health and Retirement Longitudinal Study (CHARLS) and 3916 participants were selected after screening. Five states of depressive symptoms were defined including no symptom, new symptom episode, symptom persistence, remission and relapse. We included 13 variables on demographic background, health status and functioning, and family and social connectivity, along with their interactions. Multi-state models were used to evaluate the risks of state transitions. The regularized (adaptive Lasso) partial likelihood approach was employed to simultaneously identify the important risk factors, estimate their impact on the state transition rates and determine their statistical significance. There were 1392 new depressive episodes events, 402 symptom persistence events, 639 remission events and 118 relapse events. We identified nine significant risk factors for the new onset of depressive symptoms: urban–rural residence, sex, retirement status, income, body pain, difficulty with basic daily activities, social engagement, education by income interaction and number of conditions by income interaction. The effects of the identified risk factors on new symptom episode weakened as those symptoms became persistent or went into remission. In terms of symptom relapse, sex by age was identified as a significant influencing factor. This study identified key factors and explored their effects on the various depressive symptom states among older Chinese adults. The findings could serve as a foundation for the development and implementation of targeted policies aimed at enhancing the mental well-being of China’s elderly population. Full article