Search Results (24,862)

Circulating microRNAs (miRNAs) are promising minimally invasive biomarkers for cancer risk assessment, yet prospective evidence for breast cancer (BC) remains limited. We conducted a nested case–control study within a prospective cohort to examine whether pre-diagnostic circulating miRNAs are associated with subsequent BC risk and to explore their potential relevance in prospective population-based settings. Baseline serum from 160 women (80 incident BC cases; 80 matched controls) was analyzed, with a median time to diagnosis of 8.9 years. Eight candidate miRNAs were quantified by droplet digital PCR (ddPCR) and normalized to miR-484. Group differences were evaluated by non-parametric tests, and odds ratios for BC were estimated using logistic regression models adjusted for established risk factors, with Bonferroni correction for multiple testing. Cases and controls were comparable at baseline. Among the candidates, lower circulating miR-181 levels showed a suggestive inverse association with BC risk in fully adjusted models, while lower Let7 levels showed only a non-significant, hypothesis-generating inverse trend that did not survive Bonferroni correction. No other miRNA displayed clear associations with BC risk. These findings, while preliminary, support further large-scale prospective investigations specifically designed to assess predictive performance and external validation. employing standardized pre-analytical and analytical protocols, repeated sampling, and independent replication/external validation to clarify the etiologic relevance and potential risk-prediction value of circulating miRNAs for BC. Full article

Horses presenting with temporomandibular joint (TMJ) dysfunctions are often clinically evaluated for TMJ osteoarthritis (OA). Due to the unique characteristic of TMJ-related pain, the clinical diagnosis of equine TMJ OA is challenging; however, it may be supported by computer-aided tools incorporating biomarker data. This study aims to evaluate a machine learning-based approach to address a binary classification distinguishing healthy TMJs from TMJ OA. Among 50 equine cadaver heads, 82 TMJs were included and annotated as healthy or OA based on histological and computed tomography (CT) findings. For each TMJ, nine CT findings were assessed, and synovial fluid was collected for the evaluation of twelve biomarkers. Using a biomarker dataset, correlations among biomarkers were calculated and supported with a mixed-effects logistic regression model. Using a combined dataset, twelve machine learning models, incorporating two feature selection methods and six classification algorithms, were evaluated. Specific biomarker levels showed predominately positive correlations with TMJ OA, age, and with each other; however, only age had a significant effect on OA assignment in the mixed model. The best-performing machine learning model achieved an accuracy of 0.82 and an area under the curve (AUC) of 0.85 for binary TMJ classification. The proposed classification model outperforms conventional diagnostic methods and may therefore be considered beneficial in aiding the diagnosis of equine TMJ OA. Full article

This study adheres to a logistic regression modeling protocol originally developed for long-range intelligence analyses and employs data from UN demographic estimates (the 2024 revision) to generate a set of statistical functions that suggest a moderately strong relationship between increasing median age and the probability of a persistently positive international net migration rate (NMR). According to this relationship, the post-Cold War probability (data from 1990 to 2015) of experiencing a persistently positive net migration rate (defined as a +NMR, directly followed by five consecutive years of +NMRs) rose from less than 0.12 at a population median age of 15 years, to a probability greater than 0.55 at 36 years, and then to more than 0.77 at 45 years. The author hypothesizes a speculative set of predictions aimed at providing long-term tests for this model. These predictions assume that, by a median age of 36.0 years, at least one country in the hypothesized cluster of countries will have shifted to experiencing a series of +NMRs. If, as this model predicts, the age-structurally associated transition to sustained +NMRs transpires by 2055, there could be a substantially larger pool of migrant net-receiving states in parts of Asia, Latin America, and North Africa than the UN’s future scenarios currently project. Full article

Background/Objectives: Chronic lower-limb ulcers of mixed etiology are characterized by impaired microcirculation and persistent inflammation, leading to delayed healing, frequent hospitalizations, and a high risk of limb loss. While infrared thermography reflects local perfusion status and systemic inflammatory markers capture whole-body immune activation, these dimensions are usually assessed separately. The objective of this study was to develop and internally evaluate a composite Thermal–Inflammatory Index (TI) integrating wound-bed thermography with systemic inflammatory markers to stratify disease severity and prognosis in patients with chronic lower-limb ulcers. Methods: In this prospective observational study, 82 adults with chronic lower-limb ulcers underwent baseline infrared thermographic assessment of wound-bed temperature using a standardized protocol. Concurrently, neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were measured. The Thermal–Inflammatory Index was constructed as a standardized composite of inverted wound-bed temperature, NLR, and CRP. A simplified TI score (0–3) was derived using predefined clinical thresholds. The primary endpoint was a composite adverse outcome defined as amputation or failure to achieve complete wound healing within 12 weeks. Secondary outcomes included a prolonged hospital stay (>7 days). Discriminative performance was assessed using receiver operating characteristic analysis, and associations were examined using correlation and logistic regression models. Results: Higher TI values were associated with colder wound beds, elevated systemic inflammatory markers, and increased disease burden. The TI demonstrated moderate discrimination for the composite adverse outcome (AUC 0.75) and prolonged hospitalization (AUC 0.71), performing comparably to the strongest single component (−T_bed, AUC 0.77) while integrating local and systemic information. Each one-standard-deviation increase in TI was independently associated with higher odds of the composite adverse outcome and a prolonged hospital stay. The simplified TI score showed clear stepwise gradients in adverse outcomes and length of hospitalization. Conclusions: The Thermal–Inflammatory Index integrates thermographic and inflammatory signals into a single, clinically interpretable biomarker of severity and prognosis in chronic lower-limb ulcers. TI and the simplified TI score may support early risk stratification using low-cost, bedside-accessible data. Full article

Background: The effectiveness of prostate-specific antigen (PSA)-based early detection of prostate cancer remains controversial and implementation-dependent. Screening policy changes have substantially altered PSA testing behavior in the United States, yet longitudinal evidence from non-organized European settings is limited. We assessed 12-year changes in awareness and utilization of PSA-based early detection and identified subgroups requiring targeted counseling. Methods: Two cross-sectional survey waves were conducted in 2009 (Study Phase 1) and 2021 (Study Phase 2) among men recruited via general practitioner practices in urban and rural regions of Germany. The survey was developed and reported according to the Consensus-Based Checklist for Reporting of Survey Studies (CROSS). Identical questionnaires were used across phases. Endpoints were awareness of PSA-based early detection and prior PSA testing. Univariable and multivariable logistic regression evaluated independent associations with sociodemographic and behavioral factors. To assess sensitivity to compositional differences between survey waves, post-stratified weighted analyses re-aligning Study Phase 2 to the Study Phase 1 distribution of age category, educational attainment, and smoking status were conducted. Results: The analytic cohort comprised 890 men (Study Phase 1, n = 755; Study Phase 2, n = 135). Compared with Study Phase 1, Study Phase 2 participants more frequently were non-smokers (63.0% vs. 48.5%, p < 0.001) and had a university degree (38.5% vs. 30.5%, p = 0.002). In primary multivariable analyses, higher educational attainment (OR 1.71, 95% CI 1.24–2.36) and paternity (OR 1.94, 95% CI 1.25–3.01) were independently associated with greater awareness, whereas increasing age (OR 1.39, 95% CI 1.29–1.50) and higher educational attainment (OR 1.63, 95% CI 1.19–2.24) were independently associated with utilization. Study phase was not independently associated with either endpoint in primary models. In post-stratified sensitivity analyses, study phase was positively associated with utilization, indicating sensitivity of temporal contrasts to population composition. Conclusions: In primary multivariable analyses, we did not detect statistically significant temporal differences in awareness or utilization of PSA-based early detection within this German non-organized setting. The emergence of a study phase effect in weighted sensitivity analyses suggests that apparent time trends may be influenced by compositional differences between survey waves. Persistent social gradients, particularly related to educational attainment, underscore the importance of targeted, evidence-based counseling in opportunistic early detection systems. Larger, prospectively designed studies are needed to distinguish true temporal change from sampling-related effects. Full article

During the COVID-19 pandemic, researchers have made efforts to detect COVID-19 through various methods. In the dataset used for this study, COVID-19 patients were identified using chest computed tomography (CT) images. High dimensionality is frequently an issue in machine learning image classification. Accordingly, this study implemented three dimensionality reduction methods in combination with various machine learning algorithms for improved classification. Principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and diffusion maps were applied to the dataset to extract the most important features of the chest CT images. The extracted features were given as input either to logistic regression or the extreme gradient boosting (XGBoost) algorithm to perform classification. The strongest model identified from this study was diffusion maps in combination with logistic regression. This model, evaluated against existing models from similar studies in recent years, yielded strong performance for detecting COVID-19 cases using chest CT images. Our proposed model achieved 97.35% accuracy, 92.16% sensitivity, and 98.59% specificity on the held-out test set in differentiating between COVID-19-positive cases and healthy, non-COVID-19 cases. This study aimed to detect COVID-19 without the use of viral testing. Importantly, this method could assist clinicians in making an initial diagnosis, especially when viral testing is not available or timely enough for the patient’s case. This study also provides deeper insight into various dimensionality reduction methods and how compatible they are with biomedical imaging data. Models were trained using stratified cross-validation on the training set, with final performance evaluated on a held-out test set at the patient level to prevent data leakage. Additional imbalance-aware metrics were used to assess robustness given class distribution differences. Full article

This study assessed perceptions of the food environment and its association with the availability of unprocessed, minimally processed, and ultra-processed foods in the households of Brazilian schoolchildren during the COVID-19 pandemic. A cross-sectional telephone survey (n = 475) was conducted between March, April and May 2021 with a representative sample of households with public school students from two Brazilian municipalities. Household food availability was assessed using a frequency questionnaire referring to the 30 days prior to the survey. Perception of the food environment was assessed using questions that measured perceived availability, price, and quality of fruits and vegetables (FV) and ultra-processed foods (UPF) sold in the neighbourhood. To analyse the association between perceived food environment and food availability, univariate and multivariate logistic regression analyses were performed, with 95% CI. The results indicate that high availability of unprocessed or minimally processed foods was found in 7.4% of households and high availability of UPF in 92.6%. Positive perception of UPF variety in the neighbourhood was more prevalent in households with greater availability of these foods (p < 0.05). After adjustment for sociodemographic characteristics, a positive perception of FV variety was associated with lower odds of high household UPF availability (OR = 0.54; 95%CI: 0.30–0.97). Perception of the food environment is an important factor associated with household UPF availability. Policy interventions should consider promoting healthier food environments by expanding the distribution of fresh foods alongside measures that ensure economic access to these foods. Full article

Objective: The objective of this study was to develop and validate a predictive model for MTM-HCC by integrating preoperative ultrasound (US) and contrast-enhanced ultrasound (CEUS) features with relevant clinical characteristics. Methods: This retrospective study analyzed data from patients with histopathologically confirmed hepatocellular carcinoma who underwent preoperative CEUS examination at the Ultrasound Department of the Lanzhou University Second Hospital between December 2021 and March 2025. The study cohort comprised 45 patients diagnosed with MTM-HCC and 194 patients with non-MTM-HCC. Ultrasound and CEUS images were independently reviewed by two senior abdominal radiologists with extensive experience in hepatic imaging, ensuring objective feature assessment. Clinical variables and imaging characteristics were systematically compared between the two groups to identify distinguishing patterns. To evaluate the associations among clinical data, ultrasound-derived features, and MTM-HCC, univariate analyses were first performed, followed by multivariate logistic regression to construct and assess predictive models. Results: A total of 239 patients (mean age: 57.28 ± 9.60 years; 187 males and 52 females) were included in the analysis. Among them, 45 HCC patients (18.8%) were classified as MTM-HCC. Multivariate analysis identified four independent predictors: elevated alpha-fetoprotein (AFP ≥ 467 ng/mL) (OR = 8.5, 95% CI: 4.2–17.30; p < 0.001), presence of non-enhancing necrotic areas (OR = 5.92, 95% CI: 1.82–19.30, p = 0.003), intratumoral arteries (OR = 6.61, 95% CI: 2.28–19.22, p < 0.001), and peritumoral feeding arteries (OR = 3.13, 95% CI: 1.15–8.50, p = 0.025). Conclusions: An integrated prediction model that combines ultrasound imaging and clinical parameters offers a feasible, non-invasive approach for accurate preoperative identification of MTM-HCC. Full article

Background/Objectives: Post-cholecystectomy syndrome (PCS) remains diagnostically challenging due to overlapping biliary and non-biliary causes. This study aimed to evaluate whether common bile duct (CBD) diameter measured by MRCP can serve as a practical triage parameter in symptomatic PCS patients and to define a data-supported threshold for predicting clinically relevant biliary pathology. Secondary objectives included assessing correlations between MRCP findings and upper endoscopic features. Methods: In this retrospective single-center study, symptomatic adults undergoing upper endoscopy and MRCP were analyzed. Demographic, clinical, biochemical, radiologic, and endoscopic variables were recorded. Diagnostic performance was assessed using ROC analysis, and independent predictors of biliary dilatation were evaluated with multivariable logistic regression. Results: We analyzed 141 symptomatic post-cholecystectomy patients (mean age 58.2 ± 16.3 years; 67.4% female; median time since surgery 18 [9–36] months). Major symptoms: abdominal pain 84.9%, dyspepsia/bloating 47.5%, nausea/vomiting 22.3%, diarrhea 15.1%. CBD diameter measurements were available in the MRCP subgroup (n = 45); ERCP was performed selectively (n = 12). MRCP findings: CBD ≥ 7 mm 31.9%, biliary dilatation 14.9%, stricture 2.8%, suspected Oddi dysfunction 11.3%, postoperative complications 39.7%. Endoscopy: mucosal inflammation 91.5%; normal 8.5%. Significant correlations included CBD diameter vs. mucosal inflammation (r = 0.32, p = 0.001), dilatation vs. bile reflux (r = 0.28, p = 0.004), and Oddi dysfunction vs. papillary edema (r = 0.41, p = 0.001). CBD diameter showed the best diagnostic performance (AUC 0.82, 95% CI 0.74–0.90; cut-off ≥ 8.0 mm; sensitivity 78.3%; specificity 81.5%; p < 0.001). In multivariable analysis, age independently predicted biliary dilatation (OR 1.05 per year; 95% CI 1.01–1.09; p = 0.007). Conclusions: In symptomatic post-cholecystectomy patients, MRCP-measured CBD diameter provides a useful metric for risk stratification, with a threshold of ≥8 mm identifying patients more likely to harbor biliary pathology. These findings support a structured diagnostic approach that prioritizes noninvasive imaging while reserving ERCP for selected cases. Further prospective validation is warranted. Full article

Background/Objectives: Heart rate variability (HRV) and cerebral autoregulation (CAR) reflect physiologic processes that may influence neurological injury in children supported with extracorporeal membrane oxygenation (ECMO). Although abnormalities in both have been associated with adverse neurological outcomes, their physiologic relationship during ECMO remains unclear. Methods: This retrospective single-center study evaluated the association between HRV and CAR during the first 24 h of ECMO support and assessed their independent relationships with neurological outcome. Patients with at least two hours of simultaneous HRV and CAR monitoring within 24 h of ECMO initiation were included. HRV metrics were derived from artifact-free NN intervals across time, frequency, and nonlinear domains, while CAR was quantified using the cerebral oximetry index (COx), with impaired CAR defined as COx > 0.3. Associations between HRV indices and COx were examined using Spearman correlations at hourly and 24 h resolutions. Unfavorable outcome was defined as death or a Pediatric Cerebral Performance Category (PCPC) score ≥3 at discharge with deterioration from baseline. Results: Eighty-nine patients met inclusion criteria, and 16% demonstrated impaired CAR. HRV measures were reduced relative to age-adjusted norms in both CAR groups without significant differences between groups. Correlations between HRV indices and COx were consistently weak. Overall, 50% experienced unfavorable neurological outcomes. In adjusted logistic regression models, NN skewness and COx were independently associated with outcome, although only NN skewness remained significant in interaction analyses. Conclusions: HRV and CAR exhibited limited physiological coupling during early ECMO support, while each measure provided independent prognostic information with respect to neurological outcome. Full article

Background: Glycated hemoglobin (HbA1c) is widely used for the diagnosis and monitoring of diabetes mellitus; however, its interpretation is largely based on fixed diagnostic thresholds. This study moves beyond describing a glycemic continuum by translating the non-linear HbA1c–microvascular relationship into an individualized risk estimation framework. Methods: In this cross-sectional observational study, adult subjects from a real-world clinical cohort were analyzed using HbA1c as a continuous variable. Associations between HbA1c and metabolic parameters were assessed using correlation analysis. Linear regression was applied to evaluate the relationship between HbA1c and cumulative diabetes-related complication burden. Non-linear associations between HbA1c and the risk of presenting at least one complication were explored using restricted cubic spline logistic regression models. Additional risk estimation analyses focused on the HbA1c gray zone (5.5–6.4%). Results: HbA1c showed a strong continuous association with fasting plasma glucose (ρ = 0.73, p < 0.001) and was positively associated with cumulative complication burden (β = 0.016 per 1% increase in HbA1c, p = 0.009). Non-linear modeling revealed a progressive increase in complication risk beginning below the diagnostic threshold for diabetes, with an inflection of the risk curve within the HbA1c gray zone. Individuals within this interval exhibited a higher prevalence and increased odds of presenting at least one complication compared with lower HbA1c values, although some estimates did not reach statistical significance. Conclusions: HbA1c acts as a continuous and non-linear marker of metabolic stress, with potentially biologically meaningful increases in complication risk emerging below traditional diagnostic thresholds. We demonstrate a non-linear acceleration of microvascular risk within the 5.5–6.4% interval, rather than a simple linear gradient. These findings support the concept of a glycemic risk continuum and highlight the clinical relevance of the HbA1c sub-diagnostic interval for early risk stratification and preventive strategies. Full article

The Ponseti method is the gold standard for managing congenital talipes equinovarus (CTEV); however, relapses leading to surgical intervention remain a significant challenge. We investigated the factors associated with a higher risk of relapse. A retrospective study of 31 children (≤4 years) with CTEV treated with the Ponseti method between January 2014 and December 2023 was conducted. Demographic and clinical data—age at treatment, aetiology, Pirani score, number of casts, percutaneous Achilles tenotomy (PAT), surgery, bracing, relapses, and follow-up—were collected. Descriptive statistics, univariate analyses, and logistic regression were conducted to identify risk factors for relapse. Of the 31 patients, 67.7% were male, 58.1% had bilateral involvement and 71.0% had idiopathic CTEV. The median age at presentation was 2 months (IQR 1–3), and the initial Pirani score 5.0, improving to 0.0 at final follow-up (p < 0.001). The median number of casts was 7 (IQR 5–10), which correlated with the initial Pirani score (p = 0.021). PAT was performed in 77.4%; surgical intervention was required in 32.3%, most commonly repeat tenotomy (41.2%). The median bracing duration was 84 weeks (IQR 32–136) with 64.5% compliance. Relapses occurred in 35.5% of cases; 72.7% required recasting and surgery. Logistic regression identified brace compliance (p < 0.001) as the only significant factor; compliant patients had 90% lower odds of relapse. Non-compliance with bracing significantly increases the risk of relapses and surgical intervention in children with CTEV treated by the Ponseti method. Close monitoring and strict adherence to bracing are essential for an optimal outcome. Full article

Background: Sepsis remains a leading cause of mortality in intensive care units (ICUs) worldwide. Machine learning models for clinical prediction must be accurate, fair, transparent, and reliable to ensure that physicians feel confident in their decision-making processes. Methods: We used the MIMIC-IV (version 3.1) database to evaluate several machine learning architectures, including Logistic Regression, XGBoost, LightGBM, LSTM (Long Short-Term Memory) networks and Transformer models. We predicted three main clinical targets—hospital mortality, length of stay, and septic shock onset—using artificial intelligence algorithms, with respect for responsible AI principles. Model interpretability was assessed using Shapley Additive Explanations (SHAP). Results: The XGBoost model demonstrated superior performance in prediction tasks, particularly for hospital mortality (AUROC 0.874), outperforming traditional LSTM networks, Transformers, and linear baselines. The importance analysis of the variables confirmed the clinical relevance of the model. Conclusions: While XGBoost and ensemble algorithms demonstrate superior predictive power for sepsis prognosis, their clinical adoption necessitates robust explainability mechanisms to gain trust among doctors. Full article

Objective: To determine which physical clinical test best differentiates minimally impaired people with MS (pwMS) from healthy controls and to compare the discriminatory value of upper limb clinical assessments with sensor-based gait and postural control measures. Methods: Forty-one participants (21 pwMS, 20 matched healthy controls) completed a single testing session including upper limb clinical assessments (Nine-Hole Peg Test [9HPT], grip strength), gait (Timed 25-Foot Walk, Six-Minute Walk Test, and cognitive–walking dual task), and static balance assessments using wearable inertial sensors (APDM Mobility Lab system). Dual-task costs (DTCs) were calculated for gait parameters. Between-group comparisons were performed using independent t-tests. Pearson correlation analyses were conducted to examine interrelationships among gait variables, and a parsimonious binary logistic regression model was constructed, including non-dominant 9HPT and dual-task walking speed. Receiver operating characteristic (ROC) analyses were performed to evaluate discriminative performance and determine the optimal 9HPT cutoff. Results: PwMS performed significantly slower on the 9HPT for both hands (p ≤ 0.006) and demonstrated reduced walking performance and higher gait DTCs (p ≤ 0.041) compared with controls. No significant group differences were observed in grip strength or sensor-based postural control. In multivariable analysis, the overall model was significant (p < 0.001; Nagelkerke R² = 0.49), and the non-dominant 9HPT remained the only independent predictor of group status (OR = 1.75, 95% CI [1.17–2.61]), whereas dual-task walking speed was not significant after adjustment. ROC analysis demonstrated good discriminative ability for the non-dominant 9HPT (AUC = 0.84, 95% CI [0.71–0.97]) and acceptable discrimination for dual-task walking speed (AUC = 0.75, 95% CI [0.60–0.90]). The optimal 9HPT cutoff was ≥21.4 s, yielding 71% sensitivity and 100% specificity in this cohort. Conclusions: Manual dexterity of the non-dominant hand may serve as a sensitive screening marker of early functional impairment in MS, demonstrating greater discriminatory value than sensor-based gait and balance measures. These findings support the inclusion of upper limb dexterity testing in the routine assessment of minimally impaired pwMS. Validation in larger, longitudinal cohorts is warranted. Full article

Anthropogenic wildfires account for the majority of wildfire ignitions in human-dominated landscapes, yet their spatial drivers remain insufficiently understood at national scales. This study aims to identify key factors influencing anthropogenic wildfire occurrence and to develop a robust and interpretable prediction framework using nationwide data from South Korea. Wildfire occurrence records from 2011–2021 were integrated with daily meteorological, environmental, and socio-economic variables at a 1 km grid resolution. A stacking ensemble model combining Random Forest, XGBoost, LightGBM, Extra Trees, and logistic regression was implemented to improve predictive robustness under rare-event conditions. Model performance was evaluated using ROC–AUC, PR–AUC, and threshold-optimized F1-scores, and variable contributions were interpreted using feature importance and SHAP analyses. The ensemble model achieved a PR–AUC of 0.934 and an ROC–AUC of 0.941. Relative humidity and maximum temperature were identified as influential meteorological variables, while human-accessibility-related variables, particularly distance to roads and agricultural land, showed consistently high contributions to spatial ignition probability. These findings indicate that anthropogenic wildfire occurrence is shaped by interactions between fire-weather conditions and spatial patterns of human accessibility. The proposed framework provides a scalable approach for understanding anthropogenic wildfire mechanisms and supporting prevention strategies in forested landscapes. Full article