Search Results (4,683)

Background: Although immune checkpoint inhibitors (ICIs) have improved survival in advanced melanoma, predicting individual responses remains challenging; thus, practical and inexpensive biomarkers are needed. In this study, we investigated the prognostic value of the neutrophil percentage–albumin ratio (NPAR) in patients with advanced melanoma receiving ICI therapy. Methods: Fifty patients treated in our clinic were included, with a mean age of 53.3 years and 66% being male. Visceral metastases were present in 76% of the cohort. Through conducting Receiver Operating Characteristic (ROC) analysis, we determined an NPAR cut-off value of 1.81, with patients categorized into low (<1.81, n = 27)- and high (≥1.81, n = 23)-NPAR groups. The progression-free survival (PFS) and overall survival (OS) were evaluated using Kaplan–Meier and Cox regression analyses. Results: High NPAR (≥1.81) significantly shortened both PFS and OS. In the univariate analysis, high NPAR emerged as a strong risk factor for PFS (HR: 2.68, p = 0.002) and OS (HR: 3.70, p < 0.001), while multivariate analysis confirmed NPAR as an independent negative prognostic factor for PFS (HR: 2.45, p = 0.006) and OS (HR: 2.82, p = 0.003), regardless of clinical variables. Additionally, visceral metastasis was an independent negative predictor of survival. Conclusions: Pre-treatment NPAR levels may be an independent and potential predictor of survival in advanced melanoma patients receiving ICIs. This easily calculable ratio could provide a practical guide for risk stratification. Full article

Ambrosia beetle Platypus quercivorus is a vector of Japanese oak wilt. Only females possess 4−12 mycangial pits on the pronotum, which are essential for carrying symbiotic fungi and are thought to be a significant determinant of beetle fitness. However, the factors influencing the pit numbers remain poorly understood. To elucidate the effects of environmental conditions and parental traits on the pit number, we conducted a controlled breeding experiment. Collected P. quercivorus broods were categorized into three groups (large, middle and small) based on their average number of maternal mycangial pits. Excluding the middle groups, male−female pairs from the same group (large or small) were inoculated into Quercus crispula logs and incubated under four temperature regimes: 18, 22, 26, and 30 °C. We analyzed offspring pit number and body weight relative to temperature and parental traits. Both traits increased at lower temperatures likely because accelerated metabolism and developmental rates at higher temperatures, reduces the time and resources available for body growth and pit development. Notably, the traits exhibited distinct inheritance patterns: offspring pit number was significantly influenced by maternal pit number but not by the body weight of either parent. In contrast, offspring body weight was significantly correlated with maternal pit number, paternal body weight, and maternal body weight. Thus, we conclude that temperature plays a critical role in shaping these traits. Parental effects indicate that both mycangium pit number and body weight are heritable in P. quercivorus. However, their distinct inheritance patterns suggest a weak genetic association between the traits, implying that they may evolve largely independently. Full article

Background/Objectives: Sport participation and nutrition are increasingly recognized as key determinants of cognitive function and academic achievement in student populations. However, the biological mechanisms underpinning these associations remain only partially understood. This scoping review aimed to map and synthesize the current evidence on neuroendocrine and autonomic mechanisms linking physical activity, sport participation, and nutrition to cognitive and academic outcomes in students. Methods: A systematic search of electronic databases was performed following PRISMA-ScR guidelines. Studies involving student populations that examined physical activity, sport participation, or dietary patterns in relation to cognitive function and/or academic performance were included. Particular attention was given to studies reporting biological or physiological indicators of underlying mechanisms, including neuroendocrine, autonomic, and brain-based measures. Data were extracted and synthesized qualitatively, with studies categorized according to the type of mechanistic evidence. Results: A total of 76 studies met the inclusion criteria. The available evidence was more extensive for physical activity, sport participation, and fitness-related exposures than for nutrition-related variables or integrated lifestyle models. Cognitive outcomes, particularly executive function, attention, working memory, and memory performance, were assessed more frequently and showed more consistent associations with lifestyle behaviors than academic outcomes, which were less commonly and more heterogeneously evaluated. Mechanistic evidence was unevenly distributed: only a limited subset of studies included direct biological or psychophysiological measures, mainly neuroimaging, brain-derived neurotrophic factors, cortisol-related indices, or heart rate variability. In contrast, inflammatory, metabolic, and gut microbiota-related mechanisms were mostly discussed at a conceptual or indirect level. Overall, the findings indicate a broad associative literature but a relatively small body of studies directly testing biological pathways linking physical activity, nutrition, cognition, and academic performance. Conclusions: Current evidence indicates potential associations between sport participation, nutrition, cognitive outcomes, and multiple biological pathways. However, the scoping nature of the review, the predominance of observational designs, and the limited use of direct mechanistic assessments prevent firm causal conclusions. Future research should prioritize longitudinal and intervention studies integrating behavioral, nutritional, cognitive, academic, and biological measures within the same design. Full article

Background/Objectives: Preventing recurrent anaphylaxis is crucial for patient safety. This study aimed to identify predictive factors and develop a prediction model to estimate recurrence risk, thereby enhancing targeted preventive strategies. Methods: This prognostic prediction study used a retrospective observational cohort design, analyzing medical records from an anaphylaxis registry at Naresuan University Hospital, Phitsanulok, Thailand, between March 2011 and February 2021. We developed a prediction model using multivariable Cox proportional hazards regression analysis. Statistically significant and clinically relevant predictors were weighted into a risk score derived from hazard ratio regression coefficients. Model performance was evaluated using the area under the receiver operating characteristic curve (AuROC), calibration metrics, and decision curve analysis. Results: Over the 10-year period, 381 patients experienced 439 anaphylaxis episodes, including 58 recurrences (13.2%). The final model comprised six predictors: history of food, insect, and drug allergies; asthma; chest discomfort; and severe anaphylaxis. Corresponding risk scores were 4, 5, 5.5, 1, 2.5, and 1.5 points, respectively. Total scores ranged from 0 to 19.5 and were categorized into low (<3.0), moderate (3.0–9.0), and high (>9.0) risk groups. The high-risk group had a likelihood ratio positive (LHR+) of 4.65. The model demonstrated acceptable discrimination (AuROC 0.773 (95% CI: 0.714–0.832)) and good calibration. Bootstrap validation showed consistent performance (AuROC 0.773 (95% CI: 0.714–0.831)). Decision curve analysis indicated clinical utility across relevant threshold probabilities. Conclusions: This prediction model provides a simple, clinically applicable tool for estimating the risk of recurrent anaphylaxis and may support improved prevention and management strategies. Full article

Background/Objectives: Respiratory dysfunction in Parkinson’s disease (PD) is a clinically relevant but frequently underrecognized manifestation associated with functional impairment and increased risk of respiratory complications. This study compared spirometry and impulse oscillometry (IOS) in the assessment of respiratory function in PD, with particular focus on the detection of subtle or peripheral airway abnormalities. Methods: A prospective, single-center, cross-sectional study was conducted, including 108 participants (55 patients with PD and 53 control subjects). Pulmonary function was evaluated using standardized spirometry and IOS protocols. Group comparisons were performed using non-parametric tests, while multivariable regression analyses adjusted for potential confounding factors, including age, body mass index, smoking status, pollutant exposure, and cardiovascular comorbidities. Results: IOS identified a higher frequency of abnormal categorical findings compared with spirometry, including among subjects with normal spirometric values. Although dyspnea was more frequent in patients with PD in unadjusted analyses, multivariable regression demonstrated that PD was not an independent predictor of respiratory dysfunction. Pollutant exposure was significantly associated with abnormal IOS findings (p = 0.011). No significant differences were observed between PD and control groups regarding continuous spirometric or oscillometric parameters. Only a weak association between disease severity and FEV1 (%) was identified, whereas no significant correlations were observed for oscillometric parameters. Conclusions: IOS may provide complementary information regarding subtle or peripheral respiratory abnormalities in patients with PD. The findings suggest that respiratory alterations in this population are likely multifactorial and not independently determined by PD itself. Incorporating oscillometric assessment into respiratory evaluation may contribute to the identification of subtle respiratory mechanical alterations in patients with PD. Full article

Introduction: Chronic non-specific neck pain (CNSNP) is a prevalent condition where active myofascial trigger points (A-MTrPs) are commonly detected in cervical muscles and may be associated with altered electromyographic activity (EMGact). However, their association with EMGact during functional tasks remains unclear. Objectives: This study aimed to explore this relationship, hypothesizing that A-MTrPs in cervical muscles would be associated with altered EMGact. Methods: An analytical cross-sectional exploratory study was conducted in 52 patients with CNSNP. Surface EMGact of the sternocleidomastoid (SCM), anterior scalene (AS), and upper trapezius (UT) muscles was recorded during the craniocervical flexion test (CCFT) and an isometric shoulder abduction task (ABD-90). Linear mixed-effects models were constructed to identify factors associated with EMGact. Age, pain intensity, pain duration, analgesic dose, anti-inflammatory dose, and kinesiophobia score were included as covariates, while gender, physical activity level, and the presence or absence of A-MTrPs were included as categorical factors. Results: At the 22 mmHg CCFT level, analgesic consumption was positively associated with peak EMGact and average AS activation (B = 0.791 and B = 0.223, respectively) and with SCM peak EMG act (B = 0.510). At the same level, kinesiophobia was associated with average SCM EMGact (B = 0.231). At the 26 mmHg CCFT level, average AS activation remained positively associated with analgesic consumption (B = 0.148) and SCM without A-MTrPs was associated with lower EMGact compared to SCM with A-MTrPs. At the 30 mmHg CCFT level, kinesiophobia was negatively associated with average EMGact of AS. In the UT muscle, during ABD-90, kinesiophobia was negatively associated with both peak (B = −0.378) and average EMGact (B = −0.132). Conclusions: The presence of A-MTrPs may be related to SCM EMGact during CCFT in individuals with CNSNP, while analgesic consumption and kinesiophobia also could be associated with cervical muscles EMGact during functional tasks. Full article

Dental implant therapy demonstrates high long-term survival; however, biological, behavioral, and technical complications remain prevalent. The objective of this project report was to introduce GF-Predictability for Dental Implants (GF-PreDImp), a novel, comprehensive pre-surgical multidimensional scoring proposal designed to quantify implant success predictability through a structured, evidence-based system. The model integrates six domains, Biological, Behavioral, Hard tissue, Soft tissue, Implant, and Prosthetic, assessing variables into a 100-point composite index. The domains evaluate systemic conditions (20 pts), behavioral habits (20 pts), hard-tissue anatomy (20 pts), soft-tissue characteristics (15 pts), implant parameters (15 pts), and prosthetic/surgical factors (10 pts). The final GF-PreDImp score categorizes predictability into five levels: excellent (≥85), good (70–84), moderate to guarded (55–69), guarded to high risk (40–54), and poor (<40). The tool generates dynamic visual outputs, including radar charts, enabling rapid clinical interpretation. While GF-PreDImp provides a framework for individualized risk stratification, it currently serves as a design proposal. Its implementation can improve clinical decision-making and enhance long-term implant outcomes. Further clinical assessments must be done to confirm the findings in future studies. Full article

Background: We aimed to evaluate the prognostic significance of bone pain at presentation in metastatic castration-sensitive prostate cancer (mCSPC) and its association with radiographic progression-free survival (rPFS), independent of established prognostic factors. Methods: We retrospectively analyzed 205 patients with mCSPC treated at our center. Data were obtained from patient files and hospital records. rPFS was estimated using the Kaplan–Meier method and compared using the log-rank test. Baseline characteristics according to bone pain status were compared using the χ² test for categorical variables and the Wilcoxon test for continuous variables. Results: Median rPFS for the entire cohort was 23.8 months (95% CI: 18.6–29.0). In univariate analysis, median rPFS was significantly shorter in patients with bone pain compared to those without (16.9 vs. 29.5 months, p < 0.001). Other factors associated with worse rPFS included ECOG PS ≥ 1, DNA repair mutations, high disease volume, liver metastasis, hemoglobin < 12 g/dL, and albumin < 3.5 g/dL. In multivariate analysis, bone pain (HR = 2.37, 95% CI: 1.29–4.34; p = 0.005), albumin < 3.5 g/dL (HR = 1.99; p = 0.034), hemoglobin < 12 g/dL (HR = 2.00; p = 0.005), and liver metastasis (HR = 3.07; p = 0.024) remained independent predictors of shorter rPFS. Conclusions: Bone pain at presentation is an independent prognostic factor for shorter rPFS and may help guide risk stratification and treatment decisions in mCSPC. Full article

Aims: Coronary heart disease (CHD) associated with rheumatoid arthritis (RA) is a primary driver of mortality in RA patients. In this study, we sought to establish a combined rat model of CHD and RA by integrating cardiac ischemia/reperfusion (I/R), high-fat diet (HFD), and intradermal administration of bovine type II collagen emulsified in complete Freund’s adjuvant. The aim of constructing this model is to investigate and analyze the pathogenesis of RA-induced CHD under the modulation of HFD and cardiac I/R exposure. Methods and Results: Sixty-four male Sprague–Dawley rats were randomly categorized into eight groups (n = 8 per group): control, I/R, HFD, collagen-induced arthritis (CIA), I/R + CIA, HFD + CIA, I/R + HFD, and I/R + HFD + CIA groups (n = 8 per group). We applied Synchrotron radiation-based X-ray micro-computed tomography (micro-CT) to observe the structural changes within the model over time. To further elucidate molecular mechanisms, transcriptome RNA-seq analysis was carried out to identify key signaling pathways, with particular emphasis on the homeostasis of Toll-like receptor 4 (TLR4)/Myd88 signaling in the ischemic myocardium. Furthermore, we conducted in vivo shRNA-mediated knockdown of polymerase I and transcription release factor (PTRF) and evaluated the co-localization of PTRF and TLR4 through immunofluorescence experiments. It is worth mentioning that our rat model of RA-induced (CHD) under a high-fat diet effectively manifested the relevant pathological features that align with the Traditional Chinese Medicine (TCM) definition of “bi” syndrome. The results indicate that the combined stimulation of HFD and CIA significantly elevated cardiac injury markers (CK-MB, LDH, CRP, and c-TNT) and was accompanied by a more severe expansion of the infarct area and increased cardiomyocyte apoptosis compared to the I/R group alone. In addition, the histopathological evaluation revealed significantly aggravated myocardial inflammation and fibrosis deposition, accompanied by extensive areas of tissue damage, further indicating a state of heightened inflammation and severe cardiac degenerative changes. Consistently, myocardial tissues from rats in the I/R + CIA + HFD group exhibited robust activation of the TLR4/MyD88 signaling pathway and a pronounced elevation in the p-JNK/JNK ratio. Moreover, pronounced co-localization between PTRF and TLR4 was evident in small vessels surrounding the infarcted myocardium. Importantly, AAV-mediated knockdown of PTRF attenuated the HFD- and CIA-induced exacerbation of myocardial injury in I/R rats. Conclusions: We successfully established a rat model of CHD with rheumatic syndrome using I/R in combination with RA and HFD. The present findings suggest that the PTRF-related TLR4/MyD88-JNK signaling pathway may act as an important regulatory mechanism underlying myocardial injury aggravated by combined HFD and CIA stimulation. Full article

Background/Objectives: The signs and symptoms of premenstrual syndrome (PMS) can be similar to those of various other illnesses and conditions. To facilitate the detection and categorization of PMS symptoms, specific questionnaires have been developed. The aim of this study was to translate and culturally adapt the Premenstrual Symptoms Screening Tool (PSST) for the Serbian-speaking population and assess its validity and reliability. Material and Methods: Our convenience sample included 350 Serbian-speaking female health sciences students from one of the Serbian universities who had PMS symptoms at least once during the past 12 months. They completed a general socio-epidemiologic questionnaire, the PSST, and the Depression, Anxiety and Stress Scale-21 (DASS-21). The PSST was translated according to the recommended methodology, and its psychometric characteristics (internal consistency, construct, discriminant validity and convergent validity) were tested. Results: There were no major changes in the items during the process of translation or validation. The Cronbach’s alpha coefficient for the whole scale was 0.927, whereas if an item was deleted, it was >0.8 for all the items. The McDonald’s omega coefficient was 0.896, indicating good internal consistency. The CI–TC coefficients were greater than 0.40 for all the items, indicating that all items were significant elements of the PSST. Exploratory factor analysis extracted three factors. Confirmatory factor analysis revealed adequate values for all model fit estimators. The PSST significantly correlated with the DASS-21, which confirmed its adequate convergent validity. Conclusions: The Serbian version of the PSST showed good validity and therefore can be used as a screening tool for PMS in Serbian-speaking young women. Full article

Predicting student performance has emerged as an essential element of contemporary learning assessment, allowing educational organizations to determine problematic students and offer early intellectual assistance. Many machine learning (ML) methodologies prioritize predicted accuracy at the expense of interpretability and practical insights. This paper provides a framework for predicting student performance that is both risk aware and explainable utilizing a chaotic educational competition optimizer (ECO) in conjunction with a support vector classifier (SVC) to overcome existing challenges. The ECO serves as a metaheuristic feature selection technique for selecting the most significant features from a multivariate educational dataset consisting of 1195 students and 29 behavioral, demographic, and academic characteristics. Experimental findings demonstrate that ECO effectively condenses the feature space to 11 essential indications and improves generalization of model while maintaining classification robustness. Utilizing the chosen features, the ECO–SVC model attains a complete classification accuracy of 87.03%, with F1-scores of 0.92, 0.69, and 0.82 for high-, medium-, and low-performance student categories, respectively, surpassing other benchmark ML methods. The proposed framework incorporates explainable artificial intelligence (XAI) to improve transparency by utilizing local explanations and permutation-driven feature significance. The XAI research verifies that institutional support, learner engagement, and previous academic success are the most important contributing factors to predictive results. Notably the ECO functions as a classifier-independent feature selection mechanism; however, the support vector classifier (SVC) is adopted in this study due to its strong generalization capability and effectiveness in exploiting the optimized feature space. The findings are analyzed using a semiotic-linguistic framework, wherein certain qualities are correlated with symbolic, indexical, and temporal educational signs, converting numerical significance into substantive pedagogical insights. Furthermore, an initial academic risk profile strategy is established by utilizing SVC decision confidence and elucidating feature contributors. The consequent risk ratings accurately categorize students into low-, medium-, and high-risk categories, facilitating the detection of at-risk learners beyond mere final score assessment. The proposed risk-aware and explainable ECO–SVC framework enhances learning outcomes assessment by integrating interpretability, high accuracy, and proactive academic reasoning, rendering it suitable for real-life educational decision-support systems. Full article

Background and Objectives: Healthcare-associated infections (HAIs) remain a major cause of morbidity and mortality among hospitalized patients. During the COVID-19 pandemic, SARS-CoV-2 infection emerged as a major contributor to HAIs, alongside Clostridioides difficile infection (CDI) and other bacterial infections. This study aimed to evaluate the clinical characteristics and outcomes of HAIs in surgical departments and to identify factors associated with in-hospital mortality. Materials and Methods: We conducted a retrospective observational study including 170 patients with documented HAIs admitted between July 2018 and June 2022 in surgical departments of a county emergency hospital. Patients were categorized into SARS-CoV-2 infection (n = 85), CDI (n = 73), and other bacterial infections (n = 12), the latter being included for descriptive purposes only due to limited sample size. Clinical variables, comorbidities, prior antibiotic exposure, length of hospital stay, and in-hospital mortality were analyzed. Survival analysis and logistic regression were performed to identify predictors of mortality. Results: SARS-CoV-2 infection represented the largest subgroup, followed by CDI. Overall, in-hospital mortality was 15.9%, with comparable rates between SARS-CoV-2 infection (17.6%) and CDI (16.4%), while no deaths were observed in the small subgroup of other bacterial infections. CDI patients had a significantly higher burden of comorbidities (p = 0.004). Kaplan–Meier analysis did not show a statistically significant difference in survival between SARS-CoV-2 and CDI groups (log-rank p = 0.28). In univariate analysis, acute respiratory failure (OR ≈ 13.5, p < 0.001), chronic kidney disease (OR ≈ 4.4, p = 0.018), and number of comorbidities (p = 0.019) were associated with mortality, but none remained significant in multivariable analysis. Conclusions: In-hospital mortality was similar between SARS-CoV-2 infection and CDI, highlighting the persistent clinical impact of CDI in hospitalized patients. Comorbidity burden and acute complications, particularly respiratory failure, were key determinants of mortality. These findings highlight the persistent clinical impact of CDI and the role of comorbidity burden and acute complications, particularly respiratory failure, in shaping in-hospital mortality. The absence of independent predictors in multivariable analysis should be interpreted cautiously given the limited sample size. Full article

Comprehensive land consolidation (CLC) is a systematic initiative aimed at optimizing spatial patterns of land use and revitalizing idle rural land resources. It is a pivotal policy instrument for enhancing rural resilience and possesses significant practical implications. Grounded in resilience theory, this study establishes an evaluation system for rural resilience, assesses resilience levels in Huzhou from 2003 to 2023, and investigates its spatiotemporal characteristics employing the entropy-weighted TOPSIS method and geodetector model. Furthermore, this research identifies the driving factors and dynamic mechanisms through which comprehensive land consolidation impacts rural resilience. The study area is categorized into four zones based on land use types to elucidate regional heterogeneity. The findings indicate that comprehensive land consolidation markedly enhances rural resilience, which progresses from slow initial growth to accelerated improvement, ultimately culminating in leapfrog development. Spatially, rural resilience exhibits a “central-high, marginal-low” distribution, characterized by core-periphery agglomeration. Notably, the key driving factors vary significantly across different regions. Mechanistically, comprehensive land consolidation bolsters rural resilience through a sequential pathway that begins with consolidation intervention, which activates critical factors. This activation leads to structural reorganization within the rural framework, followed by the optimization of functions that enhance overall resilience. In terms of policy implications, it is essential to adopt differentiated consolidation strategies tailored to regional resource endowments, emphasizing the optimization of production-living-ecological spaces to foster integrated and sustainable rural development. Full article

This study evaluates the efficiency of fiscal support policies for village-level collective economies in S Province, a frontier region of China, over the analytical period of 2018–2023, which includes the policy implementation years (2019–2022) plus one pre-policy and one post-policy year. Integrating theories of collaborative governance, resource alertness, and inclusive rural development, we construct an efficiency measurement framework to assess policy performance across 13 regions. Static efficiency is measured using DEA-BCC and super-efficiency SE-DEA models, while dynamic total factor productivity (TFP) is analyzed via the DEA–Malmquist index. The entropy-weighted method is employed to ensure robust indicator weighting. The findings reveal the following: (1) The average super-efficiency is 0.855, indicating relatively high expenditure efficiency but significant regional disparities and room for improvement. (2) The TFP declined by an average of 9.7% over the analytical period (2018–2023), primarily due to technological regression, despite stable technical efficiency. Based on the TFP performance, regions are categorized into high-, middle-, and low-efficiency tiers. Accordingly, we propose policy recommendations including efficiency-driven funding allocation, long-term support mechanisms combining technological innovation and management empowerment, regionally differentiated strategies, and strengthened multi-stakeholder collaboration. This study provides empirical evidence for optimizing fiscal policies to promote the sustainable development of rural collective economies and advance inclusive rural development in frontier regions. Full article

As urban rail transit systems evolve within the Industrial Internet of Things (IIoT), the intelligent recovery of regenerative braking energy becomes critical for energy efficiency. However, the existing train operation optimizations primarily focus on time-domain synchronization, frequently neglecting the spatial impedance constraints of the DC traction network. This oversight creates a discrepancy between theoretical energy matching and actual absorption. To address this, this paper proposes a spatiotemporal synergistic optimization framework integrating the analysis of electrical energy transmission factors and train relative motion. First, a dynamic multi-node circuit model based on Kirchhoff’s laws is established to characterize train fleet operations. By evaluating electrical energy transmission factors, the current distribution ratio and line impedance loss are identified as primary determinants of absorption efficiency. This physically quantifies the coupling among instantaneous energy distribution, transmission loss, and source-load relative distance. Second, a time-domain integration-based gradient analysis framework is formulated to deconstruct the energy gradient into amplitude and directional components. By mapping the relative position and speed of interacting trains, their relative motion states are systematically categorized. Subsequently, an adaptive gradient optimization strategy based on these motion states is introduced, which fine-tunes dwell times to precisely guide train trajectories into a low-impedance “optimal window” for energy absorption. Finally, a case study using operational data from Luoyang Metro Line 1 validates the proposed framework. Results demonstrate that the framework achieves dual spatiotemporal matching of braking and traction trains, outperforming the traditional fixed timetable and improving the regenerative braking energy absorption rate by approximately 13%. Full article