Search Results (48,671)

Introduction: As spine surgery volume continues to grow, ensuring patient safety and minimizing complications are increasingly critical. Disruptive bleeding—defined as hemorrhagic events requiring clinical intervention—is a significant perioperative challenge. This study aimed to: (1) quantify disruptive bleeding incidence; (2) evaluate associations between patient demographics, Elixhauser Comorbidity Index (ECI), and bleeding risk; and (3) assess the impact of disruptive bleeding on mortality, ventilator use, length of inpatient stay, 90-day readmissions, and inpatient costs. Methods: A nationwide healthcare database was used to identify patients who underwent spine surgery in 2019. Patients were subdivided by the Elixhauser Comorbidity Index (ECI) from 0 to ≥6, and multivariate logistic regression was employed to analyze for potential association with disruptive bleeding. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were calculated for each ECI classification. After controlling for baseline demographics, generalized linear models were used to evaluate how disruptive bleeding influenced hospital mortality, ventilator use, 90-day readmission rates, lengths of inpatient stay, and inpatient costs. Results: Among 165,461 patients undergoing spine surgery, 15,337 (9.3%) experienced disruptive bleeding. Women and Medicare coverage were associated with higher bleeding risk (p < 0.05). Disruptive bleeding odds increased with comorbidity burden, ranging from OR = 2.31 (95% CI 1.92–2.77) for ECI = 5 to OR = 3.32 (95% CI 2.73–4.06) for ECI ≥ 6. Disruptive bleeding was associated with increased ventilator use (18.4 versus 8.2% for ECI ≥ 6; p < 0.001) and inpatient mortality (3.0 versus 0.7% for ECI ≥ 6; p < 0.001). Hospital stays were significantly prolonged (10.4 versus 6.6 days for ECI ≥ 6; p < 0.001), 90-day readmission rates were higher (19.8 versus 14.7%; p < 0.001), and inpatient costs increased substantially ($68,000 versus $37,500; p < 0.001). Conclusions: Disruptive bleeding in spine surgery is more frequent among patients with elevated comorbidity burdens and is linked to greater mortality, ventilator dependence, and healthcare resource use. These findings highlight the importance of proactive risk stratification and targeted perioperative management strategies for high-risk patients undergoing spine surgery. Full article

Background/Objectives: Healthcare-associated infections (HAIs) remain an important cause of morbidity in coronary care units (CCUs). Although left ventricular ejection fraction (LVEF) is central to cardiovascular risk stratification, its relationship with infection susceptibility in CCU patients is poorly defined. We explored the association between LVEF and HAI incidence in a real-world CCU population. Methods: We performed a retrospective cohort study including 870 consecutive adult patients admitted to a tertiary CCU. Patients were stratified by LVEF into reduced (<40%) and preserved or mildly reduced (≥40%) groups. HAIs were defined using Centers for Disease Control and Prevention/National Healthcare Safety Network (CDC/NHSN) criteria and required microbiological confirmation. Demographic data, comorbidities, exposure to invasive devices, colonization status and clinical outcomes were collected. Associations with HAIs were assessed using univariate and exploratory multivariable logistic regression. Results: Of the 870 patients, 235 (27.0%) had LVEF < 40%. The overall HAI incidence was 1.8% (16/870) and was significantly higher in patients with reduced LVEF compared with those with LVEF ≥ 40% (3.82% vs. 1.10%, p = 0.018). Patients with LVEF < 40% had greater exposure to invasive devices (OR 2.06, 95% CI 1.52–2.79, p < 0.001). The excess HAI burden was mainly driven by urinary tract infections (1.70% vs. 0.15%, p = 0.021). Colonization rates at admission were similar between groups. In univariate analysis, reduced LVEF was associated with higher HAI occurrence, but it did not remain independently associated after adjustment. Admission infection, malignancy, CPAP use, and CCU length of stay ≥5 days emerged as independent factors in the exploratory multivariable model (Nagelkerke R² = 0.247). Conclusions: Reduced LVEF is associated with higher HAI incidence in CCU patients, reflecting greater clinical severity, longer hospitalization, and increased exposure to invasive devices. Although not an independent predictor, LVEF appears to function as a clinically useful marker of vulnerability that may support early risk stratification and targeted infection-prevention strategies in CCU settings. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is associated with an increased risk of portal vein thrombosis (PVT), which may negatively affect post-liver transplant (LT) outcomes. We aimed to evaluate the impact of PVT on post-LT outcomes in MASLD versus non-MASLD recipients and assess outcomes in MASLD patients with PVT who received donation after circulatory death (DCD) grafts. Methods: Using the UNOS database, we analyzed adult LT recipients from 2002 to 2022. Kaplan–Meier and Cox regression models were used to assess one-year post-LT outcomes. Results: Among 46,933 LT recipients, 20% had MASLD (15% PVT prevalence) and 80% had non-MASLD etiologies (9% PVT prevalence). Overall, 3051 recipients (6.5%) received DCD grafts. PVT at the time of transplant was associated with significantly higher risks of all-cause mortality, graft failure, and death-censored graft failure (DCGF) in both MASLD and non-MASLD groups (p < 0.05), although no significant differences were observed between the two groups. In the DCD subgroup, MASLD recipients with PVT had a significantly higher risk of all-cause mortality compared to non-MASLD recipients without PVT (adjusted hazard ratio [aHR] 2.24, 95% CI 1.17–4.28, p = 0.01), but no differences were observed for graft failure or DCGF. Conclusions: PVT at the time of transplant is associated with poorer survival in MASLD and non-MASLD recipients. No difference was found between the two groups. In candidates receiving DCD grafts, the presence of PVT at time of transplant was associated with a marked increase in mortality risk, although this finding requires further validation in larger cohorts. Full article

Objective: The aim of this study was to investigate the association between dental caries and multidimensional poverty in Colombia using data from the National Oral Health Survey (ENSAB IV, Spanish acronym). Methods: A cross-sectional analytical study was conducted using data from 20,534 individuals in six regions of the country. Dental caries was assessed using the ICDAS system, and multidimensional poverty was measured using a proxy Multidimensional Poverty Index (MPI) adapted from the method adjusted for Colombia. Descriptive analyses and bivariate comparisons were carried out, and Poisson regression models adjusted for sociodemographic variables were applied. Results: Households containing at least one member with caries had a higher incidence (59.9%) and intensity (46.7%) of multidimensional poverty compared to those without caries (52.6% and 45.6%, respectively). Significant associations were identified between caries and deprivation in education (low educational attainment: RR = 1.27), child labor (RR = 1.16), unemployment (RR = 1.04), lack of health insurance (RR = 1.09), and inadequate housing conditions (RR = 1.19). The model that analyzed the presence of caries in a household and multidimensional poverty, when controlled for housing conditions, confirmed a positive association between the MPI and the presence of caries (IRR = 1.08; 95% CI: 1.050–1.107; p-value < 0.001). A female head of household and rural residence were also identified as variables associated with the presence of caries in a household. Conclusions: The presence of a household member with dental caries is significantly associated with multidimensional poverty in Colombia. This study highlights the need to consider oral health as a sensitive indicator of structural inequality and proposes its inclusion in social progress metrics. The findings support the design of comprehensive public health strategies that address the social determinants of oral health, especially in vulnerable populations. Full article

The accurate estimation of the perforation length is very vital to improve fluid flow as well as the management of charges. Traditional methods, including empirical correlations, analytical models, and API 19B surface tests, suffer from significant limitations in their scope, require frequent recalibration, and fail to capture the complex physics governing shaped charge penetration. This study develops and validates machine learning models for perforation length prediction using a comprehensive dataset of 1648 API 19B standardized tests encompassing diverse gun configurations, explosive properties, and completion parameters. The dataset was partitioned into 1318 tests for model training and hyperparameter optimization, with 330 independent tests reserved for blind validation. Ten regression algorithms were systematically evaluated, with XGBoost demonstrating superior performance, achieving an R² coefficient of 0.956 on blind validation. Feature importance analysis revealed explosive weight as the dominant predictor, followed by temperature rating. The application of machine learning models offers an accurate, easier, instantaneous during planning and design workflows, and cheaper way of estimation as compared to traditional methods. Full article

Introduction: Prescription monitoring programs (PMPs) are commonly used to monitor non-medical opioid use (NMOU); however, the effectiveness of PMPs for identifying cancer patients with risk factors is not well known. Methods: This study assessed the frequency and predictors of concerning PMP findings among cancer patients in a palliative care clinic and examined the ability of PMPs, clinical review, and urine drug testing to identify NMOU behaviors. This was a retrospective analysis of consecutive cancer patients seen by palliative care at a safety-net hospital over four years. Demographic, clinical, and psychosocial risk factors for NMOU were extracted from the medical record. Concerning PMP findings were based on prescriber documentation. Logistic regression models identified predictors of documented PMP concerns. Results: Among 906 patients, 844 (93%) had PMP reviews at either consultation or a follow-up visit. Of these, 31/844 (4%) demonstrated documented PMP concern. Predictors of documented PMP irregularities included a history of illicit drug use (OR 6.30, 95% CI: 2.35–17.06), opioid use for non-malignant pain (OR 19.49, 95% CI: 6.24–60.90), and a family history of illicit drug use (OR 5.42, 95% CI: 0.96–25.04). Discussion: A total of 166 patients (20%) were identified as having NMOU behaviors based on clinical review; in contrast, PMP review identified only 31 (4%) patients with NMOU behaviors, and two (6%) were missed by clinical review. Documented PMP concern was low in cancer patients. Clinical review identified most patients with NMOU behaviors, with limited contribution from PMP review. Our findings suggest that PMP should not be used in isolation when assessing opioid-related risk in this population. Full article

Traffic congestion and travel time uncertainty remain major challenges to the operational efficiency of Bus Rapid Transit (BRT) systems in urban areas of developing countries. This study proposes an integrated solution for the Trans Metro Bandung (TMB) system by leveraging Internet of Things (IoT)–based GPS data and tree-based ensemble machine learning algorithms. Spatio-temporal data collected from on-board GPS modules are processed to predict traffic congestion levels and estimate travel time across route segments. The performance of Decision Tree, Random Forest, and XGBoost models is evaluated in terms of prediction accuracy, interpretability, and computational efficiency, with particular consideration for deployment on resource-constrained hardware. Experiments conducted on 20,156 data samples show that the Decision Tree model achieves the highest congestion classification accuracy of 96.8%, while Random Forest outperforms other models in travel time regression, achieving an R² value of 0.95 and a root mean square error (RMSE) of 5.80 min. The trained models are successfully deployed on a Raspberry Pi 3B microcontroller for real-time inference, enabling fleet management and travel planning without reliance on cloud connectivity. The results demonstrate that cost-effective and interpretable machine learning solutions can deliver reliable performance in heterogeneous urban infrastructures while providing a replicable framework for medium-sized cities seeking to implement affordable smart transportation systems. Full article

Background: Broodiness is a major limiting factor for reproductive efficiency in indigenous avian breeds, a phenomenon underpinned physiologically by granulosa cell (GC) apoptosis and subsequent follicular atresia. While Serine Protease 23 (PRSS23) has been implicated in mammalian ovarian remodeling, its specific regulatory function in avian follicular dynamics remains elusive. Methods: Utilizing the Wuding chicken—an indigenous breed distinguished by robust environmental adaptability but compromised by high broodiness frequency—as a biological model, this study dissected the molecular mechanism of PRSS23-mediated follicular regression. We cloned the complete coding sequence of the Wuding chicken PRSS23 gene, characterized its spatiotemporal expression profile, and interrogated its function in primary GCs via gain- and loss-of-function assays. Results: RT-qPCR analysis revealed that PRSS23 is differentially expressed across the hypothalamic–pituitary–ovarian (HPO) axis, with ovarian expression being significantly upregulated during the broody period compared to the laying period. Mechanistically, PRSS23 overexpression significantly downregulated the expression of follicle-stimulating hormone receptor (FSHR) and key steroidogenic enzymes (STAR, CYP19A1, HSD3β1), thereby suppressing the expression of genes governing the biosynthesis potential of progesterone and estradiol. Concurrently, PRSS23 overexpression was associated with transcriptional repression of components of the PI3K/AKT/mTOR signaling cascade; this transcriptional regulation further induced cell cycle arrest at the G0/G1 phase, and activated the mitochondrial apoptotic pathway characterized by BAX upregulation and BCL2 downregulation. Conversely, siRNA-mediated knockdown of PRSS23 alleviated these inhibitory effects, promoting GC proliferation and survival. Conclusions: These findings establish PRSS23 as a pivotal pro-atretic factor in Wuding chickens, driving ovarian atrophy through the dual transcriptional-level inhibition of steroidogenesis and survival signaling pathways. This study identifies a potential molecular target for marker-assisted selection programs aimed at attenuating broodiness while preserving the superior meat quality traits of indigenous poultry. Full article

Background: Rotator cuff tendinopathy is a major cause of shoulder pain and disability. Focused extracorporeal shockwave therapy (ESWT) is an established conservative treatment option; however, the predictive factors influencing the treatment response remain poorly characterized. Objectives: To identify clinical, demographic, and metabolic predictors of pain reduction and functional improvement at four months following focused ESWT in patients with supraspinatus tendinopathy, with the goal of informing individualized treatment planning and early prognostic counseling. Methods: This retrospective cohort study analyzed patients with supraspinatus tendinopathy (calcific and non-calcific) treated with focused ESWT at a university rehabilitation center between June 2020 and December 2025. Outcomes were assessed at baseline and 4-month follow-up using the Visual Analog Scale (VAS), Roles and Maudsley, and Constant–Murley scores. Change score analysis with covariate adjustment and backward stepwise selection were performed to identify predictors of clinical improvement. Results: A total of 239 patients (97 males [40.6%], 142 females [59.4%]; mean age 60.2 ± 11.5 years; mean BMI 25.5 ± 4.0 kg/m²) were included, of whom 101 (42.3%) had calcific tendinopathy. Significant improvements were observed in all outcomes: VAS decreased from 6.50 ± 1.35 to 3.96 ± 2.09 (p < 0.001; Cohen’s d = 1.24), and Constant–Murley score increased from 60.38 ± 14.53 to 75.88 ± 15.52 (p < 0.001; Cohen’s d = 1.07). Patient-reported satisfaction (Roles and Maudsley score) showed a 91.2% success rate (excellent or good outcomes). Regression analysis identified baseline severity as the strongest predictor of improvement in all models. BMI emerged as a significant predictor of functional recovery (β = −0.95, p < 0.001 for Constant–Murley change), with each 1 kg/m² increase associated with approximately 1-point less improvement. Conclusions: Baseline clinical severity and body mass index were consistent predictors of ESWT effectiveness in rotator cuff tendinopathy. A lower BMI was associated with greater functional improvement, highlighting a potentially modifiable factor for treatment optimization. These findings support personalized treatment planning and early prognostic counseling in clinical practices. Full article

Background/Objectives: The increasing use of radiopharmaceuticals in clinical practice has raised concerns regarding potential radiation exposure for healthcare personnel handling biological samples from treated patients. The objective of this study was to assess the radioactivity levels in clinically necessary blood samples taken from patients treated with radioactive iodine-131 (I-131) or lutetium-177 (Lu-177) in a real-world setting. Methods: Prospective, tertiary care single-center study. Blood samples, at the clinically necessary time points, from 220 consecutive targeted radionuclide therapies (TRTs) used to treat 151 distinct patients between October 2021 and January 2025 were included. The influences of the eGFR and the time interval between tracer administration and blood sampling on radioactivity concentration were investigated by linear regression models. The applied amount of radioactivity was excluded as a confounder by adjusting all cases to 1 GBq. Statistical programming language R was utilized and p < 0.05 was considered significant. Results: The mean age of the patients was 62 years and 52% were male. Mean radioactivity concentrations of 6 vs. 60 kBq/mL were measured at 52 vs. 13 h after application of 1.9 vs. 6.7 GBq I-131 vs. Lu-177, respectively. Better renal function and later blood sampling were both associated with lower radioactivity concentration in blood samples (each p < 0.001). Total radioactivity levels in all samples were well below the upper limits for the disposal of biological samples (1 MBq for I-131 and 10 MBq for Lu-177). Conclusions: There was only a low exposure risk for nuclear medicine personnel and laboratory staff. These findings emphasize that handling blood samples from patients treated with I-131 and Lu-177 in clinical routine is minimal. Full article

To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault diagnosis method based on multi-source feature fusion and Stacking ensemble learning. First, a multi-source sensing system containing MEMS (Micro-Electro-Mechanical System) pressure and travel, coil, and motor current was constructed to achieve comprehensive monitoring of the mechanical and electrical states of a 220 kV circuit breaker; in particular, the introduction of non-invasive MEMS sensors effectively solves the difficulty of capturing static spring fatigue characteristics inherent in traditional methods. Second, a high-dimensional feature space was constructed using Savitzky–Golay filtering and physical feature extraction techniques. To address the characteristics of small-sample data distribution, a two-layer Stacking ensemble learning model based on 5-fold cross-validation was designed. This model utilizes the SVM (Support Vector Machine), RF (Random Forest), and KNN (K-Nearest Neighbors) as base classifiers and Logistic Regression as the meta-learner, achieving an adaptive fusion of the advantages of heterogeneous algorithms. True-type experimental results show that the average diagnostic accuracy of this method under normal conditions and four typical fault conditions reaches 96.1%, which is superior to single base models (the RF was 94.2%). Feature importance analysis further confirms that closing and opening pressures are the most critical features for distinguishing mechanical faults. This study provides effective theoretical basis and technical support for condition-based maintenance of high-voltage circuit breakers under small-sample conditions. Full article

This study quantifies the effect of air and fuel filter restriction on fuel consumption, regulated pollutants (CO and HC), and CO₂ greenhouse gas emissions under real driving conditions in a hilly high-altitude environment. Four filter configurations were evaluated: clean air filter–clean fuel filter (CAF–CFF, reference), dirty air filter–clean fuel filter (DAF–CFF), clean air filter–dirty fuel filter (CAF–DFF), and dirty air filter–dirty fuel filter (DAF–DFF). Each test was repeated three times over the same RDE route in Quito (≈2100–2900 m). Fuel consumption was estimated from ECU-based signals, and CO₂ emission factors and regulated pollutant (CO and HC) emission factors were computed from measured exhaust concentrations and distance normalization. Results were analyzed by RDE section (urban, rural, motorway) and expressed as percent changes relative to the reference configuration to directly isolate filter restriction effects. Relative to CAF–CFF, DAF–CFF produced the largest increase in average fuel consumption (+7.2%) and the largest urban CO₂ penalty (+22.7%), indicating a strong efficiency sensitivity to intake restriction under transient operation. CAF–DFF increased average fuel consumption by 6% and produced the strongest motorway penalties for CO (+77.3%) and HC (+44.4%), suggesting that fuel delivery restriction has a stronger influence on incomplete oxidation products under sustained higher load. The combined restriction (DAF–DFF) showed non-additive responses depending on the operating regime. Random Forest models were trained to estimate CO₂, CO, and HC, achieving R² values of 0.8571, 0.8229, and 0.7690, respectively, while multiple linear regression achieved an R² of 0.852 for fuel consumption. The proposed approach supports data-driven monitoring of filter restriction effects under real driving operation, while acknowledging that fuel consumption and CO₂ are obtained through different measurement and conversion paths and may not yield identical percent changes. Full article

Background: Psychosocial well-being has been increasingly recognized as a relevant factor in cardiometabolic health; however, evidence linking Purpose in Life with type 2 diabetes risk across validated prediction tools remains limited. This study examined the association between Purpose in Life and estimated diabetes risk using three established risk scores. Methods: A cross-sectional analysis was performed in 93,077 Spanish working adults aged 18–69 years participating in routine occupational health assessments. Purpose in Life was measured with the 10-item Purpose in Life scale and categorized into high, moderate, and low levels. Estimated type 2 diabetes risk was evaluated using QDScore, FINDRISC, and CANRISK. Multivariable logistic regression models were applied to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for age, sex, occupational social class, smoking status, dietary pattern, physical activity, and body mass index. Results: Lower levels of Purpose in Life were consistently associated with greater likelihood of high estimated diabetes risk across all three instruments. Compared with participants reporting high Purpose in Life, those with low Purpose in Life showed increased odds of high-risk classification for QDScore (OR 2.38; 95% CI 2.19–2.57), FINDRISC (OR 2.49; 95% CI 2.08–2.89), and CANRISK (OR 2.79; 95% CI 2.50–3.09). Clear dose–response patterns were observed across Purpose in Life categories, and associations were similar in men and women as well as across lifestyle strata. Conclusions: Reduced Purpose in Life is strongly associated with higher estimated type 2 diabetes risk across multiple validated screening tools. Although causal direction cannot be inferred from this cross-sectional design, these findings suggest that psychosocial dimensions may provide complementary information for cardiometabolic risk assessment and prevention strategies. Full article

Background: Osteotomy around the knee (OAK) is a joint-preserving surgery for knee osteoarthritis, yet some patients experience suboptimal outcomes. Preoperative identification of high-risk patients remains challenging. This study aimed to develop a machine learning model to predict clinical outcomes after OAK using preoperative gait acceleration data from inertial measurement units (IMUs). Methods: This multicenter prospective study enrolled patients undergoing OAK. Preoperative gait was recorded using synchronized IMUs placed on the lumbar spine and tibia. Lumbar and tibial signals were used for gait-cycle segmentation, while wavelet-based time–frequency features were extracted from tibial acceleration only. Outcomes were defined by achievement of the minimal clinically important difference in ≥3 KOOS subscales at 2-year follow-up (Good vs. Poor). Continuous wavelet transform features (5–20 Hz) were summarized as mean and standard deviation across six stance subphases. A Random Undersampling Boost classifier was trained and evaluated using nested leave-one-subject-out cross-validation. A sensitivity analysis using logistic regression confirmed that the IMU-based prediction score was independently associated with outcome after adjustment for baseline KOOS (p = 0.047). Results: Of 67 enrolled patients, 37 were classified as Good and 30 as Poor outcome. For machine learning analysis, 1173 tibial acceleration gait-cycle waveforms were usable. The model achieved an AUC of 0.744 (95% CI, 0.610–0.860) using a median of 15 features (range, 5–25) with sensitivity of 0.69 and specificity of 0.72. The most informative predictors were the mean magnitude in the 5–8 Hz band during loading response (0–17%) and variability in the 5–8 Hz band during late stance (67–83%). No significant differences in baseline demographics or radiographic parameters were found between outcome groups. Conclusions: Preoperative IMU-derived gait acceleration features showed moderate-to-good discrimination between outcome groups and may support preoperative risk stratification and individualized perioperative management. Full article

Distant metastasis is the main cause of breast cancer (BC) mortality, yet current prognostic models remain largely tumor-centric and underutilize stromal biology. In this study, we quantified reactive stroma, a collagen-rich and fibrotic fraction of the stromal compartment, as a subtype-independent biomarker of metastatic risk. A retrospective cohort of 182 FFPE primary BC biopsies (2006–2020) was analyzed. Total stroma was quantified on H&E-stained sections and reactive stroma on Masson’s trichrome using QuPath with pathologist validation. Cutoffs were defined using maximally selected rank statistics, and overall survival (OS) and metastasis-free survival (MFS) were evaluated by Kaplan–Meier analysis and multivariable Cox regression. RNA sequencing was performed in a subset of cases to characterize associated transcriptomic programs. While total stromal content showed univariate associations with OS and MFS, it was not independently prognostic after adjustment. In contrast, high reactive stroma (cutoff 53.2%) independently predicted shorter MFS (HR = 3.76; p < 0.001), irrespective of molecular subtype and clinicopathological variables. Tumors with high reactive stroma exhibited upregulation of extracellular matrix and profibrotic genes (including FN1, OLR1, and EDN2), enrichment of collagen remodeling and TGF-β signaling pathways, and reduced T-cell activation signatures. These findings demonstrate that quantitative assessment of reactive stroma from standard histological stains is a reproducible, subtype-independent biomarker of metastatic risk in BC and can be readily integrated into routine pathology workflows to improve risk stratification. Full article