Search Results (4,554)

In recent years, with the significant decline in fine particulate matter (PM_2.5) concentrations, ozone (O₃) has emerged as a major composite air pollutant during the warm season in China, attracting increasing attention due to its associated health burden and economic costs. This study focuses on Tianjin, using ozone monitoring data from 2017 to 2023 combined with health statistics to assess the health impacts and economic losses attributable to ozone pollution. First, ozone exposure indicators and compliance criteria were constructed based on national air quality standards, and the interannual variation and spatial differences of O₃ levels were analyzed at both citywide and district scales. Second, multiple machine learning classification models, including logistic regression, decision tree, k-nearest neighbors, and gradient boosting, were developed using ozone and meteorological variables to predict the occurrence risks of five diseases: cardiovascular diseases, respiratory diseases, hand-foot-and-mouth disease (HFMD), influenza, and dengue fever. Finally, excess cases were estimated using health impact functions, and the associated economic losses were quantified by combining the value of a statistical life (VSL) with cost-of-illness and willingness-to-pay (WTP) approaches. The results showed that the annual evaluation value of ozone in Tianjin, defined as the 90th percentile of the daily maximum 8 h average O₃ concentration, exhibited a pattern of initially increasing, then decreasing, and subsequently rebounding. It peaked at 201 µg/m³ in 2018, declined to a minimum of 164 µg/m³ in 2021, and rebounded to 188 µg/m³ in 2023. Machine-learning results indicated that the logistic regression model showed relatively stable overall performance across predictions of different diseases, while the gradient boosting tree model also achieved high accuracy in predicting certain infectious diseases. Overall, ozone pollution exhibits significant heterogeneous effects across different disease types, and the associated health-related economic losses show stage-wise fluctuations in response to pollution levels. Based on these findings, it is recommended to implement refined control measures during periods of high ozone exceedance and in key regions, while strengthening protection for vulnerable populations such as the elderly, children, and patients with respiratory diseases, in order to achieve synergistic improvements in air quality management and public health outcomes. Full article

Background: Hyperhomocysteinemia (homocysteine [Hcy] ≥15 μmol/L) is frequently observed in patients with impaired kidney function and has been associated with vascular remodeling and increased cardiovascular risk. We aimed to evaluate the relationship between Hcy, arterial stiffness, coronary artery disease (CAD), peripheral arterial disease, and biomarkers of kidney injury in patients undergoing elective coronary angiography. Methods: In this prospective observational study, 133 patients undergoing elective coronary angiography were stratified according to serum Hcy levels (Hcy <15 vs. Hcy ≥15 μmol/L). CAD severity was assessed angiographically. Arterial stiffness was evaluated using carotid–femoral pulse wave velocity (cfPWV), while peripheral arterial disease was assessed using ankle–brachial index (ABI). Kidney function was evaluated using serum creatinine, estimated glomerular filtration rate (eGFR), cystatin C, and urinary albumin-to-creatinine ratio (UACR). Correlation, multivariable regression, logistic regression, and receiver operating characteristic (ROC) analyses were performed. Results: Patients with hyperhomocysteinemia demonstrated significantly worse kidney function, including higher serum creatinine, cystatin C, and UACR levels, and lower eGFR (all p < 0.01). Patients with elevated Hcy levels also exhibited significantly higher cfPWV values (11.4 ± 3.3 vs. 9.7 ± 2.1 m/s, p < 0.001). Hcy correlated positively with cystatin C, creatinine, UACR, and cfPWV, and inversely with eGFR. In multivariable linear regression analysis, Hcy remained independently associated with increased cfPWV after adjustment for age, sex, and eGFR (β = 0.137, 95% CI 0.047–0.226, and p = 0.003). This association remained significant in sensitivity analyses incorporating hypertension, diabetes mellitus, LDL cholesterol, and statin therapy (β = 0.124, 95% CI 0.032–0.216, and p = 0.008). No independent associations were observed between Hcy and angiographic CAD severity or ABI values. ROC analysis demonstrated modest discrimination for elevated arterial stiffness (AUC = 0.66, 95% CI 0.56–0.76) and good discrimination for impaired kidney function (AUC = 0.82, 95% CI 0.69–0.92). Conclusions: Elevated Hcy levels were independently associated with impaired kidney function and increased central arterial stiffness, but not with angiographic CAD severity or peripheral arterial disease. These findings suggest that hyperhomocysteinemia may reflect cardiorenal vascular dysfunction and diffuse vascular remodeling rather than focal obstructive atherosclerotic disease. Further studies are needed to determine its clinical utility and prognostic value. Full article

Allogeneic T cell therapies have emerged as a promising strategy to overcome the logistical and manufacturing limitations of autologous approaches, enabling scalable, off-the-shelf cancer immunotherapy. While early clinical efforts have focused predominantly on αβ T cell-based platforms, including CAR- and TCR-engineered approaches, a growing spectrum of alternative cell types, such as γδ T cells, invariant natural killer T cells, mucosal-associated invariant T cells, and induced pluripotent stem cell-derived effectors, is expanding the design landscape of allogeneic therapies. However, clinical translation remains constrained by immune rejection, limited persistence, lymphodepletion-associated toxicity, manufacturing variability, and impaired efficacy in solid tumors. To address these barriers, engineering strategies have increasingly integrated T cell receptor disruption, human leukocyte antigen modulation, cytokine support, checkpoint editing, and synthetic circuit design. This review provides an oncology-focused, cross-platform framework for evaluating diverse allogeneic T cell and T cell-like platforms according to clinical maturity, safety, manufacturability, persistence, and tumor-targeting capacity. We further discuss how platform-specific biological properties and clinical evidence can be integrated with modular engineering strategies to optimize antitumor performance. These insights support a shift from platform-centric development toward a design-driven paradigm for next-generation allogeneic cellular immunotherapies with improved efficacy, safety, and scalability. Full article

A new era in wireless communication has been witnessed by the emergence of fifth generation (5G) technology, characterized by high data rates, ultra-low latency, and massive device connectivity. To address the growing demand for efficient spectrum utilization, Non-Orthogonal Multiple Access (NOMA) has been introduced as a promising multiple access scheme. This study investigates the energy efficiency (EE) and spectral efficiency (SE) performance of NOMA in comparison with Orthogonal Multiple Access (OMA) under varying bandwidth conditions. In addition to conventional analytical and simulation-based evaluations, artificial intelligence (AI) techniques, including Deep Learning (DL), Decision Tree (DT), K-Nearest Neighbours (KNN), and Logistic Regression (LR), are employed to model and predict system performance. The AI models are trained using simulation-generated datasets to capture complex relationships between bandwidth, transmit power, and user distribution. Simulation results demonstrate improvement in SE and EE of NOMA across different bandwidth scenarios. Furthermore, DL and DT models achieve higher prediction accuracy. The consistency between AI predictions and simulation outcomes confirms the robustness of the proposed framework. These findings highlight the superiority of NOMA over OMA and demonstrate the effectiveness of integrating AI techniques for performance optimization in 5G and beyond wireless networks. Full article

Background/Objective: Cervical intraepithelial neoplasia grade 2 (CIN2) shows heterogeneous clinical behavior, with substantial rates of spontaneous regression under active surveillance. Reliable molecular biomarkers are needed to distinguish regressive from transforming lesions and reduce overtreatment. We evaluated the prognostic role of host and viral DNA methylation, alone and combined with HPV genotyping, in predicting CIN2 regression. Methods: This subanalysis derives from a prospective, multicenter Italian cohort of women with histologically confirmed CIN2 managed conservatively. Among 319 enrolled women, 134 with single HPV infections and valid host (FAM19A4/miR124-2) and viral (HPV L1 region) methylation results were included. HPV genotyping was performed with partial stratification (HPV16/18 vs. non-16/18). Clinical outcomes at 24 months were classified as regression versus persistence/progression. Logistic regression models assessed associations between biomarkers and regression. Results: At 24 months, 50% of women showed regression. Host and viral methylation positivity rates were more frequent in non-regressive lesions (40.3% vs. 19.4%, p = 0.01, and 52.2% vs. 32.8%, p = 0.02, respectively). Negative host methylation was significantly associated with regression (Odds Ratio OR = 0.37, 95% CI 0.17–0.81, p = 0.02), as was negative viral methylation (OR = 0.47, 95% CI 0.23–0.96, p = 0.04). Conclusions: Both host and viral methylation are inversely associated with CIN2 regression. Combining methylation markers did not substantially improve predictive accuracy; however, methylation negativity emerged as a potential molecular reassurance marker. When integrated with HPV genotyping, the highest probability of regression was observed among women with non-HPV16/18 infections and negative methylation results. These results endorse DNA methylation testing as a molecular tool for the conservative management of CIN2. Full article

Background: Cervicogenic headache (CEH) is a secondary headache disorder originating from dysfunction in the cervical spine. In addition to pain, individuals with CEH frequently experience disturbances in postural control and sensorimotor integration, which may compromise functional capacity and quality of life. Conventional clinical assessments typically focus on pain intensity and cervical range of motion; however, these measures often fail to capture the multifactorial mechanisms underlying balance impairments in this population. Machine learning (ML) methods offer the ability to integrate multidimensional clinical data and may provide a more comprehensive approach for identifying patterns of postural stability and the factors influencing balance regulation in CEH. Methods: A secondary analysis was conducted using baseline data pooled from three registered randomized controlled trials, comprising 68 independent participants diagnosed by a neurologist according to the International Classification of Headache Disorders, 3rd edition (ICHD-3). Postural Stability Class served as the primary outcome and was derived from quantitative stability scores categorized as High, Moderate, or Low. Predictor variables included demographic characteristics (age, gender), clinical measures (pain intensity, headache frequency, symptom duration, cervical range of motion), and sensorimotor parameters (center-of-pressure sway and gaze accuracy). Five machine learning algorithms—Random Forest, XGBoost, Support Vector Machine, Logistic Regression, and Gradient Boosting—were trained and evaluated using 10-fold cross-validation with procedures implemented to reduce overfitting. Results: The Gradient Boosting classifier demonstrated the best performance, achieving an accuracy of 0.857 and an F1 score of 0.857, with a cross-validated accuracy of 0.802 ± 0.063. Random Forest and XGBoost achieved accuracies of 0.786. Feature importance analysis identified center-of-pressure sway and pain intensity as the most influential predictors of stability classification, followed by cervical flexion range of motion and gaze accuracy. Demographic variables showed minimal contribution to model performance. Conclusions: Machine learning models were able to distinguish different levels of postural stability in individuals with CEH. The findings highlight the central role of pain and sensorimotor control in balance regulation and suggest that predictive analytics may support precision physiotherapy by enabling rehabilitation strategies tailored to individual sensorimotor profiles. Full article

Background: Oxidative stress is pivotal in pulmonary hypertension. The uric acid-to-albumin ratio (UAR) is a readily available composite biomarker reflecting oxidative stress, inflammation and nutritional status. However, its clinical value for short-term risk stratification in PH remains unclear. Objective: This study aimed to evaluate the association of UAR with in-hospital mortality, clinically recorded PH severity grades, and selected cardiac structural and functional indicators in hospitalized patients with PH. Methods: This single-center retrospective cohort study included 8763 PH patients. Patients were stratified by UAR quartiles. Ordinal logistic regression, multivariable logistic regression, and linear regression were used to assess associations of UAR with clinically recorded PH severity grades, in-hospital mortality, left ventricular ejection fraction (LVEF), and right ventricular internal diameter (RVID). Restricted cubic spline analyses, subgroup analyses, receiver operating characteristic curve analyses, and incremental prediction analyses using C-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were also performed. Results: In-hospital mortality increased stepwise across UAR quartiles (0.5% vs. 1.0% vs. 1.8% vs. 2.8%, p < 0.001). In the fully adjusted model, each 1-unit increase in UAR was associated with higher odds of a more severe clinically recorded PH grade (OR = 1.11, 95% CI: 1.09–1.13, p < 0.001) and higher odds of in-hospital mortality (OR = 1.09, 95% CI: 1.04–1.14, p < 0.001). Higher UAR was also associated with lower LVEF (β = −0.53, 95% CI: −0.58 to −0.47, p < 0.001) and greater RVID (β = 0.18, 95% CI: 0.15–0.22, p < 0.001). Adding UAR to a model containing routinely available clinical, laboratory, and echocardiographic variables improved the C-statistic from 0.6922 to 0.7230, with significant improvements in NRI and IDI. Conclusions: UAR was independently associated with in-hospital mortality, clinically recorded PH severity, LVEF, and RVID, and provided incremental prognostic information. UAR may serve as a low-cost, routinely available, complementary biomarker for short-term in-hospital risk stratification in patients with PH. Full article

Background/Objectives: Tumor-infiltrating lymphocytes (TILs) are recognized predictors of response to neoadjuvant chemotherapy (NACT) and prognosis in breast cancer, particularly in triple-negative (TN) and HER2-positive subtypes. However, the additional predictive value of morphological features of the inflammatory infiltrate beyond TIL quantification is not fully established. We aimed to assess the predictive value of TILs for response to NACT in breast cancer and to determine whether morphological characteristics of the inflammatory infiltrate enhance predictive accuracy. Methods: We analyzed 477 patients with stage II–III breast cancer treated with NACT between 2009 and 2016. Diagnostic core needle biopsies were prospectively re-evaluated. TILs were quantified according to International TILs Working Group recommendations. Morphological features of the infiltrate, including cell composition (lymphocytic vs. plasma cell-rich), heterogeneity, and localization, were evaluated using standardized criteria. Pathologic complete response (pCR) was defined as absence of invasive tumor in the breast and in the axillary lymph nodes (ypT0/Tis ypN0). Univariate and multivariate logistic regression analyses were performed to assess the predictive value of TILs (quantitative and morphological assessment) to achieve pCR for the entire cohort and by surrogate molecular subtype. Results: A TIL cutoff of >20% was identified as optimal for predicting pCR. High TILs were significantly associated with high-grade tumors, elevatedKi67, HER2-positive and TN subtypes, presence of plasma cells, and intraepithelial and heterogeneous infiltrates. In the overall cohort, TILs > 20% significantly increased the likelihood of pCR (OR 3.9, 95%IC 2.5–6.0, p < 0.001) and was an independent predictor of pCR. A combined variable incorporating TIL level and homogeneity improved predictive performance, with homogeneously high TILs emerging as a strong predictor of pCR (OR 5.521, 95%CI 3.174–9.603, p < 0.01). Plasma cell-rich and intraepithelial infiltrates were also associated with higher pCR rates (respectively, OR 2.7, 95%CI 1.5–5.0, p = 0.001 and OR 2.8, 95%CI 1.6–5.0, p < 0.001). Subtype-specific analyses confirmed the predictive value of TILs in TN tumors, but not in HER2-positive ones. Notably, in luminal B-like tumors, high TILs were the only independent predictor of response (OR 17.982, 95%CI 3.115–103.815, p = 0.001). Conclusions: TIL assessment on routine H&E-stained biopsies is a robust predictor of response to NACT in breast cancer that is readily available, cost-neutral and does not require additional techniques. Integration of simple morphological features significantly enhances predictive accuracy and may refine treatment stratification, particularly in luminal B-like tumors. Full article

Background/Objectives: Inflammation plays a central role in the pathophysiology of ST-elevation myocardial infarction (STEMI) and may influence myocardial injury, ventricular dysfunction and clinical outcomes. Simple inflammatory biomarkers derived from routine laboratory tests have been proposed as potential prognostic indicators in patients undergoing primary percutaneous coronary intervention (PCI). Objective: This study aimed to evaluate the association between admission inflammatory biomarkers, echocardiographic markers of ventricular dysfunction and in-hospital mortality in patients with STEMI treated with primary PCI. Methods: We conducted a retrospective observational study including 600 consecutive patients admitted with STEMI and treated with primary PCI between January 2021 and August 2025. Inflammatory biomarkers measured at admission included C-reactive protein (CRP); neutrophil-to-lymphocyte ratio (NLR); platelet-to-lymphocyte ratio (PLR); systemic immune-inflammation index (SII) and C-reactive protein-to-lymphocyte ratio (CLR). Echocardiographic parameters and clinical outcomes were recorded. Multivariable logistic regression analysis was performed to identify independent predictors of in-hospital mortality. Results: In-hospital mortality occurred in 54 patients (9.0%). Patients with reduced left ventricular ejection fraction (LVEF ≤ 40%) had significantly higher CRP and CLR levels (p < 0.01). Inflammatory biomarkers were associated with markers of ventricular dysfunction but were not independent predictors of mortality. Age, LVEF < 40% and the number of residual coronary lesions independently predicted in-hospital death. Conclusions: In STEMI patients undergoing primary PCI, early mortality is mainly determined by age; ventricular dysfunction and residual coronary disease burden, while inflammatory biomarkers primarily reflect the severity of myocardial injury rather than independently predicting short-term mortality. Full article

Background/Objectives: Depression is a mood disorder characterized by persistent sadness and loss of interest. Pharmacy students exhibit a relatively high prevalence of depressive symptoms, which may negatively impact quality of life, academic performance, and, in severe cases, lead to suicidal ideation. Given the increasing trend of medication use among young adults, we hypothesized that this behavior may be associated with depressive symptoms, potentially reflecting attempts to manage concurrent physical symptoms or reduced psychological well-being. Therefore, the objective of this study was to assess the association between depressive symptoms and medication use among pharmacy students. Methods: A cross-sectional study was conducted among students enrolled in pharmacy-related programs from University Center for Exact Sciences and Engineering (CUCEI), University of Guadalajara. Participants completed a personalized questionnaire assessing sociodemographic variables, medication use, comorbid conditions, and depressive symptoms using the Patient Health Questionnaire-9. Descriptive statistics were used to summarize participant characteristics. Categorical variables were reported as frequencies and percentages and compared using the chi-square test. Continuous variables were summarized as means and standard deviations and compared using Student t-test. To evaluate factors associated with moderate-to-severe depressive symptoms, logistic regression and multivariable linear regression analyses were performed. Results: A total of 365 students completed our personalized questionnaire; nearly half of the sample (47.3%) presented moderate-to-severe depressive symptoms. Multivariable analyses showed that insufficient sleep, academic stress, psychological support, and the number of medications used simultaneously were significantly associated with depressive symptoms. Logistic regression identified being in a relationship and receiving psychological support for at least one year as protective factors, while employment, insufficient sleep, academic stress, and a greater number of concomitant medications were associated with increased odds of moderate-to-severe depressive symptoms. Conclusions: A modest association was observed between self-reported medication use and moderate-to-severe depressive symptoms among pharmacy students. These findings suggest that medication use patterns may warrant further investigation as a potential marker of depressive symptoms in pharmacy students. Future longitudinal studies are needed to clarify the nature and direction of this association and to determine whether medication use could contribute to the identification of students at increased risk of depression. Full article

Background/Objective: This study evaluated the diagnostic performance of vertical growth patterns and mandibular morphology, alongside the anteroposterior dysplasia indicator (APDI), for classifying skeletal malocclusions in a Saudi adult population using cephalometric analysis. Materials and Methods: This retrospective cross-sectional discriminatory performance study analyzed 162 archived lateral cephalometric radiographs of Saudi adults aged 18–44 years. The assessed variables included Frankfort-mandibular plane angle (FMA), gonial angle, ANB angle, and APDI. Statistical analysis involved descriptive statistics, ANOVA with post hoc testing, Pearson correlation, logistic regression, and receiver operating characteristic (ROC) curve analysis. Results: Significant differences among skeletal classes were observed for all evaluated variables (p < 0.05). APDI showed the largest effect size and the highest diagnostic performance, particularly for Class III malocclusion, with excellent discriminatory ability reflected by area under the curve (AUC) values, high sensitivity, and acceptable specificity at optimal cutoff points. FMA showed moderate discriminatory performance, with higher specificity but limited sensitivity, while the gonial angle exhibited comparatively weaker diagnostic performance. In logistic regression analysis, APDI was the only significant independent associated variable of Class II malocclusion. Conclusions: Within the ANB-based classification framework used in this study, APDI showed the highest discriminatory performance for skeletal malocclusion classification, supporting its role as a primary sagittal indicator. FMA contributed adjunctive information on vertical skeletal pattern, while the gonial angle showed limited diagnostic value. Combined assessment of sagittal and vertical parameters may improve cephalometric diagnosis. Full article

In bank-dominated financial systems, the accumulation of non-performing loans is a recognised source of systemic vulnerability, as correlated corporate defaults can erode bank capital, impair liquidity, and propagate stress across interconnected portfolios. Firm-level default detection thus constitutes a microprudential foundation of macroprudential stability: the reliable early identification of risky borrowers reduces both individual credit losses and the aggregate exposures that drive system-level fragility. Yet the use of structured data-mining pipelines for this task remains underexplored in Central and Eastern Europe. This study applies the CRISP-DM methodology to predict corporate loan default using data on 302 Slovak corporate borrowers, combining financial ratios from publicly available financial statements with selected company and loan-related information from internal bank records. Seven individual classifiers were developed and compared: decision trees (CART, CHAID, C5.0), logistic regression, discriminant analysis, and neural networks (MLP, RBF), together with a stacked ensemble based on their outputs. Model performance was evaluated using sensitivity, overall classification accuracy, and area under the ROC curve (AUC), with sensitivity treated as the primary criterion because of the asymmetric costs of misclassification in credit risk assessment. The results confirm that historical firm-level information provides a reliable basis for default prediction, with tree-based models consistently outperforming statistical and neural network approaches. The stacked ensemble achieved the strongest overall performance, whereas C5.0 and CHAID showed that interpretable classifiers can also deliver competitive predictive accuracy. A champion–challenger deployment architecture is proposed, in which the ensemble serves as the performance-oriented champion and interpretable models act as challengers; this arrangement contributes to the operational resilience of the credit-risk assessment process and aligns with macroprudential expectations of model governance, auditability, and explainability. The study offers a replicable methodological framework for integrating data-driven decision support into credit evaluation in comparable banking settings. Full article

Background/Objectives: Medication-related osteonecrosis of the jaw (MRONJ) is a severe complication of antiresorptive and antiangiogenic therapies, and identifying specific risk factors for recurrence remains a significant clinical challenge. This study aimed to evaluate the clinical characteristics and independent risk factors for recurrence in a large-scale cohort of MRONJ patients. Methods: This single-center retrospective study analyzed 812 patients diagnosed with MRONJ according to the 2022 AAOMS criteria at Chosun University Dental Hospital between 2020 and 2024. Demographic, clinical, radiographic, and medication-related variables were collected. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors associated with recurrence. Results: The majority of patients were female (89.9%), with a mean age of 72.9 years, and mandibular involvement was most frequent (70.8%). Tooth extraction was the most common local precipitating factor (47.0%). The overall recurrence rate was 10.1%. Multivariate analysis identified bilateral jaw involvement (OR = 4.555, p = 0.006), mandibular ramus involvement (OR = 8.222, p = 0.008), and systemic liver disease (OR = 3.703, p = 0.037) as significant independent risk factors. Conversely, routes of prior antiresorptive medication administration involving intravenous-only or combined oral/intravenous therapy, as well as hyperlipidemia and a history of dental implant surgery, were associated with lower recurrence rates. Conclusions: Anatomical complexity and systemic health status are critical predictors of MRONJ recurrence. Patients presenting with bilateral or mandibular ramus involvement, or with compromised liver function, require more aggressive surgical debridement and individualized treatment planning to reduce the risk of recurrence. Given the small affected subgroups, the effect sizes for mandibular ramus involvement and liver disease should be interpreted with caution. Full article

Background/Objectives: Y chromosome microdeletions in the azoospermia factor (AZF) regions are a major genetic cause of severe male infertility, yet their relationship with hormonal profiles in azoospermic men remains unclear. This study aimed to investigate AZF microdeletions and associated hormonal parameters in azoospermic patients. Methods: Azoospermic patients were screened for AZFa, AZFb, and AZFc microdeletions using multiplex real-time PCR targeting sequence-tagged site (STS) markers (sY84, sY127, and sY254). Patients were categorized into AZF-negative and AZF-positive groups, with the latter further stratified according to their deletion subtype. Serum follicle-stimulating hormone (FSH), testosterone, and inhibin B levels were measured. Hormonal parameters were compared between groups using the Mann–Whitney U test, and a logistic regression analysis was performed to evaluate associations between hormonal variables and AZF deletion status. Results: AZF microdeletions were detected in 18.7% (17/91) of patients. Patients without AZF deletions showed a median FSH level of 17.40 (7.12–31.27) IU/L. In contrast, AZFc deletion carriers exhibited an intermediate median FSH level of 21.10 (16.11–26.10) IU/L and lower median inhibin B concentrations (25.50 [25.25–26.00] pg/mL) compared with AZF-negative patients (56.00 [33.50–106.50] pg/mL). Median testosterone levels in AZFc patients (3.61 [2.87–4.35] ng/mL) remained within the expected physiological range. However, no statistically significant differences were observed between the AZF subgroups for age (p = 0.262), FSH (p = 0.506), testosterone (p = 0.615), or inhibin B (p = 0.524). The logistic regression analysis also showed no significant association between hormonal parameters and AZF deletion status. Conclusions: Hormonal parameters alone are insufficient to predict the presence of AZF microdeletions in azoospermic men. These findings highlight the importance of routine genetic screening for accurate diagnosis, clinical management, and reproductive counseling in male infertility. Full article

(1) Background: One-lung ventilation (OLV) is frequently required during thoracic surgery, but hypoxemia remains a common intraoperative complication. Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have emerged as inexpensive inflammatory biomarkers, although their role in predicting hypoxemia during OLV remains unclear. This study evaluated the association between preoperative NLR, PLR, and severe intraoperative hypoxemia during OLV. (2) This interim analysis included 103 patients undergoing elective thoracic surgery with OLV in a prospective observational cohort. Severe hypoxemia was defined as PaO₂/FiO₂ < 100. Group comparisons were performed using Mann–Whitney U and chi-square/Fisher’s exact tests. Hierarchical logistic regression and ROC analysis were used to evaluate predictors and model performance. (3) Results: Preoperative PLR significantly improved the predictive performance of the clinical model for severe intraoperative hypoxemia, while NLR was not associated with the outcome. BMI remained an independent predictor of hypoxemia. (4) Conclusions: PLR improved the predictive performance of the clinical model, although its inverse association with hypoxemia should be interpreted cautiously. NLR was not associated with hypoxemia during OLV. Full article