Search Results (160)

Background/Objectives: This study aimed to evaluate the prognostic effects of tumor spread through air spaces (STAS) and other clinical and pathological risk factors on disease-free survival (DFS) in patients with non-small cell lung cancer (NSCLC) who underwent curative lobectomy and had tumors measuring 4 cm or less. Methods: NSCLC patients who underwent surgery between March 2015 and May 2024 and had at least 12 months of follow-up were retrospectively analyzed. Patients with tumors measuring 4 cm or less who underwent R0 resection, lobectomy, and STAS assessment on intraoperative frozen sections were included in the study. Clinicopathological features of all patients were restaged according to the 9th edition of the TNM staging system. The Kaplan–Meier method, log-rank test, and univariate Cox regression analysis were used to determine the factors affecting DFS. Results: 88 patients were included in the study. The median age of the patients was 61 years, 77.3% were male, and 72.7% had adenocarcinoma histology. According to TNM 9, 23.9% of the cases were staged T1b, 18.2% T1c, and 58.0% T2a. STAS positivity was detected in 45 patients (51.1%). The rates of lymphovascular invasion (LVI) (40.0% vs. 18.6%; p = 0.028) and visceral pleural invasion (VPI) (57.8% vs. 27.9%; p = 0.005) were significantly higher in the STAS-positive group than in the STAS-negative group. Recurrence was observed in a total of 31 patients (35.2%) during a median follow-up period of 68.1 months. In Kaplan–Meier analysis, the median DFS was not reached for the entire cohort. The estimated median DFS in STAS-positive patients was 52.7 months, while the median was not reached in the STAS-negative group (p = 0.001). The median DFS was 52.3 months in those with lymph node positivity, while the median was not reached in those with lymph node negativity (p = 0.031). According to TNM 9, the difference in DFS between stage IA/IB and stage IIAB groups was not statistically significant (p = 0.080). In univariate Cox analysis, STAS positivity (HR = 3.79; 95% CI: 1.69–8.51; p = 0.001), lymph node positivity (HR = 2.58; 95% CI: 1.05–6.31; p = 0.038) and VPI (HR = 2.28; 95% CI: 1.07–4.86; p = 0.032) were found to be significant prognostic factors adversely affecting DFS. Age, gender, histological type, tumor location, T stage, LVI, perineural invasion (PNI), and adjuvant chemotherapy had no significant effect on DFS. Conclusions: STAS is a strong negative prognostic indicator for recurrence in patients with operated NSCLC with tumor size ≤ 4 cm. It is believed that STAS should be integrated into risk-based staging and adjuvant treatment decision-making processes in early-stage NSCLC, particularly when evaluated in conjunction with VPI and lymph node positivity. Full article

The increasing demand for sustainable construction materials has motivated the optimization of mortar mix designs to reduce cement consumption and its environmental impact while maintaining adequate mechanical performance. This study develops a machine learning (ML) model for optimizing mortar mixtures used in concrete roofing tiles by integrating aggregate particle packing techniques with non-linear regression algorithms, using an industry-grade dataset generated in the Central Laboratory of Wienerberger Ltd. Unlike most previous studies, which mainly focus on compressive strength, this research targets the transverse strength of industrial roof tile mortar. The proposed approach combines Tarantula Curve gradation limits, experimentally derived packing density ($\eta$), and ML regression within a unified and application-oriented workflow, representing a research direction rarely explored in the literature for optimizing concrete mix transverse strength. Fine concrete aggregates were characterized through a sand sieve analysis and subsequently adjusted according to the Tarantula Curve method to optimize packing density and minimize void content. Physical properties of cements and fine aggregates were assessed, and granulometric mixtures were evaluated using computational methods to calculate fineness modulus summation (FMS) and packing density. Mortar samples were tested for transverse strength at 1, 7, and 28 days using a three-point bending test, generating a robust dataset for modeling training. Three ML models—Random Forest Regressor (RFR), XG-Boost Regressor (XGBR), and Support Vector Regressor (SVR)—were evaluated, confirming their ability to capture nonlinear relationships between mix parameters and transverse strength. The analysis of input variables, which consistently ranked as the highest contributors according to impurity-based and permutation-based importance metrics, revealed that the duration of curing, density, and the summation of the fineness modulus significantly influenced the estimated transverse strength derived from the models. The integration of particle size distribution optimization and ML demonstrates a viable pathway for reducing cement content, lowering costs, and achieving sustainable mortar mix designs in the tile manufacturing industry. Full article

Construction and demolition (C&D) waste remains a critical challenge in India due to accelerated urbanisation and material-intensive construction practices. This study integrates survey-based assessment with machine learning to identify key causes of C&D waste and recommend targeted minimization strategies. Data were collected from 116 professionals representing junior, middle, and senior management, spanning age groups from 20 to 60+ years, and working across building construction, consultancy, project management, roadworks, bridges, and industrial structures. The majority of respondents (57%) had 6–20 years of experience, ensuring representation from both operational and decision-making roles. The Relative Importance Index (RII) method was applied to rank waste causes and minimization techniques based on industry perceptions. To enhance robustness, Random Forest, Gradient Boosting, and Linear Regression models were tested, with Random Forest performing best (R² = 0.62), providing insights into the relative importance of different strategies. Findings show that human skill and quality control are most critical in reducing waste across concrete, mortar, bricks, steel, and tiles, while proper planning is key for excavated soil and quality sourcing for wood. Recommended strategies include workforce training, strict quality checks, improved planning, and prefabrication. The integration of perception-based analysis with machine learning offers a comprehensive framework for minimising C&D waste, supporting cost reduction and sustainability in construction projects. The major limitation of this study is its reliance on self-reported survey data, which may be influenced by subjectivity and regional bias. Additionally, results may not fully generalize beyond the Indian construction context due to the sample size and sectoral skew. The absence of real-time site data and limited access to integrated waste management systems also restrict predictive accuracy of the machine learning models. Nevertheless, combining industry perception with robust data-driven techniques provides a valuable framework for supporting sustainable construction management. Full article

The integration of Artificial Intelligence (AI) into higher education offers new opportunities for inclusive and sustainable learning. This study investigates the impact of an AI-enabled microlearning cycle—comprising short instructional videos, formative quizzes, and structured discussions—on student engagement, inclusivity, and academic performance in postgraduate management education. A mixed-methods design was applied across two cohorts (2023, n = 138; 2024, n = 140). Data included: (1) survey responses on engagement, accessibility, and confidence (5-point Likert scale); (2) learning analytics (video views, quiz completion, forum activity); (3) academic results; and (4) qualitative feedback from open-ended questions. Quantitative analyses used Wilcoxon signed-rank tests, regressions, and subgroup comparisons; qualitative data underwent thematic analysis. Findings revealed significant improvements across all dimensions (p < 0.001), with large effect sizes (r = 0.35–0.48). Engagement, accessibility, and confidence increased most, supported by behavioural data showing higher video viewing (+19%), quiz completion (+21%), and forum participation (+65%). Regression analysis indicated that forum contributions (β = 0.39) and video engagement (β = 0.31) were the strongest predictors of grades. Subgroup analysis confirmed equitable outcomes, with non-native English speakers reporting slightly higher accessibility gains. Qualitative themes highlighted interactivity, real-world application, and inclusivity, but also noted quiz-related anxiety and a need for industry tools. The AI-enabled microlearning model enhanced engagement, equity, and academic success, aligning with SDG 4 (Quality Education) and SDG 10 (Reduced Inequalities). By combining Cognitive Load Theory, Kolb’s experiential learning, and Universal Design for Learning, it offers a scalable, pedagogically sustainable framework. Future research should explore emotional impacts, AI co-teaching models, and cross-disciplinary applications. By integrating Kolb’s experiential learning, Universal Design for Learning, and Cognitive Load Theory, this model advances both pedagogical and ecological sustainability. Full article

Background/Objectives: Durable apical support after hysterectomy is crucial to prevent subsequent vaginal vault prolapse. Abdominal sacrocolpopexy remains the gold standard but carries risks of visceral injury and wound morbidity. The vaginal natural orifice transluminal endoscopic surgery (vNOTES) approach provides a scarless, minimally invasive alternative, but data on vNOTES-assisted sacrocolpopexy (vNOTES-SC) performed concurrently with hysterectomy remain limited. Methods: A retrospective cohort of 30 women with stage II uterine prolapse underwent concomitant hysterectomy and vNOTES-assisted sacrocolpopexy between January 2023 and January 2024. Anatomical outcomes were evaluated using the Pelvic Organ Prolapse Quantification (POP-Q) system preoperatively and at 12 months postoperatively. The primary endpoint was anatomical success (C ≤ −1 cm); the secondary endpoint used the IUGA criterion (C < −TVL/2). Complications were graded using the Clavien–Dindo classification. Statistical analyses included Wilcoxon signed-rank tests, effect-size estimation, ROC analysis, logistic regression, and Spearman correlation. Results: Mean operative time was 100.2 ± 11.7 min, mean blood loss 155.3 ± 74.8 mL, and mean hospital stay 1.5 ± 0.7 days. Significant improvements were seen in Aa, Ba, C, and Bp points (p < 0.001). Anatomical success (C ≤ −1 cm) was achieved in 73.3% and clinical success in 93.3% of patients. Two patients exhibited anatomical recurrence (6.7%), whereas one patient reported symptomatic recurrence (3.3%). Using the IUGA definition, anatomical success increased to 83.3%. The difference between strict success (C ≤ −1 cm) and IUGA success (C < −TVL/2) reflects definitional sensitivity, particularly in post-hysterectomy vaginal length. All complications were minor (Grade I–II). ROC analysis showed age as a weak predictor (AUC = 0.67). Effect sizes were large for apical and anterior compartments (Cohen’s d = 1.84 for C-point). Conclusions: Concomitant hysterectomy with vNOTES-assisted sacrocolpopexy is a feasible, safe, and effective scarless approach for apical support restoration. The procedure provides significant anatomical correction and rapid recovery with low morbidity. Patients had symptomatic stage II prolapse with risk factors for early failure after native-tissue repair, supporting the selection of sacrocolpopexy for durable apical support. Larger prospective trials are needed to confirm long-term efficacy and functional outcomes. Full article

Background: Household contacts of smear-positive tuberculosis (TB) patients face an elevated risk of infection and disease progression, particularly young children and individuals living in overcrowded households. Despite WHO recommendations for systematic contact screening and provision of TB preventive therapy (TPT), implementation remains suboptimal in high-burden rural areas. This study aimed to develop a practical framework for identifying and prioritizing high-risk families by examining demographic predictors, household clustering, and machine learning-based risk models. Methods: A total of 437 household contacts linked to smear-positive index cases were assessed and classified as high or low risk. Statistical analyses included descriptive measures, χ² tests, Z-tests for age-group differences, and multivariable logistic regression. Household-level vulnerability patterns were explored using network visualizations, clustered heatmaps, and risk-ranking charts. Three machine learning models, logistic regression, random forest, and gradient boosting, were trained using demographic and household variables with 5-fold cross-validation and an 80/20 hold-out test split. Model performance was evaluated using the AUROC, AUPRC, accuracy, F1-score, calibration curves, and decision curve analysis. Results: Of the 437 contacts, 290 (66.4%) were classified as high risk. A younger age was strongly associated with high-risk status (χ² = 16.61, p = 0.005), with children aged 0–4 years being significantly more likely to be in a high-risk category (Z = 2.706). Gender showed no significant association (p = 0.523). Logistic regression identified younger age (aOR = 2.41, 95% CI: 1.48–3.94) and larger household size (aOR = 1.12 per additional member, 95% CI: 1.01–1.25) as independent predictors of the outcome. Visual analytics revealed apparent clustering of high-risk individuals within “hotspot families,” enabling prioritization through composite risk scores. Gradient boosting achieved the strongest performance (AUROC = 0.65; AUPRC = 0.76), with acceptable calibration (Brier score = 0.21) and a positive net clinical benefit in the decision curve analysis. Conclusions: TB risk is highly clustered at the household level, with large families and young children carrying disproportionate vulnerability. Combining demographic risk assessment, household-level visualization, and predictive modeling provides a practical, data-driven approach to prioritizing households during contact investigation. These findings support the WHO’s family-centered strategy and underscore the need to strengthen clinical governance and community-engaged education to optimize TB prevention in resource-limited rural settings. Full article

Introduction: Peptide Receptor Radionuclide Therapy (PRRT) with [¹⁷⁷Lu]Lu-DOTA-TATE is effective in treating advanced Neuroendocrine Tumors (NETs), yet predicting individual response in this treatment remains a challenge due to inter-lesion heterogeneity. There is a lack of standardized, effective methods for using multi-lesion radiomics to predict progression and Time to Progression (TTP) in PRRT-treated patients. This study evaluated how aggregating radiomic features from multiple PET-identified lesions can be used to predict disease progression (event [progression and death] vs. event-free) and TTP. Methods: Eighty-one NETs patients with multiple lesions underwent pre-treatment PET/CT imaging. Lesions were segmented and ranked by minimum Standard Uptake Value (SUV_min) (both descending and ascending), SUV_mean, SUV_max, and volume (descending). From each sorting, the top one, three, and five lesions were selected. For the selected lesions, radiomic features were extracted (using the Pyradiomics library) and lesion aggregation was performed using stacked vs. statistical methods. Eight classification models along with three feature selection methods were used to predict progression, and five survival models and three feature selection methods were used to predict TTP under a nested cross-validation framework. Results: The overall appraisal showed that sorting lesions based on SUV_min (descending) yields better classification performance in progression prediction. This is in addition to the fact that aggregating features extracted from all the lesions, as well as the top five lesions sorted by SUV_mean, lead to the highest overall performance in TTP prediction. The individual appraisal in progression prediction models trained on the single top lesion sorted by SUV_min (descending) showed the highest recall and specificity despite data imbalance. The best-performing model was the Logistic Regression (LR) classifier with Recursive Feature Elimination (RFE) (recall: 0.75, specificity: 0.77). In TTP prediction, the highest concordance index was obtained using a Random Survival Forest (RSF) trained on statistically aggregated features from the top five lesions ranked by SUV_mean, selected via Univariate C-Index (UCI) (C-index = 0.68). Across both tasks, features from the Gray Level Size Zone Matrix (GLSZM) family were consistently among the most predictive, highlighting the importance of spatial heterogeneity in treatment response. Conclusions: This study demonstrates that informed lesion selection and tailored aggregation strategies significantly impact the predictive performance of radiomics-based models for progression and TTP prediction in PRRT-treated NET patients. These approaches can potentially enhance model accuracy and better capture tumor heterogeneity, supporting more personalized and practical PRRT implementation. Full article

Short-term trading presents a high-dimensional prediction problem, where the profitability of trading signals depends not only on model accuracy but also on how financial labels are defined and aligned with market dynamics. Traditional approaches often apply uniform modeling choices across assets, overlooking the asset-specific nature of volatility, liquidity, and market response. In this work, we introduce a structured, label-aware machine learning pipeline aimed at maximizing short-term trading profitability across four major benchmarks: S&P 500 (SPX), NASDAQ-100 (NDX), Dow Jones Industrial Average (DJI), and the Tadāwul All-Share Index (TASI and twelve of their most actively traded constituents). Our solution systematically evaluates all combinations of six model types (logistic regression, support vector machines, random forest, XGBoost, 1-D CNN, and LSTM), eight look-ahead labeling windows (3 to 10 days), and four feature subset sizes (44, 26, 17, 8 variables) derived through Random Forest permutation-importance ranking. Backtests are conducted using realistic long/flat simulations with zero commission, optimizing for Percentage Profit and Profit Factor on a 2005–2021 train/2022–2024 test split. The central contribution of the framework is a labeling-aware search mechanism that assigns to each asset its optimal combination of model type, look-ahead horizon, and feature subset based on out-of-sample profitability. Empirical results show that while XGBoost performs best on average, CNN and LSTM achieve standout gains on highly volatile tech stocks. The optimal look-ahead window varies by market from 3-day signals on liquid U.S. shares to 6–10-day signals on the less-liquid TASI universe. This joint model–label–feature optimization avoids one-size-fits-all assumptions and yields transferable configurations that cut grid-search cost when deploying from index level to constituent stocks, improving data efficiency, enhancing robustness, and supporting more adaptive portfolio construction in short-horizon trading strategies. Full article

Background: Hepatocellular carcinoma (HCC), the most prevalent primary malignancy of the liver, is commonly treated with transarterial chemoembolization (TACE), a locoregional therapy that combines targeted intra-arterial chemotherapy with selective embolization to induce tumor ischemia and necrosis. However, current methods for monitoring the treatment response—such as the RECIST and mRECIST—often fail to detect early or subtle biological changes, such as tumor necrosis or microstructural remodeling, and therefore may underestimate the therapeutic effects, especially in cases with minimal or delayed tumor shrinkage. Thus, there is a critical need for quantitative imaging strategies that can improve early response assessment and guide more personalized treatment decision-making. The goal of this study was to assess the changes in computed tomography (CT) perfusion parameters and radiomic features in HCC before and after TACE and to evaluate the associations of these parameters/features with the tumor burden. Methods: In this retrospective, single-center study, 32 patients with histologically confirmed HCC underwent CT perfusion and radiomic analysis prior to and following TACE. Multiple quantitative perfusion parameters (arterial flow, perfusion flow, perfusion index) and radiomic features were extracted. Statistical comparisons were performed using the Wilcoxon signed-rank test and Spearman’s correlation. Radiomic feature extraction was performed in strict adherence to the Image Biomarker Standardization Initiative (IBSI) guidelines. Preprocessing steps included voxel resampling (1 × 1 × 1 mm), z-score normalization, and fixed bin-width discretization (bin width = 25). All tumor ROIs were manually segmented in consensus by two experienced radiologists to minimize inter-observer variability. Results: Arterial flow significantly decreased from a median of 56.5 to 47.7 mL/100 mL/min after TACE (p = 0.009), while nonsignificant increases in the perfusion flow (from 101.3 to 107.8 mL/100 mL/min, p = 0.44) and decreases in the perfusion index (from 38.6% to 35.7%, p = 0.25) were also observed. Perfusion flow was strongly and positively correlated with tumor size (ρ = 0.94, p < 0.001). Five radiomic texture feature values—especially those of ShortRunHighGrayLevelEmphasis (Δ = +2.11, p = 0.0001) and LargeAreaHighGrayLevelEmphasis (Δ = +75,706, p = 0.0006)—changed significantly after treatment. These radiomic feature value changes were more pronounced in tumors ≥50 mm in diameter. In addition, we performed a receiver operating characteristic (ROC) analysis of the two most discriminative radiomic features (SRHGLE and LAHGLE). We further developed a multivariable logistic regression model that achieved an AUC of 0.87, supporting the potential of these features as predictive biomarkers. Conclusions: CT perfusion and radiomics offer complementary insights into the treatment response of patients with HCC. While perfusion parameters reflect macroscopic vascular changes and are correlated with tumor burden, radiomic features can indicate microstructural changes after TACE. This combined imaging approach may improve early therapeutic assessment and support precision oncology strategies. Full article

To reveal the regulatory mechanisms and differences in sensitivity of hydrodynamic forces and sediment parameters to the sedimentary evolution of braided river channel bars, this study takes the Sulige Gas Field as a case study and conducts 21 sets of sedimentary numerical simulation experiments using the controlled variable method. The three parameters of discharge, slope gradient, and sediment grain size were fixed, while the target variable was adjusted iteratively. After the river reaches a steady state, quantitative statistics of the area and length-width ratio of 547 identified channel bars are carried out, and sensitivity evaluation is performed by combining principal component analysis and multiple linear regression. The results show that the sedimentary evolution of braided rivers follows a unified evolutionary law, the evolution of channel bars is synergistically regulated by parameter combinations. Under the action of single factors, an increase in discharge promotes the axial extension and scale expansion of channel bars; an increase in grain size enhances the morphological stability of channel bars; slope gradient controls the erosion-deposition balance through gravitational potential energy. The parameter sensitivity is ranked as slope gradient, discharge, sediment grain size. Full article

Campaigns significantly impact overall company performance, making the measurement and prediction of campaign efficiency essential. This study proposes an integrated methodology that combines efficiency measurement with efficiency prediction for airline email campaigns. In the first part of the methodology, Data Envelopment Analysis (DEA) was applied to real airline campaign data to evaluate efficiency; this is the first study to analyze email campaign efficiency in this context. In the second part of the methodology, decision tree algorithms were employed to classify historical campaign data based on DEA scores, with the aim of predicting the efficiency of future campaigns—a novel approach in this context. A core dataset of 76 airline email campaigns with six inputs and two outputs was analyzed using output-oriented CCR (Charnes, Cooper, Rhodes) and BCC (Banker, Charnes, Cooper) models; 26 and 46 campaigns were identified as efficient, respectively. The analysis was further segmented by group size, seasonality, and route type. Efficient campaigns were then ranked via super-efficiency, and sensitivity analysis assessed variable and campaign effects. For prediction, decision tree algorithms (J48 (C4.5), C5.0, and CART (Classification and Regression Trees)) were employed to classify campaigns as efficient or inefficient, using DEA efficiency scores as the target variable and DEA inputs as attributes, with classification performed for both BCC and CCR core models. Class imbalance was addressed with SMOTE, and models were evaluated under stratified 10-fold cross-validation. After balancing, the BCC core model (BCC_C) yielded the most reliable predictions (overall accuracy 76.3%), with J48 providing the most balanced results, whereas the CCR core model (CCR_C) remained weak across algorithms. Full article

Background: Multiple primary malignant tumors (MPMTs) involving the digestive tract pose diagnostic and therapeutic challenges, with survival differences between synchronous and metachronous forms not well defined. This study assessed predictors of overall survival (OS) in patients in whom at least one tumor originated in the digestive tract or accessory organs. Methods: We retrospectively reviewed 1920 oncology cases (January 2020–June 2023) from St. Nicholas Hospital, Romania. Of 118 patients with MPMTs, 45 had ≥1 digestive tract tumor. They were classified as synchronous (<2 months) or metachronous (>2 months) as per the SEER rules. Clinical, pathological, treatment, and follow-up data were analyzed; OS was evaluated using Kaplan–Meier and Cox regression. Results: Fifteen patients (33%) had synchronous tumors and 30 (67%) had metachronous tumors. Overall, 17 of 45 patients (37.8%) died by the last follow-up. The restricted mean survival time (RMST) was 31.3 months for those with synchronous vs. 68.3 months for those with metachronous tumors (HR = 2.49, 95% CI 0.95–6.50, p = 0.062; log-rank p = 0.053). Curative treatment of the first tumor was associated with markedly improved survival (RMST 58.2 vs. 29.4 months; HR = 20.5, 95% CI 3.68–114, p < 0.001). In the multivariable Cox regression analysis, advanced primary nodal stage (N2–N3) remained independently associated with reduced survival (adjusted HR 3.86, 95% CI 1.04–14.3, p = 0.044). The adjusted effect of synchronous vs. metachronous classification was attenuated (adjusted HR 2.22, 95% CI 0.84–5.86, p = 0.10). Conclusions: In this single-center, hypothesis-generating cohort, synchronous digestive-tract MPMTs were associated with shorter unadjusted survival than metachronous tumors, but advanced nodal stage and limited feasibility of curative therapy were the dominant independent predictors of poor outcome. Given the small sample size and retrospective design, these findings should be interpreted as preliminary and warrant validation in larger, multicenter cohorts. Full article

Background/Objectives: Impaired angiogenesis and vascular dysfunction are central features of chronic diseases such as cardiovascular disorders, neurodegeneration, and metabolic syndrome. Vascular endothelial growth factor (VEGF) plays a pivotal role in vascular repair and metabolic regulation, yet its responses to exercise remain inconsistently reported. This study aimed to systematically compare the effects of different exercise modalities and doses on VEGF levels across diverse populations. Methods: This review was registered in PROSPERO (CRD42025643709) and followed PRISMA guidelines. PubMed, Web of Science, Embase, and Cochrane Library were searched until 16 January 2025. Eligible studies were randomized or quasi-experimental trials reporting exercise-induced changes in serum/plasma VEGF. Data were extracted and assessed independently using JBI tools. Exercise types were categorized and doses standardized as metabolic equivalents (METs). Network meta-analysis was performed in Stata17.0 (SMD as effect size), with SUCRA used for ranking. Dose–response relationships were examined by meta-regression (remr package), and publication bias was assessed via funnel plots. Results: Twenty-eight studies (N = 1138) were included. In healthy adults, lower-limb resistance training produced the greatest VEGF increase, with benefits observed above ~600 METs-min/week and peaking near 1950 METs-min/week. Among obese individuals, combined aerobic and resistance training under hypoxic conditions showed the highest VEGF response, though dose-specific effects were not significant. In patients with chronic conditions, upper-limb resistance training within 756–950 METs-min/week was most effective, displaying a U-shaped dose–response relationship. No substantial publication bias was detected. Conclusions: The VEGF response to exercise appears to be influenced by both population characteristics and training dosage. High-intensity lower-limb resistance training may provide greater benefits for healthy adults, while obese individuals might experience enhanced responses with combined training under hypoxic conditions. For clinical populations, moderate-dose upper-limb resistance training may be particularly beneficial. Large-scale, long-term trials are needed to further clarify and refine exercise prescriptions targeting VEGF-mediated vascular adaptations. Full article

In this study, we provide, to our knowledge, one of the first multidimensional, data-driven evaluations of green education performance in Turkish higher education, combining the THE Education Score, THE Impact Score, and the UI GreenMetric Education & Research Score (GM-ED) with institutional characteristics, and situating the analysis within SDG 4 (Quality Education). While universities worldwide increasingly integrate sustainability into their missions, systematic evidence from middle-income systems remains scarce. To address this gap, we compile a dataset of 50 Turkish universities combining three global indicators—the Times Higher Education (THE) Education Score, THE Impact Score, and the UI GreenMetric Education & Research Score (GM-ED)—with institutional characteristics such as ownership and student enrollment. We employ descriptive statistics; correlation analysis; robust regression models; composite indices under equal, PCA, and entropy-based weighting; and exploratory k-means clustering. Results show that integration of sustainability into curricula and research is the most consistent predictor of SDG-oriented performance, while institutional size and ownership exert limited influence. In addition, we propose composite indices (GECIs). GECIs confirm stable top performers across methods, but mid-ranked universities are volatile, indicating that governance and strategic orientation matter more than structural capacity. The study contributes to international debates by framing green education as both a measurable indicator and a transformative institutional practice. For Türkiye, our findings highlight the need to move beyond symbolic initiatives toward systemic reforms that link accreditation, funding, and governance with green education outcomes. More broadly, we demonstrate how universities in middle-income contexts can institutionalize sustainability and provide a replicable framework for assessing progress toward SDG 4. Full article

Objective: Segmentectomy has recently been accepted as a valid anatomical resection in the early stages non-small cell lung cancer, even if different segment numbers and combinations are included. The aim of this study is to analyze prognostic factors in patients who underwent segmentectomy, with particular attention to segment numbers and characteristics. Methods: Characteristics of patients who underwent uniportal VATS segmentectomy from 1/01/2017 to 31/12/2022 were reviewed and retrospectively analyzed. Patients with nodal involvement and/or distant metastases, tumors > 4 cm, who received neoadjuvant treatment and those who underwent completion lobectomy were excluded. Operatory and pathological reports were reviewed to collect data on surgical characteristics and pathology. Segmentectomies were categorized according to numbers of resected segments as single/multiple. Clinico-pathological characteristics, number of segments and nodal parameters were associated to overall survival (OS) using Kaplan–Meier curves. The log-rank test was used to assess differences between subgroups. A multivariable model was built using Cox-regression analysis including variables with p-values < 0.10 at univariable analysis. Results: The final analysis was conducted on 95 patients who met the inclusion criteria. Multiple segmentectomies were performed in 47 (49.4%) cases, of which 37 (39%) were complex cases. At univariable analysis, tumor size ≤ 2 cm (p = 0.006, HR:0.260; 95%CI 0.099–0.686) significantly correlated with OS: patients with pT ≤ 2 cm presented a 5YOS of 85.3% vs. 48.3% of patients with pT >2 cm, with multivariable-confirmed tumor size ≤ 2 cm as an independent prognostic factor (p = 0.004, HR:0.204; 95%CI 0.069–0.607). Considering the tumor size according to number of resected segments, patients who underwent single segmentectomy presented a significantly better survival for pT ≤ 2 cm: 5YOS 91.7% vs. 41.3% for pT > 2 cm (p = 0.001). Conversely, no significant differences in OS were present in multiple segmentectomy: 5YOS 78.9% vs. 77.1% (p = 0.700). Similarly, pT ≤ 2 cm correlated with OS in complex segmentectomy (p = 0.010) but not in simple segmentectomy (p = 0.098). Conclusions: Our study confirms the distinct prognosis associated with tumor dimensions in patients who underwent uniportal VATS segmentectomy. We confirmed the tumor dimension cut-off of 2 cm as a robust prognosticator in single and complex segmentectomies. However, no significant differences in survival were observed in multiple and simple segmentectomies, implying that tumors larger than 2 cm may necessitate extended resections. Full article