Search Results (2,188)

Background: There exists some inconsistent evidence on the relationship between altered cardiac morphology, its function, and frailty. Therefore, this study aimed to assess the associations among frailty, lean body mass, central arterial stiffness, and cardiac structure and geometry in older people with a normal ejection fraction. Methods: A total of 205 patients >65 years were enrolled into this ancillary analysis of the FRAPICA study and were assessed for frailty with the Fried phenotype scale. Left ventricular dimensions and geometry were assessed with two-dimensional echocardiography. Fat-free mass was measured using three-site skinfold method. Parametric and non-parametric statistics and analysis of covariance were used for statistical calculations. Results: Frail patients were older and women comprised the majority of the frail group. Frail men and women had comparable weight, height, fat-free mass, blood pressure, central blood pressure, and carotid–femoral pulse wave velocity to their non-frail counterparts. There was a linear correlation between the sum of frailty criteria and left ventricular end-diastolic diameter (Spearman R = −0.17; p < 0.05) and relative wall thickness (Spearman R = 0.23; p < 0.05). In the analysis of covariance, frailty and gender were independently associated with left ventricular mass (gender: β of −0.37 and 95% CI of −0.50–−0.24 at p < 0.001), the left ventricular mass index (gender: β of −0.23 and 95% CI of −0.37–−0.09 at p < 0.001), and relative wall thickness (frailty: β of −0.15 and 95% CI of −0.29–−0.01 at p < 0.05; gender: β of 0.23 and 95% CI of 0.09–0.36 at p < 0.01). Frailty was associated with a shift in heart remodeling toward concentric remodeling/hypertrophy. Conclusions: Frailty is independently associated with thickening of the left ventricular walls and a diminished left ventricular end-diastolic diameter, which are features of concentric remodeling or hypertrophy. This association appears to be more pronounced in women. Such adverse cardiac remodeling may represent another phenotypic feature linked to frailty according to the phenotype frailty criteria. Full article

The purpose of this study is to determine the optimal location for siting an onshore wind farm on the island of Skyros, thereby maximizing performance and minimizing the project’s environmental impacts. Seven evaluation criteria are defined across various sectors, including environmental and economic sectors, and six criteria weighting methods are applied in combination with four multicriteria decision-making (MCDM) ranking methods for suitable areas, resulting in twenty-four ranking models. The alternatives considered in the analysis were defined through the application of constraints imposed by the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD RES), complemented by exclusion criteria documented in the international literature, as well as a minimum area requirement ensuring the feasibility of installing at least four wind turbines within the study area. The correlations between their results are then assessed using the Spearman coefficient. Geographic information systems (GISs) are utilized as a mapping tool. Through the application of the methodology, it emerges that area A9, located in the central to northern part of Skyros, is consistently assessed as the most suitable site for the installation of a wind farm based on nine models combining criteria weighting and MCDM methods, which should be prioritized as an option for early-stage wind farm siting planning. The results demonstrate an absolute correlation among the subjective weighting methods, whereas the objective methods do not appear to be significantly correlated with each other or with the subjective methods. The ranking methods with the highest correlation are PROMETHEE II and ELECTRE III, while those with the lowest are TOPSIS and VIKOR. Additionally, the hierarchy shows consistency across results using weights from AHP, BWM, ROC, and SIMOS. After applying multiple methods to investigate correlations and mitigate their disadvantages, it is concluded that when experts in the field are involved, it is preferable to incorporate subjective multicriteria analysis methods into decision-making problems. Finally, it is recommended to use more than one MCDM method in order to reach sound decisions. Full article

Background/Objectives: Early and accurate characterization of endometrial cancer (EC) is crucial for patient management, but current imaging modalities lack in diagnostic accuracy and ability to assess molecular profiles. The aim of this study is to evaluate hybrid [¹⁸F]FDG PET/MRI’s diagnostic accuracy in EC staging and role in predicting tumor aggressiveness, molecular characterization, and recurrence. Methods: A prospective study (ClinicalTrials.gov, ID:NCT04212910) evaluating EC patients undergoing [¹⁸F]FDG PET/MRI before surgery (2018–2024). Histology, immunohistochemistry, and patients’ follow-up (mean FU time: 3.13y) were used as the reference standard. [¹⁸F]FDG PET/MRI, PET only, and MRI only were independently reviewed to assess the diagnostic accuracy (ACC), sensitivity (SN), specificity (SP), and positive/negative predictive value (PPV, NPV). Imaging parameters were extracted from [¹⁸F]FDG PET and pcT1w, T2w, DWI, and DCE MRI. Spearman’s correlations, Fisher’s exact test, ROC-AUC analysis, Kaplan–Meier survival curves, log-rank tests and Cox proportional hazards models were applied. Results: Eighty participants with primary EC (median age 63 ± 12 years) were enrolled, with 17% showing LN involvement. [¹⁸F]FDG PET/MRI provided ACC = 98.75%, SN = 98.75%, and PPV = 100% for primary tumor detection, and ACC = 92.41%, SN = 84.62%, SP = 93.94%, PPV = 73.33%, and NPV = 96.88% for LN detection. PET/MRI parameters predicted LN involvement (AUC = 79.49%), deep myometrial invasion (79.78%), lymphovascular space invasion (82.00%), p53abn (71.47%), MMRd (74.51%), relapse (82.00%), and postoperative administration of adjuvant therapy (79.64%). Patients with a tumor cranio-caudal diameter ≥ 43 mm and MTV ≥ 13.5 cm³ showed increased probabilities of recurrence (p < 0.001). Conclusions: [¹⁸F]FDG PET/MR showed exceptional accuracy in EC primary tumor and LN detection. Derived parameters demonstrated potential ability in defining features of aggressiveness, molecular alterations, and tumor recurrence. Full article

Purpose: This study examined the relationships among fear of cancer recurrence (FCR), unmet healthcare needs, and quality of life (QOL) among breast cancer survivors post-treatment, particularly within the Thai cultural and healthcare context, where limited research has been conducted. Methods: A cross-sectional descriptive correlational design with purposive sampling was used. A total of 122 breast cancer survivors, 1–5 years prior, were recruited from the Breast Clinic and Chemotherapy Unit at the National Cancer Institute. Instruments included a demographic questionnaire, the FCR Inventory Short Form, the Cancer Survivors’ Unmet Needs measure, and the EORTC QOL-C30 with the breast cancer module (QLQ-BR23). Cronbach’s α ranged from 0.82 to 0.92. Data were analyzed using descriptive statistics, Spearman’s rank correlation, and Pearson’s correlation coefficient. Results: Participants reported moderate levels of FCR (M = 13.39, SD = 4.50), low unmet healthcare needs (M = 25.63, SD = 14.82), and moderate overall QOL (M = 54.82, SD = 0.22). FCR was negatively correlated with overall QOL (r = −0.248, p <0.01) and functional QOL (r = −0.242, p < 0.01). Unmet healthcare needs were also negatively correlated with overall QOL (r = −0.261, p < 0.01). Multiple linear regression analysis revealed that both FCR and unmet healthcare needs had a significantly negative relationship with overall QOL (p < 0.05). Conclusions: FCR and unmet healthcare needs independently impair QOL among breast cancer survivors. Early, culturally appropriate survivorship care in Asian contexts is essential to address these needs and improve QOL. Full article

Background/Objectives: The tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is characterized by an enriched stroma, hampering the effectiveness of therapy. This co-clinical study aimed to (1) provide insight into early post-treatment changes in the TME using multiparametric magnetic resonance imaging (mpMRI)-derived quantitative imaging biomarkers (QIBs) in a preclinical PDAC model treated with radiotherapy and correlate these QIBs with histology; (2) evaluate the feasibility of obtaining these QIBs in patients with PDAC using clinically approved mpMRI data acquisitions. Methods: Athymic mice (n = 12) at pre- and post-treatment as well as patients with PDAC (n = 11) at pre-treatment underwent mpMRI including diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) data acquisition sequences. DW and DCE data were analyzed using monoexponential and extended Tofts models, respectively. DeepLIIF quantified the total percentage (%) of tumor cells in hematoxylin and eosin (H&E)-stained tissues from athymic mice. Spearman correlation and Wilcoxon signed rank tests were performed for statistical analysis. Results: In the preclinical PDAC model, mean pre- and post-treatment ADC and K^trans values differed significantly (p < 0.01), changing by 20.50% and 20.41%, respectively, and the median total tumor cells quantified by DeepLIIF was 24% (range: 15–53%). Post-treatment ADC values and relative change in v_e (rΔv_e) showed a significant negative correlation with total tumor cells (ρ = −0.77, p < 0.014 for ADC and ρ = −0.77, p = 0.009 for rΔv_e). In patients with PDAC, pre-treatment mean ADC and K^trans values were 1.76 × 10⁻³ (mm²/s) and 0.24 (min⁻¹), respectively. Conclusions: QIBs in both preclinical and clinical settings underscore their potential for future co-clinical research to evaluate emerging drug combinations targeting both tumor and stroma. Full article

Background: Post-COVID-19 syndrome (PCS) is defined as the persistence or development of new symptoms 3 months after the initial infection with the SARS-CoV-2 virus, these clinical aspects being most often associated with functional respiratory changes, as well as imagistic modifications. This study aimed to evaluate longitudinal changes in pulmonary function among patients with PCS, in relation to the severity of the acute COVID-19 episode and the time elapsed since infection. Methods: A retrospective, observational study was conducted at the Clinical Hospital of Pulmonary Diseases Iași, Romania, between January 2021 and December 2022, including 97 adult patients with confirmed PCS. Demographic, clinical, and functional data were collected from medical records. Pulmonary function tests (PFTs) were performed according to ATS/ERS standards, assessing Forced Vital Capacity (FVC), Forced Expiratory Volume in the First Second (FEV₁), FEV₁/FVC ratio (Tiffeneau Index), Maximal Expiratory Flow at 50% and 25% of FVC (MEF50, MEF25), Diffusing Capacity of the Lung for Carbon Monoxide (adjusted for haemoglobin) (DLCO), Carbon Monoxide Transfer Coefficient (KCO), Alveolar Volume (AV), Total Lung Capacity (TLC) and Residual Volume (RV). Patients were grouped by time elapsed since infection (1–3, 4–7, 9–12, and up to 22 months). Statistical analyses included the Mann–Whitney U test, Spearman’s correlation, ROC curve analysis, and Principal Component Analysis (PCA). Results: A progressive improvement in FVC was observed up to 9–18 months post-infection (p < 0.05), while FEV₁ remained stable, suggesting a predominantly restrictive ventilatory pattern. Patients with moderate acute COVID-19 presented significantly lower FVC%, FEV₁%, DLCO%, and KCO% values compared with those with mild disease (p < 0.05). Diffusion abnormalities (DLCO and KCO) persisted beyond 12 months, indicating lasting alveolar-capillary impairment. ROC analysis identified TLC (AUC = 0.857), AV (AUC = 0.855), and KCO (AUC = 0.805) as the most discriminative parameters for residual dysfunction. PCA revealed three major functional domains—airflow limitation, diffusion capacity, and lung volume—explaining up to 70% of total variance. Conclusions: We are facing the emergence of a new phenomenon, namely a secondary post-COVID-19 pandemic of patients confronting with persistent post-COVID-19 symptoms who present with functional respiratory changes and who require careful monitoring in dynamics, personalized treatments and a multidisciplinary approach. Full article

High-precision short-term photovoltaic (PV) power prediction has become a critical technology in ensuring grid accommodation capacity, optimizing dispatching decisions, and enhancing plant economic benefits. This paper proposes a long short-term memory (LSTM)-based short-term PV power prediction method with the genetic algorithm (GA)-optimized adaptive fusion of space-based cloud imagery and ground-based meteorological data. The effective integration of satellite cloud imagery is conducted in the PV power prediction system, and the proposed method addresses the issues of low accuracy, poor robustness, and inadequate adaptation to complex weather associated with using a single type of meteorological data for PV power prediction. The multi-source heterogeneous data are preprocessed through outlier detection and missing value imputation. Spearman correlation analysis is employed to identify meteorological attributes highly correlated with PV power output. A dedicated dataset compatible with LSTM algorithm-based prediction models is constructed. An LSTM prediction model with a GA algorithm-based adaptive multi-source heterogeneous data fusion method is proposed, and the ability to construct a precise short-term PV power prediction model is demonstrated. Experimental results demonstrate that the proposed method outperforms single-source LSTM, single-source CNN-LSTM, and dual-source CNN-Transformer models in prediction accuracy, achieving an RMSE of 0.807 kWh and an MAPE of 6.74% on a critical test day. The proposed method enables real-time precision forecasting for grid dispatch centers and lightweight edge deployment at PV plants, enhancing renewable energy integration while effectively mitigating grid instability from power fluctuations. Full article

Background: Accurate assessment of prolonged disorders of consciousness (pDOC) is a critical clinical challenge. Misdiagnosis in pDOC can occur in up to 40% of cases, highlighting the need for more objective and reproducible biomarkers to support neurophysiological scales, thereby improving diagnosis and guiding therapeutic and prognostic decisions. Electroencephalography (EEG) microstate analysis is a promising, non-invasive method for tracking large-scale brain dynamics, but research in pDOC has predominantly relied on a canonical 4-class model. This methodological constraint may limit the ability to capture the full complexity of neural alterations present in these patients. Objective: This pilot study aimed to offer an objective method for assessing consciousness, complementing and enhancing the existing approaches established in the literature. The classical 4-class and an extended 7-class microstate model were compared to determine which more accurately characterizes the complexity of resting-state brain dynamics across different levels of consciousness in pDOC patients and healthy controls (HCs). Methods: Retrospective resting-state EEG (rsEEG) data from a cohort of pDOC patients and HC subjects were analyzed. Microstate analysis was performed using both 4-class and 7-class templates. The models were evaluated and compared based on three criteria: spatial correspondence with canonical maps (shared variance), the number of significant intra-group correlations between temporal features (Spearman test), and their ability to discriminate between the pDOC and HC groups (Wilcoxon test). Results: The 7-class microstate model provided a more accurate description of brain activity for most participants, with a greater number of microstate classes exceeding the 50% shared variance threshold compared to the 4-class model. In the pDOC group, both the 4-class and 7-class models showed a mean shared variance <50% in class D, which is associated with executive functioning across both templates. For the HC group, a prevalence of classes B and D emerged in both models, indicating higher engagement of executive functions. Furthermore, the 7-class model allowed for a group-specific analysis, which demonstrated that microstates A and F were consistently shared among 86% of pDOC patients. This suggests the potential preservation of specific intrinsic brain networks, particularly the sensory and default networks, even in the presence of severely impaired consciousness. Moreover, the 7-class model yielded a higher number of significant correlations within both groups and identified a broader set of temporal features that were significantly different between pDOC patients and HCs. These results highlight the enhanced sensitivity of the 7-class model in distinguishing subtle brain dynamics and improving the diagnostic capability for pDOC. Conclusions: The 7-class microstate model provides a more fine-grained and sensitive characterization of brain activity in both pDOC patients and healthy individuals. It demonstrated better performance in capturing individual brain dynamics, identifying shared network patterns, and discriminating between clinical populations. These findings suggest that the extended 7-class model holds greater potential for clinical utility and could lead to the development of more robust biomarkers for assessing consciousness. Full article

Background/Objectives: Parkinson’s disease is the second most common neurodegenerative disorder, affecting patients’ daily lives in multiple domains, including functional status, health-related quality of life, and psychological well-being. This study examined the relationship between self-reported global activity level, functional status, Health Related QoL (HRQoL), and psychological state among patients with Parkinson’s disease in Greece. Methods: Thirty volunteers (mean age = 69.07, SD = 11.24), members of the Greek Parkinson’s Patients and Caregivers Association, completed (a) the Parkinson’s Disease Questionnaire to evaluate HRQoL and (b) the Hospital Anxiety and Depression Scale (HADS) to assess psychological state. Participants then performed (a) the Five Times Sit to Stand Test (FTSST) and (b) the Berg Balance Scale (BBS) to evaluate functional status. All questionnaires and the test used in the present study have been validated in Greek. Correlation analysis with Spearman r tests with Bonferroni correction was performed between the above variables. Subsequent linear regression models were used to identify independent predictors of HRQoL and balance using SPSS 29.0.2.0. Results: Participants reported elevated anxiety (M = 9.67, SD = 4.44) and depressive symptoms (M = 8.97, SD = 4.08), alongside relatively high HRQoL scores (M = 40.09, SD = 18.40). Impaired functional performance was observed, with 22 participants failing to complete the FTSST within 16 s and 16 scoring below 40 on the BBS. Functional status was strongly correlated with HRQoL (r = −0.696, p < 0.001) and activity level (r = −0.521, p < 0.008). Depression was also significantly associated with poorer HRQoL (r = 0.618, p < 0.008) and lower activity levels (r = −0.545, p < 0.008). Regression analyses revealed that balance (β = −0.526), disease duration (β = 0.437), anxiety (β = 0.411), and lower limb function (β = −0.351) were significant independent predictors of HRQoL (R² = 0.785; F(9, 20) = 12.69; p < 0.001), while HRQoL (β = −0.738) and lower limb function (β = −0.391) independently predicted balance (R² = 0.699; F(9, 20) = 4.72; p = 0.002), suggesting a bidirectional relationship between physical function and subjective well-being. Conclusions: Activity level, HRQoL, functional status, and psychological state in patients with Parkinson’s disease are interrelated factors. Increased levels of anxiety and depression, as well as reduced HRQoL, were observed. The findings point to a potentially reinforcing cycle between poor balance and diminished quality of life, with anxiety and age playing key roles. Overall, the results illustrate that functional, psychological, and HRQoL measures interact in complex ways, emphasizing the multidimensional profile of patients with Parkinson’s disease. Further studies with larger samples are required to confirm these findings. Full article

Background: Systemic inflammation plays a central role in the pathogenesis and progression of type 2 diabetes mellitus (T2DM). Hematologic inflammatory indices-such as the Systemic Immune-Inflammation Index (SII), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Monocyte-to-Lymphocyte Ratio (MLR)-have emerged as accessible markers of chronic inflammation, yet longitudinal comparisons across oral antidiabetic therapies remain limited. This study uniquely integrates longitudinal correlation and network analyses in a large real-world T2DM cohort, allowing assessment of the temporal stability and class-specific inflammatory patterns across four oral antidiabetic therapies. Methods: This retrospective, longitudinal study analyzed 13,425 patients with T2DM treated with Biguanidines, Dipeptidyl Peptidase-4 (DPP-4) inhibitors, Sodium–Glucose Cotransporter-2 (SGLT-2) inhibitors or Thiazolidinediones (TZDs) between 2020 and 2024. Data were retrieved from the Probel^® Hospital Information System and included baseline, early (30–180 days), and late (180–360 days) follow-up laboratory results. Systemic inflammatory indices were computed from hematologic parameters, and correlations among inflammatory and biochemical markers were assessed using Spearman’s coefficients. Results: At baseline, all hematologic indices were strongly intercorrelated (SII–NLR r = 0.83, p < 0.001; SII–PLR r = 0.73, p < 0.001), with moderate associations to C-reactive protein (CRP; r ≈ 0.3–0.4) and weak or no correlations with Ferritin (r ≈ −0.1). These relationships remained stable throughout follow-up, confirming reproducibility of systemic inflammatory coupling. Longitudinally, SII and NLR showed modest early increases followed by significant declines at one year (p < 0.05), while PLR and MLR remained stable. Class-specific differences were observed: SGLT-2 inhibitors and TZDs demonstrated stronger and more integrated anti-inflammatory networks, whereas Biguanidines and DPP-4 inhibitors exhibited moderate coherence. Principal Component Analysis (PCA) explained 62.4% of total variance and revealed distinct clustering for TZD and SGLT-2 groups, reflecting class-specific inflammatory modulation. Conclusions: Systemic inflammatory indices (SII, NLR, PLR) provide reproducible and accessible measures of low-grade inflammation in T2DM. Despite overall inflammation reduction with treatment, drug-specific patterns emerged-SGLT-2 inhibitors and TZDs showed greater anti-inflammatory coherence, while Biguanidines and DPP-4 inhibitors maintained moderate effects. Full article

The growing reliance on solar energy necessitates robust and interpretable forecasting models for stable grid management. Current research frequently employs Explainable AI (XAI) to glean insights from complex black-box models, yet the reliability and consistency of these explanations remain largely unvalidated. Inconsistent feature attributions can mislead grid operators by incorrectly identifying the dominant drivers of solar generation, thereby affecting operational planning, reserve allocation, and trust in AI-assisted decision-making. This study addresses this critical gap by conducting a systematic statistical evaluation of feature rankings generated by multiple XAI methods, including model-agnostic (SHAP, PDP, PFI, ALE) and model-specific (Split- and Gain-based) techniques, within a time-series regression context. Using a LightGBM model for one-day-ahead solar power forecasting across four sites in Calgary, Canada, we evaluate consensus and divergence using the Friedman test, Kendall’s W, and Spearman’s rank correlation. To ensure the generalizability of our findings, we further validate the results using a CatBoost model. Our results show a strong overall agreement across methods (Kendall’s W: 0.90–0.94), with no statistically significant difference in ranking (p > 0.05). However, pairwise analysis reveals that the “Split” method frequently diverges from other techniques, exhibiting lower correlation scores. These findings suggest that while XAI consensus is high, relying on a single method—particularly the split count—poses risks. We recommend employing multi-method XAI and using agreement as an explicit diagnostic to ensure transparent and reliable solar energy predictions. Full article

Advancing the Sustainable Development Goals (SDGs)—framed around social, environmental, and governance dimensions—offers societies across the world the possibility of achieving long-term prosperity and ensuring that future generations enjoy a high quality of life. Governments pursue the 17 SDGs in accordance with their own socioeconomic and cultural contexts, institutional capacities, and available resources. Because countries differ substantially in structure and capability, their progress toward these goals varies, making the systematic measurement and analysis of SDG performance essential for appropriate timing and efficient resource allocation. This study proposes a hybrid assessment system to evaluate the sustainable development performance of upper-middle-income developing countries under both fuzzy and non-fuzzy environments. This integrated evaluation system consists of four main stages. In the first stage, evaluation criteria and alternative countries are specified, relevant data are obtained, and an initial decision matrix is developed. In the second stage, an efficiency analysis is conducted to identify countries that are efficient and those that are not. In the third stage, evaluation criteria are weighted using AHP and Fuzzy AHP methods. In the final stage, the TOPSIS and Fuzzy TOPSIS methods are used to rank efficient countries depending on sustainable development performance criteria. As a result, six countries were identified as inefficient countries based on sustainable development: China, Kazakhstan, Mongolia, Paraguay, Namibia and Turkmenistan. The AHP and Fuzzy AHP methods produced similar criterion weight values compared to each other. The criteria were prioritized from most important to least one as follows: Life expectancy, expected years of schooling, mean years of schooling, gross national income per capita, CO₂ emissions per capita, and material footprint per capita. While some countries achieved similar rankings using the TOPSIS and Fuzzy TOPSIS methods, most countries achieved different rankings because of the multidimensional nature of sustainable development. When the rankings obtained from the fuzzy and non-fuzzy approaches were compared, a noticeable level of overlap was observed, with a Spearman’s rank correlation coefficient of 68.73%. However, the fuzzy TOPSIS method is considered more reliable for assessing sustainable development performance due to its ability to handle data uncertainty, imprecision, and the multidimensional nature of SDG indicators. The results of this study demonstrate that analyses related to sustainable development, which may not contain precise and clear values and have a complex structure encompassing many areas such as social, environmental, and governance, should preferably be conducted within a fuzzy logic framework to ensure more robust and credible evaluations. Full article

Background/Objectives: Obstructive sleep apnea (OSA) is characterized by recurrent episodes of complete or partial upper airway collapse during sleep, leading to apnea or hypopnea and recurrent oxygen desaturation. Intermittent hypoxia (IH) and sleep fragmentation have been proposed as key mechanisms contributing to the adverse cardiovascular consequences observed in OSA. The present study aimed to identify clinical variables independently associated with IH in patients with OSA and to examine their relationships with common comorbid conditions. Methods: This retrospective study included 569 adult patients diagnosed with obstructive sleep apnea (OSA) by overnight polysomnography (apnea–hypopnea index [AHI] ≥ 5 events/hour) between February 2020 and January 2025 at the Sleep Laboratory of Trakya University Hospital. Demographic characteristics, body mass index (BMI), AHI values, comorbid medical conditions, average nocturnal oxygen saturation, and the duration of intermittent hypoxia (time below 90% SpO₂ [T90]) were retrieved from the laboratory database. Normality of distribution was assessed using the Kolmogorov–Smirnov test. Group differences were evaluated using the Mann–Whitney U test and the Kruskal–Wallis test with Dunn–Bonferroni post hoc analysis. Correlations were examined using Spearman’s correlation analysis, and variables independently associated with average nocturnal oxygen saturation and intermittent T90 were assessed using multivariable linear regression analysis. Results: The presence of hypertension, diabetes mellitus, and comorbid conditions was associated with significant differences in T90 among patients with OSA. T90 also differed significantly across AHI severity grades. Significant negative correlations were observed between nocturnal oxygen saturation and BMI, hypertension, diabetes, comorbidities, and age. Nocturnal oxygen saturation values likewise differed significantly across BMI-defined obesity groups. In the multivariable regression analysis, BMI, AHI, and age were independently associated with lower nocturnal oxygen saturation and longer T90. Conclusions: This study provides important insight into the complex relationships among OSA severity, patient demographics, comorbidities, and intermittent hypoxia. In multivariable analysis, BMI, AHI, and age showed independent associations with reduced nocturnal oxygen saturation and prolonged T90. These findings highlight the importance of a multidimensional clinical assessment in OSA and support the use of intermittent hypoxia metrics as additional indicators of disease burden and potential clinical impact. Full article

Background/Objectives: Heart failure is a major global health problem. Among the available treatment options, cardiac resynchronization therapy (CRT) has been shown to improve both quality of life (QoL) and mortality; however, not all patients respond adequately. Our study aimed to identify echocardiographic parameters that predict a positive response to CRT. Methods: A total of 33 patients (10 women and 23 men) were prospectively recruited, all met the standard criteria for CRT implantation. Biochemical, clinical, QoL, 6 min walk test, and echocardiographic evaluations were performed prior to CRT implantation and reassessed after 6 months. A ≥15% reduction in left ventricular end-systolic volume was taken as the defining parameter of positive response. Based on response level, patients were divided into two groups: responders and non-responders. Results: Comparing the overall population before and after CRT, a positive impact was observed on biochemical, electrocardiographic, and echocardiographic parameters. Fourteen patients (42%) were classified as responders and nineteen (58%) as non-responders. Only two basal echocardiographic parameters showed significant baseline differences between groups: Global Longitudinal Strain (GLS) and the Kapetanakis index. ROC curve analysis showed that baseline GLS and Kapetanakis index had excellent discriminative ability for predicting CRT response. Also, binary logistic regression analysis identified the association of GLS and Kapetanakis index with CRT response. Finally, Rho Spearman analysis showed a positive correlation between the degree of response to CRT and the QoL, (ρ) of 0.663 with p = 0.001. Conclusions: Our findings confirm the overall clinical, biochemical, echocardiographic, and QoL benefits of CRT. In addition, two echocardiographic parameters proved to be potential response predictors. Full article

The canopy temperature of winter jujube serves as a direct indicator of plant water status and transpiration efficiency, making its accurate prediction a critical prerequisite for effective water management and optimized growth conditions in greenhouse environments. This study developed a data-driven model to forecast canopy temperature. The model serially integrates a Long Short-Term Memory (LSTM) network and a Random Forest (RF) algorithm, leveraging their complementary strengths in capturing temporal dependencies and robust nonlinear fitting. A three-stage framework comprising temporal feature extraction, multi-source feature fusion, and direct prediction was implemented to enable reliable nowcasting. Data acquisition and preprocessing were tailored to the greenhouse environment, involving multi-sensor data and thermal imagery processed with Robust Principal Component Analysis (RPCA) for dimensionality reduction. Key environmental variables were selected through Spearman correlation analysis. Experimental results demonstrated that the proposed LSTM–RF model achieved superior performance, with a determination coefficient (R²) of 0.974, mean absolute error (MAE) of 0.844 °C, and root mean square error (RMSE) of 1.155 °C, outperforming benchmark models including standalone LSTM, RF, Transformer, and TimesNet. SHAP (SHapley Additive exPlanations)-based interpretability analysis further quantified the influence of key factors, including the “thermodynamic state of air” driver group and latent temporal features, offering actionable insights for irrigation management. The model establishes a reliable, interpretable foundation for real-time water stress monitoring and precision irrigation control in protected winter jujube production systems. Full article