Search Results (380)

Background/Objectives: Metabolic syndrome (MetS) increases the risk of type 2 diabetes and cardiovascular disease. We aimed to identify the key metabolic predictors of MetS in a Central European cohort and to compare classical statistics with modern machine learning (ML) models. Methods: We analysed 956 adults from the Lower Silesia Healthy Donors cohort. Clinical, anthropometric, biochemical, and lifestyle variables were collected using standardised procedures. Group differences were tested with Mann–Whitney U tests and effect sizes. A multivariable logistic regression (outcome: binary MetS defined as ≥3 harmonised components, MetS_bin) estimated adjusted odds ratios. In parallel, ML models (logistic regression, Random Forest, XGBoost, LightGBM, CatBoost) were trained with stratified 5-fold cross-validation. Performance was evaluated by accuracy, F1-macro, and area under the receiver-operating characteristic curve (ROC AUC). Model interpretability used SHAP values. Results: Overweight/obese participants had higher fasting glucose (median 92.0 vs. 84.6 mg/dL), fasting insulin (9.9 vs. 6.6 µU/mL), and systolic blood pressure (134 vs. 121 mmHg) and lower HDL cholesterol (53 vs. 66 mg/dL) compared to normal-BMI individuals (all p < 0.001, r ≈ 0.39–0.41). Participants with a higher waist circumference also showed markedly increased HOMA-IR (2.16 vs. 1.34; p < 0.001). In multivariable logistic regression, waist circumference, BMI, triglycerides, HDL cholesterol, fasting glucose, and systolic blood pressure were independently associated with MetS, yielding a test ROC-AUC of 0.98 and PR-AUC of 0.88. Machine learning models further improved discrimination: Random Forest, XGBoost, LightGBM, and CatBoost all achieved very high performance (test ROC-AUC ≥ 0.99, PR-AUC ≥ 0.98), with CatBoost showing the best cross-validated PR-AUC (~0.99) and favourable calibration. SHAP analyses consistently highlighted fasting glucose, triglycerides, HDL cholesterol, waist circumference, and systolic blood pressure as the most influential predictors. Conclusions: Combining classical regression with modern gradient-boosting models substantially improves the identification of individuals at risk of MetS. CatBoost, XGBoost, and LightGBM delivered near-perfect discrimination in this Central European cohort while remaining explainable with SHAP. This framework supports clinically meaningful risk stratification—including a “subclinical” probability zone—and may inform targeted prevention strategies rather than purely reactive treatment. Full article

Over recent decades, planners in the U.S. have increasingly adopted mixed-use projects to reduce automobile dependency and strengthen local community identity, although results remain inconsistent across cities. Urban health and fitness outcomes are shaped by complex interactions between the built environment, socioeconomic factors, and demographic characteristics. This study introduces a Health and Fitness Index (HFI) for 28,758 U.S. ZIP codes, derived from normalized measures of walkability, healthcare facility density, and carbon emissions, to assess spatial disparities in health-supportive environments. Using four modeling approaches—lasso regression, multiple linear regression, decision trees, and k-nearest neighbor classifiers—we evaluated the predictive importance of 15 urban and socioeconomic variables. Multiple linear regression produced the strongest generalization performance (R² = 0.60, RMSE = 0.04). Key positive predictors included occupied housing units, business density, land-use mix, household income, and racial diversity, while income inequality and population density were negatively associated with health outcomes. This study evaluates five statistical formulations (Metropolis Hybrid Models) that incorporate different combinations of walkability, land-use mix, environmental variables, and socioeconomic indicators to test whether relationships between urban form and socioeconomic conditions remain consistent under different variable combinations. In cross-sectional multivariate regression, although mixed-use development in high-density areas is strongly associated with healthcare facilities, these areas tend to serve younger and more racially diverse populations. Decision tree feature importance rankings and clustering profiles highlight structural inequalities across regions, suggesting that enhancing business diversity, land-use integration, and income equity could significantly improve health-supportive urban design. This research provides a data-driven framework for urban planners to identify underserved neighborhoods and develop targeted interventions that promote walkability, accessibility to health infrastructure, and sustainability. It contributes to the growing literature on urban health analytics, integrating machine learning, spatial clustering, and multidimensional urban indicators to advance equitable and resilient city planning. Full article

Background/Objectives: Cognitive impairment is frequent in multiple sclerosis, yet routine screening is inconsistently implemented. We aimed to characterize cognitive impairment using CogEval in a Mexican cohort and to identify clinical and functional correlates. Methods: We conducted a cross-sectional study at UMAE No. 71 (Torreón, Mexico). Adults with MS (n = 81) underwent CogEval screening (classified as normal, mild, or severe). Disability, upper-limb dexterity (9-Hole Peg Test, mean of both hands), and gait speed (Timed 25-Foot Walk) were assessed. Bivariate tests and multivariable logistic regression examined associations with cognitive impairment. Results: Participants were 61.7% women; mean age was 35.7 ± 9.9 years. Median EDSS was 2.0 (IQR 1.0–4.0); 28.4% had EDSS ≥ 4. CogEval identified impairment in 49.4% (40/81), with 62.5% severe and 37.5% mild. In bivariate analyses, impairment was associated with higher EDSS (p < 0.001), slower 9-HPT (p < 0.001), and slower T25FW (p = 0.0058), but not with age, sex, or disease duration. In adjusted models, EDSS (OR 1.86, 95% CI 1.14–3.03; p = 0.012) and 9-HPT per second (OR 1.31, 95% CI 1.09–1.58; p = 0.005) independently predicted impairment, whereas T25FW and age were not significant. Discrimination was good (AUC = 0.863). Conclusions: About half of this Mexican MS cohort screened positive for cognitive impairment, particularly those with greater disability and reduced manual dexterity. CogEval appears feasible for routine screening and may help prioritize comprehensive neuropsychological assessment and rehabilitation. Full article

Background: Theta-band cordance is a quantitative EEG (qEEG) metric that integrates absolute and relative spectral power and correlates with regional cerebral perfusion. Although widely applied in psychiatric and neurophysiological research, its longitudinal trajectory in healthy adults remains largely unknown. This study aimed to characterize multi-year changes in theta cordance across cortical regions, determine which areas show stability versus decline, and evaluate whether individuals maintain a trait-like cordance profile over time. Methods: Nineteen cognitively healthy, medication-free adults underwent resting-state EEG recordings at two time points, separated by an average of 6.4 years (range: 1.9–14.8). Theta cordance (4–8 Hz) was computed at 19 scalp electrodes using the Leuchter algorithm and aggregated into eight lobar regions (left/right frontal, temporal, parietal, occipital). Paired-samples t-tests assessed longitudinal changes. Inter-regional Pearson correlations examined evolving connectivity patterns. Canonical correlation analysis (CCA), validated via LOOCV and bootstrap confidence intervals, evaluated multivariate stability between baseline and follow-up cordance profiles. Results: Theta cordance remained normally distributed at both time points. Significant longitudinal decreases emerged in the right temporal (t(18) = 5.34, p < 0.001, d = 1.23) and right frontal (t(18) = 2.65, p = 0.016, d = 0.61) regions, while other lobes showed no significant change. Midline Cz demonstrated a robust increase over time (p < 0.001). CCA revealed a strong cross-time association (Rc = 0.999, p = 0.029), indicating preservation of a stable, frontally anchored cordance profile despite regional right-hemisphere decline. Inter-regional correlation matrices showed both preserved posterior synchrony and emerging inverse anterior–posterior and cross-hemispheric relationships, suggesting age-related reorganization of cortical connectivity. Conclusions: Theta cordance exhibits a mixed pattern of trait-like stability and region-specific aging effects. A dominant, stable fronto-central profile persists across years, yet the right frontal and right temporal cortices show significant decline, consistent with lateralized vulnerability in normative aging. Evolving inter-regional correlation patterns further indicate network-level reorganization. Longitudinal cordance assessment may provide a noninvasive marker of functional brain aging and help differentiate normal aging trajectories from early pathological change. This longitudinal quantitative EEG (qEEG) study examined theta-band cordance dynamics across cortical regions in healthy adults over an average follow-up of 6.4 years (range: 1.9–14.8). Resting-state EEGs were recorded at two time points from 19 participants and analyzed using Leuchter’s cordance algorithm across 19 scalp electrodes. Regional cordance values were computed for frontal, temporal, parietal, and occipital lobes. Paired-samples t-tests revealed significant longitudinal decreases in theta cordance in the right frontal (p = 0.016, d = 0.61) and right temporal lobes (p < 0.001, d = 1.23), while other regions remained stable. Inter-regional Pearson correlations showed strong bilateral synchrony in posterior regions and emergent inverse anterior–posterior relationships over time. Canonical correlation analysis revealed a robust multivariate association (Rc = 0.999, p = 0.029) between baseline and follow-up patterns. Partial correlations (controlling for follow-up interval) identified region-specific trait stability, highest in left occipital and right frontal cortices. These findings suggest that theta cordance reflects both longitudinally stable neural traits and regionally specific aging effects in cortical physiology. 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

Modern automotive design has increasingly embraced plastics for bumper construction; however, it can lead to material degradation. To overcome these limitations, the automotive industry is turning to fiber–resin material, namely carbon–epoxy composites. Our research focuses on determining the effects of fiber orientation and angle alignment on the structural stress of the car bumper, examining the hybrid material (carbon–epoxy reinforced by CFRP) in static structural tests, and performing dynamic impact tests at various speeds, applying the Tsai–Wu criterion as a basic failure model. However, Tsai–Wu’s failure in numerical analysis highlights the limitation of not being able to experimentally distinguish between failure modes and their interaction coefficients. To address this issue, we employ ANSYS^® 2024 R1 with a Fortran program, which enables more accurate estimation of failure behavior, resulting in an average error of 13.19%. To identify research gaps, machine learning (ML) plays a vital role in predicting parameter values and assessing data normality using various algorithms. By combining ML and FEA simulations, the result shows strong data performance. Bridging from 2 mm mesh sizing of 50% carbon–epoxy woven/50% CFRP laminate in 6 mm thickness at 0° orientation shows the most distributed shear stresses and deformation, which converged toward stable values. For comprehensive research, total deformation was included in ML analysis as a second target to build a multivariate analysis. Overall, Random Forest (RF) is the best-performing model, indicating superior robustness for modeling shear stress and total deformation. Full article

Music data exhibits numerous distinct symmetric and asymmetric patterns—ranging from symmetric pitch sequences and rhythmic cycles to asymmetric phrase structures and dynamic shifts. These varied and often subjective patterns present notable challenges for data analysis, such as distinguishing meaningful structural features from noise and adapting analytical methods to accommodate both regularity and irregularity. To tackle this challenge, we present a novel uncertain hypothesis test, referred to as the conservative hypothesis test, which is designed to assess the validity of statistical hypotheses associated with the symmetric and asymmetric patterns exhibited by two multivariate normal uncertain populations. Specifically, we extend the uncertain hypothesis test for the mean difference between two single-characteristic normal uncertain populations to the multivariate case, filling a research gap in uncertainty theory. Building on this two-population multivariate hypothesis test, we propose the conservative hypothesis test—a feasible uncertain hypothesis testing method for multivariable scenarios, developed based on multiple comparison procedures. To demonstrate the practical utility of these methods, we apply them to music-related statistical data, assessing whether two groups of evaluators use consistent criteria to score music. In essence, the hypothesis tests proposed in this paper hold significant value for social sciences, particularly music statistics, where data inherently contains ambiguity and uncertainty. Full article

Background/Objective: Transformational leadership (TL) by nurse managers is a modifiable organizational factor consistently linked to improved staff outcomes. However, evidence from the Arab Gulf region, particularly the Kingdom of Saudi Arabia (KSA), is limited. This study aimed to assess the relationship between nurse managers’ TL and staff nurses’ intent to stay and determine which TL dimensions predict intent to stay. Methods: A cross-sectional online survey was conducted among staff nurses at a three-hospital academic medical city in Riyadh, KSA. A total of 523 eligible respondents successfully completed the survey, utilizing probabilistic cluster sampling to guarantee representation from various nursing units within the medical city. Nurse managers’ TL was assessed across five dimensions by using the multifactor leadership questionnaire, and staff nurses’ intention to stay was measured using intent to stay scale. Descriptive statistics summarized the respondents’ demographic profile, nurse managers’ TL and staff nurses’ intent to stay. Normality was evaluated using Shapiro–Wilk and Kolmogorov–Smirnov tests. Relationships were examined using Spearman’s rank correlation, and multivariable ridge regression modeled the predictive contributions of the overall TL and its five dimensions to intent to stay. Results were considered significant if p < 0.05. Results: Nurse managers’ TL exhibited a positive association with staff nurses’ intention to stay in their current positions (r = 0.22, p < 0.001). Moreover, every dimension of TL demonstrated a strong positive relationship with the intent to stay (all p-values < 0.001). Multivariable ridge regression analysis revealed that the overall TL was a significant predictor of the intent to stay (β = 0.13, p < 0.001). Conclusions: The findings corroborate prior evidence linking TL to retention intentions. This underscores the practical salience of leadership competencies and attributes of nursing leaders, particularly TL, which recognizes the individual needs of staff nurses. This recognition subsequently fosters retention intentions, cultivates supportive nursing work environment, and enhances overall organizational success. Full article

Bulk-fill composite resins (BFCRs) have emerged as efficient alternatives to conventional restorative systems, enabling placement in thicker increments without compromising polymerization; however, their comparative mechanical performance under clinically demanding conditions remains uncertain. This study aimed to evaluate and compare the mechanical properties—flexural strength (FS), elastic modulus (EM), strain (ε), and displacement (δ)—of three high-viscosity bulk-fill resins: Filtek One™ Bulk Fill (3M ESPE), Tetric^® N-Ceram Bulk Fill (Ivoclar Vivadent), and Opus™ Bulk Fill (FGM). Thirty specimens (n = 10 per group) were fabricated according to ISO 4049:2019 and subjected to three-point bending tests. Statistical analysis included Shapiro–Wilk testing for normality, one-way analysis of variance (ANOVA) with Tukey’s post hoc comparisons, multivariate analysis of variance (MANOVA), and Spearman’s correlation. Filtek One™ Bulk Fill exhibited the highest FS 142.5 megapascals (MPa) and EM 4.2 gigapascals (GPa), with significant differences compared to Tetric^® N-Ceram Bulk Fill and Opus™ Bulk Fill (p < 0.001). Opus™ Bulk Fill demonstrated greater deformation capacity before fracture (p = 0.015). MANOVA revealed a significant effect of resin type on overall mechanical behavior (Wilks’ λ = 0.132; p < 0.001). Strong correlations were observed between strength and stiffness (ρ = 0.82), and between stiffness and deformation (ρ = –0.68). These findings confirm that BFCRs differ significantly in mechanical behavior, with Filtek One™ Bulk Fill exhibiting superior stiffness and resistance, while Opus™ Bulk Fill showed greater deformation capacity. Such differences support material selection based on the functional and anatomical demands of restorations, contributing to improved clinical performance and longevity. Full article

Background/Objectives: Prognostic assessment in endometrial cancer (EC) is based on clinical and pathological features such as histological type, FIGO stage, tumor grade, LVSI, P53 status, and hormone receptor expression. Recent molecular research has distinguished four EC subtypes, with MMR status (pMMR vs. dMMR) providing clinically relevant stratification due to its predictive value for immunotherapy. The present study aims to compare dMMR and pMMR tumors in terms of the prevalence of adverse histopathological prognostic factors. Methods: This retrospective study included 179 patients with endometrioid endometrial carcinoma (EEC) treated at the authors’ institution (1 January 2023–31 August 2025). Patients were classified by MMR status (pMMR vs. dMMR) based on immunohistochemistry, and clinicopathological variables, including FIGO stage, myometrial invasion depth, tumor grade, LVSI, ER/PR expression, and P53 status, were analyzed. Normality was assessed using the Shapiro–Wilk test. Categorical variables were tested with chi-square or Fisher’s exact tests, reporting odds ratios with 95% CI, while continuous variables were compared using the Mann–Whitney test and presented as median (IQR) with the Hodges–Lehmann difference and 95% CI. Multivariable logistic regression with Wald tests was performed. Results: dMMR tumors accounted for 29.05% of all cases. Patients in the dMMR group were significantly more likely to present with FIGO stage III/IV disease (p = 0.036) and to exhibit LVSI (p = 0.008). No differences were observed between the groups with respect to tumor grade, estrogen receptor positivity, progesterone receptor positivity, or the prevalence of deep myometrial invasion. The most frequent pattern of protein loss in the dMMR population was concurrent loss of MLH1 and PMS2. Conclusions: In the studied population, dMMR tumors more frequently exhibited adverse prognostic features of EC, such as advanced stage of disease and lymphovascular space invasion. This suggests the potential for effective immunotherapy in this patient group. Full article

This work presents a time-domain approach for characterizing the Ground Reaction Forces (GRFs) exerted by a pedestrian during running. It is focused on the vertical component, but the methodology is adaptable to other components or activities. The approach is developed from a statistical perspective. It relies on experimentally measured force-time series obtained from a healthy male pedestrian at eight step frequencies ranging from 130 to 200 steps/min. These data are subsequently used to build a stochastic data-driven model. The model is composed of multivariate normal distributions which represent the step patterns of each foot independently, capturing potential disparities between them. Additional univariate normal distributions represent the step scaling and the aerial phase, the latter with both feet off the ground. A dimensionality reduction procedure is also implemented to retain the essential geometric features of the steps using a sufficient set of random variables. This approach accounts for the intrinsic variability of running gait by assuming normality in the variables, validated through state-of-the-art statistical tests (Henze-Zirkler and Shapiro-Wilk) and the Box-Cox transformation. It enables the generation of virtual GRFs using pseudo-random numbers from the normal distributions. Results demonstrate strong agreement between virtual and experimental data. The virtual time signals reproduce the stochastic behavior, and their frequency content is also captured with deviations below 4.5%, most of them below 2%. This confirms that the method effectively models the inherent stochastic nature of running human gait. Full article

Modeling loss data is a crucial aspect of actuarial science. In the insurance industry, small claims occur frequently, while large claims are rare. Traditional heavy-tail distributions, such as Weibull, Log-Normal, and Inverse Gaussian distributions, are not suitable for describing insurance data, which often exhibit skewness and fat tails. The literature has explored classical and Bayesian inference methods for the parameters of composite distributions, such as the Exponential–Pareto, Weibull–Pareto, and Inverse Gamma–Pareto distributions. These models effectively separate small to moderate losses from significant losses using a threshold parameter. This research aims to introduce a new composite distribution, the Gamma–Pareto distribution with two parameters, and employ a numerical computational approach to find the maximum likelihood estimates (MLEs) of its parameters. A novel computational approach for a nonlinear regression model where the loss variable is distributed as the Gamma–Pareto and depends on multiple covariates is proposed. The maximum likelihood (ML) and Approximate Bayesian Computation (ABC) methods are used to estimate the regression parameters. The Fisher information matrix, along with a multivariate normal distribution as the prior distribution, is utilized through the ABC method. Simulation studies indicate that the ABC method outperforms the ML method in terms of accuracy. Full article

Background/Objectives: Head and neck cancers significantly affect patients’ functional and psychosocial well-being. Multidisciplinary tumor boards have a central role in optimizing treatment strategies, but the relationship between tumor characteristics, comorbidities, and quality of life (QoL) remains insufficiently explored. Methods: We conducted a retrospective study of 94 patients with head and neck cancers evaluated by the oncology committee of Coltea Clinical Hospital in 2024. QoL was assessed post-surgery using the EORTC QLQ-C30 and H&N35 questionnaires. Descriptive statistics, non-parametric tests, correlations, and multivariate regression analyses were performed to examine associations between clinical variables and QoL outcomes. Results: The cohort comprised 82 men (87.2%) and 12 women (12.8%), with a mean age of 61.5 ± 9.8 years. The most common tumor site was the larynx (43.6%). Global QoL was low (mean = 42.3, SD = 11.7), and fatigue scores were high (mean = 61.5, SD = 13.5). All EORTC domains showed non-normal distributions (Shapiro–Wilk, p < 0.05). Kruskal–Wallis analysis revealed significantly lower QoL scores in patients with metastatic adenopathy with aunknown primary (p = 0.03). Spearman’s correlation indicated a moderate negative association between Charlson Comorbidity Index and QoL (r = −0.38, p = 0.01). Multivariate regression confirmed comorbidities (β = −2.5, p = 0.02) and tumor type (metastatic adenopathy, β = −8.0, p = 0.04) as independent predictors of reduced QoL. Conclusions: Patients with advanced disease and higher comorbidity burden experience significantly poorer QoL after head and neck cancer surgery. Tumor board decisions facilitate individualized treatment planning; however, systematic integration of QoL metrics is essential to optimize both oncological and functional outcomes. Full article

Background: The fetal mechanical PR interval (mPR), measured using pulsed-wave Doppler, is a widely used parameter to assess atrioventricular conduction in fetuses, particularly in cases at risk of developing atrioventricular (AV) block. However, the physiological factors that influence mPR readings are not fully understood. This study aimed to identify determinants affecting the measurement of the mPR interval using the mitral valve/aorta (MV/Ao) Doppler method in a cohort of structurally normal fetuses. Methods: We retrospectively analyzed 925 fetuses with normal echocardiographic findings and no structural cardiac or extracardiac anomalies. Correlation analysis, group comparisons, trend testing, and multivariable modeling were performed to assess the impact of biometric and Doppler parameters on mPR interval measurements. Results: The median mPR interval across the cohort was 116 ms (interquartile range: 108–123 ms). Fetuses were categorized into four gestational age groups (≤19 weeks, 20–23 weeks, 24–27 weeks, and ≥28 weeks). Significant differences in mPR were observed between gestational age groups (p < 0.01), with a positive trend across increasing gestational age (p < 0.0001). The strongest correlation was an inverse relationship between mPR and fetal heart rate (FHR) (ρ = −0.256, p < 0.01). Multivariable regression identified five independent predictors of mPR: lower FHR, greater biparietal diameter (BPD), larger pulmonary valve diameter (PVD), increased fronto-occipital diameter (FOD), and lower umbilical artery pulsatility index (UA PI). The final model explained approximately 9.9% of the variance in mPR interval (R² = 0.099). Conclusions: The fetal mPR interval increases with gestational age and is primarily influenced by fetal heart rate, even after adjusting for other factors. Certain biometric and Doppler parameters also contribute modestly to mPR variation. These findings highlight the importance of accounting for physiological variability when interpreting mPR measurements in clinical fetal cardiology. Full article

Background/Objectives: Accurate prognostication for interstitial lung disease (ILD) remains challenging, limiting clinicians’ ability to optimize patient management strategies. This study aimed to evaluate the prognostic value of baseline quantitative CT-derived indices, including total lung volume (TLVcm³), normal lung volume% (NLV%), and fibrotic lung volume% (FLV%), for predicting three-year mortality in interstitial lung disease (ILD) patients. Methods: A total of 101 ILD patients were retrospectively enrolled. Baseline CT-derived indices, including TLVcm³, NLV% (−950 to −700 HU), and FLV% (−600 to +50 HU), were measured on chest CT. Baseline forced vital capacity(FVC)% predicted and diffuse capacity of lungs for carbon monoxide (DLCO)% predicted were collected. Survival analysis used Kaplan–Meier’s curves and log-rank tests. Uni- and multivariate Cox’s proportional hazards regression were performed. Pearson’s correlation was used between CT-derived indices, FVC% predicted, and DLCO% predicted. Results: During 3-year follow-up, 30 of 101 patients (29.70%) died. Deceased patients had a significantly lower baseline NLV% (59.27% ± 7.61% vs. 65.02% ± 7.82%, p = 0.001) and a higher FLV% (17.64% ± 7.98% vs. 13.34% ± 7.48%, p = 0.011) compared with survivors. Multivariate analysis identified baseline NLV% (adjusted hazard ratio 0.88, 95% CI: 0.78–0.99, p = 0.034) and DLCO% predicted (adjusted hazard ratio 0.97, 95% CI: 0.95–0.99, p = 0.007) as independent predictors of three-year mortality. Patients with NLV% ≤ 64.15 and FLV% ≥ 14.12 showed significantly worse survival outcomes (21.78% vs. 7.92%, p < 0.001;19.80% (20/101) vs. 9.90% (10/101), p < 0.001). CT-derived indices moderately correlated with FVC% predicted and DLCO% predicted. Conclusions: Baseline FLV% ≥ 14.12 and NLV% ≤ 64.15 can effectively stratify and differentiate outcomes in ILD patients. Baseline NLV% has the potential as a prognostic indicator for 3-year survival in ILD. Full article