Search Results (613)

Background/Objectives: The prognostic and predictive role of BMI in patients with HR+/HER2− MBC remains controversial, particularly in the era of CDK4/6 inhibitors. This study aimed to evaluate the association between baseline BMI and clinical outcomes in patients treated with palbociclib in a real-world setting. Methods: We conducted a multicenter retrospective observational cohort study including 326 patients with HR+/HER2− MBC treated with palbociclib in combination with endocrine therapy across six oncology centers in Romania. Only patients who received palbociclib for at least three months were included. Patients were stratified according to BMI into <25 kg/m² and ≥25 kg/m² groups. PFS and OS were the primary endpoints, while ORR and CBR were secondary endpoints. Results: Among the 326 patients, 66.56% were classified as overweight or obese (BMI ≥ 25 kg/m²). Median PFS was 23.66 months in the BMI < 25 group and 26.78 months in the BMI ≥ 25 group, with no statistically significant difference (HR 0.86; 95% CI 0.62–1.20; p = 0.373). Median OS was not reached in the BMI < 25 group and was 43.73 months in the BMI ≥ 25 group, also without a significant difference (HR 0.82; 95% CI 0.52–1.30; p = 0.397). ORR (29.07% vs. 28.89%) and CBR (90.70% vs. 88.33%) were comparable between BMI groups. In multivariate analysis, liver metastases and brain metastases were independently associated with worse outcomes, whereas BMI was not an independent prognostic factor. Conclusions: In this selected real-world cohort of patients with HR+/HER2− MBC who tolerated at least three months of palbociclib, baseline BMI was not associated with treatment response, PFS, or OS. While clinically informative, these results should not be interpreted as definitive evidence that body weight has no influence on palbociclib efficacy, given the methodological constraints of the analysis. BMI alone may be insufficient to capture the complex interplay between body composition and treatment outcomes, highlighting the need for more refined biomarkers of body composition in this setting. Full article

Objective: To develop machine learning models by integrating transvaginal ultrasound (TVUS) with clinical indicators, conduct visual analysis of the models, and systematically assess their diagnostic efficacy in differentiating early cervical neoplastic lesions. Methods: A total of 144 eligible patients (84 cases of early cervical cancer and 60 cases of cervical myoma) admitted to the First Affiliated Hospital of Chongqing Medical University from January 2018 to August 2025 were retrospectively enrolled in this study. Their clinical data, human papillomavirus (HPV) test results, Thinprep Cytologic Test (TCT) findings, TVUS images and magnetic resonance (MR) imaging data were collected and subjected to comprehensive statistical analysis. Univariate and multivariate Logistic Regression analyses were performed to identify independent differentiating factors for lesion classification. Eleven machine learning models were subsequently constructed, and their diagnostic performance was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and the DeLong test. Finally, a nomogram was developed based on the optimal-performing model for clinical visualization. Results: The TVUS–clinical indicator integration model identified five independent differentiating factors: HPV status, TCT findings, menopausal status, ultrasonic tumor blood supply, and ultrasonic tumor morphology. In contrast, the MR–clinical indicator integration model screened out three independent factors: HPV status, TCT findings, and intratumoral signal intensity on MR T2-weighted imaging (T2WI). The TVUS integration model demonstrated marginally superior diagnostic performance, with a sensitivity of 0.988, specificity of 0.983, and an area under the ROC curve (AUC) of 0.991, compared with the MR integration model (sensitivity: 0.952, specificity: 0.950, AUC: 0.975); however, this difference in AUC values was not statistically significant (p = 0.911). Among the 11 machine learning models, the Logistic Regression model exhibited optimal classification performance and stability. DCA curves confirmed that all constructed models outperformed single-index diagnostic strategies in clinical decision-making for lesion differentiation. A nomogram was further established based on the Logistic Regression model for intuitive clinical application. Conclusions: Multiple machine learning models integrating TVUS with clinical indicators are successfully developed, and a corresponding nomogram is constructed in this study. Full article

Background/Objectives: Early detection of postnatal growth failure (PGF) is essential for optimizing nutritional management in preterm infants, as PGF is associated with adverse neurodevelopmental outcomes. Early prediction remains difficult because postnatal growth is influenced by multiple clinical factors including gestation age, birth weight, nutritional status, and comorbidities. Machine-learning approaches have been proposed to predict complex neonatal outcomes. This study compared the predictive performance of neonatologists with that of a machine-learning model for predicting PGF. Methods: PGF was defined as a decrease in weight z-score greater than 1.28 at discharge compared with birth. A machine-learning model based on extreme gradient boosting (XGBoost) was trained using a dataset of 7954 very low birth weight (VLBW) infants. Nine neonatologists independently assessed 100 clinical cases through a questionnaire-based evaluation, including 50 patients with PGF. Predictive performance was evaluated using seven metrics: area under the receiver operating characteristic curve (AUROC), accuracy, error rate, positive predictive value (PPV), sensitivity, specificity, and F1 score. Results: The neonatologists had a median of 5 years (range: 4–10 years) of clinical experience. The median prediction score among the neonatologists was 52/100 (range, 44–60), whereas the XGBoost model achieved 79/100. The XGBoost model achieved an AUROC of 0.79, accuracy of 0.79, error rate of 0.21, sensitivity of 0.82, and an F1 score of 0.80, demonstrating superior overall performance compared to the neonatologists. In addition, the XGBoost model had a lower error rate than the neonatologists (0.21 vs. 0.49), whereas specificity (0.76 vs. 0.86) and PPV (0.77 vs. 0.53) did not differ significantly. Conclusions: The machine-learning model demonstrated superior or comparable predictive performance to that of neonatologists in detecting PGF. Machine-learning-based prediction models may support early risk stratification and targeted nutritional management in VLBW infants. Full article

Existing studies of street-related emotional perception mainly rely on static scene evaluations, which cannot capture the cumulative effects of environmental exposure during continuous walking. To address this limitation, this study proposes a method for predicting pedestrian physiological responses in sequential audiovisual street environments. Four real-world walking routes were selected, with outbound and return directions treated as independent paths, yielding eight paths and 32 valid samples. EEG, ECG, sound pressure level, first-person video, and GPS data were synchronously collected to construct a 1 s multimodal time-series dataset. Pearson correlation, Kendall correlation, and mutual information analyses were used to examine linear, monotonic, and nonlinear relationships between environmental variables and physiological indicators, and the resulting weights were incorporated into a Long Short-Term Memory (LSTM) model for multi-step prediction. Visual elements and noise exposure were the main factors influencing physiological responses. Among the models, the mutual-information-weighted LSTM performed best, achieving an R² of 0.77 for heart rate variability (RMSSD), whereas prediction of the EEG ratio (β/α and θ/β) remained limited. An additional independent street sample outside the training set was then used to generate a dual-dimensional EEG-ECG physiological response map, demonstrating the model’s potential for identifying emotional risk segments and supporting street-level micro-renewal. Full article

Background: The Middle East and North Africa (MENA) region faces a high cardiovascular disease (CVD) burden alongside endemic serum 25(OH)D (vitamin D) deficiency. This systematic review examines the relationship between vitamin D deficiency and CVD risk factors in MENA populations. Methods: PubMed, Cochrane Library, and Scopus were searched from inception to 18 October 2024, for observational studies in the MENA region examining vitamin D deficiency and cardiovascular risk factors in adults. Independent data extraction was conducted. Study quality was appraised using the Joanna Briggs Institute tool and the Newcastle–Ottawa Scale, with risk of bias visualized using Robvis. Weighted mean differences in cholesterol, body mass index (BMI), and HbA1c between those with and without vitamin D deficiency were computed with random-effects meta-analysis. The protocol was registered in PROSPERO (ID: CRD42025615188) and funded by the Royal College of Surgeons in Ireland—Medical University of Bahrain. Results: Seventeen studies from nine MENA countries were included, predominantly cross-sectional, involving community-based and disease-specific cohorts. Vitamin D deficiency was highly prevalent and consistently associated with higher adiposity and central obesity. Several studies reported significant links between deficiency and poor glycemic control, particularly in obese and prediabetic groups. Meta-analysis demonstrated significantly higher total cholesterol (MD = 0.32; 95% CI = 0.11 to 0.52, p < 0.001), BMI (MD = 1.81; 95% CI = 0.68 to 2.94, p < 0.001), and HbA1c levels (MD = 0.31; 95% CI = 0.06 to 0.57, p = 0.02) in vitamin D deficient individuals, with notable heterogeneity. Conclusions: Vitamin D deficiency is highly prevalent in the MENA region and consistently associated with adiposity-related risk factors. Despite heterogeneity, findings underscore the need for public health strategies and further research to clarify causal pathways and population-specific interventions. Full article

Background/Objectives: The objective of this investigation was to assess the prevalence of sarcopenia and identify its related risk factors in patients with rheumatoid arthritis (RA) using data from the nationally representative 2008–2011 Korea National Health and Nutrition Examination Survey (KNHANES). Methods: We analyzed data from the 2008–2011 KNHANES to identify the factors associated with sarcopenia in patients with RA. Sarcopenia was defined as the ratio of appendicular skeletal muscle mass to total body weight (multiplied by 100), with cut-offs of <29.0% for men and <22.9% for women. To identify the specific factors independently associated with sarcopenia, a multivariate logistic regression model was employed, accounting for sample weights and the complex survey design. Results: Among 238 patients with RA included in the analysis, 44 (weighted prevalence: 22.7%) had sarcopenia. The sarcopenia group had a higher proportion of males (55.0% vs. 15.3%, p < 0.001), body mass index (BMI) (26.1 vs. 23.3 kg/m², p < 0.001), and waist circumference (86.9 vs. 79.0 cm, p < 0.001) than the non-sarcopenia group. After adjustment for potential confounders, including age, sex, obesity, physical activity, and daily protein intake, male sex (odds ratio [OR]: 4.17; 95% confidence interval [CI]: 1.48–11.77, p = 0.007) and obesity (OR: 3.06; 95% CI: 1.16–8.07, p = 0.024) were independently associated with sarcopenia. In sex-specific analyses, low physical activity was significantly associated with sarcopenia only in male patients (OR: 13.00; 95% CI: 1.90–88.75, p = 0.012). Conclusions: Our findings indicate that being male and having a higher BMI are significant independent indicators of sarcopenia risk within the Korean RA population. This highlights their critical role in the development of sarcopenia among RA patients. Full article

Background/Objectives: Traumatic spinal fractures are common injuries, and a proportion of these cases require surgical stabilization using various operative systems. This study aimed to analyze the epidemiology of surgical site infections (SSIs) following exclusively trauma-related spinal surgery and to identify potential risk factors for their occurrence, as there is a lack of studies focusing on non-elective trauma-related spinal surgeries and SSI in the literature. Methods: This retrospective single-center analysis examined 710 patients with traumatic spinal injuries treated surgically between 2012 and 2022 at the Level I Trauma Center at the Department of Orthopedics and Trauma Surgery of the University Hospital Wiener Neustadt, Austria. To investigate SSI risk factors, comparative statistical analyses and logistic regression were used, with a level of statistical significance of α = 0.05. Results: In total, 28 cases (with an incidence of 3.94%) developed SSI, and these cases were characterized by a significantly higher body weight/BMI, longer operative times, and more stabilized segments and implanted hardware. They were also more likely to have undergone open surgery, laminectomy in combination with dorsal stabilization, intensive care treatment, or to present with neurological deficits or ankylosing spondylitis. SSIs occurred most frequently in the thoracolumbar and cervicothoracic junctions, and were predominantly caused by Staphylococcus epidermidis, Staphylococcus aureus, and Cutibacterium acnes. As independent risk factors, a higher BMI (OR = 1.188) and the use of cross-connectors (OR = 4.948) were identified, whereas other initially significant variables did not remain significant after adjustment. Conclusions: There are surgery-related and potentially modifiable variables and non-modifiable patient-related risk factors for the occurrence of SSI. Patients with SSIs stayed an average of 25.3 days in hospital and had a mortality rate of 17.9%. Full article

With urbanization transforming from scale expansion to high-quality development and the increasing prominence of the ecological environment constraints of drainage basins, systematically identifying the mechanism of action of non-point source pollution from a high-quality development perspective is significant for coordinating urban development and environmental protection. Based on remote sensing data on atmospheric pollution and multi-source spatial big data such as nighttime light (NTL), LandScan population, point of interest (POI), and land use data from 2013 to 2025, this study applies methods including deposition flux analysis, deep learning fusion, bivariate spatial autocorrelation, and geographically weighted regression (GWR) to empirically analyze the spatiotemporal evolution characteristics, spatial correlation, and local impacts of high-quality urban development on non-point source pollution in the Chenghai drainage basin. We find that, firstly, non-point source pollution and high-quality urban development in the Chenghai drainage basin both present significant stage-specific and spatial heterogeneity. In other words, the two are not mutually independent spatial elements in space; instead, they are closely and significantly correlated, with their correlation types showing obvious spatial agglomeration characteristics. Secondly, the impact of high-quality urban development on non-point source pollution evolves in stages. It gradually shifts from a whole-region, homogeneous, strongly positive driving force to spatial differentiation. Specifically, from 2013 to 2017, the whole-region regression coefficients are generally greater than 0.5, meaning that urban development represents a strong, whole-region driving force promoting pollution. However, after 2017, this impact evolves into a stable spatial differentiation pattern. It mainly shows that the northern urban core area, where coefficients are greater than 0.5, maintains a continuous strong positive driving force. Meanwhile, the peripheral area, where coefficients are generally lower than 0, creates a negative inhibition effect. Based on the above rules, further analysis shows that the impact of high-quality urban development on non-point source pollution is absolutely not a simple linear relationship. Instead, it is a result of the coupling effect of multiple factors, including development stage, spatial location, and governance level. Therefore, to positively affect the ecological environment through high-quality development, model transformation and precise governance are essential. The findings of this study deepen our understanding of the transformation of urban development models and the response mechanism of non-point source pollution. They also provide a scientific basis and decision support for promoting the coordinated governance of high-quality urban development and non-point source pollution by region and stage in plateau lake drainage basins, as well as for improving the sustainable development of drainage basins. Full article

Background/Objectives: Vaccine hesitancy remains a significant public health challenge worldwide, yet nationally representative data from Central Asia are scarce. Evidence on the multidimensional structure of vaccination attitudes and their social patterning in Kazakhstan is limited. The study aimed to assess the distribution of anti-vaccination attitudes among adults in Kazakhstan and to examine their associations with socio-demographic, behavioural, clinical, and territorial characteristics. Methods: We conducted a cross-sectional, nationally representative survey of adults aged 18–69 years across all 17 regions of Kazakhstan between May and October 2025 (n = 6712). A multistage, stratified cluster sampling design was applied, and analyses incorporated sampling weights and design-based corrections. Vaccination attitudes were measured using the 12-item Vaccination Attitudes Examination (VAX) scale, comprising four subscales: mistrust of vaccine benefit, worries about unforeseen future effects, concerns about commercial profiteering, and preference for natural immunity. Internal consistency and confirmatory factor analysis were performed. Design-adjusted linear regression models were used to identify factors independently associated with each subscale and the overall VAX score. Results: The weighted mean overall VAX score was 3.70 (95% CI 3.67–3.73) on a 1–6 scale. The highest scores were observed for worries about unforeseen future effects (4.12; 95% CI 4.10–4.14), followed by preference for natural immunity (3.93; 95% CI 3.87–3.98), concerns about commercial profiteering (3.49; 95% CI 3.45–3.52), and mistrust of vaccine benefit (3.27; 95% CI 3.23–3.31). Internal consistency was high for the overall scale (Cronbach’s α = 0.861), and the four-factor structure demonstrated acceptable fit (CFI = 0.965; TLI = 0.952; RMSEA = 0.071). In multivariable design-adjusted models, age showed a generally consistent gradient, with lower scores in younger groups and the clearest differences observed among the youngest respondents. Married/cohabiting respondents had lower adjusted scores than single respondents across all subscales and for the overall VAX score. Men had lower adjusted worries scores than women, but sex was not independently associated with the overall VAX score. Diabetes was associated with higher adjusted mistrust, concerns about commercial profiteering, and overall VAX score, but not with worries or preference for natural immunity. Territorial differences were domain-specific: urban residence was associated with lower mistrust and higher worries, while macro-region was significant at the factor level only for worries. Conclusions: Anti-vaccination attitudes in Kazakhstan exhibit a multidimensional structure and clear socio-demographic patterning. Concerns about long-term safety were the most prominent attitudinal domain, whereas mistrust of vaccine benefit was comparatively less pronounced. Territorial differences were domain-specific rather than uniform, supporting the need for targeted communication strategies tailored to specific attitudinal domains and population subgroups. Full article

Background: We aimed to investigate the prevalence and clinical significance of pontine microtubular signal intensity (MSI), presumed dilated perivascular or perineural spaces, in patients with hemifacial spasm (HFS) using high-resolution MRI using proton density-weighted imaging (HR-PDI). Methods: We retrospectively analyzed 438 patients with unilateral HFS who underwent microvascular decompression (MVD) and preoperative HR-PDI. MSI was defined as a linear or curvilinear hyperintense lesion along the presumed course of the intraparenchymal facial nerve fascicles within the pons on HR-PDI. The presence and laterality of MSI were evaluated by consensus between two reviewers and classified according to their relationship to the symptomatic side of HFS as ipsilateral (same side as the facial spasm), contralateral (opposite side), or bilateral. Clinical characteristics, surgical findings, and postoperative outcomes were compared according to the presence of ipsilateral MSI. A control group of 307 subjects who underwent HR-PDI for non-central neurologic symptoms was included to assess the prevalence of MSI. Multivariable logistic regression analysis was performed to identify factors associated with immediate postoperative improvement after MVD. Results: MSI was more frequently observed in patients with HFS than in controls after adjusting age and sex (OR, 3.78; 95% CI, 2.747–5.197; p < 0.001). Ipsilateral MSI was identified in 267 of 438 patients (61.0%). Patients with ipsilateral MSI showed a significantly higher frequency of contralateral MSI (p < 0.001) and vertebralartery-related compression (p = 0.002). Immediate postoperative improvement after MVD was less frequent in patients with ipsilateral MSI than in those without MSI (77.5% vs. 86.5%, p = 0.019). Multivariable logistic regression analysis demonstrated that ipsilateral MSI was independently associated with a lower likelihood of immediate postoperative improvement (OR, 0.411; 95% CI, 0.222–0.759; p = 0.005). However, long-term surgical outcomes were not significantly different according to the presence of MSI. Conclusions: Pontine MSI on HR-PDI is more frequently observed in patients with HFS and is associated with a lower likelihood of immediate postoperative improvement and a tendency toward delayed recovery after MVD but not with poorer long-term outcomes. These findings suggest that MSI may represent microstructural or neurofluidic alterations along the pontine facial nerve pathway and may serve as an imaging marker of delayed recovery dynamics. Full article

Background: Breast cancer brain metastases (BCBMs) are clinically challenging, and treatment decisions are influenced by tumor biology. Because receptor profiles may differ between primary breast tumors and brain metastases and brain biopsy may be impractical, non-invasive imaging biomarkers may provide useful biologic correlates. We evaluated whether diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC) histogram metrics from BCBM were associated with primary tumor estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status; the Ki-67 proliferation index; and luminal status. Methods: This retrospective exploratory single-center study included 72 adults with BCBM who underwent standardized 1.5T brain magnetic resonance imaging. The largest lesion in each patient was segmented on ADC maps in FireVoxel. ADC histogram features, including percentiles, were extracted. Using primary tumor biomarker status as the reference, candidate metrics were screened by univariable logistic regression. Parsimonious multivariable models included age, log-transformed lesion volume, and a single selected ADC percentile scaled by ×10. Discriminatory performance was assessed using area under the receiver operating characteristic curve (AUC); thresholds were derived with the Youden index. No external validation was performed. Results: Low-percentile ADC metrics were associated with ER positivity, PR positivity, and luminal disease, whereas no meaningful ADC histogram discrimination was observed for HER2. In multivariable models, ADC10×10 predicted ER positivity (odds ratio [OR] 0.441; AUC 0.847) and PR positivity (OR 0.478; AUC 0.819). Ki-67 positivity was best predicted by ADC75×10 (OR 3.095; AUC 0.905), although this finding should be interpreted cautiously. Luminal status (non-luminal vs. luminal) was predicted by ADC10×10 (OR 2.251; AUC 0.832). Conclusions: ADC histogram analysis from DWI in BCBM showed exploratory associations with primary tumor hormone receptor status and luminal subtype, but not HER2. These findings support ADC histogram features as candidate imaging biomarkers, but the Ki-67 result and all model performance estimates require cautious interpretation and independent external validation in multicenter cohorts. Full article

Background/Objectives: Knowledge of plant-based diets is gaining increasing significance in adolescents due to the growing popularity of vegetarian and vegan dietary patterns. To date, there has been limited research examining the level of awareness and understanding of these diets among secondary school students, as well as the factors influencing their knowledge. The aim of the study was to determine the prevalence of plant-based diets and to assess knowledge regarding these dietary patterns among high school students, as well as to identify factors associated with both diet adherence and achieving sufficient nutritional knowledge. Methods: A cross-sectional study was conducted among 341 high school students. Data were collected using a self-administered paper questionnaire that included demographic information, self-reported body weight and height, adherence to plant-based diets, and knowledge of vegetarian and vegan nutrition. Nutritional knowledge was assessed using a structured 19-item questionnaire (25 scorable items) and verified for reliability (test–retest, Krippendorff’s alpha = 0.88). Based on a 25-point scale, a score of >60% (16–25 points) was categorized as ‘sufficient’ knowledge. Statistical analyses included the chi-square test, Mann–Whitney and Kruskal–Wallis non-parametric tests, and multivariable logistic regression to estimate adjusted odds ratios (aOR) for factors associated with sufficient knowledge. Results: The prevalence of plant-based diets in the study group was 16.1% (n = 55), with a significantly higher frequency observed among female students and those with sufficient nutritional knowledge. The majority of students (81.2%) achieved sufficient knowledge. Higher scores were observed among female students, those in higher grade levels, and those individuals adhering to plant-based diets (p < 0.05). Multivariate regression analysis indicated that male sex (aOR = 0.38 compared to females), higher grade level (aOR = 3.66 for grade 3 vs. grade 1; aOR = 3.62 for grade 4 vs. grade 1), residence in a rural area (aOR = 0.50), and non-adherence to a plant-based diet (aOR = 0.32) were independently associated with sufficient knowledge. Conclusions: The majority of high school students demonstrate sufficient knowledge regarding plant-based diets, with significant variations associated with sex, grade level, place of residence, and experience with plant-based diets. These findings underscore the need for targeted educational interventions, particularly among male students, those in lower grade levels, and individuals residing in rural areas. Full article

Background: Obesity, type 2 diabetes mellitus, and hypertension are increasingly prevalent components of metabolic syndrome and major contributors to cardiovascular disease and chronic kidney disease progression; however, in end-stage kidney disease an “obesity paradox” has been described, with higher body mass index (BMI) sometimes associated with improved survival on hemodialysis. Material and methods: This retrospective, single-center Eastern European cohort study aimed to characterize mortality and its causes around hemodialysis initiation in the contemporary era of cardiometabolic prevention and to test whether the obesity paradox persists at this high-risk transition. Adult patients initiating dialysis at the “Pius Brânzeu” Emergency Clinical Hospital (Timișoara, Romania) between January 2022 and December 2025 (n = 268; median age 66 years; 61% male; median eGFR 6.4 mL/min/1.73 m²) were analyzed using Kaplan–Meier methods and Cox regression, with comprehensive baseline clinical, laboratory, echocardiographic, medication, infection, and vascular access data; follow-up was obtained at 3, 6, 12, 24, and 36 months. Results: Late referral was common (61% < 3 months of nephrology follow-up), dialysis initiation was predominantly urgent (only 16% scheduled), and central venous catheters were the main access (81%), with substantial comorbidity burden (cardiovascular disease 71%, hypertension 90%) and frequent infections at initiation. BMI categories were non-obese (<25 kg/m², 30%), overweight (25–29.9 kg/m², 48%), and obese (≥30 kg/m², 22%); diabetes prevalence rose with BMI (32% to 58%). Unadjusted mortality did not differ by BMI (19.8%, 18.8%, 15.3%; log-rank p = 0.622), yet multivariable Cox models showed overweight status independently reduced mortality (HR 0.22 at 3 months, 0.29 at 1 year, 0.31 at 3 years vs. non-obese), whereas obesity was not protective. Early mortality was driven mainly by age ≥ 65 years, while diabetes and chronic obstructive pulmonary disease predicted later mortality; longer pre-dialysis follow-up time was strongly protective (HR per year 0.70 at 3 years), and higher intact parathyroid hormone showed an inverse association with 1-year mortality. Conclusions: These findings show a modified obesity paradox at dialysis initiation in which moderate excess weight, but not obesity, is associated with improved adjusted survival, underscoring the clinical importance of earlier nephrology engagement and individualized nutritional and risk-factor management during the pre-dialysis and early dialysis periods. Full article

Background: Chorioamnionitis (CA) is a major pathological cause of preterm labor and is associated with both short- and long-term adverse outcomes in neonates, including early-onset sepsis (EOS) and late-onset sepsis (LOS). Neonatal sepsis remains a significant contributor to morbidity and mortality in neonatal intensive care units (NICUs). Aim: This study aimed to evaluate the association between maternal chorioamnionitis and the incidence of early-onset and late-onset neonatal sepsis in preterm neonates born at <32 weeks’ gestation. Furthermore, the study investigated maternal and neonatal factors affecting the presentation of sepsis. Methods: A retrospective cohort study was conducted on the medical records of preterm neonates born between 2020 and 2022. Inclusion criteria were gestational age < 32 weeks, available microbiological or histological examination for chorioamnionitis, and complete maternal medical records. Infants were categorized into two groups based on the presence (CA group) or absence (non-CA group) of histological and/or microbial chorioamnionitis. Descriptive statistical analyses were performed, including calculation of frequencies and percentages for categorical variables and means with standard deviations and ranges for continuous variables. Results: A total of 189 neonates were included, with a mean birth weight of 1286 ± 405 g and a mean gestational age of 29.2 ± 2.1 weeks. The CA group consisted of 55 neonates (29.1%), while 134 (70.9%) were in the non-CA group. Early-onset sepsis (EOS) occurred in 23 neonates (12.2%), with a significantly higher incidence in the CA group compared to the non-CA group (21% vs. 8%, p = 0.014). Late-onset sepsis (LOS) developed in 66 neonates (34.9%), but no significant difference in incidence was observed between the two groups (p = 0.402). Parsimonious logistic regression analysis identified maternal chorioamnionitis as an independent predictor of EOS (Odds Ratio 2.07, 95% CI 1.85–5.08; p = 0.009). Conclusions: Intrauterine infection and inflammation caused by chorioamnionitis are linked to an increased risk of early-onset sepsis in neonates born before 32 weeks’ gestation. However, chorioamnionitis does not appear to significantly influence the incidence of late-onset sepsis, which appears to be more closely associated with postnatal factors. Full article

Background/Objectives: Body mass index (BMI) trajectories and transitions across adulthood are dynamic processes influenced by aging and social- and health-related factors, yet long-term patterns in older adults from middle-income countries remain insufficiently characterized. The objective of this study was to characterize long-term BMI trajectories and transitions, and to identify sociodemographic and clinical factors associated with adverse BMI patterns among Mexican adults aged ≥50 years followed over 20 years. Methods: This study used data from the Mexican Health and Aging Study (ENASEM), a nationally representative longitudinal cohort. Participants aged ≥50 years with repeated BMI measurements across survey waves were included. BMI trajectories and transitions between BMI categories were described, and multinomial regression models were used to examine factors associated with upward transitions and unstable high-BMI patterns. Results: Distinct BMI trajectory patterns were identified over the 20-year follow-up. Participants in stable normal-weight trajectories were younger, more frequently female, and had higher educational attainment and income. In contrast, those with stable overweight/obesity or fluctuating–adverse BMI patterns had higher baseline BMI and a greater prevalence of diabetes, hypertension, and multimorbidity. In multivariable analyses, age contributed to trajectory differences; however, sex, socioeconomic factors, baseline BMI, and chronic conditions remained independently associated with adverse BMI patterns. Conclusions: BMI trajectories in later life are heterogeneous and reflect the combined influence of aging, socioeconomic conditions, and chronic disease burden. Identifying groups at risk of adverse BMI patterns may support the development of targeted interventions to reduce obesity-related health consequences in older adults. Full article