Search Results (4,381)

Background and Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is now widely acknowledged as belonging to the broader category of metabolic disorders, being closely associated with obesity, insulin resistance, and chronic systemic inflammation. Recent evidence indicates that in MASLD, alterations in the gut–liver axis—particularly increased intestinal permeability may serve as a crucial mechanistic link between metabolic dysfunction and hepatic steatosis. Zonulin, a physiological modulator of intestinal tight junctions, has been suggested as an indicator of compromised barrier integrity; however, its specific role in MASLD remains to be fully elucidated. Materials and Methods: We conducted a prospective observational study including 52 adult patients diagnosed with MASLD. Hepatic steatosis was evaluated using the SteatoTest (FibroMax panel), while fecal zonulin levels were measured by ELISA at baseline. Clinical, anthropometric, and metabolic parameters were assessed. We used ROC curve analysis to explore zonulin’s predictive value for moderate-to-severe steatosis (≥S2). Results: Elevated fecal zonulin (>107 ng/mL) occurred in 26.9% of participants. In a binary logistic model with SteatoTest ≥ S2 as outcome, zonulin was independently associated with clinically significant steatosis (OR per 1 ng/mL = 1.017; 95% CI 1.002–1.032; p = 0.029). Discrimination for ≥S2 was AUC = 0.680 (95% CI 0.535–0.825; p = 0.015). The Youden-optimal cut-off was 57.0 ng/mL (sensitivity 68.2%, specificity 63.3%) versus 40.9%/83.3% at the manufacturer’s 107 ng/mL threshold. Conclusions: Fecal zonulin shows modest discriminatory ability for steatosis and is best used as an adjunct to non-invasive assessment; cohort-specific calibration (57.0 ng/mL) outperformed the generic 107 ng/mL threshold. Full article

Background and Objectives: Postoperative physical recovery, particularly the acquisition of independent ambulation, is a critical milestone in rehabilitation following living donor liver transplantation (LDLT). Although liver function markers are conventionally used to assess hepatic physiology, emerging evidence has suggested their potential role as prognostic indicators of physical performance. Materials and Methods: This study investigated the association between liver function parameters at the initiation of postoperative physical therapy (total bilirubin [T-Bil], aspartate aminotransferase [AST], and alanine aminotransferase [ALT]) and the independent walking ability of 63 patients who underwent LDLT. A logistic regression model was constructed using these variables, and a receiver-operating characteristic (ROC) curve analysis was performed to evaluate its discriminative performance. Predicted probabilities of each patient were calculated, and the optimal cutoff value was determined based on the Youden Index. Results: The multivariate logistic regression model demonstrated a statistically significant association between liver function markers and the ambulation status of a cohort of 63 patients. The ROC curve analysis yielded an area under the ROC curve (AUC) of 0.8416 (95% confidence interval [CI]: 0.715–0.968), indicating strong predictive performance. The optimal cutoff value was 0.865, with sensitivity and specificity of 74.1% and 88.9%, respectively. The bootstrap CI for sensitivity at this threshold ranged from 0.6111 to 0.8519. The Hosmer–Lemeshow test indicated good model fit (p = 0.363), and the correct classification rate was 87.3%. Conclusions: Liver function test results may be indicators of hepatic dysfunction as well as functional biomarkers that could predict ambulatory outcomes following LDLT. This predictive model may enhance early clinical decision-making regarding rehabilitation and discharge planning. Future prospective studies should be performed to validate the generalizability of these results to broader clinical contexts. Full article

Background and Objectives: This study evaluated the correlation between hyperferritinemia and markers of liver steatosis, fibrosis, and risk of liver-related events in patients with type 2 diabetes mellitus (T2DM) and Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD). Material and Methods: This study included 271 patients that underwent a comprehensive medical evaluation. Hyperferritinemia was defined by values >200 ng/mL (females) and >300 ng/mL (males). Liver fibrosis and steatosis were evaluated by several non-invasive indexes, and Liver Risk Score (LRS) was calculated to determine the risk of liver-related events. Their correlation with serum ferritin was investigated by bivariate and multiple regression analyses. Receiver Operating Characteristic (ROC) analyses were used to assess the accuracy to predict advanced fibrosis and increased LRS. Statistical significance was set at p < 0.05. Results: The median serum ferritin level was 94.4 [128.1] ng/mL. Metabolic hyperferritinemia was present in 12.54% of patients. Patients with hyperferritinemia had higher liver enzymes, HbA1c, HOMA-IR, and increased markers of liver steatosis and fibrosis, with a higher prevalence of advanced fibrosis (OR = 3.744 [1.481, 9.460], p = 0.0081). LRS was highest in patients with hyperferritinemia (7.99 ± 2.01 vs. 7.12 ± 1.32 vs. 6.54 ± 1.06, p < 0.0001). Serum ferritin levels were correlated with LRS (β = 0.190 [0.001; 0.003], p < 0.001), liver fibrosis (Fibrotic NASH Index) (β = 0.198 [0.000; 0.001], p < 0.001), and steatosis, while haptoglobin concentrations were correlated negatively with them. Serum ferritin predicted the moderate risk of liver-related outcomes with an acceptable performance (area under the ROC curve = 0.726 [0.590; 0.862], p = 0.001). Conclusions: Hyperferritinemia is associated with liver fibrosis and steatosis and a higher risk of liver-related events in patients with T2DM and MASLD. Full article

Background and Objectives: Although adjacent vertebral fractures (AVF) frequently occur after balloon kyphoplasty (BKP), their risk factors remain unclear. This retrospective study aimed to identify risk factors for AVF and evaluate the utility of Hounsfield unit (HU) values on preoperative vertebral computed tomography (CT) scans as predictors of its occurrence. Materials and Methods: We retrospectively evaluated 180 patients (46 male and 134 female individuals; mean age: 80.3 years; range: 60–94 years) who underwent BKP for osteoporotic vertebral fractures (OVFs) between 2021 and 2023 with at least 6 months of follow-up. The patients were categorized into the AVF (n = 31) and non-AVF (n = 149) groups. Analyzed variables included patient characteristics, fracture level, prior fractures, posterior wall injury, intravertebral cleft, vacuum phenomenon in adjacent intervertebral discs, injury-to-surgery interval, cement volume, kyphosis angles, wedge ratios, and HU values. HU values were measured at three levels on preoperative CT scans in the vertebrae above and below the treated segment. Cutoff HU values predictive of AVF were determined using receiver operating characteristic (ROC) curve analysis. Results: AVF incidence was 17.2% (31/180), with 71.0% occurring in the vertebrae above the treated level. HU values in all measured slices were significantly lower in the AVF group. The mean HU values in the upper vertebra were 61.1 ± 6.03 (AVF) and 84.7 ± 2.75 (non-AVF), and in the lower vertebra, 51.5 ± 8.44 and 81.0 ± 3.85, respectively. ROC analysis showed cutoff HU values of 79.3 and 61.0 for the upper and lower vertebrae, respectively. HU values were identified as independent AVF risk factors. Conclusions: Preoperative vertebral HU values are independent AVF predictors. Values below 79.3 in the upper or 61.0 in the lower vertebrae were linked to higher AVF risk, suggesting HU measurement is a simple, useful tool for preoperative risk assessment. Full article

Background and Objectives: Type 2 diabetes (T2DM) substantially increases cardiovascular risk; beyond the well-recognized left-ventricular involvement in diabetic cardiomyopathy, emerging data indicate subclinical right-ventricular (RV) dysfunction may also be present. This study aimed to evaluate whether speckle-tracking echocardiography identifies subclinical right-ventricular systolic dysfunction in type 2 diabetes, despite normal conventional indices and preserved global systolic function. Materials and Methods: We conducted a cross-sectional, single-center study in accordance with STROBE recommendations, enrolling 77 participants, 36 adults with T2DM, and 41 non-diabetic controls, between December 2024 and July 2025. All participants underwent comprehensive transthoracic echocardiography, including conventional parameters (tricuspid annular plane systolic excursion (TAPSE), tricuspid annular systolic velocity (TV S’), right ventricular fractional area change (RVFAC)) and deformation imaging (right ventricular global longitudinal strain (RV GLS), right ventricular free wall longitudinal strain (RVFWS)) using speckle-tracking echocardiography. Biochemical and clinical data, including glycosylated hemoglobin (HbA1c), were recorded. Correlation and ROC curve analyses were performed to explore associations and predictive value. Results: The mean age was comparable between the two groups (62.08 ± 9.54 years vs. 60.22 ± 13.39 years; p = 0.480). While conventional RV parameters did not differ significantly between groups, diabetic patients had significantly lower RV GLS (−13.86 ± 6.07% vs. −18.59 ± 2.27%, p < 0.001) and RVFWS (−15.64 ± 4.30% vs. −19.03 ± 3.53%, p < 0.001). HbA1c levels correlated positively with RV strain impairment (RVFWS r = 0.41, p < 0.001). Both RV GLS and RVFWS were independent predictors of RV dysfunction in logistic regression analysis. ROC analysis showed good diagnostic performance for RV GLS, AUC = 0.84 with an optimal cut-off −17.2% (sensitivity 86.1% and specificity 80.5%) and RVFWS, AUC = 0.76 with cut-off −17.6% (sensitivity 77.8; specificity 80.5%) in identifying early myocardial involvement. Conclusions: RV systolic dysfunction may occur early in T2DM, even when traditional echocardiographic indices remain within normal limits. Speckle-tracking echocardiography, particularly RV GLS and RVFWS, offers sensitive detection of subclinical myocardial impairment, reinforcing its value in early cardiovascular risk stratification among diabetic patients. Full article

Early Autism Spectrum Disorder (ASD) detection is important for early intervention. This study investigates the potential of eye-tracking (ET) data combined with machine learning (ML) models to classify ASD and Typically Developed (TD) children. Using a publicly available dataset, five ML models were evaluated: Support Vector Machine (SVM), Random Forest, Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Random Forest improved with Convolutional Filters (ConvRF). The models were trained and tested using a set of evaluation metrics, including accuracy, precision, recall, F1-score, and ROC Area Under the Curve (AUC). Among these, the ConvRF model attained superior performance, achieving a recall of 90% and an AUC of 88%, indicating its robustness in identifying ASD children. These results highlight the model’s effectiveness in ensuring high sensitivity, which is critical for early ASD detection. This study shows the promise of combining ML and eye-tracking technology as accessible non-invasive tools for enhancing early ASD detection, resulting in timely and personalized interventions. Limitations and recommendations for future research are also included. Full article

Glial fibrillary acidic protein (GFAP) has been shown to be a reliable biomarker for detecting neurological disorders. Recently, we developed the Lumipulse G GFAP plasma assay, which is a commercially available tool. Compared to existing assays, the LUMIPLSE G platform offers the high-throughput, rapid, and fully automated quantification of biomarkers, enabling more standardized and accessible clinical study. In this study, we evaluated this assay using cerebrospinal fluid (CSF) samples. Assessing GFAP in CSF may provide more direct insights into central nervous system pathology than plasma and could improve the characterization of Alzheimer’s disease (AD) stages and support treatment monitoring. The LUMIPULSE G system is a chemiluminescent enzyme immunoassay (CLEIA) platform equipped with full automation, utilizing specialized cartridges to process samples within 30 min. The assay, which employs a pair of proprietary monoclonal antibodies targeting GFAP, was evaluated for clinical performance using 30 CSF samples from patients diagnosed with AD, patients with mild cognitive impairment (MCI), and cognitively unimpaired (CU) individuals, with 10 samples from each group. In addition, levels of β-amyloid 1–40 (Aβ40), β-amyloid 1–42 (Aβ42), and pTau181 were simultaneously measured. The Lumipulse G GFAP assay significantly differentiated (p < 0.05) between the amyloid accumulation and non-amyloid accumulation groups, as classified based on the CSF Aβ test. Furthermore, GFAP showed a moderate correlation with pTau181 (r = 0.588), as determined based on Spearman’s rank correlation coefficient. Moreover, receiver operating characteristic (ROC) analysis was performed to determine the performance of GFAP in distinguishing amyloid-positive and amyloid-negative subjects, with an area under the curve (AUC) of 0.72 (0.50–0.93). When stratified by CSF pTau181 positivity, GFAP demonstrated an improved diagnostic accuracy, achieving an AUC of 0.86 (95% CI: 0.68–1.00). This study demonstrates that the Lumipulse G GFAP assay, when applied to CSF samples, has the potential to differentiate AD from non-AD cases, particularly suggesting its utility in detecting tau-related pathology. While GFAP has previously been established as a biomarker for AD, our findings highlight that combining GFAP with other biomarkers such as Aβ40, Aβ42, and pTau181 may enhance the understanding of AD pathogenesis, disease staging, and possibly treatment responses. These findings suggest that GFAP may serve as a complementary biomarker reflecting astroglial reactivity associated with tau positivity, alongside established biomarkers such as Aβ40, Aβ42, and pTau181. However, since GFAP levels may also be elevated in other neurological disorders beyond AD, further investigation into these conditions is required. Full article

Objective: The aim of this study was to evaluate the diagnostic accuracy of conventional CT values and the dual-energy computed tomography (DECT)-derived effective atomic number (Zeff), fat fraction (FF) and iodine concentration (IC) for the differentiation of hypodense liver lesions in patients with colorectal cancer (CRC). Methods: One hundred and twenty patients (mean age: 65 ± 12 years) affected by CRC who underwent dual-source DECT as part of tumor staging between December 2015 and June 2023 were retrospectively evaluated. Spectral datasets were reconstructed for each patient and regions of interest were applied at the level of hypodense liver lesions to collect CT, Zeff, FF and IC values. To assess diagnostic accuracy, receiver operating characteristic (ROC) curves were constructed to evaluate the area under the curve (AUC), sensitivity, and specificity using biopsy or MRI (in cases when biopsy was not indicated or feasible) as a reference standard. The Youden index was used to identify optimal cut-off values for potential clinical applications. Results: A total of 223 lesions (147 metastases and 76 cysts) were evaluated. CT, FF and IC values differed significantly between metastases and cysts (p < 0.0001), showing high diagnostic accuracy. FF showed significantly higher diagnostic accuracy compared to all other parameters (all p ≤ 0.0074), with an AUC value of 0.97 (95% CI: 0.94–0.99). For a cut-off > 15.9, the sensitivity reached 91.8% (95% CI: 86.2–95.7) and the specificity reached 98.7% (95% CI: 92.9–100). Zeff numbers did not differ considerably (p = 0.781) between the two entities and demonstrated a lower AUC (0.511; 95% CI: 0.44–0.58). Conclusions: FF measurements proved to have high diagnostic accuracy compared to CT values, IC, and Zeff in the evaluation of hypodense liver lesions in patients suffering from colorectal carcinoma. Full article

Skin cancer is the most frequently diagnosed form of cancer worldwide. Diagnostic uncertainty can arise when macroscopic or dermoscopic evaluations do not clearly differentiate between benign and malignant lesions. Laser-induced plasma spectroscopy (LIPS), traditionally used in fields like materials science and environmental analysis, has recently gained attention for its applications in human tissue assessment. LIPS works by generating a (micro)plasma when a laser interacts with tissue, producing element-specific light emissions that can be analyzed in real time. In this study, we explored the potential of LIPS to differentiate between benign and malignant skin lesions using the Spectra-Scope^® Score (SSS). Our results revealed a clear distinction: benign lesions showed a median SSS of 1.7, while suspicious and malignant lesions had a significantly higher median score of 8.1 (p < 0.001). Receiver operating characteristic (ROC) curve analysis demonstrated strong diagnostic performance, with an area under the curve (AUC) of 0.82 (p < 0.001). The findings of this preliminary study support the high accuracy of LIPS in identifying malignancy and underscore its promise as a non-invasive, real-time diagnostic aid. Integrating SSS into clinical workflows could enhance the early detection of skin cancer and reduce reliance on invasive diagnostic procedures. However, further validation is needed to fully establish its role in routine dermatological practice. Full article

Lung cancer is one of the leading causes of cancer-related mortality worldwide. The diagnosis of this disease remains a challenge due to the subtle and ambiguous nature of early-stage symptoms and imaging findings. Deep learning approaches, specifically Convolutional Neural Networks (CNNs), have significantly advanced medical image analysis. However, conventional architectures such as ResNet50 that rely on first-order pooling often fall short. This study aims to overcome the limitations of CNNs in lung cancer classification by proposing a novel and dynamic model named LungSE-SOP. The model is based on Second-Order Pooling (SOP) and Squeeze-and-Excitation Networks (SENet) within a ResNet50 backbone to improve feature representation and class separation. A novel Dynamic Feature Enhancement (DFE) module is also introduced, which dynamically adjusts the flow of information through SOP and SENet blocks based on learned importance scores. The model was trained using a publicly available IQ-OTH/NCCD lung cancer dataset. The performance of the model was assessed using various metrics, including the accuracy, precision, recall, F1-score, ROC curves, and confidence intervals. For multiclass tumor classification, our model achieved 98.6% accuracy for benign, 98.7% for malignant, and 99.9% for normal cases. Corresponding F1-scores were 99.2%, 99.8%, and 99.9%, respectively, reflecting the model’s high precision and recall across all tumor types and its strong potential for clinical deployment. Full article

Objective This study aimed to show the clinicopathological characteristics of large retroperitoneal liposarcoma (RLS) and to develop a customized nomogram model for patients with large RLS. Methods A total of 1735 patients diagnosed with RLS were selected from the public SEER database. Among them, 1113 patients with a maximum tumor diameter greater than 150 mm were included for further analysis. Nomogram models were developed based on Lasso and multivariate Cox regression analyses. A total of 166 patients that presented in the same period at our institution were used for external validations. Results A larger tumor size in RLS was associated with worse survival outcomes. Lasso and Cox regression analyses consistently identified age, TNM stage, occurrence pattern, histology, and surgery as important prognostic factors for OS. The constructed model demonstrated robust predictive performance, with better time-ROC (time-dependent receiver operating characteristic) for 1-year (83.1%), 3-year (83.8%), and 5-year (81.4%) survival in the training cohort. The concordance index (C-index) was approximately 0.80 in both the training and validation cohorts, reflecting excellent discriminatory ability of the model. Survival risk stratification analysis revealed significant differences in survival outcomes of large RLS (HR = 4.12 [3.31–5.12], p < 0.001, in the training cohort). Decision curve analysis (DCA) confirmed that the nomogram provided greater net benefits across a range of threshold probabilities. Conclusion This study identified important prognostic factors for survival in patients with large RLS and developed a reliable nomogram for predicting OS. The model’s strong predictive performance supports its use in personalized treatment strategies, improving prognosis assessment and clinical decision making for these patients. Full article

Background/Objectives: The COVID-19 pandemic highlighted the limitations of pulse oximetry in detecting occult hypoxemia. The superiority of the alveolar gas monitor (AGM) compared to pulse oximetry (SpO₂) in predicting respiratory deterioration among COVID-19-positive individuals has previously been demonstrated. Here, we combine COVID-19 and non-COVID-19 individuals as a combined cohort of participants to determine if the AGM has similar utility across a larger, more generalizable cohort. Methods: Adult patients (n = 75) at risk of respiratory deterioration in the emergency department (ED) underwent prospective assessments of their oxygen deficit (OD) and SpO₂, simultaneously measured during quiet breathing on the AGM. The OD and SpO₂ were then compared for their ability to predict the dichotomous outcome of the need for supplemental oxygen. The administration of supplemental oxygen was ordered by the clinical care team with no knowledge of the patients’ enrollment in this study. Results: In the logistic regression analysis, both SpO₂ and OD significantly predicted the need for supplemental oxygen among COVID-19-negative individuals. However, in the multivariable regression, only OD (p < 0.001) significantly predicted the need for supplemental oxygen, while SpO₂ (p = 0.05) did not in the combined cohort of COVID-19-negative and -positive individuals. Receiver operating characteristic (ROC) curve analysis demonstrated the superior discriminative ability of OD (area under ROC curve = 0.937) relative to SpO₂ (area under ROC curve = 0.888) to predict the need for supplemental oxygen. Conclusions: The noninvasive AGM, which combines the measurement of exhaled partial pressures of gas with SpO₂, outperforms SpO₂ alone in predicting the need for supplemental oxygen among individuals in the ED at risk of respiratory deterioration regardless of the etiology for their symptoms (COVID-19-positive or -negative). Full article

Background and aim: Inflammatory bowel diseases (IBD) are chronic conditions that affect the gastrointestinal tract. The chronic inflammatory state promotes a catabolic environment that contributes to undernutrition, while mucosal damage often impairs nutrient absorption. The aim of this study is to evaluate the relationship between nutritional status—including micronutrient deficiencies—and clinical as well as laboratoristics disease activity in a cohort of patients with IBD. Methods: This is a cross-sectional study conducted across three care centers in Italy. Baseline data, clinical disease activity, and laboratory test results were collected. Micronutrient evaluation included measurements of iron, ferritin, vitamin B12, vitamin D, and folate. In addition, hemoglobin and albumin levels were assessed. Pearson correlation analysis was performed to explore the relationship between disease activity and nutritional status. Additionally, receiver operating characteristics (ROC) analysis were performed to identify patients with active diseases. Results: 110 IBD patients (40 Crohn Disease; 70 Ulcerative Colitis) were included. The serum level of Hb, iron, ferritin and vitamin D was different among the active and inactive group (p: 0.007; p: 0.001; p: 0.005; p: 0.003) while no difference was found among the other micronutrients evaluated (folic acid, vitamin B12) and albumin. Iron and vitamin D levels demonstrated the highest accuracy in the ROC analysis, with Area Under the Curve (AUC) of 0.76 (p < 0.001) and 0.68 (p = 0.013), respectively. Vitamin D and Ferritin showed the better performance (based on calprotectin levels). However, their AUC were sub-optimal (AUC 0.68; p < 0.001; AUC 0.66; p = 0.19. Conclusions: Hemoglobin, iron, ferritin, and vitamin D were associated with disease activity status. However, despite this correlation, their accuracy in discriminating between active and inactive disease appeared to be suboptimal. Folic acid, vitamin B12, and albumin showed poor concordance with disease activity status. Full article

Calprotectin, a heterodimer of the S100A8 and S100A9 proteins, has been implicated in cancer-related inflammation and metastasis. Its role in melanoma progression, particularly in organ-specific metastasis, remains underexplored. In this retrospective study, plasma calprotectin levels were measured in 201 individuals, including healthy controls (n = 22), melanoma patients without evidence of metastasis (n = 71), and patients with metastatic melanoma (n = 108). Calprotectin concentrations were determined using the ELISA assay. Receiver operating characteristic (ROC) curve analyses were used to evaluate its diagnostic value, both alone and in combination with established biomarkers S100B and LDH. Plasma calprotectin levels were significantly elevated in patients with metastatic melanoma compared to non-metastatic patients (p < 0.001). Calprotectin showed moderate diagnostic value (AUC = 0.672), which improved to 0.755 when combined with S100B and LDH. Organ-specific analysis revealed that patients with liver metastases exhibited the highest calprotectin concentrations, with good discriminatory power (AUC = 0.710). No significant association was found between calprotectin levels and the type of metastasis identified (lymphatic vs. hematogenous). Logistic regression analysis showed that calprotectin levels above 2728 ng/mL were associated with a 7.4-fold increased risk of liver metastasis. Calprotectin is a promising blood-based biomarker that may enhance the detection of metastatic melanoma, particularly in cases with liver involvement. These findings suggest that calprotectin could be integrated into multivariable prediction models to improve risk stratification in clinical practice. Full article

Background/Objectives: Nurses play a vital role in healthcare, yet their demanding working conditions, including long hours, shift work, and stress, can negatively impact health behaviors. In Poland, empirical data on nurses’ eating habits and body composition remain limited. Therefore, this study aimed to evaluate body composition and dietary habits among nurses, and to identify significant relationships and associations between these variables. Methods: A cross-sectional observational study was conducted among 460 Polish nurses. The mean age of the respondents was 45.07 years (SD ± 11.98). Body composition was assessed using the Tanita MC-780 PLUS MA analyzer, and eating behaviors were measured with the standardized My Eating Habits questionnaire (MEH). Advanced statistical analyses including k-means clustering, ANOVA, chi-square tests, Spearman’s correlation, ROC curves, decision tree modeling, and heatmap visualization were used to identify associations. Results: The MEH scores among nurses indicated average eating behavior. However, excess body fat, overweight/obesity, shift work, and holding multiple jobs were significantly associated with emotional overeating, habitual overeating, and restrictive eating. Decision tree analysis identified Body Mass Index (BMI), fat-free mass (FFM) and comorbidities as key predictors of problematic eating patterns. Interaction effects showed that shift work combined with higher BMI further increased the risk of maladaptive behaviors. Heatmaps confirmed the strongest MEH scores in participants with elevated BMI and FFM. Conclusions: The findings underscore the need for targeted workplace interventions promoting healthy eating and weight control among nurses. Recognizing risk factors such as excess weight or multiple job holding can aid in designing effective prevention and health promotion strategies tailored to healthcare professionals. Full article