Tag: nevin manimala

Environ Monit Assess. 2025 Nov 2;197(12):1288. doi: 10.1007/s10661-025-14752-6.

ABSTRACT

Global human population growth results in increased emissions of chemical pollutants like heavy metals such as cadmium (Cd) and lead (Pb). These two non-essential elements have strong bioavailability and toxicity, causing harmful health effects on humans, and the wider environment. Synanthropic wildlife species like the white-eared opossum may act as sentinels of environmental contamination, since they have a high incidence in urban areas and close contact with humans. The aim of this study was to conduct toxicological and histopathological analyses of white-eared opossums (Didelphis albiventris) that live in the city of Campo Grande, capital of Mato Grosso do Sul state in Brazil. In addition, we surveyed Cd and Pb in soil and water from sites where D. albiventris were captured. A total of 23 animals were captured and, after recording biological parameters, were euthanized and necropsied. Liver and central nervous system (CNS) samples were sent for toxicological analysis of Cd and Pb. Fragments of the liver, brain, kidney, and reproductive system were collected for histopathological evaluation. The presence of Cd and Pb in the liver and CNS was identified, with a high concentration of Pb in the CNS. Additionally, we found higher concentrations of Pb in both soil and water samples than in the animals. In the histopathological analysis, mild to moderate degenerative tissues lesions were found and may be compatible with damage caused by the presence of Cd and Pb. Nevertheless, our statistical analysis indicated that contamination by Cd and Pb did not threaten the health of the sampled animals. This study is the first in Brazil to detect background levels of Cd and Pb in the liver and CNS of D. albiventris, correlating these concentrations with histopathological lesions. The findings further emphasize the importance of understanding the interactions among the environment, humans, wildlife, and domestic animals within the One Health framework.

PMID:41177812 | DOI:10.1007/s10661-025-14752-6

Influenza Other Respir Viruses. 2025 Nov;19(11):e70157. doi: 10.1111/irv.70157.

ABSTRACT

BACKGROUND: The proportion of healthcare workers (HCWs) who receive and recommend seasonal influenza vaccination to their patients remains low in China. This study aims to understand why HCWs infrequently use and recommend the influenza vaccine and how to improve utilization.

METHODS: A cross-sectional questionnaire survey and a focus group interview were conducted among primary HCWs in Hubei Province in September 2018 and May 2019. We analyzed qualitative data using descriptive methods and a general inductive approach following a quantitative analysis. In addition, we used the Health Belief Model (HBM) framework to summarize predictors of HCW vaccination and recommendation.

RESULTS: Primary HCWs acquired basic knowledge about influenza infection and vaccination and were less likely to receive and recommend influenza vaccination. However, from the focus group, HCWs reported influenza was a mild disease and would not recommend vaccination for patients who looked healthy. HCWs raised concerns about adverse events, cost-effectiveness, and contraindications to influenza vaccination. HCWs reported, “I would be more likely to recommend vaccination if my employer required that I do so.”

CONCLUSIONS: Health education materials for HCWs could be improved by providing scientific evidence on the burden of influenza disease, the benefits of vaccination, and national and international policies on influenza vaccination. In addition, interventions that may improve influenza vaccination coverage include workplace requirements for influenza vaccination of HCWs and requirements for HCWs to recommend influenza vaccination to high-risk groups in addition to providing no-cost and on-site vaccination.

PMID:41177808 | DOI:10.1111/irv.70157

Curr Med Chem. 2025 Oct 29. doi: 10.2174/0109298673406989251010070419. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to evaluate cyclophilin (CypA) levels in patients with diabetes mellitus (DM) before and after treatment. Metabolic variables, such as weight, blood pressure, and plasma glucose, were assessed in these patients.

METHOD: This prospective cross-sectional study was conducted over 24 weeks. We included 38 patients with DM. After confirming the diagnosis of type 2 diabetes, SGLT2i (empagliflozin vs dapagliflozin) therapy was prescribed to the patients. Weight, body mass index (BMI), waist circumference, body fat ratio, fasting plasma glucose, glycated hemoglobin (HbA1c, %), and CypA levels were measured at 0, 12, and 24 weeks. Patients in the drug subgroup were divided into 2 groups: Empagliflozin (Empa, n=16) and Dapagliflozin (Dapa, n=22).

RESULTS: Weight (p<0.001), body mass index (p<0.001), percentage of body fat (p<0.001), diastolic blood pressure (p=0.006), fasting plasma glucose (p<0.001), HbA1c (p<0.001), serum creatinine (p<0.001), and CypA (p<0.001) levels after the SGLT2i therapy were statistically decreased compared to pre-treatment values in all patients. When comparing drug subgroups, significant decreases in weight (p=0.013) and percentage body fat (p=0.01) were observed in the Empa group compared with the Dapa group at 24 weeks. Changes in FPG (p=0.399), HbA1c (p=0.102), and CypA (p=0.329) between the two groups seemed to be similar.

CONCLUSION: Beyond the improvement of metabolic parameters, SGLT2 treatment reduced CypA levels in patients with DM regardless of drug subgroups. These drugs may further prevent the presence of cardiovascular diseases.

PMID:41177793 | DOI:10.2174/0109298673406989251010070419

J Sci Med Sport. 2025 Oct 22:S1440-2440(25)00479-7. doi: 10.1016/j.jsams.2025.10.011. Online ahead of print.

ABSTRACT

OBJECTIVES: To investigate the prevalence of Relative Energy Deficiency in Sport (REDs) in elite adult team ball sport athletes and critically evaluate the methods used to assess prevalence.

DESIGN: Systematic review.

METHODS: Six databases were searched in October 2024 for original articles published in English from 2005 onwards. Eligible studies measured prevalence of REDs, low energy availability (LEA), or the Triad in elite team ball sport athletes aged ≥18 years.

RESULTS: Fourteen studies met the eligibility criteria (n = 2 case; n = 2 longitudinal; n = 10 cross-sectional), including 265 athletes representing 12 team ball sports. The 12 included cross-sectional and longitudinal studies used six different methods to identify REDs/LEA prevalence as 0-80 %. Seven studies used energy availability calculations, identifying clinical LEA (<30 kcal·kg FFM^-1·day^-1) in 26.3-63.6 % of athletes. The LEA in Females Questionnaire identified LEA in 29.6-80.0 % of participants across 4 studies. Two studies evaluated REDs via blood/salivary markers, with low total-testosterone in 0-36.4 % of participants. One study found 50 % with low free-testosterone, 9.1 % with low free-T3, and 13.6 % with elevated LDL cholesterol. The REDs Specific Screening Tool identified 33.3 % of athletes in one study at medium risk of REDs. The Exercise Dependence Scale and Eating Disorder Examination Questionnaire were distributed in combination in one study, finding prevalence of REDs in 4.3 % and 25.5 % of participants, respectively.

CONCLUSIONS: REDs appears ubiquitous in elite team ball sports, but research remains limited. A criterion approach for evaluating REDs/LEA prevalence is needed for accurate, reliable, and consistent reporting and cross-study comparisons.

PMID:41177746 | DOI:10.1016/j.jsams.2025.10.011

J Prosthet Dent. 2025 Nov 1:S0022-3913(25)00831-5. doi: 10.1016/j.prosdent.2025.10.034. Online ahead of print.

ABSTRACT

STATEMENT OF PROBLEM: Current caries management has emphasized minimally invasive, biologically driven strategies that demand a higher level of precision in caries diagnosis. Artificial intelligence (AI)-driven tools for classifying caries on cone beam computed tomography (CBCT) scans may improve diagnostic accuracy and streamline clinical treatment planning. However, clinically oriented and interpretable AI solutions remain lacking.

PURPOSE: The purpose of this study was to develop and validate an interpretable AI framework, CariesAI-3D, for accurate and robust classification of dentin caries severity on CBCT images.

MATERIAL AND METHODS: A high-quality CBCT dataset comprising 2148 CBCT images of single teeth was established, including sound teeth, moderate caries, deep caries, and extremely deep caries. The dataset was divided into a 5-fold cross-validation set (1826) for model training and validation and an independent test set (322) for final evaluation. CariesAI-3D was developed as a multitask learning network incorporating a spatial-attention feature fusion module (SA-FFM) for caries classification. Its performance was evaluated against 6 baseline models (ResNet-18, ResNet-34, ResNet-50, DenseNet-121, DenseNet-169, and MobileNet-V2) using cross-validation. An ablation study was conducted to evaluate the effectiveness of the SA-FFM. Caries classification performance was assessed using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic (ROC) curve (AUC). The mean absolute difference (MAD) between cross-validation and independent test sets was calculated to quantify model generalization. Statistical significance was assessed using a corrected resampled t test (α=.05).

RESULTS: CariesAI-3D significantly outperformed the baseline models on the cross-validation set, achieving an accuracy of 0.886, precision of 0.882, recall of 0.873, and F1-score of 0.876. The ablation study confirmed that CariesAI-3D with SA-FFM demonstrated better accuracy than both the backbone model and the model with the element-wise feature addition. Furthermore, CariesAI-3D exhibited strong generalization on the independent test set, achieving class-wise AUC values between 0.947 and 0.998, with metric-wise MAD ranging from 0.011 to 0.033. Class activation mapping (CAM) demonstrated that the model’s predictions were highly correlated with caries and pulp regions.

CONCLUSIONS: By integrating multitask learning with an SA-FFM, CariesAI-3D achieved the accurate and interpretable classification of dentin caries severity on CBCT images, demonstrating significant advancements over conventional methods.

PMID:41177738 | DOI:10.1016/j.prosdent.2025.10.034

J Prosthet Dent. 2025 Nov 1:S0022-3913(25)00837-6. doi: 10.1016/j.prosdent.2025.10.030. Online ahead of print.

ABSTRACT

STATEMENT OF PROBLEM: Using nonoriginal abutments compatible with dental implants has become increasingly common. However, with subtle differences in structures and materials, the long-term reliability of nonoriginal components is still a concern.

PURPOSE: The purpose of this in vitro study was to evaluate the micromorphology, internal accuracy, reverse torque, and mechanical properties of 3 nonoriginal and 1 original abutment-implant connections.

MATERIAL AND METHODS: Three brands of nonoriginal abutments (Bioconcept, Bai; Denfec, Gao; and Kerunxi, Ke) and the original abutment (Straumann, ITI) were connected to primitive internal CrossFit connection implants. The elemental composition and surface topography of abutments were analyzed. Cyclic loading was applied to mimic long-term clinical use. The reverse torque value (RTV) was measured in pre-fatigue (RTV1) and post-fatigue (RTV2) specimens. Longitudinally sectioned specimens were used to assess internal accuracy. Post-fatigue fracture strength was tested via uniaxial compression. Statistical analysis was performed using the 1-sample t test, Mann-Whitney U test, Welch ANOVA test, Kruskal-Wallis test, 2-way ANOVA test, or Aligned Rank Transform (ART) ANOVA test (α=.05).

RESULTS: Elemental composition and surface topography differed among groups. No significant intergroup difference in RTV1 was found (P>.05). RTV2 decreased in every group compared with the applied torque value but was higher in Gao than in ITI (P=.002) and Ke (P=.017). Screw jamming was observed in Gao and Ke. All groups showed an extremely high tight contact rate in the platform area. Nonoriginal abutments demonstrated comparable with or higher post-fatigue fracture strength than the original component.

CONCLUSIONS: The tested nonoriginal abutments achieved essentially similar performance to that of the original ones in terms of internal accuracy, RTV, and mechanical properties, suggesting clinical suitability. However, differences existed in elemental composition and surface morphology. Screw jamming was also observed in nonoriginal abutments after fatigue. The selection of nonoriginal abutments still requires careful consideration.

PMID:41177736 | DOI:10.1016/j.prosdent.2025.10.030

Ultrasound Med Biol. 2025 Nov 1:S0301-5629(25)00390-4. doi: 10.1016/j.ultrasmedbio.2025.10.001. Online ahead of print.

ABSTRACT

OBJECTIVE: Increasing muscle mass is the main objective of individuals engaged in resistance training (RT) programs. Traditional imaging techniques such as computed tomography, magnetic resonance imaging and dual-energy X-ray absorptiometry (DXA) are commonly used to quantify fat-free mass (FFM), but their high cost, limited accessibility and technical complexity often restrict their routine use. Ultrasound (US), by contrast, offers a non-invasive, portable and cost-effective alternative and therefore can play an important role in monitoring musculoskeletal adaptations because it provides objective measures of muscle mass and muscle quality. Since the literature does not provide an adequate US-based model to estimate whole-body (WB) and appendicular FFM in resistance-trained adult men, we aimed to develop and validate a US-based equation to predict WB and thigh FFM in resistance-trained men.

METHODS: Seventy-nine men (31.4 ± 7.0 y, 76.3 ± 10.2 kg, 174.3 ± 6.0 cm) with prior RT experience underwent DXA assessments to calculate WB and thigh FFM. Vastus lateralis (VL) and rectus femoris (RF) muscle thickness (MT) were assessed at rest by B-mode US imaging. Stepwise regression analysis was performed to develop US-based equations, and the developed models were cross-validated using the PRESS approach.

RESULTS: VL and RF MT directly correlated with body mass and WB and thigh FFM. Regression analysis showed that RF-MT alone explains 18.9% and 19% of the WB and thigh-FFM, respectively, while VL-MT alone explains 26.5% and 20.1% of the WB and thigh-FFM, respectively. Four models were developed (two for WB and two for thigh-FFM, each based on either VL or RF values), with the best performance being observed for the VL approach: WB-FFM (kg)R² = 0.882 and standard error of estimate (SEE) = 2.99 kg, thigh-FFM (kg)R² = 0.849 and SEE = 0.667 kg.

CONCLUSION: The present study introduces a statistically robust and ecologically applicable mathematical model based on US measurements, specifically on the VL-MT, for assessing FFM in resistance-trained men. This model may also be valuable for detecting muscle asymmetries and monitoring adaptations to training and rehabilitation programs.

PMID:41177731 | DOI:10.1016/j.ultrasmedbio.2025.10.001

Clin Ther. 2025 Nov 1:S0149-2918(25)00345-5. doi: 10.1016/j.clinthera.2025.09.019. Online ahead of print.

ABSTRACT

PURPOSE: Human epidermal growth factor receptor 2 (HER2 [ERBB2]) gene mutations occur in ∼3-5% of non-small cell lung cancer (NSCLC) cases and are associated with poor prognosis. However, real-world data on patients with HER2-mutant (HER2m) NSCLC are needed.

METHODS: This retrospective, observational study evaluated characteristics, treatment patterns, and clinical outcomes of patients with advanced nonsquamous HER2m NSCLC from the Institut Curie (France) and Thoraxklinik Heidelberg (Germany) between 2011 and 2022.

FINDINGS: Of the 55 patients (Curie: n = 17; Heidelberg: n = 38) included in the study, median age at diagnosis was 66 years (range, 22-90), 63.6% were female, and 50.9% had no history of smoking. Forty-eight (87.3%) patients received ≥1 line of therapy, 29 (52.7%) received ≥2 lines of therapy, and 19 (34.5%) received ≥3 lines of therapy. The most common first-line treatment was platinum-based and non-platinum-based chemotherapy (54.2%, n/n = 26/48); treatment patterns in the second- and third-line settings were more diverse than in the first-line setting. Median overall survival was 14.2 months (95% confidence interval [CI] 11.2, 23.2; n = 55) from the diagnosis of advanced disease and 16.5 months (12.3, 29.8; n = 48) from the start of first-line treatment. Median progression-free survival was 5.1 months (95% CI 3.5, 8.5; n = 48) and 4.0 months (2.4, 6.3; n = 29) from the start of first- and second-line treatment, respectively.

IMPLICATIONS: Patients with advanced HER2m NSCLC had a poor prognosis despite treatment with standard-of-care regimens available during the study period. These findings highlight the need for novel therapeutic options to improve clinical outcomes for patients with HER2m NSCLC.

PMID:41177727 | DOI:10.1016/j.clinthera.2025.09.019

Comput Biol Med. 2025 Nov 1;198(Pt B):111240. doi: 10.1016/j.compbiomed.2025.111240. Online ahead of print.

ABSTRACT

Glioblastoma is the most common and deadly brain cancer, known for its rapid progression and heterogeneity at microscopic and macroscopic levels. This heterogeneity is influenced by factors such as tumor cell density, involvement of normal tissue, and gene expression profiles. Mutations in EGFR gene are associated with shorter recurrence intervals and poorer survival outcomes in GBM patients. Non-invasive imaging techniques like MRI can provide valuable insights into EGFR mutations. To reduce the risks of brain biopsies and sampling errors, this study introduces an AI-based decision support system (DSS) for classifying EGFR mutations in GBM patients through automated segmentation of tumorous regions using MRI. The DSS employs deep neural networks (Inception ResNet-v2, DenseNet-121, and ResNet-50) trained on a GBM dataset from Memorial Hospital in Istanbul, which includes three MRI input types: expert segmented, without segmentation, and without tumor. Information criteria (IC) were used to guide model selection by balancing predictive performance and structural complexity. DenseNet-121 showed superior performance, with accuracy scores of 0.952, 0.942, and 0.938 for expert segmented, without segmentation, and absence of tumor inputs, respectively. Precision and recall metrics were also highest for DenseNet-121, especially with expert-segmented inputs. A multivariate statistical analysis confirmed significant differences across model performances. The results underscore the value of integrating information criteria into deep learning pipelines to enhance model robustness and interpretability in medical imaging applications.

PMID:41176824 | DOI:10.1016/j.compbiomed.2025.111240

Eur J Radiol. 2025 Oct 2;194:112463. doi: 10.1016/j.ejrad.2025.112463. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop a deep learning model utilizing conventional MRI sequences integrated with Transformer-based feature fusion to differentiate spinal osteolytic bone metastases (OBM) from multiple myeloma (MM).

MATERIALS AND METHODS: This retrospective study included 663 patients (mean age: 62.13 ± 9.57 years; 378 males) from two medical centers, comprising 342 cases of OBM and 321 cases of MM. All patients underwent MRI examinations with T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and fat-suppressed T2-weighted imaging (T2WI-fs) sequences. Deep learning features were extracted with DenseNet169 to construct classification models, including Multilayer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boosting, AdaBoost, and Naive Bayes. Both feature-level fusion and Transformer-based fusion methods were applied to enhance diagnostic performance. Models were trained (n = 421) and externally tested (n = 242). Their performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA), with the AUC serving as the primary evaluation metric. The Delong test was used to compare model performance.

RESULTS: Among the single-sequence models, T2WI-fs achieved the highest performance, with an AUC of 0.765 and an accuracy of 0.726. Among the fusion methods, the T2WI+T2WI-fs_Transformer fusion model performed best, with an AUC of 0.783, an accuracy of 0.723, followed by the T2WI+T2WI-fs_early fusion model (AUC = 0.762). Although the differences among models were not statistically significant in the external test set (all P > 0.05), the Transformer fusion model demonstrated superior clinical net benefit and robust generalizability.

CONCLUSION: Transformer-based feature fusion of conventional MRI sequences enables accurate, non-invasive differentiation between spinal OBM and MM, providing significant clinical utility.

PMID:41176822 | DOI:10.1016/j.ejrad.2025.112463