Category: Nevin Manimala

Radiologie (Heidelb). 2025 Aug 24. doi: 10.1007/s00117-025-01499-x. Online ahead of print.

ABSTRACT

BACKGROUND: The Bosniak classification system is widely used to assess malignancy risk in renal cystic lesions, yet inter-observer variability poses significant challenges. Large language models (LLMs) may offer a standardized approach to classification when provided with textual descriptions, such as those found in radiology reports.

OBJECTIVE: This study evaluated the performance of five LLMs-GPT‑4 (ChatGPT), Gemini, Copilot, Perplexity, and NotebookLM-in classifying renal cysts based on synthetic textual descriptions mimicking CT report content.

METHODS: A synthetic dataset of 100 diagnostic scenarios (20 cases per Bosniak category) was constructed using established radiological criteria. Each LLM was evaluated using zero-shot and few-shot prompting strategies, while NotebookLM employed retrieval-augmented generation (RAG). Performance metrics included accuracy, sensitivity, and specificity. Statistical significance was assessed using McNemar’s and chi-squared tests.

RESULTS: GPT‑4 achieved the highest accuracy (87% zero-shot, 99% few-shot), followed by Copilot (81-86%), Gemini (55-69%), and Perplexity (43-69%). NotebookLM, tested only under RAG conditions, reached 87% accuracy. Few-shot learning significantly improved performance (p < 0.05). Classification of Bosniak IIF lesions remained challenging across models.

CONCLUSION: When provided with well-structured textual descriptions, LLMs can accurately classify renal cysts. Few-shot prompting significantly enhances performance. However, persistent difficulties in classifying borderline lesions such as Bosniak IIF highlight the need for further refinement and real-world validation.

PMID:40851045 | DOI:10.1007/s00117-025-01499-x

Oncogene. 2025 Aug 24. doi: 10.1038/s41388-025-03546-2. Online ahead of print.

ABSTRACT

Alterations in neurotransmitter signaling can influence colorectal cancer (CRC). In a large, randomized Phase III clinical trial (CALGB/SWOG 80405) involving patients with metastatic CRC, high expression of gamma-aminobutyric acid (GABA) pathway gene GAD1 and low expression of ABAT, indicative of a GABAergic environment, were associated with worse progression-free survival and overall survival outcomes. A metastasis map of human cancer cell lines (MetMap) and functional studies using a microfluidic tumor-on-chip platform demonstrated that high GAD1 expression correlates with increased metastatic potential. Knockdown and pharmacological inhibition of GAD1 reduced tumor invasion, while exogenous GABA promoted invasion. Tumor-derived GABA was elevated in Ras-altered tumors. Furthermore, analysis of publicly available data confirmed that higher GAD1 expression is associated with worse outcomes in Ras-mutant tumors. These findings establish a role for GABA signaling in tumor invasiveness, particularly in Ras-altered CRC. This study demonstrates using clinical data to inform new discoveries and highlights the need for advanced preclinical model systems that more accurately reflect human physiology to explore these findings.

PMID:40851030 | DOI:10.1038/s41388-025-03546-2

Sci Rep. 2025 Aug 25;15(1):31157. doi: 10.1038/s41598-025-12758-1.

ABSTRACT

This study intends to effectively forecast solubility parameter of diverse polymers by creating machine learning models that can grasp the complex relationships between essential input factors like molecular weight, melting point, boiling point, liquid molar volume, radius of gyration, dielectric constant, dipole moment, refractive index, van der Waals area and reduced volume, and parachor, alongside the target variable, which is solubility coefficient of polymers. The goal is to create strong models that accurately capture these intricate relationships to facilitate accurate forecasts of the solubility parameter for polymers. Multiple machine learning algorithms, ranging from basic methods like Linear Regression to advanced techniques such as Artificial Neural Networks (ANNs), Ridge Regression, Lasso Regression, Support Vector Machines (SVMs), Linear Regression, Random Forests (RFs), Gradient Boosting Machines (GBM), K-Nearest Neighbors (KNN), Elastic Net, Decision Trees, Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Convolutional Neural Networks (CNNs), and Extreme Gradient Boosting (XGBoost) were utilized. These methods were utilized to create data-driven models that adeptly seize the intricate connections between input characteristics and output variable, facilitating precise predictions of the solubility parameter for polymers. The efficacy of the developed models was rigorously evaluated using statistical metrics such as R², RMSE, and MRD%, along with visual tools including cross-plots, deviation plots, and SHAP analysis to enhance interpretability and predictive reliability. To guarantee the dataset’s reliability, consisting of 1,799 datapoints on the solubility parameter of polymers, the Monte Carlo outlier detection algorithm was utilized. This stage verified the dataset’s accuracy and appropriateness for model training and evaluation. Results indicated that the models CatBoost, ANN, and CNN surpassed other techniques, attaining superior accuracy shown by the highest R-squared values and the lowest error rates. Sensitivity analysis showed that every input feature impacted the target variable, while SHAP analysis determined that dielectric constant was the most significant factor influencing the solubility parameter of polymers. These results highlight the efficiency of the utilized machine learning methods and emphasize the vital importance of these input parameters in establishing the solubility parameter of polymers. This method not only verifies that the models can make accurate predictions but also provides valuable insights into the impact of input features on solubility parameters of polymers, enhancing algorithm interpretability and scientific understanding.

PMID:40851024 | DOI:10.1038/s41598-025-12758-1

BMC Pediatr. 2025 Aug 25;25(1):649. doi: 10.1186/s12887-025-06017-5.

ABSTRACT

AIM: This study explores the potential of various biomarkers to facilitate the differential diagnosis of late-onset sepsis (LOS) from non-LOS infections in hospitalized pediatric patients.

METHODS: We conducted a retrospective cohort study using electronic medical records from our hospital from January 2022 to December 2023, and divided the patients into LOS (n = 178) and non-LOS (n = 159) groups. Data collected included demographic information, levels of inflammatory and metabolic biomarkers. Descriptive statistics were used for demographic data, and multivariable logistic regression followed by ROC curve analysis was used to assess the diagnostic value of these biomarkers.

RESULTS: Significant differences were observed in the levels of PCT, CRP, Lac, HBP, TNF-α, IL-6, IL-1β, IL-10, and IL-12 between the LOS and non-LOS groups (all p < 0.001). Multivariate logistic regression identified PCT, CRP, IL-6, IL-1β, IL-12, and Lac as independent predictors of LOS. ROC curve analysis showed high diagnostic values for PCT, Lac, and IL-1β. A combined diagnostic model of CRP, Lac, and IL-1β achieved the highest performance with an AUC of 0.958, sensitivity of 97.8%, and specificity of 91.8%. Additionally, Gram-negative LOS was associated with higher levels of PCT, CRP, and IL-6 compared to Gram-positive LOS. PCT levels demonstrated moderate diagnostic performance in differentiating LOS caused by Gram-positive vs. Gram-negative bacteria (AUC = 0.626).

CONCLUSION: The combination of CRP, Lac, and IL-1β serves as a robust set of biomarkers for the differential diagnosis of LOS in pediatric ICU settings. Furthermore, PCT also serves as a critical biomarker for differentiating between Gram-negative and Gram-positive bacterial causes, aiding in more targeted clinical management.

PMID:40851014 | DOI:10.1186/s12887-025-06017-5

Sci Rep. 2025 Aug 24;15(1):31110. doi: 10.1038/s41598-025-12437-1.

ABSTRACT

Fiber Bragg gratings represent a pivotal advancement in the field of photonics and optical fiber technology. The numerical modeling of fiber Bragg gratings is essential for understanding their optical behavior and optimizing their performance for specific applications. In this paper, numerical solutions for the revered optical fiber Bragg gratings that are considered with a cubic-quintic-septic form of nonlinear medium are constructed first time by using an iterative technique named as residual power series technique (RPST) via conformable derivative. The competency of the technique is examined by several numerical examples. By considering the suitable values of parameters, the power series solutions are illustrated by sketching 2D, 3D, and contour profiles. The results obtained by employing the RPST are compared with exact solutions to reveal that the method is easy to implement, straightforward and convenient to handle a wide range of fractional order systems in fiber Bragg gratings. The obtained solutions can provide help to visualize how light propagates or deforms due to dispersion or nonlinearity.

PMID:40851009 | DOI:10.1038/s41598-025-12437-1

Isr J Health Policy Res. 2025 Aug 25;14(1):52. doi: 10.1186/s13584-025-00714-3.

ABSTRACT

BACKGROUND: The Child Dental Care Reform introduced in Israel in 2010 aimed to provide universal dental coverage for children, addressing high caries morbidity and inequalities in access to care. The reform initially covered ages 0-8 and expanded to include all children up to age 18 by 2019. This study examines age-related patterns of dental service utilization during the first decade of its implementation.

METHODS: This retrospective study analyzed anonymized dental service data from 2011 to 2022, submitted by the four Health Maintenance Organizations to the Israeli Ministry of Health. The data included the number of children treated, categorized by age group, and the types of treatments provided.

RESULTS: Service utilization showed distinct age-related patterns, with rates peaking at age 8 (48%) and gradually declining through adolescence (p < 0.001). Restorative care consistently outnumbered preventive care across all age groups (p < 0.001), with children aged 3-5 receiving the most restorative procedures per child. Preventive treatments increased with age, from 1.0 per patient in young children to 1.5 in teenagers, transitioning from mainly dental examinations in younger children to hygienist visits in adolescents. Restorative treatments included dental restorations (peaking at 50% at ages 8-9), extractions (25% at ages 10-11), and pulp treatments (25% at ages 6-8). Emergency dental visits were most common in infants and increased by 83% over the course of a decade (p < 0.001). General anesthesia utilization increased significantly in the younger age groups, with the 4-5 age group showing the most dramatic increase (2.39-fold increase, p < 0.001).

CONCLUSION: This study highlights distinct age-related patterns in dental service utilization among children in Israel, emphasizing the need for targeted prevention strategies and policy reforms to address current challenges disparities, including the increasing rate of treatment under general anesthesia. Preventive interventions, such as community water fluoridation and early childhood programs, alongside improved access to specialized dental care, are essential for fostering better long-term oral health outcomes. Integrating quality indicators will facilitate better incorporation of dental services into the national health system, ensuring comprehensive and equitable oral care.

PMID:40851003 | DOI:10.1186/s13584-025-00714-3

Sci Rep. 2025 Aug 25;15(1):31167. doi: 10.1038/s41598-025-16865-x.

ABSTRACT

This study aimed to compare the 48-month clinical performance and wear of Bis-GMA-based and Bis-GMA-free nanoceramic resin composites in Class I posterior restorations. In a randomized clinical trial, 64 patients received occlusal restorations with either Zenit (Bis-GMA-based) or Neo Spectra ST (Bis-GMA-free) nanoceramic composites (n = 32). Clinical performance was evaluated using modified USPHS criteria at four timepoints (baseline, 12, 24, 48 months). Intraoral scans were analyzed using 3D digital superimposition techniques to assess linear and volumetric quantification of wear across follow-up periods. The results revealed that marginal discoloration was slightly more frequent in the Zenit group at 48 months, though not statistically significant. Clinical outcomes were comparable between groups. The amount of linear deviation measured in Zenit samples was higher than in Neo Spectra, whereas the volumetric deviation was greater in Neo Spectra. However, neither difference was statistically significant. Both composites demonstrated clinically acceptable performance over a 48-month period in Class I posterior restorations. Some marginal discoloration was observed with both materials. The differing matrix-to-filler ratios of the two nanoceramic resin composites may have contributed to compensating for volumetric wear. Intraoral scanning and digital analysis enable accurate, non-invasive wear monitoring. Neo Spectra ST offers superior esthetic stability and clinical handling. Neo Spectra™ ST may offer a clinically advantageous option for posterior restorations requiring esthetic durability and operator-friendly handling. Additionally, digital intraoral scanning combined with registration software provides a promising, non-invasive approach for monitoring restorative wear in clinical practice.Clinical trial registration: This study was registered on clinical trial ( http://www.ClinicalTrials.gov ) at February 4, 2021 with ID: NCT04738604.

PMID:40850984 | DOI:10.1038/s41598-025-16865-x

Sci Rep. 2025 Aug 24;15(1):31083. doi: 10.1038/s41598-025-16578-1.

ABSTRACT

To assess the function of the erector spinae muscle’s cross-sectional area (ESM_CSA)as a biomarker for the outcome of AECOPD hospitalized patients. Based on chest CT imaging, ESM_CSA were caculated following admission. Cox regression analyses, including univariate and multivariate approaches, were utilized to determine risk factors associated with 1-year mortality and initial hospitalization in patients with AECOPD. Additionally, Poisson regression was implemented to assess the rate of rehospitalization. There were 236 AECOPD patient included in the present study, including 59 and 177 patients in the ESM_CSA lower group and normal groups respectively. Seventeen patients died within 1 year after discharged from the hospital, and the 1-year mortality rates were 15.3% and 4.5% for the ESM_CSA lower group and normal group. A total of 112 patients suffered from 273 rehospitalizations for AECOPD within 1 year after discharged from hospital. Cox regression analysis showed that ESM_CSA were associated with the 1-year first hospitalization for AECOPD. Poisson regression analysis showed that ESM_CSA were associated with the rate of rehospitalization for AECOPD (IRR = 0.57, 95% CI 0.45-0.73 for univariate analysis, and IRR = 0.56, 95% CI 0.43-0.72for multivariate analysis). Both univariate (HR = 0.29 95% CI: 0.11-0.75) and multivariate Cox regression analyses (HR = 0.35, 95% CI: 0.12-0.99) showed that ESM_CSA was associated with 1-year mortality. Lower ESM_CSA was a risk factor of 1-year mortality and 1-year rehospitalization for AECOPD.

PMID:40850983 | DOI:10.1038/s41598-025-16578-1

Sci Rep. 2025 Aug 25;15(1):31162. doi: 10.1038/s41598-025-15334-9.

ABSTRACT

The Poisson-Inverse Gaussian regression model is a widely used method for analyzing count data, particularly in over-dispersion. However, the reliability of parameter estimates obtained through maximum likelihood estimation in this model can be compromised when multicollinearity exists among the explanatory variables. Multicollinearity means that high correlations between explanatory variables inflate the variance of the maximum likelihood estimates and increase the mean squared error. To handle this problem, the Poisson-Inverse Gaussian ridge regression estimator has been proposed as a viable alternative. This paper introduces a generalized ridge estimator to estimate regression coefficients in the Poisson-Inverse Gaussian regression model under multicollinearity. The performance of the proposed estimator is evaluated through a comprehensive simulation study, covering various scenarios and employing the mean squared error as the evaluation criterion. Furthermore, the practical applicability of the estimator is demonstrated using two real-life datasets, with its performance again assessed based on mean squared error. Theoretical analyses, supported by simulation and empirical findings, suggest that the proposed estimator outperforms existing methods, offering a more reliable solution in multicollinearity.

PMID:40850969 | DOI:10.1038/s41598-025-15334-9

Adm Policy Ment Health. 2025 Aug 25. doi: 10.1007/s10488-025-01466-7. Online ahead of print.

ABSTRACT

To improve access to mental health care for children and adolescents, it is necessary to identify the barriers faced by their caregivers. The aim of this study is to identify these barriers in Greece and to investigate the reliability and validity of the modified version of the Barriers to Access to Care Evaluation scale (BACE) – the BACE Proxy Report (BACE-PR). A total of 265 caregivers who reported that their offspring had mental health difficulties completed the BACE-PR. Descriptive statistics were used to identify the major barriers to accessing care. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to investigate the factor structure of the instrument. Item parameters were assessed via Item Response Theory. Interpretability was assessed by linking summed scores to IRT-based scores. Caregivers reported care costs, their willingness to resolve problems on their own, and their own concern that their children might be seen as weak, as the major barriers to services access. Obsessive compulsive symptoms and self-harm were the conditions for which caregivers reported the highest level of barriers. EFA and CFA suggested that a one-factor solution fit the data well (RMSEA = 0.048, CFI = 0.991, TLI = 0.990). Internal consistency was found to be high (ω = 0.96). Average z-scores provided five meaningful levels of caregivers’ perceived barriers compared to the national average. Caregivers face a variety of barriers to access mental health care for their children, and this could partly explain the treatment gap in the Greek mental health sector. Our study provides evidence for the reliability and validity of the BACE-PR scale, which can aid to identify caregiver-perceived barriers and to design interventions to improve access to mental health care.

PMID:40850964 | DOI:10.1007/s10488-025-01466-7