Nevin Manimala

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

Sci Rep. 2025 Aug 25;15(1):31156. doi: 10.1038/s41598-025-16042-0.

ABSTRACT

The primary objective of modern healthcare systems is to enhance public health by providing efficient, reliable, and well-structured solutions. Improving patient satisfaction through tailored medical services has driven rapid advancements in healthcare, leading to increased competition and system complexity. However, the expansion of healthcare services introduces challenges such as high data volume, latency, response time constraints, and security vulnerabilities. To address these issues, fog computing offers an effective solution by processing data closer to end devices, reducing latency, and enabling real-time responses. This research proposes a robust brain tumor detection framework within a fog-based smart healthcare infrastructure. The process begins with data placement leveraging an improved evolutionary technique for Image Processing (HETS-IP) to optimize fog node placement based on key parameters such as energy efficiency and latency. Specifically, the Particle Swarm Optimization (PSO) algorithm is enhanced with a direct binary encoding technique, in which solutions are represented as binary strings, making it suitable for problems where decisions are discrete. This approach allows efficient optimization in binary decision spaces and improves adaptability for complex placement problems. Once data placement is committed, the tumor detection framework is performed directly at fog nodes to enhance real-time processing. This phase will begin with preprocessing, where a bilateral filter is applied to reduce noise while preserving critical edge details. Next, feature extraction is utilized to derive statistical texture features, which capture diagnostic information essential for distinguishing between tumor types. The process continues by classification using a deep Convolutional Neural Network (CNN) with sequential architecture to classify tumors. Simulation results demonstrate that HETS-IP outperforms traditional evolutionary algorithms, including Ant Colony Optimization (ACO), Genetic Algorithm-Simulated Annealing (GASA), and Genetic Algorithm (GA). On average, HETS-IP reduces energy consumption by 5%, 9%, and 14% and decreases makespan by 4%, 6%, and 11%, respectively. Additionally, the proposed approach achieves an accuracy of 97% and a precision of 96%, ensuring highly reliable brain tumor detection.

PMID:40850959 | DOI:10.1038/s41598-025-16042-0

Immunol Res. 2025 Aug 25;73(1):123. doi: 10.1007/s12026-025-09679-6.

ABSTRACT

Psoriasis is one of the most common chronic inflammatory skin diseases and is characterised by the uncontrolled proliferation of keratinocytes and their abnormal differentiation. Sirtuins are a group of enzymes that play an important role in post-translational modifications of proteins, such as deacetylation, poly-ADP-ribosylation, demalonylation and lipoamidation. They are found in various cell types and are involved in ribosomal DNA recombination, gene silencing and DNA repair. This study aimed to examine the plasma levels of sirtuin 3, 4 and 5 in patients with psoriasis and correlate these levels with clinical parameters. The study included 43 patients with plaque-type psoriasis and 28 healthy controls. The plasma concentrations of sirtuin 3 were statistically significantly increased in patients with psoriasis compared to the control subjects. The plasma concentrations of sirtuin 4 and 5 were statistically significantly lower in patients with psoriasis than in the control group. No statistically significant correlations were found between the plasma levels of sirtuin 3 and 4 and the psoriasis activity tools of PASI, DLQI and the BSA index or the selected clinical parameters in patients with psoriasis. Plasma concentrations of sirtuin 5 correlated statistically significantly with the BSA index, haemoglobin and leukocytes. The results of this study suggest the involvement of sirtuin 3, 4 and 5 in the pathogenesis of psoriasis. However, an explanation of the role of sirtuins in psoriasis requires further research.

PMID:40850958 | DOI:10.1007/s12026-025-09679-6

Med Phys. 2025 Sep;52(9):e18063. doi: 10.1002/mp.18063.

ABSTRACT

BACKGROUND: Diagnosing and characterizing breast lesions and tumors remains a common challenge in clinical practice. Ultrasound imaging stands out for its safety, real-time capability, and affordability. However, the image quality of conventional ultrasound examination is limited, and the diagnosis of ultrasonographic images depends heavily on the experience of the sonographer. Therefore, improving ultrasound images and extracting tissue information from ultrasound signals to provide auxiliary means is crucial for accurate breast tumor diagnosis.

PURPOSE: Medical ultrasound imaging has been widely used in clinical diagnosis. However, traditional ultrasound has limitations in the diagnosis of breast soft tissue diseases. This study proposed a high scattering sensitivity fuzzy entropy (FE) imaging method to enhance image contrast and improve detectability for breast tumors. Moreover, this imaging method can make a preliminary classification and characterization of benign and malignant breast lesions through quantitative analysis of ultrasound radio frequency data and the calculation of the entropy value without biopsy examination.

METHODS: To achieve the fuzzy entropy imaging, a sliding window is selected to traverse across the image with a step of one sampling point while the entropy value within the sliding window is calculated. This entropy value is assigned to the center pixel of the window. The parametric image was obtained after the entropy values of all pixels were calculated. During the clinical experiments, the breast lesions were classified as benign or malignant by biopsy examination. After entropy imaging, the average entropy value of the lesion area was calculated. The entropy values of all cases of benign and malignant tumors were averaged, respectively, to verify whether the fuzzy entropy can characterize the breast lesions. All the statistical analysis was conducted by one-sample t-test to obtain the mean value and standard deviation. The Tukey test was performed, and the effect size of Cohen’s d was calculated to verify whether there was a significant difference between the entropy value of benign lesions and malignant lesions.

RESULTS: In the clinical breast imaging experiment, the FE method obtained the highest Matthews correlation coefficient (MCC) of 0.875 ± 0.047 (p < 0.0001) and F1 score of 0.876 ± 0.049 (p < 0.0001). The MCC and F1 scores of FE imaging were significantly different from those of other entropy imaging methods in the Tukey test (p < 0.0001). The effect sizes of Cohen’s d of F1 score of FE method compared with the WSE method and hNSE method were 1.498 and 1.107, respectively. The contrast-to-noise ratio (CNR) of FE images increased by 124.37% (p < 0.0001) compared with B-mode images (5.210 ± 3.136, p < 0.0001). The above results show that the FE method has good comprehensive performance in improving the detection accuracy and contrast of breast lesions. The fuzzy entropy value of benign tumors (0.033 ± 0.0.14, p < 0.0001) is higher than that of malignant tumors (0.022 ± 0.013, p < 0.0001) with both statistical and practical significance, indicating that the benign and malignant tumors can be characterized and classified by fuzzy entropy value.

CONCLUSIONS: The proposed ultrasound fuzzy entropy breast imaging method can effectively improve the ultrasound imaging performance and the ability to detect lesions, because fuzzy entropy can measure the microscopic chaos of breast tissue and enhance the scattering information characteristics in the signal. Meanwhile, fuzzy entropy imaging can classify benign and malignant lesions, because fuzzy entropy considers the causality within the ultrasound signal, avoiding information aliasing and loss, so that it can detect weaker information in the signal and can reflect organizational information more accurately.

PMID:40849881 | DOI:10.1002/mp.18063

Mycopathologia. 2025 Aug 24;190(5):77. doi: 10.1007/s11046-025-00982-2.

ABSTRACT

BACKGROUND: With the increasing incidence of fungal infection in China, the need for rapid and accurate diagnosis of mycosis is crucial. Therefore, it is necessary to understand the diagnosis capacity for mycosis.

METHODS: A cross-sectional online survey was conducted across all 31 provincial-level regions in China from August 2023 to April 2024. The survey comprised 77 questions evaluating fungal diagnostic methods, including culture, microscopy, molecular tests, and related biomarkers. Data from 1,009 valid responses were stratified by hospital tier (tertiary A vs. non-tertiary A) and regional GDP levels to analyze on-site testing capacity and outsourcing patterns.

RESULTS: Among the 1,009 respondents, 78.5% were from tertiary A hospitals. Mycology testing was more commonly performed in tertiary hospitals compared to other. Traditional mycological diagnostic methods showed no significant differences in application across regions, regardless of economic development. However, disparities emerged in novel tests, particularly molecular diagnostics: hospitals in low-GDP regions were more likely to outsource molecular testing or lack in-house capacity.

CONCLUSIONS: China’s fungal diagnostic capacity remains concentrated in tertiary A hospitals and high-GDP regions. Future efforts should prioritize expanding molecular testing access and optimizing resource distribution across all healthcare settings.

PMID:40849872 | DOI:10.1007/s11046-025-00982-2

Discov Oncol. 2025 Aug 24;16(1):1606. doi: 10.1007/s12672-025-03472-5.

ABSTRACT

BACKGROUND: Gastric cancer (GC) has a high mortality rate due to the diagnosis in advanced stages. Aberrant DNA methylation is the earliest event in carcinogenesis and can be noninvasively detected in cell-free DNA (cfDNA) from gastric cancer patients.

METHODS: A total of 143 serum samples were analyzed, including 33 GC patients, 30 chronic gastritis (ChG) patients, and 80 healthy individuals. Additionally, tissue samples were collected from 30 GC patients (stages I-IV) and 38 ChG patients. Methylation patterns of ten genes were examined in GC cells, as well as in serum and tissue samples from GC, ChG, and control groups using methylation-specific qPCR. Statistical evaluations were conducted on various parameters including Ct differences, categorical variables, sensitivity, and specificity.

RESULTS: APC, CDH1, RASSF1A, hMLH1, RUNX3, p16, SFRP2, RNF180, PCDH10, and RPRM were all significantly hypermethylated in the tissues of GC patients compared to those with ChG (P < 0.001). SFRP2, RPRM, APC, PCDH10, and RNF180 genes were analyzed in sera of 3 groups. Among them, SFRP2 methylation was detected in 71.87% of GC, 16.6% of ChG and 8.8% of the control group. The methylation frequencies of RPRM were 66.6% in GC, 13.3% in ChG, and 7.5% in the control group. In a dual-gene panel assay combining SFRP2 and RPRM, the sensitivity and specificity for detecting gastric cancer in serum samples were 57.58% and 96.25%, respectively, when comparing the cancer and control groups. The sensitivity was 78.79%, the specificity was 90.00% and AUC was 0.931 for GC and control groups (P < 0.0001). The sensitivity was 78.79%, the specificity was 83.33% and AUC was 0.879 for the discrimination of GC and ChG (P < 0.0001).

CONCLUSIONS: Methylation of 10 genes were studied and a prototype early diagnosis tool for GC utilizing SFRP2 and RPRM with high sensitivity and specificity was developed.

PMID:40849852 | DOI:10.1007/s12672-025-03472-5

Odontology. 2025 Aug 24. doi: 10.1007/s10266-025-01179-1. Online ahead of print.

ABSTRACT

To investigate the influence of different resin composites used for sealing the screw-access hole of zirconia abutments on the fatigue behavior of lithium disilicate ceramic. Eighty 3YSZ abutment discs (IPS e.max ZirCAD MO, Ivoclar AG) (Ø = 10 mm; 3 mm thickness; Ø = 2.5 mm access channel) and lithium disilicate restorative discs (IPS e.max CAD, Ivoclar AG) (Ø = 10 mm; 1 mm thickness) were obtained and randomly allocated into four groups based on the sealing protocol (2 mm of thickness): Ctrl (PFTE Tape); PFTE tape + nanohybrid resin; PFTE tape + bulk-fill resin; and PFTE tape + Flow resin. After cementation procedures, monotonic (n = 5) and cyclic fatigue tests were conducted (n = 15; initial load of 100 N for 5000 cycles, increments of 100 N every 10,000 cycles at 20 Hz, immersed in distilled water) until failure. Fractographic and finite element analysis were also performed. One-way ANOVA and Tukey post-hoc tests were carried out for the monotonic data, while Kaplan-Meier and Mantel-Cox tests were used for survival rates. No statistically significant effect of the presence neither the type of resin composite material was observer after the monotonic tests. For the fatigue test, the Bulk and Nano groups exhibited significantly better performance than the Ctrl and Flow (Ctrl: 1100 N ≤ Flow: 1213 N < Nano: 1340 N ≤ Bulk: 1380 N, p ≤ 0.05). Nanohybrid or bulk-fill resin composites are recommended for sealing the abutment screw-access hole and optimize the performance of lithium disilicate restorations.

PMID:40849850 | DOI:10.1007/s10266-025-01179-1

J Relig Health. 2025 Aug 24. doi: 10.1007/s10943-025-02421-4. Online ahead of print.

ABSTRACT

Death literacy is a novel concept that refers to knowledge, skills, and experiences related to end-of-life and death care. The purpose of this study was to identify nursing students’ death literacy levels and examine the effects of sociodemographic characteristics, end-of-life care experiences, and spiritual well-being on death literacy. Data for this descriptive and correlational study were collected using a descriptive information form, the death literacy index, and the spiritual well-being scale. The study was carried out between 15 March and 30 May 2024 with the participation of nursing students (n = 930) enrolled in the Nursing Departments of two universities in Western Turkey. The participants’ death literacy was moderate, while their spiritual well-being was high. According to the results of the hierarchical linear regression analysis, the statistically significant factors affecting death literacy among nursing students were gender (β = 0.149), class year (β = 0.107), supporting someone with a life-threatening illness (β = 0.077), supporting a grieving person (β = 0.079), and the harmony with nature subdimension of spiritual well-being (β = 0.181). Transcendence was initially a significant predictor of death literacy; however, its direct effect diminished and became non-significant when the harmony with nature subdimension was added to the model. This suggests that transcendence may influence death literacy indirectly through its association with harmony with nature. In addition, the anomie subdimension was not found to be a significant predictor of death literacy. Having high levels of death literacy and spiritual well-being may help nursing students provide patients and patients’ relatives with higher-quality care. Therefore, the integration of these concepts into nursing education will increase the quality of patient care by helping nurses become more qualified and sensitive in their provision of end-of-life care.

PMID:40849847 | DOI:10.1007/s10943-025-02421-4