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Background The floating population in China consists primarily of internal immigrants and represents a typical health vulnerable group. Poor health literacy has recently become an obstacle in the accessibility and utilization of health services for the vulnerable population, leading to adverse health outcomes. This study aimed to examine whether health literacy affected health outcomes in China’s floating population and whether health service utilization had a mediating effect between health literacy and health outcomes. Method The current study utilized a cross-sectional stratified, multistage, proportional to scale (PPS) study in Zhejiang Province, China, in November and December 2019. In total, 657 valid self-reported questionnaires were recovered and used for data collection. Questionnaires included questions regarding sociodemographic characteristics, health literacy, health outcomes, and health service utilization. Confirmatory factor analysis was used to test questionnaire validity; descriptive statistics were used to understand the demographic characteristics of the floating population; and structural equation modeling was used to determine whether health service utilization mediated health literacy and health outcomes. Results We report positive correlations between health literacy, health service utilization, and health outcomes. Mediation analysis demonstrated that health service utilization had partial mediating effects between health literacy and health outcomes. In the relationship between health literacy and health outcomes, the indirect effects of health service utilization accounted for 6.6–8.7% of the total effects. Conclusion Complete health literacy, through health care literacy and health promotion literacy, affects the mobile population’s initiative to use health services, which, in turn, affects health outcomes. Thus, improving the health literacy of the floating population will help to improve health outcomes. Furthermore, health service providers should enhance the diversity of health service supply to ensure that the floating population has the external resources to improve personal health literacy.

Identification of potential factors that can stratify a tumor’s response to specific therapies will aid in the selection of cancer therapy. The aim was to highlight the role of programmed cell death 1 ligand 1 (PD-L1) in bladder cancer. In this study, 92 of muscle-invasive bladder cancers and 28 of non- muscle invasive bladder cancers were selected for immunohistochemical staining analysis. Furthermore, human and murine bladder cancer cell lines were used to examine the correlation between PD-L1 and radiation response. Our data revealed that PD-L1 was overexpressed in the bladder tumor specimens compared with adjacent non-malignant specimens. Furthermore, the staining of PD-L1 was significantly linked to higher clinical stage, lower complete response rates and reduced disease-free survival rates. By in vitro and in vivo experiments, irradiation up-regulated the expression of PD-L1 in tumor cells and its increase correlated with the irradiation dose. In immunocompetent mouse models, blocking PD-L1 induced a longer tumour growth delay following irradiation. The inhibition of T cell functions including proliferation and cytotoxicity against tumor cells was responsible to the effects of PD-L1 on radiation response. In conclusion, PD-L1 could be a significant clinical predictor for clinical stage and treatment response of bladder cancer.

Background People in Taiwan enjoy comprehensive National Health Insurance coverage. However, under the global budget constraint, hospitals encounter enormous challenges. This study was designed to examine Taiwan medical centers' efficiency and factors that influence it. Methods We obtained data from open sources of government routine publications and hospitals disclosed by law to the National Health Insurance Administration, Ministry of Health and Welfare, Taiwan. The dynamic data envelopment analysis ( DDEA) model was adopted to estimate all medical centers' efficiencies during 2015–2018. Beta regression models were used to model the efficiency level obtained from the DDEA model. We applied an input-oriented approach under both the constant returns-to-scale (CRS) and variable returns-to-scale (VRS) assumptions to estimate efficiency. Results The findings indicated that 68.4% (13 of 19) of medical centers were inefficient according to scale efficiency. The mean efficiency scores of all medical centers during 2015–2018 under the CRS, VRS, and Scale were 0.85, 0.930, and 0.95,respectively. Regression results showed that an increase in the population less than 14 years of age, assets, nurse-patient ratio and bed occupancy rate could increase medical centers' efficiency. The rate of emergency return within 3-day and patient self-pay revenues were associated significantly with reduced hospital efficiency ( p  < 0.05). The result also showed that the foundation owns medical center has the highest efficiency than other ownership hospitals. Conclusions The study results provide information for hospital managers to consider ways they could adjust available resources to achieve high efficiency.

Gene expression studies often use bulk RNA sequencing of mixed cell populations because single cell or sorted cell sequencing may be prohibitively expensive. However, mixed cell studies may miss expression patterns that are restricted to specific cell populations. Computational deconvolution can be used to estimate cell fractions from bulk expression data and infer average cell-type expression in a set of samples (e.g., cases or controls), but imputing sample-level cell-type expression is required for more detailed analyses, such as relating expression to quantitative traits, and is less commonly addressed. Here, we assessed the accuracy of imputing sample-level cell-type expression using a real dataset where mixed peripheral blood mononuclear cells (PBMC) and sorted (CD4, CD8, CD14, CD19) RNA sequencing data were generated from the same subjects (N=158), and pseudobulk datasets synthesised from eQTLgen single cell RNA-seq data. We compared three domain-specific methods, CIBERSORTx, bMIND and debCAM/swCAM, and two cross-domain machine learning methods, multiple response LASSO and ridge, that had not been used for this task before. We also assessed the methods according to their ability to recover differential gene expression (DGE) results. LASSO/ridge showed higher sensitivity but lower specificity for recovering DGE signals seen in observed data compared to deconvolution methods, although LASSO/ridge had higher area under curves than deconvolution methods. Machine learning methods have the potential to outperform domain-specific methods when suitable training data are available.

Monitoring dynamic changes in oxygen consumption rates (OCR) of a living organism in real time provide an indirect method of monitoring changes in mitochondrial function during development, aging, or malfunctioning processes. In this study, we developed a microfluidic device integrated with an optical detection system to measure the OCR of a single developing Caenorhabditis elegans (C. elegans) from postembryonic development to aging stages in real time via phase-based phosphorescence lifetime measurement. The device consists of two components: an acrylic microwell deposited with an oxygen-sensitive luminescent layer for oxygen (O2) measurement and a microfluidic module with a pneumatically driven acrylic lid to controllably seal the microwell. We successfully measured the basal respiration (basal OCR, in pmol O2/min/worm) of a single C. elegans inside a microwell from the stages of postembryonic development (larval stages) through adulthood to aged adult. Sequentially adding metabolic inhibitors to block bioenergetic pathways allowed us to measure the metabolic profiles of a single C. elegans at key growth and aging stages, determining the following fundamental parameters: basal OCR, adenosine triphosphate (ATP)-linked OCR, maximal OCR, reserve respiratory capacity, OCR due to proton leak, and non-mitochondrial OCR. The bioenergetic health index (BHI) was calculated from these fundamental parameters to assess the bioenergetic health of a single developing C. elegans from the postembryonic development to aging stages. The changes in BHI are correlated to C. elegans development stage, with the highest BHI = 27.5 for 4-day-old adults, which possess well-developed bioenergetic functionality. Our proposed platform demonstrates for the first time the feasibility of assessing the BHI of a single C. elegans from postembryonic development to aging stages inside a microfluidic device and provides the potential for a wide variety of biomedical applications that relate mitochondrial malfunction and diseases.

Purpose This study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT). Materials and methods This study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression ( p -value < 0.05). These features were then employed to train and evaluate Logistic Regression (LR) and Random Forest (RF) models, using tenfold cross-validation to ensure robust assessment of model efficacy. Results In this study, 102 out of 120 VMAT-treated breast cancer patients were included in the detailed analysis. Thirty-two percent of these patients developed Grade 2 + RD. Age and BMI were identified as significant clinical predictors. Through feature selection, we narrowed down the vast pool of radiomic and dosiomic data to 689 features, distributed across 10 feature subsets for model construction. In the LR model, the J subset, comprising DVH, Radiomics, and Dosiomics features, demonstrated the highest predictive performance with an AUC of 0.82. The RF model showed that subset I, which includes clinical, radiomic, and dosiomic features, achieved the best predictive accuracy with an AUC of 0.83. These results emphasize that integrating radiomic and dosiomic features significantly enhances the prediction of Grade 2 + RD. Conclusion Integrating clinical, radiomic, and dosiomic characteristics into AI models significantly improves the prediction of Grade 2 + RD risk in breast cancer patients post-VMAT. The RF model analysis demonstrates that a comprehensive feature set maximizes predictive efficacy, marking a promising step towards utilizing AI in radiation therapy risk assessment and enhancing patient care outcomes.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines have been used worldwide to control the coronavirus disease pandemic. However, several adverse effects have been reported following vaccination. Therefore, further research on the adverse effects in individuals predisposed to life-threatening conditions is needed. Herein, we present a 39-year-old woman without any systemic disease who developed fulminant type 1 diabetes mellitus (T1DM) (low glycohemoglobin levels, despite hyperglycemia and diabetic ketoacidosis (DKA)) following SARS-CoV-2 vaccination. The patient was initially misdiagnosed as having fresh type 2 diabetes mellitus after the first episode of DKA, which was resolved by short-term insulin therapy and treated with oral anti-diabetic agents after the DKA was resolved. This made her develop a second episode of DKA shortly after treatment. The course and presentation of our case are noteworthy for alerting clinicians to vaccine-related fulminant T1DM.

To evaluate the potential benefit of HyperArc (HA) fractionated stereotactic radiotherapy (FSRT) for the benign brain lesion. Sixteen patients with a single deep-seated, centrally located benign brain lesion treated by CyberKnife (CK, G4 cone-based model) were enrolled. Treatment plans for HA with two different optimization algorithms (SRS NTO and ALDO) and coplanar RapidArc (RA) were generated for each patient to meet the corresponding treatment plan criteria. These four FSRT treatment plans were divided into two groups—the homogeneous delivery group (HA-SRS NTO and coplanar RA) and the inhomogeneous delivery group (HA-ALDO and cone-based CK)—to compare for dosimetric outcomes. For homogeneous delivery, the brain V5, V12, and V24 and the mean brainstem dose were significantly lower with the HA-SRS NTO plans than with the coplanar RA plans. The conformity index, high and intermediate dose spillage, and gradient radius were significantly better with the HA-SRS NTO plans than with the coplanar RA plans. For inhomogeneous delivery, the HA-ALDO exhibited superior PTV coverage levels to the cone-based CK plans. Almost all the doses delivered to organs at risk and dose distribution metrics were significantly better with the HA-ALDO plans than with the cone-based CK plans. Good dosimetric distribution makes HA an attractive FSRT technique for the treatment of benign brain lesions.

Background Stereotactic body radiation therapy (SBRT) has been an emerging non-invasive treatment modality for patients with hepatocellular carcinoma (HCC) when curative treatments cannot be applied. In this study, we report our clinical experience with Cyberknife SBRT for unresectable HCC and evaluate the efficacy and clinical outcomes of this highly sophisticated treatment technology. Methods Between 2008 and 2012, 115 patients with unresectable HCC treated with Cyberknife SBRT were retrospectively analyzed. Doses ranged from 26 Gy to 40 Gy were given in 3 to 5 fractions for 3 to 5 consecutive days. The cumulative probability of survival was calculated according to the Kaplan-Meier method and compared using log-rank test. Univariate and multivariate analysis were performed using Cox proportional hazard models. Results The median follow-up was 15.5 months (range, 2-60 months). Based on Response Evaluation and Criteria in Solid Tumors (RECIST). We found that 48.7 % of patients achieved a complete response and 40 % achieved a partial response. Median survival was 15 months (4-25 months). Overall survival (OS) at 1- and 2-years was 63.5 %(54-71.5 %) and 41.3 % (31.6-50.6 %), respectively, while 1- and 2- years Progression-free Survival (PFS) rates were 42.8 %(33.0-52.2 %) and 38.8 % (29.0-48.4 %). Median progression was 6 months (3-16 months). In-field recurrence free survival at 1 and 2 years was 85.3 % (76.2-91.1 %) and 81.6 % (72.2-88.6 %), respectively, while the 1- and 2-years out-field recurrence free survival were 52.5 % (41.2-60.8 %) and 49.5 %(38.9-59.2 %), respectively. Multivariate analysis revealed that Child-Pugh score (A vs. B), Portal vein tumor thrombosis (positive vs. negative), Tumor size (≤4 cm vs >4-9 cm /≥10 cm), and tumor response after SBRT (CR vs. PR/stable) were independent predictors of OS. Acute toxicity was mostly transient and tolerable. Conclusions Cyberknife SBRT appears to be an effective non-invasive treatment for local unresectable HCC with low risk of severe toxicity. These results suggested that Cyberknife SBRT can be a good alternative treatment for unresectable HCC unsuitable for standard treatment.