Additional file 1 of Relationship between social capital and quality of life among adult stroke patients: a cross-sectional study in Anhui Province, China

Additional file 1: The location of sampling areas in Anhui province, China

Additional file 2 of Relationship between social capital and quality of life among adult stroke patients: a cross-sectional study in Anhui Province, China

Additional file 2: The Questionnaire of this study (English version)

Data_Sheet_1_Association Between Platelet Levels and 28-Day Mortality in Patients With Sepsis: A Retrospective Analysis of a Large Clinical Database MIMIC-IV.docx

BackgroundThis research focused on evaluating the correlation between platelet count and sepsis prognosis, and even the dose-response relationship, in a cohort of American adults.MethodPlatelet counts were recorded retrospectively after hospitalization for patients admitted to Beth Israel Deaconess Medical Center’s intensive care unit between 2008 and 2019. On admission to the intensive care unit, sepsis patients were divided into four categories based on platelet counts (very low 9/L, intermediate-low 50 × 109–100 × 109/L, low 100 × 109–150 × 109/L, and normal ≥ 150 × 109/L). A multivariate Cox proportional risk model was used to calculate the 28-day risk of mortality in sepsis based on baseline platelet counts, and a two-piece linear regression model was used to calculate the threshold effect.ResultsThe risk of 28-day septic mortality was nearly 2-fold higher in the platelet very low group when compared to the low group (hazard ratios [HRs], 2.24; 95% confidence interval [CI], 1.92–2.6). Further analysis revealed a curvilinear association between platelets and the sepsis risk of death, with a saturation effect predicted at 100 × 109/L. When platelet counts were below 100 × 109/L, the risk of sepsis 28-day death decreased significantly with increasing platelet count levels (HR, 0.875; 95% CI, 0.84–0.90).ConclusionWhen platelet count was less than 100 × 109/L, it was a strong predictor of the potential risk of sepsis death, which is declined by 13% for every 10 × 109/L growth in platelets. When platelet counts reach up to 100 × 109/L, the probability of dying to sepsis within 28 days climbs by 1% for every 10 × 109/L increase in platelet count.</p

Data_Sheet_1_Radiomics-based infarct features on CT predict hemorrhagic transformation in patients with acute ischemic stroke.CSV

ObjectiveTo develop and validate a model based on the radiomics features of the infarct areas on non-contrast-enhanced CT to predict hemorrhagic transformation (HT) in acute ischemic stroke.Materials and methodsA total of 118 patients diagnosed with acute ischemic stroke in two centers from January 2019 to February 2022 were included. The radiomics features of infarcted areas on non-contrast-enhanced CT were extracted using 3D-Slicer. A univariate analysis and the least absolute shrinkage and selection operator (LASSO) were used to select features, and the radiomics score (Rad-score) was then constructed. The predictive model of HT was constructed by analyzing the Rad-score and clinical and imaging features in the training cohort, and it was verified in the validation cohort. The model was evaluated with the receiver operating characteristic curve, calibration curve and decision curve, and the prediction performance of the model in different scenarios was further discussed hierarchically.ResultsOf the 118 patients, 52 developed HT, including 21 cases of hemorrhagic infarct (HI) and 31 cases of parenchymal hematoma (PH). The Rad-score was constructed from five radiomics features and was the only independent predictor for HT. The predictive model was constructed from the Rad-score. The area under the curve (AUCs) of the model for predicting HT in the training and validation cohorts were 0.845 and 0.750, respectively. Calibration curve and decision curve analyses showed that the model performed well. Further analysis found that the model predicted HT for different infarct sizes or treatment methods in the training and validation cohorts with 78.3 and 71.4% accuracy, respectively. For all samples, the model predicted an AUC of 0.754 for HT in patients within 4.5 h since stroke onset, and predicted an AUC of 0.648 for PH.ConclusionThis model, which was based on CT radiomics features, could help to predict HT in the setting of acute ischemic stroke for any infarct size and provide guiding suggestions for clinical treatment and prognosis evaluation.</p

Table_1_Radiomics-based infarct features on CT predict hemorrhagic transformation in patients with acute ischemic stroke.docx

ObjectiveTo develop and validate a model based on the radiomics features of the infarct areas on non-contrast-enhanced CT to predict hemorrhagic transformation (HT) in acute ischemic stroke.Materials and methodsA total of 118 patients diagnosed with acute ischemic stroke in two centers from January 2019 to February 2022 were included. The radiomics features of infarcted areas on non-contrast-enhanced CT were extracted using 3D-Slicer. A univariate analysis and the least absolute shrinkage and selection operator (LASSO) were used to select features, and the radiomics score (Rad-score) was then constructed. The predictive model of HT was constructed by analyzing the Rad-score and clinical and imaging features in the training cohort, and it was verified in the validation cohort. The model was evaluated with the receiver operating characteristic curve, calibration curve and decision curve, and the prediction performance of the model in different scenarios was further discussed hierarchically.ResultsOf the 118 patients, 52 developed HT, including 21 cases of hemorrhagic infarct (HI) and 31 cases of parenchymal hematoma (PH). The Rad-score was constructed from five radiomics features and was the only independent predictor for HT. The predictive model was constructed from the Rad-score. The area under the curve (AUCs) of the model for predicting HT in the training and validation cohorts were 0.845 and 0.750, respectively. Calibration curve and decision curve analyses showed that the model performed well. Further analysis found that the model predicted HT for different infarct sizes or treatment methods in the training and validation cohorts with 78.3 and 71.4% accuracy, respectively. For all samples, the model predicted an AUC of 0.754 for HT in patients within 4.5 h since stroke onset, and predicted an AUC of 0.648 for PH.ConclusionThis model, which was based on CT radiomics features, could help to predict HT in the setting of acute ischemic stroke for any infarct size and provide guiding suggestions for clinical treatment and prognosis evaluation.</p

Identification of a Specific Phage as Growth Factor Alternative Promoting the Recruitment and Differentiation of MSCs in Bone Tissue Regeneration

Inefficient use and loss of exogenously implanted mesenchymal stem cells (MSCs) are major concerns in MSCs-based bone tissue engineering. It is a promising approach to overcome the above issues by recruiting and regulation of endogenous MSCs. However, there are few substances that can recruit MSCs effectively and specifically to the site of bone injury. In this study, we identified a phage clone (termed P11) with specific affinity for MSCs through phage display biopanning, and further investigated the effects of P11 on the cytological behavior of MSCs and macrophages. The results showed that P11 could bind MSCs specifically and promote the proliferation and migration of MSCs. Meanwhile, P11 could polarize macrophages to the M1 phenotype and significantly changed their morphology, which further enhanced the chemotaxis of MSCs. Additionally, RNA-seq results revealed that P11 could promote the secretion of osteogenesis-related markers in MSCs through the TPL2-MEK-ERK signaling pathway. Altogether, P11 has great potential to be used as growth factor alternatives in bone tissue engineering, with the advantages of cheaper and stable activity. Our study also advances the understanding of the effects of phages on macrophages and MSCs, and provides a new idea for the development in the field of phage-based tissue engineering

Probable targets and mechanism of ginsenoside Rg1 for non-alcoholic fatty liver disease: a study integrating network pharmacology, molecular docking, and molecular dynamics simulation

Ginsenoside Rg1 (GRg1), a key bioactive component of medicinal herbs, has shown beneficial effects on non-alcoholic fatty liver disease (NAFLD) and numerous other conditions. Nevertheless, the specific targets that are actively involved and the potential mechanisms underlying NAFLD treatment remain unclear. This study aimed to elucidate the therapeutic effects and mechanism of GRg1 in alleviating NAFLD using a combined approach of network pharmacology and molecular biology validation. The analysis yielded 294 targets for GRg1 and 1293 associated with NAFLD, resulting in 89 overlapping targets. Through protein-protein interactions (PPI) network topology analysis, 10 key targets were identified. Upon evaluating the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analysis, GRg1 may exert therapeutic effects on NAFLD by negatively regulating the apoptotic process, insulin and endocrine resistance, the AGE-RAGE signaling pathway in diabetic complications, and the Estrogen, PI3K/Akt, and MAPK pathways. The three differential gene targets for Akt1, EGFR, and IGF1 were identified through the compound-target network in conjunction with the aforementioned methods. The molecular docking and molecular dynamics (MD) simulations showed that AKT1 and EGFR had a strong binding affinity with GRg1. Overall, our findings point to a novel therapeutic strategy involving NAFLD, with further in vivo and in vitro studies promising to deepen our comprehension and validate its potential advantages. Communicated by Ramaswamy H. Sarma</p

sj-pdf-1-jcb-10.1177_0271678X231208168 - Supplemental material for Group 2 innate lymphoid cells suppress neuroinflammation and brain injury following intracerebral hemorrhage

Supplemental material, sj-pdf-1-jcb-10.1177_0271678X231208168 for Group 2 innate lymphoid cells suppress neuroinflammation and brain injury following intracerebral hemorrhage by Mingming Liu, Danni Wang, Lin Xu, Yan Pan, Huachen Huang, Minshu Li and Qiang Liu in Journal of Cerebral Blood Flow & Metabolism</p

The relationship between alcohol consumption and chronic kidney disease in patients with nonalcoholic fatty liver disease

Objective: To examine the impact of moderate alcohol consumption on the progression of chronic kidney disease (CKD) in individuals diagnosed with non-alcoholic fatty liver disease (NAFLD), as NAFLD has been identified as an autonomous risk factor for CKD and previous research has demonstrated a reduction in overall mortality in NAFLD patients who consume alcohol in moderation. Methods: This study included participants from ten consecutive rounds of the National Health and Nutrition Examination Survey (NHANES:1998–2018). Multivariate logistic regression models were employed to assess the impact of moderate alcohol consumption on chronic kidney disease (CKD) in both male and female populations. Subgroup analysis was conducted by categorizing patients with non-alcoholic fatty liver disease (NAFLD) based on the Fibrosis-4 (FIB-4) index. Results: 17040 participants were eligible to be included in the study. The logistic regression analysis model showed that moderate alcohol consumption was a protective factor for CKD in male NAFLD patients, with an unadjusted OR: 0.37 (0.22,0.65), and p p = 0.02), but the association was not significant in the high risk of liver fibrosis group. In female patients, both moderate alcohol consumption and excessive alcohol consumption were not significantly associated with CKD in either the low-risk group or the high-risk group. Conclusion: Moderate alcohol consumption is associated with a lower prevalence of CKD in men with NAFLD.</p

Media 1: Comprehensive spectral endoscopy of topically applied SERS nanoparticles in the rat esophagus

Originally published in Biomedical Optics Express on 01 September 2014 (boe-5-9-2883