Role of Zhiqiao Chuanlian decoction in the treatment of food accumulation fever: Network pharmacology and animal experiments.

Objective: Food accumulation fever (FAF), a common clinical disease in children, is generally induced by the excessive intake of high-calorie or high-fat foods. Zhiqiao Chuanlian decoction (ZQCLD) is a classical traditional Chinese medicine (TCM) that may have therapeutic effects on FAF. Methods: Network pharmacological analyses of ZQCLD and FAF were conducted. Animal experiments lasted for 14 days. Rats in the model, positive control, and low-, medium-, and high-dose groups were fed a high-calorie diet. On days 11-14, the positive group was given a domperidone solution. The low-, medium-, and high-dose groups were administered different concentrations of ZQCLD. The body temperature, gastric emptying rate, and intestinal propulsion rate were measured. Relevant indicators were determined by ELISA. Results: The main target proteins included IL-1ß, C-C motif chemokine 2 (CCL2), prostaglandin G/H synthase 2 (PTGS2), transcription factor AP-1 (JUN), haem oxygenase 1 (HMOX1), interferon-gamma (IFN-γ), peroxisome proliferator-activated receptor-gamma (PPAR-γ), and inducible nitric oxide synthase (NOS2/iNOS). Compared with those in the control group, body weight, gastric emptying rate, intestinal propulsion rate, and neuronal nitric oxide synthase (NOS1/nNOS) levels were significantly lower in the model group, whereas body temperature and endotoxin, interleukin-1ß (IL-1ß), PGE2, and iNOS levels were increased. In each treatment group, body temperature and PGE2 levels returned to normal levels. Compared with those in the model group, the gastric emptying rates in the positive group and the low- and medium-dose groups increased; the intestinal propulsion rates were higher in the medium- and high-dose groups, whereas the endotoxin and IL-1ß levels were lower; and the nNOS level was higher in the high-dose group, whereas the iNOS level was lower. Conclusions: ZQCLD may treat FAF by regulating jejunal IL-1ß and nNOS, serum endotoxin, and hypothalamic PGE2 and iNOS levels.

Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED).

BACKGROUND: Cell-free DNA (cfDNA) contains a large amount of molecular information that can be used for multi-cancer early detection (MCED), including changes in epigenetic status of cfDNA, such as cfDNA fragmentation profile. The fragmentation of cfDNA is non-random and may be related to cfDNA methylation. This study provides clinical evidence for the feasibility of inferring cfDNA methylation levels based on cfDNA fragmentation patterns. We performed whole-genome bisulfite sequencing and whole-genome sequencing (WGS) on both healthy individuals and cancer patients. Using the information of whole-genome methylation levels, we investigated cytosine-phosphate-guanine (CpG) cleavage profile and validated the method of predicting the methylation level of individual CpG sites using WGS data. RESULTS: We conducted CpG cleavage profile biomarker analysis on data from both healthy individuals and cancer patients. We obtained unique or shared potential biomarkers for each group and built models accordingly. The modeling results proved the feasibility to predict the methylation status of single CpG sites in cfDNA using cleavage profile model from WGS data. CONCLUSION: By combining cfDNA cleavage profile of CpG sites with machine learning algorithms, we have identified specific CpG cleavage profile as biomarkers to predict the methylation status of individual CpG sites. Therefore, methylation profile, a widely used epigenetic biomarker, can be obtained from a single WGS assay for MCED.

Risk Factors and Prediction Models for Nonalcoholic Fatty Liver Disease Based on Random Forest.

Objective: To establish a risk prediction model of nonalcoholic fatty liver disease (NAFLD) and provide management strategies for preventing this disease. Methods: A total of 200 inpatients and physical examinees were collected from the Department of Gastroenterology and Endocrinology and Physical Examination Center. The data of physical examination, laboratory examination, and abdominal ultrasound examination were collected. All subjects were randomly divided into a training set (70%) and a verification set (30%). A random forest (RF) prediction model is constructed to predict the occurrence risk of NAFLD. The receiver operating characteristic (ROC) curve is used to verify the prediction effect of the prediction models. Results: The number of NAFLD patients was 44 out of 200 enrolled patients, and the cumulative incidence rate was 22%. The prediction models showed that BMI, TG, HDL-C, LDL-C, ALT, SUA, and MTTP mutations were independent influencing factors of NAFLD, all of which has statistical significance (P < 0.05). The area under curve (AUC) of logistic regression and the RF model was 0.940 (95% CI: 0.870~0.987) and 0.945 (95% CI: 0.899~0.994), respectively. Conclusion: This study established a prediction model of NAFLD occurrence risk based on the RF, which has a good prediction value.