Integrated Bioinformatics Analysis for Revealing CBL is a Potential Diagnosing Biomarker and Related Immune Infiltration in Parkinson's Disease.

Purpose: There is growing evidence that the immune system plays an important role in the progression of Parkinson's disease, the second most common neurodegenerative disorder. This study aims to address the comprehensive understanding of the immunopathogenesis of Parkinson's disease and explore new inflammatory biomarkers. Patients and Methods: In this study, Immune-related differential expressed genes (DEIRGs) were obtained from GEO database and Immport database. The hub gene was screened in DEIRGs using LASSO regression and random forest algorithm, and the mRNA expression of the identified hub gene was validated using clinical blood samples. Results: We obtained a total of 157 DEIRGs that played an important role in the immune response. The results of immune cell infiltration analysis showed that the degree of memory B cells infiltration was higher in PD patients, while the degree of Monocytes, resting mast cells and M0 macrophages infiltration was lower (p<0.05). A total of 8 hub genes were screened by machine learning methods, and RT-PCR results showed that the expression level of CBL gene in PD was significantly increased (p<0.05). Conclusion: Our findings suggest that CBL is a new potential diagnostic biomarker for PD and that abnormal immune cell infiltration may influence PD development.

A pH-responsive, injectable and self-healing chitosan-coumarin hydrogel based on Schiff base and hydrogen bonds.

Smart hydrogels have shown great potential applications in disease treatment due to their controlled and local drug-release ability. Herein, a smart hydrogel with pH-responsive, injectable, and self-healing properties for controlled release of taxifolin (TFL) was prepared by freezing-thawing and photo-crosslinking methods. The crosslinking network of hydrogels (CS-CA hydrogels) was constructed by the hydrogen bonds, Schiff base bonds, and cyclobutane rings using chitosan (CS) and coumarin (CA) as raw materials. The CS-CA hydrogel demonstrated a compressive strength of 1.04 MPa, a self-healing efficiency of 99.9 %, and could maintain structural and functional integrity after injection. In addition, the drug release rate and shape of the CS-CA hydrogels were tunable due to its pH sensitivity. The TFL cumulative release reached 60 % within 12 h at pH = 4, and after equilibration, the cumulative release of TFL at pH = 4 (80 %) was significantly higher than at pH = 9.2 (50 %). The CCK8 experiment showed that the resulting hydrogel had no cytotoxicity. Meanwhile, subcutaneous implantation experiments in mice showed that the CS-CA hydrogels had favorable biodegradability and compatibility.

Humic acids alleviate aflatoxin B1-induced hepatic injury by reprogramming gut microbiota and absorbing toxin.

Aflatoxin B1 (AFB1) is a hepatotoxic fungal metabolite that is widely present in food and can cause liver cancer. As a potential detoxifier, naturally occurring humic acids (HAs) may be able to reduce inflammation and restructure the gut microbiota composition; however, little is known about the mechanism of HAs detoxification as applied to liver cells. In this study, HAs treatment alleviated AFB1-induced liver cell swelling and the infiltration of inflammatory cells. HAs treatment also reinstated various enzyme levels in the liver disturbed by AFB1 and substantially alleviated AFB1-caused oxidative stress and inflammatory responses by enhancing immune functions in mice. Moreover, HAs increased the length of the small intestinal and villus height to restore intestinal permeability, which is impaired by AFB1. In addition, HAs reconstructed the gut microbiota, increasing the relative abundance of Desulfovibrio, Odoribacter, and Alistipes. In vitro and in vivo assays demonstrated that HAs could efficiently remove AFB1 by absorbing the toxin. Therefore, HAs treatment can ameliorate AFB1-induced hepatic injury by enhancing gut barrier function, regulating gut microbiota, and adsorbing toxin.

The Trend-in-trend Research Design for Causal Inference.

Cohort studies can be biased by unmeasured confounding. We propose a hybrid ecologic-epidemiologic design called the trend-in-trend design, which requires a strong time trend in exposure, but is unbiased unless there are unmeasured factors affecting outcome for which there are time trends in prevalence that are correlated with time trends in exposure across strata with different exposure trends. Thus, the conditions under which the trend-in-trend study is biased are a subset of those under which a cohort study is biased. The trend-in-trend design first divides the study population into strata based on the cumulative probability of exposure given covariates, which effectively stratifies on time trend in exposure, provided there is a trend. Next, a covariates-free maximum likelihood model estimates the odds ratio (OR) using data on exposure prevalence and outcome frequency within cumulative probability of exposure strata, across multiple periods. In simulations, the trend-in-trend design produced ORs with negligible bias in the presence of unmeasured confounding. In empiric applications, trend-in-trend reproduced the known positive association between rofecoxib and myocardial infarction (observed OR: 1.2, 95% confidence interval: 1.1, 1.4), and known null associations between rofecoxib and severe hypoglycemia (OR = 1.1 [0.92, 1.3]) and nonvertebral fracture (OR = 0.84 [0.64, 1.1]). The trend-in-trend method may be useful in settings where there is a strong time trend in exposure, such as a newly approved drug or other medical intervention. See video abstract at, http://links.lww.com/EDE/B178.