Plastrum Testudinis Extracts Promote NSC Differentiation into Dopaminergic Neuron by Regulating the Interaction of TET1 and FoxA2

Abstract

In recent years, stem cells have gained much attention for the treatment of neurodegenerative diseases. However, inducing neural stem cell directionally differentiation is a difficult problem in the treatment of Parkinson's disease (PD) by stem cell therapy. Plastrum Testudinis (PT) can enhance the number of TH-positive neurons in the PD rat brain substantia nigra, but the underlying mechanism has not been clarified. Here, we aimed at further investigating the mechanism by which PT can promote NSC differentiation into dopaminergic neurons. A rat model of PD was used for detecting the effect of PT on the rat brain substantia nigra in vivo. e results showed the expressions of tyrosine hydroxylase (TH) and TET1 enzyme were increased after treatment with PT. Consequently, Plastrum Testudinis extracts (PTEs) were used for inducing NSC differentiation into dopaminergic neurons ex vivo. During differentiation of NSCs induced by PTE, TH expression was increased, with a concomitant increase in both TET1 and FoxA2. Next, we performed coimmunoprecipitation analysis to examine the interaction between TET1 protein and FoxA2 protein. Our results show that PTE can increase the binding rate of TET1 and FoxA2. us, our findings show that PTE can increase the efficiency of NSCs to directionally differentiate into dopaminergic neurons and provide experimental evidence for PT in the treatment of Parkinson's disease.

Relationship between social support and fear of cancer recurrence among Chinese cancer patients: A systematic review and meta-analysis

“…The meta-analysis was performed using the “meta” package in R version 4.1.3. Heterogeneity in systematic reviews was generally described as clinical, methodological, and statistical heterogeneity (the result of clinical and/or methodological diversity among individual studies) ( 46 ), and was assessed by the I 2 statistic and Q -test ( P -value); if P > 0.10 and I 2 < 50%, a fixed effects model was chosen; otherwise, a random effects model was adopted. When heterogeneity occurred, meta regression, subgroup analysis, and sensitivity analysis were performed to assess the source of heterogeneity.…”

“…In contrast, meta-regression, which could reflect the relationship between one or more covariates (could be categorical or continuous variables) and outcome variables by establishing a regression equation, was performed to investigate the sources and size of heterogeneity among individual studies. The selected covariates could be some characteristics among the study or trial level, such as study design, intervention dose, administration route, treatment duration, gender, age, ethnicity of the patient, and research sample size; or they can be the combined characteristics of cases included within a single study, such as the average age and average height of patients ( 46 , 47 ). The meta-regression criteria were (1) P ≤ 0.10 for the Q test or I 2 greater than 50%; (2) P ≥ 0.05 for Egger’s test; (3) and response variables reported in at least 10 studies.…”

“…The meta-regression criteria were (1) P ≤ 0.10 for the Q test or I 2 greater than 50%; (2) P ≥ 0.05 for Egger’s test; (3) and response variables reported in at least 10 studies. To ensure each covariate was scientifically sound, the covariates should be identified based on clinical assumptions and biology ( 46 ). Therefore, based on the above literature ( 46 , 47 ) and previous related studies ( 48 – 78 ), we assumed that the heterogeneity might arise from the age, gender (male and female), sample size (<200 and ≥200), types of cancer (breast cancer group, mixed-type group, and other-type group), publication year, region (north and south), follow-up, random sampling, instruments used to measure FCR and SS, and source of SS.…”

“…To ensure each covariate was scientifically sound, the covariates should be identified based on clinical assumptions and biology ( 46 ). Therefore, based on the above literature ( 46 , 47 ) and previous related studies ( 48 – 78 ), we assumed that the heterogeneity might arise from the age, gender (male and female), sample size (<200 and ≥200), types of cancer (breast cancer group, mixed-type group, and other-type group), publication year, region (north and south), follow-up, random sampling, instruments used to measure FCR and SS, and source of SS. In this study, meta-regression was performed to explore whether the differences in categorical covariates such as SS measurement, FCR measurement, types of cancer, follow-up, and region, whereas sample size and publication year were used as continuous covariates, when covariates were statistically significant, with P ≤ 0.05.…”