Objective:To investigate the effects of sodium butyrate(NaB)on pathological processes such as autophagy and inflammation in an Alzheimer's disease(AD)model and related mechanisms.Methods:Aβ 25-35's action on PC12 cells was used to establish an in vitro AD model, in which microglial BV2 were treated with NaB.Based on treatments, there were a PC12 cells group, a PC12 cells+ Aβ 25-35 group, a PC12 cells+ Aβ 25-35+ NaB group, a BV2 cells group, a BV2 cells+ Aβ 25-35 group, and a BV2 cells+ Aβ 25-35+ NaB group.The expression of markers of the autophagy pathway and inflammatory proteins was evaluated by Western blot, immunofluorescence and EdU staining.Results:NaB was able to promote cell proliferation in the AD model and reduce the level of Tau protein hyperphosphorylation.NaB also inhibited the inflammatory response of microglia and reduce the expression of inflammatory response marker NLRP3(148.313±0.973 vs.113.291±1.218, t=38.91, P<0.001). At the same time, NaB promoted the expression of autophagy pathway proteins(including Beclin-1, LAMP-1 and LC3 Ⅱ/Ⅰ)in microglia. Conclusions:NaB can mitigate pathological changes in an AD model by promoting autophagy and reducing the inflammatory response.

Medications are the main means for the treatment of mental illness, and it is of great significance to be familiar with the adverse reactions of psychotropic drugs for the formulation and optimization of treatment plans.The prolongation of the QT interval corrected by heart rate(QTc interval)is one of the most common cardiotoxic reactions after taking psychotropic drugs and may lead to torsade de pointes and increase the risk of sudden cardiac death.The elderly population is more prone to QTc interval prolongation.In this review, we focus on the relationship between common psychotropic drugs and QTc interval prolongation and influencing factors in the elderly population, so as to help clinicians avoid risk in drug selection.

The etiology of Alzheimer′s disease (AD) is unknown. A growing number of studies have shown that the gut microbiome is closely related to the occurrence and development of AD. The gut microbiome affects the central nervous system by gut-brain axis, which also affects cognitive function, mental and behavior symptoms. In addition, The gut microbiome is closely associated with clinical biomarkers of AD. Cognitive function can be enhanced by improving the gut microbiome of AD patients. The, research progress in this field provides new study ideas for the prevention and treatment of AD, particularly for early diagnosis.

Objective:To construct and evaluate a predictive diagnosis model of Alzheimer′s disease(AD) based on serum microRNA (miRNA).Methods:Downloaded the serum miRNA chip data of 1 021 AD patients and 288 healthy controls from the gene expression omnibus (GEO) database, a total of 1 309 samples were obtained. Matched AD patients and healthy controls by age, and screened out 494 samples for training and verifying the diagnostic model. Sorted the miRNA expression value from high to low and selected the top 1 000 probes for the next step. According to the ratio of 7∶3, all 494 samples were randomly divided into a training group and a validation group. LASSO regression was used to screen miRNAs, combined with gender, APOE4 genotype and miRNA data, stepwise regression was used to further screen independent variables and construct a multi-factor diagnostic model. Receiver operating characteristic (ROC) curves and calibration curves were used to verify the accuracy of the model in the training group, validation group, training group+validation group, and 1 309 total samples; A Nomogram was drawn for actual case prediction.Results:The area under the Curve of ROC of the multi-factorial diagnostic model in the training group, the validation group, the training+validation group and the total samples of 1 309 cases were 0.870, 0.831, 0.842 and 0.826, respectively, showing high predictive effectiveness. An AD case was used to show an example of applying the method and its predicted results (the total score of the patient was 521, and the probability of predicted NC was 0.022 8, indicating that the sample was AD, which was consistent with the actual diagnosis).Conclusions:The diagnostic model based on 11 serum miRNA, gender, and apolipoprotein E (ApoE4) genotypes can well predict AD and may provide the possibility for clinical prediction of AD.

The etiology of Alzheimer′s disease (AD) is unknown. A growing number of studies have shown that the gut microbiome is closely related to the occurrence and development of AD. The gut microbiome affects the central nervous system by gut-brain axis, which also affects cognitive function, mental and behavior symptoms. In addition, The gut microbiome is closely associated with clinical biomarkers of AD. Cognitive function can be enhanced by improving the gut microbiome of AD patients. The, research progress in this field provides new study ideas for the prevention and treatment of AD, particularly for early diagnosis.

Objective:To construct and evaluate a predictive diagnosis model of Alzheimer′s disease(AD) based on serum microRNA (miRNA).Methods:Downloaded the serum miRNA chip data of 1 021 AD patients and 288 healthy controls from the gene expression omnibus (GEO) database, a total of 1 309 samples were obtained. Matched AD patients and healthy controls by age, and screened out 494 samples for training and verifying the diagnostic model. Sorted the miRNA expression value from high to low and selected the top 1 000 probes for the next step. According to the ratio of 7∶3, all 494 samples were randomly divided into a training group and a validation group. LASSO regression was used to screen miRNAs, combined with gender, APOE4 genotype and miRNA data, stepwise regression was used to further screen independent variables and construct a multi-factor diagnostic model. Receiver operating characteristic (ROC) curves and calibration curves were used to verify the accuracy of the model in the training group, validation group, training group+validation group, and 1 309 total samples; A Nomogram was drawn for actual case prediction.Results:The area under the Curve of ROC of the multi-factorial diagnostic model in the training group, the validation group, the training+validation group and the total samples of 1 309 cases were 0.870, 0.831, 0.842 and 0.826, respectively, showing high predictive effectiveness. An AD case was used to show an example of applying the method and its predicted results (the total score of the patient was 521, and the probability of predicted NC was 0.022 8, indicating that the sample was AD, which was consistent with the actual diagnosis).Conclusions:The diagnostic model based on 11 serum miRNA, gender, and apolipoprotein E (ApoE4) genotypes can well predict AD and may provide the possibility for clinical prediction of AD.

Alzheimer Disease(AD) presently remains an irreversible disease.Consequently,with current research findings and clinical practices,we propose a new prevention and treatment perspective,summarized in four aspects:early-stage,comprehensive,systematic,and long term,which are interrelated and yet show varying significance depending on the circumstances.We hope this view will help improve AD-related practices in prevention,clinical management,research and education,better equipping us to face challenges posed by AD.

Objective To evaluate the preventive effects of Haishe capsules on the conversion of amnestic mild cognitive impairment (aMCI) to Alzheimer's disease(AD).Methods Patients (n=120) with aMCI from our department were recruited and randomly divided into the treatment group and the control group (n=60 in each group).The treatment group was given 0.9 gram of Haishe capsules three times a day while the control group received no drug treatment.Data on the conversion ratio,memory and cognitive function were comparedbetween the groups in a 24-months follow-up.Results By the end of the study,12 patients in the treatment group and 15 in the control group dropped out.Valid data for 93 patients were available for statistical analysis (48 in the treatment group and 45 in the control group).The number of aMCI patients who converted to AD was 6,with a conversion ratio of 12.5％ (6/48);and the number of patients who went through conversion in the control group was 13,with a conversion ratio of 28.8％ (13/45).The difference in conversion between the two groups was statistically significant (x2 =3.83,P＜0.05).After 24 months,MMSE scores for the treatment group (25.52± 1.07) had no significant change compared with baseline levels,while MMSE scores for the control group decreased significantly(24.75--1.49) and were markedly lower than thosefor the treatment group (t=2.85,P＜0.05).MoCA scores for the treatment group (19.39 ±2.01) did not show decline until the end of the study,while those for the control group started to decrease about half way through the study and were lower than scores for the treatment group (t =2.41,P＜0.05).Compared with baseline levels,ADAS-Cogscores for the treatment group (7.62± 1.06) did not increase significantly during the course of the study.ADAS-Cogscores forthe control group were higher at both half way (7.70±0.75) and the end of the study (8.18±0.80)than base line levels,and there was a statistically significant difference in end-of-study ADAS-Cog scores between the two groups(t =-2.6,P＜ 0.05).Conclusions Haishe capsules not only effectively maintain memory and cognitive function,but also delay the conversion from aMCI to AD.