Alzheimer's disease (AD) is a neurodegenerative disease with clinical hallmarks of progressive cognitive impairment. Synergistic effects of the Aβ-Tau cascade reaction are tightly implicated in AD pathology, and microglial NLRP3 inflammasome activation drives neuronal tauopathy. However, the underlying mechanism of how Aβ mediates NLRP3 inflammasome remains unclear. Herein, we determined that oligomeric Aβ (o-Aβ) bound to microglial Kv2.1 and promoted Kv2.1-dependent potassium efflux to activate NLRP3 inflammasome resulting in neuronal tauopathy by using Kv2.1 inhibitor drofenine (Dfe) as a probe. The underlying mechanism has been intensively investigated by assays with Kv2.1 knockdown in vitro (si-Kv2.1) and in vivo (AAV-ePHP-si-Kv2.1). Dfe deprived o-Aβ of its capability to promote microglial NLRP3 inflammasome activation and neuronal Tau hyperphosphorylation by inhibiting the Kv2.1/JNK/NF-κB pathway while improving the cognitive impairment of 5×FAD-AD model mice. Our results have highly addressed that the Kv2.1 channel is required for o-Aβ-driven microglial NLRP3 inflammasome activation and neuronal tauopathy in AD model mice and highlighted that Dfe as a Kv2.1 inhibitor shows potential in the treatment of AD.

Objective To build the early predictive model for chronic kidney disease(CKD)in hypertension and diabetes patients in the community.Methods The CKD patients were recruited from 4 health care centers in 4 urban areas in Kunming.The control group was residents without hypertension and diabetes(n = 1267).The disease group was residents with hypertension and/or diabetes(n = 566).The questionnaire survey,physical examination,laboratory testing,and 5 SNPs gene types in the PVT1 gene.The risk factors,which were filtered with logistics regression,were used to build predictive models.Four machine learning algorithms were built:support vector machine(SVM),random forest(RF),Na?ve Bayes(NB),and artificial neural network(ANN)models.Results Thirteen indicators included in the final diagnostic model:age,disease type,ethnicity,blood urea nitrogen,creatinine,eGFR from MDRD,ACR,eGFR from EPI2009,PAM13 score,sleep quality survey,staying-up late,PVT1 SNP rs11993333 and rs2720659.The accuracy,specificity,Kappa value,AUC of ROC,and PRC of ANN are greater than those of the other 3 models.The sensitivity of RF is the highest among 4 types of machine learning.Conclusions The ANN predictive model has a good ability of efficiency and classification to predict CKD with hypertension and/or diabetes patients in the community.

Objective The metabolites of tolvaptan in rats were identified by ultra-performance liquid chromatography-quadrupole-exactive orbitrap high-resolution mass spectrometry(UFLC-Q-Exactive Orbitrap MS),and the possible metabolic pathways of tolvaptan in rats were discussed.Methods Plasma,urine and fecal samples from rats were collected after a single oral administration of 60 mg·kg-1 tolvaptan solid dispersion solution.The protein in the samples was precipitated with acetonitrile.UFLC-Q-exactive orbitrap MS technology was adopted for the sample analysis and the data were processed by Xcalibur 2.0 software.Results According to the retention time,precise relative molecular mass,characteristic fragment ions and related literature reports of each compound,35 metabolites were identified in rat biological samples.Moreover,23,26 and 30 metabolites in the plasma,urine and feces were identified,respectively.The major metabolic pathways of tolvaptan were identified as hydroxylation,carboxylation,hydrolysis,dehydrogenation,glucuronidation and acetylation.Conclusion Our study confirmed the major metabolites of tolvaptan in rats,enriched the metabolite spectrum of tolvaptan in vivo,and provided an experimental basis for the in-depth study of the pharmacodynamic substance basis of tolvaptan.

Objective To discuss the perioperative nursing care of elderly postlingual deafness patients with cochlear implantation. Methods Cochlear implementation had been carried out in 15 elderly postilingual deafness patients from February 2010 to April 2014, and we observed complications according to their characteristics and the specificity of cochlear implantation, monitored the perioperative blood pressure and pulse, and provided the targeted psychological interventions and rehabilitation guidance. Results All of patients had steady mood during perioperative period without complications. Two patients acquired blood pressure beyond 150mmHg when admitted, but decreased to normal before operation;three patients presented slight dizziness and nausea. The patient′s satisfaction was 100%. Conclusions Based on elderly physiological and psychological characteristics, we improve psychological and fundamental nursing targeted for perioperative patients, which was a significant guarantee for a quick recovery and a good prognosis.