Objective To investigate precipitants of epileptic seizure, and to explore the correlation between various precipitants and relationship between precipitants and clinical features of epilepsy.Methods Data were collected from 154 patients attending a tertiary-care epilepsy clinic of Nanjing Brain Hospital between April 2015 and April 2016.The patients with epilepsy were older than 16 years, had a clinical history of one year or more, and one seizure at least a year and one seizure at least in the latest three months.An enclosed questionnaire was combined with open interview to identify and characterize seizure precipitants and clinical characteristics of patients.Patients were asked respectively whether there were some precipitants three months before and during last three months.Correlation between seizure precipitants and relationship between precipitants and clinical characteristics, such as age, gender, course, seizure frequency and so on, were calculated.Results A total of 125 (81.2%) participants reported at least one precipitant.Common precipitants (in descending order) were as follows: emotional stress (56.0%), sleep disorder (38.4%), fatigue (27.2%), missed medication (20.0%).There were one to six different precipitants for one patient, and 60.8% of patients had two or more precipitants.There was a correlation between emotional stress and sleep disorders as well as fatigue (χ2=4.665, 8.668;P<0.05).Patients with idiopathic epilepsy were more sensitive to sleep disorders.There was no relationship between total precipitants and clinical features such as age, gender, age of onset, duration, type of seizure, seizure frequency, number of drug taking and so on.Conclusions Seizure precipitants were found widespread.The most common precipitants were found to be emotional stress, sleep disorders, fatigue and missed medication.There existed a correlation between emotional stress and sleep disorders as well as fatigue.There was no connection between total precipitants and patient′s demographic characteristics as well as clinical features.However, the type of seizure precipitants was different in patients with different demographic and clinical characteristics.

Benign prostatic hyperplasia(BPH)is one of the major chronic complications of type 2 diabetes mellitus(T2DM),and sex steroid hormones are common risk factors for the occurrence of T2DM and BPH.The profiles of sex steroid hormones are simultaneously quantified by LC-MS/MS in the clinical serum of patients,including simple BPH patients,newly diagnosed T2DM patients,T2DM complicated with BPH patients and matched healthy individuals.The G protein-coupled estrogen receptor(GPER)inhibitor G15,GPER knockdown lentivirus,the YAP1 inhibitor verteporfin,YAP1 knockdown/overexpression lentivirus,targeted metabolomics analysis,and Co-IP assays are used to investigate the molecular mechanisms of the disrupted sex steroid hormones homeostasis in the pathological process of T2DM complicated with BPH.The homeostasis of sex steroid hormone is disrupted in the serum of patients,accompanying with the proliferated prostatic epithelial cells(PECs).The sex steroid hormone metabolic profiles of T2DM patients complicated with BPH have the greatest degrees of separation from those of healthy individuals.Elevated 17β-estradiol(E2)is the key contributor to the disrupted sex steroid hormone homeostasis,and is significantly positively related to the clinical characteristics of T2DM patients complicated with BPH.Activating GPER by E2 via Hippo-YAP1 signaling exacerbates high glucose(HG)-induced PECs prolifer-ation through the formation of the YAP1-TEAD4 heterodimer.Knockdown or inhibition of GPER-mediated Hippo-YAP1 signaling suppresses PECs proliferation in HG and E2 co-treated BPH-1 cells.The anti-proliferative effects of verteporfin,an inhibitor of YAP1,are blocked by YAP1 overexpression in HG and E2 co-treated BPH-1 cells.Inactivating E2/GPER/Hippo/YAP1 signaling may be effective at delaying the progression of T2DM complicated with BPH by inhibiting PECs proliferation.

This study aimed to explore key quality control factors that affected the prognosis of intensive care unit (ICU) patients in Chinese mainland over six years (2015-2020). The data for this study were from 31 provincial and municipal hospitals (3425 hospital ICUs) and included 2 110 685 ICU patients, for a total of 27 607 376 ICU hospitalization days. We found that 15 initially established quality control indicators were good predictors of patient prognosis, including percentage of ICU patients out of all inpatients (%), percentage of ICU bed occupancy of total inpatient bed occupancy (%), percentage of all ICU inpatients with an APACHE II score ⩾15 (%), three-hour (surviving sepsis campaign) SSC bundle compliance (%), six-hour SSC bundle compliance (%), rate of microbe detection before antibiotics (%), percentage of drug deep venous thrombosis (DVT) prophylaxis (%), percentage of unplanned endotracheal extubations (%), percentage of patients reintubated within 48 hours (%), unplanned transfers to the ICU (%), 48-h ICU readmission rate (%), ventilator associated pneumonia (VAP) (per 1000 ventilator days), catheter related blood stream infection (CRBSI) (per 1000 catheter days), catheter-associated urinary tract infections (CAUTI) (per 1000 catheter days), in-hospital mortality (%). When exploratory factor analysis was applied, the 15 indicators were divided into 6 core elements that varied in weight regarding quality evaluation: nosocomial infection management (21.35%), compliance with the Surviving Sepsis Campaign guidelines (17.97%), ICU resources (17.46%), airway management (15.53%), prevention of deep-vein thrombosis (14.07%), and severity of patient condition (13.61%). Based on the different weights of the core elements associated with the 15 indicators, we developed an integrated quality scoring system defined as F score=21.35%xnosocomial infection management + 17.97%xcompliance with SSC guidelines + 17.46%×ICU resources + 15.53%×airway management + 14.07%×DVT prevention + 13.61%×severity of patient condition. This evidence-based quality scoring system will help in assessing the key elements of quality management and establish a foundation for further optimization of the quality control indicator system.
Humans ; China/epidemiology* ; Cross Infection/epidemiology* ; Intensive Care Units/statistics & numerical data* ; Quality Control ; Quality Indicators, Health Care/statistics & numerical data* ; Sepsis/therapy* ; East Asian People/statistics & numerical data*