Abuse of amphetamine-based stimulants is a primary public health concern. Recent studies have underscored a troubling escalation in the inappropriate use of prescription amphetamine-based stimulants. However, the neurophysiological mechanisms underlying the impact of acute methamphetamine exposure (AME) on sleep homeostasis remain to be explored. This study employed non-human primates and electroencephalogram (EEG) sleep staging to evaluate the influence of AME on neural oscillations. The primary focus was on alterations in spindles, delta oscillations, and slow oscillations (SOs) and their interactions as conduits through which AME influences sleep stability. AME predominantly diminishes sleep-spindle waves in the non-rapid eye movement 2 (NREM2) stage, and impacts SOs and delta waves differentially. Furthermore, the competitive relationships between SO/delta waves nesting with sleep spindles were selectively strengthened by methamphetamine. Complexity analysis also revealed that the SO-nested spindles had lost their ability to maintain sleep depth and stability. In summary, this finding could be one of the intrinsic electrophysiological mechanisms by which AME disrupted sleep homeostasis.
Animals ; Methamphetamine ; Electroencephalography ; Male ; Sleep/drug effects* ; Central Nervous System Stimulants ; Delta Rhythm/drug effects* ; Sleep Stages/drug effects*

ObjectiveTo investigate the regulatory effect of Danggui Shaoyaosan on adenosine monophosphate-activated protein kinase （AMPK）/mammalian target of rapamycin （mTOR）/Unc-51-like kinase-1 （ULK1） signaling pathway in the rat model of metabolism-associated fatty liver disease （MAFLD）. MethodSixty SD rats were randomized into control， model， western medicine （polyene phosphatidylcholine capsules，0.144 g·kg^-1）， and low-， medium-， and high-dose （2.44， 4.88， 9.76 g·kg^-1， respectively） Danggui Shaoyaosan groups. After being fed with a high-fat diet for 8 weeks， the rats in each group were administrated with corresponding drugs for 4 weeks. At the end of drug treatment， serum and liver tissue were collected for subsequent determination of related indicators. ResultCompared with the control group， the model group showed increased contents of total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C） and activities of alanine aminotransferase （ALT） and aspartate aminotransferase （AST） in the serum， increased contents of TC， TG， and free fatty acids （FFAs） in the liver （P<0.01）， and decreased content of high-density lipoprotein cholesterol （HDL-C） in the serum （P<0.01）. Furthermore， the model group showed down-regulated protein levels of p-AMPK， microtubule-associated protein 1 light chain 3B （LC3B） Ⅱ， Beclin1， and ULK1 （P<0.01） and up-regulated protein levels of p-mTOR and ubiquitin-binding protein p62 in the liver （P<0.01）. The hepatic steatosis was obvious and the NAFLD activity score （NAS） and oil red O staining area increased in the model group， （P<0.05， P<0.01）. Compared with the model group， Danggui Shaoyaosan reduced the contents of TC and TG and the activities of ALT and AST in the serum， lowered the levels of TC， TG， and FFA in the liver， down-regulated the protein levels of p-mTOR and p62 （P<0.01）， elevated the serum HDL-C level， and up-regulated the protein levels of p-AMPK， LCBⅡ， Beclin1， and ULK1 in the liver （P<0.05， P<0.01）. Moreover， it alleviated hepatic steatosis and decreased the NAS and oil red O staining area （P<0.05， P<0.01）. ConclusionDanggui Shaoyaosan has therapeutic effect on MAFLD rats by regulating AMPK/mTOR/ULK1 signaling pathway to enhance autophagy.

Objectives:To investigate the association between social determinants of health(SDOH)and incident stroke and analyze the main risk factors for stroke among resident with different SDOH levels. Methods:From 2012 to 2015,30 036 residents(≥35 years old)from 30 districts in 14 provincial-level administrative divisions in China were enrolled this study based on stratified multi-stage-random-sampling method.The prevalence of cardiovascular diseases and related risk factors were investigated,and stroke events were followed up in 2018 to 2019.Principal component analysis was performed to establish SDOH scores based on 9 indicators related to socioeconomic and healthcare resources,participants were divided into low SDOH group(n=8 343)when it was≥-2.01 to<-1.14,middle SDOH group(n=7 257)when it was≥-1.14 to<0.10,and high SDOH group(n=8 457)when it was≥0.10 to≤5.79.Multivariate Cox regression was applied to estimate the association of SDOH levels with incident stroke.The random survival forest method was used to analyze the major risk factors in different SDOH levels. Results:A total of 24 057 participants were finally included,669(2.8%)participants developed stroke during a mean of(4.7±0.8)years follow-up.The incidence densities of stroke in the low,medium,and high SDOH groups were 468.39,628.85,and 700.39/100 000 person-years,respectively(Pdifference<0.05,Ptrend=0.01).Compared with individuals with low SDOH level group,fully HR for incident stroke among those with medium and high were 1.91(95%CI:1.54-2.36)and 1.59(95%CI:1.30-1.95),respectively(Ptrend<0.001).Advanced age is the primary risk factor for stroke in the population,especially in districts with high SDOH level.In districts with medium SDOH level,diabetes is an important risk factor for stroke.High blood pressure and alcohol consumption are important modifiable risk factors in low SDOH level districts. Conclusions:Present study shows that higher levels of SDOH are associated with increased risk of stroke.The main risk factors for stroke differ among participants with different SDOH level districts.Targeted interventions should be implemented to improve the prevention and treatment of stroke in populations with different levels of SDOH.

Objective:To investigate the alteration of glymphatic system based on diffusion tensor image-analysis along the perivascular space(DTI-ALPS)in bipolar disorder Ⅰ(BD-Ⅰ).Methods:A total of 44 BD-Ⅰ patients(BD-Ⅰ group) admitted to the Affiliated Brain Hospital of Guangzhou Medical University from January 2012 to December 2017 were selected.In addition, totally 30 healthy controls (HC group) were recruited. The diffusion tensor image data were analyzed retrospectively, and along the perivascular space (ALPS) index was calculated. Hamilton anxiety scale (HAMA), 17-item Hamilton depression rating scale (HAMD-17), Young mania rating scale (YMRS) and global assessment function (GAF) were used to evaluate the severity of anxiety, depression, mania and social function respectively. SPSS 25.0 software was used for t-test, Z-test and chi-square test, and the differences in clinical data and DTI-ALPS index between the two groups were compared. The partial correlation test was used to analyze the correlations between DTI-ALPS index and the clinical indicators such as HAMA, HAMD-17, YMRS and GAF. Results:The DTI-ALPS indexes in left(1.69±0.17), right(1.44±0.15) and bilateral cerebral hemispheres(1.56±0.15) of BD-Ⅰ group were lower than those in HC group ((1.71±0.15), (1.46±0.13) and (1.58±0.12)), but the differences were not statistically significant ( t=-0.441, -0.545, -0.556, all P>0.05). After controlling for gender, age, years of education and course of disease, there were significant negative correlations between bilateral average DTI-ALPS index and somatic anxiety ( r=-0.334, P=0.038), as well as between right DTI-ALPS index and somatic anxiety( r=-0.349, P=0.030) in BD-Ⅰ group. Conclusion:The dysfunction of cerebral glymphatic system is not obvious in BD-Ⅰ patients, but their anxiety may be related to dysfunction cerebral glymphatic system.

MiRNAs are a class of small non-coding RNAs,which regulate gene expression post-transcriptionally by partial complementary base pairing.Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients.In recent years,artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research.Compared to traditional bioinformatic tools,miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy,and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding.Additionally,the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers.In this article,we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms,focusing on the potential of machine learning and deep learning in cancer-related miRNA research.

Objective To establish a nomogram model for efficiently predicting overall survival(OS)and cancer-specific survival(CSS)in patients with CRCHBM.Method 2239 patients from 2010 to 2019 were retrospectively analyzed from the Surveillance,Epidemiology,and End Results Program(SEER)databases and Wuhan Union Hospital Cancer Center.SEER is randomly assigned to the training and internal validation cohorts,and the Wuhan database serves as the external validation.Cox regression analyses were used to determine the independent clinicopathological prognosis factors affecting OS and CSS,and a nomogram was constructed to predict OS and CSS.The clinical utility of columnar plots was assessed using calibration curves,area under the curve(AUC),and decision curve analysis(DCA).Result OS column line graphs were constructed based on nine independent predictors:age,tumor location,degree of differentiation,tumor size,TNM stage,chemotherapy,primary focus surgery,number of lymph nodes sampled,and serum carcinoembryonic antigen(CEA)level.The C-index of the nomogram to predict the 1-,3-,and 5-year OS were 0.764,0.790,and 0.805 in the training group,0.754,0.760,and 0.801 in the internal validation group,and 0.822,0.874,and 0.906 in the external validation group.CSS column line graphs were constructed based on 3 independent predictors of TNM staging,radiotherapy and chemotherapy.The 1-,3-,and 5-year CSS AUROC values of the training group were 0.791,0.757,and 0.782,respectively.0.682,0.709,0.625 in the internal validation group and 0.759,0.702,0.755 in the external validation group,respectively.The results of receiver operating characteristic curve(ROC),ROC and DCA showed that the use of our model was more effective in predicting OS and CSS than other single clinicopathological features.Conclusion In summary,the nomogram based on significant clinicopathological features can be conveniently used to predict OS and CSS individually in patients with CRCHBM.

OBJECTIVE: This study aimed to explore the association between humidity exposure and the risk of cardiovascular disease (CVD), utilizing follow-up data and relative humidity (RH) metric assessments. METHODS: We extracted the baseline data from the China Hypertension Survey (CHS) of 24,510 enrolled participants aged ≥ 35 years without a history of CVD between 2012 and 2015 and followed them up from 2018 to 2019. The National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA) provided the quality-controlled relative humidity (RH) datasets. Cox proportional hazards models were used to estimate hazard ratios ( HRs) for CVD in relation to RH. RESULTS: During the follow-up period (2018-2019), 973 patients with CVD were identified. The HR of CVD risk was 1.17 (95% CI: 1.04-1.31) per 10% increase in summer mean RH. Compared with participants in the 3 ^rd quintile group, those in the 1 ^st and 5 ^th quintiles of RH had a higher risk of CVD. For summer mean RH, the HRs (95% CIs) for the 1 ^st and 5 ^th quintiles were 1.34 (1.04-1.71) and 1.44 (1.14-1.83), respectively. The relationship ("U" shape) between summer mean RH and the risk of CVD was nonlinear. Stratified analyses indicated that the risk of CVD was substantially influenced by the summer mean RH in female, older individuals, and those in southern China. CONCLUSION Unsuitable (too high or low) humidity environments affect the risk of CVD. Our study highlights those future policies for adapting to climate change should consider the humidity-CVD relationship.
Humans ; Humidity/adverse effects* ; Cardiovascular Diseases/etiology* ; Female ; Male ; Middle Aged ; Prospective Studies ; China/epidemiology* ; Adult ; Aged ; Risk Factors ; Proportional Hazards Models ; Seasons

This paper interprets the content and recommendations of the guidelines on infection prevention and control in long-term care facilities put forward by the World Health Organization （WHO） during the 2019 coronavirus disease （COVID-19） pandemic， and actively explores the key points of nursing and infection prevention and control measures for the long-term care facilities under the background of repeated outbreaks， with the aim of providing care measures and infection prevention and control measures that suit our national conditions to improve the living standards of the elderly and protect them from viral infection amid the recurring pandemic.

Taking the course of Chinese traditional medicine as an example, this paper discusses the construction and implementation effect of online teaching mode from the following four aspects: online teaching curriculum design, teaching implementation, teaching effect evaluation, and teaching reflection, with a view to providing beneficial reference for the follow-up hybrid teaching and promoting the construction of hybrid first-class courses by summarizing the experience of online teaching.

Objective:To investigate the prevalence of albuminuria in Chinese residents aged >35 years and its potential association with cardiovascular disease (CVD).Methods:A total of 34 647 Chinese subjects aged ≥35 years were selected by stratified multi-stage random sampling from 2012 to 2015. Data were collected through questionnaires, physical examinations, and laboratory tests. Albuminuria was categorized into 3 types according to urinary albumin-to- creatinine ratio: normal (<30 mg/g), microalbuminuria (MAU, 30-300 mg/g), and macroalbuminuria (≥300 mg/g). Measurement data were expressed as xˉ±s, and t-tests were used for comparisons between indicators. Qualitative data were expressed as rate or constituent ratio, and the χ2 test or Kruskal-Wallis test was used to examine differences. Logistic regression was used for multivariate analyses. SAS 9.4 software was used for statistical analyses, and P<0.05 was considered statistically significant. Results:The prevalence of abnormal albuminuria was 19.1%; the prevalence was 17.2% for MAU and lower in males (13.8%) than females (20.1%, P<0.01). The risk of CVD was higher among subjects with MAU ( OR=1.23, 95% CI 1.12-1.35) and macroalbuminuria ( OR=1.86, 95% CI 1.50-2.32). When MAU was complicated by hypertension and diabetes mellitus, the CVD risk was 1.76 times higher. Conclusions:The prevalence of MAU is high among Chinese subjects aged 35 years and over. Those with MAU have higher CVD risk, especially those with hypertension and diabetes mellitus.