ObjectiveTo explore the elements， distribution and characteristics of traditional Chinese medicine （TCM） syndromes in depressive episodes of bipolar disorder （BD）. MethodsBasic information， along with the four examination information， the Hamilton Depression Scale and Young Mania Rating Scale scores， were collected from 293 outpatients with BD at Beijing Anding Hospital， Capital Medical University. The four examination information with an occurrence rate greater than 12% were retained. The R language “dist” function was used to calculate the distances between samples using the Euclidean distance method. The hierarchical clustering of the four examination information was performed using the “hclust” function and the squared Euclidean distance method. A team of five researchers was formed to determine the nature and location of the essential elements of TCM syndrome in BD based on the clustering results. The PC algorithm was used to construct a Bayesian network model of the essential elements. The working group combined the essential elements of TCM syndromes in the Bayesian network according to the reference model results， and then extracted common TCM syndromes. The score of each patient based on the essential elements was matched with the common TCM syndromes to determine the syndrome type of each patient. The working group then performs conformity and revision based on this， obtaining the final distribution of TCM syndromes for the patients. ResultsThere were 77 common TCM symptoms in BD with a frequency greater than 12%. The top 15 symptoms with higher frequencies were slippery pulse， mental fatigue and lack of strength， wiry pulse， excessive rumination， preference for solitude， vexation， agitation and irritability， dry mouth， palpitations， profuse dreaming， unwarranted worries， chest oppression， thin white coating， amnesia， frequent sighing， and poor appetite. TCM syndrome elements of BD can be grouped into 11 categories. The nature of disease-related essential elements included fire， qi deficiency， blood deficiency， qi counterflow， yin deficiency， dampness， heat， fire from constraint， and phlegm. The location of disease-related essential elements included heart， liver， spleen， stomach， kidney， bladder channel， and gallbladder. By constructing a Bayesian network model and considering the opinions from the experts， six common syndromes of BD were identified， among which the highest proportion was heart-stomach heat accumulation， accounting for 27.99% （82 cases）， followed by heart-spleen deficiency （55 cases， 18.77%）， non-interaction between the heart and the kidney （49 cases， 16.72%）， liver constraint and blood deficiency （42 cases， 14.33%）， heart qi deficiency （37 cases， 12.63%）， and damp-heat in the liver and gallbladder （28 cases， 9.56%）. ConclusionsThe nature of disease-related elements of BD are predominantly fire and heat， while the location of disease-related essential elements are primarily associated with the heart， liver， and spleen. The most common TCM syndromes are heart-stomach heat accumulation and heart-spleen deficiency.

Objective To investigate the relationship between insomnia,gastrointestinal symptoms,and glycosylated hemoglobin(HbA1c)levels in individuals diagnosed with type 2 diabetes mellitus(T2DM),as well as the related influencing factors.Methods A total of 910 T2DM patients treated in our multicenter from January 2022 to December 2022 were enrolled in this study.General information(gender,age,smoking and drinking history,exercise,course of disease,treatment and complications),HbA1c,Athens Insomnia Scale(AIS)scores and Gastrointestinal Symptoms Rating Scale(GSRS)scores of patients were collected.The differences of sleep and gastrointestinal symptoms between groups were analyzed,and the correlation between the differences and HbA1c was analyzed.Furthermore,the risk factors for non-standard HbA1c were analyzed.Results The AIS score and GSRS score in the HbA1c control group were less than those in the non-standard group(P＜0.01).Insomnia was reported by 37.0％of T2DM patients,and the HbA1c level in the insomnia group was significantly higher than that in the non-insomnia group(10.00％±2.38％vs.8.26％±1.73％,P＜0.01).Gastrointestinal symptoms were present in 57.5％of T2DM patients,and the HbA1c levels in the group with gastrointestinal symptoms were significantly higher than those in the group without gastrointestinal symptoms(9.26％±2.23％vs.8.43％±1.98％,P＜0.01).Furthermore,26.3％of T2DM patients experienced both insomnia and gastrointestinal symptoms.Remarkably,the HbA1c levels in the group with both insomnia and gastrointestinal symptoms were significantly higher than those in the group without either condition(10.18％±2.44％vs.8.45％±1.86％,P＜0.01).Correlation analysis demonstrated a significant association between sleep quality,gastrointestinal function,and HbA1c levels(P＜0.01).The logistic regression analysis result revealed that age,GSRS score,AIS score,and the presence of insomnia combined with gastrointestinal symptoms were independent risk factors for predicting HbA1c≥6.5％(P＜0.01).Having both insomnia and gastrointestinal symptoms concurrently was the strongest risk factor for substandard HbA1c control,and the risk of blood sugar control may increase about 5 times when both appear together.Conclusion Insomnia and gastrointestinal symptoms are common comorbidities in T2DM patients,showing a cross-interfering relationship,and they appear together with poor blood sugar control,interact causally,and amplify each other.

With the deepening of the reform of the medical and health system and the continuous optimization of the medical order,it is especially important to organize the development of admission and discharge standards and improve the service of preparing patients for discharge.In recent years,the research and application of machine learning technology in the medical field has been intensifying,and it has unique advantages in processing data and risk prediction research.Therefore,this paper reviews the development process,types of machine leaming,the content and effects of its application in patient discharge preparation services,and the current problems,in order to provide references for healthcare professionals to implement the best clinical decisions and further improve the patient discharge preparation service model.

Objective:To detect the status of PIK3CA in triple-negative breast cancer (TNBC) , and to analyze the relationships between PIK3CA mutation and clinical features and its impact on prognosis.Methods:From January 1, 2016 to December 31, 2018, 50 patients with primary TNBC admitted to Xinxiang Central Hospital of Henan Province were collected. The PIK3CA mutation status was detected, and the relationships between PIK3CA mutation and clinical characteristics of patients with TNBC and its impact on prognosis were analyzed.Results:PIK3CA gene mutation was detected in 9 of 50 TNBC patients, with a mutation frequency of 18.0%. H1047R mutation was found in 4 cases, E545K mutation in 3 cases and E542K mutation in 2 cases. PIK3CA gene mutation was not associated with age ( χ2=3.55, P=0.060) , tumor location ( χ2=1.01, P=0.315) , tumor size ( χ2<0.01, P>0.999) , lymph node status ( χ2=0.76, P=0.385) , clinical stage ( χ2=0.65, P=0.420) , Ki-67 value ( χ2<0.01, P>0.999) , P53 status ( χ2=0.02, P=0.894) and human epidermal growth factor receptor-2 (HER-2) status ( χ2=1.65, P=0.200) . Prognostic analysis showed that 3-year disease-free survival rates of wild-type PIK3CA patients was significantly higher than that of mutant PIK3CA patients (80.5% vs. 11.1%, χ2=28.23, P<0.001) . Conclusion:The frequency of PIK3CA gene mutation is higher in TNBC patients. There is no correlation between PIK3CA mutation and clinicopathologic features in TNBC patients. PIK3CA gene mutation may be significantly associated with poor prognosis of TNBC patients.

Objective:To investigate how medical students process Western medicine(WM)terms vs.traditional Chinese medicine(TCM)terms and how this is impacted by analogical priming from the perspective of cognitive neuroscience.Methods:The experiments were designed as a 2(TCM terms vs.WM terms)x 2(correct terms vs.incorrect terms)x 2(analogical priming task vs.non-priming task)scheme.A total of 26 medical stu-dents completed a non-priming judgment task and an analogical priming judgment task on medical terms.During the tasks,the participants were asked to make correct/incorrect judgments on WM terms and TCM terms,and their behavioral data and event-related potentials(ERPs)were recorded.Results:Behaviorally,the response speed and accuracy of WM terms were higher than those of TCM terms(both P＜.001),indicating a prominent concreteness effect.Analogical priming shortened the response time to medical terms(P＜.001),and the response time to TCM terms was shortened more significantly(P=.001).For ERPs,WM terms induced a larger P200(an early positive ERP component),a smaller N400(a negative ERP component),and a higher late positive ERP component,indicating supe-riority of attention adjustment,smaller-scale semantic activation,and a higher cost of late semantic analysis and integration.However,the analogical priming eliminated the difference between WM and TCM terms in P200 and N400 while maintaining it in the LPC.This suggests that WM terms are sensitive to analogical attention adjustment,and TCM terms are susceptible to analogical semantic integration.Conclusion:Our findings suggest that WM and TCM disease behavior terms may initially differ in concreteness or the use of a verbal-linguistic system.Analogy is more conducive to understanding TCM terms.This research provides important neuroscientific evidence of the difference in thinking between TCM and WM.

This study aimed to quantitatively assess the effectiveness of the Wuhan lockdown measure on controlling the spread of coronavirus diesase 2019 (COVID-19). : Firstly，estimate the daily new infection rate in Wuhan before January 23，2020 when the city went into lockdown by consulting the data of Wuhan population mobility and the number of cases imported from Wuhan in 217 cities of Mainland China. Then estimate what the daily new infection rate would have been in Wuhan from January 24 to January 30th if the lockdown measure had been delayed for 7 days，assuming that the daily new infection in Wuhan after January 23 increased in a high，moderate and low trend respectively (using exponential, linear and logarithm growth models). Based on that，calculate the number of infection cases imported from Wuhan during this period. Finally，predict the possible impact of 7-day delayed lockdown in Wuhan on the epidemic situation in China using the susceptible-exposed-infectious-removed (SEIR) model. : The daily new infection rate in Wuhan was estimated to be 0.021%，0.026%，0.029%，0.033% and 0.070% respectively from January 19 to January 23. And there were at least 20 066 infection cases in Wuhan by January 23，2020. If Wuhan lockdown measure had been delayed for 7 days，the daily new infection rate on January 30 would have been 0.335% in the exponential growth model，0.129% in the linear growth model，and 0.070% in the logarithm growth model. Correspondingly，there would have been 32 075，24 819 and 20 334 infection cases travelling from Wuhan to other areas of Mainland China，and the number of cumulative confirmed cases as of March 19 in Mainland China would have been 3.3-3.9 times of the officially reported number. Conclusions: Timely taking city-level lockdown measure in Wuhan in the early stage of COVID-19 outbreak is essential in containing the spread of the disease in China.
COVID-19 ; China/epidemiology* ; Cities ; Communicable Disease Control ; Humans ; SARS-CoV-2

To predict the epidemiological trend of coronavirus disease 2019 (COVID-19) by mathematical modeling based on the population mobility and the epidemic prevention and control measures. : As of February 8，2020，the information of 151 confirmed cases in Yueqing，Zhejiang province were obtained，including patients' infection process，population mobility between Yueqing and Wuhan，etc. To simulate and predict the development trend of COVID-19 in Yueqing, the study established two-stage mathematical models，integrating the population mobility data with the date of symptom appearance of confirmed cases and the transmission dynamics of imported and local cases. : It was found that in the early stage of the pandemic，the number of daily imported cases from Wuhan (using the date of symptom appearance) was positively associated with the number of population travelling from Wuhan to Yueqing on the same day and 6 and 9 days before that. The study predicted that the final outbreak size in Yueqing would be 170 according to the number of imported cases estimated by consulting the population number travelling from Wuhan to Yueqing and the susceptible-exposed-infectious-recovered (SEIR) model; while the number would be 165 if using the reported daily number of imported cases. These estimates were close to the 170，the actual monitoring number of cases in Yueqing as of April 27，2020. : The two-stage modeling approach used in this study can accurately predict COVID-19 epidemiological trend.
COVID-19 ; China/epidemiology* ; Disease Outbreaks ; Humans ; Models, Theoretical ; Pandemics ; SARS-CoV-2

At present, traditional methods on statistics have limitations in controlling time- varying confounding. This paper introduces an analysis method, parametric g-formula, which would adjust time-varying confounding, and also exemplifies the steps of its implementation for purpose to provide a new reference for researchers to deal with long-term observational data.

In May 2019, the International Diabetic Foot Working Group (IWGDF) updated and issued guidelines for the prevention and management of diabetic foot disease. This guide puts forward some suggestions for the diagnosis, treatment and effective prevention of diabetic foot: the prevention of diabetic foot should start with high-risk foot, early screening and treatment of diabetic foot infection, foot ulcer and peripheral vascular disease and early comprehensive treatment. Effective prevention and early treatment can reduce the incidence of cardiovascular and cerebrovascular events in patients with diabetes, reduce the amputation rate and mortality, and improve the prognosis and quality of life of patients.

Hypoxia is beneficial for the differentiation of stem cells transplanted for myocardial injury, but mechanisms underlying this benefit remain unsolved. Here, we report the impact of hypoxia-induced Jagged1 expression in cardiomyocytes (CMs) for driving the differentiation of cardiac stem cells (CSCs). Forced hypoxia-inducible factor 1 (HIF-1) expression and physical hypoxia (5% O) treatment could induce Jagged1 expression in neonatal rat CMs. Pharmacological inhibition of HIF-1 by YC-1 attenuated hypoxia-promoted Jagged1 expression in CMs. An ERK inhibitor (PD98059), but not inhibitors of JNK (SP600125), Notch (DAPT), NF-B (PTDC), JAK (AG490), or STAT3 (Stattic) suppressed hypoxia-induced Jagged1 protein expression in CMs. c-Kit CSCs isolated from neonatal rat hearts using a magnetic-activated cell sorting method expressed GATA4, SM22 or vWF, but not Nkx2.5 and cTnI. Moreover, 87.3% of freshly isolated CSCs displayed Notch1 receptor expression. Direct co-culture of CMs with BrdU-labeled CSCs enhanced CSCs differentiation, as evidenced by an increased number of BrdU/Nkx2.5 cells, while intermittent hypoxia for 21 days promoted co-culture-triggered differentiation of CSCs into CM-like cells. Notably, YC-1 and DAPT attenuated hypoxia-induced differentiation. Our results suggest that hypoxia induces Jagged1 expression in CMs primarily through ERK signaling, and facilitates early cardiac lineage differentiation of CSCs in CM/CSC co-cultures HIF-1/Jagged1/Notch signaling.