Objective:To compare the differences in clinical characteristics among the patients at clinical high risk for bipolar disorder(CHR-BD),the patients with bipolar disorder(BD),and the healthy controls(HC)at low risk,and to provide the basis for the diognasis and treatment of CHR-BD.Methods:For the first time,the BD risk criteria and prospective structured assessment tools were jointly used in outpatients aged 16-30 years,and 43 CHR-BD patients were included to ensure the accuracy of the assessment.Meanwhile,33 BD patients and 32 HC subjects were also enrolled.The clinical symptoms,neurocognitive function,and global functional levels of the subjects in the three groups were evaluated using observer-rated and self-rated tools.The CHR-BD and BD groups were combined,and Logistic regression analysis was used to identify the independent influencing factors related to diagnostic status;Pearson or Spearman correlation analysis was used to analyze the correlations between the global functional levels and the symptoms or neurocognitive characteristics of the patients in CHR-BD and BD groups.Results:There were statistically significant differences in the scores of symptom and global functional level scales among HC,CHR-BD,and BD groups(P<0.05).Compared with HC group,the scores of mood symptoms(anxiety,depression,and mania/hypomania),psychotic symptoms,total affective temperament questionnaire scores,and some dimensions(cyclothymic,depressive,irritable,and anxious temperaments)in CHR-BD and BD groups were significantly increased(P<0.001),while the global functional levels were significantly decreased(P<0.001).Compared with BD group,the lowest global functional level score in the past year in CHR-BD group was significantly increased(P=0.022),while the current global functional level score was significantly decreased(P=0.005).No significant differences were observed in neurocognitive function scores among the three groups(P>0.05).The lowest global functional level score in the past year was an independent influencing factor for BD diagnosis[odds ratio(OR)=0.952,95%confidence interval(CI):0.917-0.988,P=0.010].In both CHR-BD and BD patients,the current global functional levels were negatively correlated with depressive(r=-0.417,P=0.005;r=-0.617,P<0.001)and anxiety symptoms(r=-0.360,P=0.018;r=-0.506,P=0.003).In BD patients,the current global functional level was negatively correlated with lifetime manic/hypomanic symptoms(r=-0.360,P=0.039),psychotic symptoms(r=-0.502,P=0.003),and affective temperament scores(r=-0.479,P=0.005),while the lowest global functional level in the past year was negatively correlated with lifetime manic/hypomanic symptoms(r=-0.391,P=0.024).Conclusion:CHR-BD patients share similar mood symptom characteristics with BD patients,and their global functional levels are negatively correlated with depressive and anxiety symptoms.BD patients exhibit worse lowest global functional levels in the past year,and their global functional levels are negatively correlated with manic/hypomanic symptoms.

Objective:To provide a scoping review of domestic and international studies on the influencing factors of frailty in stroke inpatients, and to provide guidance for interventional studies on debility in stroke inpatients in China.Methods:A systematic search of domestic and international literature databases, including China National Knowledge Infrastructure, WanFang, VIP, China Biomedical Literature Database, PubMed, Web of Science, Embase, Cochrane Library, CINAHL was conducted about the status and its influencing factors of frailty in stroke inpatients. The retrieval time limit was from database establishment to November 23, 2023. The included literature was extracted, analyzed, and summarized according to the reporting specifications of the scope review guidelines.Results:Thirty-six articles were included, all of the 36 papers reported the prevalence of frailty in patients with stroke. The top three countries with the highest prevalence were China (15.3%-86.9%), the United Kingdom (28%-78%), and Italy (31.37%). The risk factors of stroke frailty were categorized mainly into 4 themes: social demographic factors, physical health factors, disease-related factors, and psychological and social background factors.Conclusions:The confidence of frailty in stroke patients of a higher level at home and abroad, and the risk factors involved are complex and diverse. It is suggested that future studies need to conduct large-sample, multicenter longitudinal studies to clarify the causal relationship, strengthen the exploration of controversial factors of stroke debility, and carry out more targeted intervention studies, so as to reduce the incidence of frailty and improve the recovery process of patients.

Objective:To provide a scoping review of domestic and international studies on the influencing factors of frailty in stroke inpatients, and to provide guidance for interventional studies on debility in stroke inpatients in China.Methods:A systematic search of domestic and international literature databases, including China National Knowledge Infrastructure, WanFang, VIP, China Biomedical Literature Database, PubMed, Web of Science, Embase, Cochrane Library, CINAHL was conducted about the status and its influencing factors of frailty in stroke inpatients. The retrieval time limit was from database establishment to November 23, 2023. The included literature was extracted, analyzed, and summarized according to the reporting specifications of the scope review guidelines.Results:Thirty-six articles were included, all of the 36 papers reported the prevalence of frailty in patients with stroke. The top three countries with the highest prevalence were China (15.3%-86.9%), the United Kingdom (28%-78%), and Italy (31.37%). The risk factors of stroke frailty were categorized mainly into 4 themes: social demographic factors, physical health factors, disease-related factors, and psychological and social background factors.Conclusions:The confidence of frailty in stroke patients of a higher level at home and abroad, and the risk factors involved are complex and diverse. It is suggested that future studies need to conduct large-sample, multicenter longitudinal studies to clarify the causal relationship, strengthen the exploration of controversial factors of stroke debility, and carry out more targeted intervention studies, so as to reduce the incidence of frailty and improve the recovery process of patients.

Polyhydroxyalkanoates (PHAs) have obtained much attention in biomaterial fields due to their similar physicochemical properties to those of the petroleum-derived plastics. Poly(3-hydroxybutyrate-co-lactate) [P(3HB-co-LA)] is one member of the PHAs family, and has better toughness and transparency compared to existing polylactic acid (PLA) and poly[(R)-3-hydroxybutyrate] [P(3HB)]. First, we confirmed the one-step biosynthesis of P(LA-co-3HB) with the lactate fraction of 23.8 mol% by introducing P(3HB-co-LA) production module into Escherichia coli MG1655. Then, the lactate fraction was increased to 37.2 mol% in the dld deficient strain WXJ01-03. The genes encoding the thioesterases, ydiI and yciA, were further knocked out, and the lactate fraction in the P(3HB-co-LA) was improved to 42.3 mol% and 41.1 mol% respectively. Strain WXJ03-03 with dld, ydiI and yciA deficient was used for the production of the LA-enriched polymer, and the lactate fraction was improved to 46.1 mol%. Notably, the lactate fraction in P(3HB-co-LA) from xylose was remarkably higher than from glucose, indicating xylose as a potent carbon source for P(3HB-co-LA) production. Therefore, the deficiency of thioesterase may be considered as an effective strategy to improve the lactate fraction in P(3HB-co-LA) in xylose fermentation.
Escherichia coli/genetics* ; Hydroxybutyrates ; Lactic Acid ; Polyesters ; Polyhydroxyalkanoates ; Xylose

Objective:To estimate the predictive performance of the population pharmacokinetics software JPKD-vancomycin on predicting the vancomycin steady-state trough concentration, and to analyze the related factors affecting the predictive performance.Methods:The clinical data of patients who were treated with vancomycin and received therapeutic drug monitoring (TDM) admitted to Suzhou Hospital Affiliated to Nanjing Medical University from July 2013 to December 2018 were enrolled. All patients were designed an empirical vancomycin regimen (initial regimen) according to vancomycin medication guidelines. Steady-state trough concentrations of vancomycin were determined at 48 hours after the first dose and 0.5 hour before the next dose. Dosage regimen was adjusted when steady-state trough concentration was not in 10-20 mg/L (adjustment regimen), and then the steady-state trough concentration was determined again 48 hours after adjustment. First, the JPKD-vancomycin software was used to calculate the initial regimen and predict the steady-state trough concentration according to the results calculated by classic pharmacokinetic software Vancomycin Calculator. Second, the JPKD-vancomycin software was used to adjust the vancomycin dosage regime and predict the steady-state trough concentration of adjustment regimen. The weight residual (WRES) between the predicted steady-state trough concentration (C pre) and the measured steady-state trough concentration (C real) was used to evaluate the ability of the JPKD-vancomycin software for predicting the vancomycin steady-state trough concentration. The TDM results of initial regimen were divided into accurate prediction group (WRES < 30%) and the inaccurate prediction group (WRES ≥ 30%) according to the WRES value. Patient and disease characteristics including gender, age, weight, height, the length of hospital stay, comorbidities, vasoactive agent, mechanical ventilation, smoking history, postoperative, obstetric patients, trauma, laboratory indicators, vancomycin therapy and TDM results were collected from electronic medical records. Univariate and multivariate Logistic regression analysis was used to screen the related factors that influence the predictive performance of JPKD-vancomycin software, and the receiver operating characteristic (ROC) curve was drawn to evaluate its predictive value. Results:A total of 310 patients were enrolled, and 467 steady-state trough concentrations of vancomycin were collected, including 310 concentrations of initial regimen and 157 concentrations of adjustment regimen. Compared with the initial regimen, the WRES of adjusted regimen was significantly reduced [14.84 (6.05, 22.89)% vs. 20.41 (11.06, 45.76)%, P < 0.01], and the proportion of WRES < 30% increased significantly [82.80% (130/157) vs. 63.87% (198/310), P < 0.01]. These results indicated that JPKD-vancomycin software had a better accuracy prediction for steady-state trough concentration of the adjusted regimen than the initial regimen. There were 198 concentrations in the accurate prediction group and 112 in the inaccurate prediction group. Univariate Logistic regression analysis showed that women [odds ratio ( OR) = 0.466, 95% confidence interval (95% CI) was 0.290-0.746, P = 0.002], low body weight ( OR = 0.974, 95% CI was 0.953-0.996, P = 0.022), short height ( OR = 0.963, 95% CI was 0.935-0.992, P = 0.014), low vancomycin clearance (CL Van; OR < 0.001, 95% CI was 0.000-0.231, P = 0.023) and postoperative patients ( OR = 1.695, 95% CI was 1.063-2.702, P = 0.027) were related factors affecting the predictive performance of JPKD-vancomycin software. Multivariate Logistic regression analysis indicated that women ( OR = 0.449, 95% CI was 0.205-0.986, P = 0.046), low CL Van ( OR < 0.001, 95% CI was 0.000-0.081, P = 0.015) and postoperative patients ( OR = 2.493, 95% CI was 1.455-4.272, P = 0.001) were independent risk factors for inaccurate prediction of JPKD-vancomycin software. The ROC analysis indicated that the area under ROC curve (AUC) of the CL Van for evaluating the accuracy of JPKD-vancomycin software in predicting vancomycin steady-state trough concentration was 0.571, the sensitivity was 56.3%, and the specificity was 57.1%. The predictive performance of JPKD-vancomycin software was decreased when CL Van was lower than 0.065 L·h -1·kg -1. Conclusions:JPKD-vancomycin software had a better predictive performance for the vancomycin steady-state trough concentrations of adjustment regimen than initial regimen. JPKD-vancomycin software had a poor predictive performance when the patient was female, having low CL Van, and was postoperative. The predictive performance of JPKD-vancomycin software was decreased when CL Van was lower than 0.065 L·h -1·kg -1.

Objective To observe changes of spatial learning-memory in rats with chronic hypoxic hypercapnia and the effect of gingkgo biloba extra. Methods After established the rat model of chronic hypoxic hypercapnia,seventy-two rats were randomly divided into four groups normal control (NC),hypoxic-hypercapnia 4-week (4HH),hypoxic-hy-percapnia 4-week+gingkgo biloba extra (EGb)high dose(100 mg/kg)group[4HH+EGb(H)] and hypoxic-hypercapnia 4-week+EGb low dose (50 mg/kg) group[4HH+EGb(L)]. Praxiology in rats was asessed by the Morris water maze and step down test. Results The spatial learning-memory in rats exposed to chronic hypoxic-hypercapnia 4-week(4HH group)were displayed significant impairment in their performance,the longer mean escape latencies and swim path dis tances,the more error times. 4HH+EGb(H) and 4HH+EGb(L)groups shortened the reaction time of leaning, pro longed the latent time of memory, reduced times of mistakes. Conclusions EGb can enhance the capacity of learning-memory in the rats exposed chronic hypoxic hypercapnia.