To compare the therapeutic effects of short-term spinal cord stimulation (stSCS) and long-term spinal cord stimulation (ltSCS) on patients with prolonged disorders of consciousness(pDoC).

Objective:To explore the latent classes of health behavior and explore the predictive factors among stroke patients.Methods:A total of 1 250 participants were recruited using cluster random sampling in September 2022. The general information, the modified Rankin scale(mRS), stroke prevention knowledge questionnaire(SPKQ), health behavior scale for stroke patients (HBS-SP), and short form-health belief model scale (SF-HBMS) were administered in the cross-sectional survey. Mplus 8.3 software was used to conduct a latent class analysis (LCA) on the health behavior of stroke patients, and SPSS 27.0 software was used to carry out multinomial Logistic regression to analyze the predictive factors of different latent classes of health behavior of stroke patients.Results:The health behavior of stroke patients obtained three latent classes: low health behaviors-lack of health responsibility group (66.9%, n=794), moderate health behaviors-poor compliance group (11.9%, n=141), and good health behaviors-insufficient exercise group (21.2%, n=251). Compared with good health behaviors-insufficient exercise group, stroke patients with shorter duration education time ( B=-0.589, OR=0.555, P=0.036), hemorrhagic stroke ( B=0.082, OR=1.086, P<0.001), fewer comorbidities ( B=-0.022, OR=0.978, P=0.026), higher mRS score ( B=-0.046, OR=1.047, P=0.004), lower SPKQ score ( B=-0.055, OR=0.947, P=0.016), and lower SF-HBMS score ( B=-0.085, OR=0.919, P<0.001) were more likely to be included in moderate health behaviors-poor compliance group. However, stroke patients with shorter duration education time ( B=-0.026, OR=0.974, P=0.003), rural areas dwelling ( B=0.800, OR=2.225, P=0.004), fewer comorbidities ( B=-0.056, OR=0.945, P<0.001), lower SPKQ score ( B=-0.101, OR=0.904, P<0.001), and lower SF-HBMS score ( B=-0.071, OR=0.931, P<0.001) were more likely to be included in low health behaviors-lack of health responsibility group. Conclusion:The health behavior of stroke patients has three latent classes. A targeted intervention should be carried out according to the characteristics of different classes to improve their health behavior levels.

Objective:To explore the potential facilitators and barriers to healthy behavior among stroke patients.Methods:Semi-structured interviews were conducted among 16 stroke patients from September 2022 to March 2023 using an objective sampling method.The interview guide was developed using the theoretical domain framework(TDF). Interviews were transcribed and refined the theme using directed content and induction analysis.Using the TDF as the initial coding framework, the themes were then merged into the most relevant domains.Finally, the correspondence between theoretical domains and the healthy behavior of stroke patients was determined based on the frequency and relationship between themes.Results:This study identified nine theoretical domains that affected the healthy behavior of stroke patients: knowledge, skills, motivation and goals, social influences, social/professional role and identity, environment context and resources, belief about capability, consequence belief and behavioral regulation.Conclusion:The healthy behavior of stroke patients is complex and influenced by several factors.The nine theoretical domains identified in this study will provide recommendations for future healthy behavior interventions for stroke patients.

Objective:To explore the anxiety level, influencing factors among surrogate decision-makers of patients with acute ischemic stroke during thrombolysis decision-making, and their correlation with decision-making duration.Methods:Acute ischemic stroke patients and their surrogate decision-makers who visited the Emergency Department of the First Affiliated Hospital of Zhengzhou University from September 2019 to December 2021 were selected as the research subjects.Sociodemographic data and disease related data of patients and surrogate decision-makers were collected.Surrogate decision-makers were evaluated with the state-trait anxiety inventory, decision participation expectation scale, Wake Forest physician trust scale, and perceived social support scale.SPSS 26.0 software was used for data processing.Pearson correlation analysis, Spearman correlation analysis and ridge regression analysis were used for statistical analysis.Results:The score of state anxiety of decision-makers was (49.47±9.04), and 18.2% (70/383) of decision-makers had a decision duration exceeding 15 minutes.The score of state anxiety of decision-makers was positively correlated with decision duration ( r=0.189, P<0.001). The influencing factors of state anxiety level of decision-makers included sociodemographic factors (age of decision-makers and patients, relationship between payers and patients, whether decision-makers bear the current medical expenses, type of medical insurance for patients), psychological factors (trust level in physicians, perceived social support), factors related to patient disease (numbers of stroke relapses, National Institutes of Health stroke scale scores for patients), characteristics of the decision-making process (whether patients participate in the decision-making process, and the role of decision-makers in the decision-making process) (all P<0.05). Conclusion:Most surrogate decision-makers experience anxiety.Medical staff should pay attention to the emotions of decision-makers and adopt appropriate communication skills when communicating with informed consent for thrombolysis, alleviate the anxiety of surrogate decision-makers, so as so reduce the decision-making duration.

Objective:To investigate the current comorbidity status among hypertension, diabetes, and dyslipidemia in residents aged 35-75 years in Tianjin and to explore the main influencing factors to provide a scientific basis for the prevention and treatment of chronic disease comorbidity.Methods:From June 2019 to November 2023, 10 districts (Hedong, Hexi, Dongli, Beichen, Nankai, Xiqing, Wuqing, Baodi, Jizhou, and Binhai New District) in Tianjin were selected as the project sites. The community and natural village was used as the primary sampling unit, and each project site selected the screening sites by cluster random sampling method. Residents aged 35-75 who lived in the screening sites for 6 months and above were surveyed by questionnaire, physical examination, and biochemical tests. The chi-square test, analysis of variance, and multivariate unconditional logistic regression analysis were used for statistical analysis. Age-standardized prevalence was based on the data of the sixth national census.Results:A total of 146 832 participants were included in this study, including 61 994 males (42.22%) and 84 838 females (57.78%), with an age of (56.83±8.84) years. The number of people with only one disease was 55 485 (37.79%), the number of people with two diseases was 36 942 (25.16%), and the number of people with three diseases was 9 683 (6.59%). The prevalence of hypertension combined with dyslipidemia was the highest (17.23%), and the standardized prevalence were 14.44%. The prevalence rates of three diseases and hypertension combined with diabetes was 6.59% and 4.98%, respectively, and the standardized prevalence was 5.42% and 4.11%, respectively. The prevalence of diabetes combined with dyslipidemia was 2.95%, and the standardized prevalence was 2.45%. Multivariate unconditional logistic regression analysis showed that advanced age (65- 75 years old: OR=2.69, 95% CI: 2.28-3.18), overweight/obesity (overweight: OR=2.21, 95% CI: 2.02-2.41; obesity: OR=4.50, 95% CI: 4.03-5.02), daily smoking ( OR=1.96, 95% CI: 1.72-2.24), regular and heavy drinking ( OR=1.63, 95% CI: 1.18-2.27), family history of hypertension/diabetes/hyperlipidemia (family history of hypertension: OR=81.17, 95% CI: 74.68-88.22; family history of diabetes: OR=15.26, 95% CI: 13.71-16.99; family history of hyperlipidemia: OR=7.13, 95% CI: 5.92-8.59), tea drinking (occasional tea drinking group: OR=1.74, 95% CI: 1.52-2.00; frequent tea drinking group: OR=2.23, 95% CI: 1.92-2.59) were risk factors for the comorbidity of hypertension, diabetes and dyslipidemia (all P<0.05), while higher education level was a protective factor (senior high school/technical secondary school: OR=0.79, 95% CI: 0.72-0.86; college/bachelor's degree and above: OR=0.60, 95% CI: 0.53-0.68, all P<0.001). Conclusions:The comorbidity rate of hypertension, diabetes, and dyslipidemia is high in residents aged 35-75 years in Tianjin. It is necessary to strengthen the co-management of blood pressure, blood glucose, and blood lipid in key populations with old age, overweight/obesity, junior high school education or below, daily smoking, daily drinking, occasional or frequent tea drinking, and family history of hypertension/diabetes/dyslipidemia, and promote a healthy lifestyle.

Objective:To understand the interaction effect of general obesity, central obesity, and dyslipidemia on the risk of hypertension to provide scientific evidence for the early prevention and control of hypertension.Methods:From 2019 to 2023, 10 of the 16 districts in Tianjin were selected as project sites. A community and a natural village were selected as monitoring sites in each project site using a multi-stage cluster random sampling method. A questionnaire, physical, and biochemical examination were conducted on permanent residents aged 35-75 who had lived in the surveillance sites for more than half a year. The chi-square test univariate and multivariate logistic regression were used for statistical analysis. The multiplicative and additive models were used to calculate the interaction between general obesity and dyslipidemia, as well as central obesity and dyslipidemia, respectively.Results:A total of 177 160 subjects were included in the study, with an age of (56.44±8.62) years old. There were 29 535 (16.67%) patients with general obesity, 67 338 (38.01%) patients with central obesity, 64 906 (36.64%) patients with dyslipidemia, and 90 266 (50.95%) patients with hypertension. Multiplication interaction analysis results showed that, after adjusting for gender, age, culture level, marriage status, drinking, smoking, and diabetes, the multiplicative interactions between general obesity and dyslipidemia, and central obesity and dyslipidemia on hypertension were statistically significant (all P<0.001), and the adjusted OR and 95% CI were 2.57 (2.47-2.68) and 2.14 (2.08-2.20), respectively. The results of the additive interaction analysis demonstrated that after adjusting for relevant variables, the relative excess risk of interaction ( RERI), the attributable proportion of interaction ( API), and the synergy index ( SI) of the interaction between generalized obesity and dyslipidemia were 0.48 (95% CI: 0.33-0.63), 0.15 (95% CI: 0.11-0.19), and 1.27 (95% CI: 1.18-1.36), respectively; the RERI, API, and SI of the interaction between central obesity and dyslipidemia were 0.37 (95% CI: 0.28-0.46), 0.13 (95% CI: 0.10-0.16), and 1.25 (95% CI: 1.18-1.32), respectively. Conclusions:There might be multiplicative and additive interactions between general obesity, central obesity, and dyslipidemia on the risk of hypertension. Simultaneous control of BMI, waist circumference, and blood lipid levels may effectively reduce the risk of hypertension.

Objective:To explore the latent profile types of capability-opportunity-motivation-behavior in stroke patients and analyze the influencing factors of different latent profiles.Methods:From January to October 2023, totally 596 stroke patients from the Neurology Department of five ClassⅢ Grade A hospitals in Henan Province were selected by stratified random sampling. The patients were surveyed using a general information questionnaire, the Stroke Prevention Knowledge Questionnaire (SPKQ), the Social Support Rating Scale (SSRS), the WHO's Quality of Life Questionnaire- Brief Version (WHOQOL-BREF), the Short Form Health Belief Model Scale (SF-HBMS), and the Health Promoting Lifestyle ProfileⅡ (HPLPⅡ). Latent profile analysis was used to classify the capability-opportunity-motivation-behavior characteristics of stroke patients, and multiple logistic regression was conducted to explore the influencing factors of different latent profiles.Results:Three latent profiles of capability-opportunity-motivation-behavior in stroke patients were identified, including low capability-opportunity-motivation-behavior with high health beliefs (32.4%, 193/596), moderate capability-opportunity-motivation-behavior with insufficient health beliefs (47.5%, 283/596), and high capability-opportunity-motivation-behavior with lack of social support (20.1%, 120/596). Multiple logistic regression analysis showed that educational level, smoking history, family history, body mass index, and Charlson Comorbidity Index score were influencing factors of different latent profiles ( P<0.05) . Conclusions:Stroke patients exhibit distinct classifications of capability-opportunity-motivation-behavior. Targeted interventions should be conducted based on the characteristics of each category to improve health behavior management outcomes in patients.

Objective:To explore the effects of depressive symptoms on sleep quality in adolescents with depressive disorder, and the mediating roles of cognitive fusion and sleep belief.Methods:A sample of 210 adolescents with first episode depressive disorder aged 12-18 years were recruited to complete 17-item Hamilton depression scale (HAMD-17), Pittsburgh sleep quality index (PSQI), cognitive fusion questionnaire (CFQ), and dysfunctional beliefs and attitudes about sleep scale (DBAS-16) from November 2021 to July 2022. SPSS 26.0 software was used to perform descriptive analysis and correlation analysis. The mediating effect was tested by Bootstrap analysis using PROCESS V 3.4 Macro program.Results:The incidence of low sleep quality in adolescents with depressive disorder was 69.0%(145/210). HAMD-17 score was (22.4±7.9), PSQI score was (9.7±3.7), CFQ score was (51.6±7.8), DBAS-16 score was (43.5±8.4).PSQI was positively correlated with the scores of HAMD-17 and CFQ( r=0.613, 0.463, both P<0.001).HAMD-17 was positively correlated with CFQ score ( r=0.488, P<0.001).DBAS-16 was negatively correlated with scores of PSQI, HAMD-17 and CFQ( r=-0.326, -0.284, -0.354, all P<0.001). The direct effect of depression on sleep quality was 0.230(95% CI=0.169-0.293). The indirect effect of depression on sleep quality through two pathways, the separate mediating effect value of cognitive fusion was 0.041 (95% CI=0.011-0.074), and the chain mediating effect value of cognitive fusion and sleep beliefs was 0.008(95% CI=0.001-0.020). Conclusion:Depressive symptoms can directly affect sleep quality of depressive disorder adolescents and indirectly through cognitive fusion and sleep beliefs.

Abstract: Objective To compare the diagnostic efficacy of the upgraded version of the GeneXpert automated fluorescent quantitative PCR system (GeneXpert MTB/RIF Ultra, GeneXpert Ultra) and the original version of the GeneXpert system (GeneXpert MTB/RIF, Xpert), real-time fluorescent quantitative nucleic acid detection (FQ-PCR), real-time fluorescent thermostatic amplification of Mycobacterium tuberculosis RNA (SAT-RNA), real-time fluorescent thermostatic amplification detection of DNA (thermostatic amplification method) and traditional BACTEC MGIT 960 liquid culture (culture method) for special specimens of tuberculosis, in order to analyze its application value in clinical detection. Methods Using prospective research methods, a total of 170 special specimens (including 47 pleural and ascites effusion samples, and 34 24-hour urinary sediment specimens, 49 tissue specimens and 40 fester specimens) were collected i'an Chest Hospital from January to September 2021. GeneXpert Ultra, Xpert, FQ-PCR, SAT-RNA, isothermal amplification, and traditional culture were used for detection. Clinical diagnosis was used as the standard, and sensitivity, specificity, positive predictive value, negative predictive value, coincidence rate, and Kappa value were compared among the methods. Results The sensitivities of GeneXpert Ultra, Xpert, FQ-PCR, SAT-RNA, isothermal amplification, and traditional culture were 65.18% (73/112), 49.11% (55/112), 37.50% (42/112), 19.64% (22/112), 8.04% (9/112), and 22.32% (25/112), respectively. The sensitivity of GeneXpert Ultra was higher than that of the other five methods, and the differences were statistically significant (χ2=66.25, 42.10, 28.89, 13.09, 4.92, 15.18, all P<0.05). GeneXpert Ultra result analysis showed that: 5.48%(4/73) cases had trace, that is, trace Mycobacterium tuberculosis load, 79.45% (58/73) cases were extremely low, 10.96% (8/73) cases were low, 2.74% (2/73) were medium, , and 1.36% (1/73) were high load. In 4 trace samples, the Xpert detection was negative for all. Of the 73 GeneXpert Ultra positive reports, 63 were rifampicin-sensitive, 6 were rifampicin-resistant, and 4 were rifampicin-resistant but of unclear resistance. Of the 55 Xpert positive reports, 45 were rifampicin-sensitive, 2 were rifampicin-resistant, and 8 were rifampicinresistant but of unclear resistance.. Conclusions The new generation of GeneXpert MTB/RIF Ultra has high sensitivity, specificity and drug resistance detection rate, and its advantage is even more apparent in the pathogenic diagnosis of special specimens of tuberculosis. It can be used as one of the preferred methods in samples with low bacterial load.

Objective:To investigate the characteristics of obesity in adolescents with major depressive disorder (MDD) and its association with the depressed severity.Methods:A total of 278 adolescents with MDD were recruited according to the inclusion and exclusion criteria. Their demographic data were collected and 24-item Hamilton depression scale (HAMD-24) was used to evaluate their severity of depression. According to the body mass index (BMI) classification standard of adolescents in China, all subjects were classified into four groups(wasting group, normal BMI group, overweight group and obesity group). SPSS 26.0 statistical software was used for data analysis. The Kruskal-Wallis test and the Chi-square test were separately used for the comparison of the four groups.Spearman correlation was used to explore the relationship between BMI and HAMD-24 scores and severity.Results:Among 278 adolescents with MDD, the prevalence of body abnormality was 32.4% (90/278), among which wasting, overweight and obesity were 9.0% (25/278), 14.4% (40/278) and 9.0% (25/278) respectively. There were statistically differences in gender ( χ2=17.018, P<0.001), HAMD-24 scores ( H=9.427, P=0.024) and depressed severity( H=8.508, P=0.037) among the four groups. Multiple comparisons showed that there were only statistically differences between obesity group and normal BMI group, that was the prevalence of obesity in males was higher than that in females ( χ2=13.631, P<0.001), and the level in HAMD-24 scores ( Z=2.956, P=0.003) and depressed severity ( Z=2.832, P=0.005) was lower in obesity group than that in normal BMI group.Spearman correlation analysis showed that BMI was negatively correlated with HAMD-24 scores ( r=-0.162, P=0.007). Conclusion:There is gender difference in obesity rates among the adolescent patients with first-episode untreated MDD. And the obese patients are less depressed than those with normal BMI.