Objective To investigate the clinical features of chronic hepatitis C(CHC)patients with hepatitis C virus genotype 3(HCV GT3)infection and the risk factors for disease progression.Methods A multicenter retrospective cohort study was conducted among 1 002 CHC patients from 11 clinical centers in Northwest China from December 2017 to November 2023,and according to their genotype,they were divided into GT1,GT2,GT3,and GT6 groups.Clinical features were compared between the patients with different genotypes.The one-way analysis of variance was used for comparison of normally distributed continuous data between groups,and the Scheffe test was used for further comparison between two groups.The Kruskal-Wallis H test was used for comparison of data with skewed distribution between groups;the chi-square test or Fisher test was used for comparison of categorical data between groups.The multivariate logistic regression analysis was used to explore the influencing factors for the progression of CHC to liver cirrhosis.Results In terms of the genotype,there were 427 patients with GT1 infection,242 with GT2 infection,299 with GT3 infection(210 patients with GT3a infection,87 with GT3b infection,and 2 with unclassified genotype),and 34 with GT6 infection.The patients with GT3 infection had a significantly younger age than those with GT1 infection(51.3±0.5 years vs 53.2±0.6 years,P<0.05)or GT2 infection(51.3±0.5 years vs 53.7±0.8 years,P<0.05),and for the patients with liver cirrhosis,the patients with GT3 infection had a significantly younger age than those with GT1 infection(52.1±0.5 years vs 59.4±0.9 years,P<0.001)or GT2 infection(52.1±0.5 years vs 58.1±1.1 years,P<0.001).Among the patients with GT3 infection,male patients accounted for 77.9%and the patients with liver cirrhosis accounted for 46.2%,which were significantly higher than those among the patients with GT1,GT2 or GT6 infection(all P<0.001).At baseline,the patients with GT3 infection had significantly higher levels of alanine aminotransferase(ALT)and aspartate aminotransferase(AST)than those with GT1 or GT2 infection,significantly higher aspartate aminotransferase-to-platelet ratio index(APRI)and fibrosis-4(FIB4)than those with GT1,GT2 or GT6 infection,a significantly lower platelet count(PLT)than those with GT2 or GT6 infection,a significantly higher level of alpha-fetoprotein than those with GT2 or GT6 infection,and a significantly lower level of albumin(Alb)than those with GT6 infection(all P<0.05).There were no significant differences between the patients with GT3a infection and those with GT3b infection in age,sex,the proportion of patients with liver cirrhosis,comorbidities,HCV RNA quantification,PLT,ALT,AST,alkaline phosphatase,Alb,APRI,and FIB-4(all P>0.05).The multivariate logistic regression analysis showed that PLT≤150×109/L(odds ratio[OR]=10.72,95%confidence interval[CI]:5.76-35.86,P<0.001)and Alb≤35 g/L(OR=3.74,95%CI:1.22-11.45,P=0.021)were risk factors for liver cirrhosis.Conclusion Most CHC patients with GT3 infection are male in Northwest China,and compared with the patients with other genotypes,such patients tend to have a younger age of onset and higher degrees of liver inflammation activity and fibrosis.Low PLT and a low level of Alb are risk factors for progression to liver cirrhosis in CHC patients with GT3 infection.

An automated identification method for adolescent schizophrenia based on brain electroencephalogram(EEG)entropy features is proposed for further improving the diagnostic accuracy of adolescent schizophrenia.The raw EEG signals are decomposed into 5 commonly used rhythm bands:Delta,Theta,Alpha,Beta,and Gamma.The permutation entropy,fuzzy entropy,and sample entropy are extracted from each rhythm band and then organized into a feature matrix structured by electrode location×frequency band.Finally,an ECA-CNN model integrating efficient channel attention(ECA)and convolutional neural network(CNN)is constructed for feature classification and realizing the automated identification of adolescent schizophrenia.The results demonstrate that the proposed ECA-CNN model has higher recognition accuracy than the traditional machine learning models,achieving an accuracy of 99.08%,a sensitivity of 99.27%,a specificity of 98.85%,a precision of 99.01%,a F1 score of 99.14%,and a Kappa coefficient of 0.9814.This study provides a new idea and method for the diagnosis of adolescent schizophrenia.

To address the challenges of extracting nonlinear features from motor imagery electroencephalogram(EEG)signals and effectively capturing functional connectivity between EEG channels,a classification and recognition method for motor imagery EEG signals is proposed based on mutual information and adaptive graph convolutional network.The proposed method extracts frequency domain information by sub-frequency banding on the original motor imagery EEG signals,uncovers the nonlinear relationships within EEG signals by an adjacency matrix constructed with mutual information neural estimation method,and finally achieve null-frequency feature extraction by capturing the dynamic correlation strength between channels with an adaptive graph convolutional network incorporating convolutional block attention module.On the BCI Competition Ⅳ 2a and BCI Competition Ⅲ 3a datasets,the proposed method has average accuracies of 83.14％and 88.19％,respectively,demonstrating that it can effectively reveal functional connectivity between EEG channels,providing a new approach for decoding motor imagery EEG signals.

Objective To investigate the clinical features of chronic hepatitis C(CHC)patients with hepatitis C virus genotype 3(HCV GT3)infection and the risk factors for disease progression.Methods A multicenter retrospective cohort study was conducted among 1 002 CHC patients from 11 clinical centers in Northwest China from December 2017 to November 2023,and according to their genotype,they were divided into GT1,GT2,GT3,and GT6 groups.Clinical features were compared between the patients with different genotypes.The one-way analysis of variance was used for comparison of normally distributed continuous data between groups,and the Scheffe test was used for further comparison between two groups.The Kruskal-Wallis H test was used for comparison of data with skewed distribution between groups;the chi-square test or Fisher test was used for comparison of categorical data between groups.The multivariate logistic regression analysis was used to explore the influencing factors for the progression of CHC to liver cirrhosis.Results In terms of the genotype,there were 427 patients with GT1 infection,242 with GT2 infection,299 with GT3 infection(210 patients with GT3a infection,87 with GT3b infection,and 2 with unclassified genotype),and 34 with GT6 infection.The patients with GT3 infection had a significantly younger age than those with GT1 infection(51.3±0.5 years vs 53.2±0.6 years,P<0.05)or GT2 infection(51.3±0.5 years vs 53.7±0.8 years,P<0.05),and for the patients with liver cirrhosis,the patients with GT3 infection had a significantly younger age than those with GT1 infection(52.1±0.5 years vs 59.4±0.9 years,P<0.001)or GT2 infection(52.1±0.5 years vs 58.1±1.1 years,P<0.001).Among the patients with GT3 infection,male patients accounted for 77.9%and the patients with liver cirrhosis accounted for 46.2%,which were significantly higher than those among the patients with GT1,GT2 or GT6 infection(all P<0.001).At baseline,the patients with GT3 infection had significantly higher levels of alanine aminotransferase(ALT)and aspartate aminotransferase(AST)than those with GT1 or GT2 infection,significantly higher aspartate aminotransferase-to-platelet ratio index(APRI)and fibrosis-4(FIB4)than those with GT1,GT2 or GT6 infection,a significantly lower platelet count(PLT)than those with GT2 or GT6 infection,a significantly higher level of alpha-fetoprotein than those with GT2 or GT6 infection,and a significantly lower level of albumin(Alb)than those with GT6 infection(all P<0.05).There were no significant differences between the patients with GT3a infection and those with GT3b infection in age,sex,the proportion of patients with liver cirrhosis,comorbidities,HCV RNA quantification,PLT,ALT,AST,alkaline phosphatase,Alb,APRI,and FIB-4(all P>0.05).The multivariate logistic regression analysis showed that PLT≤150×109/L(odds ratio[OR]=10.72,95%confidence interval[CI]:5.76-35.86,P<0.001)and Alb≤35 g/L(OR=3.74,95%CI:1.22-11.45,P=0.021)were risk factors for liver cirrhosis.Conclusion Most CHC patients with GT3 infection are male in Northwest China,and compared with the patients with other genotypes,such patients tend to have a younger age of onset and higher degrees of liver inflammation activity and fibrosis.Low PLT and a low level of Alb are risk factors for progression to liver cirrhosis in CHC patients with GT3 infection.

An automated identification method for adolescent schizophrenia based on brain electroencephalogram(EEG)entropy features is proposed for further improving the diagnostic accuracy of adolescent schizophrenia.The raw EEG signals are decomposed into 5 commonly used rhythm bands:Delta,Theta,Alpha,Beta,and Gamma.The permutation entropy,fuzzy entropy,and sample entropy are extracted from each rhythm band and then organized into a feature matrix structured by electrode location×frequency band.Finally,an ECA-CNN model integrating efficient channel attention(ECA)and convolutional neural network(CNN)is constructed for feature classification and realizing the automated identification of adolescent schizophrenia.The results demonstrate that the proposed ECA-CNN model has higher recognition accuracy than the traditional machine learning models,achieving an accuracy of 99.08%,a sensitivity of 99.27%,a specificity of 98.85%,a precision of 99.01%,a F1 score of 99.14%,and a Kappa coefficient of 0.9814.This study provides a new idea and method for the diagnosis of adolescent schizophrenia.

To address the challenges of extracting nonlinear features from motor imagery electroencephalogram(EEG)signals and effectively capturing functional connectivity between EEG channels,a classification and recognition method for motor imagery EEG signals is proposed based on mutual information and adaptive graph convolutional network.The proposed method extracts frequency domain information by sub-frequency banding on the original motor imagery EEG signals,uncovers the nonlinear relationships within EEG signals by an adjacency matrix constructed with mutual information neural estimation method,and finally achieve null-frequency feature extraction by capturing the dynamic correlation strength between channels with an adaptive graph convolutional network incorporating convolutional block attention module.On the BCI Competition Ⅳ 2a and BCI Competition Ⅲ 3a datasets,the proposed method has average accuracies of 83.14％and 88.19％,respectively,demonstrating that it can effectively reveal functional connectivity between EEG channels,providing a new approach for decoding motor imagery EEG signals.

To address the problem of low accuracy in multi-classification recognition of motor imagery electroencephalogram(EEG)signals,a recognition method is proposed based on differential entropy and convolutional neural network for 4-class classification of motor imagery.EEG signals are extracted into 4 frequency bands(Alpha,Beta,Theta,and Gamma)through the filter,followed by the computation of differential entropy for each frequency band.According to the spatial characteristics of brain electrodes,the data structure is reconstructed into three-dimensional EEG signal feature cube which is input into convolutional neural network for 4-class classification.The method achieves an accuracy of 95.88%on the BCI Competition IV-2a public dataset.Additionally,a 4-class classification motor imagery dataset is established in the laboratory for the same processing,and an accuracy of 94.50%is obtained.The test results demonstrate that the proposed method exhibits superior recognition performance.

Objective:To analyze the effect of implementing information-motivation-behavior skill model (IMB) home care in elderly patients with chronic obstructive pulmonary disease (COPD).Methods:From October 2017 to October 2018, patients with COPD who were discharged after treatment in Wuxi Fifth People′s Hospital were included and divided into control group and observation group by block randomization method. The control group was given routine health education, discharge guidance and follow-up guidance after discharge. The observation group received the information-motivation-behavior home care based on IMB. The general information before intervention, the level of disease cognition, quality of life before and after the intervention and the health behavior after the intervention were compared between the two groups.Results:The Bristol COPD Knowledge Questionnaire (BCKQ) score in the observation group was higher than that in the control group [(58.36±6.68) vs. (52.14±5.80) points] ( P<0.05). The scores of physical activity, health responsibility, stress management, nutrition, and spiritual growth in the Health Promoting Lifestyle Profile-Ⅱ (HPLR-Ⅱ) after the intervention in the observation group were higher than those in the control group [(26.01±3.95) vs. (23.25±3.48) points, (38.65±4.33) vs. (34.64±4.05) points, (16.98±2.51) vs. (14.20±1.80) points, (19.87±2.20) vs. (15.65±3.51) points, (20.32±2.85) vs. (17.35±2.89) points] ( P<0.05). The symptoms, mobility, and disease impact scores of the St George′s Respiratory Questionnaire (SGRQ) in the observation group were lower than those in the control group [(40.32±4.30) vs. (45.36±4.50) points, (43.21±4.87) vs.(45.33±4.25) points, (38.41±4.37) vs. (42.35±4.01) points] (all P<0.05). Conclusion:Implementing the home care model based on IMB in elderly patients with COPD can improve patients′ disease awareness and improve their health behaviors and quality of life.

Objective To explore the moderating effects of ocean sound on nocturnal sleep quality and negative emotion among ICU awake patients. Methods A total of 60 cases hospitalized in ICU were averagely divided into observation group and control group by random number table. The patients of observation group were given ocean sound intervention based on control group, while the patients of control group were given conventional emotional nursing method and routine sleep nursing method. Before and after interventions, Richards Campbell sleep scale ( RCSQ) , self rating depression scale ( SDS) , self rating anxiety scale ( SAS) were used to assess the effects. Results After one week interventions in the observation group, the scores of SDS, SAS, and the nocturnal sleep quality of RCSQ were lower than those of the control group (P<0. 05). Conclusions Ocean sound intervention can improve the ICU awake patients′ nocturnal sleep quality, and slow down the negative emotional process.

Objective To investigate the level and influencing factors of falls efficacy among older patients with type 2 diabetes (T2DM) .Methods A total of 218 older patients with T2DM were investigated by the modified falls efficacy scale(MFES) and Morse Fall Scale (MFS) ,questionnaire and observation were both used .Results The average score of falls efficacy was 8 .15 ± 2 .91 .The scores were lowest in the items of walking up and down stairs and going to bed and getting out of bed ,and highest were in the items of dressing and sitting down and standing up from a chair .The multivariate linear regression analysis showed that dura‐tion of diabetes ,diabetic complications and fall history were the main factors influencing their falls efficacy .Conclusion Falls in eld‐erly T2DM patients were in the medium level ,and it′s closely related with duration of diabetes ,its complications and fall history .