ObjectiveTo systematically analyze the epidemiological characteristics and cause-of-death distribution among death cases with hepatitis C in Hongkou District of Shanghai, and to provide a scientific basis for optimizing healthcare resources allocation and targeted hepatitis C prevention and control measures. MethodsA retrospective cross-sectional study was conducted by integrating historical surveillance data from China Information System of Disease Prevention and Control and Shanghai Hongkou District Death Medical Registration System. Data on demographic characteristics, hepatitis C-related clinical and management records, and underlying causes of death for cases with hepatitis C between 2012 and 2024 in Hongkou District of Shanghai, were collected. Descriptive analyses were performed to analyze the epidemiological characteristics and cause-of-death distribution of death cases, and comparative analyses were conducted across different subgroups. ResultsA total of 204 hepatitis C-related deaths were identified in Hongkou District, Shanghai, from 2012 to 2024. The average age at death was (69.49±12.75) years The majority decedents were males (71.57%) and retired (73.53%). The top three underlying causes of death were malignant tumors (45.10%), cerebrovascular diseases (15.20%) and cardiovascular diseases (12.25%), collectively accounting for 72.55% of all deaths. Deaths attributed to hepatitis C accounted for 9.80% (20/204), with a mean age at death of (63.41±11.81) years. No statistically significant differences were observed in the proportion of hepatitis C-attributed deaths across different subgroups (all P>0.05). The proportion of premature deaths was 55.88% (114/204), with a mean age at death of (60.02±6.89) years. The proportion of premature deaths was higher among males (60.27%), laboratory-diagnosed patients (62.69%), patients with other liver diseases (72.06%), and those non-compliant with follow-up (70.97%) compared to their respective counterparts (all P<0.05). Additionally, homemakers /unemployed patients (100.00%) and employed patients (88.89%) had a significantly higher proportion of premature deaths compared to retired patients (42.67%) (P<0.001). There was a statistically significant difference in the distribution of causes of death between the premature death group and the non-premature death group (χ²=14.93， P=0.048). The top three causes of premature deaths were malignant tumors (50.00%), hepatitis C (12.28%) and cerebrovascular diseases (10.53%). Regarding the proportion of deaths occuring prematurely, other viral hepatitis had the highest percentage (75.00%), followed by diabetes mellitus (71.43%) and hepatitis C (70.00%). ConclusionThe majority of death cases with hepatitis C were males and retirees in Hongkou District, Shanghai. The leading cause of death was malignant tumors, while hepatitis C ranked as the fourth underlying cause, as well as served as the second leading cause of premature death following malignant tumors. Premature death was closely associated with gender, occupation, diagnostic classification, presence of other liver diseases, and follow-up compliance, highlighting the importance of enhanced health management and targeted interventions among high-risk groups.

Traditional Chinese medicine(TCM) resources are the essential material foundation for the development of TCM. The national survey of TCM resources serves as a periodic summary of these resources, ensuring the continuity, prosperity, and development of TCM in China. Since 1949, four national surveys of TCM resources have been conducted. The fourth survey incorporated an investigation into traditional knowledge related to TCM resources, including the traditional medicinal knowledge of Chinese ethnic minorities, with the goal of systematically exploring, preserving, and inheriting this knowledge. This manuscript provides an overview of the basic findings from the first three national surveys of TCM resources, while also clarifying the concepts, categories, forms, carriers, and acquisition pathways of traditional knowledge related to TCM resources. A preliminary summary of the findings from traditional knowledge investigations reported in current literature is also presented. Based on the fourth survey, this manuscript emphasizes the urgency of developing public medical knowledge through empirically-based investigations, the excavation, and compilation of traditional knowledge. It also outlines the potential for conducting "precise" investigations based on first-hand data obtained from the survey, as well as facilitating the discovery and evaluation of new medicines using traditional knowledge related to ethnic minority medicinal practices. This manuscript is expected to provide valuable insights for promoting the health and industrial development of ethnic minority populations in the post-"survey" phase.
Humans ; Medicine, Chinese Traditional ; China/ethnology* ; Minority Groups ; Ethnicity ; Drugs, Chinese Herbal/therapeutic use* ; Health Knowledge, Attitudes, Practice/ethnology* ; Surveys and Questionnaires

Objective:Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions.Methods:Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis.Results:In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion:Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.

Apolipoprotein D(APOD)is a protein that is widely present in animal tissues and is in-volved in the reproductive regulation of the body.In order to investigate the expression regularity of APOD in bactrian camel epididymis and its regulation effect on sperm maturation,this study took the epididymis of bactrian camel during estrus and anestrus as materials,and first cloned the complete sequence of APOD coding sequence(CDS)region.The physicochemical properties of AP-OD were analyzed by ProParam,SOPMA,SWISS-MODEL and MEGA7.0 software.Meanwhile,the expression and distribution of APOD in epididymis were detected by qRT-PCR,Western blot and IHC.The cloning results showed that:the length of the CDS region of APOD gene was 624 bp,encoding 207 amino acids.The APOD sequence of Bactrian camel was highly conserved with the nucleotide and amino acid sequence of alpaca,and the homology of APOD sequence with elk was the lowest.The results of qRT-PCR showed that the mRNA levels of APOD in the head,body and tail of epididymis in estrus were significantly higher than those in estrus(P＜0.01).Western blot results showed that the APOD protein expression and mRNA expression trend was similar in the head and body of the epididymis during anestrus,but the APOD expression level in the tail of the epididymis during anestrus was opposite to the mRNA expression level(P＜0.05).The results of H&E and IHC showed that there were significant differences in epididymal tissue between estrus and anestrus.In addition,APOD showed positive reactions in epididymal epithelial cells,smooth muscle cells,sperm and connective tissue to varying degrees,suggesting that APOD may be in-volved in the maturation of sperm during estrus and anestrus,providing evidence for further explo-ring the regulatory mechanism of APOD's involvement in seasonal estrus.

Objective:To analyze the epidemiological and clinical characteristics of influenza A/B virus (IAV/IBV) infection in children aged 0-4 years in Jining city from 2022 to 2024.Methods:A retrospective analysis was conducted on the data of 3 106 influenza affected children who visited two monitoring outpost hospitals in Jining city from January 2022 to January 2024. They were separated into two groups based on the type of virus infection: IAV group (n=1 829) and IBV group (n=1 277). Two groups were compared for general information, epidemiological characteristics, and laboratory test indicators.Result:The majority of influenza patients were boys (59.01%) and 1-4 years old (63.01%), with peak body temperature mainly ranging from 37.8 to 38.9 ℃ (59.30%). Coughing (69.00%) and runny nose (66.03%) were the main manifestations. The onset season was concentrated in winter (42.43%) and spring (40.44%), and the exposure target were family members (51.90%), and the proportion of influenza vaccine injections (20.90%) was relatively low. The proportions of visit time≤3 d, peak body temperature≥39.0 ℃, myocardial damage, liver function damage, white blood cell count (WBC) of 4-10×10 9/L, and neutrophil percentage (N)>70% in the IAV group were higher than those in the IBV group ( P<0.05). The proportions of preschool children and WBC>10×10 9/L in the IBV group were higher than those in the IAV group ( P<0.05). Conclusions:Children aged 0-4 years who are infected with IAV in Jining City are more common in terms of high fever, early medical attention, impaired heart and liver function, normal WBC, and abnormal N elevation compared to those infected with IBV. However, children aged 0-4 who are infected with IBV have abnormally high WBC and are more common in daycare.

Apolipoprotein D(APOD)is a protein that is widely present in animal tissues and is in-volved in the reproductive regulation of the body.In order to investigate the expression regularity of APOD in bactrian camel epididymis and its regulation effect on sperm maturation,this study took the epididymis of bactrian camel during estrus and anestrus as materials,and first cloned the complete sequence of APOD coding sequence(CDS)region.The physicochemical properties of AP-OD were analyzed by ProParam,SOPMA,SWISS-MODEL and MEGA7.0 software.Meanwhile,the expression and distribution of APOD in epididymis were detected by qRT-PCR,Western blot and IHC.The cloning results showed that:the length of the CDS region of APOD gene was 624 bp,encoding 207 amino acids.The APOD sequence of Bactrian camel was highly conserved with the nucleotide and amino acid sequence of alpaca,and the homology of APOD sequence with elk was the lowest.The results of qRT-PCR showed that the mRNA levels of APOD in the head,body and tail of epididymis in estrus were significantly higher than those in estrus(P＜0.01).Western blot results showed that the APOD protein expression and mRNA expression trend was similar in the head and body of the epididymis during anestrus,but the APOD expression level in the tail of the epididymis during anestrus was opposite to the mRNA expression level(P＜0.05).The results of H&E and IHC showed that there were significant differences in epididymal tissue between estrus and anestrus.In addition,APOD showed positive reactions in epididymal epithelial cells,smooth muscle cells,sperm and connective tissue to varying degrees,suggesting that APOD may be in-volved in the maturation of sperm during estrus and anestrus,providing evidence for further explo-ring the regulatory mechanism of APOD's involvement in seasonal estrus.

Objective:To analyze the epidemiological and clinical characteristics of influenza A/B virus (IAV/IBV) infection in children aged 0-4 years in Jining city from 2022 to 2024.Methods:A retrospective analysis was conducted on the data of 3 106 influenza affected children who visited two monitoring outpost hospitals in Jining city from January 2022 to January 2024. They were separated into two groups based on the type of virus infection: IAV group (n=1 829) and IBV group (n=1 277). Two groups were compared for general information, epidemiological characteristics, and laboratory test indicators.Result:The majority of influenza patients were boys (59.01%) and 1-4 years old (63.01%), with peak body temperature mainly ranging from 37.8 to 38.9 ℃ (59.30%). Coughing (69.00%) and runny nose (66.03%) were the main manifestations. The onset season was concentrated in winter (42.43%) and spring (40.44%), and the exposure target were family members (51.90%), and the proportion of influenza vaccine injections (20.90%) was relatively low. The proportions of visit time≤3 d, peak body temperature≥39.0 ℃, myocardial damage, liver function damage, white blood cell count (WBC) of 4-10×10 9/L, and neutrophil percentage (N)>70% in the IAV group were higher than those in the IBV group ( P<0.05). The proportions of preschool children and WBC>10×10 9/L in the IBV group were higher than those in the IAV group ( P<0.05). Conclusions:Children aged 0-4 years who are infected with IAV in Jining City are more common in terms of high fever, early medical attention, impaired heart and liver function, normal WBC, and abnormal N elevation compared to those infected with IBV. However, children aged 0-4 who are infected with IBV have abnormally high WBC and are more common in daycare.

Objective:Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions.Methods:Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis.Results:In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion:Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.