1.Identification and expression analysis of B3 gene family in Panax ginseng.
Yu-Long WANG ; Ai-Min WANG ; Jing-Hui YU ; Si-Zhang LIU ; Ge JIN ; Kang-Yu WANG ; Ming-Zhu ZHAO ; Yi WANG ; Mei-Ping ZHANG
China Journal of Chinese Materia Medica 2025;50(16):4593-4609
Panax ginseng as a perennial herb of Araliaceae, exhibits pharmacological effects such as central nervous system stimulation, anti-tumor properties, and cardiovascular and cerebrovascular protection. The B3 gene family plays a crucial role in growth and development, antioxidant activity, stress resistance, and secondary metabolism regulation of plants and has been extensively studied in various plants. However, the identification and analysis of the B3 gene family in P. ginseng have not been reported. In this study, a total of 145 B3 genes(PgB3s) with complete open reading frames(ORF) were identified from P. ginseng and classified into five subfamilies based on domain types. Through correlation analysis with ginsenoside content, SNP/InDels analysis, and interaction analysis with key enzyme genes, 15 PgB3 transcripts were found to be significantly correlated with ginsenoside content and exhibited a close interaction network with key enzyme genes involved in ginsenoside biosynthesis, which indicated that these genes may participate in the regulation of ginsenoside biosynthesis. Additionally, this study found that PgB3 genes exhibited induced expression in response to methyl jasmonate(MeJA) stress, which aligned with the presence of abundant stress response elements in their promoters, confirming the important role of the B3 gene family in P. ginseng in stress resistance. The results of this study revealed the potential functions of PgB3 genes in ginsenoside biosynthesis and stress response, providing a significant theoretical basis for further research on the functions of PgB3 genes and their regulatory mechanisms.
Panax/metabolism*
;
Gene Expression Regulation, Plant
;
Plant Proteins/metabolism*
;
Ginsenosides/biosynthesis*
;
Multigene Family
;
Phylogeny
2.Surveillance of bacterial resistance in tertiary hospitals across China:results of CHINET Antimicrobial Resistance Surveillance Program in 2022
Yan GUO ; Fupin HU ; Demei ZHU ; Fu WANG ; Xiaofei JIANG ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Yuling XIAO ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Jingyong SUN ; Qing CHEN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yunmin XU ; Sufang GUO ; Yanyan WANG ; Lianhua WEI ; Keke LI ; Hong ZHANG ; Fen PAN ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Wei LI ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Qian SUN ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanqing ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Wenhui HUANG ; Juan LI ; Quangui SHI ; Juan YANG ; Abulimiti REZIWAGULI ; Lili HUANG ; Xuejun SHAO ; Xiaoyan REN ; Dong LI ; Qun ZHANG ; Xue CHEN ; Rihai LI ; Jieli XU ; Kaijie GAO ; Lu XU ; Lin LIN ; Zhuo ZHANG ; Jianlong LIU ; Min FU ; Yinghui GUO ; Wenchao ZHANG ; Zengguo WANG ; Kai JIA ; Yun XIA ; Shan SUN ; Huimin YANG ; Yan MIAO ; Mingming ZHOU ; Shihai ZHANG ; Hongjuan LIU ; Nan CHEN ; Chan LI ; Jilu SHEN ; Wanqi MEN ; Peng WANG ; Xiaowei ZHANG ; Yanyan LIU ; Yong AN
Chinese Journal of Infection and Chemotherapy 2024;24(3):277-286
Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5%were gram-positive and 70.5%were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3%,76.7%and 77.9%,respectively.Overall,94.0%of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8%of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2%in the isolates from children and 95.7%in the isolates from adults.The resistance rate to carbapenems was lower than 13.1%in most Enterobacterales species except for Klebsiella,21.7%-23.1%of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1%to 13.3%.The prevalence of meropenem-resistant strains decreased from 23.5%in 2019 to 18.0%in 2022 in Pseudomonas aeruginosa,and decreased from 79.0%in 2019 to 72.5%in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.
3.Endophytic fungi from Scutellaria baicalensis and the enzyme inhibitory activities of their secondary metabolites
De-Min LI ; Xiao-Di MA ; Kang-Xu WANG ; Mei-Yuan LI ; Man-Ping LUO ; Ying-Ying MENG ; Ai-Mei YANG ; Bei WANG ; Xin-Guo ZHANG
Chinese Traditional Patent Medicine 2024;46(8):2644-2649
AIM To study endophytic fungi from Scutellaria baicalensis Georgi.and the enzyme inhibitory activities of their secondary metabolites.METHODS Six different media were used to isolate and purify endophytic fungi from S.baicalensis by tissue homogenate method.The activities of secondary metabolites were evaluated by targeting different enzymes.The highly active strains were identified by molecular biology combined with morphology,and the highly active chemical components were tracked and separated by modern chromatographic separation technology.RESULTS Sixty-four endophytic fungal strains were isolated from S.baicalensis,and one hundred and twenty-eight secondary metabolites were obtained by fermentation.The samples with certain inhibitory activities against adenosine deaminase(ADA),β-lactamase and tyrosinase(TYR)accounted for 14.06%,3.91%and 18.75%,respectively.Strain HTS-23-2 showed high TYR inhibitory activity,and 99%homology with Aspergillus flavus by molecular identification.One compound was isolated from the fermentation samples and identified as kojic acid.CONCLUSION S.baicalensis harbors a rich diversity of endophytic fungi,which serve as a valuable resource for active substances.
4.Epidemiologic characteristics and influencing factors of influenza outbreaks in Guangdong Province, 2015-2022.
Ya Li ZHUANG ; Jie LU ; Shu Kai WU ; Zhan Hui ZHANG ; Zhi Mei WEI ; Yi Hong LI ; Ting HU ; Min KANG ; Ai Ping DENG
Chinese Journal of Epidemiology 2023;44(6):942-948
Objective: To grasp the epidemiological characteristics of influenza outbreaks in Guangdong Province by analyzing the outbreaks of influenza-like cases reported in Guangdong Province from January 2015 to the end of August 2022. Methods: In response to the outbreak of epidemics in Guangdong Province from 2015 to 2022, information on on-site epidemic control was collected, and epidemiological analysis was conducted to describe the characteristics of the epidemics. The factors that influence the intensity and duration of the outbreak were determined through a logistic regression model. Results: A total of 1 901 influenza outbreaks were reported in Guangdong Province, with an overall incidence of 2.05%. Most outbreak reports occurred from November to January of the following year (50.24%, 955/1 901) and from April to June (29.88%, 568/1 901). A total of 59.23% (1 126/1 901) of the outbreaks were reported in the Pearl River Delta region, and primary and secondary schools were the main places where outbreaks occurred (88.01%, 1 673/1 901). Outbreaks with 10-29 cases were the most common (66.18%, 1 258/1 901), and most outbreaks lasted less than seven days (50.93%,906/1 779). The size of the outbreak was related to the nursery school (aOR=0.38, 95%CI:0.15-0.93), the Pearl River Delta region (aOR=0.60, 95%CI:0.44-0.83), the time interval between the onset of the first case and the time of report (>7 days compared with ≤3 days: aOR=3.01, 95%CI:1.84-4.90), the influenza A(H1N1) (aOR=2.02, 95%CI:1.15-3.55) and the influenza B (Yamagata) (aOR=2.94, 95%CI: 1.50-5.76). The duration of outbreaks was related to school closures (aOR=0.65, 95%CI: 0.47-0.89), the Pearl River Delta region (aOR=0.65, 95%CI: 0.50-0.83) and the time interval between the onset of the first case and the time of report (>7 days compared with ≤3 days: aOR=13.33, 95%CI: 8.80-20.19; 4-7 days compared with ≤3 days: aOR=2.56, 95%CI: 1.81-3.61). Conclusions: An influenza outbreak in Guangdong Province exhibits two peaks, one in the winter and spring seasons and the other in the summer. Primary and secondary schools are high-risk areas, and early reporting of outbreaks is critical for controlling influenza outbreaks in schools. Furthermore, comprehensive measures should be taken to prevent the spread of the epidemic.
Humans
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Influenza A Virus, H1N1 Subtype
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Influenza, Human/epidemiology*
;
Disease Outbreaks
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Epidemics
;
China/epidemiology*
5.In silico screening method for non‑responders to cardiac resynchronization therapy in patients with heart failure: a pilot study
Minki HWANG ; Jae‑Sun UHM ; Min Cheol PARK ; Eun Bo SHIM ; Chan Joo LEE ; Jaewon OH ; Hee Tae YU ; Tae‑Hoon KIM ; Boyoung JOUNG ; Hui‑Nam PAK ; Seok‑Min KANG ; Moon‑Hyoung LEE
International Journal of Arrhythmia 2022;23(1):2-
Background:
Cardiac resynchronization therapy (CRT) is an effective treatment option for patients with heart failure (HF) and left ventricular (LV) dyssynchrony. However, the problem of some patients not responding to CRT remains unresolved. This study aimed to propose a novel in silico method for CRT simulation.
Methods:
Three-dimensional heart geometry was constructed from computed tomography images. The finite ele‑ ment method was used to elucidate the electric wave propagation in the heart. The electric excitation and mechani‑ cal contraction were coupled with vascular hemodynamics by the lumped parameter model. The model parameters for three-dimensional (3D) heart and vascular mechanics were estimated by matching computed variables with measured physiological parameters. CRT effects were simulated in a patient with HF and left bundle branch block (LBBB). LV end-diastolic (LVEDV) and end-systolic volumes (LVESV), LV ejection fraction (LVEF), and CRT responsiveness measured from the in silico simulation model were compared with those from clinical observation. A CRT responder was defined as absolute increase in LVEF ≥ 5% or relative increase in LVEF ≥ 15%.
Results:
A 68-year-old female with nonischemic HF and LBBB was retrospectively included. The in silico CRT simu‑ lation modeling revealed that changes in LVEDV, LVESV, and LVEF by CRT were from 174 to 173 mL, 116 to 104 mL, and 33 to 40%, respectively. Absolute and relative ΔLVEF were 7% and 18%, respectively, signifying a CRT responder.In clinical observation, echocardiography showed that changes in LVEDV, LVESV, and LVEF by CRT were from 162 to 119 mL, 114 to 69 mL, and 29 to 42%, respectively. Absolute and relative ΔLVESV were 13% and 31%, respectively, also signifying a CRT responder. CRT responsiveness from the in silico CRT simulation model was concordant with that in the clinical observation.
Conclusion
This in silico CRT simulation method is a feasible technique to screen for CRT non-responders in patients with HF and LBBB.
7.Overexpression of NAT10 induced platinum drugs resistance in breast cancer cell.
Pan QI ; Ya Ke CHEN ; Rui Li CUI ; Rui Juan HENG ; Sheng XU ; Xiao Ying HE ; Ai Min YUE ; Jiang Kun KANG ; Hao Han LI ; Yong Xin ZHU ; Cong WANG ; Yu Lu CHEN ; Kua HU ; Yan Yan YIN ; Li Xue XUAN ; Yu SONG
Chinese Journal of Oncology 2022;44(6):540-549
Objective: To observe the platinum drugs resistance effect of N-acetyltransferase 10 (NAT10) overexpression in breast cancer cell line and elucidate the underlining mechanisms. Methods: The experiment was divided into wild-type (MCF-7 wild-type cells without any treatment) group, NAT10 overexpression group (H-NAT10 plasmid transfected into MCF-7 cells) and NAT10 knockdown group (SH-NAT10 plasmid transfected into MCF-7 cells). The invasion was detected by Transwell array, the interaction between NAT10 and PARP1 was detected by co-immunoprecipitation. The impact of NAT10 overexpression or knockdown on the acetylation level of PARP1 and its half-life was also determined. Immunostaining and IP array were used to detect the recruitment of DNA damage repair protein by acetylated PARP1. Flow cytometry was used to detect the cell apoptosis. Results: Transwell invasion assay showed that the number of cell invasion was 483.00±46.90 in the NAT10 overexpression group, 469.00±40.50 in the NAT10 knockdown group, and 445.00±35.50 in the MCF-7 wild-type cells, and the differences were not statistically significant (P>0.05). In the presence of 10 μmol/L oxaliplatin, the number of cell invasion was 502.00±45.60 in the NAT10 overexpression group and 105.00±20.50 in the NAT10 knockdown group, both statistically significant (P<0.05) compared with 219.00±31.50 in wild-type cells. In the presence of 10 μmol/L oxaliplatin, NAT10 overexpression enhanced the binding of PARP1 to NAT10 compared with wild-type cells, whereas the use of the NAT10 inhibitor Remodelin inhibited the mutual binding of the two. Overexpression of NAT10 induced PARP1 acetylation followed by increased PARP1 binding to XRCC1, and knockdown of NAT10 expression reduced PARP1 binding to XRCC1. Overexpression of NAT10 enhanced PARP1 binding to LIG3, while knockdown of NAT10 expression decreased PARP1 binding to LIG3. In 10 μmol/L oxaliplatin-treated cells, the γH2AX expression level was 0.38±0.02 in NAT10 overexpressing cells and 1.36±0.15 in NAT10 knockdown cells, both statistically significant (P<0.05) compared with 1.00±0.00 in wild-type cells. In 10 μmol/L oxaliplatin treated cells, the apoptosis rate was (6.54±0.68)% in the NAT10 overexpression group and (12.98±2.54)% in the NAT10 knockdown group, both of which were statistically significant (P<0.05) compared with (9.67±0.37)% in wild-type cells. Conclusion: NAT10 overexpression enhances the binding of NAT10 to PARP1 and promotes the acetylation of PARP1, which in turn prolongs the half-life of PARP1, thus enhancing PARP1 recruitment of DNA damage repair related proteins to the damage sites, promoting DNA damage repair and ultimately the survival of breast cancer cells.
Breast Neoplasms/enzymology*
;
Cell Line, Tumor
;
Drug Resistance, Neoplasm
;
Female
;
Humans
;
MCF-7 Cells
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N-Terminal Acetyltransferases/metabolism*
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Organoplatinum Compounds/pharmacology*
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Oxaliplatin/pharmacology*
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X-ray Repair Cross Complementing Protein 1
8.A multicenter epidemiological study of acute bacterial meningitis in children.
Cai Yun WANG ; Hong Mei XU ; Jiao TIAN ; Si Qi HONG ; Gang LIU ; Si Xuan WANG ; Feng GAO ; Jing LIU ; Fu Rong LIU ; Hui YU ; Xia WU ; Bi Quan CHEN ; Fang Fang SHEN ; Guo ZHENG ; Jie YU ; Min SHU ; Lu LIU ; Li Jun DU ; Pei LI ; Zhi Wei XU ; Meng Quan ZHU ; Li Su HUANG ; He Yu HUANG ; Hai Bo LI ; Yuan Yuan HUANG ; Dong WANG ; Fang WU ; Song Ting BAI ; Jing Jing TANG ; Qing Wen SHAN ; Lian Cheng LAN ; Chun Hui ZHU ; Yan XIONG ; Jian Mei TIAN ; Jia Hui WU ; Jian Hua HAO ; Hui Ya ZHAO ; Ai Wei LIN ; Shuang Shuang SONG ; Dao Jiong LIN ; Qiong Hua ZHOU ; Yu Ping GUO ; Jin Zhun WU ; Xiao Qing YANG ; Xin Hua ZHANG ; Ying GUO ; Qing CAO ; Li Juan LUO ; Zhong Bin TAO ; Wen Kai YANG ; Yong Kang ZHOU ; Yuan CHEN ; Li Jie FENG ; Guo Long ZHU ; Yan Hong ZHANG ; Ping XUE ; Xiao Qin LI ; Zheng Zhen TANG ; De Hui ZHANG ; Xue Wen SU ; Zheng Hai QU ; Ying ZHANG ; Shi Yong ZHAO ; Zheng Hong QI ; Lin PANG ; Cai Ying WANG ; Hui Ling DENG ; Xing Lou LIU ; Ying Hu CHEN ; Sainan SHU
Chinese Journal of Pediatrics 2022;60(10):1045-1053
Objective: To analyze the clinical epidemiological characteristics including composition of pathogens , clinical characteristics, and disease prognosis acute bacterial meningitis (ABM) in Chinese children. Methods: A retrospective analysis was performed on the clinical and laboratory data of 1 610 children <15 years of age with ABM in 33 tertiary hospitals in China from January 2019 to December 2020. Patients were divided into different groups according to age,<28 days group, 28 days to <3 months group, 3 months to <1 year group, 1-<5 years of age group, 5-<15 years of age group; etiology confirmed group and clinically diagnosed group according to etiology diagnosis. Non-numeric variables were analyzed with the Chi-square test or Fisher's exact test, while non-normal distrituction numeric variables were compared with nonparametric test. Results: Among 1 610 children with ABM, 955 were male and 650 were female (5 cases were not provided with gender information), and the age of onset was 1.5 (0.5, 5.5) months. There were 588 cases age from <28 days, 462 cases age from 28 days to <3 months, 302 cases age from 3 months to <1 year of age group, 156 cases in the 1-<5 years of age and 101 cases in the 5-<15 years of age. The detection rates were 38.8% (95/245) and 31.5% (70/222) of Escherichia coli and 27.8% (68/245) and 35.1% (78/222) of Streptococcus agalactiae in infants younger than 28 days of age and 28 days to 3 months of age; the detection rates of Streptococcus pneumonia, Escherichia coli, and Streptococcus agalactiae were 34.3% (61/178), 14.0% (25/178) and 13.5% (24/178) in the 3 months of age to <1 year of age group; the dominant pathogens were Streptococcus pneumoniae and the detection rate were 67.9% (74/109) and 44.4% (16/36) in the 1-<5 years of age and 5-<15 years of age . There were 9.7% (19/195) strains of Escherichia coli producing ultra-broad-spectrum β-lactamases. The positive rates of cerebrospinal fluid (CSF) culture and blood culture were 32.2% (515/1 598) and 25.0% (400/1 598), while 38.2% (126/330)and 25.3% (21/83) in CSF metagenomics next generation sequencing and Streptococcus pneumoniae antigen detection. There were 4.3% (32/790) cases of which CSF white blood cell counts were normal in etiology confirmed group. Among 1 610 children with ABM, main intracranial imaging complications were subdural effusion and (or) empyema in 349 cases (21.7%), hydrocephalus in 233 cases (14.5%), brain abscess in 178 cases (11.1%), and other cerebrovascular diseases, including encephalomalacia, cerebral infarction, and encephalatrophy, in 174 cases (10.8%). Among the 166 cases (10.3%) with unfavorable outcome, 32 cases (2.0%) died among whom 24 cases died before 1 year of age, and 37 cases (2.3%) had recurrence among whom 25 cases had recurrence within 3 weeks. The incidences of subdural effusion and (or) empyema, brain abscess and ependymitis in the etiology confirmed group were significantly higher than those in the clinically diagnosed group (26.2% (207/790) vs. 17.3% (142/820), 13.0% (103/790) vs. 9.1% (75/820), 4.6% (36/790) vs. 2.7% (22/820), χ2=18.71, 6.20, 4.07, all P<0.05), but there was no significant difference in the unfavorable outcomes, mortility, and recurrence between these 2 groups (all P>0.05). Conclusions: The onset age of ABM in children is usually within 1 year of age, especially <3 months. The common pathogens in infants <3 months of age are Escherichia coli and Streptococcus agalactiae, and the dominant pathogen in infant ≥3 months is Streptococcus pneumoniae. Subdural effusion and (or) empyema and hydrocephalus are common complications. ABM should not be excluded even if CSF white blood cell counts is within normal range. Standardized bacteriological examination should be paid more attention to increase the pathogenic detection rate. Non-culture CSF detection methods may facilitate the pathogenic diagnosis.
Adolescent
;
Brain Abscess
;
Child
;
Child, Preschool
;
Escherichia coli
;
Female
;
Humans
;
Hydrocephalus
;
Infant
;
Infant, Newborn
;
Male
;
Meningitis, Bacterial/epidemiology*
;
Retrospective Studies
;
Streptococcus agalactiae
;
Streptococcus pneumoniae
;
Subdural Effusion
;
beta-Lactamases
9.Analysis on characteristics and influencing factors of COVID-19 confirmed cases with viral nucleic acid re-positive after discharge in Guangdong Province.
Xiao Hua TAN ; Min KANG ; Ai Ping DENG ; Bai Sheng LI ; Min LUO ; Yao YI ; YaLi ZHUANG ; YingTao ZHANG ; Tie SONG
Chinese Journal of Preventive Medicine 2022;56(1):49-55
Objective: To analyze the epidemiological characteristics and influencing factors of COVID-19 confirmed cases with viral nucleic acid re-positive in anal and/or throat swabs after discharge during the domestic imported epidemic stage in Guangdong Province in early 2020. Methods: The COVID-19 confirmed cases with the onset time before March 1, 2020 in Guangdong Province were collected to analyze the demographic data, epidemiological characteristics, and specimen collection and testing data after discharge. Logistic regression model was used for influencing factors analysis of re-positive cases. Results: A total of 1 286 COVID-19 confirmed cases were included, the M(Q1,Q3) of age was 44(32,58)years, 617 cases were male, 224 cases were re-positive in anal and/or throat swabs with the re-positive rate 17.42%. The M(Q1,Q3) of age of re-positive cases was 35(23, 50) years, which was younger than that of re-negative cases age was those 46(33, 59) years (P<0.001). With the increase of age, re-positive rate decreased (χ2trend=52.73, P<0.001). 85.27% (191/224) of re-positive cases were found in 14 d after discharge, the duration time of re-positive status was 13(7, 24) d, and 81.69% (183/224) of re-positive cases were re-tested negative in 28 d after re-positive date. No fever and other symptoms had been observed among re-positive cases during the whole follow-up. No secondary infectious cases had been found among close contacts after 14 d of centralized isolation and sampling screening. Univariate logistic regression model analysis revealed that the influencing factors of the re-positive cases included age, occupation, clusters, clinical types, and admission time. Multivariate logistic regression model analysis revealed that age was an independent risk factor. Conclusions: SARS-CoV-2 viral nucleic acid re-positive is found in COVID-19 confirmed cases after discharge in Guangdong Province. Most re-positive cases are confirmed among 14 d after discharge and re-test to negative among 28 d after re-positive date. Age is an risk factor for re-positive cases after discharge.
COVID-19
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China/epidemiology*
;
Epidemics
;
Humans
;
Male
;
Nucleic Acids
;
SARS-CoV-2
10.Course of disease and related epidemiological parameters of COVID-19: a prospective study based on contact tracing cohort.
Yan ZHOU ; Wen Jia LIANG ; Zi Hui CHEN ; Tao LIU ; Tie SONG ; Shao Wei CHEN ; Ping WANG ; Jia Ling LI ; Yun Hua LAN ; Ming Ji CHENG ; Jin Xu HUANG ; Ji Wei NIU ; Jian Peng XIAO ; Jian Xiong HU ; Li Feng LIN ; Qiong HUANG ; Ai Ping DENG ; Xiao Hua TAN ; Min KANG ; Gui Min CHEN ; Mo Ran DONG ; Hao Jie ZHONG ; Wen Jun MA
Chinese Journal of Preventive Medicine 2022;56(4):474-478
Objective: To analyze the course of disease and epidemiological parameters of COVID-19 and provide evidence for making prevention and control strategies. Methods: To display the distribution of course of disease of the infectors who had close contacts with COVID-19 cases from January 1 to March 15, 2020 in Guangdong Provincial, the models of Lognormal, Weibull and gamma distribution were applied. A descriptive analysis was conducted on the basic characteristics and epidemiological parameters of course of disease. Results: In total, 515 of 11 580 close contacts were infected, with an attack rate about 4.4%, including 449 confirmed cases and 66 asymptomatic cases. Lognormal distribution was fitting best for latent period, incubation period, pre-symptomatic infection period of confirmed cases and infection period of asymptomatic cases; Gamma distribution was fitting best for infectious period and clinical symptom period of confirmed cases; Weibull distribution was fitting best for latent period of asymptomatic cases. The latent period, incubation period, pre-symptomatic infection period, infectious period and clinical symptoms period of confirmed cases were 4.50 (95%CI:3.86-5.13) days, 5.12 (95%CI:4.63-5.62) days, 0.87 (95%CI:0.67-1.07) days, 11.89 (95%CI:9.81-13.98) days and 22.00 (95%CI:21.24-22.77) days, respectively. The latent period and infectious period of asymptomatic cases were 8.88 (95%CI:6.89-10.86) days and 6.18 (95%CI:1.89-10.47) days, respectively. Conclusion: The estimated course of COVID-19 and related epidemiological parameters are similar to the existing data.
COVID-19
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Cohort Studies
;
Contact Tracing
;
Humans
;
Incidence
;
Prospective Studies

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