The peroxisome proliferator-activated receptor gamma(peroxisome proliferator-activated receptor gamma,coactivator 1 beta,PPARGC1B)gene is an intranuclear receptor transcription fac-tor responsible for regulating the expression of target genes.To comprehend the characteristics and mutations of the PPARGC1B gene within the Sichuan yak population,the SNP loci of the PPARGC1B gene were identified through direct sequencing of PCR products.Additionally,the cod-ing region of the PPARGC1B gene was obtained via PCR amplification and sequencing.Bioinforma-tics analyses were conducted to predict protein-coding and mRNA secondary structure.This study identified four exon SNP mutation sites(E9-189A→C,E9-387G→A,E9-542C→T,and E9-554T→C)based on the single nucleotide polymorphism analysis of the PPARGC1B gene in Sichuan yaks.Notably,the E9-387G→A and E9-554T→C loci exhibited significant correlations with shear force and backfat thickness in Sichuan yaks.Subsequently,bioinformatics analysis of the four mutation sites revealed that the PPARGC1B protein is an acidic,unstable,non-transmembrane,and non-secretory hydrophilic protein with a coiled helix structure.It lacks a signal peptide and transmembrane region,predominantly functions in the nucleus,and features 106 phosphorylation sites,one glycosylation site,and one conserved RRM structure.The secondary structure comprises mainly α-helix and random coils.Although the protein structure of the PPARGC1B gene remained unchanged post-mutation,there were significant differences in mRNA secondary structure.These findings suggest that the polymorphic loci of the PPARGC1B gene in Sichuan yaks could serve as a theoretical basis for enhancing meat quality traits through molecular biological methods,presen-ting practical applications in breeding.

Human still has limited understanding of respiratory infectious diseases, especially emerging acute respiratory infectious diseases. Once the pandemic of this kind of infectious disease occur, it would be a serious challenge to health, political security, the economic development, and social stability. People hope to detect the changes in infectious diseases in early phase through surveillance and give early warning in time. In the field of public health, more attention has been paid to syndrome surveillance as an effective supplement to traditional surveillance. This paper summarizes the current surveillance system of infectious diseases abroad, introduces the syndrome surveillance system of acute respiratory infectious disease and its application in China, and discusses the development of syndrome surveillance and early warning technology for acute respiratory infectious diseases in the future.

Intelligent syndromic surveillance is an important part of multi-point triggering and multi-channel surveillance system of intelligent early warning of infectious diseases in China, and an inevitable development process of traditional syndromic surveillance as the constant emergence of new technologies. Intelligent syndromic surveillance collects not only the medical data of patients seeking medical care in hospitals but also massive non-medical information. However, along with its rapid development, challenges in intelligent syndromic surveillance have emerged, such as information explosion, cost-effective balance, information sharing, data security and privacy. This paper summarizes the concept and development of intelligent syndromic surveillance to provide references for the method and technique development of intelligent early warning of infectious diseases and new thought for the prevention and control of infectious diseases in China and in the world.

This paper summarizes the basic principles and models of early warning for infectious disease outbreaks, introduces the early warning systems for infectious disease based on different data sources and their applications, and discusses the application potential of big data and their analysing techniques, which have been studied and used in the prevention and control of COVID-19 pandemic, including internet inquiry, social media, mobile positioning, in the early warning of infectious diseases in order to provide reference for the establishment of an intelligent early warning mechanism and platform for infectious diseases based on multi-source big data.

This paper reviews the limitations of current infectious disease surveillance and early warning system in China, analyzes the concepts and countermeasures of the establishment of an intelligent early warning platform of infectious diseases based on multi-point trigger mechanism and multi-channel surveillance mechanism and proposes the realization routes for the purpose of facilitating capacity building and improvement of surveillance and early warning of infectious diseases in China.

Microbes play important roles in human health and disease. The interaction between microbes and hosts is a reciprocal relationship, which remains largely under-explored. Current com-putational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes, microbial core genes, and disease phenotypes. We developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease associ-ation data. MicroPhenoDB provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body sites. MicroPhenoDB also pro-vides 696,934 relationships between 27,277 unique clade-specific core genes and 685 microbes. Dis-ease phenotypes are classified and described using the Experimental Factor Ontology (EFO). A refined score model was developed to prioritize the associations based on evidential metrics. The sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and assembly. Micro-PhenoDB offers data browsing, searching, and visualization through user-friendly web interfaces and web service application programming interfaces. MicroPhenoDB is the first database platform to detail the relationships between pathogenic microbes, core genes, and disease phenotypes. It will accelerate metagenomic data analysis and assist studies in decoding microbes related to human dis-eases. MicroPhenoDB is available through http://www.liwzlab.cn/microphenodb and http://lilab2. sysu.edu.cn/microphenodb.

BACKGROUND:Human umbilical cord mesenchymal stem cells (hUC-MSCs) may be mutated duringin vitro culture based on the spontaneous malignant transformation of adult stem cells and tumor stem cell theory, and there may be a risk of tumorigenesis after in vivo transplantation. Therefore, to establish and perfect the in vitro safety testing procedures will actively promote the clinical application of stem cells. OBJECTIVE:To investigate the tumorigenic mechanism of hUC-MSCs and the expression level of DNA methyltransferase (DNMTs) in hUC-MSCs. METHODS:Primary hUC-MSCs were isolated and expanded by tissue adherent culture. 3-Methycholanthrene was used to cause the malignant transformation in hUC-MSCs (experimental group), followed by morphological observation and tumorigenesis experiment in nude mice. Then, the tumor tissues were obtained and identified by pathological examination and primary cell culture, and the levels of DNMTs mRNA in hUC-MSCs treated with 3-methycholanthrene and dimethyl sulfoxide (control group) were detected by real-time RT-PCR and compared. RESULTS AND CONCLUSION:hUC-MSCs treated with 3-methycholanthrene led to malignant transformation, which showed malignant growth and non-integer ploidy changes in the cell nuclei, and formed a malignant tumor in immune-deficient mice after injection. Compared with the control group, the cells in the experimental group showed higher expression of DNMTs mRNA as detected by real-time RT-PCR. To conclude, hUC-MSCs can trigger malignant transformation in the morphology and the epigenetics under certain conditions. DNMTs can be a candidate for prevention against malignant transformation of transplanted stem cells.

OBJECTIVETo explore the effect of signal strength indictor (SSI) in improving sensitivity of China Infectious Diseases Automated-alert and Response System (CIDARS).

METHODSDiarrhea cases in 2007-2011 and early warning signals in 2010-2011 were selected by using random digital table method. Then, SSI and event-related ratio (ER) were calculated. The relationship between ER and SSI was analyzed, and the effect of SSI on ER was explored by using multiple logistic regression analysis.

RESULTS9 620 early warning signals in 2010-2011 were generated in two years. Of these, 74, or 0.77% were defined as suspected outbreak signal. The median of SSI related with suspected outbreak signal was 4.0, which was much higher than non-suspected outbreak signal (1.7). ER was significantly correlated with SSI (r=0.917). SSI classification has a good correlation between the ER, ER exceeded 20 after SSI reached 20. The multivariate logistic regression analysis showed OR of SSI related with suspected outbreak signal was 2.52 (95% CI 2.04-3.12). Compared with non-epidemic season, the relationship of SSI and ER in epidemic season was much higher.

CONCLUSIONSSI was closely related with ER. The relationship was much closer in large scale outbreak and epidemic season, and compared to non-epidemic,the effect of epidemic season is more obvious.

China ; Communicable Diseases ; diagnosis ; epidemiology ; Diarrhea ; diagnosis ; epidemiology ; Disease Outbreaks ; Humans ; Population Surveillance

OBJECTIVEProviding evidences for further modification of China Infectious Diseases Automated-alert and Response System (CIDARS) via analyzing the outbreak detection performance of Moving Percentile Method (MPM) by optimizing thresholds in different provinces.

METHODSWe collected the amount of MPM signals, response results of signals in CIDARS, cases data in nationwide Notifiable Infectious Diseases Reporting Information System, and outbreaks data in Public Health Emergency Reporting System of 16 infectious diseases in 31 provinces in Chinese mainland from January 2011 to October 2013. The threshold with the optimal sensitivity, the shortest time to detect outbreak and the least number of signals was considered as the best threshold of each disease in Chinese mainland and in each province.

RESULTSAmong all the 16 diseases, the optimal thresholds of 10 diseases, including dysentery, dengue, hepatitis A, typhoid and paratyphoid, meningococcal meningitis, Japanese encephalitis, scarlet fever, leptospirosis, hepatitis, typhus in country level were the 90(th) percentile (P90), which was the same as provincial level for those diseases.For the other 6 diseases, including other infectious diarrhea, influenza, acute hemorrhagic conjunctivitis, mumps, rubella and epidemic hemorrhagic fever, the nationwide optimal thresholds were the 80th percentile (P80), which was different from that by provinces for each disease. For these 6 diseases, the number of signals generated by MPM with the optimal threshold for each province was decreased by 23.71% (45 557), 15.59% (6 124), 14.07% (1 870), 9.44% (13 881), 8.65% (1 294) and 6.03% (313) respectively, comparing to the national optimal threshold, while the sensitivity and time to detection of CIDARS were still the same.

CONCLUSIONOptimizing the threshold by different diseases and provinces for MPM in CIDARS could reduce the number of signals while maintaining the same sensitivity and time to detection.

China ; Communicable Diseases ; Disease Notification ; Disease Outbreaks ; prevention & control ; Humans ; Population Surveillance ; methods

OBJECTIVEFor providing evidences for further modification of China Infectious Diseases Automated-alert and Response System (CIDARS) by comparing the early-warning performance of the temporal model and temporal-spatial model in CIDARS.

METHODSThe application performance for outbreak detection of temporal model and temporal-spatial model simultaneously running among 208 pilot counties in 20 provinces from 2011 to 2013 was compared; the 16 infectious diseases were divided into two classes according to the disease incidence level; cases data in nationwide Notifiable Infectious Diseases Reporting Information System was combined with outbreaks reported to Public Health Emergency Reporting System, by adopting the index of the number of signals, sensitivity, false alarm rate and time for detection.

RESULTSThe overall sensitivity of temporal model and temporal-spatial model for 16 diseases was 96.23% (153/159) and 90.57% (144/159) respectively, without significant difference (Z = -1.604, P = 0.109), and the false alarm rate of temporal model (1.57%, 57 068/3 643 279) was significantly higher than that of temporal-spatial model (0.64%, 23 341/3 643 279) (Z = -3.408, P = 0.001), while the median time for detection of these two models was not significantly different, which was 3.0 days and 1.0 day respectively (Z = -1.334, P = 0.182).For 6 diseases of type I which represent the lower incidence, including epidemic hemorrhagic fever,Japanese encephalitis, dengue, meningococcal meningitis, typhus, leptospirosis, the sensitivity was 100% for both models (8/8, 8/8), and the false alarm rate of both temporal model and temporal-spatial model was 0.07% (954/1 367 437, 900/1 367 437), with the median time for detection being 2.5 days and 3.0 days respectively. The number of signals generated by temporal-spatial model was reduced by 2.29% compared with that of temporal model.For 10 diseases of type II which represent the higher incidence, including mumps, dysentery, scarlet fever, influenza, rubella, hepatitis E, acute hemorrhagic conjunctivitis, hepatitis A, typhoid and paratyphoid, and other infectious diarrhea, the sensitivity of temporal model was 96.03% (145/151), and the sensitivity of temporal-spatial model was 90.07% (136/151), the number of signals generated by temporal-spatial model was reduced by 59.36% compared with that of temporal model. Compared to temporal model, temporal-spatial model reduced both the number of signals and the false alarm rate of all the type II diseases;and the median of outbreak detection time of temporal model and temporal-spatial model was 3.0 days and 1.0 day, respectively.

CONCLUSIONOverall, the temporal-spatial model had better outbreak detection performance, but the performance of two different models varies for infectious diseases with different incidence levels, and the adjustment and optimization of the temporal model and temporal-spatial model should be conducted according to specific infectious disease in CIDARS.

China ; Communicable Diseases ; Disease Notification ; Disease Outbreaks ; prevention & control ; Humans ; Models, Theoretical ; Population Surveillance ; methods ; Spatio-Temporal Analysis