Objective:To analyze the association between the pre-pregnancy oral microbiota of women and fetal overgrowth, and the possible mechanisms involved.Methods:A nested case-control study design based on a pre-pregnancy cohort was used to select 51 mothers who delivered macrosomia and/or large-for-gestational-age (LGA) infants from the population recruited at the Maternal and Child Health Care Hospital of Jiading District in Shanghai from October 2016 to December 2021 as the case group. A control group was formed by selecting 204 mothers who delivered infants with normal birth weight and appropriate for gestational age during the same period, in a 1:4 ratio. The LGA subgroup consisted of 48 mothers who delivered LGA infants from the total population, and a corresponding control group of 192 was randomly selected from the remaining mothers who delivered non-LGA infants in a 1∶4 ratio for the LGA subgroup analysis. The 16S rRNA gene sequencing technique was utilized to detect pre-pregnancy saliva samples to compare the characteristics of the oral microbiota, differential microorganisms, and differential functional pathways between groups. Nonparametric Wilcoxon rank-sum tests, two independent samples t-tests, or Chi-square (or Fisher's exact) tests were used for statistical analysis. Factor analysis was conducted on the pre-pregnancy diet data of women, and the primary dietary pattern of each study subject was identified based on the highest score of the dietary pattern factors. For microbiota count data, α and β diversity indices were calculated using R and QIIME2 software, and the corresponding microbiota functional count data were acquired through PICRUSt2. Results:(1) General data: There was no significant difference in the time interval from pre-pregnancy sampling to pregnancy and from sampling to delivery between the two groups. In the case group, there were three cases of macrosomia and 48 cases (94.1%) of LGA. The corresponding control group for the LGA subgroup consisted of 192 cases. There were no significant differences in dietary patterns between the case group and the control group. (2) α diversity analysis: The species richness index of the case group was lower than that of the control group [(367.27±84.57) vs. (408.71±93.08), multivariate analysis, P=0.009], while no significant differences were found between the two groups in the Shannon and Simpson indices; the species richness index of the LGA subgroup was also lower than that of the corresponding control group [(371.04±83.92) vs. (408.04±94.21), multivariate analysis, P=0.033], with no significant differences in the Shannon and Simpson indices. (3) β diversity analysis: There was a statistically significant difference in the unweighted UniFrac distance of the oral microbiota between the case group and the control group ( R2=0.006, F=1.479, P=0.048). No significant differences were found in the β diversity indices of the oral microbiota between the LGA subgroup and the corresponding control group. (4) Differential microbiota analysis: There were 14 differential microbiotas from phylum to genus between the case group and the control group. At the genus level, members of the G1 genus of the Streptococcaceae were enriched in the case group, while the Lautropia, Dialister, Leptotrichia, and Rothia were enriched in the control group. In the LGA subgroup and its corresponding control group, there were 14 differential microbiota from phylum to genus; at the genus level, Leptotrichia, Rothia, G6 genus of the Saccharibacteria, and Selenomonas were enriched in the control group (all LDA value>2, and all P<0.05). (5) Differential functional analysis: In the case group, metabolic pathways such as nicotinate degradation [log 2 fold change ( FC)=3.510, q=0.005], de novo synthesis of pyrimidine nucleotides (log 2FC=0.078, q=0.005), and L-tyrosine degradation pathway (log 2FC=0.710, q=0.034) were enriched in the oral microbiota of women. In the LGA subgroup, compared to the corresponding control group, metabolic pathways related to nicotinate degradation were enriched in the oral microbiota (log 2FC=3.660, q=0.012). Conclusions:There are differences in the structure of the pre-pregnancy oral microbiota of mothers with overgrown fetuses compared to those with normally grown fetuses, and mothers of normally grown fetuses show higher diversity in their pre-pregnancy oral microbiota. The enrichment of certain pathogenic bacteria and the reduction of symbiotic bacteria in the pre-pregnancy oral microbiota are associated with fetal overgrowth, and this association may be mediated by functional pathways such as nicotinate degradation.

With the development of society and the awakening of civic consciousness,hospital volunteer service has be-come an indispensable part of modern medical and health system,which plays an important role in improving service mode,im-proving patients'medical experience,and establishing the public welfare image of hospitals.Based on the humanistic hospital construction thinking and combined with the connotation and goal of humanistic hospital construction,this paper is committed to building the hospital volunteer service brand,and summarizes the volunteer management system,service content and process,in order to provide theoretical reference and guidance for the standardization and systematization of hospital volunteer service brand construction.

ObjectiveTo analyze the epidemic characteristics of measles and rubella in Pudong New Area of Shanghai from 2013 to 2022， and to provide data support for the elimination of measles and rubella. MethodsEnzyme linked immunosorbent assay was used to detect IgM antibodies in serum samples. The sequence of 630 nucleotides at the C-terminal of N gene of measles virus was amplified by reverse transcription-polymerase chain reaction and the phylogenic tree was constructed. ResultsA total of 1 529 suspected cases of measles were detected from 2013 to 2022， among which the positive rate of measles IgM antibody was 33.55% （513/1 529）. The highest positive rate （20.73%） was from March to May ， and the positive rate of rubella IgM antibody was 6.80% （104/1 529）. The positive rate of both IgM was higher in males than that in females （P<0.05）. The IgM against measles was mainly detected in 0‒ years old （63.16%， 96/152） and 20‒ years old （45.61%， 161/353）. The IgM against rubella was mainly detected in 10‒20 years old （27.27%， 18/66）. The IgM antibody could be detected more easily from 4 to 28 days after eruption， and the IgM antibody positive rate of measles/rubella from 2020 to 2022 was significantly lower than previous years （2013‒2019）. There were 2 D8 genotype strains， and the rest were H1a gene subtypes. ConclusionThe positive rate of IgM antibodies against measles/rubella in Pudong New Area of Shanghai decreased significantly. People aged 0‒ years and 20‒ years old are more susceptible to measles， and rubella is concentrated in 10‒ years old. It is necessary to strengthen the vaccination of school-age children， in order to achieve the goal of eliminating measles. The age group with high risk of exposure should be checked for vaccination status to ensure the enhanced immunization， and the surveillance of imported measles cases should be strengthened.

Objective:To construct a risk prediction model for the inter-hospital transfer of critically ill children using machine learning methods, identify key medical features affecting transfer outcomes, and improve the success rate of transfers.Methods:A prospective study was conducted on critically ill children admitted to the pediatric transfer center of Hunan Children's Hospital from January 2020 to January 2021. Medical data on critical care features and relevant data from the Pediatric Risk of Mortality (PRISMⅢ) scoring system were collected and processed. Three machine learning models, including logistic regression, decision tree, and Relief algorithm, were used to construct the risk prediction model. A back propagation neural network was employed to build a referral outcome prediction model to verify and analyze the selected medical features from the risk prediction model, exploring the key medical features influencing inter-hospital transfer risk.Results:Among the 549 transferred children included in the study, 222 were neonates (40.44%) and 327 were non-neonates (59.56%). There were 50 children in-hospital deaths, resulting in a mortality rate of 9.11%. After processing 151 critical care medical feature data points, each model selected the top 15 important features influencing transfer outcomes, with a total of 34 selected features. The decision tree model had an overlap of 72.7% with PRISMⅢ indicators, higher than logistic regression (36.4%) and Relief algorithm (27.3%). The training prediction accuracy of the decision tree model was 0.94, higher than the accuracy of 0.90 when including all features, indicating its clinical utility. Among the top 15 important features selected by the decision tree model, the impact on transfer outcomes was ranked as follows based on quantitative feature violin plots: base excess, total bilirubin, ionized calcium, total time, arterial oxygen pressure, blood parameters (including white blood cells, platelets, prothrombin time/activated partial thromboplastin time), carbon dioxide pressure, blood glucose, systolic blood pressure, heart rate, organ failure, lactate, capillary refill time, temperature, and cyanosis. Eight of these important features overlapped with PRISMⅢ indicators, including systolic blood pressure, heart rate, temperature, pupillary reflex, consciousness, acidosis, arterial oxygen pressure, carbon dioxide pressure, blood parameters, and blood glucose. The decision tree was used to select the top 15 medical features with high impact on the neonatal and non-neonatal datasets, respectively. A total of 19 features were selected, among which there were 8 differences and 11 overlap terms between the important features of the neonatal and non-neonatal.Conclusions:Machine learning models could serve as reliable tools for predicting the risk of inter-hospital transfer of critically ill children. The decision tree model exhibits superior performance and helps identify key medical features affecting inter-hospital transfer risk, thereby improving the success rate of inter-hospital transfers for critically ill children.

ObjectiveTo understand the serotype distribution and drug resistance of salmonella contaminated in commercially available food. MethodsSalmonella detection， including the serotypes， was conducted in food products sold in Pudong New Area from 2020 to 2022. The antimicrobial susceptibility test of 15 antibiotics was conducted by the broth microassay. ResultsA total of 118 salmonella strains were detected in 2 497 pieces of food， with a total detection rate of 4.7%. The dominant detection categories were poultry meat， livestock meat and aquatic products. The 118 salmonella strains could be divided into 24 serotypes， Salmonella enteritidis （26.4%）， Salmonella Typhimurium （16.2%） and Salmonella delpy （14.4%） were the main dominant types. Salmonella had the highest resistance rate to ampicillin （63.6%）， followed by tetracycline， chloramphenicol， cotrimoxazole and nalidixic acid. Among the three dominant serotypes， the multidrug resistance rate of Salmonella typhimurium was the highest （89.5%）， followed by Salmonella delpy （70.6%） and Salmonella enteritidis （61.3%）. ConclusionLivestock， poultry meat， and aquatic products are seriously contaminated by salmonella with diverse serotypes. The livestock meat is mainly contaminated by Salmonella typhimurium and Salmonella delpy， and the poultry meat is mainly contaminated by Salmonella enteritidis. The drug resistance spectrum is wide and the multi-drug resistance rate is high. Different from the livestock and aquatic isolates， poultry meat-derived strains have high tolerance to ampicillin， nalidixic acid and polymyxin， and carry certain potential food safety risks.

Background Stroke has become a main cause of death in China. With global warming, the studies on temperature and stroke have attracted much attention. Objective To analyze he relationships between heatwave and the years of life lost (YLL) by different subtypes of stroke by controlling temporal and spatial effects with Bayesian spatio-temporal model, and to study the modifiers of the health effect of heatwave. Methods The daily information of stroke deaths, meteorological data, and air pollutant data in 40 districts and counties of Guangdong Province were collected during the warm seasons (from May to October) in the years from 2014 to 2017. The individual YLL was first calculated by matching age and gender according to the life table, and then the daily YLL rate (person-years/100 000 people) was obtained by summarizing the daily YLL and correcting it with the population of each district or county. Bayesian spatio-temporal model was used to fit a proposed exposure-response relationship between heatwave and the YLL rates of different subtypes of stroke. Finally, stratified analyses were conducted by age (<65 years, ≥65 years), gender (male, female), and region (Pearl River Delta and non-Pearl River Delta regions) to identify the major modifiers for the association between heatwave and stroke mortality. Results During the warm seasons from 2014 to 2017, a total of 23 heatwave events occurred in the 40 districts or counties of Guangdong Province, cumulatively lasting for 145 d. A total of 30 852 stroke deaths were recorded in the same time periods. The average daily YLL rate of total stroke was (2.39±3.63) person-years/100 000 people, and those for hemorrhagic stroke and ischemic stroke were (1.54±2.99) person-years/100 000 people and (0.84±1.85) person-years/100 000 people, respectively. Heatwave was associated with increased YLL rate of stroke in residents, and it had a greater impact on ischemic stroke with a lag effect. The largest cumulative effect of heatwave was at lag 0-1 day, which was associated with an increased YLL rate of total stroke and ischemic stroke by 0.17 (95%CI: 0.03-0.29) person-years/100 000 people and 0.13 (95%CI: 0.06-0.20) person-years/100 000 people, respectively. The results of stratified analyses showed that heatwave had a larger effect on ischemic stroke in residents of aged 65 years or older, male, and non-Pearl River Delta regions, and the rates of YLL increased by 1.11 (95%CI: 0.58-1.55), 0.13 (95%CI: 0.03-0.23), and 0.20 (95%CI: 0.07-0.32) person-years/100 000 people, respectively; Heatwave only had an effect on hemorrhagic stroke in residents aged 65 years or older with an increased YLL rate of 0.79 (95%CI: 0.26-1.31) person-years/100 000 people. Conclusion Heatwave could elevate the level of years of life lost associated with stroke in Guangdong residents, with greater impacts on ischemic stroke of the aged, men, and residents in non-Pearl River Delta regions, and on hemorrhagic stroke in the elderly.

Articular cartilage injury is common in orthopedics. Improper exercises and physical trauma can lead to the injury of cartilage. Since articular cartilage lacks blood supply, once damaged, it is difficult for the cartilage to repair itself. If not treated effectively, cartilage injuries will develop into severe osteoarthritis affecting the whole joint. Arthroscopic microfracture technique can achieve better therapeutic effects than regular joint debridement, with simple procedures, minimal invasion, and low cost. However, the microfracture technique is limited by the patients′ age (under 45 years old) and the size of the cartilage defect area (less than 4 cm 2) Additionally, postoperative patients need to conduct strict and long-term rehabilitation trainings. Generally speaking, the short-term prognosis of microfracture is satisfactory. However, the repair tissue is mainly composed of fibrocartilage, which is inferior to hyaline cartilage because of its poor mechanical properties and anti-wear abilities. Therefore, the long-term effect is controversial. To conclude, arthroscopic microfracture is a recommended method for young patients with small cartilage defect areas, but its exact long-term clinical effects still need to be verified by further research. This paper reviews the operation protocol, clinical efficacy, and the mechanism of arthroscopic microfracture surgery, and aims to provides theoretical basis for its application in clinical treatment.

Objective: To investigate the correlation between skin elasticity and setup error in optical surface image-guided radiotherapy. Methods: The skin elasticity (R7) data of the head, chest and abdomen were extracted and analyzed its correlation with age by systematic literature review. Fifty-four patients diagnosed with nasopharyngeal carcinoma, breast cancer and cervical cancer were recruited in this study. Firstly, the patients were positioned based on the room laser and markers. Subsequently, the patient position was verified by the Varian On-Board Imager, and then C-Rad Catalyst was adopted to obtain surface images in two states (mask or non-mask) as reference images. In the subsequent fraction treatment, after initial positioning, the local calibration was performed by Catalyst, and setup errors in three directions were recorded. Meanwhile, the patient setup was verified by CBCT twice a week. The Pearson correlation analysis was performed to analyze the correlation between setup error and age. Results: The skin elasticity was negatively correlated with aging (P<0.01). The correlation coefficient between random error and age in head-and-neck cancer were 0.645, 0.624 and 0.866 in the AP, SI and LR directions (all P<0.05) for male patients without mask, respectively. The system error was significantly correlated with age in the LR direction (P<0.05) for male patients, and in the AP direction (P<0.05) for female patients with head-and-neck cancer without mask. The setup error had a significant correlation with skin elasticity in male patients with head-and-neck cancer, and the sequence of absolute value of correlation coefficient was LR>SI>AP. Conclusion In optical surface-guided radiotherapy of head and neck cancer, skin elasticity may be a significant index for assessing the setup errors in male patients.

Objective:To identify the key genes affecting the outcome of sepsis using weighted gene co-expression network analysis.Methods:The peripheral blood gene chip data GSE54514 from septic patients and healthy volunteers were obtained from the gene expression database of the American Center for Biotechnology Information.An R package for weighted gene co-expression network analysis was used to construct a co-expression network of differentially expressed genes between sepsis patients and healthy volunteers to identify key modules associated with the outcome of sepsis.Then gene functional enrichment analysis was performed to figure out the possible behavior of genes in the most significant modulerelated tooutcomes of sepsis.Hub genes were selected from the most significant module according to module membership and degree of protein-protein interaction network.Results:A total of 622 differentially expressed genes identified from the microarray data of GSE36895 in septic patients and healthy volunteers were used to construct a co-expression network, and the module with the most significant correlation with the outcome of sepsis was identified.GO enrichment analysis showed that the genes in this module were related to activation of myeloid cells and neutrophils, however, the KEGG pathway enrichment analysis showed that these genes played an important role in virus infection processes.Fifteen hub genes were finally selected from the module with the most significant correlation with the outcome of sepsis by constructing a protein-protein interaction network.Conclusion:Fifteen key genes related to the outcome of sepsis are identified via bioinformatics methods, and the mechanism is related to regulating the immune response to infection.

Objective To explore the application values of GeneXpert MTB/RIF assay (Xpert assay) for rifampicin resistance and multidrug resistant tuberculosis (MDR-TB).Discordance of Xpert and L-J proportion DST of rifampicin was analyzed.Methods Specimens of 1 300patients from January 2014to June 2016in our hospital were collected for solid culture, Xpert assay and L-J proportion drug susceptibility test (DST).Rifampicin resistance detected by Xpert assay was compared with L-J proportion method as a gold standard.Sequencing of rpoB gene and determination of minimum inhibition concentration were accomplished on the discordant MTB strains between Xpert and L-J proportion DST.Results Compared with the DST, the sensitivity, specificity, positive and negative predictive value of Xpert assay for rifampicin resistance were 99.31％, 97.82％, 92.88％and 99.80％, respectively.Among 1 300culture-positive specimens, mutations were detected from 309specimens by Xpert assay, which included 125initial treatment and 184retreatment patients.Among309specimens with rpoB gene mutations, mutations detected by probes E, D and B were common, and the rates were 65.70％, 14.56％and 10.68％, respectively.Totally 239patients were MDR-TB[77.35％ (239/309) ], of which 94initial treatment patients[75.20％ (94/125) ]and 145retreatment patients[78.80％ (145/184) ]were MDR-TB.Among 22strains which were detected rpoB gene mutations by Xpert, but sensitive by L-J proportion DST, 6strains had no mutation in rpoB gene rifampicin resistance determining region (RRDR);16strains had mutations, which were mainly located in L511Pcodon (8strains) and L533Pcodon (4strains).Among 2strains which had no rpoB gene mutation by Xpert, but were resistant by L-J proportion DST, 1strain had no mutation in rpoB gene RRDR region;1strain had mutation which was located in E546G codon outside RRDR region.Conclusion Xpert assay can be used to rapidly detect rifampicin resistance and to screen MDR-TB.Mutations in codon 511and 533are related to low-level resistance to rifampicin.