Objective: To analyze the school tuberculosis (TB) outbreaks and preventive treatment in Hefei from 2022 to 2024, so as to provide reference for TB prevention and control in schools. Methods: Data were collected on all school based TB outbreaks occurring during 2022-2024 in Hefei, defined as ≥2 epidemiologically linked TB cases within the same school during a single semester. Statistical analyses were performed using the Chi square test. Results: Close contacts exhibited significantly higher TB incidence (2.88%) and latent mycobacterium tuberculosis infection (LTBI) rates (13.80%) in the school TB outbreaks, compared to non close contacts (0.12% and 2.63%, respectively). Among close contacts, secondary school students showed lower TB incidence (0.48%) and LTBI prevalence (3.42%) than both primary school or younger children (0.68%, 6.95%) and college students ( 0.78% , 6.50%), with statistically significant differences ( χ 2=360.91, 6.37; 791.71, 102.03, all P <0.05). The proportion of LTBI individuals recommended for preventive therapy was higher in primary school or younger groups (98.59%) than in secondary (95.25%) or college students (86.34%) ( χ 2=25.86, P <0.01). However, among those recommended, close contacts had higher uptake (85.82%) and completion rates (87.25%) of preventive therapy than non close contacts (69.63% and 70.57%); similarly, secondary school students demonstrated higher uptake (91.21%) and completion rates (86.45%) compared to primary school or younger (88.57%, 83.87%) and college students (57.28%, 64.08%) ( χ 2=30.52, 26.72; 125.17, 38.84, all P <0.01). Subsequent TB incidence among LTBI close contacts (13.30%) and among those who did not complete preventive therapy (22.73%) were significantly higher than among non close contacts (2.80%, 2.41%), respectively ( χ 2=32.19, 13.87, both P <0.05). Conclusions In school TB outbreaks, close contacts face higher LTBI prevalence and subsequent TB risk than non close contacts. College students show notably low adherence to preventive therapy. It is necessary to take targeted measures to improve the compliance of preventive measures among students.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

OBJECTIVES: To investigate the clinical sub-phenotype (SP) of pediatric acute kidney injury (AKI) and their association with clinical outcomes. METHODS: General status and initial values of laboratory markers within 24 hours after admission to the pediatric intensive care unit (PICU) were recorded for children with AKI in the derivation cohort (n=650) and the validation cohort (n=177). In the derivation cohort, a least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify death-related indicators, and a two-step cluster analysis was employed to obtain the clinical SP of AKI. A logistic regression analysis was used to develop a parsimonious classifier model with simplified metrics, and the area under the curve (AUC) was used to assess the value of this model. This model was then applied to the validation cohort and the combined derivation and validation cohort. The association between SPs and clinical outcomes was analyzed with all children with AKI as subjects. RESULTS: In the derivation cohort, two clinical SPs of AKI (SP1 and SP2) were identified by the two-step cluster analysis using the 20 variables screened by LASSO regression, namely SP_d1 group (n=536) and SP_d2 group (n=114). The simplified classifier model containing eight variables (P<0.05) had an AUC of 0.965 in identifying the two clinical SPs of AKI (P<0.001). The validation cohort was clustered into SP_v1 group (n=156) and SP_v2 group (n=21), and the combined derivation and validation cohort was clustered into SP1 group (n=694) and SP2 group (n=133). After adjustment for confounding factors, compared with the SP1 group, the SP2 group had significantly higher incidence rates of multiple organ dysfunction syndrome and death during the PICU stay (P<0.001), and SP2 was significantly associated with the risk of death within 28 days after admission to the PICU (P<0.001). CONCLUSIONS This study establishes a parsimonious classifier model and identifies two clinical SPs of AKI with different clinical features and outcomes.The SP2 group has more severe disease and worse clinical prognosis.
Humans ; Acute Kidney Injury/diagnosis* ; Prognosis ; Male ; Female ; Child ; Child, Preschool ; Phenotype ; Infant ; Logistic Models ; Adolescent

OBJECTIVES: To analyze the potential causal relationship between gut microbiota and food allergy (FA) using two-sample Mendelian randomization (MR) methods. METHODS: Data from genome-wide association studies on gut microbiota and FA were utilized. MR analysis was conducted employing inverse variance weighting, MR-Egger regression, and weighted median methods to assess the causal relationship between gut microbiota and FA. Cochrane's Q test was used to evaluate heterogeneity of instrumental variables, MR-PRESSO analysis was conducted to test for outliers and pleiotropy, and MR-Egger regression was employed to assess horizontal pleiotropy. The "leave-one-out" method was used to evaluate the impact of removing individual single nucleotide polymorphisms on the causal relationship. RESULTS: Inverse variance weighting analysis revealed that the phylum Verrucomicrobia, family Verrucomicrobiaceae, order Verrucomicrobiales, genus Ruminococcaceae UCG013, and genus Akkermansia were negatively associated with FA (P<0.05). Sensitivity analyses confirmed the reliability of the findings, indicating no heterogeneity or pleiotropy present. CONCLUSIONS There is a causal relationship between gut microbiota and FA, with Verrucomicrobia, Verrucomicrobiaceae, Verrucomicrobiales, Ruminococcaceae UCG013, and Akkermansia potentially reducing the risk of developing FA. These findings provide potential targets for the treatment and prevention of FA; however, further research is needed to explore the specific mechanisms by which the microbiota influence FA.
Humans ; Mendelian Randomization Analysis ; Gastrointestinal Microbiome ; Food Hypersensitivity/microbiology* ; Genome-Wide Association Study ; Polymorphism, Single Nucleotide

OBJECTIVES: To investigate the molecular epidemiology of children with phenylketonuria (PKU) in Gansu, China, providing foundational data for intervention strategies. METHODS: A retrospective analysis was conducted on 1 159 PKU families who attended Gansu Provincial Maternity and Child Care Hospital from January 2012 to December 2024. Sanger sequencing, multiplex ligation-dependent probe amplification, whole exome sequencing, and deep intronic variant analysis were used to analyze the PAH gene. RESULTS: For the 1 159 children with PKU, 2 295 variants were identified in 2 318 alleles, resulting in a detection rate of 99.01%. The detection rates were 100% (914/914) in 457 classic PKU families, 99.45% (907/912) in 456 mild PKU families, and 96.34% (474/492) in 246 mild hyperphenylalaninemia families. The 2 295 variants detected comprised 208 distinct mutation types, among which c.728G>A (14.95%, 343/2 295) had the highest frequency, followed by c.611A>G (4.88%, 112/2 295) and c.721C>T (4.79%, 110/2 295). The cumulative frequency of the top 23 hotspot variants reached 70.28% (1 613/2 295), and most variant alleles were detected in exon 7 (29.19%, 670/2 295). CONCLUSIONS Deep intronic variant analysis of the PAH gene can improve the genetic diagnostic rate of PKU. The development of targeted detection kits for PAH hotspot variants may enable precision screening programs and enhance preventive strategies for PKU.
Humans ; Phenylketonurias/epidemiology* ; Female ; Male ; Retrospective Studies ; Phenylalanine Hydroxylase/genetics* ; Mutation ; Child, Preschool ; China/epidemiology* ; Child ; Infant

OBJECTIVE To standardize the strategies for prevention and control of Chikungunya(CHIK)in healthcare in-stitutions so as to reduce the risk of transmission in the institutions.METHODS A working group comprising the ex-perts in hospital infection control,infectious diseases,and microbiology systematically reviewed domestic and international evidence and current guidelines,integrated China's vector ecology and healthcare realities,conducted two rounds of Delphi to achieve expert consensus,and graded the evidence and recommendation strength using the Oxford Centre for Evidence Based Medicine system.RESULTS The consensus issues 18 actionable recommendations on triage,patient mosquito-proof isolation,integrated vector control,protection of susceptible populations,environmental cleaning and disinfection,specimen management,medical textile handling,and outbreak emergency response,with each statement assigned an evi-dence level and recommendation strength.CONCLUSION This consensus is for the first time in China to provide evidence-graded strategies for control of CHIK in healthcare institutions,offering work flow-oriented,implementable guidance for clinicians,laboratorians,and infection-control personnel under different risk scenarios and enhancing the comprehensive coping capacity of the healthcare institutions.

Objective:To explore the impact of the stimulator of interferon genes(STING)-interferon regulatory factor 3(IRF3)pathway on the senescence of rapid pacing HL-1 myocytes.Methods:HL-1 cells were divided into five groups: the control group(HL-1 cells without any treatment), pacing group(HL-1 cells paced for 48 hours), STING siRNA group(HL-1 cells paced for 48 hours and transfected with STING siRNA), NC siRNA group(HL-1 cells paced for 48 hours and transfected with NC siRNA), and H151 inhibitor group(HL-1 cells paced for 48 hours with the addition of 1 μmol/L STING inhibitor H151). Mitochondrial membrane potential was assessed in control and pacing group cells, and mitochondrial MitoTracker and TFAM co-localization staining was performed on these cells.Cellular senescence was evaluated using β-galactosidase staining in each group, and the positive rate of cellular senescence was observed and calculated.Western blotting was employed to detect the expression levels of STING, IRF3, P-IRF3, P16, P21, and P53 proteins in all groups.Immunofluorescence was utilized to examine the expression distribution of STING and P21 across the various groups.ELISA was performed to measure the concentrations of interleukin(IL)-1β, IL-6, and IL-8 in the cell supernatants from each group as part of the senescence-associated secretory phenotype(SASP).Results:Compared with the control group, the ratio of mitochondrial JC-1 multimer to monomer was significantly decreased in the pacing group( t=16.42, P<0.05), the co-localization of mitochondrial MitoTracker and TFAM in the cells was significantly weakened, the proportion of cells with positive cellular senescence-associated β-galactosidase staining significantly increased in the pacing group, the expression levels of STING, P-IRF3/IRF3, P16, P21, and P53 proteins were significantly elevated in the pacing group, and the concentrations of IL-1β, IL-6, and IL-8 in the cell supernatants were markedly increased.Compared with the pacing group, the proportion of cells with positive cellular senescence-associated β-galactosidase staining decreased in the STING siRNA group and H151 inhibitor group ( F= 18.13, P<0.05), the expression levels of STING, P-IRF3/IRF3, P16, P21, and P53 were reduced in the STING siRNA group and H151 inhibitor group ( F=16.84, 26.56, 74.70, 31.80, 31.23, all P<0.05), and the concentrations of IL-1β, IL-6, and IL-8 in the cell supernatants decreased( F=197.80、1 339.00、1 308.00, all P<0.001). Conclusions:Rapid pacing of HL-1 cells can promote mtDNA release into the cytoplasm, activate the STING-IRF3 pathway, accelerate cellular senescence, and enhance the secretion of SASP.Inhibiting the expression of STING can delay the senescence induced by the rapid pacing of HL-1 cells and reduce SASP secretion.

Objective:To investigate the methods and skills required for the safe and swift removal of primary or secondary sacral tumors located between the second (inclusive) and fourth sacral vertebrae.Methods:The clinical images, pathology reports, surgical procedures, operation durations, intraoperative bleeding volumes, and postoperative functional follow-up data of 26 patients undergoing sacral tumor resection at the First Affiliated Hospital of Zhengzhou University and Xinjiang Production and Construction Corps Hospital between May 2020 and February 2025 were retrospectively examined. Additionally, the safety measures for sacral tumor resection and techniques for expedited specimen removal were evaluated.Results:According to magnetic resonance imaging (MRI) findings, all 26 patients presented with sacral tumors located between the second (inclusive) and fourth sacral vertebrae. Specifically, 9 patients were diagnosed with primary sacral tumors, pathologically confirmed as chordomas, while 17 patients had secondary sacral tumors. Among the secondary tumor cases, 12 were attributed to recurrent rectal cancer invading the sacrum, and 5 were due to malignant teratomas invading the sacrum. The 26 patients underwent a treatment strategy that began with managing the relationship between the internal iliac artery, vein branches, and the tumor, followed by the resection of the sacrum. During surgery, the bilateral sciatic foramina were accurately positioned, and the presacral fascia was dissected subsequent to the fracture of the sacrum. Among the 26 patients, 9 underwent sacral tumor resection directly through the posterior sacral approach. The average operation time for these patients was (71.1±4.9) minutes, with average blood loss of (186.7±72.8) milliliters. On the other hand, 17 patients underwent sacral tumor resection by transitioning from the supine position to the prone knife position through a combined abdominal and sacral approach. The average operation time for this group was (213.5±19.3) minutes, with average blood loss of (480.0±93.0) milliliters, significantly longer than that of the posterior sacral approach. The follow-up period ranged from 1 to 48 months, with a median of 20 months, ending on March 31, 2025. During this time, 26 patients achieved autonomous defecation with the aid of medication. None of the patients reported any functional movement disorders or pain in their lower limbs. It was observed that two out of the 26 patients developed distant metastasis, while the remaining 24 patients survived without any tumors.Conclusion:By pretreated the relationship between the internal iliac vessels and sacral tumors prior to resecting sacral tumors, utilizing the approach of initially fracturing the sacrum followed by rupturing the presacral fascia, the tumor can be entirely eliminated, resulting in a brief surgical procedure, reduced intraoperative bleeding, and minimal postoperative complications.