ObjectiveTo investigate the epidemiological and clinical characteristics of human bocavirus (HBoV) in hospitalized children with acute lower respiratory tract infection (ALRTI) at a single-center children’s hospital in Shanghai, thereby providing evidence for the diagnosis, treatment, and prevention of HBoV infection. MethodsA retrospective study was conducted on 19 537 hospitalized children with ALRTI at Shanghai Children’s Hospital from January 2021 to December 2023. Multiplex polymerase chain reaction (PCR) combined with capillary electrophoresis was used to detect HBoV and 12 other common respiratory viruses /atypical pathogens. The positive detection rate, demographic characteristics (sex, age), temporal distribution (year, season) of HBoV, as well as the clinical characteristics of severe and non-severe pneumonia were analyzed. ResultsThe overall HBoV-positive rate was 2.57% (503/19 537), with 59.44% (299/503) being single infections and 40.56% (204/503) being co-infections. The positive detection rate was significantly higher in boys than that in girls (2.78% vs 2.33%, χ²=3.88, P=0.049). The highest infection rate was observed in toddlers, followed by infants (χ²=379.57, P<0.001). The positive rate peaked in 2021 and reached its lowest point in 2023 (χ²=45.49, P<0.001), with epidemics mainly prevalent in summer and autumn. The main clinical symptoms were cough (90.06%, 453/503), fever (75.94%, 382/503), and wheezing (39.96%, 201/503). Children with severe pneumonia showed a higher incidence of wheezing compared with the non-severe group (P<0.001), while underlying diseases and co-infections had no significant association with disease severity (P>0.05). ConclusionHBoV was an important pathogen of ALRTI in children, predominantly affecting infants and toddlers, with higher susceptibility in boys and seasonal peaks in autumn and summer. The main clinical manifestations included cough, fever, and wheezing, with wheezing being more prevalent in children with severe pneumonia.

Objective:To analyze the distribution and epidemiological characteristics of non-bacterial pathogens in hospitalized children with acute respiratory infections at a tertiary pediatric hospital in Shanghai during 2023.Methods:A retrospective study was conducted on 10 591 children with acute respiratory tract infections who were hospitalized in Shanghai Children's Hospital from January to December 2023. A multiplex PCR combined with capillary electrophoresis platform was used to detect 11 common non-bacterial respiratory pathogens（including viruses and atypical pathogens）. Statistical analysis was carried out using SPSS 29.0 software. Qualitative data were presented as numbers and percentages，and the Chi-square test was employed to make comparisons between groups，aiming to analyze the differences in the distribution of different pathogens according to gender，age group，and season. Additionally，based on the severity of the disease，patients were calssified into a severe pneumonia group and a non-severe pneumonia group to further explore the characteristics of the pathogen spectrum of severe pneumonia.Results:The total detection rate of pathogens was 54.39%（5 760/10 591），and the proportion of mixed infections was 12.76%（735/5 760）. The dominant pathogens and their proportions were as follows： Mycoplasma pneumoniae（19.20%，2 034/10 591），human rhinovirus（12.16%，1 288/10 591），influenza A virus（8.31%，880/10 591），and respiratory syncytial virus（8.14%，862/10 591）. Epidemiological characteristics showed that：（1）In terms of age： Mycoplasma pneumoniae was more common in older children（29.55%，901/3 049，in the school-age group，χ 2 = 653.67， P<0.001）. Influenza A virus had a high incidence in the adolescent group（11.34%，45/397，χ 2=48.69， P<0.001）. Respiratory syncytial virus was most susceptible in the infant group（20.94%，280/1 337，χ 2=739.92， P<0.001）. Human rhinovirus showed the characteristic of general susceptibility across all ages.（2）Monthly and seasonal distribution： Mycoplasma pneumoniae had a seasonal epidemic in summer and autumn（it began to rise in May and peaked in October at 34.22%，439/1 283）；influenza A virus had a bimodal distribution in spring and winter（the peak was 37.15% in March，315/848）；respiratory syncytial virus had a dominant epidemic in spring and summer（the detection rate was 21.24% in May，206/970），and human rhinovirus was prevalent throughout the year.（3）Clinical correlation：The detection rate of pathogens in the severe pneumonia group was significantly higher than that in the non-severe group：84.19%（426/506） vs 2.89%（5 334/10 085），χ 2=56.23， P<0.001. Conclusions:In 2023，the pathogen spectrum of hospitalized children with acute respiratory infections in the Shanghai area exhibits an epidemic pattern dominated by Mycoplasma pneumoniae，and its transmission dynamics are significantly age-dependent. This study delineates the pathogen-host-environment tripartite interactions，establishing an evidence-based foundation for formulating precision diagnostic-therapeutic algorithms and seasonal nosocomial infection prevention frameworks.

Objective:To analyze the distribution and epidemiological characteristics of non-bacterial pathogens in hospitalized children with acute respiratory infections at a tertiary pediatric hospital in Shanghai during 2023.Methods:A retrospective study was conducted on 10 591 children with acute respiratory tract infections who were hospitalized in Shanghai Children's Hospital from January to December 2023. A multiplex PCR combined with capillary electrophoresis platform was used to detect 11 common non-bacterial respiratory pathogens（including viruses and atypical pathogens）. Statistical analysis was carried out using SPSS 29.0 software. Qualitative data were presented as numbers and percentages，and the Chi-square test was employed to make comparisons between groups，aiming to analyze the differences in the distribution of different pathogens according to gender，age group，and season. Additionally，based on the severity of the disease，patients were calssified into a severe pneumonia group and a non-severe pneumonia group to further explore the characteristics of the pathogen spectrum of severe pneumonia.Results:The total detection rate of pathogens was 54.39%（5 760/10 591），and the proportion of mixed infections was 12.76%（735/5 760）. The dominant pathogens and their proportions were as follows： Mycoplasma pneumoniae（19.20%，2 034/10 591），human rhinovirus（12.16%，1 288/10 591），influenza A virus（8.31%，880/10 591），and respiratory syncytial virus（8.14%，862/10 591）. Epidemiological characteristics showed that：（1）In terms of age： Mycoplasma pneumoniae was more common in older children（29.55%，901/3 049，in the school-age group，χ 2 = 653.67， P<0.001）. Influenza A virus had a high incidence in the adolescent group（11.34%，45/397，χ 2=48.69， P<0.001）. Respiratory syncytial virus was most susceptible in the infant group（20.94%，280/1 337，χ 2=739.92， P<0.001）. Human rhinovirus showed the characteristic of general susceptibility across all ages.（2）Monthly and seasonal distribution： Mycoplasma pneumoniae had a seasonal epidemic in summer and autumn（it began to rise in May and peaked in October at 34.22%，439/1 283）；influenza A virus had a bimodal distribution in spring and winter（the peak was 37.15% in March，315/848）；respiratory syncytial virus had a dominant epidemic in spring and summer（the detection rate was 21.24% in May，206/970），and human rhinovirus was prevalent throughout the year.（3）Clinical correlation：The detection rate of pathogens in the severe pneumonia group was significantly higher than that in the non-severe group：84.19%（426/506） vs 2.89%（5 334/10 085），χ 2=56.23， P<0.001. Conclusions:In 2023，the pathogen spectrum of hospitalized children with acute respiratory infections in the Shanghai area exhibits an epidemic pattern dominated by Mycoplasma pneumoniae，and its transmission dynamics are significantly age-dependent. This study delineates the pathogen-host-environment tripartite interactions，establishing an evidence-based foundation for formulating precision diagnostic-therapeutic algorithms and seasonal nosocomial infection prevention frameworks.

Objective:To investigate the value of tumor volume to uterine volume ratio (N/U) combined with the expression of alpha-fetoprotein (AFP), sugar antigen 199 (CA199) and human epididymal secretory protein 4 (HE4) in evaluating the pathologic grade and prognosis of endometrial carcinoma (EC) .Methods:A total of 160 EC patients admitted to Linyi Central Hospital from Jan. 2021 to Dec. 2023 were divided into low-grade group and high-grade group according to FIGO grading method, and were divided into poor prognosis group and good prognosis group according to cancer death, recrudescence. The levels of N/U, AFP, CA199 and HE4 in patients with different pathologic grades and prognosis were compared. COX regression was used to analyze the influencing factors of EC adverse prognosis, ROC curve was used to analyze the value of N/U combined with serum AFP, CA199 and HE4 in predicting EC adverse prognosis, and a nomogram model was constructed.Results:Pathological examination of 160 EC patients showed that 12 cases were non-endometrioid adenocarcinoma, 148 cases were endometrioid adenocarcinoma, 41 cases were high-grade and 119 cases were low-grade.According to the follow-up, 94 of the 160 EC patients had good prognosis and 66 had poor prognosis. The levels of N/U, AFP, CA199 and HE4 in the poor prognosis group were higher than those in the good prognosis group ( P<0.05). COX regression analysis showed that high levels of N/U, AFP, CA199 and HE4 were all factors affecting the poor prognosis of EC patients ( P<0.05). The AUC value of combined detection of N/U, AFP, CA199 and HE4 in predicting adverse prognosis of EC patients was higher than that of single detection ( Z=3.140, 3.658, 4.277, 4.378, P<0.05) .The ROC curve AUC (95% CI) of the training set and the validation set were 0.84 (0.77-0.92) and 0.90 (0.81-0.98) respectively for the training set and the validation set to predict the adverse prognosis of EC patients. Calibration curve results showed that the calibration curve for EC patients predicted by the nomogram model was close to the ideal curve ( P=0.521, 0.743). The DCA curve shows that the probability threshold of the nomogram model has a higher positive net return at 20%~100%. Conclusion:The levels of N/U, AFP, CA199 and HE4 in EC patients are related to the pathologic grade, and the combined detection of these indicators can predict the poor prognosis of EC patients, and the nomogram model constructed based on these indicators has high predictive value.

Objective:To investigate the value of tumor volume to uterine volume ratio (N/U) combined with the expression of alpha-fetoprotein (AFP), sugar antigen 199 (CA199) and human epididymal secretory protein 4 (HE4) in evaluating the pathologic grade and prognosis of endometrial carcinoma (EC) .Methods:A total of 160 EC patients admitted to Linyi Central Hospital from Jan. 2021 to Dec. 2023 were divided into low-grade group and high-grade group according to FIGO grading method, and were divided into poor prognosis group and good prognosis group according to cancer death, recrudescence. The levels of N/U, AFP, CA199 and HE4 in patients with different pathologic grades and prognosis were compared. COX regression was used to analyze the influencing factors of EC adverse prognosis, ROC curve was used to analyze the value of N/U combined with serum AFP, CA199 and HE4 in predicting EC adverse prognosis, and a nomogram model was constructed.Results:Pathological examination of 160 EC patients showed that 12 cases were non-endometrioid adenocarcinoma, 148 cases were endometrioid adenocarcinoma, 41 cases were high-grade and 119 cases were low-grade.According to the follow-up, 94 of the 160 EC patients had good prognosis and 66 had poor prognosis. The levels of N/U, AFP, CA199 and HE4 in the poor prognosis group were higher than those in the good prognosis group ( P<0.05). COX regression analysis showed that high levels of N/U, AFP, CA199 and HE4 were all factors affecting the poor prognosis of EC patients ( P<0.05). The AUC value of combined detection of N/U, AFP, CA199 and HE4 in predicting adverse prognosis of EC patients was higher than that of single detection ( Z=3.140, 3.658, 4.277, 4.378, P<0.05) .The ROC curve AUC (95% CI) of the training set and the validation set were 0.84 (0.77-0.92) and 0.90 (0.81-0.98) respectively for the training set and the validation set to predict the adverse prognosis of EC patients. Calibration curve results showed that the calibration curve for EC patients predicted by the nomogram model was close to the ideal curve ( P=0.521, 0.743). The DCA curve shows that the probability threshold of the nomogram model has a higher positive net return at 20%~100%. Conclusion:The levels of N/U, AFP, CA199 and HE4 in EC patients are related to the pathologic grade, and the combined detection of these indicators can predict the poor prognosis of EC patients, and the nomogram model constructed based on these indicators has high predictive value.

Objective To investigate the dosimetric effects of tumor treating fields(TTFields)patches on different radiotherapy modes for glioblastoma(GBM)patients who wear TTFields patches during radiotherapy,thereby providing dosimetric guidance for determining the appropriate radiotherapy mode.Methods With the TTFields data from GBM patients,artifact-free radiotherapy CT images were obtained utilizing 3D-printed TPU TTFields patches(3D-Print-TTFields)and anthropomorphic phantoms,and then a TTFields-synchronized radiotherapy image model was constructed.Furthermore,the treatment planning system was used to construct a dosimetric calculation model for TTFields-synchronized radiotherapy by simulating and fitting the ray attenuation rate of TTFields patches measured by accelerators.Using these models,3 kinds of radiotherapy plans were simulated and developed.Specifically,P1 simulated the conventional radiotherapy mode;P2 simulated the TTFields-combined radiotherapy mode(TTF-Com-RT),in which patients underwent radiotherapy using the P1 plan while wearing TTFields patches;and P3 simulated the TTFields-synchronized radiotherapy(TTF-Syn-RT)mode where the TTFields patches were worn throughout the entire radiotherapy process.The paired t-test was used to analyze dosimetric parameters such as target dose(D95),average scalp dose(D-skin),conformity index(CI)and homogeneity index(HI)in 3 plans(P1,P2,and P3),as well as the D95 and D-skin parameters for intensity-modulated radiotherapy(IMRT)and volumetric modulated arc therapy(VMAT)techniques in the P3 plan.Results The D95 simulated by P2 decreased by 1.35%as compared with P1(P<0.05),and the D95 simulated by P3 was 1.31%higher than that in P2(P<0.05).Compared with P1,P2 and P3 increased the D-skin by 12.56%and 14.30%,respectively(P<0.05),and the D-skin simulated by P3 increased by 1.55%as compared with P2(P<0.05).However,there were trivial differences in D95 between P3 and P1,CI and HI among all plans,D95 and D-skin between IMRT and VMAT techniques in P3 plan(P>0.05).Conclusion Based on GBM patient data,CT simulation images obtained from 3D-Print-TTFields combined with anthropomorphic phantom are artifact-free and meet radiotherapy requirements.The target and scalp dose differences between TTF-Com-RT and TTF-Syn-RT are less than 2%,and the dosimetric difference of TTF-Syn-RT using IMRT/VMAT techniques is insignificant.Therefore,clinicians can choose radiotherapy modes and techniques according to actual needs.

Objective To investigate the dosimetric effects of tumor treating fields(TTFields)patches on different radiotherapy modes for glioblastoma(GBM)patients who wear TTFields patches during radiotherapy,thereby providing dosimetric guidance for determining the appropriate radiotherapy mode.Methods With the TTFields data from GBM patients,artifact-free radiotherapy CT images were obtained utilizing 3D-printed TPU TTFields patches(3D-Print-TTFields)and anthropomorphic phantoms,and then a TTFields-synchronized radiotherapy image model was constructed.Furthermore,the treatment planning system was used to construct a dosimetric calculation model for TTFields-synchronized radiotherapy by simulating and fitting the ray attenuation rate of TTFields patches measured by accelerators.Using these models,3 kinds of radiotherapy plans were simulated and developed.Specifically,P1 simulated the conventional radiotherapy mode;P2 simulated the TTFields-combined radiotherapy mode(TTF-Com-RT),in which patients underwent radiotherapy using the P1 plan while wearing TTFields patches;and P3 simulated the TTFields-synchronized radiotherapy(TTF-Syn-RT)mode where the TTFields patches were worn throughout the entire radiotherapy process.The paired t-test was used to analyze dosimetric parameters such as target dose(D95),average scalp dose(D-skin),conformity index(CI)and homogeneity index(HI)in 3 plans(P1,P2,and P3),as well as the D95 and D-skin parameters for intensity-modulated radiotherapy(IMRT)and volumetric modulated arc therapy(VMAT)techniques in the P3 plan.Results The D95 simulated by P2 decreased by 1.35%as compared with P1(P<0.05),and the D95 simulated by P3 was 1.31%higher than that in P2(P<0.05).Compared with P1,P2 and P3 increased the D-skin by 12.56%and 14.30%,respectively(P<0.05),and the D-skin simulated by P3 increased by 1.55%as compared with P2(P<0.05).However,there were trivial differences in D95 between P3 and P1,CI and HI among all plans,D95 and D-skin between IMRT and VMAT techniques in P3 plan(P>0.05).Conclusion Based on GBM patient data,CT simulation images obtained from 3D-Print-TTFields combined with anthropomorphic phantom are artifact-free and meet radiotherapy requirements.The target and scalp dose differences between TTF-Com-RT and TTF-Syn-RT are less than 2%,and the dosimetric difference of TTF-Syn-RT using IMRT/VMAT techniques is insignificant.Therefore,clinicians can choose radiotherapy modes and techniques according to actual needs.

OBJECTIVE: To identify the optimal machine learning algorithm for predicting post-stroke epilepsy (PSE) within one year following acute stroke, establish a nomogram model based on this algorithm, and perform external validation to achieve accurate prediction of secondary epilepsy. METHODS: A total of 870 acute stroke patients admitted to the emergency department of Xiang'an Hospital of Xiamen University from June 2019 to June 2023 were enrolled for model development (model group). An external validation cohort of 435 acute stroke patients admitted to the Fifth Hospital of Xiamen during the same period was used to validate the machine learning algorithms and nomogram model. Patients were classified into control and epilepsy groups based on the development of PSE within one year. Clinical and laboratory data, including baseline characteristics, stroke location, vascular status, complications, hematologic parameters, and National Institutes of Health Stroke Scale (NIHSS) score, were collected for analysis. Nine machine learning algorithms such as logistic regression, CN2 rule induction, K-nearest neighbors, adaptive boosting, random forest, gradient boosting, support vector machine, naive Bayes, and neural network were applied to evaluate predictive performance. The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) was used to identify the optimal algorithm. Logistic regression was used to screen risk factors for PSE, and the top 10 predictors were selected to construct the nomogram model. The predictive performance of the model was evaluated using the ROC curve in both the model and validation groups. RESULTS: Among the 870 patients in the model group, 29 developed PSE within one year. Among the nine algorithms tested, logistic regression demonstrated the best performance and generalizability, with an AUC of 0.923. Univariate logistic regression identified several risk factors for PSE, including platelet count, white blood cell count, red blood cell count, glycated hemoglobin (HbA1c), C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen, creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), serum sodium, lactic acid, anion gap, NIHSS score, brain herniation, periventricular stroke, and carotid artery plaque. Further multivariate logistic regression analysis showed that white blood cell count, HDL, fibrinogen, lactic acid and brain herniation were independent risk factors [odds ratio (OR) were 1.837, 198.039, 47.025, 11.559, 70.722, respectively, all P < 0.05]. In the external validation group, univariate logistic regression analysis showed that platelet count, white blood cell count, CRP, triacylglycerol, APTT, D-dimer, fibrinogen, CK, CK-MB, LDH, NIHSS score, and cerebral herniation were risk factors for PSE one year after acute stroke. Further multiple logistic regression analysis showed that APTT and cerebral herniation were independent predictors (OR were 0.587 and 116.193, respectively, both P < 0.05). The nomogram model, constructed using 10 key variables-brain herniation, periventricular stroke, carotid artery plaque, white blood cell count, triglycerides, thrombin time, D-dimer, serum sodium, lactic acid, and NIHSS score-achieved an AUC of 0.908 in the model group and 0.864 in the external validation group. CONCLUSIONS The logistic regression-based prediction model for epilepsy one year after acute stroke, developed using machine learning algorithms, showed optimal predictive performance. The nomogram model based on the logistic regression-derived predictors showed strong discriminative power and was successfully validated externally, suggesting favorable clinical applicability and generalizability.
Humans ; Machine Learning ; Stroke/complications* ; Nomograms ; Epilepsy/etiology* ; Algorithms ; Male ; Female ; Logistic Models ; Middle Aged ; Aged ; Risk Factors ; Bayes Theorem

Objective:To investigate the clinical characteristics of children with severe human metapneumovirus (HMPV) infection and identify the risk factors associated with critical illness.Methods:A retrospective cohort study was conducted, enrolling 157 hospitalized children with severe HMPV infection, who tested positive for HMPV nucleic acid via PCR-capillary electrophoresis fragment analysis of nasopharyngeal secretions at Shanghai Children′s Hospital from January 2021 to December 2023.Clinical features, co-infections, treatment, and outcomes were collected. Based on the diagnostic criteria for severe HMPV infection, the patients were categorized into a critical illness group and a non-critical illness group. Intergroup comparisons were performed using the χ2 test or the Mann-Whitney U test. Multivariate Logistic regression analysis was employed to identify risk factors for critical HMPV infection and to establish a predictive model.The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis and calibration curves. Results:Among the 157 cases of severe HMPV infection, there were 67 males and 90 females, with an onset age of 39.0 (20.0, 55.5) months. Single-pathogen infection was observed in 125 cases (79.6%), while mixed infections accounted for 32 cases (20.4%).Severe pneumonia was diagnosed in 136 cases (86.6%).The predominant manifestations of severe HMPV infection included fever 152 cases (96.8%), cough 151 cases (96.2%), and wheezing 94 cases (59.9%).Sixty-eight patients (43.3%) required non-invasive respiratory support, 58 cases (36.9%) were admitted to the intensive care unit, and 22 cases (14.0%) underwent mechanical ventilation. Of the total, 149 cases (94.9%) were discharged with improvement, 8 cases (5.1%) were discharged against medical advice, and there were no fatal cases. The cohort was further stratified into a critical illness group 31 cases and a non-critical illness group 126 cases. Compared to the non-critical illness group, the critical illness group exhibited significantly higher rates of respiratory distress, lethargy, and intercostal retractions, along with a higher proportion of underlying comorbidities, and elevated levels of C-reactive protein and procalcitonin (all P<0.05).Conversely, albumin and hemoglobin levels were significantly lower in the critical illness group (both P<0.05). ROC curve analysis revealed that the optimal cutoff value for the duration of fever in predicting severe HMPV infection was 4.5 days.The multivariate binary Logistic regression analysis revealed that prolonged fever duration (>4.5 days) ( OR=28.00, 95% CI 5.09-153.93, P<0.001), anorexia ( OR=11.72, 95% CI 1.26-108.75, P=0.030), and immune dysfunction ( OR=36.71, 95% CI 1.55-867.31, P=0.026) were independent risk factors for severe HMPV infection. A predictive model for critical illness was constructed based on these independent risk factors. ROC curve analysis demonstrated excellent discriminative ability, with an area under the curve of 0.96 (95% CI 0.92-1.00, P<0.001). The optimal predictive probability threshold was 0.17, yielding a sensitivity of 0.93 and specificity of 0.92. The calibration curve closely approximated the ideal curve, indicating good model calibration ( P=0.157). Conclusions:Severe HMPV infection is predominantly observed as a single infection and is prone to progress to severe pneumonia, with fever, cough, and wheezing as the main clinical manifestations. A subset of cases progresses to critical illness, though the overall prognosis is favorable. Prolonged fever duration (>4.5 days), anorexia, and immune dysfunction were independent risk factors for critical illness.The risk prediction model constructed for pediatric critical HMPV infection demonstrated robust discriminative ability with excellent calibration.

Objective:To analyze the distribution of tumor necrosis factor-alpha（TNF-α）308 gene loci polymorphism in children with febrile seizures（FS）and to explore the correlation between TNF-α 308 gene polymorphisms and FS in children.Methods:A total of 320 children diagnosed with FS in the Department of Emergency，Shanghai Children's Hospital from September 1st，2020 to June 30th，2021 were enrolled as the study subjects，which were divided into simple febrile seizures（SFS）group（232 cases）and complex febrile seizures（CFS）group（88 cases）based on their clinical characteristics，and the clinical characteristics and laboratory indexes of the two groups were compared. Children with no history of convulsions were selected as the control group（160 cases）. The high-resolution melting and gene sequencing technology were used to analyze the polymorphism of TNF-α 308 gene in each group and the distribution of different gene types and allele frequencies among the groups was compared. A multivariate Logistic regression model was constructed to analyze the relationship between TNF-α 308 gene polymorphism and FS.Results:The age，mean corpuscular volume，mean corpuscular hemoglobin and platelet distribution width of the CFS group were significantly higher than those in the SFS group，and the difference was statistically significant（all P<0.05）.There was no significant difference in gender distribution，family history of FS，history of FS，body temperature at time of convulsions，WBC，Hb，CRP and PLT between the two groups（all P>0.05）.The genotype frequency distribution of TNF-α 308 polymorphism in the three groups was in line with the Hardy-Weinberg equilibrium（ P>0.05）.The AA genotype of TNF-α 308 locus was not detected in the study.Compared with the control group［17 cases（10.6%）］，the distribution proportion of GA genotype in the CFS group［22cases（25.0%）］and the SFS group［52cases（22.4%）］was increased，and the difference was statistically significant（ χ2＝11.126， P＝0.004）；Compared with the control group［17 frequencies（5.3%）］，the frequency distribution proportion of allele A in the CFS group［22 frequencies（12.5%）］and SFS group［52 frequencies（11.2%）］was also increased，and the difference was statistically significant（ χ2＝9.960， P＝0.007）. Adding control factors such as gender，age，family history of FS，body temperature at time of convulsions and blood routine markers，the multivariate Logistic regression model was constructed to show that there was no statistically significant association between TNF-α 308 genotype and CFS in children（ OR＝1.805，95% CI：0.926~3.519， P＝0.083）. Conclusion:In this study，there was no significant correlation between TNF-α 308 gene loci polymorphism and CFS in children.