Objective To investigate the influence of neutrophil-lymphocyte ratio (NLR) on radiosensitivity and prognosis,the relationship between NLR and clinical features,and the clinical value of NLR in patients with nasopharyngeal carcinoma (NPC).Methods 2006 to 2011 in the cancer center of Zhongshan University admitted to the newly diagnosed nasopharyngeal cancer patients in 266 cases.The association of pretreatment NLR with radiotherapy doses 20,40,and 60 Gy and therapeutic effect at 3 months after radiotherapy was analyzed,as well as the influence of NLR on overall survival (OS),local recurrence-free (LRF),and distant metastasis-free (DMF) rates.The Kaplan-Meier method was used to calculate survival rates and the log-rank test was used for survival difference analysis.Results NLR showed differences across patients with different T stages and sexes (P=0.039,0.032).The patients with NLR≤3 had significantly higher OS,LRF,and DMF rates compared with those with NLR＞ 3 (P=0.004,0.025,0.045).As NLR increased,the radiosensitivity in patients with NPC was reduced gradually,and radiosensitivity showed a significant difference between sensitive group and moderately sensitive group (P=0.043).When the radiotherapy dose was 40 Gy,the tumor regression group had a lower NLR than the residual tumor group (P=0.025).Conclusions In patients with NPC,an increased pretreatment NLR is an adverse prognostic factor,and NLR can be used as a simple and convenient method to evaluate the prognosis of patients with NPC.

Objective:To detect the genes of common genetic diseases in newborns with the high-throughput sequencing technology based on target gene capture, to study the incidence rate of such diseases, the carrying rate and variant types of pathogenic mutations related to such diseases, and to explore the application value of the high-throughput sequencing technology in screening genetic diseases of newborns.Methods:The heel blood of 1 793 newborns born in Guangdong province from June 2019 to April 2020 were collected, and the exon regions of 138 common genetic disease-related genes in neonates were detected using the high-throughput sequencing technology based on target gene capture.The pathogenicity of the mutations was interpreted according to the " Classification Criteria and Guidelines for Genetic Variation(2017)" , in which known disease and probable disease were considered as positive mutations.The positive mutations were verified by Sanger sequencing technology, and the test results were analyzed with statistical methods.Results:Among the 1 793 newborns, 978 were male and 815 were female.A total of 158 positive cases were screened(8.81%), and 11 positive diseases were detected.Among the positive diseases, there were 41 cases(2.29%)of autosomal recessive deafness type 1A, 40 cases(2.23%)of Gilbert syndrome or Crigler-Najjar syndrome, and 33 cases(1.84%)of glucose-6-phosphate dehydrogenase deficiency(1.84%), 19 cases(1.06%)of familial hypercho-lesterolemia, 18 cases(1.00%) of sodium taurocholate cotransporter peptide deficiency disease, 2 cases(0.11%)of mitochondrial non-syndromic deafness, 2 cases(0.11%)of Citrin deficiency, 1 case(0.06%)of holocarboxylase synthase deficiency, 1 case(0.06%)of β-thalassemia and 1 case(0.06%)of metachromatic leukodystrophies.Of all studied cases, 972 carried one or more positive mutations, involving 85 kinds of diseases in total.The diseases with a high carrying rate were Gilbert syndrome or Crigler-Najjar syndrome(359 cases, 20.02%), autosomal recessive deafness type 1A(302 cases, 16.84%), and sodium taurocholate cotransport peptide deficiency disease(291 cases, 16.22%). The high-frequency mutation sites were UGT1A1 gene c. 211G> A, GJB2 gene c .109G> A and SLC10A1 gene c. 800C> T. Conclusions:The common genetic diseases detected in neonates from Guangdong province are autosomal recessive deafness type 1A, Gilbert syndrome or Crigler-Najjar syndrome, glucose-6-phosphate dehydrogenase deficiency, familial hypercholesterolemia, and sodium taurocholate cotransport peptide deficiency.There are high-frequency carrying mutation sites in the population.Preliminary genetic screening of common neonatal genetic diseases can accumulate data and experience for the development of newborn genetic screening.

Objective:To explore the value of different radiomics models based on machine learning in predicting the risk of distant recurrence and metastasis of triple-negative breast cancer after neoadjuvant therapy.Methods:The clinical and imaging data of 150 patients with triple-negative breast cancer (TNBC) confirmed by histopathology were retrospectively analyzed. All patients underwent neoadjuvant chemotherapy and surgical resection from August 2011 to May 2017 in Fudan University Shanghai Cancer Center and Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. One hundred and nine patients from Shanghai Fudan University Shanghai Cancer Center were used as the training group, and 41 patients from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine were used as the validation group. The features were extracted from dynamic contrast-enhanced MRI (DCE-MRI) before treatment and were added with time domain features innovatively. Least absolute shrinkage and selection operator cross validation and recursive feature elimination were applied to select features. Six different supervised machine learning algorithms (logistic regression, linear discriminant analysis, k-nearest neighbor, naive bayesian, decision tree, support vector machine) were used to predict the prognosis. ROC curve, accuracy and F1 measure were used to evaluate the performance of the six algorithms, and also verified by the validation group.Results:The support vector machine algorithm had the best predictive effect in the recurrence and metastasis model based on 15 features, with the highest area under curve (training group was 0.917, validation group was 0.859), and the highest accuracy rate (training group was 87.5%, validation group was 82.9%) and the highest F1 measure (training group was 0.800, validation group was 0.741). In addition, of the 15 imaging features, 12 were the time domain features and 3 were spatial features.Conclusion:With the help of the time domain features and machine learning algorithms, radiomics signatures based on preoperative DCE-MRI can help predict the distant prognosis for TNBC after neoadjuvant chemotherapy and provide support for clinical decision making and follow-up management.