This study was designed to investigate the association of genetic polymorphisms of cytochrome P450 subtype 2E1 (CYP2E1) and glutathione S-transferase mu 1 (GSTM1) with susceptibility to antituberculosis drug-induced hepatotoxicity (ADIH) in Chinese tuberculosis patients. All patients were treated with a combination of isoniazid, rifampicin, pyrazinamide and ethambutol. Genomic DNA from 104 patients with ADIH and 111 without ADIH was analysed for the frequency of CYP2E1 RsaI and GSTM1 RsaI genotypes by polymerase chain reaction and restriction fragment length polymorphism. The association of polymorphisms with susceptibility to ADIH was calculated using the chi(2)-test and logistic regression analysis. The CYP2E1 RsaI polymorphisms were significantly associated with ADIH and the c1/c1 genotype was an independent risk factor for ADIH. Compared with the GSTM1 RsaI present genotype, the GSTM1 RsaI null genotype tended to increase susceptibility to ADIH, but the association with ADIH was not significant. The results indicate that CYP2E1 RsaI genotype c1/c1 is a potential risk factor for ADIH in the Chinese population. The tendency of the GSTM1 RsaI null genotype to increase susceptibility to ADIH needs further study.

Mitochondrial myopathy encephalopathy lactic acidosis and stroke-like episodes (MELAS) is an important cause of stroke-mimicking diseases that predominantly affect patients before 40 years of age. MELAS results from gene mutations in either mitochondrial DNA (mtDNA) or nuclear DNA (nDNA) responsible for the wide spectrum of clinical symptoms and imaging findings. Neurological manifestations can present with stroke-like episodes (the cardinal features of MELAS), epilepsy, cognitive and mental disorders, or recurrent headaches. Magnetic resonance imaging (MRI) is an important tool for detecting stroke-like lesions, accurate recognition of imaging findings is important in guiding clinical decision making in MELAS patients. With the development of neuroimaging technologies, MRI plays an increasingly important role in course monitoring and efficacy assessment of the disease. In this article, we provide an overview of the neuroimaging features and the application of novel MRI techniques in MELAS syndrome.

Background: Early identification and prevention of hypoxic-ischemic encephalopathy (HIE) in newborns may reduce neonatal mortality and neurological dysfunction. Objective: To analyze the diagnostic and prognostic values of urinary S100B level and lactate/creatinine ratio in newborns with HIE. Methods: Seventy-eight full-term newborns with HIE and 25 normal newborns were enrolled. The Neonatal Behavioral Neurological Assessment (NBNA) and Developmental Screening Test were scored. The concentration of urinary S100B protein was determined using the S100B enzyme-linked immunosorbent assay and the levels of urinary lactate and creatinine were measured with the enzyme colorimetric method. Results: Urinary S100B level on days 1–3 after birth and lactate/creatinine ratio on day 1 were significantly higher in newborns with HIE than those in the control group. Both indexes were positively correlated with the clinical grading of HIE. A cutoff value for the S100B level of 0.47 μg/l on day 3 after birth had a sensitivity of 90% and specificity of 92% for prediction of HIE. A lactate/creatinine ratio of more than 0.55 on day 1 showed the highest sensitivity (92%) and specificity (90%). A combination of both indexes improved the sensitivity and specificity to 99 and 97%, respectively. A negative correlation of both lactate/creatinine ratio on day 1 and S100B level on days 1–3 after birth with the NBNA score was identified on days 3, 7 and 14 after birth. The Developmental Screening Test score of 36 newborns with HIE within 6 months after birth showed that 65% of infants with moderate and high HIE had an abnormal developmental quotient. Conclusion: These data suggest that early measurement of both S100B level and lactate/creatinine ratio in the urine of newborns with HIE is a practical convenient and sensitive way to improve diagnosis on the third day of life and prognostic prediction of HIE.

PurposeMYCN amplification plays a critical role in defining high-risk subgroup of patients with neuroblastoma. We aimed to develop and validate the CT-based machine learning models for predicting MYCN amplification in pediatric abdominal neuroblastoma.MethodsA total of 172 patients with MYCN amplified (n = 47) and non-amplified (n = 125) were enrolled. The cohort was randomly stratified sampling into training and testing groups. Clinicopathological parameters and radiographic features were selected to construct the clinical predictive model. The regions of interest (ROIs) were segmented on three-phrase CT images to extract first-, second- and higher-order radiomics features. The ICCs, mRMR and LASSO methods were used for dimensionality reduction. The selected features from the training group were used to establish radiomics models using Logistic regression, Support Vector Machine (SVM), Bayes and Random Forest methods. The performance of four different radiomics models was evaluated according to the area under the receiver operator characteristic (ROC) curve (AUC), and then compared by Delong test. The nomogram incorporated of clinicopathological parameters, radiographic features and radiomics signature was developed through multivariate logistic regression. Finally, the predictive performance of the clinical model, radiomics models, and nomogram was evaluated in both training and testing groups.ResultsIn total, 1,218 radiomics features were extracted from the ROIs on three-phrase CT images, and then 14 optimal features, including one original first-order feature and eight wavelet-transformed features and five LoG-transformed features, were identified and selected to construct the radiomics models. In the training group, the AUC of the Logistic, SVM, Bayes and Random Forest model was 0.940, 0.940, 0.780 and 0.927, respectively, and the corresponding AUC in the testing group was 0.909, 0.909, 0.729, 0.851, respectively. There was no significant difference among the Logistic, SVM and Random Forest model, but all better than the Bayes model (p <0.005). The predictive performance of the Logistic radiomics model based on three-phrase is similar to nomogram, but both better than the clinical model and radiomics model based on single venous phase.ConclusionThe CT-based radiomics signature is able to predict MYCN amplification of pediatric abdominal NB with high accuracy based on SVM, Logistic and Random Forest classifiers, while Bayes classifier yields lower predictive performance. When combined with clinical and radiographic qualitative features, the clinics-radiomics nomogram can improve the performance of predicting MYCN amplification.