OBJECTIVES: To study the difference in intestinal flora between children with focal epilepsy and healthy children and the change in intestinal flora after treatment in children with epilepsy. METHODS: A total of 10 children with newly diagnosed focal epilepsy were recruited as the case group and were all treated with oxcarbazepine alone. Their clinical data were recorded. Fecal specimens before treatment and after 3 months of treatment were collected. Fourteen aged-matched healthy children were recruited as the control group. Total bacterial DNA was extracted from the fecal specimens for 16S rDNA sequencing and bioinformatics analysis. RESULTS: After 3 months of carbamazepine treatment, the seizure frequency was reduced by >50% in the case group. At the phylum level, the abundance of Actinobacteria in the case group before treatment was significantly higher than that in the control group (P<0.05), and it was reduced after treatment (P<0.05). At the genus level, the abundances of Escherichia/Shigella, Streptococcus, Collinsella, and Megamonas in the case group before treatment were significantly higher than those in the control group (P<0.05), and the abundances of these bacteria decreased significantly after treatment (P<0.05). CONCLUSIONS There is a significant difference in intestinal flora between children with focal epilepsy and healthy children. Oxcarbazepine can significantly improve the symptoms and intestinal flora in children with epilepsy.
Aged ; Bacteria/genetics* ; Child ; DNA, Bacterial ; Epilepsies, Partial/drug therapy* ; Gastrointestinal Microbiome ; Humans ; RNA, Ribosomal, 16S/genetics*

OBJECTIVESTo evaluate the diagnostic value of arterial spin labeling (ASL) technology in newborns with hypoxic ischemic encephalopathy (HIE).

METHODSeven full-term newborn infants without any history of asphyxia and other nervous system diseases were selected as the control and 33 full-term newborn infants were assigned into HIE group. The patients in HIE group were further divided into three subgroups (19 cases of mild, 6 cases of moderate and 8 cases of severe HIE) based on their clinical diagnosis. The control group and HIE group were examined with GE Signa EXCITE HD 3.0T superconducting MRI scanner with a head phase array coil. Both groups were scanned with conventional axial MRI (T1FLAIR, T2WI and T2FLAIR), 1HMRS (PRESS sequence) and ASL (FAIR). Original images of 1HMRS and ASL were processed by Functool software of ADW 4.3 workstation. ASL perfusion images were observed and the signal intensity values of the region of interest (bilateral gray, white matter and basal ganglia) of the two groups were quantitatively measured, and mean value were calculated and compared between groups. Statistical analysis was performed with SPSS 13.0 software, and statistically significant difference was set at P < 0.05.

RESULTThe perfusion images of two groups were obtained perfectly. The signal intensity values of bilateral gray, white matter and basal ganglia of control group were 125.34 ± 11.76, 73.42 ± 11.67 and 173.65 ± 15.49, respectively and there was a statistically significant difference between the different areas. The signal intensity values of bilateral gray, white matter and basal ganglia of HIE group were 153.47 ± 11.72, 71.35 ± 10.37 and 217.13 ± 12.51, respectively. There was a statistically significant difference (P < 0.05) in the average signal intensity value of gray matter and basal ganglia, but there were no statistically significant difference (P > 0.05) in white matter between the two groups.

CONCLUSIONASL Perfusion technique can assess HIE comprehensively and accurately. Furthermore, it can evaluate the brain damage of hypoxic ischemia. The results provide a strong basis for clinical treatment.

Case-Control Studies ; Electron Spin Resonance Spectroscopy ; Female ; Humans ; Hypoxia-Ischemia, Brain ; diagnosis ; Infant, Newborn ; Male ; Spin Labels

BACKGROUNDVarious etiologies that contribute to the loss of pregnancy have been proposed. Despite the lack of established and uniform screening methods for recurrent pregnancy loss (RPL), there are several factors considered to be definite (or probable) causes of RPL. Data describing the etiological characteristics of RPL consist almost entirely of Caucasian populations. As we were interested in the Chinese population, the goal of this study was to determine the etiological characteristics of RPL in the Chinese patients.

METHODSThe study was performed retrospectively by analyzing 1122 women with RPL. Patients were divided into three groups according to their number of pregnancy losses. Diagnostic tests included the following 9 critiria: parental genetics, uterine anatomy, autoimmune factors, alloimmune factors, thrombophilic factors, endocrine parameters, genital infection, toxoplasma, rubella, cytomegalovirus, herpes (TORCH) titers and RH blood groups. The criteria for abnormal results were defined before diagnosis.

RESULTSWe found that 87.1% (977/1122) patients had no more than 3 abnormal aspects, and the proportion of total abnormal results was similar among groups. The prevalence of abnormal results for each test did not differ among groups, except in the cases of parental genetics, uterine anatomy and presence of mixed lymphocyte reaction blocking antibodies (MLR-Bf). Absence of MLR-Bf, as well as abnormally increased levels of CD3⁺CD19⁺ and CD56⁺CD16⁺ cells, was commonly detected in Chinese RPL patients.

CONCLUSIONImmunological disorders play an important role in RPL among Chinese patients.

Abortion, Habitual ; etiology ; Adult ; Female ; Humans ; Pregnancy ; Retrospective Studies ; Young Adult

BACKGROUND: The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images. METHODS: Taking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR-), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists. RESULTS: The accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87-0.91, 0.89-0.92, 0.87-0.91, and 0.86-0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%). CONCLUSIONS: The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists. TRIAL REGISTRATION Chictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.
Area Under Curve ; Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; China ; Deep Learning ; Humans ; ROC Curve ; Sensitivity and Specificity