Application of hepatitis B immunoglobulin in prevention of mother-to-child transmission of chronic hepatitis B in HBsAg- and HBeAg-positive mother.

The aim of our study was to compare the efficacy of two dosages of hepatitis B immunoglobulin (HBIG) combined with HBV vaccine (HBVac) to prevent mother-to-child transmission (MTCT) of hepatitis B in HBsAg- and HBeAg-positive mother. We enrolled 331 mother-infant pairs with HBsAg- and HBeAg-positive maternal state from the Women's Hospital School of Medicine of Zhejiang University. Newborns were randomly distributed into two groups according to the dosages of HBIG injection: 100 IU and 200 IU. Newborns from both groups were injected with HBVac in the same doses. We compared the immune outcomes between the two groups and explore the influencing factors of immune outcomes through regression analysis. There was no statistically significant relationship between HBsAg serological transmission of newborns and dosages of HBIG in HBsAg- and HBeAg-positive mother (p > .05). The Logistic regression showed that high DNA load is a risk factor for passive-active immunoprophylaxis failure for both 100 IU and 200 IU group, but higher-dosage HBIG is not necessary for higher-viral-load pregnant women with HBsAg- and HBeAg-positive. In conclusion, combined application of HBVac and a single dose of 100 IU HBIG can achieve the ideal MTCT interruption results for HBsAg- and HBeAg-positive pregnant women.IMPACT STATEMENTWhat is already known on this subject? Passive-active immunoprophylaxis is proved to be effective in preventing mother-to-child transmission of hepatitis B. Hepatitis B vaccine combined with 100 IU or 200 IU immunoglobulin is mostly recommended in China.What do the results of this study add? At present, there is still a lack scientific basis for improving existing strategies and measures to prevent mother-to-child transmission of hepatitis B in China.What are the implications of these findings for clinical practice and/or further research? 100 IU and 200 IU immunoglobulin show equivalent blocking effect, and combined use of hepatitis B vaccine and 100 IU immunoglobulin is more cost-effective.

Diagnostic performance evaluation of adult Chiari malformation type I based on convolutional neural networks.

PURPOSE: This study aimed to evaluate the diagnostic performance of convolutional neural network (CNN) models in Chiari malformation type I (CMI) and to verify whether CNNs can identify the morphological features of the craniocervical junction region between patients with CMI and healthy controls (HCs). To date, numerous indicators based on manual measurements are used for the diagnosis of CMI. However, the corresponding postoperative efficacy and prognostic evaluations have remained inconsistent. From a diagnostic perspective, CNN models may be used to explore the relationship between the clinical features and image morphological parameters. METHODS: This study included a total of 148 patients diagnosed with CMI at our institution and 205 HCs were included. T1-weighted sagittal magnetic resonance imaging (MRI) images were used for the analysis. A total of 220 and 355 slices were acquired from 98 patients with CMI and 155 HCs, respectively, to train and validate the CNN models. In addition, median sagittal images obtained from 50 patients with CMI and 50 HCs were selected to test the models. We applied original cervical MRI images (CI) and images of posterior cranial fossa and craniocervical junction area (CVI) to train the CI- and CVI-based CNN models. Transfer learning and data augmentation were used for model construction and each model was retrained 10 times. RESULTS: Both the CI- and CVI-based CNN models achieved high diagnostic accuracy. In the validation dataset, the models had diagnostic accuracy of 100% and 97% (p = 0.005), sensitivity of 100% and 98% (p = 0.016), and specificity of 100% (p = 0.929), respectively. In the test dataset, the accuracy was 97% and 96% (p = 0.25), sensitivity was 97% and 92% (p = 0.109), and specificity was 100% (p = 0.123), respectively. For patients with cerebellar subungual herniation less than 5 mm, three out of the 10 CVI-based retrained models reached 100% sensitivity. CONCLUSIONS: Our results revealed that the CNN models demonstrated excellent diagnostic performance for CMI. The models had higher sensitivity than the application of cerebellar tonsillar herniation alone and could identify features in the posterior cranial fossa and craniocervical junction area of patients. Our preliminary experiments provided a feasible method for the diagnosis and study of CMI using CNN models. However, further studies are needed to identify the morphologic characteristics of patients with different clinical outcomes, as well as patients who may benefit from surgery.