COVID-19 Omicron variant infection has minimal impact on maternal and neonatal outcomes: A cross-sectional cohort study.

AIM: To explore the impact of the Omicron variant on maternal and neonatal outcomes. DESIGN: Cross-sectional cohort study of women giving live birth in a single hospital in Shanghai in December 2022. METHODS: Demographic characteristics, maternal and neonatal outcomes and laboratory testing results were retrieved from medical records. Propensity score matching was used to match COVID-19-positive and -negative women. Differential analysis was used to assess associations between COVID-19 and in-hospital maternal and neonatal outcomes. RESULTS: A total of 1508 women were included, comprising 729 natural births, 741 caesarean sections and 38 forceps deliveries. After 1:1 matching, 310 clients were included for analysis with each 155 in COVID-19-positive and -negative groups. Higher maternal fever was found in all modes of delivery, and higher preterm birth and lower pH value of blood gas of the umbilical artery in the vaginal delivery subgroup (p < 0.05). Other maternal and neonatal outcomes showed no significant difference between COVID-19-positive and -negative clients.

A heterozygous pathogenic variant in the ATP6V1A gene triggering epilepsy in a large Chinese pedigree.

Epilepsy is one of the most common disorders in children, with an incidence rate of approximately 5%. Although an increasing number of genes have been demonstrated to be pathogenic factors in epilepsy, evidence for a potential pathogenic role of ATP6V1A remains limited. Herein, the clinical and genetic data of a 5-year-old boy who experienced seizures at 9 months of age are collected. Genetic variants are screened using whole-exome sequencing (WES), and the effects of the candidate variants are further validated at both the RNA and protein levels. WES reveals a heterozygous variant [NM_001690.4: c .1132 C>T, p.Leu378Phe] of the ATP6V1A gene. This variant is not reported in the public database, but is predicted to be deleterious by multiple software packages, and classified as a variant of unknown significance following the American College of Medical Genetics and Genomics guidelines. Quantitative PCR and western blotting further confirm its down-regulatory role in both the RNA and protein expression of ATP6V1A. This case report confirms the pathogenicity of ATP6V1A in epilepsy with solid experimental evidence, thereby expanding the phenotype spectrum of ATP6V1A variants. More importantly, we show that seizures triggered by ATP6V1A variants could be controlled by Levetiracetam, crucially rescuing the development of the patient.

The pivotal role of autophagy in bone cells:bone-related cell activity and bone metabolism / 中国组织工程研究

BACKGROUND:The importance of autophagy for maintaining cellular homeostasis and stress response has long been recognized.As a way for cells to selectively clear their damaged organelles to achieve the recycling of cellular components,autophagy has a pivotal role in bone metabolism.OBJECTIVE:To review the role and possible mechanisms of autophagy in regulating bone-related cell activity and function among bone marrow mesenchymal stem cells,osteoblasts,osteocytes,and osteoclasts.METHODS:PubMed was searched for studies related to autophagy using the keywords of "autophagy;bone marrow mesenchymal stem cells;osteoblasts;osteocytes;osteoclasts."RESULTS AND CONCLUSION:We finally included 84 papers.Autophagy plays an important role in bone metabolism.Autophagy is involved in maintaining the balance between mineralization and absorption,and then maintaining bone homeostasis.An appropriate autophagy inducer may also benefit bone remodeling.Abnormal autophagy can lead to disorders of bone balance,leading to diseases such as osteoporosis.We may prevent or treat bone-related diseases by regulating the level of autophagy as its function in maintaining the balance of mineralization and resorption in bone homeostasis.

Research on convolutional neural network and its application on medical image / 生物医学工程学杂志

Recent years, convolutional neural network (CNN) is a research hot spot in machine learning and has some application value in computer aided diagnosis. Firstly, this paper briefly introduces the basic principle of CNN. Secondly, it summarizes the improvement on network structure from two dimensions of model and structure optimization. In model structure, it summarizes eleven classical models about CNN in the past 60 years, and introduces its development process according to timeline. In structure optimization, the research progress is summarized from five aspects (input layer, convolution layer, down-sampling layer, full-connected layer and the whole network) of CNN. Thirdly, the learning algorithm is summarized from the optimization algorithm and fusion algorithm. In optimization algorithm, it combs the progress of the algorithm according to optimization purpose. In algorithm fusion, the improvement is summarized from five angles: input layer, convolution layer, down-sampling layer, full-connected layer and output layer. Finally, CNN is mapped into the medical image domain, and it is combined with computer aided diagnosis to explore its application in medical images. It is a good summary for CNN and has positive significance for the development of CNN.