Brain-derived neurotrophic factor (BDNF) exerts its survival-promoting effects on photoreceptors and retinal ganglion cells, however, delivery systems with little-to-no side effect are needed to sustain its controlled release and long-term efficacy. Our previous studies demonstrated that adipose-derived stem cells (ADSCs) are ideal delivery systems for gene therapy; moreover, ADSCs present unique properties like migration to damaged tissue sites, immunomodulation and anti-inflammation. Herein, we propose to employ ADSCs as the BDNF gene delivery vehicle. Different Analyses like flow cytometry, differentiation and cell proliferation assays etc demonstrated that BDNF were successfully transduced into ADSCs and the stemness of ADSCs was maintained even with the transduction. Real Time PCR and Western blot were used to measure mRNA and protein expressions of the BDNF-transduced ADSCs. The results demonstrated that the BDNF expression level of the lentiviral-BDNF transduced ADSCs is significantly increased and, moreover, enhanced the expression of other neurotrophic and downstream signaling factors. The data suggest that ADSCs are a reliable delivery vehicle for BDNF and could be used for the treatment of various diseases.

Objective:To explore the accuracy and clinical application value of a Multi-Agent Reinforcement Learning framework (MARL framework) in three-dimensional ultrasound to automatically locate the coronal plane of the uterus.Methods:A total of 144 female patients who underwent routine gynecological examinations in Luohu People′s Hospital during May 2020 were selected as the experimental subjects. The three-dimensional volume data of the uterus of all the experimental subjects were collected by using the Resona-8 high-end color Doppler ultrasound system. A sonographer with more than 5 years of clinical experience manually locate the coronal plane of the uterus in all collected data, and at the same time automatically locate the coronal plane of the uterus MARL framework. The coronal plane images of the uterus obtained by the two methods were saved, and the operation time of the two methods was recorded. The coronal plane uterine images obtained by the two methods were mixed together, and the images were scored 0-1 by two senior ultrasound experts in a double-blind manner. The average score greater than or equal to 0.6 points was considered qualified.Results:①In 144 volunteers, among the coronal planes of the uterus located by the two methods, 131 were qualified by the manual method, and 137 were qualified by the automatic method.There was no statistical difference between the manual and automatic coronal plane images of the uterus (χ 2=1.934, P=0.164) by the chi-square test. ②Using interquartile range analysis, the median and interquartile range of the image score of the automatic group was 0.80(0.75, 0.90), while the median and interquartile range of the image score of the manual group was 0.80(0.75, 0.90). The Wilcoxon signed rank test was used to analyze the quality of the coronal plane images obtained by manual and automatic methods, and the difference was not statistically significant ( Z=1.241, P=0.215). ③The paired t test was used to compare the time required to locate the coronal surface of the uterus, by manual method (63.65±10.182)s, by automatic method (3.25±0.294)s, the difference between the two methods was statistically significant ( t=19.52, P<0.001). Conclusions:The method based on MARL framework has a high correlation with the manual locating of the coronal plane of uterus in three-dimensional ultrasound, and greatly reduces the operation time. It can be effectively applied in clinical practice and lays a foundation for the automatic diagnosis of uterine related diseases.

SARS-CoV-2 is a new RNA virus affecting humans and spreads extensively throughout the world since its first outbreak in December,2019.Whether the transmissibility and pathogenicity of SARS-CoV-2 in humans after zoonotic transfer are actively evolving,and driven by adaptation to the new host and environments is still under debate.Understanding the evolutionary mechanism underlying epidemiological and pathological characteristics of COVID-19 is essential for predicting the epidemic trend,and providing guidance for disease control and treatments.Interrogating novel strategies for identifying natural selection using within-species polymorphisms and 3,674,076 SARS-CoV-2 genome sequences of 169 countries as of December 30,2021,we demonstrate with popula-tion genetic evidence that during the course of SARS-CoV-2 pandemic in humans,1)SARS-CoV-2 genomes are overall conserved under purifying selection,especially for the 14 genes related to viral RNA replication,transcription,and assembly;2)ongoing positive selection is actively driving the evolution of 6 genes(e.g.,S,ORF3a,and N)that play critical roles in molecular processes involving pathogen-host interactions,including viral invasion into and egress from host cells,and viral inhi-bition and evasion of host immune response,possibly leading to high transmissibility and mild symptom in SARS-CoV-2 evolution.According to an established haplotype phylogenetic relation-ship of 138 viral clusters,a spatial and temporal landscape of 556 critical mutations is constructed based on their divergence among viral haplotype clusters or repeatedly increase in frequency within at least 2 clusters,of which multiple mutations potentially conferring alterations in viral transmis-sibility,pathogenicity,and virulence of SARS-CoV-2 are highlighted,warranting attention.

A novel RNA virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is responsible for the ongoing outbreak of coronavirus disease 2019 (COVID-19). Population genetic analysis could be useful for investigating the origin and evolutionary dynamics of COVID-19. However, due to extensive sampling bias and existence of infection clusters during the epidemic spread, direct applications of existing approaches can lead to biased parameter estima-tions and data misinterpretation. In this study, we first present robust estimator for the time to the most recent common ancestor (TMRCA) and the mutation rate, and then apply the approach to analyze 12,909 genomic sequences of SARS-CoV-2. The mutation rate is inferred to be 8.69 × 10-4 per site per year with a 95％ confidence interval (CI) of [8.61 × 10-4, 8.77 × 10-4], and the TMRCA of the samples inferred to be Nov 28, 2019 with a 95％ CI of [Oct 20, 2019, Dec 9, 2019]. The results indicate that COVID-19 might originate earlier than and outside of Wuhan Seafood Market. We further demonstrate that genetic polymorphism patterns, including the enrichment of specific haplotypes and the temporal allele frequency trajectories generated from infection clusters, are similar to those caused by evolutionary forces such as natural selection. Our results show that population genetic methods need to be developed to efficiently detangle the effects of sampling bias and infection clusters to gain insights into the evolutionary mechanism ofSARS-CoV-2. Software for implementing VirusMuT can be downloaded at https://bigd.big.ac.cn/biocode/tools/BT007081.