Biology subjects

zhang, Q.

Publications and source records attributed to zhang, Q..

3 recordsLinked to original sources

bioRxiv2021

Variation of female pronucleus reveal oocyte or embryo abnormality: An expert experience deep learning of non-dark box analysis

BackgroundPronuclear assessment appears to have the ability to distinguish good and bad embryos in the zygote stage, but paradoxical results were obtained in clinical studies. This situation might be caused by the robust qualitative detection of the development of dynamic pronuclei. Here, we aim to establish a quantitative pronuclear measurement method by applying expert experience deep learning from large annotated datasets. MethodsConvinced handle-annotated 2PN images (13419) were used for deep learning then corresponded errors were recorded through handle check for subsequent parameters adjusting. We used 790 embryos with 52479 PN images from 155 patients for analysis the area of pronuclei and the pre-implantation genetic test results. Establishment of the exponential fitting equation and the key coefficient {beta} 1was extracted from the model for quantitative analysis for pronuclear(PN) annotation and automatic recognition. FindingsBased on the female original PN coefficient {beta}1, the chromosome-normal rate in the blastocyst with biggest PN area is much higher than that of the blastocyst with smallest PN area (58.06% vs. 45.16%, OR=1.68 [1.07-2.64]; P=0.031). After adjusting coefficient {beta}1 by the first three frames which high variance of outlier PN areas was removed, coefficient {beta}1 at 12 hours and at 14 hours post-insemination, similar but stronger evidence was obtained. All these discrepancies resulted from the female propositus in the PGT-SR subgroup and smaller chromosomal errors. Conclusion(s)The results suggest that detailed analysis of the images of embryos could improve our understanding of developmental biology. FundingNone

genetics↗

bioRxiv2019

An optimized 16S rRNA sequencing protocol for vaginal microbiome to avoid biased abundance estimation

We applied three 16S rRNA sequencing protocols on vaginal microbiome samples, to evaluate whether they produce unbiased estimation of vaginal microbiome composition. We modified the 27F primer (hereafter denoted as 27F). Using vaginal samples from 28 healthy women and 10 women with bacterial vaginosis, we sequenced three 16S rRNA sequencing protocols, i.e., 27F-338R, 27F-338R and 341F-806R protocols, naming after their PCR primer sets, to test whether the sequencing results are consistent with the clinical diagnostics, morphology and qPCR results. First, the 27F primer would not align with Gardnerlla vaginalis very well, leading to poor amplification of such species. By modifying the primer sequences, the modified 27F primer (27F) was able to amplify Gardnerlla vaginalis very well. Second, the DNA sequence of characteristic species Lactobacillus crispatus is identical with Lactobacillus garrinarum, leading to biased estimation of abundance of Lactobacillus crispatus when using V3-V4 as PCR target region; in contrast, such bias did not occur when using V1-V2 as a target region. Third, optimized 27F-338R avoided above-mentioned biases and restored the well-established community state types (CSTs) clustering. ImportanceVaginal microbiome has profound effects on the health of women and their newborns. Our study found that two well-established 16S rDNA sequencing protocols led to systementical biased estimation of characteristic species of vaginal microbiome. Subsequent analysis proved that the PCR primer fetching efficacy and target region identity were major contributor for such bias. With carefully selected target region and optimized PCR primer set, we were able to eliminate such biases and provide accurate estimation of vaginal microbiome, which showed high consistency with clinical diagnostics. We modified the 27F primer (27F). Using the optimized PCR primer set of 27F and 338R to target the V1-V2 hyper-variable region, our 16S rRNA sequencing correctly evaluate the composition of vaginal microbiome.

microbiology↗

bioRxiv2019

Fertility intentions and influential factors in Dalian urban city ---A cross-sectional study based on Universal Two-child policy in China

BackgroundIn October, 2015, Chinese government announced that one-child policy had finally been replaced by a universal two-child policy. However, the effects of new policy may be far less than expected. So we conducted this research to explore potential influential factors of fertility intention.\n\nMethodsThis cross-sectional study was conducted and a self-administered questionnaire was designed for collecting socio-demographic information, future fertility intention and influential factors of individual reproductive behavior. The analyses were performed using the SPSS 19.0 statistical software package.\n\nResultsA total of 1370 respondents were interviewed. Our research indicated that the mean ideal number of children was only 1.73 and urban respondents sex preference was symmetrical preference. 79.1% (884) married people had the first child already, only 7.6% (71) respondents had two children. Among 1370 participants, 30.4% respondents stated that they would have a second child; while 69.6% respondents refused to have two children in future (just wanted only a child). Binary logistic regression analysis (model 1) showed that female, older age, lower education lever, birth place was Dalian, lower family income, the ideal number of children were associated with having 1 child in the future. Model 2 (only respondents with childbearing experience) showed that female, lower family income, couldnt get additional financial support from parents were more likely refused to have two children; in additional, the ideal number of children and childbearing experience were significantly influences on future fertility intention.\n\nConclusionFertility intention and reproductive behavior still below replacement in Dalian city. Our results suggest that several factors (including socioeconomic characteristics, economic factors, desired number of child, childbearing experience) have distinctive effects on fertility intention.

epidemiology↗