The spindle of oocytes observed by polarized light microscope can predict embryo quality

Background: The aim is to evaluate spindle position of metaphase II oocyte and the development of embryos originated from oocytes with spindle and without spindle.Methods: Cross-sectional analysis Research: 250 MII oocytes were analyzed with polarized microscope in Military Institute of Clinical Embryology and Histology, Vietnam Military Medical University.Results: Spindles were detected in 170 (77.98%) of 218 metaphase II oocytes, 115 spindles (67.65%) of MII oocytes is beneath or adjacent to the first polar body, 55 oocytes had the spindle located between 300 and 1800 away from the first polar body. Fertilization rate and the rate of good quality embryos in oocytes with a visible spindle (77.98% and 61.02%) were higher than those in oocytes without a visible spindle (22.02% and 36.84%), the difference was statistically significant with p <0.001 and p <0.05.Conclusions: The spindle position of metaphase II oocytes is not always beneath or adjacent to the first polar body. Fertilization rate and the rate of good quality embryos in oocytes with a visible spindle were higher than those in oocytes without a visible spindle

Knowledge of Antiretroviral Treatment and Associated Factors in HIV-Infected Patients

This study aimed to assess the knowledge of antiretroviral (ARV) treatment and the associated factors in HIV-infected patients in Vietnam. We conducted a cross-sectional descriptive study of 350 human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) patients being treated with ARV at outpatient clinics at Soc Trang, Vietnam, from June 2019 to December 2019. Using an interview questionnaire, patients who answered at least eight out of nine questions correctly, including some required questions, were considered to have a general knowledge of ARV treatment. Using multivariate logistic regression to identify factors associated with knowledge of ARV treatment, we found that 62% of HIV-infected patients had a general knowledge of ARV treatment, with a mean score of 8.2 (SD 1.4) out of 9 correct. A higher education level (p < 0.001); working away from home (p = 0.013); getting HIV transmitted by injecting drugs or from mother-to-child contact (p = 0.023); the presence of tension, anxiety, or stress (p = 0.005); self-reminding to take medication (p = 0.024); and a high self-evaluated adherence (p < 0.001) were found to be significantly associated with an adequate knowledge of ARV treatment. In conclusion, education programs for patients, as well as the quality of medical services and support, should be strengthened

Conditional Support Alignment for Domain Adaptation with Label Shift

Unsupervised domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on the labeled samples on the source domain and unlabelled ones in the target domain. The dominant existing methods in the field that rely on the classical covariate shift assumption to learn domain-invariant feature representation have yielded suboptimal performance under the label distribution shift between source and target domains. In this paper, we propose a novel conditional adversarial support alignment (CASA) whose aim is to minimize the conditional symmetric support divergence between the source's and target domain's feature representation distributions, aiming at a more helpful representation for the classification task. We also introduce a novel theoretical target risk bound, which justifies the merits of aligning the supports of conditional feature distributions compared to the existing marginal support alignment approach in the UDA settings. We then provide a complete training process for learning in which the objective optimization functions are precisely based on the proposed target risk bound. Our empirical results demonstrate that CASA outperforms other state-of-the-art methods on different UDA benchmark tasks under label shift conditions

A Generalization Bound of Deep Neural Networks for Dependent Data

Existing generalization bounds for deep neural networks require data to be independent and identically distributed (iid). This assumption may not hold in real-life applications such as evolutionary biology, infectious disease epidemiology, and stock price prediction. This work establishes a generalization bound of feed-forward neural networks for non-stationary $\phi$-mixing data