Homocysteine (Hct) is a substance produced in the metabolism of methionine. It is an essential type of amino acid gained from the daily diet. Methylenetetrahydrofolate reductase (MTHFR) gene mutation is related to elevated total homocysteine (tHct) expressions, in particular, among women with low folate intake. Hyperhomocysteinemia (HHct) is caused by numerous factors, such as genetic defects, lack of folic acid, vitamin B6 and B12 deficiency, hypothyroidism, drugs, aging, and renal dysfunction. Increased Hct in peripheral blood may lead to vascular illnesses, coronary artery dysfunction, atherosclerotic changes, and embolic diseases. Compared to nonpregnant women, the Hct level is lower in normal pregnancies. Recent studies have reported that HHct was associated with numerous pregnancy complications, including recurrent pregnancy loss (RPL), preeclampsia (PE), preterm delivery, placental abruption, fetal growth restriction (FGR), and gestational diabetes mellitus (GDM). Besides, it was discovered that neonatal birth weight and maternal Hct levels were negatively correlated. However, a number of these findings lack consistency. In this review, we summarized the metabolic process of Hct in the human body, the levels of Hct in different stages of normal pregnancy reported in previous studies, and the relationship between Hct and pregnancy complications. The work done is helpful for obstetricians to improve the likelihood of a positive outcome during pregnancy complications. Reducing the Hct level with a high dosage of folic acid supplements during the next pregnancy could be helpful for females who have suffered pregnancy complications due to HHct.
Objectives. Serological surveys were used to infer the infection attack rate in different populations. The sensitivity of the testing assay, Abbott, drops fast over time since infection which make the serological data difficult to interpret. In this work, we aim to solve this issue. Methods. We collect longitudinal serological data of Abbott to construct a sensitive decay function. We use the reported COVID-10 deaths to infer the infections, and use the decay function to simulate the seroprevalence and match to the reported seroprevalence in 12 Indian cities. Results. Our model simulated seroprevalence match the reported seroprevalence in most (but not all) of the 12 Indian cities we considered. We obtain reasonable infection attack rate and infection fatality rate for most of the 12 Indian cities. Conclusions. Using both reported COVID-19 deaths data and serological survey data, we infer the infection attack rate and infection fatality rate with increased confidence.
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