The Effectiveness of Lifestyle Changes in Glycemic Control among Pregnant Women with Gestational Diabetes Mellitus.

Background and Objectives: Gestational diabetes mellitus (GDM) is a type of diabetes that develops during pregnancy and affects approximately 10% of pregnant women worldwide. Understanding the impact of lifestyle changes on glycemic control in GDM is important for improving maternal and fetal outcomes and reducing the risk of diabetes in both the mother and child. The aim of this study is to evaluate the effectiveness as well as the factors affecting glycemic control by lifestyle changes in pregnant women with GDM. Materials and Methods: A descriptive cross-sectional study was conducted at three hospitals in the Thai Binh Province from June 2021 to May 2022. All pregnant women at 24-28 weeks of gestation, aged 18 years or older, were enrolled. GDM was diagnosed according to the guidelines of the International Association of the Diabetes and Pregnancy Study Groups. Lifestyle changes including diet and physical exercise were carried out for two weeks. The main outcome measured was successful glycemic control according to the 2018 ADA Recommendations for the Management and Treatment of GDM. Results: 1035 women were included and 20.2% diagnosed with GDM. After two weeks of lifestyle change intervention, 82.6% of the pregnant women with GDM had successful glycemic control. Pregnant women aged under 35 years had a 3.2 times higher rate of gestational glycemic control than those older than 35 (aOR = 3.22, p-value = 0.004). Women with a pre-pregnancy BMI of less than 25 had a higher rate of gestational glycemic control than those with a BMI of over 25 (aOR = 10.84, p-value < 0.001). Compared to women who had all three diagnostic criteria for gestational diabetes, those with two diagnostic criteria and one criterion were 3.8 times and 3 times more likely to have successful blood sugar control (aOR = 3.78, p-value = 0.01 and aOR = 3.03, p-value = 0.03, respectively). Conclusion: Lifestyle changes can be an effective measure for achieving glycemic control in women with GDM. Healthcare providers should consider individualized treatment plans based on the specific needs of each patient.

Generation of on-demand quasi-Mathieu beams with a controlled generation of spatial spectrum of angular Mathieu-Gauss functions with a digital laser.

This study provided an intra-cavity method for the selective generation of all kinds of quasi-Mathieu beams. The method employed L-type digital lasers to selectively generate the Fourier spectrum of the gaussian-modulated angular Mathieu function. The lasing field then underwent a Fourier-transform with an extra-cavity lens, and was converted into quasi-Mathieu beams after passing through an axicon. The selection of the lasing quasi-Mathieu beams was controlled by the projection phase of the intra-cavity spatial light modulator (SLM) of digital lasers, which provided flexibility in dynamically generating on-demand quasi-Mathieu beams. The formalism of the resulting quasi-Mathieu beams is detailed in this paper. The nondiffracting characteristics of the resulting quasi-Mathieu beams were verified both numerically and experimentally. The capability of dynamically controlled generation and manipulation of lasing quasi-Mathieu beams by the proposed method is beneficial to practical applications of Mathieu beams.

Binding of nontarget microorganisms from food washes to anti-Salmonella and anti-E. coli O157 immunomagnetic beads: minimizing the errors of random sampling in extreme dilute systems.

For most applications, 3-5 observations, or samplings (n), are utilized to estimate total aerobic plate count in an average population (µ) that is greater than about 50 cells, or colony forming units (CFU), per sampled volume. We have chosen to utilize a 6 × 6 drop plate method for bacterial colony selection because it offers the means to rapidly perform all requisite dilutions in a 96-well format and plate these dilutions on solid media using minimal materials. Besides traditional quantitative purposes, we also need to select colonies which are well-separated from each other for the purpose of bacterial identification. To achieve this goal using the drop plate format requires the utilization of very dilute solutions (µ < 10 CFUs per sampled drop). At such low CFU densities the sampling error becomes problematic. To address this issue we produced both observed and computer-generated colony count data and divided a large sample of individual counts randomly into N subsamples each with n = 2-24 observations (N × n = 360). From these data we calculated the average total mean-normalized (x⁻(tot), n = 360) deviation of the total standard deviation (s (tot)) from each jth subsample's estimate (s ( j )), which we call Δ. When either observed or computer-generated Δ values were analyzed as a function of x⁻(tot), a set of relationships (∞ ₋2√ ⁻x(tot)) were generated which appeared to converge at an n of about 18 observations. This finding was verified analytically at even lower CFU concentrations (⁻x(tot) ≈ 1 − 10 CFUs per observation). Additional experiments using the drop plate format and n = 18 samplings were performed on food samples along with most probable number (MPN) analyses and it was found that the two enumeration methods did not differ significantly.