MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women.

Given the barriers to early detection of gestational diabetes mellitus (GDM), this study aimed to develop an artificial intelligence (AI)-based prediction model for GDM in pregnant Mexican women. Data were retrieved from 1709 pregnant women who participated in the multicenter prospective cohort study 'Cuido mi embarazo'. A machine-learning-driven method was used to select the best predictive variables for GDM risk: age, family history of type 2 diabetes, previous diagnosis of hypertension, pregestational body mass index, gestational week, parity, birth weight of last child, and random capillary glucose. An artificial neural network approach was then used to build the model, which achieved a high level of accuracy (70.3%) and sensitivity (83.3%) for identifying women at high risk of developing GDM. This AI-based model will be applied throughout Mexico to improve the timing and quality of GDM interventions. Given the ease of obtaining the model variables, this model is expected to be clinically strategic, allowing prioritization of preventative treatment and promising a paradigm shift in prevention and primary healthcare during pregnancy. This AI model uses variables that are easily collected to identify pregnant women at risk of developing GDM with a high level of accuracy and precision.

Environmental monitoring shows SARS-CoV-2 contamination of surfaces in food plants

The SARS-CoV-2 pandemic has presented new challenges to food manufacturers. In addition to preventing the spread of microbial contamination of food, with SARS-CoV-2, there is an additional focus on preventing SARS-CoV-2 infections in food plant personnel. During the early phase of the pandemic, several large outbreaks of Covid-19 occurred in food manufacturing plants resulting in deaths and economic loss. In March of 2020, we assisted in implementation of environmental monitoring programs for SARS-CoV-2 in 116 food production facilities. All participating facilities had already implemented measures to prevent symptomatic personnel from coming to work. During the study period, from March 17, 2020 to September 3, 2020, 1.23% of the 22,643 environmental samples tested positive for SARS-CoV-2, suggesting that infected individuals are actively shedding virus. Virus contamination was commonly found on frequently touched surfaces. Most plants managed to control their environmental contamination when they became aware of the positive findings. Comparisons of the personnel test results to environmental contamination in one plant showed a good correlation between the two. Our work illustrates that environmental monitoring for SARS-CoV-2 can be used as a surrogate for identifying the presence of asymptomatic and pre-symptomatic personnel in workplaces and may aid in controlling infection spread. HighlightsO_LIEnvironmental contamination by SARS-CoV-2 virus was detected in food plants C_LIO_LIOut of 22,643 environmental swabs, 278 (1.23%) were positive for SARS-CoV-2 C_LIO_LIFrequently touched surfaces had the most contamination C_LIO_LISurface testing for SARS-CoV-2 may indicate presence of asymptomatic carriers C_LI