Genomic insights of mcr-1 harboring Escherichia coli by geographical region and a One-Health perspective.

The importance of the One Health concept in attempting to deal with the increasing levels of multidrug-resistant bacteria in both human and animal health is a challenge for the scientific community, policymakers, and the industry. The discovery of the plasmid-borne mobile colistin resistance (mcr) in 2015 poses a significant threat because of the ability of these plasmids to move between different bacterial species through horizontal gene transfer. In light of these findings, the World Health Organization (WHO) recommends that countries implement surveillance strategies to detect the presence of plasmid-mediated colistin-resistant microorganisms and take suitable measures to control and prevent their dissemination. Seven years later, ten different variants of the mcr gene (mcr-1 to mcr-10) have been detected worldwide in bacteria isolated from humans, animals, foods, the environment, and farms. However, the possible transmission mechanisms of the mcr gene among isolates from different geographical origins and sources are largely unknown. This article presents an analysis of whole-genome sequences of Escherichia coli that harbor mcr-1 gene from different origins (human, animal, food, or environment) and geographical location, to identify specific patterns related to virulence genes, plasmid content and antibiotic resistance genes, as well as their phylogeny and their distribution with their origin. In general, E. coli isolates that harbor mcr-1 showed a wide plethora of ARGs. Regarding the plasmid content, the highest concentration of plasmids was found in animal samples. In turn, Asia was the continent that led with the largest diversity and occurrence of these plasmids. Finally, about virulence genes, terC, gad, and traT represent the most frequent virulence genes detected. These findings highlight the relevance of analyzing the environmental settings as an integrative part of the surveillance programs to understand the origins and dissemination of antimicrobial resistance.

Neurodevelopmental Outcomes following Intrauterine Growth Restriction and Very Preterm Birth.

OBJECTIVES: To evaluate whether intrauterine growth restriction (IUGR) adds further neurodevelopmental risk to that posed by very preterm birth alone in terms of alterations in brain growth and poorer toddlerhood outcomes. STUDY DESIGN: Participants were 314 infants of very preterm birth enrolled in the Evaluation of Preterm Imaging Study (e-Prime) who were subsequently followed up in toddlerhood. IUGR was identified postnatally from discharge records (n = 49) and defined according to prenatal evaluation of growth restriction confirmed by birth weight <10th percentile for gestational age and/or alterations in fetal Doppler. Appropriate for gestational age (AGA; n = 265) was defined as birth weight >10th percentile for gestational age at delivery. Infants underwent magnetic resonance imaging at term-equivalent age (median = 42 weeks); T2-weighted images were obtained for voxelwise gray matter volumes. Follow-up assessments were conducted at corrected median age of 22 months using the Bayley Scales of Infant and Toddler Development III and the Modified-Checklist for Autism in Toddlers. RESULTS: Infants of very preterm birth with IUGR displayed a relative volumetric decrease in gray matter in limbic regions and a relative increase in frontoinsular, temporal-parietal, and frontal areas compared with peers of very preterm birth who were AGA. At follow-up, toddlers born very preterm with IUGR had significantly lower cognitive (effect size = 0.42) and motor (effect size = 0.41) scores and were more likely to have a positive Modified-Checklist for Autism in Toddlers screening for autism (OR = 2.12) compared with peers of very preterm birth who were AGA. CONCLUSIONS: IUGR might confer a neurodevelopmental risk that is greater than that posed by very preterm alone, in terms of both alterations in brain growth and poorer toddlerhood outcomes.

Screening for active pulmonary tuberculosis: Development and applicability of artificial neural network models.

Tuberculosis (TB) remains a significant public health challenge, motivated by the diversity of healthcare epidemiological settings, as other factors. Cost-effective screening has substantial importance for TB control, demanding new diagnostic tools. This paper proposes a decision support tool (DST) for screening pulmonary TB (PTB) patients at a secondary clinic. The DST is composed of an adaptive resonance model (iART) for risk group identification (low, medium and high) and a multilayer perceptron (MLP) neural network for classifying patients as active or inactive PTB. Our tool attains an overall sensitivity (SE) and specificity (SP) of 92% (95% CI; 79-97) and 58% (95% CI; 47-68), respectively. SE values for smear-positive and smear-negative patients are 96% (95% CI; 80-99) and 82% (95% CI; 52-95), as well as higher than 83% (95% CI; 43-97) in low and high-risk cases. Even in scenarios with prevalence up to 20%, negative predictive values superior to 95% are obtained. The proposed DST provides a quick and low-cost pretest for presumptive PTB patients, which is useful to guide confirmatory testing and patient management, especially in settings with limited resources in low and middle-incoming countries.