\uc9tude des impacts de la ventilation sur la qualit\ue9 de l`air int\ue9rieur et la sant\ue9 respiratoire des enfants souffrant de symptomes reli\ue9s \ue0 l`asthme dans les habitations

Peer reviewed: NoNRC publication: Ye

lavalleelab/AMLclassifier: code updates

<p>Artificial Neural Network classifier for AML single cell RNA-seq data </p&gt

Early life exposure to green space and insulin resistance: An assessment from infancy to early adolescence.

BACKGROUND: Recent studies suggest that greater exposure to natural vegetation, or green space is associated with lower diabetes risk, possibly through increasing physical activity. However, there is limited research on green space and insulin resistance in youth. We hypothesized greater green space at early-life sensitive time periods would be associated with lower insulin resistance in youth. METHODS: We used data from Project Viva (N = 460), a pre-birth cohort study that recruited pregnant women in eastern Massachusetts, 1999-2002, and followed offspring into adolescence. We defined residential green space exposure at infancy (median age - 1.1 years), early childhood (3.2 years), mid-childhood (7.7 years), and early adolescence (12.8 years), using 30 m resolution Landsat satellite imagery to estimate the Normalized Difference Vegetation Index [NDVI]. Our main outcome was early adolescence estimated insulin resistance (HOMA-IR). We used multiple imputation to account for missing data and multiple linear regression models adjusted for age, sex, race/ethnicity, parental education, household income, and neighborhood median household income. RESULTS: The highest green space tertile had the highest percentage of white participants (85%), college-educated mothers (87%) and fathers (85%), and households with income higher than US$70,000 (86%). Unadjusted models showed that participants living in the highest green space tertile at infancy had a 0.15 unit lower HOMA-IR (95% CI: -0.23, -0.06) in early adolescence, than those living in the lowest tertile. However, in adjusted models, we did not observe evidence of associations between green space from infancy to early adolescence and HOMA-IR in early adolescence, although some point estimates were in the hypothesized direction. For example, participants in the highest green space tertile in infancy had 0.03 units lower HOMA-IR (95%CI: -0.14, 0.08) than those living in the lowest tertile. CONCLUSIONS: Exposure to green space at early life sensitive time periods was not associated with HOMA-IR in youth. Early-life longitudinal studies across diverse populations are needed to confirm or refute our results

Does in utero exposure to heavy maternal smoking induce nicotine withdrawal symptoms in neonates?

Maternal drug use during pregnancy is associated with fetal passive addiction and neonatal withdrawal syndrome. Cigarette smoking-highly prevalent during pregnancy-is associated with addiction and withdrawal syndrome in adults. We conducted a prospective, two-group parallel study on 17 consecutive newborns of heavy-smoking mothers and 16 newborns of nonsmoking, unexposed mothers (controls). Neurologic examinations were repeated at days 1, 2, and 5. Finnegan withdrawal score was assessed every 3 h during their first 4 d. Newborns of smoking mothers had significant levels of cotinine in the cord blood (85.8 +/- 3.4 ng/mL), whereas none of the controls had detectable levels. Similar findings were observed with urinary cotinine concentrations in the newborns (483.1 +/- 2.5 microg/g creatinine versus 43.6 +/- 1.5 microg/g creatinine; p = 0.0001). Neurologic scores were significantly lower in newborns of smokers than in control infants at days 1 (22.3 +/- 2.3 versus 26.5 +/- 1.1; p = 0.0001), 2 (22.4 +/- 3.3 versus 26.3 +/- 1.6; p = 0.0002), and 5 (24.3 +/- 2.1 versus 26.5 +/- 1.5; p = 0.002). Neurologic scores improved significantly from day 1 to 5 in newborns of smokers (p = 0.05), reaching values closer to control infants. Withdrawal scores were higher in newborns of smokers than in control infants at days 1 (4.5 +/- 1.1 versus 3.2 +/- 1.4; p = 0.05), 2 (4.7 +/- 1.7 versus 3.1 +/- 1.1; p = 0.002), and 4 (4.7 +/- 2.1 versus 2.9 +/- 1.4; p = 0.007). Significant correlations were observed between markers of nicotine exposure and neurologic-and withdrawal scores. We conclude that withdrawal symptoms occur in newborns exposed to heavy maternal smoking during pregnancy

Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Abstract: Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine. A systematic review of evidence, across the key pillars of prevention, diagnosis, treatment and prognosis, outlines milestones that need to be met to enable the broad clinical implementation of precision medicine in diabetes care

Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes : a systematic review

Abstract: Background Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies.Methods We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment.Results Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation.Conclusions Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops. Islet autoantibodies are markers found in the blood when insulin-producing cells in the pancreas become damaged and can be used to predict future development of type 1 diabetes. We evaluated published literature to determine whether characteristics of islet antibodies (type, levels, numbers) could improve prediction and help understand differences in how individuals with type 1 diabetes respond to treatments. We found existing evidence shows that islet autoantibody type and number are most useful to predict disease progression before diagnosis. In addition, the age when islet autoantibodies first appear strongly influences rate of progression. These findings provide important information for patients and care providers on how islet autoantibodies can be used to understand future type 1 diabetes development and to identify individuals who have the potential to benefit from intervention or prevention therapy. Felton et al. conduct a systematic review to determine the utility of islet autoantibodies as biomarkers of type 1 diabetes heterogeneity. They find that islet autoantibodies are most likely to be useful for patient stratification prior to clinical diagnosis