Multiplatform characterization of dynamic changes in breast milk during lactation

The multicomponent analysis of human breast milk (BM) by metabolic profiling is a new area of study applied to determining milk composition, and is capable of associating BM composition with maternal characteristics, and subsequent infant health outcomes. A multiplatform approach combining HPLC‐MS and ultra‐performance LC‐MS, GC‐MS, CE‐MS, and 1H NMR spectroscopy was used to comprehensively characterize metabolic profiles from seventy BM samples. A total of 710 metabolites spanning multiple molecular classes were defined. The utility of the individual and combined analytical platforms was explored in relation to numbers of metabolites identified, as well as the reproducibility of the methods. The greatest number of metabolites was identified by the single phase HPLC‐MS method, while CE‐MS uniquely profiled amino acids in detail and NMR was the most reproducible, whereas GC‐MS targeted volatile compounds and short chain fatty acids. Dynamic changes in BM composition were characterized over the first 3 months of lactation. Metabolites identified as altering in abundance over lactation included fucose, di‐ and triacylglycerols, and short chain fatty acids, known to be important for infant immunological, neurological, and gastrointestinal development, as well as being an important source of energy. This extensive metabolic coverage of the dynamic BM metabolome provides a baseline for investigating the impact of maternal characteristics, as well as establishing the impact of environmental and dietary factors on the composition of BM, with a focus on the downstream health consequences this may have for infants

Assessing the climate resilience of community-managed water supplies in Ethiopia and Nepal

Understanding the resilience of water supplies to climate change is becoming an urgent priority to ensure health targets are met. Addressing systemic issues and building the resilience of community-managed supplies, which serve millions of people in rural LMIC settings, will be critical to improve access to safe drinking water. The How Tough is WASH (HTIW) framework to assess resilience was applied to community-managed water supplies in Ethiopia and Nepal to assess the effectiveness of this framework in field conditions. The resilience of these water supplies was measured along six domains—the environment, infrastructure, management, institutional support, community governance and supply chains—that can affect how they respond to climate change effects. We found that the HTIW framework provided an objective measure of resilience and could be used to rank water supplies in order of priority for action. We also found that systemic issues could be identified. The tools and methods used in the framework were easy to deploy by field research teams. The water supplies studied in Ethiopia and Nepal had low to moderate resilience to climate change. Service management and institutional support were weak in both countries. The data from Ethiopia and Nepal suggests that many water supplies in rural and small-town communities are unlikely to be resilient to future climate change without increased investment and support. The use of simple frameworks such as HTIW will be important in supporting decisions around such investments by identifying priority communities and actions

Sustainable Healthcare Elective in Nursing: A futures-thinking approach

Worldwide, despite over two decades of literature focusing on the climate emergency and its impact on public health, action on sustainable healthcare remains relatively neglected in nursing curricula. The UK government (Department for International Development) (Agenda 2030) pledged support for the United Nations 17 Global Goals for Sustainable Development, including action on climate change (Goal 13) (DfID, 2015). The NHS Sustainable Development Unit’ suggested ‘a sustainable health and care system is achieved by delivering high quality care and improved public health without exhausting natural resources or causing severe ecological damage’. Application of sustainability principles to medical and dental practice demonstrates cost savings, carbon reduction and lean thinking, particularly in respiratory medicine and nephrology/dialysis (Centre for Sustainable Healthcare, 2018)

Novel Electrophilic and Photoaffinity Covalent Probes for Mapping the Cannabinoid 1 Receptor Allosteric Site(s)

ACKNOWLEDGMENTS The work was supported by National Institutes of Health grants DA027113 and EY024717 to G.A.T. and DA09158 to A.M. A portion of this work was submitted in 2011 by A. Kulkarni in partial fulfillment of M.S. degree requirements from Northeastern University, Boston, MA.Peer reviewedPublisher PD

Migration, Multiple Sexual Partnerships, and Sexual Concurrency in the Garífuna Population of Honduras

The Garífuna, an ethnic minority group in Honduras, have been disproportionately affected by HIV. Previous research suggests that migration and high rates of multiple sexual partnerships are major drivers of the epidemic. Using data from a 2012 population-based survey, we assessed whether temporary migration was associated with 1) multiple sexual partnerships and 2) sexual concurrency among Garífuna men and women in Honduras. Among both men and women, temporary migration in the last year was associated with an increased likelihood of multiple sexual partnerships and with concurrency, though only the association between migration and multiple sexual partnerships among men was statistically significant (Adjusted Prevalence Ratio 1.7, 95% CI 1.2-2.4). Migration may contribute to HIV/STI vulnerability among Garífuna men and women via increases in these sexual risk behaviors. Research conducted among men and women at elevated risk of HIV should continue to incorporate measures of mobility, including history of internal migration

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research

Power estimation for non-standardized multisite studies

AbstractA concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this assumption, we provide a new statistical framework and derive a power equation to define inclusion criteria for a set of sites based on the variability of their scaling factors. We estimated the scaling factors of 20 scanners with heterogeneous hardware and sequence parameters by scanning a single set of 12 subjects at sites across the United States and Europe. Regional volumes and their scaling factors were estimated for each site using Freesurfer's segmentation algorithm and ordinary least squares, respectively. The scaling factors were validated by comparing the theoretical and simulated power curves, performing a leave-one-out calibration of regional volumes, and evaluating the absolute agreement of all regional volumes between sites before and after calibration. Using our derived power equation, we were able to define the conditions under which harmonization is not necessary to achieve 80% power. This approach can inform choice of processing pipelines and outcome metrics for multisite studies based on scaling factor variability across sites, enabling collaboration between clinical and research institutions