The association of wildfire smoke with respiratory and cardiovascular emergency department visits in Colorado in 2012: a case crossover study

In 2012, Colorado experienced one of its worst wildfire seasons of the past decade. The goal of this study was to investigate the relationship of local PM2.5 levels, modeled using the Weather Research and Forecasting Model with Chemistry, with emergency department visits and acute hospitalizations for respiratory and cardiovascular outcomes during the 2012 Colorado wildfires. Methods: Conditional logistic regression was used to assess the relationship between both continuous and categorical PM2.5 and emergency department visits during the wildfire period, from June 5th to July 6th 2012. Results: For respiratory outcomes, we observed positive relationships between lag 0 PM2.5 and asthma/wheeze (1 h max OR 1.01, 95 % CI (1.00, 1.01) per 10 mu g/m(3)24 h mean OR 1.04 95 % CI (1.02, 1.06) per 5 mu g/m(3)), and COPD (1 h max OR 1.01 95 % CI (1.00, 1.02) per 10 mu g/m(3)24 h mean OR 1.05 95 % CI (1.02, 1.08) per 5 mu g/m(3)). These associations were also positive for 2-day and 3-day moving average lag periods. When PM2.5 was modeled as a categorical variable, bronchitis also showed elevated effect estimates over the referent groups for lag 0 24 h average concentration. Cardiovascular results were consistent with no association. Conclusions: We observed positive associations between PM2.5 from wildfire and respiratory diseases, supporting evidence from previous research that wildfire PM2.5 is an important source for adverse respiratory health outcomes

Adiponectin reduces glomerular endothelial glycocalyx disruption and restores glomerular barrier function in a mouse model of type 2 diabetes

Adiponectin has vascular anti-inflammatory and protective effects. Although adiponectin protects against the development of albuminuria, historically, the focus has been on podocyte protection within the glomerular filtration barrier (GFB). The first barrier to albumin in the GFB is the endothelial glycocalyx (eGlx), a surface gel-like barrier covering glomerular endothelial cells (GEnCs). In diabetes, eGlx dysfunction occurs before podocyte damage; hence, we hypothesized that adiponectin could protect from eGlx damage to prevent early vascular damage in diabetic kidney disease (DKD). Globular adiponectin (gAd) activated AMPK signaling in human GEnCs through AdipoR1. It significantly reduced eGlx shedding and the TNF-α–mediated increase in syndecan-4 (SDC4) and MMP2 mRNA expression in GEnCs in vitro. It protected against increased TNF-α mRNA expression in glomeruli isolated from db/db mice and against expression of genes associated with glycocalyx shedding (namely, SDC4, MMP2, and MMP9). In addition, gAd protected against increased glomerular albumin permeability (Ps’alb) in glomeruli isolated from db/db mice when administered intraperitoneally and when applied directly to glomeruli (ex vivo). Ps’alb was inversely correlated with eGlx depth in vivo. In summary, adiponectin restored eGlx depth, which was correlated with improved glomerular barrier function, in diabetes

Automated analysis of mitral inflow doppler using convolutional neural networks

Doppler echocardiography is commonly used for functional assessment of heart valves such as mitral valve. Currently, the measurements are made manually which is a laborious and subjective process. We have demonstrated the feasibility of using neural networks to fully automate the process of mitral valve inflow measurements. Experiments show that the automated system yields comparable performance to the experts

Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data

Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap-derived Big Data are becoming increasingly solvable with the help of scalable cyber-infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media-based and event-based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in-depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard. Biodiversity data, camera traps, data exchange, data sharing, information standardspublishedVersio

Groundwater Chemistry and Blood Pressure: A Cross-Sectional Study in Bangladesh.

Background: We assessed the association of groundwater chemicals with systolic blood pressure (SBP) and diastolic blood pressure (DBP). Methods: Blood pressure data for ≥35-year-olds were from the Bangladesh Demographic and Health Survey in 2011. Groundwater chemicals in 3534 well water samples from Bangladesh were measured by the British Geological Survey (BGS) in 1998-1999. Participants who reported groundwater as their primary source of drinking water were assigned chemical measures from the nearest BGS well. Survey-adjusted linear regression methods were used to assess the association of each groundwater chemical with the log-transformed blood pressure of the participants. Models were adjusted for age, sex, body mass index, smoking status, geographical region, household wealth, rural or urban residence, and educational attainment, and further adjusted for all other groundwater chemicals. Results: One standard deviation (SD) increase in groundwater magnesium was associated with a 0.992 (95% confidence interval (CI): 0.986, 0.998) geometric mean ratio (GMR) of SBP and a 0.991 (95% CI: 0.985, 0.996) GMR of DBP when adjusted for covariates except groundwater chemicals. When additionally adjusted for groundwater chemicals, one SD increase in groundwater magnesium was associated with a 0.984 (95% CI: 0.972, 0.997) GMR of SBP and a 0.990 (95% CI: 0.979, 1.000) GMR of DBP. However, associations were attenuated following Bonferroni-correction for multiple chemical comparisons in the full-adjusted model. Groundwater concentrations of calcium, potassium, silicon, sulfate, barium, zinc, manganese, and iron were not associated with SBP or DBP in the full-adjusted models. Conclusions: Groundwater magnesium had a weak association with lower SBP and DBP of the participants

Automated mitral inflow Doppler peak velocity measurement using deep learning

Doppler echocardiography is a widely utilised non-invasive imaging modality for assessing the functionality of heart valves, including the mitral valve. Manual assessments of Doppler traces by clinicians introduce variability, prompting the need for automated solutions. This study introduces an innovative deep learning model for automated detection of peak velocity measurements from mitral inflow Doppler images, independent from Electrocardiogram information. A dataset of Doppler images annotated by multiple expert cardiologists was established, serving as a robust benchmark. The model leverages heatmap regression networks, achieving 96% detection accuracy. The model discrepancy with the expert consensus falls comfortably within the range of inter- and intra-observer variability in measuring Doppler peak velocities. The dataset and models are open-source, fostering further research and clinical application

Automated left ventricular dimension assessment using artificial intelligence developed and validated by a UK-wide collaborative

Background: requires training and validation to standards expected of humans. We developed an online platform and established the Unity Collaborative to build a dataset of expertise from 17 hospitals for training, validation, and standardization of such techniques. Methods: The training dataset consisted of 2056 individual frames drawn at random from 1265 parasternal long-axis video-loops of patients undergoing clinical echocardiography in 2015 to 2016. Nine experts labeled these images using our online platform. From this, we trained a convolutional neural network to identify keypoints. Subsequently, 13 experts labeled a validation dataset of the end-systolic and end-diastolic frame from 100 new video-loops, twice each. The 26-opinion consensus was used as the reference standard. The primary outcome was precision SD, the SD of the differences between AI measurement and expert consensus. Results: In the validation dataset, the AI’s precision SD for left ventricular internal dimension was 3.5 mm. For context, precision SD of individual expert measurements against the expert consensus was 4.4 mm. Intraclass correlation coefficient between AI and expert consensus was 0.926 (95% CI, 0.904–0.944), compared with 0.817 (0.778–0.954) between individual experts and expert consensus. For interventricular septum thickness, precision SD was 1.8 mm for AI (intraclass correlation coefficient, 0.809; 0.729–0.967), versus 2.0 mm for individuals (intraclass correlation coefficient, 0.641; 0.568–0.716). For posterior wall thickness, precision SD was 1.4 mm for AI (intraclass correlation coefficient, 0.535 [95% CI, 0.379–0.661]), versus 2.2 mm for individuals (0.366 [0.288–0.462]). We present all images and annotations. This highlights challenging cases, including poor image quality and tapered ventricles. Conclusions: Experts at multiple institutions successfully cooperated to build a collaborative AI. This performed as well as individual experts. Future echocardiographic AI research should use a consensus of experts as a reference. Our collaborative welcomes new partners who share our commitment to publish all methods, code, annotations, and results openly

Development of a Standardized Set of Patient-centered Outcomes for Advanced Prostate Cancer: An International Effort for a Unified Approach

AbstractBackgroundThere are no universally monitored outcomes relevant to men with advanced prostate cancer, making it challenging to compare health outcomes between populations.ObjectiveWe sought to develop a standard set of outcomes relevant to men with advanced prostate cancer to follow during routine clinical care.Design, setting, and participantsThe International Consortium for Health Outcomes Measurement assembled a multidisciplinary working group to develop the set.Outcome measurements and statistical analysisWe used a modified Delphi method to achieve consensus regarding the outcomes, measures, and case mix factors included.Results and limitationsThe 25 members of the multidisciplinary international working group represented academic and nonacademic centers, registries, and patients. Recognizing the heterogeneity of men with advanced prostate cancer, the group defined the scope as men with all stages of incurable prostate cancer (metastatic and biochemical recurrence ineligible for further curative therapy). We defined outcomes important to all men, such as overall survival, and measures specific to subgroups, such as time to metastasis. Measures gathered from clinical data include measures of disease control. We also identified patient-reported outcome measures (PROMs), such as degree of urinary, bowel, and erectile dysfunction, mood symptoms, and pain control.ConclusionsThe international multidisciplinary group identified clinical data and PROMs that serve as a basis for international health outcome comparisons and quality-of-care assessments. The set will be revised annually.Patient summaryOur international group has recommended a standardized set of patient-centered outcomes to be followed during routine care for all men with advanced prostate cancer

Standardized outcome measures for pregnancy and childbirth, an ICHOM proposal

Background: Value-based health care aims to optimize the balance of patient outcomes and health care costs. To improve value in perinatal care using this strategy, standard outcomes must first be defined. The objective of this work was to define a minimum, internationally appropriate set of outcome measures for evaluating and improving perinatal care with a focus on outcomes that matter to women and their families. Methods: An interdisciplinary and international Working Group was assembled. Existing literature and current measurement initiatives were reviewed. Serial guided discussions and validation surveys provided consumer input. A series of nine teleconferences, incorporating a modified Delphi process, were held to reach consensus on the proposed Standard Set. Results: The Working Group selected 24 outcome measures to evaluate care during pregnancy and up to 6 months postpartum. These include clinical outcomes such as maternal and neonatal mortality and morbidity, stillbirth, preterm birth, birth injury and patient-reported outcome measures (PROMs) that assess health-related quality of life (HRQoL), mental health, mother-infant bonding, confidence and success with breastfeeding, incontinence, and satisfaction with care and birth experience. To support analysis of these outcome measures, pertinent baseline characteristics and risk factor metrics were also defined. Conclusions: We propose a set of outcome measures for evaluating the care that women and infants receive during pregnancy and the postpartum period. While validation and refinement via pilot implementation projects are needed, we view this as an important initial step towards value-based improvements in care