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HaploVar: an R package for defining local haplotype variants for trait association and trait prediction analyses
Get PDFMarker assisted breeding (MAB) supports breeding by identifying individuals or molecular markers associated with important traits. MAB methods include genome-wide association studies (GWAS) and genomic selection (GS). Local haplotypes are regions of DNA that are inherited together due to high levels of linkage disequilibrium. Local haplotypes can improve the prediction accuracy and power of GS and GWAS. Currently available local haplotyping tools improve GWAS power through fine-mapping of candidate regions or through haplotype-based GWAS. However, no local haplotyping tools utilize the benefits of haplotypes for GS. Here we present HaploVar, a local haplotyping tool designed to improve both GWAS and GS pipelines by identifying local haplotypes and formatting the output to be compatible with all major GWAS and GS tools. HaploVar can be used in any haplotype-based MAB study.
Availability and implementation HaploVar can be downloaded from CRAN with R >4.0.0 (DOI: 10.32614/CRAN.package.HaploVar). HaploVar and a tutorial vignette is available on GitHub (https://github.com/TessaMacNish/HaploVar). HaploVar is available under an MIT license
Low physical activity-related disease burden, 1990–2021: assessment of global trends and social determinants based on GBD 2021 data
Get PDFBackground Low physical activity (LPA) is associated with cardiovascular and cerebrovascular pathologies. This study aimed to assess the prevalence of several noncommunicable diseases relating to LPA. Methods Using the 2021 Global Burden of Disease data set, we modelled LPA-related disease burdens across 204 countries and territories, quantifying mortality counts, age-standardised mortality rates, and disability-adjusted life years (DALYs) for five noncommunicable diseases. We conducted multivariable stratification analyses to assess variations by gender, age, and sociodemographic index (SDI) quintiles. We used age-period-cohort modelling to project burden trajectories, while applying counterfactual decomposition frameworks to delineate synergistic interactions between LPA and risk factors. Results We found that LPA accounted for 555 101 related deaths globally in 2021 across the five studied pathologies, mostly among individuals aged 60–94 years. Association between LPA-related disease burden and SDI followed a U-shaped distribution across regions and diseases. Among individuals aged 60–89 years, LPA-related deaths were significantly higher in women than in men, indicating a disproportionate burden on elderly females. Ischaemic heart disease (IHD) trends stabilised in low- and middle-SDI regions but declined significantly in high-SDI regions, underscoring global health disparities. From 2007 to 2011, LPA DALYs and mortality risk ratios for IHD, stroke, and lower extremity peripheral arterial disease declined from >1 to <1, whereas diabetes mellitus exhibited an opposite trend, highlighting LPA’s persistent and significant impact on diabetes-related morbidity. Demographic shifts and epidemiological transitions were primary drivers of LPA-related disease burden across five pathologies. In high-SDI regions, epidemiological changes predominated, whereas population growth was a key factor in low- and middle-SDI regions. Synergistic interaction of these factors with LPA is projected to substantially amplify future disease burden. Conclusions Physical activity should be increased among elderly women to address health risks associated with LPA. Likewise, urgent public health interventions are needed for LPA-related diabetes. As IHD burden rises in low- and middle-SDI regions, vascular disease care strategies require optimisation. Moreover, high-SDI regions should strengthen nationwide physical activity promotion, while low- and middle-SDI areas must enhance healthcare infrastructure and manage population growth to reduce LPA-related disease burdens
Predicting Divorce and Separation from Social and Demographic in US Adults: A Linear Regression Model
Get PDF A linear regression model was developed to identify social and demographic factors associated with divorce and separation among US adults from the 2024 General Social Survey (GSS). Eleven predictors (age, sex, education, number of children, income, religiosity, happiness, job satisfaction, hours worked, race, and political affiliation) came together to create three models which were then compared, including: A simple logistic regression modeling age, a multiple logistic regression, and an Elastic Net regularized regression (ENR). 10-fold cross validation and testing using withheld test data was used to assess the performance of the model. The ENR model displayed the best performance (ROC=0.722, test data AUC=0.671), resulting in moderate prediction ability between “Together” and “Divorced/Separated” (DivSep) respondents. Age was the strongest predictor, second being sex, followed by number of children, and religious attendance, in relationship dissolution. Happiness and job satisfaction were predictive of success. The sensitivity of the model was high, approximately 0.99, while the specificity was low, likely due to data imbalance (82% Together, 18% DivSep). The results suggest the use of demographic data for prediction models may hold valuable input to relationship stability. Future studies should incorporate longitudinal data, a larger variety of psychological predictors, and more balanced data. Machine learning may have greater implications in helping decipher social patterns that are relevant to counseling, relationship research, and industry-dating-algorithms
Metronidazole exposure-response and safety in infants.
Get PDFThe nitroimidazole antibiotic, metronidazole, is frequently prescribed to infants with serious intra-abdominal infections, and multiple dosing recommendations exist. We sought to evaluate the extent to which metronidazole doses and associated exposures achieved desired efficacy and safety in infants enrolled in the Antibiotic Safety in Infants with Complicated Intra-abdominal Infections (SCAMP) trial (NCT01994993). SCAMP participants received intravenous metronidazole as part of multimodal antimicrobial therapy. Participants received a 15 mg/kg loading dose and a 7.5 mg/kg maintenance dose at 24 h. A subsequent 7.5 mg/kg maintenance dose was administered every 12 h for participants of postmenstrual age (PMA) 23 to <34 weeks; 8 h for PMA 34-40 weeks; and 6 h for PMA >40 weeks. We evaluated associations between simulated metronidazole exposures and pre-specified surrogate pharmacodynamic targets and clinical outcomes of efficacy and safety. Nearly 100% of pharmacodynamic targets were met. Infants with therapeutic success (a composite efficacy outcome, defined as the absence of death, negative bacterial blood cultures, and presumptive clinical cure at 30 days) had higher Cmin,ss, Cmax,ss, AUC00-24,ss, and AUCcum compared with infants without therapeutic success. However, the relationships between these exposure measures and therapeutic success were not significant in logistic regression analysis adjusting for gestational age. Despite generally high simulated exposures, no relationships were observed between exposures and prespecified safety events (necrotizing enterocolitis, intestinal strictures, intestinal perforation, positive blood culture, seizures, death, and intraventricular hemorrhage). Findings support metronidazole dosing as administered in term and preterm infants in the SCAMP trial
Genetic determinants and genomic consequences of non-leukemogenic somatic point mutations
Get PDFClonal hematopoiesis (CH) is defined by the expansion of a lineage of genetically identical cells in blood. Genetic lesions that confer a fitness advantage, such as leukemogenic point mutations or mosaic chromosomal alterations (mCAs), are frequent mediators of CH. However, recent analyses of both single cell-derived colonies of hematopoietic cells and population sequencing cohorts have revealed CH frequently occurs in the absence of known driver genetic lesions. To characterize CH without known driver genetic lesions, we use 51,399 deeply sequenced whole genomes from the NHLBI TOPMed sequencing initiative to perform simultaneous germline and somatic mutation analyses among individuals without leukemogenic point mutations (LPM), which we term CH-LPMneg. We quantify CH by estimating the total mutation burden. Because estimating somatic mutation burden without a paired-tissue sample is challenging, we develop a novel statistical method, the Genomic and Epigenomic informed Mutation (GEM) rate, that uses external genomic and epigenomic data sources to distinguish artifactual signals from true somatic mutations. We perform a genome-wide association study of GEM to discover the germline determinants of CH-LPMneg. We identify seven genes associated with CH-LPMneg (TCL1A, TERT, SMC4, NRIP1, PRDM16, MSRA, SCARB1).Functional analyses of SMC4 and NRIP1 implicated altered hematopoietic stem cell self-renewal and proliferation as the primary mediator of mutation burden in blood. We then perform comprehensive multi-tissue transcriptomic analyses, finding that the expression levels of 404 genes are associated with GEM. Finally, we perform phenotypic association meta-analyses across four cohorts, finding that GEM is associated with increased white blood cell count, but is not significantly associated with incident stroke or coronary disease events. Overall, we develop GEM for quantifying mutation burden from WGS and use GEM to discover the genetic, genomic, and phenotypic correlates of CH-LPMneg
Deconstruction and reconstruction of degrading effects in ultrasound imaging: aberration, multiple reverberation, and trailing reverberation
No full textUltrasound image degradation in the human body arises from the propagation and reflection of acoustical waves in a complex acoustical environment. The heterogeneous distribution of soft tissue and the variation in acoustical properties distort the ultrasonic beam causing deterioration in image quality, including loss of resolution and contrast. Here we establish a framework to construct images based on a separable (additive or multiplicative) representation of aberration, multiple reverberation, and trailing reverberation. A separable approach enables high modularity and flexibility when generating quantitatively degraded image datasets. The framework provides the capability to generate images with quantitative levels of image degradation related directly to imaging physics, thus allowing for a flexible approach for augmentation techniques in ultrasound imaging data sets, as demonstrated in the included repository code. Experimentally calibrated abdominal simulations were performed in Fullwave2 by matching relevant imaging metrics such as phase aberration, reverberation strength, speckle brightness and coherence length, to experimental measurements. Then, simulations were performed to separate and characterize the different components of image degradation. Finally, these components were scaled and combined to construct quantitatively degraded image datasets. Reverberation is shown to be depth and brightness dependent, while aberration and trailing clutter are not. This general framework was tested for values in acoustical ranges that significantly, synthetically, and independently enhance or reduce these effects compared to levels naturally occurring in the body. Identifying, quantifying, and modeling these differing and complex mechanisms of degradation can be used to develop and test rational approaches to overcome these degradation mechanisms to improve image quality, particularly for traditionally harder to image patients. Additionally, the framework to synthetically modify the effects of aberration, multiple reverberation, and trailing clutter is provided, allowing for the generation of augmented datasets with a wide range of degradation effects, based on imaging physics, to improve machine learning models
Exploring disparities in the proportion of ultra-processed foods and beverages purchased in grocery stores by US households in 2020.
Get PDFOBJECTIVE: American diets are increasingly based on ultra-processed foods (UPFs). Current research, particularly on socioeconomic differentials, is lacking. This study aimed to provide an updated examination of US household purchases of UPFs and how this differs by race-ethnicity, household income and household education. DESIGN: The NielsenIQ Consumer Panel 2020 was utilized for analysis. Each food and beverage product purchased by US households was assigned a level of processing under the Nova level of processing classification system. The volume of UPFs purchased overall and by food group was determined for each Nova processing group and examined by race-ethnicity, education, and income. Results were stratified by race-ethnicity within each income group. A P value < 0.0001 was considered significant. SETTING: This study analyzed data from the Nielsen IQ Consumer Panel 2020 which recorded household food purchases in the United States. PARTICIPANTS: The Nielsen IQ Homescan Consumer Panel is a nationally representative longitudinal survey of around 35,000 and 60,000 US households. RESULTS: Of 33,054,687 products purchased by 59,939 US households in 2020, 48% foods and 38% beverages were considered UPFs. Categories with the highest proportion of purchases deriving from UPFs included carbonated soft drinks (90%), mixed dishes and soups (81%) and sweets and snacks (71%). Slightly higher but statistically significant proportions of UPF purchases occurred in the lowest income and education groups and among non-Hispanic whites. CONCLUSIONS: It is concerning that household purchases of UPFs in the US are high. Policies that reduce consumption of UPFs may help reduce diet-related health inequalities
Structure and Stability of Phospholipid-Based Microbubbles Studied Using a Spin Probe.
Get PDFSteady-state electron paramagnetic resonance (EPR) spectroscopy is used to investigate the structure and stability of surfactant microbubbles made from distearoylphosphatidylcholine (DSPC) phospholipids and a polymeric stabilizer. The spin probe doxyl-5-stearic acid (5DSA) was incorporated into the phospholipid monolayer at an overall concentration of 3 × 10-7 M. The bubbles were characterized by optical microscopy and found to range in diameter from 0.6 to 10 μm. The EPR spectrum of the spin probe at room temperature exhibited slow motion and ordering. This behavior was simulated using the microscopic order-macroscopic disorder (MOMD) model through the EasySpin software package. During the course of 12 h in the EPR sample tube, a sharper fast-motion nitroxide spectrum appeared, indicating degradation of the bubbles over time. This is attributed to the typical process of microbubble degradation following gas exchange and interactions between the sample and capillary walls, leading to bubble collapse and the formation of a liquid phase with 5DSA incorporated into liposomes, micelles, or free molecules
The State of Artificial Intelligence in Healthcare Simulation: A 2024 International Survey
Get PDFThe role and landscape of Artificial Intelligence (AI) in simulation-based education have not been well described yet, and a comprehensive survey is essential to assess its current use and capabilities. This survey aimed to explore the application of AI in healthcare simulation, particularly to 1) identify primary users, 2) provide a picture of demographic trends, 3) examine the motivations driving adoption, 4) describe specific use cases, 5) analyze the diverse applications, 6) analyze the modalities or types of AI-driven simulations being implemented, and 7) investigate recruitment and training strategies for simulationists and staff.This open-ended survey targeted professionals involved in healthcare simulation and was widely distributed from May 27 to June 23, 2024. A total of 158 respondents from 12 countries were included in the analysis. The majority of respondents (49%) reported the use of AI for healthcare simulation, 41% did not use AI, and 10% reported that they were not sure. Of the 41% (n=66) respondents not using AI, 96% of those who responded (n=47) were interested in learning more. Seventy-one percent (71.2%, n=52 of those using AI) of respondents were planning for future use of AI. The demand for workshops, certifications, and access to technical experts was evident by all respondents, highlighting the importance of developing robust training programs to equip staff with the necessary skills to leverage AI effectively.This study gives a perspective into the rapid adaptation of AI into healthcare simulation, as well as future directions to pursue understanding and support of AI and its application in simulation-based education
Studying COVID-19 transmission in US state prisons using an agent-based modelling approach: a simulation study
Get PDFObjectives We aim to use an agent-based model to accurately predict the spread of COVID-19 within multiple US state prisons.DesignWe developed a semistochastic transmission model of COVID-19.
Setting Five regional state-owned prisons within North Carolina.
Participants Several thousand incarcerated individuals.
Primary and secondary outcome measures We measured (1) the observed and simulated average daily infection rate of COVID-19 for each prison studied in 30-day intervals, (2) the observed and simulated average daily recovery rate from COVID-19 for each prison studied in 30-day intervals, (3) the mean absolute percentage error (MAPE) of each prison’s summary statistics and the simulated results and (4) the parameter estimates of key predictors used in the model.
Introduction The COVID-19 pandemic disparately affected incarcerated populations in the USA, with severe morbidity and infection rates across the country. In response, many predictive models were developed to help mitigate risk. However, these models did not feature the systemic factors of prisons, such as vaccination rates, populations and capacities (to determine overcrowding) and design and were not generalisable to other prisons.
Methods An agent-based model that used geospatial contact networks and compartmental transmission dynamics was built to create predictive microsimulations that simulated COVID-19 outbreaks within five North Carolinian regional prisons between July 2020 and June 2021. The model used the characteristics of an outbreak’s initial case size, a given facility’s capacity and its incarcerated vaccination rate as additional parameters alongside traditional susceptible-exposed-infected-recovered transmission dynamics. By fitting the model to each prison’s data using approximate Bayesian computation methods, we derived parameter estimates that reasonably modelled real-world results. These individualised estimates were then averaged to produce generalised parameter estimates for North Carolina state prisons overall.
Results Our model had a mean average MAPE score of 23.0 across all facilities, meaning that it reasonably forecasted facilities’ average daily positive and recovery rates of COVID-19. Our model estimated an average incarcerated vaccination rate of 54% across all prisons (with a 95% CI of ±0.12). In addition, the prisons of this study were estimated to be operating at 90% of their capacity on average (95% CI ±0.16). Given the high levels of COVID-19 observed in these prisons, which averaged over one-third positive tests on respective 1-day maxima, we conclude that vaccination levels were not sufficient in curbing COVID-19 outbreaks, and high occupancy levels likely exacerbated the spread of COVID-19 within prisons.In addition, data gaps in facilities without recorded daily testing resulted in poor spread predictions, demonstrating how important consistent data release practices are in incarcerated settings for accurate tracking and prediction of outbreaks.
Conclusion The findings of this study better quantify how spatial contact networks and facility-level characteristics unique to congregate living facilities can be used to predict infectious disease spread. Our approach also highlights the need for increased vaccination efforts and potential capacity reductions to mitigate COVID-19 transmission in prisons