Biology subjects

Jenkins, M.

Publications and source records attributed to Jenkins, M..

7 recordsLinked to original sources

bioRxiv2024

First record of Culex pipiens (Diptera: Culicidae) in Alberta: Expanding distributions and ecotype patterns in a western Canadian province

Culex pipiens is an invasive mosquito found in temperate regions globally. It is considered among the most important disease vectors worldwide and is responsible for transmission of a range of pathogens, including West Nile virus, avian malaria, Saint Louis encephalitis, and filarial worms. Throughout its northern temperate range, this mosquito is found in two ecotypes: form pipiens and form molestus. In Canada, this mosquito was previously thought restricted to the Pacific coast of British Columbia and the eastern provinces of Ontario, Quebec, and the Maritimes. Through routine mosquito surveillance and targeted trapping for Cx. pipiens, we detected this mosquito in two Albertan municipalities earlier than suggested by species distribution modelling based on climate change data. We confirmed the identity of putative Cx. pipiens specimens using DNA sequencing and found that form pipiens was the most common ecotype found in Alberta. Further, we compared the frequency of ecotypes in Alberta to elsewhere in North America and found a general trend of increased form pipiens in more northern latitudes, similar to previously reported results. We discuss our findings in context of vector-borne disease activity in Canada, particularly West Nile virus.

ecology↗

bioRxiv2023

Accumulation of m6A exhibits stronger correlation with MAPT than beta-amyloid pathology in an APP NL-G-F /MAPT P301S mouse model of Alzheimer's disease

The study for the pathophysiology study of Alzheimers disease (AD) has been hampered by lack animal models that recapitulate the major AD pathologies, including extracellular {beta}-amyloid (A{beta}) deposition, intracellular aggregation of microtubule associated protein tau (MAPT), inflammation and neurodegeneration. We now report on a double transgenic APPNL-G-F MAPTP301S mouse that at 6 months of age exhibits robust A{beta} plaque accumulation, intense MAPT pathology, strong inflammation and extensive neurodegeneration. The presence of A{beta} pathology potentiated the other major pathologies, including MAPT pathology, inflammation and neurodegeneration. However, MAPT pathology neither changed levels of amyloid precursor protein nor potentiated A{beta} accumulation. The APPNL-G-F/MAPTP301S mouse model also showed strong accumulation of N6-methyladenosine (m6A), which was recently shown to be elevated in the AD brain. M6A primarily accumulated in neuronal soma, but also co-localized with a subset of astrocytes and microglia. The accumulation of m6A corresponded with increases in METTL3 and decreases in ALKBH5, which are enzymes that add or remove m6A from mRNA, respectively. Thus, the APPNL- G-F/MAPTP301S mouse recapitulates many features of AD pathology beginning at 6 months of aging.

neuroscience↗

bioRxiv2022

Past, present, and future of the Living Planet Index

As we enter the next phase of international policy commitments to halt biodiversity loss (e.g. Post-2020 Biodiversity Framework), biodiversity indicators will play an important role forming the robust basis upon which targeted, and time sensitive conservation actions are developed. Population trend indicators are perhaps the most powerful tool in biodiversity monitoring due to their responsiveness to changes over short timescales and their ability to aggregate species trends from global down to at a sub-national or even local scale. We consider how the project behind the foremost population level indicator - the Living Planet Index - has evolved over the last 25 years, its value to the field of biodiversity monitoring, and how its components have portrayed a compelling account of the changing status of global biodiversity through its application at policy, research and practice levels. We explore ways the project can develop to enhance our understanding of the state of biodiversity and share lessons learned to inform indicator development and mobilise action. O_TEXTBOXBox 1. The Living Planet Index Project The Living Planet Index project (the index, methodology, and database) and its secondary outputs (methods papers and R code, database and website, global index, and subset indices) have had wide-ranging applications within the fields of biodiversity monitoring and research, as well as across policy, education, and outreach. The Living Planet Index (LPI) is a biodiversity indicator which tracks trends in the relative abundance of wild vertebrate populations (where population is defined as to a single species in a defined location rather than the biological definition). Relative abundance captures how populations are changing over time on average in comparison to a reference point, or "baseline" (the LPI uses 1970). It is often described as analogous to a stock market index for species. The index is comprised of thousands of population time-series for vertebrate species from locations around the world; the trends from these populations are averaged to produce terrestrial, freshwater, and marine indices, which are further aggregated to a global LPI. The latest global LPI shows a decline of 68% between 1970 and 2016 globally 1. This is an average trend based on time-series data from 20,811 populations of 4,392 species of mammals, birds, reptiles, amphibians, and fish. The LPI database (LPD) can include population data for any species for which time-series population data could be found, regardless of threat status, or whether they show increasing or declining trends. These population time-series are sourced from scientific papers, online databases, government, and expert led published reports. They can be searched and downloaded from the project website (www.livingplanetindex.org). More technical information is available on the LPI stats website (http://stats.livingplanetindex.org/). C_TEXTBOX

ecology↗

bioRxiv2022

Route of mRNA vaccination modulates the establishment of pulmonary resident memory CD8 and CD4 T cells

Respiratory tract resident memory T cells (Trm), typically generated by local vaccination or infection, can accelerate control of pulmonary infections that evade neutralizing antibody. It is unknown whether mRNA vaccination establishes respiratory Trm. We generated a self-amplifying mRNA vaccine encoding the influenza A virus nucleoprotein that is encapsulated in modified dendron-based nanoparticles. Here we report how routes of immunization in mice, including contralateral versus ipsilateral intramuscular boosts, or intravenous and intranasal routes, influence influenza-specific cell-mediated and humoral immunity. Parabiotic surgeries revealed that intramuscular immunization was sufficient to establish CD8 Trm in lung and draining lymph node. Contralateral, compared to ipsilateral, intramuscular boosting broadened the distribution of LN Trm and T follicular helper cells, but slightly diminished resulting levels of serum antibody. Intranasal mRNA delivery established modest circulating CD8 and CD4 T cell memory, but augmented distribution to the respiratory mucosa. Of note, combining intramuscular immunizations with an intranasal mRNA boost achieved high levels of both circulating T cell memory and lung Trm. Thus, routes of mRNA vaccination influence humoral and cell-mediated immunity, and intramuscular prime-boosting establishes lung Trm that can be further enhanced by an additional intranasal immunization.

immunology↗

bioRxiv2020

A multi-modal machine learning approach towards predicting patient readmission

Healthcare costs that can be attributed to unplanned readmissions are staggeringly high and negatively impact health and wellness of patients. In the United States, hospital systems and care providers have strong financial motivations to reduce readmissions in accordance with several government guidelines. One of the critical steps to reducing readmissions is to recognize the factors that lead to readmission and correspondingly identify at-risk patients based on these factors. The availability of large volumes of electronic health care records make it possible to develop and deploy automated machine learning models that can predict unplanned readmissions and pinpoint the most important factors of readmission risk. While hospital readmission is an undesirable outcome for any patient, it is more so for medically frail patients. Here, we develop and compare four machine learning models (Random Forest, XGBoost, CatBoost, and Logistic Regression) for predicting 30-day unplanned readmission for patients deemed frail (Age [≥] 50). Variables that indicate frailty, comorbidities, high risk medication use, demographic, hospital and insurance were incorporated in the models for prediction of unplanned 30-day readmission. Our findings indicate that CatBoost outperforms the other three models (AUC 0.80) and prior work in this area. We find that constructs of frailty, certain categories of high risk medications, and comorbidity are all strong predictors of readmission for elderly patients.

bioinformatics↗

bioRxiv2019

Assessment of Polygenic Architecture and Risk Prediction based on Common Variants Across Fourteen Cancers

We analyzed summary-level data from genome-wide association studies (GWAS) of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) contributing to risk, as well as the distribution of their associated effect sizes. All cancers evaluated showed polygenicity, involving at a minimum thousands of independent susceptibility variants. For some malignancies, particularly chronic lymphoid leukemia (CLL) and testicular cancer, there are a larger proportion of variants with larger effect sizes than those for other cancers. In contrast, most variants for lung and breast cancers have very small associated effect sizes. For different cancer sites, we estimate a wide range of GWAS sample sizes, required to explain 80% of GWAS heritability, varying from 60,000 cases for CLL to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores, compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that polygenic risk scores have substantial potential for risk stratification for relatively common cancers such as breast, prostate and colon, but limited potential for other cancer sites because of modest heritability and lower disease incidence.

genetics↗

bioRxiv2018

Ability of known susceptibility SNPs to predict colorectal cancer risk for persons with and without a family history

BackgroundA number of single nucleotide polymorphisms (SNPs), which are common inherited genetic variants, have been identified that are associated with risk of colorectal cancer. The aim of this study was to determine the ability of these SNPs to estimate colorectal cancer (CRC) risk for persons with and without a family history of CRC, and the screening implications.\n\nMethodsWe estimated the association with CRC of a 45 SNP-based risk using 1,181 cases and 999 controls, and its correlation (r) with CRC risk predicted from detailed family history. We estimated the predicted change in the distribution across predefined risk categories, and implications for recommended age to commence screening, from adding SNP-based risk to family history.\n\nResultsThe inter-quintile risk ratio for colorectal cancer risk of the SNP-based risk was 2.46 (95% CI 1.91 - 3.11). SNP-based and family history-based risks were not correlated (r = 0.02). For persons with no first-degree relatives with CRC, recommended screening would commence 2 years earlier for women (4 years for men) in the highest quintile of SNP-based risk, and 12 years later for women (7 years for men) in the lowest quintile. For persons with two first-degree relatives with CRC, recommended screening would commence 15 years earlier for men and women in the highest quintile, and 8 years earlier for men and women in the lowest quintile.\n\nConclusionsRisk reclassification by 45 SNPs could inform targeted screening for CRC prevention, particularly in clinical genetics settings when mutations in high-risk genes cannot be identified.

epidemiology↗