4,604 research outputs found

    sj-pdf-1-vet-10.1177_03009858231225500 – Supplemental material for Mortality associated with SARS-CoV-2 in nondomestic felids

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    Supplemental material, sj-pdf-1-vet-10.1177_03009858231225500 for Mortality associated with SARS-CoV-2 in nondomestic felids by Mary Drozd, Jana M. Ritter, Jonathan Peter Samuelson, Maryanna Parker, Leyi Wang, Samantha J. Sander, Jill Yoshicedo, Louden Wright, Jenee Odani, Trent Shrader, Elizabeth Lee, Shawn R. Lockhart, Ria R. Ghai and Karen A. Terio in Veterinary Pathology</p

    Twins Early Development Study (TEDS): A genetically sensitive investigation of mental health outcomes in the mid‐twenties

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    Abstract The Twins Early Development Study (TEDS) is a longitudinal study following a cohort of twins born 1994–1996 in England and Wales. Of the 13,759 families who originally consented to take part, over 10,000 families remain enrolled in the study. The current focus of TEDS is on mental health in the mid‐twenties. Making use of over 25 years of genetically sensitive data, TEDS is uniquely placed to explore the longitudinal genetic and environmental influences on common mental health disorders in early adulthood. This paper outlines recent data collection efforts supporting this work, including a cohort‐wide mental health assessment at age 26 and a multi‐phase Covid‐19 study. It will also provide an update on data linkage efforts and the Children of TEDS (CoTEDS) project

    Vaccination, time lost from work, and COVID-19 infections: a Canadian healthcare worker retrospective cohort study

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    The COVID-19 pandemic highlighted hurdles for healthcare delivery and personnel globally. Vaccination has been an important tool for preventing severe illness and death in healthcare workers (HCWs) as well as the public at large. However, vaccination has resulted in some HCWs requiring time off work post-vaccination to recover from adverse events. We aimed to understand which HCWs needed to take time off work post-vaccination, for which vaccine types and sequence, and how post-vaccination absence impacted uptake of booster doses in a cohort of 26,267 Canadian HCWs. By March 31, 2022, more than 98% had received at least two doses of the approved COVID-19 vaccines, following a two-dose mandate. We found that recent vaccination and longer intervals between doses were associated with significantly higher odds of time-loss, whereas being a medical resident and receiving the BNT162b2 vaccine were associated with lower odds. A history of lab-confirmed SARS-CoV-2 infection was associated with lower odds of receiving a booster dose compared with no documented infection, aOR 0.61 (95% CI: 0.55, 0.68). Similarly, taking sick time following the first or second dose was associated with lower odds of receiving a booster dose, aOR 0.83 (95% CI: 0.75, 0.90). As SARS-CoV-2 becomes endemic, the number and timing of additional doses for HCWs requires consideration of prevention of illness as well as service disruption from post-vaccination time-loss. Care should be taken to ensure adequate staffing if many HCWs are being vaccinated, especially for coverage for those who are more likely to need time off to recover

    Immunogenicity and Safety of a Third COVID-19 BNT162b2 mRNA Vaccine Dose in 5- to 11-Year Olds

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    In this ongoing study, substantially increased ancestral SARSCoV-2 neutralizing responses were observed 1 month after a third 10-µg BNT162b2 dose given to 5 to 11-year olds versus neutralizing responses post-dose 2. After dose 3, increased neutralizing responses against Omicron BA.1 and BA.4/BA.5 strains were also observed. The safety/tolerability profile was acceptable.Peer reviewe

    Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients

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    Recent clinical research describes a subset of glioblastoma patients that exhibit REP prior to start of radiation therapy. Current literature has thus far described this population using clinicopathologic features. To our knowledge, this study is the first to investigate the potential of conventional ra-diomics, sophisticated multi-resolution fractal texture features, and different molecular features (MGMT, IDH mutations) as a diagnostic and prognostic tool for prediction of REP from non-REP cases using computational and statistical modeling methods. Radiation-planning T1 post-contrast (T1C) MRI sequences of 70 patients are analyzed. Ensemble method with 5-fold cross validation over 1000 iterations offers AUC of 0.793 with standard deviation of 0.082 for REP and non-REP classification. In addition, copula-based modeling under dependent censoring (where a subset of the patients may not be followed up until death) identifies significant features (p-value <0.05) for survival probability and prognostic grouping of patient cases. The prediction of survival for the patients cohort produces precision of 0.881 with standard deviation of 0.056. The prognostic index (PI) calculated using the fused features suggests that 84.62% of REP cases fall under the bad prognostic group, suggesting potentiality of fused features to predict a higher percentage of REP cases. The experimental result further shows that mul-ti-resolution fractal texture features perform better than conventional radiomics features for REP and survival outcomes

    Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients

    Get PDF
    Recent clinical research describes a subset of glioblastoma patients that exhibit REP prior to the start of radiation therapy. Current literature has thus far described this population using clinicopathologic features. To our knowledge, this study is the first to investigate the potential of conventional radiomics, sophisticated multi-resolution fractal texture features, and different molecular features (MGMT, IDH mutations) as a diagnostic and prognostic tool for prediction of REP from non-REP cases using computational and statistical modeling methods. The radiation-planning T1 post-contrast (T1C) MRI sequences of 70 patients are analyzed. An ensemble method with 5-fold cross-validation over 1000 iterations offers an AUC of 0.793 ± 0.082 for REP versus non-REP classification. In addition, copula-based modeling under dependent censoring (where a subset of the patients may not be followed up with until death) identifies significant features (p-value < 0.05) for survival probability and prognostic grouping of patient cases. The prediction of survival for the patients’ cohort produces a precision of 0.881 ± 0.056. The prognostic index (PI) calculated using the fused features shows that 84.62% of REP cases fall under the bad prognostic group, suggesting the potential of fused features for predicting a higher percentage of REP cases. The experimental results further show that multi-resolution fractal texture features perform better than conventional radiomics features for prediction of REP and survival outcomes
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