Effects of Yoga on Arm Volume among Women with Breast Cancer Related Lymphedema: A Pilot Study

Lymphedema affects 3–58% of survivors of breast cancer and can result in upper extremity impairments. Exercise can be beneficial in managing lymphedema. Yoga practice has been minimally studied for its effects on breast cancer related lymphedema (BCRL). The purpose of this study was to determine the effect of yoga on arm volume, quality of life (QOL), self-reported arm function, and hand grip strength in women with BCRL. Six women with BCRL participated in modified Hatha yoga 3×/week for 8 weeks. Compression sleeves were worn during yoga sessions. Arm volume, QOL, self-reported arm function, and hand grip strength were measured at baseline, half-way, and at the conclusion of yoga practice. Arm volume significantly decreased from baseline (2423.3 ml ± 597.2) to final measures (2370.8 ml ± 577.2) (p = .02). No significant changes in QOL (p = .12), self-reported arm function (p = .34), or hand grip strength (p = .26) were found. Yoga may be beneficial in the management of lymphedema

Implementation Research: An Efficient and Effective Tool to Accelerate Universal Health Coverage

Success in the implementation of evidence-based interventions (EBIs) in different settings has had variable success. Implementation research offers the approach needed to understand the variability of health outcomes from implementation strategies in different settings and why interventions were successful in some countries and failed in others. When mastered and embedded into a policy and implementation framework, the application of implementation research by countries can provide policy-makers and implementers with the knowledge necessary to work towards universal health coverage (UHC) with the effectiveness, efficiency, sustainability, and fidelity needed to achieve sustainable positive health outcomes for all. To achieve this goal however, work is needed by the communities of research producers and consumers to create more clarity on implementation research methodologies and to build capacity to apply them as a critical tool for countries on their path to achieving UHC

Association of Circulating Tumor DNA Testing Before Tissue Diagnosis With Time to Treatment Among Patients With Suspected Advanced Lung Cancer: The ACCELERATE Nonrandomized Clinical Trial.

IMPORTANCE Liquid biopsy has emerged as a complement to tumor tissue profiling for advanced non-small cell lung cancer (NSCLC). The optimal way to integrate liquid biopsy into the diagnostic algorithm for patients with newly diagnosed advanced NSCLC remains unclear. OBJECTIVE To evaluate the use of circulating tumor DNA (ctDNA) genotyping before tissue diagnosis among patients with suspected advanced NSCLC and its association with time to treatment. DESIGN, SETTING, AND PARTICIPANTS This single-group nonrandomized clinical trial was conducted among 150 patients at the Princess Margaret Cancer Centre-University Health Network (Toronto, Ontario, Canada) between July 1, 2021, and November 30, 2022. Patients referred for investigation and diagnosis of lung cancer were eligible if they had radiologic evidence of advanced lung cancer prior to a tissue diagnosis. INTERVENTIONS Patients underwent plasma ctDNA testing with a next-generation sequencing (NGS) assay before lung cancer diagnosis. Diagnostic biopsy and tissue NGS were performed per standard of care. MAIN OUTCOME AND MEASURES The primary end point was time from referral to treatment initiation among patients with advanced nonsquamous NSCLC using ctDNA testing before diagnosis (ACCELERATE [Accelerating Lung Cancer Diagnosis Through Liquid Biopsy] cohort). This cohort was compared with a reference cohort using standard tissue genotyping after tissue diagnosis. RESULTS Of the 150 patients (median age at diagnosis, 68 years [range, 33-91 years]; 80 men [53%]) enrolled, 90 (60%) had advanced nonsquamous NSCLC. The median time to treatment was 39 days (IQR, 27-52 days) for the ACCELERATE cohort vs 62 days (IQR, 44-82 days) for the reference cohort (P < .001). Among the ACCELERATE cohort, the median turnaround time from sample collection to genotyping results was 7 days (IQR, 6-9 days) for plasma and 23 days (IQR, 18-28 days) for tissue NGS (P < .001). Of the 90 patients with advanced nonsquamous NSCLC, 21 (23%) started targeted therapy before tissue NGS results were available, and 11 (12%) had actionable alterations identified only through plasma testing. CONCLUSIONS AND RELEVANCE This nonrandomized clinical trial found that the use of plasma ctDNA genotyping before tissue diagnosis among patients with suspected advanced NSCLC was associated with accelerated time to treatment compared with a reference cohort undergoing standard tissue testing. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT04863924

sj-pdf-1-vdi-10.1177_10406387221146247 – Supplemental material for Immunohistochemical analysis of expression of VEGFR2, KIT, PDGFR-β, and CDK4 in canine urothelial carcinoma

Supplemental material, sj-pdf-1-vdi-10.1177_10406387221146247 for Immunohistochemical analysis of expression of VEGFR2, KIT, PDGFR-β, and CDK4 in canine urothelial carcinoma by Laura C. Setyo, Shannon L. Donahoe, Patrick L. Shearer, Penghao Wang and Mark B. Krockenberger in Journal of Veterinary Diagnostic Investigation</p

Measuring associations between the microbiota and repeated measures of continuous clinical variables using a lasso-penalized generalized linear mixed model

Abstract Background Human microbiome studies in clinical settings generally focus on distinguishing the microbiota in health from that in disease at a specific point in time. However, microbiome samples may be associated with disease severity or continuous clinical health indicators that are often assessed at multiple time points. While the temporal data from clinical and microbiome samples may be informative, analysis of this type of data can be problematic for standard statistical methods. Results To identify associations between microbiota and continuous clinical variables measured repeatedly in two studies of the respiratory tract, we adapted a statistical method, the lasso-penalized generalized linear mixed model (LassoGLMM). LassoGLMM can screen for associated clinical variables, incorporate repeated measures of individuals, and address the large number of species found in the microbiome. As is common in microbiome studies, when the number of variables is an order of magnitude larger than the number of samples LassoGLMM can be imperfect in its variable selection. We overcome this limitation by adding a pre-screening step to reduce the number of variables evaluated in the model. We assessed the use of this adapted two-stage LassoGLMM for its ability to determine which microbes are associated with continuous repeated clinical measures. We found associations (retaining a non-zero coefficient in the LassoGLMM) between 10 laboratory measurements and 43 bacterial genera in the oral microbiota, and between 2 cytokines and 3 bacterial genera in the lung. We compared our associations with those identified by the Wilcoxon test after dichotomizing our outcomes and identified a non-significant trend towards differential abundance between high and low outcomes. Our two-step LassoGLMM explained more of the variance seen in the outcome of interest than other variants of the LassoGLMM method. Conclusions We demonstrated a method that can account for the large number of genera detected in microbiome studies and repeated measures of clinical or longitudinal studies, allowing for the detection of strong associations between microbes and clinical measures. By incorporating the design strengths of repeated measurements and a prescreening step to aid variable selection, our two-step LassoGLMM will be a useful analytic method for investigating relationships between microbes and repeatedly measured continuous outcomes

Measuring associations between the microbiota and repeated measures of continuous clinical variables using a lasso-penalized generalized linear mixed model.

BackgroundHuman microbiome studies in clinical settings generally focus on distinguishing the microbiota in health from that in disease at a specific point in time. However, microbiome samples may be associated with disease severity or continuous clinical health indicators that are often assessed at multiple time points. While the temporal data from clinical and microbiome samples may be informative, analysis of this type of data can be problematic for standard statistical methods.ResultsTo identify associations between microbiota and continuous clinical variables measured repeatedly in two studies of the respiratory tract, we adapted a statistical method, the lasso-penalized generalized linear mixed model (LassoGLMM). LassoGLMM can screen for associated clinical variables, incorporate repeated measures of individuals, and address the large number of species found in the microbiome. As is common in microbiome studies, when the number of variables is an order of magnitude larger than the number of samples LassoGLMM can be imperfect in its variable selection. We overcome this limitation by adding a pre-screening step to reduce the number of variables evaluated in the model. We assessed the use of this adapted two-stage LassoGLMM for its ability to determine which microbes are associated with continuous repeated clinical measures.We found associations (retaining a non-zero coefficient in the LassoGLMM) between 10 laboratory measurements and 43 bacterial genera in the oral microbiota, and between 2 cytokines and 3 bacterial genera in the lung. We compared our associations with those identified by the Wilcoxon test after dichotomizing our outcomes and identified a non-significant trend towards differential abundance between high and low outcomes. Our two-step LassoGLMM explained more of the variance seen in the outcome of interest than other variants of the LassoGLMM method.ConclusionsWe demonstrated a method that can account for the large number of genera detected in microbiome studies and repeated measures of clinical or longitudinal studies, allowing for the detection of strong associations between microbes and clinical measures. By incorporating the design strengths of repeated measurements and a prescreening step to aid variable selection, our two-step LassoGLMM will be a useful analytic method for investigating relationships between microbes and repeatedly measured continuous outcomes

Bleeding during critical illness: A prospective cohort study using a new measurement tool

Purpose: To estimate the incidence, severity, duration and consequences of bleeding during critical illness, and to test the performance characteristics of a new bleeding assessment tool. Methods: Clinical bleeding assessments were performed prospectively on 100 consecutive patients admitted to a medical-surgical intensive care unit (ICU) using a novel bleeding measurement tool called HEmorrhage MEasurement (HEME). Bleeding assessments were done daily in duplicate and independently by blinded, trained assessors. Inter-rater agreement and construct validity of the HEME tool were calculated using &phi;. Risk factors for major bleeding were identified using a multivariable Cox proportional hazards model. Results: Overall, 90% of patients experienced a total of 480 bleeds of which 94.8% were minor and 5.2% were major. Inter-rater reliability of the HEME tool was excellent (&phi; = 0.98, 95% CI: 0.96 to 0.99). A decrease in platelet count and a prolongation of partial thromboplastin time were independent risk factors for major bleeding but neither were renal failure nor prophylactic anticoagulation. Patients with major bleeding received more blood transfusions and had longer ICU stays compared to patients with minor or no bleeding. Conclusions: Bleeding, although primarily minor, occurred in the majority of ICU patients. One of five patients experienced a major bleed which was associated with abnormal coagulation tests but not with prophylactic anticoagulants. These baseline bleeding rates can inform the design of future clinical trials in critical care that use bleeding as an outcome and HEME is a useful tool to measure bleeding in critically ill patients

Oropharyngeal Shedding of Gammaherpesvirus DNA by Cats, and Natural Infection of Salivary Epithelium

Felis catus gammaherpesvirus-1 (FcaGHV1), a novel candidate oncogenic virus, infects cats worldwide. Whether the oropharynx is a site of virus shedding and persistence, and whether oronasal carcinomas harbor FcaGHV1 nucleic acid were investigated. In a prospective molecular epidemiological study, FcaGHV1 DNA was detected by cPCR in oropharyngeal swabs from 26/155 (16.8%) of cats. Oropharyngeal shedding was less frequently detected in kittens &le;3 months of age (5/94, 5.3%) than in older animals; &gt;3 months to &le;1 year: 8/26, 30.8%, (p = 0.001, OR 7.91, 95% CI (2.320, 26.979)); &gt;1 year to &le;6 years: 10/20, 50%, (p &lt; 0.001, OR 17.8 95% CI (5.065, 62.557)); &gt;6 years: 3/15, 33% (p = 0.078). Provenance (shelter-owned/privately owned) was not associated with shedding. In situ hybridization (ISH) identified FcaGHV1-infected cells in salivary glandular epithelium but not in other oronasal tissues from two of three cats shedding viral DNA in the oropharynx. In a retrospective dataset of 11 oronasopharyngeal carcinomas, a single tumor tested positive for FcaGHV1 DNA by ISH, a papillary carcinoma, where scattered neoplastic cells showed discrete nuclear hybridization. These data support the oronasopharynx as a site of FcaGHV1 shedding, particularly after maternal antibodies are expected to decline. The salivary epithelium is identified as a potential site of FcaGHV1 persistence. No evidence supporting a role for FcaGHV1 in feline oronasal carcinomas was found in the examined tumours