Substrate Micropatterning as a New in Vitro Cell Culture System to Study Myelination

Artículo de publicación ISIMyelination is a highly regulated developmental process whereby oligodendrocytes in the central nervous system and Schwann cells in the peripheral nervous system ensheathe axons with a multilayered concentric membrane. Axonal myelination increases the velocity of nerve impulse propagation. In this work, we present a novel in vitro system for coculturing primary dorsal root ganglia neurons along with myelinating cells on a highly restrictive and micropatterned substrate. In this new coculture system, neurons survive for several weeks, extending long axons on defined Matrigel tracks. On these axons, myelinating cells can achieve robust myelination, as demonstrated by the distribution of compact myelin and nodal markers. Under these conditions, neurites and associated myelinating cells are easily accessible for studies on the mechanisms of myelin formation and on the effects of axonal damage on the myelin sheath.Regenerative Medicine and Nanomedicine Initiative of the Canadian Institutes of Health Research (CIHR) RMF-7028 FONDECYT 1080252 CIHR Ministry of Industry of Canada Rio Tinto Alcan Molson Foundatio

Knowledge-based quality assurance of a comprehensive set of organ at risk contours for head and neck radiotherapy

IntroductionManual review of organ at risk (OAR) contours is crucial for creating safe radiotherapy plans but can be time-consuming and error prone. Statistical and deep learning models show the potential to automatically detect improper contours by identifying outliers using large sets of acceptable data (knowledge-based outlier detection) and may be able to assist human reviewers during review of OAR contours.MethodsThis study developed an automated knowledge-based outlier detection method and assessed its ability to detect erroneous contours for all common head and neck (HN) OAR types used clinically at our institution. We utilized 490 accurate CT-based HN structure sets from unique patients, each with forty-two HN OAR contours when anatomically present. The structure sets were distributed as 80% for training, 10% for validation, and 10% for testing. In addition, 190 and 37 simulated contours containing errors were added to the validation and test sets, respectively. Single-contour features, including location, shape, orientation, volume, and CT number, were used to train three single-contour feature models (z-score, Mahalanobis distance [MD], and autoencoder [AE]). Additionally, a novel contour-to-contour relationship (CCR) model was trained using the minimum distance and volumetric overlap between pairs of OAR contours to quantify overlap and separation. Inferences from single-contour feature models were combined with the CCR model inferences and inferences evaluating the number of disconnected parts in a single contour and then compared.ResultsIn the test dataset, before combination with the CCR model, the area under the curve values were 0.922/0.939/0.939 for the z-score, MD, and AE models respectively for all contours. After combination with CCR model inferences, the z-score, MD, and AE had sensitivities of 0.838/0.892/0.865, specificities of 0.922/0.907/0.887, and balanced accuracies (BA) of 0.880/0.900/0.876 respectively. In the validation dataset, with similar overall performance and no signs of overfitting, model performance for individual OAR types was assessed. The combined AE model demonstrated minimum, median, and maximum BAs of 0.729, 0.908, and 0.980 across OAR types.DiscussionOur novel knowledge-based method combines models utilizing single-contour and CCR features to effectively detect erroneous OAR contours across a comprehensive set of 42 clinically used OAR types for HN radiotherapy

DataSheet_1_Knowledge-based quality assurance of a comprehensive set of organ at risk contours for head and neck radiotherapy.docx

IntroductionManual review of organ at risk (OAR) contours is crucial for creating safe radiotherapy plans but can be time-consuming and error prone. Statistical and deep learning models show the potential to automatically detect improper contours by identifying outliers using large sets of acceptable data (knowledge-based outlier detection) and may be able to assist human reviewers during review of OAR contours.MethodsThis study developed an automated knowledge-based outlier detection method and assessed its ability to detect erroneous contours for all common head and neck (HN) OAR types used clinically at our institution. We utilized 490 accurate CT-based HN structure sets from unique patients, each with forty-two HN OAR contours when anatomically present. The structure sets were distributed as 80% for training, 10% for validation, and 10% for testing. In addition, 190 and 37 simulated contours containing errors were added to the validation and test sets, respectively. Single-contour features, including location, shape, orientation, volume, and CT number, were used to train three single-contour feature models (z-score, Mahalanobis distance [MD], and autoencoder [AE]). Additionally, a novel contour-to-contour relationship (CCR) model was trained using the minimum distance and volumetric overlap between pairs of OAR contours to quantify overlap and separation. Inferences from single-contour feature models were combined with the CCR model inferences and inferences evaluating the number of disconnected parts in a single contour and then compared.ResultsIn the test dataset, before combination with the CCR model, the area under the curve values were 0.922/0.939/0.939 for the z-score, MD, and AE models respectively for all contours. After combination with CCR model inferences, the z-score, MD, and AE had sensitivities of 0.838/0.892/0.865, specificities of 0.922/0.907/0.887, and balanced accuracies (BA) of 0.880/0.900/0.876 respectively. In the validation dataset, with similar overall performance and no signs of overfitting, model performance for individual OAR types was assessed. The combined AE model demonstrated minimum, median, and maximum BAs of 0.729, 0.908, and 0.980 across OAR types.DiscussionOur novel knowledge-based method combines models utilizing single-contour and CCR features to effectively detect erroneous OAR contours across a comprehensive set of 42 clinically used OAR types for HN radiotherapy.</p

Pilot randomised controlled trial of a patient navigation intervention to enhance engagement in the PrEP continuum among young Latino MSM: a protocol paper

Introduction Men who have sex with men (MSM) are one of the most at-risk group for contracting HIV in the USA. However, the HIV epidemic impacts some groups of MSM disproportionately. Latino MSM comprise 25.1% of new HIV infections among MSM between the ages of 13 and 29 years. The daily medication tenofovir/emtricitabine was approved by the Food and Drug Administration for pre-exposure prophylaxis (PrEP) in 2012 and has demonstrated strong efficacy in reducing HIV acquisition.Methods and analysis Through extensive formative research, this study uses a pilot randomised controlled trial design and will examine the feasibility and acceptability of a patient navigation intervention designed to address multiple barriers to improve engagement in the PrEP continuum among 60 Latino MSM between the ages of 18 and 29 years. The patient navigation intervention will be compared with usual care plus written information to evaluate the feasibility and acceptability of the intervention and study methods and the intervention’s potential in improving PrEP continuum behaviours. The results will be reviewed for preparation for a future full-scale efficacy trial.Ethics and dissemination This study was approved by the institutional review board at San Diego State University and is registered at ClinicalTrials.gov. The intervention development process, plan and the results of this study will be shared through peer-reviewed journal publications, conference presentations and healthcare system and community presentations.Registration details Registered under the National Institutes of Health’s ClinicalTrials.gov (NCT04048382) on 7 August 2019 and approved by the San Diego State University (HS-2017–0187) institutional review board. This study began on 5 August 2019 and is estimated to continue through 31 March 2021. The clinical trial is in the pre-results stage