UMP, SCUN seal bilateral collaborations

Universiti Malaysia Pahang (UMP) fortified its international networking when it sealed a Memorandum of Understanding (MoU) with China’s South-Central University for Nationalities (SCUN) in Beijing, China, on June 1

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Statistical Methods for Multi-Modal Image Analysis with Applications in Multiple Sclerosis and Neurodevelopment

Multi-modal neuroimaging, where several high-dimensional imaging variables are collected, has enabled the visualization and analysis of the brain structure and function in unprecedented detail. Due to methodological and computational challenges, the vast number of imaging studies evaluate data from each modality separately and do not consider information encoded in the relationships between imaging types. In this work, we propose methods that quantify the complex relationships between multiple imaging modalities and map how these relationships vary spatially across different anatomical regions of the brain. In order to understand relationships between several high-dimensional imaging variables, we use novel multi-modal image analysis techniques for feature development and image fusion in conjunction with machine learning techniques to develop automatic approaches for multiple sclerosis lesion detection. Additionally, we use multi-modal image analysis to understand the association between high-dimensional imaging variables with phenotypes of interest to investigate structure-function relationships in development, aging, and pathology of the brain. We find that by leveraging the relationship between imaging modalities, we can more accurately detect neuropathology and delineate brain trajectories to provide complementary characterizations of healthy development. We provide publicly available R packages to allow easy access and implemention of the proposed methods in new data and contexts

A general method for combining different family-based rare-variant tests of association to improve power and robustness of a wide range of genetic architectures

Current rare-variant, gene-based tests of association often suffer from a lack of statistical power to detect genotype–phenotype associations as a result of a lack of prior knowledge of genetic disease models combined with limited observations of extremely rare causal variants in population-based samples. The use of pedigree data, in which rare variants are often more highly concentrated than in population-based data, has been proposed as 1 possible method for enhancing power. Methods for combining multiple gene-based tests of association into a single summary p value are a robust approach to different genetic architectures when little a priori knowledge is available about the underlying genetic disease model. To date, however, little consideration has been given to combining gene-based tests of association for the analysis of pedigree data. We propose a flexible framework for combining any number of family-based rare-variant tests of association into a single summary statistic and for assessing the significance of that statistic. We show that this approach maintains type I error and improves the robustness, to different genetic architectures, of the statistical power of family- and gene-based rare-variant tests through application to simulated phenotype data from Genetic Analysis Workshop 19

A multistep approach to single nucleotide polymorphism–set analysis: an evaluation of power and type I error of gene-based tests of association after pathway-based association tests

The aggregation of functionally associated variants given a priori biological information can aid in the discovery of rare variants associated with complex diseases. Many methods exist that aggregate rare variants into a set and compute a single p value summarizing association between the set of rare variants and a phenotype of interest. These methods are often called gene-based, rare variant tests of association because the variants in the set are often all contained within the same gene. A reasonable extension of these approaches involves aggregating variants across an even larger set of variants (eg, all variants contained in genes within a pathway). Testing sets of variants such as pathways for association with a disease phenotype reduces multiple testing penalties, may increase power, and allows for straightforward biological interpretation. However, a significant variant-set association test does not indicate precisely which variants contained within that set are causal. Because pathways often contain many variants, it may be helpful to follow-up significant pathway tests by conducting gene-based tests on each gene in that pathway to narrow in on the region of causal variants. In this paper, we propose such a multistep approach for variant-set analysis that can also account for covariates and complex pedigree structure. We demonstrate this approach on simulated phenotypes from Genetic Analysis Workshop 19. We find generally better power for the multistep approach when compared to a more conventional, single-step approach that simply runs gene-based tests of association on each gene across the genome. Further work is necessary to evaluate the multistep approach on different data sets with different characteristics

Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation

Accurate segmentation of retinal fluids in 3D Optical Coherence Tomography images is key for diagnosis and personalized treatment of eye diseases. While deep learning has been successful at this task, trained supervised models often fail for images that do not resemble labeled examples, e.g. for images acquired using different devices. We hereby propose a novel semi-supervised learning framework for segmentation of volumetric images from new unlabeled domains. We jointly use supervised and contrastive learning, also introducing a contrastive pairing scheme that leverages similarity between nearby slices in 3D. In addition, we propose channel-wise aggregation as an alternative to conventional spatial-pooling aggregation for contrastive feature map projection. We evaluate our methods for domain adaptation from a (labeled) source domain to an (unlabeled) target domain, each containing images acquired with different acquisition devices. In the target domain, our method achieves a Dice coefficient 13.8% higher than SimCLR (a state-of-the-art contrastive framework), and leads to results comparable to an upper bound with supervised training in that domain. In the source domain, our model also improves the results by 5.4% Dice, by successfully leveraging information from many unlabeled images.Comment: Accepted for publication at MICCAI 202

Nutrición y Metabolismo - NU135 - 202102

Descripción: Este curso busca demostrar la importancia del proceso alimentación-nutrición como determinante fundamental de la salud y desarrollo del ser humano y la población, así como su importancia en el binomio salud-enfermedad Los estudiantes de las carreras de Medicina, Odontología y Terapia Física aprenderán en este curso el rol de los macro y micronutrientes en el mantenimiento de una óptima nutrición, así como los principales requerimientos y esquemas nutricionales a lo largo de las etapas de la vida, para luego concluir con las principales dietas actuales y regímenes hospitalarios más comunes. Propósito: En este curso se desarrolla la competencia general de pensamiento crítico en su segundo nivel y tiene como requisito el curso de Procesos Biológicos 2 (para Medicina) y Agresión y Defensa (para Odontología y Terapia Física). Con los conceptos adquiridos en el presente curso serán capaces de comprender la importancia y aplicabilidad de la nutrición en sus carreras, fomentando el trabajo interdisciplinario

ModelArray: An R package for statistical analysis of fixel-wise data

ABSTRACT: Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel-wise data exist, currently available tools support only a limited number of statistical models. Here we introduce ModelArray, an R package for mass-univariate statistical analysis of fixel-wise data. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. In addition, ModelArray also aims for scalable analysis. With only several lines of code, even large fixel-wise datasets can be analyzed using a standard personal computer. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel-wise data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n = 938). ModelArray revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides a flexible and efficient platform for statistical analysis of fixel-wise data