A grey wolf-based method for mammographic mass classification

Breast cancer is one of the most prevalent cancer types with a high mortality rate in women worldwide. This devastating cancer still represents a worldwide public health concern in terms of high morbidity and mortality rates. The diagnosis of breast abnormalities is challenging due to different types of tissues and textural variations in intensity. Hence, developing an accurate computer-aided system (CAD) is very important to distinguish normal from abnormal tissues and define the abnormal tissues as benign or malignant. The present study aims to enhance the accuracy of CAD systems and to reduce its computational complexity. This paper proposes a method for extracting a set of statistical features based on curvelet and wavelet sub-bands. Then the binary grey wolf optimizer (BGWO) is used as a feature selection technique aiming to choose the best set of features giving high performance. Using public dataset, Digital Database for Screening Mammography (DDSM), different experiments have been performed with and without using the BGWO algorithm. The random forest classifier with 10-fold cross-validation is used to achieve the classification task to evaluate the selected set of features’ capability. The obtained results showed that when the BGWO algorithm is used as a feature selection technique, only 30.7% of the total features can be used to detect whether a mammogram image is normal or abnormal with ROC area reaching 1.0 when the fusion of both curvelet and wavelet features were used. In addition, in case of diagnosing the mammogram images as benign or malignant, the results showed that using BGWO algorithm as a feature selection technique, only 38.5% of the total features can be used to do so with high ROC area result at 0.871

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Impact of climate indicators on the COVID-19 pandemic in Saudi Arabia

202111 bcvcNot applicableSelf-fundedEarly release12 month

The effects of different carrying methods on locomotion stability, gait spatio-temporal parameters and spinal stresses

Manual material handling (MMH) contributes to a large percentage of musculoskeletal disorders. One of its fundamental activities is load carrying that can be accomplished in several strategies, with each one imposing different types of stresses on the musculoskeletal system. Therefore, the first goal of this study was to determine the effect of different carrying methods on walking stability using motion capture analysis. Second, to analyze gait adaptations to stresses associated with load carrying in order to prevent falling. Third, to investigate the effect of these stresses coupled with human body adjustment on the forces at the L5/S1 disc. Thirty participants carried 10 and 30 lbs loads via frontal, lateral, bilateral, and posterior carriages. Frontal and lateral methods generated the most unstable conditions compared to the others. The unstable locomotion forced the gait parameters to be significantly altered in order to maintain stability. Additionally, the postures maintained in these conditions resulted in significantly high compression and shear forces acting at the L5/S1 disc when compared to the other carrying methods. Moreover, heavier weights exacerbated the effect on the dependent variables. Notably, bilateral and posterior carrying methods provided results comparable to the unloaded walking baseline. In conclusion, to reduce the potential risks associated with load carrying, the recommendation to split the load between both hands using bilateral carrying method or carrying it posteriorly should be taken into account while designing MMH activities. •Frontal and lateral carrying method generated the most unstable conditions compared to the others.•The unstable locomotion forced the gait parameters to be significantly altered in order to maintain stability.•Trunk adjustments resulted in significantly high compression and shear forces acting at the L5/S1 disc.•Bilateral and posterior carriages resulted in stable locomotion, and normal gait parameters

Altered mechano-chemical environment in hip articular cartilage: effect of obesity

The production of extracellular matrix (ECM) components of articular cartilage is regulated, among other factors, by an intercellular signaling mechanism mediated by the interaction of cell surface receptors (CSR) with insulin-like growth factor-1 (IGF-1). In ECM, the presence of binding proteins (IGFBP) hinders IGF-1 delivery to CSR. It has been reported that levels of IGF-1 and IGFBP in obese population are, respectively, lower and higher than those found in normal population. In this study, an experimental-numerical approach was adopted to quantify the effect of this metabolic alteration found in obese population on the homeostasis of femoral hip cartilage. A new computational model, based on the mechano-electrochemical mixture theory, was developed to describe competitive binding kinetics of IGF-1 with IGFBP and CSR, and associated glycosaminoglycan (GAG) biosynthesis. Moreover, a gait analysis was carried out on obese and normal subjects to experimentally characterize mechanical loads on hip cartilage during walking. This information was deployed into the model to account for effects of physiologically relevant tissue deformation on GAG production in ECM. Numerical simulations were performed to compare GAG biosynthesis in femoral hip cartilage of normal and obese subjects. Results indicated that the lower ratio of IGF-1 to IGFBP found in obese population reduces cartilage GAG concentration up to 18 % when compared to normal population. Moreover, moderate physical activity, such as walking, has a modest beneficial effect on GAG production. The findings of this study suggest that IGF-1/IGFBP metabolic unbalance should be accounted for when considering the association of obesity with hip osteoarthritis