We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as “Respiratory or thoracic disease”, supporting their link with COVID-19 severity outcome.
Somatic Copy Number Alterations (SCNA) involving either a whole chromosome or just one of the arms, or even smaller parts have been described in about 88% of human tumors. This study investigated the SCNA profile in 40 well-characterized sporadic medullary thyroid carcinomas by comparative genomic hybridization array. We found that 26/40(65%) cases had at least one SCNA. The prevalence of SCNA, and in particular of chromosome 3 and 10, was significantly higher in cases with a RET somatic mutation. Similarly, SCNA of chromosomes 3, 9, 10 and 16 were more frequent in cases with a worse outcome and an advanced disease. By the pathway enrichment analysis, we found a mutually exclusive distribution of biological pathways in metastatic, biochemically persistent and cured patients. In particular, we found gain of regions involved in the intracellular signaling and loss of regions involved in DNA repair and TP53 pathways in the group of metastatic patients. Gain of regions involved in cell cycle and senescence were observed in patients with biochemical disease. Finally, gain of regions associated to the immune system and loss of regions involved in the apoptosis pathway were observed in cured patients suggesting a role of specific SCNA and corresponding altered pathways in the outcome of sporadic MTC.
Maintaining inventory systems with deteriorating items is one of the major problems in inventory management. This study aimed to develop an inventory model for deteriorating items with a prior focus on stochastic demand, salvage value, and shortages during which partial backlogging occurred. To achieve this, we derived an innovative approach of a single-item inventory model for deteriorating items having a time-dependent deterioration rate that follows a three-parameter Weibull distribution. While the demand pattern follows a negative exponential distribution with partial backlogging and salvage value. In a real market situation, salvage value is crucial to minimize the cost of the seller on deteriorating items. To validate our model on a real market inventory problem that incurred deteriorating items, we collected numerical data from the Ose poultry store and used the Maple mathematical software package to analyze the situation. An analytical procedure for deriving the optimal inventory solutions was provided. In addition, the necessary and sufficient conditions for the optimal policy of the inventory model were confirmed. A sensitivity analysis of the optimal solution concerning various parameters of the model was carried out to evaluate the model's responsiveness to changes in the inventory parameters. The findings from our study showed that the optimal inventory policy is best achieved when the seller places an order for 50 crates of eggs approximately every 2 weeks. In conclusion, this study can be adapted to complex situations, especially during the phase of the COVID-19 pandemic when supply chains and inventories experienced risks of disruptions.
Understanding the COVID-19 severity and why it differs significantly among patients is a thing of concern to the scientific community. The major contribution of this study arises from the use of a voting ensemble host genetic severity predictor (HGSP) model we developed by combining several state-of-the-art machine learning algorithms (decision tree-based models: Random Forest and XGBoost classifiers). These models were trained using a genetic Whole Exome Sequencing (WES) dataset and clinical covariates (age and gender) formulated from a 5-fold stratified cross-validation computational strategy to randomly split the dataset to overcome model instability. Our study validated the HGSP model based on the 18 features (i.e., 16 identified candidate genetic variants and 2 covariates). We provided post-hoc model explanations through the ExplainerDashboard - an open-source python library framework, allowing for deeper insight into the prediction results. We applied the Enrichr and OpenTarget genetics bioinformatic interactive tools to associate the genetic variants for plausible biological insights, and domain interpretations such as pathways, ontologies, and disease/drugs. Through an unsupervised clustering of the SHAP feature importance values, we visualized the complex genetic mechanisms. Our findings show that while age and gender mainly influence COVID-19 severity, a specific group of patients experiences severity due to complex genetic interactions.
This study aimed to ascertained using Statistical feature selection methods and interpretable Machine learning models, the best features that predict risk status (“Low”, “Medium”, “High”) to COVID-19 infection. This study utilizes a publicly available dataset obtained via; online web-based risk assessment calculator to ascertain the risk status of COVID-19 infection. 57 out of 59 features were first filtered for multicollinearity using the Pearson correlation coefficient and further shrunk to 55 features by the LASSO GLM approach. SMOTE resampling technique was used to incur the problem of imbalanced class distribution. The interpretable ML algorithms were implored during the classification phase. The best classifier predictions were saved as a new instance and perturbed using a single Decision tree classifier. To further build trust and explainability of the best model, the XGBoost classifier was used as a global surrogate model to train predictions of the best model. The XGBoost individual’s explanation was done using the SHAP explainable AI-framework. Random Forest classifier with a validation accuracy score of 96.35 % from 55 features reduced by feature selection emerged as the best classifier model. The decision tree classifier approximated the best classifier correctly with a prediction accuracy score of 92.23 % and Matthew’s correlation coefficient of 0.8960. The XGBoost classifier approximated the best classifier model with a prediction score of 99.7 %. This study identified COVID-19 positive, COVID-19 contacts, COVID-19 symptoms, Health workers, and Public transport count as the five most consistent features that predict an individual’s risk exposure to COVID-19.
The study of optimal queuing systems in healthcare is crucial at such a time as this to help decongest the system, and minimize financial and health-related risks associated with long waiting queues. This study examined a queuing system at an outpatient hospital clinic post intending to minimize waiting time in association with financial cost and healthcare-related risks. We observed the queuing system using the sampling survey information of 200 outpatients that visited the clinic for 4 weeks. We used the initial queuing ground truth parameters as the baseline scenario and further simulated 4 other queuing scenarios using the TORA optimization software. We calculated the total expected cost associated with the server(s) (Doctors) and the patients while in the queuing system for each scenario. We further discretize their health-related complications and calculated the incidence rate of the patients while in the queuing system to evaluate their health-related risks. The findings of our study showed that the system utilization, optimal expected total cost, health-related risks (risk of discomfort and illness/infections developed while in the queue), and waiting time are optimal at the hospital clinic with 5 severs (doctors). The contribution of this study arose from the incorporation of health-related risks incidence that patients could develop while in the queuing system.
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