Network-based methods with heterogeneous data to identify severe COVID immune-related genes

Bioinformatics and systems biology play a vital role in the computational prediction of disease-associated genes using multi-omics data. The network-based approach is one of the most potent tools in disease-associated gene prediction. The two commonly used methods are neighborhood-based and network diffusion techniques. However, there is still a lack of studies comparing the performance of these methods, especially in terms of functional pathway discovery. Thus, this study demonstrated the performance comparison of these two techniques in both numerical accuracies based on the area under the receiver operating characteristic curve (AUROC) and biological meaning efficiency based on functional pathway enrichment. In this study, we analyzed data of severe COVID-19 immune-related genes using heterogeneous data. The prediction results of the COVID-19 immune-related genes in the human protein-protein interaction (PPI) network showed that the network diffusion had better performance in both AUROC and pathway enrichment even though it provided a longer computational time than the neighborhood method. © 2022 IEEE.

Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

Coronavirus 2019 (COVID -19) is the current global buzzword, putting the world at risk. The pandemic's exponential expansion of infected COVID-19 patients has challenged the medical field's resources, which are already few. Even established nations would not be in a perfect position to manage this epidemic correctly, leaving emerging countries and countries that have not yet begun to grow to address the problem. These problems can be solved by using machine learning models in a realistic way, such as by using computer-aided images during medical examinations. These models help predict the effects of the disease outbreak and help detect the effects in the coming days. In this paper, Multi-Features Decease Analysis (MFDA) is used with different ensemble classifiers to diagnose the disease's impact with the help of Computed Tomography (CT) scan images. There are various features associated with chest CT images, which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia. The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results. The model's performance is assessed using Receiver Operating Characteristic (ROC) curve, the Root Mean Square Error (RMSE), and the Confusion Matrix. It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient. © 2023 CRL Publishing. All rights reserved.

Resilience characterized and quantified from physical activity data: A tutorial in R

Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on consistent physical activity. In this tutorial, we present a method to quantify the resilience process from physical activity data. We leverage the prior operationalization of resilience, as used in various psychological domains, as area under the curve and expand it to suit the characteristics of physical activity time series. As use case to illustrate the methodology, we quantified resilience in step count time series (length = 366 observations) for eight participants following the first COVID-19 lockdown as a stressor. Steps were assessed daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. For each person's time series, we fitted multiple growth models and identified the best one using the Root Mean Squared Error (RMSE). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. By developing a methodological guide with a step-by-step implementation, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research. • R tutorial to quantify resilience from physical activity time series. • Physical activity resilience is measured using an idiographic approach. • Physical activity resilience is operationalized as the AUC. • Growth models are fitted to step count time series to define the limits of the AUC. • Further indicators of resilience are provided to describe the phenomenon. [ FROM AUTHOR]

Detection and Classification of COVID-19 Chest X-rays by the Deep Learning Technique

The Coronavirus disease (COVID-19) infection has become a pandemic, and this is the most critical problem that has occurred in Thailand and also expanded all over the world. As such, it is not astonishing to know that this virus has had a direct effect on hospitals with the delayed screening of patients because of the increasing number of daily cases and the shortage of medical personnel and restricted treatment space. Due to such restrictions, in this study, we used a clinical decision-making system with predictive algorithms. Predictive algorithms could potentially ease the strain on healthcare systems by identifying the diseases. Moreover, image classification is one interesting aspect of image processing. Convolutional neural network (CNN) is a widely used algorithm for image classification by separating the images of the COVID-19 disease, images with a lung infection, and normal images. To evaluate the predictive performance of our models, precision, F1-score, recall, receiver operating characteristic (ROC) curve (area under the ROC curve), and accuracy scores were used. It was observed that the predictive models trained on the laboratory findings could be used to predict the COVID-19 infection as well and could be helpful for medical experts to appropriately prioritize the resources. This could be employed to assist medical experts in validating their initial laboratory findings and could also be used for clinical prediction studies. © 2022 IEEE.

Effect of Body Mass Factors on Setup Displacement for Gynecological Cancers and Subsequent Impact on PTV Margin

To analyze the impact of body mass factors (BMFs) including body mass index (BMI), umbilical circumference (UC), and hip circumference (HC), on setup errors in gynecological tumors, and whether the planned tumor volumes (PTVs) are adequate for obese patients. A retrospective study was conducted among 46 consecutive women with gynecological tumors, who were treated with Volumetric Modulated Arc Therapy (VMAT) at the radiotherapy (RT) unit. Setup accuracy was verified using daily cone-beam computed tomography (CBCT). BMFs were measured at baseline for all patients, and at fractions #10 and #20. Total vector errors (TVEs) were computed and linear regression was used to analyze their relationship with baseline BMFs. Accuracy was determined for each fraction by testing two different PTVs (Cutoff I: ≤0.7cm and II: ≤1.0cm). A pooled analysis was conducted to test the association of accuracy levels (within vs beyond-PTV) with the mean and variance of BMI, UC, and HC, considering the repeated measures. Receiver operating characteristics (ROC) curve analysis was carried out to test the sensitivity of BMI, UC, and HC in predicting inaccurate setup The mean (SD) TVE was 0.86 (0.34) cm and 0.79 (0.30) cm in systematic and random settings respectively. Random TVE showed weakly positive relationships with BMI (B=0.005 [95%CI=0.001-0.010];R2=0.090;p=0.042) and UC (B=0.013 [95%CI=0.002-0.025];R2=0.119;p=0.019. The pooled analysis showed a higher mean BMI with setups beyond the PTV compared to within PTV, with a mean difference of approximately 3.50 kg/m2, (p=0.001), in the lateral direction. Similarly, measures of UC (mean difference ∼10 cm) and HC (∼8 cm) were significantly higher in setups beyond the PTV compared with accurate setups (p<0.001). With respect of the vertical direction, BMI (mean difference=7.4 kg/m2, p=0.001), UC (5.3 cm, p<0.001), and HC (16.0 cm, p<0.001) were higher in setups beyond the PTV versus those within PTV;however, this was only observed using Cutoff I. Using Cutoff II, only HC showed a statistically significant difference, with a mean difference of 11.7 cm between inaccurate setups and accurate setups (0=0.041). ROC curve analysis showed that a BMI>31.4 kg/m2 was predictive for inaccurate setup in the vertical direction with 90.0% sensitivity, with respect of Cutoff I. Furthermore, a BMI>30.3 kg/m2 was predictive for inaccurate setup in the lateral direction with 92.5% sensitivity, with respect of Cutoff II. The accuracy of RT setups in gynecological tumors are highly sensitive to patients' BMI, notably in the lateral and vertical directions. To facilitate workflow during the Covid-19 crisis, we suggest that daily CBCT should be applied on patients with a BMI>30.3 kg/m2 or the PTVs should be adapted for obese patients to enhance setup accuracy of RT [ FROM AUTHOR] Copyright of International Journal of Radiation Oncology, Biology, Physics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

Coronavirus 2019 (COVID -19) is the current global buzzword, putting the world at risk. The pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources, which are already few. Even established nations would not be in a perfect position to manage this epidemic correctly, leaving emerging countries and countries that have not yet begun to grow to address the problem. These problems can be solved by using machine learning models in a realistic way, such as by using computer-aided images during medical examinations. These models help predict the effects of the disease outbreak and help detect the effects in the coming days. In this paper, Multi-Features Decease Analysis (MFDA) is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography (CT) scan images. There are various features associated with chest CT images, which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia. The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results. The model’s performance is assessed using Receiver Operating Characteristic (ROC) curve, the Root Mean Square Error (RMSE), and the Confusion Matrix. It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient. © 2023 CRL Publishing. All rights reserved.

A Diagnostic Study on Prediction of Covid-19 by Symptoms Using Machine Learning

Effective screening helps for quick and accurate detection of COVID-19 and it also decreases the burden on the healthcare system. Prediction models with numerous criteria have been developed to estimate the probability of infection. These are designed to assist medical workers across the world in triaging victi ms, especially in places with limited medical resources. For predicting the COVID-19 using symptoms, the dataset is taken from the website of the Israeli Ministry of Health. The dataset contains 9 attributes and 2, 78, 848 samples. The raw dataset is cleaned using pre-processing techniques. The Machine learning algorithms like Random Forest, K Nearest Neighbor, Decision Tree, and hybrid Random Forest, K Nearest Neighbor, and Decision Tree are applied on the 1, 95, 194 samples to identify the model. The predicted model is tested on 83, 654 samples to ensure the quality of the designed model. The performance metrics like ROC [Receiver Operating Characteristic] curve, True Positive and Negative Rate, False Positive and Negative Rate, Positive and Negative Predictive Value, and Accuracy are applied to check the model. From the evaluation result, the proposed hybrid model gives high accuracy of 98.97%. The proposed technique might be utilized to priorities COVID-19 screening when testing capabilities are constrained., among several other things. © 2022 IEEE.

Comparison of CNN-based Approaches for Detection of COVID-19 on Chest X-ray Images

Coronavirus disease 2019 (COVID-19), a highly contagious respiratory disease, has rapidly become a global pandemic. Chest X-ray imaging could serve an important role in early diagnosis of the disease. Deep learning methods have recently shown promise in disease detection tasks. The aim of this study was to develop a deep learning-based approach for detection of COVID-19 in chest X-ray images. Data were extracted from an opensource COVID-19 database developed by Cohen JP. The data consisted of X-ray images of patients with COVID-19, with other pneumonias or with no findings. The 205 images were randomly partitioned into training, validation and test datasets containing 143, 32, and 30 images, respectively, using a 70%/15%/15% split. The performance of several deep convolutional neural network (CNN)-based architectures, including VGG16, ResNet50, DenseNet121, and InceptionV3, were evaluated on the disease detection task. These networks were first pretrained on the ImageNet dataset consisting of natural images and then further fine-tuned on the task of detecting COVID-19 in chest X-ray images. The networks were then evaluated on the test set by assessing overall accuracy, area under receiver operating characteristic curve (AUROC), sensitivity and specificity. The performance of the networks trained from scratch without pretraining on ImageNet was also compared to the performance of the networks that were first pretrained on ImageNet and then fine-tuned on the detection task. DenseNet121 had the best performance on the test set with an overall accuracy of 90.0% (95% confidence interval (CI): 78.6%, 100%), an AUROC of 0.95, a sensitivity of 91.3% and a specificity of 85.7%. The pretrained DenseNet121 also significantly outperformed the DenseNet121 trained from scratch with a 30.0% improvement in overall accuracy. The proposed deep learning-based approach showed significant promise for detection of COVID-19 in chest X-ray images. © 2020 IEEE

Lymphopenia as a Predictor for Adverse Clinical Outcomes in Hospitalized Patients with COVID-19: A Single Center Retrospective Study of 4485 Cases.

Lymphopenia is commonly present in patients with COVID-19. We sought to determine if lymphopenia on admission predicts COVID-19 clinical outcomes. A retrospective chart review was performed on 4485 patients with laboratory-confirmed COVID-19, who were admitted to the hospital. Of those, 2409 (57.3%) patients presented with lymphopenia (absolute lymphocyte count < 1.1 × 109/L) on admission, and had higher incidences of ICU admission (17.9% versus 9.5%, p < 0.001), invasive mechanical ventilation (14.4% versus 6.5%, p < 0.001), dialysis (3.4% versus 1.8%, p < 0.001) and in-hospital mortality (16.6% versus 6.6%, p < 0.001), with multivariable-adjusted odds ratios of 1.86 (95% confidence interval [CI], 1.55-2.25), 2.09 (95% CI, 1.69-2.59), 1.77 (95% CI, 1.19-2.68), and 2.19 (95% CI 1.76-2.72) for the corresponding outcomes, respectively, compared to those without lymphopenia. The restricted cubic spline models showed a non-linear relationship between lymphocyte count and adverse outcomes, with an increase in the risk of adverse outcomes for lower lymphocyte counts in patients with lymphopenia. The predictive powers of lymphopenia, expressed as areas under the receiver operating characteristic curves, were 0.68, 0.69, 0.78, and 0.79 for the corresponding adverse outcomes, respectively, after incorporating age, gender, race, and comorbidities. In conclusion, lymphopenia is a useful metric in prognosticating outcomes in hospitalized COVID-19 patients.