Pancreatic Steatosis Evaluated by Automated Volumetric CT Fat Fraction of the Pancreas: Association with Severity in COVID-19 Pneumonia.

This study investigated the relationship between the severity of pneumonia based on chest CT findings and that of pancreatic steatosis assessed using an automated volumetric measurement of the CT fat volume fraction (CT-FVF) of the pancreas, using unenhanced three-dimensional CT in polymerase chain reaction (PCR)-confirmed COVID-19 patients. The study population consisted of 128 patients with PCR-confirmed COVID-19 infection who underwent CT examinations. The CT-FVF of the pancreas was calculated using a histogram analysis for the isolation of fat-containing voxels in the pancreas. The CT-FVF (%) of the pancreas had a significantly positive correlation with the lung severity score on CT (ρ = 0.549, p < 0.01). CT-FVF (%) of the pancreas in the severe pneumonia group was significantly higher than that of the non-severe pneumonia group (21.7% vs. 7.8%, p < 0.01). The area under the curve of CT-FVF (%) of the pancreas in predicting the severity of pneumonia on CT was calculated to be 0.82, with a sensitivity of 88% and a specificity of 68% at a threshold for the severity score of 12.3. The automated volumetric measurement of the CT-FVF of the pancreas using unenhanced CT can help estimate disease severity in patients with COVID-19 pneumonia based on chest CT findings.

Concentration Measurements of Ethanol in Water Based on RFID-UHF Flexible Sensor for Sterilization Against SARS-CoV

In this work, we present a UHF-RFID-based noninvasive sensor to measure the concentration of ethanol in water using the volume fraction of liquids in mixture solutions. The sensing system operates at the UHF band (860-928 MHz). The concentration of ethanol in water affects the dielectric properties of the solution and therefore the antenna sensitivity of the RFID tag. This sensor operates by measuring the change in permittivity of a solution because of the change in concentration of ethanol in water. We propose a flexible RFID-Tag sensor a low-cost alternative to identify the possible sensitivity of tag changes and is able to detect a variation of 25% in ethanol in 9 ml of deionized water (DI-Water). The solution is useful in avoiding counterfeit ethanol solutions that may be toxic. The experimental setup is inexpensive, portable, quick, and contactless. We present results for ethanol solutions ranging from 25% to 100% in a small tube container. © 2022 IEEE.

MACHINE LEARNING AND RADIOMICS IDENTIFY NOVEL BIOMARKERS OF BONE LOSS

Purpose: Altered bone turnover is a factor in many diseases including osteoarthritis (OA), osteoporosis, inflammation, and viral infection. The absence of obvious symptoms and insufficiently sensitive biomarkers in the early stages of bone loss limits early diagnosis and treatment. Therefore, it is urgent to identify novel, more sensitive, and easy-to-detect biomarkers which can be used in the diagnosis and prognosis of bone health. Our previous data using standard micro-computed tomography (μCT) measurements showed that SARS-CoV-2 infection in mice significantly decreased trabecular bone volume at the lumbar spine, suggesting that decreased bone mass, increased fracture risk, and OA may be underappreciated long-haul comorbidities for COVID patients. In this study, we applied integrated state-of-the-art radiomics and machine learning models to identify more sensitive image-based biomarkers of SARS-CoV-2-induced bone loss from μCT images. These radiomic biomarkers can potentially provide a non-invasive way of quantifying and monitoring systemic bone loss and evaluating treatment efficacy in both research and clinical practices. Methods: All animal use was performed with approval of the Institutional Animal Care and Use Committee. To quantify SARS-CoV-2-induced bone loss, 6-week-old transgenic mice (16 male, 16 female) expressing humanized ACE2 receptors were inoculated with a 2020 strain of SARS-CoV-2 or phosphate-buffered saline (Control) [Fig. A]. Viral infection was confirmed by detection of infectious SARS-CoV-2 in throat swabs and histological identification of SARS-CoV-2 labeled cells. At 6-14 days post-infection, lumbar vertebral bodies (L5) were scanned with μCT (μCT 35, SCANCO Medical AG;6 μm nominal voxel size). The open-source research platform 3D Slicer v2020 with a built-in Python console v3.8 was used for medical image computing and fully automated segmentation of cortical and trabecular bone. Standard μCT assessment of bone microstructure was performed. Radiomic feature extraction and data processing were performed using python based PyRadiomics v3.0.1. A total of 120 radiographic features were extracted from the segmented images [Fig. B]. Principle component analysis (PCA) for feature selection, a support vector machine learning (SVML) predictive model for classification, holdback method for model validation, and all statistical analyses (significance at p<0.05) were performed using JMP Pro v15 (SAS). Results: Using standard μCT methods, SARS-CoV-2 infection significantly reduced the bone volume fraction (BV/TV) by 10 and 10.5% (p= 0.04) and trabecular thickness (Tb.Th) by 8 and 9% (p= 0.02) in male and female mice, respectively, compared to PBS control mice [Fig. C]. Radiomics detected a 20-fold greater magnitude in change over standard methods. SARS-CoV-2 infection significantly changed radiographic parameters with the largest change being a 300% increase in the second-order parameter: cluster shade [Fig. D]. The 45 radiomic features comprising the first 3 principal components were selected for inclusion in the SVML model. The SVML Model (radial basis function kernel;cost = 4.8;gamma = 0.46) produced an area under the receiver operating characteristic curve (AUC) of 1.0 which reflects a perfectly accurate test [Fig. E]. Conclusions: SARS-CoV-2 infection of humanized ACE2 expressing mice caused significant bone changes, suggesting that decreased bone mass, increased fracture risk, OA, and other musculoskeletal complications could be long-term comorbidities for people infected with COVID-19. We developed an open-source, fully automated segmentation and radiomics system to assess systemic bone loss using μCT images. When coupled with machine learning, this system was able to identify novel radiographic biomarkers of bone loss that better discriminate differences in bone microstructure between SARS-CoV-2 infected and control mice than standard bone morphometric indices. The high accuracy of the SVML model in classifying SARS-CoV-2 infected mice opens the possibility of translating these biom rkers to the clinical setting for early detection of skeletal changes associated with long-haul COVID. The methods presented here were demonstrated using SARS-CoV-2 as a model system and can also be adapted to other diseases associated with altered bone turnover. Development of machine-learning methods for radiomic applications is a crucial step toward clinically relevant radiomic biomarkers of bone health and provides a non-invasive way of quantifying and monitoring systemic bone loss and evaluating treatment efficacy. [Formula presented]

Digital Twin Geometry for Fibrous Air Filtration Media

Computational modeling of air filtration is possible by replicating nonwoven nanofibrous meltblown or electrospun filter media with digital representative geometry. This article presents a methodology to create and modify randomly generated fiber geometry intended as a digital twin replica of fibrous filtration media. Digital twin replicas of meltblown and electrospun filter media are created using Python scripting and Ansys SpaceClaim. The effect of fiber stiffness, represented by a fiber relaxation slope, is analyzed in relation to resulting filter solid volume fraction and thickness. Contemporary air filtration media may also be effectively modeled analytically and tested experimentally in order to yield valuable information on critical characteristics, such as overall resistance to airflow and particle capture efficiency. An application of the Single Fiber Efficiency model is incorporated in this work to illustrate the estimation of performance for the generated media with an analytical model. The resulting digital twin fibrous geometry compares well with SEM imagery of fibrous filter materials. This article concludes by suggesting adaptation of the methodology to replicate digital twins of other nonwoven fiber mesh applications for computational modeling, such as fiber reinforced additive manufacturing and composite materials.