The limitations and high false-negative rates (30%) of COVID-19 test kits have been a prominent challenge during the 2020 coronavirus pandemic. Manufacturing those kits and performing the tests require extensive resources and time. Recent studies show that radiological images like chest X-rays can offer a more efficient solution and faster initial screening of COVID-19 patients. In this study, we develop a COVID-19 diagnosis model using Multilayer Perceptron and Convolutional Neural Network (MLP-CNN) for mixed-data (numerical/categorical and image data). The model predicts and differentiates between COVID-19 and non-COVID-19 patients, such that early diagnosis of the virus can be initiated, leading to timely isolation and treatments to stop further spread of the disease. We also explore the benefits of using numerical/categorical data in association with chest X-ray images for screening COVID-19 patients considering both balanced and imbalanced datasets. Three different optimization algorithms are used and tested:adaptive learning rate optimization algorithm (Adam), stochastic gradient descent (Sgd), and root mean square propagation (Rmsprop). Preliminary computational results show that, on a balanced dataset, a model trained with Adam can distinguish between COVID-19 and non-COVID-19 patients with a higher accuracy of 96.3%. On the imbalanced dataset, the model trained with Rmsprop outperformed all other models by achieving an accuracy of 95.38%. Additionally, our proposed model outperformed selected existing deep learning models (considering only chest X-ray or CT scan images) by producing an overall average accuracy of 94.6% ± 3.42%.

The COVID-19 global pandemic caused by the widespread transmission of the novel coronavirus (SARS-CoV-2) has become one of modern history’s most challenging issues from a healthcare perspective. At its dawn, still without a vaccine, contagion containment strategies remained most effective in preventing the disease’s spread. Patient isolation has been primarily driven by the results of polymerase chain reaction (PCR) testing, but its initial reach was challenged by low availability and high cost, especially in developing countries. As a means of taking advantage of a preexisting infrastructure for respiratory disease diagnosis, researchers have proposed COVID-19 patient screening based on the results of Chest Computerized Tomography (CT) and Chest Radiographs (X-ray). When paired with artificial-intelligence- and deep-learning-based approaches for analysis, early studies have achieved a comparatively high accuracy in diagnosing the disease. Considering the opportunity to further explore these methods, we implement six different Deep Convolutional Neural Networks (Deep CNN) models—VGG16, MobileNetV2, InceptionResNetV2, ResNet50, ResNet101, and VGG19—and use a mixed dataset of CT and X-ray images to classify COVID-19 patients. Preliminary results showed that a modified MobileNetV2 model performs best with an accuracy of 95 ± 1.12% (AUC = 0.816). Notably, a high performance was also observed for the VGG16 model, outperforming several previously proposed models with an accuracy of 98.5 ± 1.19% on the X-ray dataset. Our findings are supported by recent works in the academic literature, which also uphold the higher performance of MobileNetV2 when X-ray, CT, and their mixed datasets are considered. Lastly, we further explain the process of feature extraction using Local Interpretable Model-Agnostic Explanations (LIME), which contributes to a better understanding of what features in CT/X-ray images characterize the onset of COVID-19.

Chest radiographs (X-rays) combined with Deep Convolutional Neural Network (CNN) methods have been demonstrated to detect and diagnose the onset of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). However, questions remain regarding the accuracy of those methods as they are often challenged by limited datasets, performance legitimacy on imbalanced data, and have their results typically reported without proper confidence intervals. Considering the opportunity to address these issues, in this study, we propose and test six modified deep learning models, including VGG16, InceptionResNetV2, ResNet50, MobileNetV2, ResNet101, and VGG19 to detect SARS-CoV-2 infection from chest X-ray images. Results are evaluated in terms of accuracy, precision, recall, and f- score using a small and balanced dataset (Study One), and a larger and imbalanced dataset (Study Two). With 95% confidence interval, VGG16 and MobileNetV2 show that, on both datasets, the model could identify patients with COVID-19 symptoms with an accuracy of up to 100%. We also present a pilot test of VGG16 models on a multi-class dataset, showing promising results by achieving 91% accuracy in detecting COVID-19, normal, and Pneumonia patients. Furthermore, we demonstrated that poorly performing models in Study One (ResNet50 and ResNet101) had their accuracy rise from 70% to 93% once trained with the comparatively larger dataset of Study Two. Still, models like InceptionResNetV2 and VGG19's demonstrated an accuracy of 97% on both datasets, which posits the effectiveness of our proposed methods, ultimately presenting a reasonable and accessible alternative to identify patients with COVID-19.

Electrospun scaffolds provide a dense framework of nanofibers with pore sizes and fiber diameters that closely resemble the architecture of native extracellular matrix. However, it generates limited three-dimensional structures of relevant physiological thicknesses. 3D printing allows digitally controlled fabrication of three-dimensional single/multimaterial constructs with precisely ordered fiber and pore architecture in a single build. However, this approach generally lacks the ability to achieve submicron resolution features to mimic native tissue. The goal of this study was to fabricate and evaluate 3D printed, electrospun, and combination of 3D printed/electrospun scaffolds to mimic the native architecture of heterogeneous tissue. We assessed their ability to support viability and proliferation of human adipose derived stem cells (hASC). Cells had increased proliferation and high viability over 21 days on all scaffolds. We further tested implantation of stacked-electrospun scaffold versus combined electrospun/3D scaffold on a cadaveric pig knee model and found that stacked-electrospun scaffold easily delaminated during implantation while the combined scaffold was easier to implant. Our approach combining these two commonly used scaffold fabrication technologies allows for the creation of a scaffold with more close resemblance to heterogeneous tissue architecture, holding great potential for tissue engineering and regenerative medicine applications of osteochondral tissue and other heterogeneous tissues.

Osteoarthritis is a degenerative joint disease that limits mobility of the affected joint due to the degradation of articular cartilage and subchondral bone. The limited regenerative capacity of cartilage presents significant challenges when attempting to repair or reverse the effects of cartilage degradation. Tissue engineered medical products are a promising alternative to treat osteochondral degeneration due to their potential to integrate into the patient's existing tissue. The goal of this study was to create a scaffold that would induce site‐specific osteogenic and chondrogenic differentiation of human adipose‐derived stem cells (hASC) to generate a full osteochondral implant. Scaffolds were fabricated using 3D‐bioplotting of biodegradable polycraprolactone (PCL) with either β‐tricalcium phosphate (TCP) or decellularized bovine cartilage extracellular matrix (dECM) to drive site‐specific hASC osteogenesis and chondrogenesis, respectively. PCL‐dECM scaffolds demonstrated elevated matrix deposition and organization in scaffolds seeded with hASC as well as a reduction in collagen I gene expression. 3D‐bioplotted PCL scaffolds with 20% TCP demonstrated elevated calcium deposition, endogenous alkaline phosphatase activity, and osteopontin gene expression. Osteochondral scaffolds comprised of hASC‐seeded 3D‐bioplotted PCL‐TCP, electrospun PCL, and 3D‐bioplotted PCL‐dECM phases were evaluated and demonstrated site‐specific osteochondral tissue characteristics. This technique holds great promise as cartilage morbidity is minimized since autologous cartilage harvest is not required, tissue rejection is minimized via use of an abundant and accessible source of autologous stem cells, and biofabrication techniques allow for a precise, customizable methodology to rapidly produce the scaffold.