ß-Glucan-Mediated Oral Codelivery of 5FU and Bcl2 siRNA Attenuates Stomach Cancer.

Based on cancer-related deaths, stomach cancer is ranked fifth, and first among Hispanics. Lack of technologies for early diagnosis and unavailability of target-specific therapeutics are largely the causes of the poor therapeutic outcomes from existing chemotherapeutics. Currently available therapeutic modalities are invasive and require systemic delivery, although the cancer is localized in the stomach at its early stage. Therefore, we hypothesize that an oral local delivery approach can extend the retention duration of the therapeutics modalities within the stomach and thereby enhance therapeutic efficacy. To accomplish this, we have developed a ß-glucan (BG)-based oral delivery vehicle that can adhere to the mucus lining of the stomach for an extended period while controlling the release of Bcl2 siRNA and 5-fluorouracil (5FU) payload for over 6 h. We found that Bcl2 siRNA selectively knocked down the Bcl2 gene in a C57BL/6 stomach cancer mouse model followed by upregulation of apoptosis and remission of cancer. BG was found to be very effective in maintaining the stability of siRNA for at least 6 h, when submerged in simulated gastric juice tested in vitro. To investigate the potential therapeutic effects in vivo, we used a stomach cancer mouse model, where C57BL/6 mice were treated with 5FU, BG/5FU, siRNA, BG/siRNA, and BG/5FU/siRNA. Higher inhibition of Bcl2 and therapeutic efficacy were observed in mice treated with BG/5FU/siRNA confirmed with Western blotting and a TUNEL assay. Significant reduction in the tumor region was observed with histology (H&E) and immunohistochemistry (Ki67, TUNEL, and Bcl2) analyses. Overall, the oral formulation shows improved efficacy with nonsignificant side effects compared to the conventional treatment tested in the gastric cancer mouse model.

Time-to-event modeling for hospital length of stay prediction for COVID-19 patients.

Providing timely patient care while maintaining optimal resource utilization is one of the central operational challenges hospitals have been facing throughout the pandemic. Hospital length of stay (LOS) is an important indicator of hospital efficiency, quality of patient care, and operational resilience. Numerous researchers have developed regression or classification models to predict LOS. However, conventional models suffer from the lack of capability to make use of typically censored clinical data. We propose to use time-to-event modeling techniques, also known as survival analysis, to predict the LOS for patients based on individualized information collected from multiple sources. The performance of six proposed survival models is evaluated and compared based on clinical data from COVID-19 patients.

An interpretable multi-task system for clinically applicable COVID-19 diagnosis using CXR.

BACKGROUND: With the emergence of continuously mutating variants of coronavirus, it is urgent to develop a deep learning model for automatic COVID-19 diagnosis at early stages from chest X-ray images. Since laboratory testing is time-consuming and requires trained laboratory personal, diagnosis using chest X-ray (CXR) is a befitting option. OBJECTIVE: In this study, we proposed an interpretable multi-task system for automatic lung detection and COVID-19 screening in chest X-rays to find an alternate method of testing which are reliable, fast and easily accessible, and able to generate interpretable predictions that are strongly correlated with radiological findings. METHODS: The proposed system consists of image preprocessing and an unsupervised machine learning (UML) algorithm for lung region detection, as well as a truncated CNN model based on deep transfer learning (DTL) to classify chest X-rays into three classes of COVID-19, pneumonia, and normal. The Grad-CAM technique was applied to create class-specific heatmap images in order to establish trust in the medical AI system. RESULTS: Experiments were performed with 15,884 frontal CXR images to show that the proposed system achieves an accuracy of 91.94% in a test dataset with 2,680 images including a sensitivity of 94.48% on COVID-19 cases, a specificity of 88.46% on normal cases, and a precision of 88.01% on pneumonia cases. Our system also produced state-of-the-art outcomes with a sensitivity of 97.40% on public test data and 88.23% on a previously unseen clinical data (1,000 cases) for binary classification of COVID-19-positive and COVID-19-negative films. CONCLUSION: Our automatic computerized evaluation for grading lung infections exhibited sensitivity comparable to that of radiologist interpretation in clinical applicability. Therefore, the proposed solution can be used as one element of patient evaluation along with gold-standard clinical and laboratory testing.