Lymphoma is a potentially life threatening disease. The goal of this study was to investigate the therapeutic potential of a natural triterpenoid, Ganoderic acid A (GA-A) in controlling lymphoma growth both in vitro and in vivo. Here, we show that GA-A treatment induces caspase-dependent apoptotic cell death characterized by a dose-dependent increase in active caspases 9 and 3, up-regulation of pro-apoptotic BIM and BAX proteins, and a subsequent loss of mitochondrial membrane potential with release of cytochrome c. In addition to GA-A’s anti-growth activity, we show that lower doses of GA-A enhance HLA class II-mediated antigen presentation and CD4+ T cell recognition of lymphoma in vitro. The therapeutic relevance of GA-A treatment was also tested in vivo using the EL4 syngeneic mouse model of metastatic lymphoma. GA-A-treatment significantly prolonged survival of EL4 challenged mice and decreased tumor metastasis to the liver, an outcome accompanied by a marked down-regulation of STAT3 phosphorylation, reduction myeloid-derived suppressor cells (MDSCs), and enhancement of cytotoxic CD8+ T cells in the host. Thus, GA-A not only selectively induces apoptosis in lymphoma cells, but also enhances cell-mediated immune responses by attenuating MDSCs, and elevating Ag presentation and T cell recognition. The demonstrated therapeutic benefit indicates that GA-A is a candidate for future drug design for the treatment of lymphoma.

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Background Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED. Methods This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. institution. A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression. Results Overall ICC was 0.820 (95% CI 0.790–0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861–0.920) for the neural network and 0.936 (95% CI 0.918–0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906). Conclusion The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.