Mechanism of interferon induction by cannabidiol (CBD) and analogues in lung cancer cells

CBD, an FDA approved drug for epilepsy, may have therapeutic potential for other diseases and is currently being tested for efficacy in cancer-related clinical trials. As the literature about CBD, especially in vitro reports, is often contradictory, increasing our understanding of its specific action on a molecular level will allow to determine whether CBD can become a useful therapy or exacerbates specific cancers in a context-dependent manner. Due to its relative lipophilicity, CBD is challenging to dispense at therapeutic concentrations;therefore, one goal is to identify cannabinoid congeners with greater efficacy and reduced drug delivery challenges. We recently showed that CBD activates interferons as a mechanism of inhibiting SARS-CoV-2 replication in lung carcinoma cells. As factors produced by the innate immune system, interferons have been implicated in both pro-survival and growth arrest and apoptosis signaling in cancer. Here we show that CBD induces interferon production and interferon stimulated genes (ISGs) through a mechanism involving NRF2 and MAVS in lung carcinoma cells. We also show that CBDV, which differs from CBD by 2 fewer aliphatic tail carbons, has limited potency, suggesting that CBD specifically interacts with one or more cellular proteins rather than having a non-specific effect. We also identified other CBD-related cannabinoids that are more effective at inducing ISGs. Taken together, these results characterize a novel mechanism by which CBD activates the innate immune system in lung cancer cells and identify related cannabinoids that have possible therapeutic potential in cancer treatment.

COVID-19 Infection Percentage Prediction via Boosted Hierarchical Vision Transformer

A better backbone network usually benefits the performance of various computer vision applications. This paper aims to introduce an effective solution for infection percentage estimation of COVID-19 for the computed tomography (CT) scans. We first adopt the state-of-the-art backbone, Hierarchical Visual Transformer, as the backbone to extract the effective and semantic feature representation from the CT scans. Then, the non-linear classification and the regression heads are proposed to estimate the infection scores of COVID-19 symptoms of CT scans with the GELU activation function. We claim that multi-tasking learning is beneficial for better feature representation learning for the infection score prediction. Moreover, the maximum-rectangle cropping strategy is also proposed to obtain the region of interest (ROI) to boost the effectiveness of the infection percentage estimation of COVID-19. The experiments demonstrated that the proposed method is effective and efficient. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.