Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.

Hepatocellular carcinoma (HCC) represents a high-fatality cancer with a 5-year survival of 22%. The Wnt/β-catenin signaling pathway presents as one of the most upregulated pathways in HCC. However, it has so far not been targetable in the clinical setting. Therefore, studying new targets of this signaling cascade from a therapeutic aspect could enable reversal, delay, or prevention of hepatocarcinogenesis. Although enormous advancement has been achieved in HCC research and its therapeutic management, since HCC often occurs in the context of other liver diseases such as cirrhosis leading to liver dysfunction and/or impaired drug metabolism, the current therapies face the challenge of safely and effectively delivering drugs to the HCC tumor site. In this review, we discuss how a targeted nano drug delivery system could help minimize the off-target toxicities of conventional HCC therapies as well as enhance treatment efficacy. We also put forward the current challenges in HCC nanomedicine along with some potential therapeutic targets from the Wnt/β-catenin signaling pathway that could be used for HCC therapy. Overall, this review will provide an insight to the current advances, limitations and how HCC nanomedicine could change the landscape of some of the undruggable targets in the Wnt/β-catenin pathway.

The remarkable impact of RNA nanomedicine during the COVID-19 pandemic has demonstrated the expansive therapeutic potential of this field in diverse disease contexts. In recent years, RNA nanomedicine targeting the liver has been paradigm-shifting in the management of metabolic diseases such as hyperoxaluria and amyloidosis. RNA nanomedicine has significant potential in the management of liver diseases, where optimal management would benefit from targeted delivery, doses titrated to liver metabolism, and personalized therapy based on the specific site of interest. In this review, we discuss in-depth the different types of RNA and nanocarriers used for liver targeting along with their specific applications in metabolic dysfunction-associated steatotic liver disease, liver fibrosis, and liver cancers. We further highlight the strategies for cell-specific delivery and future perspectives in this field of research with the emergence of small activating RNA, circular RNA, and RNA base editing approaches.

Allergic rhinitis (AR) affects 20–50% of the global population. Available treatments are limited by their adverse effects. We investigated the anti-allergic effects of catechin alone and combined with cetirizine against ovalbumin-induced AR. Rats were sensitized with ovalbumin and received catechin (14 days) and then challenged with aerosolized ovalbumin (1%) to determine AR clinical scores. Histamine, histamine release, and histidine decarboxylase (HDC) activity were determined in blood, peritoneal mast cells, and stomachs, respectively. Vascular permeability and safety were assessed using Evans blue leakage and barbiturate-induced sleeping-time assays, respectively. Catechin and cetirizine binding with HDC was investigated by docking and binding energy analyses. The clinical scores of the combination regimen were superior to either drug alone. All treatments reduced vascular leakage, with no effect on barbiturate-induced sleeping time. Only the catechin-treated rats showed reduced histamine levels and HDC activity. Docking studies revealed that catechin has a 1.34-fold higher extra-precision docking score than L-histidine. The binding energy scores for catechin-HDC, L-histidine-HDC, and histamine-HDC were −50.86, −37.64, and −32.27 kcal/mol, respectively. The binding pattern of catechin was comparable to the standard HDC inhibitor, histidine methyl ester, but with higher binding free energy. Catechin binds the catalytic residue S354, unlike cetirizine. The anti-allergic effects of catechin can be explained by HDC inhibition and possible antihistaminic activity.

[This corrects the article DOI: 10.3389/fphar.2017.00930.].