MAFLD-related hepatocellular carcinoma: Exploring the potent combination of immunotherapy and molecular targeted therapy.

Hepatocellular carcinoma (HCC) is a common cause of cancer-related mortality and morbidity globally, and with the prevalence of metabolic-related diseases, the incidence of metabolic dysfunction-associated fatty liver disease (MAFLD) related hepatocellular carcinoma (MAFLD-HCC) continues to rise with the limited efficacy of conventional treatments, which has created a major challenge for HCC surveillance. Immune checkpoint inhibitors (ICIs) and molecularly targeted drugs offer new hope for advanced MAFLD-HCC, but the evidence for the use of both types of therapy in this type of tumour is still insufficient. Theoretically, the combination of immunotherapy, which awakens the body's anti-tumour immunity, and targeted therapies, which directly block key molecular events driving malignant progression in HCC, is expected to produce synergistic effects.

A role for curcumin in preventing liver fibrosis in animals: a systematic review and meta-analysis.

Objective: This meta-analysis aimed to determine the efficacy of curcumin in preventing liver fibrosis in animal models.

Methods: A systematic search was conducted on studies published from establishment to November 2023 in PubMed, Web of Science, Embase, Cochrane Library, and other databases. The methodological quality was assessed using Sycle's RoB tool.

Global research trends in tumor stem cell-derived exosomes and tumor microenvironment: visualization biology analysis.

Bankground: The tumor microenvironment (TME) is an internal environment composed of various cells and an extracellular matrix. Cancer stem cell-derived exosomes (CSC-Exos), as essential messengers involved in various tumor processes, are important carriers for bidirectional communication between the tumor microenvironment and tumor cells and play an important role in the tumor microenvironment. Nevertheless, few bibliometric analyses have been systematically studied in this field.

Deep learning for accelerated all-dielectric metasurface design.

Deep learning has risen to the forefront of many fields in recent years, overcoming challenges previously considered intractable with conventional means. Materials discovery and optimization is one such field, but significant challenges remain, including the requirement of large labeled datasets and one-to-many mapping that arises in solving the inverse problem. Here we demonstrate modeling of complex all-dielectric metasurface systems with deep neural networks, using both the metasurface geometry and knowledge of the underlying physics as inputs.