Vol 31, No 31 (2024)

:The induction of regulated cell death ferroptosis in tumors is emerging as an intriguing strategy for cancer treatment. Numerous antitumor drugs (e.g., doxorubicin, etoposide, tyrosine kinase inhibitors, trastuzumab, arsenic trioxide, 5-fluorouracil) induce ferroptosis. Although this mechanism of action is interesting for fighting tumors, the clinical use of drugs that induce ferroptosis is hampered by cardiotoxicity. Besides in cancer cells, ferroptosis induced by chemotherapeutics can occur in cardiomyocytes, and this feature represents an important drawback of antitumor therapy. This inconvenience has been tackled by developing less or no cardiotoxic antitumor drugs or by discovering cardioprotective agents (e.g., berberine, propofol, fisetin, salidroside, melatonin, epigallocatechin- 3gallate, resveratrol) to use in combination with conventional chemotherapeutics. This review briefly summarizes the molecular mechanisms of ferroptosis and describes the ferroptosis dependent mechanisms responsible for cardiac toxicity developed by cancer- suffering patients following the administration of some chemotherapeutics. Additionally, the pharmacological strategies very recently proposed for potentially preventing this inconvenience are considered.

:Cardiovascular diseases remain the leading cause of death worldwide; therefore, there is increasing attention to developing physiological-related in vitro cardiovascular tissue models suitable for personalized healthcare and preclinical test. Recently, more complex and powerful in vitro models have emerged for cardiac research. Human cardiac organoids (HCOs) are three-dimensional (3D) cellular constructs similar to in vivo organs. They are derived from pluripotent stem cells and can replicate the structure, function, and biogenetic information of primitive tissues. High-fidelity HCOs are closer to natural human myocardial tissue than animal and cell models to some extent, which helps to study better the development process of the heart and the occurrence of related diseases. In this review, we introduce the methods for constructing HCOs and the application of them, especially in cardiovascular disease modeling and cardiac drug screening. In addition, we propose the prospects and limitations of HCOs. In summary, we have introduced the research progress of HCOs and described their innovation and practicality of them in the biomedical field.

:In many circumstances, some crucial elements of the neuronal defense system fail, slowly leading to neurodegenerative diseases. Activating this natural process by administering exogenous agents to counteract unfavourable changes seems promising. Therefore, looking for neuroprotective therapeutics, we have to focus on compounds that inhibit the primary mechanisms leading to neuronal injuries, e.g., apoptosis, excitotoxicity, oxidative stress, and inflammation. Among many compounds considered neuroprotective agents, protein hydrolysates and peptides derived from natural materials or their synthetic analogues are good candidates. They have several advantages, such as high selectivity and biological activity, a broad range of targets, and high safety profile. This review aims to provide biological activities, the mechanism of action and the functional properties of plant-derived protein hydrolysates and peptides. We focused on their significant role in human health by affecting the nervous system and having neuroprotective and brain-boosting properties, leading to memory and cognitive improving activities. We hope our observation may guide the evaluation of novel peptides with potential neuroprotective effects. Research into neuroprotective peptides may find application in different sectors as ingredients in functional foods or pharmaceuticals to improve human health and prevent diseases.

:Liver fibrosis, characterized by the overproduction of extracellular matrix proteins within liver tissue, poses a rising global health concern. However, no approved antifibrotic drugs are currently available, highlighting the critical need for understanding the molecular mechanisms of liver fibrosis. This knowledge could not only aid in developing therapies but also enable early intervention, enhance disease prediction, and improve our understanding of the interaction between various underlying conditions and the liver. Notably, natural products used in traditional medicine systems worldwide and demonstrating diverse biochemical and pharmacological activities are increasingly recognized for their potential in treating liver fibrosis. This review aims to comprehensively understand liver fibrosis, emphasizing the molecular mechanisms and advancements in exploring natural products' antifibrotic potential over the past five years. It also acknowledges the challenges in their development and seeks to underscore their potency in enhancing patient prognosis and reducing the global burden of liver disease.

:Human microbes are closely associated with a variety of complex diseases and have emerged as drug targets. Identification of microbe-related drugs is becoming a key issue in drug development and precision medicine. It can also provide guidance for solving the increasingly serious problem of drug resistance enhancement in viruses.

Methods:In this paper, we have proposed a novel model of layer attention graph convolutional network for microbe-drug association prediction. First, multiple biological data have been integrated into a heterogeneous network. Then, the heterogeneous network has been incorporated into a graph convolutional network to determine the embedded microbe and drug. Finally, the microbe-drug association scores have been obtained by decoding the embedding of microbe and drug based on the layer attention mechanism.

Results:To evaluate the performance of our proposed model, leave-one-out crossvalidation (LOOCV) and 5-fold cross-validation have been implemented on the two datasets of aBiofilm and MDAD. As a result, based on the aBiofilm dataset, our proposed model has attained areas under the curve (AUC) of 0.9178 and 0.9022 on global LOOCV and local LOOCV, respectively. Based on aBiofilm dataset, the proposed model has attained an AUC value of 0.9018 and 0.8902 on global LOOCV and local LOOCV, respectively. In addition, the average AUC and standard deviation of the proposed model for 5- fold cross-validation on the aBiofilm and MDAD datasets were 0.9141±6.8556e-04 and 0.8982±7.5868e-04, respectively. Also, two kinds of case studies have been further conducted to evaluate the proposed models.

Conclusion:Traditional methods for microbe-drug association prediction are timeconsuming and laborious. Therefore, the computational model proposed was used to predict new microbe-drug associations. Several evaluation results have shown the proposed model to achieve satisfactory results and that it can play a role in drug development and precision medicine.