Liver-targeting chimeras as a potential modality for the treatment of liver diseases.

Liver diseases pose significant challenges to global public health. In the realm of drug discovery and development, overcoming 'on-target off-tissue' effects remains a substantial barrier for various diseases. In this study, we have pioneered a Liver-Targeting Chimera (LIVTAC) approach using a proteolysis-targeting chimera (PROTAC) molecule coupled to the liver-specific asialoglycoprotein receptor (ASGPR) through an innovative linker attachment strategy for the precise induction of target protein degradation within the liver. As a proof-of-concept study, we designed XZ1606, a mammalian bromodomain and extra-terminal domain (BET)-targeting LIVTAC agent, which not only demonstrated enduring tumor suppression (over 2 months) in combination with sorafenib but also an improved safety profile, notably ameliorating the incidence of thrombocytopenia, a common and severe on-target dose-limiting toxic effect associated with conventional BET inhibitors. These encouraging results highlight the potential of LIVTAC as a versatile platform for addressing a broad spectrum of liver diseases.

Deep learning model for the prediction of all-cause mortality among long term care people in China: a prospective cohort study.

This study aimed to develop a deep learning model to predict the risk stratification of all-cause death for older people with disability, providing guidance for long-term care plans. Based on the government-led long-term care insurance program in a pilot city of China from 2017 and followed up to 2021, the study included 42,353 disabled adults aged over 65, with 25,071 assigned to the training set and 17,282 to the validation set. The administrative data (including baseline characteristics, underlying medical conditions, and all-cause mortality) were collected to develop a deep learning model by least absolute shrinkage and selection operator. After a median follow-up time of 14 months, 17,565 (41.5%) deaths were recorded. Thirty predictors were identified and included in the final models for disability-related deaths. Physical disability (mobility, incontinence, feeding), adverse events (pressure ulcers and falls from bed), and cancer were related to poor prognosis. A total of 10,127, 25,140 and 7086 individuals were classified into low-, medium-, and high-risk groups, with actual risk probabilities of death of 9.5%, 45.8%, and 85.5%, respectively. This deep learning model could facilitate the prevention of risk factors and provide guidance for long-term care model planning based on risk stratification.

An overview of PROTACs targeting MDM2 as a novel approach for cancer therapy.

MDM2 genes amplification or altered expression is commonly observed in various cancers bearing wild-type TP53. Directly targeting the p53-binding pocket of MDM2 to activate the p53 pathway represents a promising therapeutic approach. Despite the development of numerous potent MDM2 inhibitors that have advanced into clinical trials, their utility is frequently hampered by drug resistance and hematologic toxicity such as neutropenia and thrombocytopenia. The emergence of PROTAC technology has revolutionized drug discovery and development, with applications in both preclinical and clinical research. Harnessing the power of PROTAC molecules to achieve MDM2 targeted degradation and p53 reactivation holds significant promise for cancer therapy. In this review, we summarize representative MDM2 PROTAC degraders and provide insights for researchers investigating MDM2 proteins and the p53 pathway.

Intra-Host Co-Existing Strains of SARS-CoV-2 Reference Genome Uncovered by Exhaustive Computational Search.

The COVID-19 pandemic caused by SARS-CoV-2 has had a severe impact on people worldwide. The reference genome of the virus has been widely used as a template for designing mRNA vaccines to combat the disease. In this study, we present a computational method aimed at identifying co-existing intra-host strains of the virus from RNA-sequencing data of short reads that were used to assemble the original reference genome. Our method consisted of five key steps: extraction of relevant reads, error correction for the reads, identification of within-host diversity, phylogenetic study, and protein binding affinity analysis. Our study revealed that multiple strains of SARS-CoV-2 can coexist in both the viral sample used to produce the reference sequence and a wastewater sample from California. Additionally, our workflow demonstrated its capability to identify within-host diversity in foot-and-mouth disease virus (FMDV). Through our research, we were able to shed light on the binding affinity and phylogenetic relationships of these strains with the published SARS-CoV-2 reference genome, SARS-CoV, variants of concern (VOC) of SARS-CoV-2, and some closely related coronaviruses. These insights have important implications for future research efforts aimed at identifying within-host diversity, understanding the evolution and spread of these viruses, as well as the development of effective treatments and vaccines against them.

High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior.

Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of highthroughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti- SARS-CoV-2 agents.

The effect of group IV chitinase, HaCHT4, on the chitin content of the peritrophic matrix (PM) during larval growth and development of Helicoverpa armigera.

BACKGROUND: Extensive research has been conducted on insect chitinases. However, little is known about the function of chitinase in the regulation of the surface structure of the peritrophic matrix (PM) in larval midguts. The aim of this study was to analyze the effect of HaCHT4 on the chitin content and surface structure of the PM during larval growth and development of Helicoverpa armigera. RESULTS: The expression level of HaCHT4 was lower and the chitin content was higher in the early stages of fourth to sixth instar larvae, but they were reversed in the corresponding late stages. The correlation coefficient between the expression level of HaCHT4 and the chitin content was -0.585 (P < 0.05), with a higher negative correlation of -0.934 for the fourth instar (P < 0.01). Scanning electron microscopy (SEM) showed that the surface structure of PM was multi-laminated with small pores in the early stages of fourth to sixth instar larvae, and more and bigger pores in the late stages. Low expression of HaCHT4 caused by RNA interference (RNAi) resulted in the increase of chitin content in the PM, and the surface structure of PM became multilayered with smaller pore size in the late stage of fourth instar larvae. Also, induction of HaCHT4 by application of 2-tridecanone (2-TD), decreased the chitin content of PM, caused larger pores to form and lots of food bolus to attach to the PM surface, and also increased the larval susceptibility to chlorantraniliprole. CONCLUSION: These results provided strong evidence that HaCHT4 plays an important role by regulating the chitin content of the PM and its surface structure, thereby affecting the sensitivity of H. armigera to chlorantraniliprole.

Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design.

Messenger RNA (mRNA) vaccines have recently emerged as a new type of vaccine technology, showing strong potential to combat the COVID-19 pandemic. In addition to SARS-CoV-2 which caused the pandemic, mRNA vaccines have been developed and tested to prevent infectious diseases caused by other viruses such as Zika virus, the dengue virus, the respiratory syncytial virus, influenza H7N9 and Flavivirus. Interestingly, mRNA vaccines may also be useful for preventing non-infectious diseases such as diabetes and cancer. This review summarises the current progresses of mRNA vaccines designed for a range of diseases including COVID-19. As epitope study is a primary component in the in silico design of mRNA vaccines, we also survey on advanced bioinformatics and machine learning algorithms which have been used for epitope prediction, and review on user-friendly software tools available for this purpose. Finally, we discuss some of the unanswered concerns about mRNA vaccines, such as unknown long-term side effects, and present with our perspectives on future developments in this exciting area.