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Abstract Cirrhosis represents a significant global health challenge, characterized by high morbidity and mortality rates that severely impact human health. Timely and precise prognostic assessments of liver cirrhosis are crucial for improving patient outcomes and reducing mortality rates as they enable physicians to identify high-risk patients and implement early interventions. This paper features a thorough literature review on the prognostic assessment of liver cirrhosis, aiming to summarize and delineate the present status and constraints associated with the application of traditional prognostic tools in clinical settings. Among these tools, the Child–Pugh and Model for End-Stage Liver Disease (MELD) scoring systems are predominantly utilized. However, their accuracy varies significantly. These systems are generally suitable for broad assessments but lack condition-specific applicability and fail to capture the risks associated with dynamic changes in patient conditions. Future research in this field is poised for deep exploration into the integration of artificial intelligence (AI) with routine clinical and multi-omics data in patients with cirrhosis. The goal is to transition from static, unimodal assessment models to dynamic, multimodal frameworks. Such advancements will not only improve the precision of prognostic tools but also facilitate personalized medicine approaches, potentially revolutionizing clinical outcomes.

Transjugular intrahepatic portosystemic shunt (TIPS) is an essential procedure for the treatment of portal hypertension but can result in hepatic encephalopathy (HE), a serious complication that worsens patient outcomes. Investigating predictors of HE after TIPS is essential to improve prognosis. This review analyzes risk factors and compares predictive models, weighing traditional scores such as Child-Pugh, Model for End-Stage Liver Disease (MELD), and albumin-bilirubin (ALBI) against emerging artificial intelligence (AI) techniques. While traditional scores provide initial insights into HE risk, they have limitations in dealing with clinical complexity. Advances in machine learning (ML), particularly when integrated with imaging and clinical data, offer refined assessments. These innovations suggest the potential for AI to significantly improve the prediction of post-TIPS HE. The study provides clinicians with a comprehensive overview of current prediction methods, while advocating for the integration of AI to increase the accuracy of post-TIPS HE assessments. By harnessing the power of AI, clinicians can better manage the risks associated with TIPS and tailor interventions to individual patient needs. Future research should therefore prioritize the development of advanced AI frameworks that can assimilate diverse data streams to support clinical decision-making. The goal is not only to more accurately predict HE, but also to improve overall patient care and quality of life.

In this study, a Bayesian network (BN)-based inhibition model is developed for the rainstorm–landslide–debris flow (R-L-D) disaster chain in the mountainous area of the Greater Bay Area (GBA), China, using the historical disaster data. Twelve nodes are selected for the inhibition model, which are classified into four types, including Hazardous Factor, Response Operation, Disaster Evolution, and Disaster Result. By combining the proposed inhibition with the scenario analysis method, the probabilities of the BN nodes under different rainfall scenarios are analyzed, and then the inhibitory effects of the environmental geological conditions and rescue speed on the R-L-D disaster chain under the most unfavorable rainfall scenario are investigated. On this basis, an inhibition framework consisting of the early warning, inhibition, and measures layers is proposed for the R-L-D disaster chain. The results reveal that under the most unfavorable rainfall scenarios, where the rainfall intensity is greater than 100 mm/d and the rainfall duration is greater than 24 h, the probability of landslides and debris flow is 0.930 and 0.665, respectively. Improving the environmental geological conditions such as slope, lithology and geological structure can greatly inhibit the occurrence of the R-L-D disaster chain. Moreover, the improvement of geological structure conditions is the most significant, and reduces the probability of landslides and debris flow by 0.684 and 0.430, respectively, as well as reducing the probability of death and direct economic loss by 0.411 and 0.619, respectively. Similarly, increasing the rescue speed leads to a reduction in the probability of death and direct economic loss by 0.201 and 0.355, respectively. This study can provide theoretical and practical insights into the prevention and inhibition of the R-L-D disaster chain.

Abstract Intercellular communication often relies on exosomes as messengers and is critical for cancer metastasis in hypoxic tumor microenvironment. Some circular RNAs (circRNAs) are enriched in cancer cell‐derived exosomes, but little is known about their ability to regulate intercellular communication and cancer metastasis. Here, by systematically analyzing exosomes secreted by non‐small cell lung cancer (NSCLC) cells, a hypoxia‐induced exosomal circPLEKHM1 is identified that drives NSCLC metastasis through polarizing macrophages toward to M2 type. Mechanistically, exosomal circPLEKHM1 promoted PABPC1‐eIF4G interaction to facilitate the translation of the oncostatin M receptor (OSMR), thereby promoting macrophage polarization for cancer metastasis. Importantly, circPLEKHM1‐targeted therapy significantly reduces NSCLC metastasis in vivo. circPLEKHM1 serves as a prognostic biomarker for metastasis and poor survival in NSCLC patients. This study unveils a new circRNA‐mediated mechanism underlying how cancer cells crosstalk with macrophages within the hypoxic tumor microenvironment to promote metastasis, highlighting the importance of exosomal circPLEKHM1 as a prognostic biomarker and therapeutic target for lung cancer metastasis.

Abstract Long-term use of antipsychotics is a common cause of myocardial injury and even sudden cardiac deaths that often lead to drug withdrawn or discontinuation. Mechanisms underlying antipsychotics cardiotoxicity remain largely unknown. Herein we performed RNA sequencing and found that NLRP3 inflammasome-mediated pyroptosis contributed predominantly to multiple antipsychotics cardiotoxicity. Pyroptosis-based small-molecule compound screen identified cannabinoid receptor 1 (CB1R) as an upstream regulator of the NLRP3 inflammasome. Mechanistically, antipsychotics competitively bond to the CB1R and led to CB1R translocation to the cytoplasm, where CB1R directly interacted with NLRP3 inflammasome via amino acid residues 177–209, rendering stabilization of the inflammasome. Knockout of Cb1r significantly alleviated antipsychotic-induced cardiomyocyte pyroptosis and cardiotoxicity. Multi-organ-based investigation revealed no additional toxicity of newer CB1R antagonists. In authentic human cases, the expression of CB1R and NLRP3 inflammasome positively correlated with antipsychotics-induced cardiotoxicity. These results suggest that CB1R is a potent regulator of the NLRP3 inflammsome-mediated pyroptosis and small-molecule inhibitors targeting the CB1R/NLRP3 signaling represent attractive approaches to rescue cardiac side effects of antipsychotics.

BackgroundAcute coronary syndrome (ACS) consists of a range of acute myocardial ischemia-related manifestations. The adverse events of ACS are usually associated with ventricular dysfunction (VD), which could finally develop to heart failure. Currently, there is no satisfactory indicator that could specifically predict the development of ACS and its prognosis. Valosin-containing protein (VCP) has recently been proposed to protect against cardiac diseases. Hence, we aimed to assess whether VCP in serum can serve as a valuable biomarker for predicting ACS and its complication.MethodsHuman serum samples from 291 participants were collected and classified into four groups based on their clinical diagnosis, namely healthy control (n = 64), ACS (n = 40), chronic coronary syndrome (CCS, n = 99), and nonischemic heart disease (non-IHD, n = 88). Clinical characteristics of these participants were recorded and their serum VCP levels were detected by enzyme-linked immunosorbent assay (ELISA). Association of serum VCP with the development of ACS and its complication VD was statistically studied. Subsequently, GWAS and eQTL analyses were performed to explore the association between VCP polymorphism and monocyte count. A stability test was also performed to investigate whether VCP is a stable biomarker.ResultsSerum VCP levels were significantly higher in the ACS group compared with the rest groups. Besides, the VCP levels of patients with ACS with VD were significantly lower compared to those without VD. Multivariate logistic regression analysis revealed that VCP was associated with both the risk of ACS (P = 0.042, OR = 1.222) and the risk of developing VD in patients with ACS (P = 0.035, OR = 0.513) independently. The GWAS analysis also identified an association between VCP polymorphism (rs684562) and monocyte count, whereas the influence of rs684562 on VCP mRNA expression level was further verified by eQTL analysis. Moreover, a high stability of serum VCP content was observed under different preservation circumstances.ConclusionValosin-containing protein could act as a stable biomarker in predicting the development of ACS and its complication VD.

The plasma membrane of a biological cell is a complex assembly of lipids and membrane proteins, which tightly regulate transmembrane transport. When a cell is exposed to strong electric field, the membrane integrity becomes transiently disrupted by formation of transmembrane pores. This phenomenon termed electroporation is already utilized in many rapidly developing applications in medicine including gene therapy, cancer treatment, and treatment of cardiac arrhythmias. However, the molecular mechanisms of electroporation are not yet sufficiently well understood; in particular, it is unclear where exactly pores form in the complex organization of the plasma membrane. In this study, we combine coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis to identify how formation of pores depends on the local lipid organization. We show that pores do not form homogeneously across the membrane, but colocalize with domains that have specific features, the most important being high density of polyunsaturated lipids. We further show that knowing the lipid organization is sufficient to reliably predict poration sites with machine learning. Additionally, by analysing poration kinetics with Bayesian survival analysis we show that poration does not depend solely on local lipid arrangement, but also on membrane mechanical properties and the polarity of the electric field. Finally, we discuss how the combination of atomistic and coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis can guide the design of future experiments and help us to develop an accurate description of plasma membrane electroporation on the whole-cell level. Achieving this will allow us to shift the optimization of electroporation applications from blind trial-and-error approaches to mechanistic-driven design.

Cannabinoid receptors typically include type 1 (CB1) and type 2 (CB2), and they have attracted extensive attention in the central nervous system (CNS) and immune system. Due to more in-depth studies in recent years, it has been found that the typical CB1 and CB2 receptors confer functional importance far beyond the CNS and immune system. In particular, many works have reported the critical involvement of the CB1 and CB2 receptors in myocardial injuries. Both pharmacological and genetic approaches have been used for studying CB1 and CB2 functions in these studies, revealing that the brother receptors have many basic differences and sometimes antagonistic functions in a variety of myocardial injuries, despite some sequence or location identity they share. Herein, we introduce the general differences of CB1 and CB2 cannabinoid receptors, and summarize the functional rivalries between the two brother receptors in the setting of myocardial injuries. We point out the importance of individual receptor-based modulation, instead of dual receptor modulators, when treating myocardial injuries.