Association between monocyte-to-high-density lipoprotein-cholesterol ratio and gallstones in U.S. adults: findings from the National Health and Nutrition Examination Survey 2017-2020.

BACKGROUND: Studies have indicated that monocyte-to-high-density lipoprotein cholesterol ratio (MHR) can be a reliable indicator of various diseases. However, the association between MHR and gallstone prevalence remains unclear. Therefore, this study aimed to explore any potential association between MHR and gallstone prevalence. METHODS: This study used data from the National Health and Nutrition Examination Survey (NHANES) 2017-March 2020. MHR was calculated as the monocyte count ratio to high-density lipoprotein cholesterol levels. Multiple logistic regression models, Cochran-Armitage trend test, and subgroup analyses were used to examine the association between MHR and gallstones. RESULTS: This study included 5907 participants, of whom 636 (10.77%) were gallstone formers. The study participants had a mean age of 50.78 ± 17.33 years. After accounting for multiple covariables, the multiple logistic regression model showed a positive linear association between MHR and gallstone odds. The subgroup analyses and interaction testing results revealed that the association between MHR and gallstones was statistically different across strata, including sex, smoking, asthma, and hypertension. CONCLUSIONS: Gallstone prevalence positively associated with elevated MHR, indicating that MHR can be employed as a clinical indicator to assess gallstone prevalence.

Corrigendum: Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.

[This corrects the article DOI: 10.3389/fimmu.2024.1368749.].

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

CBLC promotes the development of colorectal cancer by promoting ABI1 degradation to activate the ERK signaling pathway.

CBLC (CBL proto-oncogene C) is an E3 ubiquitin protein ligase that plays a key role in cancers. However, the function and mechanism of CBLC in colorectal cancer (CRC) has not been fully elucidated. The aim of this study was to investigate the function of CBLC in CRC and its underlying molecular mechanism. High CBLC levels were certified in tumor tissues of CRC patients, and its expression was positively associated with TNM stage. Next, we explored the role of CBLC in CRC using gain or loss of function. For biological function analysis, CCK-8 cell proliferation, colony formation, flow cytometry, scratch, and transwell assays collectively suggested that CBLC overexpression promoted cell proliferation, cell cycle progression, migration and invasion. As observed, CBLC knockdown exhibited exactly opposite effects, resulting in impaired tumorigenicity in vitro. Xenograft studies displayed that CBLC overexpression accelerated tumor growth and promoted tumor metastasis to the lung, while the inhibitory effects of CBLC knockdown on tumorigenicity and metastasis ability of CRC cells was also confirmed. Furthermore, the molecular mechanism of CBLC in CRC was explored. CBLC induced the activation of ERK signaling pathway, further leading to its pro-tumor role. Notably, CBLC decreased ABI1 (Abelson interactor protein-1, a candidate tumor suppressor) protein levels through its ubiquitin ligase activity, while ABI1 upregulation abolished the effects of CBLC on the tumorigenesis of CRC. Taken together, these results demonstrate that CBLC acts as a tumor promoter in CRC through triggering the ubiquitination and degradation of ABI1 and activating the ERK signaling pathway. CBLC may be a potential novel target for CRC.

RuCo/ZrO2 Tandem Catalysts with Photothermal Confinement Effect for Enhanced CO2 Methanation.

Photothermal CO2 methanation reaction represents a promising strategy for addressing CO2-related environmental issues. The presence of efficient tandem catalytic sites with a localized high-temperature is an effective pathway to enhance the performance of CO2 methanation. Here the bimetallic RuCo nanoparticles anchored on ZrO2 fiber cotton (RuCo/ZrO2) as a photothermal catalyst for CO2 methanation are prepared. A significant photothermal CO2 methanation performance with optimal CH4 selectivity (99%) and rate (169.93 mmol gcat -1 h-1) is achieved. The photothermal energy of the RuCo bimetallic nanoparticles, confined by the infrared insulation and low thermal conductivity of the ZrO2 fiber cotton (ZrO2 FC), provides a localized high-temperature. In situ spectroscopic experiments on RuCo/ZrO2, Ru/ZrO2, and Co/ZrO2 indicate that the construction of tandem catalytic sites, where the Co site favors CO2 conversion to CO while incorporating Ru enhances CO* adsorption for subsequent hydrogenation, results in a higher selectivity toward CH4. This work opens a new insight into designing tandem catalysts with a photothermal confinement effect in CO2 methanation reaction.

Stabilized ε-Fe2C catalyst with Mn tuning to suppress C1 byproduct selectivity for high-temperature olefin synthesis.

Accurately controlling the product selectivity in syngas conversion, especially increasing the olefin selectivity while minimizing C1 byproducts, remains a significant challenge. Epsilon Fe2C is deemed a promising candidate catalyst due to its inherently low CO2 selectivity, but its use is hindered by its poor high-temperature stability. Herein, we report the successful synthesis of highly stable ε-Fe2C through a N-induced strategy utilizing pyrolysis of Prussian blue analogs (PBAs). This catalyst, with precisely controlled Mn promoter, not only achieved an olefin selectivity of up to 70.2% but also minimized the selectivity of C1 byproducts to 19.0%, including 11.9% CO2 and 7.1% CH4. The superior performance of our ε-Fe2C-xMn catalysts, particularly in minimizing CO2 formation, is largely attributed to the interface of dispersed MnO cluster and ε-Fe2C, which crucially limits CO to CO2 conversion. Here, we enhance the carbon efficiency and economic viability of the olefin production process while maintaining high catalytic activity.

Pulsed laser induced plasma and thermal effects on molybdenum carbide for dry reforming of methane.

Dry reforming of methane (DRM) is a highly endothermic process, with its development hindered by the harsh thermocatalytic conditions required. We propose an innovative DRM approach utilizing a 16 W pulsed laser in combination with a cost-effective Mo2C catalyst, enabling DRM under milder conditions. The pulsed laser serves a dual function by inducing localized high temperatures and generating *CH plasma on the Mo2C surface. This activates CH4 and CO2, significantly accelerating the DRM reaction. Notably, the laser directly generates *CH plasma from CH4 through thermionic emission and cascade ionization, bypassing the traditional step-by-step dehydrogenation process and eliminating the rate-limiting step of methane cracking. This method maintains a carbon-oxygen balanced environment, thus preventing the deactivation of the Mo2C catalyst due to CO2 oxidation. The laser-catalytic DRM achieves high yields of H2 (14300.8 mmol h-1 g-1) and CO (14949.9 mmol h-1 g-1) with satisfactory energy efficiency (0.98 mmol kJ-1), providing a promising alternative for high-energy-consuming catalytic systems.