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BackgroundNon-small cell lung cancer (NSCLC) ranks among the most prevalent and lethal malignancies globally. Fatty acids (FAs) play a significant role in diverse physiological and pathological mechanisms, yet their precise involvement in NSCLC remains poorly understood.MethodsThis study utilized a large-scale prospective cohort of 249,132 participants, observed over an average of 12 years, to investigate the relationship between different FAs and NSCLC risk. Analytical approaches included Cox proportional hazards regression, Kaplan–Meier survival analysis, accelerated failure time (AFT) modeling, and restricted cubic spline (RCS) analysis.ResultsDuring the follow-up period, 1,460 participants were diagnosed with NSCLC. Cox regression analysis demonstrated that elevated levels of docosahexaenoic acid (DHA), linoleic acid (LA), and omega-3 were inversely associated with NSCLC risk. Kaplan–Meier curves, along with AFT models, corroborated that elevated concentrations of DHA and LA significantly delayed NSCLC onset. Additionally, RCS analysis uncovered nuanced dose–response relationships between these FAs and NSCLC. Stratified analyses highlighted variability based on smoking status, gender, and body mass index subgroups.ConclusionThe concentration of specific FAs exhibits a significant association with NSCLC risk. These results offer a foundation for devising dietary FA composition adjustments aimed at reducing NSCLC risk.

Researching the structural characteristics of the spatial correlation network of cultivated land utilization ecological efficiency is of great significance to China's food security and agricultural green and low-carbon development. Taking 47 cities (autonomous prefectures) in the upper reaches of the Yangtze River as the research object, the ecological efficiency of cultivated land utilization from 2010 to 2020 was measured based on the unexpected output model (Super SBM), and the spatial correlation matrix was constructed using the revised gravity model. The structural characteristics of the spatial correlation network were analyzed using the social network model (SNA), and finally, the factors affecting the spatial correlation network of cultivated land utilization ecological efficiency in the upper reaches of the Yangtze River were analyzed through the quadratic assignment procedure (QAP) model. The results show that: (1) the ecological efficiency of cultivated land utilization in the upper reaches of the Yangtze River has been increasing year by year, but the overall level is low, and there is a large gap among provinces. Sichuan Province has the highest average value of 0.605, and Yunnan Province has the lowest average value of 0.359. (2) The ecological efficiency of cultivated land utilization in the upper reaches of the Yangtze River has broken through the provincial boundaries and has formed an obvious spatial correlation network, but the overall density is low, and the network is still relatively loose, needing further development and improvement. Chengdu, Yibin, Luzhou, and other cities are located in the center of the network and have formed four cohesive subgroups. (3)The differences in the level of agricultural economic development, the rural per capita disposable income, the differences in agricultural mechanization intensity, the regional population differences, and spatial adjacency have an impact on the spatial network of ecological efficiency of cultivated land utilization in the upper reaches of the Yangtze River. The difference in the level of agricultural economic development, the rural per capita disposable income, and the differences in agricultural mechanization intensity are negatively correlated, while the regional population differences are positively correlated with spatial adjacency.

The redox potential plays a critical role in sustaining the stability of gut microbiota. This study measured the fecal redox potential in healthy and diarrheal pigs using direct and dilution methods and investigated their correlation with microbiota. The results showed that the fluctuations in the redox potential of healthy pig feces were consistent using two different methods and the two methods are equivalent based on an equivalence test. The redox potential was positively correlated with the number of fungi and negatively related to the total bacteria. The relative or absolute abundances of many bacteria at the phyla and genus levels were associated with redox potential. In diarrheal pigs, the potentiometric trends of the two methods demonstrated an opposing pattern and the correlation with total bacteria was reversed. Precipitously elevated redox potential was detected post-diarrhea using dilution methods. The absolute abundance of Escherichia-Shigella and Fuurnierella was positively correlated with redox potential, while both relative and absolute abundances of Limosilactobacillus were positively correlated. These results suggest that both methods are suitable for detecting gut redox potential in healthy pigs, while the dilution method is more suitable for diarrheal pigs. The findings on the correlation of Limosilactobacillus, Prevotella, and Escherichia-Shigella with redox potential offer novel insights for targeted modulation of intestinal health.

AbstractBackgroundPurposeMethodsResultsConclusionThe liver, a central organ in human metabolism, is often the primary target for drugs. However, conditions such as viral hepatitis, cirrhosis, non-alcoholic fatty liver disease (NAFLD), and hepatocellular carcinoma (HCC) present substantial health challenges worldwide. Existing treatments, which suffer from the non-specific distribution of drugs, frequently fail to achieve desired efficacy and safety, risking unnecessary liver harm and systemic side effects.The aim of this review is to synthesise the latest progress in the design of liver-targeted prodrugs, with a focus on passive and active targeting strategies, providing new insights into the development of liver-targeted therapeutic approaches.This study conducted an extensive literature search through databases like Google Scholar, PubMed, Web of Science, and China National Knowledge Infrastructure (CNKI), systematically collecting and selecting recent research on liver-targeted prodrugs. The focus was on targeting mechanisms, including the Enhanced Permeability and Retention (EPR) effect, the unique microenvironment of liver cancer, and active targeting through specific transporters and receptors.Active targeting strategies achieve precise drug delivery by binding specific ligands to liver surface receptors. Passive targeting takes advantage of the EPR effect and tumour characteristics to enrich drugs in liver tumours. The review details successful cases of using small molecule ligands, peptides, antibodies and nanoparticles as drug carriers.Liver-targeted prodrug strategies show great potential in enhancing the efficacy of drug treatment and reducing side effects for liver diseases. Future research should balance the advantages and limitations of both targeting strategies, focusing on optimising drug design and targeting efficiency, especially for clinical application. In-depth research on liver-specific receptors and the development of innovative targeting molecules are crucial for advancing the field of liver-targeted prodrugs. [ABSTRACT FROM AUTHOR]

Chemoproteomic probes (CPPs) have been widely considered as powerful molecular biological tools that enable the highly efficient discovery of both binding proteins and modes of action for the studied compounds. They have been successfully used to validate targets and identify binders. The design of CPP has been considered extremely challenging, which asks for the generalization using a large number of probe data. However, none of the existing databases gives such valuable data of CPPs. Herein, a database entitled 'Chem(Pro)2' was therefore developed to systematically describe the atlas of diverse types of CPPs labelling human protein in living cell/lysate. With the booming application of chemoproteomic technique and artificial intelligence in current chemical biology study, Chem(Pro)2 was expected to facilitate the AI-based learning of interacting pattern among molecules for discovering innovative targets and new drugs. Till now, Chem(Pro)2 has been open to all users without any login requirement at: https://idrblab.org/chemprosquare/., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The measurement of cell-based molecular bioactivity (CMB) is critical for almost every step of drug development. With the booming application of AI in biomedicine, it is essential to have the CMB data to promote the learning of cell-based patterns for guiding modern drug discovery, but no database providing such information has been constructed yet. In this study, we introduce MolBiC, a knowledge base designed to describe valuable data on molecular bioactivity measured within a cellular context. MolBiC features 550 093 experimentally validated CMBs, encompassing 321 086 molecules and 2666 targets across 988 cell lines. Our MolBiC database is unique in describing the valuable data of CMB, which meets the critical demands for CMB-based big data promoting the learning of cell-based molecular/pharmaceutical pattern in drug discovery and development. MolBiC is now freely accessible without any login requirement at: https://idrblab.org/MolBiC/., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)