Structural optimization of lead compounds is a crucial step in drug discovery. One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. One of the deep molecular generative model approaches preserves the scaffold while generating drug-like molecules, thereby accelerating the molecular optimization process. Deep molecular diffusion generative models simulate a gradual process that creates novel, chemically feasible molecules from noise. However, the existing models lack direct interatomic constraint features and struggle with capturing long-range dependencies in macromolecules, leading to challenges in modifying the scaffold-based molecular structures, and creates limitations in the stability and diversity of the generated molecules. To address these challenges, we propose a deep molecular diffusion generative model, the three-dimensional (3D) equivariant diffusion-driven molecular generation (3D-EDiffMG) model. The dual strong and weak atomic interaction force-based long-range dependency capturing equivariant encoder (dual-SWLEE) is introduced to encode both the bonding and non-bonding information based on strong and weak atomic interactions. Additionally, a gate multilayer perceptron (gMLP) block with tiny attention is incorporated to explicitly model complex long-sequence feature interactions and long-range dependencies. The experimental results show that 3D-EDiffMG effectively generates unique, novel, stable, and diverse drug-like molecules, highlighting its potential for lead optimization and accelerating drug discovery.

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

Structural optimization of lead compounds is a crucial step in drug discovery.One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties.One of the deep molecular generative model approaches preserves the scaffold while generating drug-like molecules,thereby accelerating the molecular optimization process.Deep molecular diffusion generative models simulate a gradual process that creates novel,chemically feasible molecules from noise.However,the existing models lack direct interatomic constraint features and struggle with capturing long-range dependencies in macromolecules,leading to challenges in modifying the scaffold-based molecular structures,and creates limitations in the stability and diversity of the generated molecules.To address these challenges,we propose a deep molecular diffusion generative model,the three-dimensional(3D)equivariant diffusion-driven molecular generation(3D-EDiffMG)model.The dual strong and weak atomic interaction force-based long-range dependency capturing equivariant encoder(dual-SWLEE)is introduced to encode both the bonding and non-bonding information based on strong and weak atomic interactions.Addi-tionally,a gate multilayer perceptron(gMLP)block with tiny attention is incorporated to explicitly model complex long-sequence feature interactions and long-range dependencies.The experimental results show that 3D-EDiffMG effectively generates unique,novel,stable,and diverse drug-like molecules,highlighting its potential for lead optimization and accelerating drug discovery.

Vertebral refracture following percutaneous vertebral augmentation (PVA) is commonly seen in elderly patients with osteoporotic thoracolumbar compression fractures (OTLCF). It can lead to recurrent pain, loss of vertebral height, progression of kyphosis, and even neurological dysfunction, significantly impairing patients′ quality of life. Current diagnosis and treatment face multiple challenges, including high misdiagnosis rate, difficulty in choosing between surgical and non-surgical treatment options, lack of standardized surgical protocols, interference from intralesional bone cement during procedures, inadequate stability of internal fixation in osteoporotic bone, and suboptimal compliance of anti-osteoporotic therapy. Establishing a standardized diagnostic and therapeutic framework is urgently needed. To standardize the management process and improve outcomes for vertebral refractures after PVA in elderly OTLCF patients, Spinal Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized experts in the field to develop Guideline for the diagnosis and treatment of vertebral refracture after percutaneous vertebral augmentation in elderly patients with osteoporotic thoracolumbar compression fractures ( version 2025), based on current literature and clinical experience, and adhering to principles of scientific rigor and clinical applicability. A total of 11 recommendations were proposed, encompassing diagnosis, treatment, and rehabilitation of vertebral refracture after PVA in elderly patients with OTLCF, aiming to provide a foundation for a standardized management.

Vertebral refracture following percutaneous vertebral augmentation (PVA) is commonly seen in elderly patients with osteoporotic thoracolumbar compression fractures (OTLCF). It can lead to recurrent pain, loss of vertebral height, progression of kyphosis, and even neurological dysfunction, significantly impairing patients′ quality of life. Current diagnosis and treatment face multiple challenges, including high misdiagnosis rate, difficulty in choosing between surgical and non-surgical treatment options, lack of standardized surgical protocols, interference from intralesional bone cement during procedures, inadequate stability of internal fixation in osteoporotic bone, and suboptimal compliance of anti-osteoporotic therapy. Establishing a standardized diagnostic and therapeutic framework is urgently needed. To standardize the management process and improve outcomes for vertebral refractures after PVA in elderly OTLCF patients, Spinal Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized experts in the field to develop Guideline for the diagnosis and treatment of vertebral refracture after percutaneous vertebral augmentation in elderly patients with osteoporotic thoracolumbar compression fractures ( version 2025), based on current literature and clinical experience, and adhering to principles of scientific rigor and clinical applicability. A total of 11 recommendations were proposed, encompassing diagnosis, treatment, and rehabilitation of vertebral refracture after PVA in elderly patients with OTLCF, aiming to provide a foundation for a standardized management.

This study was aimed at understanding the genomic characteristics of the Vibrio cholerae O1 group isolated from humans in Fujian Province,to provide essential data for the molecular epidemiological study of cholera.From 2008 to 2022,16 strains of the V.cholerae O1 group from patients and carriers were collected,and antibiotic sensitivity was determined accord-ing to the minimum inhibitory concentration(MIC).The whole genome sequences obtained through second generation sequen-cing were analyzed in open source software,including snippy,Roary,and Prokka,as well as online analysis websites,inclu-ding NCBI and BacWGSTdb,for core-genome multilocus sequence typing(cgMLST),core-genome single nucleotide polymor-phism analysis(cgSNP),virulence gene analysis,drug resistance gene prediction,and pan-genomic diversity analysis.The whole genome sequences of V.cholerae were divided into five sequence types(STs),among which the newly discovered ST182 and ST1480 were the evolutionary branches of the current dominant clonal group ST75 in China,and were highly related to two strains isolated from Taiwan in 2010 and 2013,respectively.Both toxigenic strains and non-toxigenic strains carried a variety of virulence factors and showed gene variation to varying degrees.Thirteen drug resistance genes in seven categories were predicted,among which the distribution of colistin and tetracycline resistance genes was consistent with the drug resistance phenotype.Pan-ge-nomic analysis indicated that V.cholerae had an open pan-genome,and Roary cluster analysis showed higher resolution than cgMLST.In summary,V.cholerae O1 group isolates from humans in Fujian Province have polymorphisms in genome structure and function,and the newly discovered ST1480 clone group has epidemic potential.Therefore,the monitoring of such strains must be strengthened.

Accurate receptor/ligand binding free energy calculations can greatly accelerate drug discovery by identifying highly potent ligands. By simulating the change from one compound structure to another, the relative binding free energy (RBFE) change can be calculated based on the theoretically rigorous free energy perturbation (FEP) method. However, existing FEP-RBFE approaches may face convergence challenges due to difficulties in simulating non-physical intermediate states, which can lead to increased computational costs to obtain the converged results. To fundamentally overcome these issues and accelerate drug discovery, a new combined-structure RBFE (CS-FEP) calculation strategy was proposed, which solved the existing issues by constructing a new alchemical pathway, smoothed the alchemical transformation, increased the phase-space overlap between adjacent states, and thus significantly increased the convergence and accelerated the relative binding free energy calculations. This method was extensively tested in a practical drug discovery effort by targeting phosphodiesterase-1 (PDE1). Starting from a PDE1 inhibitor (compound 9, IC₅₀ = 16.8 μmol/L), the CS-FEP guided hit-to-lead optimizations resulted in a promising lead (11b and its mesylate salt formulation 11b-Mesylate, IC₅₀ = 7.0 nmol/L), with ∼2400-fold improved inhibitory activity. Further experimental studies revealed that the lead showed reasonable metabolic stability and significant anti-fibrotic effects in vivo.

Metabolic reprogramming is a hallmark of cancer, including lung cancer. However, the exact underlying mechanism and therapeutic potential are largely unknown. Here we report that protein arginine methyltransferase 6 (PRMT6) is highly expressed in lung cancer and is required for cell metabolism, tumorigenicity, and cisplatin response of lung cancer. PRMT6 regulated the oxidative pentose phosphate pathway (PPP) flux and glycolysis pathway in human lung cancer by increasing the activity of 6-phospho-gluconate dehydrogenase (6PGD) and α-enolase (ENO1). Furthermore, PRMT6 methylated R324 of 6PGD to enhancing its activity; while methylation at R9 and R372 of ENO1 promotes formation of active ENO1 dimers and 2-phosphoglycerate (2-PG) binding to ENO1, respectively. Lastly, targeting PRMT6 blocked the oxidative PPP flux, glycolysis pathway, and tumor growth, as well as enhanced the anti-tumor effects of cisplatin in lung cancer. Together, this study demonstrates that PRMT6 acts as a post-translational modification (PTM) regulator of glucose metabolism, which leads to the pathogenesis of lung cancer. It was proven that the PRMT6-6PGD/ENO1 regulatory axis is an important determinant of carcinogenesis and may become a promising cancer therapeutic strategy.

Vascular dementia (VaD) is the second commonest type of dementia which lacks of efficient treatments currently. Neuroinflammation as a prominent pathological feature of VaD, is highly involved in the development of VaD. In order to verify the therapeutic potential of PDE1 inhibitors against VaD, the anti-neuroinflammation, memory and cognitive improvement were evaluated in vitro and in vivo by a potent and selective PDE1 inhibitor 4a. Also, the mechanism of 4a in ameliorating neuroinflammation and VaD was systematically explored. Furthermore, to optimize the drug-like properties of 4a, especially for metabolic stability, 15 derivatives were designed and synthesized. As a result, candidate 5f, with a potent IC₅₀ value of 4.5 nmol/L against PDE1C, high selectivity over PDEs, and remarkable metabolic stability, efficiently ameliorated neuron degeneration, cognition and memory impairment in VaD mice model by suppressing NF-κB transcription regulation and activating cAMP/CREB axis. These results further identified PDE1 inhibition could serve as a new therapeutic strategy for treatment of VaD.