ObjectiveTo investigate the effects of the classical Chinese medicine compound prescription Erjingwan on the inflammatory response and apoptosis of skeletal muscle cells in a mouse model of sarcopenia and decipher the mechanism based on the silent information regulator 1 （SIRT1）/nuclear factor erythroid 2-related factor 2 （Nrf2）/heme oxygenase-1 （HO-1） signaling pathway. MethodsForty C57/BL6 male mice were randomized into a control group， a model group， and groups with different doses of Erjingwan （8，16，32 g·kg^-1）. The mouse model of sarcopenia was established by D-gal-induced skeletal muscle senescence. The body weight and grip strength of mice treated with different doses of Erjingwan were examined to evaluate their physiological functions. Hematoxylin-eosin （HE） staining and Masson staining were used to observe the pathological changes and fibrosis in the skeletal muscle of mice. Enzyme-linked immunosorbent assay （ELISA） was adopted to determine the levels of tumor necrosis factor-α （TNF-α） and interleukin-6 （IL-6） in the serum samples of mice， and biochemical tests were conducted to quantify the levels of superoxide dismutase （SOD）， malondialdehyde （MDA）， and glutathione （GSH） in the serum. The protein and mRNA levels of SIRT1， Nrf2， B-cell lymphoma （Bcl-2）， and Bcl-2-associated X protein （Bax） were determined by Western blot and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）， respectively. ResultsAfter 4 weeks of drug intervention， the model group exhibited significant reductions in body weight and grip strength （P0.01） compared with the control group. Compared with the model group， all doses of Erjingwan increased the body weight in mice at week 8 （P0.01） and grip strength from week 6 （P0.01）. HE staining revealed clear muscle fiber structure in the control group， muscle fiber rupture and atrophy in the model group， and dose-dependent repair of muscle fiber structure in the Erjingwan groups. Masson staining showed minimal collagen fibers and mild fibrosis in the control group， collagen fiber proliferation and severe fibrosis in the model group， and collagen proliferation with dose-dependent inhibition of fibrosis in the Erjingwan groups. ELISA results showed that serum levels of TNF-α and IL-6 were elevated in the model group compared with those in the control group （P0.01）. After intervention， the low-dose Erjingwan group exhibited a decreased TNF-α level （P0.05）， while the medium and high-dose groups showed decreases in both TNF-α and IL-6 levels （P0.01）. Biochemical assays revealed that the model group had decreased SOD and GSH levels （P0.01） and an increased MDA level （P0.01） compared with the control group. The medium and high-dose Erjingwan groups exhibited increases in SOD and GSH levels （P0.01） and decreases in MDA level （P0.01）， compared with the model group. WB and Real-time PCR results showed that compared with the control group， the model group presented down-regulated protein and mRNA levels of SIRT1， Nrf2， HO-1， and Bcl-2 in the muscle tissue （P0.01） and up-regulated protein and mRNA levels of Bax （P0.01）. Compared with the model group， Erjingwan at different doses up-regulated the protein levels of SIRT1， Nrf2， HO-1， and Bcl-2 （P0.01） and down-regulated the protein and mRNA levels of Bax （P0.01） in the muscle tissue. Low-dose Erjingwan elevated the mRNA levels of Nrf2 and HO-1 （P0.05， P0.01）， and medium and high-dose Erjingwan up-regulated the mRNA levels of SIRT1， Nrf2， HO-1， and Bcl-2 （P0.01）. ConclusionErjingwan reduced the content of inflammatory factors in skeletal muscle cells， improved the antioxidant capacity， and attenuated pathological changes and fibrosis in the muscle of the mouse model of sarcopenia by regulating the SIRT1/Nrf2/HO-1 pathway， inflammatory response， and apoptosis network.

ObjectiveTo investigate the effects of the classical Chinese medicine compound prescription Erjingwan on the inflammatory response and apoptosis of skeletal muscle cells in a mouse model of sarcopenia and decipher the mechanism based on the silent information regulator 1 （SIRT1）/nuclear factor erythroid 2-related factor 2 （Nrf2）/heme oxygenase-1 （HO-1） signaling pathway. MethodsForty C57/BL6 male mice were randomized into a control group， a model group， and groups with different doses of Erjingwan （8，16，32 g·kg^-1）. The mouse model of sarcopenia was established by D-gal-induced skeletal muscle senescence. The body weight and grip strength of mice treated with different doses of Erjingwan were examined to evaluate their physiological functions. Hematoxylin-eosin （HE） staining and Masson staining were used to observe the pathological changes and fibrosis in the skeletal muscle of mice. Enzyme-linked immunosorbent assay （ELISA） was adopted to determine the levels of tumor necrosis factor-α （TNF-α） and interleukin-6 （IL-6） in the serum samples of mice， and biochemical tests were conducted to quantify the levels of superoxide dismutase （SOD）， malondialdehyde （MDA）， and glutathione （GSH） in the serum. The protein and mRNA levels of SIRT1， Nrf2， B-cell lymphoma （Bcl-2）， and Bcl-2-associated X protein （Bax） were determined by Western blot and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）， respectively. ResultsAfter 4 weeks of drug intervention， the model group exhibited significant reductions in body weight and grip strength （P0.01） compared with the control group. Compared with the model group， all doses of Erjingwan increased the body weight in mice at week 8 （P0.01） and grip strength from week 6 （P0.01）. HE staining revealed clear muscle fiber structure in the control group， muscle fiber rupture and atrophy in the model group， and dose-dependent repair of muscle fiber structure in the Erjingwan groups. Masson staining showed minimal collagen fibers and mild fibrosis in the control group， collagen fiber proliferation and severe fibrosis in the model group， and collagen proliferation with dose-dependent inhibition of fibrosis in the Erjingwan groups. ELISA results showed that serum levels of TNF-α and IL-6 were elevated in the model group compared with those in the control group （P0.01）. After intervention， the low-dose Erjingwan group exhibited a decreased TNF-α level （P0.05）， while the medium and high-dose groups showed decreases in both TNF-α and IL-6 levels （P0.01）. Biochemical assays revealed that the model group had decreased SOD and GSH levels （P0.01） and an increased MDA level （P0.01） compared with the control group. The medium and high-dose Erjingwan groups exhibited increases in SOD and GSH levels （P0.01） and decreases in MDA level （P0.01）， compared with the model group. WB and Real-time PCR results showed that compared with the control group， the model group presented down-regulated protein and mRNA levels of SIRT1， Nrf2， HO-1， and Bcl-2 in the muscle tissue （P0.01） and up-regulated protein and mRNA levels of Bax （P0.01）. Compared with the model group， Erjingwan at different doses up-regulated the protein levels of SIRT1， Nrf2， HO-1， and Bcl-2 （P0.01） and down-regulated the protein and mRNA levels of Bax （P0.01） in the muscle tissue. Low-dose Erjingwan elevated the mRNA levels of Nrf2 and HO-1 （P0.05， P0.01）， and medium and high-dose Erjingwan up-regulated the mRNA levels of SIRT1， Nrf2， HO-1， and Bcl-2 （P0.01）. ConclusionErjingwan reduced the content of inflammatory factors in skeletal muscle cells， improved the antioxidant capacity， and attenuated pathological changes and fibrosis in the muscle of the mouse model of sarcopenia by regulating the SIRT1/Nrf2/HO-1 pathway， inflammatory response， and apoptosis network.

Objective:To evaluate the image quality and radiation dose in contrast-enhanced thoracic-abdominal-pelvic CT using automatic tube voltage modulation (ATVM) coupled with artificial intelligence iterative reconstruction (AIIR) versus routine tube voltage combined with Karl-3D iterative reconstruction (Karl-3D IR), and to determine the optimal noise level for AIIR in contrast-enhanced thoracic-abdominal-pelvic CT.Methods:A total of 100 patients who underwent contrast-enhanced thoracic-abdominal-pelvic CT examination in the Affiliated Hospital of Hebei University from April to October, 2023 were randomly divided into group A and group B using a random number table, with 50 patients in each group. Group A was scanned using ATVM, and images were reconstructed using AIIR with 1-5 noise levels. Group B was scanned using tube voltage 120 kVp and images were reconstructed with Karl-3D IR and noise level 5. The single-to-noise ratio (SNR), contrast-to-noise ratio (CNR), effective dose (E), and size-specific dose estimate (SSDE) were recorded or calculated for all patients or images. Subjective evaluations of all images were performed. The quality of the reconstructed images using AIIR with 1-5 noise levels were compared and the optimal noise level of AIIR for image reconstruction was determined. Image quality and radiation dose were statistically analyzed for Group A (image reconstruction with optimal AIIR noise level) and Group B.Results:The mean SNR and mean CNR of the reconstructed images using AIIR with noise levels 1, 2, and 3 in group A were higher than those using AIIR with noise levels 4 and 5. The images reconstructed using AIIR with noise levels 3 and 4 scored higher in subjective assessment than those reconstructed using AIIR with noise levels 1, 2, and 5. Therefore, noise level 3 was optimal for AIIR in reconstruction of contrast-enhanced thoracic-abdominal-pelvic CT images. The mean SNR, mean CNR, and subjective evaluation score of group A using AIIR with noise level 3 were higher than those of group B using Karl-3D IR with noise level 5 ( P<0.001). The mean SSDE and the mean E of group A were reduced by 46% and 41%, respectively, compared with those of group B. Conclusions:ATVM technology combined with the AIIR algorithm can improve image quality and reduced patient radiation dose in contrast-enhanced thoracic-abdominal-pelvic CT. Noise level 3 is optimal for AIIR in the reconstruction of arterial-phase and venous-phase contrast-enhanced thoracic-abdominal-pelvic CT images.

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms

Increasing evidence showed that histone deacetylase 6(HDAC6)dysfunction is directly associated with the onset and progression of various diseases,especially cancers,making the development of HDAC6-targeted anti-tumor agents a research hotspot.In this study,artificial intelligence(AI)technology and molecular simulation strategies were fully integrated to construct an efficient and precise drug screening pipeline,which combined Voting strategy based on compound-protein interaction(CPI)prediction models,cascade molecular docking,and molecular dynamic(MD)simulations.The biological potential of the screened compounds was further evaluated through enzymatic and cellular activity assays.Among the identified compounds,Cmpd.18 exhibited more potent HDAC6 enzyme inhibitory activity(IC50=5.41 nM)than that of tubastatin A(TubA)(IC50=15.11 nM),along with a favorable subtype selectivity profile(selectivity index ≈ 117.23 for HDAC1),which was further verified by the Western blot analysis.Additionally,Cmpd.18 induced G2/M phase arrest and promoted apoptosis in HCT-116 cells,exerting desirable antiproliferative activity(IC50=2.59 μM).Furthermore,based on long-term MD simulation trajectory,the key residues facilitating Cmpd.18's binding were identified by decomposition free energy analysis,thereby elucidating its binding mechanism.Moreover,the representative conformation analysis also indicated that Cmpd.18 could stably bind to the active pocket in an effective conformation,thus demonstrating the potential for in-depth research of the 2-(2-phenoxyethyl)pyridazin-3(2H)-one scaffold.

Advancements in artificial intelligence(AI)and emerging technologies are rapidly expanding the exploration of chemical space,facilitating innovative drug discovery.However,the transformation of novel compounds into safe and effective drugs remains a lengthy,high-risk,and costly process.Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development.Despite this need,no comprehensive tool currently supports systematic evaluation and efficient screening.Here,we present druglikeFilter,a deep learning-based framework designed to assess drug-likeness across four critical dimensions:1)physicochemical rule evaluated by systematic determination,2)toxicity alert investigated from multiple perspectives,3)binding affinity measured by dual-path analysis,and 4)compound synthesizability assessed by retro-route prediction.By enabling automated,multidimensional filtering of compound libraries,druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates,which can be freely accessed at https://idrblab.org/drugfilter/.

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Increasing evidence showed that histone deacetylase 6 (HDAC6) dysfunction is directly associated with the onset and progression of various diseases, especially cancers, making the development of HDAC6-targeted anti-tumor agents a research hotspot. In this study, artificial intelligence (AI) technology and molecular simulation strategies were fully integrated to construct an efficient and precise drug screening pipeline, which combined Voting strategy based on compound-protein interaction (CPI) prediction models, cascade molecular docking, and molecular dynamic (MD) simulations. The biological potential of the screened compounds was further evaluated through enzymatic and cellular activity assays. Among the identified compounds, Cmpd.18 exhibited more potent HDAC6 enzyme inhibitory activity (IC₅₀ = 5.41 nM) than that of tubastatin A (TubA) (IC₅₀ = 15.11 nM), along with a favorable subtype selectivity profile (selectivity index ≈ 117.23 for HDAC1), which was further verified by the Western blot analysis. Additionally, Cmpd.18 induced G2/M phase arrest and promoted apoptosis in HCT-116 cells, exerting desirable antiproliferative activity (IC₅₀ = 2.59 μM). Furthermore, based on long-term MD simulation trajectory, the key residues facilitating Cmpd.18's binding were identified by decomposition free energy analysis, thereby elucidating its binding mechanism. Moreover, the representative conformation analysis also indicated that Cmpd.18 could stably bind to the active pocket in an effective conformation, thus demonstrating the potential for in-depth research of the 2-(2-phenoxyethyl)pyridazin-3(2H)-one scaffold.

Objective:To investigate the value of a multi-parameter MRI nomogram model in evaluating the recurrence risk of hormone receptor (HR)-positive breast cancer.Methods:This study was a retrospective cross-sectional study. A retrospective analysis was conducted on the clinicopathological data (age, menopausal status, axillary lymph node metastasis, etc.) and imaging data of 220 patients with HR-positive breast cancer who underwent breast MRI examination and were pathologically confirmed at the Second Affiliated Hospital of Nantong University from January 2018 to December 2023. All patients underwent preoperative MRI examinations. Their MRI features were analyzed, and the maximum diameter of the lesion and the apparent diffusion coefficient (ADC) value were measured. Finally, the clinical treatment score (CTS5 score) after 5 years was calculated, and all patients were divided into a low recurrence risk (CTS5 score 3.13 points) and a medium to high recurrence risk (CTS5 score≥3.13 points) group. The patients were followed up through the electronic medical record system or by phone until December 31, 2024 to determine recurrence status. The patients were divided into the recurrence group and the non-recurrence group. The differences in clinicopathological data, MRI features and CTS5 scores between the recurrence group and the non-recurrence group were compared using independent sample t-tests, Mann-Whitney U tests or χ2 tests. Indicators with P0.05 in the univariate analysis were included in the multivariate logistic regression to screen the independent risk factors for predicting the recurrence of HR receptor-positive breast cancer, and a nomogram was constructed to establish the nomogram model. The receiver operating characteristic curves and the area under the curve (AUC) were used to evaluate the efficacy of the nomogram model in predicting the postoperative recurrence risk of patients with HR-positive breast cancer. The variance inflation factor (VIF) was used to evaluate the multicollinearity among independent variables. Calibration curves and decision curve analysis (DCA) were used to assess the fit and net clinical benefit of the nomogram model. Results:Among 220 patients with HR-positive breast cancer, 196 cases were in the non-recurrence group and 24 cases were in the recurrence group. There were statistically significant differences in the maximum diameter of the lesion, axillary lymph node metastasis, ADC value, CTS5 grouping, and CTS5 score between the recurrence group and the non-recurrence group ( P0.05). Multivariate logistic regression analysis showed that the maximum diameter of the lesion ( OR=1.110, 95% CI 1.169-1.503, P0.001), ADC value ( OR=0.993, 95% CI 0.993?0.989, P0.001), and axillary lymph node metastasis ( OR=8.842; 95% CI 2.120?36.884, P=0.003) were independent factors influencing postoperative recurrence in patients with HR-positive breast cancer, and a nomogram model was constructed based on this. VIF analysis showed that no significant multicollinearity was detected among the variables (VIF5). The AUC value of the nomogram model for predicting postoperative recurrence in patients with HR-positive breast cancer was 0.868 (95% CI 0.794-0.942), the sensitivity was 0.875, and the specificity was 0.781. The calibration curve showed that the prediction curve of this model for predicting postoperative recurrence in HR-positive breast cancer patients was basically consistent with the ideal curve trend. DCA showed that this model had a relatively high clinical benefit within the threshold probability range of 0.01% to 90.00%. Conclusion:The nomogram constructed based on multi-parameter MRI features can predict the postoperative recurrence risk of HR-positive breast cancer patients, with good consistency and predictive ability.

Aromatherapy refers to the method of using the aromatic components of plants in appropriate forms to act on the entire body or a specific area to prevent and treat diseases. Essential oils used in aromatherapy are hydrophobic liquids containing volatile aromatic molecules， such as limonene， linalool， linalool acetate， geraniol， and citronellol. These chemicals have been extensively studied and shown to have a variety of functions， including reducing anxiety， relieving depression， promoting sleep， and providing pain relief. Terpenoids are a class of organic molecules with relatively low lipid solubility. After being inhaled， they can pass through the nasal mucosa for transfer or penetrate the skin and enter the bloodstream upon local application. Some of these substances also have the ability to cross the blood-brain barrier， thereby exerting effects on the central nervous system. Currently， the academic community generally agrees that products such as essential oils and aromatherapy from aromatic plants have certain health benefits. However， the process of extracting a single component from it and successfully developing it into a drug still faces many challenges. Its safety and efficacy still need to be further verified through more rigorous and systematic experiments. This article systematically elaborated on the efficacy of aromatic substances， including plant extracts and natural small molecule compounds， in antibacterial and antiviral fields and the regulation of nervous system activity. As a result， a deeper understanding of aromatherapy was achieved. At the same time， the potential of these aromatic substances for drug development was thoroughly explored， providing important references and insights for possible future drug research and application.