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.

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

Periodontitis is a common oral disease characterized by progressive alveolar bone resorption and inflammation of the periodontal tissues. Dimethyl fumarate (DMF) has been used in the treatment of various immune-inflammatory diseases due to its excellent anti-inflammatory and antioxidant functions. Here, we investigated for the first time the therapeutic effect of DMF on periodontitis. In vivo studies showed that DMF significantly inhibited periodontal destruction, enhanced mitophagy, and decreased the M1/M2 macrophage ratio. In vitro studies showed that DMF inhibited macrophage polarization toward M1 macrophages and promoted polarization toward M2 macrophages, with improved mitochondrial function, inhibited oxidative stress, and increased mitophagy in RAW 264.7 cells. Furthermore, DMF increased intracellular mitochondrial Tu translation elongation factor (TUFM) levels to maintain mitochondrial homeostasis, promoted mitophagy, and modulated macrophage polarization, whereas TUFM knockdown decreased the protective effect of DMF. Finally, mechanistic studies showed that DMF increased intracellular TUFM levels by protecting TUFM from degradation via the ubiquitin-proteasomal degradation pathway. Our results demonstrate for the first time that DMF protects mitochondrial function and inhibits oxidative stress through TUFM-mediated mitophagy in macrophages, resulting in a shift in the balance of macrophage polarization, thereby attenuating periodontitis. Importantly, this study provides new insights into the prevention of periodontitis.
Dimethyl Fumarate/pharmacology* ; Mitophagy/drug effects* ; Animals ; Mice ; Macrophages/metabolism* ; Periodontitis/prevention & control* ; RAW 264.7 Cells ; Oxidative Stress/drug effects* ; Peptide Elongation Factor Tu/metabolism* ; Mice, Inbred C57BL ; Male ; Mitochondria/drug effects*

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 establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7-T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7-T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7-T8 layer from chest CT images was 95.06％,84.83％,92.27％and 95.78％,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.

Purpose: Explore the causal relationship between the gut microbiota and erectile dysfunction (ED) at phylum, class, order, family, and genus levels, and identify specific pathogenic bacteria that may be associated with the onset and progression of ED. Materials and Methods: The genetic variation data of 196 human gut microbiota incorporated in our study came from the human gut microbiome Genome Wide Association Studies (GWAS) dataset released by the MiBioGen Consortium. The GWAS statistics for ED were extracted from one study by Bovijn et al., which included 223,805 participants of European ancestry, of whom 6,175 were diagnosed with ED. Subsequently, Mendelian randomization (MR) analysis was carried out to explore whether a causal relationship exists between the gut microbiota and ED. Additionally, bidirectional MR analysis was performed to examine the directionality of the causal relationship. Results: Through MR analysis, we found that family Lachnospiraceae (odds ratio [OR]: 1.27, 95% confidence interval [CI]: 1.05–1.52, p=0.01) and its subclass genus LachnospiraceaeNC2004 group (OR: 1.17, 95% CI: 1.01–1.37, p=0.04) are associated with a higher risk of ED. In addition, genus Oscillibacter (OR: 1.17, 95% CI: 1.02–1.35, p=0.03), genus Senegalimassilia (OR: 1.32, 95% CI: 1.06–1.64, p=0.01) and genus Tyzzerella3 (OR: 1.14, 95% CI: 1.02–1.27, p=0.02) also increase the risk of ED. In contrast, the inverse variance weighted estimate of genus RuminococcaceaeUCG013 (OR: 0.77, 95% CI: 0.61–0.96, p=0.02) suggests that it has a protective effect against the occurrence of ED. Conclusions This study preliminarily identified 6 bacterial taxa that may have a causal relationship with ED, including family Lachnospiraceae, genus Lachnospiraceae NC2004 group, Oscillibacter, Senegalimassilia, Tyzzerella 3 and Ruminococcaceae UCG013. These identified important bacterial taxa may serve as candidates for microbiome intervention in future ED clinical trials.

Purpose: Explore the causal relationship between the gut microbiota and erectile dysfunction (ED) at phylum, class, order, family, and genus levels, and identify specific pathogenic bacteria that may be associated with the onset and progression of ED. Materials and Methods: The genetic variation data of 196 human gut microbiota incorporated in our study came from the human gut microbiome Genome Wide Association Studies (GWAS) dataset released by the MiBioGen Consortium. The GWAS statistics for ED were extracted from one study by Bovijn et al., which included 223,805 participants of European ancestry, of whom 6,175 were diagnosed with ED. Subsequently, Mendelian randomization (MR) analysis was carried out to explore whether a causal relationship exists between the gut microbiota and ED. Additionally, bidirectional MR analysis was performed to examine the directionality of the causal relationship. Results: Through MR analysis, we found that family Lachnospiraceae (odds ratio [OR]: 1.27, 95% confidence interval [CI]: 1.05–1.52, p=0.01) and its subclass genus LachnospiraceaeNC2004 group (OR: 1.17, 95% CI: 1.01–1.37, p=0.04) are associated with a higher risk of ED. In addition, genus Oscillibacter (OR: 1.17, 95% CI: 1.02–1.35, p=0.03), genus Senegalimassilia (OR: 1.32, 95% CI: 1.06–1.64, p=0.01) and genus Tyzzerella3 (OR: 1.14, 95% CI: 1.02–1.27, p=0.02) also increase the risk of ED. In contrast, the inverse variance weighted estimate of genus RuminococcaceaeUCG013 (OR: 0.77, 95% CI: 0.61–0.96, p=0.02) suggests that it has a protective effect against the occurrence of ED. Conclusions This study preliminarily identified 6 bacterial taxa that may have a causal relationship with ED, including family Lachnospiraceae, genus Lachnospiraceae NC2004 group, Oscillibacter, Senegalimassilia, Tyzzerella 3 and Ruminococcaceae UCG013. These identified important bacterial taxa may serve as candidates for microbiome intervention in future ED clinical trials.

Purpose: Explore the causal relationship between the gut microbiota and erectile dysfunction (ED) at phylum, class, order, family, and genus levels, and identify specific pathogenic bacteria that may be associated with the onset and progression of ED. Materials and Methods: The genetic variation data of 196 human gut microbiota incorporated in our study came from the human gut microbiome Genome Wide Association Studies (GWAS) dataset released by the MiBioGen Consortium. The GWAS statistics for ED were extracted from one study by Bovijn et al., which included 223,805 participants of European ancestry, of whom 6,175 were diagnosed with ED. Subsequently, Mendelian randomization (MR) analysis was carried out to explore whether a causal relationship exists between the gut microbiota and ED. Additionally, bidirectional MR analysis was performed to examine the directionality of the causal relationship. Results: Through MR analysis, we found that family Lachnospiraceae (odds ratio [OR]: 1.27, 95% confidence interval [CI]: 1.05–1.52, p=0.01) and its subclass genus LachnospiraceaeNC2004 group (OR: 1.17, 95% CI: 1.01–1.37, p=0.04) are associated with a higher risk of ED. In addition, genus Oscillibacter (OR: 1.17, 95% CI: 1.02–1.35, p=0.03), genus Senegalimassilia (OR: 1.32, 95% CI: 1.06–1.64, p=0.01) and genus Tyzzerella3 (OR: 1.14, 95% CI: 1.02–1.27, p=0.02) also increase the risk of ED. In contrast, the inverse variance weighted estimate of genus RuminococcaceaeUCG013 (OR: 0.77, 95% CI: 0.61–0.96, p=0.02) suggests that it has a protective effect against the occurrence of ED. Conclusions This study preliminarily identified 6 bacterial taxa that may have a causal relationship with ED, including family Lachnospiraceae, genus Lachnospiraceae NC2004 group, Oscillibacter, Senegalimassilia, Tyzzerella 3 and Ruminococcaceae UCG013. These identified important bacterial taxa may serve as candidates for microbiome intervention in future ED clinical trials.