Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction.

METHOD

We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models.

RESULT

Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis.

CONCLUSION

The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

New semi-analytical method for fast transcranial ultrasonic field simulation

Objective.To optimize and ensure the safety of ultrasound brain therapy, personalized transcranial ultrasound simulations are very useful. They allow to predict the pressure field, depending on the patient skull and probe position. Most transcranial ultrasound simulations are based on numerical methods which have a long computation time and a high memory usage. The goal of this study is to develop a new semi-analytical field computation method that combines realism and computation speed.Approach.Instead of the classic ray tracing, the ultrasonic paths are computed by time of flight minimization. Then the pressure field is computed using the pencil method. This method requires a smooth and homogeneous skull model. The simulation algorithm, so-called SplineBeam, was numerically validated, by comparison with existing solvers, and experimentally validated by comparison with hydrophone measured pressure fields through anex vivohuman skull.Main results.SplineBeam simulated pressure fields were close to the experimentally measured ones, with a focus position difference of the order of the positioning error and a maximum pressure difference lower than 6.02%. In addition, for those configurations, SplineBeam computation time was lower than another simulation software, k-Wave's, by two orders of magnitude, thanks to its capacity to compute the field only at the focal spot.Significance.These results show the potential of this new method to compute fast and realistic transcranial pressure fields. The combination of this two assets makes it a promising tool for real time transcranial pressure field prediction during ultrasound brain therapy interventions.

Material Design of Porous Hydroxyapatite Ceramics via Inverse Analysis of an Estimation Model for Bone-Forming Ability Based on Machine Learning and Experimental Validation of Biological Hard Tissue Responses

Hydroxyapatite and β-tricalcium phosphate have been clinically applied as artificial bone materials due to their high biocompatibility. The development of artificial bones requires the verification of safety and efficacy through animal experiments; however, from the viewpoint of animal welfare, it is necessary to reduce the number of animal experiments. In this study, we utilized machine learning to construct a model that estimates the bone-forming ability of bioceramics from material fabrication conditions, material properties, and in vivo experimental conditions. We succeeded in constructing two models: 'Model 1', which predicts material properties from their fabrication conditions, and 'Model 2', which predicts the bone-formation rate from material properties and in vivo experimental conditions. The inclusion of full width at half maximum (FWHM) in the feature of Model 2 showed an improvement in accuracy. Furthermore, the results of the feature importance showed that the FWHMs were the most important. By an inverse analysis of the two models, we proposed candidates for material fabrication conditions to achieve target values of the bone-formation rate. Under the proposed conditions, the material properties of the fabricated material were consistent with the estimated material properties. Furthermore, a comparison between bone-formation rates after 12 weeks of implantation in the porcine tibia and the estimated bone-formation rate. This result showed that the actual bone-formation rates existed within the error range of the estimated bone-formation rates, indicating that machine learning consistently predicts the results of animal experiments using material fabrication conditions. We believe that these findings will lead to the establishment of alternative animal experiments to replace animal experiments in the development of artificial bones.

Exploring the Effect of Xiao-Chai-Hu Decoction on Treating Psoriasis Based on Network Pharmacology and Experiment Validation

BACKGROUND

Psoriasis is a chronic, inflammatory and recurrent skin disease. Xiao-Chai-Hu Decoction (XCHD) has shown good effects against some inflammatory diseases and cancers. However, the pharmacological effect and mechanisms of XCHD on psoriasis are not yet clear.

OBJECTIVE

To uncover the effect and mechanisms of XCHD on psoriasis by integrating network pharmacology, molecular docking, and in vivo experiments.

METHODS

The active ingredients and corresponding targets of XCHD were screened through Traditional Chinese Medicine Systems Pharmacology Database and Analysis (TCMSP) and Traditional Chinese Medicine Integrated Database (TCMID). Differentially expressed genes (DEGs) of psoriasis were obtained from the gene expression omnibus (GEO) database. The XCHD-psoriasis intersection targets were obtained by intersecting XCHD targets, and DEGs were used to establish the "herb-active ingredient-target" network and Protein-Protein Interaction (PPI) Network. The hub targets were identified based on the PPI network by Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed next. Molecular docking was executed via AutoDockTools-1.5.6. Finally, in vivo experiments were carried out further to validate the therapeutic effects of XCHD on psoriasis.

RESULTS

58 active components and 219 targets of XCHD were screened. 4 top-active components (quercetin, baicalein, wogonin and kaempferol) and 7 hub targets (IL1B, CXCL8, CCND1, FOS, MMP9, STAT1 and CCL2) were identified. GO and KEGG pathway enrichment analyses indicated that the TNF signaling pathway, IL-17 signaling pathway and several pathways were involved. Molecular docking results indicated that hub genes had a good affinity to the corresponding key compounds. In imiquimod (IMQ)-induced psoriasis mouse models, XCHD could significantly improve psoriasis-like skin lesions, downregulate KRT17 and Ki67, and inhibit inflammation cytokines and VEGF.

CONCLUSION

XCHD showed the therapeutic effect on psoriasis by regulating keratinocyte differentiation, and suppressing inflammation and angiogenesis, which provided a theoretical basis for further experiments and clinical research.

Unravelling diagnostic clusters and immune landscapes of cuproptosis patterns in intervertebral disc degeneration through dry and wet experiments

Cuproptosis is a manner of mitochondrial cell death induced by copper. However, cuproptosis modulators' molecular processes in intervertebral disc degeneration (IDD) are still unclear. To better understand the processes of cuproptosis regulators in IDD, a thorough analysis of cuproptosis regulators in the diagnostic biomarkers and subtype determination of IDD was conducted. Then we collected clinical IDD samples and successfully established IDD model in vivo and in vitro, and carried out real-time quantitative polymerase chain reaction (RT-qPCR) validation of significant cuproptosis modulators. Totally we identified 8 crucial cuproptosis regulators in the present research. Using a random forest model, we isolated 8 diagnostic cuproptosis modulators for the prediction of IDD risk. Then, based on our following decision curve analysis, we selected the five diagnostic cuproptosis regulators with importance scores greater than two and built a nomogram model. Using a consensus clustering method, we divided IDD patients into two cuproptosis clusters (clusterA and clusterB) based on the important cuproptosis regulators. Additionally, each sample's cuproptosis value was evaluated using principal component analysis in order to quantify the cuproptosis clusters. Patients in clusterB had higher cuproptosis scores than patients in clusterA. Moreover, we found that clusterB was involved in the immunity of natural killer cell, while clusterA was related to activated CD4 T cell, activated B cell, etc. Notably, cuproptosis modulators detected by RT-qPCR showed generally consistent expression levels with the bioinformatics results. To sum up, cuproptosis modulators play a crucial role in the pathogenic process of IDD, providing biomarkers and immunotherapeutic approaches for IDD.

Integrated Network Pharmacology and Experimental Approach to Investigate the Protective Effect of Jin Gu Lian Capsule on Rheumatoid Arthritis by Inhibiting Inflammation via IL-17/NF-κB Pathway

Purpose

This study aimed to investigate the main pharmacological action and underlying mechanisms of Jin Gu Lian Capsule (JGL) against rheumatoid arthritis (RA) based on network pharmacology and experimental verification.

Methods

Network pharmacology approaches were performed to explore the core active compounds of JGL, key therapeutic targets, and signaling pathways. Molecular docking was used to predict the binding affinity of compounds with targets. In vivo experiments were undertaken to validate the findings from network analysis.

Results

A total of 52 targets were identified as candidate JGL targets for RA. Sixteen ingredients were identified as the core active compounds, including, quercetin, myricetin, salidroside, etc. Interleukin-1 beta (IL1B), transcription factor AP-1 (JUN), growth-regulated alpha protein (CXCL1), C-X-C motif chemokine (CXCL)3, CXCL2, signal transducer and activator of transcription 1 (STAT1), prostaglandin G/H synthase 2 (PTGS2), matrix metalloproteinase (MMP)1, inhibitor of nuclear factor kappa-B kinase subunit beta (IKBKB) and transcription factor p65 (RELA) were obtained as the key therapeutic targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that the efficacy of JGL was functionally involved in regulating immune-mediated inflammation, in which IL-17/NF-κB signaling was recommended as one of the main pathways. Molecular docking suggested that the core active compounds bound strongly to their respective targets. Experimentally, JGL treatment mitigated inflammation, showed analgesic activity, and ameliorated collagen-induced arthritis. Enzyme-linked immunosorbent assay showed that JGL effectively reduced the serum levels of cytokines, chemokines, and MMPs. Immunohistochemistry staining showed that JGL markedly reduced the expression of the targets in IL-17/NF-κB pathway including IL-17A, IL-17RA, NF-κB p65, C-X-C motif ligand 2, MMP1 and MMP13.

Conclusion

This investigation provided evidence that JGL may alleviate RA symptoms by partially inhibiting the immune-mediated inflammation via IL-17/NF-κB pathway.

Hmox1 is Identified as a Ferroptosis Hub Gene and Associated with the M1 Type Microglia/Macrophage Polarization in Spinal Cord Injury: Bioinformatics and Experimental Validation

Ferroptosis and immune cell infiltration are important pathological events in spinal cord injury (SCI), but links between ferroptosis and immune microenvironment after SCI were rare reported. In our study, 77 FRDEGs were screened at 7 days after SCI. GO analysis of FRDEGs showed that aging (GO:0007568; P-value = 1.11E-05) was the most remarkable enriched for biological process, protein binding (GO:0005515; adjusted P-value = 4.44E-06) was the most significantly enriched for molecular function, cytosol (GO:0005829; adjusted P-value = 1.51E-04) was the most prominent enriched for cellular component. Meanwhile, Ferroptosis was significantly enriched both in KEGG (rno04216; adjusted P-value = 0.001) and GSEA (NES = 1.35; adjusted P-value = 0.004) analysis. Next, Hmox1 (Log2Fold change = 6.52; adjusted P-value = 0.004) was identified as one of hub genes in SCI-induced ferroptosis. In the results of immune cell infiltration analysis, proportion of microglia/macrophage was significantly increased after SCI, and Hmox1 was found to positively correlate to the M1 type microglia/macrophage abundance. Finally, effects of Hmox1 on ferroptosis and M1 type polarization were validated in vivo and in vitro. Summarily, we found that Hmox1 was the hub gene in SCI-induced ferroptosis and associated with the M1 type polarization.

Network pharmacology identifies the inhibitory effect of Yiqiyangyinquyu prescription on salivary gland inflammation in Sjögren's syndrome

This study aimed to explore the mode of action of Yiqiyangyinquyu prescription (YP) against Sjögren's syndrome (SS) by combining network pharmacology with molecular docking techniques. YP's active components and target proteins were identified using the BATMAN-traditional Chinese medicine database. Concurrently, targets associated with SS were extracted from databases, including Genecards, Online Mendelian Inheritance in Man, and Therapeutic Target Database. The standard targets were then imported into the STRING database to construct a protein-protein interaction network. We then conducted gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses, which were succeeded by molecular docking studies to validate core active components and key targets. Finally, in vitro experiments and molecular dynamics simulation were conducted to substantiate the therapeutic efficacy of YP in treating SS. A total of 206 intersection targets and 46 active compounds were identified. Gene ontology analysis unveiled that YP targets were primarily enriched in cellular responses to chemical stress, inflammation, and cell proliferation. Key enriched signaling pathways encompassed the interleukin 17, hypoxia-inducible factor-1, tumor necrosis factor (TNF-α), and advanced glycation end products-receptor for AGEs (AGE-RAGE) signaling pathways. Molecular docking results demonstrated high-affinity between neotanshinone C, tanshiquinone B, miltionone I, TNF-α, interleukin 1 beta (IL-1β), and interleukin 6 (IL-6). Noteworthy, TNF-α, considered the most important gene in YP against SS, binds to YP most stably, which was further validated by molecular dynamics simulation. In vitro experiments confirmed YP's capacity to reduce TNF-α, IL-1β, and IL-6 expression, effectively alleviating SS-related inflammation. YP demonstrated a significant anti-inflammatory effect by suppressing inflammatory cytokines (TNF-α, IL-6, and IL-1β), providing experimental evidence for its clinical application in treating SS.

Experimental validation in a neutron exposure frame of the MINAS TIRITH for cell damage simulation

In the domains of medicine and space exploration, refining risk assessment models for protecting healthy tissue from ionizing radiation is crucial. Understanding radiation-induced effects requires biological experimentations at the cellular population level and the cellular scale modeling using Monte Carlo track structure codes. We present MINAS TIRITH, a tool using Geant4-DNA Monte Carlo-generated databases to study DNA damage distribution at the cell population scale. It introduces a DNA damage location module and proposes a method to convert double-strand breaks (DSB) into DNA Damage Response foci. We evaluate damage location precision and DSB-foci conversion parameters. MINAS TIRITH's accuracy is validated againstγ-H2AX foci distribution from cell population exposed to monoenergetic neutron beams (2.5 or 15.1 MeV) under different configurations, yielding mixed radiation fields. Strong agreement between simulation and experimental results was found demonstrating MINAS TIRITH's predictive precision in radiation-induced DNA damage topology. Additionally, modeling intercellular damage variability within a population subjected to a specific macroscopic dose identifies subpopulations, enhancing realistic fate models. This approach advances our understanding of radiation-induced effects on cellular systems for risk assessment improvement.

Network pharmacology combined with molecular docking and experimental verification to elucidate functional mechanism of Fufang Zhenzhu Tiaozhi against type 2 diabetes mellitus

Fufang Zhenzhu Tiaozhi (FTZ) capsules have been prescribed for treating glucose and lipid metabolism disorders such as type 2 diabetes mellitus (T2DM). However, the underlying mechanism remains unknown. In this study, network pharmacology and experimental verification were combined to investigate the mechanisms of FTZ in treating T2DM. A total of 176 active ingredients and 1169 corresponding targets were screened using biological databases. 598 potential targets of T2DM were retrieved from GeneCards, PharmGKB, OMIM, Drugbank, and TTD. The Venn diagram was employed to identify the 194 intersection targets, which were employed to construct the "Herb-Compound-Target" interacting networks. These common targets were also used to prepare a protein-protein interaction (PPI) network to uncover potential targets. The four core targets were docked to their corresponding targets for binding analysis. Additionally, the top-ranked poses of ingredients and the positive compounds from each protein were evaluated for stability using molecular dynamics. Our results suggest that core active ingredients such as kaempferol, luteolin, and baicalein have high binding affinity and stability with AKT1, PTGS2 (also known as COX-2), DPP4, and PAPRG. GO and KEGG analyses indicated that the treatment T2DM by FTZ might be related to different pathway like AMPK and EGFR pathways. The experimental validation results proved that kaempferol, luteolin, and baicalein could significantly inhibit the activity of DPP4 and COX-2, kaempferol and luteolin were also able to activate AKT and AMPK signaling pathway. This study further validated previous findings and enhanced our understanding of the potential effects of FTZ on T2DM.Communicated by Ramaswamy H. Sarma.

Unraveling the protective mechanisms of Chuanfangyihao against acute lung injury: Insights from experimental validation

Chuanfangyihao (CFYH) is an effective treatment for acute lung injury (ALI) in clinical practice; however, its underlying mechanism of action remains unclear. Therefore, the aim of the present study was to elucidate the pharmacological mechanism of action of CFYH in ALI through experimental validation. First, a rat model of ALI was established using lipopolysaccharide (LPS). Next, the pathological changes in the lungs of the rats and the pathological damage were scored. The wet/dry weight ratios were measured, and ROS content was detected using flow cytometry. ELISA was used to examine IL-6, TNF-α, IL-1β, IL-18, and LDH levels. Immunohistochemistry was used to detect Beclin-1 and NLRP3 expression. Western blotting was performed to analyze the expression of HMGB1, RAGE, TLR4, NF-κB p65, AMPK, p-AMPK, mTOR, p-mTOR, Beclin-1, LC3-II/I, p62, Bcl-2, Bax, Caspase-3, Caspase-1, and GSDMD-NT. The mRNA levels of HMGB1, RAGE, AMPK, mTOR, and HIF-1α were determined using reverse transcription quantitative PCR. CFYH alleviated pulmonary edema and decreased the expression of IL-6, TNF-α, TLR4, NF-κB p65, HMGB1/RAGE, ROS, and HIF-1α. In addition, pretreatment with CFYH reversed ALI-induced programmed cell death. In conclusion, CFYH alleviates LPS-induced ALI, and these findings provide a preliminary clarification of the predominant mechanism of action of CFYH in ALI.

Role of corn silk for the treatment of Alzheimer's disease: A mechanism research based on network pharmacology combined with molecular docking and experimental validation

This study aimed to research the possible mechanism and effect of active ingredients of corn silk on Alzheimer's disease (AD) by the method of network pharmacology, molecular docking, and animal experiments. The active ingredients of Corn silk were obtained by searching the TCMSP database and the targets corresponding to the active ingredients of Corn silk were obtained through the TCMSP and SwissTargetPrediction platforms, and the AD targets were obtained in the GeneCards, OMIM, and DisgeNET databases. Cytoscape was employed for creating the "active ingredient-target" relationship network; STRING and Cytoscape for creating the protein-protein interaction (PPI) network. Besides, Meta scape was used for Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the intersecting targets; AutoDockTools and Pymol for molecular docking and visualization of core ingredients and core targets; and animal experiments for verifying the anti-AD effect of luteolin. A total of 12 active ingredients of corn silk were screened, including 465 targets and 209 intersected targets. Moreover, GO functional analysis results showed that the anti-AD effect of corn silk was mainly reflected in phosphotransferase activity, response to hormone, membrane raft, etc.; KEGG results indicated the main pathways involving cancer, Alzheimer disease, etc.; and the molecular docking results revealed excellent binding of the core ingredients (α-tocopheryl quinone, luteolin, etc.) to the core targets. Besides, the outcomes of animal experiments exhibited that luteolin not only reduced the expression of inflammatory factors TNF-α and IL-1β in mice but also attenuated inflammation. With the help of network pharmacology and experimental validation, the material basis and mechanism of the anti-AD of corn silk have been explored in this study. Briefly speaking, luteolin from corn silk plays an anti-AD role by inhibiting inflammation.

Identification and verification of characteristic differentially expressed ferroptosis-related genes in osteosarcoma using bioinformatics analysis

BACKGROUND

This study identified and verified the characteristic differentially expressed ferroptosis-related genes (CDEFRGs) in osteosarcoma (OS).

METHODS

We extracted ferroptosis-related genes (FRGs), identified differentially expressed FRGs (DEFRGs) in OS, and conducted correlation analysis between DEFRGs. Next, we conducted GO and KEGG analyses to explore the biological functions and pathways of DEFRGs. Furthermore, we used LASSO and SVM-RFE algorithms to screen CDEFRGs, and evaluated its accuracy in diagnosing OS through ROC curves. Then, we demonstrated the molecular function and pathway enrichment of CDEFRGs through GSEA analysis. In addition, we evaluated the differences in immune cell infiltration between OS and NC groups, as well as the correlation between CDEFRGs expressions and immune cell infiltrations. Finally, the expression of CDEFRGs was verified through qRT-PCR, western blotting, and immunohistochemistry experiments.

RESULTS

We identified 51 DEFRGs and the expression relationship between them. GO and KEGG analysis revealed their key functions and important pathways. Based on four CDEFRGs (PEX3, CPEB1, NOX1, and ALOX5), we built the OS diagnostic model, and verified its accuracy. GSEA analysis further revealed the important functions and pathways of CDEFRGs. In addition, there were differences in immune cell infiltration between OS group and NC group, and CDEFRGs showed significant correlation with certain infiltrating immune cells. Finally, we validated the differential expression levels of four CDEFRGs through external experiments.

CONCLUSIONS

This study has shed light on the molecular pathological mechanism of OS and has offered novel perspectives for the early diagnosis and immune-targeted therapy of OS patients.

Identification and analysis of differential miRNA-mRNA interactions in coronary heart disease: an experimental screening approach

Objective

This aim of this study is to screen the differential molecules of kidney deficiency and blood stasis (KDBS) syndrome in coronary heart disease by high-throughput sequencing. In addition, the study aims to verify the alterations in the expression levels of miR-4685-3p and its regulated downstream, namely, C1QC, C4, and C5, using quantitative polymerase chain reaction (qPCR) and enzyme-linked immunosorbent assay (ELISA), and to determine whether the complement and coagulation cascade pathway is the specific pathogenic pathway.

Methods

Patients diagnosed with unstable angina pectoris with KDBS syndrome, patients with non-kidney deficiency blood stasis (NKDBS) syndrome, and a Normal group were recruited. The clinical symptoms of each group were further analyzed. Illumina's NextSeq 2000 sequencing platform and FastQC software were used for RNA sequencing and quality control. DESeq software was used for differential gene expression (DGE) analysis. qPCR and ELISA verification were performed on DGE analysis.

Results

The DGE profiles of 77 miRNA and 331 mRNA were selected. The GO enrichment analysis comprised 43 biological processes, 49 cell components, and 42 molecular functions. The KEGG enrichment results included 40 KEGG pathways. The PCR results showed that, compared with the Normal group, the miR-4685-3p levels decreased in the CHD_KDBS group (P = 0.001), and were found to be lower than those observed in the CHD_NKDBS group. The downstream mRNA C1 regulated by miR-4685-3p showed an increasing trend in the CHD_KDBS group, which was higher than that in the Normal group (P = 0.0019). The mRNA C4 and C5 in the CHD_KDBS group showed an upward trend, but the difference was not statistically significant. ELISA was utilized for the detection of proteins associated with the complement and coagulation cascade pathway. It was found that the expression level of C1 was significantly upregulated in the CHD_KDBS group compared with the Normal group (P < 0.0001), which was seen to be higher than that in the CHD_NKDBS group (P < 0.0001). The expression levels of C4 and C5 in the CHD_KDBS group were significantly lower than the Normal group, and were lower than that in the CHD_NKDBS group (P < 0.0001).

Conclusion

The occurrence of CHD_KDBS might be related to the activation of the complement and coagulation cascade pathway, which is demonstrated by the observed decrease in miR-4685-3p and the subsequent upregulation of its downstream C1QC. In addition, the expression levels of complement C4 and C5 were found to be decreased, which provided a research basis for the prevention and treatment of this disease.

Plastrum testudinis Ameliorates Oxidative Stress in Nucleus Pulposus Cells via Downregulating the TNF-α Signaling Pathway

BackgroundPlastrum testudinis (PT), a widely used traditional Chinese medicine, exerts protective effects against bone diseases such as intervertebral disc degeneration (IDD). Despite its effectiveness, the molecular mechanisms underlying the effects of PT on IDD remain unclear. Methods In this study, we used a comprehensive strategy combining bioinformatic analysis with experimental verification to investigate the possible molecular mechanisms of PT against IDD. We retrieved targets for PT and IDD, and then used their overlapped targets for protein-protein interaction (PPI) analysis. In addition, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to investigate the anti-IDD mechanisms of PT. Moreover, in vivo and in vitro experiment validations including hematoxylin-eosin (HE) and safranine O-green staining, senescence-associated β-galactosidase (SA-β-gal) assay, cell immunofluorescence staining, intracellular ROS measurement and Western blot analysis were performed to verify bioinformatics findings. Results We identified 342 and 872 PT- and IDD-related targets (32 overlapping targets). GO enrichment analysis yielded 450 terms related to oxidative stress and inflammatory response regulation. KEGG analysis identified 48 signaling pathways, 10 of which were significant; the TNF-α signaling pathway had the highest p-value, and prostaglandin G/H synthase 2 (PTGS2), endothelin-1 (EDN1), TNF-α, JUN and FOS were enriched in this pathway. Histopathological results and safranin O/green staining demonstrated that PT attenuated IDD, and SA-β-gal assay showed that PT ameliorated nucleus pulposus cell (NPC) senescence. An ROS probe was adopted to confirm the protective effect of PT against oxidative stress. Western blot analyses confirmed that PT downregulated the protein expression of PTGS2, EDN1, TNF-α, JUN and FOS in the TNF-α signaling pathway as well as cellular senescence marker p16, proinflammatory cytokine interleukin-6 (IL6), while PT upregulated the expression of NPC-specific markers including COL2A1 and ACAN in a concentration-dependent manner. Conclusions To the best of our knowledge, this study is the first to report that PT alleviates IDD by downregulating the protein expression of PTGS2, EDN1, TNF-α, JUN and FOS in the TNF-α signaling pathway and upregulating that of COL2A1 and ACAN, thus suppressing inflammatory responses and oxidative stress in NPCs.

Analysis of Influencing Factors for Stackable and Expandable Acoustic Metamaterial with Multiple Tortuous Channels

To reduce the noise generated by large mechanical equipment, a stackable and expandable acoustic metamaterial with multiple tortuous channels (SEAM-MTCs) was developed in this study. The proposed SEAM-MTCs consisted of odd panels, even panels, chambers, and a final closing plate, and these component parts could be fabricated separately and then assembled. The influencing factors, including the number of layers N, the thickness of panel t0, the size of square aperture a, and the depth of chamber T0 were investigated using acoustic finite element simulation. The sound absorption mechanism was exhibited by the distributions of the total acoustic energy density at the resonance frequencies. The number of resonance frequencies increased from 13 to 31 with the number of layers N increasing from 2 to 6, and the average sound absorption coefficients in [200 Hz, 6000 Hz] was improved from 0.5169 to 0.6160. The experimental validation of actual sound absorption coefficients in [200 Hz, 1600 Hz] showed excellent consistency with simulation data, which proved the accuracy of the finite element simulation model and the reliability of the analysis of influencing factors. The proposed SEAM-MTCs has great potential in the field of equipment noise reduction.

Capillary Underfill Flow Simulation as a Design Tool for Flow-Optimized Encapsulation in Heterogenous Integration

As the power electronics landscape evolves, pushing for greater vertical integration, capillary underfilling is considered a versatile encapsulation technique suited for iterative development cycles of innovative integration concepts. Since a defect-free application is critical, this study proposes a capillary two-phase flow simulation, predicting both the flow pattern and velocity with remarkable precision and efficiency. In a preliminary performance evaluation, Volume of Fluid (VOF) outperforms the Level-Set method in terms of accuracy and computation time. Strategies like HRIC blending, artificial viscosity, and implicit Multi-Stepping prove effective in optimizing the numerical VOF scheme. Digital mapping using physical experiments and virtual simulations validates transient flow predictions, achieving excellent agreement with deviations as low as 1.48-3.34%. The accuracy of flow predictions is thereby greatly influenced by non-Newtonian viscosity characteristics in the low shear range and time-dependent contact angle variations. The study further explores flow manipulation concepts, focusing on local flow speed adjustment, gap segmentation, and the use of arcuate shapes to influence interface confluence near the chip. Experimental validation corroborates the effectiveness of each design intervention. In conclusion, this research highlights the potential of predictive engineering to develop flow-optimized package designs that enhance reliability while supporting high manufacturing yields.

Numerical Model Validation of the Blood Flow through a Microchannel Hyperbolic Contraction

A computational fluid dynamics (CFD) model of blood flow through hyperbolic contraction with a discrete phase model (DPM) was experimentally validated. For this purpose, the positions and velocities of red blood cells (RBCs) flowing in a microchannel with hyperbolic contraction were experimentally assessed using image analysis techniques, and were subsequently compared with the numerical results. The numerically and experimentally obtained velocity fields were in good agreement, with errors smaller than 10%. Additionally, a nearly constant strain rate was observed in the contraction region, which can be attributed to the quasilinear increase in the velocity along the hyperbolic contraction. Therefore, the numerical technique used was validated due to the close similarity between the numerically and experimentally obtained results. The tested CFD model can be used to optimize the microchannel design by minimizing the need to fabricate prototypes and evaluate them experimentally.

Exploring anti-acute kidney injury mechanism of Dahuang-Gancao decoction by network pharmacology and experimental validation

This study aimed to investigate the pharmacological effects and molecular mechanisms of Dahuang-Gancao Decoction (DHGC) on acute kidney injury (AKI). Network pharmacology was utilized to analyze the key targets of DHGC against AKI. These targets were used to construct a protein-protein interaction (PPI) network, which was analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment to predict the mechanism of action. Based on the network pharmacological analysis, Sirtuin 3 (SIRT3) was identified as a key target, and apoptosis was suggested as a mechanism of DHGC for AKI treatment. Subsequently, an AKI mouse model was induced using lipopolysaccharide (LPS), and the study demonstrated that DHGC gradient intervention significantly reduced plasma urea and creatinine levels in AKI mice, ameliorated renal pathological changes, reduced apoptosis, and lowered serum inflammatory factors. The mechanism of DHGC's anti-AKI effect may lie in the activation of the SIRT3/NRF2/HO-1 signaling pathway, which plays an antiapoptotic role in renal cells. In summary, DHGC improved LPS-induced AKI in mice by activating the SIRT3/NRF2/HO-1 signaling pathway. These findings shed light on the potential clinical application of DHGC for the treatment of nephropathy.

Development of the prognostic value in lung adenocarcinoma based on anoikis-related genes and initial experimental validation

BACKGROUND

Lung adenocarcinoma (LUAD) is a highly aggressive cancer in advanced stages and has the highest cancer-related death across the world. Anoikis has emerged as a specific form of apoptotic cell death that may play a vital role in the formation and development of tumors.

METHODS

Based on The Cancer Genome Atlas dataset, we developed a novel anoikis-related genes (ARGs) signature in LUAD and evaluated the differences between low and high-risk groups in clinical characteristics, expression patterns, immune cell infiltration, and drug sensitivity, etc. According to multivariate Cox regression analysis, the risk score was identified as a significant independent prognostic factor. The possible biological pathways of ARGs' were assessed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. The immune infiltration landscape and risk score of ARGs were analyzed by ESTIMATE and CIBERSORT analysis. A nomogram grounded on six key ARGs and clinicopathological features was provided. Moreover, experiment validation of the expression patterns of six hub ARGs in lung cancer cell lines was conducted.

RESULTS

We identified 53 survival-related LUAD anoikis-related differentially expressed genes and finally six hub anoikis genes (LDHA, SLC2A1, SERPINB5, ITGB4, BRCA2, and PIK3R1) were selected to construct an ARG model. The risk model could efficiently cluster the patients into low- and high-risk groups which could accurately predict clinical outcomes for LUAD patients. There is evidence that the prognostic risk score is a remarkable prognostic factor in determining overall survival. Different immune statuses and drug sensitivity between low- and high-risk groups were explored according to functional analysis. On the basis of risk scores and LUAD clinicopathological features, a novel nomogram was developed. Ultimately, all six key genes except for PIK3R1 were proved to be upregulated in LUAD tissues and cell lines by bioinformatics analysis and experimental validation.

CONCLUSIONS

The result of the present study suggest that ARGs could be carcinogenic to LUAD and could be used as an effective stratification factor to customize therapies and forecast the survival rate in LUAD patients.

Identifying the Anti-inflammatory Effects of Astragalus Polysaccharides in Anti-N-Methyl-D-Aspartate Receptor Encephalitis: Network Pharmacology and Experimental Validation

BACKGROUND

Astragalus polysaccharides (APS), a group of bioactive compounds obtained from the natural source Astragalus membranaceus(AM), exhibits numerous pharmacological actions in the central nervous system, such as anti-inflammatory, antioxidant, and immunomodulatory properties. Despite the remarkable benefits, the effectiveness of APS in treating anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis and the corresponding mechanism have yet to be fully understood. As such, this study aims to investigate the impact of APS on anti-NMDAR encephalitis and explore the potential molecular network mechanism.

METHODS

The impact of APS intervention on mice with anti-NMDAR encephalitis was assessed, and the possible molecular network mechanism was investigated utilizing network pharmacology and bioinformatics techniques such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG),protein-protein interaction (PPI) network, and molecular docking. Enzyme-linked immunosorbent assay (ELISA) was applied to detect the expression of core target proteins.

RESULTS

APS significantly ameliorated cognitive impairment and reduced susceptibility to PTZ-induced seizures in mice with anti-NMDAR encephalitis, confirming the beneficial effect of APS on anti-NMDAR encephalitis. Seventeen intersecting genes were identified between APS and anti-NMDAR encephalitis. GO and KEGG analyses revealed the characteristics of the intersecting gene networks. STRING interaction in the PPI network was applied to find crucial molecules. The results of molecular docking suggested that APS may regulate interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) as potential targets in anti-NMDAR encephalitis. Furthermore, the levels of IL-1β, IL-6, and TNF-α detected by ELISA in anti-NMDAR encephalitis mice were significantly downregulated in response to the administration of APS.

CONCLUSION

The findings of this study demonstrate the significant role of APS in the treatment of anti-NMDAR encephalitis, as it effectively suppresses inflammatory cytokines. These results suggest that APS has the potential to be considered as a viable herbal medication for the treatment of anti-NMDAR encephalitis.

Anhydroicaritin suppresses tumor progression via the PI3K/AKT signaling pathway in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most malignant tumors. The in vitro experiments on the application of Anhydroicaritin (AHI), the active ingredient of Bushen Huayu Decoction, in HCC treatment remain limited, particularly regarding its molecular mechanism.

METHODS

The TCMSP platform was used for drug ingredient screening. The GeneCards database and DisGeNET database are used to collect liver cancer targets. PPI network construction of active component-target intersection target was completed with string database. The GO and KEGG pathway analyses were performed via bioinformatics analysis. The molecular docking was used to confirm AHI's target proteins. The in vitro experiments were performed to validate the effect of AHI on HCC cell and explore the molecular mechanism by western blotting analysis.

RESULTS

Through the intersection, 155 intersection targets are finally obtained. The top 15 active ingredients were quercetin, kaempferol, beta-sitosterol, luteolin, beta-carotene, Stigmasterol, naringenin, formononetin, baicalein, Anhydroicaritin, isorhamnetin, licochalcone, 7-O-methylisomucronulatol, aloe-emodin and 8-O-Methylreyusi. The molecular mocking analysis showed that the four active components (quercetin, kaempferol, luteolin and AHI) and targets had a good binding activity (affinity ≤ 5 kcal/mol). In vitro experiments reveled that AHI could suppress tumor proliferation, invasion and metastasis of HCC cells. Further analysis showed that AHI inhibited tumor growth by PI3K/AKT signal pathway in HCC.

CONCLUSIONS

The Bushen Huayu Decoction and its active ingredient AHI could fight HCC. The potential mechanism may be associated with inhibiting the activation of PI3K/AKT signal pathway, which may serve as a potential treatment for HCC therapy.

A rate-of-injection model for predicting single and double injection with or without fusion

Models used to predict the instantaneous injected fuel mass are of varied interest in automotive applications, including for providing inputs to CFD calculations or for engine control. While multiple injection strategies are now commonly used in diesel engines, the overall approach may be susceptible to injection fusion, which is defined as two successive injections that are partly or totally coupled due to the short time interval between each event. In this work, a new model to predict the instantaneous mass flow rate from a diesel injector is proposed based on the analytical solution of a first-order linear dynamic system exposed to an impulsion. Experiments are also conducted to quantify the main injection characteristics of a solenoid indirect-action injector under different injection pressures, backpressures and injection durations, representing a total of 33 different conditions. From these results, a model is proposed and validated against experimental data using a single injection strategy. Then, the model is enhanced to predict split injection with and without injection fusion. Successful comparisons are realized between the model and the experiment. The model is then used to successfully simulate a piezoelectric injector experiencing different levels of fusion available in the literature so as to illustrate the universality of the proposed approach.

Study on a Hexagonal Acoustic Metamaterial Cell of Multiple Parallel-Connection Resonators with Tunable Perforating Rate

The limited occupied space and various noise spectrum requires an adjustable sound absorber with a smart structure and tunable sound absorption performance. The hexagonal acoustic metamaterial cell of the multiple parallel-connection resonators with tunable perforating rate was proposed in this research, which consisted of six triangular cavities and six trapezium cavities, and the perforation rate of each cavity was adjustable by moving the sliding block along the slideway. The optimal geometric parameters were obtained by the joint optimization of the acoustic finite element simulation and cuckoo search algorithm, and the average sound absorption coefficients in the target frequency ranges of 650-1150 Hz, 700-1200 Hz and 700-1000 Hz were up to 0.8565, 0.8615 and 0.8807, respectively. The experimental sample was fabricated by the fused filament fabrication method, and its sound absorption coefficients were further detected by impedance tube detector. The consistency between simulation data and experimental data proved the accuracy of the acoustic finite element simulation model and the effectiveness of the joint optimization method. The tunable sound absorption performance, outstanding low-frequency noise reduction property, extensible outline structure and efficient space utilization were favorable to promote its practical applications in noise reduction.

Preliminary Test for 3D Surface Strain Measurement in the Tower and Foundation of Offshore Wind Turbines Using DOFS

Subjected to the relentless impacts of typhoons and rough seas, offshore wind turbines' structures, particularly the tower, foundation, and blade, are at constant risk of damage. Full-field strain monitoring helps to discover potential structural defects, thereby reducing disasters caused by overall structural failure. This study introduces a novel method for assessing strain and temperature fields on these kinds of 3D surfaces of cylindrical structures. The method harnesses the capabilities of a high spatial resolution (0.65 mm) Optical Frequency Domain Reflectometer (OFDR)-based Distributed Optical Fiber Sensor (DOFS) in conjunction with a unique helical wiring layout. The core process begins with mapping the fiber optic path onto a plane corresponding to the unfolded cylinder. Fiber optic signals are then differentiated on this plane, deriving a two-dimensional strain distribution. The plane strain field is subsequently projected onto the 3D side of the cylinder. An experiment was carried out in which a 3.5 m long optical fiber was helically wound with a 10 mm pitch on the surface of a cantilever beam of a cylinder shell with a diameter of 36 mm and a length of 300 mm. The experiment collected about 5400 measurement points on the cylindrical surface of 340 cm2, approximately 15.9 measurement points per square centimeter. The reconstructed results successfully reveal the strain field of the pipe cantilever beam under bending and torsional loads, as well as the palm-shaped temperature field. This experimental validation of the method's efficacy lays the theoretical groundwork for its application to real wind turbines.

Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex

Local field potential (LFP) recordings reflect the dynamics of the current source density (CSD) in brain tissue. The synaptic, cellular, and circuit contributions to current sinks and sources are ill-understood. We investigated these in mouse primary visual cortex using public Neuropixels recordings and a detailed circuit model based on simulating the Hodgkin-Huxley dynamics of >50,000 neurons belonging to 17 cell types. The model simultaneously captured spiking and CSD responses and demonstrated a two-way dissociation: firing rates are altered with minor effects on the CSD pattern by adjusting synaptic weights, and CSD is altered with minor effects on firing rates by adjusting synaptic placement on the dendrites. We describe how thalamocortical inputs and recurrent connections sculpt specific sinks and sources early in the visual response, whereas cortical feedback crucially alters them in later stages. These results establish quantitative links between macroscopic brain measurements (LFP/CSD) and microscopic biophysics-based understanding of neuron dynamics and show that CSD analysis provides powerful constraints for modeling beyond those from considering spikes.

Effects of Defects on Masonry Confinement with Inorganic Matrix Composites

Fabric-reinforced cementitious matrix (FRCM) composites are currently considered a suitable solution for strengthening existing structures. Confinement applications are still being investigated, since experimental programs showed significant scatter in the results and theoretical models are struggling to become established as a consequence. The main aim of this study is the identification of potential sources of scatter in the confinement efficiency of FRCM wrappings, in defects such as fiber slip within the matrix or imperfect straightening of fibers, or premature failure of fibers once exposed after complete matrix cracking. A theoretical incremental approach is proposed to simulate such effects. The approach is incremental, but not iterative, so that no convergence is required and the incremental step size has an impact only on the smoothness of the nonlinear theoretical stress vs. strain curves of the FRCM confined material, among other simulation results. Theoretical results are compared to experimental outcomes of previous tests. The main source of variability can be identified in the cited defects, and the approach can be considered satisfactory to simulate the effects of defects and the high scatter found in experimental results; however, further uncertainties in the behavior of materials can be included in future refinements of this study.

Optogenetic manipulation of inhibitory interneurons can be used to validate a model of spatiotemporal sequence learning

The brain uses temporal information to link discrete events into memory structures supporting recognition, prediction, and a wide variety of complex behaviors. It is still an open question how experience-dependent synaptic plasticity creates memories including temporal and ordinal information. Various models have been proposed to explain how this could work, but these are often difficult to validate in a living brain. A recent model developed to explain sequence learning in the visual cortex encodes intervals in recurrent excitatory synapses and uses a learned offset between excitation and inhibition to generate precisely timed "messenger" cells that signal the end of an instance of time. This mechanism suggests that the recall of stored temporal intervals should be particularly sensitive to the activity of inhibitory interneurons that can be easily targeted in vivo with standard optogenetic tools. In this work we examined how simulated optogenetic manipulations of inhibitory cells modifies temporal learning and recall based on these mechanisms. We show that disinhibition and excess inhibition during learning or testing cause characteristic errors in recalled timing that could be used to validate the model in vivo using either physiological or behavioral measurements.

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins

There are over 100 computational predictors of intrinsic disorder. These methods predict amino acid-level propensities for disorder directly from protein sequences. The propensities can be used to annotate putative disordered residues and regions. This unit provides a practical and holistic introduction to the sequence-based intrinsic disorder prediction. We define intrinsic disorder, explain the format of computational prediction of disorder, and identify and describe several accurate predictors. We also introduce recently released databases of intrinsic disorder predictions and use an illustrative example to provide insights into how predictions should be interpreted and combined. Lastly, we summarize key experimental methods that can be used to validate computational predictions. © 2023 Wiley Periodicals LLC.

Identification of ribosomal protein family as immune-cell-related biomarkers of NAFLD by bioinformatics and experimental analyses

Background

Immune cells play an integral role in the development and progression of non-alcoholic fatty liver disease (NAFLD). This study was to identify immune-cell-related biomarkers for the diagnosis and treatment of NAFLD.

Methods and findings

First, we introduced human liver transcriptome data from the GEO database (GSE48452 and GSE126848) and performed a weighted gene co-expression network analysis (WGCNA) to screen out the modules related to immune cell infiltration and to identify immune-cell-related differentially expressed genes (ICR-DEGs) associated with NAFLD progression. Further, the protein-protein interaction (PPI) network of ICR-DEGs was established to obtain hub genes and subsequently, the expression trend analysis was conducted to identify immune-cell-related biomarkers of NAFLD. Finally, the mRNA expression of biomarkers was validated in a NAFLD mouse model induced by high-fat diet (HFD) feeding. In total, we identified 66 ICR-DEGs and 13 hub genes associated with NAFLD. Among them, 9 hub genes (CD247, CD74, FCGR2B, IL2RB, INPP5D, MRPL16, RPL35, RPS3A, RPS8) were correlated with the infiltrating immune cells by the Pearson correlation analysis. Subsequently, 4 immune-cell-related biomarkers (RPL35, RPS3A, RPS8, and MRPL16) with the same expression trends in GSE48452 and GSE126848 datasets were identified. These biomarkers were enriched in immune-related pathways and had a good ability to distinguish between NASH and healthy samples. Moreover, we constructed a competing endogenous RNA (ceRNA) network of biomarkers and predicted twenty potential therapeutic drugs targeting RPS3A such as taxifolin and sitagliptin. Finally, experimental validation indicated that the hepatic mRNA expression of Rpl35, Rps3A, and Rps8 was significantly decreased in NAFLD mice.

Conclusions

This study identified four ribosomal protein genes (RPL35, RPS3A, RPS8, and MRPL16) as immune-cell-related biomarkers of NAFLD, which may actively participate in the immune processes during NAFLD progression and could serve as potential targets for the diagnosis and treatment of NAFLD.

Nephroprotective mechanisms of Rhizoma Chuanxiong and Radix et Rhizoma Rhei against acute renal injury and renal fibrosis based on network pharmacology and experimental validation

The molecular mechanisms of Rhizoma Chuanxiong (Chuanxiong, CX) and Rhei Radix et Rhizoma (Dahuang, DH) in treating acute kidney injury (AKI) and subsequent renal fibrosis (RF) were investigated in this study by applying network pharmacology and experimental validation. The results showed that aloe-emodin, (-)-catechin, beta-sitosterol, and folic acid were the core active ingredients, and TP53, AKT1, CSF1R, and TGFBR1 were the core target genes. Enrichment analyses showed that the key signaling pathways were the MAPK and IL-17 signaling pathways. In vivo experiments confirmed that Chuanxiong and Dahuang pretreatments significantly inhibited the levels of SCr, BUN, UNAG, and UGGT in contrast media-induced acute kidney injury (CIAKI) rats (p < 0.001). The results of Western blotting showed that compared with the control group, the protein levels of p-p38/p38 MAPK, p53, and Bax in the contrast media-induced acute kidney injury group were significantly increased, and the levels of Bcl-2 were significantly reduced (p < 0.001). Chuanxiong and Dahuang interventions significantly reversed the expression levels of these proteins (p < 0.01). The localization and quantification of p-p53 expression in immunohistochemistry technology also support the aforementioned results. In conclusion, our data also suggest that Chuanxiong and Dahuang may inhibit tubular epithelial cell apoptosis and improve acute kidney injury and renal fibrosis by inhibiting p38 MAPK/p53 signaling.

KIF11 is a potential prognostic biomarker and therapeutic target for adrenocortical carcinoma

Background

Adrenocortical carcinoma (ACC) is a rare endocrine neoplasia with poor prognosis. Emerging evidence suggests that kinesin family member 11 (KIF11) protein is overexpressed in several tumors and associated with the onset and progression of certain types of cancer; however, its biological functions and mechanisms in ACC progression have not been studied yet. Therefore, this study evaluated the clinical significance and therapeutic potential of the KIF11 protein in ACC.

Methods

The Cancer Genome Atlas (TCGA) database (n=79) and Genotype Tissue Expression (GTEx) database (n=128) were utilized to explore the expression of KIF11 in ACC and normal adrenal tissues. The TCGA datasets were then data mined and statistically analyzed. R survival analysis and univariate and multivariate Cox regression analyses were used to evaluate the effect of KIF11 expression on the survival rates, and a nomogram was used to predict its impact on prognosis. The clinical data from 30 ACC patients' from Xiangya Hospital were also analyzed. The effects of KIF11 on the proliferation and invasion of ACC NCI-H295R were further validated in vitro.

Results

Analytical data from the TCGA and GTEx databases showed that KIF11 expression was upregulated in ACC tissues and associated with T (primary tumor), and M (metastasis) and stages of tumor progression. Increased KIF11 expression was significantly associated with shorter overall survival, disease-specific survival, and progression-free intervals. Clinical data from Xiangya Hospital illustrated that increased KIF11 had a significantly positive correlation with shorter overall survival, T and pathological stages, and tumor recurrence risk. Monastrol, a specific inhibitor of KIF11, was further confirmed to significantly inhibit the proliferation and invasion of ACC NCI-H295R cell in vitro. The nomogram demonstrated KIF11 was an excellent predictive biomarker in patients with ACC.

Conclusions

The findings demonstrate that KIF11 could be a predictor of poor prognosis and thus possibly serve as a novel therapeutic target for ACC.

Experimental Validation and Prediction of Super-Enhancers: Advances and Challenges

Super-enhancers (SEs) are cis-regulatory elements of the human genome that have been widely discussed since the discovery and origin of the term. Super-enhancers have been shown to be strongly associated with the expression of genes crucial for cell differentiation, cell stability maintenance, and tumorigenesis. Our goal was to systematize research studies dedicated to the investigation of structure and functions of super-enhancers as well as to define further perspectives of the field in various applications, such as drug development and clinical use. We overviewed the fundamental studies which provided experimental data on various pathologies and their associations with particular super-enhancers. The analysis of mainstream approaches for SE search and prediction allowed us to accumulate existing data and propose directions for further algorithmic improvements of SEs' reliability levels and efficiency. Thus, here we provide the description of the most robust algorithms such as ROSE, imPROSE, and DEEPSEN and suggest their further use for various research and development tasks. The most promising research direction, which is based on topic and number of published studies, are cancer-associated super-enhancers and prospective SE-targeted therapy strategies, most of which are discussed in this review.

Cuproptosis Depicts Immunophenotype and Predicts Immunotherapy Response in Lung Adenocarcinoma

BACKGROUND

Although significant progress has been made in immunotherapy for lung adenocarcinoma (LUAD), there is an urgent need to identify effective indicators to screen patients who are suitable for immunotherapy. Systematically investigating the cuproptosis-related genes (CRGs) in LUAD may provide new ideas for patients' immunotherapy stratification.

METHOD

We comprehensively analyzed the landscape of 12 CRGs in a merged TCGA and GEO LUAD cohort. We investigated the associations between tumor microenvironment and immunophenotypes. We utilized a risk score to predict the prognosis and immunotherapy response for an individual patient. Additionally, we conducted CCK-8 experiments to evaluate the impact of DLGAP5 knockdown on A549 cell proliferation.

RESULT

We utilized an integrative approach to analyze 12 CRGs and differentially expressed genes (DEGs) in LUAD samples, resulting in the identification of two distinct CRG clusters and two gene clusters. Based on these clusters, we generated immunophenotypes and observed that the inflamed phenotype had the most abundant immune infiltrations, while the desert phenotype showed the poorest immune infiltrations. We then developed a risk score model for individual patient prognosis and immunotherapy response prediction. Patients in the low-risk group had higher immune scores and ESTIMATE scores, indicating an active immune state with richer immune cell infiltrations and higher expression of immune checkpoint genes. Moreover, the low-risk group exhibited better immunotherapy response according to IPS, TIDE scores, and Imvigor210 cohort validation results. In addition, our in vitro wet experiments demonstrated that DLGAP5 knockdown could suppress the cell proliferation of A549.

CONCLUSION

Novel cuproptosis molecular patterns reflected the distinct immunophenotypes in LUAD patients. The risk model might pave the way to stratify patients suitable for immunotherapy and predict immunotherapy response.

A network pharmacology-based approach to explore the active ingredients and molecular mechanism of Shen-Kui-Tong-Mai granules on a rat model with chronic heart failure

OBJECTIVES

This study aimed to comprehensively investigate the potential active components and therapeutic mechanisms of Shen-Kui-Tong-Mai granule (SKTMG) in the treatment of heart failure.

METHODS

Network pharmacology combined with ultra-high performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS), molecular docking, and in vivo validation was performed to identify the active components and the potential targets for SKTMG to improve chronic heart failure (CHF).

KEY FINDINGS

The network pharmacology identified 192 active compounds and 307 potential consensus targets for SKTMG. On the other hand, network analysis discovered 10 core target genes related to the MAPK signal pathway. These genes include AKT1, STAT3, MAPK1, P53, SRC, JUN, TNF, APP, MAPK8 and IL6. The molecular docking results revealed that the SKTMG components were luteolin, quercetin, astragaloside IV and kaempferol, which could bind AKT1, MAPK1, P53, JUN, TNF and MAPK8. Additionally, SKTMG inhibited phosphorylation of AKT, P38, P53 and c-JUN, and reduced TNF-α expression in CHF rats.

CONCLUSIONS

The present results demonstrated that network pharmacology combined with UHPLC-MS/MS, molecular docking and in vivo validation can facilitate the identification of active components and the potential targets for SKTMG to improve CHF.

Exploring the Active Compounds of Traditional Mongolian Medicine Baolier Capsule (BLEC) in Patients with Coronary Artery Disease (CAD) Based on Network Pharmacology Analysis, Molecular Docking and Experimental Validation

Objective

Baolier Capsule (BLEC) is a Traditional Mongolian Medicine comprising fifteen herbs. This study aims to illustrate the synergistic mechanism of BLEC in the treatment of Coronary Artery Disease (CAD) by using network pharmacology method, molecular docking and experimental validation.

Methods

Searching and screening the active ingredients of different herbs in BLEC and target genes related to CAD in multiple databases. Subsequently, Protein-Protein Interactions Network (PPI-Net), gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment were used to identify the key targets. AutoDock was used to verify the binding ability between the active ingredient and key target through molecular docking. Reverse Transcription-Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR) was used to verify the effect of active ingredient of BLEC on the key target gene. Finally, effect of BLEC on the degree of blood lipids and atherosclerosis was validated by animal experiment.

Results

There are 144 active components and 80 CAD-related targets that are identified in BLEC in the treatment of CAD. What is more, 8 core genes were obtained by clustering and topological analysis of PPI-Net. Further, GO and KEGG analysis showed that fluid shear stress and atherosclerosis are the key pathways for BLEC to treat CAD. These results were validated by molecular docking method. In vitro, active compounds of BLEC (Quercetin, luteolin, kaempferol, naringenin, tanshinone IIA, β-carotene, 7-O-methylisomucronulatol, piperine, isorhamnetin and Xyloidone) can inhibit 8 core gene (AKT1, EGFR, FOS, MAPK1, MAPK14, STAT3, TP53 and VEGFA) expression. Moreover, BLEC not only improve blood lipid levels but also inhibit the development of atherosclerosis in ApoE-knockout mice.

Conclusion

Our research first revealed the basic pharmacological effects and related mechanisms of in the treatment of CAD. The predicted results provide some theoretical support for BLEC or its important active ingredients to treat CAD.

Mechanism of Tao Hong Decoction in the treatment of atherosclerosis based on network pharmacology and experimental validation

Background

Atherosclerosis (AS) has long been recognized as a cardiovascular disease and stroke risk factor. A well-known traditional Chinese medicine prescription, Tao Hong decoction (THD), has been proven effective in treating AS, but its mechanism of action is still unclear.

Objective

To assess the effects, explore THD's primary mechanism for treating AS, and provide a basis for rational interpretation of its prescription compatibility.

Methods

Based on network pharmacology, we evaluated the mechanism of THD on AS by data analysis, target prediction, the construction of PPI networks, and GO and KEGG analysis. AutoDockTools software to conduct Molecular docking. Then UPLC-Q-TOF-MS was used to identify significant constituents of THD. Furthermore, an AS mice model was constructed and intervened with THD. Immunofluorescence, RT-qPCR, and Western blot were used to verify the critical targets in animal experiments.

Results

The network pharmacology results indicate that eight core targets and seven core active ingredients play an essential role in this process. The GO and KEGG analysis results suggested that the mechanism is mainly involved in Fluid shear stress and atherosclerosis and Lipid and atherosclerosis. The molecular docking results indicate a generally strong affinity. The animal experiment showed that THD reduced plaque area, increased plaque stability, and decreased the levels of inflammatory cytokines (NF-κB, IL-1α, TNF-α, IL-6, IL-18, IL-1β) in high-fat diet -induced ApoE-/-mice. Decreased levels of PTGS2, HIF-1α, VEGFA, VEGFC, FLT-4, and the phosphorylation of PI3K, AKT, and p38 were detected in the THD-treated group.

Conclusion

THD plays a vital role in treating AS with multiple targets and pathways. Angiogenesis regulation, oxidative stress regulation, and immunity regulation consist of the crucial regulation cores in the mechanism. This study identified essential genes and pathways associated with the prognosis and pathogenesis of AS from new insights, demonstrating a feasible method for researching THD's chemical basis and pharmacology.

Identification of m6a-related signature genes in esophageal squamous cell carcinoma by machine learning method

Background: We aimed to construct and validate the esophageal squamous cell carcinoma (ESCC)-related m6A regulators by means of machine leaning. Methods: We used ESCC RNA-seq data of 66 pairs of ESCC from West China Hospital of Sichuan University and the transcriptome data extracted from The Cancer Genome Atlas (TCGA)-ESCA database to find out the ESCC-related m6A regulators, during which, two machine learning approaches: RF (Random Forest) and SVM (Support Vector Machine) were employed to construct the model of ESCC-related m6A regulators. Calibration curves, clinical decision curves, and clinical impact curves (CIC) were used to evaluate the predictive ability and best-effort ability of the model. Finally, western blot and immunohistochemistry staining were used to assess the expression of prognostic ESCC-related m6A regulators. Results: 2 m6A regulators (YTHDF1 and HNRNPC) were found to be significantly increased in ESCC tissues after screening out through RF machine learning methods from our RNA-seq data and TCGA-ESCA database, respectively, and overlapping the results of the two clusters. A prognostic signature, consisting of YTHDF1 and HNRNPC, was constructed based on our RNA-seq data and validated on TCGA-ESCA database, which can serve as an independent prognostic predictor. Experimental validation including the western and immunohistochemistry staining were further successfully confirmed the results of bioinformatics analysis. Conclusion: We constructed prognostic ESCC-related m6A regulators and validated the model in clinical ESCC cohort as well as in ESCC tissues, which provides reasonable evidence and valuable resources for prognostic stratification and the study of potential targets for ESCC.

Bioinformatics identification and experimental validation of m6A-related diagnostic biomarkers in the subtype classification of blood monocytes from postmenopausal osteoporosis patients

BACKGROUND

Postmenopausal osteoporosis (PMOP) is a common bone disorder. Existing study has confirmed the role of exosome in regulating RNA N6-methyladenosine (m6A) methylation as therapies in osteoporosis. However, it still stays unclear on the roles of m6A modulators derived from serum exosome in PMOP. A comprehensive evaluation on the roles of m6A modulators in the diagnostic biomarkers and subtype identification of PMOP on the basis of GSE56815 and GSE2208 datasets was carried out to investigate the molecular mechanisms of m6A modulators in PMOP.

METHODS

We carried out a series of bioinformatics analyses including difference analysis to identify significant m6A modulators, m6A model construction of random forest, support vector machine and nomogram, m6A subtype consensus clustering, GO and KEGG enrichment analysis of differentially expressed genes (DEGs) between different m6A patterns, principal component analysis, and single sample gene set enrichment analysis (ssGSEA) for evaluation of immune cell infiltration, experimental validation of significant m6A modulators by real-time quantitative polymerase chain reaction (RT-qPCR), etc.

RESULTS

In the current study, we authenticated 7 significant m6A modulators via difference analysis between normal and PMOP patients from GSE56815 and GSE2208 datasets. In order to predict the risk of PMOP, we adopted random forest model to identify 7 diagnostic m6A modulators, including FTO, FMR1, YTHDC2, HNRNPC, RBM15, RBM15B and WTAP. Then we selected the 7 diagnostic m6A modulators to construct a nomogram model, which could provide benefit with patients according to our subsequent decision curve analysis. We classified PMOP patients into 2 m6A subtypes (clusterA and clusterB) on the basis of the significant m6A modulators via a consensus clustering approach. In addition, principal component analysis was utilized to evaluate the m6A score of each sample for quantification of the m6A subgroups. The m6A scores of patients in clusterB were higher than those of patients in clusterA. Moreover, we observed that the patients in clusterA had close correlation with immature B cell and gamma delta T cell immunity while clusterB was linked to monocyte, neutrophil, CD56dim natural killer cell, and regulatory T cell immunity, which has close connection with osteoclast differentiation. Notably, m6A modulators detected by RT-qPCR showed generally consistent expression levels with the bioinformatics results.

CONCLUSION

In general, m6A modulators exert integral function in the pathological process of PMOP. Our study of m6A patterns may provide diagnostic biomarkers and immunotherapeutic strategies for future PMOP treatment.

In Search of Hosts for Blue OLEDs: Computational Design and Experimental Validation

Considering the difficulties associated with the conventional 'trial and error' method for a complete analysis of a giant molecular space, we took the aid of computational pathway (DFT) in screening a large space search of 780 (12×13×5) molecules to search for a host for the blue emitter. The selection process was completed in three Tiers with the conditions of highest theoretical triplet energy (>2.81 eV), aligned HOMO/LUMO levels w.r.t blue dopant (FIrpic), and position of substituents to meet the optimal requirements as host materials. Tier 1 screened twelve different imidazole heterocycle derivatives as base space groups which resulted in the selection of 4,5-diphenyl-1H-imidazole. Tier 2 process converged the search to mCN-CZ having the highest triplet energy and appropriate HOMO/LUMO level relative to FIrpic and ETL. Further, the carbazole of mCN-CZ was replaced with different aromatic hydrocarbons to find the other best compound in terms of triplet energy and HOMO/LUMO. Tier 3 resulted in another promising candidate (mCN-FL) as possible host materials. The band alignment with guest predicted mCN-FL and mCN-CZ to have optimal device performances compared to CZ-CZ and the experimentally observed device performance was in accordance with virtual screening results when TAPC was utilized as the hole transporter. The device results of mCN-CZ and mCN-FL were better than the reference host TCTA. The obtained results thus proved that a virtual screening process will be a useful tool for synthetic chemists in designing task-specific materials.

A Novel Approach for Cable Tension Monitoring Based on Mode Shape Identification

Estimation and monitoring of cable tension is of great significance in the structural assessment of cable-supported bridges. For short cables, the traditional cable tension identification method via frequency measurement has large errors due to the influence of complex boundaries, which affect the accuracy of estimation. A new cable tension estimation method based on mode shape identification with a multiple sensor arrangement on the cable can take the influence of boundary conditions into account and its accuracy has been verified. However, it requires more sensors compared to the traditional frequency-based method, which will significantly increase the cost of long-term monitoring in practice. Therefore, a novel approach for cable tension monitoring considering both cost and accuracy is further proposed in this study. The approach adopts multiple sensors to measure the influence of boundary conditions. Then, only a single sensor is required for long-term monitoring of the cable. In this paper, an analytical model of the cable is firstly established. The influence of boundary conditions is calculated, which ensures the accuracy of mode shape identification. Furthermore, a field experiment is carried out to verify the effectiveness of the new approach. The results have demonstrated the effectiveness and accurateness of the proposed method in long-term short cable tension monitoring.

Experimental Validation of Shifted Position-Diffuse Reflectance Imaging (SP-DRI) on Optical Phantoms

Numerous diseases such as hemorrhage, sepsis or cardiogenic shock induce a heterogeneous perfusion of the capillaries. To detect such alterations in the human blood flow pattern, diagnostic devices must provide an appropriately high spatial resolution. Shifted position-diffuse reflectance imaging (SP-DRI) has the potential to do so; it is an all-optical diagnostic technique. So far, SP-DRI has mainly been developed using Monte Carlo simulations. The present study is therefore validating this algorithm experimentally on realistic optical phantoms with thread structures down to 10 μm in diameter; a SP-DRI sensor prototype was developed and realized by means of additive manufacturing. SP-DRI turned out to be functional within this experimental framework. The position of the structures within the optical phantoms become clearly visible using SP-DRI, and the structure thickness is reflected as modulation in the SP-DRI signal amplitude; this performed well for a shift along the x axis as well as along the y axis. Moreover, SP-DRI successfully masked the pronounced influence of the illumination cone on the data. The algorithm showed significantly superior to a mere raw data inspection. Within the scope of the study, the constructive design of the SP-DRI sensor prototype is discussed and potential for improvement is explored.

Molecular Mechanisms of Luteolin Against Atopic Dermatitis Based on Network Pharmacology and in vivo Experimental Validation

PURPOSE

To undercover the underlying mechanisms of luteolin against atopic dermatitis (AD), clinically characterized by recurrent eczematous lesions and intense itching, based on network pharmacology, molecular docking and in vivo experimental validation.

METHODS

TCMSP, STITCH and SwissTargetPrediction databases were utilized to screen the corresponding targets of luteolin. Targets related to AD were collected from DisGeNET, GeneCards and TTD databases. PPI network of intersection targets was constructed through STRING 11.0 database and Cytoscape 3.9.0 software. GO and KEGG enrichment analysis were performed to investigate the critical pathways of luteolin against AD. Further, the therapeutic effects and candidate targets/signaling pathways predicted from network pharmacology analysis were experimentally validated in a mouse model of AD induced by 2, 4-dinitrofluorobenzene (DNFB).

RESULTS

A total of 31 intersection targets were obtained by matching 151 targets of luteolin with 553 targets of AD. Among all, 20 core targets were identified by PPI network topology analysis, including IL-6, TNF, IL-10, VEGFA, IL-4, etc., and molecular docking indicated that luteolin binds strongly to these core targets. KEGG pathway enrichment analysis suggested that the intersected targets were significantly enriched in IL-17 signaling pathway, Th17 cell differentiation, Th1 and Th2 cell differentiation, JAK/STAT signaling pathway, etc. The in vivo experiment validated that luteolin could alleviate AD-like skin symptoms, as evidenced by the lower SCORAD score, the reduced infiltration of mast cells and the recovery of skin barrier function. Furthermore, luteolin restored immune balance by regulating the production of Th1/Th2/Th17-mediated cytokines, which were both the predicted core targets. Moreover, luteolin inhibited the phosphorylation of JAK2 and STAT3 in the lesional skin.

CONCLUSION

Together, the present study systematically clarifies the ameliorative effects and possible molecular mechanisms of luteolin against AD through the combination of network pharmacology and experimental validation, shedding light on the future development and clinical application of luteolin.

Assessing tele-manipulation systems using task performance for glovebox operations

Tele-manipulation is indispensable for the nuclear industry since teleoperated robots cancel the radiation hazard problem for the operator. The majority of the teleoperated solutions used in the nuclear industry rely on bilateral teleoperation, utilizing a variation of the 4-channel architecture, where the motion and force signals of the local and remote robots are exchanged in the communication channel. However, the performance limitation of teleoperated robots for nuclear decommissioning tasks is not clearly answered in the literature. In this study, we assess the task performance in bilateral tele-manipulation for radiation surveying in gloveboxes and compare it to radiation surveying of a glovebox operator. To analyze the performance, an experimental setup suitable for human operation (manual operation) and tele-manipulation is designed. Our results showed that a current commercial off-the-shelf (COTS) teleoperated robotic manipulation solution is flexible, yet insufficient, as its task performance is significantly lower when compared to manual operation and potentially hazardous for the equipment inside the glovebox. Finally, we propose a set of potential solutions, derived from both our observations and expert interviews, that could improve the performance of teleoperation systems in glovebox environments in future work.

Validating a simulation model for laser-induced thermotherapy using MR thermometry

OBJECTIVES

We want to investigate whether temperature measurements obtained from MR thermometry are accurate and reliable enough to aid the development and validation of simulation models for Laser-induced interstitial thermotherapy (LITT).

METHODS

Laser-induced interstitial thermotherapy (LITT) is applied to ex-vivo porcine livers. An artificial blood vessel is used to study the cooling effect of large blood vessels in proximity to the ablation zone. The experimental setting is simulated using a model based on partial differential equations (PDEs) for temperature, radiation, and tissue damage. The simulated temperature distributions are compared to temperature data obtained from MR thermometry.

RESULTS

The overall agreement between measurement and simulation is good for two of our four test cases, while for the remaining cases drift problems with the thermometry data have been an issue. At higher temperatures local deviations between simulation and measurement occur in close proximity to the laser applicator and the vessel. This suggests that certain aspects of the model may need some refinement.

CONCLUSION

Thermometry data is well-suited for aiding the development of simulations models since it shows where refinements are necessary and enables the validation of such models.

Theoretical Modelling of the Degradation Processes Induced by Freeze-Thaw Cycles on Bond-Slip Laws of Fibres in High-Performance Fibre-Reinforced Concrete

High-performance fibre-reinforced concrete (HPFRC) is a composite material in which the advantages of fibre-reinforced concrete (FRC) are combined with those of a high-performance concrete (HPC), which mitigates the weaknesses of conventional concrete and improves its overall performance. With the aim to reduce the long-term maintenance costs of structures, such as heavily loaded bridges, HPFRC is highly recommended due to its major durability performance. Specifically, its good antifreezing property makes it suitable for application in cold regions where cyclic freeze-thaw conditions cause the concrete to degrade. In this paper, a numerical simulation of the degradation processes induced by freeze-thaw cycles on bond-slip laws in HPFRC beam specimens has been developed so as to assess their effect on the flexural response of specimens as the fibres' volume percentage changes. Their cracking strength, postcracking strength, and toughness were predicted, with the present model being able to predict the cracking strength, postcracking strength and toughness of the HPFRC beam element under bending load conditions. Its accuracy was confirmed by comparing the model predictions with experimental results.

[Mechanism of Linderae Radix against gastric cancer based on network pharmacology and in vitro experimental validation]

The present study explored the main active ingredients and the underlying mechanism of Linderae Radix the treatment of gastric cancer by network pharmacology, molecular docking, and in vitro cell experiments. TCMSP, OMIM and GeneCards database were used to obtain the active ingredients of Linderae Radix to predict the related targets of both Linderae Radix and gastric cancer. After screening the common potential action targets, the STRING database was used to construct the PPI network for protein interaction of the two common targets. Enrichment analysis of GO and KEGG by DAVID database. Based on STRING and DAVID platform data, Cytoscape software was used to construct an &quot;active ingredient-target&quot; network and an &quot;active ingredient-target-pathway&quot; network. Molecular docking was performed using the AutoDock Vina to predict the binding of the active components to the key action targets, and finally the key targets and pathways were verified in vitro. According to the prediction results, there were 9 active components, 179 related targets of Radix Linderae, 107 common targets of Linderae Radix and gastric cancer, 693 biological processes, 57 cell compositions, and 129 molecular functions involved in the targets, and 161 signaling pathways involved in tumor antigen p53, hypoxia-indu-cible factor 1, etc. Molecular docking results showed that the core component, jimadone, had high binding activity with TP53. Finally, in an in vitro experiment, the screened radix linderae active ingredient gemmadone is used for preliminarily verifying the core targets and pathways of the human gastric cancer cell SGC-7901, The results showed that germacrone could significantly inhibit the proliferation of gastric cancer cells and induce the apoptosis of SGC-7901 by regulating the expression of p53, Bax, Bcl-2 and other key proteins. In summary, Radix Linderae can control the occurrence and development of gastric cancer through multi-components, multi-targets and multi-pathways, which will provide theoretical basis for further clinical discussion on the mechanism of Radix Linderae in treating gastric cancer.

A Methodology for Optimizing the Calibration and Validation of Reactive Transport Models for Cement-Based Materials

Reactive transport models are useful tools in the development of cement-based materials. The output of cement-related reactive transport models is primarily regarded as qualitative and not quantitative, mainly due to limited or missing experimental validation. This paper presents an approach to optimize the calibration process of reactive transport models for cement-based materials, using the results of several short-term experiments. A quantitative comparison of changes in the hydrate phases (measured using TGA and XRD) and exposure solution (measured using ICP-OES) was used to (1) establish a representative chemical model, limiting the number of hydrate phases and dissolved species, and (2) calibrate the transport processes by only modeling the initial tortuosity. A case study comprising the early age carbonation of cement is presented to demonstrate the approach. The results demonstrate that the inclusion of a microstructure model in our framework minimizes the impact of the initial tortuosity factor as a fitting parameter for the transport processes. The proposed approach increases the accuracy of reactive transport models and, thus, allowing for more realistic modeling of long-term exposure.

Experimental validation of proton physics models of Geant4 for calculating stopping power ratio

In this work, we conducted experiments to validate the proton physics models of Geant4 (version 10.6). The stopping power ratios (SPRs) of 11 inserts, such as acrylic, delrin, high density polyethylene, and polytetrafluoroethylene, etc, were measured using a superconducting synchrocyclotron that produces a scattering proton beam. The SPRs of the inserts were also calculated based on Geant4 simulation with six physics lists, i.e. QGSP_ FTFP_ BERT, QGSP_BIC_HP, QGSP_BIC, QGSP_FTFP_BERT, QSGP_BERT, and QBBC. The calculated SPRs were compared to the experimental SPRs, and relative per cent error was used to quantify the accuracy of the simulated SPRs of inserts. The comparison showed that the five physics lists generally agree well with the experimental SPRs with a relative difference of less than 1%. The lowest overall percentage error was observed for QGSP_FTFP_BERT and the highest overall percentage error was observed for QGSP_BIC_HP. The 0.1 mm range cut value consistently led to higher percentage error for all physics lists except for QGSP_BIC_HP and QBBC. Based on the validation, we recommend QGSP_BERT_HP physics list for proton dose calculation.

Elucidating the anti-aging mechanism of Si Jun Zi Tang by integrating network pharmacology and experimental validation in vivo

Si Jun Zi Tang (SJZT) is a classic Traditional Chinese Medicine (TCM) prescription used to treat aging-related diseases. However, the potential molecular mechanisms of the anti-aging effects of the bioactive compounds and their targets remain elusive. In this study, we combined network pharmacology and molecular docking with in vivo experiments to elucidate the anti-aging molecular mechanism of SJZT. A series of network pharmacology strategies were used to predict potential targets and therapeutic mechanisms of SJZT, including compound screening, pathway enrichment analysis and molecular docking studies. Based on the network pharmacology predictions and observation of outward signs of aging, the expression levels of selected genes and proteins and possible key targets were subsequently validated and analysed using qRT-PCR and immunoblotting. Using a data mining approach, 235 effective targets of SJZT and aging were obtained. AKT1, STAT3, JUN, MAPK3, TP53, MAPK1, TNF, RELA, MAPK14 and IL6 were identified as core genes in the Protein-Protein Interaction Networks (PPI) analysis. The results of the effective target Gene Ontology (Go) functional enrichment analysis suggested that SJZT may be involved aging and antiapoptotic biological processes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that the anti-aging mechanism of SJZT may be associated with the PI3K-AKT and P38 MAPK signalling pathways. Molecular docking analysis suggested that kaempferol and quercetin could fit in the binding pockets of the core targets. In addition, SJZT alleviated the aging symptoms of mice such as osteoporosis and hair loss. In conclusion, the anti-aging effect of SJZT was associated with the inhibition of the PI3K-AKT and P38 MAPK signalling pathways, and these findings were consistent with the network pharmacology prediction.