Non-invasive prediction for pathologic complete response to neoadjuvant chemoimmunotherapy in lung cancer using CT-based deep learning: a multicenter study.

Neoadjuvant chemoimmunotherapy has revolutionized the therapeutic strategy for non-small cell lung cancer (NSCLC), and identifying candidates likely responding to this advanced treatment is of important clinical significance. The current multi-institutional study aims to develop a deep learning model to predict pathologic complete response (pCR) to neoadjuvant immunotherapy in NSCLC based on computed tomography (CT) imaging and further prob the biologic foundation of the proposed deep learning signature. A total of 248 participants administrated with neoadjuvant immunotherapy followed by surgery for NSCLC at Ruijin Hospital, Ningbo Hwamei Hospital, and Affiliated Hospital of Zunyi Medical University from January 2019 to September 2023 were enrolled. The imaging data within 2 weeks prior to neoadjuvant chemoimmunotherapy were retrospectively extracted. Patients from Ruijin Hospital were grouped as the training set (n = 104) and the validation set (n = 69) at the 6:4 ratio, and other participants from Ningbo Hwamei Hospital and Affiliated Hospital of Zunyi Medical University served as an external cohort (n = 75). For the entire population, pCR was obtained in 29.4% (n = 73) of cases. The areas under the curve (AUCs) of our deep learning signature for pCR prediction were 0.775 (95% confidence interval [CI]: 0.649 - 0.901) and 0.743 (95% CI: 0.618 - 0.869) in the validation set and the external cohort, significantly superior than 0.579 (95% CI: 0.468 - 0.689) and 0.569 (95% CI: 0.454 - 0.683) of the clinical model. Furthermore, higher deep learning scores correlated to the upregulation for pathways of cell metabolism and more antitumor immune infiltration in microenvironment. Our developed deep learning model is capable of predicting pCR to neoadjuvant chemoimmunotherapy in patients with NSCLC.

High-altitude hypoxia exposure inhibits erythrophagocytosis by inducing macrophage ferroptosis in the spleen.

High-altitude polycythemia (HAPC) affects individuals living at high altitudes, characterized by increased red blood cells (RBCs) production in response to hypoxic conditions. The exact mechanisms behind HAPC are not fully understood. We utilized a mouse model exposed to hypobaric hypoxia (HH), replicating the environmental conditions experienced at 6000 m above sea level, coupled with in vitro analysis of primary splenic macrophages under 1% O2 to investigate these mechanisms. Our findings indicate that HH significantly boosts erythropoiesis, leading to erythrocytosis and splenic changes, including initial contraction to splenomegaly over 14 days. A notable decrease in red pulp macrophages (RPMs) in the spleen, essential for RBCs processing, was observed, correlating with increased iron release and signs of ferroptosis. Prolonged exposure to hypoxia further exacerbated these effects, mirrored in human peripheral blood mononuclear cells. Single-cell sequencing showed a marked reduction in macrophage populations, affecting the spleen's ability to clear RBCs and contributing to splenomegaly. Our findings suggest splenic ferroptosis contributes to decreased RPMs, affecting erythrophagocytosis and potentially fostering continuous RBCs production in HAPC. These insights could guide the development of targeted therapies for HAPC, emphasizing the importance of splenic macrophages in disease pathology.

Contraception Usage and Workforce Trends Through 2022.

This cross-sectional study uses a national data set of medical prescription claims to examine contraception service and workforce changes from January 2019 through December 2022 in the US.

Synthesis of salcaprozate sodium and its significance in enhancing pancreatic kininogenase absorption performance.

We conducted pharmacokinetic research wherein salcaprozate sodium (SNAC) was utilized as a penetration enhancer by incorporating it into pancreatic kininogenase (PK) to improve the bioavailability of pancreatic kininogenase enteric-coated tablets. We conducted in vitro studies on PK using the Caco-2 cell model and quantified PK levels using the enzyme-linked immunosorbent assay (ELISA) method. We conducted methodological verification by blending SNAC and PK powders into enteric-coated capsules, and studied the pharmacokinetic characteristics. Based on the PK transport assay, the cumulative permeation rates of the test group that employed a SNAC to PK ratio of 32:1, 16:1, 8:1, 4:1, and 2:1 were 13.574%, 7.597%, 10.653%, 3.755%, and 2.523%, respectively. We conducted a uniformity test on the powder that contained a blend of SNAC and PK. The relative standard deviations (RSDs) for both the power containing SNAC and the power not containing SNAC were less than 10%. Based on the methodological verification, in vivo pharmacokinetic study of PK met the experimental requirements. As indicated by the results of in vivo pharmacokinetic research on rats, the test group (This group used SNAC) had a PK AUC0-12 h of 5679.747 ng/L*h and t1/2 of 4.569 h, while the control group (This group did not use SNAC) had a PK AUC0-12 h of 4639.665 ng/L*h and t1/2 of 3.13 h. This study has established a low-cost, environmentally friendly, and safe SNAC synthesis route with high process yield suitable for industrial production. SNAC demonstrates an absorption-enhancing effect on PK, and the optimal ratio of SNAC to PK is determined to be 32:1.

Research progress in photodynamic therapy for Helicobacter pylori infection.

Helicobacter pylori (H. pylori) is a pathogenic microorganism that colonizes the human gastric mucosa and can lead to various gastric disorders, including gastritis, gastric ulcers, and gastric cancer. However, the increasing prevalence of antibiotic resistance in H. pylori has prompted the search for alternative treatment options. Photodynamic therapy has emerged as a potential alternative therapy, thus offering the advantage of avoiding some of the side effects associated with antibiotics and effectively targeting drug-resistant strains. In the postantibiotic era, photodynamic therapy (PDT) has shown promise as a novel treatment for H. pylori infection. This review focused on elucidating the mechanism of photodynamic therapy in the treatment of H. pylori. Additionally, we present an overview of the current research on photodynamic therapy by examining both standalone photodynamic therapy and combination therapies for H. pylori infection treatment. Furthermore, the safety profile of photodynamic therapy was also evaluated. Finally, we discuss the challenges and prospects associated with this innovative technology, with an aim to provide new insights and methodologies for the treatment of H. pylori infection.

Glucose-G protein signaling plays a crucial role in tomato resilience to high temperature and elevated CO2.

Global climate change is accompanied by carbon dioxide (CO2) enrichment and high-temperature stress; however, how plants adapt to the combined environments and the underlying mechanisms remain largely unclear. Here, we show that elevated CO2 alleviated plant sensitivity to high-temperature stress, with significantly increased apoplastic glucose (Glc) levels in tomato (Solanum lycopersicum) leaves. Exogenous Glc treatment enhanced tomato resilience to high-temperature stress under ambient CO2 conditions. Cell-based biolayer interferometry, subcellular localization, and Split-Luc assays revealed that Glc bound to tomato regulator of G protein signaling 1 (RGS1) and induced RGS1 endocytosis and thereby RGS1-G protein α subunit (GPA1) dissociation in a concentration-dependent manner. Using rgs1 and gpa1 mutants, we found that RGS1 negatively regulated thermotolerance and was required for elevated CO2-Glc-induced thermotolerance. GPA1 positively regulated the elevated CO2-Glc-induced thermotolerance. Transcriptome and chlorophyll fluorescence parameter analysis further revealed that GPA1 integrated photosynthesis- and photoprotection-related mechanisms to regulate thermotolerance. These results demonstrate that Glc-RGS1-GPA1 signaling plays a crucial role in the elevated CO2-induced thermotolerance in tomato. This information enhances our understanding of the Glc-G protein signaling function in stress resilience in response to global climate change and will be helpful for genetic engineering approaches to improve plant resilience.

Assessing the Causal Relationship between Genetically Determined Inflammatory Cytokines and Parkinson's Disease Risk: A Bidirectional Two-Sample Mendelian Randomization Study.

Background: Observational studies have suggested an association between inflammatory cytokines and Parkinson's disease (PD). This Mendelian randomization (MR) was conducted to further assess the causal correlations between inflammatory cytokines and PD. Methods: Genetic instruments associated with inflammatory cytokines were extracted from a large summary genome-wide association studies (GWAS) involving 8,293 European participants. Summary-level statistics for PD were obtained from a large-sample GWAS containing 17 studies that involved European participants. Causalities of exposures and outcomes were explored mainly using inverse variance weighted (IVW) method. Results: The IVW method indicated that basic fibroblast growth factor (FGFBasic), interleukin-2 (IL-2), and macrophage migration inhibitory factor (MIF) may be suggestively associated with the risk of PD (OR: 0.71, 95%CI: 0.52-0.96, P = 0.027; OR: 1.18, 95%CI: 1.01-1.38, P = 0.041; and OR: 1.23, 95%CI: 1.04-1.46, P = 0.018). In the reverse direction, monokine induced by interferon gamma (MIG), beta nerve growth factor (bNGF), interleukin-17 (IL-17), and interferon gamma (IFNg) are suggested to be the consequences of PD. Conclusion: Our MR analysis indicated that suggestive associations between circulating levels of FGFBasic, IL-2, and MIF and PD risk. In addition, MIG, bNGF, IL-17, and IFNg are more likely to be involved in the development of downstream PD.

A novel network pharmacology strategy to decode mechanism of Wuling Powder in treating liver cirrhosis.

BACKGROUND: Liver cirrhosis is a chronic liver disease with hepatocyte necrosis and lesion. As one of the TCM formulas Wuling Powder (WLP) is widely used in the treatment of liver cirrhosis. However, it's key functional components and action mechanism still remain unclear. We attempted to explore the Key Group of Effective Components (KGEC) of WLP in the treatment of Liver cirrhosis through integrative pharmacology combined with experiments. METHODS: The components and potential target genes of WLP were extracted from published databases. A novel node importance calculation model considering both node control force and node bridging force is designed to construct the Function Response Space (FRS) and obtain key effector proteins. The genetic knapsack algorithm was employed to select KGEC. The effectiveness and reliability of KGEC were evaluated at the functional level by using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, the effectiveness and potential mechanism of KGEC were confirmed by CCK-8, qPCR and Western blot. RESULTS: 940 effective proteins were obtained in FRS. KEGG pathways and GO terms enrichments analysis suggested that effective proteins well reflect liver cirrhosis characteristics at the functional level. 29 components of WLP were defined as KGEC, which covered 100% of the targets of the effective proteins. Additionally, the pathways enriched for the KGEC targets accounted for 83.33% of the shared genes between the targets and the pathogenic genes enrichment pathways. Three components scopoletin, caryophyllene oxide, and hydroxyzinamic acid from KGEC were selected for in vivo verification. The qPCR results demonstrated that all three components significantly reduced the mRNA levels of COL1A1 in TGF-ß1-induced liver cirrhosis model. Furthermore, the Western blot assay indicated that these components acted synergistically to target the NF-κB, AMPK/p38, cAMP, and PI3K/AKT pathways, thus inhibiting the progression of liver cirrhosis. CONCLUSION: In summary, we have developed a new model that reveals the key components and potential mechanisms of WLP for the treatment of liver cirrhosis. This model provides a reference for the secondary development of WLP and offers a methodological strategy for studying TCM formulas.

Understanding Racial/Ethinic Disparities in COVID Mortality Using a Novel Metric: COVID Excess Mortality Percentage.

Prior research on racial/ethnic disparities in COVID-19 mortality has often not considered to what extent they reflect COVID-19-specific factors, versus preexisting health differences. This study examines how racial/ethnic disparities in COVID-19 mortality vary with age, gender, and time period over April - December 2020 in the US, using mortality from other natural causes to proxy for underlying health. We study a novel measure, the COVID Excess Mortality Percentage (CEMP), defined as the COVID-19 mortality rate, divided by the non-COVID natural mortality rate, converted to a percentage, where the CEMP denominator, controls, albeit imperfectly, for differences in population health. Disparities measured using CEMP deviate substantially from those in prior research. In particular, we find very high disparities (up to 12:1) in CEMP rates for Hispanics versus Whites, particularly for non-elderly men. Asians also have elevated CEMP rates versus Whites, which were obscured in prior work by lower overall Asian mortality. Native Americans and Blacks have significant disparities compared to Whites populations, but CEMP ratios to Whites are lower than ratios reported in other work. This is because the higher COVID-19 mortality for Blacks and Native Americans comes partly from higher general mortality risk, and partly from COVID-specific risk.

The effects of resveratrol in animal models of primary osteoporosis: a systematic review and meta-analysis.

BACKGROUND: There is still a lack of sufficient evidence-based medical data on the effect of resveratrol (Res) on primary osteoporosis (OP). This meta-analysis aimed to comprehensively evaluate the role of Res in animal models of primary OP. METHODS: The PubMed, Cochrane Library, Web of Science and Embase databases were searched up to August 2023. The risk of bias was assessed by the SYRCLE RoB tool. Random- or fixed-effects models were used to determine the 90% confidence interval (CI) or standardized mean difference (SMD). Statistical analysis was performed with RevMan 5.4 and Stata 14.0. RESULTS: A total of 24 studies containing 714 individuals were included. Compared with those in the control group, the bone mineral density (BMD) (P < 0.00001), bone volume/total volume (BV/TV) (P < 0.001), trabecular thickness (Tb.Th) (P < 0.00001), and trabecular number (Tb.N) (P < 0.00001) were markedly greater, and the trabecular separation (Tb.Sp) (P < 0.00001) was significantly greater. Compared with the control group, the Res group also exhibited marked decreases in alkaline phosphatase (ALP) (P < 0.05), tartrate-resistant acid phosphatase 5b (TRAP5b) (P < 0.01), and type I collagen strong carboxyl peptide (CTX-1) (P < 0.00001) and a marked increase in osteoprotegerin (OPG) (P < 0.00001). CONCLUSION: In summary, we concluded that Res can markedly increase BMD, improve morphometric indices of trabecular microstructure and serum bone turnover markers (BTMs), and exert a protective effect in animal models of primary osteoporosis. This study can supply experimental reference for Res in primary osteoporosis treatment.

Bis-Alkoxide Dysprosium(III) Crown Ether Complexes Exhibit Tunable Air Stability and Record Energy Barrier.

High-performance and air-stable single-molecule magnets (SMMs) can offer great convenience for the fabrication of information storage devices. However, the controversial requisition of high stability and magnetic axiality is hard to balance for lanthanide-based SMMs. Here, a family of dysprosium(III) crown ether complexes possessing hexagonal-bipyramidal (pseudo-D6h symmetry) local coordination geometry with tunable air stability and effective energy barrier for magnetization reversal (Ueff ) are shown. The three complexes share the common formula of [Dy(18-C-6)L2 ][I3 ] (18-C-6 = 1,4,7,10,13,16-hexaoxacyclooctadecane; L = I, 1; L = Ot Bu 2 and L = 1-AdO 3). 1 is highly unstable in the air. 2 can survive in the air for a few minutes, while 3 remains unchanged in the air for more than 1 week. This is roughly in accordance with the percentage of buried volumes of the axial ligands. More strikingly, 2 and 3 show progressive enhancement of Ueff and 3 exhibits a record high Ueff of 2427(19) K, which significantly contributes to the 100 s blocking temperature up to 11 K for Yttrium-diluted sample, setting a new benchmark for solid-state air-stable SMMs.

Predictive modeling of neuroticism in depressed and non-depressed cohorts using voice features.

BACKGROUND: Neuroticism's impact on psychopathological and physical health issues has significant public health implications. Multiple studies confirm its predictive effect on suicide risk among depressed patients. However, previous research lacks a standardized criterion for assessing neuroticism through speech, often relying on simple features (such as pitch, loudness and MFCCs). This study aims to improve upon this by extracting features using advanced pre-trained speaker embedding models (i-vector and x-vector extractors). Additionally, unlike prior studies utilizing general population data, we explore neuroticism prediction in depressed and non-depressed subgroups. METHODS: We collected edited discourse data from clinical interviews of 3580 depressed individuals and 4016 healthy individuals from the CONVERGE study. Instead of solely extracting Low-Level Acoustic Descriptors, we incorporated i-vector and x-vector features. We compared the performance of three different features in predicting neuroticism and explored their combination to enhance model accuracy. RESULTS: The SVR model, combining three speech features with downscaled features to 300, exhibited the highest performance in predicting neuroticism scores. It achieved a coefficient of determination (R-squared) of 0.3 or higher and a correlation of 0.56 between predicted and actual values. The predictive classification accuracy of speech features for neuroticism in specific populations (healthy and depressed) exceeded 60 %. LIMITATIONS: This study included only women. CONCLUSION: Combining diverse speech features enhances the predictive capacity of models using speech features to assess neuroticism, particularly in specific populations. This study lays the foundation for future exploration of speech features in neuroticism prediction.

Mediating trade-off between activity and selectivity in alkynes semi-hydrogenation via a hydrophilic polar layer.

As a crucial industrial process for the production of bulk and fine chemicals, semi-hydrogenation of alkynes faces the trade-off between activity and selectivity due to undesirable over-hydrogenation. By breaking the energy linear scaling relationships, we report an efficient additive-free WO3-based single-atom Pd catalytic system with a vertical size effect of hydrogen spillover. Hydrogen spillover induced hydrophilic polar layer (HPL) with limited thickness on WO3-based support exhibits unconventional size effect to Pd site, in which over-hydrogenation is greatly suppressed on Pd1 site due to the polar repulsive interaction between HPL and nonpolar C=C bonds, whereas this is invalid for Pd nanoparticles with higher altitudes. By further enhancing the HPL through Mo doping, activated Pd1/MoWO3 achieves recorded performance of 98.4% selectivity and 10200 h-1 activity for semi-hydrogenation of 2-methyl-3-butyn-2-ol, 26-fold increase in activity of Lindlar catalyst. This observed vertical size effect of hydrogen spillover offers broad potential in catalytic performance regulation.

[Application advances of mass spectrometry imaging technology in environmental pollutants analysis and their toxicity research].

Environmental exposures have significant impacts on human health and can contribute to the occurrence and development of diseases. Pollutants can enter the body through ingestion, inhalation, dermal absorption, or mother-to-child transmission, and can metabolize and/or accumulate in different tissues and organs. These pollutants can recognize and interact with various biomolecules, including DNA, RNA, proteins, and metabolites, disrupting biological processes and leading to adverse effects in living organisms. Thus, it is crucial to analysis the exogenous pollutants in the body, identify potential biomarkers and investigate their toxic effects. Numerous studies have shown that the metabolism rate of environmental pollutants greatly differs in various tissues and organs, their accumulation is also heterogeneous and dynamically changing. Moreover, the synthesis and accumulation of endogenous metabolites exhibit precise spatial distributions in tissues and cells. Mapping the spatial distributions of both pollutants and endogenous metabolites can discover relevant exposure biomarkers and provide a better understanding of their toxic effects and molecular mechanisms. Mass spectrometry is currently the preferred method for the qualitative and quantitative analysis of various compounds, and has been extensively utilized in pollutant and metabolomics analyses. Mass spectrometry imaging (MSI) is an emerging technology for molecular imaging that combines the information obtained by mass spectrometry with the visualization of the two- and three-dimensional spatial distributions of various molecular species in thin sample sections. Unlike other molecular imaging techniques, MSI can perform the label-free and untargeted analysis of thousands of molecules, such as elements, metabolites, lipids, peptides, proteins, pollutants, and drugs, in a single experiment with high sensitivity and throughput. Different MSI technologies, such as matrix-assisted laser desorption ionization mass spectrometry imaging, secondary ion mass spectrometry imaging, desorption electrospray ionization mass spectrometry imaging, and laser ablation inductively coupled plasma mass spectrometry imaging, have been introduced for the mapping of compounds and elements in biological, medical, and clinical research. MSI technologies have recently been utilized to characterize the spatial distribution of pollutants in the whole body and specific tissues of organisms, assess the toxic effects of pollutants at the molecular level, and identify exposure biomarkers. Such developments have brought new perspectives to investigate the toxicity of environmental pollutants. In this review, we provide an overview of the principles, characteristics, mass analyzers, and workflows of different MSI techniques and introduce their latest application advances in the analysis of environmental pollutants and their toxic effects.

Analysis of odor compounds in Lee Kum Kee brand oyster sauce and oyster enzymatic hydrolysate: Comparison and relationship.

Oyster sauce (OS) is a highly processed oyster product. However, the significant price difference between OS and fresh oysters raises a question: Does authentic OS truly contain components from oysters or oyster enzymatic hydrolysates (OEH)? Therefore, the odor compounds of Lee Kum Kee oyster sauce (LKK), 4 OEHs, and 6 other seafood enzymatic hydrolysates (SEHs) were analyzed by using solid-phase microextraction and gas chromatography-olfactometry-mass spectrometry technology (SPME-GC-O-MS). The results of multivariate statistical analysis demonstrated the effective discrimination between LKK and OEHs from other SEHs. According to the VIP value and the differences in the composition of odor compounds among different samples, 15 essential odor compounds were screened out, which could distinguish whether the samples contained OEHs. Among them, acetic acid, 2-pentylfuran, 2-ethyl furan, 2-methylbutanal, and nonanal were only detected in LKK and OEHs, which further indicated the existence of OEH in LKK.

Black and Hispanic Representation Declined After Increased Degree Requirements for Physician Assistants.

PURPOSE: The physician assistant (PA) profession is one of the least racially and ethnically diverse health professions requiring advanced education. New PA graduates are even less diverse than the current PA workforce and less diverse than professions requiring doctoral degrees. Between 1995 and 2020, the percent of all PA graduates that were Black individuals fell from 7% to 3.1%, while Hispanic representation increased from 4.5% to 7.9%. METHODS: Using the federal Integrated Postsecondary Education Data System, we examine the impact of transitions to master's degrees for PAs on Black and Hispanic representation between 1995 and 2020, using individual universities as the unit of analysis. RESULTS: After adjusting for state and year effects, PA programs that transitioned from bachelor's to master's degrees experienced a 5.3% point decline in Black representation and a 3.8% point decline in Hispanic representation. Relative to the already low proportions of Black and Hispanic graduates in PA programs, these declines are significant. CONCLUSION: Steps should be taken to ensure that underrepresented populations have greater access to PA education.

Lipid metabolism analysis in esophageal cancer and associated drug discovery.

Esophageal cancer is an upper gastrointestinal malignancy with a bleak prognosis. It is still being explored in depth due to its complex molecular mechanisms of occurrence and development. Lipids play a crucial role in cells by participating in energy supply, biofilm formation, and signal transduction processes, and lipid metabolic reprogramming also constitutes a significant characteristic of malignant tumors. More and more studies have found esophageal cancer has obvious lipid metabolism abnormalities throughout its beginning, progress, and treatment resistance. The inhibition of tumor growth and the enhancement of antitumor therapy efficacy can be achieved through the regulation of lipid metabolism. Therefore, we reviewed and analyzed the research results and latest findings for lipid metabolism and associated analysis techniques in esophageal cancer, and comprehensively proved the value of lipid metabolic reprogramming in the evolution and treatment resistance of esophageal cancer, as well as its significance in exploring potential therapeutic targets and biomarkers.

How Mixed-Methods Research Can Improve the Policy Relevance of Impact Evaluations.

This paper describes how mixed methods can improve the value and policy relevance of impact evaluations, paying particular attention to how mixed methods can be used to address external validity and generalization issues. We briefly review the literature on the rationales for using mixed methods; provide documentation of the extent to which mixed methods have been used in impact evaluations in recent years; describe how we developed a list of recent impact evaluations using mixed methods and the process used to conduct full-text reviews of these articles; summarize the findings from our analysis of the articles; discuss three exemplars of using mixed methods in impact evaluations; and discuss how mixed methods have been used for studying and improving external validity and potential improvements that could be made in this area. We find that mixed methods are rarely used in impact evaluations, and we believe that increased use of mixed methods would be useful because they can reinforce findings from the quantitative analysis (triangulation), and they can also help us understand the mechanism by which programs have their impacts and the reasons why programs fail.

Screening of the key response component groups and mechanism verification of Huangqi-Guizhi-Wuwu-Decoction in treating rheumatoid arthritis based on a novel computational pharmacological model.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by the destruction of synovial tissue and articular cartilage. Huangqi-Guizhi-Wuwu-Decoction (HGWD), a formula of Traditional Chinese Medicine (TCM), has shown promising clinical efficacy in the treatment of RA. However, the synergistic effects of key response components group (KRCG) in the treatment of RA have not been well studied. METHODS: The components and potential targets of HGWD were extracted from published databases. A novel node influence calculation model that considers both the node control force and node bridging force was designed to construct the core response space (CRS) and obtain key effector proteins. An increasing coverage coefficient (ICC) model was employed to select the KRCG. The effectiveness and potential mechanism of action of KRCG were confirmed using CCK-8, qPCR, and western blotting. RESULTS: A total of 796 key effector proteins were identified in CRS. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses confirmed their effectiveness and reliability. In addition, 59 components were defined as KRCG, which contributed to 85.05% of the target coverage of effective proteins. Of these, 677 targets were considered key reaction proteins, and their enriched KEGG pathways accounted for 84.89% of the pathogenic genes and 87.94% of the target genes. Finally, four components (moupinamide, 6-Paradol, hydrocinnamic acid, and protocatechuic acid) were shown to inhibit the inflammatory response in RA by synergistically targeting the cAMP, PI3K-Akt, and HIF-1α pathways. CONCLUSIONS: We have introduced a novel model that aims to optimize and analyze the mechanisms behind herbal formulas. The model revealed the KRCG of HGWD for the treatment of RA and proposed that KRCG inhibits the inflammatory response by synergistically targeting cAMP, PI3K-Akt, and HIF-1α pathways. Overall, the novel model is plausible and reliable, offering a valuable reference for the secondary development of herbal formulas.