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Prediction of key characteristic genes in COPD
Following batch-effect correction of clinical data from multiple microarray platforms (Supplemental File 1–Supplemental Figure S1), a differential expression analysis was performed. 34 candidate genes (32 upregulated and 2 downregulated) were identified, including SRPX2, CLDN10, and TFF3 (Figure 1A).
To refine these candidates into a robust predictive signature, three machine learning algorithms were employed in shaping a joint model. For LASSO regression, using the λ value corresponding to the minimum cross-validation error, 16 features were selected (Figure 1B,C). For SVM, 30 characteristic genes were determined at the point of minimum screening error (Figure 1D,E), and 21 genes were identified based on their importance scores in random Forest (Figure 1F,G). By intersecting these results, 10 core characteristic genes (SRPX2, CLDN10, TFF3, HRASLS2, ALDH3A1, MUC1, MUC4, SERPINF1, IL-1R2, and FOSB) were identified (Figure 1H). These genes are considered predictive biomarkers for COPD diagnosis.
The diagnostic utility of the 10-gene signature was assessed using ROC analysis. In the discovery phase, all genes exhibited high diagnostic accuracy with Area Under the Curve (AUC) values > 0.7 (Figure 1J). To evaluate model generalizability and performance uncertainty, the biomarkers were tested against an independent validation set. Differential expression was significantly maintained for IL-1R2, SRPX2, and TFF3 (Figure 1K). Notably, FOSB, IL-1R2, MUC4, SRPX2, and TFF3 sustained AUC values > 0.7, with IL-1R2 achieving a peak AUC of 0.816 (Figure 1L). These results suggest that while these genes are strong candidates for clinical stratification, their functional roles in COPD pathogenesis remain to be elucidated through future mechanistic studies.
To explore potential therapeutic intersections, we analyzed 12 drug profiles based on clinical literature and PubChem. A total of 280 potential therapeutic targets were identified. Simultaneously, 8,722 COPD-related targets were retrieved from GeneCards, OMIM, and DisGeNET. We identified 253 overlapping targets between the drug profiles and COPD-related genes (Figure 2A). It should be noted that these "DRUG-COPD" interaction targets are derived from indirect therapeutic drug-target mapping and represent a generalized intersection rather than drug-specific experimental binding. Protein-protein interaction (PPI) analysis (Confidence > 0.7) and topological screening (Relevance Score > 150) identified TERT, BMPR2, PKHD1, SERPINA1, and IL1B as central nodes (Figure 2B,C).
By integrating the machine learning-derived predictive biomarkers with the therapeutic network analysis, the IL-1R2/IL-1β axis was identified as a critical focal point for the progression and potential treatment of COPD.
Pathological and phenotypic manifestations of the COPD rat model
The COPD rat model was constructed by intratracheal instillation of LPS combined with exposure to cigarette smoke, and the model was evaluated multidimensionally from behavioral, physiological, and biochemical indicators, as well as histopathological aspects. Compared with the control group (C), the model group (M) rats exhibited a significant decrease in body weight (**p < 0.01) (Figure 3A), and the number of activities in the M group significantly decreased compared with the premodeling (**p < 0.01) (Figure 3B).
BALF tests indicated that the levels of pro-inflammatory factors (TNF-α, IL-6, IL-8, MCP-1, and OPN) were significantly elevated in the M group (Figure 3C). Histopathological observations revealed extensive inflammatory infiltration in the lung tissue of the M group, characterized by thickening of the alveolar walls with granulocyte infiltration (Figure 3D), lymphocyte aggregation around the bronchioles, and focal macrophage infiltration. Typical pathological changes included hydropic degeneration of the bronchial epithelium (pale cytoplasmic swelling), abnormal mucus secretion in the lumen, and proliferation of epithelioid cells forming cystic structures containing necrotic debris. No significant pathological changes were observed in the lung tissue of the C group. These results were highly consistent with the clinical characteristics and pathological mechanisms of human COPD. So the animal model with typical pathological features of COPD was successfully established in this study.
Transcriptomic validation of key characteristic genes in COPD
Based on the above results, further transcriptomic research was conducted on the COPD rat model to identify relevant, predictive results for key characteristic genes. As shown in Supplemental File 1–Supplemental Table S1 and Supplemental File 1—Supplemental Figure S2, the sequencing data and quality control met the experimental requirements. Compared with the C group, a total of 173 differentially expressed genes (DEGs) (p-adj < 0.05; |log2(fold change)| > 1) were screened in the M group, including 134 up-regulated genes (including IL-1β) and 39 down-regulated genes (including IL-1R2). The results are shown in Figure 4A and Supplemental File 1—Supplemental Table S2.
Subsequently, functional annotation analysis was performed on the differentially expressed genes. The KEGG pathway annotation results of the DEGs, shown in Figure 4B, revealed the strongest association with the cytokine-cytokine-receptor interaction pathway (p-adj < 0.01), where the IL-1R2 and IL-1β changing were probably one of the primary reasons for this strong association (Figure 4D). Further construction of an interaction network for the DEGs is shown in Figure 4C. Network analysis indicated that based on a centrality measure of degree ≥ 20, IL-1β was identified as a key hub node in the interaction network. Therefore, integrated transcriptomic analysis results further confirmed the accuracy of IL-1R2/IL-1β as key characteristic genes for COPD, and the cytokine-cytokine-receptor interaction pathway is presumed as a key signaling pathway in COPD.
Organizational metabolomics and identification of key metabolic pathways in COPD rat model
The total ion current (TIC) chromatograms of the QC samples are shown in Supplemental File 1—Supplemental Figure S3A,B. The response intensity and retention times of the chromatographic peaks largely overlapped, with correlation coefficients between QC samples exceeding 0.9, indicating good experimental reproducibility (Supplemental File 1-–Supplemental Figure S3C,D). The proportion of peaks with relative standard deviation (RSD) ≤ 30% in the QC samples accounted for over 70% of the total number of peaks in the QC samples, confirming the stability of the analytical system and the suitability of the data for further analysis (Supplemental File 1-–Supplemental Figure S3E,F).
PCA analysis revealed distinct clustering within groups and clear separation between the C and M groups, indicating successful induction of metabolic disturbances by the COPD model (Figure 5A). The OPLS-DA model effectively distinguished the M group from the blank group (Figure 5B), with Q2 values of 0.659 and 0.757 in the positive and negative ion modes, respectively (Q2 > 0.5), demonstrating the model's stability and reliability. Permutation tests (Figure 5C,D) showed that the R2 and Q2 values of the random models decreased with increasing permutation retention, confirming the absence of overfitting and the robustness of the original model. A total of 29 differential metabolites were identified based on VIP > 1 and p < 0.05 (Supplemental File 1–Supplemental Table S3), as shown in Figure 5E,F.
Pearson correlation analysis was performed on the identified small-molecule metabolites to explore metabolic relationships and regulatory interactions during biological state changes. Metabolites with correlation coefficients greater than 0.8 were presumed as key markers involved in synthesis and transformation. As shown in Figure 6A,B, glutathione (oxidized), phosphocholine, and L-palmitoylcarnitine were presumed as key markers according to the average correlation coefficient greater than 0.8. In addition, topological analysis of differential metabolic pathways was performed using the MetPA analysis method. Sixteen major metabolic pathways were identified by MetPA analysis, including tyrosine metabolism, one-carbon pool by folate, and pyrimidine metabolism (Figure 6C). The pathway impact analysis showed that phenylalanine, tyrosine, and tryptophan biosynthesis had an impact value greater than 0.5 in the COPD rat model. To avoid the loss of important signals due to filtering thresholds and to capture coordinated changes across the metabolite network, major metabolic pathways were further identified using metabolite set enrichment analysis (MSEA) (Figure 6D). Among the enriched pathways, thyroid hormone synthesis emerged as one of the most crucial pathways.
Tyrosine serves as a direct substrate for the synthesis of the thyroid hormones triiodothyronine (T3) and thyroxine (T4), while phenylalanine acts as a precursor that is converted to tyrosine by phenylalanine hydroxylase, thereby contributing to thyroid hormone biosynthesis. Based on the combined results of MetPA and MSEA analyses, thyroid hormone synthesis is therefore inferred to be a key metabolic pathway involved in COPD.
Amelioration of COPD metabolic dysregulation by BTHTT via the phenylalanine-tyrosine-thyroid hormone axis
Continued tissue metabolomics analysis showed that during BTHTT treatment, metabolic markers in the treatment groups exhibited a trend toward normalization. In the H group, 16 metabolic markers were significantly reversed (p < 0.05, p < 0.01), including the key markers glutathione (oxidized), phosphocholine, and L-palmitoylcarnitine (p < 0.05, p < 0.01), as shown in Figure 6E. In contrast, only seven metabolic markers were significantly reversed in the L group, with no significant reversal observed for the key markers (Supplemental File 1-–Supplemental Table S3). These results further confirmed the superior regulatory capacity of the high-dose BTHTT treatment group. The H group significantly regulated 12 metabolic pathways by MetPA analysis, especially phenylalanine, tyrosine, and tryptophan biosynthesis (impact > 0.5) (Figure 6F). Compared with the M group, MSEA analysis of the H group showed similar significant regulation of metabolic pathways, including thyroid hormone synthesis (Figure 6G), and it was particularly notable that thyroid hormone synthesis was a significant regulatory pathway shared by both MSEA analyses. In summary, based on the significant therapeutic effects of BTHTT (H group) on COPD, it markedly reversed disturbances in multiple biomarkers and metabolic pathways. Thyroid hormone synthesis could be one of the key metabolic pathways through which BTHTT exerts its therapeutic effects in COPD.
Amelioration of pathological and phenotypic manifestations in a COPD rat model by BTHTT
After 2 weeks of intervention with BTHTT (Figure 7), the body weight of rats in the M group increased slowly, whereas weight gain in the treatment groups, especially the high-dose group (H), was significant (p < 0.01), and the overall weight returned to the level of the control group. Spontaneous activity analysis showed a significant improvement in the number of movements in the treatment groups (p < 0.01). Serum and BALF tests indicated that BTHTT intervention significantly reduced the elevated levels of TNF-α, IL-6, IL-8, MCP-1, and OPN in the M group (p < 0.05, p < 0.01), and the levels of inflammatory factors in the H group were no longer significantly different from those in the C group. Histopathological examination revealed persistent lymphocyte infiltration around the bronchioles and thickening of the alveolar walls with granulocyte infiltration in the M group. The low-dose group (L) showed reduced inflammatory infiltration (local lymphocyte/neutrophil infiltration, slight alveolar thickening), and the H group exhibited near-complete repair of pathological damage (no significant inflammatory cell infiltration or alveolar structural abnormalities). In summary, BTHTT exerted a dose-dependent therapeutic effect on COPD, with the H group demonstrating the greatest capacity for pathological repair.
Amelioration of COPD metabolic dysregulation by BTHTT via the phenylalanine–tyrosine–thyroid hormone axis
Based on the aforementioned therapeutic outcomes, further transcriptomic analysis was conducted comparing the H group with the M group. Compared with the M group, a total of 224 differentially expressed genes were identified in the H group (Supplemental File 1-–Supplemental Table S4), including 70 downregulated genes and 154 upregulated genes. Among these, the H group regulated the key characteristic genes IL-1R2 and IL-1β (Figure 8A).
Further KEGG molecular functional annotation analysis of all regulated differentially expressed genes revealed that regulation in the BTHTT H group remained primarily focused on the cytokine–cytokine receptor interaction pathway, with regulation of IL-1R2/IL-1β continuing to play a dominant role (Figure 8B). In addition, regulation of multiple genes within the CC subfamily (CCR8, CCL4, CCL4L1, CCL4L2, CCL5) and the CXC subfamily (CXCL1, CXCL3, CXCL2, CXCL4, CXCL4L1, CXCR4) also showed significant changes, which may contribute to the therapeutic effects of BTHTT (Figure 8C).
Rebalancing of IL-1β/IL-1R2 and thyroid hormone synthesis in COPD by BTHTT
Thyroid hormone receptors are expressed across various pulmonary cell types, including alveolar epithelial cells, bronchial epithelial cells, and intrapulmonary immune cells. Consequently, the lung serves as a direct target for thyroid hormones, which can be locally delivered via bronchoalveolar lavage fluid (BALF).
Furthermore, lung tissue expresses type II deiodinase (D2), an enzyme responsible for intracellular conversion of circulating inactive thyroxine (T4) into its biologically active form, triiodothyronine (T3). Therefore, the levels of T3 and T4 within BALF do not merely reflect systemic infiltration from the blood but also indicate localized uptake, metabolism, and activation within lung tissue. During states of inflammation or injury, this localized metabolic profile may undergo significant alterations. Based on the above results, we further validated the changes in IL-1β, T3, and T4 in lung tissue using BALF (Figure 9A–C). In the detection of IL-1β, the M group maintained a high level, whereas all treatment groups showed significantly reduced IL-1β levels (p < 0.05, p < 0.01). The H group exhibited the most pronounced reduction, showing no significant difference compared with the C group. In the detection of T3 and T4, the M group showed significantly decreased levels, while all treatment groups led to substantial increases in T3 and T4 (p < 0.01), with no significant difference compared with the C group. Finally, immunohistochemical analysis of IL-1R2 in lung tissue was performed to observe protein expression changes across groups. Compared with the M group, the H group showed a higher expression level of IL-1R2-positive cells in rat lung tissue (Figure 9D).
The Spearman correlation coefficient method was used to confirm the pathway correlation between IL-1β/IL-1R2 and thyroid hormone synthesis (Table 2). IL-1β was significantly negatively correlated with T3 (ρ = −0.587, p = 0.002) and T4 (ρ = −0.622, p = 0.001), indicating that higher levels of inflammation were associated with lower levels of thyroid hormones. IL-1R2 showed a significant moderate positive correlation with T3 (ρ = 0.536, p = 0.006) and T4 (ρ = 0.567, p = 0.003), suggesting that increased receptor antagonism or regulation was associated with elevated thyroid hormone levels. A strong positive correlation was observed between T3 and T4 (ρ = 0.818, p < 0.001), consistent with their known physiological relationship.
These results further indicate that the transcriptional changes in IL-1β and IL-1R2 were confirmed, indicating abnormal gene expression. Abnormal levels of T3 and T4 also suggested dysregulation of thyroid hormone synthesis identified in metabolomics analysis. BTHTT treatment significantly altered these abnormal changes. Overall, the results suggest a potential negative correlation between the cytokine–cytokine receptor interaction pathway (mainly IL-1β/IL-1R2) and thyroid hormone synthesis (mainly T3/T4) in COPD. In summary, we propose a speculative mechanistic model involving the IL-1β–IL-1R2–thyroid hormone synthesis axis, which may be modulated by BTHTT.
Integrated UHPLC-MS and chemometrics for characterization of BTHTT's in vivo/in vitro components
Ultra-high-performance liquid chromatography coupled with a speculative mechanistic model here involving the IL-1β-–IL-1R2-–thyroid hormone synthesis axis, and which may be modulated by BTHTT, coefficients greater than 0.9, indicating stable and reliable data (Supplemental File 1-–Supplemental Figure S4). The base peak chromatograms (BPC) in both positive and negative ion modes are shown in Figure 10A,B. The serum samples from the H group and the in vitro test samples exhibited significant differences in the chromatograms. Moreover, clear distinctions were observed between these groups and the blank group, as well as the blank serum plus in vitro test samples.
The acquired data, including mass, isotope distribution, and MS/MS fragmentation information, were compared with the commercial standard Traditional Chinese Medicine (TCM) database. The results were further matched with public databases, such as GNPS28, ReSpect29, and Massbank30 for compound identification and annotation. A total of 2,547 in vitro components of BTHTT were identified (1,649 in positive ion mode and 968 in negative ion mode) using a mass error threshold of < 25 ppm for MS1 and a matching score > 0.7 for MS2. NPClassifier analysis indicated that the predominant in vitro components were alkaloids (24%) and shikimate/phenylpropanoid derivatives (24%) (Figure 10C). Further analysis of BPC led to the selection of 38 high-abundance in vitro components of BTHTT (Figure 10D,E and Supplemental File 1—Supplemental Table S5), with shikimate/phenylpropanoid derivatives accounting for 51% (Figure 10F).
Based on the analysis and identification results of blank control serum and blank serum plus in vitro test samples of BTHTT, combined with the in vitro full component analysis results, a background subtraction algorithm in the chemometric module was applied to the H group to ultimately determine the in vivo components of BTHTT. A total of 283 in vivo components were identified (153 in positive ion mode and 132 in negative ion mode). NPClassifier chemical classification revealed that the predominant in vivo components were shikimate/phenylpropanoid derivatives (39%), alkaloids (20%), and terpenoids (16%) (Figure 10G).
In vivo components were further cross-referenced with public databases such as PubChem and ChemSpider. Using a mass error threshold of ppm ≤ ±5 and a matching score > 0.9 for MS2, 71 in vivo components of BTHTT were ultimately precisely identified (Supplemental File 1–Supplemental Table S6). Among them, seven high-abundance in vitro components that were also detected in vivo were identified: Vitamin B1, magnoflorine, cryptotanshinone, tanshinone IIA, 3,4-dihydroxyphenylacetic acid, salvianolic acid A, and gibberellin A4.
Prediction and analysis of potential therapeutic substance basis
To explore the structural basis of the therapeutic effects of BTHTT, we performed molecular docking of its in vivo components against the core protein target IL-1R2. Using molecular docking software via an AI supercomputing platform, we assessed the binding orientations of these compounds.
Based on a binding energy threshold of ≤-8 kcal/mol, 12 compounds were identified as having high structural compatibility with the IL-1R2 binding pocket (Supplemental File 1–Supplemental Table S7). Notably, Tanshinone IIA and Cryptotanshinone exhibited the lowest predicted binding energies. Furthermore, these two compounds are significant as they represent the primary high-content bioactive components identified in in vitro analysis, suggesting they are plausible candidates for further investigation.
To further evaluate the supportive computational evidence for these interactions, we conducted molecular dynamics (MD) simulations to assess the stability of the Tanshinone IIA–IL-1R2 and Cryptotanshinone–IL-1R2 complexes under simulated physiological conditions. As shown in Figure 11A,B, both systems reached their minimum potential energy within the first 300 ps. Following NVT equilibration, the systems maintained a stable temperature of approximately 300 K. NPT equilibration successfully stabilized the pressure at approximately 1 bar. While minor fluctuations were observed, the consistent system density confirmed effective pressure control. The Root Mean Square Deviation (RMSD) for both complexes reached a stable plateau after 17 ns, with fluctuations remaining within a narrow range of 0.2 nm. Throughout the 20 ns production run, the total energy of both systems remained steady with minimal variance.
In summary, the MD simulations indicate that Tanshinone IIA and Cryptotanshinone maintain a stable structural association with IL-1R2. These findings provide computational support for their potential role as active constituents, though subsequent in vitro and in vivo functional assays are required to establish their biological efficacy.
Despite the significant findings of this study, several limitations must be acknowledged. First, the relationship identified between inflammatory signaling and thyroid hormone levels is based on associative omics data. Further functional perturbation experiments are required to establish a definitive causal link.
Specifically, future studies should directly evaluate the hypothalamic-pituitary-thyroid (HPT) axis, alongside histological and functional assessments of the thyroid gland itself. Additionally, the identification of active compounds relied heavily on molecular docking and bioinformatic inference; therefore, targeted in vitro validation of specific compounds is necessary. While the animal model utilized simulates human Chronic Obstructive Pulmonary Disease (COPD), interspecies physiological differences may limit the direct clinical translation of these findings.
In summary, this study integrates multi-omics data and computational modeling to generate mechanistic hypotheses regarding the role of the IL-1R2/IL-1β axis and thyroid hormone metabolism in COPD. Although our results demonstrate the therapeutic potential of BTHTT and provide a paradigm for modern Traditional Chinese Medicine (TCM) research, further experimental validation is essential to confirm the proposed mechanisms.
Data Availability Statement:
Publicly available gene expression datasets used in this study were obtained from GEO under the following accession numbers: GSE11784, GSE12472, GSE16972, GSE38974, and GSE222965. Additionally, the original high-throughput sequencing data generated during the experimental phase of this study have been deposited in the NCBI BioProject database under accession number PRJNA1286104, accessible via the following link:https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1286104. All datasets are publicly available and meet the journal's data sharing requirements.

Figure 1: Machine-learning-based identification of COPD diagnostic biomarkers. (A) Volcano plot of DEGs. (B-G) Feature selection via (B,C) LASSO regression, (D,E) SVM-RFE, and (F,G) Random Forest. (H) Venn diagram showing 10 consensus genes. (I) Expression heatmap of key signatures. (J-L) ROC curve and expression analysis for (J) training and (K,L) validation cohorts. *p < 0.05, **p < 0.01, ***p < 0.001. Please click here to view a larger version of this figure.

Figure 2: Network pharmacology analysis. (A) Compound-target interactions. (B) PPI network. (C) Topological analysis of the PPI network. Please click here to view a larger version of this figure.

Figure 3: Validation of the COPD rat model. (A,B) Body weight and spontaneous activity (pre- vs. post-modeling). (C) Pro-inflammatory factors in serum and BALF. (D) H&E-stained lung sections (200×). Control shows normal architecture; Model shows (i) septal thickening, (ii) bronchial epithelial degeneration, (iii) mucin hypersecretion, and (iv) cyst-like structures. Scale bars = 50 µm. *p < 0.05, **p < 0.01. Please click here to view a larger version of this figure.

Figure 4: Transcriptomic profiling of COPD versus Control rats. (A) Volcano plot highlighting 39 downregulated and 134 upregulated genes. (B) KEGG pathway enrichment. (C) PPI network of DEGs identifying IL-1β as a central hub. (D) Cytokine-cytokine receptor interaction pathway (red: upregulated; blue: downregulated). Please click here to view a larger version of this figure.

Figure 5: Metabolic profiling of lung tissue in COPD rats. (A) PCA, (B) OPLS-DA, and (C,D) permutation tests in negative and positive ion modes. (E,F) Heatmaps of differential metabolites. Please click here to view a larger version of this figure.

Figure 6: Metabolic markers and pathway determination. (A,B) Correlation analysis in negative and positive modes. (C,D) MetPA and MSEA analysis identifying key pathways (e.g., phenylalanine, tyrosine, and tryptophan biosynthesis). (E) Heatmap of 29 metabolic markers following BTHTT treatment. (F,G) MetPA and MSEA analysis of the High-dose BTHTT group. Please click here to view a larger version of this figure.

Figure 7: Therapeutic effects of BTHTT on COPD rats. (A,B) Dose-dependent changes in body weight and spontaneous activity during treatment. (C) Pro-inflammatory factors in serum and BALF. (D) Representative H&E lung histopathology (200×). High-dose group shows near-normal architecture; Low-dose group shows localized infiltration. Scale bars = 50 µm. *p < 0.05, **p < 0.01, #p < 0.05 (vs. Control). Please click here to view a larger version of this figure.

Figure 8: Transcriptomic profiling of High-dose BTHTT versus Model group. (A) Expression levels of IL-1R2 and IL-1β. (B) KEGG enrichment. (C) Cytokine-cytokine receptor interaction pathway (red: upregulated; blue: downregulated). Please click here to view a larger version of this figure.

Figure 9: The changes in levels of IL-1β, T3, T4, and IL-1R2 in the lung tissue of rats. (A–C) Concentrations of IL-1β, T3, and T4 in BALF. (D) Immunohistochemical staining of IL-1R2 in lung tissue. *p < 0.05, **p < 0.01. Please click here to view a larger version of this figure.

Figure 10: Chemical characterization of BTHTT's in vivo/in vitro components. (A,B) BPC chromatograms of BTHTT in vitro and in vivo with positive and negative ion modes. (C) Npclassifier distribution of main chemical categories. (D,E) BPC of high-content components, peak numbers 15 and 16 correspond to Cryptotanshinone and Tanshinone IIA, respectively. (F) Classification of high-content components. (G) Classification of in vivo transitional components. Please click here to view a larger version of this figure.

Figure 11: Molecular dynamics simulations of tanshinone IIA and cryptotanshinone with IL-1R2. (A) Tanshinone IIA—IL-1R2, (B) Cryptotanshinone—IL-1R2. Please click here to view a larger version of this figure.

Figure 12: Schematic diagram of the mechanism of BTHTT-mediated thyroid hormone synthesis pathway in the treatment of COPD through IL-1R2/IL-1 β and phenylalanine, tyrosine, and trypsin biosynthesis. Please click here to view a larger version of this figure.
| Time (min) | Mobile Phase A (%) | Mobile Phase B (%) |
| Initial | 95 | 5 |
| 3 | 75 | 25 |
| 8.5 | 55 | 45 |
| 14 | 5 | 95 |
| 17 | 2 | 98 |
| 17.2 | 95 | 5 |
| 20 | 95 | 5 |
Table 1: Gradient elution method for in vivo and in vitro component analysis of BTHTT.
| IL-1β | IL-1R2 | T3 | T4 |
| Spearman Rho | IL-1β | correlation coefficient | 1 | -0.176 | -.587** | -.622** |
| Significance | . | 0.401 | 0.002 | 0.001 |
| (dual tailed) |
| N | 25 | 25 | 25 | 25 |
| IL-1R2 | correlation coefficient | -0.176 | 1 | .536** | .567** |
| Significance | 0.401 | . | 0.006 | 0.003 |
| (dual tailed) |
| N | 25 | 25 | 25 | 25 |
| T3 | correlation coefficient | -.587** | .536** | 1 | .818** |
| Significance | 0.002 | 0.006 | . | 0 |
| (dual tailed) |
| N | 25 | 25 | 25 | 25 |
| T4 | correlation coefficient | -.622** | .567** | .818** | 1 |
| Significance | 0.001 | 0.003 | 0 | . |
| (dual tailed) |
| N | 25 | 25 | 25 | 25 |
Table 2: Correlation analysis based on Spearman coefficient.
NOTE: **. At the 0.01 level (double tailed), the correlation is significant.
Supplemental File 1. Supplementary figures and tables providing additional quality control analyses, differential expression results, metabolomic profiling, and chemical characterization supporting the study.Please click here to download this file.