The following instruments were used: a low-temperature high-speed centrifuge (Eppendorf Centrifuge 5430 R; Eppendorf, Hamburg, Germany), Vanquish UHPLC system (Thermo Fisher Scientific, Waltham, MA, USA), ACQUITY UPLC HSS T3 chromatographic column (2.1 mm ×100 mm, 1.8 µm), Q-Exactive HFX mass spectrometer (Thermo Fisher Scientific), ELX-800 multifunctional microplate reader (Bio Tek, Winooski, VT, USA), LightCycler 96 fluorescent quantitative PCR instrument (Roche, Basel, Switzerland), tissue optical scanning microscope camera (ZEISS, Oberkochen, Germany), Mini-PROTEAN vertical electrophoresis apparatus, Mini Trans-Blot transfer apparatus, and SCG-W2000 chemiluminescence imaging system (all from Servicebio, Hubei, China).

The reagents utilized in liquid chromatography-tandem mass spectrometry (LC-MS) included water (MS grade, MilliporeSigma, Burlington, MA, USA), methanol and acetonitrile (MS grade, Thermo Fisher Scientific), and formic acid (MS grade, Honeywell International Inc., Charlotte, NC, USA).

MXSGD consists of four TCM components: Ephedrae Herba (Lot No. 2109170042), Armeniacae Semen Amarum (Lot No. 2022011201), Gypsum fibrosum (Lot No. 2110151), and Glycyrrhizae Radix et Rhizoma (Lot No. 220201). All Chinese medicinal materials were purchased from the First Affiliated Hospital of Hunan University of Traditional Chinese Medicine, and their quality met the standards established by the Chinese Pharmacopoeia (2020 edition). Oseltamivir phosphate capsules (75 mg, Lot No. M1073; Roche) were purchased from the People’s Hospital of Hunan Province. The virus strain A/PR/8/34 (H1N1, mouse lung-adapted) was provided by the Molecular Virology Laboratory at Hunan Normal University and amplified in chicken embryos in a BSL-2 laboratory at Hunan University of Chinese Medicine.

The reagents used in the validation experiments included influenza A virus (IAV) nucleoprotein antibody (Cat No. GTX125989; GeneTex, Irvine, CA, USA), anti-CD80 antibody (Cat No. ab254579; Abcam, Cambridge, UK), anti-mannose receptor antibody (Abcam, Cat No. ab64693), HRP peroxidase-conjugated goat anti-rabbit IgG (H+L, Cat No. GB21303; Servicebio), Cy3 conjugated goat anti-rabbit IgG (H+L, Cat No. GB23303; Servicebio), PI3K p85 alpha antibody (Cat No. AF6241; Affinity Biosciences, Cincinnati, OH, USA), phospho-pan-AKT1/2/3 antibody (Cat No. AF3262; Affinity Biosciences), pan-AKT1/2/3 antibody (Cat No. AF6261; Affinity Biosciences), RIPA lysis buffer (Cat No. G2002-100ML; Servicebio), phosphatase inhibitor (Cat No. G2007-1ML; Servicebio), BCA protein quantification assay kit (Cat No. G2026-200T; Servicebio), prestained protein marker VII (8–195 kDa; Cat No. G2087-250UL; Servicebio), enhanced chemiluminescent reagent kit (Cat No. G2161-200ML; Servicebio), RNA simple total RNA extraction kit (Cat No. DP419; TIANGEN, Beijing, China), NovoScript Plus All-in-One 1st Strand cDNA Synthesis SuperMix (gDNA Purge; Cat No. E047-01B; Novoprotein, Shanghai, China), NovoScript SYBR qPCR Super Mixture (Cat No. E096-01A; Novoprotein), mouse IL-1β enzyme-linked immunosorbent assay (ELISA) kit (Cat No. ml301814; MLBIO, Shanghai, China), mouse IL-6 ELISA kit (Cat No. ml063159; MLBIO), and mouse TNF-α ELISA Kit (Cat No. ml002095; MLBIO).

The MXSGD formulation comprised 9 g of ephedra, 9 g of apricot kernel, 18 g of gypsum, and 6 g of licorice. The medicinal substances were measured according to their compositional ratios during production. The aqueous extract of MXSGD was obtained by first boiling ephedra, following the traditional preparation described in “Shang Han Lun” and based on our previous findings (17). Ephedra was mixed with distilled water at a 1:10 weight-to-volume ratio. The mixture was first boiled at 100°C and then simmered for 25 min. Following the removal of froth, gypsum, apricot kernel, and licorice were added to the mixture, which was then boiled for an additional 30 min and subsequently filtered. For the second extraction, distilled water at seven times the original volume was added, brought to a boil at 100°C, simmered for 20 min, and filtered again. The two extracts were then combined and concentrated to yield final crude drug concentrations of 0.605 g/mL and 4.265 g/mL for animal validation and serum preparation, respectively. The extracts were shielded from light and stored at 4°C until use. Oseltamivir phosphate capsules were completely dissolved in distilled water to create a suspension with a concentration of 2.15 mg/mL.

Forty-eight specific-pathogen-free (SPF) male Sprague Dawley rats (180–220 g) were purchased from Hunan SJA Laboratory Animal Co., Ltd. [Animal Quality Certificate No. SYXK (Xiang) 2019-0009; Animal Experiment Ethical Approval No.: LL2021081101]. They were housed in the Animal Experiment Center of the Hunan University of Chinese Medicine and allowed to acclimate for two days. The experimental conditions included a 12-h light/dark cycle, unlimited access to water and food, and controlled temperature and humidity. Animals were randomly assigned to two groups: the MXSGD and control groups, each including seven rats. Following serum pharmacochemistry protocols, the MXSGD group received a dosage adjusted based on body surface area to increase the concentration of active drug components in the blood. The gavage dosage was equivalent to 10 times the standard clinical dosage. Each rat in the MXSGD group received a gavage dose of 8.53 g/day. The control group received an equivalent volume of physiological saline, delivered daily at 2 mL for 7 consecutive days. On day 7, after an 8-h fasting period with unlimited access to water, the rats were anesthetized with sodium pentobarbital, and blood was drawn from the abdominal aorta. Blood samples were incubated at 4°C for 2 h and then centrifuged at 3,000 rpm for 10 min. The obtained MXSGD-containing serum and blank serum were stored at –20°C until use.

A total of 600 μL of MXSGD aqueous extract was mixed with 400 μL of methanol and vortexed. To dilute the mixture, 200 μL of it was added to 1.4 mL of a 40% aqueous methanol solution. The mixture was vortexed once more and centrifuged at 16,000 ×g for 15 min at 4°C. The supernatant was collected as the test solution sample (MXSGD-PRE).

Approximately 200 μL of blank rat serum and 200 μL of MXSGD-containing serum were each combined with 800 μL of methanol. The two solutions were vortexed for 60 s, incubated at -20°C for 30 min, and subsequently centrifuged at 16,000 ×g for 20 min at 4°C. The supernatant was collected and vacuum-dried. The residue was dissolved in 100 μL of a 40% aqueous methanol solution, vortexed, and centrifuged again at 16,000 ×g for 15 min at 4°C. The supernatant was obtained, yielding a blank serum sample (CONTROL) and a serum sample containing MXSGD (MXSGD).

Next, 200 μL of blank rat serum was mixed with 33.4 μL of MXSGD aqueous extract. Subsequently, 800 μL of methanol was added to the mixture, which was then vortexed for 60 s. The solution was incubated at –20°C for 30 min, followed by centrifugation at 16,000 ×g for 20 min at 4°C. The supernatant was then harvested and vacuum-dried. The residue was subsequently dissolved in 100 μL of a 40% aqueous methanol solution, vortexed, and centrifuged again at 16,000 ×g for 15 min at 4°C. The supernatant was obtained, yielding a sample consisting of the blank serum combined with the test solution (CONTROL + MXSGD-PRE).

Representative components of MXSGD that entered the bloodstream, as detected via UPLC-HRMS, were selected as research subjects based on the average ion abundance values. Component IDs were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). Probable matching and docking in vivo drug targets were predicted using the PharmMapper database (http://www.lilab-ecust.cn/pharmmapper/), with targets filtered based on a Norm Fit threshold of ≥ 0.8. In the SwissTargetPrediction database (http://www.swisstargetprediction.ch), targets were selected based on a “probability” threshold of ≥ 0.2. Targets underwent additional screening using the BATMAN-TCM database (http://bionet.ncpsb.org.cn/batman-tcm/) with a score cutoff” ≥ 20 and a P-value ≥ 0.05. The target protein names were standardized using the UniProt database (https://www.uniprot.org/), and duplicate targets were consolidated and eliminated, yielding the action targets of the components of MXSGD that entered the bloodstream.

The search term “Acute Lung Injury” was employed to extract pertinent targets from the GeneCards (http://www.genecards.org/), DisGeNET (https://www.disgenet.org/), TTD (https://db.idrblab.net/ttd/), and OMIM databases (http://www.omim.org/). Duplicate targets were consolidated and eliminated, and the target nomenclature was accurately corrected using the UniProt database to identify disease-associated targets. Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html) was subsequently used to input the action targets of the components and diseases, producing a Venn diagram. The intersecting targets indicated the probable action sites of the MXSGD components in the treatment of ALI.

Based on the identified MXSGD components that entered the bloodstream, target prediction outcomes, and associated illness targets, the C–T network diagram of MXSGD was generated employing Cytoscape 3.7.1 software. The PPI network was constructed using the STRING database (https://cn.string-db.org), with a high confidence threshold (≥ 0.7) as the screening criterion. The resulting network was visualized and analyzed using Cytoscape software (version 3.7.1). The network was further evaluated using the “Network Analyzer” tool, and network topology analysis was performed using CytoNCA (version 2.1.6). Key targets were identified based on degree, betweenness, and closeness centrality metrics, with values exceeding the respective medians.

The potential targets were imported into the Database for Annotation, Visualization and Integrated Discovery (DAVID) database (https://davidbioinformatics.nih.gov/summary.jsp) to conduct GO enrichment analysis for biological function and KEGG pathway enrichment analysis. These analyses highlighted the molecular functions, biological processes, cellular components, and metabolic pathways of these genes. A significance criterion of P < 0.05 was set, and part of the results were visualized using the Internet Bioinformatics tool (https://www.bioinformatics.com.cn/).

The blood-absorbed components of MXSGD, target prediction outcomes, pathway analysis, and associated diseases were utilized to delineate the relationships among drugs–ingredients, ingredients–targets, targets–pathways, and pathways–diseases. A network diagram titled “Blood-Absorbed Components –Key Targets–Signaling Pathways” (CTP) was generated utilizing Cytoscape software (version 3.7.1).

The top five potentially active components with a high degree from the CTP network were selected for molecular docking with the top 10 core targets. The structures of prospective active constituents were acquired from the PubChem database. PDB format files for the principal targets were retrieved from the PDB database (https://www.rcsb.org). Hydrogenation, dehydration, and ligand separation were performed using the AutoDock Tools software (version 1.5.7). Molecular docking was performed using AutoDock Vina software (version 1.1.2), and the minimal binding energies were computed. The docking findings were imported into PyMol for visualization and graphing.

Overall, 48 SPF-grade BALB/c mice (16–20 g), equally divided between males and females, were purchased from Hunan SJA Laboratory Animal Co., Ltd. [Animal Quality Certificate No. SYXK (Xiang) 2020-0010; Animal Experiment Ethical Approval No.: ZYFY20230703-04). The rearing conditions comprised a 12-h light/dark cycle, unlimited access to water and food, and controlled temperature and humidity levels. All animals were randomly allocated to six groups: control, model, positive control medicine (oseltamivir, Ose), and low-dose, medium-dose, and high-dose MXSGD groups, with eight mice in each group. All groups, excluding the control group, received 0.05 mL of a 1:640 hemagglutination titer dilution of IAV (1:100) by nasal drops to establish the IAV infection model. Twenty-four hours post-infection, each group received the corresponding treatment via oral gavage at clinically equivalent doses, using a body surface area-based dose variation algorithm. The medication was administered once daily at a volume of 0.2 mL. In the positive control treatment group, each mouse received 0.433 mg of oseltamivir daily by gavage. The low, medium, and high MXSGD groups received daily doses of 0.0605, 0.121, and 0.242 g of MXSGD, respectively. An equivalent volume of physiological saline was administered to the control and model groups. After 3 or 7 consecutive days of treatment, the animals were weighed, and blood was collected from the eyeball after anesthesia. Lung tissues of the mice were excised and weighed, with a portion preserved in 4% paraformaldehyde and another portion maintained in a –80°C ultra-low-temperature freezer.

At the end of the experiment, the body weights of the mice were recorded. The lungs were harvested, and the remaining blood was absorbed using filter paper prior to weighing and documenting the lungs to compute the organ index. The lung index (%) was calculated using the following formula:

Lung tissues preserved in 4% paraformaldehyde were subjected to gradient alcohol dehydration, xylene clearing, paraffin embedding, sectioning, baking, deparaffinization, hematoxylin-eosin staining, and neutral resin mounting. Tissue samples were examined under a microscope, and pathological changes were documented using photography.

Three mice per group were randomly selected for immunofluorescence detection. After embedding, lung tissue samples were sectioned and deparaffinized for antigen retrieval. The tissue sections were blocked with 3% bovine serum albumin (BSA) for 30 min and then incubated with IAV nucleoprotein antibody (1:400) overnight at 4°C. Subsequently, they were incubated with a CY3-conjugated secondary antibody (1:300, red) at room temperature for 50 min. Nuclear staining was then performed using 4′,6-diamidino-2-phenylindole (DAPI) (blue) in the dark at room temperature for 10 min. Following the suppression of tissue autofluorescence using an autofluorescence quencher, an anti-fluorescence quenching mounting solution was used, and the samples were examined under a fluorescence microscope. Image-Pro Plus 6.0 was then used to analyze the acquired images and determine the mean optical density value (IOD/area) for each field of view.

Frozen mouse serum was retrieved, and the levels of cytokines (IL-1β, IL-6, and TNF-α) were measured according to the instructions of the ELISA kit. After obtaining absorbance values, sample concentrations were calculated using a standard curve.

Total RNA was extracted from pulmonary tissue using the TIANGEN RNAsimple Total RNA Kit. The extracted total RNA was then reverse-transcribed into cDNA using the NovoScript^® Plus All-in-one 1st Strand cDNA Synthesis SuperMix (gDNA Purge). Subsequently, PCR amplification was performed using the NovoStart^® SYBR qPCR SuperMix Plus kit. Following the PCR reaction, melting curve analysis was conducted, and the relative expression levels of target genes were calculated using the 2−^ΔΔCT method, followed by statistical analysis. The specific primers utilized are presented in Table 1 .

After embedding, lung tissue samples were sectioned and deparaffinized to facilitate antigen retrieval. The tissue sections were blocked with 3% BSA for 30 min and incubated with an anti-CD80 antibody (1:2000) overnight at 4°C. Subsequently, the sections were incubated with the appropriate horseradish peroxidase-conjugated secondary antibody (1:500, green) at ambient temperature for 50 min, followed by incubation with tryptic soy agar in the dark at room temperature for 10 min. Following incubation, tissue sections were subjected to microwave heating for antigen retrieval. An anti-mannose receptor antibody (1:200) was then added, and the sections were incubated overnight at 4°C. Sections were then incubated with the appropriate CY3-conjugated secondary antibody (1:300, red) in the dark at ambient temperature for 50 min. Following incubation, DAPI (blue) was used for nuclear staining. Tissue autofluorescence was suppressed using an autofluorescence quencher, followed by the application of an anti-fluorescence quenching mounting medium. The specimens were examined under a fluorescence microscope. Image-Pro Plus 6.0 was employed to analyze the acquired images and determine the mean optical density value (IOD/area) for each field of view.

Following sectioning and dewaxing of paraffin-embedded lung tissues, antigen retrieval was performed using 1× citrate buffer (pH 6.0) as the repair solution, followed by endogenous enzyme blocking with 3% H₂O₂ solution. Tissue sections were incubated with 10% goat serum for blocking, then subjected to antibody incubation, DAB staining, and hematoxylin counterstaining. After dehydration and mounting, protein expression was detected using the PV-9000 universal two-step detection kit, with positive signals observed under an optical microscope. Five random microscopic fields were selected per sample section, and the acquired images were analyzed using Image-Pro Plus 6.0 software to calculate the mean optical density value (IOD/area) for each field.

Among the 1,297 components identified using UPLC-HRMS, we selected the blood-absorbed components of MXSGD with high average ion abundance values using the criteria of “into blood” and “mean ≥ e⁸.” We then intersected these components with the 17 main blood-absorbed components obtained from the BPCs, resulting in 56 core blood-absorbed components ( Table 3 ). We used these 56 blood-absorbed components to retrieve component targets from the BATMAN-TCM, SwissTargetPrediction, and PharmMapper databases, removing duplicates and integrating 1,114 target points for the blood-absorbed components of MXSGD.

Following the consolidation and elimination of duplicates from all ALI targets received from the GeneCards, DisGeNET, and OMIM databases, we identified 3,037 genes associated with ALI. These genes were then intersected with 1,114 target points of the blood-absorbed components of MXSGD, revealing 338 overlapping targets.

We generated the C–T network using Cytoscape software ( Figure 4 ) and analyzed it using the Network Analyzer plugin. A higher degree indicates that a node is connected to more nodes, reflecting a more significant regulatory role in the overall network. The network contained 1,172 nodes, including one Chinese herbal formula, 56 blood-absorbed components, 1,114 action targets, and 1 disease, as well as 4,619 edges.

We imported the 338 selected intersection targets into the STRING database to determine the interaction relationships between proteins. Each circle represents a protein node participating in the interactions, and each line represents the interactions between targets. Larger circles indicate higher degree values, and thicker lines indicate higher binding scores. The resulting PPI network contained 304 nodes, with 34 targets not participating in the interactions, and 916 interaction lines. The network yielded a median degree of 4, a median betweenness of 182.669, and an average closeness of 0.051 resulting in 99 core targets ( Figure 5A ).

Among these key targets, the primary ones included tumor necrosis factor (TNF) and interleukin (IL)-1β, which are associated with inflammation; HSP90AA1 and SRC, which are associated with cell proliferation and carcinogenesis; epidermal growth factor receptor (EGFR), which is linked to vascular endothelium; and AKT, Phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), and phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), which are critical components of the PI3K/AKT pathway. This result indicates that MXSGD influences the regulation of inflammatory responses, oxidative stress, autophagy, apoptosis, and vascular function.

Further analysis of these key targets revealed that many of them were closely associated with macrophage polarization. For example, inflammation-related targets such as TNF, interferon gamma (IFNG), and prostaglandin-endoperoxide synthase 2 (PTGS2) are involved in the macrophage polarization process. Several signaling pathways associated with key targets were also identified as critical pathways regulating macrophage polarization, including AKT1, PIK3R1, PIK3CA, Phosphatase and tensin homolog (PTEN), mitogen-activated protein kinase 1 (MAPK1), MAPK14, inhibitor of nuclear factor kappa-b kinase subunit beta (IKBKB), and conserved helix-loop-helix ubiquitous kinase (CHUK). These key targets included transcription factors related to macrophage polarization, such as peroxisome proliferator-activated receptor gamma (PPARG) and JUN/FOS (AP-1 complex), and markers associated with macrophage polarization, including nitric oxide synthase 2 (NOS2).

We performed GO analysis of the principal targets of the blood-absorbed components of MXSGD for the treatment of ALI using DAVID 2021, with P < 0.05 set as the criterion, revealing a total of 915 GO items. The GO enrichment analysis is primarily categorized into three domains: biological processes (BP), cellular components (CC), and molecular functions (MF). The BP-related entries were the most abundant, totaling 696, and predominantly related to the positive regulation of gene expression, signal transduction, protein phosphorylation, negative regulation of apoptotic process, positive regulation of transcription and DNA-templated, and immunological responses. The CC-related entries totaled 81, mainly involving the cytosol, cytoplasm, nucleus, and nucleoplasm. The MF-related entries included 138 entries, primarily focusing on protein binding, ATP binding, identical protein binding, enzyme binding, and protein kinase activity and binding. These GO entries were sorted by count values, and the top 15 entries for BP, CC, and MF were selected and visualized using a bubble chart ( Figure 5B ).

We conducted KEGG analysis of the potential treatment targets of ALI by MXSGD using DAVID (2021; with P < 0.05 as the criterion), revealing 169 enriched KEGG pathways. The top 20 pathways are displayed in a bubble chart in Figure 5C .

Most of the targets were enriched in signaling pathways associated with cancer, the PI3K/AKT signaling pathway, lipid metabolism, atherosclerosis, human cytomegalovirus infection, the MAPK signaling pathway, and Alzheimer’s disease.

We constructed the CTP network using Cytoscape software ( Figure 6A ) and analyzed it using the Network Analyzer plugin. Higher node degree values indicate a greater number of connected nodes, reflecting enhanced regulatory function within the entire network. The network contained 176 nodes, including 1 Chinese herbal formula, 56 blood-absorbed components, 99 key targets, 20 signaling pathways, and 1 disease, as well as 1,124 edges. These findings highlight the characteristic therapeutic approach of TCM formulas, which act through a multi-component, multi-target, multi-pathway mechanism.

We selected degree-ranking compounds in the CTP network, including DL-ephedrine, (-)-pseudoephedrine, (+/-)-norephedrine, phellamurin, and trans-2-phenylcyclopropylamine, for molecular docking validation with EGFR, CDK2, MAPK1, GSTP1, MAPK8, TGFBR2, PIK3R2, AKT1, PIK3R1, and PIK3CA. The minimum binding energies for each protein-ligand complex were calculated. A binding energy below zero indicates spontaneous binding between ligand molecules and receptor proteins, and a binding energy below –1.2 kcal/mol suggests strong binding activity between the molecules. The smaller the binding energy, the stronger the binding ability, indicating better docking.

The four docking results with the best binding activity are visualized in Figure 6C . The four pharmacologically relevant blood-absorbed components exhibited good binding to target proteins. Specifically, the binding energies of (-)-pseudoephedrine and TGFBR2, (-)-pseudoephedrine and AKT1, (+/-)-norephedrine and TGFBR2, (+/-)-norephedrine and PIK3R2, and DL-ephedrine and TGFBR2 in the blood-absorbed components were all less than –1.2 kcal/mol. Among these, (+/-)-norephedrine exhibited the lowest binding energy with TGFBR2, followed by (+/-)-norephedrine with PIK3R2, indicating that (+/-)-norephedrine exhibited the strongest binding activity with TGFBR2 and PIK3R2. The docking results depicting the best visual binding activity are presented in Figure 6B .

We then investigated the effects of MXSGD in vivo using IAV-induced lung injury mouse models. Histological analysis revealed intact alveolar morphology and septal structure in control mice. However, the model control group exhibited significant histopathological changes in lung tissue ( Figure 7A ), characterized by pulmonary congestion and edema, extensive infiltration of lymphocytes and macrophages, and large necrotic solid lesions. Compared to that in the model group, lung tissue histopathological damage in the MXSGD treatment groups significantly improved on days 3 and 7 post-treatment, as evidenced by reduced congestion and edema, decreased inflammatory cell infiltration, and a smaller area of necrotic solid lesions. These improvements were the most pronounced in the oseltamivir (positive treatment group) and medium-dose MXSGD groups, which exhibited clear alveolar outlines and reduced inflammatory cell infiltration.

The lung index data ( Figure 7B ) indicated that the model group exhibited a significant increase in the lung index on both days 3 and 7 post-IAV infection. In contrast, subsequent treatment with MXSGD led to a reduction in the lung index across all treatment groups to varying extents. Specifically, the lung index in the model group significantly increased compared to that in the control group (P < 0.01). Moreover, both the oseltamivir and medium-dose MXSGD groups exhibited a significant reduction in the lung index compared to the model group (P < 0.01 or P < 0.05).

Our immunofluorescence analysis ( Figures 7C, D ) revealed significantly increased nuclear protein (NP) levels in lung tissues in the model groups on days 3 and 7 post-IAV infection relative to those in the control group (P < 0.01). However, following therapeutic intervention with MXSGD, the NP levels in all treatment groups decreased to varying degrees. On days 3 and 7 post-infection, the oseltamivir group demonstrated the significant decrease compared to the model group (P < 0.01).

Based on the above findings, we subsequently investigated whether MXSGD confers protection against IAV-induced ALI by modulating macrophage polarization. To this end, we measured the levels of inflammatory cytokines in mouse sera. The results are presented in Figure 8A .

Following IAV infection, the model group exhibited a significant increase in IL-1β, IL-6, and TNF-α levels on days 3 and 7 (P < 0.01). However, following treatment with oseltamivir and MXSGD, the serum levels of IL-1β, IL-6, and TNF-α significantly decreased in all drug-treated mouse groups compared to the model group at both time points (P < 0.01 or P < 0.05). These results demonstrate that treatment with MXSGD reduces the serum levels of pro-inflammatory cytokines induced by IAV in mice.

We then analyzed the gene expression of various molecules in the lung tissue of mice from different groups ( Figure 8B ). They included IL-6, a classic pro-inflammatory cytokine; iNOS, an enzyme induced by inflammatory signaling, which synergizes with pro-inflammatory cytokines to amplify inflammatory responses and serves as a marker of M1 macrophages; IL-10, a classic anti-inflammatory cytokine; and FIZZ1, a secretory protein involved in inflammatory regulation and a marker of M2 macrophages.

Compared to that in the control group, Il-6 expression was significantly increased in the model group on both days 3 and 7 post-IAV intervention (P < 0.01). Compared to that in the model group, the mRNA expression of Il-6 was decreased in the oseltamivir group and all MXSGD dose groups. On day 3, the most pronounced reduction occurred in the oseltamivir group (P < 0.05). On day 7, we observed significant decreases in the oseltamivir group and the medium-dose MXSGD group (P < 0.05; Figure 8B ).

Compared to that in the control group, iNos expression was significantly increased in the model group on both days 3 and 7 post-IAV infection (P < 0.05). All MXSGD -treated groups exhibited marked reductions in iNos expression compared to the model group. On day 3 post-infection, the most significant decrease occurred in the oseltamivir group (P < 0.05). On day 7, both the oseltamivir group and all MXSGD treatment groups demonstrated statistically significant reductions in iNos expression (P < 0.05 or P < 0.01; Figure 8B ).

The expression of Il-10 was downregulated in the model group on days 3 and 7 post-IAV infection compared to those in the control group; however, this decrease was not statistically significant. Compared to that in the model group, Il-10 expression was upregulated in all drug-treated groups. Specifically, on day 3 post-infection, the most significant increase occurred in the oseltamivir group (P < 0.05). On day 7, Il-10 expression was significantly upregulated in the oseltamivir group and all MXSGD treatment groups (P < 0.05 or P < 0.01; Figure 8C ).

On days 3 and 7 post-IAV infection, Fizz1 expression decreased in the model group compared to that in the control group, though without statistical significance. All drug-treated groups exhibited increased Fizz1 expression compared to that in the model group. On day 3 post-infection, the low-dose and medium-dose MXSGD groups exhibited the most significant elevation (P < 0.05 or P < 0.01). On day 7, all MXSGD dose groups exhibited significant upregulation in Fizz1 expression compared to the model group (P < 0.01; Figure 8C ). These findings suggest that MXSGD can regulate the gene expression of macrophage polarization markers (iNos and Fizz1) in the lung tissue of IAV-infected mice.

Based on our preliminary experimental findings, and to further investigate the mechanistic relationship between MXSGD and macrophage polarization, we selected the following groups for subsequent analysis: control, model, oseltamivir, and MXSGD medium-dose groups. We subsequently characterized macrophage phenotypes in lung tissue using immunofluorescence double staining.

In terms of M1 macrophages, immunofluorescence revealed that the model group exhibited a significant increase in the expression of the M1 macrophage marker CD80 on both days 3 and 7 post-IAV infection, compared to the control group (P < 0.01; Figure 8D ). Conversely, CD80 expression levels decreased to different extents in all MXSGD groups compared to those in the model group. On day 3 post-infection, the oseltamivir group exhibited a significant decrease in CD80 expression (P < 0.05). By day 7 post-infection, both the oseltamivir and MXSGD groups exhibited a significant decrease in CD80 expression (P < 0.01).

In terms of M2 macrophages, immunofluorescence revealed notable alterations in the M2 macrophage marker CD206. Compared to that in the control group, there were no notable alterations in CD206 levels in the model group on day 3 post-infection; however, on day 7, CD206 expression significantly increased (P < 0.01; Figure 8D ). The oseltamivir and MXSGD groups exhibited a significant increase in CD206 levels on both days 3 and 7 compared to the model group (P < 0.01). This finding indicates that the macrophages were successfully repolarized from M1 to M2.

Overall, these findings suggest that MXSGD can mitigate the inflammatory response and pulmonary damage following influenza infection by modulating macrophage polarization.

Macrophage polarization is regulated by multiple signaling pathways. In the present study, analysis of key targets revealed that several target-associated pathways critically govern this process, notably the PI3K/AKT, MAPK, and NF-κB pathways. Among them, the PI3K/AKT signaling cascade plays a central regulatory role in pulmonary injury pathologies. Thus, to validate these network pharmacology predictions, we performed IHC analysis of key proteins in the PI3K/AKT pathway on lung tissues from four experimental groups: control, model, oseltamivir, and MXSGD medium-dose groups.

Compared to that in the control group, PI3K protein expression in the model group was significantly upregulated in lung tissues on both days 3 and 7 following IAV infection (P < 0.01; Figure 9 ). However, both the oseltamivir and MXSGD groups exhibited a significant reduction in PI3K expression levels, compared to the model group (P < 0.01).

Similarly, AKT protein expression in the model group was significantly elevated on days 3 and 7 post-IAV infection compared to that in the control group (P < 0.01; Figure 9 ). However, the drug-treated groups exhibited reduced AKT expression compared to the model group. On day 3, AKT expression levels in both the oseltamivir and MXSGD groups were significantly lower than those in the model group (P < 0.01). By day 7, AKT expression significantly decreased in the oseltamivir group than that in the model group (P < 0.05).

We observed a consistent trend for phosphorylated AKT (p-AKT) expression ( Figure 9 ). Specifically, the model group displayed a significant increase in p-AKT levels at both time points compared to the control group (P< 0.01), whereas therapeutic intervention with oseltamivir or MXSGD resulted in a significant decrease in p-AKT expression, compared to the model group (P < 0.01).