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pmid:41070114

Identification of Biomarkers for Sepsis-Induced Acute Lung Injury Through Bioinformatics and Machine Learning Approaches, with Experimental Validation.

Yannian Luo · Juan Xu · Nannan He · Wen Cao

Journal of inflammation research · 2025 doi:10.2147/JIR.S539899

Paper

complete

Experiment

complete

Data

partial

Analysis

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absent

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title
Identification of Biomarkers for Sepsis-Induced Acute Lung Injury Through Bioinformatics and Machine Learning Approaches, with Experimental Validation.
doi
10.2147/JIR.S539899
pmid
41070114
authors array · 4 items
[0]
Yannian Luo
[1]
Juan Xu
[2]
Nannan He
[3]
Wen Cao
journal
Journal of inflammation research
year
2025
abstract
Background: Sepsis-induced acute lung injury (ALI) remains a life-threatening condition due to the lack of reliable early diagnostic biomarkers. Machine learning offers powerful tools for analyzing high-dimensional gene expression data and identifying potential biomarkers and therapeutic targets. Methods: Five datasets (GSE10474, GSE32707, GSE66890, GSE10361, GSE3037) were obtained from the GEO database. After assessment and normalization, GSE10474, GSE32707, and GSE66890 were combined as a training set to identify differentially expressed genes (DEGs). DEGs were intersected with genes from key modules identified by weighted gene co-expression network analysis (WGCNA), yielding 213 overlapping genes. These were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Eight machine learning algorithms (RF, SVM, GLM, GBM, KNN, NNET, LASSO, DT) were used to develop diagnostic models, which were validated on GSE10361 and GSE3037. Model performance was evaluated using a nomogram, calibration curves, and decision curve analysis (DCA). Immune and inflammatory states were assessed using the CIBERSORT algorithm. Potential therapeutic compounds were identified through the DSigDB database via the Enrichr platform. Molecular docking and molecular dynamics simulations examined interactions between Resveratrol and selected targets. In vitro experiments validated these findings. Results: A total of 213 candidate genes were identified by intersecting DEGs with WGCNA-derived MEblue module genes. GO and KEGG analyses indicated associations with immune activation and bacterial infection. Four key genes (DDAH2, PNPLA2, STXBP2, TCN1) were selected using eight machine learning algorithms. The diagnostic model showed good performance via nomogram, calibration curve, and DCA. Molecular docking revealed stable binding of Resveratrol to these genes. In vitro, Resveratrol pretreatment alleviated LPS-induced ALI by modulating the core genes. Conclusion: The four genes may serve as diagnostic biomarkers for sepsis-ALI. Resveratrol represents a potential therapeutic strategy by targeting these genes.
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Data were analyzed using one-way ANOVA with GraphPad Prism 9.0, considering p-values less than 0.05 as statistically significant.
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sections[14] "Statistical Analysis" paragraph 2
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Data were analyzed using one-way ANOVA with GraphPad Prism 9.0, considering p-values less than 0.05 as statistically significant.
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The data underlying this study’s conclusions can be obtained from the corresponding author upon reasonable request. This aligns with the principles of open science, enabling independent verification and facilitating future research by others in the field.
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ROC curve analysis further supported the discriminative power of the model.
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sections[20] "Establishment of Diagnostic Prediction Model" paragraph 2
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The nomogram achieved an AUC of 0.834, while the individual genes also exhibited good diagnostic performance: DDAH2 (AUC = 0.792), PNPLA2 (AUC = 0.709), STXBP2 (AUC = 0.721), and TCN1 (AUC = 0.770) (Figure 5D).
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ROC curve analysis further supported the discriminative power of the model.
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Diagnostic test evaluation
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published_precision_envelope
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data_shapes array · 1 item
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reference_standard_and_test
evidence object · 3 fields
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id
method-4
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specific
decision_curve
label
Model calibration / clinical utility
dsl_operations array · 2 items
[0]
calibration_bins
[1]
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missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
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predictions_and_outcomes
evidence object · 3 fields
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0.7
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medium
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decision curve analysis
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gaps array · 0 items
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
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assay_families array · 9 items
[0]
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[2]
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[3]
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[4]
pca
[5]
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[6]
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[7]
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[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
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group_summary
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bootstrap_mean_difference
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group_comparison_anova
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basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: anova.
capability object · 4 fields
tier
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capability:computational_analysis
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The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
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tier
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accessions array · 5 items
[0]
GSE10474
[1]
GSE32707
[2]
GSE66890
[3]
GSE10361
[4]
GSE3037
resolved_accessions array · 5 items
[0]
GSE10474
[1]
GSE32707
[2]
GSE66890
[3]
GSE10361
[4]
GSE3037
statements array · 1 item
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The data underlying this study’s conclusions can be obtained from the corresponding author upon reasonable request. This aligns with the principles of open science, enabling independent verification and facilitating future research by others in the field.
basis
The paper names GSE10474, GSE32707, GSE66890, GSE10361, GSE3037 in GEO database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
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welch_t
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group_comparison_anova
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source:pmid:41070114
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data:geo:GSE10361
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must_build object · 5 fields
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readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
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data:geo:GSE10361
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
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MiniMax-M3.1-Flash
provider
minimax
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minimax
read_at
2026-11-01T20:02:48Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
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5
usage object · 3 fields
prompt_tokens
10858
completion_tokens
1651
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0
stages array · 1 item
[0] object · 4 fields
id
extraction
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execution object · 5 fields
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gaps array · 1 item
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provenance object · 7 fields
entities array · 3 items
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pmid:41070114
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paper
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pmid:41070114
[1] object · 3 fields
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source:pmid:41070114
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manuscript
label
PMID 41070114 full text
[2] object · 3 fields
id
data:geo:GSE10361
type
dataset
label
GEO GSE10361
activities array · 3 items
[0] object · 4 fields
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type
classification
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type
paper
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pmid:41070114
[1] object · 3 fields
id
source:pmid:41070114
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PMID 41070114 full text
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data:geo:GSE10361
type
dataset
label
GEO GSE10361
agent
agent:corpus-assay-engine
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activity-read-41070114
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used array · 1 item
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agent:minimax-reader
[2] object · 4 fields
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activity-resolve-GSE10361
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used array · 1 item
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data:geo:GSE10361
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agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
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agent:corpus-assay-engine
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dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
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lineage object · 4 fields
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pmid:41070114
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asset:data:geo:GSE10474 licence not read
[3]
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asset:data:geo:GSE3037 licence not read
[5]
asset:data:geo:GSE3037 not downloaded
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asset:data:geo:GSE32707 licence not read
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asset:data:geo:GSE66890 licence not read
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apoptosis_assay
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99e1195c7d6f
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[0]
ts with sepsis-related ALI. Cell Culture and Viability Assay The human bronchial epithelial cell line BEA
[1]
ing treatment, 10 µL of Cell Counting Kit-8 (CCK-8; Solarbio, China) reagent was added to each
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41070114", "kind": "science-record", "revision": 4, "content_hash": "sha256:158bfc214b4e5a59de9de8403fa9fb7d21d051e7207b56402b811f031ed00717", "created_at": "2026-11-01T20:31:07Z", "updated_at": "2026-11-01T22:57:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Biomarkers for Sepsis-Induced Acute Lung Injury Through Bioinformatics and Machine Learning Approaches, with Experimental Validation.", "doi": "10.2147/JIR.S539899", "pmid": "41070114", "authors": ["Yannian Luo", "Juan Xu", "Nannan He", "Wen Cao"], "journal": "Journal of inflammation research", "year": 2025, "abstract": "Background: Sepsis-induced acute lung injury (ALI) remains a life-threatening condition due to the lack of reliable early diagnostic biomarkers. Machine learning offers powerful tools for analyzing high-dimensional gene expression data and identifying potential biomarkers and therapeutic targets.\nMethods: Five datasets (GSE10474, GSE32707, GSE66890, GSE10361, GSE3037) were obtained from the GEO database. After assessment and normalization, GSE10474, GSE32707, and GSE66890 were combined as a training set to identify differentially expressed genes (DEGs). DEGs were intersected with genes from key modules identified by weighted gene co-expression network analysis (WGCNA), yielding 213 overlapping genes. These were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Eight machine learning algorithms (RF, SVM, GLM, GBM, KNN, NNET, LASSO, DT) were used to develop diagnostic models, which were validated on GSE10361 and GSE3037. Model performance was evaluated using a nomogram, calibration curves, and decision curve analysis (DCA). Immune and inflammatory states were assessed using the CIBERSORT algorithm. Potential therapeutic compounds were identified through the DSigDB database via the Enrichr platform. Molecular docking and molecular dynamics simulations examined interactions between Resveratrol and selected targets. In vitro experiments validated these findings.\nResults: A total of 213 candidate genes were identified by intersecting DEGs with WGCNA-derived MEblue module genes. GO and KEGG analyses indicated associations with immune activation and bacterial infection. Four key genes (DDAH2, PNPLA2, STXBP2, TCN1) were selected using eight machine learning algorithms. The diagnostic model showed good performance via nomogram, calibration curve, and DCA. Molecular docking revealed stable binding of Resveratrol to these genes. In vitro, Resveratrol pretreatment alleviated LPS-induced ALI by modulating the core genes.\nConclusion: The four genes may serve as diagnostic biomarkers for sepsis-ALI. Resveratrol represents a potential therapeutic strategy by targeting these genes.", "claims": [{"id": "claim-method", "statement": "Data were analyzed using one-way ANOVA with GraphPad Prism 9.0, considering p-values less than 0.05 as statistically significant.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41070114", "locator": "sections[14] \"Statistical Analysis\" paragraph 2", "quote": "Data were analyzed using one-way ANOVA with GraphPad Prism 9.0, considering p-values less than 0.05 as statistically significant."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data underlying this study’s conclusions can be obtained from the corresponding author upon reasonable request. This aligns with the principles of open science, enabling independent verification and facilitating future research by others in the field.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41070114", "locator": "sections[28] \"Data Sharing Statement\" paragraph 2", "quote": "The data underlying this study’s conclusions can be obtained from the corresponding author upon reasonable request. This aligns with the principles of open science, enabling independent verification and facilitating future research by others in the field."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ROC curve analysis further supported the discriminative power of the model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41070114", "locator": "sections[20] \"Establishment of Diagnostic Prediction Model\" paragraph 2", "quote": "The nomogram achieved an AUC of 0.834, while the individual genes also exhibited good diagnostic performance: DDAH2 (AUC = 0.792), PNPLA2 (AUC = 0.709), STXBP2 (AUC = 0.721), and TCN1 (AUC = 0.770) (Figure 5D)."}, {"asset": "source:pmid:41070114", "locator": "sections[20] \"Establishment of Diagnostic Prediction Model\" paragraph 2", "quote": "ROC curve analysis further supported the discriminative power of the model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ROC curve analysis further supported the discriminative power of the model.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41070114"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE10361"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE10361"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41070114", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/JIR.S539899", "sha256": "sha256:b43450087b9693e467b434648c0e1e98f70a3d4031ad8ac2b05659ebfe6facab", "bytes": 41060, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/JIR.S539899", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12506793", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE10361", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10361", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE10361", "version": null, "note": "The data underlying this study’s conclusions can be obtained from the corresponding author upon reasonable request. This aligns with the principles of open science, enabling independent verification and facilitating future research by others in the field.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data underlying this study’s conclusions can be obtained from the corresponding author upon reasonable request. 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This aligns with the principles of open science, enabling independent verification and facilitating future research by others in the field.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data underlying this study’s conclusions can be obtained from the corresponding author upon reasonable request. This aligns with the principles of open science, enabling independent verification and facilitating future research by others in the field.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM264753", "GSM264987", "GSM264988", "GSM264989", "GSM264990", "GSM264991", "GSM264992", "GSM264994", "GSM264995", "GSM264996", "GSM264998", "GSM265000", "GSM265001", "GSM265004", "GSM265006", "GSM265008", "GSM265009", "GSM265011", "GSM265014", "GSM265016", "GSM265018", "GSM265019", "GSM265021", "GSM265022", "GSM265023", "GSM265024", "GSM265026", "GSM265027", "GSM265028", "GSM265029", "GSM265030", "GSM265031", "GSM265032", "GSM265033"], "sample_count": 34, "group_sizes": null, "group_sizes_basis": null, "platform": "571", "taxon": "Homo sapiens", "title": "Microarray Analysis of Acute Lung Injury", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE3037", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3037", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE3037", "version": null, "note": "The data underlying this study’s conclusions can be obtained from the corresponding author upon reasonable request. This aligns with the principles of open science, enabling independent verification and facilitating future research by others in the field.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data underlying this study’s conclusions can be obtained from the corresponding author upon reasonable request. This aligns with the principles of open science, enabling independent verification and facilitating future research by others in the field.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM66867", "GSM66868", "GSM66869", "GSM66870", "GSM66871", "GSM66872", "GSM66873", "GSM66874", "GSM66875", "GSM66876", "GSM66877", "GSM66878", "GSM66879", "GSM66880", "GSM66881", "GSM66882", "GSM66883", "GSM66884", "GSM66885", "GSM66886", "GSM66887", "GSM66888", "GSM66889", "GSM66890"], "sample_count": 24, "group_sizes": null, "group_sizes_basis": null, "platform": "96", "taxon": "Homo sapiens", "title": "Stimulation by LPS and HMGB1 in peripheral blood neutrophils from patients with sepsis-induced acute lung injury", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE32707", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32707", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE32707", "version": null, "note": "The data underlying this study’s conclusions can be obtained from the corresponding author upon reasonable request. 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