Table · dataset · 2026
<p>Public datasets included in this study.</p>
Listed in ZivaHub and HKU DataHub and DaYta Ya Rona and figshare and Loughborough Research Repository and UP Research Data Repository — shown once because both records carry DOI 10.1371/journal.pone.0358983.t001
<div> <p>Background</p><p>Patients with ST-segment elevation myocardial infarction (STEMI) continue to show substantial inflammatory heterogeneity after reperfusion therapy, and recovery trajectories may differ markedly across patients.
Description
Compared with traditional diagnostic models, characterizing the dynamic immune trajectory from acute inflammation toward recovery may better identify programs associated with impaired resolution.
This study aimed to construct an immune recovery-failure framework that is transferable across cohorts and anchored by single-cell data.</p> <p>Methods</p><p>We integrated six public GEO peripheral-blood bulk transcriptome cohorts and one public single-cell RNA-seq dataset. An acute-to-recovery immune axis was constructed from directionally concordant longitudinal and cross-sectional contrasts, and a recovery-failure phenotype was defined in the discovery longitudinal cohort from paired transcriptomic features.
Read the rest (4 more)
An 18-gene monocyte-associated module was then derived using bulk recovery-failure associations, monocyte specificity, monocyte-state differences, and pseudotime information. External cohorts were used to evaluate temporal transfer and baseline-to-follow-up associations rather than definitive clinical prediction, and the single-cell dataset was used for donor-aware cell-type anchoring and exploratory state-level interpretation.</p> <p>Results</p><p>Among 81 STEMI patients who met the longitudinal definition criteria, unsupervised stratification classified 35 as recovery-failure patients and 46 as recovery-sufficient patients.
We constructed an 18-gene monocyte-associated recovery-failure module. The module distinguished the two transcriptomically defined groups in the discovery cohort and declined from the acute phase toward recovery in external longitudinal cohorts. Baseline module burden was associated with later composition-adjusted inflammatory burden, but this was interpreted as a baseline-to-follow-up association rather than clinical event prediction.
At the single-cell level, donor-aggregated lesion-associated shifts were largest in monocytes, and the module decreased along the acute-high-to-recovery-biased monocyte pseudotime direction. In 1,000 matched random monocyte-gene modules, the observed module showed a higher aggregate recovery statistic than nearly all random signatures.</p> <p>Conclusions</p><p>We propose a STEMI immune recovery-failure framework derived from public peripheral-blood transcriptomes, evaluated across independent longitudinal datasets, and linked to monocyte state variation in a public single-cell context.
The framework is does not evidence of a causal mechanism or a validated hard-endpoint prediction model; rather, it provides a reproducible transcriptomic description of impaired inflammatory resolution that should be tested prospectively and experimentally.</p></div>
Links
Where it is published
- DOI doi.org/10.1371/journal.pone.0358983.t001 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Astronomy & Astrophysics · Biochemistry and cell biology · Biochemistry and cell biology · Biochemistry and cell biology · Biochemistry and cell biology · Biochemistry and cell biology · Biochemistry and cell biology · Bioinformatics and computational biology · Bioinformatics and computational biology · Bioinformatics and computational biology · Bioinformatics and computational biology · Bioinformatics and computational biology · Bioinformatics and computational biology · Chemistry · Chemistry · Computer Science & AI · Computer Science & AI · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Economics & Finance · Economics & Finance · Engineering · Engineering · Engineering · Engineering · Genetics · Genetics · Genetics · Genetics · Genetics · Genetics · Humanities · Humanities · Humanities · Humanities · Immunology · Immunology · Immunology · Immunology · Immunology · Immunology · Infectious diseases · Infectious diseases · Infectious diseases · Infectious diseases · Infectious diseases · Infectious diseases · Life Sciences · Life Sciences · Life Sciences · Life Sciences · Life Sciences · Life Sciences · Materials Science · Mathematics & Statistics · Medicine & Health · Medicine & Health · Medicine & Health · Medicine & Health · Medicine & Health · Medicine & Health · Ocean & Atmospheric Science · Psychology & Behavioral Science · Social Science · Social Science · Social Science · Social Science
- Inferred from text
- Longitudinal study 65% · Myocardial infarction 75% · RNA sequencing 65% · Single-cell RNA sequencing 65%
Provenance · 6 source records, 80 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/34025515 | 12 d ago | JSON v1 |
| HKU DataHub | oai:figshare.com:article/34025515 | 12 d ago | JSON v1 |
| DaYta Ya Rona | oai:figshare.com:article/34025515 | 12 d ago | JSON v1 |
| figshare | oai:figshare.com:article/34025515 | 11 d ago | JSON v1 |
| Loughborough Research Repository | oai:figshare.com:article/34025515 | 11 d ago | JSON v1 |
| UP Research Data Repository | oai:figshare.com:article/34025515 | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[disease].local:disease:myocardial-infarction | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].anzsrc:field:320211 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Infectious Diseases'] |
| concepts[field].anzsrc:field:320211 | mapping · dayta nwu ac za | vocabulary-mapper@1.0.0 | keywords['Infectious Diseases'] |
| concepts[field].anzsrc:field:320211 | mapping · repository lboro ac uk | vocabulary-mapper@1.0.0 | keywords['Infectious Diseases'] |
| concepts[field].anzsrc:field:320211 | mapping · researchdata up ac za | vocabulary-mapper@1.0.0 | keywords['Infectious Diseases'] |
| concepts[field].anzsrc:field:320211 | mapping · datahub hku hk | vocabulary-mapper@1.0.0 | keywords['Infectious Diseases'] |
| concepts[field].anzsrc:field:320211 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['Infectious Diseases'] |
| concepts[field].anzsrc:group:3101 | mapping · repository lboro ac uk | vocabulary-mapper@1.0.0 | keywords['Cell Biology'] |
| concepts[field].anzsrc:group:3101 | mapping · dayta nwu ac za | vocabulary-mapper@1.0.0 | keywords['Cell Biology'] |
| concepts[field].anzsrc:group:3101 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Cell Biology'] |
| concepts[field].anzsrc:group:3101 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['Cell Biology'] |
| concepts[field].anzsrc:group:3101 | mapping · researchdata up ac za | vocabulary-mapper@1.0.0 | keywords['Cell Biology'] |
| concepts[field].anzsrc:group:3101 | mapping · datahub hku hk | vocabulary-mapper@1.0.0 | keywords['Cell Biology'] |
| concepts[field].anzsrc:group:3102 | mapping · datahub hku hk | vocabulary-mapper@1.0.0 | keywords['Computational Biology'] |
| concepts[field].anzsrc:group:3102 | mapping · dayta nwu ac za | vocabulary-mapper@1.0.0 | keywords['Computational Biology'] |
| concepts[field].anzsrc:group:3102 | mapping · repository lboro ac uk | vocabulary-mapper@1.0.0 | keywords['Computational Biology'] |
| concepts[field].anzsrc:group:3102 | mapping · researchdata up ac za | vocabulary-mapper@1.0.0 | keywords['Computational Biology'] |
| concepts[field].anzsrc:group:3102 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Computational Biology'] |
| concepts[field].anzsrc:group:3102 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['Computational Biology'] |
| concepts[field].anzsrc:group:3105 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Genetics'] |
| concepts[field].anzsrc:group:3105 | mapping · repository lboro ac uk | vocabulary-mapper@1.0.0 | keywords['Genetics'] |
| concepts[field].anzsrc:group:3105 | mapping · datahub hku hk | vocabulary-mapper@1.0.0 | keywords['Genetics'] |
| concepts[field].anzsrc:group:3105 | mapping · researchdata up ac za | vocabulary-mapper@1.0.0 | keywords['Genetics'] |
| concepts[field].anzsrc:group:3105 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['Genetics'] |
| concepts[field].anzsrc:group:3105 | mapping · dayta nwu ac za | vocabulary-mapper@1.0.0 | keywords['Genetics'] |
| concepts[field].anzsrc:group:3204 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Immunology'] |
| concepts[field].anzsrc:group:3204 | mapping · researchdata up ac za | vocabulary-mapper@1.0.0 | keywords['Immunology'] |
| concepts[field].anzsrc:group:3204 | mapping · datahub hku hk | vocabulary-mapper@1.0.0 | keywords['Immunology'] |
| concepts[field].anzsrc:group:3204 | mapping · repository lboro ac uk | vocabulary-mapper@1.0.0 | keywords['Immunology'] |
| concepts[field].anzsrc:group:3204 | mapping · dayta nwu ac za | vocabulary-mapper@1.0.0 | keywords['Immunology'] |
| concepts[field].anzsrc:group:3204 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['Immunology'] |
| concepts[field].local:field:astronomy | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:chemistry | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:chemistry | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · dayta nwu ac za | connector:dayta_nwu_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · datahub hku hk | connector:datahub_hku_hk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:economics-finance | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:economics-finance | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:engineering | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:engineering | mapping · dayta nwu ac za | connector:dayta_nwu_ac_za@1.0.0 | |
| concepts[field].local:field:humanities | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:humanities | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · dayta nwu ac za | connector:dayta_nwu_ac_za@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · dayta nwu ac za | connector:dayta_nwu_ac_za@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · datahub hku hk | connector:datahub_hku_hk@1.0.0 | |
| concepts[field].local:field:materials-science | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:mathematics-statistics | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · dayta nwu ac za | connector:dayta_nwu_ac_za@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · datahub hku hk | connector:datahub_hku_hk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:ocean-atmospheric | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:social-science | mapping · dayta nwu ac za | connector:dayta_nwu_ac_za@1.0.0 | |
| concepts[field].local:field:social-science | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[method].local:method:longitudinal-study | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (65%) |
| concepts[modality].local:modality:rna-seq | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (65%) |
| concepts[modality].local:modality:single-cell-rna-seq | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/rights |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |