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pmid:38702721

Identification of sepsis-associated mitochondrial genes through RNA and single-cell sequencing approaches.

Shilin Li · Xiang Li · Sishi Jiang · Chenglin Wang · Yingchun Hu

BMC medical genomics · 2024 doi:10.1186/s12920-024-01891-x

Paper

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Data

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title
Identification of sepsis-associated mitochondrial genes through RNA and single-cell sequencing approaches.
doi
10.1186/s12920-024-01891-x
pmid
38702721
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Shilin Li
[1]
Xiang Li
[2]
Sishi Jiang
[3]
Chenglin Wang
[4]
Yingchun Hu
journal
BMC medical genomics
year
2024
abstract
BACKGROUND: Sepsis ranks among the most formidable clinical challenges, characterized by exorbitant treatment costs and substantial demands on healthcare resources. Mitochondrial dysfunction emerges as a pivotal risk factor in the pathogenesis of sepsis, underscoring the imperative to identify mitochondrial-related biomarkers. Such biomarkers are crucial for enhancing the accuracy of sepsis diagnostics and prognostication. METHODS: In this study, adhering to the SEPSIS 3.0 criteria, we collected peripheral blood within 24 h of admission from 20 sepsis patients at the ICU of the Southwest Medical University Affiliated Hospital and 10 healthy volunteers as a control group for RNA-seq. The RNA-seq data were utilized to identify differentially expressed RNAs. Concurrently, mitochondrial-associated genes (MiAGs) were retrieved from the MitoCarta3.0 database. The differentially expressed genes were intersected with MiAGs. The intersected genes were then subjected to GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses and core genes were filtered using the PPI (Protein-Protein Interaction) network. Subsequently, relevant sepsis datasets (GSE65682, GSE28750, GSE54514, GSE67652, GSE69528, GSE95233) were downloaded from the GEO (Gene Expression Omnibus) database to perform bioinformatic validation of these core genes. Survival analysis was conducted to assess the prognostic value of the core genes, while ROC (Receiver Operating Characteristic) curves determined their diagnostic value, and a meta-analysis confirmed the accuracy of the RNA-seq data. Finally, we collected 5 blood samples (2 normal controls (NC); 2 sepsis; 1 SIRS (Systemic Inflammatory Response Syndrome), and used single-cell sequencing to assess the expression levels of the core genes in the different blood cell types. RESULTS: Integrating high-throughput sequencing with bioinformatics, this study identified two mitochondrial genes (COX7B, NDUFA4) closely linked with sepsis prognosis. Survival analysis demonstrated that patients with lower expression levels of COX7B and NDUFA4 exhibited a higher day survival rate over 28 days, inversely correlating with sepsis mortality. ROC curves highlighted the significant sensitivity and specificity of both genes, with AUC values of 0.985 for COX7B and 0.988 for NDUFA4, respectively. Meta-analysis indicated significant overexpression of COX7B and NDUFA4 in the sepsis group in contrast to the normal group (P < 0.01). Additionally, single-cell RNA sequencing revealed predominant expression of these core genes in monocytes-macrophages, T cells, and B cells. CONCLUSION: The mitochondrial-associated genes (MiAGs) COX7B and NDUFA4 are intimately linked with the prognosis of sepsis, offering potential guidance for research into the mechanisms underlying sepsis.
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Data extraction and analysis were performed by Graphpad Prism 9.0, with the logrank test for statistical evaluation, setting p < 0.05 as the threshold for significance.
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sections[11] "Survival analysis" paragraph 2
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Data extraction and analysis were performed by Graphpad Prism 9.0, with the logrank test for statistical evaluation, setting p < 0.05 as the threshold for significance.
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We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.
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sections[30] "notes" paragraph 1
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We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.
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Patients with low expression of COX7B and NDUFA4 had higher 28-day survival rates than the high-expression group, with statistically significant differences (P < 0.05).
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Patients with low expression of COX7B and NDUFA4 exhibited higher 28-day survival rates compared to the high-expression group, showing a negative correlation with the survival rate of sepsis patients, with statistically significant differences (P < 0.05) (Fig. 5A-B).
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sections[21] "Discovery of mitochondrial genes associated with diagnosis and prognosis of sepsis" paragraph 2
quote
Patients with low expression of COX7B and NDUFA4 exhibited higher 28-day survival rates compared to the high-expression group, showing a negative correlation with the survival rate of sepsis patients, with statistically significant differences (P < 0.05) (Fig. 5A-B).
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Patients with low expression of COX7B and NDUFA4 had higher 28-day survival rates than the high-expression group, with statistically significant differences (P < 0.05).
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We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.
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[151]
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[154]
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[155]
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[156]
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[157]
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[158]
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[159]
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[160]
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[161]
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[162]
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[163]
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[164]
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[165]
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[166]
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[167]
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[168]
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[169]
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[170]
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[171]
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[172]
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[173]
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[174]
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[175]
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[176]
GSM1691861
[177]
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[178]
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[179]
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[180]
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[182]
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[183]
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[184]
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[185]
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[186]
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[187]
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[188]
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[190]
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[191]
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[192]
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[193]
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[194]
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[195]
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[196]
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[197]
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[198]
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[199]
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[200]
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[201]
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[202]
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[203]
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[204]
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[205]
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[206]
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[208]
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[209]
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[211]
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[215]
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[216]
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[221]
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[222]
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[223]
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[224]
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[225]
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[226]
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[227]
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[228]
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[229]
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[230]
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[231]
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[232]
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[233]
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[234]
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[235]
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[236]
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[237]
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[238]
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[239]
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[240]
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[241]
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[242]
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[243]
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[244]
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[245]
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[246]
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[247]
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[248]
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[249]
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[250]
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[251]
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[252]
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[253]
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[254]
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[255]
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[256]
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[257]
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[258]
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[259]
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[260]
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[261]
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[262]
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[263]
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[264]
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[265]
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[266]
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[267]
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[268]
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[269]
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[270]
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[271]
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[272]
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[273]
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[274]
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[275]
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[276]
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[277]
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[278]
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[279]
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[280]
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[281]
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[282]
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[283]
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[284]
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[285]
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[286]
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[287]
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[288]
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[289]
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[290]
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[291]
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[292]
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[293]
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[294]
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[295]
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[296]
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[297]
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[298]
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[299]
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[300]
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[301]
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[302]
GSM1691990
[303]
GSM1691991
[304]
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[305]
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[306]
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[307]
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[308]
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[309]
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[310]
GSM1691998
[311]
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[312]
GSM1692000
[313]
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[314]
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[315]
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[316]
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[317]
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[318]
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[319]
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[320]
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[321]
GSM1692009
[322]
GSM1692010
[323]
GSM1692011
[324]
GSM1692012
[325]
GSM1692015
[326]
GSM1692016
[327]
GSM1692017
[328]
GSM1692018
[329]
GSM1692019
[330]
GSM1692020
[331]
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[332]
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[333]
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[334]
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[335]
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[336]
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[337]
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[338]
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[339]
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[340]
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[341]
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[342]
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[349]
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[350]
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[359]
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[360]
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[361]
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[363]
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[364]
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[365]
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[366]
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[367]
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[368]
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[369]
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[370]
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[371]
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[372]
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[373]
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[374]
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[375]
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[376]
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[377]
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[378]
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[379]
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[380]
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[381]
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[382]
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[383]
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[384]
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[385]
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[386]
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[387]
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[388]
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[389]
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[390]
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[391]
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[392]
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[393]
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[394]
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[395]
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[396]
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[397]
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[398]
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[399]
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[400]
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[401]
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[402]
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[403]
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[404]
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[405]
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[406]
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[407]
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[408]
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[409]
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[410]
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[411]
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[412]
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[413]
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[414]
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[415]
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[416]
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[417]
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[418]
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[419]
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[420]
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[421]
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[422]
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[423]
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[424]
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[425]
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[426]
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[427]
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[428]
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[429]
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[430]
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[431]
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[432]
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[433]
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[434]
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[435]
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[436]
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[437]
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[438]
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[439]
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[440]
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[441]
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[442]
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[443]
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[444]
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[445]
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[446]
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[447]
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[448]
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[449]
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[450]
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[451]
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[452]
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[453]
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[454]
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[455]
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[456]
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[457]
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[458]
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[459]
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[460]
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[461]
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[462]
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[463]
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[464]
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[465]
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[466]
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[467]
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[468]
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[469]
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[470]
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[471]
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[472]
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[473]
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[474]
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[475]
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[476]
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[477]
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[478]
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[479]
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[480]
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[481]
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[482]
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[483]
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[484]
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[485]
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[486]
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[487]
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[488]
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[489]
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[490]
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[491]
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[492]
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[493]
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[494]
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[495]
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[496]
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[497]
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[499]
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[500]
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[501]
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[502]
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[503]
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[504]
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[505]
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[506]
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[507]
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[508]
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[509]
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[510]
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[511]
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[512]
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[513]
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[514]
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[515]
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[516]
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[517]
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[518]
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[519]
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[521]
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[522]
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[524]
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[525]
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[526]
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[527]
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[528]
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[529]
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[530]
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[531]
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[532]
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[533]
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[534]
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[535]
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[536]
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[537]
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[538]
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[539]
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[540]
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[541]
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[542]
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[543]
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[544]
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[545]
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[546]
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[547]
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[548]
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[549]
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[550]
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[551]
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[552]
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[553]
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[554]
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[555]
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[556]
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[557]
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[558]
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[559]
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[560]
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[561]
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[562]
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[563]
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[564]
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[565]
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[566]
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[567]
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[568]
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[569]
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[570]
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[571]
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[572]
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[573]
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[574]
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[575]
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[576]
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[577]
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[578]
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[579]
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[580]
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[581]
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[582]
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[583]
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[584]
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[585]
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[586]
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[587]
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[588]
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[589]
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[590]
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[591]
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[594]
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[595]
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[597]
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[599]
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[604]
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[605]
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[606]
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[607]
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[608]
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[609]
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[610]
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[611]
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[612]
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[613]
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[614]
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[615]
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[616]
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[617]
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[618]
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[619]
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[620]
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[621]
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[622]
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[623]
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[624]
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[625]
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[626]
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[627]
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[628]
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[629]
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[630]
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[631]
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[632]
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[633]
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[634]
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[635]
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[636]
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[637]
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[638]
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[639]
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[640]
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[641]
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[642]
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[643]
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[644]
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[645]
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[646]
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[647]
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[648]
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[649]
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[650]
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[651]
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[652]
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[653]
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[654]
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[655]
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[656]
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[657]
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[658]
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[659]
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[660]
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[661]
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[662]
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[663]
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[664]
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[665]
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[666]
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[667]
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[668]
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[669]
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[670]
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[671]
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[672]
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[673]
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[674]
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[675]
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[676]
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[677]
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[678]
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[679]
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[680]
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[681]
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[682]
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[683]
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[684]
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[685]
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[686]
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[687]
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[688]
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[689]
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[690]
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[691]
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[692]
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[693]
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[694]
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[695]
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[696]
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[697]
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[698]
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[699]
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[700]
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[701]
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[702]
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[703]
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[704]
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[705]
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[706]
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[707]
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[708]
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[709]
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[710]
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[711]
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[712]
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[713]
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[714]
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[715]
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[716]
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[717]
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[718]
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[719]
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[720]
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[721]
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[722]
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[723]
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[724]
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[725]
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[726]
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[727]
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[728]
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[729]
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[730]
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[731]
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[732]
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[733]
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[734]
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[735]
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[736]
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[737]
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[738]
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[739]
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[740]
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[741]
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[742]
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[743]
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[744]
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[745]
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[746]
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[747]
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[748]
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[749]
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[750]
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[751]
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[752]
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[753]
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[754]
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[755]
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[756]
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[757]
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[758]
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[759]
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[760]
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[761]
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[762]
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[763]
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[764]
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[765]
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[766]
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[767]
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[768]
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[769]
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[770]
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[771]
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[772]
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[773]
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[774]
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[775]
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[776]
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[777]
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[778]
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[779]
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[780]
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[781]
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[782]
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[783]
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[784]
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[785]
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[786]
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[787]
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[788]
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[789]
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[790]
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[791]
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[792]
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[793]
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[794]
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[795]
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[796]
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[797]
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[798]
GSM1692501
[799]
GSM1692502
[800]
GSM1692503
[801]
GSM1692504
sample_count
802
group_sizes
null
group_sizes_basis
null
platform
13667
taxon
Homo sapiens
title
Genome-wide blood transcriptional profiling in critically ill patients - MARS consortium
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[4] object · 15 fields
id
data:geo:GSE67652
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=GSE67652
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE67652
version
null
note
We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.
access_statement_kind
data_availability_statement
sample_accessions array · 24 items
[0]
GSM1652818
[1]
GSM1652819
[2]
GSM1652820
[3]
GSM1652821
[4]
GSM1652822
[5]
GSM1652823
[6]
GSM1652824
[7]
GSM1652825
[8]
GSM1652826
[9]
GSM1652827
[10]
GSM1652828
[11]
GSM1652829
[12]
GSM1652830
[13]
GSM1652831
[14]
GSM1652832
[15]
GSM1652833
[16]
GSM1652834
[17]
GSM1652835
[18]
GSM1652836
[19]
GSM1652837
[20]
GSM1652838
[21]
GSM1652839
[22]
GSM1652840
[23]
GSM1652841
sample_count
24
group_sizes
null
group_sizes_basis
null
platform
16699
taxon
Homo sapiens
title
Septic shock in advanced age: Transcriptome analysis reveals altered molecular signatures in neutrophil granulocytes
resolution_checked_at
2026-11-01T20:16:08Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[5] object · 15 fields
id
data:geo:GSE69528
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=GSE69528
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE69528
version
null
note
We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.
access_statement_kind
data_availability_statement
sample_accessions array · 138 items
[0]
GSM1702888
[1]
GSM1702889
[2]
GSM1702890
[3]
GSM1702891
[4]
GSM1702892
[5]
GSM1702893
[6]
GSM1702894
[7]
GSM1702895
[8]
GSM1702896
[9]
GSM1702897
[10]
GSM1702898
[11]
GSM1702899
[12]
GSM1702900
[13]
GSM1702901
[14]
GSM1702902
[15]
GSM1702903
[16]
GSM1702904
[17]
GSM1702905
[18]
GSM1702906
[19]
GSM1702907
[20]
GSM1702908
[21]
GSM1702909
[22]
GSM1702910
[23]
GSM1702911
[24]
GSM1702912
[25]
GSM1702913
[26]
GSM1702914
[27]
GSM1702915
[28]
GSM1702916
[29]
GSM1702917
[30]
GSM1702918
[31]
GSM1702919
[32]
GSM1702920
[33]
GSM1702921
[34]
GSM1702922
[35]
GSM1702923
[36]
GSM1702924
[37]
GSM1702925
[38]
GSM1702926
[39]
GSM1702927
[40]
GSM1702928
[41]
GSM1702929
[42]
GSM1702930
[43]
GSM1702931
[44]
GSM1702932
[45]
GSM1702933
[46]
GSM1702934
[47]
GSM1702935
[48]
GSM1702936
[49]
GSM1702937
[50]
GSM1702938
[51]
GSM1702939
[52]
GSM1702940
[53]
GSM1702941
[54]
GSM1702942
[55]
GSM1702943
[56]
GSM1702944
[57]
GSM1702945
[58]
GSM1702946
[59]
GSM1702947
[60]
GSM1702948
[61]
GSM1702949
[62]
GSM1702950
[63]
GSM1702951
[64]
GSM1702952
[65]
GSM1702953
[66]
GSM1702954
[67]
GSM1702955
[68]
GSM1702956
[69]
GSM1702957
[70]
GSM1702958
[71]
GSM1702959
[72]
GSM1702960
[73]
GSM1702961
[74]
GSM1702962
[75]
GSM1702963
[76]
GSM1702964
[77]
GSM1702965
[78]
GSM1702966
[79]
GSM1702967
[80]
GSM1702968
[81]
GSM1702969
[82]
GSM1702970
[83]
GSM1702971
[84]
GSM1702972
[85]
GSM1702973
[86]
GSM1702974
[87]
GSM1702975
[88]
GSM1702976
[89]
GSM1702977
[90]
GSM1702978
[91]
GSM1702979
[92]
GSM1702980
[93]
GSM1702981
[94]
GSM1702982
[95]
GSM1702983
[96]
GSM1702984
[97]
GSM1702985
[98]
GSM1702986
[99]
GSM1702987
[100]
GSM1702988
[101]
GSM1702989
[102]
GSM1702990
[103]
GSM1702991
[104]
GSM1702992
[105]
GSM1702993
[106]
GSM1702994
[107]
GSM1702995
[108]
GSM1702996
[109]
GSM1702997
[110]
GSM1702998
[111]
GSM1702999
[112]
GSM1703000
[113]
GSM1703001
[114]
GSM1703002
[115]
GSM1703003
[116]
GSM1703004
[117]
GSM1703005
[118]
GSM1703006
[119]
GSM1703007
[120]
GSM1703008
[121]
GSM1703009
[122]
GSM1703010
[123]
GSM1703011
[124]
GSM1703012
[125]
GSM1703013
[126]
GSM1703014
[127]
GSM1703015
[128]
GSM1703016
[129]
GSM1703017
[130]
GSM1703018
[131]
GSM1703019
[132]
GSM1703020
[133]
GSM1703021
[134]
GSM1703022
[135]
GSM1703023
[136]
GSM1703024
[137]
GSM1703025
sample_count
138
group_sizes
null
group_sizes_basis
null
platform
10558
taxon
Homo sapiens
title
A Blood Transcriptional Diagnostic Assay for Septicemic Melioidosis
resolution_checked_at
2026-11-01T20:16:08Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[6] object · 15 fields
id
data:geo:GSE95233
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=GSE95233
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE95233
version
null
note
We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.
access_statement_kind
data_availability_statement
sample_accessions array · 124 items
[0]
GSM2500349
[1]
GSM2500350
[2]
GSM2500351
[3]
GSM2500352
[4]
GSM2500353
[5]
GSM2500354
[6]
GSM2500355
[7]
GSM2500356
[8]
GSM2500357
[9]
GSM2500358
[10]
GSM2500359
[11]
GSM2500360
[12]
GSM2500361
[13]
GSM2500362
[14]
GSM2500363
[15]
GSM2500364
[16]
GSM2500365
[17]
GSM2500366
[18]
GSM2500367
[19]
GSM2500368
[20]
GSM2500369
[21]
GSM2500370
[22]
GSM2500371
[23]
GSM2500372
[24]
GSM2500373
[25]
GSM2500374
[26]
GSM2500375
[27]
GSM2500376
[28]
GSM2500377
[29]
GSM2500378
[30]
GSM2500379
[31]
GSM2500380
[32]
GSM2500381
[33]
GSM2500382
[34]
GSM2500383
[35]
GSM2500384
[36]
GSM2500385
[37]
GSM2500386
[38]
GSM2500387
[39]
GSM2500388
[40]
GSM2500389
[41]
GSM2500390
[42]
GSM2500391
[43]
GSM2500392
[44]
GSM2500393
[45]
GSM2500394
[46]
GSM2500395
[47]
GSM2500396
[48]
GSM2500397
[49]
GSM2500398
[50]
GSM2500399
[51]
GSM2500400
[52]
GSM2500401
[53]
GSM2500402
[54]
GSM2500403
[55]
GSM2500404
[56]
GSM2500405
[57]
GSM2500406
[58]
GSM2500407
[59]
GSM2500408
[60]
GSM2500409
[61]
GSM2500410
[62]
GSM2500411
[63]
GSM2500412
[64]
GSM2500413
[65]
GSM2500414
[66]
GSM2500415
[67]
GSM2500416
[68]
GSM2500417
[69]
GSM2500418
[70]
GSM2500419
[71]
GSM2500420
[72]
GSM2500421
[73]
GSM2500422
[74]
GSM2500423
[75]
GSM2500424
[76]
GSM2500425
[77]
GSM2500426
[78]
GSM2500427
[79]
GSM2500428
[80]
GSM2500429
[81]
GSM2500430
[82]
GSM2500431
[83]
GSM2500432
[84]
GSM2500433
[85]
GSM2500434
[86]
GSM2500435
[87]
GSM2500436
[88]
GSM2500437
[89]
GSM2500438
[90]
GSM2500439
[91]
GSM2500440
[92]
GSM2500441
[93]
GSM2500442
[94]
GSM2500443
[95]
GSM2500444
[96]
GSM2500445
[97]
GSM2500446
[98]
GSM2500447
[99]
GSM2500448
[100]
GSM2500449
[101]
GSM2500450
[102]
GSM2500451
[103]
GSM2500452
[104]
GSM2500453
[105]
GSM2500454
[106]
GSM2500455
[107]
GSM2500456
[108]
GSM2500457
[109]
GSM2500458
[110]
GSM2500459
[111]
GSM2500460
[112]
GSM2500461
[113]
GSM2500462
[114]
GSM2500463
[115]
GSM2500464
[116]
GSM2500465
[117]
GSM2500466
[118]
GSM2500467
[119]
GSM2500468
[120]
GSM2500469
[121]
GSM2500470
[122]
GSM2500471
[123]
GSM2500472
sample_count
124
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Fractalkine receptor CX3CR1 and leukocyte Ig-like receptor B2 LILRB2 are prognostic biomarkers in septic shock
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 12 items
[0]
asset:data:geo:GSE28750 not downloaded
[1]
asset:data:geo:GSE28750 licence not read
[2]
asset:data:geo:GSE54514 not downloaded
[3]
asset:data:geo:GSE54514 licence not read
[4]
asset:data:geo:GSE65682 not downloaded
[5]
asset:data:geo:GSE65682 licence not read
[6]
asset:data:geo:GSE67652 not downloaded
[7]
asset:data:geo:GSE67652 licence not read
[8]
asset:data:geo:GSE69528 not downloaded
[9]
asset:data:geo:GSE69528 licence not read
[10]
asset:data:geo:GSE95233 not downloaded
[11]
asset:data:geo:GSE95233 licence not read
analysis object · 4 fields
state
complete
methods array · 2 items
[0] object · 10 fields
id
method-1
family
survival_analysis
specific
log_rank_test
label
Survival analysis
dsl_operations array · 1 item
[0]
logrank_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
1
band
verbatim
quote
Data extraction and analysis were performed by Graphpad Prism 9.0, with the logrank test for statistical evaluation, setting p < 0.05 as the threshold for significance.
[1] object · 10 fields
id
method-2
family
network_analysis
specific
ppi_network
label
Network / pathway analysis
dsl_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
PPI
targets array · 1 item
[0] object · 8 fields
id
target-published-estimate
claim_id
claim-target-result
operation
build_graph
unit
p-value
tolerance
null
state
complete
note
The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold.
gaps array · 0 items
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 1 item
[0]
logrank_test
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: log_rank_test.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 6 items
[0]
GSE65682
[1]
GSE28750
[2]
GSE54514
[3]
GSE67652
[4]
GSE69528
[5]
GSE95233
resolved_accessions array · 6 items
[0]
GSE65682
[1]
GSE28750
[2]
GSE54514
[3]
GSE67652
[4]
GSE69528
[5]
GSE95233
statements array · 1 item
[0]
We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.
basis
The paper names GSE65682, GSE28750, GSE54514, GSE67652, GSE69528, GSE95233 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 1 item
[0]
logrank_test
assets array · 2 items
[0]
source:pmid:38702721
[1]
data:geo:GSE28750
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE28750
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T19:17:37Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
10310
completion_tokens
2398
cached_tokens
256
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:38702721
type
paper
label
pmid:38702721
[1] object · 3 fields
id
source:pmid:38702721
type
manuscript
label
PMID 38702721 full text
[2] object · 3 fields
id
data:geo:GSE28750
type
dataset
label
GEO GSE28750
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:38702721
type
paper
label
pmid:38702721
[1] object · 3 fields
id
source:pmid:38702721
type
manuscript
label
PMID 38702721 full text
[2] object · 3 fields
id
data:geo:GSE28750
type
dataset
label
GEO GSE28750
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-38702721
type
extraction
used array · 1 item
[0]
source:pmid:38702721
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE28750
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE28750
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
label
MiniMax literature reader
source_run_id
reading-sci-2026-11-01
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:38702721
parent_hash
sha256:ba55174ee837e4405d079d28fd540a2ab081ff827738b025d961e3ce331c38c9
fork_of
null
gaps array · 16 items
[0]
asset:data:geo:GSE28750 licence not read
[1]
asset:data:geo:GSE28750 not downloaded
[2]
asset:data:geo:GSE54514 licence not read
[3]
asset:data:geo:GSE54514 not downloaded
[4]
asset:data:geo:GSE65682 licence not read
[5]
asset:data:geo:GSE65682 not downloaded
[6]
asset:data:geo:GSE67652 licence not read
[7]
asset:data:geo:GSE67652 not downloaded
[8]
asset:data:geo:GSE69528 licence not read
[9]
asset:data:geo:GSE69528 not downloaded
[10]
asset:data:geo:GSE95233 licence not read
[11]
asset:data:geo:GSE95233 not downloaded
[12]
execution.not_run
[13]
review.human_critique
[14]
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38702721", "kind": "science-record", "revision": 4, "content_hash": "sha256:85f2db5ffb14ccc917723af425531d12097ad94f367caec6ad647b855e834ad1", "created_at": "2026-11-01T19:20:40Z", "updated_at": "2026-11-01T22:11:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of sepsis-associated mitochondrial genes through RNA and single-cell sequencing approaches.", "doi": "10.1186/s12920-024-01891-x", "pmid": "38702721", "authors": ["Shilin Li", "Xiang Li", "Sishi Jiang", "Chenglin Wang", "Yingchun Hu"], "journal": "BMC medical genomics", "year": 2024, "abstract": "BACKGROUND: Sepsis ranks among the most formidable clinical challenges, characterized by exorbitant treatment costs and substantial demands on healthcare resources. Mitochondrial dysfunction emerges as a pivotal risk factor in the pathogenesis of sepsis, underscoring the imperative to identify mitochondrial-related biomarkers. Such biomarkers are crucial for enhancing the accuracy of sepsis diagnostics and prognostication.\nMETHODS: In this study, adhering to the SEPSIS 3.0 criteria, we collected peripheral blood within 24 h of admission from 20 sepsis patients at the ICU of the Southwest Medical University Affiliated Hospital and 10 healthy volunteers as a control group for RNA-seq. The RNA-seq data were utilized to identify differentially expressed RNAs. Concurrently, mitochondrial-associated genes (MiAGs) were retrieved from the MitoCarta3.0 database. The differentially expressed genes were intersected with MiAGs. The intersected genes were then subjected to GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses and core genes were filtered using the PPI (Protein-Protein Interaction) network. Subsequently, relevant sepsis datasets (GSE65682, GSE28750, GSE54514, GSE67652, GSE69528, GSE95233) were downloaded from the GEO (Gene Expression Omnibus) database to perform bioinformatic validation of these core genes. Survival analysis was conducted to assess the prognostic value of the core genes, while ROC (Receiver Operating Characteristic) curves determined their diagnostic value, and a meta-analysis confirmed the accuracy of the RNA-seq data. Finally, we collected 5 blood samples (2 normal controls (NC); 2 sepsis; 1 SIRS (Systemic Inflammatory Response Syndrome), and used single-cell sequencing to assess the expression levels of the core genes in the different blood cell types.\nRESULTS: Integrating high-throughput sequencing with bioinformatics, this study identified two mitochondrial genes (COX7B, NDUFA4) closely linked with sepsis prognosis. Survival analysis demonstrated that patients with lower expression levels of COX7B and NDUFA4 exhibited a higher day survival rate over 28 days, inversely correlating with sepsis mortality. ROC curves highlighted the significant sensitivity and specificity of both genes, with AUC values of 0.985 for COX7B and 0.988 for NDUFA4, respectively. Meta-analysis indicated significant overexpression of COX7B and NDUFA4 in the sepsis group in contrast to the normal group (P < 0.01). Additionally, single-cell RNA sequencing revealed predominant expression of these core genes in monocytes-macrophages, T cells, and B cells.\nCONCLUSION: The mitochondrial-associated genes (MiAGs) COX7B and NDUFA4 are intimately linked with the prognosis of sepsis, offering potential guidance for research into the mechanisms underlying sepsis.", "claims": [{"id": "claim-method", "statement": "Data extraction and analysis were performed by Graphpad Prism 9.0, with the logrank test for statistical evaluation, setting p < 0.05 as the threshold for significance.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38702721", "locator": "sections[11] \"Survival analysis\" paragraph 2", "quote": "Data extraction and analysis were performed by Graphpad Prism 9.0, with the logrank test for statistical evaluation, setting p < 0.05 as the threshold for significance."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38702721", "locator": "sections[30] \"notes\" paragraph 1", "quote": "We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Patients with low expression of COX7B and NDUFA4 had higher 28-day survival rates than the high-expression group, with statistically significant differences (P < 0.05).", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "Patients with low expression of COX7B and NDUFA4 exhibited higher 28-day survival rates compared to the high-expression group, showing a negative correlation with the survival rate of sepsis patients, with statistically significant differences (P < 0.05) (Fig. 5A-B)."}, "evidence": [{"asset": "source:pmid:38702721", "locator": "sections[21] \"Discovery of mitochondrial genes associated with diagnosis and prognosis of sepsis\" paragraph 2", "quote": "Patients with low expression of COX7B and NDUFA4 exhibited higher 28-day survival rates compared to the high-expression group, showing a negative correlation with the survival rate of sepsis patients, with statistically significant differences (P < 0.05) (Fig. 5A-B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Patients with low expression of COX7B and NDUFA4 had higher 28-day survival rates than the high-expression group, with statistically significant differences (P < 0.05).", "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:38702721"], "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:GSE28750"], "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:GSE28750"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38702721", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12920-024-01891-x", "sha256": "sha256:b442363f6bd938419f2ae895784a22cae8d471d0c9b08817f3a21acf58577c63", "bytes": 38980, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12920-024-01891-x", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11067249", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE28750", "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=GSE28750", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE28750", "version": null, "note": "We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM712478", "GSM712479", "GSM712480", "GSM712481", "GSM712482", "GSM712483", "GSM712484", "GSM712485", "GSM712486", "GSM712487", "GSM712488", "GSM712489", "GSM712490", "GSM712491", "GSM712492", "GSM712493", "GSM712494", "GSM712495", "GSM712496", "GSM712497", "GSM712498", "GSM712499", "GSM712500", "GSM712501", "GSM712502", "GSM712503", "GSM712504", "GSM712505", "GSM712506", "GSM712507", "GSM712508", "GSM712509", "GSM712510", "GSM712511", "GSM712512", "GSM712513", "GSM712514", "GSM712515", "GSM712516", "GSM712517", "GSM712518"], "sample_count": 41, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Development and Validation of a Novel Molecular Biomarker Diagnostic Test for the Early Detection of Sepsis", "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:GSE54514", "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=GSE54514", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE54514", "version": null, "note": "We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. 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"GSM1692463", "GSM1692465", "GSM1692466", "GSM1692467", "GSM1692468", "GSM1692469", "GSM1692470", "GSM1692471", "GSM1692472", "GSM1692473", "GSM1692474", "GSM1692475", "GSM1692476", "GSM1692477", "GSM1692478", "GSM1692479", "GSM1692480", "GSM1692481", "GSM1692482", "GSM1692483", "GSM1692484", "GSM1692485", "GSM1692487", "GSM1692488", "GSM1692489", "GSM1692490", "GSM1692491", "GSM1692492", "GSM1692493", "GSM1692494", "GSM1692495", "GSM1692496", "GSM1692497", "GSM1692498", "GSM1692499", "GSM1692501", "GSM1692502", "GSM1692503", "GSM1692504"], "sample_count": 802, "group_sizes": null, "group_sizes_basis": null, "platform": "13667", "taxon": "Homo sapiens", "title": "Genome-wide blood transcriptional profiling in critically ill patients - MARS consortium", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE67652", "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=GSE67652", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE67652", "version": null, "note": "We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1652818", "GSM1652819", "GSM1652820", "GSM1652821", "GSM1652822", "GSM1652823", "GSM1652824", "GSM1652825", "GSM1652826", "GSM1652827", "GSM1652828", "GSM1652829", "GSM1652830", "GSM1652831", "GSM1652832", "GSM1652833", "GSM1652834", "GSM1652835", "GSM1652836", "GSM1652837", "GSM1652838", "GSM1652839", "GSM1652840", "GSM1652841"], "sample_count": 24, "group_sizes": null, "group_sizes_basis": null, "platform": "16699", "taxon": "Homo sapiens", "title": "Septic shock in advanced age: Transcriptome analysis reveals altered molecular signatures in neutrophil granulocytes", "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:GSE69528", "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=GSE69528", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE69528", "version": null, "note": "We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1702888", "GSM1702889", "GSM1702890", "GSM1702891", "GSM1702892", "GSM1702893", "GSM1702894", "GSM1702895", "GSM1702896", "GSM1702897", "GSM1702898", "GSM1702899", "GSM1702900", "GSM1702901", "GSM1702902", "GSM1702903", "GSM1702904", "GSM1702905", "GSM1702906", "GSM1702907", "GSM1702908", "GSM1702909", "GSM1702910", "GSM1702911", "GSM1702912", "GSM1702913", "GSM1702914", "GSM1702915", "GSM1702916", "GSM1702917", "GSM1702918", "GSM1702919", "GSM1702920", "GSM1702921", "GSM1702922", "GSM1702923", "GSM1702924", "GSM1702925", "GSM1702926", "GSM1702927", "GSM1702928", "GSM1702929", "GSM1702930", "GSM1702931", "GSM1702932", "GSM1702933", "GSM1702934", "GSM1702935", "GSM1702936", "GSM1702937", "GSM1702938", "GSM1702939", "GSM1702940", "GSM1702941", "GSM1702942", "GSM1702943", "GSM1702944", "GSM1702945", "GSM1702946", "GSM1702947", "GSM1702948", "GSM1702949", "GSM1702950", "GSM1702951", "GSM1702952", "GSM1702953", "GSM1702954", "GSM1702955", "GSM1702956", "GSM1702957", "GSM1702958", "GSM1702959", "GSM1702960", "GSM1702961", "GSM1702962", "GSM1702963", "GSM1702964", "GSM1702965", "GSM1702966", "GSM1702967", "GSM1702968", "GSM1702969", "GSM1702970", "GSM1702971", "GSM1702972", "GSM1702973", "GSM1702974", "GSM1702975", "GSM1702976", "GSM1702977", "GSM1702978", "GSM1702979", "GSM1702980", "GSM1702981", "GSM1702982", "GSM1702983", "GSM1702984", "GSM1702985", "GSM1702986", "GSM1702987", "GSM1702988", "GSM1702989", "GSM1702990", "GSM1702991", "GSM1702992", "GSM1702993", "GSM1702994", "GSM1702995", "GSM1702996", "GSM1702997", "GSM1702998", "GSM1702999", "GSM1703000", "GSM1703001", "GSM1703002", "GSM1703003", "GSM1703004", "GSM1703005", "GSM1703006", "GSM1703007", "GSM1703008", "GSM1703009", "GSM1703010", "GSM1703011", "GSM1703012", "GSM1703013", "GSM1703014", "GSM1703015", "GSM1703016", "GSM1703017", "GSM1703018", "GSM1703019", "GSM1703020", "GSM1703021", "GSM1703022", "GSM1703023", "GSM1703024", "GSM1703025"], "sample_count": 138, "group_sizes": null, "group_sizes_basis": null, "platform": "10558", "taxon": "Homo sapiens", "title": "A Blood Transcriptional Diagnostic Assay for Septicemic Melioidosis", "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:GSE95233", "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=GSE95233", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE95233", "version": null, "note": "We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. Our study used six datasets (GSE65682, GSE95233, GSE28750, GSE54514, GSE67652, and GSE69528), with all RNA sequencing data and clinical information coming from the GEO database.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. 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A run that reproduces the effect is judged against this threshold."}], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["logrank_test"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: log_rank_test."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE65682", "GSE28750", "GSE54514", "GSE67652", "GSE69528", "GSE95233"], "resolved_accessions": ["GSE65682", "GSE28750", "GSE54514", "GSE67652", "GSE69528", "GSE95233"], "statements": ["We intend to share individual deidentified participant data. Peripheral blood RNA sequencing data from 20 patients with sepsis and 10 NC are available in the China National GeneBank DataBase (CNGBdb) and can be found below: https://db.cngb.org/, under the accession: CNP0002611, you can access it now and it’s valid forever. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["logrank_test"], "assets": ["source:pmid:38702721", "data:geo:GSE28750"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE28750"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:17:37Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10310, "completion_tokens": 2398, "cached_tokens": 256}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:38702721", "type": "paper", "label": "pmid:38702721"}, {"id": "source:pmid:38702721", "type": "manuscript", "label": "PMID 38702721 full text"}, {"id": "data:geo:GSE28750", "type": "dataset", "label": "GEO GSE28750"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38702721", "type": "paper", "label": "pmid:38702721"}, {"id": "source:pmid:38702721", "type": "manuscript", "label": "PMID 38702721 full text"}, {"id": "data:geo:GSE28750", "type": "dataset", "label": "GEO GSE28750"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38702721", "type": "extraction", "used": ["source:pmid:38702721"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE28750", "type": "accession_resolution", "used": ["data:geo:GSE28750"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:38702721", "parent_hash": "sha256:ba55174ee837e4405d079d28fd540a2ab081ff827738b025d961e3ce331c38c9", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE28750 licence not read", "asset:data:geo:GSE28750 not downloaded", "asset:data:geo:GSE54514 licence not read", "asset:data:geo:GSE54514 not downloaded", "asset:data:geo:GSE65682 licence not read", "asset:data:geo:GSE65682 not downloaded", "asset:data:geo:GSE67652 licence not read", "asset:data:geo:GSE67652 not downloaded", "asset:data:geo:GSE69528 licence not read", "asset:data:geo:GSE69528 not downloaded", "asset:data:geo:GSE95233 licence not read", "asset:data:geo:GSE95233 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["10 healthy volunteers as a control group for RNA-seq. The RNA-seq data were utilized to identify", "lunteers as a control group for RNA-seq. The RNA-seq data were utilized to identify differentiall", "meta-analysis confirmed the accuracy of the RNA-seq data. Finally, we collected 5 blood samples", "group (P < 0.01). Additionally, single-cell RNA sequencing revealed predominant expression of these cor"], "order_basis": "paper_text_order:745", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["o the normal group (P < 0.01). 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