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pmid:35045818

Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis.

Yonghua Fan · Qiufeng Han · Jinfeng Li · Gaige Ye · Xianjing Zhang · Tengxiao Xu · Huaqing Li

BMC infectious diseases · 2022 doi:10.1186/s12879-022-07056-4

Paper

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Experiment

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Data

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title
Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis.
doi
10.1186/s12879-022-07056-4
pmid
35045818
authors array · 7 items
[0]
Yonghua Fan
[1]
Qiufeng Han
[2]
Jinfeng Li
[3]
Gaige Ye
[4]
Xianjing Zhang
[5]
Tengxiao Xu
[6]
Huaqing Li
journal
BMC infectious diseases
year
2022
abstract
BACKGROUND: Sepsis is an inflammatory response caused by infection with pathogenic microorganisms. The body shock caused by it is called septic shock. In view of this, we aimed to identify potential diagnostic gene biomarkers of the disease. MATERIAL AND METHODS: Firstly, mRNAs expression data sets of septic shock were retrieved and downloaded from the GEO (Gene Expression Omnibus) database for differential expression analysis. Functional enrichment analysis was then used to identify the biological function of DEmRNAs (differentially expressed mRNAs). Machine learning analysis was used to determine the diagnostic gene biomarkers for septic shock. Thirdly, RT-PCR (real-time polymerase chain reaction) verification was performed. Lastly, GSE65682 data set was utilized to further perform diagnostic and prognostic analysis of identified superlative diagnostic gene biomarkers. RESULTS: A total of 843 DEmRNAs, including 458 up-regulated and 385 down-regulated DEmRNAs were obtained in septic shock. 15 superlative diagnostic gene biomarkers (such as RAB13, KIF1B, CLEC5A, FCER1A, CACNA2D3, DUSP3, HMGN3, MGST1 and ARHGEF18) for septic shock were identified by machine learning analysis. RF (random forests), SVM (support vector machine) and DT (decision tree) models were used to construct classification models. The accuracy of the DT, SVM and RF models were very high. Interestingly, the RF model had the highest accuracy. It is worth mentioning that ARHGEF18 and FCER1A were related to survival. CACNA2D3 and DUSP3 participated in MAPK signaling pathway to regulate septic shock. CONCLUSION: Identified diagnostic gene biomarkers may be helpful in the diagnosis and therapy of patients with septic shock.
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In this study, Limma and metaMA packages were executed for identification of the DEmRNAs.
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In this study, Limma and metaMA packages were executed for identification of the DEmRNAs.
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All data generated or analyzed during this study are included in this published article. The data sets (GSE4607, GSE13904, GSE26378, GSE26440, GSE65682 and GSE95233) analysed during the current study are available in the GEO (Gene Expression Omnibus) database, persistent accessible web link to database is https://www.ncbi.nlm.nih.gov/geo/.
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Machine-learning analysis identified 15 diagnostic gene biomarkers for septic shock.
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sections[13] "Identification of superlative diagnostic gene biomarkers" paragraph 2
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Therefore, the first 15 DEmRNAs (KLRF1, UPP1, RAB13, KIF1B, CLEC5A, NARF, DUSP3, FCER1A, CACNA2D3, HMGN3, ECRP, HDAC4, LHFPL2, MGST1 and ARHGEF18) were selected as the superlative diagnostic gene biomarkers.
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Machine-learning analysis identified 15 diagnostic gene biomarkers for septic shock.
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[56]
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[57]
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[58]
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[59]
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[60]
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[61]
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[62]
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[63]
GSM648653
[64]
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[65]
GSM648655
[66]
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[67]
GSM648657
[68]
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[69]
GSM648659
[70]
GSM648660
[71]
GSM648661
[72]
GSM648662
[73]
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[74]
GSM648664
[75]
GSM648665
[76]
GSM648666
[77]
GSM648667
[78]
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[79]
GSM648669
[80]
GSM648670
[81]
GSM648671
[82]
GSM648672
[83]
GSM648673
[84]
GSM648674
[85]
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[86]
GSM648676
[87]
GSM648677
[88]
GSM648678
[89]
GSM648679
[90]
GSM648680
[91]
GSM648681
[92]
GSM648682
[93]
GSM648683
[94]
GSM648684
[95]
GSM648685
[96]
GSM648686
[97]
GSM648687
[98]
GSM648688
[99]
GSM648689
[100]
GSM648690
[101]
GSM648691
[102]
GSM648692
[103]
GSM648693
[104]
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[105]
GSM648695
[106]
GSM648696
[107]
GSM648697
[108]
GSM648698
[109]
GSM648699
[110]
GSM648700
[111]
GSM648701
[112]
GSM648702
[113]
GSM648703
[114]
GSM648704
[115]
GSM648705
[116]
GSM648706
[117]
GSM648707
[118]
GSM648708
[119]
GSM648709
[120]
GSM648710
[121]
GSM648711
[122]
GSM648712
[123]
GSM648713
[124]
GSM648714
[125]
GSM648715
[126]
GSM648716
[127]
GSM648717
[128]
GSM648718
[129]
GSM648719
sample_count
130
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Expression data for derivation of septic shock subgroups
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[4] object · 15 fields
id
data:geo:GSE4607
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raw
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geo-series
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application/vnd.ncbi.geo-series+json
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https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4607
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null
bytes
null
access
open
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resolved
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null
reader_status
reader_required
identifier
GSE4607
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null
note
All data generated or analyzed during this study are included in this published article. The data sets (GSE4607, GSE13904, GSE26378, GSE26440, GSE65682 and GSE95233) analysed during the current study are available in the GEO (Gene Expression Omnibus) database, persistent accessible web link to database is https://www.ncbi.nlm.nih.gov/geo/.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
All data generated or analyzed during this study are included in this published article. The data sets (GSE4607, GSE13904, GSE26378, GSE26440, GSE65682 and GSE95233) analysed during the current study are available in the GEO (Gene Expression Omnibus) database, persistent accessible web link to database is https://www.ncbi.nlm.nih.gov/geo/.
access_statement_kind
data_availability_statement
sample_accessions array · 123 items
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GSM102961
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[2]
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GSM102964
[4]
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[5]
GSM102966
[6]
GSM102967
[7]
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[8]
GSM102969
[9]
GSM102970
[10]
GSM102971
[11]
GSM102972
[12]
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[13]
GSM102974
[14]
GSM102975
[15]
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[16]
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[18]
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[19]
GSM102980
[20]
GSM102981
[21]
GSM102982
[22]
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[23]
GSM102984
[24]
GSM102985
[25]
GSM102986
[26]
GSM102987
[27]
GSM102988
[28]
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[29]
GSM102990
[30]
GSM102991
[31]
GSM102992
[32]
GSM102993
[33]
GSM102994
[34]
GSM102995
[35]
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[36]
GSM102997
[37]
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[38]
GSM102999
[39]
GSM103000
[40]
GSM103001
[41]
GSM103002
[42]
GSM103003
[43]
GSM103004
[44]
GSM103005
[45]
GSM103006
[46]
GSM103007
[47]
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[48]
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[49]
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[50]
GSM103011
[51]
GSM103012
[52]
GSM103013
[53]
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[54]
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[55]
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[56]
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[57]
GSM103018
[58]
GSM103019
[59]
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[60]
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[61]
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[62]
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[63]
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[64]
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[65]
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[66]
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[67]
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[68]
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[69]
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[70]
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[71]
GSM103032
[72]
GSM103033
[73]
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[74]
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[75]
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[76]
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[77]
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[79]
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[80]
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[86]
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[91]
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[94]
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[96]
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[97]
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[98]
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[99]
GSM103060
[100]
GSM103061
[101]
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[102]
GSM103063
[103]
GSM103064
[104]
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[105]
GSM103066
[106]
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[107]
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[108]
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[109]
GSM103070
[110]
GSM103071
[111]
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[112]
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[113]
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[114]
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[115]
GSM103076
[116]
GSM103077
[117]
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[118]
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[119]
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[120]
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[121]
GSM103082
[122]
GSM103083
sample_count
123
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Systemic inflammatory response syndrome and septic shock
resolution_checked_at
2026-11-01T17:30:42Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[5] object · 15 fields
id
data:geo:GSE65682
role
raw
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geo-series
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application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65682
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null
bytes
null
access
open
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resolved
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null
reader_status
reader_required
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GSE65682
version
null
note
All data generated or analyzed during this study are included in this published article. The data sets (GSE4607, GSE13904, GSE26378, GSE26440, GSE65682 and GSE95233) analysed during the current study are available in the GEO (Gene Expression Omnibus) database, persistent accessible web link to database is https://www.ncbi.nlm.nih.gov/geo/.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
All data generated or analyzed during this study are included in this published article. The data sets (GSE4607, GSE13904, GSE26378, GSE26440, GSE65682 and GSE95233) analysed during the current study are available in the GEO (Gene Expression Omnibus) database, persistent accessible web link to database is https://www.ncbi.nlm.nih.gov/geo/.
access_statement_kind
data_availability_statement
sample_accessions array · 802 items
[0]
GSM1602801
[1]
GSM1602802
[2]
GSM1602803
[3]
GSM1602804
[4]
GSM1602805
[5]
GSM1602806
[6]
GSM1602807
[7]
GSM1602808
[8]
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[9]
GSM1602810
[10]
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[11]
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[12]
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[13]
GSM1602814
[14]
GSM1602815
[15]
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[16]
GSM1602817
[17]
GSM1602818
[18]
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[19]
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[20]
GSM1602821
[21]
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[22]
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[23]
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[24]
GSM1602825
[25]
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[26]
GSM1602827
[27]
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[28]
GSM1602829
[29]
GSM1602830
[30]
GSM1602831
[31]
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[32]
GSM1602833
[33]
GSM1602834
[34]
GSM1602835
[35]
GSM1602836
[36]
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[37]
GSM1602838
[38]
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[39]
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[40]
GSM1602841
[41]
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[42]
GSM1602843
[43]
GSM1602844
[44]
GSM1602845
[45]
GSM1602846
[46]
GSM1602847
[47]
GSM1602848
[48]
GSM1602849
[49]
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[50]
GSM1602851
[51]
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[52]
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[53]
GSM1602854
[54]
GSM1602855
[55]
GSM1602856
[56]
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[57]
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[58]
GSM1602859
[59]
GSM1602860
[60]
GSM1602861
[61]
GSM1602862
[62]
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[63]
GSM1602864
[64]
GSM1602865
[65]
GSM1602866
[66]
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[67]
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[68]
GSM1602869
[69]
GSM1602870
[70]
GSM1602871
[71]
GSM1602872
[72]
GSM1602873
[73]
GSM1602874
[74]
GSM1602875
[75]
GSM1602876
[76]
GSM1602877
[77]
GSM1602878
[78]
GSM1602879
[79]
GSM1602880
[80]
GSM1602881
[81]
GSM1602882
[82]
GSM1602883
[83]
GSM1602884
[84]
GSM1602885
[85]
GSM1602886
[86]
GSM1602887
[87]
GSM1602888
[88]
GSM1602889
[89]
GSM1602890
[90]
GSM1602891
[91]
GSM1602892
[92]
GSM1602893
[93]
GSM1602894
[94]
GSM1602895
[95]
GSM1602896
[96]
GSM1602897
[97]
GSM1602898
[98]
GSM1602899
[99]
GSM1602900
[100]
GSM1602901
[101]
GSM1602902
[102]
GSM1602903
[103]
GSM1602904
[104]
GSM1602905
[105]
GSM1602906
[106]
GSM1602907
[107]
GSM1602908
[108]
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[109]
GSM1602910
[110]
GSM1602911
[111]
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[112]
GSM1602913
[113]
GSM1602914
[114]
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[115]
GSM1602916
[116]
GSM1602917
[117]
GSM1602918
[118]
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[119]
GSM1602920
[120]
GSM1602921
[121]
GSM1602922
[122]
GSM1602923
[123]
GSM1602924
[124]
GSM1602925
[125]
GSM1602926
[126]
GSM1602927
[127]
GSM1602928
[128]
GSM1602929
[129]
GSM1602930
[130]
GSM1602931
[131]
GSM1602932
[132]
GSM1602933
[133]
GSM1602934
[134]
GSM1602935
[135]
GSM1602936
[136]
GSM1602937
[137]
GSM1602938
[138]
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[139]
GSM1602940
[140]
GSM1602941
[141]
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[142]
GSM1602943
[143]
GSM1602944
[144]
GSM1602945
[145]
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[146]
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[147]
GSM1602948
[148]
GSM1602949
[149]
GSM1602950
[150]
GSM1602951
[151]
GSM1602952
[152]
GSM1602953
[153]
GSM1602954
[154]
GSM1602955
[155]
GSM1602956
[156]
GSM1602957
[157]
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[158]
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[159]
GSM1602960
[160]
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[161]
GSM1602962
[162]
GSM1602963
[163]
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[164]
GSM1602965
[165]
GSM1602966
[166]
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[167]
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[168]
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[169]
GSM1602970
[170]
GSM1602971
[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]
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[177]
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[178]
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[179]
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[180]
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[181]
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[182]
GSM1691867
[183]
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[184]
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[185]
GSM1691870
[186]
GSM1691871
[187]
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[188]
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[189]
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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]
GSM1691882
[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]
GSM1691890
[205]
GSM1691891
[206]
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[207]
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[208]
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[209]
GSM1691895
[210]
GSM1691896
[211]
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[212]
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[213]
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[214]
GSM1691900
[215]
GSM1691901
[216]
GSM1691902
[217]
GSM1691903
[218]
GSM1691904
[219]
GSM1691905
[220]
GSM1691906
[221]
GSM1691907
[222]
GSM1691908
[223]
GSM1691909
[224]
GSM1691910
[225]
GSM1691911
[226]
GSM1691912
[227]
GSM1691913
[228]
GSM1691914
[229]
GSM1691915
[230]
GSM1691916
[231]
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[232]
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[233]
GSM1691919
[234]
GSM1691920
[235]
GSM1691921
[236]
GSM1691922
[237]
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[238]
GSM1691924
[239]
GSM1691925
[240]
GSM1691926
[241]
GSM1691927
[242]
GSM1691928
[243]
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[244]
GSM1691930
[245]
GSM1691932
[246]
GSM1691933
[247]
GSM1691934
[248]
GSM1691935
[249]
GSM1691936
[250]
GSM1691937
[251]
GSM1691938
[252]
GSM1691939
[253]
GSM1691940
[254]
GSM1691941
[255]
GSM1691942
[256]
GSM1691943
[257]
GSM1691944
[258]
GSM1691945
[259]
GSM1691946
[260]
GSM1691947
[261]
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[262]
GSM1691949
[263]
GSM1691950
[264]
GSM1691951
[265]
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[266]
GSM1691953
[267]
GSM1691954
[268]
GSM1691955
[269]
GSM1691957
[270]
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[271]
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[272]
GSM1691960
[273]
GSM1691961
[274]
GSM1691962
[275]
GSM1691963
[276]
GSM1691964
[277]
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[278]
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[279]
GSM1691967
[280]
GSM1691968
[281]
GSM1691969
[282]
GSM1691970
[283]
GSM1691971
[284]
GSM1691972
[285]
GSM1691973
[286]
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[287]
GSM1691975
[288]
GSM1691976
[289]
GSM1691977
[290]
GSM1691978
[291]
GSM1691979
[292]
GSM1691980
[293]
GSM1691981
[294]
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[295]
GSM1691983
[296]
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[297]
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[298]
GSM1691986
[299]
GSM1691987
[300]
GSM1691988
[301]
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[302]
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[303]
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[304]
GSM1691992
[305]
GSM1691993
[306]
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[307]
GSM1691995
[308]
GSM1691996
[309]
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[310]
GSM1691998
[311]
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[312]
GSM1692000
[313]
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[314]
GSM1692002
[315]
GSM1692003
[316]
GSM1692004
[317]
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[318]
GSM1692006
[319]
GSM1692007
[320]
GSM1692008
[321]
GSM1692009
[322]
GSM1692010
[323]
GSM1692011
[324]
GSM1692012
[325]
GSM1692015
[326]
GSM1692016
[327]
GSM1692017
[328]
GSM1692018
[329]
GSM1692019
[330]
GSM1692020
[331]
GSM1692021
[332]
GSM1692022
[333]
GSM1692023
[334]
GSM1692024
[335]
GSM1692025
[336]
GSM1692026
[337]
GSM1692027
[338]
GSM1692028
[339]
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[340]
GSM1692030
[341]
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[342]
GSM1692033
[343]
GSM1692034
[344]
GSM1692035
[345]
GSM1692036
[346]
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[347]
GSM1692038
[348]
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[349]
GSM1692040
[350]
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[351]
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[352]
GSM1692043
[353]
GSM1692044
[354]
GSM1692045
[355]
GSM1692046
[356]
GSM1692047
[357]
GSM1692048
[358]
GSM1692049
[359]
GSM1692050
[360]
GSM1692051
[361]
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[362]
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[363]
GSM1692054
[364]
GSM1692055
[365]
GSM1692056
[366]
GSM1692057
[367]
GSM1692058
[368]
GSM1692059
[369]
GSM1692060
[370]
GSM1692061
[371]
GSM1692062
[372]
GSM1692063
[373]
GSM1692064
[374]
GSM1692065
[375]
GSM1692066
[376]
GSM1692067
[377]
GSM1692068
[378]
GSM1692069
[379]
GSM1692070
[380]
GSM1692071
[381]
GSM1692072
[382]
GSM1692073
[383]
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[384]
GSM1692075
[385]
GSM1692077
[386]
GSM1692078
[387]
GSM1692079
[388]
GSM1692080
[389]
GSM1692081
[390]
GSM1692082
[391]
GSM1692083
[392]
GSM1692084
[393]
GSM1692085
[394]
GSM1692086
[395]
GSM1692087
[396]
GSM1692088
[397]
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[398]
GSM1692090
[399]
GSM1692091
[400]
GSM1692092
[401]
GSM1692093
[402]
GSM1692094
[403]
GSM1692095
[404]
GSM1692096
[405]
GSM1692097
[406]
GSM1692098
[407]
GSM1692099
[408]
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[409]
GSM1692101
[410]
GSM1692102
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GSM1692103
[412]
GSM1692104
[413]
GSM1692105
[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]
GSM1692128
[436]
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[437]
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[438]
GSM1692131
[439]
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[440]
GSM1692133
[441]
GSM1692135
[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]
GSM1692146
[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]
GSM1692176
[483]
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[484]
GSM1692178
[485]
GSM1692179
[486]
GSM1692180
[487]
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[488]
GSM1692182
[489]
GSM1692183
[490]
GSM1692184
[491]
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[492]
GSM1692186
[493]
GSM1692187
[494]
GSM1692188
[495]
GSM1692189
[496]
GSM1692190
[497]
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[498]
GSM1692192
[499]
GSM1692193
[500]
GSM1692194
[501]
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[502]
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[503]
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[504]
GSM1692198
[505]
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[506]
GSM1692200
[507]
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[508]
GSM1692202
[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]
GSM1692209
[516]
GSM1692210
[517]
GSM1692211
[518]
GSM1692212
[519]
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[520]
GSM1692214
[521]
GSM1692215
[522]
GSM1692216
[523]
GSM1692217
[524]
GSM1692218
[525]
GSM1692219
[526]
GSM1692220
[527]
GSM1692221
[528]
GSM1692222
[529]
GSM1692223
[530]
GSM1692224
[531]
GSM1692225
[532]
GSM1692226
[533]
GSM1692227
[534]
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[535]
GSM1692229
[536]
GSM1692230
[537]
GSM1692231
[538]
GSM1692232
[539]
GSM1692233
[540]
GSM1692234
[541]
GSM1692235
[542]
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[543]
GSM1692238
[544]
GSM1692239
[545]
GSM1692240
[546]
GSM1692241
[547]
GSM1692242
[548]
GSM1692243
[549]
GSM1692244
[550]
GSM1692245
[551]
GSM1692246
[552]
GSM1692247
[553]
GSM1692248
[554]
GSM1692249
[555]
GSM1692250
[556]
GSM1692251
[557]
GSM1692252
[558]
GSM1692253
[559]
GSM1692254
[560]
GSM1692255
[561]
GSM1692256
[562]
GSM1692257
[563]
GSM1692258
[564]
GSM1692259
[565]
GSM1692260
[566]
GSM1692261
[567]
GSM1692262
[568]
GSM1692263
[569]
GSM1692264
[570]
GSM1692265
[571]
GSM1692266
[572]
GSM1692267
[573]
GSM1692268
[574]
GSM1692269
[575]
GSM1692270
[576]
GSM1692271
[577]
GSM1692272
[578]
GSM1692273
[579]
GSM1692274
[580]
GSM1692275
[581]
GSM1692276
[582]
GSM1692277
[583]
GSM1692278
[584]
GSM1692279
[585]
GSM1692280
[586]
GSM1692281
[587]
GSM1692282
[588]
GSM1692283
[589]
GSM1692284
[590]
GSM1692286
[591]
GSM1692287
[592]
GSM1692288
[593]
GSM1692289
[594]
GSM1692290
[595]
GSM1692291
[596]
GSM1692292
[597]
GSM1692293
[598]
GSM1692294
[599]
GSM1692295
[600]
GSM1692296
[601]
GSM1692297
[602]
GSM1692298
[603]
GSM1692299
[604]
GSM1692300
[605]
GSM1692301
[606]
GSM1692302
[607]
GSM1692303
[608]
GSM1692304
[609]
GSM1692305
[610]
GSM1692306
[611]
GSM1692307
[612]
GSM1692308
[613]
GSM1692309
[614]
GSM1692310
[615]
GSM1692311
[616]
GSM1692312
[617]
GSM1692313
[618]
GSM1692314
[619]
GSM1692315
[620]
GSM1692316
[621]
GSM1692317
[622]
GSM1692318
[623]
GSM1692319
[624]
GSM1692320
[625]
GSM1692321
[626]
GSM1692322
[627]
GSM1692323
[628]
GSM1692324
[629]
GSM1692326
[630]
GSM1692327
[631]
GSM1692328
[632]
GSM1692329
[633]
GSM1692330
[634]
GSM1692331
[635]
GSM1692332
[636]
GSM1692333
[637]
GSM1692334
[638]
GSM1692335
[639]
GSM1692336
[640]
GSM1692337
[641]
GSM1692338
[642]
GSM1692339
[643]
GSM1692340
[644]
GSM1692341
[645]
GSM1692342
[646]
GSM1692343
[647]
GSM1692344
[648]
GSM1692345
[649]
GSM1692346
[650]
GSM1692347
[651]
GSM1692348
[652]
GSM1692349
[653]
GSM1692350
[654]
GSM1692352
[655]
GSM1692353
[656]
GSM1692354
[657]
GSM1692355
[658]
GSM1692356
[659]
GSM1692357
[660]
GSM1692358
[661]
GSM1692359
[662]
GSM1692360
[663]
GSM1692361
[664]
GSM1692362
[665]
GSM1692363
[666]
GSM1692364
[667]
GSM1692365
[668]
GSM1692366
[669]
GSM1692367
[670]
GSM1692368
[671]
GSM1692369
[672]
GSM1692370
[673]
GSM1692371
[674]
GSM1692372
[675]
GSM1692373
[676]
GSM1692374
[677]
GSM1692375
[678]
GSM1692376
[679]
GSM1692377
[680]
GSM1692378
[681]
GSM1692379
[682]
GSM1692380
[683]
GSM1692381
[684]
GSM1692382
[685]
GSM1692383
[686]
GSM1692384
[687]
GSM1692385
[688]
GSM1692386
[689]
GSM1692387
[690]
GSM1692388
[691]
GSM1692389
[692]
GSM1692390
[693]
GSM1692391
[694]
GSM1692392
[695]
GSM1692393
[696]
GSM1692394
[697]
GSM1692395
[698]
GSM1692396
[699]
GSM1692397
[700]
GSM1692398
[701]
GSM1692399
[702]
GSM1692400
[703]
GSM1692402
[704]
GSM1692404
[705]
GSM1692405
[706]
GSM1692406
[707]
GSM1692407
[708]
GSM1692408
[709]
GSM1692409
[710]
GSM1692410
[711]
GSM1692411
[712]
GSM1692412
[713]
GSM1692413
[714]
GSM1692414
[715]
GSM1692415
[716]
GSM1692416
[717]
GSM1692417
[718]
GSM1692418
[719]
GSM1692419
[720]
GSM1692420
[721]
GSM1692421
[722]
GSM1692422
[723]
GSM1692423
[724]
GSM1692424
[725]
GSM1692425
[726]
GSM1692426
[727]
GSM1692427
[728]
GSM1692428
[729]
GSM1692429
[730]
GSM1692430
[731]
GSM1692431
[732]
GSM1692432
[733]
GSM1692433
[734]
GSM1692434
[735]
GSM1692435
[736]
GSM1692436
[737]
GSM1692437
[738]
GSM1692438
[739]
GSM1692439
[740]
GSM1692440
[741]
GSM1692441
[742]
GSM1692442
[743]
GSM1692443
[744]
GSM1692444
[745]
GSM1692445
[746]
GSM1692446
[747]
GSM1692447
[748]
GSM1692448
[749]
GSM1692449
[750]
GSM1692450
[751]
GSM1692451
[752]
GSM1692452
[753]
GSM1692453
[754]
GSM1692454
[755]
GSM1692455
[756]
GSM1692456
[757]
GSM1692457
[758]
GSM1692458
[759]
GSM1692459
[760]
GSM1692460
[761]
GSM1692461
[762]
GSM1692462
[763]
GSM1692463
[764]
GSM1692465
[765]
GSM1692466
[766]
GSM1692467
[767]
GSM1692468
[768]
GSM1692469
[769]
GSM1692470
[770]
GSM1692471
[771]
GSM1692472
[772]
GSM1692473
[773]
GSM1692474
[774]
GSM1692475
[775]
GSM1692476
[776]
GSM1692477
[777]
GSM1692478
[778]
GSM1692479
[779]
GSM1692480
[780]
GSM1692481
[781]
GSM1692482
[782]
GSM1692483
[783]
GSM1692484
[784]
GSM1692485
[785]
GSM1692487
[786]
GSM1692488
[787]
GSM1692489
[788]
GSM1692490
[789]
GSM1692491
[790]
GSM1692492
[791]
GSM1692493
[792]
GSM1692494
[793]
GSM1692495
[794]
GSM1692496
[795]
GSM1692497
[796]
GSM1692498
[797]
GSM1692499
[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
[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
All data generated or analyzed during this study are included in this published article. The data sets (GSE4607, GSE13904, GSE26378, GSE26440, GSE65682 and GSE95233) analysed during the current study are available in the GEO (Gene Expression Omnibus) database, persistent accessible web link to database is https://www.ncbi.nlm.nih.gov/geo/.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
All data generated or analyzed during this study are included in this published article. The data sets (GSE4607, GSE13904, GSE26378, GSE26440, GSE65682 and GSE95233) analysed during the current study are available in the GEO (Gene Expression Omnibus) database, persistent accessible web link to database is https://www.ncbi.nlm.nih.gov/geo/.
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:GSE13904 not downloaded
[1]
asset:data:geo:GSE13904 licence not read
[2]
asset:data:geo:GSE26378 not downloaded
[3]
asset:data:geo:GSE26378 licence not read
[4]
asset:data:geo:GSE26440 not downloaded
[5]
asset:data:geo:GSE26440 licence not read
[6]
asset:data:geo:GSE4607 not downloaded
[7]
asset:data:geo:GSE4607 licence not read
[8]
asset:data:geo:GSE65682 not downloaded
[9]
asset:data:geo:GSE65682 licence not read
[10]
asset:data:geo:GSE95233 not downloaded
[11]
asset:data:geo:GSE95233 licence not read
analysis object · 4 fields
state
partial
methods array · 5 items
[0] object · 10 fields
id
method-1
family
differential_expression
specific
limma_voom
label
Differential expression analysis
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
count_matrix_with_condition
evidence object · 3 fields
score
1
band
verbatim
quote
In this study, Limma and metaMA packages were executed for identification of the DEmRNAs.
[1] object · 10 fields
id
method-2
family
group_comparison
specific
anova
label
Group comparison
dsl_operations array · 4 items
[0]
group_summary
[1]
welch_t
[2]
bootstrap_mean_difference
[3]
group_comparison_anova
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
two_or_more_groups_continuous
evidence object · 3 fields
score
0.8
band
high
quote
ANOVA
[2] object · 10 fields
id
method-3
family
machine_learning
specific
support_vector_machine
label
Machine learning
dsl_operations array · 3 items
[0]
train_classifier
[1]
cross_validate
[2]
roc_auc
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
labelled_feature_matrix
evidence object · 3 fields
score
0.7
band
medium
quote
SVM
[3] object · 10 fields
id
method-4
family
machine_learning
specific
decision_tree
label
Machine learning
dsl_operations array · 3 items
[0]
train_classifier
[1]
cross_validate
[2]
roc_auc
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
labelled_feature_matrix
evidence object · 3 fields
score
0.7
band
medium
quote
decision tree
[4] object · 10 fields
id
method-5
family
diagnostic_evaluation
specific
roc
label
Diagnostic test evaluation
dsl_operations array · 6 items
[0]
confusion_counts
[1]
roc_curve
[2]
sensitivity_specificity
[3]
solve_confusion_matrix
[4]
classification_metrics
[5]
published_precision_envelope
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
reference_standard_and_test
evidence object · 3 fields
score
0.8
band
high
quote
ROC curve
targets array · 0 items
gaps array · 1 item
[0]
method.reader_differs_from_extractor
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 · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
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: limma_voom.
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
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accessions array · 6 items
[0]
GSE4607
[1]
GSE13904
[2]
GSE26378
[3]
GSE26440
[4]
GSE65682
[5]
GSE95233
resolved_accessions array · 6 items
[0]
GSE4607
[1]
GSE13904
[2]
GSE26378
[3]
GSE26440
[4]
GSE65682
[5]
GSE95233
statements array · 1 item
[0]
All data generated or analyzed during this study are included in this published article. The data sets (GSE4607, GSE13904, GSE26378, GSE26440, GSE65682 and GSE95233) analysed during the current study are available in the GEO (Gene Expression Omnibus) database, persistent accessible web link to database is https://www.ncbi.nlm.nih.gov/geo/.
basis
The paper names GSE4607, GSE13904, GSE26378, GSE26440, GSE65682, GSE95233 in GEO (Gene Expression Omnibus) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 3 items
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normalise_counts
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differential_expression_fit
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fdr_bh
assets array · 2 items
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data:geo:GSE13904
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
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MiniMax-M3.1-Flash
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minimax
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minimax
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2026-11-01T17:15:53Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
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verbatim
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5
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prompt_tokens
14803
completion_tokens
2028
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256
stages array · 1 item
[0] object · 4 fields
id
extraction
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complete
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MiniMax-M3.1-Flash
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minimax
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review.human_critique
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provenance object · 7 fields
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pmid:35045818
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paper
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pmid:35045818
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source:pmid:35045818
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manuscript
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PMID 35045818 full text
[2] object · 3 fields
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data:geo:GSE13904
type
dataset
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GEO GSE13904
activities array · 3 items
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activity-reading-sci-2026-11-01
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pmid:35045818
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source:pmid:35045818
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PMID 35045818 full text
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data:geo:GSE13904
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GEO GSE13904
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agent:corpus-assay-engine
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activity-read-35045818
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source:pmid:35045818
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agent:minimax-reader
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activity-resolve-GSE13904
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used array · 1 item
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data:geo:GSE13904
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agent:corpus-assay-engine
agents array · 2 items
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agent:corpus-assay-engine
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agent:minimax-reader
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MiniMax literature reader
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dsl_catalogue_fingerprint
343fb30756ba6ec3
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lineage object · 4 fields
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sha256:d13de1a2355cafe349b07a832ff79332051941f81088d35453a1974c75409609
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null
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asset:data:geo:GSE26378 licence not read
[3]
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[4]
asset:data:geo:GSE26440 licence not read
[5]
asset:data:geo:GSE26440 not downloaded
[6]
asset:data:geo:GSE4607 licence not read
[7]
asset:data:geo:GSE4607 not downloaded
[8]
asset:data:geo:GSE65682 licence not read
[9]
asset:data:geo:GSE65682 not downloaded
[10]
asset:data:geo:GSE95233 licence not read
[11]
asset:data:geo:GSE95233 not downloaded
[12]
execution.not_run
[13]
method.reader_differs_from_extractor
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review.human_critique
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review.independent_reader
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review.machine_critique
assays array · 1 item
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fdr_bh
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source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
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In this study, Limma and metaMA packages were executed for identification of the DEmRNAs.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35045818", "kind": "science-record", "revision": 5, "content_hash": "sha256:aff28937f1afe9f0ad079ee1bed61054c5d9c485ff82085027102ff87b633ba7", "created_at": "2026-11-01T17:36:46Z", "updated_at": "2026-11-01T22:48:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis.", "doi": "10.1186/s12879-022-07056-4", "pmid": "35045818", "authors": ["Yonghua Fan", "Qiufeng Han", "Jinfeng Li", "Gaige Ye", "Xianjing Zhang", "Tengxiao Xu", "Huaqing Li"], "journal": "BMC infectious diseases", "year": 2022, "abstract": "BACKGROUND: Sepsis is an inflammatory response caused by infection with pathogenic microorganisms. The body shock caused by it is called septic shock. In view of this, we aimed to identify potential diagnostic gene biomarkers of the disease.\nMATERIAL AND METHODS: Firstly, mRNAs expression data sets of septic shock were retrieved and downloaded from the GEO (Gene Expression Omnibus) database for differential expression analysis. Functional enrichment analysis was then used to identify the biological function of DEmRNAs (differentially expressed mRNAs). Machine learning analysis was used to determine the diagnostic gene biomarkers for septic shock. Thirdly, RT-PCR (real-time polymerase chain reaction) verification was performed. Lastly, GSE65682 data set was utilized to further perform diagnostic and prognostic analysis of identified superlative diagnostic gene biomarkers.\nRESULTS: A total of 843 DEmRNAs, including 458 up-regulated and 385 down-regulated DEmRNAs were obtained in septic shock. 15 superlative diagnostic gene biomarkers (such as RAB13, KIF1B, CLEC5A, FCER1A, CACNA2D3, DUSP3, HMGN3, MGST1 and ARHGEF18) for septic shock were identified by machine learning analysis. RF (random forests), SVM (support vector machine) and DT (decision tree) models were used to construct classification models. The accuracy of the DT, SVM and RF models were very high. Interestingly, the RF model had the highest accuracy. It is worth mentioning that ARHGEF18 and FCER1A were related to survival. CACNA2D3 and DUSP3 participated in MAPK signaling pathway to regulate septic shock.\nCONCLUSION: Identified diagnostic gene biomarkers may be helpful in the diagnosis and therapy of patients with septic shock.", "claims": [{"id": "claim-method", "statement": "In this study, Limma and metaMA packages were executed for identification of the DEmRNAs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35045818", "locator": "sections[4] \"Identification of DEmRNAs (differentially expressed mRNAs)\" paragraph 2", "quote": "In this study, Limma and metaMA packages were executed for identification of the DEmRNAs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during this study are included in this published article. The data sets (GSE4607, GSE13904, GSE26378, GSE26440, GSE65682 and GSE95233) analysed during the current study are available in the GEO (Gene Expression Omnibus) database, persistent accessible web link to database is https://www.ncbi.nlm.nih.gov/geo/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35045818", "locator": "sections[23] \"Availability of data and materials\" paragraph 2", "quote": "All data generated or analyzed during this study are included in this published article. The data sets (GSE4607, GSE13904, GSE26378, GSE26440, GSE65682 and GSE95233) analysed during the current study are available in the GEO (Gene Expression Omnibus) database, persistent accessible web link to database is https://www.ncbi.nlm.nih.gov/geo/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Machine-learning analysis identified 15 diagnostic gene biomarkers for septic shock.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35045818", "locator": "sections[13] \"Identification of superlative diagnostic gene biomarkers\" paragraph 2", "quote": "Therefore, the first 15 DEmRNAs (KLRF1, UPP1, RAB13, KIF1B, CLEC5A, NARF, DUSP3, FCER1A, CACNA2D3, HMGN3, ECRP, HDAC4, LHFPL2, MGST1 and ARHGEF18) were selected as the superlative diagnostic gene biomarkers."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Machine-learning analysis identified 15 diagnostic gene biomarkers for septic shock.", "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:35045818"], "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:GSE13904"], "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:GSE13904"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35045818", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12879-022-07056-4", "sha256": "sha256:cf4875aae0291286d3e762608c2958e1f3b25b5799473f62749cec93f5ed3ec0", "bytes": 47617, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12879-022-07056-4", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8772133", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE13904", "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=GSE13904", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE13904", "version": null, "note": "All data generated or analyzed during this study are included in this published article. 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The data sets (GSE4607, GSE13904, GSE26378, GSE26440, GSE65682 and GSE95233) analysed during the current study are available in the GEO (Gene Expression Omnibus) database, persistent accessible web link to database is https://www.ncbi.nlm.nih.gov/geo/.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All data generated or analyzed during this study are included in this published article. The data sets (GSE4607, GSE13904, GSE26378, GSE26440, GSE65682 and GSE95233) analysed during the current study are available in the GEO (Gene Expression Omnibus) database, persistent accessible web link to database is https://www.ncbi.nlm.nih.gov/geo/.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM102961", "GSM102962", "GSM102963", "GSM102964", "GSM102965", "GSM102966", "GSM102967", "GSM102968", "GSM102969", "GSM102970", "GSM102971", "GSM102972", "GSM102973", "GSM102974", "GSM102975", "GSM102976", "GSM102977", "GSM102978", "GSM102979", "GSM102980", "GSM102981", "GSM102982", "GSM102983", "GSM102984", "GSM102985", "GSM102986", "GSM102987", "GSM102988", "GSM102989", "GSM102990", "GSM102991", "GSM102992", "GSM102993", "GSM102994", "GSM102995", "GSM102996", "GSM102997", "GSM102998", "GSM102999", "GSM103000", "GSM103001", "GSM103002", "GSM103003", "GSM103004", "GSM103005", "GSM103006", "GSM103007", "GSM103008", "GSM103009", "GSM103010", "GSM103011", "GSM103012", "GSM103013", "GSM103014", "GSM103015", "GSM103016", "GSM103017", "GSM103018", "GSM103019", "GSM103020", "GSM103021", "GSM103022", "GSM103023", "GSM103024", "GSM103025", "GSM103026", "GSM103027", "GSM103028", "GSM103029", "GSM103030", "GSM103031", "GSM103032", "GSM103033", "GSM103034", "GSM103035", "GSM103036", "GSM103037", "GSM103038", "GSM103039", "GSM103040", "GSM103041", "GSM103042", "GSM103043", "GSM103044", "GSM103045", "GSM103046", "GSM103047", "GSM103048", "GSM103049", "GSM103050", "GSM103051", "GSM103052", "GSM103053", "GSM103054", "GSM103055", "GSM103056", "GSM103057", "GSM103058", "GSM103059", "GSM103060", "GSM103061", "GSM103062", "GSM103063", "GSM103064", "GSM103065", "GSM103066", "GSM103067", "GSM103068", "GSM103069", "GSM103070", "GSM103071", "GSM103072", "GSM103073", "GSM103074", "GSM103075", "GSM103076", "GSM103077", "GSM103078", "GSM103079", "GSM103080", "GSM103081", "GSM103082", "GSM103083"], "sample_count": 123, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Systemic inflammatory response syndrome and septic shock", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE65682", "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=GSE65682", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE65682", "version": null, "note": "All data generated or analyzed during this study are included in this published article. 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