cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28254233.v3
title: Supplementary Material for: Identifying Neuro-inflammatory Biomarkers of Generalized Anxiety Disorder from Lymphocyte Subsets based on machine learning approaches

description: Introduction: Activation of the inflammatory response system is involved in the pathogenesis of generalized anxiety disorder (GAD). The purpose of this study was to identify and characterize inflammatory biomarkers in the diagnosis of GAD based on machine learning algorithms. 
Methods: The evaluation of peripheral immune parameters and lymphocyte subsets was performed on patients with GAD. Multivariable linear regression was used to explore the association between lymphocyte subsets and the of severity GAD. Receiver operator characteristic (ROC) analysis was used to determine the predictive value of these immunological parameters for GAD. Machine learning technology was applied to classify the collected data from patients in the GAD and healthy control groups.
Results: Of the 340 patients enrolled, 171 were GAD patients and 169 were non-GAD patients as healthy control. The levels of neutrophil (NEU), monocytes (MON) and systemic immune-inflammation index (SII) were significantly elevated in GAD patients (P&lt;0.01), and the count and proportion of immune cells, including CD3+CD4+ T cells, CD3+CD8+ T cells, CD19+ B cells and CD3-CD16+CD56+ NK cells (P&lt;0.001) have undergone large changes. The classification analysis conducted by machine learning using a weighted ensemble-L2 algorithm demonstrated an accuracy of 95.00±2.04% in assessing the predictive value of these lymphocyte subsets in GAD. In addition, the feature importance analysis score is 0.255, which was much more predictive of GAD severity than for other lymphocyte subsets.
Conclusion: In the presented work, we show the level of lymphocyte subsets altered in GAD. Lymphocyte subsets, specifically CD3+CD4+ T %, can serve as neuroinflammatory biomarkers for GAD diagnostics. Furthermore, the application of machine learning offers a highly efficient approach for investigating neuroinflammatory biomarkers and predicting GAD. Our research has provided novel insights into the involvement of cellular immunity in GAD and highlighted the potential predictive value and therapeutic targets of lymphocyte subsets in this disorder.
DOI: 10.6084/m9.figshare.28254233
title: Supplementary Material for: Identifying Neuro-inflammatory Biomarkers of Generalized Anxiety Disorder from Lymphocyte Subsets based on machine learning approaches

description: Introduction: Activation of the inflammatory response system is involved in the pathogenesis of generalized anxiety disorder (GAD). The purpose of this study was to identify and characterize inflammatory biomarkers in the diagnosis of GAD based on machine learning algorithms. 
Methods: The evaluation of peripheral immune parameters and lymphocyte subsets was performed on patients with GAD. Multivariable linear regression was used to explore the association between lymphocyte subsets and the of severity GAD. Receiver operator characteristic (ROC) analysis was used to determine the predictive value of these immunological parameters for GAD. Machine learning technology was applied to classify the collected data from patients in the GAD and healthy control groups.
Results: Of the 340 patients enrolled, 171 were GAD patients and 169 were non-GAD patients as healthy control. The levels of neutrophil (NEU), monocytes (MON) and systemic immune-inflammation index (SII) were significantly elevated in GAD patients (P&lt;0.01), and the count and proportion of immune cells, including CD3+CD4+ T cells, CD3+CD8+ T cells, CD19+ B cells and CD3-CD16+CD56+ NK cells (P&lt;0.001) have undergone large changes. The classification analysis conducted by machine learning using a weighted ensemble-L2 algorithm demonstrated an accuracy of 95.00±2.04% in assessing the predictive value of these lymphocyte subsets in GAD. In addition, the feature importance analysis score is 0.255, which was much more predictive of GAD severity than for other lymphocyte subsets.
Conclusion: In the presented work, we show the level of lymphocyte subsets altered in GAD. Lymphocyte subsets, specifically CD3+CD4+ T %, can serve as neuroinflammatory biomarkers for GAD diagnostics. Furthermore, the application of machine learning offers a highly efficient approach for investigating neuroinflammatory biomarkers and predicting GAD. Our research has provided novel insights into the involvement of cellular immunity in GAD and highlighted the potential predictive value and therapeutic targets of lymphocyte subsets in this disorder.
DOI: 10.6084/m9.figshare.28254233.v5
title: Supplementary Material for: Identifying Neuro-inflammatory Biomarkers of Generalized Anxiety Disorder from Lymphocyte Subsets based on machine learning approaches

description: Introduction: Activation of the inflammatory response system is involved in the pathogenesis of generalized anxiety disorder (GAD). The purpose of this study was to identify and characterize inflammatory biomarkers in the diagnosis of GAD based on machine learning algorithms. 
Methods: The evaluation of peripheral immune parameters and lymphocyte subsets was performed on patients with GAD. Multivariable linear regression was used to explore the association between lymphocyte subsets and the of severity GAD. Receiver operator characteristic (ROC) analysis was used to determine the predictive value of these immunological parameters for GAD. Machine learning technology was applied to classify the collected data from patients in the GAD and healthy control groups.
Results: Of the 340 patients enrolled, 171 were GAD patients and 169 were non-GAD patients as healthy control. The levels of neutrophil (NEU), monocytes (MON) and systemic immune-inflammation index (SII) were significantly elevated in GAD patients (P&lt;0.01), and the count and proportion of immune cells, including CD3+CD4+ T cells, CD3+CD8+ T cells, CD19+ B cells and CD3-CD16+CD56+ NK cells (P&lt;0.001) have undergone large changes. The classification analysis conducted by machine learning using a weighted ensemble-L2 algorithm demonstrated an accuracy of 95.00±2.04% in assessing the predictive value of these lymphocyte subsets in GAD. In addition, the feature importance analysis score is 0.255, which was much more predictive of GAD severity than for other lymphocyte subsets.
Conclusion: In the presented work, we show the level of lymphocyte subsets altered in GAD. Lymphocyte subsets, specifically CD3+CD4+ T %, can serve as neuroinflammatory biomarkers for GAD diagnostics. Furthermore, the application of machine learning offers a highly efficient approach for investigating neuroinflammatory biomarkers and predicting GAD. Our research has provided novel insights into the involvement of cellular immunity in GAD and highlighted the potential predictive value and therapeutic targets of lymphocyte subsets in this disorder.
DOI: 10.6084/m9.figshare.28254233.v4
title: Supplementary Material for: Identifying Neuro-inflammatory Biomarkers of Generalized Anxiety Disorder from Lymphocyte Subsets based on machine learning approaches

description: Introduction: Activation of the inflammatory response system is involved in the pathogenesis of generalized anxiety disorder (GAD). The purpose of this study was to identify and characterize inflammatory biomarkers in the diagnosis of GAD based on machine learning algorithms. 
Methods: The evaluation of peripheral immune parameters and lymphocyte subsets was performed on patients with GAD. Multivariable linear regression was used to explore the association between lymphocyte subsets and the of severity GAD. Receiver operator characteristic (ROC) analysis was used to determine the predictive value of these immunological parameters for GAD. Machine learning technology was applied to classify the collected data from patients in the GAD and healthy control groups.
Results: Of the 340 patients enrolled, 171 were GAD patients and 169 were non-GAD patients as healthy control. The levels of neutrophil (NEU), monocytes (MON) and systemic immune-inflammation index (SII) were significantly elevated in GAD patients (P&lt;0.01), and the count and proportion of immune cells, including CD3+CD4+ T cells, CD3+CD8+ T cells, CD19+ B cells and CD3-CD16+CD56+ NK cells (P&lt;0.001) have undergone large changes. The classification analysis conducted by machine learning using a weighted ensemble-L2 algorithm demonstrated an accuracy of 95.00±2.04% in assessing the predictive value of these lymphocyte subsets in GAD. In addition, the feature importance analysis score is 0.255, which was much more predictive of GAD severity than for other lymphocyte subsets.
Conclusion: In the presented work, we show the level of lymphocyte subsets altered in GAD. Lymphocyte subsets, specifically CD3+CD4+ T %, can serve as neuroinflammatory biomarkers for GAD diagnostics. Furthermore, the application of machine learning offers a highly efficient approach for investigating neuroinflammatory biomarkers and predicting GAD. Our research has provided novel insights into the involvement of cellular immunity in GAD and highlighted the potential predictive value and therapeutic targets of lymphocyte subsets in this disorder.
DOI: 10.25582/data.2020-01.1936466/2366055
title: Data for EMSL Project 48666 from January 2020
DOI: 10.15468/dl.nxkrpy
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mila nealeana Backeb."
 ]
}

 The dataset includes 40 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014711-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5cn3wz
title: Occurrence Download

description: A dataset containing 3509 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-90.52687 47.57529,-87.39124 47.57529,-87.39124 49.90849,-90.52687 49.90849,-90.52687 47.57529))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dryocopus pileatus (Linnaeus, 1758)",
 "Year 2019-2024"
 ]
}

 The dataset includes 3509 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029115-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hfcjtb
title: Occurrence Download

description: A dataset containing 2217 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Thalassoma trilobatum (Lacepède, 1801)"
 ]
}

 The dataset includes 2217 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051183-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2019-10.1879349/2366056
title: Data for EMSL Project 48666 from October 2019
DOI: 10.15468/dl.iz2ak0
title: Occurrence Download

description: A dataset containing 1562 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Elephas maximus Linnaeus, 1758"
 ]
}

 The dataset includes 1562 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014793-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2020-01.1936398/2366057
title: Data for EMSL Project 48666 from January 2020
DOI: 10.15468/dl.vbuyxv
title: Occurrence Download

description: A dataset containing 195 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Thamnaconus tessellatus (Günther, 1880)"
 ]
}

 The dataset includes 195 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051187-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e62nu6
title: Occurrence Download

description: A dataset containing 148 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Megapodius layardi Tristram, 1879"
 ]
}

 The dataset includes 148 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029118-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ge1tqr
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-75.41325 4.35814,-75.4105 4.34509,-75.36106 4.34921,-75.359 4.40277,-75.36329 4.39796,-75.36655 4.39522,-75.39059 4.39796,-75.41531 4.39384,-75.41325 4.35814))",
 "HasCoordinate is true",
 "TaxonKey is Mammalia",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014724-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2019-10.1857766/2366058
title: Data for EMSL Project 48666 from October 2019
DOI: 10.15468/dl.3zr74k
title: Occurrence Download

description: A dataset containing 182 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-102.96665 15.68769,-90.61573 15.48462,-96.43839 23.84296,-96.53231 29.10215,-110.76151 29.96261,-102.96665 15.68769))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Painteria elachistophylla (A.Gray ex S.Watson) Britton & Rose"
 ]
}

 The dataset includes 182 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051194-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2020-02.1939218/2366059
title: Data for EMSL Project 48666 from February 2020
DOI: 10.15468/dl.923bhw
title: Occurrence Download

description: A dataset listing the 10 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Fossil",
 "Country is Brazil",
 "TaxonKey is one of (Characiformes, Siluriformes, Perciformes, Cypriniformes, Gymnotiformes)"
 ]
}

 The dataset's 10 records were derived from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028953-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.o7bvym
title: Occurrence Download

description: A dataset containing 3291911 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Eremaea"
 ]
}

 The dataset includes 3291911 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014721-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2020-01.1936586/2366060
title: Data for EMSL Project 48666 from January 2020
DOI: 10.15468/dl.f8t6m8
title: Occurrence Download

description: A dataset containing 55211 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Canada",
 "TaxonKey is Danaus plexippus (Linnaeus, 1758)",
 "Year 1000-2024"
 ]
}

 The dataset includes 55211 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051196-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tx5uba
title: Occurrence Download

description: A dataset containing 283 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dianthus tripunctatus Sm."
 ]
}

 The dataset includes 283 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029129-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zxu8zm
title: Occurrence Download

description: A dataset containing 3241655 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-100.86548 21.80439,-97.84204 21.70643,-97.54321 26.29735,-99.24829 26.36037,-100.86548 21.80439))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3241655 records from 626 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014726-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2020-01.1936412/2366061
title: Data for EMSL Project 48666 from January 2020
DOI: 10.15468/dl.vut6xm
title: Occurrence Download

description: A dataset containing 509 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Trachyrhamphus serratus (Temminck & Schlegel, 1850)"
 ]
}

 The dataset includes 509 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051209-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ohm3n0
title: Occurrence Download

description: A dataset containing 933208 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Atlas Flora Danica"
 ]
}

 The dataset includes 933208 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014733-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.stjzbq
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dypsis bernieriana (Baill.) Beentje & J.Dransf."
 ]
}

 The dataset includes 7 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029145-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2019-10.1858727/2366062
title: Data for EMSL Project 48666 from October 2019
DOI: 10.15468/dl.vjutyj
title: Occurrence Download

description: A dataset containing 30410 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Triaenodon obesus (Rüppell, 1837)"
 ]
}

 The dataset includes 30410 records from 146 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051212-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2020-01.1936335/2366063
title: Data for EMSL Project 48666 from January 2020
DOI: 10.15468/dl.993wkb
title: Occurrence Download

description: A dataset containing 9182 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Gallus lafayettii R.Lesson, 1831"
 ]
}

 The dataset includes 9182 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029137-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.oiqnje
title: Occurrence Download

description: A dataset containing 10137 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Base de datos de especies y especímenes de la flora costera del norte de Chile (plan RECOGE)"
 ]
}

 The dataset includes 10137 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014743-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2019-10.1879188/2366064
title: Data for EMSL Project 48666 from October 2019
DOI: 10.15468/dl.vbaqz3
title: Occurrence Download

description: A dataset containing 6409 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Occurrence evidence, Material citation, Machine Observation)",
 "Geometry POLYGON((-108 -30,-15 -30,-15 50,-108 50,-108 -30))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aulostomus maculatus Valenciennes, 1841"
 ]
}

 The dataset includes 6409 records from 90 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051215-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bszwjn
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dianthus ugamicus Vved."
 ]
}

 The dataset includes 13 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029142-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f5pkw9
title: Occurrence Download

description: A dataset containing 24191 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ursus arctos Linnaeus, 1758"
 ]
}

 The dataset includes 24191 records from 154 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014735-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2019-06.1790222/2366065
title: Data for EMSL Project 48666 from June 2019
DOI: 10.15468/dl.g88y57
title: Occurrence Download

description: A dataset containing 1102 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ulua aurochs (Ogilby, 1915)"
 ]
}

 The dataset includes 1102 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051222-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2019-10.1857657/2366066
title: Data for EMSL Project 48666 from October 2019
DOI: 10.15468/dl.awbkqp
title: Occurrence Download

description: A dataset containing 24191 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ursus arctos Linnaeus, 1758"
 ]
}

 The dataset includes 24191 records from 154 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014742-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u7y8fx
title: Occurrence Download

description: A dataset containing 12173241 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Spain",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year 1971-2010"
 ]
}

 The dataset includes 12173241 records from 269 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012963-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.