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    CogState Ltd composite z score
    Composite Z Score, supplied by CogState Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/z-scores/cogstate+composite+score/nct04939597-26-7-7
    Average 90 stars, based on 1 article reviews
    composite z score - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Baseline Neurocognitive Functioning Among Children Treated for Acute Lymphoblastic Leukemia (ALL): Dana Farber Cancer Institute ALL Consortium Study 16-001.
    Article Snippet: Background: This study describes neurocognitive functioning during the first month of induction chemotherapy for childhood acute lymphoblastic leukemia (ALL) and associations with age, sex, risk of relapse, maternal education, household material hardship (HMH), and oxidative stress.. Methods: Patients treated on protocol 16-001 (NCT03020030) across eight North American sites (2017–2022) consented for optional testing using Cogstate at six timepoints throughout treatment.. The baseline data presented were collected within the first month after diagnosis.



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    Study overview: multi-cohort paired transcriptomics of checkpoint dynamics. (a) Study design schematic. Paired tumor biopsies were collected before and after ICB therapy (anti-PD-1 or anti-PD-1/anti-CTLA-4), and transcriptomic profiles were used for checkpoint dynamics analysis and clinical response association. (b) Dataset overview. Bar chart showing the number of paired patients (pre + post biopsies) per cohort and cancer type. n = 42 from GSE91061 (melanoma), n = 11 from Sade-Feldman2018/ GSE120575 (melanoma), n = 11 from Yost2019/ GSE123813 (BCC); total n = 64. (c) Meta-analysis mean fold-change <t>(Z-score)</t> per checkpoint gene across all datasets. Asterisks indicate genes reaching padj < 0.05. Genes are colored by functional category: inhibitory checkpoints (red), co-stimulatory molecules (teal), metabolic checkpoints (salmon), exhaustion markers (blue-grey). (d) Six-step analysis pipeline: data collection (4 cohorts, n = 64 paired) → pseudo-bulk aggregation and Z-score batch correction → paired DE analysis (Stouffer Z meta-analysis) → network rewiring (co-expression, hub shift) → ensemble model (AUC = 0.812, n = 38 genes) → clinical implication (combination therapy).
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    Study overview: multi-cohort paired transcriptomics of checkpoint dynamics. (a) Study design schematic. Paired tumor biopsies were collected before and after ICB therapy (anti-PD-1 or anti-PD-1/anti-CTLA-4), and transcriptomic profiles were used for checkpoint dynamics analysis and clinical response association. (b) Dataset overview. Bar chart showing the number of paired patients (pre + post biopsies) per cohort and cancer type. n = 42 from GSE91061 (melanoma), n = 11 from Sade-Feldman2018/ GSE120575 (melanoma), n = 11 from Yost2019/ GSE123813 (BCC); total n = 64. <t>(c)</t> <t>Meta-analysis</t> mean fold-change <t>(Z-score)</t> per checkpoint gene across all datasets. Asterisks indicate genes reaching padj < 0.05. Genes are colored by functional category: inhibitory checkpoints (red), co-stimulatory molecules (teal), metabolic checkpoints (salmon), exhaustion markers (blue-grey). (d) Six-step analysis pipeline: data collection (4 cohorts, n = 64 paired) → pseudo-bulk aggregation and Z-score batch correction → paired DE analysis (Stouffer Z meta-analysis) → network rewiring (co-expression, hub shift) → ensemble model (AUC = 0.812, n = 38 genes) → clinical implication (combination therapy).
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    Study overview: multi-cohort paired transcriptomics of checkpoint dynamics. (a) Study design schematic. Paired tumor biopsies were collected before and after ICB therapy (anti-PD-1 or anti-PD-1/anti-CTLA-4), and transcriptomic profiles were used for checkpoint dynamics analysis and clinical response association. (b) Dataset overview. Bar chart showing the number of paired patients (pre + post biopsies) per cohort and cancer type. n = 42 from GSE91061 (melanoma), n = 11 from Sade-Feldman2018/ GSE120575 (melanoma), n = 11 from Yost2019/ GSE123813 (BCC); total n = 64. <t>(c)</t> <t>Meta-analysis</t> mean fold-change <t>(Z-score)</t> per checkpoint gene across all datasets. Asterisks indicate genes reaching padj < 0.05. Genes are colored by functional category: inhibitory checkpoints (red), co-stimulatory molecules (teal), metabolic checkpoints (salmon), exhaustion markers (blue-grey). (d) Six-step analysis pipeline: data collection (4 cohorts, n = 64 paired) → pseudo-bulk aggregation and Z-score batch correction → paired DE analysis (Stouffer Z meta-analysis) → network rewiring (co-expression, hub shift) → ensemble model (AUC = 0.812, n = 38 genes) → clinical implication (combination therapy).
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    Study overview: multi-cohort paired transcriptomics of checkpoint dynamics. (a) Study design schematic. Paired tumor biopsies were collected before and after ICB therapy (anti-PD-1 or anti-PD-1/anti-CTLA-4), and transcriptomic profiles were used for checkpoint dynamics analysis and clinical response association. (b) Dataset overview. Bar chart showing the number of paired patients (pre + post biopsies) per cohort and cancer type. n = 42 from GSE91061 (melanoma), n = 11 from Sade-Feldman2018/ GSE120575 (melanoma), n = 11 from Yost2019/ GSE123813 (BCC); total n = 64. (c) Meta-analysis mean fold-change (Z-score) per checkpoint gene across all datasets. Asterisks indicate genes reaching padj < 0.05. Genes are colored by functional category: inhibitory checkpoints (red), co-stimulatory molecules (teal), metabolic checkpoints (salmon), exhaustion markers (blue-grey). (d) Six-step analysis pipeline: data collection (4 cohorts, n = 64 paired) → pseudo-bulk aggregation and Z-score batch correction → paired DE analysis (Stouffer Z meta-analysis) → network rewiring (co-expression, hub shift) → ensemble model (AUC = 0.812, n = 38 genes) → clinical implication (combination therapy).

    Journal: Frontiers in Immunology

    Article Title: CheckDyn: a multi-cohort computational framework for profiling treatment-induced immune checkpoint dynamics and predicting adaptive resistance to immune checkpoint blockade

    doi: 10.3389/fimmu.2026.1847297

    Figure Lengend Snippet: Study overview: multi-cohort paired transcriptomics of checkpoint dynamics. (a) Study design schematic. Paired tumor biopsies were collected before and after ICB therapy (anti-PD-1 or anti-PD-1/anti-CTLA-4), and transcriptomic profiles were used for checkpoint dynamics analysis and clinical response association. (b) Dataset overview. Bar chart showing the number of paired patients (pre + post biopsies) per cohort and cancer type. n = 42 from GSE91061 (melanoma), n = 11 from Sade-Feldman2018/ GSE120575 (melanoma), n = 11 from Yost2019/ GSE123813 (BCC); total n = 64. (c) Meta-analysis mean fold-change (Z-score) per checkpoint gene across all datasets. Asterisks indicate genes reaching padj < 0.05. Genes are colored by functional category: inhibitory checkpoints (red), co-stimulatory molecules (teal), metabolic checkpoints (salmon), exhaustion markers (blue-grey). (d) Six-step analysis pipeline: data collection (4 cohorts, n = 64 paired) → pseudo-bulk aggregation and Z-score batch correction → paired DE analysis (Stouffer Z meta-analysis) → network rewiring (co-expression, hub shift) → ensemble model (AUC = 0.812, n = 38 genes) → clinical implication (combination therapy).

    Article Snippet: Across-dataset meta-analysis was performed using the Stouffer Z-score method, weighting each dataset by the square root of its sample size.

    Techniques: Transcriptomics, Functional Assay, Expressing

    Study overview: multi-cohort paired transcriptomics of checkpoint dynamics. (a) Study design schematic. Paired tumor biopsies were collected before and after ICB therapy (anti-PD-1 or anti-PD-1/anti-CTLA-4), and transcriptomic profiles were used for checkpoint dynamics analysis and clinical response association. (b) Dataset overview. Bar chart showing the number of paired patients (pre + post biopsies) per cohort and cancer type. n = 42 from GSE91061 (melanoma), n = 11 from Sade-Feldman2018/ GSE120575 (melanoma), n = 11 from Yost2019/ GSE123813 (BCC); total n = 64. (c) Meta-analysis mean fold-change (Z-score) per checkpoint gene across all datasets. Asterisks indicate genes reaching padj < 0.05. Genes are colored by functional category: inhibitory checkpoints (red), co-stimulatory molecules (teal), metabolic checkpoints (salmon), exhaustion markers (blue-grey). (d) Six-step analysis pipeline: data collection (4 cohorts, n = 64 paired) → pseudo-bulk aggregation and Z-score batch correction → paired DE analysis (Stouffer Z meta-analysis) → network rewiring (co-expression, hub shift) → ensemble model (AUC = 0.812, n = 38 genes) → clinical implication (combination therapy).

    Journal: Frontiers in Immunology

    Article Title: CheckDyn: a multi-cohort computational framework for profiling treatment-induced immune checkpoint dynamics and predicting adaptive resistance to immune checkpoint blockade

    doi: 10.3389/fimmu.2026.1847297

    Figure Lengend Snippet: Study overview: multi-cohort paired transcriptomics of checkpoint dynamics. (a) Study design schematic. Paired tumor biopsies were collected before and after ICB therapy (anti-PD-1 or anti-PD-1/anti-CTLA-4), and transcriptomic profiles were used for checkpoint dynamics analysis and clinical response association. (b) Dataset overview. Bar chart showing the number of paired patients (pre + post biopsies) per cohort and cancer type. n = 42 from GSE91061 (melanoma), n = 11 from Sade-Feldman2018/ GSE120575 (melanoma), n = 11 from Yost2019/ GSE123813 (BCC); total n = 64. (c) Meta-analysis mean fold-change (Z-score) per checkpoint gene across all datasets. Asterisks indicate genes reaching padj < 0.05. Genes are colored by functional category: inhibitory checkpoints (red), co-stimulatory molecules (teal), metabolic checkpoints (salmon), exhaustion markers (blue-grey). (d) Six-step analysis pipeline: data collection (4 cohorts, n = 64 paired) → pseudo-bulk aggregation and Z-score batch correction → paired DE analysis (Stouffer Z meta-analysis) → network rewiring (co-expression, hub shift) → ensemble model (AUC = 0.812, n = 38 genes) → clinical implication (combination therapy).

    Article Snippet: The CheckDyn pipeline integrates these datasets through pseudo-bulk aggregation, empirical Bayes batch correction, and Stouffer Z-score meta-analysis to quantify treatment-induced changes across a curated 38-gene checkpoint and exhaustion marker panel.

    Techniques: Transcriptomics, Functional Assay, Expressing

    Meta-analysis volcano and cross-dataset expression heatmap. (a) Meta-analysis volcano plot (3 cohorts). Genes reaching meta-padj < 0.05 (red): LAG3, PDCD1, TOX2 (top right). CD274, CD80 also approach significance. Dashed lines indicate significance thresholds. (b) Log 2 FC heatmap of significant genes per dataset. Color scale as in <xref ref-type=Figure 2a . Values are annotated per cell. LAG3 shows uniformly high log 2 FC across all three cohorts (0.49/0.76/0.74). " width="100%" height="100%">

    Journal: Frontiers in Immunology

    Article Title: CheckDyn: a multi-cohort computational framework for profiling treatment-induced immune checkpoint dynamics and predicting adaptive resistance to immune checkpoint blockade

    doi: 10.3389/fimmu.2026.1847297

    Figure Lengend Snippet: Meta-analysis volcano and cross-dataset expression heatmap. (a) Meta-analysis volcano plot (3 cohorts). Genes reaching meta-padj < 0.05 (red): LAG3, PDCD1, TOX2 (top right). CD274, CD80 also approach significance. Dashed lines indicate significance thresholds. (b) Log 2 FC heatmap of significant genes per dataset. Color scale as in Figure 2a . Values are annotated per cell. LAG3 shows uniformly high log 2 FC across all three cohorts (0.49/0.76/0.74).

    Article Snippet: The CheckDyn pipeline integrates these datasets through pseudo-bulk aggregation, empirical Bayes batch correction, and Stouffer Z-score meta-analysis to quantify treatment-induced changes across a curated 38-gene checkpoint and exhaustion marker panel.

    Techniques: Expressing