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2025 · Scientific reports · open access

Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets

Lopes et al.

The finding, in our words

A saliva-derived transcriptomic signature using support vector machine learning detected gastric cancer with an AUC of 0.87, sensitivity of 79% and specificity of 70%. Tissue-based gene expression models performed poorly when applied to saliva, indicating that saliva-specific biomarker panels are needed for effective non-invasive gastric cancer screening.

A paraphrase to the Library’s standard, never the abstract. The source is one link away and is always the authority.

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  1. 2026

    This study used home self-collected saliva samples to successfully monitor cytokines and cortisol in mothers and infants, finding that a community singing intervention significantly reduced salivary interleukin-6 levels in mothers with postnatal depression.

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    A coffee-ring biosensor detected sepsis, COVID-19 and cancer proteins from self-collected saliva down to 3 pg/ml within 12 minutes, achieving sensitivity more than 100-fold higher than lateral flow assays. The system uses a smartphone camera and AI analysis, providing a rapid, affordable platform for decentralised protein diagnostics.

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