Evaluation of Salivary Indoxyl Sulfate with Proteinuria for Predicting Graft Deterioration in Kidney Transplant Recipients
Korytowska et al.
The finding, in our words
Salivary indoxyl sulfate and proteinuria were independent predictors of graft deterioration with an area under the ROC curve of 0.71, supporting their use as non-invasive screening tools in decentralised settings.
A paraphrase to the Library’s standard, never the abstract. The source is one link away and is always the authority.
This paper describes a handheld electrochemical sensor for vitamin C measurement in saliva and urine, showing a linear range from 0.2 to 200 µm and detection limit of 0.03 µm. The device demonstrated satisfactory accuracy and recovery in human samples, suggesting potential for affordable home-based testing of vitamin C status.
Fu et al., Advanced healthcare materials (paywalled) · source ↗
A US survey of 3,400 adults aged 62 to 90 collected up to 15 biomeasures at home, including saliva, dried blood spots and urine, with Salivette and Oragene among the devices used, which shows that several patient-centric sampling methods can run together in home-based research.
Jaszczak et al., The journals of gerontology. Series B, Psychological sciences and social sciences (paywalled) · source ↗
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.
Kirkpatrick et al., Brain, behavior, and immunity (paywalled) · source ↗
In 104 healthy adults who collected both 24-hour urine and saliva at home, salivary iodine concentration measured at specific post-meal intervals showed excellent correlation with urinary iodine excretion. This suggests timed saliva microsampling could replace cumbersome urine collection as a reliable biomarker for assessing iodine intake in decentralised diagnostics.
Oblak et al., Radiology and oncology (paywalled) · source ↗
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.