Surface-enhanced Raman Spectroscopy in urinalysis of hypertension patients with kidney disease.

Arterial hypertension (AH) is a multifactorial and asymptomatic disease that affects vital organs such as the kidneys and heart. Considering its prevalence and the associated severe health repercussions, hypertension has become a disease of great relevance for public health across the globe. Conventionally, the classification of an individual as hypertensive or non-hypertensive is conducted through ambulatory blood pressure monitoring over a 24-h period. Although this method provides a reliable diagnosis, it has notable limitations, such as additional costs, intolerance experienced by some patients, and interferences derived from physical activities. Moreover, some patients with significant renal impairment may not present proteinuria. Accordingly, alternative methodologies are applied for the classification of individuals as hypertensive or non-hypertensive, such as the detection of metabolites in urine samples through liquid chromatography or mass spectrometry. However, the high cost of these techniques limits their applicability for clinical use. Consequently, an alternative methodology was developed for the detection of molecular patterns in urine collected from hypertension patients. This study generated a direct discrimination model for hypertensive and non-hypertensive individuals through the amplification of Raman signals in urine samples based on gold nanoparticles and supported by chemometric techniques such as partial least squares-discriminant analysis (PLS-DA). Specifically, 162 patient urine samples were used to create a PLS-DA model. These samples included 87 urine samples from patients diagnosed with hypertension and 75 samples from non-hypertensive volunteers. In the AH group, 35 patients were diagnosed with kidney damage and were further classified into a subgroup termed (RAH). The PLS-DA model with 4 latent variables (LV) was used to classify the hypertensive patients with external validation prediction (P) sensitivity of 86.4%, P specificity of 77.8%, and P accuracy of 82.5%. This study demonstrates the ability of surface-enhanced Raman spectroscopy to differentiate between hypertensive and non-hypertensive patients through urine samples, representing a significant advance in the detection and management of AH. Additionally, the same model was then used to discriminate only patients diagnosed with renal damage and controls with a P sensitivity of 100%, P specificity of 77.8%, and P accuracy of 82.5%.

Bagged k-nearest neighbours classification with uncertainty in the variables.

An analytical result should be expressed as x+/-U, where x is the experimental result obtained for a given variable and U is its uncertainty. This uncertainty is rarely taken into account in supervised classification. In this paper, we propose to include the information about the uncertainty of the experimental results to compute the reliability of classification. The method combines k-nearest neighbours (kNN) with a nested bootstrap scheme, in which a new bootstrap training set is generated using the classical bootstrap in the first level (B times) and a new bootstrap method, called U-bootstrap, in the second level (D times). Two bootstraps are used to reduce the effect of sampling in the first level and the effect of the uncertainty in the second one. These BxD new training bootstrap sets are used to compute the reliability of classification for an unknown object using kNN. The object is classified into the class with the highest reliability. In this method, unlike the classical kNN and Probabilistic Bagged k-nearest neighbours (PBkNN), the reliability of classification changes (increases or decreases) when the uncertainty is increased. These changes depend on the position of the unknown object with respect to the training objects. For the benchmark Wine dataset, we found similar values of classification error rate (CER) than for kNN (5.57%), but lower than Probabilistic Bagged k-nearest neighbours using Hamamoto's bootstrap (7.96%) or Efron's bootstrap (8.97%).