Utilizing machine learning algorithms for precise discrimination of glycosuria in fluorescence spectroscopic data.

Fluorescence spectroscopy coupled with a random forest machine learning algorithm offers a promising non-invasive approach for diagnosing glycosuria, a condition characterized by excess sugar in the urine of diabetic patients. This study investigated the ability of this method to differentiate between diabetic and healthy control urine samples. Fluorescent spectra were captured from urine samples using a Xenon arc lamp emitting light within the 200 to 950 nm wavelength range, with consistent fluorescence emission observed at 450 nm under an excitation wavelength of 370 nm. Healthy control samples were also analyzed within the same spectral range for comparison. To distinguish spectral differences between healthy and infected samples, the random forest (RF) and K-Nearest Neighbors (KNN) machine learning algorithms have been employed. These algorithms automatically recognize spectral patterns associated with diabetes, enabling the prediction of unknown classifications based on established samples. Principal component analysis (PCA) was utilized for dimensionality reduction before feeding the data to RF and KNN for classification. The model's classification performance was evaluated using 10-fold cross-validation, resulting in the proposed RF-based model achieving accuracy of 96 %, specificity of 100 %, sensitivity of 93 %, and precision of 100 %. These results suggest that the proposed method holds promise for a more convenient and potentially more accurate method for diagnosing glycosuria in diabetic patients.

Fabrication of nano filler doped PVA/starch biodegradable composites with enhanced thermal conduction, water barrier and antimicrobial performance for food industry.

In this work there was investigated the synergistic effect of the nanomaterials-the Montmorillonite (MMT) and the vanadium pentoxide (V2O5) on the polyvinyl alcohol (PVA)/starch composite. The composite films were prepared by the solvent casting method. The characterization of the composites showed that the addition of the MMT and the V2O5 to PVA/starch composite decreased the water solubility and water absorption capacity of the film. Both of the reinforcement materials enriched values of thermal conductivity and thermal stability of the composite. The TG/DTA and universal testing machine (UTM) analysis exhibited that MMT and V2O5 augmented the thermal robustness and tensile strength of composites and decreased the strain to break. It was also observed that greater MMT concentration accelerates mechanical strength deterioration of the film owing to agglomeration. The scanning electron microscopy (SEM) analysis reflected great change in the surface morphology of the films in the presence and absence of MMT and V2O5. This was due to the interaction amid constituents of the composite. The chemical interaction between the PVA, Starch, MMT and the V2O5 was also established via Fourier-transform infrared spectroscopy (FTIR) analysis, which revealed fluctuations in the absorbance position and intensity of the PVA/Starch. Antimicrobial activities against seven different cultures of bacteria (both-gram positive and -negative) and one fungus (Candida albicans), exposed that antimicrobial performance of the PVA amplified upon addition of the starch, MMT and V2O5, making these composites prospective candidates for the biodegradable packaging materials.

Non-invasive Characterization of Glycosuria and Identification of Biomarkers in Diabetic Urine Using Fluorescence Spectroscopy and Machine Learning Algorithm.

The current study presents a steadfast, simple, and efficient approach for the non-invasive determination of glycosuria of diabetes mellitus using fluorescence spectroscopy. A Xenon arc lamp emitting light in the range of 200-950 nm was used as an excitation source for recording the fluorescent spectra from the urine samples. A consistent fluorescence emission peak of glucose at 450 nm was found in all samples for an excitation wavelength of 370 nm. For confirmation and comparison, the fluorescence spectra of non-diabetic (healthy controls) were also acquired in the same spectral range. It was found that fluorescence emission intensity at 450 nm increases with increasing glucose concentration in urine. In addition, optimized synchronous fluorescence emission at 357 nm was used for simultaneously determining a potential diabetes biomarker, Tryptophan (Trp) in urine. It was also found that the level of tryptophan decreases with the increase in urinary glucose concentration. The quantitative estimation of urinary glucose can be demonstrated based on the intensity of emission light carried by fluorescence light. Moreover, the dissimilarities were further emphasized using the hierarchical cluster analysis (HCA) algorithm. HCA gives an obvious separation in terms of dendrogram between the two data sets based on characteristic peaks acquired from their fluorescence emission signatures. These results recommend that urinary glucose and tryptophan fluorescence emission can be used as potential biomarkers for the non-invasive analysis of diabetes.