Abstract. Patients with diabetes mellitus and hypertension (HT) diseases are predisposed to kidney diseases. The objective of this study was to identify potential biomarkers in the urine of diabetic and hypertensive patients through Raman spectroscopy in order to predict the evolution to complications and kidney failure. Urine samples were collected from control subjects (CTR) and patients with diabetes and HT with no complications (lower risk, LR), high degree of complications (higher risk, HR), and doing blood dialysis (DI). Urine samples were stored frozen (−20°C) before spectral analysis. Raman spectra were obtained using a dispersive spectrometer (830-nm, 300-mW power, and 20-s accumulation). Spectra were then submitted to principal component analysis (PCA) followed by discriminant analysis. The first PCA loading vectors revealed spectral features of urea, creatinine, and glucose. It has been found that the amounts of urea and creatinine decreased as disease evoluted from CTR to LR/HR and DI (PC1, p < 0.05), and the amount of glucose increased in the urine of LR/HR compared to CTR (PC3, p < 0.05). The discriminating model showed better overall classification rate of 70%. These results could lead to diagnostic information of possible complications and a better disease prognosis.

Due to their importance in the regulation of metabolites, the kidneys need continuous monitoring to check for correct functioning, mainly by urea and creatinine urinalysis. This study aimed to develop a model to estimate the concentrations of urea and creatinine in urine by means of Raman spectroscopy (RS) that could be used to diagnose kidney disease. Midstream urine samples were obtained from 54 volunteers with no kidney complaints. Samples were subjected to a standard colorimetric assay of urea and creatinine and submitted to spectroscopic analysis by means of a dispersive Raman spectrometer (830 nm, 350 mW, 30 s). The Raman spectra of urine showed peaks related mainly to urea and creatinine. Partial least squares models were developed using selected Raman bands related to urea and creatinine and the biochemical concentrations in urine measured by the colorimetric method, resulting in r = 0.90 and 0.91 for urea and creatinine, respectively, with root mean square error of cross-validation (RMSEcv) of 312 and 25.2 mg/dL, respectively. RS may become a technique for rapid urinalysis, with concentration errors suitable for population screening aimed at the prevention of renal diseases.

Angiotensin converting enzyme 2 (ACE2) is a component of the renin-angiotensin system (RAS) which converts Ang II, a potent vasoconstrictor peptide into Ang 1-7, a vasodilator peptide which may act as a negative feedback hormone to the actions of Ang II. The discovery of this enzyme added a new level of complexity to this system. The mesangial cells (MC) have multiple functions in glomerular physiology and pathophysiology and are able to express all components of the RAS. Despite of being localized in these cells, ACE2 has not yet been purified or characterized. In this study ACE2 from mice immortalized MC (IMC) was purified by ion-exchange chromatography. The purified enzyme was identified as a single band around 60-70 kDa on SDS-polyacrylamide gel and by Western blotting using a specific antibody. The optima pH and chloride concentrations were 7.5 and 200 mM, respectively. The N-terminal sequence was homologous with many species ACE2 N-terminal sequences as described in the literature. ACE2 purified from IMC was able to hydrolyze Ang II into Ang 1-7 and the K(m) value for Ang II was determined to be 2.87 ± 0.76 μM. In conclusion, we purified and localized, for the first time, ACE2 in MC, which was able to generate Ang 1-7 from Ang II. Ang 1-7 production associated to Ang II degradation by ACE2 may exert a protective effect in the renal hemodynamic.

Objective: To evaluate the hypothesis that light could reduce the lethality of COVID-19. Methods: Most models for projections of the spread and lethality of COVID-19 take into account the ambient temperature, neglecting light. Recent advances in understanding the mechanism of action of COVID-19 have shown that it causes a systemic infection that significantly affects the hematopoietic system and hemostasis, factors extremely dependent of light, mainly in the region of visible and infrared radiation. Results: In the COVID-19 patients hemoglobin is decreasing and protoporphyrin is increasing, generating an extremely harmful accumulation of iron ions in the bloodstream, which are able to induce an intense inflammatory process in the body with a consequent increase in C-reactive protein and albumin. Observing the unsaturation characteristics of the cyclic porphyrin ring allows it to absorb and emit radiation mainly in the visible region. This characteristic can represent an important differential to change this process in the event of an imbalance in this system, through the photobiomodulation to increase the production of adenosine triphosphate (ATP) using red and nearinfrared radiation (R-NIR) and vitamin D using ultraviolet B (UVB) radiation. These two compounds have the primary role of activating the defense mechanisms of the immune system, enabling greater resistance of the individual against the attack by the virus. According to the theory of electron excitation in photosensitive molecules, similar to hemoglobin heme, after the photon absorption there would be an increase in the stability of the iron ion bond with the center of the pyrrole ring, preventing the losses of heme function oxygen transport (HbO 2). The light is also absorbed by cytochrome c oxidase in the R-NIR region, with a consequent increase in electron transport, regulating enzyme activity and resulting in a significant increase of oxygen rate consumption by mitochondria, increasing ATP production. Conclusions: The most favorable range of optical radiation to operate in this system is between R-NIR region, in which cytochrome c oxidase and porphyrin present absorption peaks centered at 640 nm and HbO 2 with absorption peak centered at 900 nm. Based on the mechanisms described earlier, our hypothesis is that light could reduce the lethality of COVID-19.

Collagen I is not only responsible for maintaining the integrity of most tissues due to its mechanical properties, but also for its active participation in the functionality of tissues because of its interaction with cells present in the extracellular matrix. The synthesis of collagen begins with tissue injury and remains until the end of the healing process. The use of non-coherent light for healing processes is still understudied. This procedure stands out as a biostimulation method for tissue repair, which increases local circulation, cell proliferation, and collagen synthesis. This study sought to quantify collagen I in the healing process after the treatment of wounds with the light-emitting diode (LED) treatment. The histologic analysis with tissue samples stained with picrosirius red showed a statistical difference between the positive controls, LED 627 and LED 945 nm groups; the group treated with LED 627 nm showed a predominance of mature collagen. The immunohistochemical analysis showed a statistically significant high concentration of collagen I in the LED 945 nm group. The irradiation of wounds with the higher wavelength (945 nm) used in the study produced the best activity of collagen I formation in experimental model.

Raman spectroscopy has been employed in the quantitative analysis of biochemical components in human serum. This study aimed to develop a spectral model to estimate the concentration of glucose and lipid fractions in human serum, thus evaluating the feasibility of Raman spectroscopy technique for diagnostic purposes. A total of 44 samples of blood serum were collected from volunteers submitted to routine blood biochemical assay analysis. The biochemical concentrations of glucose, triglycerides, cholesterol, and high-density and low-density lipoproteins (HDL and LDL) were obtained by colorimetric method. Serum samples (200 μL) were submitted to Raman spectroscopy (830 nm, 250 mW, 50-s accumulation). The spectra of sera present peaks related to the main constituents, particularly proteins and lipids. A quantitative model based on partial least squares (PLS) regression has been developed to estimate the concentration of these compounds, taking the biochemical concentrations assayed by the colorimetric method as sample's actual concentrations. The PLS model based on leave-one-out cross-validation approach estimated the concentration of triglycerides and cholesterol with r = 0.98 and 0.96, and root mean square error of 35.4 and 15.9 mg/dL, respectively. For the other biochemicals, the r was ranging from 0.75 to 0.86. These results evidenced the possibility of performing biochemical assay in blood serum samples by Raman spectroscopy and PLS regression and may be employed as a means of diagnosis in routine clinical analysis.

No abstract

Urea and creatinine are commonly used as biomarkers of renal function. Abnormal concentrations of these biomarkers are indicative of pathological processes such as renal failure. This study aimed to develop a model based on Raman spectroscopy to estimate the concentration values of urea and creatinine in human serum. Blood sera from 55 clinically normal subjects and 47 patients with chronic kidney disease undergoing dialysis were collected, and concentrations of urea and creatinine were determined by spectrophotometric methods. A Raman spectrum was obtained with a high-resolution dispersive Raman spectrometer (830 nm). A spectral model was developed based on partial least squares (PLS), where the concentrations of urea and creatinine were correlated with the Raman features. Principal components analysis (PCA) was used to discriminate dialysis patients from normal subjects. The PLS model showed r = 0.97 and r = 0.93 for urea and creatinine, respectively. The root mean square errors of cross-validation (RMSECV) for the model were 17.6 and 1.94 mg/dL, respectively. PCA showed high discrimination between dialysis and normality (95 % accuracy). The Raman technique was able to determine the concentrations with low error and to discriminate dialysis from normal subjects, consistent with a rapid and low-cost test.