Two-trace two-dimensional correlation spectra (2T2D-COS) analysis using FTIR spectra to monitor the immune response by COVID-19.

There is a growing trend in using saliva for SARS-CoV-2 detection with reasonable accuracy. We have studied the responses of IgA, IgG, and IgM in human saliva by directly comparing disease with control analyzing two-trace two-dimensional correlation spectra (2T2D-COS) employing Fourier transform infrared (FTIR) spectra. It explores the molecular-level variation between control and COVID-19 saliva samples. The advantage of 2T2D spectra is that it helps in discriminating remarkably subtle features between two simple pairs of spectra. It gives spectral information from highly overlapped bands associated with different systems. The clinical findings from 2T2D show the decrease of IgG and IgM salivary antibodies in the 50, 60, 65, and 75-years COVID-19 samples. Among the various COVID-19 populations studied the female 30-years group reveals defense mechanisms exhibited by IgM and IgA. Lipids and fatty acids decrease, resulting in lipid oxidation due to the SARS-CoV-2 in the samples studied. Study shows salivary thiocyanate plays defense against SARS-CoV-2 in the male population in 25 and 35 age groups. The receiver operation characteristics statistical method shows a sensitivity of 98% and a specificity of 94% for the samples studied. The measure of accuracy computed as F score and G score has a high value, supporting our study's validation. Thus, 2T2D-COS analysis can potentially monitor the progression of immunoglobulin's response function to COVID-19 with reasonable accuracy, which could help diagnose clinical trials. KEY MESSAGES: The molecular profile of salivary antibodies is well resolved and identified from 2T2D-COS FTIR spectra. The IgG antibody plays a significant role in the defense mechanism against SARS-CoV-2 in 25-40 years. 2T2D-COS reveals the absence of salivary thiocyanate in the 40-75 years COVID-19 population. The receiver operation characteristic (ROC) analysis validates our study with high sensitivity and specificity.

Skin wound healing improvement in diabetic mice through FTIR microspectroscopy after implanting pluripotent stem cells.

Diabetes is a chronic degenerative disease that carries multiple complications. One of the most important complications is the diabetic cutaneous complications, such as skin lesions, ulcerations, and diabetic foot, which are present in 30%-70% of the patients. Currently, the treatments for wound healing include growth factors and cytokines, skin substitutes, hyperbaric oxygen therapy, and skin grafts. However, these treatments are ineffective due to the complex mechanisms involved in developing unhealed wounds. Considering the aforementioned complications, regenerative medicine has focused on this pathology using stem cells to improve these complications. However, it is essential to mention that there is a poor biomolecular understanding of diabetic skin and the effects of treating it with stem cells. For this reason, herein, we investigated the employment of pluripotent stem cells (PSC) in the wound healing process by carrying out morphometric, histological, and Fourier-transform infrared microspectroscopy (FTIRM) analysis. The morphometric analysis was done through a photographic follow-up, measuring the lesion areas. For the histological analysis, hematoxylin & eosin and picrosirius red stains were used to examine the thickness of the epidermis and the cellularity index in the dermis as well as the content and arrangement of collagen type I and III fibers. Finally, for the FTIRM analysis, skin cryosections were obtained and analyzed by employing a Cassegrain objective of 16× of an FTIR microscope coupled to an FTIR spectrometer. For this purpose, 20 mice were divided into two groups according to the treatment they received: the Isotonic Salt Solution (ISS) group and the PSCs group (n = 10). Both groups were induced to diabetes, and six days after diabetes induction, an excisional lesion was made in the dorsal area. Furthermore, using microscopy and FTIRM analysis, the skin healing process on days 7 and 15 post-skin lesion excision was examined. The results showed that the wound healing process over time, considering the lesion size, was similar in both groups; however, the PSCs group evidenced hair follicles in the wound. Moreover, the histological analysis evidenced that the PSCs group exhibited granulation tissue, new vessels, and better polarity of the keratinocytes. In addition, the amount of collagen increased with a good deposition and orientation, highlighting that type III collagen fibers were more abundant in the PSCs. Finally, the FTIR analysis evidenced that the PSCs group exhibited a faster wound healing process. In conclusion, the wounds treated with PSCs showed a more rapid wound healing process, less inflammatory cellular infiltration, and more ordered structures than the ISS group.

Detection of People Positive to COVID-19 through ATR-FTIR Spectra Analysis of Saliva using Machine Learning / Detección de Personas Positivas a COVID-19 mediante el Análisis de Espectros ATR-FTIR de Muestras de Saliva empleando Machine Learning

ABSTRACT COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. This virus's spread is mainly through droplets released from the nose or mouth of an infected person. Although vaccines have been developed that effectively reduce the effects that this viral infection causes, the most effective method to contain the virus's spread is numerous tests to detect and isolate possible carriers. However, the response time, combined with the cost of actual tests, makes this option impractical. Herein, we compare some machine learning methodologies to propose a reliable strategy to detect people positive to COVID-19, analyzing saliva spectra obtained by Fourier transform infrared (FTIR) spectroscopy. After analyzing 1275 spectra, with 7 strategies commonly used in machine learning, we concluded that a multivariate linear regression model (MLMR) turns out to be the best option to identify possible infected persons. According to our results, the displacement observed in the region of the amide I of the spectrum, is fundamental and reliable to establish a border from the change in slope that causes this displacement that allows us to characterize the carriers of the virus. Being more agile and cheaper than reverse transcriptase polymerase chain reaction (RT-PCR), it could be reliably applied as a preliminary strategy to RT-PCR.

RESUMEN La COVID-19 es una enfermedad infecciosa ocasionada por el virus SARS-CoV-2. La propagación de este virus se produce principalmente a través de gotitas liberadas por la nariz o la boca de una persona infectada. Aunque se han desarrollado vacunas que permiten reducir efectivamente los efectos que esta infección viral provoca, el método más eficaz para contener la propagación del virus son las numerosas pruebas para detectar y aislar los posibles portadores. Sin embargo, el tiempo de respuesta, combinado con el costo de las pruebas reales, hace que esta opción sea poco práctica. Aquí, comparamos algunas metodologías de machine learning para proponer una estrategia confiable para detectar personas positivas a COVID-19 analizando espectros de saliva obtenidos por espectroscopia infrarroja transformada de Fourier (FTIR). Tras analizar 1275 espectros, con 7 estrategias comúnmente empleadas en el área de machine learning, concluimos que un modelo de regresión lineal multivariante (MLMR) resulta ser la mejor opción para identificar posibles infectados. De acuerdo con nuestros resultados, el desplazamiento observado en la región de la amida I del espectro, resulta fundamental y confiable para establecer una frontera a partir del cambio de pendiente que este provoca. Al ser más ágil y económica que la reacción en cadena de la polimerasa con transcriptasa inversa (RT-PCR), podría aplicarse confiablemente como estrategia preliminar a RT-PCR.

ATR-FTIR spectrum analysis of saliva samples from COVID-19 positive patients.

The coronavirus disease 2019 (COVID-19) is the latest biological hazard for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Even though numerous diagnostic tests for SARS-CoV-2 have been proposed, new diagnosis strategies are being developed, looking for less expensive methods to be used as screening. This study aimed to establish salivary vibrational modes analyzed by attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy to detect COVID-19 biological fingerprints that allow the discrimination between COVID-19 and healthy patients. Clinical dates, laboratories, and saliva samples of COVID-19 patients (N = 255) and healthy persons (N = 1209) were obtained and analyzed through ATR-FTIR spectroscopy. Then, a multivariate linear regression model (MLRM) was developed. The COVID-19 patients showed low SaO2, cough, dyspnea, headache, and fever principally. C-reactive protein, lactate dehydrogenase, fibrinogen, D-dimer, and ferritin were the most important altered laboratory blood tests, which were increased. In addition, changes in amide I and immunoglobulin regions were evidenced in the FTIR spectra analysis, and the MLRM showed clear discrimination between both groups. Specific salivary vibrational modes employing ATR-FTIR spectroscopy were established; moreover, the COVID-19 biological fingerprint in saliva was characterized, allowing the COVID-19 detection using an MLRM, which could be helpful for the development of new diagnostic devices.

Characterization of the Biological Fingerprint and Identification of Associated Parameters in Stress Fractures by FTIR Spectroscopy.

INTRODUCTION: The stress fractures (SFs) are a common condition in athletes and military recruits, characterized by partial fracture caused by repetitive applications of stresses that are lower than the stress required to fracture the bone in a single loading. Fourier transform infrared (FTIR) spectroscopy gives information about the bone composition and also can determine the amount of a molecule. For this reason, the FTIR spectroscopy may be used as a tool for diagnosis of certain bone diseases related to the bone strength. In this research, we established the contributions of mineral and collagen properties to SF risk through FTIR spectroscopy, analyzing the biochemical profile differences between the healthy bone and the bone with an SF. MATERIALS AND METHODS: Previous written informed consent was obtained, and samples of the hip with an SF (n = 11) and healthy bone from the femur with traumatic fracture (n = 5) were obtained and analyzed employing FTIR spectroscopy and its biochemical mapping function. Then, using FTIR spectra and the map, the collagen content and ratios corresponding to matrix maturity, mineralization, carbonate substitution, acid phosphate substitution, and crystallinity were calculated. Moreover, a histopathological analysis through Masson's staining was conducted. RESULTS: The biochemical analysis showed that the bone with an SF presented a bone immaturity characterized by a higher content of collagen, lower matrix maturity, mineralization, carbonate and acid phosphate substitutions, and greater crystallinity compared to the healthy bone, being checked by the ratio analysis and biochemical mapping. Besides, Masson's stain showed a higher collagen content in the bone with an SF. CONCLUSIONS: The bone with an SF presented alterations in its biochemical composition, showing bone immaturity, which broadens the panorama of the condition to investigate future treatments or prophylactic techniques.