Feasibility in the use of the UV-Visible region for the characterization of glucose in deionized water using Arduino 1 / Viabilidad en el uso de la región UV-Visible para la caracterización de glucosa en agua desionizada mediante Arduino 1

ABSTRACT With an estimated approximately 2 million deaths per year, diabetes is one of the top 5 deadliest noncommunicable diseases globally. Although this disease is not fatal, the degradation of the patient's health due to a bad plan to control their glucose levels can have a fatal outcome. In order to lay the foundations for the development of a device that allows estimating glucose levels in some body fluid, we present the results obtained not only for the estimation of glucose in deionized water, but also describe the development and configuration of the created device. After analyzing 50 signals obtained from 5 different glucose concentrations, the feasibility of using the developed device for the analysis is evident, since, considering the K-Nearest Neighbors (KNN) algorithm, all the signals were associated correctly to the glucose group to which they belong.

RESUMEN Con un estimado de aproximadamente 2 millones de muertes por año, la diabetes es una de las 5 enfermedades no transmisibles más mortales a nivel mundial. Aunque esta enfermedad no es mortal, el deterioro de la salud del paciente por un mal plan para controlar sus niveles de glucosa puede tener un desenlace fatal. Con el fin de sentar las bases para el desarrollo de un dispositivo que permita estimar los niveles de glucosa en algún fluido corporal, presentamos los resultados obtenidos no solo para la estimación de glucosa en agua desionizada, sino que también describimos el desarrollo y configuración del dispositivo creado. Luego de analizar 50 señales obtenidos a partir de 5 concentraciones de glucosa diferentes, se evidencia la factibilidad de utilizar el dispositivo desarrollado para el análisis, ya que, considerando el algoritmo K-Nearest Neighbors (KNN), todas las señales se asociaron correctamente al grupo de glucosa al que pertenecen.

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.

Comparison of the Immune Response in Vaccinated People Positive and Negative to SARS-CoV-2 Employing FTIR Spectroscopy.

Various immunopathological events characterize the systemic acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Moreover, it has been reported that coronavirus disease 2019 (COVID-19) vaccination and infection by SARS-CoV-2 induce humoral immunity mediated by B-cell-derived antibodies and cellular immunity mediated by T cells and memory B cells. Immunoglobulins, cytokines, and chemokines play an important role in shaping immunity in response to infection and vaccination. Furthermore, different vaccines have been developed to prevent COVID-19. Therefore, this research aimed to analyze and compare Fourier-transform infrared (FTIR) spectra of vaccinated people with a positive (V-COVID-19 group) or negative (V-Healthy group) real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR) test, evaluating the immunoglobulin and cytokine content as an immunological response through FTIR spectroscopy. Most individuals that integrated the V-Healthy group (88.1%) were asymptomatic; on the contrary, only 28% of the V-COVID-19 group was asymptomatic. Likewise, 68% of the V-COVID-19 group had at least one coexisting illness. Regarding the immunological response analyzed through FTIR spectroscopy, the V-COVID-19 group showed a greater immunoglobulins G, A, and M (IgG, IgA, and IgM) content, as well as the analyzed cytokines interferon-gamma (IFN-γ), tumor necrosis factor-alpha (TNF-ɑ), and interleukins 1ß, 6, and 10 (IL-1ß, IL-6, and IL-10). Therefore, we can state that it was possible to detect biochemical changes through FTIR spectroscopy associated with COVID-19 immune response in vaccinated people.

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.

Attenuated total reflection FTIR dataset for identification of type 2 diabetes using saliva.

Diabetes is one of the top 5 non-communicable diseases that occur worldwide according to the World Health Organization. Despite not being a fatal disease, a late diagnosis as well as poor control can cause a fatal outcome, because of that, several studies have been carried out with the aim of proposing additional techniques to the gold standard to assist in the diagnosis and control of this disease in a non-invasive way. Considering the above, and in order to provide a solid starting point for future researches, we share a primary research dataset with 1040 saliva samples obtained by Fourier Transform Infrared Spectroscopy considering the Attenuated Total Reflectance method. Database include: gender, age, individuals (patients) with/without diabetes, the glucose value, and the result to the A1C test for the diabetic population. We believe that sharing dataset as is could increase experimentation, research, and analysis of spectra through different strategies broaden its range of applicability by chemists, doctors, physicists, computer scientists, among others, to identify the effects that the virus causes in the body and to propose possible clinical treatments as well as to develop devices that allow us to assist in the characterization of possible carriers.

Artificial intelligence model validation before its application in clinical diagnosis assistance.

The process of selecting an artificial intelligence (AI) model to assist clinical diagnosis of a particular pathology and its validation tests is relevant since the values of accuracy, sensitivity and specificity may not reflect the behavior of the method in a real environment. Here, we provide helpful considerations to increase the success of using an AI model in clinical practice.

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.

Different kinds of stem cells in the development of SARS-CoV-2 treatments.

On February 11, 2020, the World Health Organization officially announced the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as an emerging recent pandemic illness, which currently has approximately taken the life of two million persons in more than 200 countries. Medical, clinical, and scientific efforts have focused on searching for new prevention and treatment strategies. Regenerative medicine and tissue engineering focused on using stem cells (SCs) have become a promising tool, and the regenerative and immunoregulatory capabilities of mesenchymal SCs (MSCs) and their exosomes have been demonstrated. Moreover, it has been essential to establishing models to reproduce the viral life cycle and mimic the pathology of COVID-19 to understand the virus's behavior. The fields of pluripotent SCs (PSCs), induced PSCs (iPSCs), and artificial iPSCs have been used for this purpose in the development of infection models or organoids. Nevertheless, some inconveniences have been declared in SC use; for example, it has been reported that SARS-CoV-2 enters human cells through the angiotensin-converting enzyme 2 receptor, which is highly expressed in MSCs, so it is important to continue investigating the employment of SCs in COVID-19, taking into consideration their advantages and disadvantages. In this review, we expose the use of different kinds of SCs and their derivatives for studying the SARS-CoV-2 behavior and develop treatments to counter COVID-19.

A machine-learning strategy to evaluate the use of FTIR spectra of saliva for a good control of type 2 diabetes.

The World Health Organization has declared that diabetes is one of the four leading causes of death attributable to non-communicable diseases. Currently, many devices allow monitoring blood glucose levels for diabetes control based mainly on blood tests. In this paper, we propose a novel methodology based on the analysis of the Fourier Transform Infrared (FTIR) spectra of saliva using machine learning techniques to characterize controlled and uncontrolled diabetic patients, clustering patients in groups of a low, medium, and high glucose levels, and finally performing the point estimation of a glucose value. After analyzing the obtained results with Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Linear Regression (LR), we found that using ANN, it is possible to carry out the characterizations mentioned above efficiently since it allowed us to identify correctly the 540 spectra that make up our database studying the region 4000-2000 cm-1.

Caracterización biomolecular mediante microespectroscopía FTIR en la fase de remodelación de la cicatrización en un modelo murino de lesión excisional / Biomolecular characterization by FTIR microspectroscopy in the modeling phase of wound cicatrization in a murine model of excisional injury

La cicatrización de la piel es un proceso complejo y organizado que involucra tres fases: inflamatoria, proliferativa y de remodelación. Es indispensable el análisis de este proceso biomolecularmente para investigar y proponer nuevas estrategias terapéuticas que mejoren la cicatrización o promuevan la regeneración. El objetivo de este proyecto fue analizar histológica y biomolecularmente mediante microespectroscopía infrarroja por transformada de Fourier (MFTIR) y su función de mapeo bioquímico, muestras de lesiones excisionales de piel, comparando los cambios morfológicos y espectroscópicos entre piel sana y piel cicatrizada. Se estandarizó un modelo de lesión excisional de piel en ratones hembra de la cepa NIH de 8 semanas de edad (n=16), provocando una herida excisional de 1 cm2. Se analizó piel sana (día 0) y cicatrizada (día 15 post-lesión) morfométrica, histológica y biomolecularmente mediante análisis fotográfico, técnica histológica y MFTIR con su función de mapeo. El análisis morfométrico demostró una reducción del área de la herida en un 87,6 % al día 15 post-lesión. Histológicamente, en la piel cicatrizada se evidenció un adelgazamiento de la epidermis y menor celularidad en la dermis, observándose la formación de tejido de granulación y fibras de colágena desorganizadas. Espectroscópicamente, se apreciaron cambios entre los dos grupos de estudio, principalmente en las bandas de lípidos y en la región de proteínas. El cálculo de las áreas bajo la curva y el mapeo bioquímico mostraron menor concentración de queratina y colágena en la piel cicatrizada, así como desorganización de las fibras de colágena. Se demostró la capacidad de la MFTIR para caracterizar de forma precisa los cambios biomoleculares en la cicatrización, entre ellos la cantidad de queratina, colágena, y el depósito y ordenamiento de las fibras de colágena asociadas a su maduración.

The skin cicatrization is a complex and organized process that involves three phases: inflammatory, proliferative, and remodeling. It is essential to analyze this process biomolecularly, in order to investigate and propose new therapeutic strategies that improve the healing or promote regeneration. The objective of this project was to analyze histological and biomolecularly through Fourier Transform infrared microspectroscopy (FTIRM) and its biochemical mapping function, samples of an excisional skin wound, comparing the morphological and spectroscopic changes between healthy skin and scarred skin. An excisional skin wound healing model was standardized using female, NIH strain 8-week-old mice (n = 16), provoking an excisional wound of 1 cm2. Healthy skin (day 0) and scarring skin (day 15 post-injury) were morphometrical, histological, and biomolecularly analyzed by digital picture analysis, histological technique, and FTIRM with its mapping function. The morphometric analysis showed a reduction of the wound area of 87.6 % at day 15 after wound. Histologically, in the scarred skin a thinning of the epidermis was evidenced, besides reduced cellularity in the dermis, granulation tissue formation, and disorganized collagen fibers were observed. Spectroscopically, changes between the study groups were appreciated, mainly in the lipid bands and in the protein region. The calculation of the areas under the curve and the biochemical mapping showed a lower concentration of keratin and collagen in the scarred skin, as well as collagen fibers disorganization. The ability of the FTIRM to accurately characterize biomolecular changes in cicatrization process was demonstrated, such as the amount of keratin, collagen, and the deposition and ordering of the collagen fibers associated with their maturation.

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.