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.

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.

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.

The coronavirus disease 2019 (COVID-19) is the latest biological hazard for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Numerous diagnostic tests for SARS-CoV-2 have been used, which are expensive and require specialized personal. So, new diagnosis strategies are being developed, looking for less expensive methods which could be used as screening for better spread control. Many researchers have described the use of saliva as a potential indicator of COVID-19, and even the same patient could carry out its collection. In this sense, this study aimed to establish specific 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. Previous written informed consent, 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. Obesity was the main comorbidity. Various laboratory blood tests were altered. In the FTIR spectra analysis, changes in amide I and immunoglobulin regions were evidenced, 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 detection for COVID-19 using an MLRM, once it helps to reduce the number of variables, which could be helpful in the future development of diagnostic devices in a faster and cheaper way.

No 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.

Various immunopathological events characterize the SARS-CoV-2 infection. Nevertheless, it has been reported that T cell response against SARS-CoV-2 is predominated by CD4+ T cells, which could be related to the compromised long-lasting protection. Furthermore, different vaccines have been developed to prevent coronavirus disease 2019 (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) qPCR test, evaluating the long-lasting immune and T-cell responses through FTIR spectroscopy. Most individuals that integrated the V-Healthy group (88.1%) were asymptomatic; contrary, only 28% of the V-COVID-19 were asymptomatic. Likewise, 68% of the V-COVID-19 group had at least one coexisting illness. About the immunological and T-cell responses analyzed through FTIR spectroscopy, the V-COVID-19 group showed a greater content of IgG and IgA as well as the analyzed cytokines (IFN-γ, TNF-α, IL-1 β, IL-6, and IL-10). Therefore, it was possible to evaluate in saliva the long-lasting immune and T-cell responses through FTIR spectroscopy, showing a statistical significance between the groups. Moreover, we corroborated that the protective role of vaccination is not only by antibodies; the vaccine-induced T cells might contribute to vaccine efficacy against viral variants.