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.

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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 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.

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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.

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 1β, 6, and 10 (IL-1β, 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.