The blood diagnosis of diabetes mellitus (DM) is highly accurate; however, it is an invasive, high-cost, and painful procedure. In this context, the combination of ATR-FTIR spectroscopy and machine learning techniques in other biological samples has been used as an alternative tool to develop a non-invasive, fast, inexpensive, and label-free diagnostic or screening platform for several diseases, including DM. In this study, we used the ATR-FTIR tool associated with linear discriminant analysis (LDA) and a support vector machine (SVM) classifier in order to identify changes in salivary components to be used as alternative biomarkers for the diagnosis of type 2 DM. The band area values of 2962 cm⁻¹, 1641 cm⁻¹, and 1073 cm⁻¹ were higher in type 2 diabetic patients than in non-diabetic subjects. The best classification of salivary infrared spectra was by SVM, showing a sensitivity of 93.3% (42/45), specificity of 74% (17/23), and accuracy of 87% between non-diabetic subjects and uncontrolled type 2 DM patients. The SHAP features of infrared spectra indicate the main salivary vibrational modes of lipids and proteins that are responsible for discriminating DM patients. In summary, these data highlight the potential of ATR-FTIR platforms coupled with machine learning as a reagent-free, non-invasive, and highly sensitive tool for screening and monitoring diabetic patients.

Cellular plasticity enables cells to dynamically adapt their phenotype in response to environmental cues, a process central to development, tissue repair, and disease. Among the most studied plasticity programs is epithelial–mesenchymal transition (EMT), a transcriptionally controlled process by which epithelial cells acquire mesenchymal traits. Originally described in embryogenesis, EMT is now recognized as a key driver in both tumor progression and fibrotic remodeling. In cancer, EMT and hybrid epithelial/mesenchymal (E/M) states promote invasion, metastasis, stemness, therapy resistance, and immune evasion. In fibrotic diseases, partial EMT (pEMT) contributes to fibroblast activation and excessive extracellular matrix deposition, sustaining organ dysfunction mainly in the kidney, liver, lung, and heart. This review integrates recent findings on the molecular regulation of EMT, including signaling pathways (TGF-β, WNT, NOTCH, HIPPO), transcription factors (SNAIL, ZEB, TWIST), and regulatory layers involving microRNAs and epigenetic modifications. Moreover, we discuss the emergence of pEMT states as drivers of phenotypic plasticity, functional heterogeneity, and poor prognosis. By comparing EMT in cancer and fibrosis, we reveal shared mechanisms and disease-specific features, emphasizing the translational relevance of targeting EMT plasticity. Finally, we explore how cutting-edge technologies, such as single-cell transcriptomics and lineage tracing, are reshaping our understanding of EMT across pathological contexts.

Novel coronavirus disease (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Its impact on patients with comorbidities is clearly related to fatality cases, and diabetes has been linked to one of the most important causes of severity and mortality in SARS-CoV-2 infected patients. Substantial research progress has been made on COVID-19 therapeutics; however, effective treatments remain unsatisfactory. This unmet clinical need is robustly associated with the complexity of pathophysiological mechanisms described for COVID-19. Several key lung pathophysiological mechanisms promoted by SARS-CoV-2 have driven the response in normoglycemic and hyperglycemic subjects. There is sufficient evidence that glucose metabolism pathways in the lung are closely tied to bacterial proliferation, inflammation, oxidative stress, and pro-thrombotic responses, which lead to severe clinical outcomes. It is also likely that SARS-CoV-2 proliferation is affected by glucose metabolism of type I and type II cells. This review summarizes the current understanding of pathophysiology of SARS-CoV-2 in the lung of diabetic patients and highlights the changes in clinical outcomes of COVID-19 in normoglycemic and hyperglycemic conditions.

Zika virus (ZIKV) diagnosis is currently performed through an invasive, painful, and costly procedure using molecular biology. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for ZIKV diagnosis is of great relevance. It is critical to prepare a global strategy for the next ZIKV outbreak given its devastating consequences, particularly in pregnant women. Attenuated total reflection–Fourier transform infrared (ATR-FTIR) spectroscopy has been used to discriminate systemic diseases using saliva; however, the salivary diagnostic application in viral diseases is unknown. To test this hypothesis, we intradermally challenged interferon-gamma gene knockout C57/BL6 mice with ZIKV (50 µL,105 FFU, n = 7) or vehicle (50 µL, n = 8). Saliva samples were collected on day three (due to the peak of viremia) and the spleen was also harvested. Changes in the salivary spectral profile were analyzed by Student’s t test (p < 0.05), multivariate analysis, and the diagnostic capacity by ROC curve. ZIKV infection was confirmed by real-time PCR of the spleen sample. The infrared spectroscopy coupled with univariate analysis suggested the vibrational mode at 1547 cm⁻¹ as a potential candidate to discriminate ZIKV and control salivary samples. Three PCs explained 93.2% of the cumulative variance in PCA analysis and the spectrochemical analysis with LDA achieved an accuracy of 93.3%, with a specificity of 87.5% and sensitivity of 100%. The LDA-SVM analysis showed 100% discrimination between both classes. Our results suggest that ATR-FTIR applied to saliva might have high accuracy in ZIKV diagnosis with potential as a non-invasive and cost-effective diagnostic tool.

This study investigated the impacts of online and face-to-face Mat Pilates training in adults with persistent symptoms of long COVID on health outcomes. Forty-nine patients (52 ± 5.85 yr.) diagnosed with long COVID related to fatigue symptoms were randomly included in three groups: online Mat Pilates training (n = 16), face-to-face Mat Pilates training (n = 15), and a control group (n = 18) without training. Mat Pilates training was conducted three times a week for 12 weeks. Fatigue, functional capacity, anthropometrics, body composition, and cardiometabolic markers were assessed before and after the interventions. Two-factor Generalized Estimating Equation analyses identified significant differences with Bonferroni post hoc testing (p < 0.05). After the intervention, only the face-to-face Mat Pilates training group had an improved total, physical and mental fatigue, trunk isometric strength, upper limb muscle endurance strength, and aerobic capacity (p < 0.05). No changes were found in fat mass, muscle mass, free fat mass, % of fat, body mass, body mass index, or waist and hip circumferences. No significant changes were observed in blood glucose, glycated hemoglobin, triglycerides, total cholesterol, high-density lipoprotein, low-density lipoprotein, or blood pressure (p > 0.05). Our results highlight the potential of face-to-face Mat Pilates training as an effective intervention to mitigate persistent symptoms of long COVID related to fatigue and functional capacities.

In this narrative review, we aim to point out the close relationship between mpox virus (MPXV) infection and the role of saliva as a diagnostic tool for mpox, considering the current molecular approach and in the perspective of OMICs application. The MPXV uses the host cell’s rough endoplasmic reticulum, ribosomes, and cytoplasmic proteins to replicate its genome and synthesize virions for cellular exit. The presence of oral mucosa lesions associated with mpox infection is one of the first signs of infection; however, current diagnostic tools find it difficult to detect the virus before the rashes begin. MPXV transmission occurs through direct contact with an infected lesion and infected body fluids, including saliva, presenting a potential use of this fluid for diagnostic purposes. Currently available diagnostic tests for MPXV detection are performed either by real-time quantitative PCR (RT-qPCR) or ELISA, which presents several limitations since they are invasive tests. Despite current clinical trials with restricted sample size, MPXV DNA was detected in saliva with a sensitivity of 85%–100%. In this context, the application of transcriptomics, metabolomics, lipidomics, or proteomics analyses coupled with saliva can identify novel disease biomarkers. Thus, it is important to note that the identification and quantification of salivary DNA, RNA, lipid, protein, and metabolite can provide novel non-invasive biomarkers through the use of OMICs platforms aiding in the early detection and diagnosis of MPXV infection. Untargeted mass spectrometry (MS)-based proteomics reveals that some proteins also expressed in saliva were detected with greater expression differences in blood plasma when comparing mpox patients and healthy subjects, suggesting a promising alternative to be applied in screening or diagnostic platforms for mpox salivary diagnostics coupled to OMICs.