Developing affordable, rapid, and accurate biosensors is essential for SARS-CoV-2 surveillance and early detection. We created a bio-inspired peptide, using the SAGAPEP AI platform, for COVID-19 salivary diagnostics via a portable electrochemical device coupled to Machine Learning algorithms. SAGAPEP enabled molecular docking simulations against the SARS-CoV-2 Spike protein’s RBD, leading to the synthesis of Bio-Inspired Artificial Intelligence Peptide 1 (BIAI1). Molecular docking was used to confirm interactions between BIAI1 and SARS-CoV-2, and BIAI1 was functionalized on rhodamine-modified electrodes. Cyclic voltammetry (CV) using a [Fe(CN)₆]^3−/4 solution detected virus levels in saliva samples with and without SARS-CoV-2. Support vector machine (SVM)-based machine learning analyzed electrochemical data, enhancing sensitivity and specificity. Molecular docking revealed stable hydrogen bonds and electrostatic interactions with RBD, showing an average affinity of −250 kcal/mol. Our biosensor achieved 100% sensitivity, 80% specificity, and 90% accuracy for 1.8 × 10⁴ focus-forming units in infected saliva. Validation with COVID-19-positive and -negative samples using a neural network showed 90% sensitivity, specificity, and accuracy. This BIAI1-based electrochemical biosensor, integrated with machine learning, demonstrates a promising non-invasive, portable solution for COVID-19 screening and detection in saliva.

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.

Amelogenesis imperfecta (AI) is a genetic disease characterized by poor formation of tooth enamel. AI occurs due to mutations, especially in AMEL, ENAM, KLK4, MMP20, and FAM83H, associated with changes in matrix proteins, matrix proteases, cell-matrix adhesion proteins, and transport proteins of enamel. Due to the wide variety of phenotypes, the diagnosis of AI is complex, requiring a genetic test to characterize it better. Thus, there is a demand for developing low-cost, noninvasive, and accurate platforms for AI diagnostics. This case-control pilot study aimed to test salivary vibrational modes obtained in attenuated total reflection fourier-transformed infrared (ATR-FTIR) together with machine learning algorithms: linear discriminant analysis (LDA), random forest, and support vector machine (SVM) could be used to discriminate AI from control subjects due to changes in salivary components. The best-performing SVM algorithm discriminates AI better than matched-control subjects with a sensitivity of 100%, specificity of 79%, and accuracy of 88%. The five main vibrational modes with higher feature importance in the Shapley Additive Explanations (SHAP) were 1010 cm⁻¹, 1013 cm⁻¹, 1002 cm⁻¹, 1004 cm⁻¹, and 1011 cm⁻¹ in these best-performing SVM algorithms, suggesting these vibrational modes as a pre-validated salivary infrared spectral area as a potential biomarker for AI screening. In summary, ATR-FTIR spectroscopy and machine learning algorithms can be used on saliva samples to discriminate AI and are further explored as a screening tool.

Anchorage has always been one of the limitations of orthodontics and in almost all orthodontic therapy, it is necessary to obtain it. With the discovery of osseous integration, a new anchoring model, the skeletal model, emerged. In an extra alveolar skeletal anchorage, the more oblique positioning of the mini-screws can increase the risk of deformation. The objective of this work was to analyze, in an in vitro study, the mechanical performance in the insertion and removal torques and to observe if the type of alloy and the insertion angles influence the use of these mini-screws in extra alveolar positions. 40 mini-screws of similar diameter and length (2.0x12mm), but of different alloys (20 of steel and 20 of titanium) were used, all inserted by a digital torque wrench in synthetic polyurethane bone cortex (40PCF). The insertions were made in two angles, one at 30º and the other from 60º to 30º. After obtaining the data, they were subjected to statistical analysis of variance, Generalized Estimation Equations, with a 95% confidence level and a 5% significance level. Statistical differences were observed in the analyzed factors. Among the factors analyzed, type of alloy and angulation of insertion, and with the data obtained we can conclude that regardless of the alloy, angulation is an essential factor in increasing the torques.