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.

Chalcones, a naturally occurring class of molecules found in various plants, serve as both precursors and final products in the biosynthesis of flavonoids. Renowned for their diverse therapeutic actions, chalcones demonstrate anti-inflammatory, antitumoral, antimalarial and antiviral activities. The structure of chalcones allows chemical manipulation, making them attractive for metal coordination, such as with copper, an essential metal for living organisms. Here, we characterize the activity of CuL ₂ phen and CuL ₁ phen against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), in which L1 and L2 are two forms of the chalcones 3-(4-(benzyloxy)phenyl)-1-(4-fluoro-2-hydroxyphenyl)prop-2-en-1-one and 3-(4-(benzyloxy)phenyl)-1-(2-hydroxyphenyl)prop-2-en-1-one, respectively, and phen is phenanthroline. CuL ₁ phen and CuL ₂ phen anti-SARS-CoV-2 activity were studied in the viral replication cycle employing both the SARS-CoV-2-NeonGreen infectious clone and wild-type isolates. The SI of CuL ₁ phen and CuL ₂ phen was found to be 1.7 and 5.5, respectively, demonstrating that CuL ₂ phen is a more promising compound. CuL ₂ phen impaired SARS-CoV-2 entry, predicted by molecular docking calculations to disrupt the glycoprotein S and angiotensin-converting enzyme 2 (ACE2) binding, emphasized by the low EC ₅₀ in pseudotyped virus entry assay. Further, CuL ₂ phen was identified as SARS-CoV-2 post-entry inhibitor, probably due to its strong interaction with SARS-CoV-2 double stranded RNA. Altogether, the data suggest that CuL ₂ phen acts by impairing SARS-CoV-2 entry by disrupting the viral envelope as well as interrupting RNA replication through specifically intercalating into the dsRNA. The obtained results give us mechanistic insights into the activity of this promising Cu(II) metallodrug candidate in SARS-CoV-2 infection.