Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach.

The umami taste is one of the five basic taste modalities normally linked to the protein content in food. The implementation of fast and cost-effective tools for the prediction of the umami taste of a molecule remains extremely interesting to understand the molecular basis of this taste and to effectively rationalise the production and consumption of specific foods and ingredients. However, the only examples of umami predictors available in the literature rely on the amino acid sequence of the analysed peptides, limiting the applicability of the models. In the present study, we developed a novel ML-based algorithm, named VirtuousUmami, able to predict the umami taste of a query compound starting from its SMILES representation, thus opening up the possibility of potentially using such a model on any database through a standard and more general molecular description. Herein, we have tested our model on five databases related to foods or natural compounds. The proposed tool will pave the way toward the rationalisation of the molecular features underlying the umami taste and toward the design of specific peptide-inspired compounds with specific taste properties.

The Cytotoxic Effects of Cannabidiol and Cannabigerol on Glioblastoma Stem Cells May Mostly Involve GPR55 and TRPV1 Signalling.

Glioblastoma (GBM) is one of the most aggressive cancers, comprising 60-70% of all gliomas. The large G-protein-coupled receptor family includes cannabinoid receptors CB1, CB2, GPR55, and non-specific ion receptor protein transporters TRPs. First, we found up-regulated CNR1, GPR55, and TRPV1 expression in glioma patient-derived tissue samples and cell lines compared with non-malignant brain samples. CNR1 and GPR55 did not correlate with glioma grade, whereas TRPV1 negatively correlated with grade and positively correlated with longer overall survival. This suggests a tumour-suppressor role of TRPV1. With respect to markers of GBM stem cells, preferred targets of therapy, TRPV1 and GPR55, but not CNR1, strongly correlated with different sets of stemness gene markers: NOTCH, OLIG2, CD9, TRIM28, and TUFM and CD15, SOX2, OCT4, and ID1, respectively. This is in line with the higher expression of TRPV1 and GPR55 genes in GSCs compared with differentiated GBM cells. Second, in a panel of patient-derived GSCs, we found that CBG and CBD exhibited the highest cytotoxicity at a molar ratio of 3:1. We suggest that this mixture should be tested in experimental animals and clinical studies, in which currently used Δ9-tetrahydrocannabinol (THC) is replaced with efficient and non-psychoactive CBG in adjuvant standard-of-care therapy.

A survey on computational taste predictors.

Taste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs. Today, taste prediction plays a key role in several fields, e.g., medical, industrial, or pharmaceutical, but the complexity of the taste perception process, its multidisciplinary nature, and the high number of potentially relevant players and features at the basis of the taste sensation make taste prediction a very complex task. In this context, the emerging capabilities of machine learning have provided fruitful insights in this field of research, allowing to consider and integrate a very large number of variables and identifying hidden correlations underlying the perception of a particular taste. This review aims at summarizing the latest advances in taste prediction, analyzing available food-related databases and taste prediction tools developed in recent years. Supplementary Information: The online version contains supplementary material available at 10.1007/s00217-022-04044-5.

Cardiovascular risk prediction: from classical statistical methods to machine learning approaches.

Nowadays, cardiovascular risk prediction scores are commonly used in primary prevention settings. Estimating the cardiovascular individual risk is of crucial importance for effective patient management and optimal therapy identification, with relevant consequences on secondary prevention settings. To reach this goal, a plethora of risk scores have been developed in the past, most of them assuming that each cardiovascular risk factor is linearly dependent on the outcome. However, the overall accuracy of these methods often remains insufficient to solve the problem at hand. In this scenario, machine learning techniques have repeatedly proved successful in improving cardiovascular risk predictions, being able to capture the non-linearity present in the data. In this concern, we present a detailed discussion concerning the application of classical versus machine learning-based cardiovascular risk scores in the clinical setting. This review aimed to give an overview of the current risk scores based on classical statistical approaches and machine learning techniques applied to predict the risk of several cardiovascular diseases, comparing them, discussing their similarities and differences, and highlighting their main drawbacks to aid the physician having a more critical understanding of these tools.

JAK inhibitors in immune-mediated rheumatic diseases: From a molecular perspective to clinical studies.

The Janus Kinase signalling pathway is implicated in the pathogenesis of immune-related diseases. The potency of small-molecule Janus Kinase inhibitors in the treatment of inflammatory diseases demonstrates that this pathway can be successfully targeted for therapeutic purposes. The outstanding relevant questions concerning drugs' efficacy and toxicity challenge the research to enhance the selectivity of these drugs. The promising results of computational techniques, such as Molecular Dynamics and Molecular Docking, coupled with experimental studies, can improve the understanding of the molecular mechanism of Janus Kinase pathway and thus enable the rational design of new more selective inhibitor molecules.