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.

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.

Nutritional status measured by BMI is impaired and correlates with left ventricular mass in patients with systemic sclerosis.

OBJECTIVE: Systemic sclerosis (SSc) is a multisystemic chronic disease that is complicated by protein-energy malnutrition (PEM). Considering that PEM also may influence left ventricular mass (LVM), the aim of this study was to evaluate whether LVM is related to patients' nutritional status and to determine clinically relevant features of SSc. METHODS: Adult patients referring to our institution were considered. Body weight, height, body mass index (BMI), involuntary weight loss, and the presence of gastrointestinal symptoms were recorded. Echocardiography was performed to assess LVM, using the Devereux regression formula. Results were then normalized by body surface area. Pattern, skin thickening, disease activity and severity, and duration were assessed to characterize SSc. RESULTS: Ninety-four patents with SSc (81 women and 13 men; median duration of disease 7 y) were studied. The prevalence of PEM as assessed by BMI < 20 kg/m(2) was 19%, whereas 15% of patients reported involuntary weight loss of any degree. Patients who lost weight reported gastrointestinal symptoms more frequently (P < 0.05). PEM was not associated with disease activity. LVM (g/m(2)) correlated with patients' BMI (r = 0.32; P < 0.01), and the vascular domain of disease severity (DDS; r = 0.21; P < 0.05), but it showed a negative correlation with skin thickening (r = -0.21 P = 0.01). Patients with ulcers had a significantly greater LVM than patients without skin lesions. CONCLUSIONS: Our study shows that LVM correlates with patients' BMI, skin thickening, and the vascular domain of DSS. Therefore, LVM could serve as a marker of nutritional status and fibrosis in patients with SSc.