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Spices and aromatic herbs are important components of everyday nutrition in several countries and cultures, thanks to their capability to enhance the flavor of many dishes and convey significant emotional contributions by themselves. Indeed, spices as well as aromatic herbs are to be considered not only for their important values of antimicrobial agents or flavor enhancers everybody knows, but also, thanks to their olfactory and gustatory spectrum, as drivers to stimulate the consumers’ memories and, in a stronger way, emotions. Considering these unique characteristics, spices and aromatic herbs have caught the attention of consumer scientists and experts in sensory analysis for their evaluation using semi-quantitative approaches, with interesting evidence. In this pilot study as a first step, each studied botanical, belonging to Piperaceae or aromatic herbs, has been subjected to headspace solid phase micro-extraction (HS-SPME) coupled with gas-chromatography mass spectrometry (GC-MS) analysis to assess their spontaneous volatile emission, representing the complex chemical pattern, which encounters the consumers’ olfactory perception. Furthermore, the present investigation, performed on 12 individuals, outlines the administration of a pilot study, merging the typical sensory analysis with emotional data collection and the innovative contribution related to the study around the Autonomic and Central Nervous System activation in consumers, performed using wearable technologies and related signal processing. The results obtained by our study, beyond demonstrating the feasibility of the approach, confirmed, both in terms of emotional responses and biomedical signals, the significant emotional potential of spices and aromatic herbs, most of which featuring an overall positive valence, yet with inter-subjects’ variations. Future investigations should aim to increase the number of volunteers evaluated with such an approach to draw more stable conclusions and attempting a customization of product preferences based on both implicit and explicit sensory responses.

The “Exposome” is a concept that indicates the set of exposures to which a human is subjected during their lifetime. These factors influence the health state of individuals and can drive the development of Noncommunicable Diseases (NCDs). Artificial Intelligence (AI) allows one to analyze large amounts of data in a short time. As such, several authors have used AI to study the relationship between exposome and chronic diseases. Under such premises, this study reviews the use of AI in analyzing the exposome to understand its role in the development of chronic diseases, focusing on how AI can identify patterns in exposure-related data and support prevention strategies. To achieve this, we carried out a search on multiple databases, including PubMed, ScienceDirect, and SCOPUS, from 1 January 2019 to 31 May 2023, using the MeSH terms (exposome) and (‘Artificial Intelligence’ OR ‘Machine Learning’ OR ‘Deep Learning’) to identify relevant studies on this topic. After completing the identification, screening, and eligibility assessment, a total of 18 studies were included in this literature review. According to the search, most authors used supervised or unsupervised machine learning models to study multiple exposure factors’ role in the risk of developing cardiovascular, metabolic, and chronic respiratory diseases. In some more recent studies, authors also used deep learning. Furthermore, the exposome analysis is useful to study the risk of developing neuropsychiatric disorders or evaluating pregnancy outcomes and child growth. Understanding the role of the exposome is pivotal to overcome the classic concept of a single exposure/disease. The application of AI allows one to analyze multiple environmental risks and their combined effects on health conditions. In the future, AI could be helpful in the prevention of chronic diseases, providing new diagnostic, therapeutic, and follow-up strategies.

Previous research identified emotion processing (i.e., emotion recognition and gaze pattern towards emotional stimuli) deficits in youths with callous-unemotional (CU) traits, though mixed results have emerged. CU traits frequently co-occur with high levels of the other psychopathy dimensions, namely narcissism and impulsivity, and overall, psychopathic traits in youths might be better understood from a multidimensional perspective. However, little is known about the contribution of narcissism and impulsivity. Participants included a clinical sample of 116 boys (aged 7-12) with Oppositional Defiant Disorder and Conduct Disorder based on the DSM-IV criteria. Psychopathic traits were assessed with the Antisocial Process Screening Device. Gaze pattern was recorded with an eye-tracker while participants completed a computerized emotion recognition task. Partial correlations showed negative associations between CU traits and sadness recognition and between narcissism and disgust recognition. Also, CU traits were associated with reduced attention to the mouth of angry faces and the eyes of sad and disgusted faces. Impulsivity was associated with greater attention to the eyes of angry and fearful faces. Narcissism was negatively associated with disgust recognition and the number of fixation to the eyes of angry faces, and positively associated with fixation count and duration to the mouth of angry faces. Findings support the association between CU traits and emotion processing deficits even after controlling for the effects of the other psychopathy dimensions. Our results also preliminary suggested emotional processing impairments associated with narcissism and impulsivity. A better understanding of emotional processing impairments associated with youths' psychopathy dimensions would provide a new key to interpret extant literature and, more importantly, constitute a first step towards developing early tailored interventions., Author(s): Valentina Levantini [sup.1] [sup.2] , Pietro Muratori [sup.1] , Sara Calderoni [sup.1] , Emanuela Inguaggiato [sup.1] , Gabriele Masi [sup.1] , Annarita Milone [sup.1] , Alessandro Tonacci [sup.3] , [...]

Kidney diseases are a group of conditions related to the functioning of kidneys, which are in turn unable to properly filter waste and excessive fluids from the blood, resulting in the presence of dangerous levels of electrolytes, fluids, and waste substances in the human body, possibly leading to significant health effects. At the same time, the toxins amassing in the organism can lead to significant changes in breath composition, resulting in halitosis with peculiar features like the popular ammonia breath. Starting from this evidence, scientists have started to work on systems that can detect the presence of kidney diseases using a minimally invasive approach, minimizing the burden to the individuals, albeit providing clinicians with useful information about the disease’s presence or its main related features. The electronic nose (e-nose) is one of such tools, and its applications in this specific domain represent the core of the present review, performed on articles published in the last 20 years on humans to stay updated with the latest technological advancements, and conducted under the PRISMA guidelines. This review focuses not only on the chemical and physical principles of detection of such compounds (mainly ammonia), but also on the most popular data processing approaches adopted by the research community (mainly those relying on Machine Learning), to draw exhaustive conclusions about the state of the art and to figure out possible cues for future developments in the field.

Thyroid cancer (TC) is the most common endocrine malignancy, with an increased global incidence in recent decades, despite a substantially unchanged survival. While TC has an excellent overall prognosis, some types of TC are associated with worse patient outcomes, depending on the genetic setting. Furthermore, oxidative stress is related to more aggressive features of TC. Vitamin C, an essential nutrient provided with food or as a dietary supplement, is a well-known antioxidant and a scavenger of reactive oxygen species; however, at high doses, it can induce pro-oxidant effects, acting through multiple biological mechanisms that play a crucial role in killing cancer cells. Although experimental data and, less consistently, clinical studies, suggest the possibility of antineoplastic effects of vitamin C at pharmacological doses, the antitumor efficacy of this nutrient in TC remains at least partly unexplored. Therefore, this review discusses the current state of knowledge on the role of vitamin C, alone or in combination with other conventional therapies, in the management of TC, the mechanisms underlying this association, and the perspectives that may emerge in TC treatment strategies, and, also, in light of the development of novel functional foods useful to this extent, by implementing novel sensory analysis strategies.

Frontotemporal dementia (FTD) includes a number of neurodegenerative diseases, often with early onset (before 65 years old), characterized by progressive, irreversible deficits in behavioral, linguistic, and executive functions, which are often difficult to diagnose due to their similar phenotypic characteristics to other dementias and psychiatric disorders. The genetic contribution is of utmost importance, although environmental risk factors also play a role in its pathophysiology. In fact, some metals are known to produce free radicals, which, accumulating in the brain over time, can induce oxidative stress, inflammation, and protein misfolding, all of these being key features of FTD and similar conditions. Therefore, the present review aims to summarize the current evidence about the environmental contribution to FTD―mainly dealing with toxic metal exposure―since the identification of such potential environmental risk factors can lead to its early diagnosis and the promotion of policies and interventions. This would allow us, by reducing exposure to these pollutants, to potentially affect society at large in a positive manner, decreasing the burden of FTD and similar conditions on affected individuals and society overall. Future perspectives, including the application of Artificial Intelligence principles to the field, with related evidence found so far, are also introduced.

We aim to develop a deep learning-based algorithm for automated segmentation of thigh muscles and subcutaneous adipose tissue (SAT) from T1-weighted muscle MRIs from patients affected by muscular dystrophies (MDs). From March 2019 to February 2022, adult and pediatric patients affected by MDs were enrolled from Azienda Ospedaliera Universitaria Pisana, Pisa, Italy (Institution 1) and the IRCCS Stella Maris Foundation, Calambrone-Pisa, Italy (Institution 2), respectively. All patients underwent a bilateral thighs MRI including an axial T1 weighted in- and out-of-phase (dual-echo). Both muscles and SAT were manually and separately segmented on out-of-phase image sets by a radiologist with 6 years of experience in musculoskeletal imaging. A U-Net1 and U-Net3 were built to automatically segment the SAT, all the thigh muscles together and the three muscular compartments separately. The dataset was randomly split into the on train, validation, and test set. The segmentation performance was assessed through the Dice similarity coefficient (DSC). The final cohort included 23 patients. The estimated DSC for U-Net1 was 96.8%, 95.3%, and 95.6% on train, validation, and test set, respectively, while the estimated accuracy for U-Net3 was 94.1%, 92.9%, and 93.9%. Both of the U-Nets achieved a median DSC of 0.95 for SAT segmentation. The U-Net1 and the U-Net3 achieved an optimal agreement with manual segmentation for the automatic segmentation. The so-developed neural networks have the potential to automatically segment thigh muscles and SAT in patients affected by MDs.

The concept of Industry 5.0 is novel from many points of view, as it fosters the transition to a sustainable, human-centric, resilient European industry. To reach this ambitious goal, it is necessary to act simultaneously on many fronts, starting from guaranteeing basic human rights (e.g., privacy, independence, and dignity) and paying attention to the circular economy and energy efficiency. Despite being difficult to adopt in its general formulation, this concept can be scaled up to specific fields, thus producing increased value with repercussions to the whole industrial process. The winemaking industry puts Italy at the forefront globally, as it is also among the key components of the whole national agrifood/agritech value chain. However, the Italian winemaking industry is quite fragmented, with a heterogeneous mix of small and medium enterprises (SMEs) and with large companies having opposite approaches to the production process, both in terms of involvement of human resources and seasonality of efforts, due to the existing climate differences nationwide. This fact makes the adoption of common practices even harder but makes the benefits of projects promoting this process innovation more tangible. In such a framework, CANTINA 5.0 seeks to fill in this important gap, promoting the Industry 5.0 principles in a selected group of SMEs and large companies from two different Italian areas featuring different climate conditions and different seasonality, hence characterized by different wine harvesting periods and types of wine production. The present article deals with the description of this paradigm in its single parts, including the use of questionnaires and smart tools to detect the health and well-being state of factory workers and winemakers, the use of well-grounded (including gas chromatography/mass spectrometry) and novel (e.g., based on the Internet-of-Things) environmental monitoring tools applied to the cellars/production departments, and the sensory analysis of the end-products, also leveraging the chemical and emotional characteristics of wines produced using the Industry 5.0 approach.

The "Exposome" is a concept that indicates the set of exposures to which a human is subjected during their lifetime. These factors influence the health state of individuals and can drive the development of Noncommunicable Diseases (NCDs). Artificial Intelligence (AI) allows one to analyze large amounts of data in a short time. As such, several authors have used AI to study the relationship between exposome and chronic diseases. Under such premises, this study reviews the use of AI in analyzing the exposome to understand its role in the development of chronic diseases, focusing on how AI can identify patterns in exposure-related data and support prevention strategies. To achieve this, we carried out a search on multiple databases, including PubMed, ScienceDirect, and SCOPUS, from 1 January 2019 to 31 May 2023, using the MeSH terms (exposome) and ('Artificial Intelligence' OR 'Machine Learning' OR 'Deep Learning') to identify relevant studies on this topic. After completing the identification, screening, and eligibility assessment, a total of 18 studies were included in this literature review. According to the search, most authors used supervised or unsupervised machine learning models to study multiple exposure factors' role in the risk of developing cardiovascular, metabolic, and chronic respiratory diseases. In some more recent studies, authors also used deep learning. Furthermore, the exposome analysis is useful to study the risk of developing neuropsychiatric disorders or evaluating pregnancy outcomes and child growth. Understanding the role of the exposome is pivotal to overcome the classic concept of a single exposure/disease. The application of AI allows one to analyze multiple environmental risks and their combined effects on health conditions. In the future, AI could be helpful in the prevention of chronic diseases, providing new diagnostic, therapeutic, and follow-up strategies. [ABSTRACT FROM AUTHOR]

Asthma is a chronic respiratory disease affecting over 358 million people worldwide; for this reason analysing big data on asthma from different countries could give a more detailed picture of current disease burden. We aim to investigate the correlations between asthma and key socio-demographic parameters from different world databases. We found a direct correlation with the gross domestic product (GDP) per capita and its nominal counterpart, with wealthiest countries seen to have the highest prevalence of asthma, as also confirmed by a similar correlation with the human development index (HDI). A positive correlation was also seen between asthma prevalence and a number of socio-cultural data being representative of a good life quality index and prevalent in more developed and wealthier countries. Concerning medical data, an inverse relationship was seen between asthma prevalence and helminthiasis. Those data indicate a higher prevalence for asthma in more developed countries, where socio-economic status is higher and also the access to medical care is more ubiquitous. The approach used in our study highlighted the role of medical literacy and access to healthcare facilities in the correct diagnosis of asthma and vice versa. Our data appear to be suitable in terms of a health programming approach because of the high burden of disease worldwide.

Abstract Recently, a continuous growth for both neurodevelopmental and atopic diseases’ incidence was seen throughout the world. Notably, autism spectrum disorders (ASD) and atopic dermatitis (AD) attracted the attention of clinicians and scientists for their impact on the quality of life of patients, starting during childhood. Despite a number of hypotheses about common pathogenesis between the two disorders, uncertainty is still present, and data coming from various parts of the world are contradictory. Fortunately, works recently published have brought useful material for comparative analysis to the benefit of the scientific community, making large scale, country-based perspectives methodologically viable. In light of that, the present study took into account uniform data, available from country-based registries or related publications, dealing with the prevalence of the two conditions around the world, and tried to setup a simple correlation analysis between the two. According to such data, the growth of AD and ASD prevalence appear uncorrelated, leading to hypothesise that, if a common etiopathological pathway exists between the two conditions, they are likely to interact to each other due to a complex interplay of co-morbidities, genes and environmental players not enough explained by a simple correlation analysis. Such facts are worth investigation in future research.

Background: The coronavirus disease 2019 (COVID-19) has been an unprecedented challenge for the worldwide community also in terms of the access to reliable information. In that, scientific publications have represented the only trustworthy source of information about the challenges of the pandemic and the way COVID-19 and its related infection can be defeated. This study sought to analyze the correlations between 200 worldwide country-based data about scientific publications and those concerning the impact of COVID-19 to understand whether more scientifically productive countries were able to better manage the pandemic. Methods: We made use of open-access, country-based data concerning the pandemic impact and compared them with the nation-based COVID-19-related scientific production. Scientific production was obtained from PubMed for papers published until April 15, 2021, whereas epidemiological data were retrieved from the OurWorldInData portal. Statistical correlations were performed by SPSS v. 23 using two-tailed Spearman’s Test. After Bonferroni post-hoc analysis, p less than 0.05 was deemed as significant. Results: Publications data are strongly, and positively correlated with the coronavirus cases, highlighting a greater scientific attention in those countries where COVID-19 had more impact on the population, overall. In addition, the normalized number of publications per inhabitant was negatively correlated with mortality, suggesting a possible higher treatment efficacy in those countries where health literacy was higher. Conclusions: Results obtained probably suggest a higher pandemic penetration in countries with a higher degree of education, where also scientific research is more largely developed, and which are also more ready to successfully treat the affected individuals.

Thyroid cancer (TC), the most frequent malignancy of the endocrine system, has recorded an increasing incidence in the last decades. The etiology of TC remains at least partly unknown and, among modifiable risk factors, the gut microbiota and dietary nutrients (vitamins, essential microelements, polyphenols, probiotics) have been recognized to not only influence thyroid function, but exert critical effects on TC development and progression. Recent discoveries on the existence of tumor microbiota also in the TC microenvironment provide further evidence for the essential role of tumor microorganisms in TC etiology and severity, as well as acting as prognostic markers and as a potential target of adjuvant care in the treatment of TC patients. Therefore, in this review, we summarize current knowledge on the relationship of the tumor microbiome with the clinical tumor characteristics and TC progression, also illustrating the molecular mechanisms underlying this association, and how antioxidant nutrients may be used as a novel strategy to both control gut health and reduce the risk for TC. Furthermore, we discuss how new technologies might be exploited for the development of new foods with high nutritional values, antioxidant capability, and even attractiveness to the individual in terms of sensory and emotional features.

Abstract COVID-19 has represented an unprecedented challenge to be faced also concerning the spread of information, with scientific literature being often the sole source of trustworthy knowledge for the global community. However, a massive waste in research was noticed during pandemic, preventing the scientists to produce totally novel and original results, and the citizenship to have the complete support they needed from science. The present work investigated the relationship between planned funding, research grants, scientific publications and epidemiology in the 27 EU countries, retrieving a significant correlation between scientific publications and COVID-19 cases and deaths, as well as with economic data. Interestingly, planned coronavirus-devoted funds were correlated with lower GDP per capita and higher mortality, leading to the hypothesis for a lack of translation into real funds allowed to the respective country, or for a significant research waste, not transformed into a tangible product or effect. Such results could suggest the need for a different approach in the future concerning the redistribution of research funds in case of COVID-19 relapse or future pandemic events.

Air pollution exposure plays a key role in the alteration of gene expression profiles, which can be regulated by microRNAs, inducing the development of various diseases. Moreover, there is also evidence of sensitivity of miRNAs to environmental factors, including tobacco smoke. Various diseases are related to specific microRNA signatures, suggesting their potential role in pathophysiological processes; considering their association with environmental pollutants, they could become novel biomarkers of exposure. Therefore, the aim of the present work is to analyse data reported in the literature on the role of environmental stressors on microRNA alterations and, in particular, to identify specific alterations that might be related to the development of airway diseases so as to propose future preventive, diagnostic, and therapeutic strategies.

Over recent years, the number of signals of a different type that can be acquired from the human body has increased extraordinarily [...]

Early and accurate diagnosis of autism spectrum disorders (ASD) and tailored therapeutic interventions can improve prognosis. ADOS-2 is a standardized test for ASD diagnosis. However, owing to ASD heterogeneity, the presence of false positives remains a challenge for clinicians. In this study, retrospective data from patients with ASD and multi-systemic developmental disorder (MSDD), a term used to describe children under the age of 3 with impaired communication but with strong emotional attachments, were tested by machine learning (ML) models to assess the best predictors of disease development as well as the items that best describe these two autism spectrum disorder presentations. Maternal and infant data as well as ADOS-2 score were included in different ML testing models. Depending on the outcome to be estimated, a best-performing model was selected. RIDGE regression model showed that the best predictors for ADOS social affect score were gut disturbances, EEG retrievals, and sleep problems. Linear Regression Model showed that term pregnancy, psychomotor development status, and gut disturbances were predicting at best for the ADOS Repetitive and Restricted Behavior score. The LASSO regression model showed that EEG retrievals, sleep disturbances, age at diagnosis, term pregnancy, weight at birth, gut disturbances, and neurological findings were the best predictors for the overall ADOS score. The CART classification and regression model showed that age at diagnosis and weight at birth best discriminate between ASD and MSDD.

Kidney diseases are a group of conditions related to the functioning of kidneys, which are in turn unable to properly filter waste and excessive fluids from the blood, resulting in the presence of dangerous levels of electrolytes, fluids, and waste substances in the human body, possibly leading to significant health effects. At the same time, the toxins amassing in the organism can lead to significant changes in breath composition, resulting in halitosis with peculiar features like the popular ammonia breath. Starting from this evidence, scientists have started to work on systems that can detect the presence of kidney diseases using a minimally invasive approach, minimizing the burden to the individuals, albeit providing clinicians with useful information about the disease's presence or its main related features. The electronic nose (e-nose) is one of such tools, and its applications in this specific domain represent the core of the present review, performed on articles published in the last 20 years on humans to stay updated with the latest technological advancements, and conducted under the PRISMA guidelines. This review focuses not only on the chemical and physical principles of detection of such compounds (mainly ammonia), but also on the most popular data processing approaches adopted by the research community (mainly those relying on Machine Learning), to draw exhaustive conclusions about the state of the art and to figure out possible cues for future developments in the field. [ABSTRACT FROM AUTHOR]

Thyroid cancer (TC) is the most common endocrine malignancy, with an increased global incidence in recent decades, despite a substantially unchanged survival. While TC has an excellent overall prognosis, some types of TC are associated with worse patient outcomes, depending on the genetic setting. Furthermore, oxidative stress is related to more aggressive features of TC. Vitamin C, an essential nutrient provided with food or as a dietary supplement, is a well-known antioxidant and a scavenger of reactive oxygen species; however, at high doses, it can induce pro-oxidant effects, acting through multiple biological mechanisms that play a crucial role in killing cancer cells. Although experimental data and, less consistently, clinical studies, suggest the possibility of antineoplastic effects of vitamin C at pharmacological doses, the antitumor efficacy of this nutrient in TC remains at least partly unexplored. Therefore, this review discusses the current state of knowledge on the role of vitamin C, alone or in combination with other conventional therapies, in the management of TC, the mechanisms underlying this association, and the perspectives that may emerge in TC treatment strategies, and, also, in light of the development of novel functional foods useful to this extent, by implementing novel sensory analysis strategies. [ABSTRACT FROM AUTHOR]

We aim to develop a deep learning-based algorithm for automated segmentation of thigh muscles and subcutaneous adipose tissue (SAT) from T1-weighted muscle MRIs from patients affected by muscular dystrophies (MDs). From March 2019 to February 2022, adult and pediatric patients affected by MDs were enrolled from Azienda Ospedaliera Universitaria Pisana, Pisa, Italy (Institution 1) and the IRCCS Stella Maris Foundation, Calambrone-Pisa, Italy (Institution 2), respectively. All patients underwent a bilateral thighs MRI including an axial T1 weighted in- and out-of-phase (dual-echo). Both muscles and SAT were manually and separately segmented on out-of-phase image sets by a radiologist with 6 years of experience in musculoskeletal imaging. A U-Net1 and U-Net3 were built to automatically segment the SAT, all the thigh muscles together and the three muscular compartments separately. The dataset was randomly split into the on train, validation, and test set. The segmentation performance was assessed through the Dice similarity coefficient (DSC). The final cohort included 23 patients. The estimated DSC for U-Net1 was 96.8%, 95.3%, and 95.6% on train, validation, and test set, respectively, while the estimated accuracy for U-Net3 was 94.1%, 92.9%, and 93.9%. Both of the U-Nets achieved a median DSC of 0.95 for SAT segmentation. The U-Net1 and the U-Net3 achieved an optimal agreement with manual segmentation for the automatic segmentation. The so-developed neural networks have the potential to automatically segment thigh muscles and SAT in patients affected by MDs. [ABSTRACT FROM AUTHOR]

Frontotemporal dementia (FTD) includes a number of neurodegenerative diseases, often with early onset (before 65 years old), characterized by progressive, irreversible deficits in behavioral, linguistic, and executive functions, which are often difficult to diagnose due to their similar phenotypic characteristics to other dementias and psychiatric disorders. The genetic contribution is of utmost importance, although environmental risk factors also play a role in its pathophysiology. In fact, some metals are known to produce free radicals, which, accumulating in the brain over time, can induce oxidative stress, inflammation, and protein misfolding, all of these being key features of FTD and similar conditions. Therefore, the present review aims to summarize the current evidence about the environmental contribution to FTD―mainly dealing with toxic metal exposure―since the identification of such potential environmental risk factors can lead to its early diagnosis and the promotion of policies and interventions. This would allow us, by reducing exposure to these pollutants, to potentially affect society at large in a positive manner, decreasing the burden of FTD and similar conditions on affected individuals and society overall. Future perspectives, including the application of Artificial Intelligence principles to the field, with related evidence found so far, are also introduced. [ABSTRACT FROM AUTHOR]

AbstractThe coronavirus SARS-CoV-2, the aetiological agent of COVID-19 disease, is representing a worldwide threat for the medical community and the society at large so that it is being defined as “the twenty-first-century disease”. Often associated with a severe cytokine storm, leading to more severe cases, it is mandatory to block such occurrence early in the disease course, to prevent the patients from having more severe, sometimes fatal, outcomes. In this framework, early detection of “danger signals”, possibly represented by alarmins, can represent one of the most promising strategies to effectively tailor the disease and to better understand the underlying mechanisms eventually leading to death or severe consequences. In light of such considerations, the present article aims at evaluating the role of alarmins in patients affected by COVID-19 disease and the relationship of such compounds with the most commonly reported comorbidities. The conducted researches demonstrated yet poor literature on this specific topic, however preliminarily confirming a role for danger signals in the amplification of the inflammatory reaction associated with SARS-CoV-2 infection. As such, a number of chronic conditions, including metabolic syndrome, gastrointestinal and respiratory diseases, in turn, associated with higher levels of alarmins, both foster the infection and predispose to a worse prognosis. According to these preliminary data, prompt detection of high levels of alarmins in patients with COVID-19 and co-morbidities could suggest an immediate intense anti-inflammatory treatment.Key messageAlarmins have a role in the amplification of the inflammatory reaction associated with SARS-CoV-2 infectiona prompt detection of high levels of alarmins in patients with COVID-19 could suggest an immediate intense anti-inflammatory treatment

In the last few decades, while the sensory evaluation of edible products has been leveraged to make strategic decisions about many domains, the traditional descriptive analysis performed by a skilled sensory panel has been seen to be too complex and time-consuming for the industry needs, making it largely unsustainable in most cases. In this context, the study of the effectiveness of different methods for sensory training on panel performances represents a new trend in research activity. With this purpose, wearable sensors are applied to study physiological signals (ECG and skin conductance) concerned with the emotions in a cohort of volunteers undergoing a short, two-day (16 h) sensory training period related to wine tasting. The results were compared with a previous study based on a conventional three-month (65 h) period of sensory training. According to what was previously reported for long panel training, it was seen that even short, intensive sensory training modulated the ANS activity toward a less sympathetically mediated response as soon as odorous compounds become familiar. A large-scale application of shorter formative courses in this domain appears possible without reducing the effectiveness of the training, thus leading to money saving for academia and scientific societies, and challenging dropout rates that might affect longer courses.