SELF-REPORTED NON-CELIAC GLUTEN SENSITIVITY IN ITALIAN YOUNG ADULTS: A CROSS-SECTIONAL STUDY. A DIETARY FAD?

OBJECTIVES: In different countries, the exact prevalence of people that refer symptoms after gluten ingestion is increasing and the unavailability of reliable laboratory tests to diagnose the condition known as non-celiac gluten sensitivity (NCGS) has opened the door to the spread of survey-based studies to hypothesize a prevalence of this condition with highly discordant results. We aim to describe the attitude toward gluten consumption in a large population of young adults in Italy. METHODS: A questionnaire-based cross-sectional study was conducted in thirteen Italian cities to investigate the dietary attitudes of more than 9400 people distributed throughout the country about gluten consumption. Only those referring to gluten-related symptoms with a frequency equal to "always" or "most of the time" were considered self-reported NCGS (SR-NCGS) patients. RESULTS: Five thousand two hundred and thirty-four of 9432 eligible participants (55.5%) fully completed the questionnaire. Excluding those with previous gastrointestinal diagnoses of celiac disease and wheat allergy, we have finally analyzed 4987 questionnaires. Four hundred and eighty-seven participants indicated gluten-related symptoms "always" or "most of the time" (SR-NCGS subjects), while 121 already had a medical diagnosis of NCGS. The minimum prevalence figure of SR-NCGS is 6.4% (95%CI: 6.0-6.9), with a higher prevalence in females (79.9%). The most frequent gluten-related symptoms were bloating, abdominal pain and tiredness. CONCLUSIONS: The high prevalence of people reporting symptoms after gluten ingestion requires that the diagnosis of NCGS should be ascertained with a double-blind controlled study to limit the number of people who improperly approach a gluten-free diet.

Neurological Examination: An Evaluation of Video-Based Learning.

Objective This study aimed to systematically review and assess educational YouTube videos on neurological examination. Methods YouTube was screened for educational videos on neurological examination. A scoring system (involving five major and six minor criteria) was used to assess videos. Educationally useful videos were defined as those satisfying all major criteria and at least three minor criteria; 2 points were allocated for each major criterion and 1 point for each minor criterion, thereby using a score of 13 as a threshold. Results A total of 500 videos were screened, and 128 videos were included in the final selection procedure. Only 55 videos were deemed as educationally useful; 13 of these videos focused on the general neurological examination, 10 on cranial nerves, 11 on the upper limb, five on the lower limb, three on reflexes, one on upper and lower limbs, one on gait, and 11 were in the form of lectures. Six (46.15%) of the educationally useful videos about general neurological exams, including the top three videos, were created by academic institutions, and three (23.07%) were book-related. Educationally useful videos were not the most viewed videos. None of the analyzed videos included the evaluation of the autonomic nervous system in the physical examination routine. Conclusions YouTube is an increasingly common source of educational videos for medical students. However, videos found on YouTube are not peer-reviewed and may be inaccurate, and the preponderance of videos available on the platform makes it difficult for students and educators to find good educational material. We provide a list of URLs of educationally useful videos for students and educators in neurology and offer suggestions for the creation of high-quality educational videos.

What, where, and when: Spatial-temporal distribution of macro-litter on the seafloor of the western and central Mediterranean sea.

The progressive increase of marine macro-litter on the bottom of the Mediterranean Sea is an urgent problem that needs accurate information and guidance to identify those areas most at risk of accumulation. In the absence of dedicated monitoring programs, an important source of opportunistic data is fishery-independent monitoring campaigns of demersal resources. These data have long been used but not yet extensively. In this paper, MEDiterranean International Trawl Survey (MEDITS) data was supplemented with 18 layers of information related to major environmental (e.g. depth, sea water and wind velocity, sea waves) and anthropogenic (e.g. river inputs, shipping lanes, urban areas and ports, fishing effort) forcings that influence seafloor macro-litter distribution. The Random Forest (RF), a machine learning approach, was applied to: i) model the distribution of several litter categories at a high spatial resolution (i.e. 1 km2); ii) identify major accumulation hot spots and their temporal trends. Results indicate that RF is a very effective approach to model the distribution of marine macro-litter and provides a consistent picture of the heterogeneous distribution of different macro-litter categories. The most critical situation in the study area was observed in the north-eastern part of the western basin. In addition, the combined analysis of weight and density data identified a tendency for lighter items to accumulate in areas (such as the northern part of the Tyrrhenian Sea) with more stagnant currents. This approach, based on georeferenced information widely available in public databases, seems a natural candidate to be applied in other basins as a support and complement tool to field monitoring activities and strategies for protection and remediation of the most impacted areas.

From inshore to offshore: distribution of microplastics in three Italian seawaters.

A comprehensive understanding of the concentration of microplastics (MPs) in seawaters is essential to implement monitoring programs and understand the impacts on ecosystems, as required by the European legislation to protect the marine environment. In this context, the purpose of this study is to investigate the composition, quantity, and spatial distribution of microplastics from coastal to offshore areas in three Italian seawaters. In addition, the distribution of microplastics between surface and subsurface water layers was analyzed in order to better understand the dynamics of MPs in the upper layers of the water column. A total number of 6069 MPs (mean total concentration of 0.029 microplastics · m-2) were found to be heterogeneous in type, shape, and color. In general, MPs concentrations decrease with coastal distance, except when environmental forcings are predominant (such as sea currents). Moreover, the amount of surface MPs was almost four times that of subsurface microplastics, which consisted mostly of fibers. In light of these results, it becomes clear how critical it is to plan remediation actions and programs to minimize microplastic accumulations in the sea.

GLOVE: The Global Plastic Ingestion Initiative for a cleaner world.

Plastics are one of the most used materials in the world. Their indiscriminate use and inappropriate disposal have led to inevitable impacts, for instance ingestion, on the environment arousing the attention of the global community. In addition, plastic ingestion studies are often written in scientific jargon or hidden behind paywalls, which makes these studies inaccessible. GLOVE is an online and open-access dashboard database available at gloveinitiative.shinyapps.io/Glove/ to support scientists, decision-makers, and society with information collected from plastic ingestion studies. The platform was created in the R environment, with a web interface developed through Shiny. It already comprises 530 studies, including all biological groups, with 245,366 individual records of 1458 species found in marine, freshwater, and terrestrial environments. The main goal of the GLOVE dashboard database is to improve data accessibility by being a scientifically useful grounded tool for designing effective and innovative actions in the current scenario of upcoming global and local agreements and actions on plastic pollution.

Disentangling beach litter pollution patterns to provide better guidelines for decision-making in coastal management.

Beach litter represents a worldwide problem impacting both terrestrial and aquatic environments. In the present study, we assessed beach litter pollution in a prominent touristic site in Brazil, the Jericoacoara National Park. In particular, we applied a delta-generalized additive modeling (GAM) approach in order to investigate pollution hotspots and to provide better guidelines for coastal environmental managers. A total of 7549 litter items were collected, resulting hard and flexible plastics the most abundant type. Our GAM analysis revealed that the distribution of each type of litter was affected by distinct drivers in the protected area, with the extension of the beach, tourist attractions, wind angle, and the distance to water bodies and villages as the most significant explanatory variables. Our model is suitable in predicting litter pollution hotspots on beaches, which is a valuable tool for future guidelines and effective management strategies to prevent beach pollution worldwide.

Scattered accumulation hotspots of macro-litter on the seafloor: Insights for mitigation actions.

Marine litter is an ever-increasing problem that demands immediate reduction plans and mitigation actions that should act synergically to efficiently meet ambitious goals. Since the seafloor has been recognized as the major sink for marine debris, the study of litter accumulation dynamics represent a fundamental tool to evaluate possible removal actions. We analysed a 7 years (2013-2019) standardized data series collected along Sardinian fishing grounds through MEDiterranean International Trawl Survey, for which estimates of density and weight of seafloor macro-litter were calculated for over 707 hauls. Results show the absence of any temporal trend in seafloor macro-litter density and weight, but rather indicate a spatial and bathymetric segregation of different litter categories. Our data showed how different sources and physical features of macro-litter items (i.e., plastic and fishing gear, rubber, glass, metals, and cloth) led to spatially segregated accumulation hotspots. We also point out here how the identification of seafloor macro-litter hotspots using aggregated data that include plastic items could obscure the identification of other segregated but yet relevant hotspots of other macro-litter categories accumulated in the marine environment. These hotspots often occurred at shallower depths and closer to coastlines, thus representing potential spots where eventual future litter removal action could be prioritized.

Scattered accumulation hotspots of macro-litter on the seafloor: Insights for mitigation actions.

Marine litter is an ever-increasing problem that demands immediate reduction plans and mitigation actions that should act synergically to efficiently meet ambitious goals. Since the seafloor has been recognized as the major sink for marine debris, the study of litter accumulation dynamics represents a fundamental tool to evaluate future removal actions. We analysed a 7 years (2013-2019) standardized data series collected along Sardinian fishing grounds through MEDiterranean International Trawl Survey, for which estimates of density and weight of seafloor macro-litter were calculated over 707 hauls. Results show the absence of any temporal trend in seafloor macro-litter density and weight, but rather indicate a spatial and bathymetric segregation of different litter categories. Our data showed how different sources and physical features of macro-litter items (i.e., plastic and fishing gears, rubber, glass, metal and textile) led to spatially segregated accumulation hotspots. These hotspots often occurred at shallower depths and closer to coastlines, representing spots where future litter removal action could be prioritized. We also point out here how the identification of seafloor macro-litter hotspots using aggregated data that include plastic items could indeed hide the identification of hotspots of other less abundant but yet detrimental macro-litter categories accumulated in the marine environment.

All is fish that comes to the net: metabarcoding for rapid fisheries catch assessment.

Monitoring marine resource exploitation is a key activity in fisheries science and biodiversity conservation. Since research surveys are time consuming and costly, fishery-dependent data (i.e., derived directly from fishing vessels) are increasingly credited with a key role in expanding the reach of ocean monitoring. Fishing vessels may be seen as widely ranging data-collecting platforms, which could act as a fleet of sentinels for monitoring marine life, in particular exploited stocks. Here, we investigate the possibility of assessing catch composition of single hauls carried out by trawlers by applying DNA metabarcoding to the dense water draining from fishing nets just after the end of hauling operations (hereafter "slush"). We assess the performance of this approach in portraying ß-diversity and examining the quantitative relationship between species abundances in the catch and DNA amount in the slush (read counts generated by amplicon sequencing). We demonstrate that the assemblages identified using DNA in the slush satisfactorily mirror those returned by visual inspection of net content (about 71% of species and 86% of families of fish) and detect a strong relationship between read counts and species abundances in the catch. We therefore argue that this approach could be upscaled to serve as a powerful source of information on the structure of demersal assemblages and the impact of fisheries.

The effect of tobacco smoking during pregnancy and breastfeeding on human milk composition-a systematic review.

Tobacco smoking is still a widespread habit in pregnant and breastfeeding women. While the role of these risk factors on neonatal outcomes has been deeply studied, their effect on human milk composition is still not completely clear. This study aimed to report the most up to date evidence about the alteration of breast milk composition of smoking breastfeeding mothers compared to non-smoking ones. We performed a systematic review by searching PubMed, Embase, and Cochrane Library databases. Evaluated data were extracted and critically analyzed by two independent authors. PRISMA guidelines were applied, and the risk of bias was assessed (ROBINS), as was the methodological quality of the included studies (GRADE). After applying the inclusion criteria, we included 20 studies assessed as medium or high quality. In all the studies, we analyzed data regarding 1769 mothers (398 smokers and 971 nonsmokers). Smoking was associated with a lower content of lipids, calories, and proteins. Moreover, it was characterized by decreased antioxidant properties and an altered immune status. Smoking during pregnancy and breastfeeding is significantly associated with an alteration of milk metabolic properties. Further studies are needed to investigate how these changes can alter newborns' development and outcomes and which molecular patterns are involved.

The MINOUWApp: a web-based tool in support of by-catch and discards management.

Current fishing practices often do not allow adequate selection of species or sizes of fish, resulting in unwanted catches, subsequently discarded, with the consequent negative effects on both marine communities and fisheries profitability. The cross-analysis of density patches of potential unwanted catches and distribution of fishing effort can support the identification of spatial-temporal hot-spots in which the fishing pressure should be reduced to limit the amount of discards. The MinouwApp represents a technological and methodological framework to bring different, and structurally complex, sources of georeferenced data together into a simple visual interface aiming to interactively explore temporal ranges and areas of interest. The objective is to improve the understanding of fisheries dynamics, including discards, thus contributing to the implementation of discard management plans in a context of participative, ecosystem-based fisheries management strategies.

Skin Pigmentation in Gilthead Seabream (Sparus aurata L.) Fed Conventional and Novel Protein Sources in Diets Deprived of Fish Meal.

The pattern of yellowish pigmentation of the skin was assessed in gilthead seabream (Sparus aurata) fed for 12 weeks iso-proteic (45%) and iso-lipidic (20%) diets deprived of fish meal and containing either a blend of vegetable protein-rich ingredients or where graded levels of the vegetable protein blend were replaced by insect (Hermetia illucens-10%, 20% or 40%) pupae meal, poultry by-product meal (20%, 30% or 40%), red swamp crayfish meal (10%) and marine microalgae (Tisochrysis lutea and Tetraselmis suecica-10%) dried biomass. Digital images of fish fed diets differing in protein sources were analyzed by means of an automatic and non-invasive image analysis tool, in order to determine the number of yellow pixels and their dispersion on the frontal and lateral sides of the fish. The relationship between the total carotenoid concentration in the diet and the number of yellow pixels was investigated. Test diets differently affected gilthead seabream skin pigmentation both in the forefront and the operculum, due to their carotenoid content. The highest yellow pixels' number was observed with the diet containing microalgae. Fish fed poultry by-product meal were characterized by the lowest yellow pixels' number, diets containing insect meal had an intermediate coloring capacity. The vegetable control, the microalgae mix diet and the crayfish diet had significantly higher values of yellow pixels at both inspected skin sites.

An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks and other empirical models.

Sensitivity analysis applied to Artificial Neural Networks (ANNs) as well as to other types of empirical ecological models allows assessing the importance of environmental predictive variables in affecting species distribution or other target variables. However, approaches that only consider values of the environmental variables that are likely to be observed in real-world conditions, given the underlying ecological relationships with other variables, have not yet been proposed. Here, a constrained sensitivity analysis procedure is presented, which evaluates the importance of the environmental variables considering only their plausible changes, thereby exploring only ecological meaningful scenarios. To demonstrate the procedure, we applied it to an ANN model predicting fish species richness, as identifying relationships between environmental variables and fish species occurrence in river ecosystems is a recurring topic in freshwater ecology. Results showed that several environmental variables played a less relevant role in driving the model output when that sensitivity analysis allowed them to vary only within an ecologically meaningful range of values, i.e. avoiding values that the model would never handle in its practical applications. By comparing percent changes in MSE between constrained and unconstrained sensitivity analysis, the relative importance of environmental variables was found to be different, with habitat descriptors and urbanization factors that played a more relevant role according to the constrained procedure. The ecologically constrained procedure can be applied to any sensitivity analysis method for ANNs, but obviously it can also be applied to other types of empirical ecological models.

Cascaded neural networks improving fish species prediction accuracy: the role of the biotic information.

Species distribution is the result of complex interactions that involve environmental parameters as well as biotic factors. However, methodological approaches that consider the use of biotic variables during the prediction process are still largely lacking. Here, a cascaded Artificial Neural Networks (ANN) approach is proposed in order to increase the accuracy of fish species occurrence estimates and a case study for Leucos aula in NE Italy is presented as a demonstration case. Potentially useful biotic information (i.e. occurrence of other species) was selected by means of tetrachoric correlation analysis and on the basis of the improvements it allowed to obtain relative to models based on environmental variables only. The prediction accuracy of the L. aula model based on environmental variables only was improved by the addition of occurrence data for A. arborella and S. erythrophthalmus. While biotic information was needed to train the ANNs, the final cascaded ANN model was able to predict L. aula better than a conventional ANN using environmental variables only as inputs. Results highlighted that biotic information provided by occurrence estimates for non-target species whose distribution can be more easily and accurately modeled may play a very useful role, providing additional predictive variables to target species distribution models.

Dataset exploited for the development and validation of automated cyanobacteria quantification algorithm, ACQUA.

The estimation and quantification of potentially toxic cyanobacteria in lakes and reservoirs are often used as a proxy of risk for water intended for human consumption and recreational activities. Here, we present data sets collected from three volcanic Italian lakes (Albano, Vico, Nemi) that present filamentous cyanobacteria strains at different environments. Presented data sets were used to estimate abundance and morphometric characteristics of potentially toxic cyanobacteria comparing manual Vs. automated estimation performed by ACQUA ("ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning" (Gandola et al., 2016) [1]). This strategy was used to assess the algorithm performance and to set up the denoising algorithm. Abundance and total length estimations were used for software development, to this aim we evaluated the efficiency of statistical tools and mathematical algorithms, here described. The image convolution with the Sobel filter has been chosen to denoise input images from background signals, then spline curves and least square method were used to parameterize detected filaments and to recombine crossing and interrupted sections aimed at performing precise abundances estimations and morphometric measurements.

ACQUA: Automated Cyanobacterial Quantification Algorithm for toxic filamentous genera using spline curves, pattern recognition and machine learning.

Toxigenic cyanobacteria are one of the main health risks associated with water resources worldwide, as their toxins can affect humans and fauna exposed via drinking water, aquaculture and recreation. Microscopy monitoring of cyanobacteria in water bodies and massive growth systems is a routine operation for cell abundance and growth estimation. Here we present ACQUA (Automated Cyanobacterial Quantification Algorithm), a new fully automated image analysis method designed for filamentous genera in Bright field microscopy. A pre-processing algorithm has been developed to highlight filaments of interest from background signals due to other phytoplankton and dust. A spline-fitting algorithm has been designed to recombine interrupted and crossing filaments in order to perform accurate morphometric analysis and to extract the surface pattern information of highlighted objects. In addition, 17 specific pattern indicators have been developed and used as input data for a machine-learning algorithm dedicated to the recognition between five widespread toxic or potentially toxic filamentous genera in freshwater: Aphanizomenon, Cylindrospermopsis, Dolichospermum, Limnothrix and Planktothrix. The method was validated using freshwater samples from three Italian volcanic lakes comparing automated vs. manual results. ACQUA proved to be a fast and accurate tool to rapidly assess freshwater quality and to characterize cyanobacterial assemblages in aquatic environments.