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Clinical practice guidelines (CPGs) for rare diseases (RDs) are scarce, may be difficult to identify through Internet searches and may vary in quality depending on the source and methodology used. In order to contribute to the improvement of the diagnosis, treatment and care of patients, Orphanet (www.orpha.net) has set up a procedure for the selection, quality evaluation and dissemination of CPGs, with the aim to provide easy access to relevant, accurate and specific recommendations for the management of RDs. This article provides an analysis of selected CPGs by medical domain coverage, prevalence of diseases, languages and type of producer, and addresses the variability in CPG quality and availability. CPGs are identified via bibliographic databases, websites of research networks, expert centres or medical societies. They are assessed according to quality criteria derived from the Appraisal of Guidelines, REsearch and Evaluation (AGREE II) Instrument. Only open access CPGs and documents for which permission from the copyright holders has been obtained are disseminated on the Orphanet website. From January 2012 to July 2015, 277 CPGs were disseminated, representing coverage of 1,122 groups of diseases, diseases or subtypes in the Orphanet database. No language restriction is applied, and so far 10 languages are represented, with a predominance of CPGs in English, French and German (92% of all CPGs). A large proportion of diseases with identified CPGs belong to rare oncologic, neurologic, hematologic diseases or developmental anomalies. The Orphanet project on CPG collection, evaluation and dissemination is a continuous process, with regular addition of new guidelines, and updates. CPGs meeting the quality criteria are integrated to the Orphanet database of rare diseases, together with other types of textual information and the appropriate services for patients, researchers and healthcare professionals in 40 countries.

Many large carnivore populations are expanding into human-modified landscapes and the subsequent increase in coexistence between humans and large carnivores may intensify various types of conflicts. A proactive management approach is critical to successful mitigation of such conflicts. The Cantabrian Mountains in Northern Spain are home to the last remaining native brown bear (Ursus arctos) population of the Iberian Peninsula, which is also amongst the most severely threatened European populations, with an important core group residing in the province of Asturias. There are indications that this small population is demographically expanding its range. The identification of the potential areas of brown bear range expansion is crucial to facilitate proactive conservation and management strategies towards promoting a further recovery of this small and isolated population. Here, we used a presence-only based maximum entropy (MaxEnt) approach to model habitat suitability and identify the areas in the Asturian portion of the Cantabrian Mountains that are likely to be occupied in the future by this endangered brown bear population following its range expansion. We used different spatial scales to identify brown bear range suitability according to different environmental, topographic, climatic and human impact variables. Our models mainly show that: (1) 4977 km2 are still available as suitable areas for bear range expansion, which represents nearly half of the territory of Asturias; (2) most of the suitable areas in the western part of the province are already occupied (77% of identified areas, 2820 km2), 41.4% of them occurring inside protected areas, which leaves relatively limited good areas for further expansion in this part of the province, although there might be more suitable areas in surrounding provinces; and (3) in the eastern sector of the Asturian Cantabrian Mountains, 62% (2155 km2) of the land was classified as suitable, and this part of the province hosts 44.3% of the total area identified as suitable areas for range expansion. Our results further highlight the importance of increasing: (a) the connectivity between the currently occupied western part of Asturias and the areas of potential range expansion in the eastern parts of the province; and (b) the protection of the eastern sector of the Cantabrian Mountains, where most of the future population expansion may be expected.

The deposition of aggregated amyloid-β peptides derived from the pro-amyloidogenic processing of the amyloid precurson protein (APP) into characteristic amyloid plaques (APs) is distinctive to Alzheimer’s disease (AD). Alternative APP processing via the metalloprotease ADAM10 prevents amyloid-β formation. We tested whether downregulation of ADAM10 activity by its secreted endogenous inhibitor secreted-frizzled-related protein 1 (SFRP1) is a common trait of sporadic AD. We demonstrate that SFRP1 is significantly increased in the brain and cerebrospinal fluid of patients with AD, accumulates in APs and binds to amyloid-β, hindering amyloid-β protofibril formation. Sfrp1 overexpression in an AD-like mouse model anticipates the appearance of APs and dystrophic neurites, whereas its genetic inactivation or the infusion of α-SFRP1-neutralizing antibodies favors non-amyloidogenic APP processing. Decreased Sfrp1 function lowers AP accumulation, improves AD-related histopathological traits and prevents long-term potentiation loss and cognitive deficits. Our study unveils SFRP1 as a crucial player in AD pathogenesis and a promising AD therapeutic target.

Aflatoxins (AF) and ochratoxin A (OTA) are fungal metabolites that have carcinogenic, teratogenic, embryotoxic, genotoxic, neurotoxic, and immunosuppressive effects in humans and animals. The increased consumption of plant-based foods and environmental conditions associated with climate change have intensified the risk of mycotoxin intoxication. This study aimed to investigate the abilities of eleven selected LAB strains to reduce/inhibit the growth of Aspergillus flavus, Aspergillus parasiticus, Aspergillus carbonarius, Aspergillus niger, Aspergillus welwitschiae, Aspergillus steynii, Aspergillus westerdijkiae, and Penicillium verrucosum and AF and OTA production under different temperature regiments. Data were treated by ANOVA, and machine learning (ML) models able to predict the growth inhibition percentage were built, and their performance was compared. All factors LAB strain, fungal species, and temperature significantly affected fungal growth and mycotoxin production. The fungal growth inhibition range was 0–100%. Overall, the most sensitive fungi to LAB treatments were P. verrucosum and A. steynii, while the least sensitive were A. niger and A. welwitschiae. The LAB strains with the highest antifungal activity were Pediococcus pentosaceus (strains S11sMM and M9MM5b). The reduction range for AF was 19.0% (aflatoxin B1)-60.8% (aflatoxin B2) and for OTA, 7.3–100%, depending on the bacterial and fungal strains and temperatures. The LAB strains with the highest anti-AF activity were the three strains of P. pentosaceus and Leuconostoc mesenteroides ssp. dextranicum (T2MM3), and those with the highest anti-OTA activity were Leuconostoc paracasei ssp. paracasei (3T3R1) and L. mesenteroides ssp. dextranicum (T2MM3). The best ML methods in predicting fungal growth inhibition were multilayer perceptron neural networks, followed by random forest. Due to anti-fungal and anti-mycotoxin capacity, the LABs strains used in this study could be good candidates as biocontrol agents against aflatoxigenic and ochratoxigenic fungi and AFL and OTA accumulation.

Mycotoxins are secondary metabolites produced primarily by certain species of the genera Aspergillus, Fusarium, Penicillium, Alternaria, and Claviceps. Toxigenic fungi and mycotoxins are prevalent in staple foods, resulting in significant economic losses and detrimental impacts on public health and food safety. These fungi demonstrate remarkable adaptation to water and heat stress conditions associated with climate change, and the use of synthetic antifungals can lead to the selection of resistant strains. In this context, the development of novel strategies for their prevention and control of food is a priority objective. This review synthesizes the extant knowledge concerning the antifungal and anti-mycotoxin potential of the primary metal nanoparticles (silver, copper) and metal oxide nanoparticles (copper oxide and zinc oxide) studied in the literature. It also considers synthesis methods and the lack of consensus on technical definitions and regulations. Despite methodological gaps and the scarcity of publications analyzing the effect of these NPs on fungal growth and mycotoxin production simultaneously, it can be concluded that these NPs present high reactivity, stability, and the ability to combat these food risks. However, aspects related to their biosafety and consumer acceptance remain major challenges that must be addressed for their implementation in the food industry.

ContextRestoring or establishing corridors between residual forest patches is one of the most adopted strategies for the conservation of animal populations and ecosystem processes in fragmented landscapes.ObjectivesThis study aimed to assess whether it is more effective to focus restoration actions on existing corridors or to establish habitats in other strategic areas that can create new dispersal pathways to enhance connectivity.MethodsWe considered a real agroecosystem in northern Italy, based our analyses on graph-theory and habitat availability metrics, and focused on the Hazel Dormouse as the target species. We compared the connectivity increase resulting from (i) the simulated restoration of existing priority corridors, i.e., those with significant presence of forest but in which restoration actions would still result in considerable connectivity gains, or (ii) the simulated plantation of 30 hedgerows along new priority pathways, i.e., those areas with no current forest cover in which habitat creation would be more beneficial for connectivity.ResultsImplementing new priority pathways resulted in substantially larger connectivity gains (+ 38%) than when restoration efforts were concentrated in improving already existing corridors (+ 11%).ConclusionsEstablishing hedgerows along new pathways allowed enhancing the complementary and functionality of the full set of landscape corridors and proved more efficient than just strengthening the areas where dispersal flows were already concentrated. We demonstrated the importance of analytical procedures able to compare the effectiveness of different management strategies for enhancing connectivity. Our approach may be applied to multiple species sensitive to fragmentation in other heterogeneous landscapes and geographical contexts.

Unifloral honeys are highly demanded by honey consumers, especially in Europe. To ensure that a honey belongs to a very appreciated botanical class, the classical methodology is palynological analysis to identify and count pollen grains. Highly trained personnel are needed to perform this task, which complicates the characterization of honey botanical origins. Organoleptic assessment of honey by expert personnel helps to confirm such classification. In this study, the ability of different machine learning (ML) algorithms to correctly classify seven types of Spanish honeys of single botanical origins (rosemary, citrus, lavender, sunflower, eucalyptus, heather and forest honeydew) was investigated comparatively. The botanical origin of the samples was ascertained by pollen analysis complemented with organoleptic assessment. Physicochemical parameters such as electrical conductivity, pH, water content, carbohydrates and color of unifloral honeys were used to build the dataset. The following ML algorithms were tested: penalized discriminant analysis (PDA), shrinkage discriminant analysis (SDA), high-dimensional discriminant analysis (HDDA), nearest shrunken centroids (PAM), partial least squares (PLS), C5.0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost). The ML models were optimized by repeated 10-fold cross-validation primarily on the basis of log loss or accuracy metrics, and their performance was compared on a test set in order to select the best predicting model. Built models using PDA produced the best results in terms of overall accuracy on the test set. ANN, ET, RF and XGBoost models also provided good results, while SVM proved to be the worst.

Spain is a relevant producer of oats (Avena sativa), but to date there has been no study on the occurrence/co-occurrence of mycotoxins in oats marketed in Spain. The present study is addressed to overcome this lack of knowledge. One hundred oat kernel samples were acquired across different Spanish geographic regions during the years 2015–2019 and analyzed for mycotoxin content using an ultra-high performance liquid chromatography electrospray ionization tandem mass spectrometry (UPLC–ESI–MS/MS) method and matrix-matched calibration. The focus was on the regulated mycotoxins although other relevant mycotoxins were considered. The percentage of incidence (levels ≥ limit of detection), mean and range (ng/g) of mycotoxins were as follows: zearalenone (66%, mean 39.1, range 28.1–153), HT-2 toxin (47%, mean 37.1, range 4.98–439), deoxynivalenol, (34%, mean 81.4, range 19.1–736), fumonisin B1 (29%, mean 157.5, range 63.2–217.4), and T-2 toxin, (24%, mean 49.9, range 12.3–321). Fumonisin B2, 3-acetyldeoxynivalenol, aflatoxins B1, B2, and G2, and ochratoxin A were also detected at low levels, but aflatoxin G1 was not. The maximum limits established by the European Commission for unprocessed oats were not exceeded, except for zearalenone (in one sample), and the sum of aflatoxins (in two samples). Mycotoxin co-occurrence at quantifiable levels in the same sample (two to five combinations) was found in 31% of samples. The most common mixtures were those of HT-2 + T-2 toxins alone or together with deoxynivalenol and/or zearalenone.

The proliferation of toxigenic fungi in food and the subsequent production of mycotoxins constitute a significant concern in the fields of public health and consumer protection. This review highlights recent strategies and emerging methods aimed at preventing fungal growth and mycotoxin contamination in food matrices as opposed to traditional approaches such as chemical fungicides, which may leave toxic residues and pose risks to human and animal health as well as the environment. The novel methodologies discussed include the use of plant-derived compounds such as essential oils, classified as Generally Recognized as Safe (GRAS), polyphenols, lactic acid bacteria, cold plasma technologies, nanoparticles (particularly metal nanoparticles such as silver or zinc nanoparticles), magnetic materials, and ionizing radiation. Among these, essential oils, polyphenols, and lactic acid bacteria offer eco-friendly and non-toxic alternatives to conventional fungicides while demonstrating strong antimicrobial and antifungal properties; essential oils and polyphenols also possess antioxidant activity. Cold plasma and ionizing radiation enable rapid, non-thermal, and chemical-free decontamination processes. Nanoparticles and magnetic materials contribute advantages such as enhanced stability, controlled release, and ease of separation. Furthermore, this review explores recent advancements in the application of artificial intelligence, particularly machine learning methods, for the identification and classification of fungal species as well as for predicting the growth of toxigenic fungi and subsequent mycotoxin production in food products and culture media.

Grapes are frequently contaminated by Aspergillus section Nigri fungi and ochratoxin A (OTA), with A. niger also capable of producing substantial fumonisin B2 (FB2) levels. Emerging evidence suggests that aflatoxigenic fungi may eventually replace ochratoxigenic fungi in certain regions due to better adaptation to changes in climatic conditions. However, research on the toxigenic fungal community and mycotoxins in grapes from organic vineyards remains limited. Research on Spanish conventional grapes is also deficient, with most of the available literature being outdated. The present study investigates the diversity of toxigenic fungi and the presence of mycotoxins in organically cultivated grape berries in Spain, which are renowned for their significant oenological tradition. This study employed species-specific PCR protocols for fungal characterization and optimized methods for the analysis of OTA, FB2, and aflatoxin B1 (AFB1) by UPLC–ESI–MS/MS. The most prevalent species present were Aspergillus flavus, A. niger, A. parasiticus, A. steynii, A. carbonarius, and A. westerdijkiae (67.1%, 43.5%, 20.0%, 14.1%, 14.1%, and 11.8% of the samples, respectively). OTA was detected only in 16 samples (19%), averaging 0.48 ng/g and peaking at 0.7 ng/g, which were lower than previously reported for conventional grapes. There was no FB2 or AFB1 detected. This study is pioneering in its exploration of the occurrence of toxigenic mycobiota, beyond Nigri fungi, and subsequent potential for other serious mycotoxins to contaminate Spain’s organic grapes.