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Advancing automated identification of airborne fungal spores: Guidelines for cultivation and reference dataset creation
No full textThe presence of bioparticles in the air, including fungal spores, is a major concern for human and plant health and requires robust and precise monitoring systems. While a European norm based on the manual volumetric Hirst method exists, there’s a growing interest in technologies allowing automated real-time monitoring. Most of them rely on machine learning for the identification of bioaerosols. However, the diverse nature of airborne particles in terms of size, properties and composition presents challenges, among which the availability of well-curated datasets for training algorithms. While collecting reference material for pollen is relatively straightforward, current automatic monitoring methods for fungal spores rely on limited training data, hindering broader applicability. This study, which was conducted in the frame of the SYLVA project (GA no. 101086109) and the COST Action ADOPT (CA18226), aims to address this gap by outlining best practices for collecting reference material from controlled cultivation and creating datasets specifically tailored for training algorithms to classify airborne fungal spores. Critical aspects such as access to reference fungal species, in vitro cultivation, sporulation yield, clean spore isolation, dry aerosolization, and dataset cleaning have been explored for a series of 17 fungal species from the Belgian fungi collection BCCM/IHEM, including 5 Alternaria species with contrasted morphological profiles. Simple classification models were developed as proof-of-principle to assess recognition capabilities from the holography and/or fluorescence data measured by the SwisensPoleno Jupiter (Swisens AG) and laser-induced scattering and fluorescence data measured by the Plair Rapid-E+ (Plair SA). The models were trained using 80% of reference data, while 10% was used for validation to avoid overfitting during training and the remaining 10% was left aside for testing the identification performance. For Plair Rapid-E+, classification accuracy for 7 genera was shown to vary from 0.43 to 0.75 depending on the taxon (F1 score 0.577), recognizing best Botrytis cinerea and Cladosporium (class created as a mix of 3 species). For SwisensPoleno Jupiter, the initial performance obtained for classification of 8 genera by using only holographic images (F1 score 0.77) has been significantly improved by complementing them with fluorescence measurements (F1 score 0.83). Classification accuracy varied between 0.55 and 0.95 with the best performance for Curvularia lunata and Alternaria (class created as a mix of 5 species). Differentiation of species was also shown to be possible for Cladosporium, with more difficulty for some Alternaria species, while the F1 score remained good (0.72). Overall, this protocol is paving the way for more efficient, standard and accurate automatic identification of airborne fungal spores.</p
Knowledge, perceptions and practices related to mosquitoes and mosquito-borne viruses: survey in Belgium, 2022
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Characterisation and Hazard identification of substandard and Falsified Antimicrobial Drugs. The CantiBio project 2016-2020
No full textStudies on SF antimicrobials, one of the most frequently used medical products, circulating in Europe or other industrialized regions are very scarce. However, case reports of SF antimicrobials have already demonstrated associated adverse drug effects, prolonged illness, deadly outcomes and microbial resistance. The present study, based on suspected samples seized by the Belgian customs and inspection services, aimed to provide systematic and comprehensive understanding of SF antimicrobials by developing analytical methods for monitoring SF antimicrobials and identifying potential hazards to raise public awareness.
Several quality aspects were evaluated. In a first step samples were screened and the dosage of the identified antibiotics was determined. Results showed that all seized samples contained the API they claimed and that no other antimicrobial drugs could be discovered. Though, half of the samples were underdosed, resulting in a potentially lower efficacy which may aggravate illness of patients and induce bacterial resistance. Further, the collected samples were evaluated in terms of impurities and dissolution. The impurities were analyzed based on the methods of the European Pharmacopoeia (Ph. Eur.) and the dissolution methods were adopted from the United States Pharmacopeia (USP). Moreover, the dissolution profiles of SF antimicrobials were compared to the ones of their genuine counterparts. In general, about 30% of the samples contained higher than permitted amounts of impurities. Concerning the dissolution tests, 58% of samples were not compliant as the amount of drug released is under the limit at the time point described by the USP. In addition to that, low equivalences of dissolution profiles between SF antimicrobials and genuine products were demonstrated based on f1 and f2 statistics. Finally a series of tests of microbiological contamination, residual solvents and heavy metals were carried out. The results showed that 35% of samples did not comply with bio-burden testing, and one out of three injectable samples failed sterility testing with contamination of neurotoxin producing Penicillium crustosum. Moreover, it has been found that four out of 51 SF antimicrobials contained an excessive amount of the class two residual solvent dichloromethane. In the pilot study of heavy metals, one out of 15 tested samples was contaminated with vanadium and lead. These sub-quality issues of SF antimicrobials are possibly attributed to non-standard manufacturing practices, improper storage conditions and/or intentional fraud.
Finally, an approach for on site analysis of suspected antimicrobial drugs was developed, based on spectroscopy and chemometric methods, for identification of the API and dosage compliance.</p
Impact of short-term exposure to air pollution on natural mortality and vulnerable populations: a multi-city case-crossover analysis in Belgium
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Expert report on FLAKKA
No full textThis rapid assessment on the FLAKKA phenomenon in Belgium was created to support the General Drug Policy Cell. The document combines data from all available resources (hospitals, police, court investigations, prevention & harm reduction initiatives, own research, …) ranging from mid-2022 until February 2024. The main questions answered are: 1) what is FLAKKA, 2) which epidemiological data are available and 3) which actions have been or are yet to be undertaken.</p
