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Shellfish accumulate microalgal toxins, which can make them unsafe for human consumption. In France, in accordance with EU regulations, three groups of marine toxins are currently under official monitoring: lipophilic toxins, saxitoxins, and domoic acid. Other unregulated toxin groups are also present in European shellfish, including emerging lipophilic and hydrophilic marine toxins (e.g., pinnatoxins, brevetoxins) and the neurotoxin β-N-methylamino-L-alanine (BMAA). To acquire data on emerging toxins in France, the monitoring program EMERGTOX was set up along the French coasts in 2018. Three new broad-spectrum LC-MS/MS methods were developed to quantify regulated and unregulated lipophilic and hydrophilic toxins and the BMAA group in shellfish (bivalve mollusks and gastropods). A single-laboratory validation of each of these methods was performed. Additionally, these specific, reliable, and sensitive operating procedures allowed the detection of groups of EU unregulated toxins in shellfish samples from French coasts: spirolides (SPX-13-DesMeC, SPX-DesMeD), pinnatoxins (PnTX-G, PnTX-A), gymnodimines (GYM-A), brevetoxins (BTX-2, BTX-3), microcystins (dmMC-RR, MC-RR), anatoxin, cylindrospermopsin and BMAA/DAB. Here, we present essentially the results of the unregulated toxins obtained from the French EMERGTOX monitoring plan during the past five years (2018–2022). Based on our findings, we outline future needs for monitoring to protect consumers from emerging unregulated toxins.

This study deals with meaning construction in idiomatic phrases (locutions figées/figements). We focus on three French idioms involving the word cœur (à contrecœur, de bon cœur, de tout cœur). To begin, we give a brief account of idiomaticity’s typical criteria, insisting on the common idea that idioms are semantically opaque (non-compositional): their meanings are not a combination of literal meanings (Gross 1996). We argue that another point of view is productive. Indeed, using the notion of “holistic compositionality” (Gosselin 2013), we investigate the way meaning may arise from the combination of words’ abstract semantic contributions (compositionality principle) and take shape contextually (contextuality principle). The primary goal of the study is to analyze the meaning of à contrecœur, de bon cœur, de tout cœur using this approach – which happens to be more accurate than the literal meaning combination approach. To achieve this goal for each of these three idioms: (1) we examine a sample of 200 utterances (and the contexts in which they occur) coming from a large data-source (frWaC) and (2) we take advantage of previous semantic descriptions of cœur (Bertin 2018, 2019), contre (Amiot & De Mulder 2003; Paillard 2003), bon (Katz 1964) and tout (Bat-Zeev Shyldkrot 1995). These two methodological tools shed light on the operation of the semantic mechanism and emphasize the relevance of the “holistic compositionality” approach when applied to idioms’ meaning construction.

From January 2011 to March 2018, 26 patients aged from 20 to 80 years old reported being sick in France after eating sea figs of the genus Microcosmus. The patients had symptoms evoking a cerebellar syndrome: blurred or double vision, ataxia and dizziness, asthenia, headache, muscle cramps, paresthesia and digestive disorders (nausea, vomiting and diarrhea). Three of the 18 food poisoning events recorded by the Poison Control Center in Marseille and involving four patients were further investigated as the meal leftovers were collected and analyzed. A previous study ruled out the presence of the regulated lipophilic marine toxins after high-resolution mass spectrometry, but further analyses were required to look for hydrophilic cyanotoxins. The sea fig leftovers from food poisoning case Numbers 1 (January 2011), 6 (December 2012) and 17 (March 2018) of this published case series were analyzed by hydrophilic interaction liquid chromatography coupled to low- and high-resolution mass spectrometry to investigate the presence of hydrophilic cyanotoxins. The sea fig samples showed anatoxin-a (ATX-a) concentrations ranging from 193.7 to 1240.2 µg/kg. The sea fig control sample analyzed was also contaminated with ATX-a but in a much smaller concentration (22.5 µg/kg). To the best of our knowledge, this is the first report of human food poisoning involving ATX-a as the possible causative toxin where the cyanotoxin could be unequivocally identified.

Background: Breast cancer has a 14.8% incidence rate and an 8.5% fatality rate in Saudi Arabia. Mammography is useful for the early detection of breast cancer. Researchers have been developing artificial intelligence (AI) algorithms for early breast cancer diagnosis and reducing false-positive mammography results. The aim of this study was to examine the performance and accuracy of an AI system in breast cancer screening among Saudi women. Materials and Methods: This is a retrospective cross-sectional study that included 378 mammograms collected from 2017 to 2021 from government hospitals in Jeddah, Saudi Arabia. The patients’ demographic and clinical information were collected from files and electronic medical records. The radiologists’ assessments of the mammograms were based on Breast Imaging Reporting and Data System (BIRADS) scores. Follow-up or biopsy reports verified the radiologists’ findings. The MammoScreen system was the AI tool used in this study. Data were analyzed using SPSS Version 25. Results: The patients’ mean age was 50.31 years. Most patients had breast density B (42.3%) followed by A (27.2%) and C (25.9%). Most malignant cases were invasive ductal carcinomas (37.3%). Of the 181 cancer cases, 36.9% were BIRADS category V. The area under the curve for the AI detection (0.923; 95% confidence interval [CI], 0.893–0.954) was greater than that for the radiologists’ interpretation (0.838; 95% CI, 0.796–0.881). The AI detection agreed with the histopathological result in 167 positive (91.3%) and 182 negative cases (93.3%). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the AI system were 92.8%, 91.9%, 91.3%, 93.3%, and 92.3%, respectively. The radiologist’s interpretation agreed with the pathology report in 180 positive (73.8%) and 134 negative cases (100%). Its sensitivity, specificity, PPV, NPV, and accuracy were 100%, 67.7%, 73.8%, 100%, and 83.1%, respectively. Conclusions: The AI system tested in this study had better accuracy and diagnostic performance than the radiologists and thus could be used as a support diagnostic tool for breast cancer detection in clinical practice and to reduce false-positive recalls.