Acute mushroom poisoning of Amanita pseudosychnopyramis: A case report from Fujian, China with exact species identification and descriptive study

Determination of ibotenic acid and muscimol in species of the genus Amanita section Amanita from China

The genus Amanita sect. Amanita harbors approximately 150 species in the world, and 27 species have been recognized in China. Some of the species in China have continuously caused poisoning. The responsible toxins should be ibotenic acid (IBO) and muscimol (MUS). However, species of the section Amanita containing IBO and MUS and their systematic positions are unclear. In this study, the contents of IBO and MUS in 84 samples of 24 species in section Amanita were detected using UPLC‒MS/MS, and the distribution of toxin-containing species in the molecular phylogeny was analyzed by the combined (ITS, nrLSU, RPB2, TUB2 and TEF1-α) dataset using maximum likelihood (ML) analysis and Bayesian inference (BI). Our results indicated that 10 of the 24 species contained IBO and MUS ranging from 0.6125 to 32.0932 and 0.0056-5.8685 g/kg dry weight, respectively. Among these 10 species, the toxins of eight species, including Amanita altipes, A. concentrica, A. flavopantherina, A. griseopantherina, A. pseudopantherina, A. rubrovolvata, A. subglobosa and A. sychnopyramis, were detected for the first time. In addition, the IBO and MUS contents of A. subglobosa in different growth stages showed that both toxins decreased in the mature stage. The phylogenetic analysis showed that all species of sect. Amanita from China were divided into 5 groups. And IBO- and MUS-containing species were gathered in clades Ⅰ and Ⅳ, but not all of the species in the two clades contain the toxins. No presence of IBO and MUS in the species of clades Ⅱ, Ⅲ and Ⅴ were confirmed.

Mechanism and treatment of α-amanitin poisoning

Amanita poisoning has a high mortality rate. The α-amanitin toxin in Amanita is the main lethal toxin. There is no specific detoxification drug for α-amanitin, and the clinical treatment mainly focuses on symptomatic and supportive therapy. The pathogenesis of α-amanitin mainly includes: α-amanitin can inhibit the activity of RNA polymeraseII in the nucleus, including the inhibition of the largest subunit of RNA polymeraseII, RNApb1, bridge helix, and trigger loop. In addition, α-amanitin acts in vivo through the enterohepatic circulation and transport system. α-Amanitin can cause the cell death. The existing mechanisms of cell damage mainly focus on apoptosis, oxidative stress, and autophagy. In addition to the pathogenic mechanism, α-amanitin also has a role in cancer treatment, which is the focus of current research. The mechanism of action of α-amanitin on the body is still being explored.

Automatic Mushroom Species Classification Model for Foodborne Disease Prevention Based on Vision Transformer

Mushrooms are the fleshy, spore-bearing structure of certain fungi, produced by a group of mycelia and buried in a substratum. Mushrooms are classified as edible, medicinal, and poisonous. However, many poisoning incidents occur yearly by consuming wild mushrooms. Thousands of poisoning incidents are reported each year globally, and 80% of these are from unidentified species of mushrooms. Mushroom poisoning is one of the most serious food safety issues worldwide. Motivated by this problem, this study uses an open-source mushroom dataset and employs several data augmentation approaches to decrease the probability of model overfitting. We propose a novel deep learning pipeline (ViT-Mushroom) for mushroom classification using the Vision Transformer large network (ViT-L/32). We compared the performance of our method against that of a convolutional neural network (CNN). We visualized the high-dimensional outputs of the ViT-L/32 model to achieve the interpretability of ViT-L/32 using the t-distributed stochastic neighbor embedding (t-SNE) method. The results show that ViT-L/32 is the best on the testing dataset, with an accuracy score of 95.97%. These results surpass previous approaches in reducing intraclass variability and generating well-separated feature embeddings. The proposed method is a promising deep learning model capable of automatically classifying mushroom species, helping wild mushroom consumers avoid eating toxic mushrooms, safeguarding food safety, and preventing public health incidents of food poisoning. The results will offer valuable resources for food scientists, nutritionists, and the public health sector regarding the safety and quality of mushrooms.