&lt;p&gt;Myotoxic Mushroom Poisoning in Thailand: Clinical Characteristics and Outcomes&lt;/p&gt;

Trakulsrichai, Satariya; Jeeratheepatanont, Peerawich; Sriapha, Charuwan; Tongpoo, Achara; Wananukul, Winai

doi:10.2147/ijgm.s271914

Cited by 13 publications

(6 citation statements)

References 18 publications

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“…The study identified 14 clinical variables, of which AST, ALT, LDH, INR, PT, TBiL, IBiL, urea, creatinine, and CK-MB were consistent with current clinical evidence. These factors were classified as hepatic (AST, ALT [ 39 - 41 ], LDH [ 42 ], TBiL [ 39 ], and IBiL [ 43 ]), coagulation (INR [ 39 , 41 ] and PT [ 43 ]), renal (urea [ 43 ] and creatinine[ 40 , 44 ]), and cardiac impairment (CK-MB [ 42 ]). Using the SHAP method, we found that the indicators characterizing liver function impairment contributed 57.5% to the prediction results of the model and had the greatest impact.…”

Section: Discussionmentioning

confidence: 99%

Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach

Liu¹,

Lyu²,

Yang³

et al. 2023

JMIR Form Res

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Background Early triage of patients with mushroom poisoning is essential for administering precise treatment and reducing mortality. To our knowledge, there has been no established method to triage patients with mushroom poisoning based on clinical data. Objective The purpose of this work was to construct a triage system to identify patients with mushroom poisoning based on clinical indicators using several machine learning approaches and to assess the prediction accuracy of these strategies. Methods In all, 567 patients were collected from 5 primary care hospitals and facilities in Enshi, Hubei Province, China, and divided into 2 groups; 322 patients from 2 hospitals were used as the training cohort, and 245 patients from 3 hospitals were used as the test cohort. Four machine learning algorithms were used to construct the triage model for patients with mushroom poisoning. Performance was assessed using the area under the receiver operating characteristic curve (AUC), decision curve, sensitivity, specificity, and other representative statistics. Feature contributions were evaluated using Shapley additive explanations. Results Among several machine learning algorithms, extreme gradient boosting (XGBoost) showed the best discriminative ability in 5-fold cross-validation (AUC=0.83, 95% CI 0.77-0.90) and the test set (AUC=0.90, 95% CI 0.83-0.96). In the test set, the XGBoost model had a sensitivity of 0.93 (95% CI 0.81-0.99) and a specificity of 0.79 (95% CI 0.73-0.85), whereas the physicians’ assessment had a sensitivity of 0.86 (95% CI 0.72-0.95) and a specificity of 0.66 (95% CI 0.59-0.73). Conclusions The 14-factor XGBoost model for the early triage of mushroom poisoning can rapidly and accurately identify critically ill patients and will possibly serve as an important basis for the selection of treatment options and referral of patients, potentially reducing patient mortality and improving clinical outcomes.

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Section: Discussionmentioning

confidence: 99%

Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach

Liu¹,

Lyu²,

Yang³

et al. 2023

JMIR Form Res

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“…Therefore, it is likely that the two species described in this study are edible. However, the correct identification of well documented edible species is important because a number of Russula species are reported as poisonous 48 , 49 or can cause gastrointestinal irritations, and the latter ones include Russula collections identified as R. cf. rosacea 50 .…”

Section: Discussionmentioning

confidence: 99%

Two new Russula species (fungi) from dry dipterocarp forest in Thailand suggest niche specialization to this habitat type

Wisitrassameewong

Manz

Hampe³

et al. 2022

Sci Rep

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Dry dipterocarp forests are among the most common habitat types in Thailand. Russulaceae are known as common ectomycorrhizal symbionts of Dipterocarpaceae trees in this type of habitat. The present study aims to identify collections of Russula subsection Amoeninae Buyck from dry dipterocarp forests in Thailand. A multi-locus phylogenetic analysis placed Thai Amoeninae collections in two novel lineages, and they are described here as R. bellissima sp. nov. and R. luteonana sp. nov. The closest identified relatives of both species were sequestrate species suggesting that they may belong to drought-adapted lineages. An analysis of publicly available ITS sequences in R. subsect. Amoeninae did not confirm evidence of any of the new species occurring in other Asian regions, indicating that dry dipterocarp forests might harbor a novel community of ectomycorrhizal fungi. Macromorphological characters are variable and are not totally reliable for distinguishing the new species from other previously described Asian Amoeninae species. Both new species are defined by a combination of differentiated micromorphological characteristics in spore ornamentation, hymenial cystidia and hyphal terminations in the pileipellis. The new Amoeninae species may correspond to some Russula species collected for consumption in Thailand, and the detailed description of the new species can be used for better identification of edible species and food safety in the region.

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“…10,11 Well-documented examples include Amanita brunneitoxicaria, A. exitialis, A. fuliginea, A. gleocystidiosa and Russula subnigricans. 2,4,10,12,13 Mushroom poisoning cases typically fall into two primary categories: gastrointestinal irritation and neurotoxicity. 10 Various mushroom genera, including Agaricus, Chlorophyllum, Entoloma, Hygrocybe, Macrocybe, and others, are recognized sources of gastrointestinal (GI) irritants.…”

Section: Detection Of Neurotoxins Using Lc-ms/ms Methodsmentioning

confidence: 99%

Analyzing DNA barcoding and identifying toxins caused by neurotoxic mushroom poisoning using liquid chromatography tandem mass spectrometry

Phatsarapongkul,

Parnmen,

Nooron

et al. 2024

JAMS

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Background: Neurotoxic mushroom poisoning often exhibits rapid symptom onset, typically attributed to compounds such as Ibotenic acid, which affect the central nervous system. This study addresses a new case of mushroom-related food poisoning in southern Thailand. Objective: The objectives are to determine the presence of ibotenic acid in cases of mushroom-related food poisoning utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS) and to identify toxic Amanita species implicated in these cases. Materials and methods: Remnant mushroom samples obtained from three clinically reported cases were used. Nucleotide similarity was compared against the rRNA/ ITS databases using NCBI BLAST search. Phylogenetic analyses were conducted using maximum likelihood (ML) and FastTree approaches. LC-MS/MS was employed to separate of Ibotenic acid, determine its molecular weight and perform precursor ion fragmentation. Results: Analysis of the rRNA/ITS databases revealed a high nucleotide similarity between suspected mushroom samples and Amanita digitosa. Detailed phylogenetic analysis confirmed that mushroom samples from the three poisoning cases clustered with A. digitosa. LC-MS/MS analysis showed the presence of ibotenic acid, with precursor ion (m/z 159) and product ion (m/z 113.1) as the major toxic substances. Clinically, patients poisoned by ibotenic acid-containing mushrooms exhibited a short latent period with symptoms of nausea, vomiting, vertigo, delirium, confusion, and fatigue. Conclusion: The genus Amanita comprises both edible and inedible species that produce several lethal toxins. The report of ibotenic acid in A. digitosa is a novel finding, valuable for food safety monitoring and healthcare decision-maker. This is especially notable due to the accuracy and rapidity of the analytical process.

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<p>Myotoxic Mushroom Poisoning in Thailand: Clinical Characteristics and Outcomes</p>

Cited by 13 publications

References 18 publications

Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach

Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach

Two new Russula species (fungi) from dry dipterocarp forest in Thailand suggest niche specialization to this habitat type

Analyzing DNA barcoding and identifying toxins caused by neurotoxic mushroom poisoning using liquid chromatography tandem mass spectrometry

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