Correlation between early changes of serum lipids and clinical severity in patients with wasp stings

Quan, Zhenglin; Liu, Mei; Zhao, Jianmin; Yang, Xia

doi:10.1016/j.jacl.2022.09.003

Cited by 2 publications

(3 citation statements)

References 26 publications

(23 reference statements)

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“…Wasp venom initiates allergic and toxic reactions, activating immune and in ammatory pathways that increase in ammatory cytokines (IL-6, IL-8) and coagulation factors [27][28][29] ,leading to hemoglobin breakdown and enhancing in ammation through hemolysis products (heme, ferrous heme, oxygen free radicals) 30 .This process mediated by the STING-TBK1-p65/IRF3 signaling pathway and involves dysregulated lipid metabolism (HDL-C, Apo-A1) 31,32 . Our study demonstrates that elevated levels of the Systemic In ammatory Response Index (SIRI), which re ects systemic in ammation and immune status correlate with MODS and AKI in patients stung by wasps 8 .SIRI, easily assessed in emergency settings, offers a timely evaluation of the in ammatory response, proving more immediate than C-Reactive Protein (CRP) and Procalcitonin (PCT) in identifying high-risk patients.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning Based Prediction Model for Acute Kidney Injury Induced by Multiple Wasp Stings: Incorporating Four Admission Clinical Indices

Wu,

Zhang,

Zhang

et al. 2024

Preprint

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Acute kidney injury (AKI) following multiple wasp stings is a severe complication with potentially poor outcomes. Despite extensive research on AKI's risk factors, predictive models for wasp sting-related AKI are limited. This study aims to develop and validate a machine learning-based clinical prediction model for AKI in individuals with wasp stings. We retrospectively analyzed clinical data from 214 patients with wasp sting injuries. Among these patients, 34.6% (74/214) developed AKI. Using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis, the number of stings, presence of gross hematuria, systemic inflammatory response index (SIRI), and platelet count were identified as prognostic factors. A nomogram was constructed and evaluated for its predictive accuracy, showing an area under the curve (AUC) of 0.757 (95% CI 0.711 to 0.804) and a concordance index (C-index) of 0.75. The model's performance was assessed through internal validation, leave-one-out cross-validation, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Validation confirmed the model's reliability and superior discrimination ability over existing models, as demonstrated by NRI, IDI, and DCA. This nomogram accurately predicts the risk of AKI in wasp sting patients, facilitating early identification and management of those at risk.

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Section: Discussionmentioning

confidence: 99%

Machine-Learning Based Prediction Model for Acute Kidney Injury Induced by Multiple Wasp Stings: Incorporating Four Admission Clinical Indices

Wu,

Zhang,

Zhang

et al. 2024

Preprint

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“…Recently, wasp stings have become an increasingly serious public health problem because of the high incidence and mortality rates [1,2]. Hemolysis is one of the most common and prominent clinical symptoms of wasp stings, and the hemolysis incidence in severe patients (generally >30 skin sting lesions) is up to 83.5% [3]. The major complications in severe patients include acute kidney injury (AKI), liver damage and acute respiratory distress syndrome (ARDS), which are related to hemolysis; for example, hemolysis and rhabdomyolysis induced by massive wasp stings lead to the precipitation of haemoglobin and myoglobin casts in renal tubules, which cause AKI [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…(2) some wasp venom peptides can also activate phospholipase A on the erythrocyte membrane, mediating the hydrolysis of lecithin and resulting in hemolysis [8]. (3) Wasp venom peptides can prolong prothrombin time and reduce the activity of coagulation factors VIII and IX, leading to coagulation dysfunction [9,10].…”

Section: Introductionmentioning

confidence: 99%

Characterization of the Hemolytic Activity of Mastoparan Family Peptides from Wasp Venoms

Ye,

Zhang,

Luo

et al. 2023

Toxins

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Biologically active peptides have attracted increasing attention in research on the development of new drugs. Mastoparans, a group of wasp venom linear cationic α-helical peptides, have a variety of biological effects, including mast cell degranulation, activation of protein G, and antimicrobial and anticancer activities. However, the potential hemolytic activity of cationic α-helical peptides greatly limits the clinical applications of mastoparans. Here, we systematically and comprehensively studied the hemolytic activity of mastoparans based on our wasp venom mastoparan family peptide library. The results showed that among 55 mastoparans, 18 had strong hemolytic activity (EC50 ≤ 100 μM), 14 had modest hemolytic activity (100 μM < EC50 ≤ 400 μM) and 23 had little hemolytic activity (EC50 > 400 μM), suggesting functional variation in the molecular diversity of mastoparan family peptides from wasp venom. Based on these data, structure–function relationships were further explored, and, hydrophobicity, but not net charge and amphiphilicity, was found to play a critical role in the hemolytic activity of mastoparans. Combining the reported antimicrobial activity with the present hemolytic activity data, we found that four mastoparan peptides, Parapolybia-MP, Mastoparan-like peptide 12b, Dominulin A and Dominulin B, have promise for applications because of their high antimicrobial activity (MIC ≤ 10 μM) and low hemolytic activity (EC50 ≥ 400 μM). Our research not only identified new leads for the antimicrobial application of mastoparans but also provided a large chemical space to support the molecular design and optimization of mastoparan family peptides with low hemolytic activity regardless of net charge or amphiphilicity.

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Correlation between early changes of serum lipids and clinical severity in patients with wasp stings

Cited by 2 publications

References 26 publications

Machine-Learning Based Prediction Model for Acute Kidney Injury Induced by Multiple Wasp Stings: Incorporating Four Admission Clinical Indices

Machine-Learning Based Prediction Model for Acute Kidney Injury Induced by Multiple Wasp Stings: Incorporating Four Admission Clinical Indices

Characterization of the Hemolytic Activity of Mastoparan Family Peptides from Wasp Venoms

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