Comment diagnostiquer une intoxication par conotoxines ?

Le diagnostic repose sur l'historique d'exposition et les symptômes cliniques.

Quels tests sont utilisés pour détecter les conotoxines ?

Des tests biologiques et des analyses de sang peuvent être effectués.

Quels symptômes indiquent une intoxication par conotoxines ?

Les symptômes incluent paralysie, douleur intense et troubles respiratoires.

Les conotoxines sont-elles détectables dans le sang ?

Oui, des méthodes spécifiques peuvent détecter les conotoxines dans le sang.

Y a-t-il des marqueurs biologiques pour les conotoxines ?

Actuellement, il n'existe pas de marqueurs spécifiques pour les conotoxines.

Quels sont les symptômes courants des conotoxines ?

Les symptômes incluent des douleurs, des nausées, et des troubles neurologiques.

Les conotoxines provoquent-elles des troubles respiratoires ?

Oui, elles peuvent entraîner des difficultés respiratoires graves.

Peut-on ressentir des douleurs après exposition aux conotoxines ?

Oui, des douleurs aiguës et localisées sont fréquentes après exposition.

Les symptômes des conotoxines sont-ils immédiats ?

Les symptômes peuvent apparaître rapidement, souvent en quelques minutes.

Y a-t-il des symptômes neurologiques associés aux conotoxines ?

Oui, des symptômes comme la paralysie et des troubles de la coordination peuvent survenir.

Comment prévenir l'exposition aux conotoxines ?

Évitez de manipuler les cônes marins et portez des gants lors de la pêche.

Y a-t-il des mesures de sécurité pour les plongeurs ?

Oui, les plongeurs doivent être formés aux risques liés aux cônes marins.

Les conotoxines peuvent-elles être évitées par la cuisson ?

La cuisson ne neutralise pas les conotoxines, il faut éviter leur consommation.

Les enfants sont-ils plus à risque d'exposition ?

Oui, les enfants peuvent être plus vulnérables en raison de leur curiosité.

Des campagnes de sensibilisation existent-elles ?

Oui, des campagnes sensibilisent le public aux dangers des cônes marins.

Quel est le traitement principal pour l'intoxication par conotoxines ?

Le traitement est symptomatique et peut inclure des soins de soutien en milieu hospitalier.

Des antidotes existent-ils pour les conotoxines ?

Actuellement, il n'existe pas d'antidote spécifique pour les conotoxines.

Comment gérer la douleur causée par les conotoxines ?

Des analgésiques et des traitements symptomatiques sont utilisés pour gérer la douleur.

Les soins intensifs sont-ils nécessaires pour les cas graves ?

Oui, les cas graves peuvent nécessiter des soins intensifs pour surveiller la respiration.

Peut-on utiliser des médicaments pour traiter les symptômes ?

Oui, des médicaments peuvent être administrés pour traiter les symptômes spécifiques.

Quelles complications peuvent survenir après une intoxication ?

Des complications neurologiques et respiratoires peuvent survenir après l'exposition.

Les complications sont-elles réversibles ?

Certaines complications peuvent être réversibles, mais d'autres peuvent être permanentes.

Y a-t-il un risque de décès lié aux conotoxines ?

Oui, une intoxication sévère peut entraîner un risque de décès par paralysie respiratoire.

Les complications nécessitent-elles un suivi médical ?

Oui, un suivi médical est souvent nécessaire pour évaluer les complications.

Les complications peuvent-elles affecter la qualité de vie ?

Oui, les complications peuvent avoir un impact significatif sur la qualité de vie.

Qui est le plus à risque d'intoxication par conotoxines ?

Les plongeurs, pêcheurs et personnes manipulant des cônes marins sont à risque.

Les personnes allergiques sont-elles plus vulnérables ?

Oui, les personnes allergiques peuvent avoir des réactions plus sévères aux conotoxines.

L'âge influence-t-il le risque d'intoxication ?

Oui, les jeunes enfants et les personnes âgées sont plus à risque.

Les personnes avec des antécédents médicaux sont-elles plus à risque ?

Oui, les personnes avec des antécédents neurologiques peuvent être plus vulnérables.

Les régions géographiques influencent-elles le risque ?

Oui, le risque est plus élevé dans les régions côtières où vivent les cônes marins.

Conotoxines - Informations médicales vérifiées

A machine learning approach to identifying important features for achieving step thresholds in individuals with chronic stroke.

2022

DOI: 10.1371/journal.pone.0270105 PMID: 35714133

While many factors are associated with stepping activity after stroke, there is significant variability across studies. One potential reason to explain this variability is that there are certain chara... Using two step thresholds (2500 steps/day, corresponding to home vs. community ambulation and 5500 steps/day, corresponding to achieving physical activity guidelines through walking), we applied 3 dif... We analyzed data from 268 participants with stroke that included 25 demographic, performance-based and self-report variables. Step 1 of our analysis involved dimensionality reduction using lasso regul... The number of relevant predictors was reduced from 25 to 7 for home vs. community and from 25 to 16 for aerobic thresholds. Drop column feature importance revealed that 6 Minute Walk Test and speed mo... Addressing physical capacity is necessary but not sufficient to achieve important step thresholds, as ancillary characteristics, such as readiness to change activity behavior and physical health may a...

Brain Damage, Chronic Humans Machine Learning Stroke Stroke Rehabilitation +2 autres

Preprocedural determination of an occlusion pathomechanism in endovascular treatment of acute stroke: a machine learning-based decision.

Sep 2023

DOI: 10.1136/neurintsurg-2022-018946 PMID: 35710314

To evaluate whether an occlusion pathomechanism can be accurately determined by common preprocedural findings through a machine learning-based prediction model (ML-PM).... A total of 476 patients with acute stroke who underwent endovascular treatment were retrospectively included to derive an ML-PM. For external validation, 152 patients from another tertiary stroke cent... An ML-PM could determine an occlusion pathomechanism with an accuracy of 96.9% (AUC=0.95). In the model, CT angiography-determined occlusion type, atrial fibrillation, hyperdense artery sign, and occl... An ML-PM could accurately determine an occlusion pathomechanism with common preprocedural findings. A decision flowchart consisting of the four most influential findings was clinically applicable and ...

Humans Retrospective Studies Stroke Arteries Machine Learning +1 autres

Machine learning/molecular dynamic protein structure prediction approach to investigate the protein conformational ensemble.

15 06 2022

DOI: 10.1038/s41598-022-13714-z PMID: 35705565

Proteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings show that co-evolutionary analysis coupled with...

Machine Learning Molecular Dynamics Simulation Protein Conformation Protein Folding Proteins

Ensemble Machine Learning Model for Accurate Air Pollution Detection Using Commercial Gas Sensors.

10 Jun 2022

DOI: 10.3390/s22124393 PMID: 35746175

This paper presents the results on developing an ensemble machine learning model to combine commercial gas sensors for accurate concentration detection. Commercial gas sensors have the low-cost advant...

Air Pollution Environmental Monitoring Gases Machine Learning Neural Networks, Computer

Comparison of machine learning models for predicting the risk of breast cancer-related lymphedema in Chinese women.

Dec 2022

DOI: 10.1016/j.apjon.2022.100101 PMID: 36276882

Predictive models for the occurrence of cancer symptoms by using machine learning (ML) algorithms could be used to aid clinical decision-making in order to enhance the quality of cancer care. This stu... This was a retrospective cohort study of consecutive cases that had been diagnosed with breast cancer, stages I-IV. Forty-eight variables were grouped into five feature sets. Five classification model... Of 370 eligible female participants, 91 had BCRL (24.6%). The mean age of this study sample was 49.89 (SD = 7.45). All participants had had breast cancer surgery, and more than half of them had had ... This study found that in the ML training dataset, the multilayer perceptron model and the logistic regression model were the best discrimination models for predicting the outcome of BCRL, and the...

Optimization of Service Process in Emergency Department Using Discrete Event Simulation and Machine Learning Algorithm.

2022

DOI: 10.22037/aaem.v10i1.1545 PMID: 35765608

Emergency departments are operating with limited resources and high levels of unexpected requests. This study aimed to minimize patients' waiting time and the percentage of units' engagement to improv... A comprehensive combination method involving Discrete Event Simulation (DES), Artificial Neural Network (ANN) algorithm, and finally solving the model by use of Genetic Algorithm (GA) was used in this... According to the model optimization result, it was determined that the hospitalization unit, as well as the hospitalization units' doctors, were in an optimized condition, but the triage unit, as well... Using the service optimization method creates a significant improvement in patient's waiting time and stream at emergency departments, which is made possible through appropriate allocation of the huma...

Characterizing Risk of In-Hospital Mortality Following Subarachnoid Hemorrhage Using Machine Learning: A Retrospective Study.

2022

DOI: 10.3389/fsurg.2022.891984 PMID: 36034376

Subarachnoid hemorrhage has a high rate of disability and mortality, and the ability to use existing disease severity scores to estimate the risk of adverse outcomes is limited. Collect relevant infor... Patient-level data were extracted from MIMIC-IV data. The primary outcome was in-hospital mortality. The models were trained and tested on a data set (ratio 70:30) including age and key past medical h... Of the 1,787 patients included in the mimic database, a total of 379 died during hospitalization. Recursive feature abstraction (RFE) selected 20 variables. After simplification, we determined 10 feat... ML approaches significantly enhance predictive discrimination for mortality following subarachnoid hemorrhage compared to existing illness severity scores and LR. The discriminative ability of these M...

Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction.

07 Jun 2022

DOI: 10.1101/2022.06.06.22276062 PMID: 35702157

Data drift can negatively impact the performance of machine learning algorithms (MLAs) that were trained on historical data. As such, MLAs should be continuously monitored and tuned to overcome the sy... We devise a series of simulations that measure the effects of data drift in patients with sepsis. We simulate multiple scenarios in which data drift may occur, namely the change in the distribution of... Our results show that the properly retrained XGB models outperform the baseline models in all simulation scenarios, hence signifying the existence of data drift. In the major event scenario, the area ... Our simulations reveal that retraining periods of a couple of months or using several thousand patients are likely to be adequate to monitor machine learning models that predict sepsis. This indicates...

Multimodal classification of extremely preterm and term adolescents using the fusiform gyrus: A machine learning approach.

2022

DOI: 10.1016/j.nicl.2022.103078 PMID: 35687994

Extremely preterm birth has been associated with atypical visual and neural processing of faces, as well as differences in gray matter structure in visual processing areas relative to full-term peers.... Extremely preterm adolescents (n = 20) and full-term peers (n = 24) underwent structural and functional magnetic resonance imaging. Group differences in gray matter density, measured via voxel-based m... Group differences in two partially overlapping clusters emerged: one from the VBM analysis showing less density in the extremely preterm cohort and one from BOLD response to faces showing greater acti... Consistent with previous findings, we observed neural differences in extremely preterm youth in an area that plays an important role in face processing. Multimodal analyses revealed differences in str...

Adolescent Adult Brain Child Female +7 autres

Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review.

03 06 2022

DOI: 10.1186/s12984-022-01032-4 PMID: 35659246

Rehabilitation medicine is facing a new development phase thanks to a recent wave of rigorous clinical trials aimed at improving the scientific evidence of protocols. This phenomenon, combined with ne... We conducted a comprehensive search of five electronic databases using the Patient, Intervention, Comparison and Outcome (PICO) format. We extracted health conditions, population characteristics, outc... A total of 19 primary studies were included. The predictors most frequently used belonged to the areas of demographic characteristics and stroke assessment through clinical examination. Regarding the ... We identified several methodological limitations: small sample sizes, a limited number of external validation approaches, and high heterogeneity among input and output variables. Although these elemen...

Bias Humans Machine Learning Prognosis Recovery of Function +2 autres

Conotoxines : Questions médicales fréquentes

Termes MeSH sélectionnés :

Sous-catégories

Conotoxines-oméga

Conotoxine-oméga-GVIA

Sources (10000 au total)

A machine learning approach to identifying important features for achieving step thresholds in individuals with chronic stroke.

Preprocedural determination of an occlusion pathomechanism in endovascular treatment of acute stroke: a machine learning-based decision.

Machine learning/molecular dynamic protein structure prediction approach to investigate the protein conformational ensemble.

Ensemble Machine Learning Model for Accurate Air Pollution Detection Using Commercial Gas Sensors.

Comparison of machine learning models for predicting the risk of breast cancer-related lymphedema in Chinese women.

Optimization of Service Process in Emergency Department Using Discrete Event Simulation and Machine Learning Algorithm.

Characterizing Risk of In-Hospital Mortality Following Subarachnoid Hemorrhage Using Machine Learning: A Retrospective Study.

Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction.

Multimodal classification of extremely preterm and term adolescents using the fusiform gyrus: A machine learning approach.

Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review.