BACKGROUND:In clinical practice,some patients might not be able or unwilling to provide a thorough history of medication and poison exposure.The aim of this study was to use toxicological analysis to examine the clini...BACKGROUND:In clinical practice,some patients might not be able or unwilling to provide a thorough history of medication and poison exposure.The aim of this study was to use toxicological analysis to examine the clinical characteristics of patients with acute poisoning whose exposure history was uncertain from a toxicological analysis perspective.METHODS:This was a retrospective and descriptive study from an institute of poisoning.Patient registration information and test reports spanning the period from April 1,2020 to March 31,2022,were obtained.Patients with uncertain exposure histories and who underwent toxicological analysis were included.Clinical manifestations and categories of toxics were analyzed.RESULTS:Among the 195 patients with positive toxicological analysis results,the main causes of uncertain exposure history was disturbance of consciousness(62.6%),unawareness(23.6%)and unwillingness or lack of cooperation(13.8%).The predominant clinical manifestations were disturbed consciousness(62.6%),followed by vomiting and nausea(14.4%)and liver function abnormalities(8.7%).A comparison of clinical manifestations between patients with positive and negative(n=99)toxicological analyses results revealed significantly different proportions of disturbances in consciousness(63%vs.21%),dizziness(1.5%vs.5.1%),multi-organ failure(1.5%vs.7.1%),and local pain(0 vs 4%).The main categories of substances involved were psychiatric medications(23.1%),sedatives(20.5%),insecticides(13.8%),and herbicides(12.8%).CONCLUSION:The clinical manifestations of acute poisoning in patients with an uncertain exposure history are diverse and nonspecific,and toxicological analysis plays a pivotal role in the diagnosis and differential diagnosis of such patients.展开更多
The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning o...The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.展开更多
The security of Federated Learning(FL)/Distributed Machine Learning(DML)is gravely threatened by data poisoning attacks,which destroy the usability of the model by contaminating training samples,so such attacks are ca...The security of Federated Learning(FL)/Distributed Machine Learning(DML)is gravely threatened by data poisoning attacks,which destroy the usability of the model by contaminating training samples,so such attacks are called causative availability indiscriminate attacks.Facing the problem that existing data sanitization methods are hard to apply to real-time applications due to their tedious process and heavy computations,we propose a new supervised batch detection method for poison,which can fleetly sanitize the training dataset before the local model training.We design a training dataset generation method that helps to enhance accuracy and uses data complexity features to train a detection model,which will be used in an efficient batch hierarchical detection process.Our model stockpiles knowledge about poison,which can be expanded by retraining to adapt to new attacks.Being neither attack-specific nor scenario-specific,our method is applicable to FL/DML or other online or offline scenarios.展开更多
基金supported by National Natural Science Foundation of China(82172184)。
文摘BACKGROUND:In clinical practice,some patients might not be able or unwilling to provide a thorough history of medication and poison exposure.The aim of this study was to use toxicological analysis to examine the clinical characteristics of patients with acute poisoning whose exposure history was uncertain from a toxicological analysis perspective.METHODS:This was a retrospective and descriptive study from an institute of poisoning.Patient registration information and test reports spanning the period from April 1,2020 to March 31,2022,were obtained.Patients with uncertain exposure histories and who underwent toxicological analysis were included.Clinical manifestations and categories of toxics were analyzed.RESULTS:Among the 195 patients with positive toxicological analysis results,the main causes of uncertain exposure history was disturbance of consciousness(62.6%),unawareness(23.6%)and unwillingness or lack of cooperation(13.8%).The predominant clinical manifestations were disturbed consciousness(62.6%),followed by vomiting and nausea(14.4%)and liver function abnormalities(8.7%).A comparison of clinical manifestations between patients with positive and negative(n=99)toxicological analyses results revealed significantly different proportions of disturbances in consciousness(63%vs.21%),dizziness(1.5%vs.5.1%),multi-organ failure(1.5%vs.7.1%),and local pain(0 vs 4%).The main categories of substances involved were psychiatric medications(23.1%),sedatives(20.5%),insecticides(13.8%),and herbicides(12.8%).CONCLUSION:The clinical manifestations of acute poisoning in patients with an uncertain exposure history are diverse and nonspecific,and toxicological analysis plays a pivotal role in the diagnosis and differential diagnosis of such patients.
基金supported by Systematic Major Project of China State Railway Group Corporation Limited(Grant Number:P2023W002).
文摘The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.
基金supported in part by the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(Grant No.2022C03174)the National Natural Science Foundation of China(No.92067103)+4 种基金the Key Research and Development Program of Shaanxi,China(No.2021ZDLGY06-02)the Natural Science Foundation of Shaanxi Province(No.2019ZDLGY12-02)the Shaanxi Innovation Team Project(No.2018TD-007)the Xi'an Science and technology Innovation Plan(No.201809168CX9JC10)the Fundamental Research Funds for the Central Universities(No.YJS2212)and National 111 Program of China B16037.
文摘The security of Federated Learning(FL)/Distributed Machine Learning(DML)is gravely threatened by data poisoning attacks,which destroy the usability of the model by contaminating training samples,so such attacks are called causative availability indiscriminate attacks.Facing the problem that existing data sanitization methods are hard to apply to real-time applications due to their tedious process and heavy computations,we propose a new supervised batch detection method for poison,which can fleetly sanitize the training dataset before the local model training.We design a training dataset generation method that helps to enhance accuracy and uses data complexity features to train a detection model,which will be used in an efficient batch hierarchical detection process.Our model stockpiles knowledge about poison,which can be expanded by retraining to adapt to new attacks.Being neither attack-specific nor scenario-specific,our method is applicable to FL/DML or other online or offline scenarios.
