A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays,the COVID-19 virus disease is spreading rampantly.There are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited count.To diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are needed.The enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting COVID-19.The most common symptoms of COVID-19 are fever,dry cough and sore throat.These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier.Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death rate.Here,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and classification.This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models.At last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of class.With the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is estimated.The experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.

A Mathematical Model for COVID-19 Image Enhancement based on Mittag-Leffler-Chebyshev Shift

The lungs CT scan is used to visualize the spread of the disease across the lungs to obtain better knowledge of the state of the COVID-19 infection.Accurately diagnosing of COVID-19 disease is a complex challenge that medical system face during the pandemic time.To address this problem,this paper proposes a COVID-19 image enhancement based on Mittag-Leffler-Chebyshev polynomial as pre-processing step for COVID-19 detection and segmentation.The proposed approach comprises the MittagLeffler sum convoluted with Chebyshev polynomial.The idea for using the proposed image enhancement model is that it improves images with low graylevel changes by estimating the probability of each pixel.The proposed image enhancement technique is tested on a variety of lungs computed tomography(CT)scan dataset of varying quality to demonstrate that it is robust and can resist significant quality fluctuations.The blind/referenceless image spatial quality evaluator(BRISQUE),and the natural image quality evaluator(NIQE)measures for CT scans were 38.78,and 7.43 respectively.According to the findings,the proposed image enhancement model produces the best image quality ratings.Overall,this model considerably enhances the details of the given datasets,and it may be able to assist medical professionals in the diagnosing process.

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

Contribution of Metabolic Imaging in the Exploration of Cognitive Disorders Related to COVID-19

On March 11, 2019, the WHO declared COVID-19 a pandemic disease. It is a respiratory tropism SARS COV 2 infection. In the emergency of the pandemic, in medical imaging, only computed tomography (CT) of the lungs was favored to assess lung lesions. In addition, many cases of post-COVID-19 cognitive disorders have been reported. As the curve dips and services restart correctly, other imaging techniques have been used to better explore the disease. The objective of this presentation is to illustrate the contribution of metabolic imaging in the exploration of post COVID-19 cognitive disorders and to discuss the pathophysiological mechanisms. Hypometabolism brain lesions are objective signs of functional impairment whose pathophysiological mechanism is not yet fully understood. Metabolic imaging with PET-SCAN is a suitable tool for exploring these disorders, both for the severity and extent of the lesions and for the topography of the brain damage.

Identifying Severity of COVID-19 Medical Images by Categorizing Using HSDC Model

Since COVID-19 infections are increasing all over the world,there is a need for developing solutions for its early and accurate diagnosis is a must.Detectionmethods for COVID-19 include screeningmethods like Chest X-rays and Computed Tomography(CT)scans.More work must be done on preprocessing the datasets,such as eliminating the diaphragm portions,enhancing the image intensity,and minimizing noise.In addition to the detection of COVID-19,the severity of the infection needs to be estimated.The HSDC model is proposed to solve these problems,which will detect and classify the severity of COVID-19 from X-ray and CT-scan images.For CT-scan images,the histogram threshold of the input image is adaptively determined using the ICH Swarm Optimization Segmentation(ICHSeg)algorithm.Based on the Statistical and Shape-based feature vectors(FVs),the extracted regions are classified using a Hybrid model for CT images(HSDCCT)algorithm.When the infections are detected,it’s classified as Normal,Moderate,and Severe.A fused FHI is formed for X-ray images by extracting the features of Histogram-oriented gradient(HOG)and Image profile(IP).The FHI features of X-ray images are classified using Hybrid Support Vector Machine(SVM)and Deep Convolutional Neural Network(DCNN)HSDCX algorithm into COVID-19 or else Pneumonia,or Normal.Experimental results have shown that the accuracy of the HSDC model attains the highest of 94.6 for CT-scan images and 95.6 for X-ray images when compared to SVM and DCNN.This study thus significantly helps medical professionals and doctors diagnose COVID-19 infections quickly,which is the most needed in current years.

COVID-19 Classification from X-Ray Images:An Approach to Implement Federated Learning on Decentralized Dataset

The COVID-19 pandemic has devastated our daily lives,leaving horrific repercussions in its aftermath.Due to its rapid spread,it was quite difficult for medical personnel to diagnose it in such a big quantity.Patients who test positive for Covid-19 are diagnosed via a nasal PCR test.In comparison,polymerase chain reaction(PCR)findings take a few hours to a few days.The PCR test is expensive,although the government may bear expenses in certain places.Furthermore,subsets of the population resist invasive testing like swabs.Therefore,chest X-rays or Computerized Vomography(CT)scans are preferred in most cases,and more importantly,they are non-invasive,inexpensive,and provide a faster response time.Recent advances in Artificial Intelligence(AI),in combination with state-of-the-art methods,have allowed for the diagnosis of COVID-19 using chest x-rays.This article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning scheme.In order to build a progressive global COVID-19 classification model,two edge devices are employed to train the model on their respective localized dataset,and a 3-layered custom Convolutional Neural Network(CNN)model is used in the process of training the model,which can be deployed from the server.These two edge devices then communicate their learned parameter and weight to the server,where it aggregates and updates the globalmodel.The proposed model is trained using an image dataset that can be found on Kaggle.There are more than 13,000 X-ray images in Kaggle Database collection,from that collection 9000 images of Normal and COVID-19 positive images are used.Each edge node possesses a different number of images;edge node 1 has 3200 images,while edge node 2 has 5800.There is no association between the datasets of the various nodes that are included in the network.By doing it in this manner,each of the nodes will have access to a separate image collection that has no correlation with each other.The diagnosis of COVID-19 has become considerably more efficient with the installation of the suggested algorithm and dataset,and the findings that we have obtained are quite encouraging.

A Deep Learning Approach for Detecting Covid-19 Using the Chest X-Ray Images

Real-time detection of Covid-19 has definitely been the most widely-used world-wide classification problem since the start of the pandemic from 2020 until now.In the meantime,airspace opacities spreads related to lung have been of the most challenging problems in this area.A common approach to do on that score has been using chest X-ray images to better diagnose positive Covid-19 cases.Similar to most other classification problems,machine learning-based approaches have been the first/most-used candidates in this application.Many schemes based on machine/deep learning have been proposed in recent years though increasing the performance and accuracy of the system has still remained an open issue.In this paper,we develop a novel deep learning architecture to better classify the Covid-19 X-ray images.To do so,we first propose a novel multi-habitat migration artificial bee colony(MHMABC)algorithm to improve the exploitation/exploration of artificial bee colony(ABC)algorithm.After that,we optimally train the fully connected by using the proposed MHMABC algorithm to obtain better accuracy and convergence rate while reducing the execution cost.Our experiment results on Covid-19 X-ray image dataset show that the proposed deep architecture has a great performance in different important optimization parameters.Furthermore,it will be shown that the MHMABC algorithm outperforms the state-of-the-art algorithms by evaluating its performance using some wellknown benchmark datasets.

Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020.The consequences of this virus are quite frightening,especially when accompanied by an underlying disease.The novelty of the virus,the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process.Although the new test kits provide almost certain results,chest X-rays are extremely important to detect the progression and degree of the disease.In addition to the Covid-19 virus,pneumonia and harmless opacity of the lungs also complicate the diagnosis.Considering the negative results caused by the virus and the treatment costs,the importance of fast and accurate diagnosis is clearly seen.In this context,deep learning methods appear as an extremely popular approach.In this study,a hybrid model design with superior properties of convolutional neural networks is presented to correctly classify the Covid-19 disease.In addition,in order to contribute to the literature,a suitable dataset with balanced case numbers that can be used in all artificial intelligence classification studies is presented.With this ensemble model design,quite remarkable results are obtained for the diagnosis of three and four-class Covid-19.The proposed model can classify normal,pneumonia,and Covid-19 with 92.6%accuracy and 82.6%for normal,pneumonia,Covid-19,and lung opacity.

COVID-19 Detection from Chest X-Ray Images Using Convolutional Neural Network Approach

COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus, first identified in 2019. The primary mode of transmission is through respiratory droplets when an infected person coughs or sneezes. Symptoms can range from mild to severe, and timely diagnosis is crucial for effective treatment. Chest X-Ray imaging is one diagnostic tool used for COVID-19, and a Convolutional Neural Network (CNN) is a popular technique for image classification. In this study, we proposed a CNN-based approach for detecting COVID-19 in chest X-Ray images. The model was trained on a dataset containing both COVID-19 positive and negative cases and evaluated on a separate test dataset to measure its accuracy. Our results indicated that the CNN approach could accurately detect COVID-19 in chest X-Ray images, with an overall accuracy of 97%. This approach could potentially serve as an early diagnostic tool to reduce the spread of the virus.

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs.Chest X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and portability.In radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer variability.Using lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is advantageous.The current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on optimization.The data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s edges.Then,the OSDL model is applied to classify the CXRs under different severity levels based on CXR data.The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work.OSDL model,applied in this study,was validated using the COVID-19 dataset.The experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of infection.Despite,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray imaging.Thoracic imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging machines.Through this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected patients.This model is trained on a dataset containing thousands of X-ray images collected from different sources.The model was tested and evaluated on an independent dataset.In order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning technique.This experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with pneumonia.This proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging goal.The finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

基于CiteSpace及VOSviewer的COVID-19相关心律失常的文献计量学分析

目的:分析新型冠状病毒感染(COVID-19)相关心律失常的文献,探索该领域的研究现状、热点并预测未来的趋势,为后来的研究者提供借鉴。方法:选择Web of Science的核心合集数据库,每项研究都进行了文献计量和视觉分析,使用CiteSpace和VOSviewer软件生成知识图谱。结果:共鉴定出768篇文章,发文涉及美国、意大利和中国为首的319个国家/地区和4 366个机构,领先的研究机构是梅奥诊所和哈佛医学院。New England Journal of Medicine是该领域最常被引用的期刊。在6 687位作者中,Arbelo Elena撰写的研究最多,Guo T被共同引用的次数最多,心房纤颤是最常见的关键词。结论:随着COVID-19的暴发,对COVID-19所致新发/进行性心律失常事件的研究蓬勃发展,未来的研究者可能会对COVID-19感染后新发或遗留的快速性心律失常/缓慢性心律失常的发生机制进行进一步的探索。

COVID-19诊疗信息、中医证型分布及组方用药规律的文献研究

目的利用数据挖掘技术深入探究新型冠状病毒感染(COVID-19,简称新冠感染)患者的诊断、中医证型分布以及药物使用规律,以期为中医药治疗新冠感染提供有效的参考依据。方法通过搜索,可以获取来自国家卫生健康委员会和全国各地的《新型冠状病毒肺炎诊疗方案》内涉及的中医证型和中医药防治方案,以及中国生物医学文献服务系统、知网、维普、万方数据库收录的治疗新冠感染相关文献共249份。对文献通过筛选、整理和去重并建立中药复方数据库、诊疗信息数据库、证型数据库,运用频数分析、频率分析进行探究。结果新冠感染患者常见的症状频数较高的为咳嗽、咽干咽痛、发热、纳差、乏力;大多数患者呈淡红舌、红舌,脉象为滑脉、滑数脉;中医证型频数较高的有湿毒郁肺证、湿热蕴肺证、肺脾气虚证、寒湿郁肺证、气阴两伤证、兼夹瘀血证。此外,共纳入491首治疗新冠感染中药复方,涉及中药227味,得到高频中药共64个,药物类别以清热药、补益药、解表药、化痰止咳平喘药、化湿药为主;药性以温、平、寒为主,药味以甘、辛、苦为主,归经中归肺、脾、胃经中药居多;聚类分析结果根据中药性能将治疗新冠感染的高频药物聚为8类较好。结论中医药治疗新型冠状病毒感染用药具有以下特点:补益药用药次数较多体现攻邪不忘扶正;解表、清热、攻下、化湿、利湿、渗湿药物俱全体现多种逐邪之法;药类以清热药、补益药、化湿药、化痰止咳平喘药、解表药为主,彰显新冠感染基本治法为清热化湿、止咳平喘、补养气阴。可为指导临床用药及研发新药提供一定的参考与借鉴。

低频脉冲磁场诱导TRPC1改善COVID-19患者康复期下肢的肌肉无力症状

背景:肌肉无力是新型冠状病毒(COVID-19)感染后的常见症状,影响康复期人体日常活动能力。在强度1.5 mT,频率3300 Hz的低频脉冲磁场刺激下可通过诱导和激活经典瞬时感受器电位通1(classical transient receptor potential channel 1,TRPC1),提升人体骨骼肌的最大自主收缩力与力量耐力,对肌肉组织产生一系列生理支持效应,该手段是否会改善新型冠状病毒肺炎患者康复期的肌无力症状尚无研究。目的:选用低频脉冲磁场对新型冠状病毒肺炎患者下肢肌群进行磁刺激,以观察该刺激对新型冠状病毒肺炎患者康复期下肢肌群肌无力改善的影响。方法:招募胶体金法抗原检测试剂(COVID-19)为阳性并伴有肌肉无力症状的新型冠状病毒(奥密克戎毒株)感染患者14例,将所有受试者随机分成2组,分别为接受磁场刺激的试验组和接受假治疗的对照组。试验总时长3周,试验组每隔48 h对腿部进行低频脉冲磁刺激,对照组与试验组干预流程一致但给予假刺激,两组患者均不被告知磁刺激仪器是否运行,两组患者共进行9次操作,随后观察两组患者下肢局部肌群最大自主收缩力、腿部爆发力与力量耐力的变化情况。结果与结论:①在采集的8个局部肌群中,试验组患者7个局部肌群在经过3周的低频脉冲磁场刺激,最大自主收缩力值均增长。对照组除3个肌群最大自主收缩力自行增长改善以外,其他肌群肌力无提升。②试验组的左腿前群与双腿后群提升率显著高于对照组。③两组的纵跳摸高高度与膝关节峰值角速度相比试验前测均提升,试验组摸高高度提升率高于对照组。④在疲劳状态下,试验组膝关节峰值角速度下降率显著下降,对照组膝关节峰值角速度下降率无显著性变化;试验组摸高高度下降率显著下降,而对照组摸高高度下降率无显著性变化。⑤上述数据证实,在强度1.5 mT,频率3300 Hz的低频脉冲磁场刺激方案下,新型冠状病毒肺炎患者在康复期经过3周的低频脉冲磁场刺激相比人体自愈过程可使更多的下肢局部肌群肌力获得提升,对基于腿部爆发力的全身协调发力能力及功能状态明显改善。因此,低频脉冲磁场刺激可作为一种改善新冠感染患者下肢肌肉无力症状的有效、非运动的康复手段。

基于对比学习MocoV2的COVID-19图像分类

肺炎是一种常见多发感染性疾病,老年人和免疫力较弱者容易感染,尽早发现有助于后期治疗。肺部病变的位置、密度和清晰度等因素会影响肺炎图像分类的准确性。随着深度学习的发展,卷积神经网络被广泛应用于医学图像分类任务中,然而网络的学习能力依赖训练样本的数量和标签。针对电子计算机断层扫描(Computed Tomography,CT)的肺炎图像分类研究,提出一种基于自监督对比学习的网络模型(MCLSE),可以从无标记的数据中学习特征,提高网络模型的准确率。本文模型(MCLSE)首先设计辅助任务,从无标记的图像中挖掘表征完成预训练,提高模型在向量空间中学习数据映射关系的能力。其次,使用卷积神经网络提取特征,为了有效捕获更高层次的特征信息选择SENet网络改进分类模型,建模特征通道的相关性。最后,用训练好的权重加载改进后的分类模型中,下游任务中使用标记数据再次训练网络。在公开数据集SARS-CoV-2 CT和CT Scans for COVID-19 Classification上进行实验,实验结果表明MCLSE对整体样本分类的准确率分别达到99.19%和99.75%,较主流模型有很大提升。

规律运动在防治COVID-19中的研究进展

新型冠状病毒病(coronavirus disease-19,COVID-19)是由严重急性呼吸综合征冠状病毒2(SARS-CoV-2)感染引起的以呼吸系统症状为主的疾病。多项研究证明,规律运动对预防COVID-19、增强治疗效果、避免不良预后具有重要作用。本文梳理了运动在COVID-19防治前、中、后的作用及可能机制,运动可通过调节ACE2/Ang(1-7)/Mas轴、增强免疫力和心肺功能、抑制炎症因子和氧化应激、调节肠道菌群稳态以及改善心理状态等途径发挥作用,进而总结出COVID-19防治不同阶段的运动处方,在对运动防治COVID-19全面总结的同时,也为后疫情时代类似呼吸道传染疾病的预防和治疗提供借鉴和参考。

托珠单抗治疗COVID-19导致继发感染风险的Meta分析

目的通过Meta分析评估托珠单抗(tocilizumab,TCZ)治疗新型冠状病毒感染(corona virus disease2019,COVID-19)导致的继发感染风险,为托珠单抗在COVID-19患者中应用的安全性提供循证依据。方法在The Cochrane Library、PubMed、Web of Science、中国知网、中国生物医学文献数据库以及万方数据库中检索了2019年12月19日至2022年12月30日期间使用托珠单抗治疗COVID-19患者的相关研究,筛选并提取文献中发生继发感染的数据,利用RevMan 5.4.1进行Meta分析。结果共筛选了1691篇参考文献,纳入18项研究,涉及3933名患者。托珠单抗+标准治疗组继发感染发生率为19.14%(331/1729),标准治疗组继发感染发生率为12.11%(267/2204)。Meta分析结果显示,托珠单抗+标准治疗组继发感染发生率高于标准治疗组[RR=1.35,95%CI(1.05,1.74),P=0.02]。亚组分析显示,使用不同剂量的托珠单抗发生继发感染的风险不同。托珠单抗给药剂量为400~800 mg/d的亚组继发感染发生率明显高于标准治疗组,差异具有统计学意义[RR=1.48,95%CI(1.19,1.84),P=0.0004];≤400 mg/d继发感染发生率也显著高于标准治疗组,差异具有统计学意义[RR=1.87,95%CI(1.28,2.72),P=0.001];托珠单抗给药剂量为6~8 mg/kg亚组与标准治疗组比较差异无统计学意义。结论与标准治疗相比,托珠单抗可能增加COVID-19患者发生继发感染的风险,临床给药前应仔细评估使用托珠单抗治疗的利益和风险。但是,目前仍需要更多大样本、高质量的研究来进一步评估。

基于肝素酶对比试验对COVID-19重型非ICU患者抗凝治疗出血风险的评估与分析

目的探讨肝素酶对比试验(heparinase-modified TEG,hmTEG)在评估COVID-19重型非ICU患者凝血状态及抗凝治疗监测中的临床应用效果。方法回顾性分析2022年12月至2023年5月于本院确诊感染新型冠状病毒(SARS-CoV-2)的COVID-19重型非ICU患者临床资料。按依诺肝素初始剂量不同分为治疗剂量组和预防剂量组,通过比较2组患者接受肝素治疗前后,血小板计数、活化部分凝血活酶时间、凝血酶原时间、凝血酶时间、纤维蛋白原、D二聚体、TEG、hmTEG的参数变化,评估COVID-19重型非ICU患者接受不同剂量肝素抗凝后凝血功能的变化及出血风险。结果本研究共纳入179名COVID-19重型非ICU患者,其中治疗剂量组102名,预防剂量组77名。接受肝素抗凝前,除年龄(63.4±11.6 vs 59.8±9.1)D二聚体(678 ng/mL vs 621 ng/mL)和MA值[(69.1±10.2)mm vs(65.6±8.5)mm]外,治疗剂量组血小板计数、活化部分凝血活酶时间、凝血酶原时间、凝血酶时间、纤维蛋白原、R值、K时间、α角、凝血指数(CI),与预防剂量组比较均无统计学差异(P>0.05)。接受肝素抗凝后,治疗剂量组与预防剂量组hmTEG检测结果比较,CKR值[(12.2±4.1)min vs(10.2±3.3)min]、CKHR值[(8.1±3.2)min vs(7.1±2.6)min]差异有统计学意义(P<0.05),其余参数组间差异无统计学意义(P>0.05)。同时,治疗剂量组与预防剂量组比较,肝素残留或过量比例15.69%(16/102)vs 5.19%(4/77)显著增加(P<0.05)。但2组间VTE事件2.35%(2/85)vs 2.78%(2/72)、消化道出血2.35%(2/85)vs 1.39%(1/72)、ICU入住4.71%(4/85)vs 4.17%(3/72)、死亡事件3.53%(3/85)vs 2.78%(2/72)等发生率无差异(P>0.05)。结论在当前COVID-19流行趋势下,COVID-19重型非ICU患者血栓预防的肝素初始剂量选择需更为谨慎,为减少出血事件的发生,采用hmTEG对患者进行出血风险的个体化评估,更有利于肝素剂量的调整和控制。

COVID-19感染恢复后运动风险筛查与运动建议的经验与启示

COVID-19患者在感染恢复后进行运动可能出现症状加重、心血管意外和猝死等风险,严重影响其生命健康和康复进程。采用文献资料法和逻辑分析法,对COVID-19感染恢复后患者进行运动的潜在风险、运动前风险筛查标准及针对该人群的运动方案进行了系统地梳理和分析。结果表明,COVID-19感染恢复后立即运动,心血管系统和呼吸系统的风险较高,应特别注意运动强度的控制。在恢复运动前,应对运动系统、呼吸和心血管系统及神经系统等进行全面的筛查和评估。总结了COVID-19感染恢复后运动康复的经验,建议建立适用于我国国情的COVID-19感染恢复后患者运动前风险筛查评估体系,同时根据个体差异和病情程度制定运动方案,并遵循循序渐进原则开展运动以及注意运动过程中的持续监控。旨在提高COVID-19患者运动康复的安全性和有效性,并对未来可能出现的类似传染病的运动康复提供经验。