Displaying publications 1 - 20 of 22 in total

Abstract:
Sort:
  1. Fulltext Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing
    Kubicek J, Penhaker M, Krejcar O, Selamat A
    Sensors (Basel), 2021 Jan 27;21(3).
    PMID: 33513910 DOI: 10.3390/s21030847
    There are various modern systems for the measurement and consequent acquisition of valuable patient's records in the form of medical signals and images, which are supposed to be processed to provide significant information about the state of biological tissues [...].
  2. Fulltext Smart Sensor Technologies for IoT
    Brida P, Krejcar O, Selamat A, Kertesz A
    Sensors (Basel), 2021 Sep 01;21(17).
    PMID: 34502784 DOI: 10.3390/s21175890
    The recent development in wireless networks and devices leads to novel services that will utilize wireless communication on a new level [...].
  3. Decoding of the coupling between the brain and facial muscle reactions in auditory stimulation
    Soundirarajan M, Kuca K, Krejcar O, Namazi H
    Technol Health Care, 2021 Nov 19.
    PMID: 34842201 DOI: 10.3233/THC-213528
    BACKGROUND: Analysis of the reactions of different organs to external stimuli is an important area of research in physiological science.

    OBJECTIVE: In this paper, we investigated the correlation between the brain and facial muscle activities by information-based analysis of electroencephalogram (EEG) signals and electromyogram (EMG) signals using Shannon entropy.

    METHOD: The EEG and EMG signals of thirteen subjects were recorded during rest and auditory stimulations using relaxing, pop, and rock music. Accordingly, we calculated the Shannon entropy of these signals.

    RESULTS: The results showed that rock music has a greater effect on the information of EEG and EMG signals than pop music, which itself has a greater effect than relaxing music. Furthermore, a strong correlation (r= 0.9980) was found between the variations of the information of EEG and EMG signals.

    CONCLUSION: The activities of the facial muscle and brain are correlated in different conditions. This technique can be utilized to investigate the correlation between the activities of different organs versus brain activity in different situations.

  4. Fulltext Dynamics of a two-layer neuronal network with asymmetry in coupling
    Sriram S, Natiq H, Rajagopal K, Krejcar O, Krejcar O
    Math Biosci Eng, 2023 Jan;20(2):2908-2919.
    PMID: 36899564 DOI: 10.3934/mbe.2023137
    Investigating the effect of changes in neuronal connectivity on the brain's behavior is of interest in neuroscience studies. Complex network theory is one of the most capable tools to study the effects of these changes on collective brain behavior. By using complex networks, the neural structure, function, and dynamics can be analyzed. In this context, various frameworks can be used to mimic neural networks, among which multi-layer networks are a proper one. Compared to single-layer models, multi-layer networks can provide a more realistic model of the brain due to their high complexity and dimensionality. This paper examines the effect of changes in asymmetry coupling on the behaviors of a multi-layer neuronal network. To this aim, a two-layer network is considered as a minimum model of left and right cerebral hemispheres communicated with the corpus callosum. The chaotic model of Hindmarsh-Rose is taken as the dynamics of the nodes. Only two neurons of each layer connect two layers of the network. In this model, it is assumed that the layers have different coupling strengths, so the effect of each coupling change on network behavior can be analyzed. As a result, the projection of the nodes is plotted for several coupling strengths to investigate how the asymmetry coupling influences the network behaviors. It is observed that although no coexisting attractor is present in the Hindmarsh-Rose model, an asymmetry in couplings causes the emergence of different attractors. The bifurcation diagrams of one node of each layer are presented to show the variation of the dynamics due to coupling changes. For further analysis, the network synchronization is investigated by computing intra-layer and inter-layer errors. Calculating these errors shows that the network can be synchronized only for large enough symmetric coupling.
  5. Fulltext Multi-Biometric System Based on Cutting-Edge Equipment for Experimental Contactless Verification
    Kolda L, Krejcar O, Selamat A, Kuca K, Fadeyi O
    Sensors (Basel), 2019 Aug 26;19(17).
    PMID: 31455045 DOI: 10.3390/s19173709
    Biometric verification methods have gained significant popularity in recent times, which has brought about their extensive usage. In light of theoretical evidence surrounding the development of biometric verification, we proposed an experimental multi-biometric system for laboratory testing. First, the proposed system was designed such that it was able to identify and verify a user through the hand contour, and blood flow (blood stream) at the upper part of the hand. Next, we detailed the hard and software solutions for the system. A total of 40 subjects agreed to be a part of data generation team, which produced 280 hand images. The core of this paper lies in evaluating individual metrics, which are functions of frequency comparison of the double type faults with the EER (Equal Error Rate) values. The lowest value was measured for the case of the modified Hausdorff distance metric - Maximally Helicity Violating (MHV). Furthermore, for the verified biometric characteristics (Hamming distance and MHV), appropriate and suitable metrics have been proposed and experimented to optimize system precision. Thus, the EER value for the designed multi-biometric system in the context of this work was found to be 5%, which proves that metrics consolidation increases the precision of the multi-biometric system. Algorithms used for the proposed multi-biometric device shows that the individual metrics exhibit significant accuracy but perform better on consolidation, with a few shortcomings.
  6. Complexity-Based Decoding of the Coupling Among Heart Rate Variability (HRV) and Walking Path
    Mujib Kamal S, Babini MH, Krejcar O, Namazi H
    Front Physiol, 2020;11:602027.
    PMID: 33324242 DOI: 10.3389/fphys.2020.602027
    Walking is an everyday activity in our daily life. Because walking affects heart rate variability, in this research, for the first time, we analyzed the coupling among the alterations of the complexity of walking paths and heart rate. We benefited from the fractal theory and sample entropy to evaluate the influence of the complexity of paths on the complexity of heart rate variability (HRV) during walking. We calculated the fractal exponent and sample entropy of the R-R time series for nine participants who walked on four paths with various complexities. The findings showed a strong coupling among the alterations of fractal dimension (an indicator of complexity) of HRV and the walking paths. Besides, the result of the analysis of sample entropy also verified the obtained results from the fractal analysis. In further studies, we can analyze the coupling among the alterations of the complexities of other physiological signals and walking paths.
  7. Fulltext Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model
    Mambou SJ, Maresova P, Krejcar O, Selamat A, Kuca K
    Sensors (Basel), 2018 Aug 25;18(9).
    PMID: 30149621 DOI: 10.3390/s18092799
    Women's breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models.
  8. Decoding the correlation between heart activation and walking path by information-based analysis
    Kamal SM, Babini MH, Tee R, Krejcar O, Namazi H
    Technol Health Care, 2023;31(1):205-215.
    PMID: 35848002 DOI: 10.3233/THC-220191
    BACKGROND: One of the important areas of heart research is to analyze heart rate variability during (HRV) walking.

    OBJECTIVE: In this research, we investigated the correction between heart activation and the variations of walking paths.

    METHOD: We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation.

    RESULTS: According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series.

    CONCLUSION: The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.

  9. Fulltext A novel multi-scale based deep convolutional neural network for detecting COVID-19 from X-rays
    Karnati M, Seal A, Sahu G, Yazidi A, Krejcar O
    Appl Soft Comput, 2022 Aug;125:109109.
    PMID: 35693544 DOI: 10.1016/j.asoc.2022.109109
    The COVID-19 pandemic has posed an unprecedented threat to the global public health system, primarily infecting the airway epithelial cells in the respiratory tract. Chest X-ray (CXR) is widely available, faster, and less expensive therefore it is preferred to monitor the lungs for COVID-19 diagnosis over other techniques such as molecular test, antigen test, antibody test, and chest computed tomography (CT). As the pandemic continues to reveal the limitations of our current ecosystems, researchers are coming together to share their knowledge and experience in order to develop new systems to tackle it. In this work, an end-to-end IoT infrastructure is designed and built to diagnose patients remotely in the case of a pandemic, limiting COVID-19 dissemination while also improving measurement science. The proposed framework comprises six steps. In the last step, a model is designed to interpret CXR images and intelligently measure the severity of COVID-19 lung infections using a novel deep neural network (DNN). The proposed DNN employs multi-scale sampling filters to extract reliable and noise-invariant features from a variety of image patches. Experiments are conducted on five publicly available databases, including COVIDx, COVID-19 Radiography, COVID-XRay-5K, COVID-19-CXR, and COVIDchestxray, with classification accuracies of 96.01%, 99.62%, 99.22%, 98.83%, and 100%, and testing times of 0.541, 0.692, 1.28, 0.461, and 0.202 s, respectively. The obtained results show that the proposed model surpasses fourteen baseline techniques. As a result, the newly developed model could be utilized to evaluate treatment efficacy, particularly in remote locations.
  10. Fulltext FLIR vs SEEK thermal cameras in biomedicine: comparative diagnosis through infrared thermography
    Kirimtat A, Krejcar O, Selamat A, Herrera-Viedma E
    BMC Bioinformatics, 2020 Mar 11;21(Suppl 2):88.
    PMID: 32164529 DOI: 10.1186/s12859-020-3355-7
    BACKGROUND: In biomedicine, infrared thermography is the most promising technique among other conventional methods for revealing the differences in skin temperature, resulting from the irregular temperature dispersion, which is the significant signaling of diseases and disorders in human body. Given the process of detecting emitted thermal radiation of human body temperature by infrared imaging, we, in this study, present the current utility of thermal camera models namely FLIR and SEEK in biomedical applications as an extension of our previous article.

    RESULTS: The most significant result is the differences between image qualities of the thermograms captured by thermal camera models. In other words, the image quality of the thermal images in FLIR One is higher than SEEK Compact PRO. However, the thermal images of FLIR One are noisier than SEEK Compact PRO since the thermal resolution of FLIR One is 160 × 120 while it is 320 × 240 in SEEK Compact PRO.

    CONCLUSION: Detecting and revealing the inhomogeneous temperature distribution on the injured toe of the subject, we, in this paper, analyzed the imaging results of two different smartphone-based thermal camera models by making comparison among various thermograms. Utilizing the feasibility of the proposed method for faster and comparative diagnosis in biomedical problems is the main contribution of this study.

  11. Precise Temperature Measurement for Increasing the Survival of Newborn Babies in Incubator Environments
    Frischer R, Penhaker M, Krejcar O, Kacerovsky M, Selamat A
    Sensors (Basel), 2014 Dec 08;14(12):23563-23580.
    PMID: 25494352
    Precise temperature measurement is essential in a wide range of applications in the medical environment, however the regarding the problem of temperature measurement inside a simple incubator, neither a simple nor a low cost solution have been proposed yet. Given that standard temperature sensors don't satisfy the necessary expectations, the problem is not measuring temperature, but rather achieving the desired sensitivity. In response, this paper introduces a novel hardware design as well as the implementation that increases measurement sensitivity in defined temperature intervals at low cost.
  12. Fulltext Attitude Toward Protective Behavior Engagement During COVID-19 Pandemic in Malaysia: The Role of E-government and Social Media
    Mat Dawi N, Namazi H, Hwang HJ, Ismail S, Maresova P, Krejcar O
    Front Public Health, 2021;9:609716.
    PMID: 33732677 DOI: 10.3389/fpubh.2021.609716
    The coronavirus disease 2019 (COVID-19) pandemic is still evolving and affecting millions of lives. E-government and social media have been used widely during this unprecedented time to spread awareness and educate the public on preventive measures. However, the extent to which the 2 digital platforms bring to improve public health awareness and prevention during a health crisis is unknown. In this study, we examined the influence of e-government and social media on the public's attitude to adopt protective behavior. For this purpose, a Web survey was conducted among 404 Malaysian residents during the Recovery Movement Control Order (RMCO) period in the country. Descriptive and multiple regression analyses were conducted using IBM SPSS software. Social media was chosen by most of the respondents (n = 331 or 81.9%) as the source to get information related to COVID-19. Multiple regression analysis suggests the roles of e-government and social media to be significantly related to people's attitudes to engage in protective behavior. In conclusion, during the COVID-19 outbreak, public health decision makers may use e-government and social media platforms as effective tools to improve public engagement on protective behavior. This, in turn, will help the country to contain the transmission of the virus.
  13. Fulltext Smart and Climate-Smart Agricultural Trends as Core Aspects of Smart Village Functions
    Adesipo A, Fadeyi O, Kuca K, Krejcar O, Maresova P, Selamat A, et al.
    Sensors (Basel), 2020 Oct 22;20(21).
    PMID: 33105622 DOI: 10.3390/s20215977
    Attention has shifted to the development of villages in Europe and other parts of the world with the goal of combating rural-urban migration, and moving toward self-sufficiency in rural areas. This situation has birthed the smart village idea. Smart village initiatives such as those of the European Union is motivating global efforts aimed at improving the live and livelihood of rural dwellers. These initiatives are focused on improving agricultural productivity, among other things, since most of the food we eat are grown in rural areas around the world. Nevertheless, a major challenge faced by proponents of the smart village concept is how to provide a framework for the development of the term, so that this development is tailored towards sustainability. The current work examines the level of progress of climate smart agriculture, and tries to borrow from its ideals, to develop a framework for smart village development. Given the advances in technology, agricultural development that encompasses reduction of farming losses, optimization of agricultural processes for increased yield, as well as prevention, monitoring, and early detection of plant and animal diseases, has now embraced varieties of smart sensor technologies. The implication is that the studies and results generated around the concept of climate smart agriculture can be adopted in planning of villages, and transforming them into smart villages. Hence, we argue that for effective development of the smart village framework, smart agricultural techniques must be prioritized, viz-a-viz other developmental practicalities.
  14. Detection of abnormality in wireless capsule endoscopy images using fractal features
    Jain S, Seal A, Ojha A, Krejcar O, Bureš J, Tachecí I, et al.
    Comput Biol Med, 2020 12;127:104094.
    PMID: 33152668 DOI: 10.1016/j.compbiomed.2020.104094
    One of the most recent non-invasive technologies to examine the gastrointestinal tract is wireless capsule endoscopy (WCE). As there are thousands of endoscopic images in an 8-15 h long video, an evaluator has to pay constant attention for a relatively long time (60-120 min). Therefore the possibility of the presence of pathological findings in a few images (displayed for evaluation for a few seconds only) brings a significant risk of missing the pathology with all negative consequences for the patient. Hence, manually reviewing a video to identify abnormal images is not only a tedious and time consuming task that overwhelms human attention but also is error prone. In this paper, a method is proposed for the automatic detection of abnormal WCE images. The differential box counting method is used for the extraction of fractal dimension (FD) of WCE images and the random forest based ensemble classifier is used for the identification of abnormal frames. The FD is a well-known technique for extraction of features related to texture, smoothness, and roughness. In this paper, FDs are extracted from pixel-blocks of WCE images and are fed to the classifier for identification of images with abnormalities. To determine a suitable pixel block size for FD feature extraction, various sizes of blocks are considered and are fed into six frequently used classifiers separately, and the block size of 7×7 giving the best performance is empirically determined. Further, the selection of the random forest ensemble classifier is also done using the same empirical study. Performance of the proposed method is evaluated on two datasets containing WCE frames. Results demonstrate that the proposed method outperforms some of the state-of-the-art methods with AUC of 85% and 99% on Dataset-I and Dataset-II respectively.
  15. Fulltext An EEG Database and Its Initial Benchmark Emotion Classification Performance
    Seal A, Reddy PPN, Chaithanya P, Meghana A, Jahnavi K, Krejcar O, et al.
    Comput Math Methods Med, 2020;2020:8303465.
    PMID: 32831902 DOI: 10.1155/2020/8303465
    Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance. The ELM algorithm is used for channel selection followed by subband selection. The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy. The presented database would be available to the researchers for affective recognition applications.
  16. Fulltext Consequences of chronic diseases and other limitations associated with old age - a scoping review
    Maresova P, Javanmardi E, Barakovic S, Barakovic Husic J, Tomsone S, Krejcar O, et al.
    BMC Public Health, 2019 Nov 01;19(1):1431.
    PMID: 31675997 DOI: 10.1186/s12889-019-7762-5
    BACKGROUND: The phenomenon of the increasing number of ageing people in the world is arguably the most significant economic, health and social challenge that we face today. Additionally, one of the major epidemiologic trends of current times is the increase in chronic and degenerative diseases. This paper tries to deliver a more up to date overview of chronic diseases and other limitations associated with old age and provide a more detailed outlook on the research that has gone into this field.

    METHODS: First, challenges for seniors, including chronic diseases and other limitations associated with old age, are specified. Second, a review of seniors' needs and concerns is performed. Finally, solutions that can improve seniors' quality of life are discussed. Publications obtained from the following databases are used in this scoping review: Web of Science, PubMed, and Science Direct. Four independent reviewers screened the identified records and selected relevant publications published from 2010 to 2017. A total of 1916 publications were selected. In all, 52 papers were selected based on abstract content. For further processing, 21 full papers were screened."

    RESULTS: The results indicate disabilities as a major problem associated with seniors' activities of daily living dependence. We founded seven categories of different conditions - psychological problems, difficulties in mobility, poor cognitive function, falls and incidents, wounds and injuries, undernutrition, and communication problems. In order to minimize ageing consequences, some areas require more attention, such as education and training; technological tools; government support and welfare systems; early diagnosis of undernutrition, cognitive impairment, and other diseases; communication solutions; mobility solutions; and social contributions.

    CONCLUSIONS: This scoping review supports the view on chronic diseases in old age as a complex issue. To prevent the consequences of chronic diseases and other limitations associated with old age related problems demands multicomponent interventions. Early recognition of problems leading to disability and activities of daily living (ADL) dependence should be one of essential components of such interventions.

  17. A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images
    Jain S, Seal A, Ojha A, Yazidi A, Bures J, Tacheci I, et al.
    Comput Biol Med, 2021 10;137:104789.
    PMID: 34455302 DOI: 10.1016/j.compbiomed.2021.104789
    Wireless capsule endoscopy (WCE) is one of the most efficient methods for the examination of gastrointestinal tracts. Computer-aided intelligent diagnostic tools alleviate the challenges faced during manual inspection of long WCE videos. Several approaches have been proposed in the literature for the automatic detection and localization of anomalies in WCE images. Some of them focus on specific anomalies such as bleeding, polyp, lesion, etc. However, relatively fewer generic methods have been proposed to detect all those common anomalies simultaneously. In this paper, a deep convolutional neural network (CNN) based model 'WCENet' is proposed for anomaly detection and localization in WCE images. The model works in two phases. In the first phase, a simple and efficient attention-based CNN classifies an image into one of the four categories: polyp, vascular, inflammatory, or normal. If the image is classified in one of the abnormal categories, it is processed in the second phase for the anomaly localization. Fusion of Grad-CAM++ and a custom SegNet is used for anomalous region segmentation in the abnormal image. WCENet classifier attains accuracy and area under receiver operating characteristic of 98% and 99%. The WCENet segmentation model obtains a frequency weighted intersection over union of 81%, and an average dice score of 56% on the KID dataset. WCENet outperforms nine different state-of-the-art conventional machine learning and deep learning models on the KID dataset. The proposed model demonstrates potential for clinical applications.
  18. Securing Optical Networks Using Quantum-Secured Blockchain: An Overview
    Sharma P, Choi K, Krejcar O, Blazek P, Bhatia V, Prakash S
    Sensors (Basel), 2023 Jan 20;23(3).
    PMID: 36772267 DOI: 10.3390/s23031228
    The deployment of optical network infrastructure and development of new network services are growing rapidly for beyond 5/6G networks. However, optical networks are vulnerable to several types of security threats, such as single-point failure, wormhole attacks, and Sybil attacks. Since the uptake of e-commerce and e-services has seen an unprecedented surge in recent years, especially during the COVID-19 pandemic, the security of these transactions is essential. Blockchain is one of the most promising solutions because of its decentralized and distributed ledger technology, and has been employed to protect these transactions against such attacks. However, the security of blockchain relies on the computational complexity of certain mathematical functions, and because of the evolution of quantum computers, its security may be breached in real-time in the near future. Therefore, researchers are focusing on combining quantum key distribution (QKD) with blockchain to enhance blockchain network security. This new technology is known as quantum-secured blockchain. This article describes different attacks in optical networks and provides a solution to protect networks against security attacks by employing quantum-secured blockchain in optical networks. It provides a brief overview of blockchain technology with its security loopholes, and focuses on QKD, which makes blockchain technology more robust against quantum attacks. Next, the article provides a broad view of quantum-secured blockchain technology. It presents the network architecture for the future research and development of secure and trusted optical networks using quantum-secured blockchain. The article also highlights some research challenges and opportunities.
  19. Fulltext A Hybrid Lightweight System for Early Attack Detection in the IoMT Fog
    Hameed SS, Selamat A, Abdul Latiff L, Razak SA, Krejcar O, Fujita H, et al.
    Sensors (Basel), 2021 Dec 11;21(24).
    PMID: 34960384 DOI: 10.3390/s21248289
    Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT's big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift.
  20. Fulltext Phytoantioxidant Functionalized Nanoparticles: A Green Approach to Combat Nanoparticle-Induced Oxidative Stress
    Balkrishna A, Kumar A, Arya V, Rohela A, Verma R, Nepovimova E, et al.
    Oxid Med Cell Longev, 2021;2021:3155962.
    PMID: 34737844 DOI: 10.1155/2021/3155962
    Nanotechnology is gaining significant attention, with numerous biomedical applications. Silver in wound dressings, copper oxide and silver in antibacterial preparations, and zinc oxide nanoparticles as a food and cosmetic ingredient are common examples. However, adverse effects of nanoparticles in humans and the environment from extended exposure at varied concentrations have yet to be established. One of the drawbacks of employing nanoparticles is their tendency to cause oxidative stress, a significant public health concern with life-threatening consequences. Cardiovascular, renal, and respiratory problems and diabetes are among the oxidative stress-related disorders. In this context, phytoantioxidant functionalized nanoparticles could be a novel and effective alternative. In addition to performing their intended function, they can protect against oxidative damage. This review was designed by searching through various websites, books, and articles found in PubMed, Science Direct, and Google Scholar. To begin with, oxidative stress, its related diseases, and the mechanistic basis of oxidative damage caused by nanoparticles are discussed. One of the main mechanisms of action of nanoparticles was unearthed to be oxidative stress, which limits their use in humans. Secondly, the role of phytoantioxidant functionalized nanoparticles in oxidative damage prevention is critically discussed. The parameters for the characterization of nanoparticles were also discussed. The majority of silver, gold, iron, zinc oxide, and copper nanoparticles produced utilizing various plant extracts were active free radical scavengers. This potential is linked to several surface fabricated phytoconstituents, such as flavonoids and phenols. These phytoantioxidant functionalized nanoparticles could be a better alternative to nanoparticles prepared by other existing approaches.
Related Terms
    Try searching for something
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links