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BACKGROUND: There is no clear consensus regarding the superiority of a combined anterior cruciate ligament reconstruction (ACLR) with anterolateral ligament reconstruction (ALLR) versus an isolated ACLR. In this study, we compared the postoperative stability profile, complications, and patient-reported outcomes of these procedures. METHODS: Twenty-one patients with an anterior cruciate ligament (ACL) tear who were either treated by an isolated all-inside ACLR (n = 21) or a combined all-inside ACLR and ALLR (n = 20) were included. The outcomes were evaluated in the last follow-up and included the postoperative stability profile evaluated by the Lachman test, pivot shift test, and KT-1000 side-to-side difference, postoperative complications, and patient-reported outcomes evaluated by the International Knee Documentation Committee (IKDC) score and Lysholm knee scale. RESULTS: The baseline characteristics of the two groups were not significantly different. The residual Lachman and pivot shift were not significantly different between the two groups (P = 0.41 and P = 0.18, respectively). The mean KT-1000 side-to-side difference was 1.93 ± 1.9 mm in the isolated and 1.635 ± 0.91 mm in the combined group (P = 0.01). The mean improvement of the IKDC score was not significantly different between the isolated and combined groups (24.7 vs. 25.2, P = 0.28). The mean improvement of the Lysholm scale was not significantly different between the isolated and combined groups (33.5 vs. 34.1, P = 0.19). ACL re-rupture occurred in three patients of the isolated group and no patient of the combined group. CONCLUSION: The outcomes of patients in the present study support performing a combined ALL and ACL reconstruction.
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Lesões do Ligamento Cruzado Anterior , Reconstrução do Ligamento Cruzado Anterior , Humanos , Ligamento Cruzado Anterior/cirurgia , Estudos Prospectivos , Seguimentos , Articulação do Joelho/cirurgia , Lesões do Ligamento Cruzado Anterior/cirurgia , Reconstrução do Ligamento Cruzado Anterior/efeitos adversos , Reconstrução do Ligamento Cruzado Anterior/métodos , Resultado do TratamentoRESUMO
Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients' health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients' health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients' health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients' sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms.
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Many data mining methods have been proposed to generate computer-aided diagnostic systems, which may determine diseases in their early stages by categorizing the data into some proper classes. Considering the importance of the existence of a suitable classifier, the present study aims to introduce an efficient approach based on the World Competitive Contests (WCC) algorithm as well as a multi-layer perceptron artificial neural network (ANN). Unlike the previously introduced methods, which each has developed a universal model for all different kinds of data classes, our proposed approach generates a single specific model for each individual class of data. The experimental results show that the proposed method (ANNWCC), which can be applied to both the balanced and unbalanced datasets, yields more than 76% (without applying feature selection methods) and 90% (with applying feature selection methods) of the average five-fold cross-validation accuracy on the 13 clinical and biological datasets. The findings also indicate that under different conditions, our proposed method can produce better results in comparison to some state-of-art meta-heuristic algorithms and methods in terms of various statistical and classification measurements. To classify the clinical and biological data, a multi-layer ANN and the WCC algorithm were combined. It was shown that developing a specific model for each individual class of data may yield better results compared with creating a universal model for all of the existing data classes. Besides, some efficient algorithms proved to be essential to generate acceptable biological results, and the methods' performance was found to be enhanced by fuzzifying or normalizing the biological data.
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Biologia Computacional/métodos , Diagnóstico por Computador/métodos , Redes Neurais de Computação , Bases de Dados Factuais , HumanosRESUMO
BACKGROUND: The global burden of road injuries is known to follow complex geographical, temporal and demographic patterns. While health loss from road injuries is a major topic of global importance, there has been no recent comprehensive assessment that includes estimates for every age group, sex and country over recent years. METHODS: We used results from the Global Burden of Disease (GBD) 2017 study to report incidence, prevalence, years lived with disability, deaths, years of life lost and disability-adjusted life years for all locations in the GBD 2017 hierarchy from 1990 to 2017 for road injuries. Second, we measured mortality-to-incidence ratios by location. Third, we assessed the distribution of the natures of injury (eg, traumatic brain injury) that result from each road injury. RESULTS: Globally, 1 243 068 (95% uncertainty interval 1 191 889 to 1 276 940) people died from road injuries in 2017 out of 54 192 330 (47 381 583 to 61 645 891) new cases of road injuries. Age-standardised incidence rates of road injuries increased between 1990 and 2017, while mortality rates decreased. Regionally, age-standardised mortality rates decreased in all but two regions, South Asia and Southern Latin America, where rates did not change significantly. Nine of 21 GBD regions experienced significant increases in age-standardised incidence rates, while 10 experienced significant decreases and two experienced no significant change. CONCLUSIONS: While road injury mortality has improved in recent decades, there are worsening rates of incidence and significant geographical heterogeneity. These findings indicate that more research is needed to better understand how road injuries can be prevented.
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Carga Global da Doença , Saúde Global , Ferimentos e Lesões , Acidentes de Trânsito , Ásia , Humanos , Morbidade , Mortalidade/tendências , Anos de Vida Ajustados por Qualidade de Vida , Ferimentos e Lesões/mortalidadeRESUMO
OBJECTIVES: Synthetic biology is primarily an emerging research field that consists of designing new synthetic gene circuits dedicated to targeted functions and therapies such as cancer therapy. In this study, a genetic logic NOT-IF gate is used to reduce the multidrug resistance and facilitate the malignant cancer therapy. MCF7 cancer cells were cultured in RPMI-1640 medium and transfected with lentiviral vectors including MDR1 gene and the corresponding shRNA against MDR1 with controllable promoters. Transcript levels and protein levels of MDR1 gene were quantified. RESULTS: Our results showed that when doxycycline (DOX) and sodium butyrate were present and IPTG was absent, these led to a 74,354-fold increase in MDR1 gene expression. Upon IPTG treatment, the MDR1 gene expression was not detected due to the lack of the inducer. In addition, following IPTG induction in the presence of DOX and sodium butyrate and expressing shRNA, there was a 75% reduction in MDR1 gene expression compared to those cells treated only with sodium butyrate and DOX. CONCLUSIONS: We successfully designed and implemented the genetic logic NOT-IF gate at the transcriptional level using the inducible expression of both MDR1 drug resistance pump and its specific shRNA in MCF7 cancer cells, using the third generation lentiviral vectors.
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Antineoplásicos/farmacologia , Resistencia a Medicamentos Antineoplásicos/genética , Regulação Neoplásica da Expressão Gênica , Biologia Sintética/métodos , Ácido Butírico/farmacologia , Doxiciclina/farmacologia , Resistência a Múltiplos Medicamentos/genética , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Regulação Neoplásica da Expressão Gênica/genética , Vetores Genéticos/genética , Humanos , Lentivirus/genética , Células MCF-7 , Modelos Biológicos , RNA Interferente Pequeno/genética , RNA Interferente Pequeno/metabolismoRESUMO
Flying ad hoc network (FANET) is a new technology, which creates a self-organized wireless network containing unmanned aerial vehicles (UAVs). In FANET, routing protocols deal with important challenges due to limited energy, frequent link failures, high mobility of UAVs, and limited communication range of UAVs. Thus, a suitable path is always essential to transmit data between UAVs. In this paper, a local filtering-based energy-aware routing scheme (LFEAR) is proposed for FANETs. LFEAR improves the template of the route request (RREQ) packet by adding three fields, namely the energy, reliable distance, and movement similarity of the relevant route to create stable and energy-efficient paths. In the routing process, LFEAR presents a local filtering construction technique to avoid the broadcasting storm issue. This filter limits the broadcasting range of RREQs in the network. Accordingly, only UAVs inside this local filtered area can rebroadcast RREQs and other UAVs must eliminate these packets. After ending the route discovery process, the destination begins the route selection phase and extracts information about each discovered route, including the number of hops, route energy, reliable distance, and movement similarity. Then, the destination node calculates a score for each path based on the extracted information, selects the route with the highest score, and sends a route reply (RREP) packet to the source node through this route. Finally, the simulation process of LFEAR is performed using the NS2 simulator, and two simulation scenarios, namely change in network density and change in the speed of UAVs, are defined to evaluate network performance. In the first scenario, LFEAR improves energy consumption, packet delivery rate, network lifespan, and delay by 1.33%, 1.77%, 6.74%, and 1.71%, while its routing overhead is about 16.51% more than EARVRT. In the second scenario, LFEAR optimizes energy consumption and network lifetime by 5.55% and 5.67%, respectively. However, its performance in terms of routing overhead, packet delivery rate, and delay is 23%, 2.29%, and 6.67% weaker than EARVRT, respectively.
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The integration of the Internet of Things (IoT) in healthcare, especially for people with diabetes, allows for constant health monitoring. This means that doctors can watch over patients' health more closely, making sure they catch any issues early on. With this technology, healthcare workers can be more accurate and effective when keeping an eye on how patients are doing. This not only helps in keeping track of patients' health in real-time but also makes the whole process more reliable and efficient.By implementing appropriate routing techniques, the transmission of diabetic patients' data to medical centers will facilitate real-time and timely responses from healthcare professionals. The grasshopper optimization algorithm is employed in the proposed approach to cluster network nodes, resulting in the formation of a network tree that facilitates the establishment of connections between the cluster head and the base station. After identifying the cluster head and establishing the clusters, the second stage of routing is implemented by employing the Harris Hawks optimization algorithm. This algorithm ensures that the data pertaining to diabetic patients is transmitted to the treatment centers and hospitals with minimal delay. For node routing, the optimal next step is selected based on the parameters such as the residual energy of the node, the ratio of delivered data packages, and the number of the neighbors of the node. To continue, first, the MATLAB software is utilized to simulate the proposed method, and then, it is compared with other similar methods. This comparison is conducted based on various parameters, including delay, energy consumption, network throughput, and network lifespan. Compared to other methods, the proposed method demonstrates a significant 33% improvement in the average point-to-point delay parameter in the subsequent iterations or rounds.
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Algoritmos , Diabetes Mellitus , Internet das Coisas , Humanos , Diabetes Mellitus/terapia , Monitorização Fisiológica/métodosRESUMO
With electronic healthcare systems undergoing rapid change, optimizing the crucial process of recording physician prescriptions is a task with major implications for patient care. The power of blockchain technology and the precision of the Raft consensus algorithm are combined in this article to create a revolutionary solution for this problem. In addition to addressing these issues, the proposed framework, by focusing on the challenges associated with physician prescriptions, is a breakthrough in a new era of security and dependability for the healthcare sector. The Raft algorithm is a cornerstone that improves the diagnostic decision-making process, increases confidence in patients, and sets a new standard for robust healthcare systems. In the proposed consensus algorithm, a weighted sum of two influencing factors including the physician acceptability and inter-physicians' reliability is used for selecting the participating physicians. An investigation is conducted to see how well the Raft algorithm performs in overcoming prescription-related roadblocks that support a compelling argument for improved patient care. Apart from its technological benefits, the proposed approach seeks to revolutionize the healthcare system by fostering trust between patients and providers. Raft's ability to communicate presents the proposed solution as an effective way to deal with healthcare issues and ensure security.
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Algoritmos , Blockchain , Humanos , Médicos , Registros Eletrônicos de Saúde , Consenso , Segurança Computacional , Atenção à SaúdeRESUMO
Healthcare has significantly contributed to the well-being of individuals around the globe; nevertheless, further benefits could be derived from a more streamlined healthcare system without incurring additional costs. Recently, the main attributes of cloud computing, such as on-demand service, high scalability, and virtualization, have brought many benefits across many areas, especially in medical services. It is considered an important element in healthcare services, enhancing the performance and efficacy of the services. The current state of the healthcare industry requires the supply of healthcare products and services, increasing its viability for everyone involved. Developing new approaches for discovering and selecting healthcare services in the cloud has become more critical due to the rising popularity of these kinds of services. As a result of the diverse array of healthcare services, service composition enables the execution of intricate operations by integrating multiple services' functionalities into a single procedure. However, many methods in this field encounter several issues, such as high energy consumption, cost, and response time. This article introduces a novel layered method for selecting and evaluating healthcare services to find optimal service selection and composition solutions based on Deep Reinforcement Learning (Deep RL), Kalman filtering, and repeated training, addressing the aforementioned issues. The results revealed that the proposed method has achieved acceptable results in terms of availability, reliability, energy consumption, and response time when compared to other methods.
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Computação em Nuvem , Atenção à Saúde , Humanos , Reprodutibilidade dos TestesRESUMO
Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively.
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Aprendizado Profundo , Neoplasias Cutâneas , Neoplasias Cutâneas/patologia , Humanos , Aprendizado de Máquina , AlgoritmosRESUMO
Domestic violence (DV) against women in Iran is a hidden societal issue. In addition to its chronic physical, mental, industrial, and economic effects on women, children, and families, DV prevents victims from receiving mental health care. On the other hand, DV campaigns on social media have encouraged victims and society to share their stories of abuse. As a result, massive amount of data has been generated about this violence, which can be used for analysis and early detection. Therefore, this study aimed to analyze and classify Persian textual content pertinent to DV against women in social media. It also aimed to use machine learning to predict the risk of this content. After collecting 53,105 tweets and captions in the Persian language from Twitter and Instagram, between April 2020 and April 2021, 1611 tweets and captions were chosen at random and categorized using criteria compiled and approved by an expert in the field of DV. Then, using machine learning algorithms, modeling and evaluation processes were performed on the tagged data. The Naïve Base model, with an accuracy of 86.77% was the most accurate model among all machine learning models for predicting critical Persian content pertinent to domestic violence on social media. The obtained findings indicate that using a machine learning approach, the risk of Persian content related to DV in social media against women can be predicted.
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The introduction of unintended oil spills into the marine ecosystem has a significant impact on aquatic life and raises important environmental concerns. The present review summarizes the recent studies where nanocomposites are applied to treat oil spills. The review deals with the techniques used to fabricate nanocomposites and identify the characteristics of nanocomposites beneficial for efficient recovery and treatment of oil spills. It classifies the nanocomposites into four categories, namely bio-based materials, polymeric materials, inorganic-inorganic nanocomposites, and carbon-based nanocomposites, and provides an insight into understanding the interactions of these nanocomposites with different types of oils. Among nanocomposites, bio-based nanocomposites are the most cost-effective and environmentally friendly. The grafting or modification of magnetic nanoparticles with polymers or other organic materials is preferred to avoid oxidation in wet conditions. The method of synthesizing magnetic nanocomposites and functionalization polymer is essential as it influences saturation magnetization. Notably, the inorganic polymer-based nanocomposite is very less developed and studied for oil spill treatment. Also, the review covers some practical considerations for treating oil spills with nanocomposites. Finally, some aspects of future developments are discussed. The terms "Environmentally friendly," "cost-effective," and "low cost" are often used, but most of the studies lack a critical analysis of the cost and environmental damage caused by chemical alteration techniques. However, the oil and gas industry will considerably benefit from the stimulation of ideas and scientific discoveries in this field.
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Nanocompostos , Poluição por Petróleo , Poluição por Petróleo/análise , Ecossistema , Óleos , PolímerosRESUMO
Flying ad-hoc networks (FANETs) include a large number of drones, which communicate with each other based on an ad hoc model. These networks provide new opportunities for various applications such as military, industrial, and civilian applications. However, FANETs have faced with many challenges like high-speed nodes, low density, and rapid changes in the topology. As a result, routing is a challenging issue in these networks. In this paper, we propose an energy-aware routing scheme in FANETs. This scheme is inspired by the optimized link state routing (OLSR). In the proposed routing scheme, we estimate the connection quality between two flying nodes using a new technique, which utilizes two parameters, including ratio of sent/received of hello packets and connection time. Also, our proposed method selects multipoint relays (MPRs) using the firefly algorithm. It chooses a node with high residual energy, high connection quality, more neighborhood degree, and higher willingness as MPR. Finally, our proposed scheme creates routes between different nodes based on energy and connection quality. Our proposed routing scheme is simulated using the network simulator version 3 (NS3). We compare its simulation results with the greedy optimized link state routing (G-OLSR) and the optimized link state routing (OLSR). These results show that our method outperforms G-OLSR and OLSR in terms of delay, packet delivery rate, throughput, and energy consumption. However, our proposed routing scheme increases slightly routing overhead compared to G-OLSR.
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In wireless sensor networks (WSNs), existing routing protocols mainly consider energy efficiency or security separately. However, these protocols must be more comprehensive because many applications should guarantee security and energy efficiency, simultaneously. Due to the limited energy of sensor nodes, these protocols should make a trade-off between network lifetime and security. This paper proposes a cluster-tree-based trusted routing method using the grasshopper optimization algorithm (GOA) called CTTRG in WSNs. This routing scheme includes a distributed time-variant trust (TVT) model to analyze the behavior of sensor nodes according to three trust criteria, including the black hole, sink hole, and gray hole probability, the wormhole probability, and the flooding probability. Furthermore, CTTRG suggests a GOA-based trusted routing tree (GTRT) to construct secure and stable communication paths between sensor nodes and base station. To evaluate each GTRT, a multi-objective fitness function is designed based on three parameters, namely the distance between cluster heads and their parent node, the trust level, and the energy of cluster heads. The evaluation results prove that CTTRG has a suitable and successful performance in terms of the detection speed of malicious nodes, packet loss rate, and end-to-end delay.
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Gafanhotos , Animais , Algoritmos , Comunicação , InundaçõesRESUMO
Patients must always communicate with their doctor for checking their health status. In recent years, wireless body sensor networks (WBSNs) has an important contribution in Healthcare. In these applications, energy-efficient and secure routing is really critical because health data of individuals must be forwarded to the destination securely to avoid unauthorized access by malicious nodes. However, biosensors have limited resources, especially energy. Recently, energy-efficient solutions have been proposed. Nevertheless, designing lightweight security mechanisms has not been stated in many schemes. In this paper, we propose a secure routing approach based on the league championship algorithm (LCA) for wireless body sensor networks in healthcare. The purpose of this scheme is to create a tradeoff between energy consumption and security. Our approach involves two important algorithms: routing process and communication security. In the first algorithm, each cluster head node (CH) applies the league championship algorithm to choose the most suitable next-hop CH. The proposed fitness function includes parameters like distance from CHs to the sink node, remaining energy, and link quality. In the second algorithm, we employs a symmetric encryption strategy to build secure connection links within a cluster. Also, we utilize an asymmetric cryptography scheme for forming secure inter-cluster connections. Network simulator version 2 (NS2) is used to implement the proposed approach. The simulation results show that our method is efficient in terms of consumed energy and delay. In addition, our scheme has good throughput, high packet delivery rate, and low packet loss rate.
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Redes de Comunicação de Computadores , Tecnologia sem Fio , Humanos , Simulação por Computador , Algoritmos , Atenção à SaúdeRESUMO
Physical Unclonable Functions (PUFs) are widely used in cryptographic authentication and key-agreement protocols due to their unique physical properties. This article presents a comprehensive cryptanalysis of two recently developed authentication protocols, namely PLAKE and EV-PUF, both relying on PUFs. Our analysis reveals significant vulnerabilities in these protocols, including susceptibility to impersonation and key leakage attacks, which pose serious threats to the security of the underlying systems. In the case of PLAKE, we propose an attack that can extract the shared secret key with negligible complexity by eavesdropping on consecutive protocol sessions. Similarly, we demonstrate an efficient attack against EV-PUF that enables the determination of the shared key between specific entities. Furthermore, we highlight the potential for a single compromised client in the EV-PUF protocol to compromise the security of the entire network, leaving it vulnerable to pandemic attacks. These findings underscore the critical importance of careful design and rigorous evaluation when developing PUF-based authentication protocols. To address the identified vulnerabilities, we present an improved PUF-based authentication protocol that ensures robust security against all the attacks described in the context of PLAKE and EV-PUF. Through this research, we contribute to the field by exposing vulnerabilities in existing PUF-based authentication protocols and offering an improved protocol that enhances security and safeguards against various attack vectors. This work serves as a valuable reference for researchers and practitioners involved in the design and implementation of secure authentication schemes for IoT systems and dynamic charging systems for electric vehicles.
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Today, wireless sensor networks (WSNs) are growing rapidly and provide a lot of comfort to human life. Due to the use of WSNs in various areas, like health care and battlefield, security is an important concern in the data transfer procedure to prevent data manipulation. Trust management is an affective scheme to solve these problems by building trust relationships between sensor nodes. In this paper, a cluster-based trusted routing technique using fire hawk optimizer called CTRF is presented to improve network security by considering the limited energy of nodes in WSNs. It includes a weighted trust mechanism (WTM) designed based on interactive behavior between sensor nodes. The main feature of this trust mechanism is to consider the exponential coefficients for the trust parameters, namely weighted reception rate, weighted redundancy rate, and energy state so that the trust level of sensor nodes is exponentially reduced or increased based on their hostile or friendly behaviors. Moreover, the proposed approach creates a fire hawk optimizer-based clustering mechanism to select cluster heads from a candidate set, which includes sensor nodes whose remaining energy and trust levels are greater than the average remaining energy and the average trust level of all network nodes, respectively. In this clustering method, a new cost function is proposed based on four objectives, including cluster head location, cluster head energy, distance from the cluster head to the base station, and cluster size. Finally, CTRF decides on inter-cluster routing paths through a trusted routing algorithm and uses these routes to transmit data from cluster heads to the base station. In the route construction process, CTRF regards various parameters such as energy of the route, quality of the route, reliability of the route, and number of hops. CTRF runs on the network simulator version 2 (NS2), and its performance is compared with other secure routing approaches with regard to energy, throughput, packet loss rate, latency, detection ratio, and accuracy. This evaluation proves the superior and successful performance of CTRF compared to other methods.
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The Internet of Things (IoT) is a universal network to supervise the physical world through sensors installed on different devices. The network can improve many areas, including healthcare because IoT technology has the potential to reduce pressure caused by aging and chronic diseases on healthcare systems. For this reason, researchers attempt to solve the challenges of this technology in healthcare. In this paper, a fuzzy logic-based secure hierarchical routing scheme using the firefly algorithm (FSRF) is presented for IoT-based healthcare systems. FSRF comprises three main frameworks: fuzzy trust framework, firefly algorithm-based clustering framework, and inter-cluster routing framework. A fuzzy logic-based trust framework is responsible for evaluating the trust of IoT devices on the network. This framework identifies and prevents routing attacks like black hole, flooding, wormhole, sinkhole, and selective forwarding. Moreover, FSRF supports a clustering framework based on the firefly algorithm. It presents a fitness function that evaluates the chance of IoT devices to be cluster head nodes. The design of this function is based on trust level, residual energy, hop count, communication radius, and centrality. Also, FSRF involves an on-demand routing framework to decide on reliable and energy-efficient paths that can send the data to the destination faster. Finally, FSRF is compared to the energy-efficient multi-level secure routing protocol (EEMSR) and the enhanced balanced energy-efficient network-integrated super heterogeneous (E-BEENISH) routing method based on network lifetime, energy stored in IoT devices, and packet delivery rate (PDR). These results prove that FSRF improves network longevity by 10.34% and 56.35% and the energy stored in the nodes by 10.79% and 28.51% compared to EEMSR and E-BEENISH, respectively. However, FSRF is weaker than EEMSR in terms of security. Furthermore, PDR in this method has dropped slightly (almost 1.4%) compared to that in EEMSR.
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Lógica Fuzzy , Internet das Coisas , Instalações de Saúde , Algoritmos , Atenção à SaúdeRESUMO
Interferon-induced transmembrane protein 3 (IFITM3) inhibits the entry of numerous viruses through undefined molecular mechanisms. IFITM3 localizes in the endosomal-lysosomal system and specifically affects virus fusion with target cell membranes. We found that IFITM3 induces local lipid sorting, resulting in an increased concentration of lipids disfavoring viral fusion at the hemifusion site. This increases the energy barrier for fusion pore formation and the hemifusion dwell time, promoting viral degradation in lysosomes. In situ cryo-electron tomography captured IFITM3-mediated arrest of influenza A virus membrane fusion. Observation of hemifusion diaphragms between viral particles and late endosomal membranes confirmed hemifusion stabilization as a molecular mechanism of IFITM3. The presence of the influenza fusion protein hemagglutinin in post-fusion conformation close to hemifusion sites further indicated that IFITM3 does not interfere with the viral fusion machinery. Collectively, these findings show that IFITM3 induces lipid sorting to stabilize hemifusion and prevent virus entry into target cells.
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Vírus da Influenza A , Influenza Humana , Humanos , Influenza Humana/metabolismo , Internalização do Vírus , Vírus da Influenza A/metabolismo , Membrana Celular/metabolismo , Lipídeos , Proteínas de Membrana/metabolismo , Proteínas de Ligação a RNA/metabolismoRESUMO
In recent decades, the use of sensors has dramatically grown to monitor human body activities and maintain the health status. In this application, routing and secure data transmission are very important to prevent the unauthorized access by attackers to health data. In this article, we propose a secure routing scheme called SecAODV for heterogeneous wireless body sensor networks. SecAODV has three phases: bootstrapping, routing between cluster head nodes, and communication security. In the bootstrapping phase, the base station loads system parameters and encryption functions in the memory of sensor nodes. In the routing phase, each cluster head node calculates its degree based on several parameters, including, distance, residual energy, link quality, and the number of hops, to decide for rebroadcasting the route request (RREQ) message. In the communication security phase, a symmetric cryptography method is used to protect intra-cluster communications. Also, an asymmetric cryptography method is used to secure communication links between cluster head nodes. The proposed secure routing scheme is simulated in the network simulator version 2 (NS2) simulator. The simulation results are compared with the secure multi tier energy-efficient routing scheme (SMEER) and the centralized low-energy adaptive clustering hierarchy (LEACH-C). The results show that SecAODV improves end-to-end delay, throughput, energy consumption, packet delivery rate (PDR), and packet loss rate (PLR).