Drug-induced liver injury(DILI)is a major problem in the United States,commonly leading to hospital admission.Diagnosing DILI is difficult as it is a diagnosis of exclusion requiring a temporal relationship between dr...Drug-induced liver injury(DILI)is a major problem in the United States,commonly leading to hospital admission.Diagnosing DILI is difficult as it is a diagnosis of exclusion requiring a temporal relationship between drug exposure and liver injury and a thorough work up for other causes.In addition,DILI has a very variable clinical and histologic presentation that can mimic many different etiologies of liver disease.Objective scoring systems can assess the probability that a drug caused the liver injury but liver biopsy findings are not part of the criteria used in these systems.This review will address some of the recent updates to the scoring systems and the role of liver biopsy in the diagnosis of DILI.展开更多
The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the Internet.Recently,smart healthcare has emerged as a significant application of ...The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the Internet.Recently,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning systems.Smart healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical data.In smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance Imaging.The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI.Moreover,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission delays.To address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory.The results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,respectively.This validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.展开更多
Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial environments.Several IIoT nodes operate confidential data(s...Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial environments.Several IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied structure.Intrusion Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant attention.However,existing ML and DL-based IDS still face a number of obstacles that must be overcome.For instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory devices.Imbalanced and fewer data potentially lead to low performance on existing IDS.This paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant features.The proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for prediction.The performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID datasets.These datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.展开更多
BACKGROUND： Various scoring systems based on assessment of the systemic inflammatory response help assessing the prognosis of patients with pancreatic ductal adenocarcinoma.In the present systematic review we evaluat...BACKGROUND： Various scoring systems based on assessment of the systemic inflammatory response help assessing the prognosis of patients with pancreatic ductal adenocarcinoma.In the present systematic review we evaluated the validity of four pre-intervention scoring systems： Glasgow prognostic score（GPS） and its modified version（mGPS）, platelet lymphocyte ratio（PLR）, neutrophil lymphocyte ratio（NLR）, and prognostic nutrition index（PNI）.DATA SOURCES： MOOSE guidelines were followed and EMBASE and MEDLINE databases were searched for all published studies until September 2013 using comprehensive text word and MeSH terms. All identified studies were analyzed, and relevant studies were included in the systematic review.RESULTS： Six studies were identified for GPS/mGPS with3 reporting statistical significance for GPS/mGPS on both univariate analysis（UVA） and multivariate analysis（MVA）.Two studies suggested prognostic significance on UVA but not MVA, and in the final study UVA failed to show significance.Eleven studies evaluated the prognostic value of NLR. Six of them reported prognostic significance for NLR on UVA that persisted at MVA in 4 studies, and in the remaining 2 studies NLR was the only significant factor on UVA. In the remaining5 studies, all in patients undergoing resection, there was no significance on UVA. Seven studies evaluated PLR, with only one study demonstrated its prognostic significance on both UVAand MVA, the rest did not show the significance on UVA. Of the two studies identified for PNI, one demonstrated a statistically significant difference in survival on both UVA and MVA, and the other reported no significance for PNI on UVA.CONCLUSIONS： Both GPS/mGPS and NLR may be useful but further better-designed studies are required to confirm their value. PLR might be little useful, and there are at present inadequate data to assess the prognostic value of PNI. At present, no scoring system is reliable enough to be accepted into routine use for the prognosis of patients with pancreatic ductal adenocarcinoma.展开更多
Herbal and dietary supplements(HDS)are increasingly used worldwide for numerous,mainly unproven health benefits.The HDS industry is poorly regulated compared to prescription medicines and most products are easily obta...Herbal and dietary supplements(HDS)are increasingly used worldwide for numerous,mainly unproven health benefits.The HDS industry is poorly regulated compared to prescription medicines and most products are easily obtainable.Drug induced liver injury(DILI)is a well-recognized entity associated with prescription and over the counter medications and many reports have emerged of potential HDS-related DILI.There is considerable geographic variability in the risk and severity of DILI associated with HDS but the presentation of severe liver injury is similar with a hepatocellular pattern accompanied by jaundice.This type of injury can lead to acute liver failure and the need for liver transplantation.Patients will often fail to mention their use of HDS,considering it natural and therefore harmless.Hence physicians should understand that these products can be associated with DILI and explicitly ask about HDS use in any patient with otherwise unexplained acute liver injury.展开更多
Liver transplantation is the optimal treatment for many patients with advanced liver disease, including decompensated cirrhosis, hepatocellular carcinoma and acute liver failure. Organ shortage is the maindeterminant ...Liver transplantation is the optimal treatment for many patients with advanced liver disease, including decompensated cirrhosis, hepatocellular carcinoma and acute liver failure. Organ shortage is the maindeterminant of death on the waiting list and hence living donor liver transplantation(LDLT) assumes importance. Biliary complications are the most common post operative morbidity after LDLT and occur due to anatomical and technical reasons. They include biliary leaks, strictures and cast formation and occur in the recipient as well as the donor. The types of biliary complications after LDLT along with their etiology, presenting features, diagnosis and endoscopic and surgical management are discussed.展开更多
Background:Post-hepatectomy liver failure(PHLF)is the Achilles’heel of hepatic resection for colorectal liver metastases.The most commonly used procedure to generate hypertrophy of the functional liver remnant(FLR)is...Background:Post-hepatectomy liver failure(PHLF)is the Achilles’heel of hepatic resection for colorectal liver metastases.The most commonly used procedure to generate hypertrophy of the functional liver remnant(FLR)is portal vein embolization(PVE),which does not always lead to successful hypertrophy.Associating liver partition and portal vein ligation for staged hepatectomy(ALPPS)has been proposed to overcome the limitations of PVE.Liver venous deprivation(LVD),a technique that includes simultaneous portal and hepatic vein embolization,has also been proposed as an alternative to ALPPS.The present study aimed to conduct a systematic review as the first network meta-analysis to compare the efficacy,effectiveness,and safety of the three regenerative techniques.Data sources:A systematic search for literature was conducted using the electronic databases Embase,PubMed(MEDLINE),Google Scholar and Cochrane.Results:The time to operation was significantly shorter in the ALPPS cohort than in the PVE and LVD cohorts by 27 and 22 days,respectively.Intraoperative parameters of blood loss and the Pringle maneuver demonstrated non-significant differences between the PVE and LVD cohorts.There was evidence of a significantly higher FLR hypertrophy rate in the ALPPS cohort when compared to the PVE cohort,but non-significant differences were observed when compared to the LVD cohort.Notably,the LVD cohort demonstrated a significantly better FLR/body weight(BW)ratio compared to both the ALPPS and PVE cohorts.Both the PVE and LVD cohorts demonstrated significantly lower major morbidity rates compared to the ALPPS cohort.The LVD cohort also demonstrated a significantly lower 90-day mortality rate compared to both the PVE and ALPPS cohorts.Conclusions:LVD in adequately selected patients may induce adequate and profound FLR hypertrophy before major hepatectomy.Present evidence demonstrated significantly lower major morbidity and mortality rates in the LVD cohort than in the ALPPS and PVE cohorts.展开更多
The severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which caused the coronavirus disease 2019(COVID-19)pandemic,has affected more than 400 million people worldwide.With the recent rise of new Delta and Omi...The severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which caused the coronavirus disease 2019(COVID-19)pandemic,has affected more than 400 million people worldwide.With the recent rise of new Delta and Omicron variants,the efficacy of the vaccines has become an important question.The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions,particularly for healthcare workers.In this paper,we discuss the current literature on invasive/contact and non-invasive/noncontact technologies(including Wi-Fi,radar,and software-defined radio)that have been effectively used to detect,diagnose,and monitor human activities and COVID-19 related symptoms,such as irregular respiration.In addition,we focused on cutting-edge machine learning algorithms(such as generative adversarial networks,random forest,multilayer perceptron,support vector machine,extremely randomized trees,and k-nearest neighbors)and their essential role in intelligent healthcare systems.Furthermore,this study highlights the limitations related to non-invasive techniques and prospective research directions.展开更多
Liver transplantation(LT)remains the best option for patients with end-stage liver disease but the demand for organs from deceased donors continues to outweigh the available supply.The advent of highly effective anti-...Liver transplantation(LT)remains the best option for patients with end-stage liver disease but the demand for organs from deceased donors continues to outweigh the available supply.The advent of highly effective anti-viral treatments has reduced the number of patients undergoing LT for hepatitis C(HCV)and hepatitis B(HBV)related liver disease and yet the number of patients waiting for LT continues to increase,driven by an increase in the patients listed with a diagnosis of cirrhosis due to non-alcoholic steatohepatitis and alcoholrelated liver disease.In addition,human immunodeficiency virus(HIV)infection,which was previously a contra-indication for LT,is no longer a fatal disease due to the effectiveness of HIV therapy and patients with HIV and liver disease are now developing indications for LT.The rising demand for LT is projected to increase further in the future,thus driving the need to investigate potential means of expanding the pool of potential donors.One mechanism for doing so is utilizing organs from donors that previously would have been discarded or used only in exceptional circumstances such as HCV-positive,HBV-positive,and HIVpositive donors.The advent of highly effective anti-viral therapy has meant that these organs can now be used with excellent outcomes in HCV,HBV or HIV infected recipients and in some cases uninfected recipients.展开更多
The Internet of things(IoT)is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision.Despite several a...The Internet of things(IoT)is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision.Despite several advantages,the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals.A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries.To overcome the security challenges of IoT networks,this article proposes a lightweight deep autoencoder(DAE)based cyberattack detection framework.The proposed approach learns the normal and anomalous data patterns to identify the various types of network intrusions.The most significant feature of the proposed technique is its lower complexity which is attained by reducing the number of operations.To optimally train the proposed DAE,a range of hyperparameters was determined through extensive experiments that ensure higher attack detection accuracy.The efficacy of the suggested framework is evaluated via two standard and open-source datasets.The proposed DAE achieved the accuracies of 98.86%,and 98.26%for NSL-KDD,99.32%,and 98.79%for the UNSW-NB15 dataset in binary class and multi-class scenarios.The performance of the suggested attack detection framework is also compared with several state-of-the-art intrusion detection schemes.Experimental outcomes proved the promising performance of the proposed scheme for cyberattack detection in IoT networks.展开更多
AIM: To determine the safety profile of new hepatitis C virus (HCV) treatments in liver transplant (LT) recipients with recurrent HCV infection.METHODS: Forty-two patients were identified with recurrent HCV infection ...AIM: To determine the safety profile of new hepatitis C virus (HCV) treatments in liver transplant (LT) recipients with recurrent HCV infection.METHODS: Forty-two patients were identified with recurrent HCV infection that underwent LT at least 12 mo prior to initiating treatment with a Sofosbuvir-based regimen during December 2013-June 2014. Cases were patients who experienced hepatic decompensation and/or serious adverse events (SAE) during or within one month of completing treatment. Controls had no evidence of hepatic decompensation and/or SAE. HIV-infected patients were excluded. Cumulative incidence of decompensation/SAE was calculated using the Kaplan Meier method. Exact logistic regression analysis was used to identify factors associated with the composite outcome.RESULTS: Median age of the 42 patients was 60 years [Interquartile Range (IQR): 56-65 years], 33% (14/42) were female, 21% (9/42) were Hispanic, and 9% (4/42) were Black. The median time from transplant to treatment initiation was 5.4 years (IQR: 2.1-8.8 years). Thirteen patients experienced one or more episodes of hepatic decompensation and/or SAE. Anemia requiring transfusion, the most common event, occurred in 62% (8/13) patients, while 54% (7/13) decompensated. The cumulative incidence of hepatic decompensation/SAE was 31% (95%CI: 16%-41%). Risk factors for decompensation/SAE included lower pre-treatment hemoglobin (OR = 0.61 per g/dL, 95%CI: 0.40-0.88, P < 0.01), estimated glomerular filtration rate (OR = 0.95 per mL/min per 1.73 m<sup>2</sup>, 95%CI: 0.90-0.99, P = 0.01), and higher baseline serum total bilirubin (OR = 2.43 per mg/dL, 95%CI: 1.17-8.65, P < 0.01). The sustained virological response rate for the cohort of 42 patients was 45%, while it was 31% for cases.CONCLUSION: Sofosbuvir/ribavirin will continue to be used in the post-transplant population, including those with HCV genotypes 2 and 3. Management of anemia remains an important clinical challenge.展开更多
ABSTRACT： Serum aminotransferases have been used as surrogate markers for liver ischemia-reperfusion injury that follows liver surgery. Some studies have suggested that rises in serum alanine aminotransferase （ALT）...ABSTRACT： Serum aminotransferases have been used as surrogate markers for liver ischemia-reperfusion injury that follows liver surgery. Some studies have suggested that rises in serum alanine aminotransferase （ALT） correlate with patient outcome after liver resection. We assessed whether postoperative day 1 （POD 1） ALT could be used to predict patient morbidity and mortality following liver resection. We reviewed our prospectively held database and included consecutive adult patients undergoing elective liver resection in our institution between January 2013 and December 2014. Primary outcome assessed was correlation of POD 1 ALT with patient＇s morbidity and mortality. We also assessed whether concurrent radiofrequency ablation, neoadjuvant chemotherapy and use of the Pringle maneuver significantly affected the level of POD 1 ALT. A total of 110 liver resections were included in the study. The overall in-hospital patient morbidity and mortality were 31.8% and 0.9%, respectively. The median level of POD 1 ALT was 275 IU/L. No correlation was found between POD 1 serum ALT levels and patient morbidity after elective liver resection, whilst correlation with mortality was not possible because ofthe low number of mortalities. Patients undergoing concurrent radiofrequency ablation were noted to have an increased level of POD 1 serum ALT but not those given neoadjuvant chemotherapy and those in whom the Pringle maneuver was used. Our study demonstrates POD 1 serum ALT does not correlate with patient morbidity after elective liver resection.展开更多
AIM: To describe our experience using a low-acceleratingdose regimen(LADR) with pegylated interferon alpha-2a and ribavirin in treatment of hepatitis C virus(HCV) recurrence. METHODS: From 2003, a protocolized LADR st...AIM: To describe our experience using a low-acceleratingdose regimen(LADR) with pegylated interferon alpha-2a and ribavirin in treatment of hepatitis C virus(HCV) recurrence. METHODS: From 2003, a protocolized LADR strategy was employed to treat liver transplant(LT) recipients with recurrent HCV at our institution. Medical records of 182 adult patients with recurrent HCV treated with LADR between 1/2003 and 1/2011 were reviewed. Histopathology from all post-LT liver biopsies were reviewed in a blinded fashion. Paired recipient and donor IL28 B status were assessed. A novel technique was employed to ascertain recipient and donor IL28B(rs12979860) Gt data using DNA extracted from archival FFPE tissue from explanted native livers and donor gallbladders respectively. The primary endpoint was SVR; secondary endpoints examined include(1) patient and graft survival;(2) effect of anti-viral therapy on liver histology(fibrosis and inflammation);(3) incidence of on-treatment development of ACR, CDR, or PCH;(4) association of recipient and donor IL28 B genotype with SVR; and(5) incidence of antiviral therapy-associated adverse events(anemia, leukopenia, thrombocytopenia, depression) and hepatic decompensation.RESULTS: The overall SVR rate was 38%(29% Gt1, 67% Gt2, 86% Gt3 and 58% Gt4). HCV Gt(P < 0.0001), donor age(P = 0.003), cytomegalovirus mismatch(P = 0.001), baseline serum bilirubin(P = 0.002), and baseline viral load(P = 0.04) were independent predictors for SVR. SVR rates were significantly higher in the recipient-CC/donor-non CC pairs(P = 0.007). Neither baseline fibrosis nor change in fibrosis stage after anti-viral therapy were associated with SVR. Fibrosis progressed in 72% of patients despite SVR. Median graft survival was 91 mo. Five-year patient survival was superior in patients who achieved SVR(97% vs 82%, P = 0.001). Pre-treatment ALP ≥ 150 U/L(P = 0.01), total bilirubin ≥ 1.5 mg/d L(P = 0.001) and creatinine ≥ 2 mg/d L(P = 0.001) were independently associated with patient survival. Only 13% of patients achieving SVR died during the followup period. Treatment discontinuation and treatmentrelated mortality occurred in 35% and 2.2% of patients, respectively. EPO, G-CSF and blood transfusion were needed in 89%, 40% and 23% of patients, respectively. Overall hospitalization rate for treatment-related serious adverse events was 21%. Forty-six(25%) of the patients were deceased; among those who died, 25(54%) were due to liver-related complications, and 4 deaths(9%) occurred while receiving therapy(2 patients experienced hepatic decompensation and 2 sepsis). CONCLUSION: LADR strategy remains relevant in managing post-LT recurrent HCV where access to DAAs is limited. SVR is associated with improved survival, but fibrosis progression still occurs.展开更多
Due to the inherent insecure nature of the Internet,it is crucial to ensure the secure transmission of image data over this network.Additionally,given the limitations of computers,it becomes evenmore important to empl...Due to the inherent insecure nature of the Internet,it is crucial to ensure the secure transmission of image data over this network.Additionally,given the limitations of computers,it becomes evenmore important to employ efficient and fast image encryption techniques.While 1D chaotic maps offer a practical approach to real-time image encryption,their limited flexibility and increased vulnerability restrict their practical application.In this research,we have utilized a 3DHindmarsh-Rosemodel to construct a secure cryptosystem.The randomness of the chaotic map is assessed through standard analysis.The proposed system enhances security by incorporating an increased number of system parameters and a wide range of chaotic parameters,as well as ensuring a uniformdistribution of chaotic signals across the entire value space.Additionally,a fast image encryption technique utilizing the new chaotic system is proposed.The novelty of the approach is confirmed through time complexity analysis.To further strengthen the resistance against cryptanalysis attacks and differential attacks,the SHA-256 algorithm is employed for secure key generation.Experimental results through a number of parameters demonstrate the strong cryptographic performance of the proposed image encryption approach,highlighting its exceptional suitability for secure communication.Moreover,the security of the proposed scheme has been compared with stateof-the-art image encryption schemes,and all comparison metrics indicate the superior performance of the proposed scheme.展开更多
TheCOVID-19 outbreak began in December 2019 andwas declared a global health emergency by the World Health Organization.The four most dominating variants are Beta,Gamma,Delta,and Omicron.After the administration of vac...TheCOVID-19 outbreak began in December 2019 andwas declared a global health emergency by the World Health Organization.The four most dominating variants are Beta,Gamma,Delta,and Omicron.After the administration of vaccine doses,an eminent decline in new cases has been observed.The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies.However,strong variants likeDelta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination.Therefore,it is indispensable to study,analyze and most importantly,predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons.In this regard,machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes.In this study,prediction of T-cells Epitopes’response was conducted for vaccinated and unvaccinated people for Beta,Gamma,Delta,and Omicron variants.The dataset was divided into two classes,i.e.,vaccinated and unvaccinated,and the predicted response of T-cell Epitopes was divided into three categories,i.e.,Strong,Impaired,and Over-activated.For the aforementioned prediction purposes,a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers.Furthermore,the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach.Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error.展开更多
Waste Glass(WGs)and Coir Fiber(CF)are not widely utilized,even though their silica and cellulose content can be used to create construction materials.This study aimed to optimize mortar compressive strength using Resp...Waste Glass(WGs)and Coir Fiber(CF)are not widely utilized,even though their silica and cellulose content can be used to create construction materials.This study aimed to optimize mortar compressive strength using Response Surface Methodology(RSM).The Central Composite Design(CCD)was applied to determine the optimization of WGs and CF addition to the mortar compressive strength.Compressive strength and microstructure testing with Scanning Electron Microscope(SEM),Fourier-transform Infrared Spectroscopy(FT-IR),and X-Ray Diffraction(XRD)were conducted to specify the mechanical ability and bonding between the matrix,CF,and WGs.The results showed that the chemical treatment of CF produced 49.15%cellulose,with an average particle size of 1521μm.The regression of a second-order polynomial model yielded an optimum composition consisting of 12.776%WGs and 2.344%CF with a predicted compressive strength of 19.1023 MPa.C-S-H gels were identified in the mortars due to the dissolving of SiO_(2) in WGs and cement.The silica from WGs increased the C-S-H phase.CF plays a role in preventing,bridging,and branching micro-cracks before reaching maximum stress.WGs aggregates and chemically treated CF are suitable to be composited in mortar to increase compressive strength.展开更多
Image encryption has attracted much interest as a robust security solution for preventing unauthorized access to critical image data.Medical picture encryption is a crucial step in many cloud-based and healthcare appl...Image encryption has attracted much interest as a robust security solution for preventing unauthorized access to critical image data.Medical picture encryption is a crucial step in many cloud-based and healthcare applications.In this study,a strong cryptosystem based on a 2D chaotic map and Jigsaw transformation is presented for the encryption of medical photos in private Internet of Medical Things(IoMT)and cloud storage.A disorganized three-dimensional map is the foundation of the proposed cipher.The dispersion of pixel values and the permutation of their places in this map are accomplished using a nonlinear encoding process.The suggested cryptosystem enhances the security of the delivered medical images by performing many operations.To validate the efficiency of the recommended cryptosystem,various medical image kinds are used,each with its unique characteristics.Several measures are used to evaluate the proposed cryptosystem,which all support its robust security.The simulation results confirm the supplied cryptosystem’s secrecy.Furthermore,it provides strong robustness and suggested protection standards for cloud service applications,healthcare,and IoMT.It is seen that the proposed 3D chaotic cryptosystem obtains an average entropy of 7.9998,which is near its most excellent value of 8,and a typical NPCR value of 99.62%,which is also near its extreme value of 99.60%.Moreover,the recommended cryptosystem outperforms conventional security systems across the test assessment criteria.展开更多
The advancements in sensing technologies,information processing,and communication schemes have revolutionized the healthcare sector.Electronic Healthcare Records(EHR)facilitate the patients,doctors,hospitals,and other...The advancements in sensing technologies,information processing,and communication schemes have revolutionized the healthcare sector.Electronic Healthcare Records(EHR)facilitate the patients,doctors,hospitals,and other stakeholders to maintain valuable data and medical records.The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks.A single attempt of a successful Denial of Service(DoS)attack can compromise the complete healthcare system.This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things(IoMT)to address the stated challenges.The proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy manner.The efficacy of the proposed framework is evaluated in the context of service execution time and throughput.The experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.展开更多
Chaos-based cryptosystems are considered a secure mode of communication due to their reliability.Chaotic maps are associated with the other domains to construct robust encryption algorithms.There exist numerous encryp...Chaos-based cryptosystems are considered a secure mode of communication due to their reliability.Chaotic maps are associated with the other domains to construct robust encryption algorithms.There exist numerous encryption schemes in the literature based on chaotic maps.This work aims to propose an attack on a recently proposed hyper-chaotic map-based cryptosystem.The core notion of the original algorithm was based on permutation and diffusion.A bitlevel permutation approach was used to do the permutation row-and column-wise.The diffusion was executed in the forward and backward directions.The statistical strength of the cryptosystem has been demonstrated by extensive testing conducted by the author of the cryptosystem.This cryptanalysis article investigates the robustness of this cryptosystem against a chosen-plaintext attack.The secret keys of the cryptosystem were retrieved by the proposed attack with 258 chosen-plain images.The results in this manuscript suggest that,in addition to standard statistical evaluations,thorough cryptanalysis of each newly suggested cryptosystem is necessary before it can be used in practical application.Moreover,the data retrieved is also passed through some statistical analysis to compare the quality of the original and retrieved data.The results of the performance analysis indicate the exact recovery of the original data.To make the cryptosystem useful for applications requiring secure data exchange,a few further improvement recommendations are also suggested.展开更多
Breast cancer is one of the leading cancers among women.It has the second-highest mortality rate in women after lung cancer.Timely detection,especially in the early stages,can help increase survival rates.However,manu...Breast cancer is one of the leading cancers among women.It has the second-highest mortality rate in women after lung cancer.Timely detection,especially in the early stages,can help increase survival rates.However,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience.However,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages.The research presented in this paper is focused on the multi-class classification of breast cancer.The deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound dataset.The proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble model.Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results.Subsequently,entropy-based feature selection is used.Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,respectively.The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.展开更多
文摘Drug-induced liver injury(DILI)is a major problem in the United States,commonly leading to hospital admission.Diagnosing DILI is difficult as it is a diagnosis of exclusion requiring a temporal relationship between drug exposure and liver injury and a thorough work up for other causes.In addition,DILI has a very variable clinical and histologic presentation that can mimic many different etiologies of liver disease.Objective scoring systems can assess the probability that a drug caused the liver injury but liver biopsy findings are not part of the criteria used in these systems.This review will address some of the recent updates to the scoring systems and the role of liver biopsy in the diagnosis of DILI.
文摘The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the Internet.Recently,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning systems.Smart healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical data.In smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance Imaging.The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI.Moreover,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission delays.To address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory.The results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,respectively.This validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.
基金Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia,Grant/Award Number:NU/IFC/02/SERC/-/31Institutional Funding Committee at Najran University,Kingdom of Saudi Arabia。
文摘Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial environments.Several IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied structure.Intrusion Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant attention.However,existing ML and DL-based IDS still face a number of obstacles that must be overcome.For instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory devices.Imbalanced and fewer data potentially lead to low performance on existing IDS.This paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant features.The proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for prediction.The performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID datasets.These datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.
文摘BACKGROUND： Various scoring systems based on assessment of the systemic inflammatory response help assessing the prognosis of patients with pancreatic ductal adenocarcinoma.In the present systematic review we evaluated the validity of four pre-intervention scoring systems： Glasgow prognostic score（GPS） and its modified version（mGPS）, platelet lymphocyte ratio（PLR）, neutrophil lymphocyte ratio（NLR）, and prognostic nutrition index（PNI）.DATA SOURCES： MOOSE guidelines were followed and EMBASE and MEDLINE databases were searched for all published studies until September 2013 using comprehensive text word and MeSH terms. All identified studies were analyzed, and relevant studies were included in the systematic review.RESULTS： Six studies were identified for GPS/mGPS with3 reporting statistical significance for GPS/mGPS on both univariate analysis（UVA） and multivariate analysis（MVA）.Two studies suggested prognostic significance on UVA but not MVA, and in the final study UVA failed to show significance.Eleven studies evaluated the prognostic value of NLR. Six of them reported prognostic significance for NLR on UVA that persisted at MVA in 4 studies, and in the remaining 2 studies NLR was the only significant factor on UVA. In the remaining5 studies, all in patients undergoing resection, there was no significance on UVA. Seven studies evaluated PLR, with only one study demonstrated its prognostic significance on both UVAand MVA, the rest did not show the significance on UVA. Of the two studies identified for PNI, one demonstrated a statistically significant difference in survival on both UVA and MVA, and the other reported no significance for PNI on UVA.CONCLUSIONS： Both GPS/mGPS and NLR may be useful but further better-designed studies are required to confirm their value. PLR might be little useful, and there are at present inadequate data to assess the prognostic value of PNI. At present, no scoring system is reliable enough to be accepted into routine use for the prognosis of patients with pancreatic ductal adenocarcinoma.
文摘Herbal and dietary supplements(HDS)are increasingly used worldwide for numerous,mainly unproven health benefits.The HDS industry is poorly regulated compared to prescription medicines and most products are easily obtainable.Drug induced liver injury(DILI)is a well-recognized entity associated with prescription and over the counter medications and many reports have emerged of potential HDS-related DILI.There is considerable geographic variability in the risk and severity of DILI associated with HDS but the presentation of severe liver injury is similar with a hepatocellular pattern accompanied by jaundice.This type of injury can lead to acute liver failure and the need for liver transplantation.Patients will often fail to mention their use of HDS,considering it natural and therefore harmless.Hence physicians should understand that these products can be associated with DILI and explicitly ask about HDS use in any patient with otherwise unexplained acute liver injury.
文摘Liver transplantation is the optimal treatment for many patients with advanced liver disease, including decompensated cirrhosis, hepatocellular carcinoma and acute liver failure. Organ shortage is the maindeterminant of death on the waiting list and hence living donor liver transplantation(LDLT) assumes importance. Biliary complications are the most common post operative morbidity after LDLT and occur due to anatomical and technical reasons. They include biliary leaks, strictures and cast formation and occur in the recipient as well as the donor. The types of biliary complications after LDLT along with their etiology, presenting features, diagnosis and endoscopic and surgical management are discussed.
文摘Background:Post-hepatectomy liver failure(PHLF)is the Achilles’heel of hepatic resection for colorectal liver metastases.The most commonly used procedure to generate hypertrophy of the functional liver remnant(FLR)is portal vein embolization(PVE),which does not always lead to successful hypertrophy.Associating liver partition and portal vein ligation for staged hepatectomy(ALPPS)has been proposed to overcome the limitations of PVE.Liver venous deprivation(LVD),a technique that includes simultaneous portal and hepatic vein embolization,has also been proposed as an alternative to ALPPS.The present study aimed to conduct a systematic review as the first network meta-analysis to compare the efficacy,effectiveness,and safety of the three regenerative techniques.Data sources:A systematic search for literature was conducted using the electronic databases Embase,PubMed(MEDLINE),Google Scholar and Cochrane.Results:The time to operation was significantly shorter in the ALPPS cohort than in the PVE and LVD cohorts by 27 and 22 days,respectively.Intraoperative parameters of blood loss and the Pringle maneuver demonstrated non-significant differences between the PVE and LVD cohorts.There was evidence of a significantly higher FLR hypertrophy rate in the ALPPS cohort when compared to the PVE cohort,but non-significant differences were observed when compared to the LVD cohort.Notably,the LVD cohort demonstrated a significantly better FLR/body weight(BW)ratio compared to both the ALPPS and PVE cohorts.Both the PVE and LVD cohorts demonstrated significantly lower major morbidity rates compared to the ALPPS cohort.The LVD cohort also demonstrated a significantly lower 90-day mortality rate compared to both the PVE and ALPPS cohorts.Conclusions:LVD in adequately selected patients may induce adequate and profound FLR hypertrophy before major hepatectomy.Present evidence demonstrated significantly lower major morbidity and mortality rates in the LVD cohort than in the ALPPS and PVE cohorts.
文摘The severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which caused the coronavirus disease 2019(COVID-19)pandemic,has affected more than 400 million people worldwide.With the recent rise of new Delta and Omicron variants,the efficacy of the vaccines has become an important question.The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions,particularly for healthcare workers.In this paper,we discuss the current literature on invasive/contact and non-invasive/noncontact technologies(including Wi-Fi,radar,and software-defined radio)that have been effectively used to detect,diagnose,and monitor human activities and COVID-19 related symptoms,such as irregular respiration.In addition,we focused on cutting-edge machine learning algorithms(such as generative adversarial networks,random forest,multilayer perceptron,support vector machine,extremely randomized trees,and k-nearest neighbors)and their essential role in intelligent healthcare systems.Furthermore,this study highlights the limitations related to non-invasive techniques and prospective research directions.
文摘Liver transplantation(LT)remains the best option for patients with end-stage liver disease but the demand for organs from deceased donors continues to outweigh the available supply.The advent of highly effective anti-viral treatments has reduced the number of patients undergoing LT for hepatitis C(HCV)and hepatitis B(HBV)related liver disease and yet the number of patients waiting for LT continues to increase,driven by an increase in the patients listed with a diagnosis of cirrhosis due to non-alcoholic steatohepatitis and alcoholrelated liver disease.In addition,human immunodeficiency virus(HIV)infection,which was previously a contra-indication for LT,is no longer a fatal disease due to the effectiveness of HIV therapy and patients with HIV and liver disease are now developing indications for LT.The rising demand for LT is projected to increase further in the future,thus driving the need to investigate potential means of expanding the pool of potential donors.One mechanism for doing so is utilizing organs from donors that previously would have been discarded or used only in exceptional circumstances such as HCV-positive,HBV-positive,and HIVpositive donors.The advent of highly effective anti-viral therapy has meant that these organs can now be used with excellent outcomes in HCV,HBV or HIV infected recipients and in some cases uninfected recipients.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia has funded this project,under Grant No.(IFPDP-279-22).
文摘The Internet of things(IoT)is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision.Despite several advantages,the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals.A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries.To overcome the security challenges of IoT networks,this article proposes a lightweight deep autoencoder(DAE)based cyberattack detection framework.The proposed approach learns the normal and anomalous data patterns to identify the various types of network intrusions.The most significant feature of the proposed technique is its lower complexity which is attained by reducing the number of operations.To optimally train the proposed DAE,a range of hyperparameters was determined through extensive experiments that ensure higher attack detection accuracy.The efficacy of the suggested framework is evaluated via two standard and open-source datasets.The proposed DAE achieved the accuracies of 98.86%,and 98.26%for NSL-KDD,99.32%,and 98.79%for the UNSW-NB15 dataset in binary class and multi-class scenarios.The performance of the suggested attack detection framework is also compared with several state-of-the-art intrusion detection schemes.Experimental outcomes proved the promising performance of the proposed scheme for cyberattack detection in IoT networks.
基金Supported by National Institutes of Health,No.DA031095 and No.DK090317
文摘AIM: To determine the safety profile of new hepatitis C virus (HCV) treatments in liver transplant (LT) recipients with recurrent HCV infection.METHODS: Forty-two patients were identified with recurrent HCV infection that underwent LT at least 12 mo prior to initiating treatment with a Sofosbuvir-based regimen during December 2013-June 2014. Cases were patients who experienced hepatic decompensation and/or serious adverse events (SAE) during or within one month of completing treatment. Controls had no evidence of hepatic decompensation and/or SAE. HIV-infected patients were excluded. Cumulative incidence of decompensation/SAE was calculated using the Kaplan Meier method. Exact logistic regression analysis was used to identify factors associated with the composite outcome.RESULTS: Median age of the 42 patients was 60 years [Interquartile Range (IQR): 56-65 years], 33% (14/42) were female, 21% (9/42) were Hispanic, and 9% (4/42) were Black. The median time from transplant to treatment initiation was 5.4 years (IQR: 2.1-8.8 years). Thirteen patients experienced one or more episodes of hepatic decompensation and/or SAE. Anemia requiring transfusion, the most common event, occurred in 62% (8/13) patients, while 54% (7/13) decompensated. The cumulative incidence of hepatic decompensation/SAE was 31% (95%CI: 16%-41%). Risk factors for decompensation/SAE included lower pre-treatment hemoglobin (OR = 0.61 per g/dL, 95%CI: 0.40-0.88, P < 0.01), estimated glomerular filtration rate (OR = 0.95 per mL/min per 1.73 m<sup>2</sup>, 95%CI: 0.90-0.99, P = 0.01), and higher baseline serum total bilirubin (OR = 2.43 per mg/dL, 95%CI: 1.17-8.65, P < 0.01). The sustained virological response rate for the cohort of 42 patients was 45%, while it was 31% for cases.CONCLUSION: Sofosbuvir/ribavirin will continue to be used in the post-transplant population, including those with HCV genotypes 2 and 3. Management of anemia remains an important clinical challenge.
文摘ABSTRACT： Serum aminotransferases have been used as surrogate markers for liver ischemia-reperfusion injury that follows liver surgery. Some studies have suggested that rises in serum alanine aminotransferase （ALT） correlate with patient outcome after liver resection. We assessed whether postoperative day 1 （POD 1） ALT could be used to predict patient morbidity and mortality following liver resection. We reviewed our prospectively held database and included consecutive adult patients undergoing elective liver resection in our institution between January 2013 and December 2014. Primary outcome assessed was correlation of POD 1 ALT with patient＇s morbidity and mortality. We also assessed whether concurrent radiofrequency ablation, neoadjuvant chemotherapy and use of the Pringle maneuver significantly affected the level of POD 1 ALT. A total of 110 liver resections were included in the study. The overall in-hospital patient morbidity and mortality were 31.8% and 0.9%, respectively. The median level of POD 1 ALT was 275 IU/L. No correlation was found between POD 1 serum ALT levels and patient morbidity after elective liver resection, whilst correlation with mortality was not possible because ofthe low number of mortalities. Patients undergoing concurrent radiofrequency ablation were noted to have an increased level of POD 1 serum ALT but not those given neoadjuvant chemotherapy and those in whom the Pringle maneuver was used. Our study demonstrates POD 1 serum ALT does not correlate with patient morbidity after elective liver resection.
基金Supported by JTD(an employee of Mount Sinai Medical Center)in part was provided by Genentech Pharmaceuticals
文摘AIM: To describe our experience using a low-acceleratingdose regimen(LADR) with pegylated interferon alpha-2a and ribavirin in treatment of hepatitis C virus(HCV) recurrence. METHODS: From 2003, a protocolized LADR strategy was employed to treat liver transplant(LT) recipients with recurrent HCV at our institution. Medical records of 182 adult patients with recurrent HCV treated with LADR between 1/2003 and 1/2011 were reviewed. Histopathology from all post-LT liver biopsies were reviewed in a blinded fashion. Paired recipient and donor IL28 B status were assessed. A novel technique was employed to ascertain recipient and donor IL28B(rs12979860) Gt data using DNA extracted from archival FFPE tissue from explanted native livers and donor gallbladders respectively. The primary endpoint was SVR; secondary endpoints examined include(1) patient and graft survival;(2) effect of anti-viral therapy on liver histology(fibrosis and inflammation);(3) incidence of on-treatment development of ACR, CDR, or PCH;(4) association of recipient and donor IL28 B genotype with SVR; and(5) incidence of antiviral therapy-associated adverse events(anemia, leukopenia, thrombocytopenia, depression) and hepatic decompensation.RESULTS: The overall SVR rate was 38%(29% Gt1, 67% Gt2, 86% Gt3 and 58% Gt4). HCV Gt(P < 0.0001), donor age(P = 0.003), cytomegalovirus mismatch(P = 0.001), baseline serum bilirubin(P = 0.002), and baseline viral load(P = 0.04) were independent predictors for SVR. SVR rates were significantly higher in the recipient-CC/donor-non CC pairs(P = 0.007). Neither baseline fibrosis nor change in fibrosis stage after anti-viral therapy were associated with SVR. Fibrosis progressed in 72% of patients despite SVR. Median graft survival was 91 mo. Five-year patient survival was superior in patients who achieved SVR(97% vs 82%, P = 0.001). Pre-treatment ALP ≥ 150 U/L(P = 0.01), total bilirubin ≥ 1.5 mg/d L(P = 0.001) and creatinine ≥ 2 mg/d L(P = 0.001) were independently associated with patient survival. Only 13% of patients achieving SVR died during the followup period. Treatment discontinuation and treatmentrelated mortality occurred in 35% and 2.2% of patients, respectively. EPO, G-CSF and blood transfusion were needed in 89%, 40% and 23% of patients, respectively. Overall hospitalization rate for treatment-related serious adverse events was 21%. Forty-six(25%) of the patients were deceased; among those who died, 25(54%) were due to liver-related complications, and 4 deaths(9%) occurred while receiving therapy(2 patients experienced hepatic decompensation and 2 sepsis). CONCLUSION: LADR strategy remains relevant in managing post-LT recurrent HCV where access to DAAs is limited. SVR is associated with improved survival, but fibrosis progression still occurs.
基金the Deanship of Scientific Research at Najran University for funding this work under the Research Groups Funding Program Grant Code(NU/RG/SERC/12/3).
文摘Due to the inherent insecure nature of the Internet,it is crucial to ensure the secure transmission of image data over this network.Additionally,given the limitations of computers,it becomes evenmore important to employ efficient and fast image encryption techniques.While 1D chaotic maps offer a practical approach to real-time image encryption,their limited flexibility and increased vulnerability restrict their practical application.In this research,we have utilized a 3DHindmarsh-Rosemodel to construct a secure cryptosystem.The randomness of the chaotic map is assessed through standard analysis.The proposed system enhances security by incorporating an increased number of system parameters and a wide range of chaotic parameters,as well as ensuring a uniformdistribution of chaotic signals across the entire value space.Additionally,a fast image encryption technique utilizing the new chaotic system is proposed.The novelty of the approach is confirmed through time complexity analysis.To further strengthen the resistance against cryptanalysis attacks and differential attacks,the SHA-256 algorithm is employed for secure key generation.Experimental results through a number of parameters demonstrate the strong cryptographic performance of the proposed image encryption approach,highlighting its exceptional suitability for secure communication.Moreover,the security of the proposed scheme has been compared with stateof-the-art image encryption schemes,and all comparison metrics indicate the superior performance of the proposed scheme.
基金This paper is funded by the Deanship of Scientific Research at ImamMohammad Ibn Saud Islamic University Research Group No.RG-21-07-05.
文摘TheCOVID-19 outbreak began in December 2019 andwas declared a global health emergency by the World Health Organization.The four most dominating variants are Beta,Gamma,Delta,and Omicron.After the administration of vaccine doses,an eminent decline in new cases has been observed.The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies.However,strong variants likeDelta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination.Therefore,it is indispensable to study,analyze and most importantly,predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons.In this regard,machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes.In this study,prediction of T-cells Epitopes’response was conducted for vaccinated and unvaccinated people for Beta,Gamma,Delta,and Omicron variants.The dataset was divided into two classes,i.e.,vaccinated and unvaccinated,and the predicted response of T-cell Epitopes was divided into three categories,i.e.,Strong,Impaired,and Over-activated.For the aforementioned prediction purposes,a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers.Furthermore,the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach.Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error.
基金funded by the Ministry of Education,Culture,Research,and the Technology,Indonesia for Matching Fund (Kedaireka)Scheme in 2022 with Contract No.155/E1/KS.06.02/2022.
文摘Waste Glass(WGs)and Coir Fiber(CF)are not widely utilized,even though their silica and cellulose content can be used to create construction materials.This study aimed to optimize mortar compressive strength using Response Surface Methodology(RSM).The Central Composite Design(CCD)was applied to determine the optimization of WGs and CF addition to the mortar compressive strength.Compressive strength and microstructure testing with Scanning Electron Microscope(SEM),Fourier-transform Infrared Spectroscopy(FT-IR),and X-Ray Diffraction(XRD)were conducted to specify the mechanical ability and bonding between the matrix,CF,and WGs.The results showed that the chemical treatment of CF produced 49.15%cellulose,with an average particle size of 1521μm.The regression of a second-order polynomial model yielded an optimum composition consisting of 12.776%WGs and 2.344%CF with a predicted compressive strength of 19.1023 MPa.C-S-H gels were identified in the mortars due to the dissolving of SiO_(2) in WGs and cement.The silica from WGs increased the C-S-H phase.CF plays a role in preventing,bridging,and branching micro-cracks before reaching maximum stress.WGs aggregates and chemically treated CF are suitable to be composited in mortar to increase compressive strength.
基金The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Groups Funding program grant code(NU/RC/SERC/11/5).
文摘Image encryption has attracted much interest as a robust security solution for preventing unauthorized access to critical image data.Medical picture encryption is a crucial step in many cloud-based and healthcare applications.In this study,a strong cryptosystem based on a 2D chaotic map and Jigsaw transformation is presented for the encryption of medical photos in private Internet of Medical Things(IoMT)and cloud storage.A disorganized three-dimensional map is the foundation of the proposed cipher.The dispersion of pixel values and the permutation of their places in this map are accomplished using a nonlinear encoding process.The suggested cryptosystem enhances the security of the delivered medical images by performing many operations.To validate the efficiency of the recommended cryptosystem,various medical image kinds are used,each with its unique characteristics.Several measures are used to evaluate the proposed cryptosystem,which all support its robust security.The simulation results confirm the supplied cryptosystem’s secrecy.Furthermore,it provides strong robustness and suggested protection standards for cloud service applications,healthcare,and IoMT.It is seen that the proposed 3D chaotic cryptosystem obtains an average entropy of 7.9998,which is near its most excellent value of 8,and a typical NPCR value of 99.62%,which is also near its extreme value of 99.60%.Moreover,the recommended cryptosystem outperforms conventional security systems across the test assessment criteria.
文摘The advancements in sensing technologies,information processing,and communication schemes have revolutionized the healthcare sector.Electronic Healthcare Records(EHR)facilitate the patients,doctors,hospitals,and other stakeholders to maintain valuable data and medical records.The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks.A single attempt of a successful Denial of Service(DoS)attack can compromise the complete healthcare system.This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things(IoMT)to address the stated challenges.The proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy manner.The efficacy of the proposed framework is evaluated in the context of service execution time and throughput.The experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.
基金The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Groups Funding program grant code(NU/RG/SERC/11/4).
文摘Chaos-based cryptosystems are considered a secure mode of communication due to their reliability.Chaotic maps are associated with the other domains to construct robust encryption algorithms.There exist numerous encryption schemes in the literature based on chaotic maps.This work aims to propose an attack on a recently proposed hyper-chaotic map-based cryptosystem.The core notion of the original algorithm was based on permutation and diffusion.A bitlevel permutation approach was used to do the permutation row-and column-wise.The diffusion was executed in the forward and backward directions.The statistical strength of the cryptosystem has been demonstrated by extensive testing conducted by the author of the cryptosystem.This cryptanalysis article investigates the robustness of this cryptosystem against a chosen-plaintext attack.The secret keys of the cryptosystem were retrieved by the proposed attack with 258 chosen-plain images.The results in this manuscript suggest that,in addition to standard statistical evaluations,thorough cryptanalysis of each newly suggested cryptosystem is necessary before it can be used in practical application.Moreover,the data retrieved is also passed through some statistical analysis to compare the quality of the original and retrieved data.The results of the performance analysis indicate the exact recovery of the original data.To make the cryptosystem useful for applications requiring secure data exchange,a few further improvement recommendations are also suggested.
基金This research work was funded by Institutional Fund Projects under Grant No.(IFPIP:1614-611-1442)from the Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Breast cancer is one of the leading cancers among women.It has the second-highest mortality rate in women after lung cancer.Timely detection,especially in the early stages,can help increase survival rates.However,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience.However,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages.The research presented in this paper is focused on the multi-class classification of breast cancer.The deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound dataset.The proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble model.Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results.Subsequently,entropy-based feature selection is used.Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,respectively.The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.
