Diagnosis Capability Research Articles

Advances of nanoparticles as drug delivery systems for disease diagnosis and treatment

Decades have passed since the first nanoparticles-base medicine was approved for human cancer treatment, and the research and development of nanoparticles for drug delivery are always undergoing. Nowadays, the significant advances complicate nanoparticles’ branches, including liposomes, solid lipid nanoparticles, inorganic nanoparticles, micelles, nanovaccines and nano-antibodies, etc. These nanoparticles show numerous capabilities in treatment and diagnosis of stubborn diseases like cancer and neurodegenerative diseases, emerging as novel drug carriers or therapeutic agents in future. In this review, the complicated branches of nanoparticles are classified and summarized, with their property and functions concluded. Besides, there are also some delivery strategies that make nanoparticles smarter and more efficient in drug delivery, and frontiers in these strategies are also summarized in this review. Except these excellent works in newly-produced drug delivery nanoparticles, some points of view and future expectations are made in the end.

Serum long non-coding RNA SCARNA10 serves as a potential diagnostic biomarker for hepatocellular carcinoma

BackgroundCirculating long non-coding RNAs (lncRNAs) have been demonstrated to serve as diagnostic or prognosis biomarkers for various disease. We aimed to elucidate the diagnostic efficacy of serum lncRNA SCARNA10 for the hepatocellular carcinoma (HCC).MethodsIn this study, a total of 182 patients with HCC, 105 patients with benign liver disease (BLD), and 149 healthy controls (HC) were enrolled. According to different classifications, the levels of serum SCARNA10 were assessed by quantitative real-time polymerase chain reaction (qPCR). The correlations between serum SCARNA10 and clinicopathological characteristics were further analyzed. The receiver operating characteristic (ROC) curve and area under curve (AUC) were utilized to estimate the diagnostic capacity of serum SCARNA10 and its combination with AFP for HCC.ResultsThe results demonstrated that the levels of serum SCARNA10 were significantly higher in HCC patients than in patients with BLD and healthy controls, and significantly increased in HCC patients with hepatitis B or C infection, or with liver cirrhosis. Furthermore, positive correlations were noted between serum SCARNA10 level and some clinicopathological characteristics, including tumor size, differentiation degrees, tumor stage, vascular invasion, tumor metastasis and complications. ROC analysis revealed that SCARNA10 had a significantly predictive value for HCC (Sensitivity = 0.70, Specificity = 0.77, and AUC = 0.82), the combination of SCARNA10 and AFP gained the higher sensitivity (AUCSCARNA10 + AFP = 0.92 vs AUCAFP = 0.83, p < 0.01). SCARNA10 retained significant diagnosis capabilities for AFP-negative HCC patients.ConclusionsIn summary, lncRNA SCARNA10 may serve as a novel and non-invasive biomarker with relatively high sensitivity and specificity for HCC diagnosis.

Large‐scale structural health monitoring using composite recurrent neural networks and grid environments

AbstractThe demand for resilient and smart structures has been rapidly increasing in recent decades. With the occurrence of the big data revolution, research on data‐driven structural health monitoring (SHM) has gained traction in the civil engineering community. Unsupervised learning, in particular, can be directly employed solely using field‐acquired data. However, the majority of unsupervised learning SHM research focuses on detecting damage in simple structures or components and possibly low‐resolution damage localization. In this study, an unsupervised learning, novelty detection framework for detecting and localizing damage in large‐scale structures is proposed. The framework relies on a 5D, time‐dependent grid environment and a novel spatiotemporal composite autoencoder network. This network is a hybrid of autoencoder convolutional neural networks and long short‐term memory networks. A 10‐story, 10‐bay, numerical structure is used to evaluate the proposed framework damage diagnosis capabilities. The framework was successful in diagnosing the structure health state with average accuracies of 93% and 85% for damage detection and localization, respectively.

LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis

Many applications for the automated diagnosis of plant disease have been developed based on the success of deep learning techniques. However, these applications often suffer from overfitting, and the diagnostic performance is drastically decreased when used on test data sets from new environments. In this article, we propose LeafGAN, a novel image-to-image translation system with own attention mechanism. LeafGAN generates a wide variety of diseased images via transformation from healthy images, as a data augmentation tool for improving the performance of plant disease diagnosis. Due to its own attention mechanism, our model can transform only relevant areas from images with a variety of backgrounds, thus enriching the versatility of the training images. Experiments with five-class cucumber disease classification show that data augmentation with vanilla CycleGAN cannot help to improve the generalization, i.e., disease diagnostic performance increased by only 0.7&#x0025; from the baseline. In contrast, LeafGAN boosted the diagnostic performance by 7.4&#x0025;. We also visually confirmed that the generated images by our LeafGAN were much better quality and more convincing than those generated by vanilla CycleGAN. The code is available publicly at <uri>https://github.com/IyatomiLab/LeafGAN</uri>. <i>Note to Practitioners</i> Automated plant disease diagnosis systems play an important role in the agricultural automation field. Building a practical image-based automatic plant diagnosis system requires collecting a wide variety of disease images with reliable label information. However, it is quite labor-intensive. Conventional systems have reported relatively high diagnosis performance, but most of their scores were largely biased due to the &#x201C;latent similarity&#x201D; between training and test images, and their true diagnosis capabilities were much lower than claimed. To address this issue, we propose LeafGAN, which generates countless diverse and high-quality training images; it works as an efficient data augmentation for the diagnosis classifier. Such generated images can be used as useful resources for improving the performance of the cucumber disease diagnosis systems.

Fault diagnosis for vehicle air conditioning blower using deep learning neural network

This study presents a fault diagnosis system for vehicle heating, ventilation and air conditioning (HVAC) acoustic signal with various feature extractions in deep learning neural network. Traditionally, sound used for fault diagnosis or signal classification is observed the difference of energy in time or frequency domains. Unfortunately, the frequency smearing effect often arises in some critical conditions. In the present study, discrete wavelet transform (DWT) and wavelet packet transform (WPT) are proposed in fault diagnosis. Meanwhile, when using mechanical learning methods, the data are relatively large, in order to reduce the amount of data, DWT and WPT low-frequency decomposition could be used to improve the performance. Furthermore, the signal characteristics more comprehensive, this study attempts to use the feature extraction method of wavelet packet conversion to improve the signal characteristics. In the experiment process, the operation state of the blade blower in the vehicle air conditioner, four different faults were designed, test database was established through sound to classify, and identify the data using deep neural networks to achieve the purpose of blower fault diagnosis. In data analysis, the original signal is presented through wavelet packet decomposition and discrete packet conversion technology, compared with traditional time and frequency domain signals to explore the identification rate, identification speed and related issues. Experimental results show that using WPT combined with deep neural networks have good fault diagnosis and discrimination capabilities, training, and identification time is shorter than time-frequency domain signals.

Multifunctional fibrous wound dressings for refractory wound healing

AbstractRefractory wounds have always been an important issue to healthcare systems, whose healing process is always delayed by multiple factors, including bacterial infections, chronic inflammation, and excessive exudates, etc. Employing multifunctional wound dressings is recognized as an effective strategy to deal with refractory wounds, which has yielded promising outcomes in recent years. Among these advanced wound dressings, fibrous dressings have gained growing attention due to their unique merits. Such wound dressings have demonstrated great potential in delivering theranostic agents, such as antibacterial agents, anti‐inflammatory drugs, growth factors, and diagnostic probes, etc., for the purposes of accelerating wound healing. This paper reviews the development of multifunctional fibrous dressings and their applications in treating refractory wounds. The construction approaches of novel fibrous dressing with capabilities of antibacterial, anti‐inflammation, exudate management and diagnosis were also introduced. Furthermore, the existing problems and challenges are also discussed briefly.

Diagnosability and hybrid diagnosability of some classes of graphs under the BPMC model

Diagnosability is an important metric to the capability of fault identification for multiprocessor systems. For considering system-level diagnosis, Lian and Zhou proposed the (f1,f2)-BPMC model which is a novel testing model and is the generalization of the PMC model and the BGM model. For measuring the fault diagnosis capability of a multiprocessor system with faulty links, Zhu et al. (2019) introduced the h-edge tolerable diagnosability which is the generalization of the concept of the traditional diagnosability. In this paper, we discuss the diagnosability of a graph under the (f1,f2)-BPMC model, the h-edge tolerable diagnosability of a k-regular graph (k≥6) for 0≤h≤k and 1≤cn(G)≤⌊k3⌋−1 under the PMC model and the MM⁎ model, and the h-edge tolerable diagnosability of an f1-regular graph (f1≥3) for 0≤h≤f1 under the (f1,f2)-BPMC model if N(x)∩N(y)=∅ for any (x,y)∈E(G) and N(u)≠N(v) for any two distinct vertices u,v∈V(G). Some previous results in (Lian et al. (2020) [17]) and (Lin and Teng (2014) [14]) are extended.

Research on the Fault Diagnostic of the Aircraft Cross-Linking Systems

High complexity of modern civil aircraft system leads to a huge difficulty to its trouble-shooting. This paper firstly discussed the two main directions of improving the aircraft fault diagnosis capability, and then analysed the pain points encountered by the model-based troubleshooting method. For the pain points, the Colour Fuzzy Fault Petri Net (CFFPN) model was proposed. The basic principles of the model and the basic process of conducting forward and reverse reasoning diagnosis based on the model were illustrated. Then the application of reasoning process in system design integration and airline maintenance was expounded. Finally, a practical software prototype of the model was developed, and effectiveness of the method and model was demonstrated through applications in engineering practice.

A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring

Automating the task of fault detection and diagnosis is crucial in the effort to reduce the operation and maintenance cost in the nuclear industry. This paper describes a physics-based approach for system-level diagnosis in thermal–hydraulic systems in nuclear power plants. The inclusion of physics information allows for the creation of virtual sensors, which provide improved fault diagnosis capability. The physics information also serves to better constrain diagnostic solutions to the physical domain. As a demonstration, various test cases for fault diagnosis in a high-pressure feedwater system were considered. The use of virtual sensors allows constructing performance models for two first-point feedwater heaters which would not have been possible otherwise due to the limited sensor set. Real-time plant data provided by a utility partner were used to assess the diagnostic approach. The detection of an abnormal event immediate after a plant startup pointed to faulty behaviors in the two first-point feedwater heaters. This double-blind fault diagnosis was subsequently confirmed by the plant operator. In addition, several simulated sensor fault events demonstrated the capability of our algorithms in detecting and discriminating sensor faults.

General Surgery and Surgery: Textbooks for Training Foreign Medical Students

The most important thing in education is the development of future doctor is method of preliminary diagnosis of any disease, including surgical. The previously used textbooks on surgery has been described mainly the clinical manifestations of diseases and their treatment techniques, without clarifying the principles and preliminary diagnosis capabilities for students. After medical education the doctors of the general medical practice tasks determinations basic requirements of scope of knowledge and practical skills for graduating student of institute of higher education of IV level of accreditation: goal-directed methodical algorithm of questioning of the patient (getting anamnesis), physical examination, substantiation of provisional diagnosis, determinate algorithm of additional methods of investigations with analysis of received results, differential diagnosis, forming clinical diagnosis, substantiation of treatment program and it’s realization. In the future the young doctor can work not only the doctor of family medicine, but also the expert of other directions, for example - the surgeon. For preparation to practical workies application textbook “General Surgery” for students 2-3 years education and textbooks “General Surgery” and “Surgery” for 2-6 years education English language foreign students and including information on methods and principles of forming of preliminary diagnosis, differential diagnosis, clinical diagnosis and treatment of surgical patients (Ed: Bereznytskyy, Zakharash, Mishalov, Shidlovskyj, 2016, 2019).

Imaging of Prenatal and Neonatal Intra-abdominal Genitourinary Tumors: a Review of the Literature.

Our goal was to summarize current literature related to imaging of intra-abdominal genitourinary tumors diagnosed in the prenatal or neonatal period. Our specific interests included modalities used, diagnoses made, changing incidence of tumor detection, and proposed future uses of these imaging modalities. Fetal and neonatal MRI have been used as an adjunct to ultrasound for better characterization and assessment of congenital mesoblastic nephroma, juvenile granulosa cell tumor, and other tumors. Despite recent literature describing fetal and neonatal MRI, it is not yet possible to determine whether its use is changing the incidence of tumor detection. Improvements in imaging technology, specifically the use of fetal MRI, have allowed for earlier identification of genitourinary masses with improved capability for diagnosis, surveillance, surgical planning, and sometimes prenatal treatment of the malignancy and related diagnoses, with a goal of preventing pregnancy and delivery complications.

Compressed Fourier-Domain Convolutional Beamforming for Sub-Nyquist Ultrasound Imaging.

Efficient ultrasound (US) systems that produce high-quality images can improve current clinical diagnosis capabilities by making the imaging process much more affordable and accessible to users. The most common technique for generating B-mode US images is delay-and-sum (DAS) beamforming, where an appropriate delay is introduced to signals sampled and processed at each transducer element. However, sampling rates that are much higher than the Nyquist rate of the signal are required for high-resolution DAS beamforming, leading to large amounts of data, making remote processing of channel data impractical. Moreover, the production of US images that exhibit high resolution and good image contrast requires a large set of transducer elements, which further increases the data size. Previous works suggest methods for reduction in sampling rate and in array size. In this work, we introduce compressed Fourier domain convolutional beamforming, combining Fourier domain beamforming (FDBF), sparse convolutional beamforming, and compressed sensing methods. This allows reducing both the number of array elements and the sampling rate in each element while achieving high-resolution images. Using in vivo data, we demonstrate that the proposed method can generate B-mode images using 142 times less data than DAS. Our results pave the way toward efficient US and demonstrate that high-resolution US images can be produced using sub-Nyquist sampling in time and space.

A Novel Power Transformer Fault Diagnosis Model Based on Harris-Hawks-Optimization Algorithm Optimized Kernel Extreme Learning Machine

Dissolved gas analysis (DGA) method is widely used to detect the incipient fault of power transformers. This paper presents a novel DGA method for power transformer fault diagnosis based on Harris-Hawks-optimization (HHO) algorithm optimized kernel extreme learning machine (KELM). The non-code ratios of the gases are used as the characterizing vector for the KELM model, and the Harris-Hawks-optimization (HHO) algorithm is introduced to optimize the KELM parameters, which promotes the fault diagnostic performance of KELM. Based on dataset collected from IEC TC 10, the fault diagnosis capability of the proposed method is validated by different characterizing vectors and is compared with conventional KELM and other optimized KELM. Moreover, the generalization ability of the proposed method is confirmed by China DGA data. The results demonstrate that the proposed method is superior to other methods and is more effective and stable for power transformer fault diagnosis with high accuracy.

ECOVID19 – Development of Ontology-based Clinical Decision Support System for COVID-19

Introduction: Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID-19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology-based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of the Royal Oman Police (ROP) of Sultanate of Oman.Materials and Methods: The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was from the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID-19 Symptom checker for the public users with a text interface and an AI-based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition in the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X-rays. Our CDSS also has a WebRTC (Web Real-Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested.Results: The ROP hospital's specialized doctors tested our CDSS, and the user interfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances.Conclusion: The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics.

Stabilizing Enzymes in Plasmonic Silk Film for Synergistic Therapy of In Situ SERS Identified Bacteria.

Increasing antibiotic resistance becomes a serious threat to public health. Photothermal therapy (PTT) and antibacterial enzyme‐based therapy are promising nonresistant strategies for efficiently killing drug‐resistant bacteria. However, the poor thermostability of enzymes in PTT hinders their synergistic therapy. Herein, antibacterial glucose oxidase (GOx) is embedded in a Ag graphitic nanocapsule (Ag@G) arrayed silk film to fabricate a GOx‐synergistic PTT system (named silk‐GOx‐Ag@G, SGA). The SGA system can stabilize GOx by a vitrification process through the restriction of hydrogen bond and rigid β‐sheet, and keep the antibacterial activity in the hyperthermal PTT environment. Moreover, the arrayed Ag@G possesses excellent chemical stability due to the protection of graphitic shell, providing stable plasmonic effect for integrating PTT and surface enhanced Raman scattering (SERS) analysis even in the GOx‐produced H2O2 environment. With in situ SERS identification of bacterial intrinsic signals in the mouse wound model, such SGA realizes superior synergistic antibacterial effect on the infected Escherichia coli, Staphylococcus aureus, and methicillin‐resistant Staphylococcus aureus in vivo, while without causing significant biotoxicity. This system provides a therapeutic method with low resistance and in situ diagnosis capability for efficiently eliminating bacteria.

Modern capabilities in diagnosis and treatment of neurotrophic keratopathy

In recent years, the problem of diagnosing and treating neurotrophic keratopathy (NK) has become relevant in view of its prevalence reaching 1.6-11.0 per 10000 people. While previously it was associated only with neuroparalytic keratitis, at present the violation of sensitive and trophic innervation of the cornea with the development of characteristic keratopathy is observed in many diseases and injuries of the organ of vision. Diagnosis of NK is based on anamnestic information and assessment of clinical and functional parameters: determination of the stability of the tear film, tear production and assessment of staining of the ocular surface with vital dyes. The main role in the diagnosis of NK belongs to corneal sensitivity determined with the Cochet-Bonnet esthesiometer. Treatment of NK is designed to restore or increase corneal sensitivity and involves tear replacement therapy, instillations of preparations derived from patient's own blood, anti-inflammatory, metabolic and antibacterial therapy. However, instillations of human erve growth factor (NGF) - the drug Cenegermin (registered in Europe in 2017 at a dose of 20 μg/ml under the name Oxervate), a recombinant form of human rhNGF from Escherichia coli bacteria - exhibit the highest pathogenetic orientation. Its «target» is the affected nerve fibers (specific receptors for their growth factor), which makes it possible to eliminate the violation of reparative processes in neural and epithelial cells. A high and long-term clinical efficacy of a course of six (with an interval of 2 hours) instillations of the drug for 8 weeks in the treatment of children and adults with NK has been established. Among the pathogenetically justified methods of surgical treatment, there is the so-called surgical neurotization of the cornea involving the contralateral supraorbital, supratrochlear, great auricular and other nerves, which has a long-term clinical effect.

On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine

Inspired by recent progress in machine learning, a data-driven fault diagnosis and isolation (FDI) scheme is explicitly developed for failure in the fuel supply system and sensor measurements of the laboratory gas turbine system. A passive approach to fault diagnosis is implemented where a model is trained using machine learning classifiers to detect a given set of faults in real-time on which it is trained. Towards the end, a comparative study is presented using well-known classification techniques, namely Support Vector Machine, Linear Discriminant Analysis, K-Neighbors, and Decision Trees. Several simulation studies were carried out to demonstrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.

A note on diagnosis and performance degradation detection in automatic control systems towards functional safety and cyber security

This note addresses diagnosis and performance degradation detection issues from an integrated viewpoint of functionality maintenance and cyber security of automatic control systems. It calls for more research attention on three aspects: (i) application of control and detection unified framework to enhancing the diagnosis capability of feedback control systems, (ii) projection-based fault detection, and complementary and explainable applications of projection- and machine learning-based techniques, and (iii) system performance degradation detection that is of elemental importance for today’s automatic control systems. Some ideas and conceptual schemes are presented and illustrated by means of examples, serving as convincing arguments for research efforts in these aspects. They would contribute to the future development of capable diagnosis systems for functionality safe and cyber secure automatic control systems.

MMFNet: Multisensor Data and Multiscale Feature Fusion Model for Intelligent Cross-Domain Machinery Fault Diagnosis

Although cross-domain fault diagnosis has received much attention in intelligent mechanical fault diagnosis, most existing methods only achieve knowledge transfer within the data from a single sensor. For a complex industrial system, multiple sensors are usually required to monitor its operating conditions coordinately. In such a situation, the fault diagnosis capability of existing cross-domain methods might be impaired significantly. To address this issue, in this article, a multisensor data and multiscale feature fusion network (MMFNet) is proposed to achieve cross-domain fault diagnosis using multisensor data. More specifically, a multiscale feature extraction module based on the pyramid principle is designed to fuse both deep and shallow features of the complex raw signal. Then, a Transformer-based multiscale features fusion and domain adaptation module is developed to fuse multiscale features and achieve the domain invariant of the sensor data. Finally, the proposed transferable Transformer is used for multisensor features cross-domain fusion. With the developed MMFNet, the features from multiple sensors can be comprehensively captured so that a more accurate fault diagnosis can be achieved. A series of experiments on a complex planetary gearbox demonstrate the effectiveness and practicability of the proposed method for fault diagnosis.

Symmetric positive definite manifold learning and its application in fault diagnosis

Locally linear embedding (LLE) is an effective tool to extract the significant features from a dataset. However, most of the relevant existing algorithms assume that the original dataset resides on a Euclidean space, unfortunately nearly all the original data space is non-Euclidean. In addition, the original LLE does not use the discriminant information of the dataset, which will degrade its performance in feature extraction. To address these problems raised in the conventional LLE, we first employ the original dataset to construct a symmetric positive definite manifold, and then estimate the tangent space of this manifold. Furthermore, the local and global discriminant information are integrated into the LLE, and the improved LLE is operated in the tangent space to extract the important features. We introduce Iris dataset to analyze the capability of the proposed method to extract features. Finally, several experiments are performed on five machinery datasets, and experimental results indicate that our proposed method can extract the excellent low-dimensional representations of the original dataset. Compared with the state-of-the-art methods, the proposed algorithm shows a strong capability for fault diagnosis.