Diagnosis Technology Research Articles

An efficient DNAzyme-locked leakless enzyme-free amplification system for alpha-foetoprotein detection in liver cancer and breast cancer.

Alpha-foetoprotein (AFP) is taken as a diagnostic tumor marker for the screening and diagnosis of cancer. Nucleic acid-based isothermal amplification strategies are emerging as a potential technology in early screening and clinical diagnosis of AFP. The leakages between hairpins dramatically increase the background and reduce the sensitivity. Thus, it is necessary to develop some strategies to reduce the leakage for isothermal amplification strategies. A DNAzyme-locked leakless enzyme-free amplification system was developed for AFP detection in liver cancer and breast cancer. AFP could open the apt-hairpin and initiate the catalytic hairpin assembly (CHA) reaction to produce a Y-shaped duplex. Two tails of a Y-shaped duplex cleaved the two kinds of leakless hairpins. Then, the third tail of the Y-shaped duplex catalyzed the second CHA between the cleaved leakless hairpins to recover the fluorescent intensity. The limit of detection reached 5fg/mL by the two levels of signal amplifications. Importantly, the leakless hairpin design effectively reduced leakage between hairpins and weakened the background. In addition, it also showed a great promising potential for AFP detection in early screening and clinical diagnosis.

The application value of whole exome sequencing technology in diagnosis of hereditary renal cysts

The data of 57 renal cyst patients who visited the First Affiliated Hospital of Zhengzhou University from January 2023 to March 2024 were retrospectively analyzed. The age of patients ranged from three months to 60 years old, with 31 males and 26 females. The whole exome sequencing (WES) detected pathogenic or suspected pathogenic (P/LP) variants in 48 renal cystic probands, with a detection rate of 84.2% (48/57), including PKD1, PKD2, PKHD1, LRP5, COL4A4 and ALG8 gene variants as well as copy number variations (CNV). In addition, four PKD1 gene variants of uncertain significance (VUS) were detected. In five WES negative families, one PKD1 nonsense variation was detected through long-range PCR (LR-PCR)+Oxford nanopore technologies, and one heterozygous deletion in exon 22 of PKD1 gene was detected through multiplex ligation-dependent probe amplification (MLPA). In summary, WES can detect multiple types of variations, which is helpful for early diagnosis and prognosis prediction of renal cyst patients. However, there is still a risk of failing to detect PKD1 gene by WES, therefore, healthcare practitioners should beware of the negative results of WES.

Kidney autotransplantation with renal arteries injury in a patient with Takayasu arteritis

Takayasu arteritis (AT) is a systemic vasculitis of large vessels. As a rule, AT develops in patients under 50 years of age and is characterized by vasculitis, often granulomatous, with a predominant lesion of the aorta and/or its main branches. The article describes a clinical case of a patient with bilateral lesions of the renal arteries caused by AT, who underwent kidney autotransplantation. The first manifestations of psoriasis in the patient occurred after psychological stress 15 years after surgery. The described clinical observation of multi-stage surgical treatment of Takayasu arteritis, which ended favorably, is presented by us in order to demonstrate the capabilities of modern medical technologies in both diagnosis and surgical treatment of multi-vessel lesions in Takayasu arteritis. The combination of AT and psoriasis is a rare pathology, the problems of diagnosis and treatment of which require further study.

Advancement in transformer fault diagnosis technology

The transformer plays a critical role in maintaining the stability and smooth operation of the entire power system, particularly in power transmission and distribution. The paper begins by providing an overview of traditional fault diagnosis methods for transformers, including dissolved gas analysis and vibration analysis techniques, elucidating their developmental trajectory. Building upon these traditional methods, numerous researchers have aimed to enhance and optimize them through intelligent technologies such as neural networks, machine learning, and support vector machines. These researchers have addressed common issues in traditional fault diagnosis methods, such as the low correlation between characteristic parameters and faults, ambiguous fault descriptions, and the complexity of feature analysis. However, due to the complexity of transformer structures and the uncertainties in operating environments, the collection and analysis of characteristic parameters becomes highly intricate. Researchers have further refined algorithms and feature values based on intelligent diagnostic algorithms for transformers. The goal is to improve diagnostic speed, mitigate the impact of measurement noise, and further advance the adaptability of artificial intelligence technology in the field of transformers. On the other hand, the excellent multi-parameter analysis capability of artificial intelligence technology is more suitable for transformer diagnostic techniques that involve the fusion of multiple information sources. Through the powerful data acquisition, processing, and decision-making capabilities provided by intelligent algorithms, it can comprehensively analyze non-electrical parameters such as oil and gas characteristics, vibration signals, temperature, along with electrical parameters like short-circuit reactance and load ratio. Moreover, it can automatically analyze the inherent relationship between faults and characteristic quantities and provide decision-making suggestions. This technique plays a pivotal role in ensuring transformer safety and power network security, emerging as a prominent direction in transformer fault diagnosis research.

Application research of UAV infrared diagnosis technology in intelligent inspection of substations

This study proposes an improved YOLOv4 algorithm based on mixed domain attention mechanism to design an intelligent substation inspection system. The proposed method combines improvement strategies such as lightweight, depthwise separable convolution, and mixed attention mechanism. The experimental results showed that the identification accuracy of the proposed model was only reduced by 0.2% for test samples at different positions, promoting the accuracy of intelligent inspection to reach 97.5%. The mIoU, mAP, detection speed, and recognition accuracy of the model constructed by the research were 78.34%, 95.12%, 62.05 frames per second, and 95.12%, respectively. Therefore, the proposed model could comprehensively enhance the information expression and recognition accuracy of the system, while promoting intelligent inspection to achieve high accuracy.

Design and Application of Virtual Cloud OMS Computing in Smart Airport

Airport management takes airport information management as its key link, and it is also the key to ensure the safe operation of airports. As a modern information management and processing technology, cloud computing plays an indispensable role in the fields of efficient information processing, strengthening management and improving work efficiency. Under the development background of the information age, the management mode of airport operation and maintenance has also changed to some extent. In the growth of the new era, airport operation and maintenance managers need to introduce new technologies and then combine more advanced technologies with the airport operation and maintenance system (OMS) so as to promote the normal operation of the airport OMS based on advanced data acquisition, analysis, and integration technologies. This article studies the application of cloud computing technology and designs a virtual cloud OMS based on deep learning (DL) from the perspective of the security requirements of the management information system (MIS) to realize the fault diagnosis and identification of the airport MIS. The fault diagnosis technology of the airport MIS based on virtual cloud OMS and DL proposed in this study has broad prospects in practical application. It can be applied to airport operation and maintenance management, security, and other links, providing intelligent support for airport operation.

Noninvasive reconstruction of internal heat source in biological tissue using adaptive simulated annealing algorithm

The heat distribution information of human lesions is of great value for disease analysis, diagnosis, and treatment. It is a typical inverse problem of heat conduction that deriving the distribution of internal heat sources from the temperature distribution on the body surface. This paper transforms such an inverse problem of bio-heat transfer into a direct one, thereby avoiding complex boundary conditions and regularization processes. To noninvasively reconstruct the internal heat source and its corresponding 3D temperature field in biological tissue, the adaptive simulated annealing (ASA) algorithm is used in the simulation module, where the position P(x, y, z) of point heat source in biological tissue and its corresponding temperature T are set as the optimization variables. Under a certain optimized sample, one can obtain the simulated temperature distributing on the surface of the module, then subtract the simulated temperature from the measured temperature of the same surface which was measured using a thermal infrared imager. If the sum of absolute values of the difference is smaller, it indicates that the current sample is closer to the true location and temperature of the heat source. When the values of optimization variables are determined, the corresponding 3D temperature field is also confirmed. The simulation results show the simulated position and temperature of the heat source are very approximate with those of the real experimental module. The method presented in this paper has enormous potential and promising prospects in clinical research and application, such as tumor hyperthermia, disease thermal diagnosis technology, etc.

Technologies for Supporting Individuals and Caregivers Living With Fetal Alcohol Spectrum Disorder: Scoping Review.

Fetal alcohol spectrum disorder (FASD) is a common developmental disability that requires lifelong and ongoing support but is often difficult to find due to the lack of trained professionals, funding, and support available. Technology could provide cost-effective, accessible, and effective support to those living with FASD and their caregivers. In this review, we aimed to explore the use of technology available for supporting people living with FASD and their caregivers. We conducted a scoping review to identify studies that included technology for people with FASD or their caregivers; focused on FASD; used an empirical study design; were published since 2005; and used technology for assessment, diagnosis, monitoring, or support for people with FASD. We searched MEDLINE, Web of Science, Scopus, Embase, APA PsycINFO, ACM Digital Library, JMIR Publications journals, the Cochrane Library, EBSCOhost, IEEE, study references, and gray literature to find studies. Searches were conducted in November 2022 and updated in January 2024. Two reviewers (CZC and HW) independently completed study selection and data extraction. In total, 17 studies exploring technology available for people with FASD showed that technology could be effective at teaching skills, supporting caregivers, and helping people with FASD develop skills. Technology could provide support for people affected by FASD; however, currently there is limited technology available, and the potential benefits are largely unexplored.

Case Study on the Advancements in Diagnosis and Treatment Technologies for Military Personnel with Complex Explosive Injuries

Case(s): A 51-year-old military personnel with severe limb injury from mortar shelling, admitted to Orthopedic Traumatology Department, Ternopil Regional Hospital, Ukraine.Conclusions: Explosive injuries typically arise from the detonation of devices such as landmines, improvised explosive devices, or grenades, causing harm through blast, fragmentation, and thermal effects. Despite a delayed presentation to medical care, the patient underwent multiple debridement procedures, including Vacuum-Assisted Closure therapy, and vascular reconstruction, leading to limb salvage. This case underscore needs for timely medical care, addressing risks of mine blasts and cold weather injuries through training.

Chromosomal abnormalities detected by chromosomal microarray analysis and pregnancy outcomes of 4211 fetuses with high-risk prenatal indications

With the gradual liberalization of the three-child policy and the development of assisted reproductive technology in China, the number of women with high-risk pregnancies is gradually increasing. In this study, 4211 fetuses who underwent chromosomal microarray analysis (CMA) with high-risk prenatal indications were analysed. The results showed that the overall prenatal detection rate of CMA was 11.4% (480/4211), with detection rates of 5.82% (245/4211) for abnormal chromosome numbers and 5.58% (235/4211) for copy number variants. Additionally, the detection rates of clinically significant copy number variants were 3.78% (159/4211) and 1.8% (76/4211) for variants of uncertain significance. The detection rates of fetal chromosomal abnormalities were 6.42% (30/467) for pregnant women with advanced maternal age (AMA), 6.01% (50/832) for high-risk maternal serum screening (MSS) results, 39.09% (224/573) with abnormal non-invasive prenatal testing (NIPT) results, 9.21% (127/1379) with abnormal ultrasound results, and 5.1% (49/960) for other indications. Follow-up results were available for 4211 patients, including 3677 (3677/4211, 87.32%) whose infants were normal after birth, 462 (462/4211, 10.97%) who terminated their pregnancy, 51 (51/4211, 1.21%) whose infants were abnormal after birth, and 21 (21/4211, 0.50%) who refused follow-up. The results of this study demonstrate significant variation in the diagnostic rate of chromosomal microarray analysis across different indications, providing valuable guidance for clinicians to assess the applicability of CMA technology in prenatal diagnosis.

Recent progress in Surface-Enhanced Raman Spectroscopy detection of biomarkers in liquid biopsy for breast cancer.

Breast cancer is the most commonly diagnosed cancer in women globally and a leading cause of cancer-related mortality. However, current detection methods, such as X-rays, ultrasound, CT scans, MRI, and mammography, have their limitations. Recently, with the advancements in precision medicine and technologies like artificial intelligence, liquid biopsy, specifically utilizing Surface-Enhanced Raman Spectroscopy (SERS), has emerged as a promising approach to detect breast cancer. Liquid biopsy, as a minimally invasive technique, can provide a temporal reflection of breast cancer occurrence and progression, along with a spatial representation of overall tumor information. SERS has been extensively employed for biomarker detection, owing to its numerous advantages such as high sensitivity, minimal sample requirements, strong multi-detection ability, and controllable background interference. This paper presents a comprehensive review of the latest research on the application of SERS in the detection of breast cancer biomarkers, including exosomes, circulating tumor cells (CTCs), miRNA, proteins and others. The aim of this review is to provide valuable insights into the potential of SERS technology for early breast cancer diagnosis.

Advances in electrowetting-on-dielectric digital microfluidics technology for disease diagnosis and prevention applications

Rapid and accurate diagnostic technologies are crucial for early detection and diagnosis of diseases. Electrowetting-on-dielectric digital microfluidics, with its high-precision detection and high-throughput screening capabilities, significantly enhances the accuracy and efficiency in early disease diagnosis and personalized treatment, enabling swift disease detection and widespread screening. This article provides a comprehensive review of the working principles and fabrication processes of digital microfluidic chips based on electrowetting on dielectric method. It details the latest research progress in the areas of nucleic acids, proteins, and cells, organizes the commercialization of digital microfluidics technology, and finally discusses the current challenges and future directions of digital microfluidic chips.

Early Diagnosis of Sepsis: The Role of Biomarkers and Rapid Microbiological Tests.

Sepsis is a medical emergency resulting from a dysregulated response to an infection, causing preventable deaths and a high burden of morbidity. Protocolized and accurate interventions in sepsis are time-critical. Therefore, earlier recognition of cases allows for preventive interventions, early treatment, and improved outcomes. Clinical diagnosis of sepsis by clinical scores cannot be considered an early diagnosis, given that underlying molecular pathophysiological mechanisms have been activated in the preceding hour or days. There is a lack of a widely available tool enhancing preclinical diagnosis of sepsis. Sophisticated technologies for sepsis prediction have several limitations, including high costs. Novel technologies for fast molecular and microbiological diagnosis are focusing on bedside point-of-care combined testing to reach most settings where sepsis represents a challenge.

Damage localization in composite structures based on Lamb wave and modular artificial neural network

Lamb wave-based technology for damage diagnosis in composite structures has emerged as a promising tool for structural health monitoring (SHM). However, traditional SHM faces challenges in multi-sensor localization, including complex data processing, high costs, and cumbersome maintenance management. The current trend is to employ data-driven approaches, but achieving precise damage localization across the entire structure with fewer sensors still presents difficulties. To address this issue, a damage localization method based on Lamb wave envelope characteristics and modular artificial neural network (M-ANN) is developed. In this approach, the envelope characteristics are employed to characterize the damage information and reduce the data dimension. Subsequently, the M-ANN model is innovatively designed with dropout layers in each hidden layer, significantly enhancing performance on random datasets. In the composite laminate experiment using only four sensors, the proposed method achieves an average relative error of 2.96 % for random damage samples, with 95 % falling within 6.17 %, outperforming other advanced damage localization approaches. Additionally, the method is utilized to investigate the requirements for the relative density of data acquisition under different damage localization needs in composite structures.

Innovation and development of surgical techniques for pancreatic tumors

In recent years, great progress has been made in the diagnosis and treatment of pancreatic tumors. In terms of diagnosis, three-dimensional CT reconstruction, PET-CT scan, endoscopic ultrasound with needle biopsy are used to evaluate the benign or malignant stage and biological characteristics of the tumor, to make treatment decisions more scientific and reasonable. In terms of treatment, new technologies, such as arterial priority arterial sheath dissection and radical resection of the retroperitoneal lipo-lymphatic layer, have continuously emerged to improve radical curability of tumors. For benign or low-grade malignant pancreatic tumors, function-preserving surgery is adopted to avoid long-term complications. Minimally invasive pancreatic surgery has advanced in leaps and bounds. Both standard radical surgery and function-preserve surgery can be performed under a laparoscope or robot. Non-surgical treatment has developed quickly with each passing day; for locally advanced or metastatic pancreatic cancer, neoadjuvant therapy is expected to be down-staged or transformed into surgery. These advances in diagnosis and treatment technology have led to multidisciplinary teamwork. Based on accurate assessment, giving full play to the advantages of laparoscopic and robotic systems in diagnosis and treatment, attaching importance to comprehensive nonsurgical treatment and doctor-patient communication with care throughout the process, these are keys to improve the clinical efficacy of pancreatic tumors in the era of minimally invasive surgery.

Transformer Fault Diagnosis Based on Intelligent Algorithm and Partial Discharge

Abstract As an important part of power transmission and transformation equipment and power systems, transformers’ operating conditions will have a direct impact on the stability and reliability of the power system. In view of the problems of high diagnosis cost and low accuracy of diagnosis results in existing fault diagnosis technology, this paper uses the pulse current method to detect the pulse signal generated by the discharge model based on the partial discharge experimental platform data, analyzes and studies the discharge characteristics of different discharge defects, and draws For the partial discharge phase distribution (PRPD) spectrum of each defect type, the statistical characteristic parameters characterizing the characteristics of the spectrum were extracted. The partial discharge statistical characteristics were used as classification feature quantities and input into three network models: RBF, GRNN, and PSO-GRNN. The results show that PSO-GRNN has a good recognition effect and is suitable for partial discharge type identification. It has important practical significance and application value for the all-round health management of power transformers.

Wind turbine blade fault diagnosis technology based on Efficient-YOLOv5s algorithm

Abstract The daily operation and maintenance of wind turbine blades in wind power plants requires the application of target detection algorithms that are both accurate in detection and fast in training. For the current wind turbine blade fault detection algorithms in the dataset when the number of samples of the problem of slow training time through the bi-directional feature pyramid network of the fast weighted normalised fusion to improve the training speed, for the small size of the fault detection of the low accuracy of the problem through the bi-directional feature pyramid network of the better use of multiscale information to improve the detection accuracy and through the coordinate attention to generate bidirectional complementary feature maps to enhance the object of interest to further improve the accuracy of detection. In this way, an efficient wind turbine blade fault detection model (Efficent-YOLOv5s) is proposed on the basis of the YOLOv5s model. Through the experimental validation of the homemade dataset, the results show that compared with the YOLOv5s model, the detection map0.5 value and accuracy of the efficient wind turbine blade fault detection model are improved by 0.5% and 0.6%, respectively, and the average training time is shortened by 4s/epoch, which verifies that the model is effective.

Research on Transformer Fault Diagnosis Based on Voiceprint Signal

Abstract As an important part of the power system, the operating status of the transformer will have a direct impact on the stability and reliability of the power system. In view of the problems of high diagnosis cost and low accuracy of diagnosis results in existing fault diagnosis technology, this paper takes advantage of the obvious difference between the voiceprint signal of the transformer under normal and fault operating conditions and applies it to transformer fault diagnosis, which can effectively reflect its internal working status and fault conditions, helping operation and maintenance personnel promptly discover equipment defects and locate fault causes. In order to accurately realize transformer fault diagnosis, this paper uses the improved hybrid frog leaping algorithm to optimize the fault diagnosis algorithm of support vector machine parameters for fault diagnosis, which further improves the accuracy of fault diagnosis and is of great significance for accurately identifying transformer fault states.

Development of All Solid State Nanosecond Pulse Transformer Fault Detection Device Based on Marx Circuit

Abstract a nanosecond pulse generator with a fast leading edge for transformer fault diagnosis is developed. The device is composed of a full solid-state nanosecond pulse generator based on Marx circuit and with a tail cut switch and a field programmable logic gate array (FPGA) device. Among them, the nanosecond pulse source is mainly composed of DC power supply, control circuit and main circuit. The main circuit is a 9-level modular Marx circuit, using MOSFET as the main switch and tail cut switch; The control circuit generates a synchronous trigger pulse signal, which drives the MOSFET to work synchronously after being isolated through the optical fiber. The output nanosecond pulse voltage parameters are adjustable amplitude 0-8kv, pulse width 100˜1000ns, repetition frequency 1hz˜1000hz, rising edge 30ns and falling edge 40ns. The transformer fault diagnosis platform is built, and the transformer off-line and on-line fault diagnosis is carried out, which paves the way for further exploration of transformer fault diagnosis technology.

Machine Learning-Assisted High-Throughput Identification and Quantification of Protein Biomarkers with Printed Heterochains.

Advanced in vitro diagnosis technologies are highly desirable in early detection, prognosis, and progression monitoring of diseases. Here, we engineer a multiplex protein biosensing strategy based on the tunable liquid confinement self-assembly of multi-material heterochains, which show improved sensitivity, throughput, and accuracy compared to standard ELISA kits. By controlling the material combination and the number of ligand nanoparticles (NPs), we observe robust near-field enhancement as well as both strong electromagnetic resonance in polymer-semiconductor heterochains. In particular, their optical signals show a linear response to the coordination number of the semiconductor NPs in a wide range. Accordingly, a visible nanophotonic biosensor is developed by functionalizing antibodies on central polymer chains that can identify target proteins attached to semiconductor NPs. This allows for the specific detection of multiple protein biomarkers from healthy people and pancreatic cancer patients in one step with an ultralow detection limit (1 pg/mL). Furthermore, rapid and high-throughput quantification of protein expression levels in diverse clinical samples such as buffer, urine, and serum is achieved by combining a neural network algorithm, with an average accuracy of 97.3%. This work demonstrates that the heterochain-based biosensor is an exemplary candidate for constructing next-generation diagnostic tools and suitable for many clinical settings.