Diagnosis Technology Research Articles

Serum Immune-Response Protein Biomarkers Based on Olink Technology for Diagnosis of Ischemic Stroke.

The immune response plays a crucial role in the treatment of ischemic stroke (IS). Our primary objective was to explore immune proteins related to stroke and to develop a noninvasive diagnostic panel. We used the high-throughput Olink immunoassay platform to quantitatively measure 92 proteins in the serum of 88 patients with IS and 88 controls. We first selected feature proteins using least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine (SVM), and then modeled them for external validation of IS. In this study, we found that 53 proteins exhibited significant differences in the IS compared to the control group. We selected GLB1, PRDX5, DDX58, and CLEC4C as potential protein biomarkers to differentiate IS from the control group using LASSO, RF, and SVM. The diagnostic model, which included these four proteins, demonstrated excellent performance in validation data sets, achieving an AUC value of 0.899 (95%CI: 0.848-0.950). Our findings offer valuable insights into both the immune response and the diagnosis of IS. These results offer a novel approach to clinical decision-making in the diagnosis and treatment of IS.

Application of DynaCT biliary soft tissue reconstruction technology in diagnosis and treatment of hepatolithiasis.

Hepatobiliary stone disease involves an intrahepatic bile duct stone that occurs above the confluence of the right and left hepatic ducts. One-step percutaneous transhepatic cholangioscopic lithotripsy (PTCSL) using the percutaneous transhepatic one-step biliary fistulation (PTOBF) technique enables the clearing of intrahepatic stones and the resolution of strictures. However, hepatolithiasis with associated strictures still has high residual and recurrence rates after one-step PTCSL. DynaCT can achieve synchronized acquisition with a flat-panel detector during C-arm rotation. The technical approach and application value of DynaCT biliary soft tissue reconstruction technology for the diagnosis and treatment of hepatolithiasis with bile duct stenosis were evaluated in this study. To explore the value of DynaCT biliary soft tissue reconstruction technology for the diagnosis and treatment of hepatolithiasis with bile duct stenosis, and to assess the feasibility and effectiveness of the PTOBF technique guided by DynaCT biliary soft tissue reconstruction technology. The clinical data of 140 patients with complex biliary stenosis disease combined with bile duct stenosis who received PTOBF and were admitted to the First Affiliated Hospital of Guangzhou Medical University from January 2020 to December 2024 were collected. The patients were divided into two groups: DynaCT-PTOBF group (70 patients) and conventional PTOBF group (70 patients). These groups were compared in terms of the preoperative bile duct stenosis, location of the liver segment where the stone was located, intraoperative operative time, immediate stone retrieval rate, successful stenosis dilatation rate, postoperative complication rate, postoperative reoperation rate, stone recurrence rate, and stenosis recurrence rate. DynaCT biliary soft tissue reconstruction technology was successfully performed in 70 patients. The DynaCT-PTOBF group had a higher detection rate of target bile ducts where bile duct stones and biliary strictures were located than the PTOBF group. Compared with the PTOBF group, the DynaCT-PTOBF group was characterized by a significantly greater immediate stone removal rate (68.6% vs 50.0%, P = 0.025), greater immediate stenosis dilatation success rate (72.9% vs 55.7%, P = 0.034), greater final stenosis release rate (91.4% vs 75.7%, P = 0.012), shorter duration of intraoperative hemorrhage (3.14 ± 2.00 vs 26.5 ± 52.1, P = 0.039), and lower incidence of distant cholangitis (2.9% vs 11.4%, P = 0.49). There were no significant differences between the two groups in terms of the final stone removal rate, reoperation rate, or long-term complication incidence rate. DynaCT biliary soft tissue reconstruction technology guiding the PTOBF technique in patients with hepatolithiasis with bile duct stenosis is feasible and accurate. It may be beneficial for optimizing the preoperative evaluation of the PTOBF technique.

Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies

Formation damage remains a key challenge in oil and gas exploration and development, requiring effective prediction and diagnostic technologies to mitigate its impact. Despite decades of research, current techniques lack the accuracy and practicality demanded by modern oilfield operations and the future of intelligent oil and gas development. This study systematically reviews advancements in formation damage prediction and diagnostics, focusing on wellsite diagnosis, experimental methods, imaging techniques, analytical approaches, numerical modeling, and artificial intelligence applications. The advantages and limitations of these methods are analyzed to provide a comprehensive understanding of their capabilities. The paper emphasizes the need for further research to develop an intelligent expert system that integrates multiple damage factors and accounts for spatial–temporal evolution, paving the way for improved future hydrocarbon production and sustainable energy development.

Academic‐Industrial Partnerships for Translation of Technologies for Diagnosis and Treatment (NCI)

Academic‐Industrial Partnerships for Translation of Technologies for Diagnosis and Treatment (NCI)

Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture

Plant diseases cause significant damage to agriculture, leading to substantial yield losses and posing a major threat to food security. Detection, identification, quantification, and diagnosis of plant diseases are crucial parts of precision agriculture and crop protection. Modernizing agriculture and improving production efficiency are significantly affected by using computer vision technology for crop disease diagnosis. This technology is notable for its non-destructive nature, speed, real-time responsiveness, and precision. Deep learning (DL), a recent breakthrough in computer vision, has become a focal point in agricultural plant protection that can minimize the biases of manually selecting disease spot features. This study reviews the techniques and tools used for automatic disease identification, state-of-the-art DL models, and recent trends in DL-based image analysis. The techniques, performance, benefits, drawbacks, underlying frameworks, and reference datasets of more than 278 research articles were analyzed and subsequently highlighted in accordance with the architecture of computer vision and deep learning models. Key findings include the effectiveness of imaging techniques and sensors like RGB, multispectral, and hyperspectral cameras for early disease detection. Researchers also evaluated various DL architectures, such as convolutional neural networks, vision transformers, generative adversarial networks, vision language models, and foundation models. Moreover, the study connects academic research with practical agricultural applications, providing guidance on the suitability of these models for production environments. This comprehensive review offers valuable insights into the current state and future directions of deep learning in plant disease detection, making it a significant resource for researchers, academicians, and practitioners in precision agriculture.

Current status of fertility preservation procedures in gynecologic oncology: from a Chinese perspective.

The "Healthy China" initiative, along with advancements in technology for cancer diagnosis and treatment, has significantly enhanced outcomes for patients with gynecologic tumors. The trends of late marriage and delayed childbirth have led to an increasing number of women diagnosed with gynecologic cancers who are seeking fertility preservation in China. This issue is critical yet often overlooked in clinical practice. This review aims to synthesize the existing research on fertility preservation within the field of gynecologic oncology, emphasizing both clinical explorations and expert guidelines. We conducted a comprehensive literature review on fertility preservation in the context of gynecologic tumors, examining treatment approaches, commonly employed tumor management technologies, and specific techniques for preserving fertility. This extensive review highlights the importance of integrating fertility preservation strategies into treatment plans for gynecologic tumors. It explores various methods to safeguard fertility during chemotherapy, radiation therapy, and surgical interventions. For patients with early-stage cervical cancer, surgical options are available; however, these may result in obstetric complications. Neoadjuvant chemotherapy is currently under investigation as an alternative approach. Endometrial cancer can be managed through hysteroscopic resection combined with hormonal therapy. The feasibility of fertility preservation in ovarian cancer varies based on tumor type and patient age. In cases of vulvar and vaginal cancers, partial excision may be considered following a thorough evaluation. Chemotherapy for gestational trophoblastic tumors has proven effective and typically preserves fertility despite potential decreases in AMH levels. This review provides a comprehensive and current synthesis of the latest evidence and clinical practice guidelines regarding fertility preservation in gynecologic cancers. Its aim is to assist clinicians and researchers in addressing the urgent and increasing demand for effective fertility preservation strategies for their patients.

Boosting the innovation transformation of clinical medicine based on core technology in anesthesiology

The core technologies proposed in surgery have boosted the innovation transformation and disciplinary development. However, the core technologies in anesthesiology remain undefined both domestically and internationally. Through collaborative discussions among Chinese anesthesiologists, the core technologies of anesthesiology can be succinctly summarized as Relief of pain, Regulation of life, Resuscitation, and Restoration of organ function, and collectively referred to as the 4R technologies. Their primary components include: (1) Technology for sedation, analgesia and muscle relaxation; (2) Technology for the monitoring and regulation of life; (3) Technology for resuscitation and restoration of organ function; (4) Technology for diagnosis and therapy for acute and chronic pain, as well as functional regulation. Up to now, there is still a significant gap between the current situation of anesthesiology and the requirements of core technologic content. Exclusive innovation transformation can remodel the new future of anesthesiology.

Expert consensus on the standardized application of whole exome sequencing technology in diagnosis of genetic disorders

Next generation sequencing (NGS) technology is playing an increasingly important role in the diagnosis of genetic diseases. Whole exome sequencing (WES) which targets the coding regions of the genome has been widely used in the diagnosis of genetic diseases for its low cost and high efficiency. However, compared to conventional methods, the NGS process is intricate, and there is variability in the expertise of data analysts and variant interpreters, which may lead to inconsistencies in the outcomes. To ensure the quality of testing and enhance the diagnostic rate of diseases, this consensus has provided recommendations regarding the laboratory setup, operational procedures, data analysis, result interpretation, and quality control for WES, with an aim to standardize its application in the detection of genetic disorders.

Advances in the application of artificial intelligence technology in the surgical diagnosis and treatment of thyroid cancer

Advances in the application of artificial intelligence technology in the surgical diagnosis and treatment of thyroid cancer

Clinical application of time-of-flight mass spectrometry nucleic acid detection technology in diagnosis of drug-resistant pulmonary tuberculosis.

This study aims to evaluate the clinical diagnostic value of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) for tuberculosis and drug-resistant tuberculosis. Totally 201 pulmonary tuberculosis patients were recruited retrospectively. All patients underwent smear microscopy, Mycobacterium growth indicator tube (MGIT) 960 culture, loop-mediated isothermal amplification (LAMP) molecular testing, Xpert MTB/RIF (Xpert), and MassARRAY assay which is a MALDI-TOF MS based method. Among the enrolled patients, 28.36 %, 43.78 %, 47.26 %, 64.68 %, and 85.57 % tested positive by smear, culture, LAMP, Xpert, and MALDI-TOF MS, respectively. MALDI-TOF MS had significantly higher detection rates than the other methods (All P < 0.05). In the 88 culture-positive patients, the positive detection rates for smear, LAMP, Xpert, and MALDI-TOF MS were 43.2 %, 68.2 %, 85.2 %, and 94.3 %, respectively; In 113 culture-negative cases, the positive rates for the above tests were 16.8 %, 31.0 %, 48.7 %, and 78.8 %, respectively. Among the 130 Xpert-positive cases, the positive rates for smear, culture, LAMP, and MALDI-TOF MS were 40.77 %, 57.7 %, 70.77 %, and 98.46 %, respectively; In the 71 Xpert-negative cases, the positive rates for above tests were 5.6 %, 18.3 %, 4.2 %, and 62.0 %, respectively. In the 61 rifampicin-resistant cases, the successful detection rate of MALDI-TOF MS (96.72 %) was significantly higher than that of Xpert (81.97 %) (P < 0.05). The concordance between MALDI-TOF MS and pDST for detecting resistance to 11 anti-tuberculosis drugs ranged from 83.0 % to 98.9 %. Nucleic acid detection technology using time-of-flight mass spectrometry provides a powerful tool for the rapid diagnosis of tuberculosis and drug-resistant tuberculosis, with advantages of high sensitivity and reliability, warranting further promotion and application.

AI-based non-invasive imaging technologies for early autism spectrum disorder diagnosis: A short review and future directions

AI-based non-invasive imaging technologies for early autism spectrum disorder diagnosis: A short review and future directions

EARLY DIAGNOSIS OF ALZHEIMER’S DISEASE USING A SIMPLE AND SENSITIVE NANOBIOSENSOR

Abstract Existing diagnostic methods for Alzheimer’s disease (AD), such as PET/MRI imaging and cerebrospinal fluid tests, are invasive and/or costly. Recent studies have demonstrated that exosomes (nanoscale extracellular vesicles carrying specific biomarkers that can traverse blood-brain barrier) are ideal sources of AD biomarkers, which can be conveniently collected from peripheral body fluids like blood and saliva. Detection of AD exosomal biomarkers opens a new avenue for non-invasive diagnosis of AD. However, the concentrations of AD exosomal biomarkers in body fluids are extremely low. Therefore, highly sensitive analytical techniques such as mass spectrometry are required to measure the exosomal biomarkers accurately. Unfortunately, these sensitive techniques come with high costs and can be cumbersome, limiting their widespread use in clinical settings. In this research, we report a simple and low-cost nano-biosensor for ultrasensitive detection of AD exosomal biomarkers. The nano-biosensor relies on metal nanoparticles as labels or signal transducers. After conjugation of the metal nanoparticles with bioreceptors specific to AD exosomal biomarkers, the nanoparticle-bioreceptor conjugates can specifically catalyze the formation of colorimetric signal that can be conveniently quantified by an inexpensive spectrophotometer. The nano-biosensor is expected to be able to detect AD exosomal biomarkers from blood and/or saliva samples, representing a minimally invasive and accessible technology for early diagnosis of AD.

Fundamentals of Using Telemedicine for Pediatric Care

Background. The pandemic has accelerated the implementation of telemedicine and provided with important insights into the feasibility of remote medical care, including for children in terms of child development and learning, as well as the creation and adoption of clinical technologies for diagnosis, therapy, and care in a family setting. In addition, the real-life needs are still to be addressed within the implementation of telemedicine. In this regard, it is crucial to study the fundamentals and basic mechanisms for introducing telemedicine in pediatric care.Purpose. To identify the insights into the use of telemedicine in pediatrics from the standpoint of a modern model of pediatric care.Materials and methods. The author reviewed the modern scientific knowledge about the application of telemedicine in pediatric care via the content analysis of relevant publications.Results. Telemedicine has proven to be a potential strategy for improving access to care for different population groups, especially for children in need of a specialized treatment.

The Application of Imaging and Molecular Markers in Cancer Diagnosis and Treatment

Cancer is one of the leading causes of death worldwide, and its diagnosis and treatment face many challenges. In recent years, the combination of imaging techniques and molecular markers has made remarkable progress in cancer diagnosis and treatment. Imaging techniques such as CT, MRI and PET-CT can provide anatomical and functional information of tumors, while molecular markers such as EGFR and HER2 play a key role in tumor diagnosis, prognosis and treatment. However, how to effectively combine these technologies to achieve more accurate diagnosis and treatment is still a gap in research. This paper reviews the combined application of imaging technology and molecular markers in cancer diagnosis, therapeutic response monitoring and individualized therapy, analyzes the synergistic effect of multimodal imaging technology and molecular markers, and finds that it has significant advantages in early detection and individualized treatment planning. Combining imaging and molecular marker information can improve the accuracy of cancer diagnosis and guide targeted therapies. The combination of such technologies provides a new reference for precision cancer medicine in the future, but further research is still needed in the aspects of marker sensitivity and image analysis automation. In the future, the combination of new technologies such as artificial intelligence is expected to promote the development of more accurate and efficient cancer diagnosis and treatment.

An efficient optimized encryption and compression techniques to improve medical image security and transmission in the cloud

As digital technology for illness diagnosis and analysis has advanced, medical images are sent over the Internet. Cloud computing plays a major role for low-cost data storage and data sharing. In the healthcare industry, data security and privacy are key issues with cloud computing. It is imperative that healthcare professionals make sure that patient data is safe against hackers, unauthorized access, and thrift. To secure the confidential data and store the huge amount of data, encryption and compression techniques are used. This paper provides an enhanced optimized encryption and efficient hybrid compression strategies based on cloud environment. The proposed model involves various operations such as generate optimal key, encryption, compression, decompression and decryption. In order to transfer the data using high speed cloud data retrieval, we initially propose the Huffman Fano Hybrid Entropy approach. In the next step, the proposed model performs Elliptic Curve Coding based encryption technique to secure compressed medical image transmission. Here the shared secret keys are generated optimally by dynamic group based cooperative optimization algorithm, which makes use of the encryption quality measures and is called DGBCO–ECC model. On the receiving end, the image is decrypted and decompressed. The proposed model performance is validated by exploiting various parameters namely Mean Square Error, Standard Deviation of the Mean Error, Universal image quality index, Structural Similarity Index, Entropy, Peak Signal to Noise Ratio, Compression Ratio, Data Rate Saving and compression time. When the experimental outcome is contrasted with existing methods, it is found to perform better.

Potential of X-ray endovascular technologies in the correction of rare vascular complications after kidney transplantation

Objective. The paper aims at demonstrating the efficacy of endovascular interventional techniques in the treatment of rare vascular complications after kidney transplantation.Material and methods. Recipients of renal grafts obtained from a deceased donor developed the following complications: a false aneurysm of the interlobar renal artery after lymphocele puncture in one case and a subintimal hematoma of the external iliac artery with subocclusion of the latter and impaired perfusion of the renal graft in the other one.Results. These complications were successfully managed using X-ray endovascular technologies, which allowed saving the renal grafts in both cases with satisfactory long-term results.Conclusion. Early diagnosis and endovascular technologies made it possible to cope with complications minimally invasively and prevent the development of adverse events in the long term.

Artificial intelligence in oncology: Revolutionizing cancer care

With the evolution of Artificial Intelligence (AI), even cancer care approaches are evolving as it is providing innovative solutions to some of the most complex challenges in oncology. This article delves into how AI is making a profound impact across the cancer care spectrum worldwide, from early detection and precise diagnosis to the personalization of treatment and improved patient management. By harnessing AI's ability to analyze massive datasets and identify patterns beyond human perception, healthcare professionals can offer more accurate diagnoses and more effective treatments tailored to individual patient needs. This review also highlights the most recent advancements in AI-driven technologies in oncology and looks toward the future, where AI's role is expected to expand further. By discussing the potential and challenges of AI in cancer care, this article offers insights into how it is reshaping oncology practice, with the ultimate goal of enhancing patient outcomes and revolutionizing cancer treatment. This article aims to explore the transformative role of Artificial Intelligence (AI) in oncology, focusing on its impact on early cancer detection, precise diagnosis, personalized treatment, and overall patient management. It seeks to provide insights into the recent advancements of AI in cancer care, the challenges associated with its integration, and the potential future directions in oncology.A comprehensive review of literature was conducted, focusing on AI applications in oncology, including diagnostic imaging, precision oncology, and clinical decision support systems. Recent studies were analyzed to understand the role of AI-driven technologies in cancer diagnosis, treatment, and management. Inclusion criteria: Peer-reviewed articles, case studies, and reviews published in the last five years that focus on the application of AI in oncology, including early cancer detection, diagnostic accuracy, personalized treatment, and clinical decision support systems. Exclusion criteria: Articles that did not focus on oncology, did not involve AI technologies, or were not peer-reviewed were excluded from this review. AI has shown significant improvements in cancer detection and diagnostic accuracy, particularly through advanced imaging techniques and personalized treatment strategies. AI-powered diagnostic tools have revolutionized imaging by enhancing detection rates and reducing diagnostic errors. Moreover, AI has played a crucial role in tailoring therapeutic interventions based on individual patient characteristics, thus contributing to precision oncology. AI is revolutionizing cancer care by improving diagnostic precision, personalizing treatments, and enhancing patient outcomes. However, challenges such as data privacy, algorithm bias, and regulatory complexities must be addressed. Future innovations in AI, along with collaborative efforts, will further enhance cancer care and pave the way for AI-driven oncology practices globally.

Advances of New Extracellular Vesicle Isolation and Detection Technologies in Cancer Diagnosis.

Cancer is a global health issue threatening people's lives. Currently, cancer detection methods still have a lot of room for improvement in both efficiency and accuracy. The development and application of new technologies are urgently required for early cancer diagnosis and prognosis. Extracellular vesicles (EVs) are a type of phospholipid bilayer vesicle secreted by cells and play an important role in cancer development and metastasis. These small vesicles participate in cancer information transmission, antigen presentation, angiogenesis, immune response, tumor invasion, and mediate signaling pathways in the tumor microenvironment. Liquid biopsy of EV cargo contents is a fast-developing research area, holding promise for early cancer diagnosis and monitoring cancer progression in real-time. However, current EV detection technologies for clinical translation are still facing many challenges. Recent advancements in developing techniques for EV isolation and detection have made significant progress and are paving the way toward clinical application. Here, the advantages and limitations of traditional EV detection and isolation technologies in cancer diagnosis and prognosis are reviewed. The review also focuses on emerging EV detection and isolation technologies in cancer, discusses the challenges faced by current methods, and explores the perspective of new EV detection techniques for future cancer diagnosis.

Label-free rat brain traumatic penumbra imaging based on multiphoton fluorescence microscopy

Traumatic penumbra (TP) is a region with recoverable potential around the primary lesion of brain injury. Rapid and accurate imaging for identifying TP is essential for treating traumatic brain injury (TBI). In this study, we first established traumatic brain injuries (TBIs) in rats using a modified Feeney method, followed by label-free imaging of brain tissue sections with multiphoton fluorescence microscopy. The results showed that the technique effectively imaged normal and traumatic brain tissues, and revealed pathological features such as extracellular matrix changes, vascular cell proliferation, and intracellular edema in the traumatic penumbra. Compared with normal brain tissue, the extracellular matrix in the TP was sparse, cells were disorganized, and hyperplastic vascular cells emitted higher two-photon excited fluorescence (TPEF) signals. Our research demonstrates the potential of multiphoton fluorescence technology in the rapid diagnosis and therapeutic evaluation of TBI.

Ways to improve the system of prevention and treatment of environmental diseases

International experience, as well as many Russian research works, prove the necessity to concentrate efforts of healthcare professionals and researchers on development of healthcare technologies for diagnosis, prevention and treatment (including health resort treatment and rehabilitation) of so-called environmental diseases caused by unhealthy environments and adverse environmental factors (physical, chemical, biological, social ones). This paper proposes the integration of research areas in environmental medicine into profiles of various research specialties (Occupational Medicine, Ecology, etc.). It is also proposed to transform the healthcare specialty of Doctor of Occupational Medicine into Doctor of Environmental Medicine. Finding ways to improve the system of prevention and treatment of environmental diseases, discussing the issues of further integration of environmental medicine, public health, ecology, occupational medicine, restorative medicine and other biomedical, preventive and clinical disciplines may improve the diagnosis and treatment of human diseases caused by adverse effects of environmental factors, as well as contribute to the development of preventive strategies. The methods of epidemiologic (including prenosologic) diagnostics based on the assessment of functional reserves of the body should play a key role in scientific research with the implementation of its results in the practice of health professionals of various profiles, including environmental occupational pathologists, in order to improve clinical diagnostics and develop personalized treatment and rehabilitation programs for patients exposed to adverse environmental factors.