Non-invasive Disease Diagnosis Research Articles

Extracellular vesicles: Function, resilience, biomarker, bioengineering, and clinical implications

ABSTRACT Extracellular vesicles (EVs) have emerged as key players in intercellular communication, disease pathology, and therapeutic innovation. Initially overlooked as cellular debris, EVs are now recognized as vital mediators of cell-to-cell communication, ferrying a cargo of proteins, nucleic acids, and lipids, providing cellular resilience in response to stresses. This review provides a comprehensive overview of EVs, focusing on their role as biomarkers in disease diagnosis, their functional significance in physiological and pathological processes, and the potential of bioengineering for therapeutic applications. EVs offer a promising avenue for noninvasive disease diagnosis and monitoring, reflecting the physiological state of originating cells. Their diagnostic potential spans a spectrum of diseases, including cancer, cardiovascular disorders, neurodegenerative diseases, and infectious diseases. Moreover, their presence in bodily fluids such as blood, urine, and cerebrospinal fluid enhances their diagnostic utility, presenting advantages over traditional methods. Beyond diagnostics, EVs mediate crucial roles in intercellular communication, facilitating the transfer of bioactive molecules between cells. This communication modulates various physiological processes such as tissue regeneration, immune modulation, and neuronal communication. Dysregulation of EV-mediated communication is implicated in diseases such as cancer, immune disorders, and neurodegenerative diseases, highlighting their therapeutic potential. Bioengineering techniques offer avenues for manipulating EVs for therapeutic applications, from isolation and purification to engineering cargo and targeted delivery systems. These approaches hold promise for developing novel therapeutics tailored to specific diseases, revolutionizing personalized medicine. However, challenges such as standardization, scalability, and regulatory approval need addressing for successful clinical translation. Overall, EVs represent a dynamic frontier in biomedical research with vast potential for diagnostics, therapeutics, and personalized medicine.

Hyperspectral retinal imaging biomarkers of ocular and systemic diseases.

Hyperspectral imaging is a frontier in the field of medical imaging technology. It enables the simultaneous collection of spectroscopic and spatial data. Structural and physiological information encoded in these data can be used to identify and localise typically elusive biomarkers. Studies of retinal hyperspectral imaging have provided novel insights into disease pathophysiology and new ways of non-invasive diagnosis and monitoring of retinal and systemic diseases. This review provides a concise overview of recent advances in retinal hyperspectral imaging.

Effects of blood-processing protocols on cell-free DNA fragmentomics in plasma: Comparisons of one- and two-step centrifugations

BackgroundCell-free DNA (cfDNA) fragmentomic characteristics are promising analytes with abundant physiological signals for non-invasive disease diagnosis and monitoring. Previous studies on plasma cfDNA fragmentomics commonly employed a two-step centrifugation process for removing cell debris, involving a low-speed centrifugation followed by a high-speed centrifugation. However, the effects of centrifugation conditions on the analysis of cfDNA fragmentome remain uncertain. MethodsWe collected blood samples from 10 healthy individuals and divided each sample into two aliquots for plasma preparation with one- and two-step centrifugation processes. We performed whole genome sequencing (WGS) of the plasma cfDNA in the two groups and comprehensively compared the cfDNA fragmentomic features. Additionally, we reanalyzed the fragmentomic features of cfDNA from 16 healthy individuals and 16 COVID-19 patients, processed through one- and two-step centrifugation in our previous study, to investigate the impact of centrifugation on disease signals. ResultsOur results showed that there were no significant differences observed in the characteristics of nuclear cfDNA, including size, motif diversity score (MDS) of end motifs, and genome distribution, between plasma samples treated with one- and two-step centrifugation. The cfDNA size shortening in COVID-19 patients was observed in plasma samples with one- and two-step centrifugation methods. However, we observed a significantly higher relative abundance and longer size of cell-free mitochondrial DNA (mtDNA) in the one-step samples compared to the two-step samples. This difference in mtDNA caused by the one- and two-step centrifugation methods surpasses the pathological difference between COVID-19 patients and healthy individuals. ConclusionsOur findings indicate that one-step low-speed centrifugation is a simple and potentially suitable method for analyzing nuclear cfDNA fragmentation characteristics. These results offer valuable guidance for cfDNA research in various clinical scenarios.

Development and implementation of a treatment pathway to reduce coronary angiograms - lessons from a failure

BackgroundThe rates of coronary angiograms (CA) and related procedures (percutaneous intervention [PCI]) are significantly higher in Germany than in other Organisation for Economic Co-ordination and Development (OECD) countries. The current guidelines recommend non-invasive diagnosis of coronary heart disease (CHD); CA should only have a limited role in choosing the appropriate revascularisation procedure. The aim of the present study was to explore whether improvements in guideline adherence can be achieved through the implementation of regional treatment pathways. We chose four regions of Germany with high utilisation of CAs for the study. Here we report the results of the concomitant qualitative study.MethodsGeneral practitioners and specialist physicians (cardiologists, hospital-based cardiologists, emergency physicians, radiologists and nuclear medicine specialists) caring for patients with suspected CHD were invited to develop regional treatment pathways. Four academic departments provided support for moderation, provision of materials, etc. The study team observed session discussions and took notes. After the development of the treatment pathways, 45 semi-structured interviews were conducted with the participating physicians. Interviews and field notes were transcribed verbatim and underwent qualitative content analysis.ResultsPathway development received little support among the participants. Although consensus documents were produced, the results were unlikely to improve practice. The participants expressed very little commitment to change. Although this attempt clearly failed in all study regions, our experience provides relevant insights into the process of evidence appraisal and implementation. A lack of organisational skills, ignorance of current evidence and guidelines, and a lack of feedback regarding one’s own clinical behaviour proved to be insurmountable. CA was still seen as the diagnostic gold standard by most interviewees.ConclusionsOversupply and overutilisation can be assumed to be present in study regions but are not immediately perceived by clinicians. The problem is unlikely to be solved by regional collaborative initiatives; optimised resource planning within the health care system combined with appropriate economic incentives might best address these issues.

Artificial intelligence in diagnosis of neurodegenerative disorders (literature review)

Currently, there are virtually no objective diagnostic signs (markers) to diagnose neurodegenerative diseases, that is why it may take months of constant monitoring of symptoms to establish a reliable diagnosis. Now there is an increasing number of reports that artificial intelligence can provide a more accurate diagnosis of neurodegenerative diseases by identifying specific diagnostic features from electroencephalography data, neuroimaging, and wearable devices and smartphones. This study reviews the application of artificial intelligence to the diagnosis of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and Huntington's disease. Using the international databases Scopus and PubMed, as well as the Russian Science Citation Index, we analysed studies that used artificial intelligence methods to detect, monitor, or control the progression of neurodegenerative diseases. The primary focus is on analysing electroencephalography data, neuroimaging data, and data from wearable devices and smartphones. The use of the latter may allow screening, diagnosis, and monitoring of the disease at home with a minimum economic cost. Artificial intelligence can be a useful tool for early, accurate, and non-invasive diagnosis of neurodegenerative diseases, as well as for assessing the effectiveness of treatment and predicting the course of the disease. However, for the widespread implementation of artificial intelligence in clinical practice, several issues related to the quality, availability, and standardization of data, validation and interpretation of results, ethical and legal aspects need to be resolved. The use of artificial intelligence requires both specialists from the IT industry with a deep understanding of the types and kinds of neural networks and physicians with fundamental knowledge of neurology, psychiatry, molecular biology, and biophysics.

Surface protein profiling and subtyping of extracellular vesicles in body fluids reveals non-CSF biomarkers of Alzheimer's disease.

Noninvasive and effortless diagnosis of Alzheimer's disease (AD) remains challenging. Here we report the multiplexed profiling of extracellular vesicle (EV) surface proteins at the single EV level in five types of easily accessible body fluids using a proximity barcoding assay (PBA). A total of 183 surface proteins were detected on the EVs from body fluids collected from APP/PS1 transgenic mice and patients with AD. The AD-associated differentially expressed EV proteins could discriminate between the control and AD/AD model samples with high accuracy. Based on machine learning predictive models, urinary EV proteins exhibited the highest diagnostic potential compared to those on other biofluid EVs, both in mice and humans. Single EV analysis further revealed AD-associated EV subpopulations in the tested body fluids, and a urinary EV subpopulation with the signature proteins PLAU, ITGAX and ANXA1 could diagnose patients with AD in blinded datasets with 88% accuracy. Our results suggest that EVs and their subpopulations from noninvasive body fluids, particularly urine, are potential diagnostic biomarkers for AD.

Highly Accurate Profiling of Exosome Phenotypes Using Super-resolution Tricolor Fluorescence Co-localization.

Exosomes contain a wealth of proteomic information, presenting promising biomarkers for the noninvasive early diagnosis of diseases, especially cancer. However, it remains a great challenge to accurately and reliably distinguish exosomes secreted from different types of cell lines. Fluorescence immunoassay is frequently used for exosome detection. Nonspecific adsorption in immunoassays is unavoidable and affects the reliability of assay results. Despite the fact that various methods have been proposed to reduce nonspecific adsorption, a more effective method that can eliminate the influence of nonspecific adsorption is still lacking. Here, we report a more convenient way (named SR-TFC) to remove the artifacts caused by nonspecific adsorption, which combines tricolor fluorescence labeling of target exosomes, tricolor super-resolution imaging, and pixel counting. The pixel counting method (named CFPP) is realized by MATLAB and can eliminate nonspecific binding sites at the single-pixel level, which has never been achieved before and could improve the reliability of detection to the maximum extent. Furthermore, as a proof-of-concept, profiling of exosomal membrane proteins and identification of breast cancer subpopulations are demonstrated. To enable multiplex breast cancer phenotypic analysis, three kinds of specific proteins are labeled to obtain the 3D phenotypic information on various exosomes. Breast cancer subtypes can be accurately identified according to the super-resolution images of some clinically relevant exosomal proteins. Worth mentioning is that, by selecting other biomarkers, classification of other cancers could also be realized using SR-TFC. Hence, the present work holds great potential in clinical cancer diagnosis and precision medicine.

SERS-based AI diagnosis of lung and gastric cancer via exhaled breath

Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.

In Situ Detection of Nucleic Acids in Extracellular Vesicles via Membrane Fusion.

Extracellular vesicles (EVs) carry diverse biomolecules (e. g., nucleic acids, proteins) for intercellular communication, serving as important markers for diseases. Analyzing nucleic acids derived from EVs enables non-invasive disease diagnosis and prognosis evaluation. Membrane fusion, a fundamental cellular process wherein two lipid membranes merge, facilitates cell communication and cargo transport. Building on this natural phenomenon, recent years have witnessed the emergence of membrane fusion-based strategies for the detection of nucleic acids within EVs. These strategies entail the encapsulation of detection probes within either artificial or natural vesicles, followed by the induction of membrane fusion with EVs to deliver probes. This innovative approach not only enables in situ detection of nucleic acids within EVs but also ensures the maintenance of structural integrity of EVs, thus preventing nucleic acid degradation and minimizing the interference from free nucleic acids. This concept categorizes approaches into universal and targeted membrane fusion strategies, and discusses their application potential, and challenges and future prospects.

Multiplexed detection of viral proteases through dual response peptide-assisted nanopore sensing

Owing to their central roles in viral replication, main protease (Mpro) and papain-like protease (PLpro) are promising biomarkers for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, the simultaneous sensing of Mpro and PLpro activities is a valuable approach for SARS-CoV-2 detection. Herein, we developed a dual response peptide probe for simultaneously monitoring the protease activities of Mpro and PLpro using a single aerolysin nanopore. The target proteases can specifically digest the probe at the corresponding cleavage sites, releasing the output peptide fragments composed of different amino acids. These fragments generated clearly distinguishable blocking currents in nanopore recording that can be assigned to each analyte, allowing accurate differentiation among multiple proteases. The approach enabled the sensitive detection of Mpro and PLpro activity without the need for any amplification or labeling. Furthermore, with excellent discrimination ability, the method can be applied to evaluate the activity of the two model proteases against a background of exhaled breath condensate. Importantly, since peptides with specific sequences serve as catalytic substrates of multiple enzymes, the proposed strategy would be applicable for accurate and multiplex quantification of different types of enzymes, which is critical for non-invasive early disease diagnosis.

Preconcentrating sensor systems toward indoor low-concentration VOC detection by goal-oriented, sequential, inverse design strategy

The techniques capable of accurately and effectively detecting low-concentration volatile organic compounds (VOCs) are promising for environmental contaminant monitoring and non-invasive disease diagnosis. However, low-concentration measurement and selective quantification of multiple components are two major challenges for VOC detection. This work proposes the idea of a sensing system that incorporates preconcentration and multiple sensor arrays to address the two challenges. The design principles and strategies of the sensing system were discussed, as well as the key requirements and challenges of each step of the design process. We further developed a goal-oriented, sequential, inverse design method for an accurate sensing system of indoor VOCs, enabling the realization of high cross-selectivity detection. We also demonstrate the potential and feasibility of the sensing system with a preconcentrator through illustrative examples. The proposed system is expected to be scalable and intermittent, moving it closer to reliable, commercial, and portable near real-time determinations of specific VOC concentrations. It would promote the effectiveness of indoor air quality (IAQ) assessments and interventions.

Seeing is believing: pathway strategies for a personalised non-invasive diagnosis of coronary artery disease.

Coronary artery disease (CAD) diagnosis requires a precise assessment of patient profile and disease extension. While non-invasive imaging modalities offer an in-depth evaluation of CAD through differential approaches, this is based primarily on detecting coronary plaques or inducible myocardial ischaemia, thus eachoffering only a partial outlook of this condition. The improvement in appropriately identifying CAD patients at risk of developing major adverse cardiovascular events and guiding treatment outcomes will require developing a personalised diagnostic strategy for a value-based application of current technologies.

High-yield and rapid isolation of extracellular vesicles by flocculation via orbital acoustic trapping: FLOAT

Extracellular vesicles (EVs) have been identified as promising biomarkers for the noninvasive diagnosis of various diseases. However, challenges in separating EVs from soluble proteins have resulted in variable EV recovery rates and low purities. Here, we report a high-yield ( > 90%) and rapid ( < 10 min) EV isolation method called FLocculation via Orbital Acoustic Trapping (FLOAT). The FLOAT approach utilizes an acoustofluidic droplet centrifuge to rotate and controllably heat liquid droplets. By adding a thermoresponsive polymer flocculant, nanoparticles as small as 20 nm can be rapidly and selectively concentrated at the center of the droplet. We demonstrate the ability of FLOAT to separate urinary EVs from the highly abundant Tamm-Horsfall protein, addressing a significant obstacle in the development of EV-based liquid biopsies. Due to its high-yield nature, FLOAT reduces biofluid starting volume requirements by a factor of 100 (from 20 mL to 200 µL), demonstrating its promising potential in point-of-care diagnostics.

Analysis of the longitudinal stability of human plasma miRNAs and implications for disease biomarkers

There is great interest in developing clinical biomarker assays that can aid in non-invasive diagnosis and/or monitoring of human diseases, such as cancer, cardiovascular disease, and neurological diseases. Yet little is known about the longitudinal stability of miRNAs in human plasma. Here we assessed the intraindividual longitudinal stability of miRNAs in plasma from healthy human adults, and the impact of common factors (e.g., hemolysis, age) that may confound miRNA data. We collected blood by venipuncture biweekly over a 3-month period from 22 research participants who had fasted overnight, isolated total RNA, then performed miRNA qPCR. Filtering and normalization of the qPCR data revealed amplification of 134 miRNAs, 74 of which had high test–retest reliability and low percentage level drift, meaning they were stable in an individual over the 3-month time period. We also determined that, of nuisance factors, hemolysis and tobacco use have the greatest impact on miRNA levels and variance. These findings support that many miRNAs show intraindividual longitudinal stability in plasma from healthy human adults, including some reported as candidate biomarkers for Alzheimer’s disease.

Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects.

Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer's and Parkinson's diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research.

Ultrasensitive sensing urinary cystatin C via an interface-engineered graphene extended-gate field-effect transistor for non-invasive diagnosis of chronic kidney disease

Early chronic kidney disease (CKD) has strong concealment and lacks an efficient, non-invasive, and lable-free detection platform. Cystatin C (Cys C) in urine is closely related to the progress of CKD (especially at the early stage), which is an ideal endogenous marker to evaluate the impairment of renal function. Thus, the accurate detection of urinary Cys C (u-Cys C) is great significant for early prevention and treatment and delaying the course of the disease of CKD patients. Herein, we developed an extended-gate field-effect transistor (EG-FET) sensor for ultrasensitive detection of u-Cys C, which consists of a monolithic interface-engineered graphene EG electrode array and a commercially available MOSFET. Laser-induced graphene (LIG) loaded with sputtered Au NPs in the presence of adhesive Cr (Au NPs/Cr/LIG) boosts the electrical performance of the EG electrode. Meanwhile, Au NPs also serve as linkers to immobilize papain that can selectively form protein complexes with Cys C. Supported by the synergistic effect of multilevel interface-engineered graphene, our sensor exhibits a good linear correlation within the u-Cys C concentration range of 5 ag/μL to 50 ng/μL with low detection limit of 0.05 ag/μL. Our work makes accurate, specific and rapid detection of u-Cys C feasible and promising for early screening for CKD.

Machine learning aided non-invasive diagnosis of coronary heart disease based on tongue features fusion.

Coronary heart disease (CHD) is the first cause of death globally. Hypertension is considered to be the most important independent risk factor for CHD. Early and accurate diagnosis of CHD in patients with hypertension can plays a significant role in reducing the risk and harm of hypertension combined with CHD. To propose a non-invasive method for early diagnosis of coronary heart disease according to tongue image features with the help of machine learning techniques. We collected standard tongue images and extract features by Diagnosis Analysis System (TDAS) and ResNet-50. On the basis of these tongue features, a common machine learning method is used to customize the non-invasive CHD diagnosis algorithm based on tongue image. Based on feature fusion, our algorithm has good performance. The results showed that the XGBoost model with fused features had the best performance with accuracy of 0.869, the AUC of 0.957, the AUPR of 0.961, the precision of 0.926, the recall of 0.806, and the F1-score of 0.862. We provide a feasible, convenient, and non-invasive method for the diagnosis and large-scale screening of CHD. Tongue image information is a possible effective marker for the diagnosis of CHD.

Optical imaging techniques for non-invasive diagnosis and monitoring of disease

Optical imaging techniques for non-invasive diagnosis and monitoring of disease

Web photoplethysmography: opportunities and prospects

This literature review is devoted to the possibilities of using in clinical practice a new modification of photoplethysmography – its web version. The use of modern innovative techniques in the form of photo/video fixation of the human skin allows for contactless and remote assessment of the main physiological indicators of human health. This approach is of particular importance in conditions of shortage of medical workers, territorial separation of doctors and patients, restrictions in visiting medical institutions in the event of epidemics/pandemics of infectious diseases and it contributes to the development and implementation of telemedicine technologies in the daily work of medical specialists. The article discusses the possibilities of using web photoplethysmography to assess microcirculation (web capillaroscopy), heart rate and other indicators (respiratory rate, pulse rate, saturation, body temperature, etc.). Web photoplethysmography is a sensitive, simple and effective method of registering vital body functions. Its effectiveness as a possible screening method for detecting rhythm disturbances and its prospects in using blood pressure measurement has already been proven. The possibility of using not only video, but also photographic materials, for example, for the detection of cardiovascular diseases, is also described. No less promising is the possibility of using the method in conjunction with thermometry. The importance of studying aspects of web photoplethysmography is seen in the need to activate the processes of import substitution and technological sovereignty, as one of the main paradigms of our country’s development. It is obvious that the development of contactless research methods is one of the priorities in modern healthcare. Given the widespread availability of various devices, it is necessary to further explore the possibilities of using webcams and mobile devices in medical practice. The development and introduction into routine practice of medical devices that allow remote non-invasive early diagnosis of chronic non-communicable diseases is of special interest.

Doctors' original intentionality in adherence to non-invasive diagnoses benefits MAFLD patients

Precisely diagnosing metabolic dysfunction-associated fatty liver disease (MAFLD) and its severity degree can effectively delay disease progression and have important guiding values for treatment. In recent years, research on non-invasive diagnosis of metabolic dysfunction-associated fatty liver disease has made great progress, suggesting that we should not only give full play advantage to professional personnel in the field of liver disease but also actively cooperate with personnel in other fields to explore different high-performance non-invasive diagnostic methods to achieve early detection, diagnosis, and treatment.