Non-invasive Disease Diagnosis Research Articles

A planar mounted SQUID full-tensor module for magnetoenterogram denoising detection

Magntoenterogram (MENG) is a functional imaging method with great clinical potential for non-invasive diagnosis of intestinal diseases. In measurements of MENG, the cardiac artifacts are a strong biological source of interference. Based on wire-wound SQUID gradiometers, we constructed a planar mounted full-tensor compensation module for MENG system, which can obtain cardiac artifacts while suppressing ambient noise, so as to denoise MENG signals without increasing external ECG recording channels. The cardiac artifacts are regressed out from the full-tensor compensation module outputs, which can denoise the MENG measurements. Using this module, the SNR is about 70 dB better than the raw data. At last, 36-point array MENG signals were detected successfully and the contour map of jejunal slow-wave frequency (SWF) was consistent with the jejunal area (one part of the small intestine). This method will save costs and avoid the new electromagnetic interferences introduced by ECG equipment. It will lay a good foundation for the rapid clinical application of the MENG system.

A microfluidic-based chemiluminescence biosensor for sensitive multiplex detection of exosomal microRNAs based on hybridization chain reaction

The analysis of microRNAs (miRNAs) in exosomes is of great importance for noninvasive early disease diagnosis. However, current techniques to detect exosomal miRNAs is hampered either by laborious exosome isolation or low abundance of miRNAs in exosomes. Here, we developed a microfluidic chemiluminescence (CL) analysis method for the multiplexed detection of exosomal miR-21 and miR-155. The microfluidic device contained three parts: a snake-shaped channel for fully mixing chemiluminescent reagents, a ship-shaped channel modified with CD63 protein aptamer for capturing exosomes, and another two parallel ship-shaped channels for hybridization chain reaction (HCR) amplification and CL detection. The multiple signal amplification was realized by Y-shaped arrays, HCR amplification, and poly-HRP catalyzed CL reaction. Using this multiple signal amplification method, our microfluidic CL biosensor achieves a limit of detection of miRNAs of 0.49 fM, with a linear range of 1 fM–10 pM, which is better or comparable to previously reported biosensors. What's more, the proposed microfluidic biosensor exhibits great specificity and selectivity to the target miRNA. Moreover, the microfluidic CL strategy exhibited excellent accuracy and could significantly distinguish different cancer subtypes as well as cancer patients and healthy people. These results suggest that this simple, high sensitive, and more accurate analytical strategy by analyzing different types of exosomal miRNAs has the potential applications in cancer diagnosis and stage monitoring.

Thermal energy and electro-osmotic for biomimetic artificial olfactory cilia in tri-hybrid nanofluids: entropy-defying approaches

Biomimetic artificial olfactory cilia have demonstrated potential in identifying specific volatile organic compounds linked to various diseases, including certain cancers, metabolic disorders, and respiratory conditions. These sensors may facilitate non-invasive disease diagnosis and monitoring. Cilia Motility is the coordinated movement of cilia, which are hair-like projections present on the surface of particular cells in different species. Cilia serve an important part in several biological functions, including motility, fluid movement, and sensory reception. Cilia motility is a complicated process that requires the coordinated interaction of structural components and molecular pathways. Cilia are made up of a highly structured structure known as the axoneme, which is made up of microtubules grouped in a unique pattern. The axoneme is made up of nine outer doublet microtubules and a core pair of singlet microtubules. This arrangement offers structural support and serves as a scaffold for the proteins involved in ciliary movement. Our latest endeavors investigate these Multiphysics phenomena in ciliary beating flows that are inspired by biology, utilizing copper, gold, and titania nanoparticles. We examine their functions in biological systems such as peristaltic transport computationally. Our models give precise two- and three-dimensional velocity, temperature, and concentration solutions by integrating transverse magnetohydrodynamics with laser heating. Furthermore, at the channel wall expressions, the skin friction coefficient, Sherwood number, Nusselt number and optimization of entropy generation are acquired and analyzed. Important properties of the velocity and scalar profiles are revealed by a thorough analysis of dimensionless parameters. The simplified examination provides more insight into the trapping patterns that result from the complex interaction between nanofluid rheology and optics. These findings greatly contribute to our knowledge and improvement of nanofluidic transport technologies in a variety of fields supporting industry, sustainability, and medicine. Our combined computational and experimental methodology clarifies the complex dynamics in these systems and provides design guidance for the engineering of improved fluidic devices that make use of multifunctional nanomaterial interfaces and peristaltic motion.

Light-Activated Virtual Sensor Array with Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease

HighlightsPhotoresponsive black phosphorus (BP)/Ti3C2Tx composites were synthesized by a self-assembly strategy.Enhanced gas sensitive property was achieved by visible light modulation.Light activated virtual sensor array was fabricated based on BP/Ti3C2Tx composite.Diagnosis of coronary heart disease was achieved with the help of machine learning.

Sound as a bell: a deep learning approach for health status classification through speech acoustic biomarkers

BackgroundHuman health is a complex, dynamic concept encompassing a spectrum of states influenced by genetic, environmental, physiological, and psychological factors. Traditional Chinese Medicine categorizes health into nine body constitutional types, each reflecting unique balances or imbalances in vital energies, influencing physical, mental, and emotional states. Advances in machine learning models offer promising avenues for diagnosing conditions like Alzheimer's, dementia, and respiratory diseases by analyzing speech patterns, enabling complementary non-invasive disease diagnosis. The study aims to use speech audio to identify subhealth populations characterized by unbalanced constitution types.MethodsParticipants, aged 18–45, were selected from the Acoustic Study of Health. Audio recordings were collected using ATR2500X-USB microphones and Praat software. Exclusion criteria included recent illness, dental issues, and specific medical histories. The audio data were preprocessed to Mel-frequency cepstral coefficients (MFCCs) for model training. Three deep learning models—1-Dimensional Convolution Network (Conv1D), 2-Dimensional Convolution Network (Conv2D), and Long Short-Term Memory (LSTM)—were implemented using Python to classify health status. Saliency maps were generated to provide model explainability.ResultsThe study used 1,378 recordings from balanced (healthy) and 1,413 from unbalanced (subhealth) types. The Conv1D model achieved a training accuracy of 91.91% and validation accuracy of 84.19%. The Conv2D model had 96.19% training accuracy and 84.93% validation accuracy. The LSTM model showed 92.79% training accuracy and 87.13% validation accuracy, with early signs of overfitting. AUC scores were 0.92 and 0.94 (Conv1D), 0.99 (Conv2D), and 0.97 (LSTM). All models demonstrated robust performance, with Conv2D excelling in discrimination accuracy.ConclusionsThe deep learning classification of human speech audio for health status using body constitution types showed promising results with Conv1D, Conv2D, and LSTM models. Analysis of ROC curves, training accuracy, and validation accuracy showed all models robustly distinguished between balanced and unbalanced constitution types. Conv2D excelled with good accuracy, while Conv1D and LSTM also performed well, affirming their reliability. The study integrates constitution theory and deep learning technologies to classify subhealth populations using noninvasive approach, thereby promoting personalized medicine and early intervention strategies.

Fetuin-A: a relevant novel serum biomarker for non-invasive diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD): a retrospective case-control study

ObjectivesTo determine how fetuin-A contributes to diagnosing and assessing MASLD severity.MethodsFifty MASLD patients and fifty healthy control participants were involved in this retrospective case-control research. Abdominal ultrasonography, fibroscan with controlled attenuated parameter scan (CAP scan), laboratory investigation (including fetuin-A assessment), clinical examination, and history-taking were performed on every case.ResultsFetuin-A level was considerably higher in the Cases group (1154.85 ± 629.89) than in the Control group (505.29 ± 150.4) (p < 0.001). Fetuin-A had significant validity in the prediction of MASLD at a cut-off > 702.5 with 82% sensitivity, 90% specificity, and 86% overall accuracy.ConclusionOne possible marker for MASLD diagnosis could be fetuin-A. Furthermore, a substantial association between such marker and the severity of the disease as it revealed a significant correlation with ultrasound grading and fibroscan with controlled attenuated parameters.Trial registration1- Pan African Clinical Trial Registry.Unique Identifying number/registration ID: PACTR202309644280965.URL: https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=26860.Registration Approval date: 21/09/2023.2- ClinicalTrials.gov.Unique Identifying number /registration ID: NCT06097039.URL: https://clinicaltrials.gov/study/NCT06097039?cond=NCT06097039&rank=1.Registration Approval date: 25/10/2023.

Viscosity-activated carbon dots for noninvasive disease diagnosis, therapeutic efficacy assessment and anticancer drug screening

Abnormal levels of intracellular microviscosity have a close relationship with some diseases and pathologies. Therefore, quantitative imaging of viscosity at the cellular and organ levels is beneficial for disease diagnosis, curative effect evaluation, and anticancer drug screening. Herein, we synthesized viscosity-activated orange-red emitting carbon dots (named as LP-CDs) from lignin and p-phenylenediamine using a one-step method, whose fluorescence intensity, lifetime, and quantum yield significantly increase with rising viscosity. Meanwhile, the fluorescence intensity of LP-CDs also has a good linear relationship with the solution viscosity. Taking the advantages of high sensitivity, noninvasiveness, and rapid result output of fluorescence imaging, LP-CDs can visualize the cellular viscosity changes induced by inflammation and hyperglycemia, and image tumor evolution. More importantly, LP-CDs can be used as powerful sensors to screen anticancer drugs in vivo by evaluating therapeutic effects. This work provides a new scheme for constructing robust nanoprobes to achieve the diagnosis and imaging-guided surgery of viscosity related diseases.

Multimodal Cardiac Imaging Revisited by Artificial Intelligence: An Innovative Way of Assessment or Just an Aid?

Cardiovascular disease remains a leading global health challenge, necessitating advanced diagnostic approaches. This review explores the integration of artificial intelligence (AI) in multimodal cardiac imaging, tracing its evolution from early X-rays to contemporary techniques such as CT, MRI, and nuclear imaging. AI, particularly machine learningand deep learning, significantly enhances cardiac diagnostics by estimating biological heart age, predicting disease risk, and optimizing heart failure management through adaptive algorithms without explicit programming or feature engineering. Key contributions include AI's transformative role in non-invasive coronary artery disease diagnosis, arrhythmia detection via wearable devices, and personalized treatment strategies. Despite substantial progress, challenges including data standardization, algorithm validation, regulatory approval, and ethical considerations must be addressed to fully harness AI's potential. Collaborative efforts among clinicians, scientists, industry stakeholders, and regulatory bodies are essential for the safe and effective deployment of AI in cardiac imaging, promising enhanced diagnostics and personalized patient care.

A Prototype of Graphene E-Nose for Exhaled Breath Detection and Label-Free Diagnosis of Helicobacter Pylori Infection.

Helicobacter pylori (HP), a common microanaerobic bacteria that lives in the human mouth and stomach, is reported to infect ≈50% of the global population. The current diagnostic methods for HP are either invasive, time-consuming, or harmful. Therefore, a noninvasive and label-free HP diagnostic method needs to be developed urgently. Herein, reduced graphene oxide (rGO) is composited with different metal-based materials to construct a graphene-based electronic nose (e-nose), which exhibits excellent sensitivity and cross-reactive response to several gases in exhaled breath (EB). Principal component analysis (PCA) shows that four typical types of gases in EB can be well discriminated. Additionally, the potential of the e-nose in label-free detection of HP infection is demonstrated through the measurement and analysis of EB samples. Furthermore, a prototype of an e-nose device is designed and constructed for automatic EB detection and HP diagnosis. The accuracy of the prototype machine integrated with the graphene-based e-nose can reach 92% and 91% in the training and validation sets, respectively. These results demonstrate that the highly sensitive graphene-based e-nose has great potential for the label-free diagnosis of HP and may become a novel tool for non-invasive disease screening and diagnosis.

Skin Surface Sebum Analysis by ESI-MS.

The skin surface is an important sample source that the metabolomics community has only just begun to explore. Alterations in sebum, the lipid-rich mixture coating the skin surface, correlate with age, sex, ethnicity, diet, exercise, and disease state, making the skin surface an ideal sample source for future noninvasive biomarker exploration, disease diagnosis, and forensic investigation. The potential of sebum sampling has been realized primarily via electrospray ionization mass spectrometry (ESI-MS), an ideal approach to assess the skin surface lipidome. However, a better understanding of sebum collection and subsequent ESI-MS analysis is required before skin surface sampling can be implemented in routine analyses. Challenges include ambiguity in definitive lipid identification, inherent biological variability in sebum production, and methodological, technical variability in analyses. To overcome these obstacles, avoid common pitfalls, and achieve reproducible, robust outcomes, every portion of the workflow-from sample collection to data analysis-should be carefully considered with the specific application in mind. This review details current practices in sebum sampling, sample preparation, ESI-MS data acquisition, and data analysis, and it provides important considerations in acquiring meaningful lipidomic datasets from the skin surface. Forensic researchers investigating sebum as a means for suspect elimination in lieu of adequate fingerprint ridge detail or database matches, as well as clinical researchers interested in noninvasive biomarker exploration, disease diagnosis, and treatment monitoring, can use this review as a guide for developing methods of best-practice.

Emerging importance of stool preservation methods in OMICS studies with special focus on cancer biology.

The intricate consortium of microorganisms in the human gut plays a crucial role in different physiological functions. The complex known-unknown elements of the gut microbiome are perplexing and the absence of standardized procedures for collecting and preserving samples has hindered continuous research in comprehending it. The technological bias produced because of lack of standard protocols has affected the reproducibility of results. The complex nature of diseases like colorectal cancer, gastric cancer, hepatocellular carcinoma and breast cancer require a thorough understanding of its etiology for an efficient and timely diagnosis. The designated protocols for collection and preservation of stool specimens have great variance, hence generate inconsistencies in OMICS studies. Due to the complications associated to the nature of sample, it is important to preserve the sample to be studied later in a laboratory or to be used in the future research purpose. Stool preservation is gaining importance due to the increased use of treatment options like fecal microbiota transplantation to cure conditions like recurrent Clostridium difficile infections and for OMICS studies including metagenomics, metabolomics and culturomics. This review provides an insight into the importance of omics studies for the identification and development of novel biomarkers for quick and noninvasive diagnosis of various diseases.

Free-breathing, fat-corrected T1 mapping of the liver with stack-of-stars MRI, and joint estimation of T1, PDFF, , and .

Quantitative T1 mapping has the potential to replace biopsy for noninvasive diagnosis and quantitative staging of chronic liver disease. Conventional T1 mapping methods are confounded by fat and inhomogeneities, resulting in unreliable T1 estimations. Furthermore, these methods trade off spatial resolution and volumetric coverage for shorter acquisitions with only a few images obtained within a breath-hold. This work proposes a novel, volumetric (3D), free-breathing T1 mapping method to account for multiple confounding factors in a single acquisition. Free-breathing, confounder-corrected T1 mapping was achieved through the combination of non-Cartesian imaging, magnetization preparation, chemical shift encoding, and a variable flip angle acquisition. A subspace-constrained, locally low-rank image reconstruction algorithm was employed for image reconstruction. The accuracy of the proposed method was evaluated through numerical simulations and phantom experiments with a T1/proton density fat fraction phantom at 3.0 T. Further, the feasibility of the proposed method was investigated through contrast-enhanced imaging in healthy volunteers, also at 3.0 T. The method showed excellent agreement with reference measurements in phantoms across a wide range of T1 values (200 to 1000 ms, slope = 0.998 (95% confidence interval (CI) [0.963 to 1.035]), intercept = 27.1 ms (95% CI [0.4 54.6]), r2 = 0.996), and a high level of repeatability. In vivo imaging studies demonstrated moderate agreement (slope = 1.099 (95% CI [1.067 to 1.132]), intercept = -96.3 ms (95% CI [-82.1 to -110.5]), r2 = 0.981) compared to saturation recovery-based T1 maps. The proposed method produces whole-liver, confounder-corrected T1 maps through simultaneous estimation of T1, proton density fat fraction, and in a single, free-breathing acquisition and has excellent agreement with reference measurements in phantoms.

Spicy Recipe for At-Home Diagnostics: Smart Salivary Sensors for Point-of-Care Diagnosis of Jaundice.

Even though significant advances have been made, there is still a lack of reliable sensors capable of noninvasively monitoring bilirubin and diagnosing jaundice as the most common neonatal disease, particularly at the point-of-care (POC) where blood sampling from infants is accompanied by serious challenges and concerns. Herein, for the first time, using an easy-to-fabricate/use assay, we demonstrate the capability of curcumin embedded within paper for noninvasive optical monitoring of bilirubin in saliva. The highly selective sensing of the developed sensor toward bilirubin is attributed to bilirubin photoisomerization under blue light exposure, which can selectively restore the bilirubin-induced quenched fluorescence of curcumin. We also fabricated an IoT-enabled hand-held optoelectronic reader to measure and quantify the fluorescence and color signals of our sensor. Clinical analysis on the saliva of 18 jaundiced infants by using our developed smart salivary sensor proved that it is amenable to be widely exploited in POC applications for bilirubin monitoring as there are good correlations between its results with those of reference methods in saliva and blood. Meeting all WHO's REASSURED criteria by our developed sensor makes it a highly promising sensor for smart noninvasive diagnosis and therapeutic monitoring of jaundice, hepatitis, and other bilirubin-induced neurologic diseases at the POC.

3D Printed Hydrogel Sensor for Rapid Colorimetric Detection of Salivary pH.

Salivary pH is one of the crucial biomarkers used for non-invasive diagnosis of intraoral diseases, as well as general health conditions. However, standard pH sensors are usually too bulky, expensive, and impractical for routine use outside laboratory settings. Herein, a miniature hydrogel sensor, which enables quick and simple colorimetric detection of pH level, is shown. The sensor structure was manufactured from non-toxic hydrogel ink and patterned in the form of a matrix with 5 mm × 5 mm × 1 mm individual sensing pads using a 3D printing technique (bioplotting). The authors' ink composition, which contains sodium alginate, polyvinylpyrrolidone, and bromothymol blue indicator, enables repeatable and stable color response to different pH levels. The developed analysis software with an easy-to-use graphical user interface extracts the R(ed), G(reen), and B(lue) components of the color image of the hydrogel pads, and evaluates the pH value in a second. A calibration curve used for the analysis was obtained in a pH range of 3.5 to 9.0 using a laboratory pH meter as a reference. Validation of the sensor was performed on samples of artificial saliva for medical use and its mixtures with beverages of different pH values (lemon juice, coffee, black and green tea, bottled and tap water), and correct responses to acidic and alkaline solutions were observed. The matrix of square sensing pads used in this study provided multiple parallel responses for parametric tests, but the applied 3D printing method and ink composition enable easy adjustment of the shape of the sensing layer to other desired patterns and sizes. Additional mechanical tests of the hydrogel layers confirmed the relatively high quality and durability of the sensor structure. The solution presented here, comprising 3D printed hydrogel sensor pads, simple colorimetric detection, and graphical software for signal processing, opens the way to development of miniature and biocompatible diagnostic devices in the form of flexible, wearable, or intraoral sensors for prospective application in personalized medicine and point-of-care diagnosis.

Artificial Intelligence in Eye Movements Analysis for Alzheimer's Disease Early Diagnosis.

As the world's population ages, Alzheimer's disease is currently the seventh most common cause of death globally; the burden is anticipated to increase, especially among middle-class and elderly persons. Artificial intelligence-based algorithms that work well in hospital environments can be used to identify Alzheimer's disease. A number of databases were searched for English-language articles published up until March 1, 2024, that examined the relationships between artificial intelligence techniques, eye movements, and Alzheimer's disease. A novel non-invasive method called eye movement analysis may be able to reflect cognitive processes and identify anomalies in Alzheimer's disease. Artificial intelligence, particularly deep learning, and machine learning, is required to enhance Alzheimer's disease detection using eye movement data. One sort of deep learning technique that shows promise is convolutional neural networks, which need further data for precise classification. Nonetheless, machine learning models showed a high degree of accuracy in this context. Artificial intelligence-driven eye movement analysis holds promise for enhancing clinical evaluations, enabling tailored treatment, and fostering the development of early and precise Alzheimer's disease diagnosis. A combination of artificial intelligence-based systems and eye movement analysis can provide a window for early and non-invasive diagnosis of Alzheimer's disease. Despite ongoing difficulties with early Alzheimer's disease detection, this presents a novel strategy that may have consequences for clinical evaluations and customized medication to improve early and accurate diagnosis.

Human Sputum Proteomics: Advancing Non-Invasive Diagnosis of Respiratory Diseases with Enhanced Biomarker Analysis Methods

Many ailments can be diagnosed while they are asymptomatic, meaning that the patient has no signs or symptoms of a progressing disease. If caught in their initial stage of formation, these disorders can be effectively treated, leading to successful outcomes; curative therapies can halt illnesses from advancing, thus improving the quality of life and long-term survival of the patient. Still, cutting-edge upgrades in precision technologies are necessary for early, reliable, affordable, and rapid disease detection, but also vital for the well-being of people and the future of global public health. The emerging role and utility of non-invasive and repeatable diagnostic test approaches for the detection of health conditions have been exemplified by liquid biopsies based on genomic biomarkers. As such, biological fluids permit any measurable molecular indicator or signature (e.g., proteins) to provide valuable information on an individual’s wellness and/or disease. Among the bodily secretions used for non-invasive diagnostics is sputum, a complex viscous gel-like biopolymeric network that has gained growing recognition as a rich source of biomarkers of airway infections and pulmonary diseases, and serves as a determinant to reveal other illnesses. As per the World Health Organization, the burden of respiratory conditions is exacerbated by factors ranging from considerable subjection to air pollution and occupational contaminants to tobacco smoking and second-hand smoke, in addition to poor socio-economic status. Due to the likely increase in these determinants, respiratory tract ailments are on the rise, affecting the health of many individuals, in addition to putting stress on healthcare facilities and services worldwide. The aim of this study was to perform a narrative review of sputum constituents with an emphasis on proteins and glycoproteins assessed as possible biomarkers of lung and other organ diseases. A search was conducted using mucus, sputum proteomics, sputum biomarkers, and point-of-care testing as keywords employing Google, PubMed (MEDLINE), and Web of Science, selecting the most referenced and related papers of the last decade. We, therefore, highlight the need to use expectorated or induced sputum specimens as a routine sample source for testing valuable protein biomarkers to diagnose these chronic disorders, predict inflammation and disease progression, as well as monitor the effectiveness of treatments. Further, we discuss the urgent need for fast and reliable point-of-care methods to detect and quantify crucial protein biomarkers in sputum specimens, and the limitations faced when dealing with their complex matrices.

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

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.