Noninvasive Method For Diagnosis Research Articles

Drug Delivery and Monitoring through Wearable Devices with Microneedles in the Field of the Health Care System

Abstract: Microneedles represent a revolutionary advancement in drug delivery and diagnostics, offering a minimally invasive approach to accessing the intricate biological environment of the human body. These micron-sized needles, typically ranging from 25 to 1000 micrometers in length, penetrate the outermost layer of the skin, creating microchannels that facilitate the transdermal administration of therapeutics or the extraction of interstitial fluid for analysis. This innovative technology holds great promise for enhancing patient compliance, reducing side effects, and improving the overall efficiency of drug delivery. Integrating microneedles with wearable devices further amplifies their potential impact. Wearable devices provide a seamless interface for monitoring and controlling microneedle-based systems, fostering real-time data collection and personalized healthcare. Such devices can be designed to administer precise drug doses at predetermined intervals, adapting treatment regimens to individual patient needs. Additionally, the combination of microneedles and wearable devices enables continuous monitoring of biomarkers through the extraction of interstitial fluid, offering a non-invasive method for disease diagnosis and management. The review also provides a detailed overview of the mechanisms, types, fabrication techniques, applications, and patents for integrating microneedles with wearable devices. This symbiotic relationship between microneedles and wearables opens new paths for patient-centric healthcare, with the potential to transform chronic disease management and streamline therapeutic interventions. As these technologies continue to evolve, their integration may pave the way for personalized, on-demand healthcare solutions, accompanying a new era of patient well-being and treatment efficacy.

Prospect and Challenges of Volatile Organic Compound Breath Testing in Non-Cancer Gastrointestinal Disorders.

Breath analysis, despite being an overlooked biomatrix, has a rich history in disease diagnosis. However, volatile organic compounds (VOCs) have yet to establish themselves as clinically validated biomarkers for specific diseases. As focusing solely on late-stage or malignant disease biomarkers may have limited relevance in clinical practice, the objective of this review is to explore the potential of VOC breath tests for the diagnosis of non-cancer diseases: (1) Precancerous conditions like gastro-esophageal reflux disease (GERD) and Barrett's esophagus (BE), where breath tests can complement endoscopic screening; (2) endoluminal diseases associated with autoinflammation and dysbiosis, such as inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), and coeliac disease, which currently rely on biopsy and symptom-based diagnosis; (3) chronic liver diseases like cirrhosis, hepatic encephalopathy, and non-alcoholic fatty liver disease, which lack non-invasive diagnostic tools for disease progression monitoring and prognostic assessment. A literature search was conducted through EMBASE, MEDLINE, and Cochrane databases, leading to an overview of 24 studies. The characteristics of these studies, including analytical platforms, disorder type and stage, group size, and performance evaluation parameters for diagnostic tests are discussed. Furthermore, how VOCs can be utilized as non-invasive diagnostic tools to complement existing gold standards is explored. By refining study designs, sampling procedures, and comparing VOCs in urine and blood, we can gain a deeper understanding of the metabolic pathways underlying VOCs. This will establish breath analysis as an effective non-invasive method for differential diagnosis and disease monitoring.

High-Performance Metabolic Profiling of High-Risk Thyroid Nodules by ZrMOF Hybrids.

Thyroid nodules (TNs) have emerged as the most prevalent endocrine disorder in China. Fine-needle aspiration (FNA) remains the standard diagnostic method for assessing TN malignancy, although a majority of FNA results indicate benign conditions. Balancing diagnostic accuracy while mitigating overdiagnosis in patients with benign nodules poses a significant clinical challenge. Precise, noninvasive, and high-throughput screening methods for high-risk TN diagnosis are highly desired but remain less explored. Developing such approaches can improve the accuracy of noninvasive methods like ultrasound imaging and reduce overdiagnosis of benign nodule patients caused by invasive procedures. Herein, we investigate the application of gold-doped zirconium-based metal-organic framework (ZrMOF/Au) nanostructures for metabolic profiling of thyroid diseases. This approach enables the efficient extraction of urine metabolite fingerprints with high throughput, low background noise, and reproducibility. Utilizing partial least-squares discriminant analysis and four machine learning models, including neural network (NN), random forest (RF), logistic regression (LR), and support vector machine (SVM), we achieved an enhanced diagnostic accuracy (98.6%) for discriminating thyroid cancer (TC) from low-risk TNs by using a diagnostic panel. Through the analysis of metabolic differences, potential pathway changes between benign nodule and malignancy are identified. This work explores the potential of rapid thyroid disease screening using the ZrMOF/Au-assisted LDI-MS platform, providing a potential method for noninvasive screening of thyroid malignant tumors. Integrating this approach with imaging technologies such as ultrasound can enhance the reliability of noninvasive diagnostic methods for malignant tumor screening, helping to prevent unnecessary invasive procedures and reducing the risk of overdiagnosis and overtreatment in patients with benign nodules.

Exploring Salivary Biomarkers for Tumor Diagnosis: A Narrative Review.

A promising method for non-invasive cancer diagnosis and prognosis is through salivary biomarkers. By utilizing the distinct characteristics of saliva and the progress made in biomarker studies, these markers provide more accurate diagnoses for a wider range of malignancies. An attempt was made to thoroughly investigate the field of salivary biomarkers for tumor prognosis and diagnosis, with an emphasis on their use in various cancer forms. Predetermined search criteria were utilized for a systematic search across numerous databases for peer-reviewed papers from 2009 to 2021. Studies concentrating on the detection, validation, and clinical use of salivary biomarkers for different types of cancers were included in the inclusion criteria. Initially, 238 articles were found, of which 15 relevant articles satisfied the inclusion requirements. Information on study aims, methodology, findings, and conclusions were gathered for data extraction. We identified recurrent themes, patterns, and contradictions by a thematic analysis. We also assessedstate-of-the-art salivary biomarkers for tumor diagnosis and prognosis. One major finding is the identification of biomolecules in saliva linked to several cancer forms, including pancreatic, oral, breast, lung, and stomach cancers. There is an increasing amount of evidence demonstrating the value of saliva-based diagnostics in oncology. This is due to new detection methodsand developments in salivary proteomics and genomics. Identification of exosomes and microvesicles as salivary biomarker profiles offered molecular understandings of the etiology and evolution of cancer, thereby opening new avenues for diagnosis and treatment. Salivary biomarkers are a non-invasive approach for the early detection and prediction of cancer, thanks to the unique properties of saliva and advancements in biomarker research. This potential revolution could enhance patient outcomes and reduce cancer-related deaths.

Plasma-based near-infrared spectroscopy for early diagnosis of lung cancer

Lung cancer (LC) continues to be a leading death cause in China, primarily due to late diagnosis. This study aimed to evaluate the effectiveness of using plasma-based near-infrared spectroscopy (NIRS) for LC early diagnosis. A total of 171 plasma samples were collected, including 73 healthy controls (HC), 73 LC, and 25 benign lung tumors (B). NIRS was utilized to measure the spectra of samples. Pre-processing methods, including centering and scaling, standard normal variate, multiplicative scatter correction, Savitzky-Golay smoothing, Savitzky-Golay first derivative, and baseline correction were applied. Subsequently, 4 machine learning (ML) algorithms, including partial least squares (PLS), support vector machines (SVM), gradient boosting machine, and random forest, were utilized to develop diagnostic models using train set data. Then, the predictive performance of each model was evaluated using test set samples. The study was conducted in 5 comparisons as follows: LC and HC, LC and B, B and HC, the diseased group (D) and HC, as well as LC, B and HC. Among the 5 comparisons, SVM consistently generated the best performance with a certain pre-processing method, achieving overall accuracy of 1.0 (kappa: 1.0) in the comparisons of LC and HC, B and HC, as well as D and HC. Pre-processing was identified as a crucial step in developing ML models. Interestingly, PLS demonstrated remarkable stability and relatively high predictive performance across the 5 comparisons, even though it did not achieve the top results like SVM. However, none of these algorithms were able to effectively distinguish B from LC. These findings indicate that the combination of plasma-based NIRS with ML algorithms is a rapid, non-invasive, effective, and economical method for LC early diagnosis.

Integrated microfluidic chip with a MIP-gated organic electrochemical transistor for continuous, selective, and label-free quantification of tumor marker

Early diagnosis of cancer is crucial to effective treatment of the patients and to higher survival rates. Tumor marker detection as an effective and noninvasive cancer diagnosis method has important clinical significance. N1, N12-diacetylspermine (DAS) is a new tumor marker. The development of its detection methods is an urgent need for medical diagnosis and human health monitoring. The conventional detection methods of DAS are labor-intensive and time-consuming approaches. In this work, a novel electrochemical detection platform was established by integrating an organic electrochemical transistor (OECT) with a microfluidic chip and applied for trace measurement of DAS. The sensing unit is a gate electrode in the OECT modified with molecularly imprinted polymers (MIPs), which can be used as an artificial receptor to realize label-free detection. The high specific recognition ability of MIPs films combined with the amplified function of an OECT yielded a highly sensitive and selective electrochemical detection chip. The integrated microfluidic channel controlledthe exact amount of solution to be delivered and provided real-time detection of DAS. The experimental results indicated that changes in channel current was proportional to DAS concentrations from 1 nM to 10 μM, with the detection limits of 0.8 nM. It is the first demonstration of the integration of a microfluidic system with a MIP-gated OECT for real-time tumor marker detection. The study results of this work helped to develop Point-Of-Care (POC) tools for the early diagnosis of lung cancer, colon cancer and breast cancer.

Portable and highly sensitive gas sensor based on CaCl2-Cu NPs-MIL53(Al)–NH2 nanocomposite for breath ammonia analysis

Exhaled gas analysis is an effective and non-invasive method for disease diagnosis. However, due to the complexity of the exhaled gases, this is a challenge for most sensing devices. Herein, CaCl2-Cu NPs-MIL53(Al)–NH2 composites modified with screen-printed electrodes (SPEs) and ionic liquids [BMIM][BF4] were used as electrolytes to construct a novel electrochemical ammonia sensor. CaCl2-Cu NPs-MIL53(Al)–NH2/SPEs possessed excellent performance, exhibiting good linearity in the range of 0.5–500 ppm with a low limit of detection (0.25 ppm) and a fast response time for 10 ppm ammonia (5.4 s). Most importantly, CaCl2-Cu NPs-MIL53(Al)–NH2/SPEs showed excellent resistance to high humidity and interference, which is required for exhaled gas analysis. Furthermore, compared to standard spectrophotometry, this method also can provide satisfactory recovery (101.43–103.98 %), suggesting that this sensor is promising for the detection of exhaled gases in the future.

Characterization of urinary metabolites associated with malaria infection using infra-red spectroscopy and liquid chromatography–mass spectrometry in South Western Uganda

Early malaria diagnosis improves outcomes during malaria treatment; routine diagnostic techniques rely on blood samples obtained invasively. Therefore, this study used infra-red (IR) spectroscopy coupled with Principle Component Analysis (PCA) to study the urinary profile of malaria patients and that of controls aimed at understanding metabolite perturbation during malaria infection so as to contribute towards development of non-invasive malaria diagnosis methods. Freeze dried human urine samples form malaria infected individuals (cases) and controls were screened in the IR region of 4000 cm−1 to 600 cm−1 and overall spectral differences were observed at wave numbers 1618 cm−1, 1679 cm−1 (amino acids). Peaks at 3030 cm−1 (NH4+) and 940 cm−1 (O–H of carboxylic acids) showed high absorbance in patients compared to controls. Liquid-chromatography–mass spectrometry (LC–MS/MS) was used to quantify amino acids in the urine samples and the results indicated a significant increase of amino acid cystine (P = 0.012). Lysine and tyrosine also increased in patients compared to controls. The use of IR-PCA differentiated clusters of urine samples from patients with malaria from control and the demonstrated amino acid perturbation is consistent with malaria infection. This data provides baseline information for application in development of a non-invasive diagnostic tests for malaria.

Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI.

Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays a crucial role in improving patient outcomes. This study introduces a non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the detection and diagnosis of prostate cancer (PCa). IVIM imaging enables the differentiation of water molecule diffusion within capillaries and outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes a two-step segmentation approach through the use of three U-Net architectures for extracting tumor-containing regions of interest (ROIs) from the segmented images. The performance of the CAD system is thoroughly evaluated, considering the optimal classifier and IVIM parameters for differentiation and comparing the diagnostic value of IVIM parameters with the commonly used apparent diffusion coefficient (ADC). The results demonstrate that the combination of central zone (CZ) and peripheral zone (PZ) features with the Random Forest Classifier (RFC) yields the best performance. The CAD system achieves an accuracy of 84.08% and a balanced accuracy of 82.60%. This combination showcases high sensitivity (93.24%) and reasonable specificity (71.96%), along with good precision (81.48%) and F1 score (86.96%). These findings highlight the effectiveness of the proposed CAD system in accurately segmenting and diagnosing PCa. This study represents a significant advancement in non-invasive methods for early detection and diagnosis of PCa, showcasing the potential of IVIM parameters in combination with machine learning techniques. This developed solution has the potential to revolutionize PCa diagnosis, leading to improved patient outcomes and reduced healthcare costs.

Implementation of a predictive strategy in the diagnosis of inflammatory periodontal diseases

Relevance. The diagnosis of periodontal diseases, considering their severity, prevalence, progression, and staging, can be achieved by determining the levels of biomarkers or molecular imaging biomarkers in biofluids such as crevicular or sulcular fluid (GCF or GSF), saliva, and oral fluid. GCF is currently regarded as one of the diagnostically significant biological fluids for assessing the condition of periodontal tissues, not only in clinical diagnostic laboratories but also in dental offices. The implementation of sensitive, highly accurate, non-invasive, and specific methods for rapid GCF diagnosis, based on the qualitative analysis of biomarkers of cytokine imbalance, immunological disorders, changes in non-specific defence factors, and biophysical indicators, will allow for an objective assessment of the condition of periodontal tissues.Purpose. To improve the efficiency of periodontitis prevention using a developed mathematical model for personalized prediction of the course of inflammatory periodontal diseases based on the investigated biomarkers in GCF.Material and methods. The study included 101 patients: Group I consisted of 22 patients diagnosed with K05.10 (gingivitis), Group II included 31 patients diagnosed with K05.31 (mild periodontitis), and Group III comprised 18 patients diagnosed with K05.31 (moderate periodontitis). The comparison group consisted of 30 individuals with clinically healthy periodontium. All subjects underwent clinical and instrumental examination, determination of periodontal indices, GCF collection, and quantitative analysis of immune regulatory mediators (IL-1β, IL-6, TNF-α, IL-8, MCP-1, IL-17, VEGF, IL-1RA).Results. The study of immune regulatory mediators confirmed the significance of increased levels of pro- and anti-inflammatory cytokines/chemokines, as well as the reduction of the anti-inflammatory biomarker IL-1RA in GCF at the early stages of inflammatory changes in periodontal tissues. This is accompanied by the appearance of signs indicating the destruction of the dentogingival junction. Using logistic regression and training a multiclass classifier based on the support vector machine method, a model was developed to predict the risk of dentogingival junction loss in patients, potentially leading to periodontitis.Conclusion. The results of logistic regression modelling and training a multiclass classifier based on the support vector machine method demonstrate that in diagnosing the initial stages of periodontal tissue damage with the loss of the dentogingival junction (DGJ), the most effective approach is the comprehensive use of inflammatory process biomarkers and the development of multi-marker algorithms based on a computer program.

Diagnostic value of contrast-enhanced ultrasound in judging the survival of graft after heteroautoplasty for secondary hyperparathyroidism.

Secondary hyperparathyroidism (SHPT) is one of the common complications of end-stage renal disease-uremia, and is mainly manifested as parathyroid hyperplasia and abnormal secretion of parathyroid hormone (PTH). To investigate the value and advantages of contrast-enhanced ultrasound (CEUS) in evaluating the survival of autografts after parathyroidectomy + parathyroid autotransplantation. In this study, 125 patients with renal failure due to polycystic kidney disease, chronic nephritis, diabetic nephropathy, lupus nephritis, and atherosclerotic nephropathy were enrolled as the participants and each of them had 4 secondary hyperactive parathyroid glands and underwent parathyroid autotransplantation. One parathyroid gland was taken from each patient and equally divided into 4 parts and placed in the subcutaneous fat of one forearm for transplantation. CEUS was performed 14 days after the transplantation to observe the micro blood supply of the graft and assess the survival and secretory function of the transplanted parathyroid. The grafts were divided into the partial survival group and the total survival group based on the enhancement characteristics. The survival of the grafts was determined by comparing the parathyroid hormone level in bilateral elbow cephalic veins 1 month after surgery. Among the 125 patients, 112 had linear or punctate enhancement of 2-4 parathyroid glands 14 days after surgery, and 13 patients had linear or punctate enhancement of 0-1 parathyroid gland. There were statistically significant differences in the perfusion pattern, enhancement uniformity, and parathyroid hormone levels in the cephalic veins at the elbow on both the graft and non-graft sides among all groups (P< 0.05). Compared to the detection of the difference in the parathyroid hormone level in the cephalic vein of bilateral elbows 1 month after surgery, CEUS can reflect the parathyroid survival after transplantation more quickly and accurately 2weeks later, and provide a more rapid and agile non-invasive clinical diagnosis method.

Assessment of the appropriate cutoff points for anthropometric indices and their relationship with cardio-metabolic indices to predict the risk of metabolic associated fatty liver disease

BackgroundResearch on Metabolic Associated Fatty Liver Disease (MAFLD) is still in its early stages, with few studies available to identify and predict effective indicators of this disease. On the other hand, early diagnosis and intervention are crucial to reduce the burden of MAFLD. Therefore, the aim of this research was to investigate the effectiveness of eleven anthropometric indices and their appropriate cut-off values as a non-invasive method to predict and diagnose MAFLD in the Iranian population.MethodsIn this cross-sectional study, we analyzed baseline data from the Hoveyzeh Cohort Study, a prospective population-based study conducted in Iran that enrolled a total of 7836 subjects aged 35 to 70 years from May 2016 through August 2018.ResultsThe optimal cut-off values of anthropometric indices for predicting MAFLD risk were determined for waist circumference(WC) (102.25 cm for males and 101.45 cm for females), body mass index (BMI) (27.80 kg/m2 for males and 28.75 kg/m2 for females), waist-to-hip ratio (WHR) (0.96 for both males and females), waist-to-height ratio (WHtR) (0.56 for males and 0.63 for females), body adiposity index (BAI) (23.24 for males and 32.97 for females), visceral adiposity index (VAI) (1.64 for males and 1.88 for females), weight-adjusted waist index (WWI) (10.63 for males and 11.71 for females), conicity index (CI) (1.29 for males and 1.36 for females), body roundness index (BRI) (4.52 for males and 6.45 for females), relative fat mass (RFM) (28.18 for males and 44.91 for females) and abdominal volume index (AVI) (18.85 for males and for 21.37 females). VAI in males (sensitivity: 77%, specificity: 60%, Youden’s Index: 0.37) and RFM in females (sensitivity: 76%, specificity: 59%, Youden’s Index: 0.35) were found to have higher sensitivity and specificity compared to other anthropometric indices. Furthermore, anthropometric indices demonstrated statistically significant correlations with various hepatic and cardiometabolic indices. Among these, the strongest positive correlations were observed between WC, BMI, BAI, BRI, and AVI with the Hepatic Steatosis Index (HSI), TyG-BMI, and TyG-WC, as well as between VAI and the Atherogenic Index of Plasma (AIP), Lipid Accumulation Product (LAP), Cardiometabolic Index (CMI), and the Triglyceride and Glucose (TyG) Index.ConclusionAnthropometric indices are effective in predicting MAFLD risk among Iranian adults, with WWI, VAI, and RFM identified as the strongest predictors. The proposed cutoff values could serve as a straightforward and non-invasive methods for the early diagnosis of MAFLD.

Role and predictive value of microRNAs 204 and 210 in the diagnosis of pulmonary arterial hypertension and the distinction between idiopathic, systemic sclerosis, and schistosomiasis-associated pulmonary arterial hypertension

BackgroundPulmonary arterial hypertension is most of the time diagnosed late in the course of the disease and necessitates right cardiac catheterization which is an invasive and costly tool. MicroRNAs have a role in the pathogenesis of pulmonary hypertension, systemic sclerosis, and schistosomiasis and their dosages are easy and non-expensive. Therefore, determining their levels in the blood may be helpful in detecting PAH and differentiating its idiopathic form from those caused by systemic sclerosis and schistosomiasis.Purpose of the studyTo evaluate the role of microRNA (miR) 204 and miR-210 in the diagnosis of PAH and to distinguish between idiopathic PAH (IPAH), systemic sclerosis-associated PAH (SSc-PAH), and schistosomiasis-associated PAH (Sch-PAH) and to identify patients who may benefit from simple non-expensive and non-invasive methods in diagnosis of PAH.MethodsSixty patients with PAH and 30 subjects as control were enrolled in the study. PAH was diagnosed by right heart catheterization, echocardiography, and laboratory tests. Blood samples were taken from all patients for measuring miR-204 and miR-210.ResultsMiR-204 was downregulated in PAH and there was a highly significant difference between PAH and control (p = 0.003) with cut-off predictive value ≤ 0.15 µM and 70% sensitivity, 85% specificity with AUC (0.749). However, miR-204 failed to distinguish between IPAH, SSc-PAH, and Sch-PAH. MiR-210 was upregulated in PAH with a highly significant difference between PAH and control (p < 0.001) with cut-off predictive value ≥ 1.16 µM and 93.33% sensitivity, 85% specificity with AUC (0.917). MiiR-210 showed a significant difference between SSc-PAH and idiopathic PAH (P = 0.012) and between SSc-PAH and Sch-PAH (P = 0.035).ConclusionsMiR-204 and miR-210 are useful non-invasive and non-expensive markers for the diagnosis of PAH, miR-210 is an excellent predictor in the diagnosis of PAH and also miR-210 might be used to distinguish SSc-PAH from idiopathic PAH and Sch-PAH.

Refining a non-invasive prediction model for neurosyphilis diagnosis by using immunoassay to detect serum anti-TP0435 (TP17) and TP0574 (TP47) IgG antibodies: two-centre cross-sectional retrospective study in China

ObjectivesInvasive lumbar puncture is the conventional method for diagnosing neurosyphilis (NS). We investigated a non-invasive alternative method to detect serum Treponema pallidum-specific antibodies against highly immunogenic antigens TP0171 (TP15), TP0435 (TP17), and TP0574 (TP47) by using luciferase immunosorbent assay. MethodsA total of 816 HIV-negative patients suspected of NS from the Beijing and Guangzhou cohorts were retrospectively selected and tested for serum anti-TP15, TP17, and TP47 IgG antibodies. Two diagnostic prediction models were developed using stepwise logistic regression in the Beijing cohort, and evaluated in the Guangzhou cohort for external validation. ResultsSerum antibodies against TP15, TP17, and TP47 showed moderate capability for NS diagnosis in the Beijing cohort and the corresponding area under the receiver operating characteristic curves (AUCs) were 0.722 [95% confidence interval (CI): 0.680–0.762)], 0.780 (95% CI: 0.741–0.817), and 0.774 (95% CI: 0.734–0.811), respectively. An expanded NS prediction model integrated with anti-TP17 and anti-TP47 antibodies showed better performance than the base NS diagnostic model without anti-TP17 and anti-TP47 antibodies with the AUC of 0.874 (95% CI: 0.841–0.906) vs. 0.845 (95% CI: 0.809–0.881) (p = 0.007) in the development cohort, and 0.934 (95% CI: 0.909–0.960) vs. 0.877 (95% CI: 0.840–0.914) (p < 0.001) in validation cohort, respectively. Decision curve analysis revealed that the net benefit of the expanded model exceeded that of the base model when the threshold probability was between 0.10 and 0.95 in both the development and external validation cohorts. DiscussionSerum antibodies against TP17 and TP47 exhibited promising diagnostic capability for NS and significantly enhanced the predictive accuracy of model for NS diagnosis. Our study highlights the potential of serum treponemal antibody detection as a non-invasive method for NS diagnosis to substitute invasive lumbar puncture in NS diagnosis.

A Machine Learning Approach for Early Identification of Prodromal Parkinson's Disease.

Parkinson's disease (PD) affects approximately 6 million people worldwide. Data analysis of early PD symptoms using machine learning (ML) models may provide an inexpensive, non-invasive, and simple method for the remote diagnosis of early PD. The aim of this project was to analyze voice, computer keystrokes, spiral drawings, and gait data involving PD patients and controls available in public databases using ML models and identify early PD characteristics that are more pronounced than others. An ML model was developed using Random Forest to analyze existing clinical data for PD patients, prodromal PD patients with REM (rapid eye movement) sleep behavior disorder (RBD) symptoms, and non-PD healthy controls. We reviewed and collected data from the UCI (University of California Irvine) Machine Learning Repository, PPMI (Parkinson's Progression Markers Initiative), and Kaggle databases. ML analysis was carried out on voice samples in PD and RBD patients, computer keystroke data, spiral drawings, and gait datasets. The ML prediction model developed may be helpful in improving risk prediction for PD, enabling early intervention and resource prioritization. The ML study suggests that voice analysis is the most robust test, followed by computer keystroke data, spiral drawings, and gait analysis, in that order. Voice is affected even in RBD patients, revealing that it is a sensitive and early measure of prodromal PD. The low accuracy of the analysis indicates that several PD-positive samples may remain undetected and unclassified. Combining all four features, that is, voice analysis, computer keystroke data, spiral drawings, and gait analysis, may improve the overall accuracy.

A simultaneous non-invasive diagnosis method for flame temperature and velocity fields based on radiation self-calibration and optical flow

The measurement of flame temperature and velocity is crucial to combustion research, hence it is of great significance to develop a non-invasive diagnosis technology for the measurement of flame temperature and velocity. In this study, a single-camera simultaneous non-invasive diagnosis technology for flame temperature and velocity field was developed, based on radiation self-calibration and optical flow method. The diagnosis method was applied to two case studies, propane diffusion flame and solid surface spread flame. The reconstructed flame temperature field was compared with measuring results by thermocouples. Results show that the reconstructed temperature field can accurately describe the symmetrical distribution characteristics and the clear high-temperature front. The combustion zone reconstruction temperature was 1250–1380 K for propane diffusion flame and the maximum reconstruction temperature was 1400 K for solid spread flame. The maximum relative error between the reconstructed temperature and the measured values by thermocouples is less than 15 %. Additionally, the flow velocity field at the flame edge was also measured, which could provide a good qualitative characterization of flame kinematic.

Non-invasive Scores and Serum Biomarkers for Fatty Liver in the Era of Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD): A Comprehensive Review From NAFLD to MAFLD and MASLD.

The prevalence of non-alcoholic fatty liver disease (NAFLD) is rapidly increasing worldwide, making it the leading cause of liver related morbidity and mortality. Currently, liver biopsy is the gold standard for assessing individuals with steatohepatitis and fibrosis. However, its invasiveness, sampling variability, and impracticality for large-scale screening has driven the search for non-invasive methods for early diagnosis and staging. In this review, we comprehensively summarise the evidence on the diagnostic performance and limitations of existing non-invasive serum biomarkers and scores in the diagnosis and evaluation of steatosis, steatohepatitis, and fibrosis. Several non-invasive serum biomarkers and scores have been developed over the last decade, although none has successfully been able to replace liver biopsy. The introduction of new NAFLD terminology, namely metabolic dysfunction-associated fatty liver disease (MAFLD) and more recently metabolic dysfunction-associated steatotic liver disease (MASLD), has initiated a debate on the interchangeability of these terminologies. Indeed, there is a need for more research on the variability of the performance of non-invasive serum biomarkers and scores across the diagnostic entities of NAFLD, MAFLD and MASLD. There remains a significant need for finding valid and reliable non-invasive methods for early diagnosis and assessment of steatohepatitis and fibrosis to facilitate prompt risk stratification and management to prevent disease progression and complications. Further exploration of the landscape of MASLD under the newly defined disease subtypes is warranted, with the need for more robust evidence to support the use of commonly used serum scores against the new MASLD criteria and validation of previously developed scores.

#328 Non-invasive assessment of urinary exfoliated proximal tubule cells using multispectral autofluorescence imaging features for early detection of CKD

Abstract Background and Aims Early detection of CKD is important to allow for timely intervention which may reduce adverse clinical outcomes and cost of renal replacement therapies. Histopathological diagnosis of CKD remains the gold standard, but routine kidney biopsy is costly and associated with risks such as pain and bleeding. There is a need to develop accurate non-invasive methods for early diagnosis and prognostication of CKD. Kidney cell exfoliation into urine is an active process, which may generate significant information regarding an individual's kidney status. In particular, proximal tubule cells (PTCs) make up majority of the kidney mass and represents the hallmark of CKD in reflecting the degree of tubulointerstitial fibrosis and atrophy. Multispectral assessment of native cell autofluorescence has been specifically shown to be sensitive to metabolic changes and oxidative stress, which are key factors involved in kidney function decline. Hence, we evaluated whether multispectral autofluorescence signals in urinary exfoliated PTCs reflect kidney function as determined by eGFR. Method Individuals aged 18 or above were included and informed consent was obtained from all participants involved in this study. Spot urine samples were collected and stored at -80°C. On assessment, exfoliated PTCs were extracted from thawed urine using a validated specific immunomagnetic separation method based on anti-CD13 and anti-SGLT2 antibodies. Multispectral autofluorescence imaging was performed using a custom-made autofluorescence microscopy system (standard Olympus iX83™ fluorescence microscope and multispectral excitation lamp from Quantitative™, AU). Images were captured by a Nüvü™ EMCCD camera HNü 1024 cooled to -65°C in our laboratory to reduce sensor-induced noise. For the 34 image channels taken per sample, image acquisition times of up to 5 seconds per channel, with multiple averaging (3 times) was applied to optimize image quality. Image analysis was conducted through a workflow encompassing data preprocessing, data normalization and outlier handling, followed by basic model training and evaluation. An entropy-based image feature selection methodology was initially completed by hand, with results subsequently compared to that generated from the AutoGluon machine-learning framework [1]. The models developed from this process were assessed using ROC (Receiver Operating Characteristic) curves and AUC (Area Under the Curve) values. Two discriminative studies were performed – an overall study between exfoliated PTCs of individuals eGFR≥60 and eGFR &amp;lt; 60 ml/min/1.73 m2 and a subgroup study between that of individuals eGFR≥90 and eGFR 60-90 ml/min/1.73 m2. Results 40 individuals were included in our study. 20 individuals had eGFR≥60 and 20 had eGFR &amp;lt; 60 ml/min/1.73 m2. Multispectral assessment of cell autofluorescence features differentiated between exfoliated PTCs of these 2 groups with AUC value of 0.97 (95% CI 0.94-1.00) (Fig. 1), model accuracy being 82%. Subgroup analysis included 20 individuals of which 10 had eGFR≥90 and 10 had eGFR 60-90 ml/min/1.73 m2. Multispectral assessment of cell autofluorescence features differentiated between exfoliated PTCs of these 2 groups with AUC value of 0.99 (95% CI 0.99-1.00) (Fig. 2), model accuracy being 85%. Conclusion An excellent degree of differentiation between urinary PTCs derived from those with normal or high eGFR and mild degrees of CKD is now reliably demonstrated, complementing conclusions from our group's original small pilot study which evaluated between urinary PTCs from CKD and non-CKD cohorts [2]. Further studies are required to determine whether our novel method can be used to non-invasively predict individuals at risk of progressive kidney disease and to monitor whether intervention is effective.

Non-calcified plaque-based coronary stenosis grading in contrast enhanced CT

BackgroundThe high mortality rate associated with coronary heart disease has led to state-of-the-art non-invasive methods for cardiac diagnosis including computed tomography and magnetic resonance imaging. However, stenosis computation and clinical assessment of non-calcified plaques has been very challenging due to their ambiguous intensity response in CT i.e. a significant overlap with surrounding muscle tissues and blood. Accordingly, this research presents an approach for computation of coronary stenosis by investigating cross-sectional lumen behaviour along the length of 3D coronary segments. MethodsNon-calcified plaques are characterized by comparatively lower-intensity values with respect to the surrounding. Accordingly, segment-wise orthogonal volume was reconstructed in 3D space using the segmented coronary tree. Subsequently, the cross sectional volumetric data was investigated using proposed CNN-based plaque quantification model and subsequent stenosis grading in clinical context was performed. In the last step, plaque-affected orthogonal volume was further investigated by comparing vessel-wall thickness and lumen area obstruction w.r.t. expert-based annotations to validate the stenosis grading performance of model. ResultsThe experimental data consists of clinical CT images obtained from the Rotterdam CT repository leading to 600 coronary segments and subsequent 15786 cross-sectional images. According to the results, the proposed method quantified coronary vessel stenosis i.e. severity of the non-calcified plaque with an overall accuracy of 83%. Moreover, for individual grading, the proposed model show promising results with accuracy equal to 86%, 90% and 79% respectively for severe, moderate and mild stenosis. The stenosis grading performance of the proposed model was further validated by performing lumen-area versus wall-thickness analysis as per annotations of manual experts. The statistical results for lumen area analysis precisely correlates with the quantification performance of the model with a mean deviation of 5% only. ConclusionThe overall results demonstrates capability of the proposed model to grade the vessel stenosis with reasonable accuracy and precision equivalent to human experts.

Multipanel Approach including miRNAs, Inflammatory Markers, and Depressive Symptoms for Metabolic Dysfunction-Associated Steatotic Liver Disease Diagnosis during 2-Year Nutritional Intervention.

Metabolic dysfunction-associated steatotic liver disease (MASLD), with a prevalence of 30% of adults globally, is considered a multifactorial disease. There is a lack of effective non-invasive methods for accurate diagnosis and monitoring. Therefore, this study aimed to explore associations between changes in circulating miRNA levels, inflammatory markers, and depressive symptoms with hepatic variables in MASLD subjects and their combined potential to predict the disease after following a dietary intervention. Biochemical markers, body composition, circulating miRNAs and hepatic and psychological status of 55 subjects with MASLD with obesity and overweight from the FLiO study were evaluated by undergoing a 6-, 12- and 24-month nutritional intervention. The highest accuracy values of combined panels to predict the disease were identified after 24 months. A combination panel that included changes in liver stiffness, high-density lipoprotein cholesterol (HDL-c), body mass index (BMI), depressive symptoms, and triglycerides (TG) yielded an AUC of 0.90. Another panel that included changes in hepatic fat content, total cholesterol (TC), miR15b-3p, TG, and depressive symptoms revealed an AUC of 0.89. These findings identify non-invasive biomarker panels including circulating miRNAs, inflammatory markers, depressive symptoms and other metabolic variables for predicting MASLD presence and emphasize the importance of precision nutrition in MASLD management and the sustained adherence to healthy lifestyle patterns.