Noninvasive Diagnosis Research Articles

Potential application of body fluids autofluorescence in the non-invasive diagnosis of endometrial cancer.

Endometrial carcinoma (EC) is the most common cancer of the female reproductive tract in developed countries. The prognosis and 5-year survival rates are closely tied to the stage diagnosis. Current routine diagnostic methods of EC are either lacking specificity or are uncomfortable, invasive and painful for the patient. As of now, the gold diagnostic standard is endometrial biopsy. Early and non-invasive diagnosis of EC requires the identification of new biomarkers of disease and a screening test applicable to routine laboratory diagnostics. The application of untargeted metabolomics combined with artificial intelligence and biostatistics tools has the potential to qualitatively and quantitatively represent the metabolome, but its introduction into routine diagnostics is currently unrealistic due to the financial, time and interpretation challenges. Fluorescence spectral analysis of body fluids utilizes autofluorescence of certain metabolites to define the composition of the metabolome under physiological conditions. This review highlights the potential of fluorescence spectroscopy in the early detection of EC. Data obtained by three-dimensional fluorescence spectroscopy define the quantitative and qualitative composition of the complex fluorescent metabolome and are useful for identifying biochemical metabolic changes associated with endometrial carcinogenesis. Autofluorescence of biological fluids has the prospect of providing new molecular markers of EC. By integrating machine learning and artificial intelligence algorithms in the data analysis of the fluorescent metabolome, this technique has great potential to be implemented in routine laboratory diagnostics.

Enhancing Coronary Artery Diseases Screening: A Comprehensive Assessment of Machine Learning Approaches Using Routine Clinical and Laboratory Data.

Introduction: Coronary artery disease (CAD) stands among the leading global causes of mortality, underscoring the critical necessity for early detection to facilitate effective treatment. Although Coronary Angiography (CA) serves as the gold standard for diagnosis, its limitations for screening, including side effects and cost, necessitate alternative approaches. This study focuses on the development and comparison of machine learning techniques as substitutes for CA in CAD screening, leveraging routine clinical and laboratory data.Material and Methods: Various machine learning classification algorithms—decision tree, k-nearest neighbor, artificial neural network, support vector machine, logistic regression, and stacked ensemble learning—were employed to differentiate CAD and healthy subjects. Feature selection algorithms, namely LASSO and ReliefF, were utilized to prioritize relevant features. A range of evaluation metrics, including accuracy, precision, sensitivity, specificity, AUC, F1 score, ROC curve, and NPV, were applied. The SHAP technique was employed to elucidate and interpret the artificial neural network model.Results: The artificial neural network, support vector machine, and stacked ensemble learning models demonstrated excellent results in a 10-fold cross-validation evaluation using features selected by LASSO and ReliefF. With the LASSO feature selection algorithm, these models achieved accuracies of 90.38%, 90.07%, and 90.39%, sensitivities of 94.43%, 93.03%, and 93.96%, and specificities of 80.27%, 82.77%, and 81.52%, respectively. Using ReliefF, the accuracies were 88.79%, 88.77%, and 90.06%, sensitivities were 92.12%, 91.66%, and 93.98%, and specificities were 80.13%, 81.38%, and 80.13%, respectively. The SHAP technique revealed that typical and atypical chest pain, hypertension, diabetes mellitus, T inversion, and age were the most influential features in the neural network model.Conclusion: The machine learning models developed in this study exhibit high potential for non-invasive screening and diagnosis of CAD in the Z-Alizadeh Sani dataset. However, further studies are essential to validate and apply these models in real-world and clinical settings.

Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis

The carotid artery is a vital blood vessel that supplies oxygenated blood to the brain. Blockages in this artery can lead to life-threatening illnesses, making accurate diagnosis essential. While ultrasound (US) imaging is the primary diagnostic tool, evaluations by trained operators can be subjective and imprecise. An automated approach can provide a more reliable and accurate evaluation of the carotid artery’s condition. To achieve this, the artery must first be identified and isolated from US images. This paper proposes an automated segmentation method for the carotid artery in transverse B-mode ultrasound images, using a Bi-attention DoubleUnet architecture which incorporates spatial attention and channel wise attention using Bottleneck attention module. The method is evaluated on a dataset of transverse images acquired from two devices. The obtained results exhibit higher performance than existing methods with a dice index of 95.96%, IoU of 97.92%, Precision of 98.35% and recall of 97.57% on combined dataset. Another variant of doubleUnet with SE layer and without ASPP (Artous Spatial Pyramidal Pooling) is presented where the modified DoubleUnet with SE realized an average IoU of 92.805%, Dice of 96.215%, precision of 98.82%, and recall of 93.84% representing average results across datasets. These approaches can significantly improve the efficiency and accuracy of carotid artery disease diagnosis and treatment, ultimately improving patient outcomes.

A β-Galactosidase-Activated Fluorogenic Reporter for the Detection of Gastric Cancer In Vivo and in Urine.

Although gastric cancer (GC) is one of the most frequent malignant tumors in the digestive tract with high morbidity and mortality, it remains a diagnostic dilemma due to its reliance on invasive biopsy or insensitive assays. Herein, we report a fluorescent gastric cancer reporter (FGCR) with activatable near-infrared fluorescence (NIRF) signals and high renal-clearance efficiency for the detection of orthotopic GC in a murine model via real-time imaging and remote urinalysis. In the presence of gastric-tumor-associated β-galactosidase (β-Gal), FGCR can be fluorescently activated for in vivo NIRF imaging. Relying on its high renal-clearance efficiency (∼95% ID), it can be rapidly excreted through kidneys to urine for the ultrasensitive detection of tumors with a diameter down to ∼2.1 mm and for assessing the prognosis of oxaliplatin-based chemotherapy. This study not only provides a new approach for noninvasive auxiliary diagnosis and prognosis of GC but also provides guidelines for the development of fluorescence probes for cancer diagnosis.

Automated Multi-Injection Gas Chromatography-Photoionization Detection (AMI-GC-PID) for Qualitative and Quantitative Determination of Urinary Acetone

Acetone is produced from fat metabolism which is increased in diabetic patients due to the disruption of glucose metabolism. Therefore, urinary acetone is expected to be a biomarker for noninvasive diagnosis of diabetes. Although several techniques have been developed for urinary acetone, none meet the clinical needs of automated multi-injection for numerous samples. In this study, we developed automated multi-injection gas chromatography-photoionization detection (AMI-GC-PID) to determine urinary acetone. First, we optimized the sample preprocessing during urine collection and storage before detection and established a standard protocol. The urine was immediately sealed in headspace bottles, stored at 20 °C for 1 h, and equilibrated for 20 min at 80 °C before analysis. Next we evaluated the repeatability of the method and the influence of the urine matrix. The relative standard deviations (RSDs) of the intra-day (nine measurements) and inter-day (three days) measurements were less than 5%. The recovery rate was 97.4% ± 4.6%. AMI-GC-PID was applied to determine urinary acetone in 44 diabetic patients and 29 healthy subjects. The median concentration of urinary acetone was much higher in diabetic patients than in healthy controls (2019 μg/L compared 699 μg/L). The results show that AMI-GC-PID possesses suitable analytical figures of merit for urinary acetone with broad applications in the noninvasive diagnosis of diabetes.

Role of Cardiovascular Magnetic Resonance in Cardiac Amyloidosis: A Narrative Review.

Amyloidosis is a rare infiltrative condition resulting from the extracellular accumulation of amyloid fibrils at the cardiac level. It can be an acquired condition or due to genetic mutations. With the progression of imaging technologies, a non-invasive diagnosis was proposed. In this study, we discuss the role of CMR in cardiac amyloidosis, focusing on the two most common subtypes (AL and ATTR), waiting for evidence-based guidelines to be published.

Plasma miRNA expression profile in pediatric pineal pure germinomas.

Pure germinomas account for 40% of pineal tumors and are characterized by the lack of appreciable tumor markers, thus requiring a tumor biopsy for diagnosis. MicroRNAs (miRNA) have emerged as potential non-invasive biomarkers for germ cell tumors and may facilitate the non-invasive diagnosis of pure pineal germinomas. A retrospective chart review was performed on all patients treated at the Children's Cancer Hospital Egypt diagnosed with a pineal region tumor between June 2013 and March 2021 for whom a research blood sample was available. Plasma samples were profiled for miRNA expression, and DESeq2 was used to compare between pure germinoma and other tumor types. Differentially expressed miRNAs were identified. The area under the curve of the receive;r operating characteristic curve was constructed to evaluate diagnostic performance. Samples from 39 pediatric patients were available consisting of 12 pure germinomas and 27 pineal region tumors of other pathologies, including pineal origin tumors [n = 17; pineoblastoma (n = 13) and pineal parenchymal tumors of intermediate differentiation (n = 4)] and others [n = 10; low-grade glioma (n = 6) and atypical teratoid rhabdoid tumor (n = 4)]. Using an adjusted p-value <0.05, three miRNAs showed differential expression (miR-143-3p, miR-320c, miR-320d; adjusted p = 0.0058, p = 0.0478, and p = 0.0366, respectively) and good discriminatory power between the two groups (AUC 90.7%, p < 0.001) with a sensitivity of 25% and a specificity of 100%. Our results suggest that a three-plasma miRNA signature has the potential to non-invasively identify pineal body pure germinomas which may allow selected patients to avoid the potential surgical complications.

Innovation in Non-Invasive Diagnosis and Disease Monitoring for Meningiomas.

Meningiomas are tumors of the central nervous system that vary in their presentation, ranging from benign and slow-growing to highly aggressive. The standard method for diagnosing and classifying meningiomas involves invasive surgery and can fail to provide accurate prognostic information. Liquid biopsy methods, which exploit circulating tumor biomarkers such as DNA, extracellular vesicles, micro-RNA, proteins, and more, offer a non-invasive and dynamic approach for tumor classification, prognostication, and evaluating treatment response. Currently, a clinically approved liquid biopsy test for meningiomas does not exist. This review provides a discussion of current research and the challenges of implementing liquid biopsy techniques for advancing meningioma patient care.

Expanding Access to Noninvasive Prenatal Diagnosis for Monogenic Conditions to Consanguineous Families.

Cell-free fetal DNA exists within the maternal bloodstream during pregnancy and provides a means for noninvasive prenatal diagnosis (NIPD). Our accredited clinical service offers definitive NIPD for several autosomal recessive (AR) and X-linked conditions using relative haplotype dosage analysis (RHDO). RHDO involves next-generation sequencing (NGS) of thousands of common single nucleotide polymorphism (SNPs) surrounding the gene of interest in the parents and an affected or unaffected offspring to conduct haplotype phasing of the high- and low-risk alleles. NGS is carried out in parallel on the maternal cell-free DNA, and fetal inheritance is predicted using sensitive dosage calculations performed at sites where the parental genotypes differ. RHDO is not currently offered to consanguineous couples owing to the shared haplotype between parents. Here we test the expansion of RHDO for AR monogenic conditions to include consanguineous couples. The existing sequential probability ratio test analysis pipeline was modified to apply to SNPs where both parents are heterozygous for the same genotype. Quality control thresholds were developed using 33 nonconsanguineous cases. The performance of the adapted RHDO pipeline was tested on 8 consanguineous cases. The correct fetal genotype was predicted by our revised RHDO approach in all conclusive cases with known genotypes (n = 5). Haplotype block classification accuracies of 94.5% and 93.9% were obtained for the nonconsanguineous and consanguineous case cohorts, respectively. Our modified RHDO pipeline correctly predicts the genotype in fetuses from consanguineous families, allowing the potential to expand access to NIPD services for these families.

Proteomic biomarkers in seminal plasma as predictors of reproductive potential in azoospermic men.

Azoospermia, characterized by an absence of sperm in the ejaculate, represents the most severe form of male infertility. While surgical sperm retrieval in obstructive azoospermia (OA) is successful in the majority of cases, patients with non-obstructive azoospermia (NOA) show retrieval rates of only about 50% and thus frequently have unnecessary surgery. Surgical intervention could be avoided if patients without preserved spermatogenesis are identified preoperatively. This prospective study aimed to discover biomarkers in seminal plasma that could be employed for a non-invasive differential diagnosis of OA/NOA in order to rationalize surgery recommendations and improve success rates. All patients signed written informed consent, underwent comprehensive andrological evaluation, received human genetics to exclude relevant pathologies, and patients with azoospermia underwent surgical sperm retrieval. Using label-free LC-MS/MS, we compared the proteomes of seminal plasma samples from fertile men (healthy controls (HC), n=8) and infertile men diagnosed with 1) OA (n=7), 2) NOA with successful sperm retrieval (mixed testicular atrophy (MTA), n=8), and 3) NOA without sperm retrieval (Sertoli cell-only phenotype (SCO), n=7). Relative abundance changes of two candidate markers of sperm retrieval, HSPA2 and LDHC, were confirmed by Western Blot. We found the protein expression levels of 42 proteins to be significantly down-regulated (p ≤ 0.05) in seminal plasma from SCO NOA patients relative to HC whereas only one protein was down-regulated in seminal plasma from MTA patients. Analysis of tissue and cell expression suggested that the testis-specific proteins LDHC, PGK2, DPEP3, and germ-cell enriched heat-shock proteins HSPA2 and HSPA4L are promising biomarkers of spermatogenic function. Western blotting revealed a significantly lower abundance of LDHC and HSPA2 in the seminal plasma of men with NOA (SCO and MTA) compared to controls. The results indicate that certain testis-specific proteins when measured in seminal plasma, could serve as indicators of the presence of sperm in the testis and predict the success of sperm retrieval. Used in conjunction with conventional clinical assessments, these proteomic biomarkers may assist in the non-invasive diagnosis of idiopathic male infertility.

Non-invasive real-time diagnosis of PMSM faults implemented in motor control software for mission critical applications

The machine’s health should be continuously monitored, or a test should be applied on a regular basis to predict failure before fatal damage is incurred. Unexpected failures, particularly in mission-critical applications, can cause irreversible damage to the system, or even human life. This paper introduces a novel non-intrusive, real-time, online Condition Monitoring (CM), and Fault Diagnosis (FD) system for Permanent Magnet Synchronous Machines (PMSMs). Only the motor drive’s built-in sensors, such as current and position sensors, are used to detect three types of faults: inter-turn short circuit, partial demagnetization, and static eccentricity. It encompasses the implementation of algorithms within a motor drive system and the creation of failure mode models. The proposed solution adopts a hardware-free approach, utilizing current/voltage signature analysis for cost-effectiveness. It requires a small memory and short execution time, allowing it to be implemented on a simple motor controller with limited memory and calculation power. The drive system is intended for mission critical applications, therefore, computation load, code size, memory allocation, run-time optimization, etc. are the key focuses for real-time operation. It offers immediate insights into motor’s health without interrupting the drive operation. Additionally, it ensures rapid processing with modest computational requirements, making it adaptable for implementation on any PMSM controller. The non-intrusive nature of this diagnostic approach has the potential to enhance safety in systems reliant on PMSM drives. The proposed method has a high detection accuracy of 98%, is computationally efficient and can detect and classify the fault accurately. Simulation and experimental results demonstrate the efficiency of the FD algorithm for online identification and classification of machine faults. Theoretical hypotheses are proven based on experimental data.

Oxygen Vacancy-Rich Bimetallic Au@Pt Core-Shell Nanosphere-Functionalized Electrospun ZnFe2O4 Nanofibers for Chemiresistive Breath Acetone Detection.

Sensitive and selective acetone detection is of great significance in the fields of environmental protection, industrial production, and individual health monitoring from exhaled breath. To achieve this goal, bimetallic Au@Pt core-shell nanospheres (BNSs) functionalized-electrospun ZnFe2O4 nanofibers (ZFO NFs) are prepared in this work. Compared to pure NFs-650 analogue, the ZFO NFs/BNSs-2 sensor exhibits a stronger mean response (3.32 vs 1.84), quicker response/recovery speeds (33 s/28 s vs 54 s/42 s), and lower operating temperature (188 vs 273 °C) toward 0.5 ppm acetone. Note that an experimental detection limit of 30 ppb is achieved, which ranks among the best cases reported thus far. Besides the demonstrated excellent repeatability, humidity-enhanced response, and long-term stability, the selectivity toward acetone is remarkably improved after BNSs functionalization. Through material characterizations and DFT calculations, all these improvements could be attributed to the boosted oxygen vacancies and abundant Schottky junctions between ZFO NFs and BNSs, and the synergistic catalytic effect of BNSs. This work offers an alternative strategy to realize selective subppm acetone under high-humidity conditions catering for the future requirements of noninvasive breath diabetes diagnosis in the field of individual healthcare.

Quantifying H&E staining results, grading and predicting IDH mutation status of gliomas using hybrid multi-dimensional MRI.

To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas. Included were 71 glioma patients (mean age, 50.17 ± 13.38years; 35 men). HM-MRI images were collected at five different echo times (80-200ms) with seven b-values (0-3000s/mm2). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models. HM-MRI metrics FDvery-slow and FDslow demonstrated a significant correlation with the H&E staining results (p < .05). Besides, FDvery-slow showed the highest area under ROC curve (AUC = 0.854) for grading, while Dslow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics FDvery-slow and T2Dslow improved the diagnostic performance for grading (AUC = 0.876). HM-MRI can aid in non-invasive diagnosis of gliomas.

Pyrene Derivative Incorporated Ni MOF as an Enzyme Mimic for Noninvasive Salivary Glucose Detection Toward Diagnosis of Diabetes Mellitus.

Herein, we demonstrate the detection of glucose in a noninvasive and nonenzymatic manner by utilizing an extended gate field-effect transistor (EGFET) based on the organic molecule pyrene phosphonic acid (PyP4OH8) incorporated nickel metal-organic framework (NiOM-MOF). The prepared electrode responds selectively to glucose instead of sucrose, fructose, maltose, ascorbic acid, and uric acid in a 1× phosphate buffer saline solution. Also, utilizing the scanning Kelvin probe system, the sensing electrode's work function (Φ) is measured to validate the glucose-sensing mechanism. The sensitivity, detection range, response time, limit of detection, and limit of quantification of the electrode are determined to be 24.5 μA mM-1 cm-2, 20 μM to 10 mM, less than 5 s, 2.73 μM, and 8.27 μM, respectively. Most interestingly, the developed electrode follows the Michaelis-Menten kinetics, and the calculated rate constant (km) 0.07 mM indicates a higher affinity of NiOM-MOF toward glucose. The real-time analysis has revealed that the prepared electrode is sensitive to detect glucose in real human saliva, and it can be an alternative device for the noninvasive detection of glucose. Overall, the outcomes of the EGFET studies demonstrate that the prepared electrodes are well-suited for expeditious detection of glucose levels in saliva.

A candidate panel of eight urinary proteins shows potential of early diagnosis and risk assessment for diabetic kidney disease in type 1 diabetes

Diabetic kidney disease (DKD) poses a significant health challenge for individuals with diabetes. At its initial stages, DKD often presents asymptomatically, and the standard for non-invasive diagnosis, the albumin-creatinine ratio (ACR), employs discrete categorizations (normal, microalbuminuria, macroalbuminuria) with limitations in sensitivity and specificity across diverse population cohorts. Single biomarker reliance further restricts the predictive value in clinical settings. Given the escalating prevalence of diabetes, our study uses proteomic technologies to identify novel urinary proteins as supplementary DKD biomarkers. A total of 158 T1D subjects provided urine samples, with 28 (15 DKD; 13 non-DKD) used in the discovery stage and 131 (45 DKD; 40 pDKD; 46 non-DKD) used in the confirmation. We identified eight proteins (A1BG, AMBP, AZGP1, BTD, RBP4, ORM2, GM2A, and PGCP), all of which demonstrated excellent area-under-the-curve (AUC) values (0.959 to 0.995) in distinguishing DKD from non-DKD. Furthermore, this multi-marker panel successfully segregated the most ambiguous group (microalbuminuria) into three distinct clusters, with 80% of subjects aligning either as DKD or non-DKD. The remaining 20% exhibited continued uncertainty. Overall, the use of these candidate urinary proteins allowed for the better classification of DKD and offered potential for significant improvements in the early identification of DKD in T1D populations.

Carcinoma hepatocelular

Hepatocellular carcinoma (HCC) is the most common malignant liver tumor. Between 80% and 90% of cases occur in patients with advanced chronic liver disease, traditionally of viral etiology. Its clinical manifestations are very nonspecific and, in most cases, it is asymptomatic. Early diagnosis of HCC is essential, hence the screening strategies based on periodic abdominal ultrasound in patients with chronic liver disease. A peculiarity of this tumor is the possibility of making a non-invasive radiological diagnosis based on dynamic imaging tests (computed tomography or magnetic resonance imaging), reserving a histological study for cases with inconclusive results. The prognosis of HCC depends on the tumor stage as well as characteristics of the underlying liver disease and the patient's functional status, which will determine the different treatment options.

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.

Available medical imaging modalities for melanoma screening

The prevalence of melanoma of the skin has seen a significant rise in recent decades, constituting approximately one-third of all diagnosed cancer cases. Melanoma, the most fatal variant among cutaneous malignancies, exhibits a 4% probability of occurrence over an individual’s lifetime. The increasing incidence and mortality rates of skin cancer impose a substantial burden on healthcare resources and the economy. In recent years, several optical modalities, including dermoscopy, reflectance confocal microscopy (RCM), optical coherence tomography, multiphoton excited fluorescence imaging, and dermatofluorescence, have been extensively studied and utilized to improve the non-invasive diagnosis of skin cancer. This review article provides an analysis of the approach employed in the recently developed optical non-invasive diagnostic technologies. It explores the clinical uses of these techniques, while also examining their respective advantages and disadvantages. Furthermore, the paper explores the possibility for additional advancements in these technologies in the future.

474 An Investigation of Novel Urinary Cell mRNA Profiles for Noninvasive Diagnosis of Acute Rejection in Kidney Transplant Recipients

OBJECTIVES/GOALS: RNA-seq of urine and kidney allograft biopsies (bx) found that urinary cell immune landscape reflects intragraft molecular events and we discovered a shared set of 127 mRNAs in urine matched to T cell mediated and antibody mediated rejection bx. We prioritized ITM2A, SLAMF6 and IKZF3 mRNAs and herein investigate if these accurately predict rejection. METHODS/STUDY POPULATION: We collected urine samples from adult kidney allograft (KA) recipients at the time of KA bx. KA bx were classified by pathologists by Banff criteria. Total RNA was isolated from KA bx-matched urine samples. Absolute copy numbers of ITM2A, SLAMF6, and IKZF3 mRNAs and 18S rRNA were measured using our customized RT-qPCR assays. Logistic regression used to derive an equation for a combined signature score of 18S-normalized urinary cell mRNA levels of ITM2A, IKZF3, and SLAMF6 that best predicts Acute Rejection (AR= both T cell mediated rejection and antibody mediated rejection). Area under the ROC curve (AUC) was calculated to discriminate between AR and No Rejection (NR) biopsies for 18S-normalized urinary cell levels of ITM2A, IKZF3 and SLAMF6 and the composite signature score. AUCs were compared by DeLong Method. RESULTS/ANTICIPATED RESULTS: Urinary cell 18S-normalized levels of ITM2A, IKZF3, and SLAMF6 mRNAs in urine discriminated KA recipients with AR biopsies (n=95) from those with NR biopsies (n=160) (All P values &lt;0.05, Mann-Whitney test) and the AUC was 0.69 (95%CI, 0.62 to 0.76) for ITM2A, 0.61 (95%CI, 0.53 to 0.68) for IKZF3, and 0.60 (95%CI, 0.53 to 0.68) for SLAMF6. The derived combination signature score of urinary cell 18S-normalized levels of ITM2A, IKZF3, and SLAMF6 mRNA discriminated KA recipients with AR from those with NR (P&lt;0.0001, Mann Whitney test) and the combined signature score AUC was 0.72 (95%CI, 0.65 to 0.79). The combination signature score discriminated AR vs NR better than IKZF3 and SLAMF6 alone, but was not significantly different than ITM2A alone (DeLong method). (Additional results/figures to be included in poster) DISCUSSION/SIGNIFICANCE: Our RNA-seq offered a unique opportunity to diagnose AR by measuring the mRNAs in urine. We now found that urinary cell mRNA levels of ITM2A, IKZF3, SLAMF6 and the combined signature are diagnostic of AR, a major and serious post-transplant complication. This allows for much-needed KA molecular surveillance and personalization of immunosuppression.

Computational hemodynamics and hemoacoustic study on carotid bifurcation: Effect of stenosis and branch angle

Investigation of sound-signal-based noninvasive diagnosis of arterial stenosis is an active area of research. This study focuses on computational investigation of hemodynamic and hemoacoustic parameters within the carotid bifurcation. The objective is to analyze the effect of 40 distinct geometric configurations on indicative sound signals, useful for understanding the feasibility of stethoscope-based diagnosis of stenosis. The study employs an in-house flow-solver based on the semi-implicit pressure-projection method on a curvilinear grid. Physiological condition-based pulsatile flow waveforms and three-element Windkessel model-based pressure are utilized at the inlet and outlets of the bifurcating carotid artery. The research involves assessment of parameters like wall shear stress (WSS) and integrated pressure force rate (IPFR) fast Fourier transform (FFT) spectrum. Geometric configurations are varied based on stenosis level S (0, 45%, 60%, and 70%), bifurcation angle BA (30°, 40°, 50°, and 65°), and length of stenosis L (1, 1.5, and 2). In the investigated geometries, WSS exhibits a distinct behavior, reaching a peak at stenosis and subsequently transitioning to a negative value. Furthermore, IPFR-spectrum analysis reveals distinguishable frequencies for S≥ 40%, hinting at the potential for stethoscope-based diagnosis. A novel correlation between the cutoff frequencies of IPFR FFT-spectrum and arterial geometry is established, which reflect the influence of artery geometry on sound signals. Computational fluid dynamics (CFD)-based flow-visualization approach is proposed to calculate characteristic frequencies, which are close to IPFR spectrum frequencies. Our study contributes to a framework for potential sound-based classification of plaque-induced constrictions.