Diagnostic Analysis Research Articles

DNA Methylation Profiles Are Stable in H3 K27M-Mutant Diffuse Midline Glioma Neurosphere Cell Lines.

Diffuse midline gliomas are among the deadliest human cancers and have had little progress in treatment in the last 50 years. Cell cultures of these tumors have been developed recently, but the degree to which such cultures retain the characteristics of the source tumors is unknown. DNA methylation profiling offers a powerful tool to look at genome-wide epigenetic changes that are biologically meaningful and can help assess the similarity of cultured tumor cells to their in vivo progenitors. Paraffinized diagnostic tissue from three diffuse intrinsic pontine gliomas with H3 K27M mutations was compared with subsequent passages of neurosphere cell cultures from those tumors. Each cell line was passaged 3-4 times and analyzed with DNA methylation arrays and standard algorithms that provided a comparison of diagnostic classification and cluster analysis. All samples tested maintained high classifier scores and clustered within the reference group of H3 K27M-mutant diffuse midline gliomas. There was a gain of 1q in all cell lines, with two cell lines initially manifesting the gain of 1q only during culture. In vitro cell cultures of H3 K27M-mutant gliomas maintain high degrees of similarity in DNA methylation profiles to their source tumor, confirming their fidelity even with some chromosomal changes.

Bifunctional enzyme-mimicking metal-organic frameworks for sensitive acetylcholine analysis

The development of nanomaterials with multi-enzyme-like activity is crucial for addressing challenges in multi-enzyme-based biosensing systems, including cross-talk between different enzymes and the complexities and costs associated with detection. In this study, Pt nanoparticles (Pt NPs) were successfully supported on a Zr-based metal-organic framework (MOF-808) to create a composite catalyst named MOF-808/Pt NPs. This composite catalyst effectively mimics the functions of acetylcholinesterase (AChE) and peroxidase (POD). Leveraging this capability, we replaced AChE and POD with MOF-808/Pt NPs and constructed a biosensor for sensitive detection of acetylcholine (ACh). The MOF-808/Pt NPs catalyze the hydrolysis of ACh, resulting in the production of acetic acid. The subsequent reduction in pH value further enhances the POD-like activity of the MOFs, enabling signal amplification through the oxidation of a colorimetric substrate. This biosensor capitalizes on pH variations during the reaction to modulate the different enzyme-like activities of the MOFs, simplifying the detection process and eliminating cross-talk between different enzymes. The developed biosensor holds great promise for clinical diagnostic analysis and offers significant application value in the field.

Technical Advances in Circulating Cell-Free DNA Detection and Analysis for Personalized Medicine in Patients' Care.

Circulating cell-free DNA (cfDNA) refers to small fragments of DNA molecules released after programmed cell death and necrosis in several body fluids such as blood, saliva, urine, and cerebrospinal fluid. The discovery of cfDNA has revolutionized the field of non-invasive diagnostics in the oncologic field, in prenatal testing, and in organ transplantation. Despite the potential of cfDNA and the solid results published in the recent literature, several challenges remain, represented by a low abundance, a need for highly sensitive assays, and analytical issues. In this review, the main technical advances in cfDNA analysis are presented and discussed, with a comprehensive examination of the current available methodologies applied in each field. Considering the potential advantages of cfDNA, this biomarker is increasing its consensus among clinicians, as it allows us to monitor patients' conditions in an easy and non-invasive way, offering a more personalized care. Nevertheless, cfDNA analysis is still considered a diagnostic marker to be further validated, and very few centers are implementing its analysis in routine diagnostics. As technical improvements are enhancing the performances of cfDNA analysis, its application will transversally improve patients' quality of life.

Comprehensive effects of aerodynamic performance and dynamic strength of aircraft wings after bird strike in numerical simulation

ABSTRCT To enhance the bird strike resistance of fixed-wing aircraft wings, a combined computational approach integrating nonlinear solid mechanics and fluid dynamics was employed to analyze the bird strike characteristics of both traditional aluminum alloy skins and novel composite materials skins. Additionally, a standardized procedure was established to simulate aircraft structural damage more comprehensively, swiftly, and accurately during bird strikes, along with diagnostic analysis of flow field changes near the wing post-impact. This method not only quantifies wing strength damage but also establishes corresponding relationships between damage severity and aerodynamic performance degradation. The research findings indicate that the deformation of composite material skin wings is only a quarter of that of aluminum alloy skin wings. Composite material skins absorb less harmful energy compared to aluminum alloy skins, with consistently lower stiffness reduction rates at various bird strike velocities. Velocity distribution on deformed wings with composite material skins exhibits minimal deviation compared to normal wings, thereby preventing airflow separation. The research results provide more comprehensive guidance for the selection of materials for bird strike resistance of aircraft wings and the airworthiness evaluation after bird strikes. Highlights This paper establishes a standardized procedure that enables a more comprehensive, faster, and higher-precision simulation of structural damage to the aircraft during a bird strike on the wing and allows for the diagnostic analysis of the flow field changes near the wing after bird strike. By coupling nonlinear solid mechanics and fluid dynamics computational methods, the aerodynamic performance of the wings at different angles of attack post-deformation is investigated. This approach not only quantifies the strength damage to the wings but also establishes corresponding relationships between the degree of damage and the deterioration in aerodynamic performance. The research yields insights into the aerodynamic performance degradation patterns with varying angles of attack for both composite material skin and aluminum alloy skin deformed wings. These findings can serve as valuable references for pilots in emergency situations, aiding in their decision-making regarding aircraft control.

Multiomics Analysis Revealed Colorectal Cancer Pathogenesis.

Gut microbiota-derived microbial compounds may link to the pathogenesis of colorectal cancer (CRC). However, the role of the host-microbiome in the incidence and progression of CRC remains elusive. We performed 16S rRNA sequencing, metabolomics, and proteomic studies on samples from 85 CRC patients who underwent colonoscopy examination and found two distinct changed patterns of microbiome in CRC patients. The relative abundances of Catabacter and Mogibacterium continuously increased from intramucosal carcinoma to advanced stages, whereas Clostridium, Anaerostipes, Vibrio, Flavonifractor, Holdemanella, and Hungatella were significantly altered only in intermediate lesions. Fecal metabolomics analysis exhibited consistent increases in bile acids, indoles, and urobilin as well as a decrease in heme. Serum metabolomics uncovered the highest levels of bilin, glycerides, and nucleosides together with the lowest levels of bile acids and amino acids in the stage of intermediate lesions. Three fecal and one serum dipeptides were elevated in the intermediate lesions. Proteomics analysis of colorectal tissues showed that oxidation and autophagy through the PI3K/Akt-mTOR signaling pathway contribute to the development of CRC. Diagnostic analysis showed multiomics features have good predictive capability, with AUC greater than 0.85. Our overall findings revealed new candidate biomarkers for CRC, with potentially significant diagnostic and prognostic capabilities.

Winter Insolation Modulates Boreal Tropical Monsoonal Temperatures in the Late Pleistocene

AbstractDuring past glacial‐interglacial cycles, the boreal summer insolation is the most crucial external forcing for climate change. However, the question of whether summer insolation is a key forcing on temperatures in the boreal tropics remains under debate, hampering our understanding of climate change at low latitudes. To shed further light on this issue, we performed a series of equilibrium simulations with the NorESM‐L model over the past 425 ka. Our simulations show that in the boreal tropical monsoon region, the simulated annual temperature is anti‐phased towards the boreal summer insolation. This antiphase relation is also supported by some available geological data. Additional diagnostic analyses reveal that the tropical warmth throughout the year is more reliant on winter temperatures than on summer temperatures. This stands in contrast to the situation in middle to high latitudes, particularly in the Mediterranean region. Further correlation analysis and spectrum analysis suggest that the annual temperature in boreal tropics is highly linked to local winter insolation. Our results highlight complex hydrothermal configurations in the boreal tropics, suggesting a decoupling of temperature and precipitation. Specifically, variations in annual temperature and precipitation in the boreal tropics are driven by distinct patterns of seasonal insolation. We deduce that the unique hydrothermal configurations in North Africa may have influenced the dispersal of early humans out of Africa.

Selection of prenatal screening with nuchal translucency > 95th centile and below 99th centile: a 4-year observational study with real-world data.

We sought to analyze the genetic outcomes of fetuses with nuchal translucency (NT) > 95th centile, and determine whether prenatal genetic counseling, chromosomal microarray analysis (CMA) or non-invasive prenatal testing (NIPT) are truly beneficial for the outcomes of fetuses with increased NT > 95th centile and below 99th centile. A total of 535 pregnant women were included in this study, with a fetal NT > 95th centile at 11-13+6weeks of gestation from January 2017 to December 2020. 324 pregnant women with fetal NT > 95th centile and below 99th centile combined with other risk factors and NT > 99th centile received prenatal diagnostic karyotype analysis and CMA, and 211 pregnant women with fetal isolated increased NT > 95th centile and below 99th centile were selected to carry out NIPT. A total of 211 pregnant women who underwent NIPT were included in the study, NIPT results showed that 8 high-risk cases were confirmed by prenatal diagnosis. Overall, the detection rate of NIPT was 3.79%. A total of 324 pregnant women with fetal NT > 95th centile and below 99th centile, along with other risk factors, and those with fetal NT > 99th centile, received karyotype analysis and CMA for prenatal diagnosis. Among them, a total of 73 genetic abnormalities were detected, including 45 cases of chromosomal aneuploidy, 7 cases of structural abnormalities, and 21 cases of copy number variations (CNVs) with a size of less than 10Mb. In addition, the 73 women with genetic abnormalities are divided into three groups based on the NT measurement (Group 1: Fetuses with NT > 95th centile and below 99th centile, Group 2: Fetuses with NT > 99th centile, and Group 3: Fetuses with NT > 99th centile). 13.11% (8/61) of pathogenic genetic abnormalities (6 chromosomal aneuploidy, 1 structural abnormality, and 1 likely pathogenic CNV) will be missed if genetic counseling and prenatal genetic testing were not conducted in fetuses with increased NT > 95th centile and below 99th centile combined with other risks. Pathogenic CNVs were the most common abnormalities in group 3, and one likely pathogenic CNV was detected in group 1 and group 3, respectively, and a total of 14 CNVs of unknown clinical significance (VOUS) were detected. Through this study, we demonstrated that the critical value of NT > 95th centile for invasive detection or NIPT. Invasive testing combined with CMA may be recommended for fetuses with NT > 95th centile and below 99th centile and with other risks. But when isolated NT > 95th centile and below 99th centile, NIPT would be appropriate.

Revisiting Mac-2-Binding Protein Glycosylation Isomer (M2BPGi) for Diagnosing High-Risk Liver Fibrosis: A Stepwise Diagnostic Analysis

Abstract* Background The level of liver fibrosis is the basis for the treatment of chronic hepatitis B (CHB), and it is necessary to adapt non-invasive liver fibrosis modalities. We aimed to investigate the use of M2BPGi as a single or combined diagnostic modality for liver fibrosis in CHB patients through a stepwise diagnostic analysis. Methods Cross-sectional data were taken from patients between October 2021 and August 2022. Demographic data, blood profile, liver function, and liver stiffness were measured in CHB patients over 18 years old, willing to take part in the research, and had complete data. APRI, FIB-4, and AAR were calculated using the well-known formulas. Serum M2BPGi-levels were converted into a cut-off index (COI). The patients were divided into low-risk (LR) and high-risk fibrosis (HR) groups. A cut-off for each predictor variable to differentiate between the LR and HR groups was determined. The obtained cut-off was assessed for its association with the grouping of liver elastography results. Models to diagnose the liver stiffness measurement (LSM) ≥8 kPa were created and compared through multivariate and ROC analyses. Results The number of patients that met the inclusion and exclusion criteria was 143 (HR = 65, LR = 78). The cut-off for diagnosing LSM ≥8kPa was 0.311, 0.742, 0.635, and 1.434 for APRI, FIB-4, AAR, and M2BPGi, respectively. This cut-off was significantly associated with the results of the HR and LR groupings. A multivariate analysis found that FIB4, AAR, and M2BPGi added significantly to the model. Statistically, the most optimal use of M2BPGi was combined with FIB-4, with an AUC of 0.835. Conclusions The optimal cut-off of M2BPGi for diagnosing high-risk liver fibrosis in this study was 1.434. M2BPGi should be used with FIB-4 as a diagnostic tool for diagnosing liver fibrosis, especially in the absence of a liver biopsy or elastography.

A framework for identification of brain tumors from MR images using progressive segmentation

PurposeThis study addresses the critical health issue of brain tumors, focusing on enhancing the accuracy of tumor segmentation from Magnetic Resonance Imaging (MRI) images. The primary research question investigates the effectiveness of a novel Hybrid Watershed–Clustering framework and its underlying Progressive Segmentation of the MR Images using the Radius and Intensity Measure (PS-RIM) algorithm. The aim is to improve the detection and segmentation of brain tumors within MR images, surpassing the efficacy of current methodologies.MethodsThe methodology involves a three-stage process. In the preprocessing stage, noise reduction and intensity normalization techniques are applied to clarify the images. The next stage is region-based segmentation, which includes morphological processing, edge detection, and thresholding to delineate tumor areas accurately. The final post-processing stage enhances segmentation accuracy and reduces false positives by integrating clustering machine learning techniques, specifically the K-Means cluster algorithm, to refine tumor identification.ResultsThe framework's comprehensive evaluation across various MR images shows a significant improvement in accuracy over existing segmentation methods. The PS-RIM algorithm within the framework effectively captures the diverse presentations of tumor appearances in MR images. The research recorded an impressive accuracy rate of 98.11% in tumor detection, demonstrating enhanced identification and segmentation quality.ConclusionsThe study concludes that the proposed Hybrid Watershed–Clustering framework, powered by the PS-RIM algorithm, markedly improves the detection and differentiation of brain tumors in MR images. It exhibits exceptional accuracy, resilience, and computational efficiency. These findings hold substantial potential for advancing computer vision and image analysis in medical diagnostics, which could improve patient outcomes in managing brain tumors.Graphical abstract

Molecular pathological analysis through the ages

Molecular pathological examinations of tumor samples encompass awide range of diagnostic analyses. Especially in recent years, numerous new biomarkers have come to the forefront-the analysis of which is crucial for therapy decisions. Within the field of molecular pathology, the demands of next generation sequencing (NGS)-based requirements have experienced massive growth in recent years. To meet this demand, methods are constantly being adapted and further developed. The following sections aim to illuminate how this trend arises and which analyses are gaining importance. The article provides an overview of the essential nucleic acid-based analysis techniques in the field of massive parallel sequencing. Terms such as DNA- and RNA-based techniques, as well as the associated analysis methods, are described, particularly with regard to their use in routine molecular pathological diagnostics. The breadth of genomic sequencing has been steadily growing in recent years, particularly due to the increasing relevance of personalized medicine, along with the rising approvals of targeted therapeutics. This necessitates, among other things, the analysis of new biomarkers. The diagnostics as part of interdisciplinary molecular tumor boards (MTB) are now based on large gene panels (> 1 megabase). Furthermore, through the "Modellvorhaben Genomsequenzierung" §64e, whole exome or whole genome sequencing has been made available for oncological patients. Given these developments, it is evident that future analyses will require the integration of additional omics fields, such as whole transcriptome analysis, epigenomics, and proteomics. The challenges of personalized medicine along with the necessity of simultaneously assessing numerous new biomarkers require the implementation and execution of new techniques in molecular pathology whose complexity is steadily increasing.

Midsummer precipitation prediction over eastern China by the dynamic downscaling method

AbstractThis study assesses the midsummer precipitation prediction over eastern China by the dynamic downscaling method. Based on the Climate Forecast System version 2 of the National Centers for Environmental Prediction and the Weather Research and Forecasting Model, the prediction performance of global and regional models on the July precipitation over eastern China is further analyzed by hindcast experiments from 1982 to 2010 and prediction experiments from 2011 to 2021. The results suggest that the regional model forced by the global model can noticeably improve the prediction skill for precipitation in eastern China, especially in the region from the South of North China to the Yangtze River Basin, referred as the northern China in this paper. In addition, we perform a diagnostic analysis of the reason for the improvement of the model prediction skill. The results indicate that the high resolution of the regional model and the refinement of physical process parameterizations contribute to improving the simulation ability for the East Asian atmospheric circulation pattern, heat flux, especially for the meridional teleconnection pattern in East Asia and the sensible heat flux in the northern China, thus further improving precipitation prediction.

Re-emergence of foot-and-mouth disease in the Republic of Korea caused by the O/ME-SA/Ind-2001e lineage.

The O/ME-SA/Ind-2001e foot-and-mouth disease virus (FMDV) lineage is a pandemic strain that has recently become dominant within East and Southeast Asia. During May 2023, this viral lineage spread to the Republic of Korea, where 11 outbreaks were detected on cattle and goat farms located in Cheongju and Jeungpyeong. Infected animals displayed typical FMD signs including vesicular lesions with drooling and anorexia. Molecular diagnostic testing and genetic analysis (VP1 sequencing) showed that the causative FMDVs belonged to the O/ME-SA/Ind-2001e lineage and shared the closest nucleotide identity (97.95-99.21%) to viruses that have been collected from Mongolia and South-East Asian countries. Phylogenetic analyses showed that these sequences were distinct to those collected from the previous Korean O/ME-SA/Ind-2001e lineage outbreaks in 2019, demonstrating that these cases are due to a new incursion of the virus into the country. Prompt implementation of emergency vaccination using antigenically matched serotype O vaccines (r1 value: 0.74-0.93), together with intensive active surveillance on farms surrounding the infected premises has successfully prevented further spread of FMD. These recent FMD outbreaks reinforce the importance of research to understand the risks associated with transboundary pathways in the region, in order to reduce the possibility of a further reintroduction of FMD into the Republic of Korea.

Nonlinear recurrence analysis of piezo sensor placement for unmanned aerial vehicle motor failure diagnosis

This paper is focused on the diagnostics of multicopter UAV propulsion system, in which the temporary transient states occur during operation in faulty conditions (eg. not all motor phases working properly). As a diagnostic sensor, the piezo strip has been used, which is very sensitive to any vibrations of the multi-rotor frame. The paper concerns the precise location of the sensor for more effective monitoring of the propulsion system state. For this purpose, a nonlinear analysis of the vibration times series was carefully presented. The obtained non-linear time series were studied with the recurrence analysis in short time windows, which were sensitive to changes in Unmanned Aerial Vehicle motor speeds. The tests were carried out with different percentage of the pulse width modulation signal used for the operation of the brushless motor and for different locations of the piezosensor (side and top planes of the multicopter arm). In the article, it was shown that the side location of the piezosensor is more sensitive to changes in the Unmanned Aerial Vehicle propulsion system, which was studied with the Principal Component Analysis method applied for four main recurrence quantifications. The research presented proves the possibility of using nonlinear recurrence analysis for propulsion system diagnostics and helps to determine the optimal sensor location for more effective health monitoring of multicopter motor.

Combining anatomical and biochemical markers in the detection and risk stratification of coronary artery disease.

We aimed to test the hypothesis if combining coronary artery calcium score (Ca-score) as a quantitative anatomical marker of coronary atherosclerosis with high-sensitivity cardiac troponin as a quantitative biochemical marker of myocardial injury provided incremental value in the detection of functionally relevant coronary artery disease (fCAD) and risk stratification. Consecutive patients undergoing myocardial perfusion single-photon emission computed tomography (MPS) without prior CAD were enrolled. The diagnosis of fCAD was based on the presence of ischaemia on MPS and coronary angiography; fCAD was centrally adjudicated in the diagnostic and prognostic domain. Diagnostic accuracy was evaluated using the area under the receiver-operating characteristic curve (AUC). The composite of cardiovascular death and non-fatal acute myocardial infarction (AMI) within 730 days was the primary prognostic endpoint. Among 1715 patients eligible for the diagnostic analysis, 399 patients had fCAD. The combination of Ca-score and high-sensitivity cardiac troponin T (hs-cTnT) had good diagnostic accuracy for the diagnosis of fCAD (AUC 0.79, 95% confidence interval (CI) 0.77-0.81), but no incremental value compared with the Ca-score alone (AUC 0.79, 95% CI 0.77-0.81, P = 0.965). Similar results were observed using high-sensitivity cardiac troponin I (AUC 0.80, 95% CI 0.77-0.82) instead of hs-cTnT. Among 1709 patients (99.7%) with available follow-up, 59 patients (3.5%) suffered the composite primary prognostic endpoint (non-fatal AMI, n = 34; CV death, n = 28). Both Ca-score and hs-cTnT had independent prognostic value. Increased risk was restricted to patients with elevation in both markers. The combination of the Ca-score with hs-cTnT increases the prognostic accuracy for future events but does not provide incremental value vs. the Ca-score alone for the diagnosis of fCAD. Clinical trial registration: NCT00470587.

Usefulness and Limitations of PFGE Diagnosis and Nucleotide Sequencing Method in the Analysis of Food Poisoning Pathogens Found in Cooking Employees.

In the case of a food poisoning outbreak, it is essential to understand the relationship between cooking workers and food poisoning. Many biological diagnostic methods have recently been developed to detect food poisoning pathogens. Among these diagnostic tools, this study presents PCR-based pulsed-field gel electrophoresis and nucleotide sequencing diagnostic analysis results for diagnosing food poisoning outbreaks associated with cooking employees in Chungcheongnam-do, Republic of Korea. Pulsed-field gel electrophoresis was useful in identifying the food poisoning outbreaks caused by Staphylococcus aureus and Enteropathogenic Escherichia coli. In the case of Norovirus, nucleotide sequencing was used to identify the relationship between cooking workers and the food poisoning outbreak. However, it is difficult to determine whether cooking employees directly caused the food poisoning outbreaks based on these molecular biological diagnostic results alone. A system is needed to integrate epidemiological and diagnostic information to identify a direct correlation between the food poisoning outbreak and cooking employees.

PAPAYA: A library for performance analysis of SQL-based RDF processing systems

Prescriptive Performance Analysis (PPA) has shown to be more useful than traditional descriptive and diagnostic analyses for making sense of Big Data (BD) frameworks’ performance. In practice, when processing large (RDF) graphs on top of relational BD systems, several design decisions emerge and cannot be decided automatically, e.g., the choice of the schema, the partitioning technique, and the storage formats. PPA, and in particular ranking functions, helps enable actionable insights on performance data, leading practitioners to an easier choice of the best way to deploy BD frameworks, especially for graph processing. However, the amount of experimental work required to implement PPA is still huge. In this paper, we present PAPAYA,11 https://github.com/DataSystemsGroupUT/PAPyA a library for implementing PPA that allows (1) preparing RDF graphs data for a processing pipeline over relational BD systems, (2) enables automatic ranking of the performance in a user-defined solution space of experimental dimensions; (3) allows user-defined flexible extensions in terms of systems to test and ranking methods. We showcase PAPAYA on a set of experiments based on the SparkSQL framework. PAPAYA simplifies the performance analytics of BD systems for processing large (RDF) graphs. We provide PAPAYA as a public open-source library under an MIT license that will be a catalyst for designing new research prescriptive analytical techniques for BD applications.

Enhancing Healthcare with AI: Diagnostic Imaging and Patient Sentiment Analysis

Enhancing Healthcare with AI: Diagnostic Imaging and Patient Sentiment Analysis

Enhancing Healthcare with AI: Diagnostic Imaging and Patient Sentiment Analysis

Enhancing Healthcare with AI: Diagnostic Imaging and Patient Sentiment Analysis

Signal Processing Techniques of Lamb Waves for Structural Health Monitoring System - A Review

The structural health monitoring (SHM) system using Lamb wave approach has drawn a lot of interest because of its outstanding performance in terms of accuracy and adaptability. The implemented signal processing techniques in the diagnostic analysis are specifically reviewed in this work. The primary factors of the wide range of applied signal processing techniques are due to the multimode and dispersive behaviour of the Lamb waves. Several Lamb modes occur simultaneously, and because they have different dispersive characteristics, they can produce complicated superimposed signals. To effectively diagnose the observed signals, several mode separations approaches have been proposed. Generally, time-frequency representation is applied for signal processing techniques. The findings demonstrated that the proposed procedures were successful in decomposing the superimposed mode into individual modes for further analysis. All these works have shown how SHM systems based on Lamb waves have evolved over time to control and monitor the condition of the structure.

Plasma profile reconstruction supported by kinetic modeling

Combining the analysis of multiple diagnostics and well-chosen prior information in the framework of Bayesian probability theory, the Integrated Data Analysis code (IDA Fischer et al 2010 Fusion Sci. Technol. 58 675–84) can provide density and temperature radial profiles of fusion plasmas. These IDA-fitted measurements are then used for further analysis, such as discharge simulations and other experimental data analysis. Since IDA considers measurement data, which is frequently fragmentary, with statistical and systematic uncertainties, which are often difficult to quantify, from a heterogeneous set of diagnostics, the fitted profiles and their gradients may be in contradiction to well-established expectations from transport theory. Using the modeling suite ASTRA coupled with the quasi-linear transport solver TGLF, we have created a loop in which simulated profiles and their uncertainties are fed back into IDA as an additional prior, thus providing constraints about the physically reasonable parameter space. We apply this physics-motivated prior to several different plasma scenarios and find improved heat flux match, while still matching the experimental data. This work feeds into a broader effort to make IDA more robust against measurement uncertainties or lack of measurements by combining multiple transport solvers with different levels of complexity and computing costs in a multi-fidelity approach.