Separate Tests Research Articles

Do Different Next-to-Skin Garments Change Thermal Sensation, Jump Height and Landing Knee Valgus After Cold Exposure?

Exposure to the cold can negatively affect muscle performance. This study compared the effects of two different full-length, lower body, next-to-skin garments on thermal sensation, countermovement jump (CMJ) height and knee frontal plane angle upon landing following cold exposure against a control. After familiarisation, 13 male and 11 female recreationally active adults attended three separate laboratory testing sessions where a randomly assigned next-to-skin garment was used (compression, thermal and control (shorts)). A pre- and post-testing protocol comprising CMJ and drop landings interspersed with a sedentary cooling period of 40 min at 0 °C was adopted. High-speed motion analysis and subjective ratings of thermal sensation were recorded. Exposure to the cold significantly reduced thermal sensation (p < 0.001) scores and CMJ height (p < 0.001). Only female participants felt significantly warmer (p ≤ 0.009) in the next-to-skin garments. Losses in CMJ height were significantly reduced by the next-to-skin garments compared to the control with the thermal garment producing better results. There was little change in knee frontal plane angle upon landing in all the garments tested. Ambient cooling at 0 °C for 40 min had a significant effect on CMJ height and thermal sensation but not knee valgus upon landing. Participants in winter sports should consider next-to-skin garments in conjunction with proper warm-ups and re-warming techniques to protect themselves from the negative effects of the cold.

Countermovement Jump and Isometric Strength Test-Retest Reliability in English Premier League Academy Football Players.

To examine the test-retest reliability of countermovement jump (CMJ) and isometric strength testing measures in elite-level under-18 and under-23 academy football players. A total of 36 players performed 3 maximal CMJs and isometric abductor (IABS), adductor (IADS), and posterior chain (IPCS) strength tests on 2 separate test days using dual force plates (CMJ and IPCS) and a portable strength testing device (IABS and IADS). Relative (intraclass correlation coefficient) and absolute (coefficient of variation, standard error of the measurement, and minimal detectable change [MDC%]) reliabilities for 34 CMJ, 10 IABS, 10 IADS, and 11 IPCS measures were analyzed using between-sessionsbest, mean, and within-session methods. For all methods, relative reliability was good to excellent for all CMJ and all IADS measures and poor to good for all IABS and IPCS measures. Absolute reliability was good (ie,coefficient of variation < 10%) for 27 (best) and 28 (mean) CMJ variables and for 6 (IABS and IADS) and 2 (IPCS) isometric measures. Commonly used CMJ measures (jump height, eccentric duration, and flight-time:contraction-time ratio) had good to excellent relative reliability and an MDC% range of 14.6% to 23.7%. Likewise, commonly used isometric peak force measures for IABS, IADS, and IPCS had good to excellent relative reliability and an MDC% range of 22.2% to 26.4%. Commonly used CMJ and isometric strength measures had good test-retest reliability but might be limited by their MDC%. Rate-of-force-development measures (for all isometric tests) and impulse measures (IPCS) are limited by poor relative and absolute reliability and high MDC%. MDC% statistics should be considered in the context of typical responsiveness.

Yoga-based lifestyle intervention for healthy ageing in older adults: a two-armed, waitlist randomized controlled trial with multiple primary outcomes.

Yoga-based clinical research has shown considerable promise in varied ageing-related health outcomes in older adults. However, robust frameworks have yet to be used in intervention research to endorse yoga as a healthy ageing intervention to test the multidimensional construct of healthy ageing. This was an assessor-masked, randomized controlled trial conducted among 258 sedentary, community-dwelling older adults aged 60-80years, randomly allocated to 26-week yoga-based intervention (YBI) (n = 132) or waitlist control (WLC) (n = 126). The effectiveness of YBI was assessed through two separate global statistical tests, generalized estimating equations and rank sum-based test, against a comprehensive healthy aging panel comprised of ten markers representing the domains of physiological and metabolic, cognitive, physical capability, psychological, and social well-being. The secondary outcomes were individual primary marker scores, Klotho, inflammatory markers, and auxiliary blood markers. We could establish the healthy aging effect of the 26-week YBI over WLC using two models of global statistical test (GEE, β = 0.29; 95% CI = 0.20 to 0.38, p < 0.001), and rank sum-based test (β = 0.28, 95% CI = 0.19 to 0.36, p < 0.001). There were also significant improvements in direction of benefit at individual levels of all the aging markers. Exploratory evaluation with adopted indices from contemporary clinical trials also validated the potential of YBI for healthy aging; HATICE adapted composite score (mean difference = - 0.18; 95% CI = - 0.26 to - 0.09, p < 0.001) and healthy ageing index (mean difference = - 0.33; 95% CI = - 0.63 to - 0.02, p = 0.03). The global effect of YBI across multiple ageing-related outcomes provides a proof of concept for further large-scale validation. The findings hold a great translational value given the accelerated pace of population aging across the globe. Trial registration: CTRI/2021/02/031373.

Reliability and Validity of Four Step Tests in Older Adults With Dementia.

To determine the test-retest and inter-rater reliability, concurrent and discriminative validity of the Four Square Step Test (FSST), the Choice Stepping Reaction Time Test (CSRTT), the Maximum Step Length Test (MSLT), and the Alternate Step Test (AST) in older adults with dementia. Thirty-seven older adults with dementia who could walk independently for at least 10m were recruited at community centers and day care centers for older adults. The participants completed the step tests conducted by two independent raters on three separate testing occasions within 3weeks. In addition, the physical and cognitive function of the participants were evaluated at baseline. The FSST, CSRTT, and MSLT showed good-to-excellent test-retest reliability (intraclass correlation coefficient [ICC]=0.83-0.91), and the AST exhibited fair test-retest reliability (ICC=0.70). All the step tests showed good-to-excellent inter-rater reliability (ICC=0.75-0.94). The step tests had moderate-to-strong correlations with various physical and cognitive measures (Pearson correlation coefficients=0.34-0.72). The MSLT side step and AST could differentiate between individuals who did and did not use a walking stick to ambulate (p≤0.046). The FSST, CSRTT, and AST could differentiate between individuals with and without a major neurocognitive impairment (p≤0.005). The FSST, CSRTT, and MSLT were reliable and valid for examining the stepping performance of older adults with dementia. Clinicians can use these tests to evaluate the physical and cognitive function of this population and identify those with significant cognitive impairment. Clinical Trial Registration number: NCT04296123.

Nanoscale Poly(Dopamine)‐Modified ZIF‐8 Particle Hybrid Solvent‐Resistant Thin Film Nanocomposite (TFN) Membranes via Interfacial Polymerization on a Robust Polyether Ether Ketone (PEEK) Substrate

ABSTRACTThis study introduces an innovative approach to fabricating a poly ether ketone (PEEK)‐based thin‐film nanocomposite (TFN) nanofiltration membrane. Ultrafine polydopamine (PDA) modified zeolitic imidazolate framework‐8 (ZIF‐8) nanoparticles were integrated into the polyamide (PA) layer through a process of interfacial polymerization. The impacts of PDA@ZIF‐8 nanoparticle loading on composite membranes were examined, and their separation performances with different solvents and small molecular organics with various molecular weights and charges were evaluated. The PDA modification is credited with inducing a thinner, less crosslinked PA layer, culminating in the formation of a selective barrier that is both loose and ultra‐thin, improving the separation capabilities of membranes. The TFN‐3 membrane, with a 0.01 wt% loading of PDA@ZIF‐8 nanoparticles, emerged as a standout, demonstrating notably enhanced hydrophilicity. The pure water and organic solvent permeances through the TFN‐3 membrane were remarkably enhanced, with recorded values of 6.19 ± 0.26 L h−1 m−2 bar−1 for pure water and 3.44 ± 0.21 L h−1 m−2 bar−1 for methanol. Furthermore, the TFN‐3 membrane showcased impressive molecular retention in ethanol solutions for compounds with a molecular weight cut‐off (MWCO) of 464 g/mol. The PEEK‐based TFN‐3 membrane also exhibited exceptional creep resistance, ensuring consistent performance during long‐term testing. In a 5‐day ethanol separation test, the TFN‐3 membrane maintained a Rose Bengal (RB) retention rate exceeding 99%, underscoring its stability and reliability. The newly developed PEEK‐based TFN membrane not only promises robust solvent resistance and stability but also delivers efficient separation and purification capabilities. It holds great potential for offering effective and sustainable solutions across a spectrum of industries, particularly in pharmaceutical production and chemical processing, where the separation, recovery, and purification of solvents are paramount.

Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study

BackgroundMethods for accurately predicting the prognosis of patients with recurrent hepatolithiasis (RH) after biliary surgery are lacking. This study aimed to develop a model that dynamically predicts the risk of hepatolithiasis recurrence using a machine-learning (ML) approach based on multiple clinical high-order correlation data.Materials and methodsData from patients with RH who underwent surgery at five centres between January 2015 and December 2020 were collected and divided into training and testing sets. Nine predictive models, which we named the Correlation Analysis and Recurrence Evaluation System (CARES), were developed and compared using machine learning (ML) methods to predict the patients’ dynamic recurrence risk within 5 post-operative years. We adopted a k-fold cross validation with k = 10 and tested model performance on a separate testing set. The area under the receiver operating characteristic curve was used to evaluate the performance of the models, and the significance and direction of each predictive variable were interpreted and justified based on Shapley Additive Explanations.ResultsModels based on ML methods outperformed those based on traditional regression analysis in predicting the recurrent risk of patients with RH, with Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) showing the best performance, both yielding an AUC (Area Under the receiver operating characteristic Curve) of∼0.9 or higher at predictions. These models were proved to have even better performance on testing sets than in a 10-fold cross validation, indicating that the model was not overfitted. The SHAP method revealed that immediate stone clearance, final stone clearance, number of previous surgeries, and preoperative CA19-9 index were the most important predictors of recurrence after reoperation in RH patients. An online version of the CARES model was implemented.ConclusionThe CARES model was firstly developed based on ML methods and further encapsulated into an online version for predicting the recurrence of patients with RH after hepatectomy, which can guide clinical decision-making and personalised postoperative surveillance.

Characterization and Processing of Low-Grade Middle Group 2 Chromite Ore by Gravity Shaking Table and a Comparative SLon Magnetic Separation: A Case Study

Chromite is considered a strategic mineral in the global economy. It is mainly used as an essential raw material in the production of stainless steel and other metal alloys due to its corrosion and heat resistance properties. High-grade chromite resources are gradually depleting; with the increasing chromite demand in metallurgical applications, studies have focused on exploring low-grade and alternative chromite sources. This study proposes a cost-effective processing flowsheet for the low-grade middle group 2 (MG2) chromite layer, a poorly explored chromatite seam within the South African bushveld igneous complex (BIC). The study involved mineralogical characterization followed by gravity and magnetic separation of the low-grade MG2 ore at 18.18% Cr2O3. Characterization by XRD and Auto-SEM revealed that the ore mainly consists of pyroxene, chromite, and feldspar, with other minerals in trace quantities. The gravity separation test by shaking table upgraded the chromite (Cr2O3) to 42.0% at high chromite recoveries, whereas the laboratory Slon wet high-intensity magnetic separation method (SLon WHIMS) upgraded the chromite in the feed to 42.95% grade at lower chromite recoveries. Desliming the sample before the gravity and magnetic separation tests significantly improved the separation. The magnetic separation tests further demonstrated that chromite within the MG2 layer is sensitive to magnetic separation due to its high iron content. The adapted flowsheet is proposed as a cost-effective flowsheet for processing the low-grade MG2 layer. The flow sheet can be optimized by conducting the SLon WHIMS tests at high intensities followed by fine gravity tests by spiral circuits to maximize the chromite recovery while achieving commercial chromite grades and a Cr:Fe ratio greater than 1.5.

Resolution-dependent MRI-to-CT translation for orthotopic breast cancer models using deep learning

Objective.This study aims to investigate the feasibility of utilizing generative adversarial networks (GANs) to synthesize high-fidelity computed tomography (CT) images from lower-resolution MR images. The goal is to reduce patient exposure to ionizing radiation while maintaining treatment accuracy and accelerating MR image acquisition. The primary focus is to determine the extent to which low-resolution MR images can be utilized to generate high-quality CT images through a systematic study of spatial resolution-dependent magnetic resonance imaging (MRI)-to-CT image conversion.Approach.Paired MRI-CT images were acquired from healthy control and tumor models, generated by injecting MDA-MB-231 and 4T1 tumor cells into the mammary fat pad of nude and BALB/c mice to ensure model diversification. To explore various MRI resolutions, we downscaled the highest-resolution MR image into three lower resolutions. Using a customized U-Net model, we automated region of interest masking for both MRI and CT modalities with precise alignment, achieved through three-dimensional affine paired MRI-CT registrations. Then our customized models, Nested U-Net GAN and Attention U-Net GAN, were employed to translate low-resolution MR images into high-resolution CT images, followed by evaluation with separate testing datasets.Main Results.Our approach successfully generated high-quality CT images (0.142mm2) from both lower-resolution (0.282mm2) and higher-resolution (0.142mm2) MR images, with no statistically significant differences between them, effectively doubling the speed of MR image acquisition. Our customized GANs successfully preserved anatomical details, addressing the typical loss issue seen in other MRI-CT translation techniques across all resolutions of MR image inputs.Significance.This study demonstrates the potential of using low-resolution MR images to generate high-quality CT images, thereby reducing radiation exposure and expediting MRI acquisition while maintaining accuracy for radiotherapy.

Full-scale testing and monitoring of geosynthetics-stabilized flexible pavement in Alberta, Canada

Freeze-thaw (F-T) cycles are a primary contributor of pavement damages in seasonal frost regions. Geosynthetics stabilization has been a promising solution for enhancing the roadways performance in cold regions. However, in comparison with the practical applications, research on the geosynthetics stabilization in cold-region roads is scarce and its efficacy is yet to be quantified. This study presents the full-scale test on geosynthetics-stabilized sections in a flexible pavement in Sturgeon County, Alberta. It focused on the investigation of three separate test sections with bases stabilized by two types of geocells and one geogrid composite, each fully instrumented with earth pressure cells, thermocouples, and moisture sensors. This experimental program consisted of plate loading tests and trafficking tests on each test section before and after the first F-T season, and monitoring of soil temperatures, moisture contents, and loads transferred to subbases while the sections were open to general traffic. The results showed seasonal F-T cycles resulted in increased pavement settlement, decreased load transfer ratio, and increased stress distribution angle under the plate loading. The traffic-induced stress on the subbases increased during the spring thaw but decreased afterwards.

Computational analysis of variability and uncertainty in the clinical reference on magnetic resonance imaging radiomics: modelling and performance

To conduct a computational investigation to explore the influence of clinical reference uncertainty on magnetic resonance imaging (MRI) radiomics feature selection, modelling, and performance. This study used two sets of publicly available prostate cancer MRI = radiomics data (Dataset 1: n = 260; Dataset 2: n = 100) with Gleason score clinical references. Each dataset was divided into training and holdout testing datasets at a ratio of 7:3 and analysed independently. The clinical references of the training set were permuted at different levels (increments of 5%) and repeated 20 times. Four feature selection algorithms and two classifiers were used to construct the models. Cross-validation was employed for training, while a separate hold-out testing set was used for evaluation. The Jaccard similarity coefficient was used to evaluate feature selection, while the area under the curve (AUC) and accuracy were used to assess model performance. An analysis of variance test with Bonferroni correction was conducted to compare the metrics of each model. The consistency of the feature selection performance decreased substantially with the clinical reference permutation. AUCs of the trained models with permutation particularly after 20% were significantly lower (Dataset 1 (with ≥ 20% permutation): 0.67, and Dataset 2 (≥ 20% permutation): 0.74), compared to the AUC of models without permutation (Dataset 1: 0.94, Dataset 2: 0.97). The performances of the models were also associated with larger uncertainties and an increasing number of permuted clinical references. Clinical reference uncertainty can substantially influence MRI radiomic feature selection and modelling. The high accuracy of clinical references should be helpful in building reliable and robust radiomic models. Careful interpretation of the model performance is necessary, particularly for high-dimensional data.

Evaluating the diffusion of Kr in UO2 and ADOPTTM using time-of-flight elastic recoil detection analysis (ToF-erda)

ABSTRACT A combination of 300 keV 84Kr ion implantation and Time-of-Flight Elastic Recoil Detection Analysis is utilised to investigate the diffusion of Kr in UO2 and ADOPTTM fuels. Composition depth-profiles on the nanometer scale were obtained, both for as-implanted samples and after annealing at 800°C for 1 h. Observed drifts in the 84Kr profiles could be associated with short-range diffusion mechanisms. The approach employed here provides the possibility to make direct comparisons with atomistic scale modelling data, and can be of service as a separate effect test in line with the Accelerated Fuel Qualification initiative.

Out of (the) bag—encoding categorical predictors impacts out-of-bag samples

Performance of random forest classification models is often assessed and interpreted using out-of-bag (OOB) samples. Observations which are OOB when a tree is trained may serve as a test set for that tree and predictions from the OOB observations used to calculate OOB error and variable importance measures (VIM). OOB errors are popular because they are fast to compute and, for large samples, are a good estimate of the true prediction error. In this study, we investigate how target-based vs. target-agnostic encoding of categorical predictor variables for random forest can bias performance measures based on OOB samples. We show that, when categorical variables are encoded using a target-based encoding method, and when the encoding takes place prior to bagging, the OOB sample can underestimate the true misclassification rate, and overestimate variable importance. We recommend using a separate test data set when evaluating variable importance and/or predictive performance of tree based methods that utilise a target-based encoding method.

Prediction of viscoelastic and printability properties on binder-free TiO2-based ceramic pastes by DIW through a machine learning approach

Ceramic 3D printing has become an increasingly popular manufacturing technique due to its potential to create complex geometries with high precision. However, predicting the printability of ceramic pastes remains a challenge, as it depends on various rheological properties. In this study, we propose a feed-forward deep neural network model that predicts the printability of ceramic pastes based on two suggested criteria, a shear-thinning ability and a gel point greater than 1×103 Pa. The model is trained on a dataset built from rheological and viscoelastic characterizations of pastes, and validated on a separate test set. Our results show that the proposed learning model achieves small relative error in predicting the gel point of the ceramic pastes, with a mean value of 8.99181 and a standard deviation of 1.812864. This model has the potential to improve the efficiency and quality of ceramic 3D printing by enabling rapid and accurate predictions of paste printability.

Increasing the capabilities of a test ballistic missile to separate test objects

Increasing the capabilities of a test ballistic missile to separate test objects

AI-BASED AUTOMATED VARUS/VALGUS ALIGNMENT MEASUREMENT IN STANDARD KNEE RADIOGRAPHS

IntroductionAccurate assessment of alignment in pre-operative and post-operative knee radiographs is important for planning and evaluating knee replacement surgery. Existing methods predominantly rely on manual measurements using long-leg radiographs, which are time-consuming to perform and are prone to reliability errors. In this study, we propose a machine-learning-based approach to automatically measure anatomical varus/valgus alignment in pre-operative and post-operative standard AP knee radiographs.MethodWe collected a training dataset of 816 pre-operative and 457 one-year post-operative AP knee radiographs of patients who underwent knee replacement surgery. Further, we have collected a separate distinct test dataset with both pre-operative and one-year post-operative radiographs for 376 patients. We manually outlined the distal femur and the proximal tibia/fibula with points to capture the knee joint (including implants in the post-operative images). This included point positions used to permit calculation of the anatomical tibiofemoral angle. We defined varus/valgus as negative/positive deviations from zero. Ground truth measurements were obtained from the manually placed points. We used the training dataset to develop a machine-learning-based automatic system to locate the point positions and derive the automatic measurements. Agreement between the automatic and manual measurements for the test dataset was assessed by intra-class correlation coefficient (ICC), mean absolute difference (MAD) and Bland-Altman analysis.ResultAnalysing the agreement between the manual and automated measurements, ICC values were excellent pre-/post-operatively (0.96, CI: 0.94-0.96) / (0.95, CI: 0.95-0.96). Pre-/post-operative MAD values were 1.3°±1.4°SD / 0.7°±0.6°SD. The Bland-Altman analysis showed a pre-/post-operative mean difference (bias) of 0.3°±1.9°SD/-0.02°±0.9°SD, with pre-/post-operative 95% limits of agreement of ±3.7°/±1.8°, respectively.ConclusionThe developed machine-learning-based system demonstrates high accuracy and reliability in automatically measuring anatomical varus/valgus alignment in pre-operative and post-operative knee radiographs. It provides a promising approach for automating the measurement of anatomical alignment without the need for long-leg radiographs.AcknowledgementsThis research was funded by the Wellcome Trust [223267/Z/21/Z].

Detection of Potato Pathogen Clavibacter sepedonicus by CRISPR/Cas13a Analysis of NASBA Amplicons.

The ring rot of potato caused by the bacterial pathogen Clavibacter sepedonicus is a quarantine disease posing a threat to the potato industry worldwide. The sensitive and selective detection of C. sepedonicus is of a high importance for its effective control. Here, the detection system is reported to determine viable bacteria of C. sepedonicus in potato tubers, based on the coupling of CRISPR/Cas13a nuclease with NASBA (Nucleic Acid Sequence Based Amplification)-the method of isothermal amplification of RNA. Detection can be conducted using both instrumental and non-instrumental (visual inspection of test tubes under blue light) modes. When NASBA and Cas13a analyses were carried out in separate test tubes, the limit of detection (LOD) for the system was 1000 copies of purified target 16S rRNA per NASBA reaction or about 24 colony-forming units (CFUs) of C. sepedonicus per 1 g of tuber tissue. The testing can also be conducted in the "one-pot" format (a single test tube), though with lower sensitivity: LOD was 10,000 copies of target RNA or about 100 CFU per 1 g of tuber tissue for both instrumental and visual detection modes. The overall time of NASBA/Cas13a analysis did not exceed 2 h. The developed NASBA/Cas13a detection system has the potential to be employed as a routine test of C. sepedonicus, especially for on-site testing.

Abstract A074: Early detection of breast implant associated anaplastic large cell lymphoma by multiplex lateral flow assay of CD30 and IL-10

Abstract Introduction: More than 1300 women have developed a unique breast implant associated anaplastic large cell lymphoma (BIA-ALCL) around breast implants. BIA-ALCL presents as an accumulation of fluid (seroma) a median of 9 years after implant placement. When detected early, survival with surgery alone is nearly 100%. Remaining patients present with masses and/or lymph node metastases requiring radiation, chemotherapy and/or immunotherapy and fatalities have occurred. Methods: Preliminary studies identified CD30 and IL-10 as the most sensitive and specific biomarkers for BIA-ALCL in seroma fluids. Therefore, we constructed a multiplex LFA to detect both CD30 and IL-10 in 30 mL seroma fluid within 20 minutes. LFA is a point of care immunoassay using capture and detection antibodies that detect separate epitopes of the target molecule. The detection antibody is linked to a colored particle; in this multiplex assay CD30 antibody was linked to blue and IL-10 to red particles. Separate positive test lines (blue and red) indicate presence of CD30 or IL-10. A control line confirms migration of the seroma fluid past the test lines. To determine the dynamic range of sensitivity we spiked recombinant CD30 and IL-10 into benign seromas. Test and control lines were quantitated with NIH Image J software and ratios of test line to control line calculate for each sample. Forty-eight hour supernatants of supernatants of BIA-ALCL lines TLBR1 and TLBR2 were tested for IL-10 and CD30. Fifteen patients, 8 with BIA-ALCL and 7 with benign seromas provided consent for testing of their seroma fluids. Results: Multiplex CD30/IL10 LFA produced positive test lines for neat and 1:10 dilutions of 48 hour supernatnats of BIA-ALCL lines TLBR1 and TLBR2. Both IL-10 and CD-30 could be detected on the multiplex LFA strip using a 1:3 dilution of seroma fluids. The multiplex LFA shows both IL-10 and CD30 are significantly higher in malignant samples (IL-10 TL/CL: malignant, 0.2±0.16 n=8 vs benign, 0.004±0.007 n=7, p&amp;lt;0.05; CD30 TL/CL: malignant, 0.38±0.2 vs benign, 0.02±0.02, p&amp;lt;0.001, two-way ANOVA). Importantly, when either IL-10 or CD30 yields a faint positive test line, the other is not detected reducing the possibility of false positives. Conclusions: The CD30/IL-10 multiplex LFA had 100% positive and negative predictive value in this pilot study. The assay was able to distinguish lymphoma from recurrent breast cancer and more common benign seromas. The use of this multiplex assay mitigates positive false positive results from more sensitive CD30 LFA which has been detected in some benign seromas. This LFA will potentially allow early pre-operative detection of ALCL allowing surgeons to plan their surgical approach. The LFA can replace more costly time-consuming flow cytometry, immunohistochemistry, cyto- and histopathologic studies which require at least 10 ml of fluid. The LFA can be performed outside of medical centers in rural and underserved areas. A larger prospective multicenter study including intra-operative sampling is planned to validate these results. Citation Format: Peng Xu, Katerina Kourentzi, Richard Willson, Marshall Kadin. Early detection of breast implant associated anaplastic large cell lymphoma by multiplex lateral flow assay of CD30 and IL-10 [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A074.

M-CLUSTER: multistage clustering for unsupervised train wheel condition monitoring

Wheel out-of-roundness (OOR) is a common wheel defect that raises maintenance costs and increases the risk of failure or damage to track components. This paper proposes a novel multistage clustering framework (M-CLUSTER) for unsupervised condition monitoring of train wheels. The framework initially extracts time-domain features from raw acceleration responses collected by rail-mounted sensors. Sensitive features are then selected through an unsupervised feature selection algorithm called local learning-based clustering (LLC). Next, a detector model is trained using density-based spatial clustering of applications with noise (DBSCAN), a data clustering method effective for clusters with similar density. Since this algorithm does not originally involve separate training and testing phases, a new two-step mechanism is introduced: (1) training on a healthy dataset and (2) testing on an unlabelled dataset. Finally, the severity of train wheel defects is classified by K-means, with cluster validity indices (CVI) automatically determining the number of severity clusters (classes). The framework’s efficiency is demonstrated through the detection of defective wheels using the Alfa Pendular passenger model. Results indicate that M-CLUSTER accurately identifies train wheel flats and polygonal wear without labelled data, achieving 98% accuracy by selecting 10 features from the set.

Abstract 4131622: Opportunistic Screening of Chronic Liver Disease With Deep Learning Enhanced Echocardiography

Introduction: Chronic liver disease affects more than 1.5 billion adults worldwide, but the majority of cases are asymptomatic and undiagnosed. Echocardiography is broadly performed and visualizes the liver; however, this information is not diagnostically leveraged. Hypothesis and Aims: We hypothesized that a deep-learning algorithm can detect chronic liver diseases using subcostal echocardiography images that contains hepatic tissue. To develop and evaluate a deep learning algorithm on subcostal echocardiography videos to enable opportunistic screening for chronic liver disease. Methods: We identified adult patients who received echocardiography and abdominal imaging (either abdominal ultrasound or abdominal magnetic resonance imaging) with ≤30 days between tests. A convolutional neural network pipeline was developed to predict the presence of cirrhosis or steatotic liver disease (SLD) using echocardiogram images. The model performance was evaluated in a held-out test dataset, dataset in which diagnosis was made by magnetic resonance imaging, and external dataset. Results: A total of 2,083,932 echocardiography videos (51,608 studies) from Cedars-Sinai Medical Center (CSMC) were used to develop EchoNet-Liver, an automated pipeline that identifies high quality subcostal images from echocardiogram studies and detects presence of cirrhosis or SLD. In a total of 11,419 quality-controlled subcostal videos from 4,849 patients, a chronic liver disease detection model was able to detect the presence of cirrhosis with an AUC of 0.837 (0.789 - 0.880) and SLD with an AUC of 0.799 (0.758 - 0.837). In a separate test cohort with paired abdominal MRIs, cirrhosis was detected with an AUC of 0.726 (0.659-0.790) compared to MR elastography and SLD was detected with an AUC of 0.704 (0.689-0.718). In the external test cohort of 66 patients (n = 130 videos), the model detected cirrhosis with an AUC of 0.830 (0.738 - 0.909) and SLD with an AUC of 0.768 (0.652 – 0.875). Conclusions: Deep learning assessment of clinically indicated echocardiography enables opportunistic screening of SLD and cirrhosis. Application of this algorithm may identify patients who may benefit from further diagnostic testing and treatment for hepatic disease.

Can computer vision / artificial intelligence locate key reference points and make clinically relevant measurements on axillary radiographs?

Computer vision and artificial intelligence (AI) offer the opportunity to rapidly and accurately interpret standardized x-rays. We trained and validated a machine learning tool that identified key reference points and determined glenoid retroversion and glenohumeral relationships on axillary radiographs. Standardized pre and post arthroplasty axillary radiographs were manually annotated locating six reference points and used to train a computer vision model that could identify these reference points without human guidance. The model then used these reference points to determine humeroglenoid alignment in the anterior to posterior direction and glenoid version. The model's accuracy was tested on a separate test set of axillary images not used in training, comparing its reference point locations, alignment and version to the corresponding values assessed by two surgeons. On the test set of pre- and post-operative images not used in the training process, the model was able to rapidly identify all six reference point locations to within a mean of 2mm of the surgeon-assessed points. The mean variation in alignment and version measurements between the surgeon assessors and the model was similar to the variation between the two surgeon assessors. This article reports on the development and validation of a computer vision/artificial intelligence model that can independently identify key landmarks and determine the glenohumeral relationship and glenoid version on axillary radiographs. This observer-independent approach has the potential to enable efficient human observer independent assessment of shoulder radiographs, lessening the burden of manual x-ray interpretation and enabling scaling of these measurements across large numbers of patients from multiple centers so that pre and postoperative anatomy can be correlated with patient reported clinical outcomes. Level III Study of Diagnostic Test.