Accurate Prediction Of Response Research Articles

Analysis of geometric uncertainties in 3D thermo-fluid problems solved by RBF-FD meshless method

The tolerances of production processes can lead to uncertainties in the behaviour and in the features of the manufactured products. From the point of view of the design of engineering components it is therefore of valuable practical interest to be able to quantify such uncertainties as well as the expected, i.e., averaged, performances. Such uncertainty quantification is carried out in this work by means of the Non-Intrusive Polynomial Chaos (PC) method in order to estimate the propagation of geometrical uncertainties of the boundaries, i.e., when the boundaries are described by stochastic variables. Existing deterministic solvers can be used with the PC method because of its non-intrusive formulation, allowing an accurate and practical prediction of the random response through a simple set of deterministic response simulations. The Radial Basis Function Finite Differences (RBF-FD) method is employed as a black box solver for the computation of the required set of responses defined over deterministic boundaries. The RBF-FD method belongs to the class of meshless methods which do not require a computational mesh/grid, therefore its main capability is to easily deal with practical problems defined over complex-shaped domains. The geometrical flexibility of the RBF-FD method is even more advantageous when coupled to the Non-Intrusive PC method for uncertainty quantification since different deterministic solutions over different geometries are required. The applicability of the proposed approach to practical problems is presented through the prediction of geometric uncertainty effects for a steady-state forced convection problem in a 3D complex-shaped domain.

Deep-Learning, Radiomics and Clinic Based Fusion Models for Predicting Response to Infliximab in Crohn's Disease Patients: A Multicentre, Retrospective Study.

Accurate prediction of treatment response in Crohn's disease (CD) patients undergoing infliximab (IFX) therapy is essential for clinical decision-making. Our goal was to compare the performance of the clinical characteristics, radiomics and deep learning model from computed tomography enterography (CTE) for identifying individuals at high risk of IFX treatment failure. This retrospective study enrolled 263 CD patients from three medical centers between 2017 and 2023 patients received CTE examinations within 1 month before IFX commencement. A training cohort was recruited from center 1 (n=166), while test cohort from centers 2 and 3 (n=97). The deep learning model and radiomics were constructed based on CTE images of lesion. The clinical model was developed using clinical characteristics. Two fusion methods were used to create fusion model: the feature-based early fusion model and the decision-based late fusion model. The performances of the predictive models were evaluated. The early fusion model achieved the highest area under characteristics curve (AUC) (0.85-0.91) among all patient cohorts, significantly outperforming deep learning model (AUC=0.72-0.82, p=0.06-0.03, Delong test) and radiomics model (AUC=0.72-0.78, p=0.06-0.01). Compared to early fusion model, the AUC values for the clinical and late fusion models were 0.71-0.91 (p=0.01-0.41), and 0.81-0.88 (p=0.49-0.37) in the test and training set, respectively. Moreover, the early fusion had the lowest value of Brier's score 0.15-0.12 in all patient set. The early fusion model, which integrates deep learning, radiomics, and clinical data, can be utilized to predict the response to IFX treatment in CD patients and illustrated clinical decision-making utility.

Predictive Factors for the Efficacy of Radioactive Iodine Treatment of Graves' Disease: An Experience From 613 Chinese Patients.

Objective: The utilization of radioactive iodine-131I (RAI) has long been established as a cost-effective and conventional treatment for managing Graves' disease (GD). However, the accurate prediction of the clinical response to RAI treatment remains difficult. The successful resolution of GD through RAI therapy is typically characterized by the induction of hypothyroidism or euthyroidism. Thus, the principal aim of this study was to identify plausible predictors of RAI efficacy in the treatment of GD. Methods: The clinical data of 613 GD patients, who underwent RAI treatment for the first time, were retrospectively analyzed, including age, gender, duration of hyperthyroidism, presence or absence of ocular signs, thyroid volume, thyroid weight, thyroid function (FT3, FT4, and TSH), radioactive iodine uptake (RAIU) at 2 h/6 h/24 h (2-h/6-h/24-h RAIU) prior to RAI treatment, the highest RAIU (RAIUmax), and administered activity of 131I and 131I activity per gram of thyroid tissue. Success of RAI treatment was defined as achieving hypothyroidism or euthyroidism for more than 1 year after the initial treatment. Univariate and multivariate logistics regression analyses were conducted to identify factors that influence the efficacy of RAI treatment for GD. And at last, based on the results of the multivariate logistic regression analysis, a nomogram model was established. Results: In this study, the success rate of RAI treatment for GD was 91.2% (559/613). Univariate analysis demonstrated that several factors, including age (p=0.005), thyroid volume (p=0.001), thyroid-stimulating hormone (TSH, p=0.042), ratio of RAIU at 6 h to 24 h (6-h/24-h RAIU, p=0.048), total 131I activity (p=0.026), and 131I activity per gram of thyroid tissue (p=0.001), were significantly associated with treatment outcome. Multivariate logistic regression analysis indicated thyroid volume and 131I activity per gram of thyroid tissue as significant independent predictors of radioactive iodine therapy (RIT) efficacy. The area under the ROC curve of the established nomogram model was 0.769 (95% confidence interval [CI]: 0.692-0.846), indicating that the model has good discriminatory ability. Conclusion: Calculated-dose RAI is effective in the treatment of GD. The smaller thyroid volume and the higher 131I activity per gram of thyroid tissue are predictors of RAI efficacy in the treatment of GD.

Among- and Within-Individual Variance in Metabolic Thermal Reaction Norms.

AbstractIn ectotherms, temperature has a strong effect on metabolic rate (MR), yet the extent to which the thermal sensitivity of MR varies among versus within individuals is largely unknown. This is of interest because significant among-individual variation is a prerequisite for the evolution of metabolic thermal sensitivity. Here, we estimated the repeatability (R) of the thermal sensitivity of MR in individual virgin, adult male Drosophila melanogaster () by taking repeated overnight measures of their MRs at two temperatures (~24°C and ~27°C). At the population level, thermal sensitivity decreased with locomotor activity, and older individuals showed a higher thermal sensitivity of MR than younger individuals. Taking these effects (and body mass) into account, we detected significant repeatability in both the centered intercept () and the slope () of the metabolic thermal reaction norms, which respectively represent average MR and thermal sensitivity of MR. Furthermore, individuals with a higher overall MR also displayed greater increases in MR as temperature increased from ~24°C to ~27°C (). Average MR and thermal sensitivity of MR were also positively correlated within individuals (). Our study represents a point of departure for future larger studies, in which more complex protocols (e.g., wider temperature range, breeding design) can be applied to quantify the causal components of variation in thermal sensitivity that are needed to make accurate predictions of adaptive responses to global warming.

Improving prediction of treatment response and prognosis in colorectal cancer with AI-based medical image analysis

<p>The heterogeneous response and prognosis of patients with colorectal cancer (CRC) to standard treatment regimens remains a challenge for clinical management. Individually weak prognostic markers, defined by gene mutations and protein expression, are difficult to apply in routine clinical practice because of their high acquisition cost and mediocre prediction accuracy. Visual evaluation of medical images, including radiology and digital pathology images, is an important part of CRC management. With the rapid development of artificial intelligence (AI), high-dimensional imaging features other than visual information are increasingly being used to develop imaging markers. At different stages of treatment, accurate predictions of treatment response and prognosis may help in selecting patients and tailoring their treatment. Here, we review the current state of AI applied to the medical imaging of CRC and describe its recent progress in short-term response and long-term survival prediction. In addition, we illustrate how these AI-based approaches may affect clinical decision-making. Although few approaches have been applied in routine clinical practice, their results are promising. Finally, we discuss the challenges in applying AI in clinical practice and possible future solutions from three perspectives: model interpretability, model generalizability, and patient privacy protection. This comprehensive assessment underscores the transformative potential of AI in CRC management and emphasizes the need for further exploration and integration into routine clinical workflows.</p>

DGDRP: drug-specific gene selection for drug response prediction via re-ranking through propagating and learning biological network.

Introduction: Drug response prediction, especially in terms of cell viability prediction, is a well-studied research problem with significant implications for personalized medicine. It enables the identification of the most effective drugs based on individual genetic profiles, aids in selecting potential drug candidates, and helps identify biomarkers that predict drug efficacy and toxicity.A deeper investigation on drug response prediction reveals that drugs exert their effects by targeting specific proteins, which in turn perturb related genes in cascading ways. This perturbation affects cellular pathways and regulatory networks, ultimately influencing the cellular response to the drug. Identifying which genes are perturbed and how they interact can provide critical insights into the mechanisms of drug action. Hence, the problem of predicting drug response can be framed as a dual problem involving both the prediction of drug efficacy and the selection of drug-specific genes. Identifying these drug-specific genes (biomarkers) is crucial because they serve as indicators of how the drug will affect the biological system, thereby facilitating both drug response prediction and biomarker discovery.Methods: In this study, we propose DGDRP (Drug-specific Gene selection for Drug Response Prediction), a graph neural network (GNN)-based model that uses a novel rank-and-re-rank process for drug-specific gene selection. DGDRP first ranks genes using a pathway knowledge-enhanced network propagation algorithm based on drug target information, ensuring biological relevance. It then re-ranks genes based on the similarity between gene and drug target embeddings learned from the GNN, incorporating semantic relationships. Thus, our model adaptively learns to select drug mechanism-associated genes that contribute to drug response prediction. This integrated approach not only improves drug response predictions compared to other gene selection methods but also allows for effective biomarker discovery.Discussion: As a result, our approach demonstrates improved drug response predictions compared to other gene selection methods and demonstrates comparability with state-of-the-art deep learning models. Case studies further support our method by showing alignment of selected gene sets with the mechanisms of action of input drugs.Conclusion: Overall, DGDRP represents a deep learning based re-ranking strategy, offering a robust gene selection framework for more accurate drug response prediction. The source code for DGDRP can be found at: https://github.com/minwoopak/heteronet.

A predicting method for the mechanical property response of the marine riser based on the simulation and data-driven models

The accurate and real-time prediction of the mechanical property response for the safety assessment of the marine riser is necessary during offshore oil and gas production. However, a finite element simulation is time-consuming, which obstructs the real-time monitoring and evaluation of the marine riser. Therefore, to obtain the real-time mechanical response of the marine riser, a predicting method based on the simulation and data-driven models is proposed. To obtain the prediction data, the finite element simulation model under the typical working conditions is established by the Abaqus software, and then the sensitivity analysis of influence factors is conducted due to the varied working conditions. The orthogonal experimental method is used to select the typical working conditions of the marine riser, and the selection of the sensitive influence factors is ensured based on the typical working conditions, the range analysis, and the grey relational method. To obtain enough training data, the Latin hypercube sampling method is used to enlarge the data for the analysis process. To find a more reliable prediction algorithm, four typical single surrogate models, which are the polynomial response surface model, the radial basis model, the kriging model, and the support vector machine regression model, are selected to establish a predicting model library. Meanwhile, a combining surrogate model, including the average weight surrogate model and the adaptive weight surrogate model, is established based on the combining weight to further improve the predicting accuracy. As a result, an optimal surrogate model is selected from the above four single and two combining surrogate models based on four evaluation criteria including the mean absolute percentage error, the mean absolute error, the root mean square error, and the goodness of fit. The cases show that the optimal surrogate model has a highly rapid response, with average relative errors are 8.17%, 3.42%, and 11.40% for the maximum bending moment, the maximum equivalent stress, and the maximum lateral deformation, respectively. In the above study, the accurate and reliable prediction method of the mechanical property responses will provide a theoretical basis for ensuring the safety of the marine riser.

Implementation of the critical wave groups method with computational fluid dynamics and neural networks

Accurate and efficient prediction of extreme ship responses continues to be a challenging problem in ship hydrodynamics. Probabilistic frameworks in conjunction with computationally efficient numerical hydrodynamic tools have been developed that allow researchers and designers to better understand extremes. However, the ability of these hydrodynamic tools to represent the physics quantitatively during extreme events is limited. Previous research successfully implemented the critical wave groups (CWG) probabilistic method with computational fluid dynamics (CFD). Although the CWG method allows for less simulation time than a Monte Carlo approach, the large quantity of simulations required is cost prohibitive. The objective of the present paper is to reduce the computational cost of implementing CWG with CFD, through the construction of long short-term memory (LSTM) neural networks. After training the models with a limited quantity of simulations, the models can provide a larger quantity of predictions to calculate the probability. The new framework is demonstrated with a 2-D midship section of the Office of Naval Research Tumblehome (ONRT) hull in Sea State 7 and beam seas at zero speed. The new framework is able to produce predictions that are representative of a purely CFD-driven CWG framework, with two orders of magnitude of computational cost savings.

Aligning theoretical and empirical representations of soil carbon-to-nitrogen stoichiometry with process-based terrestrial biogeochemistry models

Soil carbon-nitrogen (C:N) stoichiometry acts as a control over decomposition and soil organic matter formation and loss, making it a key soil property for understanding ecosystem dynamics and projected ecosystems responses to global environmental change. However, the controls of soil C:N and how they respond to increasing pressures from global change agents are not fully understood. The “foundational” controls on soil C:N, namely plant and microbial C:N, have been used to predict soil C:N, but fail to accurately simulate all ecosystems and may be insufficient for predictions under global environmental change. We present an “emerging” representation of controls of soil C:N that includes plant-microbe-mineral feedbacks that have been shown to regulate soil C:N. We argue that including representation of these emerging drivers in process-based terrestrial biogeochemistry models, which include biological N fixation, mycorrhizae, priming, root exudation of organic acids, and mineralogy (including soil texture, mineral composition, and aggregation), will improve mechanistic representation of soil C:N and associated processes. Such improvements will produce models that will better simulate a variety of ecological states and predict soil C:N when global changes modify plant-microbe-mineral interactions. Here, we align our empirical understanding of controls of soil C:N with those controls represented in models, identifying contexts where emerging drivers might be particularly important to represent (e.g., priming and root exudation in nutrient-limited conditions) and areas of future work. Additionally, we show that implementing emerging drivers of soil C:N results in different simulated outcomes at steady state and in response to elevated atmospheric CO2. Our review and preliminary simulations support the need to incorporate emerging drivers of soil C:N into process-based terrestrial biogeochemistry models, allowing for both theoretical exploration of mechanisms and potentially more accurate predictions of land biogeochemical responses to global change.

Predictive markers for anti-inflammatory treatment response in thyroid eye disease.

Anti-inflammatory treatment is the primary and vital therapeutic approach for active, moderate-to-severe thyroid eye disease (TED). Accurate pretreatment prediction of treatment response is of paramount importance for the prognosis of patients. However, relying solely on the clinical activity score asa determinant of activity has led to unsatisfactory treatment outcomes. In recent years, significant advancements have been made in identifying predictive markers for anti-inflammatory treatment response in TED, clinical markers, body fluid biomarkers and imaging biomarkers. Several clinical studies have developed prediction models based on these markers. However, there is still a lack of comprehensive elucidation or comparison between the different markers. Therefore, this review aims to provide a detailed analysis of the definition, characteristics, and application of predictive markers for anti-inflammatory treatment response in TED. Through detailed literature search, 26 articles applying anti-inflammatory treatment effect prediction with a total of 1948 TED patients were used for analysis and discussion. By gaining a better understanding of the current research on predictive markers, we can accelerate and guide the exploration of treatment prediction strategies, leading us towards an era of precise therapy for TED.

Deep Learning-Based Metasurface Design for Smart Cooling of Spacecraft

A reconfigurable metasurface constitutes an important block of future adaptive and smart nanophotonic applications, such as adaptive cooling in spacecraft. In this paper, we introduce a new modeling approach for the fast design of tunable and reconfigurable metasurface structures using a convolutional deep learning network. The metasurface structure is modeled as a multilayer image tensor to model material properties as image maps. We avoid the dimensionality mismatch problem using the operating wavelength as an input to the network. As a case study, we model the response of a reconfigurable absorber that employs the phase transition of vanadium dioxide in the mid-infrared spectrum. The feed-forward model is used as a surrogate model and is subsequently employed within a pattern search optimization process to design a passive adaptive cooling surface leveraging the phase transition of vanadium dioxide. The results indicate that our model delivers an accurate prediction of the metasurface response using a relatively small training dataset. The proposed patterned vanadium dioxide metasurface achieved a 28% saving in coating thickness compared to the literature while maintaining reasonable emissivity contrast at 0.43. Moreover, our design approach was able to overcome the non-uniqueness problem by generating multiple patterns that satisfy the design objectives. The proposed adaptive metasurface can potentially serve as a core block for passive spacecraft cooling applications. We also believe that our design approach can be extended to cover a wider range of applications.

Feature selection based on unsupervised clustering evaluation for predicting neoadjuvant chemoradiation response for patients with locally advanced rectal cancer

Accurate response prediction allows for personalized cancer treatment of locally advanced rectal cancer (LARC) with neoadjuvant chemoradiation. In this work, we designed a convolutional neural network (CNN) feature extractor with switchable 3D and 2D convolutional kernels to extract deep learning features for response prediction. Compared with radiomics features, convolutional kernels may adaptively extract local or global image features from multi-modal MR sequences without the need of feature predefinition. We then developed an unsupervised clustering based evaluation method to improve the feature selection operation in the feature space formed by the combination of CNN features and radiomics features. While normal process of feature selection generally includes the operations of classifier training and classification execution, the process needs to be repeated many times after new feature combinations were found to evaluate the model performance, which incurs a significant time cost. To address this issue, we proposed a cost effective process to use a constructed unsupervised clustering analysis indicator to replace the classifier training process by indirectly evaluating the quality of new found feature combinations in feature selection process. We evaluated the proposed method using 43 LARC patients underwent neoadjuvant chemoradiation. Our prediction model achieved accuracy, area-under-curve (AUC), sensitivity and specificity of 0.852, 0.871, 0.868, and 0.735 respectively. Compared with traditional radiomics methods, the prediction models (AUC = 0.846) based on deep learning-based feature sets are significantly better than traditional radiomics methods (AUC = 0.714). The experiments also showed following findings: (1) the features with higher predictive power are mainly from high-order abstract features extracted by CNN on ADC images and T2 images; (2) both ADC_Radiomics and ADC_CNN features are more advantageous for predicting treatment responses than the radiomics and CNN features extracted from T2 images; (3) 3D CNN features are more effective than 2D CNN features in the treatment response prediction. The proposed unsupervised clustering indicator is feasible with low computational cost, which facilitates the discovery of valuable solutions by highlighting the correlation and complementarity between different types of features.

Abstract A136: Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles

Abstract Single-cell sequencing techniques have greatly advanced our current understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies and therapeutic responses. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key step in development of such approaches centers around accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method detects shared expression patterns between the two data sources and utilizes such information to project cellular drug response. This method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0); this holds when using single-cell RNA-seq from both cell line and in vivo models. More importantly, we examine our method’s utility with three distinct prospective tests, and in each our predicted results are accurate and mirrored biological expectations. In the first two tests, we investigated predicting drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. In both, our results showed high consistency with results from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify drugs predicted effective on the resistant line, and validated the predictions with in vitro experiments. Together, scIDUC quickly and directly translates scRNA-seq data into meaningful cellular drug response for individual cells, displaying the potential to be used by researchers as a first-line tool for nuanced and heterogeneity-aware drug discovery. Citation Format: Weijie Zhang, Danielle Maeser, Adam Lee, Yingbo Huang, Robert F Gruener, Sampreeti Jena, Anand G Patel, R. Stephanie Huang. Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A136.

Structural vibration damping by the use of poro-elastic layers: a summary

A properly designed limp porous materials such as fibrous layers can provide damping equivalent to conventional viscoelastic dampers while providing advantages such as light weight and effective sound absorption. This then allows porous layers to be used as multi-functional noise and vibration control solutions in automotive and aerospace applications. Further, the addition of bulk elasticity to the solid phase of the porous medium is beneficial since it improves damping performance compared to equivalent limp treatments. In the work summarized here, porous media, such as fibers and foams, were designed to serve as treatments for various vibrating structures to examine their damping effectiveness. Both analytical modeling and numerical simulation based on finite element methods were involved depending on the complexity of the structure. Specifically, a Fourier transform-based computational method was introduced as the key step to allow accurate prediction of a panel's spatial response based on its wavenumber-frequency spectrum. Parametric studies have been conducted to identify the optimal bulk properties that would allow a porous layer to provide the largest possible damping within a target frequency region. Finally, design concepts for achieving the maximum damping potential of porous layers are summarized.

Refined self-attention mechanism based real-time structural response prediction method under seismic action

Accurate prediction of structural response under earthquake is of great significance for structural damage and performance evaluation. In order to improve the efficiency of structure time history response prediction, this paper proposes a novel SeisFormer model based on the self-attention mechanism and deep learning technology. Through autoregressive prediction, the SeisFormer can achieve real-time prediction of the response time histories of a large number of nodes in the structure under seismic action and can effectively solve the problem of data scarcity. Four case studies are performed to verify the accuracy and efficiency of the proposed methodology, including validation on datasets obtained from elastoplastic seismic analysis of a single-story structure, a three-story structure, and an eleven-story structure, and measured data of a shaking table test model. In addition, this paper further studies the prediction accuracy of the SeisFormer through ablation experiments and comparative experiments. The experimental results show that the SeisFormer can accurately predict the acceleration, velocity, and displacement time histories of numerous nodes in the structure. The prediction accuracy outperforms the LSTM model, and the prediction speed is 193–109,824 times faster than finite element method. Furthermore, with data augmentation through autoregressive prediction, the SeisFormer model can achieve efficient and accurate predictions when training data is exceptionally scarce, enabling engineering applications.

An accurate prediction of electronic structure, mechanical stability and optical response of BaCuF3 fluoroperovskite for solar cell application

This study utilizes the first-principles modelling approach based on the density functional theory (DFT) framework to investigate the structural, mechanical, and optoelectronic properties of the BaCuF3 fluoroperovskite. Initially, the three distinct cubic structures of BaCuF3 were simulated based on the positioning of F atoms. This investigation aimed to determine the most stable configuration for BaCuF3. The structural stability of all compounds has been evaluated through the formation enthalpy calculations. In addition, the mechanical stability is assessed by the investigation of elastic stiffness constants. The mechanical stability of the α-BaCuF3 and β-BaCuF3 compounds has been observed. However, the γ-BaCuF3 compound is deemed unstable due to its failure to meet the Born-stability criterion (C44 < 0). The ductility of α-BaCuF3 and the brittleness of β-BaCuF3 have been determined by exploring Pugh's and passion ratios and the Cauchy pressure. The electronic characteristics have been evaluated by examining electronic band structure, density of states and partial density of states. The compounds α-BaCuF3 and β-BaCuF3 have been seen to have indirect and direct band gaps of 0.14 eV (M–Γ) and 1.22 eV (Γ–Γ), respectively. Several optical parameters have been calculated and compared. The materials that have been chosen exhibit significant optical conductivity and absorption coefficients when exposed to high-energy photons while also demonstrating transparency at lower incident photon energy ranges. Our investigations into the optical properties have determined that α-BaCuF3 and β-BaCuF3 exhibit favourable characteristics for solar cell implementation, high-frequency UV, and optoelectronic devices.

Characterization of cancer-associated fibroblasts (CAFs) and development of a CAF-based risk model for triple-negative breast cancer

Triple-negative breast Cancer (TNBC) is a highly malignant cancer with unclear pathogenesis. Within the tumor microenvironment (TME), cancer-associated fibroblasts (CAFs) vitally influence tumor onset and progression. Thus, this research aimed to identify distinct subgroups of CAF using single-cell and TNBC-related information from the GEO and TCGA databases, respectively. The primary aim was to establish a novel predictive model based on the CAF features and their clinical relevance. Moreover, the CAFs were analyzed for their immune characteristics, response to immunotherapy, and sensitivity to different drugs. The developed predictive model demonstrated significant effectiveness in determining the prognosis of patients with TNBC, TME, and the immune landscape of the tumor. Of note, the expression of GPR34 was significantly higher in TNBC tissues compared to that in other breast cancer (non-TNBC) tissues, indicating that GPR34 plays a crucial role in the onset and progression of TNBC. In summary, this research has yielded a novel predictive model for TNBC that holds promise for the accurate prediction of prognosis and response to immunotherapy in patients with TNBC.

Mechanical Responses of Ductile Aluminum Alloy under Biaxial Non-Proportional Tensile Reverse Loading Patterns

This paper deals with the study of the mechanical responses of ductile metals under biaxial non-proportional cyclic loading tests. The biaxially loaded HC specimens manufactured from 4 mm thick aluminum alloy sheets (EN AW 6082-T6) are subjected to various loading paths, including monotonic and cyclic loading conditions. The aim is to investigate the plastic, damage, and fracture behavior of the material under these different loading scenarios. In terms of numerical aspects, a modified anisotropic two-surface cyclic plastic–damage continuum model is used to predict the material behavior in the load-displacement field and different strain fields. Numerically predicted stress states are analyzed in detail to gain a better understanding of the damage mechanisms. Moreover, the scanning electronic microscopy (SEM) pictures taken from the fracture surfaces confirm the dependency of the damage mechanisms on the loading histories. The present work indicates the importance of considering different loading conditions for the accurate prediction of material responses.

Abstract 15033: Development of an Interpretable Machine Learning Model for Predicting Individual Response to Antihypertensive Treatments

Introduction: Accurate prediction of individual response to anti-hypertensive treatment is crucial in enhancing the safety and efficiency of offering appropriate regimens to attain desired blood pressure (BP) goals. Hypothesis: Machine learning (ML) based models could accurately predict the BP reduction of different antihypertensive regimens. Methods: We utilized data from the LIGHT study, a pragmatic, cluster-randomized trial conducted in 94 primary care institutions in China, involving 13146 visits of 5192 patients who were required to attend the clinic for hypertension management every 3 months during an overall study period of 1 year. Clinical and laboratory variables, baseline BP, and anti-hypertensive therapies at baseline and at follow-up were used to develop models for predicting post-treatment individual BP response. Five ML algorithms were applied as candidate models, and mean squared error (MSE) was used to evaluate the model performance ( panel A ). Results: The mean (standard deviation) age of the study population at baseline was 64.2 (14.0) years, and 2207 (42.5%) were female. 29 features selected by the least absolute shrinkage and selection operator method and purposeful selection were used to build the model, including age, sex, weight, waist circumference, BMI, baseline systolic and diastolic BP, smoking status, visit interval, history of coronary heart disease, diabetes, and dyslipidemia, and baseline and prescript antihypertensive drugs. Among all candidate models, the Catboost model had the best performance with a training MSE of 149.6 and a validation MSE of 135.2 ( panel B ). The most important features include baseline BP, age, weight, and BMI ( panel C and D ). Conclusions: We developed an ML-based model for predicting the response to antihypertensive regimens for assisting clinicians to achieve individualized antihypertensive treatment effectively and safely.

Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes

Rhenium-186 (186Re) labeled nanoliposome (RNL) therapy for recurrent glioblastoma patients has shown promise to improve outcomes by locally delivering radiation to affected areas. To optimize the delivery of RNL, we have developed a framework to predict patient-specific response to RNL using image-guided mathematical models. MethodsWe calibrated a family of reaction-diffusion type models with multi-modality imaging data from ten patients (NCR01906385) to predict the spatio-temporal dynamics of each patient's tumor. The data consisted of longitudinal magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) to estimate tumor burden and local RNL activity, respectively. The optimal model from the family was selected and used to predict future growth. A simplified version of the model was used in a leave-one-out analysis to predict the development of an individual patient's tumor, based on cohort parameters. ResultsAcross the cohort, predictions using patient-specific parameters with the selected model were able to achieve Spearman correlation coefficients (SCC) of 0.98 and 0.93 for tumor volume and total cell number, respectively, when compared to the measured data. Predictions utilizing the leave-one-out method achieved SCCs of 0.89 and 0.88 for volume and total cell number across the population, respectively. ConclusionWe have shown that patient-specific calibrations of a biology-based mathematical model can be used to make early predictions of response to RNL therapy. Furthermore, the leave-one-out framework indicates that radiation doses determined by SPECT can be used to assign model parameters to make predictions directly following the conclusion of RNL treatment. Statement of SignificanceThis manuscript explores the application of computational models to predict response to radionuclide therapy in glioblastoma. There are few, to our knowledge, examples of mathematical models used in clinical radionuclide therapy. We have tested a family of models to determine the applicability of different radiation coupling terms for response to the localized radiation delivery. We show that with patient-specific parameter estimation, we can make accurate predictions of future glioblastoma response to the treatment. As a comparison, we have shown that population trends in response can be used to forecast growth from the moment the treatment has been delivered.In addition to the high simulation and prediction accuracy our modeling methods have achieved, the evaluation of a family of models has given insight into the response dynamics of radionuclide therapy. These dynamics, while different than we had initially hypothesized, should encourage future imaging studies involving high dosage radiation treatments, with specific emphasis on the local immune and vascular response.