Prediction Of Response Research Articles

Label-Free Prediction of Immunotherapy Response in Lung Cancer

Label-Free Prediction of Immunotherapy Response in Lung Cancer

Numerical prediction and experimental verification of dynamic deflection responses of damaged hybrid (luffa and carbon-nanotube-reinforced) polymeric composite panel structure

Numerical prediction and experimental verification of dynamic deflection responses of damaged hybrid (luffa and carbon-nanotube-reinforced) polymeric composite panel structure

3D keloid spheroid model: Development and application for personalized drug response prediction

Research on keloid is limited by the lack of proper in vitro and animal model reflecting in vivo status. Based on heterogeneity of keloid and important role of endothelial cells in its pathogenesis, a novel 3D in vitro keloid spheroid prepared with keloid fibroblasts and endothelial cells was evaluated in this study. Commercial cell lines of keloid fibroblasts and endothelial cells were used at various cellular ratios to generate keloid spheroids to determine the optimal condition. Keloid spheroids from three keloid patients were also made and their usefulness as in vitro models, including their responses to drugs, were assessed. Spheroids with higher endothelial cell proportions exhibited increased viability and propagation ability. Patient-derived keloid spheroids showed heterogeneity which might reflect individual clinical conditions. The optimal ratio of fibroblasts to endothelial cells was determined to be 4:1 for keloid spheroids based on gene expression and viability analyses. Patient-derived keloid spheroid showed better keloidal changes in genetic expressions than 2D monolayer culture. Spheroids exhibited varied responses and resistance to each drug used for keloids, depending on the cell type used. 3D keloid spheroids might provide an effective in vitro model for investigating disease pathogenesis and appropriate treatment modalities for future precision medicine.

An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer

Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods prioritize extracting multi-modal features to enhance predictive performance, with limited consideration on real-world clinical applicability, particularly in longitudinal NAT scenarios with multi-modal data. Here, we propose the Multi-modal Response Prediction (MRP) system, designed to mimic real-world physician assessments of NAT responses in breast cancer. To enhance feasibility, MRP integrates cross-modal knowledge mining and temporal information embedding strategy to handle missing modalities and remain less affected by different NAT settings. We validated MRP through multi-center studies and multinational reader studies. MRP exhibited comparable robustness to breast radiologists, outperforming humans in predicting pathological complete response in the Pre-NAT phase (ΔAUROC 14% and 10% on in-house and external datasets, respectively). Furthermore, we assessed MRP’s clinical utility impact on treatment decision-making. MRP may have profound implications for enrolment into NAT trials and determining surgery extensiveness.

Dynamics of cell-free tumor DNA correlate with early MRI response during chemoradiotherapy in rectal cancer

BackgroundIn locally advanced rectal cancer, the prediction of tumor response during and after neoadjuvant treatment remains challenging. In terms of organ preservation, adaptive radiotherapy, and intensified (total) neoadjuvant therapies, biomarkers are desirable for patient stratification.MethodsIn 16 patients, weekly blood samples (n = 86) to detect cell-free tumor DNA (ctDNA) during long-course neoadjuvant chemoradiotherapy were analyzed. Data were correlated with initial tumor volumes, MRI response in week 2 and 5 of radiotherapy as well as with pathologic tumor response after resection and outcome parameters.ResultsMost patients showed decreasing ctDNA during the course of radiochemotherapy. However, we found heterogenous dynamics of ctDNA and could identify three groups: (1) decline (2) no clear decline and/or late shedding (3) persistence of ctDNA. In seven patients we could detect significant amounts of ctDNA in week 5 or week 6 of treatment. In our pilot cohort, we did not find significant correlations of ctDNA dynamics with pathologic response or outcome parameters. However, patients with distinct decline of ctDNA had larger tumor volumes prior to treatment, and MRI imaging in week 2 and 5 revealed bigger absolute decrease of tumor volumes. If significant levels of ctDNA were found in week 5 and / or 6, patients showed less absolute tumor volume decrease in week 2 and 5.ConclusionsWeekly measurement of ctDNA during radiochemotherapy is feasible and might represent a promising biomarker. Bigger initial primary tumors showed different ctDNA shedding profiles compared with smaller primary tumors and correlations of ctDNA dynamics with early imaging response were found.

Construction and validation of a prognostic signature based on microvascular invasion and immune-related genes in hepatocellular carcinoma

Background: Microvascular invasion (MVI) is an independent risk factor of poor prognosis in hepatocellular carcinoma (HCC) and can be used to guide the diagnosis and treatment of HCC. The immune system serves as an integral role in the incidence and progression of HCC. However, the molecular biology correlation between MVI and tumor immunity and the value of combining the two parameters to predict patient prognosis and HCC response to treatment remain to be evaluated. Results: In this study, we used univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis to establish the MVI and immune-related gene index (MIRGPI) including eight genes. We demonstrated that the MIRGPI was an independent risk factor in predicting the prognosis of HCC. Subsequently, our research established a nomogram model combining pathologic characteristics and verified its good clinical application value. In addition, our study found that the TP53 gene had a higher mutation frequency and a lower degree of immune infiltration in the high-risk group. The low-risk group had higher sensitivity to immunotherapy, sorafenib, and TACE treatment, and the high-risk group had higher sensitivity to common chemotherapeutic agents. Finally, SEMA3C was found to facilitate the proliferation, migration and invasive ability of HCC by in vitro and in vivo experiments, and its mechanism may be associated with the activation of the NF-Κb/EMT signaling pathway. Conclusions: In summary, the MIRGPI signature we developed is a reliable marker for the prediction of prognosis and treatment response, and is important for the prognostic assessment and individualized treatment of HCC.

Inductive graph‐based long short‐term memory network for the prediction of nonlinear floor responses and member forces of steel buildings subjected to orthogonal horizontal ground motions

Inductive graph‐based long short‐term memory network for the prediction of nonlinear floor responses and member forces of steel buildings subjected to orthogonal horizontal ground motions

Fluid responsiveness in pediatrics: an unsolved challenge

Predicting fluid responsiveness is a major challenge in the pediatric population as vascular and pulmonary compliance differ from the adults. However it is a crucial thing to avoid the harmful fluid overload. We count on different variables to identify responders being the dynamic parameters the ones with more evidence, specially the Respiratory Variation In Aortic Blood Flow Velocity based on echocardiography. Other variables rely on the arterial waveform, like Pulse Pressure Variation or Stroke Volume Variation seem to have limitations but new tests like VTC are arriving to overcome their drawbacks. We review the actual evidence regarding fluid responsiveness prediction in children and the anatomic and physiologic peculiarities of children that explain why they do not respond like adults and why we should study them in particular.

Non‐linear time history analyses of a rigid block isolated with unbonded fiber‐reinforced elastomeric isolators (UFREIs): A comparison between 3D finite element and phenomenological models

AbstractNumerical modeling represents a pivotal tool in the seismic analysis and design of structural systems, enabling the detailed prediction and examination of structural responses under seismic loading. This research conducts a comparative analysis of two numerical modeling approaches aimed at simulating the seismic response of unbonded fiber‐reinforced elastomeric isolators (UFREIs). The research focuses on a finite element (FE) model developed using Abaqus and a developed phenomenological model implemented in OpenSees, outlining the development and calibration processes for each. The FE model is developed based on simple rubber material testing data, while the phenomenological model is calibrated using experimental results from cyclic shear tests conducted on the UFREI device and the FE model. The primary objective of this study is to assess the effectiveness of these modeling approaches in predicting UFREI behavior under seismic conditions. This evaluation entails comparing model predictions with experimental data obtained from unidirectional shake table tests performed on a rigid block isolated by two UFREIs. This paper highlights the distinct advantages and limitations of each model in simulating UFREI dynamic responses during seismic events. Furthermore, it provides insights into the modeling techniques and discusses the computational demands and data requirements of each model, thereby aiding in their application to various aspects of seismic analysis and design.

Liquid Biopsy-Based Detection and Response Prediction for Depression.

Proactively predicting antidepressant treatment response before medication failures is crucial, as it reduces unsuccessful attempts and facilitates the development of personalized therapeutic strategies, ultimately enhancing treatment efficacy. The current decision-making process, which heavily depends on subjective indicators, underscores the need for an objective, indicator-based approach. This study developed a method for detecting depression and predicting treatment response through deep learning-based spectroscopic analysis of extracellular vesicles (EVs) from plasma. EVs were isolated from the plasma of both nondepressed and depressed groups, followed by Raman signal acquisition, which was used for AI algorithm development. The algorithm successfully distinguished depression patients from healthy individuals and those with panic disorder, achieving an AUC accuracy of 0.95. This demonstrates the model's capability to selectively diagnose depression within a nondepressed group, including those with other mental health disorders. Furthermore, the algorithm identified depression-diagnosed patients likely to respond to antidepressants, classifying responders and nonresponders with an AUC accuracy of 0.91. To establish a diagnostic foundation, the algorithm applied explainable AI (XAI), enabling personalized medicine for companion diagnostics and highlighting its potential for the development of liquid biopsy-based mental disorder diagnosis.

Conformal field theories in a magnetic field

We study the properties of 2+1d conformal field theories (CFTs) in a background magnetic field. Using generalized particle-vortex duality, we argue that in many cases of interest the theory becomes gapped, which allows us to make a number of predictions for the magnetic response, background monopole operators, and more. Explicit calculations at large N for Wilson-Fisher and Gross-Neveu CFTs support our claim, and yield the spectrum of background (defect) monopole operators. Finally, we point out that other possibilities exist: certain CFTs can become metallic in a magnetic field. Such a scenario occurs, for example, with a Dirac fermion coupled to a Chern-Simons gauge field, where a non-Fermi liquid is argued to emerge. Published by the American Physical Society 2024

Use of albumin and CRP related immuno-nutritional markers for prediction of locoregional response to treatment in unresectable hepatocellular carcinoma

Objective:We analysed the relationship between albumin and CRP related inflammation markers (Controlling nutritional status(Conut)score, lymphocyte albumin factor(LA), albumin-bilirubin score (ALBI), highly sensitive modified Glasgow prognostic score (Hs-mGPS), Glasgow prognostic score(GPS)) and locoregional treatment response in HCC. Materials and methods:Among 180 HCC patients, 63 patients who underwent locoregional therapy were included in this study. Albumin and CRP related immuno-nutrition scores were calculated by recording routine laboratory tests between the fourth and eighth week after treatment. The predictive and prognostic value of these markers for overall survival(OS) and disease free survival after treatment(DFS) were analysed. Results:The mean age was 63 years (min-max:26-87) and 59 (93.7%) of the patients were male. The mean follow up period was 25 months and 53 patients were deceased (84.1%). mOS: 18.56 months (min-max: 13.13-23.99); mDFS: 7 months (min-max: 3.63-10.37) after locoregional treatment. Cut-off values of prognostic markers were determined by Roc analysis. Statistically significant correlation was found between age(p=0.019), Conut(p=0.001), GPS(p=0.028), Hs-mGPS(p=0.012), LA(p=0.017) and ALBI(p=0.002) and mOS. The relationship between Conut(p=0.002), GPS(p

Transcriptional Profiling of Tofacitinib Treatment in Juvenile Idiopathic Arthritis: Implications for Treatment Response Prediction.

To assess changes in gene expression following tofacitinib treatment and investigate transcription patterns as potential predictors of treatment response in patients with active juvenile idiopathic arthritis (JIA). Whole blood samples were collected from JIA patients at baseline and after 18 weeks of open-label tofacitinib treatment (clinical trial NCT02592434). Patients who achieved a JIA-American College of Rheumatology (ACR) response of 70 or above at week 18 were classified as treatment-responders (TR) while those with at most a JIA-ACR30 response were classified as poor-responders (PR). Differential gene expression and gene ontology (GO) over-representation analyses were performed to compare RNA expression between week 18 and baseline samples, as well as between PR and TR samples at baseline. Samples from 67 patients at baseline and 60 at week 18 were analyzed. Following 18 weeks of tofacitinib treatment across all JIA subjects, 883 genes showed significant differential expression (week 18 - baseline). The most strongly downregulated genes were over-represented within IL-7, type I, and type II interferon pathways, while upregulated genes were enriched in ontologies related to neuronal cell processes and cell signaling. Comparing PR and TR at baseline, 663 genes showed differential expression. Upregulated genes were over-represented within ontologies including activation of MAPK activity (p=9.40x10-5), myeloid cell development (p=8.13 x10-5), activation of GTPase activity (p=0.00015), and organelle transport along microtubules (p=0.00021). Tofacitinib treatment in JIA downregulated genes in interferon and IL-7 signaling pathways regardless of effectiveness. Furthermore, baseline upregulation of MAPK signaling may predict poor response to tofacitinib treatment in JIA.

Prediction of Treatment Response Based on Nutritional Status and Tumor Immunity in Oropharyngeal Cancer Patients Treated With Chemoradiotherapy.

Radiotherapy (RT) for advanced oropharyngeal cancer (OPC) is effective, especially when combined with chemotherapy (CRT). However, its success can vary depending on factors, such as tumor stage, HPV infection (p16 status), and the patient's nutritional and immune status. This study examined the controlling nutritional status (CONUT) score and tumor immunity as predictive factors for treatment outcomes in OPC, aiming to develop a personalized risk score. A retrospective analysis was conducted on 84 patients with OPC treated with definitive RT or CRT, and survival outcomes were compared based on various factors, including BMI, CONUT score, CD8 expression, and HLA class II expression. We observed better overall survival (OS) rates in CD8-positive patients and those with higher HLA class II expression. The univariate analysis identified stage, p16 status, BMI, CONUT score, and CD8 expression as significantly associated with OS. In multivariate analysis, stage, BMI, and CONUT score remained significant predictors of OS. A risk scoring system was developed based on stage, p16 status, BMI, CONUT score, and CD8 expression. Patients were categorized into low-risk and high-risk groups, with significantly better survival in the low-risk group. A combined risk score incorporating clinical, nutritional, and immune factors can improve the prediction of treatment outcomes for OPC patients. This risk stratification may enable personalized treatment plans and improve ΟS rates.

A fast-aware multi-target response prediction approach and its application in aeronautical engineering

Response prediction is a fundamental yet challenging task in aeronautical engineering, requiring an accurate selection of sensor positions correlated with the target responses to achieve precise predictions. Unfortunately, in large-scale structures, the rigorous selection of reliable sensor candidates for multi-target responses remains largely unexplored. In this paper, we propose a flexible and generalized framework for selecting the most relevant sensors to the multi-target response and predicting the target response, referred to as the Fast-aware Multi-Target Response Prediction (FMTRP) approach in the spirit of divide-and-conquer. Specifically, first, a multi-task learning module is designed to predict multi-point response tasks at the same time. Simultaneously, we meticulously devise adaptive mechanisms to facilitate loss-term reweighting and encourage prioritization of challenging tasks in multiple prediction tasks. Second, to ensure ease of interpretation, we introduce a hybrid penalty to select sensors at the group-sparsity, individual-sparsity and element-sparsity levels. Finally, due to the substantial number of candidate sensors posing a significant computational burden, we develop a more efficient search strategy and support computation to make the proposed approach applicable in practice, leading to substantial runtime improvements. Extensive experiments on aircraft standard model response datasets and large airliner test flight datasets validate the effectiveness of the proposed approach in identifying sensor locations and simultaneously predicting responses at multiple points. Compared to state-of-the-art methods, the proposed approach achieves an accuracy of over 99% in sinusoidal excitation and exhibits the shortest runtime (3.514 s).

Performance Assessment and Quality Control of Fluorescence Molecular Endoscopy With a Multi-Parametric Rigid Standard.

Fluorescence molecular endoscopy (FME) is emerging as a "red-flag" technique with potential to deliver earlier, faster, and more personalized detection of disease in the gastrointestinal tract, including cancer, and to gain insights into novel drug distribution, dose finding, and response prediction. However, to date, the performance of FME systems is assessed mainly by endoscopists during a procedure, leading to arbitrary, potentially biased, and heavily subjective assessment. This approach significantly affects the repeatability of the procedures and the interpretation or comparison of the acquired data, representing a major bottleneck towards the clinical translation of the technology. Herein, we propose a robust methodology for FME performance assessment and quality control that is based on a novel multi-parametric rigid standard. This standard enables the characterization of an FME system's sensitivity through a single acquisition, performance comparison of multiple systems, and, for the first time, quality control of a system as a function of time and number of usages. We show the photostability of the standard experimentally and demonstrate how it can be used to characterize the performance of an FME system. Moreover, we showcase how the standard can be employed for quality control of a system. In this study, we find that the use of composite fluorescence standards before endoscopic procedures can ensure that an FME system meets the performance criteria and that components prone to performance degradation are replaced in time, avoiding disruption of clinical endoscopy logistics. This will help overcome a major barrier for the translation of FME into the clinics.

Multi-axial fatigue of high-strength concrete: Model-enabled interpretation of punch-through shear test response

This paper aims to fill the gap in multi-axial fatigue characterization of concrete by presenting a comprehensive numerical analysis of data from the punch-through shear test (PTST), which allows control over combined shear and compression loads in the tested ligament. To accurately reproduce the degradation process in this multi-axial configuration, a material model must capture two key effects: (i) dissipative behavior at the inter-aggregate level during subcritical pulsating loading and (ii) fatigue-induced tri-axial stress redistribution in the test ligament. The recently published MS1 model by the authors effectively incorporates these features, thereby enabling a deeper interpretation of the experimental data. The former effect is seamlessly integrated into the model using the Lemaitre-type fatigue hypothesis, facilitating the direct derivation of general evolution equations. The latter effect is efficiently represented through the microplane homogenization framework. The model’s predictions for PTST response under monotonic shear loading with varying levels of radial confinement align well with experimental results. Similarly, the simulated fatigue response accurately reproduces the experimentally observed S-N (Wöhler) curves. Further studies demonstrate the model’s ability to predict the effect of fatigue loading sequence. A thermodynamically-based formulation provides an energetic interpretation of this effect, offering a quantitative breakdown of energy dissipation attributed to individual dissipative mechanisms over the lifetime of the material. The studies show that damage-induced dissipation remains almost constant regardless of different fatigue loading histories.

A critical discussion of elasto-visco-plastic self-consistent (EVPSC) models

Two recently proposed elasto-visco-plastic self-consistent (EVPSC) models, based on solving for stress or stress increment, are discussed, and their results are compared with the ones of the visco-plastic self-consistent (VPSC) approach. The advantages and shortcomings of the models are demonstrated, with emphasis on how the grain responses are affected by the time increment and inclusion-medium interaction strength used. Experimental data obtained for an FCC stainless steel is used as the benchmark system for performing the simulations and assessing the reliability of the results. Predictions of stress-strain response of the aggregate, standard deviations in intergranular stress and strain rate, along with the evolution of internal lattice strain are presented and compared, with special focus on their dependence on the incremental approach chosen. We find that the chosen time increment may significantly affect predicted grain responses. To avoid such effect a method based on using two separate time increments is proposed, one for approximating the stress rate and another for actual integration of stress, strain, and state variables. The proposed method successfully eliminates the dependence of results on the time increment used, thus allowing more reliable computations while preserving the computational efficiency of the elasto-visco-plastic model.

Functional ecology of plant communities as a guide for vegetation management

Plant functional traits determine how individual plants cope with varying environmental conditions, and extensive literature supports their role in influencing ecosystem properties. The study of plant functional traits could help the prediction of species responses to disturbances and environmental change, thus providing invaluable knowledge to refine conservation strategies of species and vegetation. However, applications of functional ecology for vegetation management are still underrepresented.The articles in this Special Issue of FLORA offer cutting-edge insights on the variation of plant traits and ecological strategies in response to environmental drivers, such as anthropogenic disturbance, climate and land use change. The results demonstrate how trait-based approaches can be used for the prediction of climate and land use change impacts on vulnerable sets of species or on biogeochemical cycles, reinforcing the concept that functional plant ecology can help identify the most effective procedures for vegetation management, such as the choice of mowing intensity in grasslands or planning vegetation recovery after wildfires.Our knowledge of plant trait coordination at the community level is still undefined, especially concerning the integration of above- and belowground traits. This represents a missed opportunity for vegetation management, especially considering that a complete understanding of the dynamics of the entire ecosystem is crucial for complete understanding of ecosystem properties. Since plant functional traits can represent a straightforward tool for monitoring changes in ecosystem structure and functions, their use could support vegetation monitoring as required by different programs of nature conservation. It would be useful to embrace a perspective that integrates plant traits with systematic conservation planning, ensuring effective and sustainable conservation efforts.

An analysis of floating wind turbine towing operations using OpenFAST

Abstract The floating wind industry is slowly entering the commercialisation phase and array-scale floating wind farms are imminent. Irrespective of the type of floater used, towing operations represent a crucial marine activity required during installation, major repairs and decommissioning phases. Numerical analysis of towing operations is vital for predicting hydrodynamic motions and towing loads, enabling the identification of metocean limits crucial for achieving optimised safe and efficient execution. This paper presents a unique approach to utilise OpenFAST for the examination of towing operations. Compared to other numerical analysis tools available in the market, OpenFAST stands out as a free, open-source tool specifically designed for wind turbine analysis. The study introduces and evaluates an analytical method using OpenFAST, demonstrating its efficacy in modelling towing dynamics, predicting forces and assessing motion responses of a semi-submersible floating wind turbine in varying wave conditions. The numerical simulations are compared against the results from an experimental campaign. The capability of the method in simulating towing dynamics and the prediction of motion responses are discussed.