Computer-based Model Research Articles

Detailed Images of Deep Brain Stimulation Leads Using Micro-CT

Introduction: One of the challenges in directional deep brain stimulation (DBS) is to determine the orientation of implanted electrodes relative to targeted regions. Post-operative images must be aligned with a model of the implanted lead, usually a computer-based model provided by the manufacturer. This paper shows that models can alternatively be obtained by capturing images of individual leads using micro-CT, a high-resolution CT technique. Contrary to computer-aided design models, lead models generated this way provide realistic X-ray contrast and finer details. Methods: We scanned DBS leads from various vendors using a Bruker SkyScan 1276 micro-CT system. To reduce beam-hardening artefacts, samples were scanned at maximum X-ray tube voltage (100 kV) and with copper filtering. Images were made publicly available for download and 3D visualisation. Conclusion: Detailed images of single DBS leads can be generated using standard micro-CT systems. Their use as reference models could improve lead orientation algorithms, in particular those dedicated to X-ray modalities. Furthermore, the possibility to share models online could broaden access for clinical research.

Towards Computer-Supported Functional Modelling in Engineering Design Education

The growing need for solutions that can support the computer-based and distant assessment of functional models has resulted in ad hoc implementations of various diagramming tools. These tools are typically not intended for the purpose of functional modelling and lack the flexibility and efficiency of the traditional pen-and-paper approach. This paper reports on an experimental study of 42 students who were introduced to functional modelling through either printed vocabulary materials for pen-and-paper modelling or a specifically developed software application for computer-based modelling. All participants received an identical task—model an electric citrus juicer—with a brief description of how one operates and a photograph of a commercial example. The results show no significant difference in their total scores. However, the pen-and-paper group performed significantly better when it came to the selection of appropriate functions and creating plausible function–flow pairs. These results suggest that the current version of the software alters the functional modelling process in which the students typically engage. Also, it has been hypothesised that the software tool’s lack of flexibility and dynamism in presenting the predefined function vocabulary, when compared to the traditional printouts, might result in earlier fixation and the selection of less appropriate functions. On the other hand, the computer-supported approach can be better controlled and is less prone to critical errors, such as disregarding functional modelling conventions.

Heterogeneous Nuclear Ribonucleoprotein A1 Knockdown Alters Constituents of Nucleocytoplasmic Transport.

Changes in nuclear morphology, alterations to the nuclear pore complex (NPC), including loss, aggregation, and dysfunction of nucleoporins (Nups), and nucleocytoplasmic transport (NCT) abnormalities have become hallmarks of neurodegenerative diseases. Previous RNA sequencing data utilizing knockdown of heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) identified enrichment for pathways and changes in RNAs related to nuclear morphology and showed differential expression of key nuclear targets. This suggests that dysfunction of hnRNP A1, which is observed in neurodegenerative diseases, may contribute to abnormalities in nuclear morphology, NPC, and NCT. We performed knockdown of hnRNP A1 in Neuro-2A cells, a neuronal cell line, to examine nuclear morphology, NPC, and NCT. First, we examined nuclear morphology using Lamin B, wherein we observed increased nuclear envelope abnormalities in cells with hnRNP A1 knockdown as compared to control. To quantify changes in Lamin B, we designed and validated an automated computer-based model, which quantitatively confirmed our observations. Next, we investigated the impact of hnRNP A1 knockdown on components of the NPC and NCT. In line with the previous literature, we found changes in Nups, including altered distribution and reduced protein expression, as well as disrupted NCT. Finally, we validated our findings in multiple sclerosis (MS) brains, a disease with a significant neurodegenerative component caused by hnRNP A1 dysfunction, where neuronal nuclear envelope alterations were significantly increased as compared to controls. Together, these data implicate hnRNP A1 as an important contributor to nuclear morphology, Nup expression and distribution, and NCT and suggest that hnRNP A1 dysfunction may lead to defects in these processes in neurodegenerative diseases.

Environmentally friendly molecularly imprinted polymers as an insert for SPE type columns in the gentamicin monitoring process

The quantity and variety of micro-pollutants infiltrating water resources have increased rapidly in recent times. The appearance of many harmful substances in the waters has resulted in so-called chemical cocktails which significantly contribute to the deterioration of water quality. Additionally, the variety of these compounds, often similar to each other in terms of molecular weights, makes their separation and identification very difficult. In this paper we present the possibility of using self-regenerating mechanism of molecularly imprinted polymers to measure the concentration of micropollutants in the aquatic environment. Molecularly imprinted polymers toward gentamicin were prepared by monomer polymerization in aqueous solution at ambient temperature. Results from computer-based molecular modelling demonstrated potential binding sites between gentamicin and functional monomers in water. Various compositions of polymerization mixtures were tested. The ratio of monomers to each other was 1.1:1.4:0.0015 and 1:1:1 for N-isopropylacrylamine:acrylamide:acrylic acid, respectively. For each composition, various amounts of the standard were tested: 0, 3, 5, 7, 10,15 mol% in relation to monomers. The best results were obtained for 5 % gentamicin with an excess of acrylamide in relation to the other monomers. Sorption for this system was 0.783 mg/g at ambient temperature and desorption 0.593 at 4 °C. The synthesized materials, thanks to the incorporation of thermosensitive poly(N-isopropylacrylamide) into their structures, were able to release 89 % of adsorbed gentamicin. This made it possible to use the designed SPE columns repeatably with similar efficiency. The prepared materials were selective in the presence of other antibiotics like amoxicillin and norfloxacin.

Specialized educational program for high-grade liver injury management: a three-dimensional printed model approach.

The study aims to present a specialized educational program using a 3D printed model for managing Grade IV and V liver injuries. Hepatic packing, a common technique, may not always achieve sufficient hemostasis in these cases, warranting alternative solutions such as mesh liver wrapping. However, mastering this procedure is challenging due to limited teaching resources and the need for repeated practice. A computer-based model was created from an abdominal CT scan to produce a real-sized injured liver model using thermoplastic elastomer TPU-95. Trainees received systematic instruction from an instructor, allowing them to perform the procedure under supervision and independently. Eight surgical residents at Hillel Yaffe Medical Center participated in the program, with the majority successfully completing the procedure under supervision. Furthermore, trainees demonstrated reduced procedure times when performing independently, indicating improved proficiency. This educational approach offers a simple and repeatable method for continuous training in managing high-grade liver injuries, holding potential for enhanced patient outcomes.

Computer Simulation for Effective Pharmaceutical Kinetics and Dynamics: A Review.

Computer-based modelling and simulation are developing as effective tools for supplementing biological data processing and interpretation. It helps to accelerate the creation of dosage forms at a lower cost and with the less human effort required to conduct the work. This paper aims to provide a comprehensive description of the different computer simulation models for various drugs along with their outcomes. The data used are taken from different sources, including review papers from Science Direct, Elsevier, NCBI, and Web of Science from 1995-2020. Keywords like - pharmacokinetic, pharmacodynamics, computer simulation, whole-cell model, and cell simulation, were used for the search process. The use of computer simulation helps speed up the creation of new dosage forms at a lower cost and less human effort required to complete the work. It is also widely used as a technique for researching the structure and dynamics of lipids and proteins found in membranes. It also facilitates both the diagnosis and prevention of illness. Conventional data analysis methods cannot assess and comprehend the huge amount, size, and complexity of data collected by in vitro, in vivo</i>, and ex vivo</i> experiments. As a result, numerous in silico</i> computational e-resources, databases, and simulation software are employed to determine pharmacokinetic (PK) and pharmacodynamic (PD) parameters for illness management. These techniques aid in the provision of multiscale representations of biological processes, beginning with proteins and genes and progressing through cells, isolated tissues and organs, and the whole organism.

Development of radiofluorinated MLN-4760 derivatives for PET imaging of the SARS-CoV-2 entry receptor ACE2.

The angiotensin converting enzyme 2 (ACE2) plays a regulatory role in the cardiovascular system and serves SARS-CoV-2 as an entry receptor. The aim of this study was to synthesize and evaluate radiofluorinated derivatives of the ACE2 inhibitor MLN-4760. [18F]F-MLN-4760 and [18F]F-Aza-MLN-4760 were demonstrated to be suitable for non-invasive imaging of ACE2, potentially enabling a better understanding of its expression dynamics. Computational molecular modeling, based on the structures of human ACE2 (hACE2) and mouse ACE2 (mACE2), revealed that the ACE2-binding modes of F-MLN-4760 and F-Aza-MLN-4760 were similar to that of MLN-4760. Co-crystallization of the hACE2/F-MLN-4760 protein complex was performed for confirmation. Displacement experiments using [3H]MLN-4760 enabled the determination of the binding affinities of the synthesized F-MLN-4760 and F-Aza-MLN-4760 to hACE2 expressed in HEK-ACE2 cells. Aryl trimethylstannane-based and pyridine-based radiofluorination precursors were synthesized and used for the preparation of the respective radiotracers. [18F]F-MLN-4760 and [18F]F-Aza-MLN-4760 were evaluated with regard to the uptake in HEK-ACE2 and HEK-ACE cells and in vitro binding to tissue sections of HEK-ACE2 xenografts and normal organs of mice. Biodistribution and PET/CT imaging studies of [18F]F-MLN-4760 and [18F]F-Aza-MLN-4760 were performed using HEK-ACE2 and HEK-ACE xenografted nude mice. Crystallography data revealed an equal hACE2-binding mode for F-MLN-4760 as previously found for MLN-4760. Moreover, computer-based modeling indicated that similar binding to hACE2 and mACE2 holds true for both, F-MLN-4760 and F-Aza-MLN-4760, as is the case for MLN-4760. The IC50 values were three-fold and seven-fold higher for F-MLN-4760 and F-Aza-MLN-4760, respectively, than for MLN-4760. [18F]F-MLN-4760 and [18F]F-Aza-MLN-4760 were obtained in 1.4 ± 0.3 GBq and 0.5 ± 0.1 GBq activity with > 99% radiochemical purity in a 5.3% and 1.2% radiochemical yield, respectively. Uptake in HEK-ACE2 cells was higher for [18F]F-MLN-4760 (67 ± 9%) than for [18F]F-Aza-MLN-4760 (37 ± 8%) after 3-h incubation while negligible uptake was seen in HEK-ACE cells (< 0.3%). [18F]F-MLN-4760 and [18F]F-Aza-MLN-4760 accumulated specifically in HEK-ACE2 xenografts of mice (13 ± 2% IA/g and 15 ± 2% IA/g at 1h p.i.) with almost no uptake observed in HEK-ACE xenografts (< 0.3% IA/g). This was confirmed by PET/CT imaging, which also visualized unspecific accumulation in the gall bladder and intestinal tract. Both radiotracers showed specific and selective binding to ACE2 in vitro and in vivo. [18F]F-MLN-4760 was, however, obtained in higher yields and the ACE2-binding affinity was superior over that of [18F]F-Aza-MLN-4760. [18F]F-MLN-4760 would, thus, be the candidate of choice for further development in view of its use for PET imaging of ACE2.

A circular economy integration approach into vertical farming with computer-based simulation model for resource optimization and waste reduction

Integrating circular economy into vertical farming holds promises to transform the current economic landscape and bolster environmental sustainability. However, knowledge gaps persist regarding the efficiency of vertical farming methods within the circular economy context, particularly in the development of a simulation model aimed at resource optimization and waste reduction. This study aimed to analyze the vertical farming methods in the circular economy framework and introduce a zero-waste circular economy model both experimentally and through simulation. In the study, a circular economy model was proposed and implemented, utilizing waste generated from hydroponics and aquaponics, alongside vermicompost tea and aerobic compost tea/quick compost, as nutrients for six distinct halophyte plants in the secondary farming setup. The model serves as a valuable tool for identifying and assessing critical factors influencing the circular economy principle, as well as for optimizing resource allocation and management. Over six weeks, plant growth was monitored and compared across the vertical farming methods. Results show that both hydroponic and aquaponic waste proved superior growth compared to the alternatives, highlighting vertical farming's potential for the circular economy. Using the results from the experimental observation, a simulation model was developed. The simulation model's accuracy was validated by comparing outcomes with actual data. Furthermore, the model was used to optimize for balancing resources and capacities in each stage of vertical farming. The vertical farming circular economy model proposed in this study can serve as a blueprint for agriculturalists and decision-makers to adopt more sustainable and efficient farming practices that not only reduce waste but also generate profits.

Fuzzy Logic Controller-based Intelligent Irrigation System Using Solar Radiation Data

Solar radiation is a critical factor influencing agricultural productivity and water resource management. Irrigation systems play a pivotal role in maintaining crop health and yield, and optimizing their operation requires accurate solar radiation data. This abstract explores the significance of solar radiation data in enhancing the efficiency of irrigation systems. Irrigating agricultural fields using an intelligent information system plays a crucial role. This study introduces an irrigation control system employing closed-loop control to use the available water resources efficiently. Continuous data collection from field sensors was done and transmitted to a central station in wireless mode. The data was then retrieved and processed in a computer-based or microcontroller-based solution model, enhancing system autonomy, reliability, and cost-effectiveness. Weather conditions are translated into fuzzy set values. The pump, water outlet valves, and sprinklers are set into motion according to control commands. This research simplifies the need for manual labor and reduces water wastage.

Explainable prediction model for the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma using CNN on CT images

Several studies have emphasised how positive and negative human papillomavirus (HPV+ and HPV−, respectively) oropharyngeal squamous cell carcinoma (OPSCC) has distinct molecular profiles, tumor characteristics, and disease outcomes. Different radiomics-based prediction models have been proposed, by also using innovative techniques such as Convolutional Neural Networks (CNNs). Although some of these models reached encouraging predictive performances, there evidence explaining the role of radiomic features in achieving a specific outcome is scarce. In this paper, we propose some preliminary results related to an explainable CNN-based model to predict HPV status in OPSCC patients. We extracted the Gross Tumor Volume (GTV) of pre-treatment CT images related to 499 patients (356 HPV+ and 143 HPV−) included into the OPC-Radiomics public dataset to train an end-to-end Inception-V3 CNN architecture. We also collected a multicentric dataset consisting of 92 patients (43 HPV+ , 49 HPV−), which was employed as an independent test set. Finally, we applied Gradient-weighted Class Activation Mapping (Grad-CAM) technique to highlight the most informative areas with respect to the predicted outcome. The proposed model reached an AUC value of 73.50% on the independent test. As a result of the Grad-CAM algorithm, the most informative areas related to the correctly classified HPV+ patients were located into the intratumoral area. Conversely, the most important areas referred to the tumor edges. Finally, since the proposed model provided additional information with respect to the accuracy of the classification given by the visualization of the areas of greatest interest for predictive purposes for each case examined, it could contribute to increase confidence in using computer-based predictive models in the actual clinical practice.

Drug-target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism.

Accurately identifying the drug-target interactions (DTIs) is one of the crucial steps in the drug discovery and drug repositioning process. Currently, many computational-based models have already been proposed for DTI prediction and achieved some significant improvement. However, these approaches pay little attention to fuse the multi-view similarity networks related to drugs and targets in an appropriate way. Besides, how to fully incorporate the known interaction relationships to accurately represent drugs and targets is not well investigated. Therefore, there is still a need to improve the accuracy of DTI prediction models. In this study, we propose a novel approach that employs Multi-view similarity network fusion strategy and deep Interactive attention mechanism to predict Drug-Target Interactions (MIDTI). First, MIDTI constructs multi-view similarity networks of drugs and targets with their diverse information and integrates these similarity networks effectively in an unsupervised manner. Then, MIDTI obtains the embeddings of drugs and targets from multi-type networks simultaneously. After that, MIDTI adopts the deep interactive attention mechanism to further learn their discriminative embeddings comprehensively with the known DTI relationships. Finally, we feed the learned representations of drugs and targets to the multilayer perceptron model and predict the underlying interactions. Extensive results indicate that MIDTI significantly outperforms other baseline methods on the DTI prediction task. The results of the ablation experiments also confirm the effectiveness of the attention mechanism in the multi-view similarity network fusion strategy and the deep interactive attention mechanism. https://github.com/XuLew/MIDTI.

Advanced machine learning approaches for predicting permeability in reservoir pay zones based on core analyses

Permeability is the most important petrophysical characteristic for determining how fluids pass through reservoir rocks. This study aims to develop and assess intelligent computer-based models for predicting permeability. The research focuses on three novel models—Decision Tree, Bagging Tree, and Extra Trees—while also investigating previously applied techniques such as random forest, support vector regressor (SVR), and multiple variable regression (MVR). The primary dataset consists of 197 data points from a heterogeneous petroleum reservoir in the Jeanne d'Arc Basin, including laboratory-derived permeability (K), oil saturation (SO), water saturation (SW), grain density (ρgr), porosity (φ), and depth. The most effective machine learning models are identified by a thorough analysis that makes use of a variety of statistical metrics, such as the coefficient of the determinant (R2), mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), maximum error (maxE), and minimum error (minE). Additionally, core features are ranked based on their importance in permeability modeling. This study deviates from conventional approaches by proposing an efficient means of forecasting permeability, reducing reliance on labor-intensive and time-consuming laboratory work. The findings reveal that MVR is unsuitable for permeability prediction, with all developed models outperforming it. Extra Trees emerges as the most accurate model, with an R2 of 0.976, while random forest and bagging tree exhibit slightly lower R2 values of 0.961 and 0.964, respectively. The ranking of these algorithms based on performance criteria is as follows: extra trees, bagging tree, random forest, SVR, decision tree, and MVR. The study also presents a detailed analysis of the impact of input parameters, highlighting porosity (φ) and water saturation (SW) as the most influential, while grain density (ρgr), oil saturation (SO), and depth are considered less important. This study contributes to the petroleum industry's knowledge by showcasing the inadequacy of MVR and highlighting the superior performance of machine learning models, particularly Extra Trees. The proposed models employed in this study can help engineers and researchers determine reservoir permeability quickly and accurately by using a few core attributes, reducing the dependency on resource-intensive and time-consuming laboratory work.

Generation of Character Designs Based on Pre-Islamic Beliefs of Turks

Turkish communities had lived under the belief system of Turkic mythology, known as Tengriism, in a significant part of their history. It is noteworthy that Turkic mythology elements, which have taken such a place in the cultural life of the Turks, are not sufficiently used as visual design tools. Considering the significance of preserving cultural heritage, Turkic mythology is rarely shown in animations, video games, or movies. The primary gods, goddesses, and supernatural creatures of Turkic mythology are not directly depicted, despite the existence of some animations relating to Turkic cultures such as Keloğlan, Nasreddin Hodja, and Dede Korkut. The main concern of this article is that the elements of Turkic mythology are unable to find enough depiction. Therefore some dominant creatures belonging to Turkic mythology were transformed into character designs. As the method, two-dimensional computer-based modelling techniques were selected to compose character designs of selected Turkic mythology creatures.

In vitro calibration and in vivo validation of phenomenological corrosion models for resorbable magnesium-based orthopaedic implants

Metallic bioresorbable orthopaedic implants based on magnesium, iron and zinc-based alloys that provide rigid internal fixation without foreign-body complications associated with permanent implants have great potential as next-generation orthopaedic devices. Magnesium (Mg) based alloys exhibit excellent biocompatibility. However, the mechanical performance of such implants for orthopaedic applications is contingent on limiting the rate of corrosion in vivo throughout the bone healing process. Additionally, the surgical procedure for the implantation of internal bone fixation devices may impart plastic deformation to the device, potentially altering the corrosion rate of the device. The primary objective of this study was to develop a computer-based model for predicting the in vivo corrosion behaviour of implants manufactured from a Mg-1Zn-0.25Ca ternary alloy (ZX10). The proposed corrosion model was calibrated with an extensive range of mechanical and in vitro corrosion testing. Finally, the model was validated by comparing the in vivo corrosion performance of the implants during preliminary animal testing with the corrosion performance predicted by the model. The proposed model accurately predicts the in vitro corrosion rate, while overestimating the in vivo corrosion rate of ZX10 implants. Overall, the model provides a “first-line of design” for the development of new bioresorbable Mg-based orthopaedic devices. Statement of significanceBiodegradable metallic orthopaedic implant devices have emerged as a potential alternative to permanent implants, although successful adoption is contingent on achieving an acceptable degradation profile. A reliable computational method for accurately estimating the rate of biodegradation in vivo would greatly accelerate the development of resorbable orthopaedic implants by highlighting the potential risk of premature implant failure at an early stage of the device development. Phenomenological corrosion modelling approach is a promising computational tool for predicting the biodegradation of implants. However, the validity of the models for predicting the in vivo biodegradation of Mg alloys is yet to be determined. Present study investigates the validity of the phenomenological modelling approach for simulating the biodegradation of resorbable metallic orthopaedic implants by using a porcine model that targets craniofacial applications.

Computer Aided Diagnosis of Brain Tumour Using Walrus Optimization Algorithm

Brain tumour segmentation is one of the most challenging problems in medical image analysis. To detect brain cancers, radiologists must use a computer-based tumour classification model. In medical imaging research, several computer-aided diagnostic (CAD) models are available to help radiologists with their patients. The reason for brain growth division is to deliver exact outline of brain tumour regions. This study suggests a Walrus Optimisation Algorithm approach for detecting brain tumours using MRI (Magnetic Resonance Imaging). Separating the characteristics into four categories has been utilized: no growth, gliomas, meningiomas, and pituitary cancers. The deep learning based model inception AlexNet and ResNet-18 is trained on an augmented training dataset. The CNN classifier is used for characteristics map improvement, while the LSTM (Long momentary memory) classifier is utilized for order. Besides, the boundary remembered for the classifiers is chosen aimlessly utilizing the Walrus Improvement Calculation to build the presentation of the CNN-LSTM classifier. The performance of the tumour diagnosis is assessed using the metrics: overall classification accuracy of 98.8%, precision of 96.23%, recall of 97.01%, specificity of 98%.

Analysis and Development of a Decision Support System for Selection of Prospective KIP Scholarship Recipients

Global Institute of Technology and Business Campus (Global Institute) is one of the campuses that organizes the KIP (Kartu Indonesia Pintar) scholarship program. This program aims to increase access to education for children from disadvantaged families and provide them with financial support. The research methods used are needs analysis, system design, and prototype implementation. This research collects data from various sources, including personal data of prospective scholarship recipients, academic data, family income data, and other relevant factors. The data collected is then analyzed using appropriate decision making methods, such as the Analytical Hierarchy Process (AHP) and TOPSIS methods. A Decision Support System is an interactive platform that helps make decisions efficiently and effectively in the process by presenting alternatives resulting from data processing, information, and the design of interactive computer-based models or systems. This platform provides support to decision makers in utilizing data and models to overcome unstructured problems.

Level of Acceptability of Computer-Assisted Assessment (CAA) and Student’s Academic Performance in Key Stage 2 of Ilalim Elementary School for The School Year 2022-2023

In this research study, the researcher aimed to determine the level of acceptability of computer-assisted assessment and learners’ academic performance of 127 Key Stage 2 Learners of Ilalim Elementary School for the School Year 2022-2023. A descriptive survey instrument was used to determine the level of acceptability adapted from the computer-based assessment acceptance model (CBAAM) and was modified by the researcher. Data revealed that the use of computer-assisted assessment of the Key Stage 2 learners was Moderately Acceptable with a mean of 4.01, indicating that the application purposively used it to improve their knowledge and skills. The learners gained an Outstanding academic performance as reflected in their general weighted average from the four grading periods. Significant differences were found in the level of acceptability of the use of computer-assisted assessment on the profile variables of the learner-respondents. Moreover, the results showed a positive correlation between the level of acceptability of the use of computer-assisted assessment on the learner’s academic performance. Based on the gathered data from the survey, computer-assisted assessment provides the learners with a positive experience when using the application through its friendly user interface. Teachers may continue to use the computer-assisted assessment application to motivate their learners to increase their engagement in learning their subject. It is recommended that teachers may further enhance the delivery of questions in the CAA application to improve learners’ academic performance in the future.

Aceptación y uso de la evaluación basada en computadora en estudiantes universitarios

The rapid development of information and communication technologies (ICT) has opened new possibilities for teaching and assessment practices in higher education, including computerbased assessment (CBA). Since the effective development of this depends on student acceptance, the present study analyzed the constructs that affect the intention to use CBA through the computer-based assessment acceptance model (CBAAM) at a private university in Mexico. The methodology was quantitative and divided into two phases: 1) application of an assessment with multiple-choice questions with automatic grading, and 2) testing of the measurement and structural model of the CBAAM instrument with 84 first-semester engineering students. The results indicate that playful perception has a direct effect on the use of CBA, while facilitating conditions, computer self-efficacy, perceived ease of use, goal expectation, social influence, and content only have indirect effects. The acceptance model studied explains approximately 47% of the variation in the intention to use. It is recommended to investigate other variables that affect the purpose of use and to apply the model in other contexts for further confirmation.

Abstract 4751: The antiviral, Daclatasvir, downregulates Tribbles 2 pseudokinase and reverses enzalutamide resistance in prostate cancer

Abstract Background: FDA-approved enzalutamide is commonly prescribed for advanced prostate cancer. However, enzalutamide-resistant prostate cancer (ERPC) invariably develops, which leads to aggressive, lethal disease. Recently, we found that the Tribbles 2 (TRIB2) pseudokinase is overexpressed in ERPC cells and confers resistance to enzalutamide by promoting lineage plasticity to neuroendocrine differentiation. Though TRIB2 emerged as an excellent molecular target for ERPC, suitable inhibitors are not commercially available for effective targeting. Methods: Compounds were tested using a luciferase-tagged TRIB2 fusion protein-based assay system. Binding of drugs with TRIB2 protein was analyzed by thermal shift assays and by advanced computer-based homology modeling. Degradation of TRIB2 protein was measured by Western blot. Drug effects on re-sensitization of ERPC cells and synergy with enzalutamide were determined through cell viability and apoptosis assays. To gauge the in vivo effects of Daclatasvir (DCV), ERPC tumor xenograft-bearing mice were treated with varying doses of DCV via oral gavage. Tumor growth was calculated by measuring volumes and molecular markers in tumors were analyzed by immunohistochemistry. Results: By designing a luciferase-tagged TRIB2 fusion protein-based assay system, we screened a library of about 1,600 FDA-approved compounds and found that DCV, effectively inhibits the TRIB2-luciferase activity (more than 70% inhibition in 24 hours at 10 micromolar) but does not inhibit the activity of free luciferase. Notably, DCV directly binds to pure TRIB2 protein, leading to its destabilization and a reduction in the half-maximal melting temperature (Tm) from 41oC to 37oC, as confirmed by thermal shift assays. Interestingly, we found that DCV degrades TRIB2 proteins via activation of proteasomes and re-sensitizes ERPC cells to enzalutamide. DCV downregulates the master neuronal transcription factor, BRN2, and the stemness factor, SOX2, and synergizes with enzalutamide at lower, sub-lethal doses to decrease the viability of prostate cancer cells by inducing apoptosis. Finally, DCV was found to effectively inhibit the growth of ERPC tumors and decrease the protein level of TRIB2 in mice when delivered via oral gavage. Conclusion: These findings indicate that DCV effectively downregulates TRIB2 both in vitro and in vivo and suggest that further testing of DCV may help design an innovative therapeutic approach for management of enzalutamide-resistant, aggressive, lethal prostate cancer. Citation Format: Jitender Monga, Rohith Guddeti, Craig Rogers, Shirish Gadgeel, Dhananjay Chitale, Jagadananda Ghosh. The antiviral, Daclatasvir, downregulates Tribbles 2 pseudokinase and reverses enzalutamide resistance in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4751.

Evaluating the Agricultural Production Systems sIMulator (APSIM) wheat module for California

Context Computer-based crop simulation models are important tools for agricultural research and management. APSIM (Agricultural Production Systems sIMulator) is commonly used around the world but has not been widely validated in North America. Aims The objective of this work was to evaluate the reliability of APSIM for simulating wheat production in California, with the aim of providing guidance for future field research aimed at model calibration and validation. Methods Environmental and management data from state-wide wheat variety trials of common wheat (Triticum aestivum L.) were used to parameterise the APSIM-Wheat module (ver. 7.10 r4220). Simulated yield and protein data were compared with observed field trial results to test the reliability of APSIM simulations. Key results The most reliable simulation of grain yield had a root-mean-square error of 1040 kg/ha and normalised root-mean-square error of 16% relative to actual field data. Preliminary calibration of the model for Californian wheat varieties did not improve simulation accuracy or precision. Conclusions The accuracy or precision of the simulations was comparable to that of other tests of the APSIM-Wheat module in environments where it has not been previously calibrated but was considered too low to be reliable. The lack of reliability was due to the poor representation of local Californian wheat genotypes by existing APSIM cultivars, as well as possible lack of precision and accuracy of field data. Implications APSIM could be a valuable tool for wheat research and management in California; however, further research is needed to generate suitable field data for model calibration and validation.