Results Of Model Research Articles

Does change in trauma-related shame predict change in PTSD symptomatology?

Trauma-related shame (TR-shame) is associated with Posttraumatic Stress Disorder (PTSD) symptoms. However, research findings are inconsistent regarding the role of TR-shame in PTSD treatment. The purpose of this study was to examine if change in TR-shame predicted change in PTSD symptoms in treatment. A sample of 462 adults who received PTSD treatment at a Partial Hospitalization Program completed questionnaires measuring TR-shame (i.e., Trauma-Related Shame Inventory; TRSI) and PTSD symptoms (PTSD Checklist for DSM-5; PCL-5). Latent growth curve models were estimated to examine if the rate of change in TRSI predicted the rate of change in PCL-5 by using structural equation modeling. Furthermore, a latent regression model was estimated to predict the intercept and slope of the PCL-5. Model fits for the PCL-5 and the TRSI linear models were acceptable and both linear slopes were significant. On average, PCL-5 scores reduced 22.18 points from admission to discharge, while TRSI scores reduced 21.9 points from admission to discharge. The results of latent curve regression model suggested that the TRSI linear slope and intercept predicted the PCL-5 linear slope and intercept, respectively. Additionally, post-hoc analyses suggested that the variance in PCL-5 factors at discharge that were explained by TRSI intercept and linear slope ranged from 18.6% to 34.9%. The results of this study indicated that the rate of change in TR-shame predicted the rate of change in PTSD symptoms. Given the negative impact of TR-shame on PTSD symptoms, TR-shame should be a target in treatment for PTSD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.

Analytical Analysis of the Bladeless Rotor Turbine Design for Enhanced Performance

The growing desire for clean and efficient energy solutions has fueled considerable advances in turbine technology. This study proposed a conceptual approach to enhancing the performance of bladeless turbines using an analytical technique, and the analytical solutions bear a resemblance to the experimental setup for a fluid dynamics investigation of a rotor for real-flow effects. The Navier-Stokes equations for fluid flow in cylindrical coordinates were simplified to two-dimensional flow by ignoring axial direction and velocity, and direct integration was used in conjunction with series expansion solutions. The experiment data were used to verify the rotor's fluid dynamics analytical solutions. The model's result was compared to previously calculated solutions of the fluid dynamics of a disc rotor in a bladeless turbine. The disc turbine models were used to predict radial velocity, tangential velocity, pressure gradient, volumetric flow rate, rotor torque, shear stress on the inner and outer disc walls, and rotor efficiency. The model was validated using experimental data with an efficiency of 23.9%, the theoretical solution model was 34%, and the analytical efficiency was 24.3%. The efficiency comparison of the analytical solutions model to the theoretical solutions model revealed a substantial difference, however, the correlation between computed theoretical and analytical results is significant. Previous studies used computed solutions for models, but current analytical solutions outperformed them. The model's output will be valuable to engineers building the disc turbine. It demonstrates a strong link between the analytical and experimental research of the bladeless turbine.

Empowered corrosion-resistant products through HCP crystal network: a topological assistance

Human computer interaction (HCI) aims to enhance product effectiveness and efficiency by empowering users. This research examines corrosion resistance in alloys, a concern due to technological advancements. Metals and alloys are susceptible to degradation, leading to functionality loss, structural collapse, and environmental contamination. Improving corrosion resistance is crucial for product efficiency. In this paper, HCI identifies requirements that emphasize taking an existing hexagonal closely packed (HCP) network, investigating the network for requirements in the form of vertices and edges, mapping different vertices and edges of the network graph with topological invariants, solving the network graph by invariants, and providing results for modeling and design of advanced networks and architectures. The HCI also ensures and investigates the optimization of results produced under the specifications. The study examines network graph results for irregularities, providing guidelines for engineers and manufacturers to create advanced alloy architectures with characteristics through mathematical and graphical methods.

Automatically Temporal Labeled Data Generation Using Positional Lexicon Expansion for Focus Time Estimation of News Articles

Many facts change over time, which is a fundamental aspect of our physical environment. In the case of pandemic articles, the user is not interested in the creation date of the document but in the facts and the cause of the last pandemic. Fake news can be better combated by having a document with a temporal focus. Currently, neither the sequence of events nor the temporal focus is considered when obtaining news documents. Despite the limited number of temporal aspects in the available datasets, it is difficult to test and evaluate the temporal conclusions of the model. The goal of this work is to develop a temporal focus news article retrieval model based on co-training to advance research in semi-supervised learning. A mapping of the dataset is performed using (1) the evolving focus time of news articles and (2) the semi-supervised method based on coincidence contexts for learning low-dimensional continuous vectors for learning neural contrast embedding models generating focus time-based query in sequential news articles to facilitate temporal understanding by learning low-dimensional continuous vectors. A diverse dataset of news articles is used to evaluate the effectiveness of the proposed method. With semi-supervised learning and lexicon expansion, the result of the developed model can achieve 89%. The method performed better than previous baselines and traditional machine learning models with improvements of 12.65% and 4.7%, respectively.

Acceleration mechanisms of energetic ion debris in laser-driven tin plasma EUV sources

Laser-driven tin plasmas are driving new-generation nanolithography as sources of extreme ultraviolet (EUV) radiation centered at 13.5 nm. A major challenge facing industrial EUV source development is predicting energetic ion debris produced during the plasma expansion that may damage the sensitive EUV channeling multilayer optics. Gaining a detailed understanding of the plasma dynamics and ion acceleration mechanisms in these sources could provide critical insights for designing debris mitigation strategies in future high-power EUV sources. We develop a fully kinetic model of tin-EUV sources using one-dimensional particle-in-cell simulations to study ion debris acceleration, which will be valuable for cross-validation of radiation-hydrodynamic simulations. An inverse-bremsstrahlung heating operator is used to model the interaction of a tin target with an Nd:YAG laser, and thermal conduction is included through a Monte Carlo Coulomb collision operator. While the large-scale evolution is in reasonable agreement with analogous hydrodynamic simulations, the significant timescale for collisional equilibration between electrons and ions allows for the development of prominent two-temperature features. A collimated flow of energetic ions is produced with a spectrum that is significantly enhanced at high energies compared to fluid simulations. The dominant acceleration mechanism is found to be a large-scale electric field supported mainly by the electron pressure gradient, which is enhanced in the kinetic simulations due to the increased electron temperature. We discuss the implications of these results for future modeling of tin-EUV sources and the development of debris mitigation schemes.

Minimally Invasive Delivery of Percutaneous Ablation Agent via Magnetic Colloidal Hydrogel Injection for Treatment of Hepatocellular Carcinoma.

Percutaneous thermotherapy, a minimally invasive operational procedure, is employed in the ablation of deep tumor lesions by means of target-delivering heat. Conventional thermal ablation methods, such as radiofrequency or microwave ablation, to a certain extent, are subjected to extended ablation time as well as biosafety risks of unwanted overheating. Given its effectiveness and safety, percutaneous thermotherapy gains a fresh perspective, thanks to magnetic hyperthermia. In this respect, an injectable- and magnetic-hydrogel-construct-based thermal ablation agent is likely to be a candidate for the aforementioned clinical translation. Adopting a simple and environment-friendly strategy, a magnetic colloidal hydrogel injection is introduced by a binary system comprising super-paramagnetic Fe3O4 nanoparticles and gelatin nanoparticles. The colloidal hydrogel constructs, unlike conventional bulk hydrogel, can be easily extruded through a percutaneous needle and then self-heal in a reversible manner owing to the unique electrostatic cross-linking. The introduction of magnetic building blocks is exhibited with a rapid magnetothermal response to an alternating magnetic field. Such hydrogel injection is capable of generating heat without limitation of deep penetration. The materials achieve outstanding therapeutic results in mouse and rabbit models. These findings constitute a new class of locoregional interventional thermal therapies with minimal collateral damages.

Development and Validation of an Algorithm Model for Predicting Heat Sink Effects during Pulmonary Thermal Ablation

The aim of this study was to develop an algorithm model to predict the heat sink effect during thermal ablation of lung tumors and to assist doctors in the formulation and adjustment of surgical protocols. The heat sink effect is an important factor affecting the therapeutic effect of tumor thermal ablation. At present, there is no algorithm model to predict the intraoperative heat sink effect automatically, which needs to be measured manually, which lacks accuracy and consumes time. To construct a segmentation model based on a convolutional neural network that can automatically identify and segment pulmonary nodules and vascular structure and measure the distance between the nodule and vascular. First, the classical Faster RCNN model was used as the nodule detection network. After obtaining the bounding box of pulmonary nodules, the VSPP-NET model was used to segment nodules in the bounding box. The distance from the nodule to the vasculature was measured after the surrounding vasculature was segmented by the VSPP-NET model. The lung CT images of 392 patients with pulmonary nodules were used as the training data for the algorithm. 68 cases were used as algorithm validation data, 29 as nodule algorithm test data, and 80 as vascular algorithm test data. We compared the heat sink effect of 29 cases of data with the results of the algorithm model and expert segmentation and compared the difference between the two results. In pulmonary CT image vasculature segmentation, the recall and precision of the algorithm model reached >0.88 and >0.78, respectively. The average time for automatic segmentation of each image model is 29 seconds, and the average time for manual segmentation is 158 seconds. The output image of the model shows that the results of nodule segmentation and nodule distance measurement are satisfactory. In terms of heat sink effect prediction, the positive rate of the algorithm group was 28.3%, and that of the expert group was 32.1%, with no significant difference between the two groups (p=0.687). The algorithm model developed in this study shows good performance in predicting the heat sink effect during pulmonary thermal ablation. It can improve the speed and accuracy of nodule and vessel segmentation, save ablation planning time, reduce the interference of human factors, and provide more reference information for surgeons to make ablation plans to improve the ablation effect.

Role of international remittances in poverty alleviation: A panel data study of developing countries

Remittances are the funds sent by individuals to their home country from the host country and are the main source of foreign funding after FDI. Remittances are a crucial source of subsistence for the countries that are still in developing phase. Due to of the increasing scale and effect on the origin country, the international remittance flows into the developing country have gained the attention of numerous analysts from all over the world. This study examines the role of foreign remittances in poverty alleviation utilizing the panel data set of Asian countries, precisely nine developing countries. For the dependent variable this study uses poverty gap and the independent variables are Remittances as percentage of GDP, Per capita GDP, External debt as percentage of GNI and Trade openness as percentage of GDP. The findings reveal that foreign emittances have a significant role in alleviating poverty gap. The results of the random effect model showed that a 1% increase in international remittances as percentage of GDP leads to a decrease of 65.932% in poverty gap at $1.90 poverty line in the sample developing economies from 1990-2019. In addition, the results indicate, trade openness and per capita GDP decrease poverty and external debt seems to increase poverty.

Emotion language use in narratives of the 9/11 attacks predicts long-term memory.

Despite considerable cognitive neuroscience research demonstrating that emotions can influence the encoding and consolidation of memory, research has failed to demonstrate a relationship between self-reported ratings of emotions collected soon after a traumatic event and memory for the event over time. This secondary analysis of data from a multisite longitudinal study of memories of the September 11, 2001 terrorist attacks, asked the question of whether emotional language use could predict memory over time. In the 2 weeks following the 9/11 attacks, participants (N = 691; Mage = 36.8; 72% identifying as male; 76% identifying as white) wrote narratives about how they learned of the attacks and the impact of the attacks on them. Language features of these narratives were extracted using the Linguistic Inquiry Word Count program and used to predict three types of memory: (a) event memory accuracy, (b) flashbulb memory consistency, and (c) emotion memory consistency. These outcomes were assessed at the time of writing, 1, 3, and 10 years after the 9/11 attacks. Results of linear mixed-effects models indicate that greater use of negative emotion words in narratives predicts better event memory accuracy 3 and 10 years after the attacks and worse flashbulb memory consistency 10 years after the attacks. However, emotion word use does not predict emotion memory consistency across time. We also examine whether other exploratory linguistic predictors are associated with memory over time. These findings suggest that written language may serve as a potential early indicator of memory over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Detection of diseases in rice leaf using convolutional neural network with transfer learning based on ResNeXt

Rice is the most cultivated crop in the agricultural field and is a major food source for more than 60% of the population in India. Occurrence of disease in rice leaves, majorly affects the rice quality, production and also the farmers’ income. Nowadays, new variety of diseases in rice leaves are identified and detected periodically throughout the world. Manual monitoring and detection of plant diseases proves to be time consuming for the farmer and also a costly affair for using chemicals in the disease treatment. In this paper, a deep learning method of convolutional neural networks (CNN) with a transfer learning technique is proposed for the detection of a variety of diseases in rice leaves. This method uses the ResNeXt network model for classifying the images of disease-affected plants. The proposed model’s performance is evaluated using accuracy, precision, recall, F1-score and specificity. The experimental results of ResNeXt model measured for accuracy, precision, recall, F1-score, and specificity, are respectively 99.22%, 92.87%, 91.97%, 90.95%, and 99.05%, which proves greater accuracy improvement than the existing methods of SG2-ADA, YOLOV5, InceptionResNetV2 and Raspberry Pi.

Identification and Verification of Potential Ferroptosis-Related Biomarkers in Cervical Cancer

This study screened important genes contributing to morbidity from differential ferroptosis-related genes (FRGs) in cervical cancer and to establish a risk assessment model with ferroptosis-related LncRNAs. Total RNA sequencing data were extracted from The cancer genome atlas (TCGA), Gene Expression Omnibus (GEO) and Genotype-Tissue Expression (GTEx). By differential analysis, we identified ferroptosis-related hub genes close to prevalence of cervical cancer. According to receiver operator curves (ROC) curves, hub genes have good diagnostic performance. The diagnostic potential of hub genes for occurrence of the disease was further assessed and verified. Further, a risk-assessing model based on ferroptosis-related LncRNAs was established. Finally, the differential expressions of hub genes were verified through qRT-PCR. Five hub genes were identified, and we found through GO, KEGG and immune infiltration, that the hub genes are connection with cervical cancer. The Area Under Curve (AUC) values were all greater than 0.8 in ROC curve, and the hub genes presented differences between disease and control groups in validation set GSE29570. We created a risk assessment model with 16 ferroptosis-related LncRNAs. There was a difference in survival between high-risk and low-risk groups. The AUC result for risk assessment model reached 0.792, and there were significant expression differences of Hub genes in Huvec and Hela cells. The study screened 5 hub genes and constructed the risk-assessment model based on 16 LncRNAs associated with ferroptosis genes.

Implementation of a PAFV turbulence model for airfoil flow simulation on OpenFOAM

To further develop a more effective turbulence model and to improve the calculation accuracy of the flow around airfoil, a new PAFV turbulence model has here been constructed by using a deformation rate tensor and the grouping of an average fluctuation velocity. To evaluate the applicability of the PAFV turbulence model, the numerical calculations of flow around the airfoil have here been implemented, which was based on the OpenFOAM calculation platform. On the basis of grid independence research, the model was used to calculate the low-speed flow-around problem for the plano-convex airfoil NACA4412 and the symmetric airfoil NACA0012. It was also compared with the S-A (Spalart-Allmaras) and SST (Shear Stress Transport) k-ω turbulence models. Firstly, the maximum lift angle-of-attack case of the NACA4412 airfoil was calculated. Thereafter, numerical calculations were performed for the flow around the airfoil in the multi-angle-of-attack case of the NACA0012 airfoil. The results showed that the NACA4412 airfoil had an obviously separated vortex at the trailing edge of the airfoil at the maximum lift angle of attack. Also, there was a certain velocity loss downstream of the trailing edge, which was calculated by all three models. However, the results of the PAFV turbulence model were found to be better than those of the S-A and SST turbulence models. The three turbulence models showed comparable accuracies for the calculations of the surface pressure coefficients of the NACA0012 airfoil. However, the S-A and SST k-ω turbulence models were slightly better for the calculations of the mean velocity profiles of the NACA0012 airfoil. Also, the PAFV turbulence model was more accurate for the calculations of the lift and drag coefficients. In conclusion, the PAFV model can make effective predictions of the airfoil low-speed flow around the problem at hand, which in turn preliminarily verifies the applicability of this turbulence model for the low-speed flow around the airfoil problems.

The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models

Thompson sampling (TS) has been known for its outstanding empirical performance supported by theoretical guarantees across various reward models in the classical stochastic multi-armed bandit problems. Nonetheless, its optimality is often restricted to specific priors due to the common observation that TS is fairly insensitive to the choice of the prior when it comes to asymptotic regret bounds. However, when the model contains multiple parameters, the optimality of TS highly depends on the choice of priors, which casts doubt on the generalizability of previous findings to other models. To address this gap, this study explores the impact of selecting noninformative priors, offering insights into the performance of TS when dealing with new models that lack theoretical understanding. We first extend the regret analysis of TS to the model of uniform distributions with unknown supports, which would be the simplest non-regular model. Our findings reveal that changing noninformative priors can significantly affect the expected regret, aligning with previously known results in other multiparameter bandit models. Although the uniform prior is shown to be optimal, we highlight the inherent limitation of its optimality, which is limited to specific parameterizations and emphasizes the significance of the invariance property of priors. In light of this limitation, we propose a slightly modified TS-based policy, called TS with Truncation (TS-T), which can achieve the asymptotic optimality for the Gaussian models and the uniform models by using the reference prior and the Jeffreys prior that are invariant under one-to-one reparameterizations. This policy provides an alternative approach to achieving optimality by employing fine-tuned truncation, which would be much easier than hunting for optimal priors in practice.

Research Advances in Stem Cell Therapy for Erectile Dysfunction.

Erectile dysfunction (ED) is a common clinical condition that mainly affects men aged over 40 years. Various causes contribute to the progression of ED, including pelvic nerve injury, diabetes, metabolic syndrome, age, Peyronie's disease, smoking, and psychological disorders. Current treatments for ED are limited to symptom relief and do not address the root cause. Stem cells, with their powerful ability to proliferate and differentiate, are a promising approach for the treatment of male ED and are gradually gaining widespread attention. Current uses for treating ED have been studied primarily in experimental animals, with most studies observing improvements in erectile quality as well as improvements in erectile tissue. However, research on stem cell therapy for human ED is still limited. This article summarizes the recent literature on basic stem cell research on ED, including cavernous nerve injury, aging, diabetes, and sclerosing penile disease, and describes mechanisms of action and therapeutic effects of various stem cell therapies in experimental animals. Stem cells are also believed to interact with host tissue in a paracrine manner, and improved function can be supported through both implantation and paracrine factors. To date, stem cells have shown some preliminary promising results in animal and human models of ED.

Gas effects on horizontal ribbon growth

Three-phase (solid, melt, and gas) and two-phase (solid and melt) models of horizontal ribbon growth were compared to identify the significance of different gas effects. The boundary conditions at the melt–gas and solid–gas interfaces for two-phase simulations were obtained from decoupled simulations of the gas phase. The results showed that the gas shear stress strongly changes the flow and temperature fields and the position of the triple-phase line. Also, the gas pressure distribution determined the vertical position of the triple-phase line. In the absence of growth angle effects, the results of the two-phase model with specified convective heat transfer coefficient, shear stress, and pressure as boundary conditions along the gas phase interface closely matched that of the three-phase model. Even with non-zero growth angle effects, the two-phase model with all the boundary conditions agreed well with three-phase simulation results, despite increased deviations at higher pull speeds. Finally, the results indicated that gas-induced velocities are significant compared to the Marangoni and buoyancy velocities, which could lead to flow instabilities and the variations in solid shape as observed in HRG experiments.

Abstract 1658: CFT1946, a potent, selective BRAF V600X mutant-specific degrader demonstrates superior activity as a single agent to clinically approved BRAF inhibitors and standard of care combinations in preclinical models of BRAF V600X melanoma, CRC, NSCLC, and brain metastasis

Abstract Activating mutations in BRAF at residue V600 (typically V600E) lead to dysregulation of the MAPK pathway and occur in approximately 8% of all human cancers including 60% of melanoma, up to 12% of colorectal cancer (CRC), and 4% of NSCLC. Currently approved BRAF inhibitors (BRAFi) are selective for BRAF V600X mutant proteins and are typically used in combination with MEK inhibitors (MEKi) in melanoma and NSCLC, or anti-EGFR antibodies such as cetuximab in CRC. However, their activity is limited by primary or acquired resistance often mediated by RAF dimer-inducing mechanisms. Furthermore, progression of BRAF V600X melanoma after BRAFi/MEKi treatment frequently involves brain metastasis, and currently approved BRAFi have relatively poor brain penetration. CFT1946 is a potent, orally bioavailable, cereblon-based BiDACTM degrader that selectively degrades BRAF V600X mutant protein and is currently under investigation in a Phase I clinical trial. CFT1946 has the potential to overcome limitations of clinically approved BRAFi, as degradation of BRAF V600X should abrogate RAF dimer-driven resistance and paradoxical activation. Indeed, we have previously demonstrated that CFT1946 is efficacious in an A375 BRAF V600E/NRAS Q61K xenograft model of BRAFi resistant melanoma. Here we substantially expand our preclinical characterization of CFT1946 across multiple models of BRAF V600X-driven cancers including BRAF V600X-driven CRC and NSCLC, additional BRAFi-resistant melanoma models, and a brain metastatic melanoma model. Single agent CFT1946 outperformed the SOC encorafenib + cetuximab combination in a panel of BRAF-V600X CRC xenograft models. Pathway analysis in BRAF V600X CRC models revealed that CFT1946 suppresses EGFR-mediated MAPK pathway reactivation, a mechanism known to diminish the impact of approved BRAF inhibitors in this indication. CFT1946 also demonstrated regression in a BRAF-V600X NSCLC PDX model where the SOC dabrafenib + trametinib showed only modest tumor growth inhibition. Consistent with our previous results in the A375 melanoma model, single agent CFT1946 showed superior activity versus the SOC dabrafenib + trametinib combination in all additional melanoma models tested, with complete regression achieved using a CFT1946 + trametinib combination in a PDX model bearing a BRAF-V600E kinase duplication that showed minimal response to dabrafenib + trametinib. Finally, using an A375 intracranial model, treatment with CFT1946 gave robust, dose dependent efficacy and survival advantage over encorafenib. The promising activity of CFT1946 in a broad range of BRAF V600X preclinical models supports its ongoing clinical investigation in BRAF V600 mutant solid tumors (NCT05668585). Citation Format: Bridget Kreger, Jacob R. Stephenson, Mathew E. Sowa, Samantha A. Perino, Laura L. Poling, Eunju Hurh, Michael J. Thomenius, Yanke Liang, Stewart L. Fisher, Roy M. Pollock. CFT1946, a potent, selective BRAF V600X mutant-specific degrader demonstrates superior activity as a single agent to clinically approved BRAF inhibitors and standard of care combinations in preclinical models of BRAF V600X melanoma, CRC, NSCLC, and brain metastasis [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 1658.

Abstract 6028: Preferential tumor-to-normal tissue biodistribution and single-dose efficacy with ABD147, a DLL3-targeted engineered antibody-based radiotherapeutic, in preclinical small cell lung cancer models

Abstract Targeted radiotherapies represent an emerging treatment modality for aggressive cancers with limited treatment options, such as small cell lung cancer (SCLC). ABD147 is a Delta Ligand 3 (DLL3)-targeted antibody conjugate designed to carry and deliver cytotoxic radioactive isotopes to DLL3-expressing tumor cells. DLL3 is commonly expressed on the cell surface of neuroendocrine cancer cells, including SCLC, but has limited and predominantly intracellular expression in non-malignant tissues. The ABD147 antibody specifically binds human DLL3 with high affinity and is internalized by DLL3-expressing cancer cells. To minimize radiation exposure to normal tissue, ABD147 has been engineered to clear quickly from the blood compartment. Using indium-111 to determine ABD147 pharmacokinetics and biodistribution in mice, we demonstrate rapid blood and normal tissue clearance of 111In-ABD147, as designed, following a single intravenous dose administration. However, 111In-ABD147 retains high tumor accumulation (activity concentration of up to 30% ID/g) in xenograft mouse models of SCLC despite low DLL3 surface antigen expression on tumor cells (≤ 3000 copies). 111In-ABD147 shows a favorable tumor-to-normal tissue distribution with predominant liver clearance. Following ABD147 therapeutic radioisotope delivery of actinium-225 or lutetium-177 (225Ac-ABD147, 177Lu-ABD147), we demonstrate single-dose tumor regression and dose-dependent sustained anti-tumor efficacy, corresponding to an extension of survival out to 84 days in multiple SCLC xenograft models. In summary, ABD147 can preferentially deliver radionuclides to DLL3-expressing tumor tissue while substantially reducing the systemic radioactive exposure typical of conventional IgG radioconjugates. Following promising results in preclinical models, including potent anti-tumor activity, 225Ac-ABD147 is now progressing into clinical development. Citation Format: Iva Kulic, Etienne S. Melese, Emma Cummins, Alex Mandel, Raja Viswas, Michael Abrams, Adam Judge. Preferential tumor-to-normal tissue biodistribution and single-dose efficacy with ABD147, a DLL3-targeted engineered antibody-based radiotherapeutic, in preclinical small cell lung cancer models [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 6028.

Current design of rectangular steel silos: limitations and improvement

This study proposes a modification for the current design approach for square and rectangular silos that accounts for silos’ wall flexibility. First, the authors investigated the effect of wall stiffness symbolized by the wall width-to-thickness ratio (a/t) and silo’s dimensions, on the wall-filling pressure using a recently validated 3D finite element model (FEM). The model was then employed to predict the pressures acting on silos’ walls accounting for the stress state in stored granular materials. Most design formulas and guidelines assume silos’ walls to be rigid. This assumption is acceptable for the case of rigid wall concrete silos; however, it is questionable for semi-rigid, flexible wall metal silos. Consequentially, it is crucial to determine the minimum wall stiffness necessary to secure the applicability of the current design rigid wall assumptions and to propose a way to deal with semi-rigid and flexible walls. To this end, several wall pressure distributions that correspond to filling steel silos with varied wall thicknesses were studied. A new adjustment to the Janssen technique was proposed for a better estimate of the wall-filling pressures for square and rectangular silos. In the case of prismatic silos, the Eurocode uses the Janssen equation together with an equivalent radius of a corresponding circular silo (with the same hydraulic radius) to determine the wall pressure. This method predicts pressure values that are practically accurate for rigid-wall silos, but its accuracy decreases for semi-rigid and flexible-wall silos. As an enhancement, the Janssen equation was modified in this research to generate more accurate pressure estimates based on the equivalent volume concept. The finite element results of several developed models with the same granular material were compared to the estimations of the newly established approach to verify the broad range of its applicability.

Cost-Effectiveness of Parathyroid Cryopreservation and Autotransplantation

Delayed autotransplantation of cryopreserved parathyroid tissue (DACP) is the only surgical treatment for permanent postoperative hypoparathyroidism. Studies suggest that only a small minority of cryopreserved samples are ultimately autotransplanted with highly variable outcomes. For these reasons, many have questioned the economic utility of the process, although, to the authors' knowledge, this has never been formally studied. To report the clinical outcomes of parathyroid cryopreservation and DACP at a large academic institution and to determine the cost-effectiveness of this treatment. An institutional review board-approved, retrospective review of patients at a single institution who underwent DACP over a 17-year period was conducted with a median follow-up of 48.2 months. A forward-looking cost-utility analysis was then performed to determine the economic utility of cryopreservation/DACP vs usual care (monitoring and supplementation). Patients who had parathyroid tissue in cryopreserved storage between August 2005 to September 2022 at a single-center, academic, quaternary care center were identified. Parathyroid cryopreservation and DACP. Graft functionality, clinical outcomes, and cost utility using a willingness-to-pay threshold of $100 000 per quality-adjusted life-year (QALY). A total of 591 patients underwent cryopreservation. Of these, 10 patients (1.7%; mean [SD] age, 45.6 [17.9] years; 6 male [60%]) underwent DACP. A minority of autografts (2 [20%]) were subsequently fully functional, one-half (5 [50%]) were partially functional, and 3 (30%) were not functional. The cost-utility model estimated that at a large academic center over 10 years, the additional cost of 591 patients undergoing cryopreservation and 10 patients undergoing autotransplantation would be $618 791.64 (2022 dollars) and would add 8.75 QALYs, resulting in a cost per marginal QALY of $70 719.04, which is less than the common willingness-to-pay threshold of $100 000/QALY. The reimplantation rate of cryopreserved tissue was low (<2%), but when implanted, autografts were at least partially functional 70% of the time. In the first-ever, to the authors' knowledge, formal cost analysis for this treatment, results of the current model suggest that cryopreservation and autotransplantation were cost-effective compared with the usual care for hypoparathyroidism at a large, academic institution. It is recommended that each surgical center consider whether the economic and logistical commitments necessary for cryopreservation are worthwhile for their individual needs.