Search

Introduction The utility of pancreaticoduodenectomy (PD) for high-grade traumatic injuries remains unclear and data surrounding its use are limited. We hypothesized that PD does not result in improved outcomes when compared with non-PD surgical management of grade IV–V pancreaticoduodenal injuries.Methods This is a retrospective, multicenter analysis from 35 level 1 trauma centers from January 2010 to December 2020. Included patients were ≥15 years of age with the American Association for the Surgery of Trauma grade IV–V duodenal and/or pancreatic injuries. The study compared operative repair strategy: PD versus non-PD.Results The sample (n=95) was young (26 years), male (82%), with predominantly penetrating injuries (76%). There was no difference in demographics, hemodynamics, or blood product requirement on presentation between PD (n=32) vs non-PD (n=63). Anatomically, PD patients had more grade V duodenal, grade V pancreatic, ampullary, and pancreatic ductal injuries compared with non-PD patients (all p

The onset of the COVID-19 pandemic has compelled a swift transformation in higher-education methodologies, particularly in the domain of course modality. This study highlights the potential for artificial intelligence and machine learning to improve decision-making in advanced engineering education. We focus on the potential for large existing datasets to align institutional decisions with student and faculty preferences in the face of rapid changes in instructional approaches prompted by the COVID-19 pandemic. To ascertain the preferences of students and instructors regarding class modalities across various courses, we utilized the Cognitive Process-Embedded Systems and e-learning conceptual framework. This framework effectively delineates the task execution process within the scope of technology-enhanced learning environments for both students and instructors. This study was conducted in seven Iranian universities and their STEM departments, examining their preferences for different learning styles. After analyzing the variables by different feature selection methods, we used three ML methods—decision trees, support vector machines, and random forest—for comparative analysis. The results demonstrated the high performance of the RF model in predicting curriculum style preferences, making it a powerful decision-making tool in the evolving post-COVID-19 educational landscape. This study not only demonstrates the effectiveness of ML in predicting educational preferences but also contributes to understanding the role of self-regulated learning in educational policy and decision-making in higher education.

Penetrating injury to the inferior vena cava (IVC) is associated with high morbidity and mortality. Luminal narrowing can occur following lateral venorrhaphy and can lead to future morbidity. This case report discusses the success of patch repair following lateral venorrhaphy in two trauma patients. We describe the use of patch repair to eliminate stenosis of the IVC resulting from primary repair in the setting of traumatic injury. Furthermore, trauma patients are known to be at high risk for venous thromboembolism, and we describe the use of low molecular weight heparin as chemical prophylaxis for prevention of this complication following patch repair.

We report here the discovery and optimization of a novel T cell retargeting anti-GUCY2C x anti-CD3ε bispecific antibody for the treatment of solid tumors. Using a combination of hybridoma, phage display and rational design protein engineering, we have developed a fully humanized and manufacturable CD3 bispecific antibody that demonstrates favorable pharmacokinetic properties and potent in vivo efficacy. Anti-GUCY2C and anti-CD3ε antibodies derived from mouse hybridomas were first humanized into well-behaved human variable region frameworks with full retention of binding and T-cell mediated cytotoxic activity. To address potential manufacturability concerns, multiple approaches were taken in parallel to optimize and de-risk the two antibody variable regions. These approaches included structure-guided rational mutagenesis and phage display-based optimization, focusing on improving stability, reducing polyreactivity and self-association potential, removing chemical liabilities and proteolytic cleavage sites, and de-risking immunogenicity. Employing rapid library construction methods as well as automated phage display and high-throughput protein production workflows enabled efficient generation of an optimized bispecific antibody with desirable manufacturability properties, high stability, and low nonspecific binding. Proteolytic cleavage and deamidation in complementarity-determining regions were also successfully addressed. Collectively, these improvements translated to a molecule with potent single-agent in vivo efficacy in a tumor cell line adoptive transfer model and a cynomolgus monkey pharmacokinetic profile (half-life>4.5 days) suitable for clinical development. Clinical evaluation of PF-07062119 is ongoing.

The role of brain-derived neurotrophic factor (BDNF) signaling in chronic pain has been well documented. Given the important central role of BDNF in long term plasticity and memory, we sought to engineer a high affinity, peripherally-restricted monoclonal antibody against BDNF to modulate pain. BDNF shares 100% sequence homology across human and rodents; thus, we selected chickens as an alternative immune host for initial antibody generation. Here, we describe the affinity optimization of complementarity-determining region-grafted, chicken-derived R3bH01, an anti-BDNF antibody specifically blocking the TrkB receptor interaction. Antibody optimization led to the identification of B30, which has a > 300-fold improvement in affinity based on BIAcore, an 800-fold improvement in potency in a cell-based pERK assay and demonstrates exquisite selectivity over related neurotrophins. Affinity improvements measured in vitro translated to in vivo pharmacological activity, with B30 demonstrating a 30-fold improvement in potency over parental R3bH01 in a peripheral nerve injury model. We further demonstrate that peripheral BDNF plays a role in maintaining the plasticity of sensory neurons following nerve damage, with B30 reversing neuron hyperexcitability associated with heat and mechanical stimuli in a dose-dependent fashion. In summary, our data demonstrate that effective sequestration of BDNF via a high affinity neutralizing antibody has potential utility in modulating the pathophysiological mechanisms that drive chronic pain states.

The onset of the COVID-19 pandemic has compelled a swift transformation in higher-education methodologies, particularly in the domain of course modality. This study highlights the potential for artificial intelligence and machine learning to improve decision-making in advanced engineering education. We focus on the potential for large existing datasets to align institutional decisions with student and faculty preferences in the face of rapid changes in instructional approaches prompted by the COVID-19 pandemic. To ascertain the preferences of students and instructors regarding class modalities across various courses, we utilized the Cognitive Process-Embedded Systems and e-learning conceptual framework. This framework effectively delineates the task execution process within the scope of technology-enhanced learning environments for both students and instructors. This study was conducted in seven Iranian universities and their STEM departments, examining their preferences for different learning styles. After analyzing the variables by different feature selection methods, we used three ML methods—decision trees, support vector machines, and random forest—for comparative analysis. The results demonstrated the high performance of the RF model in predicting curriculum style preferences, making it a powerful decision-making tool in the evolving post-COVID-19 educational landscape. This study not only demonstrates the effectiveness of ML in predicting educational preferences but also contributes to understanding the role of self-regulated learning in educational policy and decision-making in higher education. [ABSTRACT FROM AUTHOR]