1,250 research outputs found
Hellas, her monuments and scenery by Thomas Chase, M.A. Trübner and Co., 60, Paternoster Row, London. Cambridge Sever and Francis 1863
No full textPreface: by Chase, THomasDedication: by the author to Cornelius Conway Felton, .D.Content description: TitlePagination: PP8+220PVolumes: 1Edition:1stText Genre:Prose / Journa
Parameter Sets, Dose Sums, Average Cure Times 060118.xlsx
No full textTwo data sets (A-D and F-I) for simulation of mutants cured by each of 434 chemotherapy dose schedules in a colon crypt model. The chemotherapy, initiated when the crypt was filed with 20% of mutants, removed all mutants and allowed normal crypt cell dynamics to recover. The dose schedule parameters (columns A-C, F-H) were Duration (Time steps), Interval (Time steps), and Lethality (arbitrary units). The accumulated doses (Column D, Dose Sum), and Average Cure Time (Column I) are each the average of 50 stochastic simulations for the indicated Duration, Interval, and Lethality.Supplementary data for manuscript prepared for publication: Chase Cockrell and David E. Axelrod, Optimization of Dose Schedules for Chemotherapy of Early Colon Cancer Determined by High Performance Computer Simulations
3D Rotation Plot of Times to Cure 060118.avi
No full textMovie showing rotation of three-dimensional plot of time to cure colon crypts of mutant cells. Each point represents one of 434 parameter sets of chemotherapy dose schedules that cure all mutant cells and allows recovery of normal crypt cell kinetics. Axes are Duration (Time Steps) 0-15, Interval (Time Steps) 0-50, and Lethality (arbitrary units) 0-20. Points are color-coded based on the average time to cure a colon crypt of mutant cells, with red representing a treatment schedule that quickly eliminates the mutant cells and dark blue representing a treatment which takes longer to eliminate mutant cells.Supplementary data for manuscript prepared for publication: Chase Cockrell and David E. Axelrod, Optimization of Dose Schedules for Chemotherapy of Early Colon Cancer Determined by High Performance Computer Simulations
Colon Crypt Model 060518 C++
No full textC++ code ported from NetLogo code. Colon Crypt Model 110514 G.nlogo written in the application NetLogo. The NetLogo code and model are available at https://doi.org/doi:10.7282/T3KH0QKV. The colon crypt model is described in the publication: Bravo R, Axelrod D. A calibrated agent-based computer model of stochastic cell dynamics in normal human colon crypts useful for in silico experiments. Theoret Biol Med Model. 2013;10:66-89, http://www.tbiomed.com/content/10/1/66TXT-1. axelrod_colonCancer_MPI.cpp (21 KB) -- TXT-2. Parameters.h (2 KB) -- TXT-3. agents_axelrodCC.h (537 bytes).Supplementary data for manuscript prepared for publication: Chase Cockrell and David E. Axelrod, Optimization of Dose Schedules for Chemotherapy of Early Colon Cancer Determined by High Performance Computer Simulations
The Burn Agent-Based Model (BABM): Developing a Computational Agent-based Model of the Burn Wound Healing Process to Examine Fibrosis and Re-epithelialization After a Partial-thickness Burn Injury
No full textIntroduction: Partial-thickness burn injuries, depending on depth, are distinct from deeper burns in that they can preserve the hair follicle bulb as a source of epithelial stem cells and a modulator of mechanical forces and immune cells. This changes the dynamics of wound healing and is an important consideration for therapeutic decisions. Regardless, patients require appropriate interventions to facilitate proper restoration of the skin to prevent complications like wound infection or sepsis that can occur as sequelae of the disrupted epithelial barrier and systemic inflammatory cascades. Herein, we developed a computational agent-based model of the partial-thickness burn wound environment, the Burn Agent-based Model (BABM), in order to better characterize the complex systems of wound healing and facilitate investigation of novel therapeutic strategies. Methods: The model was developed using NetLogo and simulates a two-dimensional cross-section of epidermis and dermis containing multiple hair follicles. It includes multiple cell types present in those layers, such as keratinocytes, epithelial stem cells, neutrophils, macrophages, and fibroblasts, as well as key cytokines and chemokines. Burn injuries were implemented as a mechanical “shearing” of the entire epidermis and the superficial portion of the dermis with damage to the immediately deeper tissue. The model encompasses the first three weeks of the post-burn period, including hemostasis and coagulation, inflammation, proliferation, and re-epithelialization. Results: We successfully created an ABM of the partial-thickness burn wound healing process at the cellular and cytokinetic level. The model implements the mitotic function of epithelial stem cells particularly within hair follicle bulbs, paracrine influences of injured keratinocytes, phagocytic and inflammatory actions of neutrophils and macrophages, migration and extra-cellular matrix deposition by fibroblasts, and the vascular dynamics of dermal capillaries representing the zones of coagulation, stasis, and hyperemia. In addition, the model incorporates multiple cytokines, notably IL1, IL6, TNFa, TGFb, EGF, FGF, PDGF, and VEGF. The model was calibrated to closely follow the cytokine profiles obtained from cytokine multiplex assays of patients hospitalized for burn injuries. Conclusion: The BABM provides a powerful in-silico framework to study burn wound healing by simulating a small segment of injured skin that can be scaled near infinitely to represent a wound of any size but does not include the wound edge. As the model is refined and further validated, our goal is to allow for model calibration with individualized patient data in order to create a personalized “digital twin” that closely mimics the patient’s physiology and may augment diagnostic or therapeutic decision-making in the clinical setting
The Family History of Chase Theodore Uhlich
No full textThe Family History of
Chase T. Uhlich
28 November 2022
Chase T. Uhlich authored this family history as part of the course requirements for HIST 550/700 Your Family in History offered online in Fall 2022 and was submitted to the Pittsburg State University Digital Commons. Please contact the author directly with any questions or comments: [email protected]
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
Letter from Irah Chase to T.Z.R. Jones
No full text3 p.Irah Chase, the father of the author of Letter 286, Thomas S. Chase, writes another letter in a series concerning his daughter Emma to T.Z.R. Jones. The “mutual misunderstanding” between Emma and Mrs. Stone mentioned by Thomas is also the subject of this letter, and Irah suggests that Emma return for the next term in order to repair her relation with Mrs. Stone. Irah also clarifies the status of Mollie, T.Z.R. Jones’ daughter, who was mentioned in Letter 286. Mollie is a friend of Emma’s, and Irah does not want to see them separated. The rest of the letter consists of Irah articulating a sincere reconciliation grounded in Christian faith, culminating in the written recitation of the Lutheran hymn Blest Be the Tie That Binds by John Fawcett
Data adjuvant therapy dose schedules
No full textData file of simulated dose schedules that prevent the recurrence of colon cancer by apoptotic adjuvant therapy. Includes numerical data in columns for Interval, Duration, Treatment, and 50-year dose sum, ranked by 50-year dose sum. Supplementary file for article tentatively titled “Prevention of Colon Cancer Recurrence from Minimal Residual Disease: Computer Optimized Dose Schedule of Intermittent Apoptotic Adjuvant Therapy.”No restriction on public acces
Model-based machine learning to identify clinical relevance in a high-resolution simulation of sepsis and trauma
No full textIntroduction: Sepsis is a devastating, costly, and complicated disease. It represents the summation of varied host immune responses in a clinical and physiological diagnosis. Despite extensive research, there is no current mediator-directed therapy, nor a biomarker panel able to categorize disease severity or reliably predict outcome. Although still distant from direct clinical translation, dynamic computational and mathematical models of acute systemic inflammation and sepsis are being developed. Although computationally intensive to run and calibrate, agent-based models (ABMs) are one type of model well suited for this. New analytical methods to efficiently extract knowledge from ABMs are needed. Specifically, machine-learning techniques are a promising option to augment the model development process such that parameterization and calibration are performed intelligently and efficiently. Methods: We used the Keras framework to train an Artificial Neural Network (ANN) for the purpose of identifying critical biological tipping points at which an in silico patient would heal naturally or require intervention in the Innate Immune Response Agent-Based Model (IIRABM). This ANN, determines simulated patient “survival” from cytokine state based on their overall resilience and the pathogenicity of any active infections experienced by the patient, defined by microbial invasiveness, toxigenesis, and environmental toxicity. These tipping points were gathered from previously generated datasets of simulated sweeps of the 4 IIRABM initializing parameters. Results: Using mean squared error as our loss function, we report an accuracy of greater than 85% with inclusion of 20% of the training set. This accuracy was independently validated on withheld runs. We note that there is some amount of error that is inherent to this process as the determination of the tipping points is a computation which converges monotonically to the true value as a function of the number of stochastic replicates used to determine the point. Conclusion: Our method of regression of these critical points represents an alternative to traditional parameter-sweeping or sensitivity analysis techniques. Essentially, the ANN computes the boundaries of the clinically relevant space as a function of the model’s parameterization, eliminating the need for a brute-force exploration of model parameter space. In doing so, we demonstrate the successful development of this ANN which will allows for an efficient exploration of model parameter space
Camp Chase surgeon's report
No full textThis Civil War-era surgeon's report dated August 4, 1862, describes the conditions of the training camp and Confederate prison at Camp Chase, located in Columbus, Ohio. Post-surgeon L. C. Brown outlines the layout of the camp, hygienic issues of concern to its inhabitants, and the general health of those housed at the camp and in its hospital.
Established in 1861, Camp Chase served as a recruitment and training center for the Union Army and as a prison camp for captured Confederate soldiers during the Civil War
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