46 research outputs found
Phase 1 study of inotuzumab ozogamicin combined with R-GDP for the treatment of patients with relapsed/refractory CD22+ B-cell non-Hodgkin lymphoma
No full textObjective: To evaluate safety, tolerability, and preliminary activity of inotuzumab ozogamicin (InO) plus rituximab, gemcitabine, dexamethasone, and cisplatin (R-GDP) in patients with relapsed/refractory CD22+ B-cell non-Hodgkin lymphoma (NHL). Methods: Patients received InO plus R-GDP (21-day cycle; six-cycle maximum) using up-and-down dose-escalation schema for gemcitabine and cisplatin to define the highest dosage regimen(s) with acceptable toxicity (Part 1; n = 27). Part 2 (n = 10) confirmed safety and tolerability; Part 3 (n = 18) evaluated preliminary efficacy. Results: Among 55 patients enrolled, 42% were refractory at baseline (median 2 [range, 1-6] prior therapies); 38% had diffuse large B-cell lymphoma (DLBCL). The highest dosage regimen with acceptable toxicity was InO 0.8 mg/m2, rituximab 375 mg/m2, cisplatin 50 mg/m2, gemcitabine 500 mg/m2 (day 1 only) and dexamethasone 40 mg (days 1-4); this was confirmed in Part 2, in which three patients had dose-limiting toxicities (grade 4 thrombocytopenia [n = 2], febrile neutropenia [n = 2]). Most frequent treatment-related adverse events were thrombocytopenia (any grade, 85%; grade ≥3, 75%) and neutropenia (69%; 62%). Overall (objective) response rate (ORR) was 53% (11 complete, 18 partial responses); ORR was 71%, 33%, and 62% in patients with follicular lymphoma (n = 14), DLBCL (n = 21), and mantle cell lymphoma (n = 13), respectively. Conclusions: InO 0.8 mg/m2 plus R-GDP was associated with manageable toxicity, although gemcitabine and cisplatin doses were lower than in the standard R-GDP regimen due to hematologic toxicity. Evidence of antitumor activity was observed; however, these exploratory data should be interpreted with caution due to the small sample size and short follow-up duration (Clinicaltrials.gov number: NCT01055496).</p
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On the structural response of eukaryotic cells
No full texttextThe actin, microtubule and intermediate filament cytoskeletal polymer assemblies, along with their accessory proteins, govern the mechanical or structural
response of an eukaryotic cell to an external stress. Using statistical mechanics
tools, this dissertation investigates the molecular properties, such as mesh size, persistence length and filament length, that determine the structural strength of the in
vivo polymer networks, with an emphasis on the actin network or the cortex of cells.
Our study of actin shows how the wide range of shear moduli from 1 Pa to 1 kPa that
spans the viscous sol-like state to the elastic gel-like state witnessed in eukaryotic
cells can be achieved through transient crosslinking and the spatial distribution of
actin and actin crosslinking proteins alone. Thus, this gel-sol transition is achieved
without the action of any severing or capping proteins that depolymerize the actin
network.
In order to understand how the microscopic quantities controlling the structural properties of these in vivo polymers are related to the deformation of a cell
observed experimentally, a cell model is created by us. It starts with modeling the
actin cortex as a thick shell and increases in complexity to include microtubules
and the nucleus. Our cell model predicts that the structural response of the cell
amplifies changes in molecular properties such as the in vivo actin concentration.
Hence, the sensitivity of the structural response to cytoskeletal changes can be used
to distinguish between different cells such as normal and cancer cells and can serve
as an indicator of disease.Physic
Two-fluid Physical Modeling of Superconducting Resonators in the ARTEMIS Framework
No full textInput files, data files and scripts to replicate results in "Two-fluid Physical Modeling of Superconducting Resonators in the ARTEMIS Framework" (Jambunathan et el.)
Please direct any questions to the corresponding author, Revathi Jambunathan (rjambunathan [at] lbl.gov)
Artemis simulations were run using the development branch of artemis https://github.com/ECP-WarpX/artemis
Some simulations in this paper were performed with different commit hashes of artemis and amrex, however, the input files should still work with the most recent development branch of artemis.
In the attached tar file, the input files, data, and scripts used to analyse simulation results shown in Figures 1, 2, 3, 4, and 6 of the paper are provided. To run the simulations on perlmutter GPUs, the code is compiled with USE_GPU=TRUE and USE_LLG=FALS
On the designs of early phase oncology studies
No full textThis thesis focuses on the design, statistical operating characteristics and interpretation of early phase oncology clinical trials. Anti-cancer drugs are generally highly toxic and it is imperative to deliver a dose to the patient that is low enough to be safe but high enough to produce a clinically meaningful response. Thus, a study of dose limiting toxicities (DLTs) and a determination of the maximum tolerated dose (MTD) of a drug that can be used in later phase trials is the focus of most Phase I oncology trials. We first comprehensively compare the statistical operating characteristics of various early phase oncology designs, finding that all the designs examined select the MTD more accurately when there is a clear separation between the true DLT rate at the MTD and the rates at the dose levels immediately above and below. Among the rule-based designs studied, we found that the 3+3 design under-doses a large percentage of patients and is not accurate in selecting the MTD for all the cases considered. The 5+5 a design picks the MTD as accurately as the model based designs for the true DLT rates generated using the chosen log-logistic and linear dose-toxicity curves, but requires enrolling a larger number of patients. The model based designs examined, mTPI, TEQR, BOIN, CRM and EWOC designs, perform well on the whole, assign the maximum percentage of patients to the MTD, and pick the MTD fairly accurately. However, the limited sample size of these Phase I oncology trials makes it difficult to accurately predict the MTD. Hence, we next study the effect of sample size and cohort size on the accuracy of dose selection in early phase oncology designs, finding that an adequate sample size is crucial. We then propose some integrated Phase 1/2 oncology designs, namely the 20+20 accelerated titration design and extensions of the mTPI and TEQR designs, that consider both toxicity and efficacy in dose selection, utilizing a larger sample size. We demonstrate that these designs provide an improvement over the existing early phase designs.2019-12-01T00:00:00
The Forces Behind Cell Movement
No full textCell movement is a complex phenomenon primarily driven by the actin network beneath the cell membrane, and can be divided into three general components: protrusion of the leading edge of the cell, adhesion of the leading edge and deadhesion at the cell body and rear, and cytoskeletal contraction to pull the cell forward. Each of these steps is driven by physical forces generated by unique segments of the cytoskeleton. This review examines the specific physics underlying these phases of cell movement and the origins of the forces that drive locomotion.</p
Disruptive Technology: Do Robots Want Your Job?
No full textKeynote talk with Martin Ford, author of Rise of the Robots. Part of the “Deep Humanities,” One-Day Symposium: FrankenSTEM? Technology Ethics in Silicon Valley, organized by Dr. Revathi Krishnaswamy & Dr. Katherine D. Harris, Department of English and Comparative Literature, San Jose State University.
May 1, 2018, 7pm, The Tech Museum of Innovation, San Jose.https://scholarworks.sjsu.edu/frankenstein200_flyers/1003/thumbnail.jp
A schematic (based on a figure in ) showing how the cell adheres to the substrate
No full text<p><b>Copyright information:</b></p><p>Taken from "The Forces Behind Cell Movement"</p><p></p><p>International Journal of Biological Sciences 2007;3(5):303-317.</p><p>Published online 1 Jun 2007</p><p>PMCID:PMC1893118.</p><p>© Ivyspring International Publisher. This is an open-access article distributed under the terms of the Creative Commons License (http://creativecommons.org/licenses/by-nc-nd/3.0/). Reproduction is permitted for personal, noncommercial use, provided that the article is in whole, unmodified, and properly cited.</p> Cell-substrate attachments are formed when actin bundles connect to the substrate at certain sites via adhesion molecules such as vinculin, talin and integrin
A schematic of the three stages of cell movement, based on ,: after determining its direction of motion, the cell extends a protusion in this direction by actin polymerization at the leading edge
No full text<p><b>Copyright information:</b></p><p>Taken from "The Forces Behind Cell Movement"</p><p></p><p>International Journal of Biological Sciences 2007;3(5):303-317.</p><p>Published online 1 Jun 2007</p><p>PMCID:PMC1893118.</p><p>© Ivyspring International Publisher. This is an open-access article distributed under the terms of the Creative Commons License (http://creativecommons.org/licenses/by-nc-nd/3.0/). Reproduction is permitted for personal, noncommercial use, provided that the article is in whole, unmodified, and properly cited.</p> It then adheres its leading edge to the surface on which it is moving and de-adheres at the cell body and rear. Finally, it pulls the whole cell body forward by contracile forces generated at the cell body and rear of the cell
