DigitalCommons@The Texas Medical Center
Not a member yet
41791 research outputs found
Sort by
Titer-First Vaccination Protocol in an Employee Health Clinic
No full textPurpose:
The purpose of this project was to evaluate the clinical and financial outcomes of implementing a titer-first vaccination protocol for new employees at an Employee Health Clinic. This project sought to improve cost-effectiveness and reduce unnecessary vaccinations. Background:
This project was conducted at a large healthcare organization in Houston, Texas, to address the cost burden of traditional vaccination protocols. Pre-vaccination serologic testing helps identify existing immunity, reducing unnecessary vaccinations and overall clinic costs. Methodology:
The titer-first vaccination protocol includes serologic testing for measles, mumps, rubella (MMR), varicella, and hepatitis B, followed by vaccine administration for those without immunity. Data on vaccine costs and dose administration was collected for June, July, and August across two fiscal years. Post-intervention data was compared to pre-intervention data. Results:
The clinic achieved a cost savings of $7,065.53 and a 44.26% reduction in vaccine doses administered after implementing the Titer-First Vaccination Protocol. Improved data tracking was noted with EMR use (EPIC), despite minor challenges such as early documentation discrepancies and occasional scheduling delays. Implications:
The titer-first vaccination protocol effectively reduces unnecessary vaccinations and costs while maintaining employee immunization standards. This model is scalable to other institutions with supportive infrastructure
Restoring p53 Wild-Type Conformation in TP53-Y220C-Mutant Acute Myeloid Leukemia
No full textTP53-Y220C is a recurrent hotspot mutation in cancers and leukemias. It is observed predominantly in acute myeloid leukemia (AML)/myelodysplastic syndromes among hematological malignancies and is associated with poor outcome. The mutation creates a structural pocket in the p53 protein. PC14586 (rezatapopt) is a small molecule designed to bind to this pocket and thus restore a p53-wild type (p53-WT) conformation. We demonstrate that PC14586 converts p53-Y220C into a p53-WT conformation and activates p53 transcriptional targets, but surprisingly induces limited/no apoptosis in TP53-Y220C AML. Mechanistically, MDM2 induced by PC14586-activated conformational p53-WT and the nuclear exporter XPO1 reduce the transcriptional activities of p53, which are fully restored by inhibition of MDM2 and/or XPO1. Importantly, p53-WT protein can bind to BCL-2, competing with BAX in the BH3 binding pocket of BCL-2 and also binds to BCL-xL and MCL-1. However, such binding by PC14586-activated conformational p53-WT is not detected. Pharmacological inhibition of the BCL-2/BAX interaction with venetoclax fully compensates for this deficiency, induces massive cell death in AML cells and stem/progenitor cells in vitro and prolongs survival of TP53-Y220C AML xenografts in vivo. Collectively, we identified transcription-dependent and -independent mechanisms that limit the apoptogenic activities of reactivated conformational p53-WT and suggest approaches to optimize apoptosis induction in TP53-mutant leukemia. A clinical trial of PC14586 in TP53-Y220C AML/myelodysplastic syndromes has recently been initiated (NCT06616636)
Long-Chain Fatty Acids Promote Candida albicans Gut Colonization by Restructuring the Fungal Cell Surface
No full textLong-chain fatty acids promote Candida albicans gut colonization by restructuring the fungal cell surface
Musfirat Shubaita, BS
Advisory Professor: J. Christian Perez, Ph.D.
The yeast Candida albicans is a facultative anaerobe residing in the human digestive tract. Although fungal genetic determinants of mammalian host colonization have been identified, little is known about signals and molecules in the intestinal environment that influence C. albicans proliferation. Previously unpublished data from the Perez lab showed that oleic acid, one of the most abundant long-chain fatty acids in nature, promotes C. albicans colonization of the murine intestine. The goal of this research is to investigate the role of oleic acid in C. albicans gut colonization. I hypothesized that oleic acid promotes, either directly or indirectly, the expression of C. albicans molecules that facilitate the persistence of this fungus in the intestinal domain. I found that β-oxidation, a catabolic process used to break down fatty acids for energy production, was dispensable for C. albicans to colonize mice fed a high-oleic acid diet. Transcriptome analysis revealed that, under anaerobic conditions, oleic acid promoted the expression of several C. albicans transcription factors, which are known to positively regulate intestinal colonization. Furthermore, oleic acid induced the expression of a variety of adhesins and cell surface components. Finally, I identified SOK1 as an oleic acid-induced kinase that dictates cell wall mannan exposure and binding to intestinal mucin under anaerobic conditions. Taken together, these findings indicate that in environments largely void of oxygen (like the colon), dietary oleic acid promotes a C. albicans cell surface configuration that enhances gut occupation
LiGeR-HN Phase III Trials of Petosemtamab + Pembrolizumab and Petosemtamab Monotherapy in Recurrent or Metastatic HNSCC
No full textPatients with recurrent/metastatic (r/m) head and neck squamous cell carcinoma (HNSCC) have limited treatment options and a dismal prognosis, especially when their cancer is resistant to standard treatments like anti-programmed cell death protein 1 and platinum-based therapies. Petosemtamab - a human, common light chain, bispecific antibody with enhanced antibody-dependent cellular cytotoxicity targeting epidermal growth factor receptor (EGFR) and leucine-rich repeat-containing G-protein coupled receptor 5 (LGR5) - demonstrated antitumor activity in r/m HNSCC. In many tumor types, including HNSCC, EGFR is an oncogenic driver, while LGR5 is upregulated. LGR5 can potentiate the wingless-type integration site (WNT)/β-catenin signaling pathway in response to ligand binding, stimulating cancer stem cell proliferation and self-renewal. This article describes two registration-intent, open-label, randomized phase III trials evaluating efficacy and safety of petosemtamab. LiGeR-HN1 (NCT06525220) evaluates petosemtamab plus pembrolizumab versus pembrolizumab as first-line therapy for patients with programmed cell death ligand 1-positive r/m HNSCC. LiGeR-HN2 (NCT06496178) evaluates petosemtamab versus investigator\u27s choice of monotherapy (cetuximab, methotrexate, or docetaxel) in patients with previously treated r/m HNSCC. Primary endpoints in both trials are objective response rate per Response Evaluation Criteria in Solid Tumors version 1.1 by blinded independent central review, and overall survival. Both trials are recruiting at the time of publication
Optimizing Inventory Management to Reduce Medication Waste at a Low-Resource Clinic
No full textIntroduction
This project implemented standard operating procedures for inventory management in a low-resource clinic, incorporating First Expired, First Out (FEFO), Just-in-Time (JIT) ordering, and barcode scanning to reduce medication waste and optimize inventory processes. The aim was to reduce medication waste and optimize inventory processes.
Methodology
The Lean Six Sigma framework guided this project. Root causes of waste included inaccurate demand forecasting, lack of expiration tracking, insufficient staff training, poor stock rotation, and absence of standardized procedures. Baseline waste and practices were recorded. Medications were organized with nearing expirations upfront (FEFO), PAR levels were set, and JIT ordering implemented. Waste was tracked weekly, practice changes documented, staff feedback obtained, while software selection proved challenging due to budget and resistance. Medication waste was analyzed descriptively; inventory practices were assessed through observation and feedback.
Results
Medication waste decreased significantly. Standard operating procedures established a dedicated inventory manager, scheduled tracking, and proactive control using real-time data.
Implications for Practice
Organized procedures optimize inventory management. FEFO and JIT methods effectively reduced waste. Limitations included underuse of barcode scanning and low staff compliance, necessitating manual counts. Future projects should address compliance
Correction To: Selinexor in Combination With Carboplatin and Paclitaxel in Patients With Advanced Solid Tumors: Results of a Single-Center, Multi-Arm Phase Ib Study
No full textLoncastuximab in High-Risk and Heavily Pretreated Relapsed/Refractory Diffuse Large B-Cell Lymphoma: A Realworld Analysis From 21 Us Centers
No full textOutcomes in patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) are poor. Loncastuximab- teserine (Lonca) is an antibody-drug conjugate which was approved by the Food and Drug Administration for the treatment of patients with R/R DLBCL who have received at least two prior lines of therapy, based on the results of the LOTIS-2 trial. However, there are limited data regarding its efficacy in the real-world setting. This retrospective study included 21 US centers and evaluated outcomes of patients with R/R DLBCL treated with Lonca. Our analysis comprises 187 patients with notably higher-risk baseline features compared to those of the LOTIS-2 population, including a higher proportion of patients with bulky disease (17% vs. 0%), high-grade B-cell histology (22% vs. 8%), and increased number of prior lines of therapy (median 4 vs. 3). The complete response rate was 14% and overall response rate was 32%. The median event-free survival and overall survival were 2.1 and 4.6 months, respectively. Those with bulky disease and high-grade B-cell histology had significantly worse outcomes, and those with non-germinal center cell of origin and a complete response to the most recent line of therapy demonstrated superior outcomes. In summary, in this largest retrospective cohort study of Lonca in the real-world setting, the response rates, event-free survival and overall survival were lower than those reported in LOTIS-2, which is likely reflective of its use in higher risk and more heavily pre-treated patients in the real world compared to the patients enrolled on a clinical study
Memantine Inhibits Calcium-Permeable Ampa Receptors
No full textMemantine is an US Food and Drug Administration (FDA) approved drug that is thought to selectively inhibit NMDA-subtype of ionotropic glutamate receptors (NMDARs). NMDARs enable calcium influx into neurons and are critical for normal brain function. However, increasing evidence shows that calcium influx in neurological diseases is augmented by calcium-permeable AMPA-subtype ionotropic glutamate receptors (AMPARs). Here, we demonstrate that these calcium-permeable AMPARs (CP-AMPARs) are inhibited by memantine. Electrophysiology unveils that memantine inhibition of CP-AMPARs is dependent on their calcium permeability and the presence of their neuronal auxiliary subunit transmembrane AMPAR regulatory proteins (TARPs). Through cryo-electron microscopy we elucidate that memantine blocks CP-AMPAR ion channels in a unique mechanism of action from NMDARs. Furthermore, we demonstrate that memantine inhibits a gain of function AMPAR mutation found in a patient with a neurodevelopmental disorder. Our findings unlock potential exploitation of this site to design more specific drugs targeting CP-AMPARs
Dose Prediction via Deep Learning to Enhance Treatment Planning of Lung Radiotherapy Including Simultaneous Integrated Boost Techniques
No full textBackground: Recent studies have shown deep learning techniques are able to predict three-dimensional (3D) dose distributions of radiotherapy treatment plans. However, their use in dose prediction for treatments with varied prescription doses including simultaneous integrated boost (SIB), that is, using multiple prescription doses within the same plan, and benefit in improving plan quality should be validated.
Purpose: To investigate the feasibility and potential benefit of using deep learning to predict dose distribution of volumetric modulated arc therapy (VMAT) including SIB techniques and improve treatment planning for patients with lung cancer.
Methods: The dose prediction model was trained with 93 retrospective clinical VMAT plans for patients with lung cancer from our institutional patient database. The prescription doses of these plans ranged from 35 to 72 Gy, with various fractionation schemes. We used a 3D U-Net architecture to predict 3D dose distributions with 75 plans for training and 18 plans for testing. Model input consisted of computed tomography (CT) images, target and normal tissue contours and prescription doses. We first evaluated model accuracy by comparing the predicted and clinical plan doses for the test set, and then performed replanning according to predicted dose distributions. Furthermore, we evaluated the model prospectively in an additional set of 10 patients from our institution by two approaches where dose prediction was either blinded or provided to treatment planners. We then assessed whether dose prediction could identify suboptimal plan quality and how it affects plan quality if adopted in clinical planning workflow.
Results: The dose prediction model achieved good agreement between the predicted and clinical plan dose distributions, with a mean dose difference of -0.49 ± 0.54 Gy across the test set. The replanning study guided by dose prediction showed that a small subset of the original plans could benefit from improvements regarding sparing of the spinal cord and esophagus. The analysis of the prospective dataset, with initial and final clinical plans generated in the absence of dose prediction, showed that the predicted doses were able to identify possible improvements of target coverage and normal tissue sparing in the initial plans similar to those made by the final plans for majority of the patients, but in varied magnitudes. Moreover, the plans generated with dose prediction guidance were able to consistently improve normal tissue sparing compared to the plans generated without dose prediction guidance.
Conclusions: We demonstrated that our deep learning model can consistently predict high quality VMAT lung plans for a variety of prescription doses. The dose prediction tool was also effective in identifying suboptimal plan quality, suggesting its potential benefit in automated treatment planning and evaluation
Do We Need to Add the Type of Treatment Planning System, Dose Calculation Grid Size, and CT Density Curve to Predictive Models?
No full textBackground: Generalizability and domain dependency are critical challenges in developing predictive models for healthcare, particularly in medical diagnostics and radiation oncology. Predictive models designed to assess tumor recurrence rely on comprehensive and high-quality datasets, encompassing treatment planning parameters, imaging protocols, and patient-specific data. However, domain dependency, arising from variations in dose calculation algorithms, computed tomography (CT) density conversion curves, imaging modalities, and institutional protocols, can significantly undermine model reliability and clinical utility.
Methods: This study evaluated dose calculation differences in the head and neck cancer treatment plans of 19 patients using two treatment planning systems, Pinnacle 9.10 and RayStation 11, with similar dose calculation algorithms. Variations in the dose grid size and CT density conversion curves were assessed for their impact on domain dependency.
Results: Results showed that dose grid size differences had a more significant influence within RayStation than Pinnacle, while CT curve variations introduced potential domain discrepancies. The findings underscore the critical role of precise and standardized treatment planning in enhancing the reliability of predictive modeling for tumor recurrence assessment.
Conclusions: Incorporating treatment planning parameters, such as dose distribution and target volumes, as explicit features in model training can mitigate the impact of domain dependency and enhance prediction accuracy. Solutions such as multi-institutional data harmonization and domain adaptation techniques are essential to improve model generalizability and robustness. These strategies support the better integration of predictive modeling into clinical workflows, ultimately optimizing patient outcomes and personalized treatment strategies