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Longitudinal Multi-Omics in Alpha-Synuclein Drosophila Model Discriminates Disease- From Age-Associated Pathologies in Parkinson’s Disease
No full textParkinson\u27s disease (PD) starts decades before symptoms appear, usually in the later decades of life, when age-related changes are occurring. To identify molecular changes early in the disease course and distinguish PD pathologies from aging, we generated Drosophila expressing alpha-synuclein (αSyn) in neurons and performed longitudinal bulk transcriptomics and proteomics on brains at six time points across the lifespan and compared the data to healthy control flies as well as human post-mortem brain datasets. We found that translational and energy metabolism pathways were downregulated in αSyn flies at the earliest timepoints; comparison with the aged control flies suggests that elevated αSyn accelerates changes associated with normal aging. Unexpectedly, single-cell analysis at a mid-disease stage revealed that neurons upregulate protein synthesis and nonsense-mediated decay, while glia drive their overall downregulation. Longitudinal multi-omics approaches in animal models can thus help elucidate the molecular cascades underlying neurodegeneration vs. aging and co-pathologies
Improving Scheduling Accuracy through a Standardized Dermatology Consult Order
No full textImproving Scheduling Accuracy through a Standardized Dermatology Consult Order
PURPOSE
This quality improvement (QI) project aimed to improve scheduling accuracy and reduce referral errors in a dermatology department by implementing a standardized consult order form within the electronic health record (EHR).
BACKGROUND
Inaccurate dermatology referrals led to misdirected consultations, increased reconsultation rates, and treatment delays. A lack of standardization contributed to inefficiencies, impacting patient outcomes and workflow. Implementing a structured referral form aimed to enhance triaging accuracy and improve timely dermatologic care.
METHODOLOGY
A pre- and post-intervention design was used. An eight-week baseline data collection period (n=288) assessed existing referral errors. The intervention replaced the free-text consult order with a standardized form featuring quick buttons for referral reasons and mandatory clinical fields. An eight-week post-intervention evaluation (n=301) followed. Data were extracted from EHR reports and validated. Minor staff resistance and workflow adaptation challenges were addressed through training and real-time feedback.
RESULTS
Reconsultation rates decreased from 6% to 4% (33% reduction), with external reconsults declining by 37.5%. Clinicians reported improved efficiency and scheduling accuracy, while staff feedback indicated enhanced referral clarity. These improvements are expected to be sustained with continued staff adherence and periodic workflow assessments.
IMPLICATIONS
The structured consult order improved referral accuracy and efficiency and can serve as a model for other specialties. However, variability in provider adoption may impact broader implementation. Future efforts should refine triage protocols and address department-specific challenges to enhance scalability and long-term impact
Timely Transfusions in Trauma: Instituting a Transfusion Guideline for Trauma Nurses
No full textPURPOSE This project aims to reduce delays in the blood transfusion process in three non-monitored trauma units at a large academic Level I Trauma Center by decreasing the mean RN transfusion process times (RN-TPT) by 20 minutes. The project focuses on optimizing transfusion rates, nursing practices, and electronic documentation integration to enhance transfusion timeliness and improve overall patient care.
BACKGROUND Delays in the blood transfusion process can negatively affect patient outcomes and increase healthcare costs. In three non-monitored trauma units at a Level I Trauma Center, inefficiencies in transfusion rates, nursing practices, and electronic documentation were identified. Barriers included concerns over transfusion rates, complications, and lab collection workflows.
METHODOLOGY Donabedian’s Structure-Process-Outcome model guided the project. The structure focused on creating a transfusion guideline, and the process involved staff education and a lab draw turnaround time campaign. Outcomes were tracked through a retrospective chart review of 237 transfusions and hemoglobin result times. SurveyMonkey assessed nursing confidence in transfusion competency via a pre- and post-survey. Data limitations included missing data, sample size variation, nursing practice differences, and EMR integration.
RESULTS Nursing confidence in transfusion competency significantly increased, particularly among nurses with less than one year of experience. The median RN-TPT for healthy patients improved by 22 minutes following guideline implementation and the hemoglobin campaign but only improved by 5 minutes during the EMR integration phase, suggesting data collection variation or lack of sustained efforts. Additionally, transfusions completed before 10 AM increased by 36%, and nursing compliance with transfusion guidelines rose from 34% to 57%.
IMPLICATIONS Nursing guidelines are recommended to improve confidence and adherence to best practices, particularly among new nurses. No complications related to increased transfusion rates were observed, negating initial nursing fears about the transfusion process. Process changes should be minimized during EMR conversion to prevent performance anxiety and ensure process sustainability
Development of Lineal Energy Spectrum-Based Biological Effects Models for Protons
No full textIn this dissertation, methods are developed and described to allow for the rapid calculation of microdosimetric spectra (specifically, lineal energy) for protons. SuperTrack, a GPU-accelerated tool for calculation of microdosimetric spectra was developed and is capable of computing lineal energy spectra up to 5000x faster than using Geant4 directly. Proton lineal energy spectra generated by SuperTrack are indistinguishable from those generated by Geant4. With SuperTrack, large libraries of lineal energy spectra for monoenergetic protons spanning 0-300 MeV have been developed. The proton lineal energy spectra calculated by SuperTrack have been compared to experimental measurements made by a tissue equivalent proportional counter and demonstrate reasonable agreement. A method to sum monoenergetic lineal energy spectra to yield the lineal energy spectrum of a polyenergetic beam is described and validated. The summation approach for calculation of lineal energy spectra, along with the libraries generated by SuperTrack have been incorporated into a treatment planning system, RayStation IonPG-2023B.
Having made the rapid calculation of proton lineal energy spectra possible, investigations to establish and determine the optimal mathematical formulation of a mathematical radiobiological model for the prediction of the biological effects of protons began. Using previously gathered clonogenic cell survival response data of H460, H1437, U87, and AGO cell lines following proton irradiation, mathematical models describing the relative biological effectiveness of proton therapy as a function of lineal energy and linear energy transfer were developed. It was determined that the potential benefits of lineal energy spectrum-based radiobiological models for protons may only be meaningful in conditions where cells are subject to multiple irradiation conditions with differing underlying proton energy spectra at the same linear energy transfer.
Following this, mathematical models to predict in-vivo treatment outcomes following proton therapy were developed. Four distinct analysis approaches were applied to a cohort of pediatric ependyoma patients treated with proton therapy, first identified in a prior study by Peeler et al. 2016. The analysis approaches attempted to determine whether a correlation with increasing linear energy transfer and the appearance of hyperintense regions on T2-weighted magnetic resonance imaging post-treatment were correlated. I found that analysis approaches which grouped voxel-level response data from all patients together indicated that higher linear energy transfer was correlated with increasing risk of post-treatment image change. However, analysis methods which considered the risk of each individual patient’s risk of developing image changes found that most patients did not demonstrate increasing image change risk with increasing linear energy transfer. Additional work remains to be done to extend the lineal energy spectrum-based models developed to predict clonogenic cell survival to the prediction of in-vivo treatment response
STAT3, NF-κB, and Estrogen Receptor Beta: The Balance of Inflammation in K-ras Mutant Lung Adenocarcinoma
No full textK-ras mutant lung adenocarcinoma (KM-LUAD) is a difficult-to-treat cancer subtype in which chronic inflammation pervades the tumor immune microenvironment (TIME). Pro-inflammatory pathways dampen the response to treatments, including immune checkpoint inhibitors, necessitating therapies that target this inflammatory signaling network in the TIME. This network is underpinned by interaction and coordination of two inflammatory pathways: signal transducer and activator of transcription 3 (STAT3) and nuclear factor kappa B (NF-κB). The balance of these transcription factors determines the degree of anti- vs. pro-tumor immunity, and a skewing towards STAT3 is known to promote tumor development and a pro-tumor TIME. It is also well documented in lung cancer that there are disparities in incidence and survival based on sex, with more cases of lung cancer occurring in female patients but yielding a lower mortality rate than males. A proposed mechanism for this disparity involves the interaction of estrogen signaling with STAT3 and NF-κB. From these observations, two independent but related research projects were developed. The first wing of this study was to test the anti-tumor and early immunotherapeutic efficacy of TTI-101, a selective small-molecule inhibitor of canonical STAT3 signaling, in a K-rasG12D mutant lung cancer mouse model (CC-LR). Treatment of CC-LR mice with TTI-101 resulted in reduced tumor burden while increasing dendritic cell (DC) and T helper 1 (Th1) infiltration into the TIME. TTI-101 treatment decreased pY-STAT3 expression in tumors with accompanying increases in several NF-κB anti-tumor target genes including CXCL9, a chemokine for primed T cells. Transcriptional profiling of the TIME revealed improved immune activation and anti-tumor skewing, as well as B cell signaling enrichment. Analysis of human LUAD data demonstrated negative correlations between STAT3 and Th1/DC infiltration, with DC infiltration also conferring improved survival in LUAD patients with low STAT3. The second wing of this study was to generate a KM-LUAD mouse model with conditional deletion of estrogen receptor β (ERβ), the main form of ER expressed in lung tissue. The resulting mice developed a lower tumor burden than matched controls, and RNA and protein analyses indicated a shift from STAT3- to NF-κB-driven inflammation. The results of these two research arms highlight the importance of STAT3 in driving early tumorigenesis and the anti-tumor benefits of repolarization of the TIME towards NF-κB-driven inflammation. These results also offer a preventative treatment window for high-risk individuals and patients with early-stage KM-LUAD, as well as potential stratification criteria and therapeutic targets
Investigative Needle Core Biopsies Support Multimodal Deep-Data Generation in Glioblastoma
No full textGlioblastoma (GBM) is an aggressive primary brain cancer with few effective therapies. Stereotactic needle biopsies are routinely used for diagnosis; however, the feasibility and utility of investigative biopsies to monitor treatment response remains ill-defined. Here, we demonstrate the depth of data generation possible from routine stereotactic needle core biopsies and perform highly resolved multi-omics analyses, including single-cell RNA sequencing, spatial transcriptomics, metabolomics, proteomics, phosphoproteomics, T-cell clonotype analysis, and MHC Class I immunopeptidomics on standard biopsy tissue obtained intra-operatively. We also examine biopsies taken from different locations and provide a framework for measuring spatial and genomic heterogeneity. Finally, we investigate the utility of stereotactic biopsies as a method for generating patient-derived xenograft (PDX) models. Multimodal dataset integration highlights spatially mapped immune cell-associated metabolic pathways and validates inferred cell-cell ligand-receptor interactions. In conclusion, investigative biopsies provide data-rich insight into disease processes and may be useful in evaluating treatment responses
A quality improvement project on implementing a nurse-driven Foley catheter removal protocol using modified HOUDINI criteria for oncology patients to reduce the Catheter-Associated Urinary Tract Infection (CAUTI) and catheter days in the Intensive Care Unit (ICU).
No full textAbstract
A quality improvement project on implementing a nurse-driven Foley catheter removal protocol using modified HOUDINI criteria for oncology patients to reduce the Catheter-Associated Urinary Tract Infection (CAUTI) and catheter days in the Intensive Care Unit (ICU).
Purpose: This scholarly project aimed to develop modified criteria for oncology patients regarding Foley catheter removal guidance and implement a nurse initiative in the ICU to reduce CAUTI and catheter days.
Background: CAUTIs are considered preventable nosocomial infections; hence, a nurse-driven Foley catheter removal protocol has become an essential strategy in healthcare to reduce CAUTIs. The project was implemented in a 43-bed ICU in an academic hospital at the Texas Medical Center. The 12-week pilot project included adult patients with a primary diagnosis of cancer.
Methodology: A quantitative design with a PDSA framework was employed. The institutional infection control trend reports were utilized to obtain data on CAUTI rates and catheter days for 9 months, providing pre-and post-intervention data. A QR code and Qualtrics survey were used to monitor protocol compliance due to the lack of EPIC integration of the new criteria.
Results: The nurse-driven protocol maintained zero CAUTIs and reduced catheter days by 9.4% by the end of implementation which further reduced to 713 catheter days (a 68.8% reduction) during the post-intervention phase.
Implications of practice: Implementing an evidence-based practice will reduce CAUTI and catheter days. The trend suggests that the protocol can facilitate the early removal of Foley catheters
Fc Gamma Receptors Facilitate Antigenic Modulation of LILRB4 and Function as Predictive Biomarkers in Acute Monocytic Leukemia
No full textAcute monocytic leukemia (monocytic AML) is a subtype of AML marked by a proliferation of abnormal monoblasts. This subtype represents approximately 10% of AML cases. The prognosis of monocytic AML is poor, with a 5-year survival of ~30%. Most patients are diagnosed at an age when they are unlikely to survive first-line non-targeted cytotoxic chemotherapy as a bridge to hematopoietic stem cell transplant (HSCT). Even patients who achieve remission commonly relapse. This population of patients would greatly benefit from precision-targeted therapies but currently there are none approved for monocytic AML.
Leukocyte immunoglobulin-like receptor B4 (LILRB4) is an immune checkpoint expressed specifically on the cell surface of monocytic AML with limited expression on other cell types, making it an excellent target for antibody-based precision therapy. We have developed an antibody targeting this receptor, which makes use of an Fc gamma receptor scaffolding mechanism to interact with its target. This mechanism is specific to antibodies targeting cells of the hematopoietic lineage, which possess Fc gamma receptors. It has been described in the literature as antigenic modulation or antibody bipolar bridging and has been shown to reduce the effector function of anti-CD20 antibodies targeting B cell malignancies.
Through this Fc-mediated mechanism, the anti-LILRB4 mAb and others bind their target receptor, inducing internalization of the antigen complex. In the case of anti-LILRB4, this antigenic modulation mechanism leads to improved disinhibition of T cell cytotoxicity and reduced Fc-mediated effector function, as observed with anti-CD20 antibodies. Fc gamma receptors thus represent clinically useful predictive biomarkers for the efficacy of antibodies targeting receptors found on monocytic AML and other hematologic malignancies. Appropriate use of these biomarkers will help clinicians better identify patients who will respond to these precision-targeted antibody therapies in the future, leading to superior outcomes in clinical trials and much-needed approvals of novel therapeutic antibodies
A Deconvolution Framework That Uses Single-Cell Sequencing Plus a Small Benchmark Data Set for Accurate Analysis of Cell Type Ratios in Complex Tissue Samples
No full textBulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we utilize an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using this well-matched, that is, benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using two benchmark data sets of healthy retinas and ovarian cancer tissues suggest much-improved deconvolution accuracy. Leveraging tissue-specific benchmark data sets, we applied DeMixSC to a large cohort of 453 age-related macular degeneration patients and a cohort of 30 ovarian cancer patients with various responses to neoadjuvant chemotherapy. Only DeMixSC successfully unveiled biologically meaningful differences across patient groups, demonstrating its broad applicability in diverse real-world clinical scenarios. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched data set to resolve this challenge. The developed DeMixSC framework is generally applicable for accurately deconvolving large cohorts of disease tissues, including cancers, when a well-matched benchmark data set is available
A Phase IB Trial of Selinexor in Combination With Immune Checkpoint Blockade in Patients With Advanced Renal Cell Carcinoma
No full textBackground: Selinexor (SEL) is a nuclear exportin 1 inhibitor that blocks the transport of nuclear proteins, including tumor suppressors, to the cytoplasm. Preclinical data suggest that the combination of SEL with checkpoint blockade may result in improved response to immunotherapy.
Methods: NCT02419495 was a multiarm phase IB study of SEL in combination with other standard regimens in patients with advanced malignancies. Arm M utilized twice weekly oral SEL and intravenous nivolumab (NIVO). Arm N utilized weekly oral SEL with NIVO plus ipilimumab (IPI). The primary objective of this study was to evaluate the safety of SEL + NIVO and SEL + NIVO+IPI. Secondary objectives included determining the objective response rate (ORR) and progression-free survival (PFS).
Results: Twenty-nine patients were enrolled in the study, of which 26 (90%) had clear cell RCC (ccRCC). Most patients (72%, n = 21) had prior systemic therapies. All patients (100%) developed at least one treatment-emergent adverse event, and 93% had a treatment-related adverse event (TRAE). Grade ≥ 3 TRAE occurred in 31% of patients, including 10% with hyponatremia, 7% with neutropenia, and 7% with thromboembolic events. At a median follow-up of 12.4 months, the ORR in 27 patients evaluable for response was 19% (n = 5). An additional 17 patients (63%) had stable disease (SD) as the best response. The median PFS for the overall cohort was 14.5 months (95% CI 5.2-17.4 months; SEL + NIVO+IPI: 12.2 months, SEL + NIVO: 14.5 months). The median overall survival was 27.8 months (95% CI 15.3-32.5; SEL + NIVO+IPI: unreached, SEL + NIVO: 21.3 months).
Conclusions: SEL in combination with NIVO or NIVO+IPI had a potentially favorable safety profile and showed modest clinical activity in patients with advanced renal cell carcinoma.
Trial registration: This clinical trial was registered on clinicaltrials.gov (NCT02419495)