We are thrilled to invite you to the information session for the Nonprofit Management Program! No matter where you are in your academic journey, this program is designed to cater to your unique interests and goals.

Register Here

We are thrilled to invite you to the information session for the Nonprofit Management Program! No matter where you are in your academic journey, this program is designed to cater to your unique interests and goals.

Register Here

In addition to the pre-arrival webinars, the Graduate College is offering an additional series of webinars to help students get acquainted with campus resources and services as they prepare to begin their graduate study. These webinars are optional but highly encouraged.

Discover Blackboard: Drexel's Learning Management System (LMS)
Tuesday, July 16, 2024
9 to 10 a.m. Eastern Daylight Time (EDT)

In this workshop, students will learn the ins and outs about Blackboard Learn, Drexel's fully supported Learning Management System (LMS), also known as "Drexel Learn" or "Learn" for short. Learn is managed by Drexel University Information Technology and is fully integrated with the DrexelOne portal and the Banner Student Information System (SIS). All Drexel courses have a section created in DrexelLearn each term in which you will be enrolled. This webinar is curated by the Director of Instructional Technology from Information Technology, Dr. Michael Shelmet.

JOIN THE WEBINAR [ZOOM]

Introducing Drexel Libraries and Services & Navigating the Waters of Academic Integrity with Artificial Intelligence (AI)
Thursday, July 25, 2024
9 to 10 a.m. Eastern Daylight Time (EDT)

In this workshop, students will learn about the Drexel Libraries and services, Drexel's Academic Integrity policy, and Academic Integrity Pertaining to Artificial Intelligence (AI). Drexel's renowned Librarian, Jay Bhatt, will discuss the importance of academic integrity in the digital age, highlight the usage of ChatGPT and other large language models (LLMs) in academia. Key topics will include the importance of citations, the impact of scientific misconduct, and the role of tools like Retraction Watch in maintaining research integrity. The discussion will cover the responsible use of LLMs in coding and writing academic papers, emphasizing the need for verifying AI-generated citations against credible sources.

JOIN THE WEBINAR [ZOOM]

The webinar sessions will be recorded and archived on this website for later viewing.

If you have any questions about these webinars, please contact graduatecollege@drexel.edu.

BIOMED PhD Thesis Defense

Title:
Controlling Inflammation To Promote Tissue Regeneration

Speaker:
Erin O’Brien, PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Advisor:
Kara Spiller, PhD
URBN Professor of Biomedical Innovation
School of Biomedical Engineering, Science and Health Systems
Drexel University
 
Details:
Dysfunctional tissue repair manifests in a number of conditions, including aging, diabetes, and catastrophic injuries. As key directors of the immune response, macrophages are responsible for sustaining a pro-regenerative environment within injuries, partly via modulation of other immune cells and progenitor cells. Normally, pro-inflammatory (M1) macrophages dominate the site of injury early on, and are subsequently replaced by reparative (M2) macrophages. M2 macrophages, typically activated with IL-4, may derive from circulating monocytes or M1 macrophages that have switched phenotypes. However, it is unknown whether these populations are different in terms of reparative function. Furthermore, in injuries where regeneration is stalled, M1 macrophages are dysfunctional and fail to switch to the M2 phenotype, resulting in chronic inflammation.

This thesis sought to first understand the mechanisms underlying the “M1-to-M2” switch, then leverage it in a macrophage cell therapy to promote tissue regeneration. First, the responses of unactivated (M0) and M1 macrophages to IL-4 were compared in terms of gene, protein, and functional expression. Next, the crosstalk between macrophages and T helper cells was investigated using direct co-culture of human cells in vitro and a cutaneous wound model in mice. Finally, these findings informed the design of a macrophage cell therapy in which mRNA-loaded lipid nanoparticles intracellularly maintained a reparative macrophage phenotype in a murine model of volumetric muscle loss. Altogether, this thesis demonstrates the need to control the macrophage M1-to-M2 switch to promote healing, and offers a potential strategy to do so.

BIOMED PhD Thesis Defense

Title:
Macrophage-Targeted Therapeutics To Attenuate the Development of Ischemic Heart Failure

Speaker:
Shreya Soni, PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Advisor:
Christopher B. Rodell, PhD
Assistant Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University

Details:
Heart failure (HF) remains the leading cause of death worldwide and is predicted to afflict over 8 million Americans by 2030. Nearly 70% of HF cases are the direct result of myocardial infarction (MI), causing a cascade of cell death, loss of muscle contraction, and maladaptive tissue remodeling. Inflammation has recently been recognized as a critical regulator of adverse left ventricular remodeling (LVR) post-MI. Following injury, classically activated (inflammatory) macrophages initially dominate the immune microenvironment, necessary for early tissue remodeling. With injury resolution, alternatively activated (reparatory) macrophages later dominate the local immune landscape, mitigating inflammation and promoting repair. In many cases, this transition is impeded by the establishment of a chronic hyper-inflammatory milieu. The long-term inflammatory response is driven by multiple parallel mechanisms, including local cytokine toxicity, continual recruitment of inflammatory cells to the injury, an autoimmune reaction against the myocardium, and even genetic factors.

Therapeutic strategies have sought to leverage immune modulation to improve outcomes and have shown promise in both pre-clinical and clinical studies. However, phenotypic modulation of macrophages by small-molecule drugs remains under-explored, and specific delivery of these agents to immune cells at the injury site remains challenging. To address these critical needs, we i) identify small-molecule drugs that directly combat the hyper-inflammatory environment and ii) aim to prevent HF development through delivery of the chosen drug via a biomaterial-based vehicle. First, we sought to identify an anti-inflammatory drug that could inhibit the damaging inflammatory macrophage phenotype and promote the healing reparatory phenotype. Using a two-step drug screening process, which involved a developed reporter assay as well as follow-up quantitative polymerase chain reaction (qPCR) and nanoString analysis, we identified celastrol as a potent inhibitor of inflammatory signaling (IC50 < 100 nM, NF-κB inhibition) that likewise promoted a pro-healing phenotype.

We then developed cyclodextrin nanoparticles (CDNPs) that could bind to and sequester celastrol (Keq = 0.474 mM) via non-covalent guest–host interactions. β-Cyclodextrin is widely used to improve drug solubility and bioavailability. Here, we observed that the CDNPs likewise enable macrophage targeting, as the particles are recognized by cell surface receptors (scavenger and mannose receptors), resulting in rapid macrophage-targeted delivery that further improved the anti-inflammatory effects of celastrol in vitro.

In a murine model of ischemia reperfusion injury, macrophage populations were observed to increase over the first two days after MI (>2-fold), dominated by inflammatory (Nos2+) cells that precipitate HF. Intravenous injection of CDNPs two days post-injury resulted in selective myocardial accumulation of the nanoparticles (a >2.5-fold increase relative to sham controls) due to macrophage uptake. At 24 hours post-treatment, CDNP+celastrol delivery re-oriented macrophages towards a reparatory (Arg1+CD206+) phenotype. Histological, geometric, and functional assessment of the LV at day 28 demonstrated reduced fibrosis, prevention of ventricular dilation (end-diastolic volume, end-systolic volume), and retained function (ejection fraction) after treatment of CDNP+celastrol, all similar to that of the sham controls.

While CDNPs can be administered systemically (by intravenous injection), we also developed a locally-deliverable injectable polymer–nanoparticle (iPNP) hydrogel, composed of CDNPs dynamically crosslinked by adamantane-modified hyaluronic acid (Ad-HA). The iPNP hydrogel possesses shear-thinning (>90% loss in G' at 500% strain) and self-healing (>98% recovery within seconds) capabilities, allowing for local delivery via minimally invasive injection. After optimizing hydrogel properties (i.e., polymer concentration, polymer-to-nanoparticle ratio), the chosen formulation, loaded with celastrol, demonstrated sustained knockdown of inflammatory pathway activity (>80%) over 14 days in vitro. Additionally, the hydrogel demonstrated prolonged degradation over 28 days after subcutaneous injection in a control mouse. This local delivery system is a promising strategy to achieve local immune modulation, such as by intramyocardial injection, to concentrate drug effects at the site of injury.

In conclusion, we have identified celastrol as a potent immunomodulatory small molecule drug, capable of modulating macrophage phenotypes in vitro. For macrophage-targeted delivery of celastrol, we have developed two drug-delivery platforms: CDNPs for systemic delivery and iPNP hydrogels for local delivery. Both drug-loaded vehicles knock down inflammatory signaling significantly in vitro. Furthermore, treatment in a mouse model of myocardial injury demonstrated the ability of celastrol-loaded CDNPs to locally modulate macrophage phenotype, preventing LVR. Thus, the CDNP and iPNP hydrogel are versatile drug-delivery platforms for macrophage-targeted therapy to modulate the post-injury immune microenvironment, including for the prevention of ischemic HF.

BIOMED PhD Thesis Defense

Title:
Predicting Failure of Non-Invasive Ventilation in Children with a Risk Stratification Model

Speaker:
Natalie Napolitano, PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Advisor:
Amy Throckmorton, PhD
Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University

Details:
Respiratory distress and the need for assistance with breathing is one of the most frequent reasons a child is admitted into the pediatric intensive care unit (PICU). Mechanical assistance with breathing is performed non-invasively or invasively and the determination of which level of support is required and when to transition from one level to another is unknown.   Although non-invasive ventilation (NIV) has been shown to improve outcomes of care and shorten time in the hospital when successful, the failure of NIV has been reported to cause an increase time on advanced respiratory support, time in the intensive care unit, and time in the hospital. Therefore, it is theorized that if we can predict which patients will not be successfully treated with NIV and at what time-point we should make this decision, we can improve the long-term outcomes of patients and support their faster recovery. The historical definition of NIV failure is flawed and not in line with the traditional framework of therapy failure determination. There is little evidence to support how best to manage NIV, including best approach for success and the appropriate timing of transition to a higher level of care.

Motivated by the need strive for more evidence-based approach to the delivery of respiratory support in the PICU. here we redefined NIV failure in children using the incidence of unfavorable long-term outcomes, provide a landscape of NIV use in a large, busy, tertiary care, referral children’s hospital, and developed a failure prediction model to assist with determination of when to change from NIV to invasive ventilation (IV) to optimize favorable long-term outcome. A new compound outcome definition of NIV failure was designed, and a patient cohort was defined and separated into success and failure groups for comparison. Statistical comparisons were made between groups utilizing Wilcoxon Rank Sum test for non-normally distributed continuous variables and Chi-squared test for categorial variables with our large sample size. A p-value of 0.5 indicates a significant difference between groups.

A retrospective observational study was performed with patient data from the PICU at the Children’s Hospital of Philadelphia from January 1, 2015 – December 31, 2019. The first case of NIV before tracheal intubation (TI) during the first PICU stay was used for data analysis. Complex NIV failure was defined by the incidence of any of the following criteria occurring during the PICU admission: (1) PICU mortality, (2) new tracheostomy, (3) IV for 7 or more days, (4) relevant tracheal intubation associated events during TI (cardiac arrest, hypotension requiring intervention, dysrhythmia, emesis with aspiration, or pneumothorax/ pneumomediastinum), or (5) severe desaturation during TI (SpO2 greater than 90% after preoxygenation and lowest SpO2 during intubation of less than 70%). During the study period, 3,273 patients had 3,844 courses of NIV, during 3,967 PICU admissions that met criteria for inclusion. Of these, 231 (6%) courses met criteria for failure. These cases had a significantly higher rate of TI (72% vs 0.6%, P=<0.001), demonstrated longer time on respiratory support (342.35 days vs 26.68 days, P<0.001), and more time in the PICU (23.31 days vs 2.43 days, P<0.001).

Per these data sets and detailed analysis, nine physiologic metrics or features were identified as important to predicting the worsening of the clinical condition and thus to be important to predicting failure of NIV. These features were isolated for each hour for the first 24-hours of therapy and used in a logistic regression prediction model. This failure prediction model benchmarked and accurately predicted complex NIV failure at 6-hours of therapy (AUPRC = 0.32 and AUROC = 0.803).

Known gaps in the clinical management of NIV failure of definition, physiologic metrics, and timing of  NIV failure have been addressed in the development of (1) a new prediction model with metrics of NIV therapy failure and (2) a new clinical tool that will be used to better inform clinical teams who are treating children with respiratory distress and who require the proper data and trends for clinical decision making.