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Item Intravenous cangrelor infusion for the treatment of traumatic intracranial internal carotid artery dissection: illustrative case(2025-06-30)BACKGROUND: Antithrombotic initiation in patients with traumatic arterial dissections is weighed against the risk of bleeding complications. Cangrelor is a direct P2Y12 receptor blocker with rapid onset and reversibility. As such, its usage in polytrauma cases such as these, in which patients have both an elevated bleeding risk and an acute need for thromboembolic prevention, is advantageous. OBSERVATIONS: This case details cangrelor usage in a patient with a traumatic left cavernous internal carotid artery dissection as well as acute intracranial hemorrhage and other systemic injuries. No hemorrhagic or thromboembolic complications occurred during cangrelor therapy, which was eventually transitioned to oral aspirin. Outpatient follow-up angiography revealed successful healing of the carotid dissection. LESSONS: The authors present a case demonstrating the clinical utility of cangrelor therapy to treat traumatic arterial dissections and prevent thrombus formation while also maintaining the ability to achieve rapid reversal if bleeding were to occur or urgent surgical procedures were needed.Item A STICKY SITUATION: ESSENTIAL ROLES OF ADHESION G PROTEIN COUPLED RECEPTORS ADGRF5 AND ADGRG1 IN CARDIAC HOMEOSTASIS AND FAILURE(Temple University. Libraries, 2025-05)Rationale: Cardiovascular Disease (CVD) continues to be the leading cause of death both in the US and worldwide. This is due in part to the complex and diverse molecular causes of CVD, an aging population, and treatment strategies which address symptoms rather than the underlying molecular mechanisms that drive disease pathology. Therefore, there is a critical translational need to identify novel molecular targets which can be exploited for therapeutic potential. G-Protein Coupled Receptors (GPCRs) serve as pharmacological targets of more than 30% of all FDA-approved drugs, including key treatments for heart failure like β-blockers. GPCRs constitute the largest protein-coding superfamily in the human genome, with widespread tissue expression and vital roles in various functions, including sensory perception and immune response. However, a subgroup of GPCRs known as Adhesion GPCRs (AGPCRs) has yet to be explored for their pharmacological potential, representing a promising but underutilized area for drug development. Objective: This thesis focuses on investigating roles of AGPCR family members ADGRF5 and ADGRG1 in cardiomyocytes, in both healthy and failing hearts, to identify novel targets which mediate cardiac pathophysiology. Methods & Results: RNA sequencing was performed on healthy left ventricular tissue from adult C57BL/6 mice, allowing for the ranking of AGPCR family members by their expression levels. This analysis revealed ADGRF5 and ADGRG1 as novel targets of interest due to their high cardiac tissue expression. Further validation using previously published single-cell RNA sequencing data confirmed that both cardiac ADGRF5 and ADGRG1 expression is indeed specific to cardiomyocytes. To investigate their functional roles in the heart, cardiomyocyte-specific knockout mouse models were developed using the αMHC promoter to induce the genetic deletion of ADGRF5 or ADGRG1 through the Cre-LoxP recombination system. First, cardiomyocyte-specific genetic deletion of ADGRG1 initially did not impact cardiac structure or function. However, these mice gradually developed increased systolic and diastolic left ventricular (LV) volumes and internal diameters over time. Importantly, when subjected to chronic pressure overload, the deletion of ADGRG1 in CMs accelerated the onset of cardiac dysfunction, accompanied by reduced CM hypertrophy, heightened cardiac inflammation, and increased mortality. These findings suggest that ADGRG1 plays a crucial role in the early adaptation to chronic cardiac stress. Overall, this study provided a compelling proof-of-concept that targeting CM-expressed AGPCRs may present a novel therapeutic strategy for managing the progression of heart failure. In our next study, cardiomyocyte-specific genetic deletion of ADGRF5 did not initially alter cardiac structure or function. However, as these mice aged, we observed notable differences in cardiac remodeling, including increased fibrosis, myocyte hypertrophy, and higher mortality rates. Additionally, the knockout mice exhibited an increased incidence of premature ventricular contractions, which in some animals led to fatal ventricular arrhythmias. To investigate the underlying molecular mechanisms of these changes, bulk RNA sequencing was performed on left ventricular tissue from both knockout mice and their littermates, revealing 58 differentially expressed genes. Validation of these differentially expressed targets was confirmed in isolated adult mouse cardiomyocytes (AMCMs) versus controls and revealed upregulation of the various fetal cardiac genes, including ion channel subunits Scn1b, Cacnah1, and Hcn2 as well as hypertrophic genes, Anp and Bnp. Concurrent with these findings, in vitro studies using (NRVMs) established that indeed, upon increasing infection of adenovirus encoding ADGRF5 resulted in step wise decreases in expression of these genes. Additionally, using this in vitro system, we identified that ADGRF5 couples with Gαq/11-mediated signaling in healthy myocytes and with the use of a Gq/11 inhibitor, expression of these genes is restored, suggesting that ADGRF5 intracellular signaling contributes to their expression. Further mechanistic studies using Givinostat, a pan-HDAC inhibitor, revealed that these gene expression changes occurred in an HDAC sensitive matter. Additionally, when knockout mice and controls were interrogated via transverse aortic constriction, a model for pressure-overload induced HF in vivo, knockout mice possessed a slight acceleration toward functional cardiac failure and accelerated myocyte growth which coincided with enhanced expression of Nppa and Nppb gene expression. Conversely, by directing overexpression of ADGRF5-CTF to myocytes specifically via AAV9-cTnT-ADGRF5-CTF-Flag vs LacZ delivery, injected retro-orbitally 3-weeks post TAC-induction, revealed that ADGRF5 prevented the onset of cardiac failure in wild type mice. Taken together, this study reveals that ADGRF5 maintains cardiac homeostasis through repression of harmful developmental cardiac genes, thus repressing hypertrophic gene activation and electrophysiological imbalance. Conclusion: In conclusion, this work displays the importance of uninvestigated AGPCRs and their critical (patho)physiological contributions. Additionally, AGPCRs may offer a new avenue for the development of CVD therapeutics.Item Advancing Data-driven and Hybrid Modeling for Environmental Biotechnology: Principles and Practices(Temple University. Libraries, 2024-12)Environmental biotechnologies are of paramount importance for the removal of contaminants and the recovery of resources in engineered processes and environmental management. However, the involved bioprocesses encounter challenges due to the variability and complexity of microbial populations, which result in operational uncertainties. Mathematical models, including mechanistic and data-driven approaches, are indispensable tools for optimizing these processes. While mechanistic models offer insights into microbial kinetics, they are constrained by challenges associated with parameter calibration and limited applicability to novel conditions. This study introduces a data-driven model, capable of handling diverse datasets, for identifying phytopathogenic fungal conidia in agricultural settings. The integration of Raman spectroscopy with machine learning enabled the rapid classification of the data. Among the evaluated models, XGBoost demonstrated superior performance, achieving 95% accuracy and addressing key limitations, such as loss of significant features and compromised interpretability, of conventional methods for dimensionality reduction in fungal pathogen identification. However, data-driven models face challenges in extrapolation and often lack interpretability. To address these limitations, hybrid models have been developed to combine the strengths of both approaches in order to deliver robust and interpretable predictions. Hybrid modeling strategies were devised for illustrating microbial dynamics in bioelectrochemical systems (BES) and activated sludge (AS). For BES, Bayesian networks incorporated with a mechanistic framework demonstrated a 72% Bray-Curtis similarity for microbial community predictions and an RMSE of 0.8 for current production. For AS, a sliding-window transformation was conducted for the expansion of the dataset. The core populations were classified into guilds using Bayesian networks, and the ANN validators demonstrated an improvement in community structure predictions following the incorporation of kinetic parameter inputs (Bray-Curtis similarity: 0.70 vs. 0.66). The modeling strategy was then adapted for real-time BES performance prediction by integrating mechanistic and artificial neural network models, and achieved the following: internal resistance (RMSE=3.9%), organic removal (R²=0.90), and current production (R²=0.94). A guideline was finally proposed to standardize the modeling processes, promoting applications of robust hybrid strategies in environmental biotechnologies.Item MAXIMIZING LEARNING EFFICIENCY WITH LIMITED LABELED DATA: APPLICATIONS TO HEALTHCARE AND EDUCATION(Temple University. Libraries, 2024-12)Document classification is essential in domains such as healthcare and education, encompassing three major steps: annotation, training accurate models, and evaluation. Each of these steps is labor-intensive and time-consuming, requiring substantial amounts of labeled data, which is both costly and resource-demanding. This dissertation addresses these challenges by presenting innovative methodologies to enhance annotation efficiency, model training in low-resource settings, and automated scoring. In the healthcare domain, we tackle the challenges of annotation and training with limited resources. First, we develop a visualization approach for rapid labeling of clinical notes for smoking status extraction. The annotation process is labor-intensive and time-consuming; thus, we introduce a tool that accelerates annotation by clustering similar sentences and highlighting important keywords. This reduces the cognitive load on annotators, resulting in faster and more efficient labeling. Next, we address the problem of training accurate classifiers in low-resource settings with limited labeled data. In our first approach, MERIT (Minimal Supervision Through Label Augmentation for Biomedical Relation Extraction), we propose using shortest dependency path (SDP) representation and specific distance thresholds to propagate labels and augment high-quality labeled data. This method improves classifier accuracy compared to using limited labeled data alone. We extend this in our second approach by developing an iterative algorithm to learn automatic thresholds for label propagation. This method is tested in various scenarios, including semi-supervised learning, supervised learning, and in-context learning, demonstrating significant improvements in model performance. In the education domain, we focus on the problem of assessing narratives generated by school-aged children, a task that is both expensive and time-consuming for teachers. We leverage large language models (LLMs) to learn the scoring patterns of teachers accurately, offering a reliable tool for automated narrative scoring. This approach reduces the subjectivity and resource requirements of manual scoring, providing a scalable and consistent alternative. Experimental results across these methodologies demonstrate their effectiveness in improving annotation speed, data utilization, and model accuracy. This dissertation contributes to advancing document classification in low-resource settings, offering practical solutions for critical tasks in healthcare and education.Item Photogalvanic Study of the Structure/Topology Relationship in Chiral Multifold Semimetals and Development of Novel Broadband Ultrafast Probes(Temple University. Libraries, 2025-05)We report on a spectroscopic, enantiomer-dependent study of the linear (LPGE) and circular photogalvanic effect (CPGE) in the structurally chiral multifold fermion material PdGa using THz emission. Both the bulk and surface responses were measured in right and left-handed enantiomers over the $0.47-1.1$~eV incoming photon energy range. The effect of structural handedness was to invert the polarity of the emitted THz transients, yielding spectra roughly equal in magnitude but opposite in sign for all responses measured. All spectra showed evidence of a previously-reported interband resonance deriving from interband transitions midway between the $\Gamma$ and R points of the Brillouin zone. The CPGE spectra deriving from the helicoidally dispersing Fermi arc surface states closely matched that of the bulk, while the preferred axis of radiation for both LPGE and CPGE surface responses rotated in opposite directions in the opposite enantiomers, revealing the complementary helicoidally dispersing nature of their surface states. Turning our focus to the technical development of the spectroscopy of condensed phase systems, we present a novel time-resolved, optical pump/NIR supercontinuum probe spectrometer suitable for oscillators. A NIR supercontinuum probe spectrum ($850 - 1250$~nm) is generated in a photonic crystal fiber, dispersed across a DMD and then raster scanned into a single element detector at a 5~Hz rate. Dual modulation of pump and probe beams at disparate frequencies permits simultaneous measurement of both the bare reflectance $R$ and its photoinduced change $\Delta R$ through lock-in detection, allowing for continuously self-normalized measurement of $\Delta R/R$. Example data are presented on a germanium wafer sample that demonstrate for signals of order $\Delta R/R\sim 10^{-3}$, 2.87~nm spectral resolution and $\lesssim 400$~fs temporal resolution pre-recompression and comparable sensitivity to standard time-resolved, amplifier-based pump-probe techniques.