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Purpose: Sepsis is a clinical syndrome defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis is a highly heterogeneous syndrome with distinct phenotypes that impact immune function and response to infection. To develop targeted therapeutics, immunophenotyping is needed to identify distinct functional phenotypes of immune cells. In this study, we utilized our Organ-on-Chip assay to categorize sepsis patients into distinct phenotypes using patient data, neutrophil functional analysis, and proteomics. Methods: Following informed consent, neutrophils and plasma were isolated from sepsis patients in the Temple University Hospital ICU (n=45) and healthy control donors (n=7). Human lung microvascular endothelial cells (HLMVEC) were cultured in the Organ-on-Chip and treated with buffer or cytomix ((TNF/IL-1b/IFNg). Neutrophil adhesion and migration across HLMVEC in the Organ-on-Chip were used to categorize functional neutrophil phenotypes. Quantitative label-free global proteomics was performed on neutrophils to identify differentially expressed proteins. Plasma levels of sepsis biomarkers and neutrophil extracellular traps (NETs) were determined by ELISA. Results: We identified three functional phenotypes in critically ill ICU sepsis patients based on ex vivo neutrophil adhesion and migration patterns. The phenotypes were classified as: Hyperimmune characterized by enhanced neutrophil adhesion and migration, Hypoimmune that was unresponsive to stimulation, and Hybrid with increased adhesion but blunted migration. These functional phenotypes were associated with distinct proteomic signatures and differentiated sepsis patients by important clinical parameters related to disease severity. The Hyperimmune group demonstrated higher oxygen requirements, increased mechanical ventilation, and longer ICU length of stay compared to the Hypoimmune and Hybrid groups. Patients with the Hyperimmune neutrophil phenotype had significantly increased circulating neutrophils and elevated plasma levels NETs. Conclusion: Neutrophils and NETs play a critical role in vascular barrier dysfunction in sepsis and elevated NETs may be a key biomarker identifying the Hyperimmune group. Our results establish significant associations between specific neutrophil functional phenotypes and disease severity and identify important functional parameters in sepsis pathophysiology that may provide a new approach to classify sepsis patients for specific therapeutic interventions. [ABSTRACT FROM AUTHOR]

Background: Pelvic venous obstruction (PVO), defined as greater than 50% stenosis or occlusion of pelvic veins, is a known risk factor for deep vein thrombosis (DVT). DVT is a known risk factor for chronic thromboembolic pulmonary hypertension (CTEPH), but the prevalence of PVO in CTEPH is unknown. Methods: This cross-sectional study at Temple University's tertiary referral center for Pulmonary Hypertension, Right Heart Failure, and CTEPH sought to identify the presence of PVO in patients with CTEPH who underwent cardiac catheterization, pulmonary angiography, and venography. Results: A total of 193 CTEPH patients were referred for pulmonary angiography, and among these, 148 underwent venography. PVO was identified in 65 (44%) patients. Lower extremity (LE) DVT was associated with PVO (p = 0.004). The severity of pulmonary hypertension was similar with and without PVO (mean pulmonary artery pressure 43.0 ± 10.3 mm Hg vs. 43.8 ± 12.4 mm Hg, p = 0.70), as was the need for pulmonary thromboendarterectomy (69.2% vs. 61.4%, p = 0.32). Conclusions: Pelvic vein obstruction is common and a novel clinical association in patients with CTEPH, particularly in patients with a history of LE DVT. PVO and its role in CTEPH warrants further study, including the potential role of revascularization to mitigate further risk. [ABSTRACT FROM AUTHOR]

Background: Gastroparesis has a substantial impact on the quality of life but has limited treatment options, which makes it a public health concern. No bibliometric studies on gastroparesis have been published thus far. Thus, this article aims to summarize and analyze research hotspots to provide a reference for clinical researchers. Materials and methods: Gastroparesis-related research articles were searched in the Web of Science Core Collection (WOSCC), and relevant information was extracted after screening. A total of 1033 documents were analyzed with the bibliometric method using Microsoft Excel, Citespace, and VOSviewer. Results: Overall, our search retrieved 1033 papers contributed by 966 research institutions from 53 countries. Since 1980, publications in this field have increased rapidly. United States (n = 645) and Temple University (n = 122) were the most productive country and institution, respectively. Parkman, with 96 publications, was the most prominent author. Conclusions: Research hotspots in gastroparesis can be summarized into four domains: innovation in diagnostic modalities, change of oral therapeutic agents, choice of surgical interventions, and pathological mechanisms. Future research on gastroparesis should focus on the quality of life of patients, diagnostic techniques, pyloromyotomy, and transpyloric stent placement. [ABSTRACT FROM AUTHOR]

Tina Sloan Green, Nikki Franke, Alpha Alexander, and Linda Greene represent an integral part of a culture of Black women in sports who created a place and space for themselves and others in opposition to the long history of racism and sexism that suffused sports in the United States and global world. As founders of the Black Women in Sport Foundation (BWSF), their activism and organizing on behalf of Black women and girls in, and beyond sport, is as varied as it is vast. While the founders have been interviewed about the BWSF numerous times throughout their respective careers, those interviews fail to capture the paths that led them to successful careers or the incorporation of the BWSF. Using oral history narratives, this paper contends that their experiences from childhood to young adulthood offer incredible insights about the origins and evolution of their critical consciousness around race and gender that emerged during their formative years. It illuminates the familial, communal, educational, and sporting legacies of BWSF founders from childhood to the mid-to-late 1970s, when their worlds collided at Temple University. Their histories underscore how they navigated and negotiated the ideologies of racism and sexism from childhood to adulthood. As young Black women who lived before the passage of Title IX, their stories depict the early struggles and successes of women and girls' participation in sports and broader society. Individually and collectively, BWSF founders' oral history narratives offer a great understanding of Black women in sports and society in the past and present. [ABSTRACT FROM AUTHOR]

The struggle for civil rights has never been black and white; multiracial coalitions have been imperative in fighting for equality under the Constitution. With the creation of the new Hon. Nelson A. Diaz Professorship in Law, Temple University Beasley School of Law has brought important recognition to the role Latinxs have played in this movement. It has spotlighted a trailblazer in the Latinx community and committed to support other leaders who follow in his footsteps. The authors participated in a panel discussion in the Spring of 2023, which celebrated Judge Diaz and Latinx leadership in the law. This Essay will build on that event, detailing Judge Diaz's legacy of advocacy and service and exploring the many Latinx contributions to the civil rights movement that the Projessorship seeks to showcase. With Judge Diaz's generous gift, Temple has invested in Latinx representation and recognition, broadening our understanding of who fights for our civil rights, and for whom. [ABSTRACT FROM AUTHOR]

t-distributed stochastic neighbor embedding (t-SNE) is a method for reducing high-dimensional data to a low-dimensional representation, and is mostly used for visualizing data. In parametric t-SNE, a neural network learns to reproduce this mapping. When used for EEG analysis, the data are usually first transformed into a set of features, but it is not known which features are optimal. The principle of t-SNE was used to train convolutional neural network (CNN) encoders to learn to produce both a high- and a low-dimensional representation, eliminating the need for feature engineering. To evaluate the method, the Temple University EEG Corpus was used to create three datasets with distinct EEG characters: (1) wakefulness and sleep; (2) interictal epileptiform discharges; and (3) seizure activity. The CNN encoders produced low-dimensional representations of the datasets with a structure that conformed well to the EEG characters and generalized to new data. Compared to parametric t-SNE for either a short-time Fourier transform or wavelet representation of the datasets, the developed CNN encoders performed equally well in separating categories, as assessed by support vector machines. The CNN encoders generally produced a higher degree of clustering, both visually and in the number of clusters detected by k-means clustering. The developed principle is promising and could be further developed to create general tools for exploring relations in EEG data. [ABSTRACT FROM AUTHOR]

Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last hours or days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent seizure detector is challenging as seizures exhibit diverse characteristics across patients and recording devices. In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG and intracranial EEG (iEEG). First, we deploy a convolutional neural network with transformers and belief matching loss to detect seizures in single-channel EEG segments. Next, we extract regional features from the channel-level outputs to detect seizures in multi-channel EEG segments. At last, we apply post-processing filters to the segment-level outputs to determine seizures' start and end points in multi-channel EEGs. Finally, we introduce the minimum overlap evaluation scoring as an evaluation metric that accounts for minimum overlap between the detection and seizure, improving upon existing assessment metrics. We trained the seizure detector on the Temple University Hospital Seizure (TUH-SZ) dataset and evaluated it on five independent EEG datasets. We evaluate the systems with the following metrics: sensitivity (SEN), precision (PRE), and average and median false positive rate per hour (aFPR/h and mFPR/h). Across four adult scalp EEG and iEEG datasets, we obtained SEN of 0.617–1.00, PRE of 0.534–1.00, aFPR/h of 0.425–2.002, and mFPR/h of 0–1.003. The proposed seizure detector can detect seizures in adult EEGs and takes less than 15 s for a 30 min EEG. Hence, this system could aid clinicians in reliably identifying seizures expeditiously, allocating more time for devising proper treatment. [ABSTRACT FROM AUTHOR]

In this work, an attempt has been made to develop an automated system for detecting electroclinical seizures such as tonic-clonic seizures, complex partial seizures, and electrographic seizures (EGSZ) using higher-order moments of scalp electroencephalography (EEG). The scalp EEGs of the publicly available Temple University database are utilized in this study. The higher-order moments, namely skewness and kurtosis, are extracted from the temporal, spectral, and maximal overlap wavelet distributions of EEG. The features are computed from overlapping and non-overlapping moving windowing functions. The results show that the wavelet and spectral skewness of EEG is higher in EGSZ than in other types. All the extracted features are found to have significant differences (p < 0.05), except for temporal kurtosis and skewness. A support vector machine with a radial basis kernel designed using maximal overlap wavelet skewness yields a maximum accuracy of 87%. In order to improve the performance, the Bayesian optimization technique is utilized to determine the suitable kernel parameters. The optimized model achieves the highest accuracy of 96% and an MCC of 91% in three-class classification. The study is found to be promising, and it could facilitate the rapid identification process of life-threatening seizures. [ABSTRACT FROM AUTHOR]

The 23rd Annual Law Student Tax Challenge winners have been announced by the American Bar Association (ABA) Tax Times. Natalie Romano-Nuesch and Victor Ficarra from Temple University Beasley School of Law won first place in the J.D. Division, while Joachim Saldaha and Norma Chayo Dichi from Georgetown University Law Center won first place in the LL.M. Division. The competition allows law students to research, write about, and present their analyses of a real-life tax planning problem. The winners presented their oral arguments before a panel of tax lawyers and tax court judges at the Section of Taxation 2024 Midyear Tax Meeting in San Francisco, CA. [Extracted from the article]

With modern population growth and an increase in the average lifespan, more patients are becoming afflicted with neurodegenerative diseases such as dementia and Alzheimer's. Patients with a history of epilepsy, drug abuse, and mental health disorders such as depression have a larger risk of developing Alzheimer's and other neurodegenerative diseases later in life. Utilizing recordings of patients' brain waves obtained from the Temple University abnormal electroencephalogram (EEG) corpus, deep leaning long short-term memory neural networks are utilized to classify and predict patients' brain ages. The proposed deep learning neural network model structure and brain wave-processing methodology leads to an accuracy of 90% in patients' brain age classification across six age groups, with a mean absolute error value of 7 years for the brain age regression analysis. The achieved results demonstrate that the use of raw patient-sourced brain wave information leads to higher performance metrics than methods utilizing other brain wave-preprocessing methods and outperforms other deep learning models such as convolutional neural networks. [ABSTRACT FROM AUTHOR]

Monitoring patients at risk of epileptic seizure is critical for optimal treatment and ensuing the reduction of seizure risk and complications. In general, seizure detection is done manually in hospitals and involves time-consuming visual inspection and interpretation by experts of electroencephalography (EEG) recordings. The purpose of this study is to investigate the pertinence of band-limited spectral power and signal complexity in order to discriminate between seizure and seizure-free EEG brain activity. The signal complexity and spectral power are evaluated in five frequency intervals, namely, the delta, theta, alpha, beta, and gamma bands, to be used as EEG signal feature representation. Classification of seizure and seizure-free data was performed by prevalent potent classifiers. Substantial comparative performance evaluation experiments were performed on a large EEG data record of 341 patients in the Temple University Hospital EEG seizure database. Based on statistically validated criteria, results show the efficiency of band-limited spectral power and signal complexity when using random forest and gradient-boosting decision tree classifiers (95% of the area under the curve (AUC) and 91% for both F-measure and accuracy). These results support the use of these automatic classification schemes to assist the practicing neurologist interpret EEG records more accurately and without tedious visual inspection. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of EEG recordings requires the expertise of trained neurologists, a resource which is scarce in the developing world. Neurologists spend a significant portion of their time sifting through EEG recordings looking for abnormalities. Most recordings turn out to be completely normal, owing to the low yield of EEG tests. To minimize such wastage of time and effort, automatic algorithms could be used to provide pre-diagnostic screening to separate normal from abnormal EEG. Data driven machine learning offers a way forward however, design and verification of modern machine learning algorithms require properly curated labeled datasets. To avoid bias, deep learning based methods must be trained on large datasets from diverse sources. This work presents a new open-source dataset, named the NMT Scalp EEG Dataset, consisting of 2,417 recordings from unique participants spanning almost 625 h. Each recording is labeled as normal or abnormal by a team of qualified neurologists. Demographic information such as gender and age of the patient are also included. Our dataset focuses on the South Asian population. Several existing state-of-the-art deep learning architectures developed for pre-diagnostic screening of EEG are implemented and evaluated on the NMT, and referenced against baseline performance on the well-known Temple University Hospital EEG Abnormal Corpus. Generalization of deep learning based architectures across the NMT and the reference datasets is also investigated. The NMT dataset is being released to increase the diversity of EEG datasets and to overcome the scarcity of accurately annotated publicly available datasets for EEG research. [ABSTRACT FROM AUTHOR]

Pharmacists around the world are playing an active role in educating the public about COVID-19. First-year pharmacy students taking a healthcare course at Temple University in Philadelphia were asked to write papers based on the nature of a pharmacist's contributions during the pandemic. 90% of the essays outlined the importance of community pharmacists, as they are the most accessible healthcare professional, providing immediate social interaction and drug expertise. They help minimize the need for hospital visits, limiting their time in public, therefore reducing their risk of becoming infected. For the pharmacies that have a drive-thru window, over-the-counter medications and other necessities can be sold to decrease the likelihood of transmission. Many students also proposed the opportunity for more leniency in prescribing power. Unless pharmacists are within a collaborative practice agreement, they cannot prescribe even maintenance medication. Due to the pandemic, it has become increasingly hard to reach the doctor or see them in person, as most offices have switched to telehealth. Clinical pharmacists operating out of hospitals have the opportunity to oversee proposed treatment options or experimental drugs, such as remdesivir. Retail pharmacies are emerging as key resources in this pandemic, and it is important that students see this as a vibrant and important form of pharmacy practice. [ABSTRACT FROM AUTHOR]

This paper investigates how an organization tries to restore trust after an organizational-level scandal by examining the trust repair process. The study adopts a qualitative research approach to analyze the case of the scandal at Temple University’s Fox School of Business concerning the submission of false data to the ranking body. This paper shows the importance of trust between education institutions and the society and how it is difficult to rebuild it after violation. While dealing with trust rebuilding at the organizational level, we should consider not only the prior reputation of the organization, but also appropriate timing of trust repair mechanisms and number of actors involved in the trust failure situation. This paper also presents the trust rebuilding actions in a novel institutional setting, that is, in case of the business school. [ABSTRACT FROM AUTHOR]

Warren Hope, former editor of the Newsletter and co-author of The Shakespeare Controversy, died May 23, 2022, as the result of lung disease. A true scholar and man of letters, [...]