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Inhalation of nicotine-containing electronic cigarette vapor exacerbates the features of COPD by inducing ferroptosis in βENaC-overexpressing mice
Introduction
Chronic obstructive pulmonary disease (COPD) is currently listed as the 3rd leading cause of death in the United States. Accumulating data shows the association between COPD occurrence and the usage of electronic nicotine delivery systems (ENDS) in patients. However, the underlying pathogenesis mechanisms of COPD have not been fully understood.
Methods
In the current study, bENaC-overexpressing mice (bENaC mice) were subjected to whole-body ENDS exposure. COPD related features including emphysema, mucus accumulation, inflammation and fibrosis are examined by tissue staining, FACS analysis, cytokine measurement. Cell death and ferroptosis of alveolar epithelial cells were further evaluated by multiple assays including staining, FACS analysis and lipidomics.
Results
ENDS-exposed mice displayed enhanced emphysema and mucus accumulation, suggesting that ENDS exposure promotes COPD features. ENDS exposure also increased immune cell number infiltration in bronchoalveolar lavage and levels of multiple COPD-related cytokines in the lungs, including CCL2, IL-4, IL-13, IL-10, M-CSF, and TNF-α. Moreover, we observed increased fibrosis in ENDS-exposed mice, as evidenced by elevated collagen deposition and a-SMA+ myofibroblast accumulation. By investigating possible mechanisms for how ENDS promoted COPD, we demonstrated that ENDS exposure induced cell death of alveolar epithelial cells, evidenced by TUNEL staining and Annexin V/PI FACS analysis. Furthermore, we identified that ENDS exposure caused lipid dysregulations, including TAGs (9 species) and phospholipids …
Characterizing Public Sentiments and Drug Interactions during COVID-19: A Pretrained Language Model and Network Analysis of Social Media Discourse
Objective
Harnessing drug-related data posted on social media in real time can offer insights into how the pandemic impacts drug use and monitor misinformation. This study developed a natural language processing (NLP) pipeline tailored for the analysis of social media discourse on COVID-19 related drugs.
Methods
This study constructed a full pipeline for COVID-19 related drug tweet analysis, utilizing pre-trained language model-based NLP techniques as the backbone. This pipeline is architecturally composed of four core modules: named entity recognition (NER) and normalization to identify medical entities from relevant tweets and standardize them to uniform medication names, target sentiment analysis (TSA) to reveal sentiment polarities associated with the entities, topic modeling to understand underlying themes discussed by the population, and drug network analysis to potential adverse drug reactions (ADR) and drug-drug interactions (DDI). The pipeline was deployed to analyze tweets related to COVID-19 and drug therapies between February 1, 2020, and April 30, 2022.
Results
From a dataset comprising 2,124,757 relevant tweets sourced from 1,800,372 unique users, our NER model identified the top five most-discussed drugs: Ivermectin, Hydroxychloroquine, Remdesivir, Zinc, and Vitamin D. Sentiment and topic analysis revealed that public perception was predominantly shaped by celebrity endorsements, media hotspots, and governmental directives rather than empirical evidence of drug efficacy. Co-occurrence matrices and complex network analysis further identified emerging patterns of DDI and ADR that could be critical for …
Revisiting Bundle Recommendation for Intent-aware Product Bundling
Product bundling represents a prevalent marketing strategy in both offline stores and e-commerce systems. Despite its widespread use, previous studies on bundle recommendation face two significant limitations. Firstly, they rely on noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session. Secondly, they target specific tasks by holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. This paper proposes to take a step back and considers the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich metadata, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion and ranking, to …
Intensive ambulance-delivered blood-pressure reduction in hyperacute stroke
Background
Treatment of acute stroke, before a distinction can be made between ischemic and hemorrhagic types, is challenging. Whether very early blood-pressure control in the ambulance improves outcomes among patients with undifferentiated acute stroke is uncertain.
Methods
We randomly assigned patients with suspected acute stroke that caused a motor deficit and with elevated systolic blood pressure (≥150 mm Hg), who were assessed in the ambulance within 2 hours after the onset of symptoms, to receive immediate treatment to lower the systolic blood pressure (target range, 130 to 140 mm Hg) (intervention group) or usual blood-pressure management (usual-care group). The primary efficacy outcome was functional status as assessed by the score on the modified Rankin scale (range, 0 [no symptoms] to 6 [death]) at 90 days after randomization. The primary safety outcome was any serious adverse …
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Academic Papers and Presentations by Dr. Jenny Yang

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