Biology subjects

Gao, C. A.

Publications and source records attributed to Gao, C. A..

3 recordsLinked to original sources

bioRxiv2026

Pathogen-specific host responses define distinct pneumonia endotypes in the human lung

Pneumonia is the leading cause of death from infectious disease worldwide. The diagnosis and treatment of patients with pneumonia lag behind other major conditions, relying on syndromic definitions that lack molecular resolution and ignore underlying endotypes. We sought to test the hypothesis that dynamic pathogen-specific host responses in the alveolar space represent distinct pneumonia endotypes linked to different clinical features and outcomes. We prospectively enrolled a cohort of 690 patients (including immunocompromised patients) with known or suspected pneumonia receiving mechanical ventilation in whom the etiology of pneumonia was determined by gold-standard analysis of distal lung fluid obtained by bronchoalveolar lavage (BAL) combined with clinical adjudication. From these patients, we analyzed 792 BAL fluid samples, including 310 serial samples, using flow cytometry (482 patients) and single-cell RNA-sequencing (170 patients; 263 samples, complemented by 9 healthy controls and 25 post-COVID-19 patients, yielding [~]2.4 million single cells across 28 cell types), and extracted daily clinical data from the electronic health record (>15,000 patient-days). We used machine learning models to identify pathogen-specific host responses in the transcriptome of alveolar immune cells that were associated with changes in alveolar cell abundance and clinical features. Our results suggest that therapeutic strategies for pneumonia should be individualized to specific host-pathogen interactions.

immunology↗

bioRxiv2023

A distinctive evolution of alveolar T cell responses is associated with clinical outcomes in unvaccinated patients with SARS-CoV-2 pneumonia

Pathogen clearance and resolution of inflammation in patients with pneumonia require an effective local T cell response. Nevertheless, local T cell activation may drive lung injury, particularly during prolonged episodes of respiratory failure characteristic of severe SARS-CoV-2 pneumonia. While T cell responses in the peripheral blood are well described, the evolution of T cell phenotypes and molecular signatures in the distal lung of patients with severe pneumonia caused by SARS-CoV-2 or other pathogens is understudied. Accordingly, we serially obtained 432 bronchoalveolar lavage fluid samples from 273 patients with severe pneumonia and respiratory failure, including 74 unvaccinated patients with COVID-19, and performed flow cytometry, transcriptional, and T cell receptor profiling on sorted CD8+ and CD4+ T cell subsets. In patients with COVID-19 but not pneumonia secondary to other pathogens, we found that early and persistent enrichment in CD8+ and CD4+ T cell subsets correlated with survival to hospital discharge. Activation of interferon signaling pathways early after intubation for COVID-19 was associated with favorable outcomes, while activation of NF-{kappa}B-driven programs late in disease was associated with poor outcomes. Patients with SARS-CoV-2 pneumonia whose alveolar T cells preferentially targeted the Spike and Nucleocapsid proteins tended to experience more favorable outcomes than patients whose T cells predominantly targeted the ORF1ab polyprotein complex. These results suggest that in patients with severe SARS-CoV-2 pneumonia, alveolar T cell interferon responses targeting structural SARS-CoV-2 proteins characterize patients who recover, yet these responses progress to NF-{kappa}B activation against non-structural proteins in patients who go on to experience poor clinical outcomes.

systems biology↗

bioRxiv2022

Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers

BackgroundLarge language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing. MethodsWe gathered ten research abstracts from five high impact factor medical journals (n=50) and asked ChatGPT to generate research abstracts based on their titles and journals. We evaluated the abstracts using an artificial intelligence (AI) output detector, plagiarism detector, and had blinded human reviewers try to distinguish whether abstracts were original or generated. ResultsAll ChatGPT-generated abstracts were written clearly but only 8% correctly followed the specific journals formatting requirements. Most generated abstracts were detected using the AI output detector, with scores (higher meaning more likely to be generated) of median [interquartile range] of 99.98% [12.73, 99.98] compared with very low probability of AI-generated output in the original abstracts of 0.02% [0.02, 0.09]. The AUROC of the AI output detector was 0.94. Generated abstracts scored very high on originality using the plagiarism detector (100% [100, 100] originality). Generated abstracts had a similar patient cohort size as original abstracts, though the exact numbers were fabricated. When given a mixture of original and general abstracts, blinded human reviewers correctly identified 68% of generated abstracts as being generated by ChatGPT, but incorrectly identified 14% of original abstracts as being generated. Reviewers indicated that it was surprisingly difficult to differentiate between the two, but that the generated abstracts were vaguer and had a formulaic feel to the writing. ConclusionChatGPT writes believable scientific abstracts, though with completely generated data. These are original without any plagiarism detected but are often identifiable using an AI output detector and skeptical human reviewers. Abstract evaluation for journals and medical conferences must adapt policy and practice to maintain rigorous scientific standards; we suggest inclusion of AI output detectors in the editorial process and clear disclosure if these technologies are used. The boundaries of ethical and acceptable use of large language models to help scientific writing remain to be determined.

scientific communication and education↗