Publications by authors named "Alec Xiang"

We compared the outcome of COVID-19 in immunosuppressed solid organ transplant (SOT) patients to a transplant naïve population. In total, 10 356 adult hospital admissions for COVID-19 from March 1, 2020 to April 27, 2020 were analyzed. Data were collected on demographics, baseline clinical conditions, medications, immunosuppression, and COVID-19 course.

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Skin disease is a prevalent condition all over the world. Computer vision-based technology for automatic skin lesion classification holds great promise as an effective screening tool for early diagnosis. In this paper, we propose an accurate and interpretable deep learning pipeline to achieve such a goal.

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Macrophage polarization is critical to inflammation and resolution of inflammation. We previously showed that high-mobility group box 1 (HMGB1) can engage receptor for advanced glycation end product (RAGE) to direct monocytes to a proinflammatory phenotype characterized by production of type 1 IFN and proinflammatory cytokines. In contrast, HMGB1 plus C1q form a tetramolecular complex cross-linking RAGE and LAIR-1 and directing monocytes to an antiinflammatory phenotype.

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Synopsis of recent research by authors named "Alec Xiang"

  • - Alec Xiang's research primarily focuses on the intersection of immunology and machine learning, particularly examining the effects of immunosuppression on health outcomes and developing advanced diagnostic tools for skin diseases.
  • - In his study on COVID-19 patients, Xiang compared the outcomes of solid organ transplant recipients to non-transplant patients, providing valuable insights into the vulnerabilities of immunosuppressed individuals during the pandemic.
  • - His work on skin lesion classification employs deep learning techniques to enhance the accuracy and interpretability of diagnoses, aiming to establish a reliable automated screening method for early detection of skin diseases.