HMGB1-C1q complexes regulate macrophage function by switching between leukotriene and specialized proresolving mediator biosynthesis.

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. Lipid mediators, as well as cytokines, help establish a milieu favoring either inflammation or resolution of inflammation. This study focuses on the induction of lipid mediators by HMGB1 and HMGB1 plus C1q and their regulation of IRF5, a transcription factor critical for the induction and maintenance of proinflammatory macrophages. Here, we show that HMGB1 induces leukotriene production through a RAGE-dependent pathway, while HMGB1 plus C1q induces specialized proresolving lipid mediators lipoxin A4, resolvin D1, and resolvin D2 through a RAGE- and LAIR-1-dependent pathway. Leukotriene exposure contributes to induction of IRF5 in a positive-feedback loop. In contrast, resolvins (at 20 nM) block IRF5 induction and prevent the differentiation of inflammatory macrophages. Finally, we have generated a molecular mimic of HMGB1 plus C1q, which cross-links RAGE and LAIR-1 and polarizes monocytes to an antiinflammatory phenotype. These findings may provide a mechanism to control nonresolving inflammation in many pathologic conditions.

An early experience on the effect of solid organ transplant status on hospitalized COVID-19 patients.

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. Primary outcome was combined death or mechanical ventilation. We assessed the association between primary outcome and prognostic variables using bivariate and multivariate regression models. We also compared the primary endpoint in SOT patients to an age, gender, and comorbidity-matched control group. Bivariate analysis found transplant status, age, gender, race/ethnicity, body mass index, diabetes, hypertension, cardiovascular disease, COPD, and GFR <60 mL/min/1.73 m2 to be significant predictors of combined death or mechanical ventilation. After multivariate logistic regression analysis, SOT status had a trend toward significance (odds ratio [OR] 1.29; 95% CI 0.99-1.69, p = .06). Compared to an age, gender, and comorbidity-matched control group, SOT patients had a higher combined risk of death or mechanical ventilation (OR 1.34; 95% CI 1.03-1.74, p = .027).

Towards Interpretable Skin Lesion Classification with Deep Learning Models.

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. Comparing with existing research, we would like to highlight the following aspects of our model. 1) Rather than a single model, our approach ensembles a set of deep learning architectures to achieve better classification accuracy; 2) Generative adversarial network (GAN) is involved in the model training to promote data scale and diversity; 3) Local interpretable model-agnostic explanation (LIME) strategy is applied to extract evidence from the skin images to support the classification results. Our experimental results on real-world skin image corpus demonstrate the effectiveness and robustness of our method. The explainability of our model further enhances its applicability in real clinical practice.