Computational pathology reveals unique spatial patterns of immune response in H&E images from COVID-19 autopsies: preliminary findings

Computational pathology: A survey review and the way forward

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

Diffuse alveolar damage patterns reflect the immunological and molecular heterogeneity in fatal COVID-19

Background

Severe COVID-19 lung disease exhibits a high degree of spatial and temporal heterogeneity, with different histological features coexisting within a single individual. It is important to capture the disease complexity to support patient management and treatment strategies. We provide spatially decoded analyses on the immunopathology of diffuse alveolar damage (DAD) patterns and factors that modulate immune and structural changes in fatal COVID-19.

Methods

We spatially quantified the immune and structural cells in exudative, intermediate, and advanced DAD through multiplex immunohistochemistry in autopsy lung tissue of 18 COVID-19 patients. Cytokine profiling, viral, bacteria, and fungi detection, and transcriptome analyses were performed.

Findings

Spatial DAD progression was associated with expansion of immune cells, macrophages, CD8+ T cells, fibroblasts, and (lymph)angiogenesis. Viral load correlated positively with exudative DAD and negatively with disease/hospital length. In all cases, enteric bacteria were isolated, and Candida parapsilosis in eight cases. Cytokines correlated mainly with macrophages and CD8+T cells. Pro-coagulation and acute repair were enriched pathways in exudative DAD whereas intermediate/advanced DAD had a molecular profile of elevated humoral and innate immune responses and extracellular matrix production.

Interpretation

Unraveling the spatial and molecular immunopathology of COVID-19 cases exposes the responses to SARS-CoV-2-induced exudative DAD and subsequent immune-modulatory and remodeling changes in proliferative/advanced DAD that occur side-by-side together with secondary infections in the lungs. These complex features have important implications for disease management and the development of novel treatments.

Funding

CNPq, Bill and Melinda Gates Foundation, HC-Convida, FAPESP, Regeneron Pharmaceuticals, and the Swedish Heart & Lung Foundation.

Possible harm from glucocorticoid drugs misuse in the early phase of SARS-CoV-2 infection: a narrative review of the evidence

Since the publication of the RECOVERY trial, the use of glucocorticoid drugs (GC) has spread for the treatment of severe COVID-19 worldwide. However, the benefit of dexamethasone was largest in patients who received mechanical ventilation or supplemental oxygen therapy, while no benefit was found among patients without hypoxemia. In addition, a positive outcome was found in patients who received dexamethasone after several days of symptoms, while possible harm could exist if administered early. The right time interval for GC administration is still a matter of debate. Previous studies showed that an early GC use during the first phase of the disease, when viral replication peaks, may negatively affect the innate immune response through several mechanisms, such as the inhibition of pro-inflammatory and antiviral cytokine production and signaling pathway, including type I interferon, that is fundamental to counteract the virus and that was found to be impaired in several patients with life-threatening COVID-19. The GC misuse can lead to a more severe disease even in patients who do not have the established risk factors, such as obesity and cardiovascular diseases. In our focused review, we describe the role of immune response in viral infections, especially SARS-CoV-2, and discuss the potential harms of GC misuse in COVID-19.