The role of preoperative blood eosinophil counts in distinguishing chronic rhinosinusitis with nasal polyps phenotypes.

BACKGROUND: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a common heterogenous disease in the patients with chronic airway diseases. This study investigated the role of blood eosinophil count (BEC) in the classification of CRSwNP and its recurrence in eosinophilic CRSwNP. METHODS: Sixty-five patients who underwent nasal endoscopic resection of CRSwNP were recruited and divided into eosinophilic CRSwNP and non-eosinophilic CRSwNP groups based on the levels (10% cutoff) of eosinophil infiltration as indicated by hematoxylin and eosin (H&E) staining. RESULTS: We recruited 30 patients in the eosinophilic CRSwNP group and 35 patients in the non-eosinophilic CRSwNP group. The outcome of preoperative visual analogue scale (VAS) score, preoperative Lund-Mackay score, and preoperative Lund-Kennedy score between the 2 groups were comparable. The level of BEC in the eosinophilic CRSwNP group was significantly higher than that of non-eosinophilic CRSwNP group (0.79 ± 0.27 × 109 /L vs 0.30 ± 0.22 × 109 /L; p < 0.001). We observed a statistical significance in the number of H&E eosinophils (29.11 ± 2.93 vs 3.17 ± 0.51; p < 0.001) and CRSwNP phenotypes (eosinophilic/non-eosinophilic, 28/3 vs 2/32; p < 0.001) when the cutoff value of BEC was set at 0.39 × 109 /L. The disease-free recurrence (DFR) was found to be statistically significant when the cutoff value of BEC was 0.73 × 109 /L in eosinophilic CRSwNP (p = 0.009). CONCLUSION: Results indicate that BEC may be capable of distinguishing CRSwNP phenotypes as well as predicting polyp recurrence in eosinophilic CRSwNP. Given the relatively small sample size, further studies will be necessary to confirm a role for BEC as a systemic biomarker in CRSwNP.

The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak - an update on the status.

An acute respiratory disease, caused by a novel coronavirus (SARS-CoV-2, previously known as 2019-nCoV), the coronavirus disease 2019 (COVID-19) has spread throughout China and received worldwide attention. On 30 January 2020, World Health Organization (WHO) officially declared the COVID-19 epidemic as a public health emergency of international concern. The emergence of SARS-CoV-2, since the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002 and Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012, marked the third introduction of a highly pathogenic and large-scale epidemic coronavirus into the human population in the twenty-first century. As of 1 March 2020, a total of 87,137 confirmed cases globally, 79,968 confirmed in China and 7169 outside of China, with 2977 deaths (3.4%) had been reported by WHO. Meanwhile, several independent research groups have identified that SARS-CoV-2 belongs to ß-coronavirus, with highly identical genome to bat coronavirus, pointing to bat as the natural host. The novel coronavirus uses the same receptor, angiotensin-converting enzyme 2 (ACE2) as that for SARS-CoV, and mainly spreads through the respiratory tract. Importantly, increasingly evidence showed sustained human-to-human transmission, along with many exported cases across the globe. The clinical symptoms of COVID-19 patients include fever, cough, fatigue and a small population of patients appeared gastrointestinal infection symptoms. The elderly and people with underlying diseases are susceptible to infection and prone to serious outcomes, which may be associated with acute respiratory distress syndrome (ARDS) and cytokine storm. Currently, there are few specific antiviral strategies, but several potent candidates of antivirals and repurposed drugs are under urgent investigation. In this review, we summarized the latest research progress of the epidemiology, pathogenesis, and clinical characteristics of COVID-19, and discussed the current treatment and scientific advancements to combat the epidemic novel coronavirus.

Infiltration pattern of gammadelta T cells and its association with local inflammatory response in the nasal mucosa of patients with allergic rhinitis.

BACKGROUND: gammadelta (γδ) T cells play important roles in allergic lower airway inflammation. However, little is known about their infiltration pattern in the nasal mucosa during upper airway inflammation. This study investigated γδ T cell distribution in nasal tissues of allergic rhinitis (AR) patients and the relationship between γδ T cells and other inflammatory cell types. METHODS: A total of 30 patients with septal deviation were examined, including 22 with and 8 without AR. The localization of γδ T cells and other cells (eosinophils, neutrophils, mast cells, macrophages, B cells, cluster of differentiation [CD]4+ T cells, CD8+ T cells, regulatory T cells [Tregs], interferon [IFN]-γ+ cells, interleukin [IL]17+ cells, and IL10+ cells) was evaluated by histological analysis and immunohistochemistry. T helper cell (Th)1/Th2/Th17 and Treg gene expression was analyzed by quantitative polymerase chain reaction (PCR). RESULTS: γδ T cells were localized in the epithelium or subepithelial region of nasal mucosa, and their infiltration was higher in AR patients relative to control subjects. The number of γδ T cells was associated with the presence of eosinophils, macrophages, mast cells, B cells, CD8+ T cells, Forkhead box (Fox)p3+ Tregs, IL17+ cells, and IL10+ cells but not of neutrophils or IFN-γ+ cells. The messenger RNA (mRNA) level of a γδ T cell subunit was positively correlated with those of Th1 genes (T-bet and IFN-γ), Th2 cytokine (C-C motif chemokine ligand 18), and Treg genes (Foxp3 and IL10). CONCLUSION: γδ T cells play multiple roles in mucosal inflammation in AR including immune surveillance and adaptive and innate immune responses.

SLIDE - a web-based tool for interactive visualization of large-scale - omics data.

Summary: Data visualization is often regarded as a post hoc step for verifying statistically significant results in the analysis of high-throughput datasets. This common practice leaves a large amount of raw data behind, from which more information can be extracted. However, existing solutions do not provide capabilities to explore large-scale raw datasets using biologically sensible queries, nor do they allow user interaction based real-time customization of graphics. To address these drawbacks, we have designed an open-source, web-based tool called Systems-Level Interactive Data Exploration, or SLIDE to visualize large-scale -omics data interactively. SLIDE's interface makes it easier for scientists to explore quantitative expression data in multiple resolutions in a single screen. Availability and implementation: SLIDE is publicly available under BSD license both as an online version as well as a stand-alone version at https://github.com/soumitag/SLIDE. Supplementary information: Supplementary data are available at Bioinformatics online.