No abstract

BackgroundNeutrophil-lymphocyte ratio (NLR) has been shown to be associated with prognosis in various solid tumors. This study aimed to evaluate the prognostic role of NLR in patients with laryngeal squamous cell carcinoma (LSCC).MethodsA total of 141 LSCC patients were retrospectively reviewed. Patients’ demographics were analyzed along with clinical and pathologic data. The optimal cutoff value of NLR was determined using receiver operating characteristic (ROC) curve analysis. The impact of the NLR and other potential prognostic factors on disease-free survival (DFS) and overall survival (OS) was assessed using the Kaplan-Meier method and multivariate Cox regression analysis.ResultsThe optimal cutoff value of the NLR was 2.17. In the NLR ≤ 2.17 group, the 1-, 3-, and 5-year DFS rates were 88.2, 73.9 and 69.1 %, respectively, while in the NLR > 2.17 group, the DFS rates were 83.0, 54.6 and 49.2 %, respectively. Correspondingly, the 1-, 3-, and 5-year OS rates were 98.9, 85.1 and 77.4 % in the NLR ≤ 2.17 group and 97.9, 63.8 and 53.3 % in the NLR > 2.17 group, respectively. The multivariate Cox proportional hazard model analysis showed that NLR > 2.17 was a prognostic factor for both DFS [hazard ratio (HR) = 1.869; 95 % confidence interval (CI) 1.078–3.243; P = 0.026] and OS (HR =2.177; 95 % CI 1.208–3.924; P = 0.010).ConclusionOur results showed that elevated preoperative NLR was an independent predictor of poor prognosis for patients with LSCC after surgical resection.

IntroductionThe pathways underlying chronic rhinosinusitis with nasal polyps (CRSwNP) are unclear. We conducted genome-wide gene expression analysis to determine pathways and candidate gene sets associated with CRSwNP.MethodsWe performed whole-transcriptome RNA sequencing on 42 polyp (CRSwNP-NP) and 33 paired nonpolyp inferior turbinate (CRSwNP-IT) tissues from patients with CRSwNP and 28 inferior turbinate samples from non-CRS controls (CS-IT). We analysed the differentially expressed genes (DEGs) and the gene sets that were enriched in functional pathways.ResultsPrincipal component-informed analysis revealed cilium function and immune regulation as the two main Gene Ontology (GO) categories differentiating CRSwNP patients from controls. We detected 6182 and 1592 DEGs between CRSwNP-NP versus CS-IT and between CRSwNP-NP versus CRSwNP-IT tissues, respectively. Atopy status did not have a major impact on gene expression in various tissues. GO analysis on these DEGs implicated extracellular matrix (ECM) disassembly, O-glycan processing, angiogenesis and host viral response in CRSwNP pathogenesis. Ingenuity Pathway Analysis identified significant enrichment of type 1 interferon signalling and axonal guidance canonical pathways, angiogenesis, and collagen and fibrotic changes in CRSwNP (CRSwNP-NP and CRSwNP-IT) tissues compared with CS-IT. Finally, gene set enrichment analysis implicated sets of genes co-regulated in processes associated with inflammatory response and aberrant cell differentiation in polyp formation.ConclusionsGene signatures involved in defective host defences (including cilia dysfunction and immune dysregulation), inflammation and abnormal metabolism of ECM are implicated in CRSwNP. Functional validation of these gene expression patterns will open opportunities for CRSwNP therapeutic interventions such as biologics and immunomodulators.

Objectives/Hypothesis To create a new strategy for monitoring pediatric otitis media (OM), we developed a brief, reliable, and objective method for automated classification using convolutional neural networks (CNNs) with images from otoscope. Study Design Prospective study. Methods An otoscopic image classifier for pediatric OM was built upon the idea of deep learning and transfer learning using the two most widely used CNN architectures named Xception and MobileNet‐V2. Otoscopic images, including acute otitis media (AOM), otitis media with effusion (OME), and normal ears were obtained from our institution. Among qualified otoendoscopic images, 10,703 images were used for training, and 1,500 images were used for testing. In addition, 102 images captured by smartphone with WI‐FI connected otoscope were used as a prospective test set to evaluate the model for home screening and monitoring. Results For all diagnoses combined in the test set, the Xception model and the MobileNet‐V2 model had similar overall accuracies of 97.45% (95% CI 96.81%–97.94%) and 95.72% (95% CI 95.12%–96.16%). The overall accuracies of two models with smartphone images were 90.66% (95% CI 90.21%–90.98%) and 88.56% (95% CI 87.86%–90.05%). The class activation map results showed that the extracted features of smartphone images were the same as those of otoendoscopic images. Conclusions We have developed deep learning algorithms for the successfully automated classification of pediatric AOM and OME with otoscopic images. With a smartphone‐enabled wireless otoscope, artificial intelligence may assist parents in early detection and continuous monitoring at home to decrease the visit frequencies. Level of Evidence NA Laryngoscope, 131:E2344–E2351, 2021