Although eosinophilic inflammation is characteristic of asthma pathogenesis, neutrophilic inflammation is also marked, and eosinophils and neutrophils can coexist in some cases. Based on the proportion of sputum cell differentiation, asthma is classified into eosinophilic asthma, neutrophilic asthma, neutrophilic and eosinophilic asthma, and paucigranulocytic asthma. Classification by bronchoalveolar lavage is also performed. Eosinophilic asthma accounts for most severe asthma cases, but neutrophilic asthma or a mixture of the two types can also present a severe phenotype. Biomarkers for the diagnosis of neutrophilic asthma include sputum neutrophils, blood neutrophils, chitinase-3-like protein, and hydrogen sulfide in sputum and serum. Thymic stromal lymphoprotein (TSLP)/T-helper 17 pathways, bacterial colonization/microbiome, neutrophil extracellular traps, and activation of nucleotide-binding oligomerization domain-like receptor family, pyrin domain-containing 3 pathways are involved in the pathophysiology of neutrophilic asthma and coexistence of obesity, gastroesophageal reflux disease, and habitual cigarette smoking have been associated with its pathogenesis. Thus, targeting neutrophilic asthma is important. Smoking cessation, neutrophil-targeting treatments, and biologics have been tested as treatments for severe asthma, but most clinical studies have not focused on neutrophilic asthma. Phosphodiesterase inhibitors, anti-TSLP antibodies, azithromycin, and anti-cholinergic agents are promising drugs for neutrophilic asthma. However, clinical research targeting neutrophilic inflammation is required to elucidate the optimal treatment.

An evidence-based diagnostic algorithm for adult asthma is necessary for effective treatment and management. We present a diagnostic algorithm that utilizes a random forest (RF) and an optimized eXtreme Gradient Boosting (XGBoost) classifier to diagnose adult asthma as an auxiliary tool. Data were gathered from the medical records of 566 adult outpatients who visited Kindai University Hospital with complaints of nonspecific respiratory symptoms. Specialists made a thorough diagnosis of asthma based on symptoms, physical indicators, and objective testing, including airway hyperresponsiveness. We used two decision-tree classifiers to identify the diagnostic algorithms: RF and XGBoost. Bayesian optimization was used to optimize the hyperparameters of RF and XGBoost. Accuracy and area under the curve (AUC) were used as evaluation metrics. The XGBoost classifier outperformed the RF classifier with an accuracy of 81% and an AUC of 85%. A combination of symptom–physical signs and lung function tests was successfully used to construct a diagnostic algorithm on importance features for diagnosing adult asthma. These results indicate that the proposed model can be reliably used to construct diagnostic algorithms with selected features from objective tests in different settings.

Epidermal growth factor receptor (EGFR) mutation is the best marker of sensitivity to the EGFR tyrosine kinase inhibitor gefitinib, but a marker for the anti-EGFR antibody cetuximab has not been identified in lung cancer. The present study investigated markers for sensitivity to cetuximab. Sensitivity to cetuximab and gefitinib was compared with EGFR expression, EGFR and KRAS mutation, and EGFR gene copy numbers in lung cancer cell lines. We also studied the effect of these agents on the activation of EGFR, ERK, AKT, and STAT3 in cetuximab-sensitive and -resistant cell lines. We found one cetuximab-sensitive cell line with EGFR mutation among 19 lung cancer cell lines. Analysis of molecules downstream from EGFR revealed that AKT phosphorylation was suppressed in this cell line. Augmentation of AKT phosphorylation by transfection of a plasmid induced resistance to cetuximab. Acquisition of cetuximab resistance was associated with AKT activation in this cell line, while pharmacological inhibition of AKT markedly enhanced the growth inhibitory effect of cetuximab. Dephosphorylation of AKT in association with EGFR mutation is a candidate marker for sensitivity to cetuximab, and combined use of an AKT pathway inhibitor with cetuximab could be a novel therapeutic strategy for lung cancer.

Light detection and ranging (LIDAR) can estimate daily volumes of sand dust particles from the East Asian desert to Japan. The objective of this study was to investigate the relationship between sand dust particles and pulmonary function, and respiratory symptoms in adult patients with asthma. One hundred thirty-seven patients were included in the study. From March 2013 to May 2013, the patients measured their morning peak expiratory flow (PEF) and kept daily lower respiratory symptom diaries. A linear mixed model was used to estimate the correlation of the median daily levels of sand dust particles, symptoms scores, and PEF. A heavy sand dust day was defined as an hourly concentration of sand dust particles of >0.1 km⁻¹. By this criterion, there were 8 heavy sand dust days during the study period. Elevated sand dust particles levels were significantly associated with the symptom score (0.04; 95% confidence interval (CI); 0.03, 0.05), and this increase persisted for 5 days. There was no significant association between PEF and heavy dust exposure (0.01 L/min; 95% CI, −0.62, 0.11). The present study found that sand dust particles were significantly associated with worsened lower respiratory tract symptoms in adult patients with asthma, but not with pulmonary function.