Abstract. Previous studies have reported that regulatory T cells (Tregs), which are physiologically engaged in the maintenance of immunological self-tolerance, have a critical role in the regulation of the antitumor immune response. Targeting Tregs has the potential to augment cancer vaccine approaches. The current study therefore aimed to evaluate the role of cytokine-induced killer (CIK) cell infusion in modulating Tregs in patients with non-small cell lung cancer (NSCLC). A total of 15 patients with advanced NSCLC were treated by an infusion of CIK cells derived from autologous peripheral blood mononuclear cells (PBMCs). By using flow cytometry and liquid chip analysis, subsets of T cells and natural killer (NK) cells in peripheral blood, and plasma cytokine profiles in the treated patients, were analyzed at 2 and 4 weeks after CIK cell infusion. Cytotoxicity of PBMCs (n=15) and NK cells (n=6) isolated from NSCLC patients was evaluated before and after CIK cell therapy. Progression-free survival (PFS) and overall survival (OS) were also assessed. Analysis of the immune cell populations before and after treatment showed a significant increase in NK cells (P<0.05) concomitant with a significant decrease in Tregs (P<0.01) at 2 weeks post-infusion of CIK cells compared with the baseline. NK group 2D receptor (NKG2D) expression on NK cells was also significantly increased at 2 weeks post-infusion compared with the baseline (P<0.05). There was a positive correlation between NKG2D expression and the infusion number of CIK cells (P<0.05).

The host tolerance mechanisms to avian influenza virus (H5N1) infection that limit tissue injury remain unknown. Emerging evidence indicates that cystic fibrosis transmembrane conductance regulator (CFTR), a cAMP-dependent Clchannel, modulates airway inflammation. Janus tyrosine kinase (JAK) 3, a JAK family member that plays a central role in inflammatory responses, prominently contributes to the dysregulated innate immune response upon H5N1 attachment; therefore, this study aims to elucidate whether JAK3 activation induced by H5N1 hemagglutinin (HA) inhibits cAMP-dependent CFTR channels. We performed short-circuit current, immunohistochemistry and molecular analyses of the airway epithelium in Jak3 +/+ and Jak3 +/mice. We demonstrate that H5N1 HA attachment inhibits cAMP-dependent CFTR Clchannels via JAK3-mediated adenylyl cyclase (AC) suppression, which reduces cAMP production. This inhibition leads to increased nuclear factor-kappa B (NF-κB) signaling and inflammatory responses. H5N1 HA is detected by TLR4 expressed on respiratory epithelial cells, facilitating JAK3 activation. This activation induces the interaction between TLR4 and Gαi protein, which blocks ACs. Our findings provide novel insight into the pathogenesis of acute lung injury via the inhibition of cAMP-dependent CFTR channels, indicating that the administration of cAMP-elevating agents and targeting JAK3 may activate host tolerance to infection for the management of influenza virus-induced fatal pneumonia.

IntroductionSpirometry, a pulmonary function test, is being increasingly applied across healthcare tiers, particularly in primary care settings. According to the guidelines set by the American Thoracic Society (ATS) and the European Respiratory Society (ERS), identifying normal, obstructive, restrictive, and mixed ventilatory patterns requires spirometry and lung volume assessments. The aim of the present study was to explore the accuracy of deep learning-based analytic models based on flow–volume curves in identifying the ventilatory patterns. Further, the performance of the best model was compared with that of physicians working in lung function laboratories.MethodsThe gold standard for identifying ventilatory patterns was the rules of ATS/ERS guidelines. One physician chosen from each hospital evaluated the ventilatory patterns according to the international guidelines. Ten deep learning models (ResNet18, ResNet34, ResNet18_vd, ResNet34_vd, ResNet50_vd, ResNet50_vc, SE_ResNet18_vd, VGG11, VGG13, and VGG16) were developed to identify patterns from the flow–volume curves. The patterns obtained by the best-performing model were cross-checked with those obtained by the physicians.ResultsA total of 18,909 subjects were used to develop the models. The ratio of the training, validation, and test sets of the models was 7:2:1. On the test set, the best-performing model VGG13 exhibited an accuracy of 95.6%. Ninety physicians independently interpreted 100 other cases. The average accuracy achieved by the physicians was 76.9 ± 18.4% (interquartile range: 70.5–88.5%) with a moderate agreement (κ = 0.46), physicians from primary care settings achieved a lower accuracy (56.2%), while the VGG13 model accurately identified the ventilatory pattern in 92.0% of the 100 cases (P &lt; 0.0001).ConclusionsThe VGG13 model identified ventilatory patterns with a high accuracy using the flow–volume curves without requiring any other parameter. The model can assist physicians, particularly those in primary care settings, in minimizing errors and variations in ventilatory patterns.