Increased chemokines levels in patients with chronic obstructive pulmonary disease: correlation with quantitative computed tomography metrics

Preventing CXCL12 elevation helps to reduce acute exacerbation of COPD in individuals co-existing type-2 diabetes: A bioinformatics and clinical pharmacology study

AIMS

To investigate the immunology shared mechanisms underlying chronic obstructive pulmonary disease (COPD) and type 2 diabetes mellitus (T2DM) and examine the impact of anti-diabetic drugs on acute exacerbation of COPD (AECOPD).

METHODS

We analyzed GSE76925, GSE76894, GSE37768, and GSE25724 to identify differentially expressed genes. Hub-genes were identified through protein-protein interaction network analysis and evaluated by the receiver operating characteristic curve. CXCL12 emerged as a robust biomarker, and its correlation with lung function and CD8+ T cells were further quantified and validated. The activated signaling pathways were inferred through Gene set enrichment analysis (GSEA). The retrospective clinical analysis was executed to identify the influence of dipeptidyl peptidase-4 inhibitors (DPP-4i) on CXCL12 and evaluate the drug's efficacy in AECOPD.

RESULTS

The significant up-regulation of CXCL12 expression in patients with two diseases were revealed. CXCL12 exhibited a negative correlation with pulmonary function (r = -0.551, p < 0.05). Consistent with analysis in GSE76925 and GSE76894, the positive correlation between the proportion of CD8+ T cells was demonstrated(r=0.469, p<0.05). GSEA identified "cytokines interaction" as an activated signaling pathway, and the clinical study revealed the correlation between CXCL12 and IL-6 (r=0.668, p<0.05). In patients with COPD and T2DM, DDP-4i treatment exhibited significantly higher serum CXCL12, compared to GLP-1RA. Analysis of 187 COPD patients with T2DM indicated that the DPP-4i group had a higher frequency of AECOPD compared to the GLP-1RA group (OR 1.287, 95%CI [1.018-2.136]).

CONCLUSIONS

CXCL12 may represent a therapeutic target for COPD and T2DM. GLP-1RA treatment may be associated with lower CXCL12 levels and a lower risk of AECOPD compared to DPP-4i treatment.

CLINICAL TRIAL REGISTRATION

China Clinical Trial Registration Center（ChiCTR2200055611）.

Increased inflammatory mediators levels are associated with clinical outcomes and prolonged illness in severe COVID-19 patients

OBJECTIVE

The purpose of this study was to identify potential predictors of clinical outcome in severe COVID-19 patients and to investigate the relationship between immunological parameters and duration of illness.

METHODS

This single-center study retrospectively recruited 73 patients with severe or critical COVID-19. Immunological indicators include white blood cell count, neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio, and circulating inflammatory mediators were observed for their association with disease severity, mortality and duration of illness of COVID-19.

RESULTS

Serum inflammatory mediators levels of C-reactive protein (P = 0.015), interleukin 6 (IL-6) (P < 0.001), CX3CL1 (P < 0.001), D-dimer (P < 0.001) and procalcitonin (PCT) (P < 0.001) were increased in critical illness patients compared to those severe COVID-19 patients. CX3CL1 has the highest C-index (0.75) to predict in-hospital mortality in patients with COVID-19. Furthermore, this study shows for the first time that the duration of illness in severe COVID-19 patients is associated with serum levels of CX3CL1 (P = 0.037) and D-dimer (P = 0.014).

CONCLUSION

CX3CL1, D-dimer, PCT, and IL-6 could effectively predict mortality in severe COVID-19 patients. In addition, only the circulating levels of CX3CL1 and D-dimer were significantly associated with duration of illness.

Chemokine Homeostasis in Healthy Volunteers and during Pancreatic and Colorectal Tumor Growth in Murine Models

Chemokines are involved in the humoral regulation of body homeostasis. Changes in the blood level of chemokines were found in cancer, atherosclerosis, diabetes, and other systemic diseases. It is essential to distinguish the effects of co-morbid pathologies and cancer on the level of chemokines in the blood. We aimed to analyze, by multiplex cytometry, the levels of chemokines in the blood of healthy young volunteers as well as of intact mice and mice with CT26 colon and Pan02 pancreatic tumors. Two types of chemokines were identified both in human and murine plasmas: homeostatic ones, which were found in high concentrations (>100 pg/mL), and inducible ones, which can be undetectable or determined at very low levels (0−100 pg/mL). There was a high variability in the chemokine levels, both in healthy humans and mice. To analyze chemokine levels during tumor growth, C57BL/6 and BALB/c were inoculated with Pan02 or CT26 tumor cells, accordingly. The tumors significantly differed in the growth and the mortality of mice. However, the blood chemokine levels did not change in tumor-bearing mice until the very late stages. Taken collectively, blood chemokine level is highly variable and reflects in situ homeostasis. Care should be taken when considering chemokines as prognostic parameters or therapeutic targets in cancer.

N-Acetylcysteine (NAC) Inhibits Synthesis of IL-18 in Macrophage by Suppressing NLRP3 Expression to Reduce the Production of IFN-γ from NK Cells

Background

N-Acetylcysteine (NAC) had exerted antioxidation and anti-inflammation effects on chronic obstructive pulmonary disease (COPD) patients. However, its effect in regulating interleukin- (IL-) 18 was not fully understood. This study was designed to evaluate the specific mechanism of NAC regulating IL-18.

Materials and Methods

A total of 112 COPD patients and 103 health individuals were recruited in the study. Cytokine level in patients' serum was measured by enzyme-linked immunosorbent assay (ELISA). A COPD mouse model was established by administration of lipopolysaccharide (LPS) and cigarette smoke. The expression of cytokines was measured by ELISA and flow cytometry. Inflammasome-related protein was measured by Western blot.

Result

NAC could effectively improve the immune status of COPD patients as well as the COPD mouse model by downregulating proinflammation and inflammation cytokines including IL-1β, interferon- (IFN-) γ, tumor necrosis factor- (TNF-) α, and IL-18. It also had the capability to suppress synthesis of IL-18 in macrophage to inhibit the secretion of IFN-γ from natural killer (NK) cells through influencing the inflammasome-related protein in macrophages.

Conclusion

NAC could effectively inhibit the production of IL-18 by suppressing NLRP3 expression in macrophages to reduce the production of IFN-γ in NK cells.

Computed tomography-based deep-learning prediction of induction chemotherapy treatment response in locally advanced nasopharyngeal carcinoma

BACKGROUND

Deep learning methods have great potential to predict treatment response. The objective of this study was to evaluate and validate the predictive performance of the computed tomography (CT)-based model using deep learning features for identification of responders and nonresponders to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC).

MATERIALS AND METHODS

All eligible patients were included retrospectively between January 2012 and December 2018, and assigned to the training (n = 208) or the testing cohort (n = 89). We extracted deep learning features of six pretrained convolutional neural networks (CNNs) via transfer learning method, and handcrafted radiomics features manually. Support vector machine (SVM) was adopted as the classifier. All predictive models were evaluated using the area under the receiver operating characteristics curve (AUC), by which an optimal model was selected. We also built clinical and clinical-radiological models for comparison.

RESULTS

The model with features extracted from ResNet50 (RN-SVM) had optimal performance among all models with features extracted from pretrained CNNs with an AUC of 0.811, accuracy of 68.54%, sensitivity of 61.54%, specificity of 87.50%, positive predictive value (PPV) of 93.02%, and negative predictive value (NPV) of 45.65% in the testing cohort. The handcrafted radiomics model was slightly inferior to the RN-SVM model with an AUC of 0.663 and accuracy of 60.67% in the testing cohort. All the imaging-derived models had better predictive performance than the clinical model.

CONCLUSION

The noninvasive deep learning method could provide efficient prediction of treatment response to IC in locally advanced NPC and might be a practicable approach in therapeutic strategy decision-making.

Evaluation of small airway function and its application in patients with chronic obstructive pulmonary disease (Review)

Chronic obstructive pulmonary disease (COPD) is a chronic airway inflammatory disease characterized by incomplete reversible airflow limitation. The diagnosis of COPD is mainly based on pulmonary function examination. In recent years, it has been indicated that small airway dysfunction occurs in patients with all stages of COPD, even in high-risk smoking groups who have not yet met the diagnostic criteria for COPD. Early recognition of small airway dysfunction and early initiation of small airway targeted therapy have become foci of research. In the present review, the methods of evaluating small airway function were summarized and their merits and shortcomings were discussed. Furthermore, the potential of targeted treatment of small airways in patients with COPD was outlined.