Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide, with approximately 70% to 80% of adults with COPD being undiagnosed. Patients with undiagnosed COPD are at increased risk of poor outcomes and a worsened quality of life, making early detection a crucial strategy to mitigate the impact of COPD and reduce the burden on healthcare systems. In the past decade, increased interest has been focused on the development of effective strategies and instrument for COPD early detection. However, identifying undiagnosed cases of COPD is still challenging. Both screening and case-finding approaches have been adopted to identify undiagnosed COPD, with case-finding being recommended by the 2023 Global Initiative for Chronic Obstructive Lung Disease (GOLD) guideline and the updated United States Preventive Services Task Force (USPTF) recommendation. Nonetheless, the approaches, criteria, and instruments used for early detection of COPD are varied. However, advances in the taxonomy and risk factors of COPD are continuously being investigated. It is important to continuously assess the current state of knowledge on COPD early detection, given the challenges associated with identifying undiagnosed COPD. This review aims to highlight recent advances in early detection of COPD. To discuss the current challenge and opportunity in COPD early detection, providing an overview of existing literature on COPD case-finding strategies, including the approaches, criteria for subjects, and instruments. The review also summarizes the current progress in COPD case-findings and proposes a COPD case-finding flowchart as an efficient method for identifying at risk COPD patients.

Background: The study developed accurate explainable machine learning (ML) models for predicting first-time acute exacerbation of chronic obstructive pulmonary disease (COPD, AECOPD) at an individual level. Methods: We conducted a retrospective case–control study. A total of 606 patients with COPD were screened for eligibility using registry data from the COPD Pay-for-Performance Program (COPD P4P program) database at Changhua Christian Hospital between January 2017 and December 2019. Recursive feature elimination technology was used to select the optimal subset of features for predicting the occurrence of AECOPD. We developed four ML models to predict first-time AECOPD, and the highest-performing model was applied. Finally, an explainable approach based on ML and the SHapley Additive exPlanations (SHAP) and a local explanation method were used to evaluate the risk of AECOPD and to generate individual explanations of the model’s decisions. Results: The gradient boosting machine (GBM) and support vector machine (SVM) models exhibited superior discrimination ability (area under curve [AUC] = 0.833 [95% confidence interval (CI) 0.745–0.921] and AUC = 0.836 [95% CI 0.757–0.915], respectively). The decision curve analysis indicated that the GBM model exhibited a higher net benefit in distinguishing patients at high risk for AECOPD when the threshold probability was <0.55. The COPD Assessment Test (CAT) and the symptom of wheezing were the two most important features and exhibited the highest SHAP values, followed by monocyte count and white blood cell (WBC) count, coughing, red blood cell (RBC) count, breathing rate, oral long-acting bronchodilator use, chronic pulmonary disease (CPD), systolic blood pressure (SBP), and others. Higher CAT score; monocyte, WBC, and RBC counts; BMI; diastolic blood pressure (DBP); neutrophil-to-lymphocyte ratio; and eosinophil and lymphocyte counts were associated with AECOPD. The presence of symptoms (wheezing, dyspnea, coughing), chronic disease (CPD, congestive heart failure [CHF], sleep disorders, and pneumonia), and use of COPD medications (triple-therapy long-acting bronchodilators, short-acting bronchodilators, oral long-acting bronchodilators, and antibiotics) were also positively associated with AECOPD. A high breathing rate, heart rate, or systolic blood pressure and methylxanthine use were negatively correlated with AECOPD. Conclusions: The ML model was able to accurately assess the risk of AECOPD. The ML model combined with SHAP and the local explanation method were able to provide interpretable and visual explanations of individualized risk predictions, which may assist clinical physicians in understanding the effects of key features in the model and the model’s decision-making process.

Patients with chronic obstructive pulmonary disease (COPD) have chronic, irreversible airway inflammation; currently, there is no effective or curative treatment and the main goals of COPD management are to mitigate symptoms and improve patients’ quality of life. Stem cell based therapy offers a promising therapeutic approach that has shown potential in diverse degenerative lung diseases. Preclinical studies have demonstrated encouraging outcomes of mesenchymal stem/stromal cells (MSCs) therapy for lung disorders including emphysema, bronchopulmonary dysplasia, fibrosis, and acute respiratory distress syndrome. This review summarizes available data on 15 studies currently registered by the ClinicalTrials.gov repository, which used different stem cell therapy protocols for COPD; these included bone marrow mononuclear cells (BMMCs), bone marrow-derived MSCs, adipose-derived stem/stromal cells (ADSCs), and adipose-derived MSCs. Published results of three trials indicate that administering BMMCs or MSCs in the setting of degenerative lung disease is safe and may improve patients’ condition and quality of life; however, larger-scale studies are needed to evaluate efficacy. Results of another completed trial (NCT01872624) are not yet published, and eleven other studies are ongoing; these include MSCs therapy in emphysema, several studies of ADSCs in COPD, another in idiopathic pulmonary fibrosis, and plerixafor mobilization of CD117 stem cells to peripheral blood.

Background/Aim: Niclosamide is an antihe-minthic drug that has shown cytotoxic effects on non-small cell lung carcinoma (NSCLC) cells. However, the exact mechanisms underlying the anti-tumour activity of niclosamide in NSCLC cancer cells remains to be defined. The aim of this study was to evaluate the antitumor activity of niclosamide in human A549 and CL1-5 non-small cell lung cancer cells using in vitro and in vivo. Materials and Methods: We investigated the effects of niclosamide on cell viability, apoptosis, the mitochondrial membrane potential (MMP; Δϕm), and autophagy and apoptosis-related protein expression in human A549 and CL1-5 non-small cell lung cancer cells. Results: Niclosamide induced mainly caspase-independent apoptosis through apoptosis-inducible factor (AIF) translocation to the nucleus upon mitochondria damage. Moreover, niclosamide-induced autophagy may act as adaptive response against apoptosis. AMPK/AKT/mTOR pathway were involved in niclosamide-induced cell death and autophagy in response to ATP depletion. Furthermore, niclosamide efficiently suppressed tumor growth and induce autophagy in vivo. Conclusion: Niclosamide induced apoptosis by activating the intrinsic and caspase-independent pathway in human A549 and CL1-5 non-small cell lung cancer cells. Therefore, niclosamide is a potential candidate for anti-NSCLC therapy.

Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. It has also imposed a substantial economic and social burden on the health care system. In Taiwan, a nationwide COPD pay-for-performance (P4P) program was designed to improve the quality of COPD-related care by introducing financial incentives for health care providers and employing a multidisciplinary team to deliver guideline-based, integrated care for patients with COPD, reducing adverse outcomes, especially COPD exacerbation. However, the results of a survey of the effectiveness of the pay-for-performance program in COPD management were inconclusive. To address this knowledge gap, this study evaluated the effectiveness of the COPD P4P program in Taiwan. Methods: This retrospective cohort study used data from Taiwan’s National Health Insurance claims database and nationwide COPD P4P enrollment program records from June 2016 to December 2018. Patients with COPD were classified into P4P and non-P4P groups. Patients in the P4P group were matched at a ratio of 1:1 based on age, gender, region, accreditation level, Charlson Comorbidity Index (CCI), and inhaled medication prescription type to create the non-P4P group. A difference-in-difference analysis was used to evaluate the influence of the P4P program on the likelihood of COPD exacerbation, namely COPD-related emergency department (ED) visit, intensive care unit (ICU) admission, or hospitalization. Results: The final sample of 14,288 patients comprised 7144 in each of the P4P and non-P4P groups. The prevalence of COPD-related ED visits, ICU admissions, and hospitalizations was higher in the P4P group than in the non-P4P group 1 year before enrollment. After enrollment, the P4P group exhibited a greater decrease in the prevalence of COPD-related ED visits and hospitalizations than the non-P4P group (ED visit: − 2.98%, p< 0.05, 95% confidence interval [CI]: − 0.277 to − 0.086; hospitalization: − 1.62%, p< 0.05, 95% CI: − 0.232 to − 0.020), whereas no significant difference was observed between the groups in terms of the changes in the prevalence of COPD-related ICU admissions. Conclusion: The COPD P4P program exerted a positive net effect on reducing the likelihood of COPD exacerbation, namely COPD-related ED visits and hospitalizations. Future studies should examine the long-term cost-effectiveness of the COPD P4P program.