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Feasibility and Utility of a Smartphone Application-Based Longitudinal Cough Monitoring in Chronic Cough Patients in a Real-World Setting.

This study evaluated the feasibility and utility of longitudinal cough frequency monitoring with the Hyfe Cough Tracker, a mobile application equipped with cough-counting artificial intelligence algorithms, in real-world patients with chronic cough. Patients with chronic cough (> 8-week duration) were monitored continuously for cough frequency with the Hyfe app for at least one week. Cough was also evaluated using the Leicester Cough Questionnaire (LCQ) and daily cough severity scoring (0-10). The study analyzed adherence rate, the correlation between objective cough frequency and subjective scores, day-to-day variability, and patient experience. Of 65 subjects consecutively recruited, 43 completed the study. The median cough monitoring duration was 13.9days, with a median adherence of 91%. Study completion was associated with baseline cough severity, and the adherence rate was higher in younger subjects. Cross-sectional correlation analyses showed modest correlations between objective and subjective cough measures at the group level. However, in time series correlation analyses, correlations between objective and subjective measures widely varied across individuals. Cough frequency had greater day-to-day variability than daily cough severity scores in most subjects. A patient experience survey found that 70% of participants found the cough monitoring helpful, 86% considered it acceptable, and 84% felt it was easy to use. Monitoring cough frequency longitudinally for at least one week may be feasible. The substantial day-to-day variability in objective cough frequency highlights the need for continuous monitoring. Grasping the implications of daily cough variability is crucial in both clinical practice and clinical trials.

Targeting CDH17 with Chimeric Antigen Receptor-Redirected T Cells in Small Cell Lung Cancer.

Chimeric antigen receptor T cell (CAR-T) therapy stands as a precise and targeted approach in the treatment of malignancies. In this study, we investigated the feasibility of targeting Cadherin 17 (CDH17) with CDH17 CAR-T cells as a therapeutic modality for small cell lung cancer (SCLC). CDH17 expression levels were assessed in human SCLC tumor tissues and cell lines using qPCR and Western blot. Subsequently, we established CDH17 CAR-T cells and assessed their cytotoxicity by co-culturing them with various SCLC cell lines at different effector-to-target (E:T) ratios, complemented by ELISA assays. To ascertain the specificity of CDH17 CAR-T cells, we conducted experiments on SCLC cells with and without CDH17 expression (shRNAs). Furthermore, we employed an SCLC xenograft model to evaluate the in vivo efficacy of CDH17 CAR-T cells. Our results revealed a significant upregulation of CDH17 in both SCLC tissues and cell lines. CDH17 CAR-T cells exhibited robust cytotoxic activity against SCLC cells in vitro, while demonstrating no cytotoxicity towards CDH17-deficient SCLC cells and HEK293 cells that lack CDH17 expression. Importantly, the production of IFN-γ and TNF-α by CDH17 CAR-T cells correlated with their cytotoxic potency. Additionally, treatment with CDH17 CAR-T cells significantly decelerated the growth rate of SCLC-derived xenograft tumors in vivo. Remarkably, no significant difference in body weight was observed between the control group and the group treated with CDH17 CAR-T cells. The preclinical data open further venues for the clinical use of CDH17 CAR-T cells as an immunotherapeutic strategy for SCLC treatment.

Lung Microbiome as a Treatable Trait in Chronic Respiratory Disorders.

Once thought to be a sterile environment, it is now established that lungs are populated by various microorganisms that participate in maintaining lung function and play an important role in shaping lung immune surveillance. Although our comprehension of the molecular and metabolic interactions between microbes and lung cells is still in its infancy, any event causing a persistent qualitative or quantitative variation in the composition of lung microbiome, termed "dysbiosis", has been virtually associated with many respiratory diseases. A deep understanding of the composition and function of the "healthy" lung microbiota and how dysbiosis can cause or participate in disease progression will be pivotal in finding specific therapies aimed at preventing diseases and restoring lung function. Here, we review lung microbiome dysbiosis in different lung pathologies and the mechanisms by which these bacteria can cause or contribute to the severity of the disease. Furthermore, we describe how different respiratory disorders can be caused by the same pathogen, and that the real pathogenetic mechanism is not only dependent by the presence and amount of the main pathogen but can be shaped by the interaction it can build with other bacteria, fungi, and viruses present in the lung. Understanding the nature of this bacteria crosstalk could further our understanding of each respiratory disease leading to the development of new therapeutic strategies.

Current Applications of Artificial Intelligence in Sarcoidosis.

Sarcoidosis is a complex disease which can affect nearly every organ system with manifestations ranging from asymptomatic imaging findings to sudden cardiac death. As such, diagnosis and prognostication are topics of continued investigation. Recent technological advancements have introduced multiple modalities of artificial intelligence (AI) to the study of sarcoidosis. Machine learning, deep learning, and radiomics have predominantly been used to study sarcoidosis. Articles were collected by searching online databases using keywords such as sarcoid, machine learning, artificial intelligence, radiomics, and deep learning. Article titles and abstracts were reviewed for relevance by a single reviewer. Articles written in languages other than English were excluded. Machine learning may be used to help diagnose pulmonary sarcoidosis and prognosticate in cardiac sarcoidosis. Deep learning is most comprehensively studied for diagnosis of pulmonary sarcoidosis and has less frequently been applied to prognostication in cardiac sarcoidosis. Radiomics has primarily been used to differentiate sarcoidosis from malignancy. To date, the use of AI in sarcoidosis is limited by the rarity of this disease, leading to small, suboptimal training sets. Nevertheless, there are applications of AI that have been used to study other systemic diseases, which may be adapted for use in sarcoidosis. These applications include discovery of new disease phenotypes, discovery of biomarkers of disease onset and activity, and treatment optimization.

Increased Fungal Infection Mortality Induced by Concurrent Viral Cellular Manipulations.

Certain respiratory fungal pathogen mono-infections can cause high mortality rates. Several viral pathogen mono-infections, including influenza viruses and coronaviruses including SARS-CoV-2, can also cause high mortality rates. Concurrent infections by fungal pathogens and highly manipulative viral pathogens can synergistically interact in the respiratory tract to substantially increase their mortality rates. There are at least five viral manipulations which can assist secondary fungal infections. These viral manipulations include the following: (1) inhibiting transcription factors and cytokine expressions, (2) impairing defensive protein expressions, (3) inhibiting defenses by manipulating cellular sensors and signaling pathways, (4) inhibiting defenses by secreting exosomes, and (5) stimulating glucocorticoid synthesis to suppress immune defenses by inhibiting cytokine, chemokine, and adhesion molecule production. The highest mortality respiratory viral pandemicsup to now have had substantially boosted mortalities by inducing secondary bacterial pneumonias. However, numerous animal species besides humans are also carriers of endemic infections by viral and multidrug-resistant fungal pathogens. The vast multi-species scope of endemic infection opportunities make it plausible that the pro-fungal manipulations of a respiratory virus can someday evolve to enable a very high mortality rate viral pandemic inducing multidrug-resistant secondary fungal pathogen infections. Since such pandemics can quickly spread world-wide and outrun existing treatments, it would be worthwhile to develop new antifungal treatments well before such a high mortality event occurs.

Baseline Cohort Profile of the Korean Chronic Cough Registry: A Multicenter, Prospective, Observational Study.

The Korean Chronic Cough Registry study was initiated to characterize patients with chronic cough (CC) and investigate their outcomes in real-world clinical practice. This report aims to describe the baseline cohort profile and study protocols. This multicenter, prospective observational cohort study included newly referred CC patients and those already being treated for refractory or unexplained chronic cough (RUCC). Cough status was assessed using a visual analog scale, the Leicester Cough Questionnaire (LCQ), and the Cough Hypersensitivity Questionnaire (CHQ). A total of 610 patients (66.9% women; median age 59.0 years) were recruited from 18 centers, with 176 being RUCC patients (28.9%). The median age at CC onset was 50.1 years, and 94.4% had adult-onset CC (≥ 19 years). The median cough duration was 4 years. Compared to newly referred CC patients, RUCC patients had a longer cough duration (6.0 years vs. 3.0 years) but had fewer symptoms and signs suggesting asthma, rhinosinusitis, or gastroesophageal acid reflux disease. Subjects with RUCC had lower LCQ scores (10.3 ± 3.3 vs. 11.6 ± 3.6; P < 0.001) and higher CHQ scores (9.1 ± 3.9 vs. 8.4 ± 4.1; P = 0.024). There were no marked differences in the characteristics of cough between refractory chronic cough and unexplained chronic cough. Chronic cough typically develops in adulthood, lasting for years. Cough severity and quality of life impairment indicate the presence of unmet clinical needs and insufficient cough control in real-world clinical practice. Longitudinal follow-up is warranted to investigate the natural history and treatment outcomes.

Genetic Association of Circulating Adipokines with Risk of Idiopathic Pulmonary Fibrosis: A Two-Sample Mendelian Randomization Study.

The causal relationships between circulating adipokines and idiopathic pulmonary fibrosis (IPF) are yet to be established. We performed a two-sample Mendelian randomization (MR) study to investigate the causal roles of adipokines on IPF risk. We analyzed the summary data from genome-wide association studies (GWAS), including adiponectin, leptin, resistin and monocyte chemoattractant protein-1 (MCP-1) and IPF. The inverse-variance weighted (IVW) method was considered as the major method and the MR-Egger, weighted median, simple mode and weighted mode were utilized as complementary methods. We also performed the sensitivity analyses, including heterogeneity test, horizontal pleiotropy test and leave-one-out analysis. The selected number of single nucleotide polymorphisms (SNPs) was 13 for adiponectin, 6 for leptin,12 for resistin, and 6 for MCP-1, respectively. The results showed a causal effect of the circulating adiponectin levels on the risk of IPF (OR 0.645, 95% CI 0.457-0.911, P = 0.013). However, we did not observe significant associations of genetic changes in serum leptin (OR 1.018, 95% CI 0.442-2.346, P = 0.967), resistin (OR 1.002, 95% CI 0.712-1.408, P = 0.993), and MCP-1 (OR 1.358, 95% CI 0.891-2.068, P = 0.155) with risk of developing IPF. There was no evidence of heterogeneity or horizontal pleiotropy. The sensitivity analyses confirmed that our results were stable and reliable. The increase in serum adiponectin was associated causally with a decreased risk of developing IPF. There is no evidence to support a causal association between leptin, resistin or MCP-1 with risk of IPF. Further studies are needed to confirm our findings.

ETS1 Ameliorates Hyperoxia-Induced Bronchopulmonary Dysplasia in Mice by Activating Nrf2/HO-1 Mediated Ferroptosis.

Bronchopulmonary dysplasia (BPD) is associated with hyperoxia-induced oxidative stress-associated ferroptosis. This study examined the effect of E26 oncogene homolog 1 (ETS1) on oxidative stress-associated ferroptosis in BPD. Hyperoxia-induced A549 cells and neonatal mice were used to establish BPD models. The effects of ETS1 on hyperoxia-induced ferroptosis-like changes in A549 cells were investigated by overexpression of ETS1 plasmid transfection and erastin treatment. Glucose consumption, lactate production, and NADPH levels were assessed by the glucose, lactate, and NADP+/NADPH assay kits, respectively. The potential regulatory relationship between ETS1 and Nrf2/HO-1 was examined by treating hyperoxia-induced A549 cells with the Nrf2 inhibitor ML385. ETS1 effect on the Nrf2 promoter was explored by dual-luciferase reporter and chromatin immunoprecipitation assay. The effect of ETS1 on the symptoms of BPD mice was examined by injecting an adenovirus overexpressing ETS1. ETS1 overexpression increased hyperoxia-induced cell viability, glucose consumption, lactate production, and NADPH levels and reduced inflammation and apoptosis in A549 cells. In animal experiments, ETS1 overexpression prevented weight loss, airway enlargement, and reductions in radial alveolar counts in BPD mice, while reducing the mean linear intercept, mean alveolar diameter and inflammation. ETS1 overexpression suppressed PTGS2 and CHAC1 expression, reduced ROS, MDA and ferrous iron (Fe2+) production and increased GSH levels in hyperoxia-induced A549 cells and BPD mice. In addition, ETS1 can bind to the Nrf2 promoter region and thus promote Nrf2 transcription. ETS1 overexpression increased the mRNA and protein levels of Nrf2, HO-1, xCT, and GPX4 in hyperoxia-induced A549 cells and BPD mice. In hyperoxia-induced A549 cells, erastin and ML385 treatment abolished the effect of ETS1 overexpression. ETS1 is important in oxidative stress-related ferroptosis in a hyperoxia-induced BPD model, and the effect is partially mediated by the Nrf2/HO-1 axis.

Open Access
Investigation of Early-Stage Non-Small Cell Lung Cancer Patients with Different T2 Descriptors: Real Word Data From a Large Database.

The current study evaluated a large cohort of T2N0M0 NSCLC patients with different T2 descriptors to investigate the prognostic disparities and further externally validate the T category of these patients. The Kaplan-Meier Method with the log-rank test was used to plot survival curves. The propensity score matching (PSM) method was used to reduce bias. Univariable and multivariable Cox analyses were used to determine prognostic factors. A total of 13,015 eligible T2N0M0 NSCLC patients were included. There were 5,287, 2,577 and 5,151 patients in the T2a, T2b and non-sized determined T2N0M0 (T2non-sized) groups, respectively. Before PSM, the survival of T2non-sized patients was comparable to that of T2a patients (P = 0.080) but was superior to that of T2b patients (P < 0.001). After PSM, the survival of T2non-sized patients was inferior to that of T2a patients (P = 0.028) but was similar to that of T2b patients (P = 0.325). The T category was further subdivided based on the specific non-sized T2 descriptors and tumor size. The results of the multivariate Cox analysis found that the prognosis of T2 tumors with visceral pleural invasion (size: 0-30mm) was better than that of T2a tumors, and the prognosis of T2 tumors with visceral pleural invasion (size: 30-40mm) was inferior to that of T2a tumors but comparable to that of T2b tumors. T2 tumors with visceral pleural invasion (size: 30-40mm) should be assigned to the T2b category, and those with a size interval of 0-30mm should be assigned to a better prognostic T2a category.