Combining wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.

STUDY OBJECTIVES: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of two levels of OSA severity (i.e., moderate-to-severe and severe OSA) in accordance with clinical practice standards. METHODS: We conducted a prospective, simultaneous study using the wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index (AHI), results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared. RESULTS: A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between AHI and RDIPSG_TST were identified. In terms of the agreement between the two devices, the average difference between PSG-based AHI and radar-based RDIPSG_TST was 0.59 events/h, while 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. CONCLUSIONS: The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy, and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work.

Prediction value of neutrophil and eosinophil count at risk of COPD exacerbation.

INTRODUCTION: Predicting acute exacerbations (AEs) in chronic obstructive pulmonary disease (COPD) is crucial. This study aimed to identify blood biomarkers for predicting COPD exacerbations by inflammatory phenotypes. MATERIALS AND METHODS: We analyzed blood cell counts and clinical outcomes in 340 COPD patients aged 20-90 years. Patients were categorized into eosinophilic inflammation (EOCOPD) and non-eosinophilic inflammation (N-EOCOPD) groups. Blood cell counts, eosinophil-to-lymphocyte ratio (ELR), neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-eosinophil ratio (NER) were calculated. Linear and logistic regression models assessed relationships between health outcomes and blood cell counts. RESULTS: EOCOPD patients had distinct characteristics compared to N-EOCOPD patients. Increased neutrophil % and decreased lymphocyte % were associated with reduced pulmonary function, worse quality of life and more exacerbations, but they did not show statistical significance after adjusting by age, sex, BMI, smoking status, FEV1% and patient's medication. Subgroup analysis revealed a 1.372-fold increase in the OR of AE for every 1 unit increase in NLR in EOCOPD patients (p < .05). In N-EOCOPD patients, every 1% increase in blood eosinophil decreased the risk of exacerbation by 59.6%. CONCLUSIONS: Our study indicates that distinct white blood cell profiles in COPD patients, with or without eosinophilic inflammation, can help assess the risk of AE in clinical settings.

Association between air pollutant exposure, body water distribution and sleep disorder indices in individuals with low-arousal-threshold obstructive sleep apnoea.

BACKGROUND: Air pollution may alter body water distribution, it may also be linked to low-arousal-threshold obstructive sleep apnoea (low-ArTH OSA). Here, we explored the mediation effects of air pollution on body water distribution and low-ArTH OSA manifestations. METHODS: In this retrospective study, we obtained sleep centre data from healthy participants and patients with low-ArTH OSA (N=1924) in northern Taiwan. Air pollutant exposure at different time intervals (1, 3, 6 and 12 months) was estimated using the nearest station estimation method, and government air-quality data were also obtained. Regression models were used to assess the associations of estimated exposure, sleep disorder indices and body water distribution with the risk of low-ArTH OSA. Mediation analysis was performed to explore the relationships between air pollution, body water distribution and sleep disorder indices. RESULTS: First, exposure to particulate matter (PM) with a diameter of ≤10 µm (PM10) for 1 and 3 months and exposure to PM with a diameter of ≤2.5 µm (PM2.5) for 3 months were significantly associated with the Apnoea-Hypopnoea Index (AHI), Oxygen Desaturation Index (ODI), Arousal Index (ArI) and intracellular-to-extracellular water ratio (I-E water ratio). Significant associations were observed between the risk of low-ArTH OSA and 1- month exposure to PM10 (OR 1.42, 95% CI 1.09 to 1.84), PM2.5 (OR 1.33, 95% CI 1.02 to 1.74) and ozone (OR 1.27, 95% CI 1.01 to 1.6). I-E water ratio alternation caused by 1-month exposure to PM10 and 3-month exposure to PM2.5 and PM10 had partial mediation effects on AHI and ODI. CONCLUSION: Air pollution can directly increase sleep disorder indices (AHI, ODI and ArI) and alter body water distribution, thus mediating the risk of low-ArTH OSA.

Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles.

Objective: Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. Methods: Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. Results: InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. Conclusions: The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.

A mobile-based airway clearance care system using deep learning-based vision technology to support personalized home-based pulmonary rehabilitation for COAD patients: Development and usability testing.

Background: Excessive mucus secretion is a serious issue for patients with chronic obstructive airway disease (COAD), which can be effectively managed through postural drainage and percussion (PD + P) during pulmonary rehabilitation (PR). Home-based (H)-PR can be as effective as center-based PR but lacks professional supervision and timely feedback, leading to low motivation and adherence. Telehealth home-based pulmonary (TH-PR) has emerged to assist H-PR, but video conferencing and telephone calls remain the main approaches for COAD patients. Therefore, research on effectively assisting patients in performing PD + P during TH-PR is limited. Objective: This study developed a mobile-based airway clearance care for chronic obstructive airway disease (COAD-MoAcCare) system to support personalized TH-PR for COAD patients and evaluated its usability through expert validation. Methods: The COAD-MoAcCare system uses a mobile device through deep learning-based vision technology to monitor, guide, and evaluate COAD patients' PD + P operations in real time during TH-PR programs. Medical personnel can manage and monitor their personalized PD + P and operational statuses through the system to improve TH-PR performance. Respiratory therapists from different hospitals evaluated the system usability using system questionnaires based on the technology acceptance model, system usability scale (SUS), and task load index (NASA-TLX). Results: Eleven participant therapists were highly satisfied with the COAD-MoAcCare system, rating it between 4.1 and 4.6 out of 5.0 on all scales. The system demonstrated good usability (SUS score of 74.1 out of 100) and a lower task load (NASA-TLX score of 30.0 out of 100). The overall accuracy of PD + P operations reached a high level of 97.5% by comparing evaluation results of the system by experts. Conclusions: The COAD-MoAcCare system is the first mobile-based method to assist COAD patients in conducting PD + P in TH-PR. It was proven to be usable by respiratory therapists, so it is expected to benefit medical personnel and COAD patients. It will be further evaluated through clinical trials.

Associations between air pollution, intracellular-to-extracellular water distribution, and obstructive sleep apnea manifestations.

Background: Exposure to air pollution may be a risk factor for obstructive sleep apnea (OSA) because air pollution may alter body water distribution and aggravate OSA manifestations. Objectives: This study aimed to investigate the mediating effects of air pollution on the exacerbation of OSA severity through body water distribution. Methods: This retrospective study analyzed body composition and polysomnographic data collected from a sleep center in Northern Taiwan. Air pollution exposure was estimated using an adjusted nearest method, registered residential addresses, and data from the databases of government air quality motioning stations. Next, regression models were employed to determine the associations between estimated air pollution exposure levels (exposure for 1, 3, 6, and 12 months), OSA manifestations (sleep-disordered breathing indices and respiratory event duration), and body fluid parameters (total body water and body water distribution). The association between air pollution and OSA risk was determined. Results: Significant associations between OSA manifestations and short-term (1 month) exposure to PM2.5 and PM10 were identified. Similarly, significant associations were identified among total body water and body water distribution (intracellular-to-extracellular body water distribution), short-term (1 month) exposure to PM2.5 and PM10, and medium-term (3 months) exposure to PM10. Body water distribution might be a mediator that aggravates OSA manifestations, and short-term exposure to PM2.5 and PM10 may be a risk factor for OSA. Conclusion: Because exposure to PM2.5 and PM10 may be a risk factor for OSA that exacerbates OSA manifestations and exposure to particulate pollutants may affect OSA manifestations or alter body water distribution to affect OSA manifestations, mitigating exposure to particulate pollutants may improve OSA manifestations and reduce the risk of OSA. Furthermore, this study elucidated the potential mechanisms underlying the relationship between air pollution, body fluid parameters, and OSA severity.

Association of Low Arousal Threshold Obstructive Sleep Apnea Manifestations with Body Fat and Water Distribution.

Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E-I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E-I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA.

Impact of lifetime air pollution exposure patterns on the risk of chronic disease.

Long-term exposure to air pollution can lead to cardiovascular disease, metabolic syndrome, and chronic respiratory disease. However, from a lifetime perspective, the critical period of air pollution exposure in terms of health risk is unknown. This study aimed to evaluate the impact of air pollution exposure at different life stages. The study participants were recruited from community centers in Northern Taiwan between October 2018 and April 2021. Their annual averages for fine particulate matter (PM2.5) exposure were derived from a national visibility database. Lifetime PM2.5 exposures were determined using residential address information and were separated into three stages (<20, 20-40, and >40 years). We employed exponentially weighted moving averages, applying different weights to the aforementioned life stages to simulate various weighting distribution patterns. Regression models were implemented to examine associations between weighting distributions and disease risk. We applied a random forest model to compare the relative importance of the three exposure life stages. We also compared model performance by evaluating the accuracy and F1 scores (the harmonic mean of precision and recall) of late-stage (>40 years) and lifetime exposure models. Models with 89% weighting on late-stage exposure showed significant associations between PM2.5 exposure and metabolic syndrome, hypertension, diabetes, and cardiovascular disease, but not gout or osteoarthritis. Lifetime exposure models showed higher precision, accuracy, and F1 scores for metabolic syndrome, hypertension, diabetes, and cardiovascular disease, whereas late-stage models showed lower performance metrics for these outcomes. We conclude that exposure to high-level PM2.5 after 40 years of age may increase the risk of metabolic syndrome, hypertension, diabetes, and cardiovascular disease. However, models considering lifetime exposure showed higher precision, accuracy, and F1 scores and lower equal error rates than models incorporating only late-stage exposures. Future studies regarding long-term air pollution modelling are required considering lifelong exposure pattern. .1.

Targeting Lung-Gut Axis for Regulating Pollution Particle-Mediated Inflammation and Metabolic Disorders.

Cigarette smoking (CS) or ambient particulate matter (PM) exposure is a risk factor for metabolic disorders, such as insulin resistance (IR), increased plasma triglycerides, hyperglycemia, and diabetes mellitus (DM); it can also cause gut microbiota dysbiosis. In smokers with metabolic disorders, CS cessation decreases the risks of serious pulmonary events, inflammation, and metabolic disorder. This review included recent studies examining the mechanisms underlying the effects of CS and PM on gut microbiota dysbiosis and metabolic disorder development; one of the potential mechanisms is the disruption of the lung-gut axis, leading to gut microbiota dysbiosis, intestinal dysfunction, systemic inflammation, and metabolic disease. Short-chain fatty acids (SCFAs) are the primary metabolites of gut bacteria, which are derived from the fermentation of dietary fibers. They activate G-protein-coupled receptor (GPCR) signaling, suppress histone deacetylase (HDAC) activity, and inhibit inflammation, facilitating the maintenance of gut health and biofunction. The aforementioned gut microbiota dysbiosis reduces SCFA levels. Treatment targeting SCFA/GPCR signaling may alleviate air pollution-associated inflammation and metabolic disorders, which involve lung-gut axis disruption.

YAP-regulated type II alveolar epithelial cell differentiation mediated by human umbilical cord-derived mesenchymal stem cells in acute respiratory distress syndrome.

Acute respiratory distress syndrome (ARDS) contributes to higher mortality worldwide. Human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) have immunomodulatory and regenerative potential. However, the effects of hUC-MSCs as an ARDS treatment remain unclear. We investigated the role of hUC-MSCs in the differentiation of type II alveolar epithelial cells (AECII) by regulating Yes-associated protein (YAP) in ARDS. Male C57BL/6JNarl mice were intratracheally (i.t.) administered lipopolysaccharide (LPS) to induce an ARDS model, followed by a single intravenous (i.v.) dose of hUC-MSCs. hUC-MSCs improved pulmonary function, decreased inflammation on day 3, and mitigated lung injury by reducing the lung injury score and increasing lung aeration (%) in mice on day 7 (p < 0.05). hUC-MSCs inactivated YAP on AECII and facilitated cell differentiation by decreasing Pro-surfactant protein C (Pro-SPC) and galectin 3 (LGALS3) while increasing podoplanin (T1α) in lungs of mice (p < 0.05). In AECII MLE-12 cells, both coculture with hUC-MSCs after LPS exposure and the YAP inhibitor, verteporfin, reduced Pro-SPC and LGALS3, whereas the YAP inhibitor increased T1α expression (p < 0.05). In conclusion, hUC-MSCs ameliorated lung injury of ARDS and regulated YAP to facilitate AECII differentiation.

Corrigendum: Heparin binding epidermal growth factor-like growth factor is a prognostic marker correlated with levels of macrophages infiltrated in lung adenocarcinoma.

[This corrects the article DOI: 10.3389/fonc.2022.963896.].

Heparin binding epidermal growth factor-like growth factor is a prognostic marker correlated with levels of macrophages infiltrated in lung adenocarcinoma.

Background: The interactions between tumor cells and the host immune system play a crucial role in lung cancer progression and resistance to treatment. The alterations of EGFR signaling have the potential to produce an ineffective tumor-associated immune microenvironment by upregulating a series of immune suppressors, including inhibitory immune checkpoints, immunosuppressive cells, and cytokines. Elevated Heparin-binding EGF-like growth factor (HB-EGF) expression, one EGFR ligand correlated with higher histology grading, worse patient prognosis, and lower overall survival rate, acts as a chemotactic factor. However, the role of heparin-binding epidermal growth factor-like growth factor (HB-EGF) in the accumulation of immune cells in the tumor microenvironment remains unclear. Methods: The clinical association of HB-EGF expression in lung cancer was examined using the Gene Expression Omnibus (GEO) repository. HB-EGF expression in different cell types was determined using single-cell RNA sequencing (scRNA-seq) dataset. The correlation between HB-EGF expression and cancer-immune infiltrated cells was investigated by performing TIMER and ClueGo pathways analysis from TCGA database. The chemotaxis of HB-EGF and macrophage infiltration was investigated using migration and immunohistochemical staining. Results: The high HB-EGF expression was significantly correlated with poor overall survival in patients with lung adenocarcinoma (LUAD) but not lung squamous cell carcinoma (LUSC). Moreover, HB-EGF expression was correlated with the infiltration of monocytes, macrophages, neutrophils, and dendritic cells in LUAD but not in LUSC. Analysis of scRNA-seq data revealed high HB-EGF expression in lung cancer cells and myeloid cells. Results from the pathway analysis and cell-based experiment indicated that elevated HB-EGF expression was associated with the presence of macrophage and lung cancer cell migration. HB-EGF was highly expressed in tumors and correlated with M2 macrophage infiltration in LUAD. Conclusions: HB-EGF is a potential prognostic marker and therapeutic target for lung cancer progression, particularly in LUAD.

Climate-mediated air pollution associated with COPD severity.

Air pollution has been reported to be associated with chronic obstructive pulmonary disease (COPD). Our study aim was to examine the mediating effects of air pollution on climate-associated health outcomes of COPD patients. A cross-sectional study of 117 COPD patients was conducted in a hospital in Taiwan. We measured the lung function, 6-min walking distance, oxygen desaturation, white blood cell count, and percent emphysema (low attenuation area, LAA) and linked these to 0-1-, 0-3-, and 0-5-year lags of individual-level exposure to relative humidity (RH), temperature, and air pollution. Linear regression models were conducted to examine associations of temperature, RH, and air pollution with severity of health outcomes. A mediation analysis was conducted to examine the mediating effects of air pollution on the associations of RH and temperature with health outcomes. We observed that a 1 % increase in the RH was associated with increases in forced expiratory volume in 1 s (FEV1), eosinophils, and lymphocytes, and a decrease in the total-lobe LAA. A 1 °C increase in temperature was associated with decreases in oxygen desaturation, and right-, left-, and upper-lobe LAA values. Also, a 1 µg/m3 increase in PM2.5 was associated with a decrease in the FEV1 and an increase in oxygen desaturation. A 1 µg/m3 increases in PM10 and PM2.5 was associated with increases in the total-, right-, left, upper-, and lower-lobe (PM2.5 only) LAA. A one part per billion increase in NO2 was associated with a decrease in the FEV1 and an increase in the upper-lobe LAA. Next, we found that NO2 fully mediated the association between RH and FEV1. We found PM2.5 fully mediated associations of temperature with oxygen saturation and total-, right-, left-, and upper-lobe LAA. In conclusion, climate-mediated air pollution increased the risk of decreasing FEV1 and oxygen saturation and increasing emphysema severity among COPD patients. Climate change-related air pollution is an important public health issue, especially with regards to respiratory disease.

Association of air pollution exposure with exercise-induced oxygen desaturation in COPD.

BACKGROUND: There is a link between exposure to air pollution and the increased prevalence of chronic obstructive pulmonary disease (COPD) and declining pulmonary function, but the association with O2 desaturation during exercise in COPD patients with emphysema is unclear. Our aims were to estimate the prevalence of O2 desaturation during exercise in patients with COPD, and determine the association of exposure to air pollution with exercise-induced desaturation (EID), the degree of emphysema, and dynamic hyperinflation (DH). METHODS: We assessed the effects of 10-year prior to the HRCT assessment and 7 days prior to the six-minute walking test exposure to particulate matter with an aerodynamic diameter of < 10 µm (PM10) or of < 2.5 µM (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in patients with emphysema in this retrospective cohort study. EID was defined as a nadir standard pulse oximetry (SpO2) level of < 90% or a delta (△)SpO2 level of ≥ 4%. Ambient air pollutant (PM2.5, PM10, O3, and NO2) data were obtained from Taiwan Environmental Protection Administration (EPA) air-monitoring stations, usually within 10 km to each participant's home address. RESULTS: We recruited 141 subjects with emphysema. 41.1% of patients with emphysema exhibited EID, and patients with EID had more dyspnea, worse lung function, more severe emphysema, more frequent acute exacerbations, managed a shorter walking distance, had DH, and greater long-term exposure to air pollution than those without EID. We observed that levels of 10-year concentrations of PM10, PM2.5, and NO2 were significantly associated with EID, PM10 and PM2.5 were associated with the severity of emphysema, and associated with DH in patients with emphysema. In contrast, short-term exposure did not have any effect on patients. CONCLUSION: Long-term exposure to ambient PM10, PM2.5 and NO2, but not O3, was associated with EID.

lnc-IL7R Expression Reflects Physiological Pulmonary Function and Its Aberration Is a Putative Indicator of COPD.

Despite rapidly evolving pathobiological mechanistic demystification, coupled with advances in diagnostic and therapeutic modalities, chronic obstructive pulmonary disease (COPD) remains a major healthcare and clinical challenge, globally. Further compounded by the dearth of available curative anti-COPD therapy, it is posited that this challenge may not be dissociated from the current lack of actionable COPD pathognomonic molecular biomarkers. There is accruing evidence of the involvement of protracted 'smoldering' inflammation, repeated lung injury, and accelerated lung aging in enhanced predisposition to or progression of COPD. The relatively novel uncharacterized human long noncoding RNA lnc-IL7R (otherwise called LOC100506406) is increasingly designated a negative modulator of inflammation and regulator of cellular stress responses; however, its role in pulmonary physiology and COPD pathogenesis remains largely unclear and underexplored. Our previous work suggested that upregulated lnc-IL7R expression attenuates inflammation following the activation of the toll-like receptor (TLR)-dependent innate immune system, and that the upregulated lnc-IL7R is anti-correlated with concomitant high PM2.5, PM10, and SO2 levels, which is pathognomonic for exacerbated/aggravated COPD in Taiwan. In the present study, our quantitative analysis of lnc-IL7R expression in our COPD cohort (n = 125) showed that the lnc-IL7R level was significantly correlated with physiological pulmonary function and exhibited COPD-based stratification implications (area under the curve, AUC = 0.86, p < 0.001). We found that the lnc-IL7R level correctly identified patients with COPD (sensitivity = 0.83, specificity = 0.83), precisely discriminated those without emphysematous phenotype (sensitivity = 0.48, specificity = 0.89), and its differential expression reflected disease course based on its correlation with the COPD GOLD stage (r = −0.59, p < 0.001), %LAA-950insp (r = −0.30, p = 0.002), total LAA (r = −0.35, p < 0.001), FEV1(%) (r = 0.52, p < 0.001), FVC (%) (r = 0.45, p < 0.001), and post-bronchodilator FEV1/FVC (r = 0.41, p < 0.001). Consistent with other data, our bioinformatics-aided dose−response plot showed that the probability of COPD decreased as lnc-IL7R expression increased, thus, corroborating our posited anti-COPD therapeutic potential of lnc-IL7R. In conclusion, reduced lnc-IL7R expression not only is associated with inflammation in the airway epithelial cells but is indicative of impaired pulmonary function, pathognomonic of COPD, and predictive of an exacerbated/ aggravated COPD phenotype. These data provide new mechanistic insights into the ailing lung and COPD progression, as well as suggest a novel actionable molecular factor that may be exploited as an efficacious therapeutic strategy in patients with COPD.

Lnc-IL7R alleviates PM2.5-mediated cellular senescence and apoptosis through EZH2 recruitment in chronic obstructive pulmonary disease.

BACKGROUND: Long-term exposure to PM2.5 (particulate matter with an aerodynamic diameter of ≤ 2.5 µm) is associated with pulmonary injury and emphysema in patients with chronic obstructive pulmonary disease (COPD). We investigated mechanisms through which the long noncoding RNA lnc-IL7R contributes to cellular damage by inducing oxidative stress in COPD patients exposed to PM2.5. METHODS: Associations of serum lnc-IL7R levels with lung function, emphysema, and previous PM2.5 exposure in COPD patients were analyzed. Reactive oxygen species and lnc-IL7R levels were measured in PM2.5-treated cells. The levels of lnc-IL7R and cellular senescence-associated genes, namely p16INK4a and p21CIP1/WAF1, were determined through lung tissue section staining. The effects of p16INK4a or p21CIP1/WAF1 regulation were examined by performing lnc-IL7R overexpression and knockdown assays. The functions of lnc-IL7R-mediated cell proliferation, cell cycle, senescence, colony formation, and apoptosis were examined in cells treated with PM2.5. Chromatin immunoprecipitation assays were conducted to investigate the epigenetic regulation of p21CIP1/WAF1. RESULTS: Lnc-IL7R levels decreased in COPD patients and were negatively correlated with emphysema or PM2.5 exposure. Lnc-IL7R levels were upregulated in normal lung epithelial cells but not in COPD cells exposed to PM2.5. Lower lnc-IL7R expression in PM2.5-treated cells induced p16INK4a and p21CIP1/WAF1 expression by increasing oxidative stress. Higher lnc-IL7R expression protected against cellular senescence and apoptosis, whereas lower lnc-IL7R expression augmented injury in PM2.5-treated cells. Lnc-IL7R and the enhancer of zeste homolog 2 (EZH2) synergistically suppressed p21CIP1/WAF1 expression through epigenetic modulation. CONCLUSION: Lnc-IL7R attenuates PM2.5-mediated p21CIP1/WAF1 expression through EZH2 recruitment, and its dysfunction may augment cellular injury in COPD.

Associations of the distance-saturation product and low-attenuation area percentage in pulmonary computed tomography with acute exacerbation in patients with chronic obstructive pulmonary disease.

Background: Chronic obstructive pulmonary disease (COPD) has high global health concerns, and previous research proposed various indicators to predict mortality, such as the distance-saturation product (DSP), derived from the 6-min walk test (6MWT), and the low-attenuation area percentage (LAA%) in pulmonary computed tomographic images. However, the feasibility of using these indicators to evaluate the stability of COPD still remains to be investigated. Associations of the DSP and LAA% with other COPD-related clinical parameters are also unknown. This study, thus, aimed to explore these associations. Methods: This retrospective study enrolled 111 patients with COPD from northern Taiwan. Individuals' data we collected included results of a pulmonary function test (PFT), 6MWT, life quality survey [i.e., the modified Medical Research Council (mMRC) scale and COPD assessment test (CAT)], history of acute exacerbation of COPD (AECOPD), and LAA%. Next, the DSP was derived by the distance walked and the lowest oxygen saturation recorded during the 6MWT. In addition, the DSP and clinical phenotype grouping based on clinically significant outcomes by previous study approaches were employed for further investigation (i.e., DSP of 290 m%, LAA% of 20%, and AECOPD frequency of ≥1). Mean comparisons and linear and logistic regression models were utilized to explore associations among the assessed variables. Results: The low-DSP group (<290 m%) had significantly higher values for the mMRC, CAT, AECOPD frequency, and LAA% at different lung volume scales (total, right, and left), whereas it had lower values of the PFT and 6MWT parameters compared to the high-DSP group. Significant associations (with high odds ratios) were observed of the mMRC, CAT, AECOPD frequency, and PFT with low- and high-DSP groupings. Next, the risk of having AECOPD was associated with the mMRC, CAT, DSP, and LAA% (for the total, right, and left lungs). Conclusion: A lower value of the DSP was related to a greater worsening of symptoms, more-frequent exacerbations, poorer pulmonary function, and more-severe emphysema (higher LAA%). These readily determined parameters, including the DSP and LAA%, can serve as indicators for assessing the COPD clinical course and may can serve as a guide to corresponding treatments.

Clinical Experience with Ropeginterferon Alfa-2b in the Off-Label Use for the Treatment of COVID-19 Patients in Taiwan.

INTRODUCTION: This study, for the first time to our knowledge, evaluated the efficacy of ropeginterferon alfa-2b, a long-acting pegylated interferon (IFN)-alfa, in the treatment of COVID-19. METHODS: We retrospectively evaluated ropeginterferon alfa-2b administered subcutaneously at a single dose of 250 µg for the treatment of mild and moderate COVID-19. Primary outcome was to compare the overall negative conversion time from the confirmed, last positive SARS-CoV-2 RT-PCR to the first RT-PCR negative conversion between patients receiving ropeginterferon alfa-2b plus standard of care (SOC) and those receiving SOC alone. RESULTS: Thirty-five patients with mild COVID-19 and 37 patients with moderate disease were included. Of them, 19 patients received SOC plus ropeginterferon alfa-2b and 53 patients received SOC alone. All patients with moderate disease in the ropeginterferon alfa-2b group showed RT-PCR negative conversion within 8 days, while a significant portion of patients in the SOC alone group failed to do so. For patients with moderate disease and age ≤ 65 years old, the ropeginterferon alfa-2b group had statistically significant shorter median RT-PCR conversion time than the SOC alone group (7 vs. 11.5 days, p < 0.05). CONCLUSIONS: Ropeginterferon alfa-2b showed the potential for the treatment of moderate COVID-19 patients. A randomized, controlled Phase III study is planned to further assess the effectiveness of ropeginterferon alfa-2b in COVID-19 patients.

3-Nitrobenzanthrone promotes malignant transformation in human lung epithelial cells through the epiregulin-signaling pathway.

Exposure to environmental and occupational contaminants leads to lung cancer. 3-Nitrobenzanthrone (3-nitro-7H-benz[de]anthracen-7-one, 3-NBA) is a potential carcinogen in ambient air or diesel particulate matter. Studies have revealed that short-term exposure to 3-NBA induces cell death, reactive oxygen species activation, and DNA adduct formation and damage. However, details of the mechanism by which chronic exposure to 3-NBA influences lung carcinogenesis remain largely unknown. In this study, human lung epithelial BEAS-2B cells were continuously exposed to 0-10-µM 3-NBA for 6 months. NanoString analysis was conducted to evaluate gene expression in the cells, revealing that 3-NBA-mediated transformation results in a distinct gene expression signature including carbon cancer metabolism, metastasis, and angiogenesis. Alterations in tumor-promoting genes such as EREG (epiregulin), SOX9, E-cadherin, TWIST, and IL-6 were involved in epithelial cell aggressiveness. Kaplan-Meier plotter analyses indicated that increased EREG and IL-6 expressions in early-stage lung cancer cells are correlated with poor survival. In vivo xenografts on 3-NBA-transformed cells exhibited prominent tumor formation and metastasis. EREG knockout cells exposed to 3-NBA for a short period exhibited high apoptosis and low colony formation. By contrast, overexpression of EREG in 3-NBA-transformed cells markedly activated the PI3K/AKT and MEK/ERK signaling pathways, resulting in tumorigenicity. Furthermore, elevated IL-6 and EREG expressions synergistically led to STAT3 signaling activation, resulting in clonogenic cell survival and migration. Taken together, chronic exposure of human lung epithelial cells to 3-NBA leads to malignant transformation, in which the EREG signaling pathway plays a pivotal mediating role. • Short-term exposure of lung epithelial cells to 3-NBA can lead to ROS production and cell apoptosis. • Long-term chronic exposure to 3-NBA upregulates the levels of tumor-promoting genes such as EREG and IL-6. • Increased EREG expression in 3-NBA-transformed cells markedly contributes to tumorigenesis through PI3K/AKT and MEK/ERK activation and synergistically enhances the IL-6/STAT3 signaling pathway, which promotes tumorigenicity.