Shu-Chuan Ho

While the association between air pollution and chronic obstructive pulmonary disease (COPD) is well-documented, limited studies investigate the effects of

Background: High mortality is common in mechanically ventilated patients with severe community-acquired pneumonia (CAP). This study sought to create prediction models and determine factors for the pneumonia patients under difficult ventilator weaning in a respiratory care center. Methods: In total, 353 CAP and hospital-acquired pneumonia (HAP) patients admitted to a respiratory care center (RCC) from January 1, 2015 to December 31, 2017 were included in this retrospective cohort study. Mortality and weaning risks factors were collected and analyzed for validating the prediction models. The study focuses primarily on CAP patients, with HAP data used to external validation. Results: Among 270 CAP patients in model testing and validation, CURB-65 (Confusion, Uremia, Respiratory rate, Blood pressure, aged ≥65 years) and CUB-65 similarly predicted RCC survival (AUROCs ~65%). Three new RCC survival prediction models incorporated age ≥65, hypotension, BUN >19 mg/dl, ventilator type (replacing respiratory rate), and GCS ≤ 8 (replacing confusion), and either white blood cells (WBCs) or hemoglobin (Hb) were additionally included in model 1 or model 2. In CAP test samples, AUROCs with CAP test sample were 77.51% (model 1), 75.99% (model 2), and 77.97% (model 3). All three new models showed higher AUROCs than CURB-65, with significantly improved predictability in models 1 ( p = 0.0354) and 3 ( p = 0.0383). All three models demonstrated satisfactory performance (AUROC ≥ 80%) for predicting RCC survival in the CAP validation sample. Conclusions: These new models more accurately predicted survival during RCC admission. Furthermore, they offer clinicians a better predictive tool for survival in CAP patients facing difficult ventilator weaning.

Background: Adherence to prescribed inhaler use is critical for effective asthma management and to prevent exacerbations. However, overall adherence to inhaler use among asthma patients is low and the most frequent measures of adherence are considered not sufficiently reliable. The aim of this study was to investigate adherence rates and their impact on asthma management via an electronic monitoring system. Methods: This was a prospective observational study of adults diagnosed with asthma who were equipped with electronic monitoring sensors whose data were recorded from August 2019 to June 2020. The study participants were also divided into two groups: allergic and nonallergic. The study lasted 90 days, and participants’ demographic information, inhaled corticosteroid (ICS) dosage, lung function and asthma control test scores, blood samples, exhaled nitric oxide concentrations, and inhaler usage records from the electronic monitoring system were collected. Results: In total, 39 asthma patients completed the follow-up period. The study results revealed a significant difference in the adherence rates between the allergy and nonallergy groups at 30 days, with respective adherence rates of 49.9% and 22.8%, and at 90 days, the rates were 37.3% in the allergy group and 19.3% in the nonallergy group. The study results also revealed a significant positive correlation between medication adherence and the allergy group, and adherence was negatively correlated with the ICS dosage. Conclusion: In this study, the average medication adherence rates among asthma patients were low and even lower in the nonallergy group than in the allergy group. Electronic monitoring can provide a clearer understanding of patients’ medication patterns and could serve as a valuable tool for improving asthma management in the future.

Rationale: :Prolonged exposure to airborne particulate matter (PM) is associated with emphysema and chronic obstructive pulmonary disease (COPD); however, the precise underlying mechanism remains unclear.Objectives: In a previous high-throughput screen, we identified inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4) as a biomarker of long-term PM exposure. We hypothesized that ITIH4 is implicated in PM-related emphysema.Methods: We investigated the association between ITIH4 expression and ambient PM exposure through a clinical cohort analysis (220 patients with COPD and 61 healthy participants) andin vitrostudies.Measurements and Main Results: The COPD cohort studies revealed significant correlations between emphysema severity, ambient PM exposure, and serum ITIH4 levels. In primary small airway epithelial cells from COPD patients with low basal levels of ITIH4, exposure to PM and oxidative stress led to increased apoptosis. However, ITIH4 overexpression significantly inhibited oxidative stress–induced apoptosis in normal and COPD airway epithelial cells. Acute exposure to hydrogen peroxide resulted in the rapid degradation of ITIH4 protein with no effect on transcriptional level, althoughITIH4gene expression is downregulated in the lung tissue of patients with COPD. A human apoptosis antibody array revealed that ITIH4 overexpression attenuated hydrogen peroxide–induced apoptotic signaling. Furthermore, extracellular ITIH4 protein confers cytoprotective functions in cells exposed to PM or oxidative stress. Mechanistically, ITIH4 attenuated oxidative stress–induced JNK activation and β-catenin decrease. A deficiency of ITIH4 exacerbated the effects of oxidative stress.Conclusions: We identified a novel pathogenetic mechanism involving ITIH4, where chronic exposure to air pollution induces or promotes emphysema.

Background/Objectives: Chronic obstructive pulmonary disease (COPD) remains a leading cause of death worldwide, with emphysema progression providing valuable insights into disease development. Clinical assessment approaches, including pulmonary function tests and high-resolution computed tomography, are limited by accessibility constraints and radiation exposure. This study, therefore, proposed an alternative approach by integrating the novel biomarker long non-coding interleukin-7 receptor α-subunit gene (lnc-Il7R), along with other easily accessible clinical and biochemical metrics, into machine learning (ML) models. Methods: This cohort study collected baseline characteristics, COPD Assessment Test (CAT) scores, and biochemical details from the enrolled participants. Associations with emphysema severity, defined by a low attenuation area percentage (LAA%) threshold of 15%, were evaluated using simple and multivariate-adjusted models. The dataset was then split into training and validation (80%) and test (20%) subsets. Five ML models were employed, with the best-performing model being further analyzed for feature importance. Results: The majority of participants were elderly males. Compared to the LAA% lnc-Il7R (all p < 0.01). Fold changes in lnc-IL7R were strongly and negatively associated with LAA% (p < 0.01). The random forest (RF) model achieved the highest accuracy and area under the receiver operating characteristic curve (AUROC) across datasets. A feature importance analysis identified lnc-IL7R fold changes as the strongest predictor for emphysema classification (LAA% ≥15%), followed by CAT scores and BMI. Conclusions: Machine learning models incorporated accessible clinical and biochemical markers, particularly the novel biomarker lnc-IL7R, achieving classification accuracy and AUROC exceeding 75% in emphysema assessments. These findings offer promising opportunities for improving emphysema classification and COPD management.

Objective: Common examinations for diagnosing obstructive sleep apnea (OSA) are polysomnography (PSG) and home sleep apnea testing (HSAT). However, both PSG and HSAT require that sensors be attached to a subject, which may disturb their sleep and affect the results. Hence, in this study, we aimed to verify a wireless radar framework combined with deep learning techniques to screen for the risk of OSA in home-based environments. Methods: This study prospectively collected home-based sleep parameters from 80 participants over 147 nights using both HSAT and a 24-GHz wireless radar framework. The proposed framework, using hybrid models (ie, deep neural decision trees), identified respiratory events by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine correlations and agreement of the apnea-hypopnea index (AHI) with results obtained through HSAT and the radar-based respiratory disturbance index based on the time in bed from HSAT (bRDI_TIB). Additionally, Youden’s index was used to establish cutoff thresholds for the bRDI_TIB, followed by multiclass classification and outcome comparisons. Results: A strong correlation (ρ = 0.87) and high agreement (93.88% within the 95% confidence interval; 138/147) between the AHI and bRDI_TIB were identified. The moderate-to-severe OSA model achieved 83.67% accuracy (with a bRDI_TIB cutoff of 21.19 events/h), and the severe OSA model demonstrated 93.21% accuracy (with a bRDI_TIB cutoff of 28.14 events/h). The average accuracy of multiclass classification using these thresholds was 78.23%. Conclusion: The proposed framework, with its cutoff thresholds, has the potential to be applied in home settings as a surrogate for HSAT, offering acceptable accuracy in screening for OSA without the interference of attached sensors. However, further optimization and verification of the radar-based total sleep time function are necessary for independent application.

Background: The endeavor of liberating patients from ventilator dependence within respiratory care centers (RCCs) poses considerable challenges. Multiple factors contribute to this process, yet establishing an effective regimen for pulmonary rehabilitation (PR) remains uncertain. This retrospective study aimed to evaluate existing rehabilitation protocols, ascertain associations between clinical factors and patient outcomes, and explore the influence of these protocols on the outcomes of the patients to shape suitable rehabilitation programs. Methods: Conducted at a medical center in northern Taiwan, the retrospective study examined 320 newly admitted RCC patients between January 1, 2015, and December 31, 2017. Each patient received a tailored PR protocol, following which researchers evaluated weaning rates, RCC survival, and 3-month survival as outcome variables. Analyses scrutinized differences in baseline characteristics and prognoses among three PR protocols: protocol 1 (routine care), protocol 2 (routine care plus breathing training), and protocol 3 (routine care plus breathing and limb muscle training). Results: Among the patients, 28.75% followed protocol 1, 59.37% protocol 2, and 11.88% protocol 3. Variances in age, body-mass index, pneumonia diagnosis, do-not-resuscitate orders, Glasgow Coma Scale scores (≤ 14), and Acute Physiology and Chronic Health Evaluation II (APACHE) scores were notable across these protocols. Age, APACHE scores, and abnormal blood urea nitrogen levels (> 20 mg/dL) significantly correlated with outcomes—such as weaning, RCC survival, and 3-month survival. Elevated mean hemoglobin levels linked to increased weaning rates (p = 0.0065) and 3-month survival (p = 0.0102). Four adjusted models clarified the impact of rehabilitation protocols. Notably, the PR protocol 3 group exhibited significantly higher 3-month survival rates compared to protocol 1, with odds ratios (ORs) ranging from 3.87 to 3.97 across models. This association persisted when comparing with protocol 2, with ORs between 3.92 and 4.22. Conclusion: Our study showed that distinct PR protocols significantly affected the outcomes of ventilator-dependent patients within RCCs. The study underlines the importance of tailored rehabilitation programs and identifies key clinical factors influencing patient outcomes. Recommendations advocate prospective studies with larger cohorts to comprehensively assess PR effects on RCC patients.

Background: Chronic obstructive pulmonary disease (COPD) is characterized, at least in part, by autoimmunity through amplified T helper 1 and 17 (Th1 and Th17) immune responses. The loss of immune tolerance controlled by programmed death-ligand 1 (PD-L1) may contribute to this. Objectives: We studied the tolerogenic role of PD-L1⁺ dendritic cells (DCs) and their subtypes in relation to specific T cell immunity and the clinical phenotypes of COPD. Methods: We used flow cytometry to analyze PD-L1 expression by the DCs and their subtypes in the peripheral blood mononuclear cells (PBMCs) from normal participants and those with COPD. T cell proliferation and the signature cytokines of T cell subtypes stimulated with elastin as autoantigens were measured using flow cytometry and enzyme-linked immunosorbent assays (ELISA), respectively. Measurement and main results: A total of 83 participants were enrolled (normal, n = 29; COPD, n = 54). A reduced PD-L1⁺ conventional dendritic cell 1 (cDC1) ratio in the PBMCs of the patients with COPD was shown (13.7 ± 13.7%, p = 0.03). The decrease in the PD-L1⁺ cDC1 ratio was associated with a rapid decline in COPD (p = 0.02) and correlated with the CD4⁺ T cells (r = −0.33, p = 0.02). This is supported by the NCBI GEO database accession number GSE56766, the researchers of which found that the gene expressions of PD-L1 and CD4, but not CD8 were negatively correlated from PBMC in COPD patients (r = −0.43, p = 0.002). Functionally, the PD-L1 blockade enhanced CD4⁺ T cell proliferation stimulated by CD3/elastin (31.2 ± 22.3%, p = 0.04) and interleukin (IL)-17A production stimulated by both CD3 (156.3 ± 54.7, p = 0.03) and CD3/elastin (148 ± 64.9, p = 0.03) from the normal PBMCs. The PD-L1 blockade failed to increase IL-17A production in the cDC1-depleted PBMCs. By contrast, there was no significant change in interferon (IFN)-γ, IL-4, or IL-10 after the PD-L1 blockade. Again, these findings were supported by the NCBI GEO database accession number GSE56766, the researchers of which found that only the expression of RORC, a master transcription factor driving the Th17 cells, was significantly negatively correlated to PD-L1 (r = −0.33, p = 0.02). Conclusions: Circulating PD-L1⁺ cDC1 was reduced in the patients with COPD, and the tolerogenic role was suppressed with susceptibility to self-antigens and linked to rapid decline caused by Th17-skewed chronic inflammation.

Background: High-grade inflammation represents a critical contribution to the onset of depression and might be manageable by physical activity (PA). Nevertheless, no study has examined synergistic interactions of insufficient PA and high values of the systemic immune-inflammation index (SII) on psychological problems. Objective: We investigated independent and synergistic interactions of insufficient PA and high SII levels on stress, anxiety, and depression in T2DM patients. Methods: A cross-sectional research design with 294 T2DM patients was conducted. An XP-100 automated hematology analyzer was used to evaluate inflammatory biomarkers. Depression, Anxiety, and Stress Scale-21 items and a standardized questionnaire about PA were respectively used to measure psychological problems and metabolic equivalent of task (MET)-h/week. Results: A multiple linear regression demonstrated that patients with insufficient PA were significantly more likely to have higher stress ( β = 1.84, 95% confidence interval (CI) = 1.03–2.65), anxiety ( β = 1.88, 95% CI = 1.81–2.96), and depression ( β = 2.53, 95% CI = 0.82–4.24) than those with active PA. A high SII level was a key predictor and was most strongly associated with stress ( β = 2.61, 95% CI = 2.02–3.20), anxiety ( β = 3.16, 95% CI = 2.37–3.94), and depression ( β = 3.72, 95% CI = 2.49–4.96) compared to those who had low SII levels. Notably, additive interaction results showed that combining insufficient PA and a high SII level had a significantly escalated 1.71-fold risk of stress, 1.82-fold risk of anxiety, and 2.69-fold risk of depression. Conclusions: Active PA and a low SII had a positive synergistic effect of decreasing psychological problems.