Airway mucus dysfunction and impaired immunological defenses are hallmarks of several lung diseases, including asthma, cystic fibrosis, and chronic obstructive pulmonary diseases, and are mostly causative factors in bacterial-biofilm-associated respiratory tract infections. Bacteria residing within the biofilm architecture pose a complex challenge in clinical settings due to their increased tolerance to currently available antibiotics and host immune responses, resulting in chronic infections with high recalcitrance and high rates of morbidity and mortality. To address these unmet clinical needs, potential anti-biofilm therapeutic strategies are being developed to effectively control bacterial biofilm. This review focuses on recent advances in the development and application of nanoparticulate drug delivery systems for the treatment of biofilm-associated respiratory tract infections, especially addressing the respiratory barriers of concern for biofilm accessibility and the various types of nanoparticles used to combat biofilms. Understanding the obstacles facing pulmonary drug delivery to bacterial biofilms and nanoparticle-based approaches to combatting biofilm may encourage researchers to explore promising treatment modalities for bacterial-biofilm-associated chronic lung infections.

Background: Artificial intelligence (AI) is transforming various fields, with health care, especially diagnostic specialties such as radiology, being a key but controversial battleground. However, there is limited research systematically examining the response of “human intelligence” to AI. Objective: This study aims to comprehend radiologists’ perceptions regarding AI, including their views on its potential to replace them, its usefulness, and their willingness to accept it. We examine the influence of various factors, encompassing demographic characteristics, working status, psychosocial aspects, personal experience, and contextual factors. Methods: Between December 1, 2020, and April 30, 2021, a cross-sectional survey was completed by 3666 radiology residents in China. We used multivariable logistic regression models to examine factors and associations, reporting odds ratios (ORs) and 95% CIs. Results: In summary, radiology residents generally hold a positive attitude toward AI, with 29.90% (1096/3666) agreeing that AI may reduce the demand for radiologists, 72.80% (2669/3666) believing AI improves disease diagnosis, and 78.18% (2866/3666) feeling that radiologists should embrace AI. Several associated factors, including age, gender, education, region, eye strain, working hours, time spent on medical images, resilience, burnout, AI experience, and perceptions of residency support and stress, significantly influence AI attitudes. For instance, burnout symptoms were associated with greater concerns about AI replacement (OR 1.89; P<.001), less favorable views on AI usefulness (OR 0.77; P=.005), and reduced willingness to use AI (OR 0.71; P<.001). Moreover, after adjusting for all other factors, perceived AI replacement (OR 0.81; P<.001) and AI usefulness (OR 5.97; P<.001) were shown to significantly impact the intention to use AI. Conclusions: This study profiles radiology residents who are accepting of AI. Our comprehensive findings provide insights for a multidimensional approach to help physicians adapt to AI. Targeted policies, such as digital health care initiatives and medical education, can be developed accordingly.

Patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) are prone to diaphragmatic dysfunction. However, dynamic assessment of diaphragmatic function is complex and difficult, and whether the assessment of diaphragmatic function can reflect clinical indicators such as lung function in AECOPD patients remains unclear. We studied diaphragm stiffness and diaphragm stiffening rate (DSR) in AECOPD patients with acute exacerbations ≥ 2 times within 1 year and their correlation with clinical data, the diaphragmatic thickening fraction (DTF), lung function, and blood gas values. In total, 112 AECOPD patients in group C and Group D who had acute exacerbations ≥ 2 times within 1 year in the Global Initiative for Chronic Obstructive Lung Disease Guideline A (low risk, few symptoms), B (low risk, many symptoms), C (High risk, few symptoms), D (High risk, many symptoms) grouping system were included in the study. Their general clinical data, chronic obstructive pulmonary disease assessment test (CAT), modified medical research council (mMRC), number of acute exacerbations in 1 year, DTF, lung function, and blood gas analysis were collected. The diaphragm shear wave elasticity at functional residual capacity (DsweFRC) and DSR were measured by ultrasound. The DsweFRC and DSR of Group D were higher than those of Group C (P < .05). DsweFRC, DSR were negatively correlated with DTF, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC) and FEV1/FVC (r ranged from −0.293 to −0.697, all P < .05), and positively correlated with CAT score, mMRC score, and arterial carbon dioxide pressure (r ranged from 0.274 to 0.462, all P < .05) in both groups; the correlation coefficients of DsweFRC, DSR and DTF, FEV1/FVC in group D were greater than those in group C. There was no correlation between DsweFRC, DSR and arterial oxygen partial pressure in both groups (P > .05). The DsweFRC, DSR increased with the number of acute exacerbations per year in both groups. We found that diaphragmatic stiffness in AECOPD patients increased with the number of acute exacerbations within 1 year, correlated with DTF, CAT, mMRC, lung function, and arterial carbon dioxide pressure and provides a simple and practical method for dynamically assessing diaphragmatic function and disease severity in AECOPD patients.