Tuberculosis and Respiratory Diseases最新文献

Background: In the absence of significant pleural effusion, conventional medical thoracoscopy (MT) is often not feasible due to the risk of lung injury. Dry MT mitigates these risks by inducing artificial pneumothorax through needle insufflation or blunt dissection. Although the Veress needle is commonly used by surgeons to create pneumoperitoneum before laparoscopic surgeries, its application in dry MT has not been widely reported in recent times.

Methods: We report on a series of 31 patients who underwent dry MT with artificial pneumothorax induction using Veress needle under thoracic ultrasonography (TUS) guidance. A procedure was considered technically successful if it met all the following criteria: successful pneumothorax induction, allowing smooth insertion of the semi-rigid thoracoscope; absence of immediate significant procedural-related complications; and no delayed complications such as persistent air leaks, defined as leakage lasting more than 5 days necessitating extended chest tube placement.

Results: Complete pneumothorax induction was achieved in 25 cases, resulting in an 80.6% technical success rate; however, biopsies were successfully performed in all cases. The most frequent histopathological diagnoses were malignancy (n=9, 29.0%), followed by inflammatory pleuritis (n=8, 25.8%) and tuberculosis (n=8, 25.8%). No procedural complications were reported.

Conclusion: These results indicate that TUS-guided dry MT utilizing a Veress needle is technically feasible and secure when performed by experienced MT practitioners in TUS.

Bronchiectasis has an increasing prevalence and substantial clinical and economic burden. Therefore, physicians should identify patients with bronchiectasis at high risk of disease progression to ensure optimal management in advance. The heterogeneity of bronchiectasis means it is unlikely that any single parameter could identify highrisk patients; therefore, disease severity is usually assessed using validated composite tools, such as the Bronchiectasis Severity Index, FACED, and Bronchiectasis Aetiology Comorbidity Index, to predict long-term outcomes in bronchiectasis. Disease severity, however, implies an advanced process with lung destruction. Earlier intervention may prevent disease progression and improve outcomes. To identify patients at risk, rather than patients with established advanced disease, we need to shift our focus from disease severity to disease activity. Disease activity denotes the activation level of underlying pathophysiological processes and can be measured using clinical presentations and biomarkers. This review discusses a paradigm shift in bronchiectasis management, focusing on disease activity rather than severity, to prevent disease progression.

Background: Kelch-like ECH-associated protein 1 (KEAP1)-nuclear factor erythroid- 2-related factor 2 (NRF2) pathway is a major regulator protecting cells from oxidative and metabolic stress. Studies have revealed that this pathway is involved in mediating resistance to cytotoxic chemotherapy and immunotherapy; however, its implications in oncogene-addicted tumors are largely unknown. This study aimed to elucidate whether this pathway could be a potential therapeutic target for epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer.

Methods: We measured the baseline expression of NRF2 using EGFR-mutant parental cells and acquired gefitinib resistant cells. We investigated whether NRF2 inhibition affected cell death in vitro and tumor growth in vivo using a xenograft mouse model, and compared the transcriptional changes before and after NRF2 inhibition.

Results: Baseline NRF2 expression was enhanced in PC9 and PC9 with gefitinib resistance (PC9/GR) cells than in other cell lines, with a more prominent expression in PC9/ GR. The NRF2 inhibitor induced NRF2 downregulation and cell death in a dose-dependent manner. Cotreatment with an NRF2 inhibitor enhanced osimertinib-induced cell death in vitro, and potentiated tumor growth inhibition in a PC9/GR xenograft model. Finally, RNA sequencing revealed that NRF2 inhibition resulted in the altered expression of multiple genes involved in various signaling pathways.

Conclusion: We identified that NRF2 inhibition enhanced cell death and inhibited tumor growth in tyrosine kinase inhibitor (TKI)-resistant lung cancer with EGFR-mutation. Thus, NRF2 modulation may be a novel therapeutic strategy to overcome the resistance to EGFR-TKIs.

Background: Several novel molecular platforms using nucleic acid amplification tests have been developed for the diagnosis of pulmonary tuberculosis (PTB) and rapid detection of isoniazid and rifampin resistance. Among them, the BD MAX MDR-TB assay (BD MAX) has shown high sensitivity and specificity; however, its diagnostic accuracy performed on bronchoscopy specimens has not been reported.

Methods: We retrospectively reviewed the medical records of patients with suspected PTB who underwent bronchoscopy. Patients who underwent BD MAX testing of bronchoscopy specimens were included in the final analysis. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for PTB diagnosis were calculated using a positive culture of Mycobacterium tuberculosis as the reference standard.

Results: Of 114 patients, 34 had culture-confirmed PTB. The sensitivity, specificity, PPV, and NPV of BD MAX performed on bronchoscopy specimens for the diagnosis of PTB were 79.4%, 88.8%, 75.0%, and 91.0%, respectively. The sensitivity of BD MAX was superior to that of acid-fast bacillus smear (79.4% vs. 38.2%, p<0.001).

Conclusion: BD MAX performed on bronchoscopy specimens showed high accuracy for diagnosing PTB. BD MAX can be performed on bronchoscopy specimens in patients with suspected PTB.

Background: Respiratory infection is a major cause of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). We investigated the presence of bacterial and viral pathogens and clinical features in patients with AECOPD.

Methods: This retrospective study included 1,186 patients diagnosed with AECOPD from 28 hospitals in South Korea between 2015-2018. Pathogen identification rates, basic characteristics and clinical features, and associated factors for infection with potentially drug-resistant (PDR) pathogens were evaluated using microbiological tests.

Results: Bacteria, viruses, and both were found in 262 (22.1%), 265 (22.5%), and 129 (10.9%) patients, respectively. The most common pathogens were Pseudomonas aeruginosa (17.8%), Mycoplasma pneumoniae (11.2%), Streptococcus pneumoniae (9.0%), influenza A virus (19.0%), rhinovirus (15.8%), and respiratory syncytial virus (6.4%). A history of pulmonary tuberculosis (OR 1.66; P=0.046), bronchiectasis (OR 1.99; P=0.032), and triple inhaler use within six months (OR 2.04; P=0.005) were significant associated factors for PDR pathogen infection. Hospital stay length (15.9 days vs. 12.4 days; P=0.018) and ICU admission rates (15.9% vs. 9.5%; P=0.030) were increased in patients infected with PDR pathogens.

Conclusions: This study indicates that various types of pathogens are implicated during AECOPD. However, further research is needed to confirm whether these pathogens influence AECOPD development and progression.

Thoracic radiology is a primary field where artificial intelligence (AI) has been extensively researched. Recent advancements in AI demonstrate potential improvements in radiologists' performance. AI facilitates the detection and classification of abnormalities, as well as the quantification of both normal and abnormal anatomical structures. Furthermore, it enables prognostication based on these quantitative values. In this review article, the recent achievements of AI in thoracic radiology will be reviewed, mainly focused on deep learning, and the current limitations and future directions of this cutting-edge technique will be discussed.

A significant portion of newly diagnosed lung cancer cases occur in populations exposed to air pollution. The World Health Organization has identified air pollution as a human carcinogen, prompting many countries to implement monitoring systems for ambient particulate matter (PM). PM consists of a complex mix of organic and inorganic particles, both solid and liquid, present in the air. Given the carcinogenic properties of PM and the prevalence of lung cancer in exposed populations, it is crucial to explore their connection and clinical implications to effectively prevent lung cancer in this group. This review examines the link between ambient PM and lung cancer. Epidemiological studies have shown a dose-response relationship between PM exposure and lung cancer risk. PM exposure leads to oxidative stress, disrupting the body's redox balance and causing DNA damage, a key factor in cancer development. Recent findings on the strong correlation between ambient PM and adenocarcinoma suggest that understanding the specific molecular and pathological background of pollution-related lung cancer is important. In addition to efforts to control emission sources at the international level, a more individualized approach is necessary to prevent PM-related lung cancer development.

Backgrounds: The development of frailty at hospital discharge affects the clinical outcomes in severe coronavirus disease (COVID-19) survivors who had no frailty before hospitalization. We aimed to describe the prevalence of new frailty using the Clinical Frailty Scale (CFS) and evaluate its associated factors in patients with severe COVID-19 without pre-existing frailty before hospitalization.

Methods: We performed a secondary analysis of clinical data from a nationwide retrospective cohort collected from 22 hospitals between January 1, 2020 and August 31, 2021. The patients were at least 19 years old and survived until discharge after admission to the intensive care unit (ICU) because of severe COVID-19. Development of new frailty was defined as a CFS score ≥ 5 at hospital discharge.

Results: Among 669 severe COVID-19 survivors without pre-existing frailty admitted to the ICU, the mean age was 65.2 ± 12.8 years, 62.5% were male, and 50.2% received mechanical ventilation (MV). The mean CFS score at admission was 2.4 ± 0.9, and new frailty developed in 27.8% (186/483). In multivariate analysis, older age, cardiovascular disease, CFS score of 3-4 before hospitalization, increased C-reactive protein level, longer duration of corticosteroid treatment, and use of MV and extracorporeal membrane oxygenation were identified as factors associated with new-onset frailty.

Conclusion: Our study suggests that new frailty is not uncommon and is associated with diverse factors in survivors of severe COVID-19 without pre-existing frailty.

Background: High-resolution chest computed tomography (CT) is a crucial assessment tool for diagnosis of Birt-Hogg-Dubé syndrome (BHD). This study aims to analyze the differences of lung cyst between BHD and other cystic lung diseases.

Methods: From January 2020 to December 2022, we retrospectively screened all patients who underwent chest CT at Gangnam Severance Hospital. We included the patients with multiple lung cysts for the analysis of chest CT images.

Results: Over a three-year period, out of 52,823 patients who underwent a chest CT scan, 301 patients (0.6%) with multiple lung cysts were enrolled, of which 24 (8.0%) were diagnosed with BHD. Notably, 95.8% and 83.3% of BHD patients exhibited bilateral cysts and basal predominance, and had larger cysts with a maximal diameter (averaging 32.1mm [interquartile range 26.5mm to 43.5mm]) than lymphangioleiomyomatosis (17.0mm [13.2;19.1], p<0.001) and others' group (11.3mm [7.9;17.0], p<0.001). Additionally, 95.8% of BHD patients has a diverse range in cyst sizes and morphologies. Multivariate logistic regression analysis identified bilateral cysts (OR 12.393, 95% CI: 1.613-274.682, p=0.038), basal predominance (OR 8.511, 95% CI: 2.252-39.392, p=0.002), maximum diameter (OR 1.053, 95% CI: 1.009-1.108, p=0.032), and diversity of morphology (OR 19.513, 95% CI: 2.833-398.119, p=0.010) as factors associated with BHD diagnosis. By stepwise selection, a multivariate prediction model for BHD diagnosis was established, demonstrating a sensitivity of 95.83%, a specificity of 81.22%, and an AUC of 0.951 (95% CI: 0.914-0.987).

Conclusion: Distinguishing features of lung cyst from other cystic lung diseases include bilateral cysts, basal dominance, large size, and irregular shape. The predictive model can assist in identifying undiagnosed patients with BHD.