Chinese medical journal pulmonary and critical care medicine最新文献

Lung cancer is the leading cause of cancer-related deaths worldwide. Tobacco smoking and air pollution are believed to be responsible for more than 90% of lung cancers. Respiratory pathogens are also known to be associated with the initiation and development of lung cancer. Despite the fact that the bacterial biomass in the lungs is lower than that in the intestinal tract, emerging evidence indicates that the lung is colonized by a diverse array of microbes. However, there is limited knowledge regarding the role of dysbiosis of the lung microbiota in the progression of lung cancer. In this review, we summarize the current information about the relationship between the microbiome and lung cancer. The objective is to provide an overview of the core composition of the microbiota in lung cancer as well as the role of specific dysbiosis of the lung microbiota in the progression of lung cancer and treatment of the disease.

Lung cancer has the highest mortality rate among all cancers in the world. Hence, early diagnosis and personalized treatment plans are crucial to improving its 5-year survival rate. Chest computed tomography (CT) serves as an essential tool for lung cancer screening, and pathology images are the gold standard for lung cancer diagnosis. However, medical image evaluation relies on manual labor and suffers from missed diagnosis or misdiagnosis, and physician heterogeneity. The rapid development of artificial intelligence (AI) has brought a whole novel opportunity for medical task processing, demonstrating the potential for clinical application in lung cancer diagnosis and treatment. AI technologies, including machine learning and deep learning, have been deployed extensively for lung nodule detection, benign and malignant classification, and subtype identification based on CT images. Furthermore, AI plays a role in the non-invasive prediction of genetic mutations and molecular status to provide the optimal treatment regimen, and applies to the assessment of therapeutic efficacy and prognosis of lung cancer patients, enabling precision medicine to become a reality. Meanwhile, histology-based AI models assist pathologists in typing, molecular characterization, and prognosis prediction to enhance the efficiency of diagnosis and treatment. However, the leap to extensive clinical application still faces various challenges, such as data sharing, standardized label acquisition, clinical application regulation, and multimodal integration. Nevertheless, AI holds promising potential in the field of lung cancer to improve cancer care.

Lung cancer remains the leading cause of cancer-related deaths worldwide. Despite the recent advances in cancer therapies, the 5-year survival of non-small cell lung cancer (NSCLC) patients hovers around 20%. Inherent and acquired resistance to therapies (including radiation, chemotherapies, targeted drugs, and combination therapies) has become a significant obstacle in the successful treatment of NSCLC. c-Myc, one of the critical oncoproteins, has been shown to be heavily associated with the malignant cancer phenotype, including rapid proliferation, metastasis, and chemoresistance across multiple cancer types. The c-Myc proto-oncogene is amplified in small cell lung cancers (SCLCs) and overexpressed in over 50% of NSCLCs. c-Myc is known to actively regulate the transcription of cancer stemness genes that are recognized as major contributors to tumor progression and therapeutic resistance; thus, targeting c-Myc either directly or indirectly in mitigation of the cancer stemness phenotype becomes a promising approach for development of a new strategy against drug resistant lung cancers. This review will summarize what is currently known about the mechanisms underlying c-Myc regulation of cancer stemness and its involvement in drug resistance and offer an overview on the current progress and future prospects in therapeutically targeting c-Myc in both SCLC and NSCLC.

Chronic Obstructive Pulmonary Disease (COPD) is characterized by persistent respiratory symptoms and airflow limitation. Acute exacerbation of COPD (AECOPD) is an acute worsening of respiratory symptoms, which needs additional treatment and can result in worsening health status, increasing risks of hospitalization and mortality. Therefore, it is necessary to early recognize and diagnose exacerbations of COPD. This review introduces the updated definition of COPD exacerbations, the current clinical assessment tools, and the current potential biomarkers. The application of mobile health care in COPD management for early identification and diagnosis is also included in this review.

Steroid resistance represents a major clinical problem in the treatment of severe asthma, and therefore a better understanding of its pathogenesis is warranted. Recent studies indicated that histone deacetylase 2 (HDAC2) and interleukin 17A (IL-17A) play important roles in severe asthma. HDAC2 activity is reduced in patients with severe asthma and smoking-induced asthma, perhaps accounting for the amplified expression of inflammatory genes, which is associated with increased acetylation of glucocorticoid receptors. Neutrophilic inflammation contributes to severe asthma and may be related to T helper (Th) 17 rather than Th2 cytokines. IL-17A levels are elevated in severe asthma and correlate with the presence of neutrophils. Restoring the activity of HDAC2 or targeting the Th17 signaling pathway is a potential therapeutic approach to reverse steroid insensitivity.

Tissue inhibitors of metalloproteases (TIMPs) have caught the attention of many scientists due to their role in various physiological and pathological processes. TIMP-1, 2, 3, and 4 are known members of the TIMPs family. TIMPs exert their biological effects by, but are not limited to, inhibiting the activity of metalloproteases (MMPs). The balance between MMPs and TIMPs is critical for maintaining homeostasis of the extracellular matrix (ECM), while the imbalance between MMPs and TIMPs can lead to pathological changes, such as cancer. In this review, we summarized the current knowledge of TIMP-1 in several pulmonary diseases namely, acute lung injury (ALI)/acute respiratory distress syndrome (ARDS), pneumonia, asthma, chronic obstructive pulmonary disease (COPD), cystic fibrosis, and pulmonary fibrosis. Considering the potential of TIMP-1 serving as a non-invasive diagnostic and/or prognostic biomarker, we also reviewed the circulating TIMP-1 levels in translational and clinical studies.

The pandemic of coronavirus disease 2019 (COVID‑19), caused by a novel severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2), has caused an enormous impact on the global healthcare. SARS-CoV-2 infection primarily targets the respiratory system. Although most individuals testing positive for SARS-CoV-2 present mild or no upper respiratory tract symptoms, patients with severe COVID-19 can rapidly progress to acute respiratory distress syndrome (ARDS). ARDS-related pulmonary fibrosis is a recognized sequelae of COVID-19. Whether post-COVID-19 lung fibrosis is resolvable, persistent, or even becomes progressive as seen in human idiopathic pulmonary fibrosis (IPF) is currently not known and remains a matter of debate. With the emergence of effective vaccines and treatments against COVID-19, it is now important to build our understanding of the long-term sequela of SARS-CoV-2 infection, to identify COVID-19 survivors who are at risk of developing chronic pulmonary fibrosis, and to develop effective anti-fibrotic therapies. The current review aims to summarize the pathogenesis of COVID-19 in the respiratory system and highlights ARDS-related lung fibrosis in severe COVID-19 and the potential mechanisms. It envisions the long-term fibrotic lung complication in COVID-19 survivors, in particular in the aged population. The early identification of patients at risk of developing chronic lung fibrosis and the development of anti-fibrotic therapies are discussed.

Pyroptosis is a type of programed cell death that differs from apoptosis, ferroptosis, or necrosis. Numerous studies have reported that it plays a critical role in tumorigenesis and modification of the tumor microenvironment in multiple tumors. In this review, we briefly describe the canonical, non-canonical, and alternative mechanisms of pyroptotic cell death. We also summarize the potential roles of pyroptosis in oncogenesis, tumor development, and lung cancer treatment, including chemotherapy, radiotherapy, targeted therapy, and immunotherapy. Pyroptosis has double-edged effects on the modulation of the tumor environment and lung cancer treatment. Further exploration of pyroptosis-based drugs could provide novel therapeutic strategies for lung cancer.

Background

Candida species (Candida spp.) are commonly isolated microorganisms from lower respiratory tract (LRT) specimens of patients with hospital-acquired pneumonia (HAP); however, the clinical significance remains controversial. This study aimed to investigate the correlation between Candida spp. in the LRT and the clinical features and prognosis of HAP.

Methods

This retrospective analysis included eligible patients with HAP from the database of a prospective study carried out between 2018 and 2019 in nine Chinese hospitals. Data on demographics, clinical characteristics, and prognosis were collected and analyzed. Propensity score matching (PSM) was used to balance the baseline characteristics.

Results

A total of 187 HAP patients were enrolled. After PSM of severity score, 27 cases with positive sputum culture of Candida spp. were compared with the control group at a ratio of 1:1. The Candida-positive group had more bacterial isolates in blood culture than the Candida-negative group (39.1% [9/23] vs. 7.7% [2/26], ${\chi }^2$ = 6.928, effect size [ES] = 0.38, 95% CI: 0.12–0.61, P = 0.008). The proportion of patients with chronic lung diseases was significantly higher in the Candida-positive group (55.6% [15/27] vs. 22.2% [6/27], ${\chi }^2$ = 6.312, ES = 0.34, 95% CI: 0.07–0.59, P = 0.012). The 30-day prognosis of HAP was significantly different between the two groups (80.8% [21/26] vs. 38.5% [10/26], ${\chi }^2$ = 9.665, ES = 0.43, 95% CI: 0.19–0.66, P = 0.002). Univariable logistic regression analysis showed that LRT Candida spp. colonization was a risk factor for 30-day mortality of HAP (OR = 6.720, 95% CI: 1.915–23.577, P = 0.003).

Conclusions

Candida spp. in the LRT was associated with 30-day mortality of HAP. Patients with chronic underlying lung diseases tend to have Candida spp. colonization.