Chronic inflammatory respiratory diseases, such as asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis, present ongoing challenges in terms of effective treatment and management. These diseases are characterized by persistent inflammation in the airways, leading to structural changes and compromised lung function. There are several treatments available for them, such as bronchodilators, immunomodulators, and oxygen therapy. However, there are still some shortcomings in the effectiveness and side effects of drugs. To achieve optimal therapeutic outcomes while minimizing systemic side effects, targeted therapies and precise drug delivery systems are crucial to the management of these diseases. This comprehensive review focuses on the role of drug delivery systems in chronic inflammatory respiratory diseases, particularly nanoparticle-based drug delivery systems, inhaled corticosteroids (ICSs), novel biologicals, gene therapy, and personalized medicine. By examining the latest advancements and strategies in these areas, we aim to provide a thorough understanding of the current landscape and future prospects for improving treatment outcomes in these challenging conditions.

Increased risks of certain cancers have been observed in patients with hepatitis C virus (HCV) infection. However, data on other cancer sites/types are lacking. We analyzed systematically the risk of developing 35 common cancers in patients with HCV infection using a nationwide Swedish database. Patients with HCV infection were identified from the Swedish Hospital Inpatient and Outpatient Register and Primary Health Care Database, and followed until the diagnosis of cancer. Standardized incidence ratios (SIRs) were calculated for subsequent 35 common cancer sites/types between 1990 and 2010 in patients with HCV infection in Sweden. Increased risks were recorded for six cancers. The highest SIR was seen for liver cancer (36.67; 95% CI: 33.20–40.40). The decreased risk was for prostate cancer (0.73; 95% CI: 0.59–0.90) and melanoma (0.50; 95% CI: 0.30–0.79). A significant sex‐difference for cancer was observed only for liver cancer (40.72; 95% CI: 36.36–45.45 for men and 27.21; 95% CI: 21.90–33.41 for women). Also, increased SIRs were noted only for liver cancer during the entire period of follow‐up. HCV infection was associated with an increased incidence of liver cancer and additionally five other types of cancer. Active surveillance of other cancers may be needed in order to be diagnosed at an earlier stage.

Aspiration Pneumonia (AP) is a leading cause of death in patients with Acute Ischemic Stroke (AIS). Early detection, diagnosis and effective prevention measures are crucial for improving patient prognosis. However, there is a lack of research predicting AP occurrence after AIS. This study aimed to identify risk factors and develop a nomogram model to determine the probability of developing AP after AIS. A total of 3258 AIS patients admitted to Jinshan Hospital of Fudan University between January 1, 2016, and August 20, 2022, were included. Among them, 307 patients were diagnosed with AP (AP group), while 2951 patients formed the control group (NAP group). Univariate and multivariate logistic regression analyses were conducted to identify relevant risk factors for AP after AIS. These factors were used to establish a scoring system and develop a nomogram model using R software. Univariate analysis revealed 20 factors significantly associated (P < 0.05) with the development of AP after AIS. These factors underwent multivariate logistic regression analysis, which identified age (elderly), National Institute of Health Stroke Scale (NIHSS) score, dysphagia, atrial fibrillation, cardiac insufficiency, renal insufficiency, hepatic insufficiency, elevated Fasting Blood Glucose (FBG), elevated C-Reactive Protein (CRP), elevated Neutrophil percentage (NEUT%), and decreased prealbumin as independent risk factors. A nomogram model incorporating these 11 risk factors was constructed, with a C-index of 0.872 (95 % CI: 0.845–0.899), indicating high accuracy. Calibration and clinical decision analyses demonstrated the model's reliability and clinical value. A nomogram model incorporating age, NIHSS score, dysphagia, atrial fibrillation, cardiac insufficiency, renal insufficiency, hepatic insufficiency, FBG, CRP, NEUT%, and prealbumin effectively predicts AP risk in AIS patients. This model provides guidance for early intervention strategies, enabling the identification of high-risk individuals for timely preventive measures.