Over the past few years, the billows of the digital trends and the exploding growth of electronic networks, such as worldwide web, global mobility networks, etc., have drastically changed our daily lifestyle. In view of the widespread applications of digital images, medical images, which are produced by a wide variety of medical appliances, are stored in digital form gradually. These digital images are very easy to be modified imperceptively by malicious intruders for illegal purposes. The well-known adage that "seeing is believing" seems not always a changeless truth. Therefore, protecting images from being altered becomes an important issue. Based on the lossless data-embedding techniques, two detection and restoration systems are proposed to cope with forgery of medical images in this paper. One of them has the ability to recover the whole blocks of the image and the other enables to recover only a particular region where a physician will be interested in, with a better visual quality. Without the need of comparing with the original image, these systems have a great advantage of detecting and locating forged parts of the image with high possibility. And then it can also restore the counterfeited parts. Furthermore, once an image is announced authentic, the original image can be derived from the stego-image losslessly. The experimental results show that the restored version of a tampered image in the first method is extremely close to the original one. As to the second method, the region of interest selected by a physician can be recovered without any loss, when it is tampered.

Damage to the bronchial epithelium leads to persistent inflammation and airway remodelling in various respiratory diseases, such as asthma and chronic obstructive pulmonary disease. To date, the mechanisms underlying bronchial epithelial cell damage and death by common allergens remain largely unknown. The aim of the present study was to investigate Der f1, an allergen of Dermatophagoides farinae, which may result in the death of human bronchial epithelial cells (HBECs). Der f1 induces BECs to undergo the inflammatory cell death referred to as pyroptosis, induced by increasing lactate dehydrogenase release and propidium iodide penetration. Stimulation by Der f1 enhances interleukin (IL)-1β cleavage and release, which is associated with caspase-1 activation. In addition, the NOD-like receptor family pyrin domain-containing 3 (NLRP3), is required for the activation of caspase-1 through increasing the formation of the inflammasome complex. Consistent with these findings, pre-treatment of HBECs with a caspase-1 inhibitor, or silencing of NLRP3 by siRNA transfection, reduced Der f1-mediated IL-1β and pyroptosis. Therefore, the common allergen Der f1 was not only found to induce allergy, but also led to pyroptosis and IL-1β secretion via the NLRP3-caspase-1 inflammasome in HBECs. This newly identified connection of the Der f1 allergen with BEC damage and inflammation may play an important role in the pathogenesis of asthma.

The aims of this study were to investigate the outcomes of patients requiring prolonged mechanical ventilation (PMV) and to identify risk factors associated with its mortality rate. All patients admitted to the respiratory care centre (RCC) who required PMV (the use of MV ≥21 days) between January 2006 and December 2014 were enrolled. A total of 1,821 patients were identified; their mean age was 69.8 ± 14.2 years, and 521 patients (28.6%) were aged >80 years. Upon RCC admission, the APACHE II scores were 16.5 ± 6.3, and 1,311 (72.0%) patients had at least one comorbidity. Pulmonary infection was the most common diagnosis (n = 770, 42.3%). A total of 320 patients died during hospitalization, and the in-hospital mortality rate was 17.6%. A multivariate stepwise logistic regression analysis indicated that patients were more likely to die if they who were >80 years of age, had lower albumin levels (<2 g/dl) and higher APACHE II scores (≥15), required haemodialysis, or had a comorbidity. In conclusion, the in-hospital mortality for patients requiring PMV in our study was 17%, and mortality was associated with disease severity, hypoalbuminaemia, haemodialysis, and an older age.

Chronic diseases have been among the major concerns in medical fields since they may cause a heavy burden on healthcare resources and disturb the quality of life. In this paper, we propose a novel framework for early assessment on chronic diseases by mining sequential risk patterns with time interval information from diagnostic clinical records using sequential rules mining, and classification modeling techniques. With a complete workflow, the proposed framework consists of four phases namely data preprocessing, risk pattern mining, classification modeling, and post analysis. For empiricasl evaluation, we demonstrate the effectiveness of our proposed framework with a case study on early assessment of COPD. Through experimental evaluation on a large-scale nationwide clinical database in Taiwan, our approach can not only derive rich sequential risk patterns but also extract novel patterns with valuable insights for further medical investigation such as discovering novel markers and better treatments. To the best of our knowledge, this is the first work addressing the issue of mining sequential risk patterns with time-intervals as well as classification models for early assessment of chronic diseases.