In recent years, the growth of population, increase in environmental pollution, and changes in human’s life style, a dramatic increase in the number of ASTHMATIC PATIENTs has been observed. In addition, the Lack of suitable solution for the cure of asthma shows us the necessity of recent researches for controlling of the exacerbation of asthma in order to improve PATIENTs’ quality of life. Environmental condition in a spatio-temporal perspective enables us to do some predictive analytics in order to providing this ability to the ASTHMATIC PATIENTs to have self-checking in addition to the medication by personal physician. Ubiquitous PATIENT MONITORING system which is enabled by geospatial perspective can assist us to provide such services to the ASTHMATIC PATIENTs in both outdoor and indoor environments. In this paper, we monitor ubiquitously ASTHMATIC PATIENTs in a Ubiquitous GIS environment by considering 8 important environmental asthma triggers including CO, NO, O3, PM10, SO2, Temperature, Humidity, and Pressure, and 4 medical records of current status of ASTHMATIC PATIENTs. In fact, to achieve a better modeling with divided our research to two parts: User Model and Contextual Model. For implementation of Ubiquitous Asthma model (UbiAsthma), this survey is done by consideration of 30 PATIENTs in Tehran city. To develop our prediction model in UbiAsthma, at first we used VIKOR method to reclassified current PATIENTs’ medical classification to a novel and applicable classification for providing context-aware services. Secondly, after classification of PATIENTs, one PATIENT is selected and the information related to FVC and FEV1 of the PATIENT in 86 different locations within 47 days is collected. The PATIENT was equipped by CO pollutant sensor (MQ9) and Temperature, Humidity, and Pressure sensors collection by smart-phone in order to having of a real-time database for the PATIENT’s trajectory. Also, we utilized O3, NO, SO2, and PM10 for all 86 locations as static database in our model. After data collection and manipulation, Artificial Neural Network (ANN) is used for doing predictive analysis for ASTHMATIC PATIENTs’ status. The result of test and train of ANN method shows that UbiAsthma model has 0.0230 evaluation error in prediction of ASTHMATIC PATIENTs’ status, which is a considerable output in our model.

Asthma is a heterogeneous disease, in which ASTHMATIC PATIENTs present with different clinical phenotypes, variable endotypes, and different response to asthma medicines. Thus, we are faced with an asthma paradox; asthma is diagnosed subjectively by clinical history and treated with biologically active drugs. To solve this paradox, we need objective airway biomarkers to tailor the proper medications to the proper PATIENT. Biomarkers should have one or more of the following characteristics: 1) could differentiate poor symptoms perceivers from over-perceivers, 2) could predict disease activity and hence disease outcome, 3) could clarify asthma phenotype responders from non-responders, and finally 4) could characterize different clinical asthma phenotypes. Therefore, we have conducted a review of literature trying to apply those four parameters to different airway inflammatory biomarkers. We found that FeNO fulfilled the four proposed clinical parameters of airway inflammatory biomarkers whereas; serum periostin was the single best systemic biomarker of airway luminal and tissue eosinophilia in severe uncontrolled TH2 asthma phenotype. Thus, this may be considered a trial towards tailoring the proper medication to the proper PATIENT. However, application of biomarkers in clinical practice requires easier and cheaper techniques together with standardized methods for sample collection and analysis.

Strongyloides stercoralis is a common cause of gastrointestinal infection. Symptoms are usually mild, but in the setting of impaired host immunity, severe and disseminated illnesses may occur. The present report describes a male PATIENT with a history of asthma and corticosteroid therapy, now presented with dyspnea and abdominal pain. Examinations show Strongyloides stercoralis larvae in Bronchoalveolar lavage fluid specimens. The PATIENTs who have undergone immunosuppressive therapy are highly indicated for Strongyloides stercoralis screening which prevents hyperinflation in endemic areas.