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Data de publicació electrònica: 22-11-2022 Background: The self-reporting of asthma frequently leads to patient misidentification in epidemiological studies. Strategies combining the triangulation of data sources may help to improve the identification of people with asthma. We aimed to combine information from the self-reporting of asthma, medication use and symptoms to identify asthma patterns in the users of an mHealth app. Methods: We studied MASK-air® users who reported their daily asthma symptoms (assessed by a 0-100 visual analogue scale - "VAS Asthma") at least three times (either in three different months or in any period). K-means cluster analysis methods were applied to identify asthma patterns based on: (i) whether the user self-reported asthma; (ii) whether the user reported asthma medication use and (iii) VAS asthma. Clusters were compared by the number of medications used, VAS asthma levels and Control of Asthma and Allergic Rhinitis Test (CARAT) levels. Findings: We assessed a total of 8,075 MASK-air® users. The main clustering approach resulted in the identification of seven groups. These groups were interpreted as probable: (i) severe/uncontrolled asthma despite treatment (11.9-16.1% of MASK-air® users); (ii) treated and partly-controlled asthma (6.3-9.7%); (iii) treated and controlled asthma (4.6-5.5%); (iv) untreated uncontrolled asthma (18.2-20.5%); (v) untreated partly-controlled asthma (10.1-10.7%); (vi) untreated controlled asthma (6.7-8.5%) and (vii) no evidence of asthma (33.0-40.2%). This classification was validated in a study of 192 patients enrolled by physicians. Interpretation: We identified seven profiles based on the probability of having asthma and on its level of control. mHealth tools are hypothesis-generating and complement classical epidemiological approaches in identifying patients with asthma.

Background Lymph nodes (LNs) are the main doorway for tumor cell metastases from the primary site and its evaluation is a major prognostic factor. The One Step Nucleic Acid Amplification (OSNA) is being adopted worldwide for sentinel-LNs (SLNs) staging in breast cancer (BC). SLNs´ OSNA lysate may be used for gene expression studies, being the potentially ideal samples to search for new markers related to immune response. Using a targeted gene expression approach, we aim to identify transcriptomic patterns of SLNs immune response and biomarkers that may improve risk stratification and personalized therapy for patients with Luminal A BC. Methods This was an observational, prospective, pilot study that included 32 patients with Luminal A early-stage BC: 16 patients with OSNA negative SLNs and 16 patients with OSNA positive SLNs. After the OSNA assay, rather than being discarded, the remaining OSNA lysates were prepared for target RNA sequencing (RNA-seq) analysis, using the Oncomine™ Immune Response Research Assay. Identification of differentially expressed genes (DEGs) for group comparisons was performed by DESeq2 R package (version 1.36.0) in R (version 4.2.0). Data analysis was performed using STATA software, version 13.1, and statistical significance was set at p KRT7, VTCN1, CD44, GATA3, ALOX15B, RORC and NECTIN2. In macrometastatic SLNs, LRG1, CD276, FOXM1 and IGF1R were also upregulated. In metastatic SLNs, higher values of total tumor load (TTL) correlated with a higher expression of most DEGs. Hierarchical clustering analysis revealed three different clusters, not coincident with pN0, pN1mi and pN1 classification. Conclusions A better understanding of the complex interplay between cancer cells and host immunity is essential for the choice of personalized treatments in Luminal A BC. The identified DEGs codify proteins mainly involved in cancer aggressiveness and with impact in immune response. These DEGs may improve prognosis accuracy and increase the efficacy and safety of targeted therapies in Luminal A BC patients. As OSNA assay is being implemented for SLNs staging in other cancers, RNA-seq in the OSNA lysate could also have a wider utility.

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