A data-mining algorithm to assess key factors in asthma diagnosis

A scoping review of asthma and machine learning

OBJECTIVE

The objective of this study was to determine the extent of machine learning (ML) application in asthma research and to identify research gaps while mapping the existing literature.

DATA SOURCES

We conducted a scoping review. PubMed, ProQuest, and Embase Scopus databases were searched with an end date of September 18, 2020.

STUDY SELECTION

DistillerSR was used for data management. Inclusion criteria were an asthma focus, human participants, ML techniques, and written in English. Exclusion criteria were abstract only, simulation-based, not human based, or were reviews or commentaries. Descriptive statistics were presented.

RESULTS

A total of 6,317 potential articles were found. After removing duplicates, and reviewing the titles and abstracts, 102 articles were included for the full text analysis. Asthma episode prediction (24.5%), asthma phenotype classification (16.7%), and genetic profiling of asthma (12.7%) were the top three study topics. Cohort (52.9%), cross-sectional (20.6%), and case-control studies (11.8%) were the study designs most frequently used. Regarding the ML techniques, 34.3% of the studies used more than one technique. Neural networks, clustering, and random forests were the most common ML techniques used where they were used in 20.6%, 18.6%, and 17.6% of studies, respectively. Very few studies considered location of residence (i.e. urban or rural status).

CONCLUSIONS

The use of ML in asthma studies has been increasing with most of this focused on the three major topics (>50%). Future research using ML could focus on gaps such as a broader range of study topics and focus on its use in additional populations (e.g. location of residence).

Supplemental data for this article is available online at http://dx.doi.org/ .

PI3K/AKT/mTOR and TLR4/MyD88/NF-κB Signaling Inhibitors Attenuate Pathological Mechanisms of Allergic Asthma

Asthma is an inflammatory airway disease wherein bronchoconstriction, airway inflammation, and airway obstruction during asthma attacks are the main problems. It is recognized that imbalance of Th1/Th2 and Th17/Treg is a critical factor in asthma pathogenesis. Manipulation of these with signaling molecules such as mTOR, PI3K, Akt, and MyD88 can control asthma. Mouse model of allergic asthma was produced and treated with ketamine, metformin, metformin and ketamine, triciribine, LY294002, and torin2. MCh challenge test, BALf's Eos Count, the IL-4, 5, INF-γ, eicosanoid, total IgE levels were determined. The MUC5a, Foxp3, RORγt, PI3K, mTOR, Akt, PU.1, and MyD88 gene expressions and histopathology study were done. Asthma groups that were treated with all six components had reduced Penh value, total IgE, IL-4 and IL-5 levels, MUC5a, RORγt, MyD88 and mTOR expression, goblet cell hyperplasia, and mucus hyper-secretion. The eosinophil percentage and Cys-LT level were decreased by metformin and ketamine, triciribine, LY294002, and torin2. The level of IFN-γ was increased in triciribine, LY294002, and torin2. Metformin, metformin and ketamine, triciribine, LY294002, and torin2 reduced Akt and PI3K expression, peribronchial and perivascular inflammation, and increased expression of Foxp3. Torin2 had an effect on PU.1 expression. Inhibition of PI3K/AKT/mTOR and TLR4/MyD88/NF-κB signaling with targeted molecules can attenuate asthma pathology and play an important role in airways protection.