Systematic Approach to Asthma of Varying Severity

Asthma prediction via affinity graph enhanced classifier: a machine learning approach based on routine blood biomarkers

BACKGROUND

Asthma is a chronic respiratory disease affecting millions of people worldwide, but early detection can be challenging due to the time-consuming nature of the traditional technique. Machine learning has shown great potential in the prompt prediction of asthma. However, because of the inherent complexity of asthma-related patterns, current models often fail to capture the correlation between data samples, limiting their accuracy. Our objective was to use our novel model to address the above problem via an Affinity Graph Enhanced Classifier (AGEC) to improve predictive accuracy.

METHODS

The clinical dataset used in this study consisted of 152 samples, where 24 routine blood markers were extracted as features to participate in the classification due to their ease of sourcing and relevance to asthma. Specifically, our model begins by constructing a projection matrix to reduce the dimensionality of the feature space while preserving the most discriminative features. Simultaneously, an affinity graph is learned through the resulting subspace to capture the internal relationship between samples better. Leveraging domain knowledge from the affinity graph, a new classifier (AGEC) is introduced for asthma prediction. AGEC's performance was compared with five state-of-the-art predictive models.

RESULTS

Experimental findings reveal the superior predictive capabilities of AGEC in asthma prediction. AGEC achieved an accuracy of 72.50%, surpassing FWAdaBoost (61.02%), MLFE (60.98%), SVR (64.01%), SVM (69.80%) and ERM (68.40%). These results provide evidence that capturing the correlation between samples can enhance the accuracy of asthma prediction. Moreover, the obtained [Formula: see text] values also suggest that the differences between our model and other models are statistically significant, and the effect of our model does not exist by chance.

CONCLUSION

As observed from the experimental results, advanced statistical machine learning approaches such as AGEC can enable accurate diagnosis of asthma. This finding holds promising implications for improving asthma management.

The Utilization of Plant-Material-Loaded Vesicular Drug Delivery Systems in the Management of Pulmonary Diseases

Medicinal plants have been utilized to treat a variety of conditions on account of the bioactive properties that they contain. Most bioactive constituents from plants are of limited effectiveness, due to poor solubility, limited permeability, first-pass metabolism, efflux transporters, chemical instability, and food-drug interactions However, when combined with vesicular drug delivery systems (VDDS), herbal medicines can be delivered at a predetermined rate and can exhibit site-specific action. Vesicular drug delivery systems are novel pharmaceutical formulations that make use of vesicles as a means of encapsulating and transporting drugs to various locations within the body; they are a cutting-edge method of medication delivery that combats the drawbacks of conventional drug delivery methods. Drug delivery systems offer promising strategies to overcome the bioavailability limitations of bioactive phytochemicals. By improving their solubility, protecting them from degradation, enabling targeted delivery, and facilitating controlled release, drug delivery systems can enhance the therapeutic efficacy of phytochemicals and unlock their full potential in various health conditions. This review explores and collates the application of plant-based VDDS with the potential to exhibit protective effects against lung function loss in the interest of innovative and effective treatment and management of respiratory illnesses.

Difference of Serum Cytokine Profile in Allergic Asthma Patients According to Disease Severity

Purpose

Allergic asthma is a heterogeneous disease with complex underlying mechanisms. Cytokines are key mediators in immune system and potential indicators of disease status. The aim of this study is to compare the difference of serum cytokine profile in allergic asthma patients with different disease severity and explore candidate biomarkers for disease monitoring and targeting therapeutic agents.

Patients and Methods

A total of 40 allergic asthmatics (mild, n=22; moderate-to-severe, n=18) were included in this study. Serum samples, lung function and exhaled nitric oxide data were collected from each subject. A Meso Scale Discovery (MSD) electrochemiluminescence platform was applied to access serum levels of 33 cytokines. Serum cytokine profile was compared between mild and moderate-to-severe allergic asthmatics, and the correlation between serum cytokine levels, lung function and exhaled nitric oxide were analyzed.

Results

Moderate-to-severe allergic asthmatics displayed higher levels of eotaxin-1, eotaxin-2, MCP-1, MCP-2, MCP-3, YKL-40 and lower IL-23, IL-31 and TRAIL in serum in comparison with mild allergic asthmatics. Serum YKL-40, eotaxin-1 and MCP-1 had the best ability to discriminate mild and moderate-to-severe allergic asthmatics, with an AUC of 0.833, 0.811 and 0.760. Serum IP-10 was positively correlated with FeNO levels, while FnNO displayed a strong positive correlation with serum IL-25.

Conclusion

Compared with mild allergic asthmatics, significant increase in serum eotaxin-1, eotaxin-2, MCP-1, MCP-2, MCP-3, YKL-40 and decrease in serum IL-23, IL-31 and TRAIL was noted in moderate-to-severe allergic asthmatics. YKL-40, eotaxin-1 and MCP-1 might be candidate biomarkers in reflecting severity in allergic asthma patients.

Mold, Mycotoxins and a Dysregulated Immune System: A Combination of Concern?

Fungi represent one of the most diverse and abundant eukaryotes on earth. The interplay between mold exposure and the host immune system is still not fully elucidated. Literature research focusing on up-to-date publications is providing a heterogenous picture of evidence and opinions regarding the role of mold and mycotoxins in the development of immune diseases. While the induction of allergic immune responses by molds is generally acknowledged, other direct health effects like the toxic mold syndrome are controversially discussed. However, recent observations indicate a particular importance of mold/mycotoxin exposure in individuals with pre-existing dysregulation of the immune system, due to exacerbation of underlying pathophysiology including allergic and non-allergic chronic inflammatory diseases, autoimmune disorders, and even human immunodeficiency virus (HIV) disease progression. In this review, we focus on the impact of mycotoxins regarding their impact on disease progression in pre-existing immune dysregulation. This is complemented by experimental in vivo and in vitro findings to present cellular and molecular modes of action. Furthermore, we discuss hypothetical mechanisms of action, where evidence is missing since much remains to be discovered.

Characterizing outpatient problem list completeness and duplications in the electronic health record

Objective

The study sought to characterize rates of problem list completeness and duplications in common chronic diseases and to identify any relationships that they may have with respect to disease type, demographics, and disease severity.

Materials and Methods

We performed a retrospective analysis of electronic health record data from Partners HealthCare. We selected 8 common chronic diseases and identified patients with each of those diseases. We then analyzed each patient’s problem list for completeness and duplications and also collected information regarding demographics and disease severity. Rates of completeness and duplications were calculated for each disease and compared according to disease type, demographics, and disease severity.

Results

A total of 327 695 unique patients and 383 404 problem list entries were identified. Problem list completeness varied from 72.9% in hypertension to 93.5% in asthma, whereas problem list duplications varied from 4.8% in hypertension to 28.2% in diabetes. There was a variable relationship between demographic factors and rates of completeness and duplication. Rates of completeness were positively correlated with disease severity for most diseases. Rates of duplication were consistently positively correlated with disease severity.

Conclusions

Incompleteness and duplications are both important issues in problem lists. These issues vary widely across different diseases and can also be impacted by patient demographics and disease severity. Further studies are needed to investigate the effect of individual user behaviors and organizational policies on problem list utilization, which will aid the development of interventions that improve the utility of problem lists.

YKL-40 mediates airway remodeling in asthma via activating FAK and MAPK signaling pathway

YKL-40 is a chitinase-like protein which was significantly elevated in asthma patients and related closely to asthma severity and airway remodeling. Airway remodeling in asthma involves complicated physical and pathological processes, including increased airway smooth muscle mass due to proliferation, migration of airway smooth muscle cells, epithelial-mesenchymal transition (EMT) and sub-epithelial fibrosis. However, the precise effect and underlying mechanism of YKL-40 in this pathological alteration remained unelucidated. In this study, we demonstrated that YKL-40 could promote asthma airway remodeling by increasing airway smooth muscle mass, inducing EMT and sub-epithelial fibrosis. Furthermore, we identified that FAK and MAPK signaling pathways are activated in the process. Inhibiting FAK or MAPK pathway could significantly ameliorate airway remodeling induced by excessive secretion of YKL-40 in vitro. and in vivo. In conclusion, this study shed light upon the effects of YKL-40 in asthma airway remodeling and provided potential novel targets in asthma patients with high YKL-40 level.