Using the AllerSearch Smartphone App to Assess the Association Between Dry Eye and Hay Fever: mHealth-Based Cross-Sectional Study.

BACKGROUND: Dry eye (DE) and hay fever (HF) show synergistic exacerbation of each other's pathology through inflammatory pathways. OBJECTIVE: This study aimed to investigate the association between DE and HF comorbidity and the related risk factors. METHODS: A cross-sectional observational study was conducted using crowdsourced multidimensional data from individuals who downloaded the AllerSearch smartphone app in Japan between February 2018 and May 2020. AllerSearch collected the demographics, medical history, lifestyle and residential information, HF status, DE symptoms, and HF-related quality of life. HF symptoms were evaluated using the nasal symptom score (0-15 points) and nonnasal symptom score (0-12 points). HF was defined by the participants' responses to the questionnaire as HF, non-HF, or unknown. Symptomatic DE was defined as an Ocular Surface Disease Index total score (0-100 points), with a threshold score of 13 points. HF-related quality of life was assessed using the Japanese Allergic Conjunctival Disease Standard Quality of Life Questionnaire (0-68 points). We conducted a multivariable linear regression analysis to examine the association between the severity of DE and HF symptoms. We subsequently conducted a multivariable logistic regression analysis to identify the factors associated with symptomatic DE (vs nonsymptomatic DE) among individuals with HF. Dimension reduction via Uniform Manifold Approximation and Projection stratified the comorbid DE and HF symptoms. The symptom profiles in each cluster were identified using hierarchical heat maps. RESULTS: This study included 11,284 participants, classified into experiencing HF (9041 participants), non-HF (720 participants), and unknown (1523 participants) groups. The prevalence of symptomatic DE among individuals with HF was 49.99% (4429/9041). Severe DE symptoms were significantly associated with severe HF symptoms: coefficient 1.33 (95% CI 1.10-1.57; P<.001) for mild DE, coefficient 2.16 (95% CI 1.84-2.48; P<.001) for moderate DE, and coefficient 3.80 (95% CI 3.50-4.11; P<.001) for severe DE. The risk factors for comorbid symptomatic DE among individuals with HF were identified as female sex; lower BMI; medicated hypertension; history of hematologic, collagen, heart, liver, respiratory, or atopic disease; tomato allergy; current and previous mental illness; pet ownership; living room and bedrooms furnished with materials other than hardwood, carpet, tatami, and vinyl; discontinuation of contact lens use during the HF season; current contact lens use; smoking habits; and sleep duration of <6 hours per day. Uniform Manifold Approximation and Projection stratified the heterogeneous comorbid DE and HF symptoms into 14 clusters. In the hierarchical heat map, cluster 9 was comorbid with the most severe HF and DE symptoms, and cluster 1 showed severe HF symptoms with minimal DE-related symptoms. CONCLUSIONS: This crowdsourced study suggested a significant association between severe DE and HF symptoms. Detecting DE among individuals with HF could allow effective prevention and interventions through concurrent treatment for ocular surface management along with HF treatment.

A working hypothesis visualization method for fNIRS measurements using Monte Carlo simulation.

In neuroscience, clarifying the functional localization of the cerebrum using functional near-infrared spectroscopy (fNIRS) is one of the important works. To better understand and trust fNIRS data, neuroscientists formulate hypothesis about the underlying neural processes. However, visualizing and validating these hypotheses is not easy due to the complex nature of brain activity and the limitations of fNIRS measurements. In this paper, we suggest the novel Monte Carlo tool designed to assist fNIRS study for neuroscientists and to deal with these problems. The tool provides a user-friendly interface for generating realistic virtual brain activity patterns based on a specified hypothesis. By setting up a region of interest in the standard brain based on the hypothesis, the simulation models the propagation of light through the brain accurately and mimics the hemodynamic response observed in fNIRS measurements. By visually displaying simulation data and identifying the major activation point, neuroscientists can validate and refine hypothesis and obtain a better understanding of the neural mechanisms underlying the fNIRS signals.•A Monte Carlo simulation method reflecting the functional localization of the cerebrum for fNIRS measurements.•Method for setting ROI corresponding to the functional localization of the cerebrum in the standard brain.•Visualization of Monte Carlo simulation results and anatomical evaluation method of activation points.