Using a Biofeedback-Based Mindfulness Practice System to Enhance Mindfulness and Alleviate Anxiety in College Students: A Randomized Controlled Trial.

Objective: College students experience intense anxiety, for which biofeedback mindfulness techniques show effectiveness in relief. However, typical biofeedback products often lead to user fatigue and boredom because of a single or fixed feedback and lack of focus on mindfulness enhancement. Materials and Methods: In this research, we developed Mindjourney, a VR-based respiratory feedback mindfulness system, designed to enhance mindfulness and alleviate anxiety through continuous/noncontinuous feedback and nonjudgmental reward/punishment for self-perception and attention management. A randomized controlled trial involved 72 college students, split equally into short-term (n = 34, age: 23.11 ± 1.729) and 4-week long-term (n = 38, age: 24.12 ± 1.408) groups, with equal randomization for intervention and control groups. Pre/postintervention tests were measured by using Trait Anxiety Inventory (TAI) and Five Facet Mindfulness Questionnaire (FFMQ) for long-term groups and Galvanic Skin Response and State Anxiety Inventory (SAI) for short-term groups. Results: Results showed that the long-term intervention group showed a significant increase in mindfulness (P = 0.001 for FFMQ total score). Furthermore, observe and act with awareness subscales showed significant increase after intervention (P = 0.034 for observe, P < 0.001 for act with awareness) compared with the control group. Both intervention groups demonstrated a significant decrease in anxiety levels compared with the control groups (P = 0.049 for SAI, P = 0.01 for TAI). Moreover, participants expressed high interest in this biofeedback mindfulness system and willingness for long-term usage. Conclusion: The proposed biofeedback mindfulness practice system could potentially facilitate mindfulness practice and serve as a convenient tool for anxiety relief in campus college students.

Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates

Microvascular thrombosis is a typical symptom of COVID-19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID-19 samples and 101 non-COVID-19 thrombosis samples, resulting in a total of 6.3 million bright-field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on derived single-cell features for each population, we trained machine learning models for classification between COVID-19 and non-COVID-19 thrombosis, resulting in a patient testing accuracy of 75%. This result indicates that platelet formation differs between COVID-19 and non-COVID-19 thrombosis. All analysis steps were optimized for efficiency and implemented in an easy-to-use plugin for the image viewer napari, allowing the entire analysis to be performed within seconds on mid-range computers, which could be used for real-time diagnosis.