Public knowledge and willingness in the use of public access defibrillation of Hubei Province in China: A cross-sectional study.

To understand the current status of public knowledge of automated external defibrillator (AED) and their willingness to use public AED in Hubei Province, along with the influencing factors. A self-designed questionnaire was used for convenience sampling of the public in Hubei Province. The questionnaire consists of three parts: basic information, AED knowledge questions, and willingness to use public AED and influencing factors. Data was collected between May 2022 and March 2023. A total of 1561 valid questionnaires were collected from 1602 distributed. In the study conducted in Hubei Province, it was found that 875 respondents (56.05%) had knowledge of automated external defibrillator, and they achieved an average score of 39.27 ± 29.17. The pass rate for the survey was 28.11%. Several factors were identified as significant influencing factors, including gender, age, education level, occupation related to medicine, residential location in the past three years, family members with cardiovascular disease, marital status, residential population density, whether there are family members over 65 years old, and participation in AED-related training (P < .05).Furthermore, 692 respondents (72.99%) expressed their willingness to cardiopulmonary resuscitation for someone experiencing cardiac arrest. On the other hand, 686 respondents (43.95%) had no knowledge of AED. Among those who were not willing to perform defibrillation, the highest percentages cited "fear of incorrect use" (129, 31.2%) and "fear of harming the patient" (121, 29.3%) as their reasons. The study also found statistically significant differences in the willingness to use public AED based on participation in training, education level, residential location, family members with cardiovascular disease, population density, and the presence of elderly family members aged 65 or over (P < .05). In conclusion, the study highlights the general lack of public knowledge regarding AED in Hubei Province. However, there is a strong willingness among respondents to provide help during cardiac arrest situations. To improve the chances of survival for cardiac arrest patients, it is crucial to strengthen public AED training programs.

Aberrant temporal-spatial complexity of intrinsic fluctuations in major depression.

Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.

Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach.

INTRODUCTION: Developing a machine learning-based approach which could provide quantitative identification of major depressive disorder (MDD) is essential for the diagnosis and intervention of this disorder. However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. In the present work, we exploit the hidden information embedded in dynamic functional connectivity (DFC) and developed an accurate and objective image-based diagnosis system for MDD. METHODS: MRI images were collected from 99 participants including 56 healthy controls and 43 MDD patients. DFC was calculated using a sliding-window algorithm. A non-linear support vector machine (SVM) approach was then used with the DFC matrices as features to distinguish MDD patients from healthy controls. The spatiotemporal characteristics of the most discriminative features were then investigated. RESULTS: The area under the curve (AUC) of the SVM classifier with DFC measures reached 0.9913, while this value is only 0.8685 for the algorithm using SFC measures. Spatially, the most discriminative 28 connections distributed in the visual network (VN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), limbic network (LN), frontoparietal network (FPN), and default mode network (DMN), etc. Notably, a large portion of these connections were associated with the FPN, DMN, and VN. Temporally, the most discriminative connections transited from the cortex to deeper regions. CONCLUSION: The results clearly suggested that DFC is superior to SFC and provide a reliable quantitative identification method for MDD. Our findings may furnish a better understanding of the neural mechanisms of MDD as well as improve accurate diagnosis and early intervention of this disorder.

Label-free sensing of the binding state of MUC1 peptide and anti-MUC1 aptamer solution in fluidic chip by terahertz spectroscopy.

The aptamer and target molecule binding reaction has been widely applied for construction of aptasensors, most of which are labeled methods. In contrast, terahertz technology proves to be a label-free sensing tool for biomedical applications. We utilize terahertz absorption spectroscopy and molecular dynamics simulation to investigate the variation of binding-induced collective vibration of hydrogen bond network in a mixed solution of MUC1 peptide and anti-MUC1 aptamer. The results show that binding-induced alterations of hydrogen bond numbers could be sensitively reflected by the variation of terahertz absorption coefficients of the mixed solution in a customized fluidic chip. The minimal detectable concentration is determined as 1 pmol/µL, which is approximately equal to the optimal immobilized concentration of aptasensors.