High body mass index with the risk of allergic conjunctivitis in children: a case-control study in Southwest China.

Purpose: To assess the correlation of body mass index (BMI), diet and lifestyle with allergic conjunctivitis (AC) in children.

Methods: This prospective case-control study included 105 children with AC and 105 age- and sex-matched children with no AC. Clinical data were collected, including BMI, dietary habits, physical activity, and sleep time.

High Serum Allergen-Specific IgE of House Dust Mite in Predicting the Risk of Comorbidity in Children with Allergic Conjunctivitis.

Purpose: To investigate the patterns of allergens in allergic conjunctivitis (AC) and the association with allergic comorbidity.

Methods: This retrospective cross-sectional study enrolled 2972 children with AC. Clinical data, including sex, age, allergic comorbidities (allergic asthma, allergic rhinitis, and atopic dermatitis), and serum allergen-specific immunoglobulin E (sIgE), were collected from the electronic medical record (EMR).

Study of the Correlation between the Motor Ability of the Individual Upper Limbs and Motor Imagery Induced Neural Activities.

Motor imagery based brain-computer interfaces (MI-BCIs) have excellent application prospects in motor enhancement and rehabilitation. However, MI-induced electroencephalogram features applied to MI-BCI usually vary from person to person. This study aimed to investigate whether the motor ability of the individual upper limbs was associated with these features, which helps understand the causes of inter-subject variability.

Hybrid Attention Network for Epileptic EEG Classification.

Automatic seizure detection from electroencephalography (EEG) based on deep learning has been significantly improved. However, existing works have not adequately excavate the spatial-temporal information between EEG channels. Besides, most works mainly focus on patient-specific scenarios while cross-patient seizure detection is more challenging and meaningful.

Automatic Seizure Identification from EEG Signals Based on Brain Connectivity Learning.

Epilepsy is a neurological disorder caused by brain dysfunction, which could cause uncontrolled behavior, loss of consciousness and other hazards. Electroencephalography (EEG) is an indispensable auxiliary tool for clinical diagnosis. Great progress has been made by current seizure identification methods.

Determination of significant parameters in remote ischemic postconditioning for ischemic stroke in experimental models: A systematic review and meta-analysis study.

Objectives: To systematically review studies using remote ischemia postconditioning (RIPostC) for ischemic stroke in experimental models and obtain factors that significantly influence treatment outcomes.

Materials And Methods: Peer-reviewed studies were identified and selected based on the eligibility criteria, followed by extraction of data on potentially influential factors related to model preparation, postconditioning, and measure time based on outcome measures including infarct size, neurological scales, and cell tests with autophagy, apoptosis, normal-neuron, and damaged-neuron counting. Then, all data were preprocessed, grouped, and meta-analyzed with the indicator of the standardized mean difference.

Severe ophthalmia neonatorum in Southwest China: a 5-year review of demographics, microbiological results, and risk factors.

Purpose: To evaluate the microbiological characteristics and risk factors of severe ophthalmia neonatorum (ON) in Southwest China.

Methods: In this retrospective review, data on demography, microbiological results, and risk factors were analyzed. Data were obtained from medical records of patients with severe ON treated at Children's Hospital of Chongqing Medical University from January 2015 to December 2019.