Research on brain functional network property analysis and recognition methods targeting brain fatigue.

At present, researches on brain fatigue recognition are still in the stage of single task and simple brain region network features, while researches on high-order brain functional network features and brain region state mechanisms during fatigue in multi-task scenarios are still insufficient, making it difficult to meet the needs of fatigue recognition under complex conditions. Therefore, this study utilized functional near-infrared spectroscopy (fNIRS) technology to explore the correlation and differences in the low-order and high-order brain functional network attributes of three task induced mental fatigue, and to explore the brain regions that have a major impact on mental fatigue. Self-training algorithms were used to identify the three levels of brain fatigue.

GFANC-RL: Reinforcement Learning-based Generative Fixed-filter Active Noise Control.

The recent Generative Fixed-filter Active Noise Control (GFANC) method achieves a good trade-off between noise reduction performance and system stability. However, labelling noise data for training the Convolutional Neural Network (CNN) in GFANC is typically resource-consuming. Even worse, labelling errors will degrade the CNN's filter-generation accuracy.

Development and validation of a visualized prediction model for early miscarriage risk in patients undergoing IVF/ICSI procedures: a real-world multi-center study.

Background: This study focuses on the risk of early miscarriage in patients undergoing fertilization (IVF) or intracytoplasmic sperm injection (ICSI). These patients commonly experience heightened stress levels and may discontinue treatment due to emotional burdens associated with repeated failures. Despite the identification of numerous potential factors contributing to early miscarriage, there exists a research gap in integrating these factors into predictive models specifically for IVF/ICSI patients.

Gait parameter fitting and adaptive enhancement based on cerebral blood oxygen information.

Accurate recognition of patients' movement intentions and real-time adjustments are crucial in rehabilitation exoskeleton robots. However, some patients are unable to utilize electromyography (EMG) signals for this purpose due to poor or missing signals in their lower limbs. In order to address this issue, we propose a novel method that fits gait parameters using cerebral blood oxygen signals.

Effects of leukocyte-rich platelet-rich plasma and leukocyte-poor platelet-rich plasma on the healing of bone-tendon interface of rotator cuff in a mice model.

Platelet-rich plasma (PRP) is widely used clinically to treat tendon injuries, and often contains leukocytes. However, the debate regarding the concentration of leukocytes in PRP is still ongoing. This study aimed to evaluate the therapeutic effects of leukocyte-rich platelet-rich plasma (LR-PRP) and leukocyte-poor platelet-rich plasma (LP-PRP) on the healing of the bone-tendon interface (BTI) of the rotator cuff.

Effect of Mechanical Stimulation Combined With Platelet-Rich Plasma on Healing of the Rotator Cuff in a Murine Model.

Background: Mechanical stimulation and platelet-rich plasma (PRP) have been shown to be beneficial for healing of the bone-tendon interface (BTI), but few studies have explored the efficacy of a combination of these applications. We investigated the effect of mechanical stimulation combined with PRP on rotator cuff repair in mice.

Hypothesis: Mechanical stimulation combined with PRP can enhance BTI healing in a murine model of rotator cuff repair.

Autologous Freeze-Dried, Platelet-Rich Plasma Carrying Icariin Enhances Bone-Tendon Healing in a Rabbit Model.

Background: Tendon-bone interface (TBI) injuries are common in sports activities. Owing to the limited regenerative ability of the TBI, its functional healing remains a difficulty in clinical practice. Icariin (ICA) provides strong stimulation for osteogenesis.