Magneto-responsive biocomposites in wound healing: from characteristics to functions.

The number of patients with non-healing wounds continuously increases, and has become a prominent societal issue that imposes a heavy burden on both patients and the entire healthcare system. Although traditional dressings play an important role in wound healing, the complexity and diversity of the healing process pose serious challenges in this field. Magneto-responsive biocomposites, with their excellent biocompatibility, remote spatiotemporal controllability, and unique convenience, demonstrate enticing advantages in the field of wound dressings. However, current research on magneto-responsive biocomposites as wound dressings lacks comprehensive and in-depth reviews, which to some extent, restricts the deeper understanding and further development of this field. Based on this, this paper reviews the latest advances in magnetic responsive wound dressings for wound healing. First, we review the process of skin wound healing and parameters for assessing repair progress. Then, we systematically discuss the preparation strategies and unique characteristics of magneto-responsive biocomposites, focusing on magneto-induced orientation, magneto-induced mechanical stimulation, and magnetocaloric effect. Subsequently, this review elaborates the multiple mechanisms of magneto-responsive biocomposites in promoting wound healing, including regulating cell behavior, enhancing electrical signal, controlling drug release, and accelerating tissue reconstruction. Finally, we further propose the development direction and future challenges of magnetic responsive biomaterials as wound dressings in clinical application.

Unilaterally extrapedicular versus transpedicular kyphoplasty in treating osteoporotic lumbar fractures: a randomized controlled study.

BACKGROUND: The unilaterally extrapedicular approach is adopted increasingly to perform balloon kyphoplasty in treating osteoporotic lumbar fractures, which is intended to improve radiological and clinical efficacy. We compared the efficacy and safety of this method with a unilaterally transpedicular approach. METHODS: We conducted a single-center, randomized controlled trial enrolling participants with a one-level osteoporotic lumbar fracture in less than 1 month. Patients were randomly assigned to undergo kyphoplasty via either a unilaterally extrapedicular approach (treatment group) or a unilaterally transpedicular approach (control group). The primary outcome was the difference in change from baseline to 1 month in visual analog scale (VAS) scores between the two groups. Secondary outcome measures included vertebral height ratio, operation time, fluoroscopic times, hemoglobin loss, and cement leakage between groups. Data were analyzed by intention to treat principle. RESULTS: A total of 80 participants were assigned to the treatment group (n = 40) and control group (n = 40), with three and two patients lost to follow-up during 12 months in the two groups, respectively. At 1 month postoperatively, the treatment group showed a greater reduction in VAS score from baseline, compared with the control group (mean difference between groups = 0.63, 95%CI 0.19-1.06). There were no significant between-group differences in restoration in anterior, middle, and posterior vertebral body (P > 0.05). No significant differences were found in the rate of cement leakage and perioperative hemoglobin loss (P > 0.05). CONCLUSION: Compared with balloon kyphoplasty via the unilaterally transpedicular approach in treating lumbar OVCFs, the unilaterally extrapedicular approach appears to be promising in achieving effective pain relief, adequate cement infusion, short operation time, less fluoroscopy exposure, and comparable risk of cement leakage and vessel injury. It is an alternative approach for lumbar OVCFs treated with kyphoplasty.

A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction.

Electronic health records (EHRs) have been widely used to help physicians to make decisions by predicting medical events such as diseases, prescriptions, outcomes, and so on. How to represent patient longitudinal medical data is the key to making these predictions. Recurrent neural network (RNN) is a popular model for patient longitudinal medical data representation from the view of patient status sequences, but it cannot represent complex interactions among different types of medical information, i.e., temporal medical event graphs, which can be represented by graph neural network (GNN). In this paper, we propose a hybrid method of RNN and GNN, called RGNN, for next-period prescription prediction from two views, where RNN is used to represent patient status sequences, and GNN is used to represent temporal medical event graphs. Experiments conducted on the public MIMIC-III ICU data show that the proposed method is effective for next-period prescription prediction, and RNN and GNN are mutually complementary.

Genetic algorithm for multiple bus line coordination on urban arterial.

Bus travel time on road section is defined and analyzed with the effect of multiple bus lines. An analytical model is formulated to calculate the total red time a bus encounters when travelling along the arterial. Genetic algorithm is used to optimize the offset scheme of traffic signals to minimize the total red time that all bus lines encounter in two directions of the arterial. The model and algorithm are applied to the major part of Zhongshan North Street in the city of Nanjing. The results show that the methods in this paper can reduce total red time of all the bus lines by 31.9% on the object arterial and thus improve the traffic efficiency of the whole arterial and promote public transport priority.