In recent years, osteoarthritis of the knee, a common degenerative joint disease, often occurs in the elderly population. This disease has a significant impact on the quality of life of patients. For treating knee osteoarthritis, physical therapy is highly regarded as a very effective treatment method. This article delves deeply into commonly used physical therapy methods and analyzes their therapeutic effects, cost-effectiveness, and applicability, aiming to find treatments with broader applicability and better cost-effectiveness. The goal is to help a large number of patients effectively alleviate the discomfort caused by knee osteoarthritis, enhance the clinical therapeutic effects, and introduce home treatment methods to reduce financial burdens. The article also compares various physical therapy methods and finds that moxibustion and electrotherapy are more suitable for home use. Other treatment methods provide a reliable scientific basis for patient treatment.

Purpose: We aimed to evaluate the efficacy of gentamicin compared to corticosteroids for the treatment of Meniere’s disease.Methods: An extensive search was conducted in PubMed, Embase, and Web of Science until May 2024. For continuous outcomes, pooled effect estimates were determined by calculating the weighted mean difference (WMD), while for binary outcomes, the risk ratio (RR) was used, each accompanied by their respective 95% confidence intervals (CIs). Heterogeneity among the studies was assessed using Cochran’s I² and Q statistics.Results: A total of 12 studies were selected, involving 694 patients. Our analysis found that the gentamicin group demonstrates superior vertigo control rates compared to the corticosteroid group (RR: 1.36, 95% CI: 1.13 to 1.65, p < 0.001). In subgroup analysis, the gentamicin group showed a higher vertigo control rates at 6 months compared to the corticosteroid group (RR: 1.69, 95% CI: 1.28 to 2.24, p < 0.001); however, there was no statistically significant difference between the two groups at 12 months (RR: 1.48, 95% CI: 0.88 to 2.49, p = 0.14). Regarding changes in pure tone average, the corticosteroid group was superior to the gentamicin group (WMD: 4.41, 95% CI: 3.31 to 5.52, p < 0.001).Conclusion: Our study suggests that the intratympanic gentamicin group achieves higher vertigo control rates, whereas the corticosteroid group demonstrates better improvement in pure tone averages. However, the high heterogeneity in vertigo control rates warrants caution. Larger sample-sized randomized controlled trials are needed to further validate these findings.

Background: Stroke patients often experience significant impairments, making rapid and accurate detection crucial for timely intervention and early warning. However, existing diagnostic methods such as advanced neuroimaging are often time-consuming, highly dependent on operator expertise, or costly and complex to deploy, limiting their scalability in resource-restricted settings. Eye movement patterns in stroke patients present a promising opportunity for efficient detection, given their close ties to underlying neurocognitive mechanisms and potential diagnostic sensitivity. Nevertheless, the lack of a feasible task paradigm and robust detection strategy has hindered the practical application of eye movement-based stroke identification. This study aimed to capture eye movement dysfunction associated with stroke through an ecological paradigm and develop a machine learning model with improved diagnostic accuracy. Methods: We recorded eye movement signals in stroke patients ( N = 16) and healthy controls ( N = 23) during free visual exploration of familiar dramatic character faces. A diverse set of eye movement features, encompassing saccadic, fixation, and scanpath features, was extracted and analyzed. These features were subsequently employed to construct machine learning models for the recognition of stroke patients. Results: We identified distinctive eye movement patterns in the stroke group, including prolonged fixation duration, restricted saccadic movements, and reduced scanpath length, which reflect underlying visual processing impairments. Furthermore, by integrating these multi-dimensional eye movement features, our machine learning model achieved a high accuracy of 87.18% and an excellent area under the receiver operating characteristic curve (AUROC) of 0.92 in distinguishing stroke patients. Discussion: This study demonstrates that ecologically valid eye-tracking, combined with multi-type feature analytics, serves as a practical screening tool with the potential to significantly improve identification accuracy and alleviate logistical burdens in community and primary care settings.

The five-pattern personality traits rooted in the theory of traditional Chinese medicine (TCM) have promising prospects for clinical application. However, they are currently assessed using a self-report scale, which may have certain limitations. Eye tracking technology, with its non-intrusive, objective, and culturally neutral characteristics, has become a powerful tool for revealing individual cognitive and emotional processes. Therefore, applying this technology for personality assessment is a promising approach. In this study, participants observed five emotional faces (anger, happy, calm, sad, and fear) selected from the Chinese Facial Affective Picture System. Utilizing artificial intelligence algorithms, we evaluated the feasibility of automatically identifying different traits of the five-pattern personality traits from participants’ eye movement patterns. Based on the analysis of five supervised learning algorithms, we draw the following conclusions: The Lasso feature selection method and Logistic Regression achieve the highest prediction accuracy for most of the traits (TYa, SYa, SYi, TYi). This study develops a framework for predicting five-pattern personality traits using eye movement behavior, offering a novel approach for personality assessment in TCM.