ABSTRACT
Vogt-Koyanagi-Harada disease (VKH) is a neurological disorder that impacts vision and hearing by causing the immune system to attack melanocytes. Symptoms of the condition include flu-like symptoms, eye pain, headache, and dizziness, which may progress to vitiligo and hearing impairment. The diagnostic criteria include ocular involvement, generalized choroiditis, tinnitus, meningitis, and skin depigmentation. The treatment includes corticosteroids and immunosuppressive drugs. VKH is believed to be an autoimmune condition, possibly triggered by hereditary factors and cross-reactivity with cytomegalovirus. VKH is common in East Asia and India and has a genetic link to certain alleles. Inflammation generated by Th1 in melanocytes results in the production of granulomas. An analysis of a 48-year-old female with VKH disease revealed symptoms of anterior uveitis and subsequent glaucoma. The treatment involved the administration of systemic steroids and intratympanic steroid injections. Biochemical indicators showed signs of inflammation. Timely identification and therapy are essential for managing VKH. Further research is necessary to enhance outcomes for patients with VKH disease.
ABSTRACT
Artificial intelligence (AI) and machine learning (ML) show promise in various medical domains, including medical imaging, precise diagnoses, and pharmaceutical research. In neuroscience and neurosurgery, AI/ML advancements enhance brain-computer interfaces, neuroprosthetics, and surgical planning. They are poised to revolutionize neuroregeneration by unraveling the nervous system's complexities. However, research on AI/ML in neuroregeneration is fragmented, necessitating a comprehensive review. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations, 19 English-language papers focusing on AI/ML in neuroregeneration were selected from a total of 247. Two researchers independently conducted data extraction and quality assessment using the Mixed Methods Appraisal Tool (MMAT) 2018. Eight studies were deemed high quality, 10 moderate, and four low. Primary goals included diagnosing neurological disorders (35%), robotic rehabilitation (18%), and drug discovery (12% each). Methods ranged from analyzing imaging data (24%) to animal models (24%) and electronic health records (12%). Deep learning accounted for 41% of AI/ML techniques, while standard ML algorithms constituted 29%. The review underscores the growing interest in AI/ML for neuroregenerative medicine, with increasing publications. These technologies aid in diagnosing diseases and facilitating functional recovery through robotics and targeted stimulation. AI-driven drug discovery holds promise for identifying neuroregenerative therapies. Nonetheless, addressing existing limitations remains crucial in this rapidly evolving field.
ABSTRACT
Atrial fibrillation (AF) is a type of cardiac arrhythmia causing shortness of breath, lightheadedness, and palpitations. It may go unrecognized and asymptomatic among many patients. AF is not a potentially fatal arrhythmia; its hemodynamic, structural, and hemocoagulative effects have a significant impact on the standard of life, which can lead to various complications such as stroke. A stroke caused by AF leads to additional burdens on both patients and the global economy. Patients with AF can prevent strokes with oral anticoagulants; however, ensuring diligent adherence to medication is crucial for maximizing treatment efficacy. Since they have a lighter treatment load than warfarin, non-vitamin K antagonist oral anticoagulants (NOACs) are also recommended with better hope for medication adherence. Various anticoagulants such as warfarin and ximelagatran, among many more, are prescribed to patients who have the potential to reduce the incidence of stroke as well as alleviate their likelihood of developing other thromboembolic events that can decrease their quality of life. Economic and psychological burdens associated with diminished functionality can be prevented by anticoagulant therapy among AF patients, therefore reducing their economic and social burden. This is due to the negative association between stroke among AF patients and anticoagulation consumption.