Post-LASIK dry eye disease: A comprehensive review of management and current treatment options.

Laser-assisted in situ keratomileusis (LASIK) is a unique corneal stromal laser ablation method that uses an excimer laser to reach beneath corneal dome-shaped tissues. In contrast, surface ablation methods, such as photorefractive keratectomy, include removing epithelium and cutting off the Bowman's layer and the stromal tissue of the anterior corneal surface. Dry eye disease (DED) is the most common complication after LASIK. DED is a typical multi-factor disorder of the tear function and ocular surface that occurs when the eyes fail to produce efficient or adequate volumes of tears to moisturize the eyes. DED influences quality of life and visual perception, as symptoms often interfere with daily activities such as reading, writing, or using video display monitors. Generally, DED brings about discomfort, symptoms of visual disturbance, focal or global tear film instability with possible harm to the ocular surface, the increased osmolarity of the tear film, and subacute inflammation of the ocular surface. Almost all patients develop a degree of dryness in the postoperative period. Detection of preoperative DED and committed examination and treatment in the preoperative period, and continuing treatments postoperatively lead to rapid healing, fewer complications, and improved visual outcomes. To improve patient comfort and surgical outcomes, early treatment is required. Therefore, in this study, we aim to comprehensively review studies on the management and current treatment options for post-LASIK DED.

The Association between COVID-19 and Reactive Arthritis: A Systematic Review of Case Reports and Case Series.

INTRODUCTION: Reactive arthritis (ReA) is a joint inflammation that follows an infection at a distant site, often in the gastrointestinal or urogenital tract. Since the emergence of COVID-19 in January 2020, several case reports have suggested a relation between reactive arthritis and severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), due to the novelty of the disease, most findings were reported in the form of case reports or case series, and a comprehensive overview is still lacking. METHODS: We searched PubMed/Medline and Embase to identify studies addressing the association between ReA and COVID-19. The following terms were used: ("Reactive Arthritis" OR "Post-Infectious Arthritis" OR "Post Infectious Arthritis") AND ("COVID-19" OR "SARS-CoV-2" OR "2019-nCoV"). RESULTS: A total number of 35 reports published up to February 16th, 2022, were included in this study. A wide range of ages was affected (mean 41.0, min 4 max 78), with a higher prevalence of males (61.0%) from 16 countries. The number and location of the affected joints were different in included patients, with a higher prevalence of polyarthritis in 41.5% of all cases. Cutaneous manifestations and visual impairments were found as the most common associated symptoms. Most patients (95.1%) recovered, with a mean recovery time of 24 days. Moreover, arthritis induced by COVID-19 seems to relieve faster than ReA, followed by other infections. CONCLUSION: ReA can be a possible sequel of COVID-19 infection. Since musculoskeletal pain is a frequent symptom of COVID-19, ReA with rapid onset can easily be misdiagnosed. Therefore, clinicians should consider ReA a vital differential diagnosis in patients with post-COVID-19 joint swelling. Additional studies are required for further analysis and to corroborate these findings.

Audio deepfakes: A survey.

A deepfake is content or material that is synthetically generated or manipulated using artificial intelligence (AI) methods, to be passed off as real and can include audio, video, image, and text synthesis. The key difference between manual editing and deepfakes is that deepfakes are AI generated or AI manipulated and closely resemble authentic artifacts. In some cases, deepfakes can be fabricated using AI-generated content in its entirety. Deepfakes have started to have a major impact on society with more generation mechanisms emerging everyday. This article makes a contribution in understanding the landscape of deepfakes, and their detection and generation methods. We evaluate various categories of deepfakes especially in audio. The purpose of this survey is to provide readers with a deeper understanding of (1) different deepfake categories; (2) how they could be created and detected; (3) more specifically, how audio deepfakes are created and detected in more detail, which is the main focus of this paper. We found that generative adversarial networks (GANs), convolutional neural networks (CNNs), and deep neural networks (DNNs) are common ways of creating and detecting deepfakes. In our evaluation of over 150 methods, we found that the majority of the focus is on video deepfakes, and, in particular, the generation of video deepfakes. We found that for text deepfakes, there are more generation methods but very few robust methods for detection, including fake news detection, which has become a controversial area of research because of the potential heavy overlaps with human generation of fake content. Our study reveals a clear need to research audio deepfakes and particularly detection of audio deepfakes. This survey has been conducted with a different perspective, compared to existing survey papers that mostly focus on just video and image deepfakes. This survey mainly focuses on audio deepfakes that are overlooked in most of the existing surveys. This article's most important contribution is to critically analyze and provide a unique source of audio deepfake research, mostly ranging from 2016 to 2021. To the best of our knowledge, this is the first survey focusing on audio deepfakes generation and detection in English.