Ischemic stroke in the setting of supratherapeutic International Normalized Ratio following coronavirus disease 2019 infection: a case report.

BACKGROUND: SARS-CoV-19 infection is associated with an increased risk of thrombotic events. We present a case of acute middle cerebral artery ischemic stroke in a patient with SARS-CoV-19 infection despite being on warfarin with supratherapeutic INR (International Normalized Ratio). CASE PRESENTATION: A 68-year-old Caucasian female with multiple comorbidities was admitted to the hospital with symptoms of upper respiratory tract infection. A rapid antigen test confirmed the diagnosis of COVID-19 pneumonia, and intravenous remdesivir was initiated. On the fifth day of admission, the patient experienced sudden onset confusion, slurred speech, left-sided hemiplegia, right-sided eye deviation, and left-sided facial droop. Imaging studies revealed an occlusion of the distal anterior M2 segment of the right middle cerebral artery, and an MRI of the brain confirmed an acute right MCA infarction. Notably, the patient was receiving warfarin therapy with a supratherapeutic INR of 3.2. CONCLUSIONS: This case report highlights the potential for thromboembolic events, including stroke, in patients with COVID-19 infection, even when receiving therapeutic anticoagulation therapy. Healthcare providers should be vigilant for signs of thrombosis in COVID-19 patients, particularly those with pre-existing risk factors. Further research is necessary to understand the pathophysiology and optimal management of thrombotic complications in COVID-19 patients.

Machine learning models to detect anxiety and depression through social media: A scoping review.

Despite improvement in detection rates, the prevalence of mental health disorders such as anxiety and depression are on the rise especially since the outbreak of the COVID-19 pandemic. Symptoms of mental health disorders have been noted and observed on social media forums such Facebook. We explored machine learning models used to detect anxiety and depression through social media. Six bibliographic databases were searched for conducting the review following PRISMA-ScR protocol. We included 54 of 2219 retrieved studies. Users suffering from anxiety or depression were identified in the reviewed studies by screening their online presence and their sharing of diagnosis by patterns in their language and online activity. Majority of the studies (70%, 38/54) were conducted at the peak of the COVID-19 pandemic (2019-2020). The studies made use of social media data from a variety of different platforms to develop predictive models for the detection of depression or anxiety. These included Twitter, Facebook, Instagram, Reddit, Sina Weibo, and a combination of different social sites posts. We report the most common Machine Learning models identified. Identification of those suffering from anxiety and depression disorders may be achieved using prediction models to detect user's language on social media and has the potential to complimenting traditional screening. Such analysis could also provide insights into the mental health of the public especially so when access to health professionals can be restricted due to lockdowns and temporary closure of services such as we saw during the peak of the COVID-19 pandemic.