Background Low-profile, self-expandable stents are used to treat wide-neck aneurysms located on the smaller distal intracranial arteries. This study aimed to assess the usefulness of time-of-flight (TOF) and contrast-enhanced (CE) magnetic resonance angiography (MRA) for follow-up after LEO Baby stent (LBS)-assisted coil embolization. Methods Twenty-four aneurysms treated with LBS-assisted coil embolization were evaluated. Researchers reviewed TOF MRA and CE MRA images in terms of occlusion and stent patency. Aneurysm occlusion was graded according to Raymond–Roy classification as follows: total occlusion (grade 1), residual neck (grade 2), and residual aneurysm (grade 3). Stent patency was scored as follows: occlusion (1), stenosis (2), and normal (3). Interobserver and intermodality agreement values were determined by weighted kappa (κ) statistics. Results Intermodality and interobserver values of TOF MRA and CE MRA with digital subtraction angiography (DSA) were perfect (κ = 1.00, p < 0.001) in terms of aneurysm occlusion. Rate of stent occlusion and stenosis in DSA, TOF, and MRA, respectively, were as follows: 0 and 12.5%, 16.6 and 70.8%, and 0 and 62.5%. Intermodality agreement values of TOF MRA and CE MRA with DSA were insignificant in terms of stent patency (κ = 0.065, p = 0.27; κ = 0.158, p = 0.15, respectively). Interobserver agreement was substantial in both TOF MRA (κ = 0.71, p < 0.001) and CE MRA (κ = 0.64, p = 0.001). Conclusions Both TOF and CE MRA techniques have strong concordance with DSA for the detection of aneurysm occlusion status. CE MRA can be used as a first-line noninvasive imaging modality due to its superiority to TOF MRA with respect to the visualization of in-stent signals.

Acute non-traumatic liver disorders can originate from abnormalities of the hepatic artery, portal vein and hepatic veins. Ultrasonography and computed tomography can be used in non-traumatic acute vascular liver disorders according to patient status, indication and appropriateness of imaging modality. Awareness of the imaging findings, in the appropriate clinical context, is crucial for prompt and correct diagnosis, as delay may cause severe consequences with significant morbidity and mortality. This review article will discuss imaging algorithms, and multimodality imaging findings for suspected acute vascular disorders of the liver.

A clinical or radiological worsening of already existing lesions or an emergence of new lesions after beginning treatment in patients with tuberculosis (TB) is referred to as the paradoxical response. This has aroused suspicion regarding the accuracy of diagnosis, the possibilities of treatment failure, or the presence of another underlying disease, and thus it is an important topic for clinicians to understand. In this article, the development of a paradox reaction in a 14-year-old male patient diagnosed with and treated for tuberculosis meningitis is reported. This pediatric patient with a healthy immune system is treated with steroids successfully and reported to elucidate the importance of managing the paradox of TB progression in spite of the appropriate anti-TB medications.

We present a patient with idiopathic sciatic neuropathy with demonstrative magnetic resonance imaging findings.

Natural Language Processing (NLP) is an interdisciplinary field between linguistics and computer science. Its main aim is to process natural (human) language using computer programs. Text classification is one of the main tasks of this field, and they are widely used in many different applications such as spam filtering, sentiment analysis, and document categorization. Nonetheless, there is only very little text classification work in the law domain and even less for the Turkish language. This may be attributed to the complexity within the domain. The length, complexity of documents, and use of extensive technical jargon are some of the reasons that distinguish this domain from others. Similar to the medical domain, understanding these documents requires extensive specialization. Another reason can be the scarcity of publicly available datasets. In this study, we compile sizeable unsupervised and supervised datasets from publicly available sources and experiment with several classification algorithms ranging from traditional classifiers to much more complicated deep learning and transformer-based models along with different text representations. We focus on classifying Court of Cassation decisions for their crime labels. Interestingly, the majority of the models we experiment with could be able to obtain good results. This suggests that although understanding the documents in the legal domain is complicated and requires expertise from humans, it may be relatively easier for machine learning models despite the extensive presence of the technical terms. This seems to be especially the case for transformer-based pre-trained neural language models which can be adapted to the law domain, showing high potential for future real-world applications.