Intravenous Thrombolysis in Acute Ischemic Stroke. Acute ischemic stroke is a major cause of mortality and morbidity in the world. Intravenous thrombolysis with recombinant tissue plasminogen activator remains the standard treatment for acute ischemic stroke for any patient presenting within 4.5 hours from symptom onset. However, it is more effective and safe when treatment starts early. This therapy for acute ischemic stroke has been administered in Vojvodina since 2008. Various factors influence the outcome after intravenous thrombolysis. Timely recanalization and reperfusion is associated with better clinical outcomes. Mechanical Thrombectomy - a New Therapeutic Modality for the Treatment of Acute Ischemic Stroke. Nevertheless, the rate of recanalization and favorable outcomes for patients with acute ischemic stroke due to large vessel occlusion are low after intravenous thrombolysis. In such patients mechanical thrombectomy has demonstrated significantly higher rates of recanalization and improved outcomes compared with intravenous thrombolysis alone. This endovascular reperfusion therapy began to be implemented in Vojvodina in 2016. Conclusion. Intravenous thrombolysis continues to play a key role in the treatment of all acute ischemic stroke patients, but mechanical thrombectomy should be the ?gold standard? in the cases with large vessel occlusion.

Introduction: While the machine learning (ML) model’s black‐box nature presents a significant barrier to effective clinical application, the dynamic nature of stroke patients’ recovery further undermines the reliability of established predictive scores and models, making them less suitable for accurate prediction and appropriate patient care. This research is aimed at building and evaluating an interpretable ML‐based model, which would perform outcome prediction at different time points of patients’ recovery, giving more secure and understandable output through interpretable packages. Materials and Methods: A retrospective analysis was conducted on acute ischemic stroke (AIS) patients treated with alteplase at the Neurology Clinic of the University Clinical Center of Vojvodina (Novi Sad, Serbia), for 14 years. Clinical data were grouped into four categories based on collection time—baseline, 2‐h, 24‐h, and discharge features—serving as inputs for three different classifiers—support vector machine (SVM), logistic regression (LR), and random forest (RF). The 90‐day modified Rankin scale (mRS) was used as the outcome measure, distinguishing between favorable (mRS ≤ 2) and unfavorable outcomes (mRS ≥ 3). Results: The sample was described with 49 features and included 355 patients, with a median age of 67 years (interquartile range (IQR) 60–74 years), 66% being male. The models achieved strong discrimination in the testing set, with area under the curve (AUC) values ranging from 0.80 to 0.96. Additionally, they were compared with a model based on the DRAGON score, which showed an AUC of 0.760 (95% confidence interval (CI), 0.640–0.862). The decision‐making process was more thoroughly understood using interpretable packages: Shapley additive explanation (SHAP) and local interpretable model–agnostic explanation (LIME). They revealed the most significant features at both the group and individual patient levels. Conclusions and Clinical Implications: This study demonstrated the moderate to strong efficacy of interpretable ML‐based models in predicting the functional outcomes of alteplase‐treated AIS patients. In all constructed models, age, onset‐to‐treatment time, and platelet count were recognized as the important predictors, followed by clinical parameters measured at different time points, such as the National Institutes of Health Stroke Scale (NIHSS) and systolic and diastolic blood pressure values. The dynamic approach, coupled with interpretable models, can aid in providing insights into the potential factors that could be modified and thus contribute to a better outcome.

Introduction. Autoimmune encephalitis associated with ovarian teratoma is a serious and potentially fatal pathology. While this clinical entity is known to neurologists, the available literature rarely mentions the role of a gynecologist in diagnostic imagining and treatment. Although several months have passed from the appearance of the symptoms to surgical treatment, this case shows that even then a complete recovery is possible. Case presentation. The patient was a 28-year-old female, brought to the hospital because a sudden onset of unusual behavior - an acute psychosis with suicidal thoughts and auditory hallucinations. Soon after the admission she became delirious, uncooperative and agitated. Blood check, neurological assessment and cranial computed tomography yielded normal results. Therefore, a psychiatric disorder was suspected. Electroencephalogram revealed a diffuse encephalitic insufficiency. As cerebrospinal fluid was negative for infections, the autoimmune etiology of the disease was suspected. Abdominal computer tomography showed a complex right ovarian mass measuring 50 x 40 x 30 mm, confirmed by vaginal ultrasound. Laparoscopy with right adnexectomy was performed. The pathohistological finding showed a mature teratoma. In the meantime, the result of the cerebrospinal fluid test came positive for anti NMDAR antibodies. Six months after surgery, the patient was in a good mental and neurological status without symptoms. Conclusion. Gynecologists should be aware of the presence of ovarian tumors in encephalitis cases. A timely diagnosis of the underlying gynecological cause of a neurological condition, allows for prompt treatment and can remarkably improve clinical conditions and, thus, be lifesaving.