Migraine is a primary headache that involves genetic and environmental factors. In studies conducted in different countries, migraine was shown to be underdiagnosed, treated insufficiently, and highly related to disability. The primary aim of this study was to identify the competence in making a diagnosis of migraine by primary care physicians who provide basic health care to patients. Primary care physicians (266 individuals) working in the primary health service centers located within the borders of Kayseri province were included in our study. The research was conducted by using techniques such as face-to-face meetings with the primary care physicians and by participants filling in questionnaires. A neurologist evaluated the questionnaire form. The information provided by the participants was evaluated according to the migraine without aura diagnostic criteria prepared by the International Headache Society (ICHD-3 Beta). Only 10.5% participants were able to give the complete diagnostic criteria of migraine without aura. The most well-known properties were unilateral (53.4%) and pulsating headaches (47%). This study showed that educational programs are required regarding migraines for primary care physicians, supported by complete educational material.

Background: The prognostic value of the neutrophil–lymphocyte ratio (NLR) in ischemic stroke remains debated due to cohort variability and treatment heterogeneity across studies. This study evaluates the relationship between admission NLR, stroke severity and 90-day outcomes in patients with anterior circulation large vessel occlusion (LVO) undergoing early, successful revascularization. Methods: A retrospective multicenter study was conducted with 1082 patients treated with mechanical thrombectomy for acute ischemic stroke. The relationship between admission NLR, baseline National Institutes of Health Stroke Scale (NIHSS), 24 h NIHSS and 90-day modified Rankin Scale (mRS) outcomes was analyzed using logistic regression. Results: Admission NLR correlated weakly but significantly with both baseline (p = 0.018) and 24 h (p = 0.005) NIHSS scores, reflecting stroke severity. However, multivariate analysis showed that higher 24 h NIHSS scores (OR 0.831, p = 0.000) and prolonged puncture-to-recanalization times (OR 0.981, p = 0.000) were independent predictors of poor 90-day outcomes, whereas NLR was not (p = 0.557). Conclusions: Admission NLR is associated with stroke severity but does not independently predict clinical outcomes at 90 days in patients achieving early and successful revascularization. These findings underscore the critical role of inflammation in the acute phase of stroke but suggest that its prognostic value for long-term outcomes is limited in this context.

Migraine is a neurological disorder that is associated with severe headaches and seriously affects the lives of patients. Diagnosing Migraine Disease (MD) can be laborious and time-consuming for specialists. For this reason, systems that can assist specialists in the early diagnosis of MD are important. Although migraine is one of the most common neurological diseases, there are very few studies on the diagnosis of MD, especially electroencephalogram (EEG)-and deep learning (DL)-based studies. For this reason, in this study, a new system has been proposed for the early diagnosis of EEG- and DL-based MD. In the proposed study, EEG signals obtained from the resting state (R), visual stimulus (V), and auditory stimulus (A) from 18 migraine patients and 21 healthy control (HC) groups were used. By applying continuous wavelet transform (CWT) and short-time Fourier transform (STFT) methods to these EEG signals, scalogram-spectrogram images were obtained in the time-frequency (T-F) plane. Then, these images were applied as inputs in three different convolutional neural networks (CNN) architectures (AlexNet, ResNet50, SqueezeNet) that proposed deep convolutional neural network (DCNN) models and classification was performed. The results of the classification process were evaluated, taking into account accuracy (acc.), sensitivity (sens.), specificity (spec.), and performance criteria, and the performances of the preferred methods and models in this study were compared. In this way, the situation, method, and model that showed the most successful performance for the early diagnosis of MD were determined. Although the classification results are close to each other, the resting state, CWT method, and AlexNet classifier showed the most successful performance (Acc: 99.74%, Sens: 99.9%, Spec: 99.52%). We think that the results obtained in this study are promising for the early diagnosis of MD and can be of help to experts.