Stroke is a serious health problem all over the world. Ischemia causes 85% of strokes and 75% of these ischemic strokes occur within the area supplied by the internal carotid artery (ICA). This study included 47 acute stroke patients who were in the large-artery atherosclerosis group according to Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification and who had an infarct in the area supplied by the internal carotid artery. We sought to determine whether there was a significant correlation between the infarct volume of the patients as measured by diffusion-weighted magnetic resonance imaging (DW MRI), their National Institutes of Health Stroke Scale (NIHSS), and degree of carotid stenosis as identified by carotid computed tomography angiography (CTA). A significant correlation was observed between the percentage of carotid artery stenosis and infarct volume (p<0.001). In addition, there was a significant positive correlation between the NIHSS and infarct volume; the correlation was of moderate strength (r=0.366, p=0.001). Our findings indicate that the percentage of carotid artery stenosis could be useful in predicting the infarct volume of the stroke.

Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra (SN), pathological accumulation of alpha-synuclein, and chronic neuroinflammation. The aim of this study is to evaluate the serum levels of systemic inflammatory markers such as neutrophil–lymphocyte ratio (NLR), neutrophil-HDL ratio (NHR), monocyte-HDL ratio (MHR), platelet–lymphocyte ratio (PLR), IL-6, IGF-1, systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) in patients with PD, and to analyze the relationship between these markers and the clinical stage of the disease as well as its motor and non-motor symptoms. Methods: Fifty-one patients diagnosed with PD and forty-nine HC matched for age and sex were evaluated prospectively. Results: NLR, NHR, and IGF-1 levels were found to be significantly higher in the PD group compared to the HC group (p < 0.05). There was no significant difference between the two groups in terms of PLR, MHR, SII, and SIRI. No significant relationship was found between the inflammatory markers and disease duration, clinical scales, or symptoms. Conclusions: These findings support the role of systemic inflammation in the pathophysiology of PD. Further multi-center, long-term follow-up studies—including simultaneous measurements of central nervous system inflammation markers—are needed for translation into clinical practice.

Background: Multiple sclerosis (MS) is a disease of the central nervous system characterized by inflammation, demyelination, and axonal degeneration. This study aimed to investigate the relationship between inflammatory indexes and MS disease activity and progression. Methods: A prospective cohort study was conducted at the Kocaeli University Neurology Clinic, involving 108 patients diagnosed with MS. Data related to patient demographics, clinical presentations, radiological findings, and laboratory results were recorded. Inflammatory markers such as NLR (neutrophil-to-lymphocyte ratio), PLR (platelet-to-lymphocyte ratio), MLR (monocyte-to-lymphocyte ratio), and indexes such as SII (systemic immune inflammation index), SIRI (systemic immune response index), and AISI (systemic total aggregation index) were examined to determine their correlation with MS disease activity and disability. When assessing the influence of SII, AISI, and SIRI in predicting NEDA, it was found that all three indexes significantly predict NEDA. All indexes demonstrated a significant relationship with the EDSS score. Notably, SII, SIRI, and AISI were significant predictors of NEDA, and all inflammatory indexes showed a strong intercorrelation. This study investigates the role of inflammation markers in MS patients. It suggests that one or more of these non-invasive, straightforward, and practical markers could complement clinical and radiological parameters in monitoring MS.

Bmj, Bosphorus Medical Journal

Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by motor symptoms, but early diagnosis is challenging due to symptom overlap with other conditions and a lack of definitive biomarkers (clinical assessments). In this study, we propose a novel multimodal artificial intelligence (AI)-based decision support system aimed at the early diagnosis of idiopathic PD. To the best of our knowledge, this is the first framework to enable the synchronous analysis of four distinct modalities: walking, facial expression, voice, and posture, whereas prior studies have typically focused on unimodal or partially multimodal approaches. We also constructed a new dataset by establishing a controlled clinical testing environment equipped with an L-shaped walking track and an integrated audiovisual recording system to capture natural walking, turning, facial, vocal, and postural characteristics. For each modality, specialized AI models were developed and evaluated. For the walking modality, the proposed Bidirectional GRU model achieved the best performance in terms of both $$F_1$$ score (92.74%) and area under the curve (AUC) (97.86%), demonstrating superior gait-based classification performance. Similarly, in the face modality, the ensemble model integrating eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Categorical Boosting (CatBoost) yielded the highest $$F_1$$ score (92.31%) while also achieving the best AUC (97.96%). For the voice and posture modalities, although the highest $$F_1$$ scores were not obtained, the RF-based models achieved the highest AUC values (99.85% and 97.56%, respectively) within their respective modality comparisons in the literature, reflecting strong class separability and discriminative capability.