Multiple screen addiction is a growing public health problem, especially among young people. Early detection and classification of screen addiction are important for the prevention of neurological complaints. The current study aimed to determine the status of multiple screen addiction with machine learning and to identify the relationship between multiple screen addiction and neurological complaints. This cross-sectional and correlational study was conducted with 406 participants. The data were collected from November 13, 2023, to March 4, 2024, in the neurology outpatient clinic of a training and research hospital. The dataset created in the study was trained with machine learning algorithms, and classification was provided. K-Nearest Neighbors (KNN), Ensemble, Support Vector Machine (SVM), Decision Tree machine learning algorithms, and Confusion Matrix were used for classification. In the study, KNN was the algorithm with the highest classification performance, while Decision Tree was the algorithm with the worst performance. Moreover, it was determined that multiple screen addiction and neurological complaints were related, and these complaints were successfully evaluated through machine learning classification. Multi-screen addiction is closely linked to numerous neurological complaints, and machine learning can effectively uncover and analyze this association. As a foundational step, this study paves the way for family physicians and healthcare professionals to utilize machine learning in calculating individuals' addiction scores, monitoring addiction patterns, and providing personalized recommendations.

Objective: Early recurrence of epileptic seizures within the same day of emergency department (ED) admission poses clinical management challenges. While several blood‐based parameters have been proposed as prognostic markers in various neurological disorders, their predictive utility in same‐day seizure recurrence remains underexplored. We aimed to investigate whether inflammatory parameters derived from hemogram parameters can predict seizure recurrence within the same day in patients presenting with epileptic seizures or not. Methods: We included 267 patients who presented to our ED with epileptic seizures within the last 2 years. Patients were grouped into recurrence ( n = 68) and non‐recurrence ( n = 199) cohorts. Hemogram‐derived indices, including neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), lymphocyte‐to‐monocyte ratio (LMR), monocyte‐to‐neutrophil ratio (MNR), platelet‐to‐neutrophil ratio (PNR), immune‐platelet inflammation value (IPIV), pan‐immune inflammation value (PIV), systemic immune‐inflammation index (SII) and systemic inflammation response index (SIRI) were calculated and compared between groups. Statistical analyses included receiver operating characteristic (ROC) curve assessment for predictive accuracy. Results: NLR, PLR, SII, and SIRI values were significantly elevated in the recurrence group ( p < 0.05), indicating heightened systemic inflammation. Traditional parameters such as white blood cell count (WBC), glucose, and lactate did not significantly differ. Area under the curve (AUC) values in ROC curve analyses revealed that the NLR (AUC = 0.618) and SII (AUC = 0.601) had moderate discriminative ability, although no parameter achieved a substantial predictive value (AUC ≥ 0.7). Significance: Elevated inflammatory indices, particularly NLR and SII, are associated with same‐day seizure recurrence, suggesting their potential utility in ED triage. However, none demonstrated sufficient stand‐alone predictive power, highlighting the need for prospective studies incorporating novel biomarkers. Plain Language Summary: This study looked at whether certain blood test values can help predict if a person who comes to the emergency department (ED) with an epileptic seizure will have another seizure on the same day. Researchers focused on inflammation‐related markers found in routine blood tests, such as the neutrophil‐to‐lymphocyte ratio (NLR) and systemic immune‐inflammation index (SII). We found that these values were higher in patients who had repeated seizures within the same day. However, while these markers showed some association with seizure recurrence, they were not accurate enough to reliably predict it on their own. These findings suggest that simple blood tests might provide helpful clues in emergency settings, but more research is needed to find stronger and more reliable indicators for early seizure recurrence.

Aim: Fibrinogen and albumin are proteins that play a role in inflammation. In this study, it was aimed to investigate the role of fibrinogen, albumin, and fibrinogen/albumin ratio (FAR) levels as markers of disease severity and prognosis in coronavirus disease 2019 (COVID-19) patients. Material and Methods: Seventy-one patients aged between 19 and 84 years diagnosed with COVID-19 who were hospitalized in Sakarya University Training and Research Hospital, Neurology Department between March and May 2020 were analyzed retrospectively. Fibrinogen, albumin, FAR, D-dimer, platelet, and C-reactive protein (CRP) levels of the patients were compared according to the length of hospitalization duration, survival, and clinical severity of COVID-19. Results: Twenty-eight (%39.4) of the patients were male and 43 (%60.6) were female, and the mean age was 55.7±20.7 years. There was a significant difference between the groups of COVID-19 clinical severity in terms of age, fibrinogen, albumin, FAR, D-dimer, and CRP values (all p values were <0.001). Also, significantly higher fibrinogen, FAR, D-dimer, and CRP values were found in patients hospitalized longer, while the albumin level was lower in these patients (all p values were <0.001). FAR values were higher and albumin values were lower in non-surviving patients compared to surviving patients (p=0.025 and p<0.001, respectively). Conclusion: FAR levels may be useful in predicting mortality risk in COVID-19 patients. In addition, it may be helpful and useful in determining the prognosis since it has higher levels as the severity of the disease and the length of hospital stay increase.

Objective: The most common entrapment neuropathy seen by the clinician is Carpal tunnel syndrome (CTS). CTS is graded as mild, moderate, and severe according to the results obtained on electroneuromyography (ENMG) by clinicians. We aimed to show the effectiveness of the use of artificial intelligence in clinical diagnosis in the grading of CTS. Methods: In our study, the data of 315 people with a pre-diagnosis of CTS were used and classified into four classes based on AI as CTS grade. Machine Learning (ML) algorithms Ensemble, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (Tree) algorithms were used in classification processes. 10% Hold-out validation was used and the learning rate was determined as 0.1. As a result of the classification, accuracy, precision, sensitivity, specificity, and F1-score performance values were obtained. Results: SVM made the best estimation and KNN made the worst estimation in the 0 class. The best estimate in class 1 belongs to the Support Vector Machine. Ensemble and Tree made the best guesses in the 2nd and 3rd grades. In our study, the best algorithm with an overall success rate is SVM with 93.55%. Conclusions: The results showed that ML algorithm models consistently provided better predictive results and would assist physicians in determining the medical treatment modality of CTS. Artificial intelligence (AI) techniques are reliable methods that assist clinicians to deliver quality healthcare.

Parkinsonism is a syndrome that occurs as a combination of six cardinal signs; resting tremor, rigidity, bradykinesia, loss of postural reflex, flexion posture and freezing (motor block). Parkinson disease occurs with the loss of brain cells which are generating dopamine. The most important primary motor symptoms of Parkinson’s disease are shaking of hands, slowness of movement, and speech changes. Sound changes are not recognized at the early stages of the disease while it becomes evident at the progressive stages. However, speech changes can be detected with some acoustic parameters. This study aims to detect Parkinson’s disease by using voice recordings. In this study, 342 voice recordings that belong to 174 healthy subjects and 168 Parkinson’s disease patients are used. 21 features are extracted from each voice record. The classification of subjects as healthy or with Parkinson disease is achieved by using logistic regression, k-nearest neighboring and ensemble gentle boost techniques. Furthermore, ten-fold and leave-one-out cross validation techniques are applied to improve the performance and reliability of the classifier. Sensitivity, specificity, maximum and average accuracy values are calculated to evaluate the success of the system. The obtained results show that the proposed system can be utilized by the neurologists to diagnose Parkinson’s disease at its early stages.

OBJECTIVE: Zonulin is a protein, synthesized in intestinal cells, that reversibly regulates the permeability of the intestinal epithelium. Elevated levels of zonulin are associated with increased intestinal permeability. It is shown that some neurological disorders are related to zonulin. We aim to research the possible associations between the zonulin levels and the symptom intensity in restless legs syndrome.