Objective: Ubiquitin C-terminal Hydrolase-L1 (UCH-L1) enzyme levels were investigated in patients with epilepsy, epileptic seizure, remission period, and healthy individuals. Methods: Three main groups were evaluated, including epileptic seizure, patients with epilepsy in the non-seizure period, and healthy volunteers. The patients having a seizure in the Emergency department or brought by a postictal confusion were included in the epileptic attack group. The patients having a seizure attack or presenting to the Neurology outpatient department for follow up were included in the non-seizure (remission period) group. Results: The UCH-L1 enzyme levels of 160 patients with epilepsy (80 patients with epileptic attack and 80 patients with epilepsy in the non-seizure period) and 100 healthy volunteers were compared. Whereas the UCH-L1 enzyme levels were 8.30 (IQR=6.57‒11.40) ng/mL in all patients with epilepsy, they were detected as 3.90 (IQR=3.31‒7.22) ng/mL in healthy volunteers, and significantly increased in numbers for those with epilepsy (p<0.001). However, whereas the UCH-L1 levels were 8.50 (IQR=6.93‒11.16) ng/mL in the patients with epileptic seizures, they were 8.10 (IQR=6.22‒11.93) ng/mL in the non-seizure period, and no significant difference was detected (p=0.6123). When the UCH-L1 cut-off value was taken as 4.34 mg/mL in Receiver Operating Characteristic (ROC) Curve analysis, the sensitivity and specificity detected were 93.75 and 66.00%, respectively (AUG=0.801; p<0.0001; 95%CI 0.747‒0.848) for patients with epilepsy. Conclusion: Even though UCH-L1 levels significantly increased more in patients with epilepsy than in healthy individuals, there was no difference between epileptic seizure and non-seizure periods.

We investigated the effects of N-acetyl cysteine (NAC) on transient receptor potential melastatin 2 (TRPM2) channel expression in rat kidney and liver tissues following experimental malathion intoxication. We used seven groups of six male Wistar albino rats: control group, NAC, pralidoxime + atropine, malathion, malathion + pralidoxime + atropine, malathion + pralidoxime + atropine + NAC, and malathion + NAC. Single doses of 100 mg/kg N-acetyl cysteine, 40 mg/kg pralidoxime, 2 mg/kg atropine and 1/3 the lethal dose of malathion were administered. No difference in malondialdehyde (MDA) levels, apoptosis or TRPM2 immunoreactivity was found in liver tissue among the groups. In kidney tissue, MDA levels, apoptosis and TRPM2 immunoreactivity were increased significantly in the malathion and malathion + NAC groups compared to the control group. We found that organophosphate intoxication did not affect MDA, apoptosis or TRPM2 immunoreactivity in rat liver during the acute period. By contrast, we found that in kidney tissue, MDA, apoptosis, and TRPM2 immunoreactivity were increased significantly following administration of malathion. Also, NAC given in addition to pralidoxime and atropine reduced MDA to control levels.

Background/Objectives: Heart attacks are the leading cause of death in the world. There are two important classes of heart attack: ST-segment Elevation Myocardial Infarction (STEMI) and Non-ST-segment Elevation Myocardial Infarction (NSTEMI) patient groups. While the STEMI group has a higher mortality rate in the short term, the NSTEMI group is considered more dangerous and insidious in the long term. Blocked coronary arteries can be predicted from ECG signals in STEMI patients but not in NSTEMI patients. Therefore, coronary angiography (CAG) is inevitable for these patients. However, in the elderly and some patients with chronic diseases, if there is a single blockage, the CAG procedure poses a risk, so medication may be preferred. In this study, a novel deep learning-based approach is used to automatically detect the occluded main coronary artery or arteries in NSTEMI patients. For this purpose, a new seven-class dataset was created with expert cardiologists. Methods: A new Multi Input-Multi Scale (MI-MS) ConvMixer model was developed for automatic detection. The MI-MS ConvMixer model allows simultaneous training of 12-channel ECG data and highlights different regions of the data at different scales. In addition, the ConMixer structure provides high classification performance without increasing the complexity of the model. Moreover, to maximise the classifier performance, the WSSE algorithm was developed to adjust the classification prediction value according to the feature importance weights. Results: This algorithm improves the SVM classifier performance. The features extracted from this model were classified with the WSSE algorithm, and an accuracy of 88.72% was achieved. Conclusions: This study demonstrates the potential of the MI-MS ConvMixer model in advancing ECG signal classification for diagnosing coronary artery diseases, offering a promising tool for real-time, automated analysis in clinical settings. The findings highlight the model’s ability to achieve high sensitivity, specificity, and precision, which could significantly improve.

Background/Objectives: Rib fracture detection holds critical importance in the field of medical image processing. Methods: In this study, two different data augmentation methods, traditional data augmentation (Albumentations) and focused data augmentation (focused augmentation), were compared using computed tomography (CT) images for the detection of rib fractures on YOLOv8n, YOLOv8s, and YOLOv8m models. While the traditional data augmentation method applies general transformations to the entire image, the focused data augmentation method performs specific transformations by targeting only the fracture regions. Results: The model performance was evaluated using the Precision, Recall, mAP@50, and mAP@50–95 metrics. The findings revealed that the focused data augmentation method achieved superior performance in certain metrics. Specifically, analysis on the YOLOv8s model showed that the focused data augmentation method increased the mAP@50 value by 2.18%, reaching 0.9412, and improved the recall value for fracture detection by 5.70%, reaching 0.8766. On the other hand, the traditional data augmentation method achieved better results in overall precision metrics with the YOLOv8m model and provided a slight advantage in the mAP@50 value. Conclusions: The study indicates that focused data augmentation can contribute to achieving more reliable and accurate results in medical imaging applications.

Amaç: Bu çalışmanın amacı acil serviste spontan pnömomediastinum (SPM) tanısı alan hastaların retrospektif olarak değerlendirilmesidir. Gereç ve Yöntem: Tek merkezli retrospektif olarak planlanan çalışmamızda Ocak 2010 - Aralık 2017 tarihleri arasında acil serviste SPM tanısı alan hastaların verileri analiz edildi. Sekonder pnömomediastinum tanısı alan hastalar (travmatik, girişimsel işlem yâda cerrahi sonrası, enfeksiyonlar) çalışma dışı bırakıldıBulgular: SPM tanısı alan 31 hastanın yaş ortalaması 48.35±20.80/yıl olup %74’ü (n=23) erkek idi. Erkeklerde SPM gelişimi anlamlı oranda fazla idi En sık eşlik eden hastalık kronik obstruktif akciğer hastalığı idi. En sık semptom nefes darlığı idi. Hastaların % 61,29’u sigara kullanıcısıydı. Hastalığı en fazla tetikleyen faktör %32 oranında fiziksel efor idi. Hastaların %35.5’i sonbahar % 32.3’ü ilkbahar mevsiminde acil servise müracaat ettiği tespit edildi. Hamman's işareti hastaların %25.8’inde mevcuttu. Hastanede yatış süresi ortalama 5.61±5.57 iken acil serviste mortalite görülmedi.Sonuç: Acil serviste akut göğüs ağrısı ve nefes darlığı ile başvuran hastalarda ayırıcı tanı da SPM düşünülmelidir.

Correction: PRISMA-7 (for frailty assessment) and SARC-F (for evaluation of sarcopenia risk) in predicting emergency department readmission and mortality. Mehmet Cagri Goktekin,Evrim Gul,Feyza Aksu…