Amaç: Bu çalışmanın amacı; 24.01.2020 tarihinde merkez üssü Elazığ Sivrice ilçesi olan, toplamda 44 kişinin vefat ettiği ve 1500 üzerinde insanın yaralandığı deprem sonrası ortaya çıkan psikiyatrik semptomları ortaya koyabilmektir. Gereç ve Yöntem: Çalışma depremden 2,5 ay sonra yapılmıştır. Tüm katılımcılara; sosyodemografik veri formu, Beck Anksiyete Ölçeği (BAÖ), Beck Depresyon Ölçeği (BDÖ), Peritravmatik Disosiyasyon Ölçeği (PTDÖ), DSM-5 Travma Sonrası Stres Bozukluğu Kontrol Listesi (TSSBKL) uygulandı. Bulgular: Çalışmaya 360 kişi; 284 kişi deprem mağduru ve 76 kişi de kontrol grubu olarak alındı. Katılımcıların yaş ortalaması 30.9±9.9 olarak hesaplandı. Deprem mağdurlarından yakınını kaybeden 11 kişi (%3.87) olduğu saptandı. Katılımcıların BAÖ ve BDÖ skorları gruplar arasında farklılık göstermedi. PTDÖ ve TSSBKL için deprem mağdurlarının hesaplanan skorları çok daha yüksekti. Sonuç: Çalışmamızda deprem sonrası hayatta kalanların depresyon ve ansiyete düzeyleri artmasa da travma ve dissosiyasyon skorlarının artmış olduğu görüldü. Elde ettiğimiz sonuçlara dayanarak; deprem gibi felaketler sonrası hayatta kalanların psikiyatrik ve sosyal olarak iyi desteklenmeleri, hem kayıplarına alışabilmeleri hem de psikiyatrik olarak rahatsızlanmamaları açısından önemlidir.

Background and Aim: In the era of deep learning, numerous models have emerged in the literature and various application domains. Transformer architectures, particularly, have gained popularity in deep learning, with diverse transformer-based computer vision algorithms. Attention convolutional neural networks (CNNs) have been introduced to enhance image classification capabilities. In this context, we propose a novel attention convolutional model with the primary objective of detecting bipolar disorder using optical coherence tomography (OCT) images. Materials and Methods: To facilitate our study, we curated a unique OCT image dataset, initially comprising two distinct cases. For the development of an automated OCT image detection system, we introduce a new attention convolutional neural network named “TurkerNeXt”. This proposed Attention TurkerNeXt encompasses four key modules: (i) the patchify stem block, (ii) the Attention TurkerNeXt block, (iii) the patchify downsampling block, and (iv) the output block. In line with the swin transformer, we employed a patchify operation in this study. The design of the attention block, Attention TurkerNeXt, draws inspiration from ConvNeXt, with an added shortcut operation to mitigate the vanishing gradient problem. The overall architecture is influenced by ResNet18. Results: The dataset comprises two distinctive cases: (i) top to bottom and (ii) left to right. Each case contains 987 training and 328 test images. Our newly proposed Attention TurkerNeXt achieved 100% test and validation accuracies for both cases. Conclusions: We curated a novel OCT dataset and introduced a new CNN, named TurkerNeXt in this research. Based on the research findings and classification results, our proposed TurkerNeXt model demonstrated excellent classification performance. This investigation distinctly underscores the potential of OCT images as a biomarker for bipolar disorder.

Purpose: In our study, we aimed to investigate the ganglion cell-inner plexiform layer thickness (GCIPL), retinal nerve fiber layer thickness (RNFL), mean macular volume (MMV), central macular thickness (MMV), mean macular thickness (MMT), and choroidal thickness (CT) values with Optical Coherence Tomography (OCT) in patients who are diagnosed with Alcohol Use Disorder (AUD). Material and Methods: The study included 43 patients who were diagnosed with AUD, and 43 healthy controls. Detailed biomicroscopic examinations of all the participants, visual acuity, intraocular pressure, anterior and posterior segment examinations, and then, OCT measurements were carried out. Results: Although the measured values for RNFL in the superior and temporal quadrant are within normal limits, they were slightly higher compared to those in the control group (p values 0.127 and 0.191 for superior quadrant and temporal quadrant, respectively). The CT measurements in all quadrants were higher than the control group; however these measurements were not statistically significant (p > 0.05). When the relation between clinical features and OCT findings of the patients were examined, it was determined that the ages of the patients were statistically significantly and inversely correlated with the temporal CT and also the nasal and temporal quadrants of RNFL. Conclusions: Our study is the first study that examines the retinal GCIPL and CT with OCT in patients who are diagnosed with AUD. In our results, it was determined that there were no statistically significant differences between the participants in terms of OCT parameters. Further studies with larger sampling groups evaluating neurotransmission findings may provide wider results.

Lymphocyte,methamphetamine,neutrophil,platelet,substance

Aim: The aim of this study was to examine the relationship between peripheral inflammatory markers and aggression and impulsivity in schizophrenia patients with and without criminal histories. Materials and Methods: The study was conducted with patients with schizophrenia involved in crimes and hospitalized in the Forensic Psychiatry ward of Elazığ Fethi Sekin City Hospital and patients with schizophrenia not involved in crimes and hospitalized in the psychiatry ward of Elazığ Mental Health and Diseases Hospital. All participants completed the Buss–Waren Aggression Scale (BWAS), the Barratt Impulsiveness Scale-11 (BIS-11), and the Positive and Negative Symptom Scale (PANSS). Before treatment, venous blood samples were taken for laboratory measurements on the first day of hospitalization. Results: All participants were male. The mean age of those involved in a crime was 39 ± 9.7 years, while the mean age of those not involved in a crime was 41.2 ± 10.7 years. The PANSS all subscale and total scores of the patients with schizophrenia who were involved in a crime were significantly higher than the group who were not involved (p values were p < 0.001, p = 0.001, p = 0.043, p = 0.001, respectively). The BWAS—physical aggression (p = 0.007) and total scores of the scale (p = 0.046) and BIS-11—inability to plan (p = 0.002) scores of the group involved in a crime were higher than the group not involved. As for laboratory parameters, MCH, MCHC, PDW, eosinophils, basophils, RDW-CV, and RDW-SD values were significantly higher in those involved in crime, while MPV, creatinine, albumin, and LDH values were lower. CRP and CRP/albumin values were significantly higher, while neutrophil/albumin values were significantly lower in those who committed murder in the first degree than those who committed other crimes. Conclusion: Based on our results, we found that inflammatory agents were significantly increased in forensic schizophrenia patients with high aggression scores. Significant correlations between some inflammatory factors and impulsivity and aggression scores and differences in these factors according to crime types showed that these factors might be related to violence and criminal behavior. There is a need for further large-scale studies on this subject at different stages of the disease.