Background: Klotho and its relationship with neurotrophic factors and cognition in schizophrenia has not yet been investigated. In this study, the hypothesis that the blood serum levels of BDNF, GDNF, NGF and Klotho in schizophrenia patients and healthy controls would be related to cognitive functions was investigated. Methods: In this study, two groups were assessed: schizophrenia patients (case group) who were hospitalised in the Psychiatry Clinic of Sakarya University Training and Research Hospital and healthy volunteers (control group). The patients were evaluated on the 1st and 20th days of their hospitalisation with the Positive and Negative Syndrome Scale (PANSS), the Brief Psychiatric Rating Scale (BPRS), the General Assessment of Functioning Scale (GAF) and the Clinical Global Impression Scale (CGI). For cognitive assessment, both groups were evaluated with the Wechsler Memory Scale-Visual Production Subtest (Wechsler Memory Scale III-Visual Reproduction Subtest) and the Stroop test. Results: BDNF, GDNF, NGF and Klotho levels were lower in schizophrenia patients than in healthy controls. In the schizophrenia patients, on the 20th day of treatment, there was a statistically significant increase in BDNF compared to the 1st day of treatment. BDNF, GDNF and Klotho showed positive correlations with some cognitive functions in the healthy controls. BDNF, GDNF, NGF and Klotho levels were intercorrelated and predictive of each other in both groups. Conclusion: This study suggests a relationship between cognitive functions, neurotrophic factors and Klotho. Most of the results are the first of their kind in the extant literature, while other results are either similar to or divergent from those generated in previous studies. Therefore, new, enhanced studies are needed to clarify the role of Klotho and neurotrophic factors in schizophrenia.

BACKGROUND: Anxiety disorders are common in women. This sensitivity extends into the perinatal period as well. Thus, screening for anxiety disorders during the aforementioned period is important for the proper management and treatment of conditions. This study was conducted to assess the validity and reliability of the Perinatal Anxiety Screening Scale, which was determined to be beneficial for the purposes listed above. METHOD: For this study, the “Perinatal Anxiety Screening Scale” (PASS) was translated into Turkish and relabelled “Perinatal Anksiyete Tarama Ölçeği” (PASS-TR). 312 perinatal women were then evaluated with: the ICD 10 diagnosis system, SCID-1, the Hamilton Anxiety Scale, Hamilton Depression Scale, Beck Anxiety Scale, and PASS-TR. The resulting data was examined using Pearson Correlation analysis, Reliability tests, ROC analysis, and Factor analysis. The generated sub-dimensions were re-examined again by confirmatory factor analysis and Root Mean Square Error of Approximation (RMSEA), Root Mean Square Residual (RMR), Standardized Root Mean Square Residual (SRMR) χ²/sd, the Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Comparative Fit Index (CFI), Akaike's Information Criterion (AIC), and the Bayesian Information Criterion (BIC). RESULTS: In this assessment, Cronbach's Alpha value for the scale is = 0.95, and the sub-dimensions obtained by explanatory factor analysis are: (1) general anxiety and specific fear, (2) perfectionism and control, (3) social anxiety and adjustment disorder, (4) acute anxiety and trauma. The cut-off score for the scale is 16. As a result, it was determined that PASS-TR is an accurate method for the scanning of anxiety disorders in the perinatal period. CONCLUSION: PASS-TR can be validly and reliably used to scan for anxiety disorders amongst perinatal women.

The long-term use of opioids leads alternations in both innate-adaptive immune systems and other diagnostic hematologic cells. The purpose of this study is to evaluate the alterations of these parameters in patients with heroin/opioid addictions. Adults, meeting the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria of the American Psychiatric Association regarding opioid use disorder (Heroin Group or HG, n = 51) and healthy controls (Control Group or CG, n = 50), were included in the study. All hematological parameters, inflammation indexes (neutrophil-lymphocyte ratio and platelet-lymphocyte ratio), and iron panel were compared with the controls. Mean corpuscular volume, red blood cell distribution width, mean corpuscular hemoglobin content, unsaturated iron-binding capacity, and total iron-binding capacity were significantly higher in HG compared to CG, while red blood cell count, hemoglobin, hematocrit, and serum iron levels were significantly lower. Additionally, platelet and platelet distribution width were significantly high while mean platelet volume was low in HG. Regarding the parameters related to immunity, white blood cell, neutrophil count, and neutrophil percentage were significantly high while lymphocyte percentage and basophils count were significantly low. Besides, inflammatory indexes were significantly higher in HG compared to CG. Intravenous administration of heroin resulted in lower levels of hemoglobin, hematocrit, and mean corpuscular volume than inhalation and intranasal administration. Our data demonstrated that chronic use of opioids is related to all of the hematologic series. The chronic use of opioid alters the immunologic balance in favor of innate immunity cells and changes the hematometric/morphometric characteristics of erythrocytes. What is more, the route of heroin administration should be taken into consideration as well. This study may lead to a better understanding of the hematological effects of heroin/opioid use in patients with relevant addictions.

Alcohol and substance use disorder (ASUD) is a chronic condition featuring relapses and remissions. Due to their multifactorial nature, the causes of relapses in ASUD are not fully understood and it is important to update the information. Therefore, we aimed to provide an update on research examining factors associated with relapses, organized into sections. Factors such as early age of onset, dysfunction in the brain reward system, poor physical health, sleep disturbance, comorbid psychiatric disorders, severity of ASUD, craving, low self-efcacy, negative life events, and low socioeconomic status have been consistently shown to increase the relapse rate of ASUD. Conversely, factors such as positive family functioning, strong social support, treatment motivation and regular medication appear to decrease relapse rates. Studies on gender and genetic factors have yielded mixed results and no consistent relationship with relapses has been found. While pharmaceutical agents such as methadone, buprenorphine and naltrexone are efective in preventing relapse in opioid and alcohol use disorders; there are no agents proven to be efective in other substance use disorders. Psychotherapeutic approaches, including motivational interviewing, cognitive behavioral therapy, 12-step programs and contingency management have been demonstrated to be efective. The treatment of addiction to substances is intricate and necessitates biopsychosocial interventions. However, despite all treatments, high relapse rates indicate the necessity for social support and rehabilitation services. In the light of the data obtained, the factors afecting relapses in patients with ASUD can be determined and appropriate interventions and therapeutic approaches can be used to prevent relapses. Cite this article as: Yazıcı AB, Bardakçı MR. Factors associated with relapses in alcohol and substance use disorder. Eurasian J Med., 2023;55(Suppl. Issue: 1): S75-S81.

Aim: Neurodegenerative processes are effective in schizophrenia. However, the underlying causes of the symptoms and associated factors have not yet been fully elucidated. Recent research has focused on the relationship between neurodegeneration and vitamin D, Klotho and homocysteine levels. In this study, we aimed to investigate this relationship in schizophrenia. Method: This study included 30 schizophrenic inpatients, 30 schizophrenic outpatients in remission and 28 healthy volunteers as the control group. The psychiatric diagnoses of our patients were evaluated according to DSM-IV criteria. The Positive and Negative Syndrome Scale (PANSS), the Global Assessment of Functioning (GAF) scale and the Clinical Global Impression (CGI) scale were used for clinical measurements. Serum Klotho, homocysteine, vitamin D, vitamin B12 and folic acid levels were analyzed using ELISA and compared with clinical properties. Results: The PANSS scores and CGI scores were higher in schizophrenic inpatients than outpatients, and the GAF scores were lower (p < 0.05). Three groups were compared for Klotho, homocysteine and vitamin D serum levels; Klotho levels were elevated but the difference was not statistically significant (p > 0.05). However, vitamin B12, folic acid and homocysteine levels were higher in schizophrenic patients than the control group (p < 0.05). Conclusions: Higher levels of homocysteine with concomitant higher levels of vitamin B12 and folic acid suggest a relationship of this pathway with schizophrenia. Differences in Klotho levels were elevated but it was not significant. Replication studies to investigate probable associations with larger samples are needed.

One of the forces driving science and industry is machine learning, but the proliferation of Big Data necessitates paradigm shifts from conventional approaches in applying machine learning techniques to this massive amount of data with varying velocity. Computers are now capable of accurately diagnosing a variety of medical conditions thanks to the availability of immense healthcare datasets and advancements in machine learning techniques. The study’s primary aim is to identify the most compelling questions on anxiety and depression in pregnant women by extracting features through performance-optimized algorithms. In this way, it is aimed to reach the result in a shorter time with fewer questions. The next goal of this work is to create an instant remote health status prediction system for depression and anxiety in pregnant women based on the Apache Spark Big Data processing engine, which concentrates on using machine learning models on streaming Big Data. In this scalable system, the application receives data from pregnant women to forecast the patient’s health condition. It then applies the machine learning algorithm that produces the best results for this dataset. With the assistance of this big data platform, the time-consuming anxiety and depression detection procedure in a pregnant woman can be replaced with a computer-based technique that works in an instant with a respectable amount of accuracy.