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Recent studies have prompted a shift in the understanding of attention deficit hyperactivity disorder (ADHD) from models positing dysfunction of individual brain areas to those that assume alterations in large-scale brain networks. Despite this shift, the underlying neural mechanism of ADHD in the adult population remains uncertain. With functional magnetic resonance imaging (fMRI), this study examined brain connectivity of dorsal and ventral attention networks. Adults with and without ADHD completed a Go/No-Go task inside the scanner and the functional connectivity of attention networks was analysed. The generalized psychophysiological interaction analysis indicated differences involving the dorsal attention network. For the ADHD group, an interaction effect revealed altered dorsal attention-default mode network connectivity modulation, particularly between the right frontal eye field and posterior cingulate gyrus. We conclude that dorsal attention network dysfunction may be involved in sustained attention deficits in adult-ADHD. This study sheds light into network-level alterations contributing to the understanding of adult-ADHD, which may be a potential avenue for future research and clinical interventions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistant depression (TRD), which is characterized by an inadequate response to antidepressant treatment. Identifying the TRD population is crucial in terms of saving time and resources in depression treatment. Recently several studies employed various methods on EEG datasets for automatic depression detection or treatment outcome prediction. However, no previous study has used the deep learning (DL) approach and EEG signals for detecting treatment resistance. Method: 77 patients with TRD, 43 patients with non-TRD, and 40 healthy controls were compared using GoogleNet convolutional neural network and DL on EEG data. Additionally, Class Activation Maps (CAMs) acquired from the TRD and non-TRD groups were used to obtain distinctive regions for classification. Results: GoogleNet classified the healthy controls and non-TRD group with 88.43%, the healthy controls and TRD subjects with 89.73%, and the TRD and non-TRD group with 90.05% accuracy. The external validation accuracy for the TRD-non-TRD classification was 73.33%. Finally, the CAM analysis revealed that the TRD group contained dominant features in class detection of deep learning architecture in almost all electrodes. Limitations: Our study is limited by the moderate sample size of clinical groups and the retrospective nature of the study. Conclusion: These findings suggest that EEG-based deep learning can be used to classify treatment resistance in depression and may in the future prove to be a useful tool in psychiatry practice to identify patients who need more vigorous intervention., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objective: Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this condition overlap and co-occur with those of other psychiatric illnesses, making diagnosis difficult. The availability of biomarkers could be useful for aiding in diagnosis, although prior neuroimaging studies were unable to provide such biomarkers. Method: In this study, patients with OCD were classified from healthy controls using 2 different hybrid deep learning models: one-dimensional convolutional neural networks (1DCNN) together with long-short term memory (LSTM) and gradient recurrent units (GRU), respectively. Results: Both models exhibited exceptional classification accuracies in cross-validation and external validation phases. The mean classification accuracies in the cross-validation stage were 90.88% and 85.91% for the 1DCNN-LSTM and 1DCNN-GRU models, respectively. The inferior frontal, temporal, and occipital electrodes were predominant in providing discriminative features. Conclusion: Our findings underscore the potential of hybrid deep learning architectures utilizing EEG data to effectively differentiate patients with OCD from healthy controls. This promising approach holds implications for advancing clinical decision-making by offering valuable insights into diagnostic markers for OCD.

Background: Dentists, through inappropriate antibiotic prescription, may contribute to the global problem of antibiotic resistance (AR)., Objectives: Understanding dentists' antibiotic prescription patterns, source of knowledge, and the driving forces behind their prescription practices may be crucial for the effective implementation of the rational use of antibiotics (RUA) in dentistry., Material and Methods: Active members of the Turkish Dental Association were invited to participate in an electronic survey comprising questions focusing on their role, knowledge and perceptions regarding RUA, the perceived barriers to adapting RUA in daily dental work, and the actual antibiotic prescription practices. The potential impact of age, gender, professional experience, and the mode of dental practice was also evaluated. Dentists' prescription practices for periodontal disease/conditions were evaluated as well., Results: Based on 1,005 valid responses, there was consensus on the necessity of RUA (99.1%); however, its implementation was low. The main barriers were dentists' own safety concerns (74.4%), strong patients' demands (42.2%) and the fact that prescribing antibiotics became a professional habit (35.8%). Different educational background resulted in clear variances in everyday prescription practices., Conclusions: The implementation of RUA was not sufficient and the perceived barriers had an impact on daily prescribing habits. Support for dental professionals through the efficient dissemination of evidencebased clinical guidelines and decision-making aids is likely to require additional help from professional organizations in order to actively combat AR.

Introduction: Parkinson disease is the world's second most prevalent neurological disease. In this disease, intracytoplasmic neuronal inclusions are observed in enteric neurons in the gastrointestinal tract, and the composition of the intestinal microbiome is altered. These changes correlate with the motor phenotype. A systematic review was conducted to determine the effect of using probiotics in Parkinson disease., Methods: Scopus, PubMed, Web of Science, the Cochrane Library, ScienceDirect, and Ovıd-LWW were searched until April 2021. A total of 27395 records were found according to inclusion and exclusion criteria with the following outcomes: Parkinson disease rating, oxidative stress, and gastrointestinal system markers. Data search, article selection, and data extraction assessments were performed according to the PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines. The Jadad scale was used to rate the evidence's quality., Results: Our study information was gathered from 5 randomized controlled trials involving 350 individuals with Parkinson disease receiving probiotic supplements. Parkinson disease rating and non-motor symptoms test were performed in the samples. Also, oxidative stress (glutathione, malondialdehyde) and gastrointestinal system symptoms (bowel opening frequency, gut transit time, complete bowel movement, spontaneous bowel movements) were evaluated during 4-12 weeks of using probiotics in these patients., Conclusion: While all high-quality studies demonstrate improvement in disease symptoms of the patients, currently sufficient data are not available to recommend the use of probiotics for people with Parkinson disease in clinical practice., (Copyright© 2024 Iranian Neuroscience Society.)

Introduction: Bipolar disorder (BD) is associated with cognitive abnormalities that may persist during euthymia and are linked to poor occupational performance. The cognitive differences between phases of BD are not well known. Therefore, a cross-sectional study with a relatively large population was conducted to evaluate the differences among BD phases in a wide range of neurocognitive parameters., Methods: Neuropsychological profile of 169 patients with a diagnosis of BD in manic, depressive, mixed, and euthymic phases between the ages of 18 and 70 years were compared to 45 healthy individuals' between ages of 24 and 69 years. The working memory (digit-span backward test), face recognition, executive functions (verbal fluency and Stroop test), face recognition, and visual and verbal memory (immediate and delayed recall) were evaluated. For BD subgroup analyses, we used the Kruskal-Wallis (KW) test. Then, for the comparison of BD versus healthy individuals, we used the Mann-Whitney U (MWU) test., Results: Analyses based on non-parametric tests showed impairments in BD for all tests. There were no significant differences between phases., Conclusion: Cognitive performance in patients with BD appears to be mostly unrelated to the phase of the disorder, implying that cognitive dysfunction in BD is present even during remission.

Chimeric antigen receptor T (CAR-T) cell therapy holds great promise as an innovative immunotherapeutic approach for cancer treatment. To optimize the production and application of CAR-T cells, we evaluated the in vivo stability and efficacy capacities of CAR-T cells developed under different conditions. In this study, CAR-T cells were activated using Phytohemagglutinin (PHA) or anti-CD3&anti-CD28 and were compared in an in vivo CD19+B-cell cancer model in mouse groups. Our results demonstrated that CAR-T cells activated with PHA exhibited higher stability and anti-cancer efficacy compared to those activated with anti-CD3&anti-CD28. Specifically, CAR19BB-T cells activated with PHA exhibited continuous proliferation and long-term persistence without compromising their anti-cancer efficacy. Kaplan-Meier survival analysis revealed prolonged overall survival in the CAR-T cell-treated groups compared to the only tumor group. Furthermore, specific LTR-targeted RT-PCR analysis confirmed the presence of CAR-T cells in the treated groups, with significantly higher levels observed in the CAR19BB-T (PHA) group compared to other groups. Histopathological analysis of spleen, kidney, and liver tissue sections indicated reduced inflammation and improved tissue integrity in the CAR-T cell-treated groups. Our findings highlight the potential benefits of using PHA as a co-stimulatory method for CAR-T cell production, offering a promising strategy to enhance their stability and persistence. These results provide valuable insights for the development of more effective and enduring immunotherapeutic approaches for cancer treatment. CAR-T cells activated with PHA may offer a compelling therapeutic option for advancing cancer immunotherapy in clinical applications., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Bipolar disorder (BD) is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity in the diagnosis of Bipolar disorder (BD) due to its overlapping symptoms with other mood disorders prompted researchers and clinicians to seek new and advanced techniques for the precise detection of Bipolar disorder (BD). One of these methods is the use of advanced machine learning algorithms such as deep learning (DL). However, no study of BD has previously adopted DL techniques using EEG signals. Method: EEG signals of 169 BD patients and 45 controls were cleaned from the artifacts and processed using two different DL methods: a one-dimensional convolutional neural network (1D-CNN) combined with the long-short term memory (LSTM) and a two-dimensional convolutional neural network (2D-CNN). Additionally, Class Activation Maps (CAMs) acquired from the bipolar and control groups were used to obtain distinctive regions to specify a particular class in an image. Results: Group identifications were confirmed with 95.91% overall accuracy through the 2D-CNN method, demonstrating very high sensitivity and lower specificity. Also, the overall accuracy obtained from the 1D-CNN + LSTM method was 93%. We also found that F4, C3, F7, and F8 electrode activities produce predominant features to detect the bipolar group. Conclusion: To our knowledge, this study used EEG-based DL analysis for the first time in BD. Our results suggest that the raw EEG-based DL algorithm can successfully differentiate individuals with BD from controls. Class Activation Map (CAM) analysis suggests that prefrontal changes are predominant in EEG data of patients with BD., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Aim: To summarize the nutritional supplementation on biochemical parameters, cognition, function, Alzheimer's Disease (AD) biomarkers and nutritional status., Materials and Methods: PubMed, Web of Science, Korean Journal Database, Russian Science Citation Index, SciELO Citation Index, Cochrane Library and Scopus databases were searched until 16 April 2021. 22.193 records in total were reached according to inclusion and exclusion criteria. Included Studies were evaluated through the Modified Jadad Scale and gathered under four subheadings., Results: Forty-eight studies with a total of 7009 AD patients were included. Souvenaid, ONS (368 ± 69 kcal), Vegenat-med, 500 mg Resveratrol, ONS (200 mL) were effective nutritional supplements on promoting weight gain and protecting malnutrition status but showed conflicting results in Body mass index, Mid-Upper-Arm Circumference and Triceps Skin Fold Thickness. ONS and a lyophilized whole supplementation Vegenat-med intake made an increase in MNA scores. While all nutritional supplements showed controversial results in biochemical parameters but caused a decrease in Hcy levels which caused reductions in brain Aβ plaque (increase serum Aβ), p-Tau and cognitive improvement. Folic acid and vitamin D decreased serum APP, BACE1, BACE1mRNA. Resveratrol, Hericium erinaceus mycelia, vitamin D and Betaine supplements improved cognitive, functional prognosis and quality of life unlike other nutritional supplements had no effect on cognitive scales., Conclusions: Better designed trials with holistic measures are needed to investigate the effect of nutritional support on the AD biomarkers, cognitive status, biochemical parameters and functional states. Also, more beneficial results can be obtained by examining the simultaneous effects of nutritional supplements with larger sample groups.

Objective: This study aimed to explore pharmacists' counseling for dementia by developing a measurement tool based on the Theory of Planned Behavior., Methods: A survey was applied online to community pharmacists; in total, 190 community pharmacists participated in the study. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and Mann-Whitney U test were performed., Results: A six-factor structure was obtained from the EFA, and the CFA confirmed the structure. These factors explained 71.294% of the total variance. The Cronbach's alpha coefficients for the factors ranged from 0.728 to 0.913. In addition, the pharmacists rated themselves as having positive attitudes and intentions toward counseling for dementia. Differences in these factors were found between the groups based on their participation in vocational training on dementia., Conclusion: The developed measurement tool had a satisfactory construct and convergent and discriminant validities and reliabilities., Practice Implications: The developed measurement tool can be used to investigate pharmacists' self-reported counseling for dementia and can also be applied to the counseling behavior of pharmacists in various health conditions., Competing Interests: Declaration of Competing Interest The authors have no conflict of interest to declare., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Eating disorders have become increasingly prevalent over the years; the age at which they appear has decreased, and they can lead to serious illness or death. Therefore, the number of studies on the matter has increased. Eating disorders like anorexia nervosa (AN) and bulimia nervosa (BN) are affected by many factors including mental illnesses that can have serious physical and psychological consequences. Accordingly, the present study aimed to compare the clinical and metabolic features of patients with AN and BN and identify potential biomarkers for distinguishing between the two disorders., Methods: Clinical data of 41 participants who sought treatment for eating disorders between 2012 and 2022, including 29 AN patients and 12 BN patients, were obtained from NPIstanbul Brain Hospital in Istanbul, Turkey. The study included the clinical variables of both outpatient and inpatient treatments. Principal component analysis (PCA) was utilized to gain insights into differentiating AN and BN patients based on clinical characteristics, while machine learning techniques were applied to identify eating disorders., Findings: The study found that thyroid hormone levels in patients with AN and BN were influenced by non-thyroidal illness syndrome (NTIS), which could be attributed to various factors, including psychiatric disorders, substance abuse, and medication use. Lipid profile comparisons revealed higher triglyceride levels in the BN group ( P <0.05), indicating increased triglyceride synthesis and storage as an energy source. Liver function tests showed lower levels of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in BN patients ( P <0.05), while higher prolactin levels ( P <0.05) suggested an altered hypothalamic-pituitary-gonadal axis. Imbalances in minerals such as calcium and magnesium ( P <0.05) were observed in individuals with eating disorders. PCA effectively differentiated AN and BN patients based on clinical features, and the Naïve Bayes (NB) model showed promising results in identifying eating disorders., Conclusion: The findings of the study provide important insights into AN and BN patients' clinical features and may help guide future research and treatment strategies for these conditions., Competing Interests: Competing Interests The authors have no conflict of interest to declare., (© 2023 Kerman University of Medical Sciences.)

Diagnosis of patients with bipolar disorder may be challenging and delayed in clinical practice. Neuropsychological impairments and brain abnormalities are commonly reported in bipolar disorder (BD); therefore, they can serve as potential biomarkers of the disorder. Rather than relying on these predictors separately, using both structural and neuropsychiatric indicators together could be more informative and increase the accuracy of the automatic disorder classification. Yet, to our information, no Artificial Intelligence (AI) study has used multimodal data using both neuropsychiatric tests and structural brain changes to classify BD. In this study, we first investigated differences in gray matter volumes between patients with bipolar I disorder (n = 37) and healthy controls (n = 27). The results of the verbal and non-verbal memory tests were then compared between the two groups. Finally, we used the artificial neural network (ANN) method to model all the aforementioned values for group classification. Our voxel-based morphometry results demonstrated differences in the left anterior parietal lobule and bilateral insula gray matter volumes, suggesting a reduction of these brain structures in BD. We also observed a decrease in both verbal and non-verbal memory scores of individuals with BD (p < 0.001). The ANN model of neuropsychiatric test scores combined with gray matter volumes has classified the bipolar group with 89.5% accuracy. Our results demonstrate that when bilateral insula volumes are used together with neuropsychological test results the patients with bipolar I disorder and controls could be differentiated with very high accuracy. The findings imply that multimodal data should be used in AI studies as it better represents the multi-componential nature of the condition, thus increasing its diagnosability., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

Rationale: Alcohol and substance use disorders are types of brain diseases that have psychological components which damage many life areas of the affected individual. Since investigating alcohol use alone is insufficient in the diagnostic evaluation process, self-awareness and the individual's long-term psychological well-being are important in the treatment process. Primary prevention is used for preventing disease in healthy people, whereas secondary prevention is used for early diagnosis of people at risk. Tertiary prevention is important to prevent the recurrence of the disease. Since substance use disorders are a chronic problems, a new need has emerged for tertiary protection in rehabilitation standards., Methodology: In this study, we aimed to develop two scales that can provide ideas about rehabilitation standards by determining the awareness of individuals with or without alcohol and substance use disorders. By so, experts in the field can have information about the risk status of their patients in the follow-up process of rehabilitation, with the data obtained from the harm perception and result awareness dimensions in the scales. The sample consisted of 1134 participants, 41 of whom had substance use disorders., Results: Among the two scales developed in the study, the Uskudar Result Awareness Scale (USRAS) consisting of 25 items and 6 factors explained 58.4% of the total variance. The Uskudar Harm Perception Scale (USHPS), consisting of 36 items and 10 factors, explained 56.3% of the total variance. Confirmatory factor analysis of the two scales resulted in acceptable goodness-of-fit values. ( X 2 / df < 3; RMSEA < 0.08; NFI > 0.90; NNFI > 0.95; CFI > 0.95; GFI > 0.90; AGFI > 0.85)., Discussion: Comparisons showed that the resulting awareness of the non-SUD group was moderate (X = 3.81), whereas the SUD group had a low result awareness (X = 3.20); the effect size of the difference between the two groups was found to be high (d = 1.45; >0.8). On the other hand, the harm perception of the non-SUD group was found in the low-risk group (X = 3.78); the harm perception of the SUD group was found in the moderate-risk group (X = 3.43). According to Cohen's d calculations, the effect size of the difference between the two groups is high (d = 1.43; >0.8). It was concluded that both of the scales are valid and safe. They can be included in the treatment process and future studies.

Background and Objective: Globally, gastric carcinoma (Gca) ranks fifth in terms of incidence and third in terms of mortality. Higher serum tumor markers (TMs) than those from healthy individuals, led to TMs clinical application as diagnostic biomarkers for Gca. Actually, there is no accurate blood test to diagnose Gca., Methods: Raman spectroscopy is applied as an efficient, credible, minimally invasive technique to evaluate the serum TMs levels in blood samples. After curative gastrectomy, serum TMs levels are important in predicting the recurrence of gastric cancer, which must be detected early. The experimentally assesed TMs levels using Raman measurements and ELİSA test were used to develop a prediction model based on machine learning techniques. A total of 70 participants diagnosed with gastric cancer after surgery (n = 26) and healthy (n = 44) were comrpised in this study., Results: In the Raman spectra of gastric cancer patients, an additional peak at 1182 cm -1 was observed and, the Raman intensity of amide III, II, I, and CH 2 proteins as well as lipids functional group was higher. Furthermore, Principal Component Analysis (PCA) showed, that it is possible to distinguish between the control and Gca groups using the Raman range between 800 and 1800 cm -1 , as well as between 2700 and 3000 cm -1 . The analysis of Raman spectra dynamics in gastric cancer and healthy patients showed, that the vibrations at 1302 and 1306 cm -1 were characteristic for cancer patients. In addition, the selected machine learning methods showed classification accuracy of more than 95%, while obtaining an AUROC of 0.98. Such results were obtained using Deep Neural Networks and the XGBoost algorithm., Conclusions: The obtained results suggest, that Raman shifts at 1302 and 1306 cm -1 could be spectroscopic markers of gastric cancer., Competing Interests: Declaration of Competing Interest All authors have read the journal's policy on disclosing potential conflicts of interest and declare that there is no conflict of interest. This study does not funded by any profit or non-profit agencies or none of organization has no role in the study conception/design, methods, data analysis or manuscript preparation., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.

Colorectal cancer is the second most common cause of cancer-related deaths worldwide. To follow up on the progression of the disease, tumor markers are commonly used. Here, we report serum analysis based on Raman spectroscopy to provide a rapid cancer diagnosis with tumor markers and two new cell adhesion molecules measured using the ELİSA method. Raman spectra showed higher Raman intensities at 1447 cm -1 1560 cm -1 , 1665 cm -1, and 1769 cm -1 , which originated from CH 2 proteins and lipids, amide II and amide I, and CO lipids vibrations. Furthermore, the correlation test showed, that only the CEA colon cancer marker correlated with the Raman spectra. Importantly, machine learning methods showed, that the accuracy of the Raman method in the detection of colon cancer was around 95 %. Obtained results suggest, that Raman shifts at 1302 cm -1 and 1306 cm -1 can be used as spectroscopy markers of colon cancer., Competing Interests: Declaration of competing interest The authors have no disclosures., (Copyright © 2023. Published by Elsevier Inc.)

Background: Physicians follow-up a symptom-based approach in the diagnosis of psychiatric diseases. According to this approach, a process based on internationally valid diagnostic tools such as The Diagnostic and Statistical Manual of Mental Disorders (DSM) or International Classification of Diseases (ICD), patient reports and the observation and experience of the physician is monitored. As in other fields of medicine, the search for biomarkers that can be used in processes related to diseases continues in psychiatry and various researches are carried out in this field., Objectives: Within the scope of this study, a dataset containing electroencephalogram (EEG) measurements of individuals diagnosed with different psychiatric diseases were analyzed by machine learning methods and the diseases were differentiated/classified with the models obtained. Thus, it was investigated whether EEG data could be a biomarker for psychiatric diseases., Materials and Methods: In the dataset analyzed within the scope of the study, for 550 patients (81 bipolar disorder, 95 attention deficit and hyperactivity disorder - ADHD, 67 depression, 34 obsessive compulsive disorder - OCD, 75 opioid, 146 posttraumatic stress disorder - PTSD, 52 schizophrenia) and 84 healthy individuals, there are 634 samples (rows), 77 variables (columns) in total. 76 of the variables consist of absolute power values belonging to 4 frequency bands (alpha, beta, delta, theta) collected from 19 different electrodes. 80 % of the dataset was used for training the models and 20 % of the data was used for testing the performance of the models. The 5-fold cross validation (CV) method, which repeats 3 times in the training dataset, was used and with this method, the hyperparameters used in the models were also optimized. Different models have been established with the selected hyperparameters and the performance of these models has been tested with the test dataset. C5.0, random forest (RF), support vector machine (SVM) and artificial neural networks (ANN) were used to build the models., Results: Within the scope of the study, the absolute power values obtained from EEG measurements performed using 19 electrodes were analyzed by machine learning methods. It was concluded that classification between disease groups was feasible with a high accuracy (C5.0-0.841, SVM&#95;radial - 0.841, RF - 0.762). It was observed that ADHD, depression and schizophrenia diseases can be differentiated better (F-score = 1, balanced accuracy = 1) once the results were evaluated on a class category basis according to the F- measure and balanced accuracy values., Discussion and Conclusion: Through the medium of the analyzes made within the scope of this study, it was investigated whether EEG data could be used as a biomarker for the detection and diagnosis of psychiatric diseases. The findings obtained from this study revealed that by using EEG data as a biomarker, it can be highly predicted whether a person has a psychiatric disease or not. Once evaluated with broad strokes, it is feasible to assert that it is possible to analyze whether the person who consults a physician with a complaint is ranked among the psychiatric disease class with EEG measurement. When trying to differentiate between numerous and diverse disease categories, it may be claimed that some diseases (ADHD, depression, schizophrenia) can be distinguished better by coming to the fore on a model basis. Considering the findings, it is anticipated that the analyzes obtained as a result of this study will contribute to the studies to be conducted using machine learning in the field of psychiatry., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The development of genetic modification techniques has led to a new era in cancer treatments that have been limited to conventional treatments such as chemotherapy. intensive efforts are being performed to develop cancer-targeted therapies to avoid the elimination of non-cancerous cells. One of the most promising approaches is genetically modified CAR-T cell therapy. The high central memory T cell (Tcm) and stem cell-like memory T cell (Tscm) ratios in the CAR-T cell population increase the effectiveness of immunotherapy. Therefore, it is important to increase the populations of CAR-expressing Tcm and Tscm cells to ensure that CAR-T cells remain long-term and have cytotoxic (anti-tumor) efficacy. In this study, we aimed to improve CAR-T cell therapy's time-dependent efficacy and stability, increasing the survival time and reducing the probability of cancer cell growth. To increase the sub-population of Tcm and Tscm in CAR-T cells, we investigated the production of a long-term stable and efficient cytotoxic CAR-T cell by modifications in the cell activation-dependent production using Phytohemagglutinin (PHA). PHA, a lectin that binds to the membranes of T cells and increases metabolic activity and cell division, is studied to increase the Tcm and Tscm population. Although it is known that PHA significantly increases Tcm cells, B-lymphocyte antigen CD19-specific CAR-T cell expansion, its anti-cancer and memory capacity has not yet been tested compared with aCD3/aCD28-amplified CAR-T cells. Two different types of CARs (aCD19 scFv CD8-(CD28 or 4-1BB)-CD3z-EGFRt)-expressing T cells were generated and their immunogenic phenotype, exhausted phenotype, Tcm-Tscm populations, and cytotoxic activities were determined in this study. The proportion of T cell memory phenotype in the CAR-T cell populations generated by PHA was observed to be higher than that of aCD3/aCD28-amplified CAR-T cells with similar and higher proliferation capacity. Here, we show that PHA provides long-term and efficient CAR-T cell production, suggesting a potential alternative to aCD3/aCD28-amplified CAR-T cells.

Objectives: Immune enhancers are attracting attention day by day. Besides, during the coronavirus disease-2019 (COVID-19) pandemic, there has been an increasing demand for immune enhancers. Pharmacists are seen as trustable providers of complementary and alternative medicines, dietary and herbal supplements, immune-enhancers, and so on. This study aims to prioritization criteria that affect community pharmacists' recommending behavior regarding immune enhancers., Materials and Methods: This paper adopts the analytic hierarchy process (AHP) to rank different criteria substantial for affecting community pharmacists' recommending behavior regarding immune enhancers. In this direction, firstly seven criteria were identified through literature review and views of pharmacists who have community pharmacy experiences. These are; (i) ease of access, (ii) selling price, (iii) package, (iv) content (appropriateness to patient health status), (v) expectation of patient, (vi) quality, and (vii) trust in the manufacturer. Then, a questionnaire including criteria was prepared and delivered to community pharmacists. The data obtained from 93 participants were transferred to the Super Decisions software. The hierarchical structure of the AHP was established and pair-wise comparisons were made., Results: This study showed that the most important criterion was the ease of access (28%). Secondly, pharmacists give importance to the content of the product, while advising immune-enhancers (22%). Besides, it was determined that the least important criterion was the package of the product (4%)., Conclusion: This study will contribute to the literature by facilitating the process of assessing factors that pharmacists pay attention to while recommending immune-enhancing products. Additionally, the present study results will shed light on firms producing such products, to shape their supply chain management strategies, especially for marketing and sales., Competing Interests: Conflict of Interest: No conflict of interest was declared by the authors., (©Turk J Pharm Sci, Published by Galenos Publishing House.)

Objectives: QEEG reflects neuronal activity directly rather than using indirect parameters, such as blood deoxygenation and glucose utilization, as in fMRI and PET. The correlation between QEEG spectral power density and Symptom Check List-90-R may help identify biomarkers pertaining to brain function, associated with affective disorder symptoms. This study aims at determining whether there is a relation between QEEG spectral power density and Symptom Check List-90-R symptom scores in affective disorders., Methods: This study evaluates 363 patients who were referred for the initial application and diagnosed with affective disorders according to DSM-V, with QEEG and Scl-90-R. Spectral power density was calculated for the 18 electrodes representing brain regions., Results: Somatization scores were found to be correlated with Pz and O1 theta, O1 and O2 high beta. Whereas FP1 delta activities were correlated with anxiety, F3, F4, and Pz theta were correlated with obsession scores. Interpersonal sensitivity scores were found to be correlated with F4 delta, P3, T5, P4, T6 alpha and T5, and T6 theta activities. While depression scores were correlated with P3 and T4 delta, as well as T4 theta, there was a correlation between anger and F4, as well as T4 alpha and F8 high beta activities. Paranoia scores are correlated with FP1, F7, T6 and F8 theta, T5 and F8 delta, and O2 high beta activities., Conclusions: According to our results, anxiety, obsession, interpersonal sensitivity, depression, anger, and paranoia are related to some spectral powers of QEEG. Delta-beta coupling seems to be a neural biomarker for affective dysregulation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Kesebir, Yosmaoglu and Tarhan.)

Introduction: The present study aimed to investigate sex differences in response to repetitive transcranial magnetic stimulation (rTMS) in Major Depressive Disorder (MDD) patients. Identifying the factors that mediate treatment response to rTMS in MDD patients can guide clinicians to administer more appropriate, reliable, and personalized interventions., Methods: In this paper, we developed a novel pipeline based on convolutional LSTM-based deep learning (DL) to classify 25 female and 25 male patients based on their rTMS treatment response., Results: Five different classification models were generated, namely pre-/post-rTMS female (model 1), pre-/post-rTMS male (model 2), pre-rTMS female responder versus pre-rTMS female nonresponders (model 3), pre-rTMS male responder vs. pre-rTMS male nonresponder (model 4), and pre-rTMS responder versus nonresponder of both sexes (model 5), achieving 93.3%, 98%, 95.2%, 99.2%, and 96.6% overall test accuracy, respectively., Conclusion: These results indicate the potential of our approach to be used as a response predictor especially regarding sex-specific antidepressant effects of rTMS in MDD patients., (© 2022 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

In previous studies, decreased vitamin B12 and increased plasma homocysteine levels were reported as risk factors for dementia. The aim of this study was to clarify this relationship in earlier ages. Twenty-one healthy middle-aged adults (9 females, 12 males) with a mean age of 46.21 ± 7.99 were retrospectively included in the study. A voxel-based morphometry analysis was performed to measure brain volume. Plasma homocysteine, vitamin B12 levels, verbal and non-verbal memory test performances were recorded. Correlation analyses showed that increased plasma homocysteine was associated with lower memory score. Decreased vitamin B12 level was found to be associated with smaller brain volume in temporal regions. These results suggest that vitamin B12 and plasma homocysteine levels are associated with brain and cognition as early as middle adulthood. Future studies are needed to clarify whether they might be utilized as early hematological biomarkers to predict cognitive decline and neural loss., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

By in vitro fertilization, oocytes can be removed and the embryo can be cultured, and then trans cervically replaced when they reach cleavage or at the blastocyst stage. The characterization of the follicular fluid is important for the treatment process. Women who applied to the Academic Hospital in vitro fertilization (IVF) Center diagnosed with idiopathic female infertility (IFI) were sought in the patient group. Demographics and clinical gonadotropin measurements of the study population were recorded. Of the 116 follicular fluid samples (n=58 male-induced infertility; n=58 control) were analyzed using the FTIR system. To identify FTIR spectral characteristics of follicular fluids associated with an ovarian reserve and reproductive hormone levels from control and IFI, six machine learning methods and multivariate analysis were used. To assess the quantitative information about the total biochemical composition of a follicular fluid across various diagnoses. FTIR spectra showed a higher level of vibrations corresponding to lipids and a lower level of amide vibrations in the IFI group. Furthermore, the T square plot from Partial Last Square (PLS) analysis showed, that these vibrations can be used to distinguish IFI from the control group which was obtained by principal component analysis (PCA). Proteins and lipids play an important role in the development of IFI. The absorption dynamics of FTIR spectra showed wavenumbers with around 100% discrimination probability, which means, that the presented wavenumbers can be used as a spectroscopic marker of IFI. Also, six machine learning methods showed, that classification accuracy for the original set was from 93.75% to 100% depending on the learning algorithm used. These results can inform about IFI women's follicular fluid has biomacromolecular differentiation in their follicular fluid. By using a safe and effective tool for the characterization of changes in follicular fluid during in vitro fertilization, this study builds upon a comprehensive examination of the idiopathic female infertility remodeling process in human studies. We anticipate that this technology will be a valuable adjunct for clinical studies., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Cholangiocarcinoma (CCA) is a type of cancer, which 5-year survival is lower than 20 %, and which is detected mostly in advanced stage of the disease. Unfortunately, there are no diagnostic tools, which could show changes in the body indicating the development of the disease. Therefore, in this study, we investigate Raman spectroscopy as a promising analytical tool in medical diagnostics and as a method, which would allow to distinguish between healthy patients and patients suffering from cholangiocarcinoma. The obtained Raman spectra showed, that lower intensities of peaks corresponding to amino acids and proteins, as well as higher intensities of peaks originating from lipids vibrations were observed in healthy individuals in comparison with cancer patients. Moreover, Partial Last Square (PLS), Principal Component Analysis (PCA) and Hierarchical Component Analysis (HCA) of Raman spectra indicate that the ranges between 800 cm -1 and 1800 cm -1 , 3477 cm -1 -3322 cm -1 and 1394 cm -1 -1297 cm -1 allow to distinguish cancer patients from healthy ones. The obtained results showed, that Raman spectroscopy is a good candidate, to become in future one of the diagnostic tools of Cholangiocarcinoma., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Objective: The aim was to evaluate the effectiveness of Arabin pessary use in patients with cervical insufficiency or short cervix before the 24th week of gestation and the impact of cervical examination findings prior to pessary application on the outcome in terms of the gestational week., Materials and Methods: In our study, among the pregnancies between the 12th and 24th gestational weeks, 60 pregnant women with a preterm delivery history and/or cervical length less than 25 mm were included. Among these 60 patients, 43 of them had a short cervix, 17 of them had cervical insufficiency. Routine medical and obstetric history was obtained. In the vaginal examination, the cervix was evaluated in terms of patency, dilatation, and position. Cervical length, presence of debris, and funneling were evaluated by transvaginal ultrasound. After receiving patients' approval a cervical pessary was applied to patients. Pessaries of pregnant women with 37 weeks of gestation were removed. Before reaching the 37th gestation week, pessaries were withdrawn in patients who had ongoing vaginal bleeding, premature rupture of membranes, and preterm contractions unresponsive to tocolytic treatment., Results: Thirty-one pregnant women (51.7%) out of 60 pregnant women who underwent pessary, delivered at 37 weeks and below. Delivery rates in the short cervical measurement group and cervical insufficiency group at ≤28 weeks, ≤34 weeks and ≤37 weeks were respectively ( n  = 21) 34.8% vs 36.3% ( p  = 0.976), ( n  = 29) 41.8% vs 64.7% ( p  = 0.111), ( n  = 31) 44.2% vs 70.6% ( p  = 0.888). The presence of cervical funneling before pessary application shows a statistically significant difference in terms of patient's giving birth before or after 28 weeks ( p  = 0.033). In patients with cervical funneling, there was a significant increase in a birth before 28 weeks. Depending on whether or not patients applying with pain need for tocolysis, it shows the statistically meaningful difference in terms of the patients giving birth before or after 34 weeks ( p  = 0.001) (OR 7, 61, 95% GA 2.4-24.6). In the group without the need for tocolysis, there is a meaningful increase in birth after 34 weeks., Conclusions: Our findings showed that, alongside the defined cervical risk factors, cervical funneling and the need for tocolysis are remarkable prognostic variables in pessary application.

Herein, we show differences in blood serum of asymptomatic and symptomatic pregnant women infected with COVID-19 and correlate them with laboratory indexes, ATR FTIR and multivariate machine learning methods. We collected the sera of COVID-19 diagnosed pregnant women, in the second trimester (n = 12), third-trimester (n = 7), and second-trimester with severe symptoms (n = 7) compared to the healthy pregnant (n = 11) women, which makes a total of 37 participants. To assign the accuracy of FTIR spectra regions where peak shifts occurred, the Random Forest algorithm, traditional C5.0 single decision tree algorithm and deep neural network approach were used. We verified the correspondence between the FTIR results and the laboratory indexes such as: the count of peripheral blood cells, biochemical parameters, and coagulation indicators of pregnant women. CH 2 scissoring, amide II, amide I vibrations could be used to differentiate the groups. The accuracy calculated by machine learning methods was higher than 90%. We also developed a method based on the dynamics of the absorbance spectra allowing to determine the differences between the spectra of healthy and COVID-19 patients. Laboratory indexes of biochemical parameters associated with COVID-19 validate changes in the total amount of proteins, albumin and lipase., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Endometriomas are typically an advanced form of endometriosis that leads to the formation of scar tissue, adhesions, and an inflammatory reaction. There is no certain serum marker for the diagnosis of endometriosis. This study aims to research the correlation between the amount of peaks corresponding to proteins and lipids with the volume of endometrioma and determine the chemical structure of blood serum collected from women suffering from endometriosis patients with endometrioma and healthy subjects using Fourier Transform Infrared (FTIR) spectroscopy. FTIR spectroscopy is used as a non-invasive diagnostic technique for the discrimination of endometriosis women with endometrioma and control blood sera. The FTIR spectra of 100 serum samples acquired from 50 patients and 50 healthy individuals were used for this study. For this purpose, multivariate analyses such as Principal Component Analysis (PCA), Partial Last Square analysis (PLS) with Variables Importance in Projection (VIP), and probability models, were performed. Our results showed that FTIR range 1500 cm -1 and 1700 cm -1 and around 2700 cm -1 - 3000 cm -1 , regions may be used for the diagnosis of endometriosis. Also, we find that proteins and lipids fraction increase with the volume of endometrioma. Moreover, PLS and VIP analysis suggested that lipids could be helpful in the diagnosis of endometriosis women with endometrioma., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Electroencephalogram (EEG)-based automated depression diagnosis systems have been suggested for early and accurate detection of mood disorders. EEG signals are highly irregular, nonlinear, and nonstationary in nature and are traditionally studied from a linear viewpoint by means of statistical and frequency features. Since, linear metrics present certain limitations and nonlinear methods have proven to be an efficient tool in understanding the complexities of the brain in the identification of underlying behavior of biological signals, such as electrocardiogram, EEG and magnetoencephalogram and thus, can be applied to all nonstationary signals. Various nonlinear algorithms can be used in the analysis of EEG signals. In this research paper, we aim to develop a novel methodology for EEG-based depression diagnosis utilizing 2 advanced computational techniques: frequency-domain extended multivariate autoregressive (eMVAR) and deep learning (DL). We proposed a hybrid method comprising a pretrained ResNet-50 and long-short term memory (LSTM) to capture depression-specific information and compared with a strong conventional machine learning (ML) framework having eMVAR connectivity features. The following 8 causality measures, which interpret the interaction mechanisms among spectrally decomposed oscillations, were used to extract features from multivariate EEG time series: directed coherence (DC), directed transfer function (DTF), partial DC (PDC), generalized PDC (gPDC), extended DC (eDC), delayed DC (dDC), extended PDC (ePDC), and delayed PDC (dPDC). The classification accuracies were 84% with DC, 85% with DTF, 95.3% with PDC, 95.1% with gPDC, 84.8% with eDC, 84.6% with dDC, 84.2% with ePDC, and 95.9% with dPDC for the eMVAR framework. Through a DL framework (ResNet-50 + LSTM), the classification accuracy was achieved as 90.22%. The results demonstrate that our DL methodology is a competitive alternative to the strong feature extraction-based ML methods in depression classification.

The COVID-19 pandemic has accelerated neurological, mental health disorders, and neurocognitive issues. However, there is a lack of inexpensive and efficient brain evaluation and screening systems. As a result, a considerable fraction of patients with neurocognitive or psychobehavioral predicaments either do not get timely diagnosed or fail to receive personalized treatment plans. This is especially true in the elderly populations, wherein only 16% of seniors say they receive regular cognitive evaluations. Therefore, there is a great need for development of an optimized clinical brain screening workflow methodology like what is already in existence for prostate and breast exams. Such a methodology should be designed to facilitate objective early detection and cost-effective treatment of such disorders. In this paper we have reviewed the existing clinical protocols, recent technological advances and suggested reliable clinical workflows for brain screening. Such protocols range from questionnaires and smartphone apps to multi-modality brain mapping and advanced imaging where applicable. To that end, the Society for Brain Mapping and Therapeutics (SBMT) proposes the Brain, Spine and Mental Health Screening (NEUROSCREEN) as a multi-faceted approach. Beside other assessment tools, NEUROSCREEN employs smartphone guided cognitive assessments and quantitative electroencephalography (qEEG) as well as potential genetic testing for cognitive decline risk as inexpensive and effective screening tools to facilitate objective diagnosis, monitor disease progression, and guide personalized treatment interventions. Operationalizing NEUROSCREEN is expected to result in reduced healthcare costs and improving quality of life at national and later, global scales.

Objectives: Colleague solidarity, which emphasizes trust, independent thinking skills, and sharing, enables the problems encountered in the health service delivery to be dealt with effectively. This study aims to identify the current situation regarding colleague solidarity among pharmacists, which is also included in Turkey's pharmacy legislation., Materials and Methods: "Colleague Solidarity Scale Among Nurses" was used in this study conducted with the questionnaire technique. The scale comprises of 23 items and was scored using a 5-point Likert scale. In addition, there were three demographic questions and six questions to get information from participants related to collegial solidarity in the questionnaire., Results: As a result of the exploratory factor analysis (Kaiser-Meyer-Olkin: 0.837), three factors reported 51.029% of the total variance. The t-test indicated a significant difference between gender groups only in the negative opinions about solidarity (NOS) factor (p=0.000). Females exhibited more negative thoughts about solidarity. The ANOVA showed a significant difference in the academic solidarity (AS) factor (p=0.007) among the participants' works in community pharmacies and universities. Pharmacists working in universities had higher means in the AS factor. Moreover, the number of working years made significant differences in the emotional solidarity factor (p=0.000) and NOS factor (p=0.002). Additionally, it was found that the average responses in all factors of the participants who thought that they supported their colleagues in need and that solidarity with their colleagues increased significantly during the COVID-19 pandemic period (p<0.05)., Conclusion: The findings suggest that colleague solidarity among pharmacists should be addressed profoundly as an element specified in legislation and education processes. It is crucial to determine the level of colleague solidarity and improve it using this scale for different practice areas in pharmacy.

Objective: Decorin is a leucine-rich proteoglycan, affects the proliferation, migration, and invasion of extravillous trophoblasts (EVTs). In this study, we aimed to determine the localization of decorin in the implantation site in human tubal ectopic pregnancy, to compare decorin expression levels in ectopic and intrauterine pregnancy, and to investigate the relationship between implantation depth of the tubal wall and expression levels of decorin., Methods: 15 patients underwent salpingectomy for tubal ectopic pregnancy and 15 underwent curettage for voluntary interruption of pregnancy were included. All blocks were stained with decorin immunohistochemical staining. Trophoblastic cells of tubal Stage I-III and tubal epithelial and stromal cells were analyzed in terms of presence and intensity of decorin staining., Results: Decorin was expressed in both tubal and intrauterine trophoblasts, stroma, and surface epithelium during the first trimester of pregnancy. Decorin staining intensity was significantly lower in the villous cytotrophoblasts and syncytiotrophoblasts in tubal ectopic pregnancies, compared to intrauterine pregnancies (p = 0.001 for both). Decorin staining intensity also significantly lower in the extravillous cytotrophoblasts and syncytiotrophoblasts in the tubal ectopic pregnancies (p = 0.002 and p = 0.001, respectively). There was no significant difference in the staining intensity of the trophoblasts and surface epithelial between Stage II and Stage III tubal invasion; however, the decorin expression was lower in the stroma in Stage III (p = 0.094)., Conclusion: Decorin expression is significantly lower in trophoblastic cells of tubal ectopic pregnancies than the intrauterine pregnancies. Although it remains limited to explain the underlying cellular mechanisms, decorin seems to play a role in the development of tubal pregnancy., Competing Interests: Declaration of Competing of Interest The authors declare that they have no conflict of interest., (Copyright © 2021 Elsevier Masson SAS. All rights reserved.)

Objective: To evaluate immunohistochemical (IHC) staining of decorin and vascular endothelial growth factor (VEGF) of ovarian and endometrial tissues in patients with and without endometriosis.  Study Design: Descriptive study., Place and Duration of Study: Department of Obstetrics and Gynecology, ZeynepKamil Training and Research Hospital, Istanbul, Turkey, between Istanbul, TurkeyJanuary 2018 and June 2019., Methodology: Thirty patients, who underwent total abdominal hysterectomy (TAH) + bilateral salpingo-oophorectomy (BSO)/unilateral salpingo-oophorectomy (USO) and were in the proliferative phase of menstrual cycle,were included. The study population consisted of 20 patients (patient group) with an endometriomaand the control group consisted of 10 patients who were operated for benign gynecological pathologies.The ovarian and endometrial tissue specimens were collected from the archives. IHC staining was performed using decorin and VEGF., Results: Decorin analysis showed a significantly higher intensity of staining in both endometrial and ovarian tissues in control group than patient group. Patients with endometriosis had a lower intensity of staining of decorin and a higher intensity of staining of VEGF compared to control group. There was a negative, statistically significant concordance between VEGF and decorin staining of endometrial tissues of both groups (concordance rate -0.560, p=0.001). Therewas a negative, statistically significant concordance between VEGF and decorin staining of ovarian tissues of both groups (concordance rate -0.564, p<0.001)., Conclusion: Angiogenesis plays a critical role in endometriosisand interaction between decorin and VEGF,which suggests that decorin may be a promising molecule for the treatment of endometriosis. Key Words: Decorin, Endometriosis, Immunohistochemical staining, Vascular endothelial Growth factor.

Colon and gastric cancers are the widespread benign types of cancers which are synchronous and metachronous neoplasms. In terms of the progression and progress of the disease, metabolic processes and differentiation in protein structures have an important role in for treatment of the disease. In this study we proposed to investigate the metabolic process and the differentiation of protein secondary structure among colon and gastric cancer as well as healthy controls using biochemistry and Fourier Transform InfraRed spectroscopy (FTIR) methods. For this purpose, we measured blood serum of 133 patients, which were conducted upon oncology department (45 colon cancer, 45 gastric cancer and 43 control individuals). The obtained spectroscopic results and biochemical assays showed significant reduction in the amount of functional groups in cancer groups contrary with total protein measurements and structure of protein differences between colon and gastric cancers. Differentiations were visible in serum levels of CEA, CA-125, CA-15-3, CA-19-9 AFP (Alpha fetoprotein) of gastric and colon cancer patients as well as in amide III and secondly described amide I regions. Our findings suggest that amide I bonds in colon cancer cells can be helpful in diagnosis of colon cancer. Indeed, our results showed that metabolic processes were higher in gastric cancer group than in colon cancer. Hence, FTIR spectroscopy and curve-fitting analysis of amide I profile can be successfully applied as tools for identifying quantitative and qualitative changes of proteins in human cancerous blood serum. However, what is very important, in PCA analysis we see, that the scatter plot of PC1 (variability 80%) and PC2 (variability 15%) show that the data related to the control and two cancer groups are clustered together with different magnitudes and directions., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Diabetic wounds have a slow healing process and easy to be infected. In addition to current drug treatments, supportive approaches are needed for diabetic wound treatment. In this study, we aimed to load Aloe Vera (AV) and Hypericum perforatum oil (HPO) with PCL/Ge (Poly (ɛ-caprolactone)/Gelatine) polymeric biodegradable by electrospinning method into nanofiber dressings on an experimental diabetic wound model to compare the diabetic wound healing effect. Changes in the amount and chemical structure of phospholipids, proteins, and lipids were investigated in the blood and serum samples of the animals using Fourier transform infrared (FTIR) analysis. To evaluate biological events associated with the wound repair process in inflammatory phase we used oxidant and antioxidant status to determine the healing status of wounds such as Total antioxidant status (TAS), Total oxidant level (TOS) and tumor necrosis factor alpha (TNF-α) levels. TOS level increased in DM groups and decreased in the AV and HPO group. Oxidative stress index decreased and TNF-α level increased in the HPO group. FTIR spectra showed changes in the phospholipids, proteins, and carbon chain of lipids in the whole blood as well as serum of DM rats. FTIR spectra combined with Principal component analysis (PCA) showed, that treated DM rats by AV and HPO caused return chemical structure of blood and serum to this observed in control group. Higher similarity with control group for HPO rats was observed. HPO is better than AV in the alternative for healing on diabetic wound. Thus, we have demonstrated that IR spectroscopy and multivariate data analysis and biochemical assays are consistent and correlative with each other., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Objective . Psychogenic nonepileptic seizures (PNES), is one of the clinical manifestations of conversion disorder that epileptiform discharges do not accompany. Factors capable of increasing susceptibility to these seizures have not been adequately investigated yet. This study aims to investigate the quantitative electroencephalography (QEEG) findings for PNES by evaluating the resting EEG spectral power changes during the periods between seizures. Methods . Thirty-nine patients (29 females, 10 males) diagnosed with PNES (group 1) and 47 patients (23 females, 24 males) without any psychiatric diagnosis (group 2) were included in the study. The patients underwent a psychiatric examination at their first visit, were diagnosed and their EEGs were recorded. Using fast Fourier transformation (FFT), spectral power analysis was calculated for delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (15-30 Hz), high-beta (25-30 Hz), gamma-1 (31-40 Hz), gamma-2 (41-50 Hz), and gamma (30-80 Hz) frequency bands. Results . Six separate EEG band power, namely (C3-high beta, C3-gamma, C3-gamma-1, C3-gamma-2, P3-gamma, P3 gamma-1), were found to be higher in the patients diagnosed with PNES than in the control group. Conclusion . Our findings show that PNES correlate with high-frequency oscillations on central motor and somatosensory cortices.

In the current study, promising glass composites based on vanadium pentoxide (V 2 O 5 )-doped zinc borate (ZnB) were investigated in terms of their nuclear-radiation-shielding dynamics. The mass and linear attenuation coefficient, half-value layer, mean free path, tenth-value layer, effective atomic number, exposure-buildup factor, and energy-absorption-buildup factor were deeply simulated by using MCNPX code, Phy-X PSD code, and WinXcom to study the validation of ZBV1, ZBV2, ZBV3, and ZBV4 based on (100-x)(0.6ZnO-0.4B 2 O 3 )(x)(V 2 O 5 ) (x = 1, 2, 3, 4 mol%) samples against ionizing radiation. The results showed that attenuation competencies of the studied glasses slightly changed while increasing the V 2 O 5 content from 1 mol% to 4 mol%. The domination of ZnO concentration in the composition compared to B 2 O 3 makes ZnO substitution with V 2 O 5 more dominant, leading to a decrease in density. Since density has a significant role in the attenuation of gamma rays, a negative effect was observed. It can be concluded that the aforementioned substitution can negatively affect the shielding competencies of studied glasses.

Depression is a very common disease with increasing incidence resulting from complex interactions of genetic, environmental, and immunological processes. To this day, the etiopathogenesis and treatment of depression unfortunately seem to be stuck in the synaptic gap. Despite highly potent antidepressants, the treatment rate cannot reach 100%, the treatment resistant group cannot be eliminated, and relapse cannot be prevented. These problems lead researchers to further and different research to understand and treat psychopathology. Immune dysfunction and neuroinflammation have been one of the main issues that psychiatry has focused on in recent years and helps us to understand depression. Extraneuronal components of all neuropsychiatric disorders, especially depression, have begun to be revealed in detail thanks to a better understanding of the immune system and an increase in experimental and technological possibilities. There is increasing evidence of a causal relationship between the etiopathogenesis of major depression and low-level chronic neuroinflammation. In this article, the role of neuroinflammation in the etiopathogenesis of depression and the possibilities of vaccination and immunotherapy are discussed.

The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.

Neurological disorders significantly impact the world's economy due to their often chronic and life-threatening nature afflicting individuals which, in turn, creates a global disease burden. The Group of Twenty (G20) member nations, which represent the largest economies globally, should come together to formulate a plan on how to overcome this burden. The Neuroscience-20 (N20) initiative of the Society for Brain Mapping and Therapeutics (SBMT) is at the vanguard of this global collaboration to comprehensively raise awareness about brain, spine, and mental disorders worldwide. This paper aims to provide a comprehensive review of the various brain initiatives worldwide and highlight the need for cooperation and recommend ways to bring down costs associated with the discovery and treatment of neurological disorders. Our systematic search revealed that the cost of neurological and psychiatric disorders to the world economy by 2030 is roughly $16T. The cost to the economy of the United States is $1.5T annually and growing given the impact of COVID-19. We also discovered there is a shortfall of effective collaboration between nations and a lack of resources in developing countries. Current statistical analyses on the cost of neurological disorders to the world economy strongly suggest that there is a great need for investment in neurotechnology and innovation or fast-tracking therapeutics and diagnostics to curb these costs. During the current COVID-19 pandemic, SBMT, through this paper, intends to showcase the importance of worldwide collaborations to reduce the population's economic and health burden, specifically regarding neurological/brain, spine, and mental disorders.

Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response-based filtering process was employed rather than traditional time and frequency domain methods. Finite impulse response subbands were analyzed further to obtain feature vectors of different entropy markers and these features were fed into a classifier namely multilayer perceptron. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies. The results also indicate that entropy estimators can distinguish normal and opioid use disorder subjects. EEG data and theta frequency band have distinctive capability for almost all types of entropies while nonextensive Tsallis entropy outperforms compared with other types of entropies.

Objective: This study aimed to identify an Electroencephalography (EEG) complexity biomarker that could predict treatment resistance in Obsessive compulsive disorder (OCD) patients. Additionally, the statistical differences between EEG complexity values in treatment-resistant and treatment-responsive patients were determined. Moreover, the existence of correlations between EEG complexity and Yale-Brown Obsessive Compulsive Scale (YBOCS) score were evaluated., Methods: EEG data for 29 treatment-resistant and 28 treatment-responsive OCD patients were retrospectively evaluated. Approximate entropy (ApEn) method was used to extract the EEG complexity from both whole EEG data and filtered EEG data, according to 4 common frequency bands, namely delta, theta, alpha, and beta. The random forests method was used to classify ApEn complexity., Results: ApEn complexity extracted from beta band EEG segments discriminated treatment-responsive and treatment-resistant OCD patients with an accuracy of 89.66% (sensitivity: 89.44%; specificity: 90.64%). Beta band EEG complexity was lower in the treatment-resistant patients and the severity of OCD, as measured by YBOCS score, was inversely correlated with complexity values., Conclusions: The results indicate that, EEG complexity could be considered a biomarker for predicting treatment response in OCD patients., Significance: The prediction of treatment response in OCD patients might help clinicians devise and administer individualized treatment plans., (Copyright © 2020 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Gestational diabetes mellitus (GDM) is a common complication during pregnancy. Evaluation of the quantitative physical activity in diabetic pregnant women is critical. The aim of this study was to test the reliability and validity of the Pregnancy Physical Activity Questionnaire (PPAQ) in Turkish patients with GDM. A total of 120 pregnant women between the ages of 18 and 44 years with GDM were included. The reliability of the questionnaire was measured by internal consistency and analysis of 2-week test-retest reliability. Of the patients, 74 completed the test-retest procedure. Concurrent validity was examined by comparing the PPAQ with the Short Form of the International Physical Activity Questionnaire (IPAQ) in 36 patients. Test-retest intraclass correlation coefficient scores varied between 0.72 and 0.95. The Spearman rank correlation analysis showed that the PPAQ total activity values were statistically significantly correlated with the total values of IPAQ-Short Form ( r  = 0.410 and p  = .030). In conclusion, the Turkish version of the PPAQ is a valid and reliable tool for the measurement of the physical activity level of pregnant women with GDM.Impact statement What is already know n on this subject? The pregnancy physical activity questionnaire (PPAQ) developed in 2004 by Chasan-Taber et al.; is a simple and short questionnaire measuring the frequency, duration, and intensity of physical activity in pregnant women. To date, this questionnaire has been translated into many languages and has been used in a number of studies. What do the results of this study add? The aim of this study was to test the reliability and validity of the PPAQ in Turkish pregnant women with GDM. On the basis of our study results, we suggest that the Turkish version of the PPAQ is a valid and reliable tool for the measurement of the physical activity level of pregnant women with GDM. What are the implications of these findings for clinical practice and/or further research? Evaluation of the quantitative physical activity in diabetic pregnant women may contribute to gain a better understanding of the role of physical activity during treatment and may be useful to compare the results of different studies carried out in different places more effectively.

Objectives . Certain studies have claimed that borderline personality disorder (BPD) could be evaluated as a subtype of bipolar disorder (BD), whereas others have argued that BPD should be regarded as an independent disorder because of its distinct clinical features. The aim of this study was to investigate if there was a difference between these 2 disorders biologically based on EEG recordings. Methods . A total of 111 subjects (11 healthy, 25 BPD, 75 BD) who had resting EEG recordings were included. The EEGs were analyzed to compute absolute power values. Results . One-way analysis of variance results revealed statistically significant differences among the 3 groups on 55 out of 229 EEG variables. However, post hoc analysis indicated that all of the significant changes were between healthy and patient groups and no significant differences were found between 2 clinical groups. Conclusion . The findings suggested that these 2 clinical entities are biologically similar; however, further research should be performed to explain the basis clinical differences between the 2 disorders.

Event-related mental task information collected from electroencephalography (EEG) signals, which are functionally related to different brain areas, possesses complex and non-stationary signal features. It is essential to be able to classify mental task information through the use in brain-computer interface (BCI) applications. This paper proposes a wavelet packet transform (WPT) technique merged with a specific entropy biomarker as a feature extraction tool to classify six mental tasks. First, the data were collected from a healthy control group and the multi-signal information comprised six mental tasks which were decomposed into a number of subspaces spread over a wide frequency spectrum by projecting six different wavelet basis functions. Later, the decomposed subspaces were subjected to three entropy-type statistical measure functions to extract the feature vectors for each mental task to be fed into a backpropagation time-recurrent neural network (BPTT-RNN) model. Cross-validated classification results demonstrated that the model could classify with 85% accuracy through a discrete Meyer basis function coupled with a Renyi entropy biomarker. The classifier model was finally tested in the Simulink platform to demonstrate the Fourier series representation of periodic signals by tracking the harmonic pattern. In order to boost the model performance, ant colony optimization (ACO)-based feature selection method was employed. The overall accuracy increased to 88.98%. The results underlined that the WPT combined with an entropy uncertainty measure methodology is both effective and versatile to discriminate the features of the signal localized in a time-frequency domain.

Logistic regression (LR) and artificial neural networks (ANNs) are widely referred approaches in medical data classification studies. LR, a statistical fitting model, is suggested in medical problems because of its well-established methodology and coefficients contributing to the evaluation of clinical interpretations. ANNs are graphical models structured with node networks interconnected with arcs each of which is expressed in terms of weights discovered throughout the modeling process. Since ANNs have a complex structure with its layers and nodes in the layers, which provides ANNs the ability to model any data with complex relationships. Among the various models having origins in statistics and computer science, LR and ANNs have prevailed in the area of mass medical data classification. In this study, we introduce the 2 aforementioned approaches in order to generate a model dichotomizing 75 opioid-dependent patients and 59 control subjects from each other. Quantitative electroencephalography (QEEG) absolute power value of each electrode were calculated for 4 consecutive frequency bands namely delta, theta, alpha, and beta with the frequencies, 0.5 to 4, 4 to 8, 8 to 12, and 12 to 20 Hz, respectively. Significant independent variables contributing to the classification were underlined in LR while a feature selection (FS) method, genetic algorithm, is being applied to the ANN model to reveal more informative features. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores, and Gini coefficient. Although ANN-based classifier outperformed compared with LR, both models performed satisfactorily for absolute power measure in beta frequency band. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies.

Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder and is characterized by symptoms of inattention and/or hyperactivity and impulsivity. In the current study, we obtained quantitative EEG (QEEG) recordings of 51 children aged between 6 and 12 years before the initiation of methylphenidate treatment. The relationship between changes in the scores of ADHD symptoms and initial QEEG features (power/power ratios values) were assessed. In addition, the children were classified as responder and nonresponder according to the ratio of their response to the medication (>25% improvement after medication). Logistic regression analyses were performed to analyze the accuracy of QEEG features for predicting responders. The findings indicate that patients with increased delta power at F8, theta power at Fz, F4, C3, Cz, T5, and gamma power at T6 and decreased beta powers at F8 and P3 showed more improvement in ADHD hyperactivity symptoms. In addition, increased delta/beta power ratio at F8 and theta/beta power ratio at F8, F3, Fz, F4, C3, Cz, P3, and T5 showed negative correlations with Conners' score difference of hyperactivity as well. This means, those with greater theta/beta and delta/beta powers showed more improvement in hyperactivity following medication. Theta power at Cz and T5 and theta/beta power ratios at C3, Cz, and T5 have significantly classified responders and nonresponders according to the logistic binary regression analysis. The results show that slow and fast oscillations may have predictive value for treatment response in ADHD. Future studies should seek for more sensitive biomarkers.

Background: Quantitative EEG (qEEG) analysis can be used to evaluate brain correlates of human psychology in all aspects. As the gamma oscillations of qEEG rhythms are related to depression, and particularly to treatment resistance, they may also be related to suicidality., Aim: The present study aimed to identify the neural correlates of suicidal ideation and suicide attempt in depression using qEEG, based on the hypothesis that gamma rhythm in patients with depression would be higher in patients with suicidal ideation and suicide attempt., Method: qEEG were recorded in 533 participants (276 female). Groups were divided into the following: Non-suicidal (n = 218), Suicide Ideation (n = 211), Suicide Attempt (n = 74), and control (n = 30)., Results: High-gamma power at the F4, Fz, C4, Cz, O2, F8, T5 and T6 regions was significantly higher in the Suicide Ideation than the other groups., Conclusion: If confirmed by further studies, high-gamma rhythm has the potential to be used as a biomarker for screening suicidality., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Objective: The aim of this study was to investigate whether differentiation of delta-beta cross frequency coupling (CFC) in bipolar II depressive episode with mixed features and consecutive remission was observed., Methods: 8 patient diagnosed bipolar disorder type II, depressive episode with mixed features according to DSM-V, were examined during the current episode, at baseline and end of the third week of lamotrigine use. Hamilton Depression Rating Scale (HDRS) and Mood Disorder Questionnaire (MDQ) were applied to each case at the baseline and end of the third week and QEEG was performed. Temperament was evaluated with TEMPS-A., Results: dPAC-DB and AAC-DB were significantly higher in FP2 at the end of 3rd weeks of lamotrigine use. A linear relationship was found between F3 dPAC-DB and MDQ scores, before and after treatment. P3 dPAC-DB was correlated with HDRS scores initially only. Cyclothymic temperament scores was found related with dPAC-DB and AAC-DB in F4., Conclusion: In bipolar disorder, delta-beta CFC was first investigated in this study. CFCs may have a neural projection of the physiopathology of bipolar disorder. At the point we reached in this study, we can show it as both state and trait.