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Gokben Hizli Sayar, Eylem Ozten, Oguz Tan, Nevzat Tarhan Uskudar University, Neuropsychiatry Istanbul Hospital, Department of Psychiatry, Istanbul, Turkey Purpose: The aim of the study reported here was to examine the safety and effectiveness of high-frequency repetitive transcranial magnetic stimulation (rTMS) in elderly patients with depression. Patients and methods: Sixty-five depressed elderly patients received rTMS over their left prefrontal cortex for 6 days per week, from Monday to Saturday, for 3 weeks. The rTMS intensity was set at 100% of the motor threshold and 25 Hz stimulation with a duration of 2 seconds and was delivered 20 times at 30-second intervals. A full course comprised an average of 1000 magnetic pulses. Depression was rated using the Hamilton Depression Rating Scale (HAMD) before and after treatment. Response was defined as a 50% reduction in HAMD score. Patients with HAMD scores < 8 were considered to be in remission. Results: The mean HAMD score for the study group decreased from 21.94 ± 5.12 before treatment to 11.28 ± 4.56 after rTMS (P < 0.001). Following the treatment period, 58.46% of the study group demonstrated significant mood improvement, as indexed by a reduction of more than 50% on the HAMD score. Nineteen of these 38 patients attained remission (HAMD score < 8), while 41.54% of all study patients achieved a partial response. None of the patients had a worsened HAMD score at the end of the treatment. Treatment was generally well tolerated and no serious adverse effects were reported. Conclusion: In this study, rTMS was found to be a safe, well-tolerated treatment, and a useful adjunctive treatment to medications in elderly treatment-resistant depressed patients. This study contributes to the existing evidence on the antidepressant effect of rTMS in the treatment of depression in patients over 60 years of age. Keywords: high-frequency repetitive TMS, rTMS, Hamilton Depression Rating Scale

Our motivation for estimating a demand system for gasoline and cars is its strategic relevance to policy objectives such as pollution control: if demand is responsive to pricing, demand reductions for polluting goods will provide an important share of the pollution reductions; otherwise, cleaner technologies will have to do most of the job. We estimate a model of gasoline demand and car ownership in Mexico, using a panel of annual observations by state. Key features that we introduce include instrumental variables on differenced data and the treatment of possible dynamics, measurement errors in the data, and unobserved individual state characteristics. We use tests of serial correlation in the residuals to model the dynamics properly. The resulting demand system is quite responsive to pricing even in the short term (-0.6 for the own-price elasticity of gasoline), but we emphasize a medium- to long-term perspective of 5-10 years as most relevant for policy. Five- to ten-year elasticity estimates are in the range of -1.25 to -1.13. Applying these elasticity estimates to data on pollution control options for the vehicle fleet in Mexico City, the costs of reaching a target for pollution reductions would be 45% more expensive if one were not willing to use a demand management instrument such a gasoline tax in the control program. JEL classification: C33; H3; Q25; Q41; R41 Keywords: Demand estimation; Dynamic panel data; Pollution; Gasoline taxes

This study aimed to perform an extensive characterization of a 74.75TeO2–0.25V2O5–(25 − x)B2O3-xNd2O3 glass system with (x = 0, 0.5, 1.0, and 1.5 mol%) for radiation shielding properties. Linear and mass attenuation coefficients were determined using Phy-X PSD software and compared with the simulation using Monte Carlo software MCNPX (version 2.7.0). Half value layer, mean free path, tenth value layer, effective atomic number, exposure buildup factor, and energy absorption buildup factors of VTBNd0.0, VTBNd0.5, VTBNd1.0, and VTBNd1.5 glasses were determined, respectively. The results showed that boron (III) oxide and neodymium (III) oxide substitution has an obvious impact on the gamma ray attenuation properties of the studied glasses. It can be concluded that the VTBNd1.5 sample with the highest content of neodymium (III) oxide (1.5 mol%) is the superior sample for shielding of gamma radiation in the investigated energy range. [ABSTRACT FROM AUTHOR]

We present a 10-month-old child with central pontine myelinolysis (CPM) secondary to chronic active hepatitis due to cytomegalovirus (CMV) infection. A total of 35 paediatric cases of pontine and/or extrapontine myelinolysis are reported and, to our knowledge, CPM secondary to CMV hepatitis in an infant has not been previously reported. The MRI findings are highlighted. [ABSTRACT FROM AUTHOR]

Objectives: To determine the chemical composition of pure and mixed urinary calculi by multislice helical computed tomography (CT) in an in vitro setting. CT is becoming the preferred radiologic examination in diagnosis and management of calculus disease. The management of calculus disease can be facilitated by ascertaining the stone composition. Unnecessary shock wave treatment could be avoided if the fragility of the stones could be predicted at diagnosis. Methods: A total of 107 stones (86 pure and 21 mixed) were measured in a multislice helical scanner within an air-filled environment. The scans were performed at two energy levels, 80 kV and 120 kV, with a 1-mm slice thickness. The chemical compositions of the urinary stones were assessed on the basis of the differences in the densities measured in Hounsfield units. The stones were assigned to six different groups according to the density measured in Hounsfield units: uric acid, struvite, cystine, calcium phosphate, calcium oxalate monohydrate, and calcium oxalate dihydrate. Results: The differences in the density measured by multislice helical CT at 120 kV for the six groups of pure stones were statistically significant. The densities of the different regions within the mixed stones were also significantly different statistically from each other. The densities of the different regions measured in the mixed stones were not different from the densities of the corresponding pure stones. This finding supports the idea that different chemical compositions within a stone can be identified by their densities measured by multislice helical CT. Conclusions: The chemical compositions of both pure and mixed stones can be determined by multislice helical CT in an in vitro setting. The feasibility of in vivo determinations remains to be clarified. [ABSTRACT FROM AUTHOR]

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.)