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The primary aim of this work was to determine the effects of production parameters, such as wood species and timber strength classes, on some mechanical properties of cross-laminated timber (CLT) panels using artificial neural network (ANN) prediction models. Subsequently, using the models obtained from the analyses, the goal was to identify the optimum layer combinations of timber strength classes used in the middle and outer layers that would provide the highest mechanical properties for CLT panels. CLT panels made from spruce and alder timbers, as well as hybrid panels created from combinations of these two wood species, were produced. The strength classes of the timbers were determined non-destructively according to the TS EN 338 (2016) standard using an acoustic testing device. The bending strength and modulus of elasticity values of the CLT panels were determined destructively according to the TS EN 408 (2019) standard. According to ANN results, the optimum timber strength classes and layer combinations were determined for bending strength as C24-C27-C24 for spruce CLT, D18-D24-D18 for alder CLT, C30-D40-C30 and D18-C30-D18 for hybrid panels; and for modulus of elasticity, C22-C27-C22 for spruce, D35-D30-D35 for alder, C16-D24-C16, and D24-C24-D24 for hybrid panels.

Shear walls in lightweight carcass timber construction systems; The skeleton system, in which solid material is generally used, consists of covering or covering elements and fasteners. As a covering element, plywood is used because it resists the horizontal deformation of the structure, such as earthquake and wind, and prevents it from collapsing. There are many factors that affect the structural properties of the curtain wall and the technological properties of plywood. Factors such as wood type, board thicknesses, fiber direction, defects, type of fasteners, location and distances between them are effective on the durability, strength and amount of horizontal deformation of the shear wall. The combination of these factors determines the overall performance of the shear wall and the amount of horizontal deformation. In this study, it was aimed to determine the amount of horizontal deformation in shear walls under lateral load. The study examines the impact of structural and technological factors on plywood shear walls made of black pine and spruce wood species. The research evaluates a number of variables such as wood type (black pine and spruce), plywood board thicknesses, fiber orientation (perpendicular and parallel to the fibers), imperfections, fastener size (6d and 8d). Insulation materials used in shear walls (such as Extruded Polystyrene (XPS), Expanded Polystyrene (EPS)) were also taken into consideration and the amount of horizontal deformation under lateral load was. The results of the research showed that shear walls covered with black pine plywood generally undergo more horizontal deformation than spruce, and shear walls produced in parallel with the fibers generally have higher horizontal deformation values.

PURPOSEThis study aimed to evaluate the potential of machine learning-based models for predicting carcinogenic human papillomavirus (HPV) oncogene types using radiomics features from magnetic resonance imaging (MRI).METHODSPre-treatment MRI images of patients with cervical cancer were collected retrospectively. An HPV DNA oncogene analysis was performed based on cervical biopsy specimens. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1) and T2-weighted images (T2WI). A third feature subset was created as a combined group by concatenating the CE-T1 and T2WI subsets. Feature selection was performed using Pearson’s correlation coefficient and wrapper- based sequential-feature selection. Two models were built with each feature subset, using support vector machine (SVM) and logistic regression (LR) classifiers. The models were validated using a five-fold cross-validation technique and compared using Wilcoxon’s signed rank and Friedman’s tests.RESULTSForty-one patients were enrolled in the study (26 were positive for carcinogenic HPV oncogenes, and 15 were negative). A total of 851 features were extracted from each imaging sequence. After feature selection, 5, 17, and 20 features remained in the CE-T1, T2WI, and combined groups, respectively. The SVM models showed 83%, 95%, and 95% accuracy scores, and the LR models revealed 83%, 81%, and 92.5% accuracy scores in the CE-T1, T2WI, and combined groups, respectively. The SVM algorithm performed better than the LR algorithm in the T2WI feature subset (P = 0.005), and the feature sets in the T2WI and the combined group performed better than CE-T1 in the SVM model (P = 0.033 and 0.006, respectively). The combined group feature subset performed better than T2WI in the LR model (P = 0.023).CONCLUSIONMachine learning-based radiomics models based on pre-treatment MRI can detect carcinogenic HPV status with discriminative accuracy.

INTRODUCTION: The inflammatory system has recently received attention in the pathophysiology of Bipolar disorder(BD). Mean platelet volume(MPV), platelet distribution width(PDW), and red cell distribution width(RDW) are low-cost hematological biomarkers used to assess the level of inflammation. MPV, PDW, and RDW values in different BD periods were investigated in this study. METHODS: This retrospective study included 108 BD patients(76 manic and 32 depressive) who were hospitalized in the psychiatry clinic of the Faculty of Medicine. MPV, PDW and RDW values at various stages of BP were compared with the healthy control group. RESULTS: Before treatment, PDW values in BD in mania and depression were lower than in the healthy control group(medians: 15.30, 15.50, and 16.20,p

Background: In basketball, agility is essential, characterized by the ability to change direction swiftly and accelerate. Traditional tests like the Illinois Agility Test (IAT) and the Reactive Agility Test (RAT) may not fully capture the agility demands specific to basketball. Purpose: This study aimed to introduce the Change of Direction and Acceleration Test (CODAT), designed specifically for young basketball players. It evaluates CODAT’s effectiveness by comparing it with IAT and RAT through comprehensive analysis. Methods: We assessed 87 pre-adolescent male basketball players, aged 9 to 13 years, with an average biological age of 11.2 years and an average estimated Peak Height Velocity (PHV) of 12.5 ± 0.5 years, using CODAT, IAT, and RAT. We employed regression analysis and the Bland–Altman method to determine CODAT’s reliability and validity. Results: The findings indicate that CODAT offers superior reliability and validity in measuring basketball-specific agility. Consistent scores highlight its potential as an effective tool for agility assessment in basketball training and talent identification. Conclusions: CODAT represents a significant advancement in agility assessment for young basketball players, advocating for its integration into sports science practices to better address the specialized demands of basketball agility.

Objectives: To evaluate the performances of machine learning using semantic and radiomic features from magnetic resonance imaging data to distinguish cystic pituitary adenomas (CPA) from Rathke’s cleft cysts (RCCs). Materials and Methods: The study involved 65 patients diagnosed with either CPA or RCCs. Multiple observers independently assessed the semantic features of the tumors on the magnetic resonance images. Radiomics features were extracted from T2-weighted, T1-weighted, and T1-contrast-enhanced images. Machine learning models, including Support Vector Machines (SVM), Logistic Regression (LR), and Light Gradient Boosting (LGB), were then trained and validated using semantic features only and a combination of semantic and radiomic features. Statistical analyses were carried out to compare the performance of these various models. Results: Machine learning models that combined semantic and radiomic features achieved higher levels of accuracy than models with semantic features only. Models with combined semantic and T2-weighted radiomics features achieved the highest test accuracies (93.8%, 92.3%, and 90.8% for LR, SVM, and LGB, respectively). The SVM model combined semantic features with T2-weighted radiomics features had statistically significantly better performance than semantic features only (p = 0.019). Conclusion: Our study demonstrates the significant potential of machine learning for differentiating CPA from RCCs.

Background: Bipolar disorder is a chronic psychiatric disorder with depression and manic episodes. It is one of the leading causes of disease-related disability worldwide. Despite the presence of various alternative drug options for bipolar disorder, some patients do not adequately benefit from the treatment. Therefore, possible underlying mechanisms need to be clarified. Recently, studies on the relationship between bipolar disorder and vitamin D (Vit D) have attracted attention. Although many studies have found an association between depression and Vit D deficiency, little is known about the relationship between manic episodes and Vit D. The aim of this study was to compare Vit D and related metabolites of bipolar manic episodes prior to treatment, bipolar remission after treatment, and healthy control groups. Methods: This case–control study consisted of 34 bipolar manic episode patients and 34 healthy controls. Disease activity was evaluated with the Hamilton Depression Rating Scale (HAM-D) and Young Mania Rating Scale (YMRS). Firstly, serum 25-hydroxy vitamin D (25-OHD), calcium (Ca) and phosphorus (P) levels of patients in the bipolar manic episode were measured and compared with healthy control. Secondly, serum 25-OHD, Ca and P levels in the euthymic periods of the same patients were measured and compared with healthy control. Results: Bipolar manic episode Vit D levels were lower when compared to healthy controls; while there was no difference in terms of Ca and P levels. There was no significant difference between the bipolar euthymic period patients and the healthy control group in terms of 25-OHD, Ca and P levels. Conclusion: Our results demonstrated low serum Vit D concentrations in the acute manic episode of bipolar disorder. Decreased Vit D level may play a role in the onset of the manic episode, or malnutrition and insufficient sunlight during the manic episode may have caused Vit D deficiency. Future studies are needed to exclude potential confounding factors and to compare all mood episodes.

Purpose: This study aims to evaluate the potential of machine learning algorithms built with radiomics features from computed tomography urography (CTU) images that classify RB1 gene mutation status in bladder cancer. Method: The study enrolled CTU images of 18 patients with and 54 without RB1 mutation from a public database. Image and data preprocessing were performed after data augmentation. Feature selection steps were consisted of filter and wrapper methods. Pearson’s correlation analysis was the filter, and a wrapper-based sequential feature selection algorithm was the wrapper. Models with XGBoost, Random Forest (RF), and k-Nearest Neighbors (kNN) algorithms were developed. Performance metrics of the models were calculated. Models’ performances were compared by using Friedman’s test. Results: 8 features were selected from 851 total extracted features. Accuracy, sensitivity, specificity, precision, recall, F1 measure and AUC were 84%, 80%, 88%, 86%, 80%, 0.83 and 0.84, for XGBoost; 72%, 80%, 65%, 67%, 80%, 0.73 and 0.72 for RF; 66%, 53%, 76%, 67%, 53%, 0.60 and 0.65 for kNN, respectively. XGBoost model had outperformed kNN model in Friedman’s test (p = 0.006). Conclusions: Machine learning algorithms with radiomics features from CTU images show promising results in classifying bladder cancer by RB1 mutation status non-invasively.

PurposeThe aim of this study is to determine the presence and evaluate the features of potential predatory journals in the radiology field.MethodsThe presence of the keywords related to radiology listed in the name of journals was investigated in Beall’s list. We have searched and recorded the features and the information of the included journals listed under the following headings: address and location, publishing features, editorial board, indexing features, submission, and peer-review processes.ResultsA total of 66 radiology journals from 27 publishers were identified from the updated version of the original Beall's list. Regarding the publishers, 33 journals (50%) reported an address in the United States of America, while others were from United Kingdom, India, Hong Kong, Iran, and Canada. While 44 journals' (67%) website reported a contact address, no addresses were declared in the website of 21 journals (32%). The median time of publication activity was 3.5 years (interquartile range [IQR], 1–5 years; range, 0–16 years). Thirty-five journals (53%) indicated their publication ethics policy on the website. Forty-seven (71%) journals reported a regular editorial board (EB) list. The competency of the EB was considered as "inappropriate" in 27 (41%) journals. Only 18% of the total number of EB members had affiliations related to radiology (n=286/1566). Forty journals (61%) did not report any indexing and database coverage. We found 26 journals (39%) which had a DOI number in its latest 5 articles. Fifty-nine (89%) journals clearly reported article processing change (APC) on the webpage. The median APC value was 641.43 USD (IQR, 300–918.75 USD; range, 100–2588 USD). Considering the latest 5 articles, the number of journals with radiologic images in all of the articles was 8 (12%). Mean peer-review time was 63.5 days (IQR, 21.75–87.5 days; range, 1–237 days) for the journals which indicated the submission and acceptance dates clearly.ConclusionWe demonstrated the several main characteristics of potential predatory journals in the radiology field such as reliability of the reported address, APC, publication frequencies, indexing features, features of published article and peer-review time which were all found to be similar to the characteristics of potential predatory journals in other biomedical fields.

Brain MR images are the most suitable method for detecting chronic nerve diseases such as brain tumors, strokes, dementia, and multiple sclerosis. They are also used as the most sensitive method in evaluating diseases of the pituitary gland, brain vessels, eye, and inner ear organs. Many medical image analysis methods based on deep learning techniques have been proposed for health monitoring and diagnosis from brain MRI images. CNNs (Convolutional Neural Networks) are a sub-branch of deep learning and are often used to analyze visual information. Common uses include image and video recognition, suggestive systems, image classification, medical image analysis, and natural language processing. In this study, a new modular deep learning model was created to retain the existing advantages of known transfer learning methods (DenseNet, VGG16, and basic CNN architectures) in the classification process of MR images and eliminate their disadvantages. Open-source brain tumor images taken from the Kaggle database were used. For the training of the model, two types of splitting were utilized. First, 80% of the MRI image dataset was used in the training phase and 20% in the testing phase. Secondly, 10-fold cross-validation was used. When the proposed deep learning model and other known transfer learning methods were tested on the same MRI dataset, an improvement in classification performance was obtained, but an increase in processing time was observed.

Background: Insulin-like growth factor-I is a liver-derived humoral factor, which has important anabolic and metabolic actions and is predominantly bound by insulin-like growth factor binding protein-3. Low serum concentrations of both insulin-like growth factor-I and insulin-like growth factor binding protein-3 have been reported in patients with chronic liver disease, especially cirrhosis, but their conditions in chronic hepatitis are uncertain. The aim of this study was to evaluate the effect of chronic hepatitis on serum concentrations of insulin-like growth factor-I and insulin-like growth factor binding protein-3 and their association with hepatic inflammation activity and fibrosis. Methods: Serum insulin-like growth factor-I and insulin-like growth factor binding protein-3 were measured by RIA (ng/ml) in 17 patients with mild to severe chronic viral hepatitis (12 chronic hepatitis C, 5 chronic hepatitis B) and 16 healthy subjects. The hepatic inflammation activity and the severity of fibrosis were evaluated using Desmet classification. Results: Both insulin-like growth factor-I and insulin-like growth factor binding protein-3 levels did not correlate with inflammation activity, fibrosis or transaminase levels. In the chronic hepatitis group, insulin-like growth factor-I levels were significantly higher than the control group (mean, 263.8 ± 27.33 versus 127.14 ± 10.83 ng/ml, P < 0.001, respectively), whereas insulin-like growth factor binding protein-3 levels were significantly lower when compared with the controls (1643.47 ± 60.68 versus 2728.87­± 284.61 ng/ml, P < 0.05, respectively). Conclusions: These results suggest that the concomitant states of serum insulin-like growth factor-I and insulin-like growth factor binding protein-3 levels in patients with chronic hepatitis may be different from cirrhotic patients and high serum IGF-I levels may be a specific finding of the stage of chronic hepatitis before developing cirrhosis. [ABSTRACT FROM AUTHOR]

Perkütanan böbrek girişimlerinden sonra postoperative kanamanın major nedeni psödo anevrizmadır. Mikroperkütan nefrolitotomi böbreğin alt pol kaliks taşlarının tedavisinde kullanılan tedavi yöntemidir. Literatürde küçük serilerden oluşan ve mikroperkütan nefrolitotominin uygulanabilir ve güvenli olduğunu bildiren az sayıda makale mevcuttur. Bu makalede literatürdeki mikroperkütan nefrolitotomi sonrası gelişen ilk psödoanevrizma ve tedavisi sunulmaktadır.

Postoperative bleeding due to pseudoaneurysm formation is the major cause of percutaneous kidney interventions. Micro percutaneous nephrolithotomy (microperc) is a one step procedure that used mostly lower pole stones. There are small series that reports technical feasibility and safety of microperc using the All-seeing needle. Here we state the first report of pseudoaneurysm and its treatment after microperc in the literature. [Cukurova Med J 2014; 39(3.000): 616-619]