Istanbul Arel University

Uncertainties persist regarding the allocation of apical lymph nodes in colorectal cancer, the approaches to lymph node dissection and mesocolic excision, which may contribute to inconsistent surgical practices. The aim of this study is to assess surgeons' practices in lymph node dissection and mesocolic excision approaches and to identify areas lacking standardization. A multinational pilot survey of 22 colorectal surgeons from 6 countries was conducted during the FICARE colorectal meeting. The survey consisted of 21 Likert-scale questions on surgical practices and lymph node allocation in colorectal cancer surgery. Majority of the respondents (90.9%) recognized conceptual differences in apical lymph node stratification between right- and left-sided colon cancers, whereas D3 LND for left-sided cancer should include mesocolic tissue along the inferior mesenteric artery from its origin to the last sigmoid artery. Complete lymph node dissection requires excision of mesocolic tissue along inferior mesenteric artery for left colon cancer and superior mesenteric artery for right colon cancer according to 81.8% of respondents. At the same time, 95.5% agreed that intermediate and paracolic lymph nodes are located within a 10-cm resection margin proximally and distally from tumor, while 81.9% of respondents supported the concept of tumor-specific mesocolic excision to be sufficient enough for adequate paracolic and intermediate lymph node dissection. A multinational snapshot showed an existing contraindication in surgeons' perception of lymph node stratification and the variability in mesocolic excision and LND. Further Delphi consensus is needed to prove the suggested concepts.

This study investigates the accuracy limitations of classical far-field approximation methods commonly employed in linear array antenna analysis, particularly in regions close to the far-field boundary. In the classical approach, the distances between array elements and the observation point are represented by approximate expressions, which may result in zero-valued electric fields at certain angles and lead to inaccurate predictions of main-lobe and side-lobe directions. To address this issue, an alternative approach for approximating the distance-dependent amplitude terms is proposed. The proposed method retains the closed-form calculability of the classical technique while more accurately representing amplitude variations.The suggested method greatly increases accuracy close to the far-field border by lowering main-lobe and side-lobe deviations, according to numerical data for linear dipole arrays. Both the traditional and suggested approaches converge to the reference far-field solution as the observation distance grows; comparable patterns are seen for endfire arrays.These results show that the suggested method helps anticipate array antenna patterns more accurately, especially in near-far-field zones, providing a useful technique for antenna array analysis and design applications.

Osmanlı bürokrasisinin kurumsal yapısını anlamada birincil elden veri sunan kaynaklardan biri Sicill-i Ahvâl Defterleridir. Devlet hizmetinde bulunan memurların meslekî ve sosyal geçmişlerinin sistematik biçimde kaydedildiği bu defterler, memurların biyografik bilgilerinin yanı sıra Osmanlı idari zihniyetinin memur tanımı, liyakat ölçütleri ve terfi mekanizmalarına ilişkin önemli ipuçları barındırır. 1879’da tesis edilen Sicill-i Ahvâl Komisyonu’nun kayıt sistematiği, özellikle II. Abdülhamid döneminde merkeziyetçi yönetim anlayışının güçlenmesiyle daha da kurumsallaşmıştır. Memurların kimlik, eğitim, meslekî performans ve terfi süreçlerine dair çok katmanlı veriler üretilmiştir. Bu defterlerde yer alan kayıtlar memurun doğum yeri, aile arka planı, eğitim aldığı kurumlar, yabancı dil bilgisi, göreve başlama yaşı, tayin ve terfi seyri gibi unsurlar yalnızca bireysel kariyerleri değil, aynı zamanda taşra ve merkez bürokrasisi arasındaki güç dengelerini, sosyal mobilite kanallarını ve modernleşme politikalarının saha etkisini analiz etmeye imkân tanımaktadır. Bu çalışma, söz konusu defterler üzerinden Serfiçe doğumlu 16 memurun meslekî profillerini inceleyerek Osmanlı taşra elit üretim mekanizmasına dair yapısal bir okuma ortaya koymayı amaçlamaktadır.

Incisional hernia is a common postoperative complication, particularly following abdominal surgeries, and is frequently associated with recurrence and impaired healing due to postoperative infections. In this study, a dual-layered hernia repair biopatch was developed by integrating a 3D-printed polycaprolactone/gelatin (PCL/Ge) scaffold, providing mechanical support, with an electrospun nanofibrous layer composed of PCL/Ge/κ-carrageenan (κ-C) to promote wound healing. To impart antimicrobial functionality, the scaffolds were functionalized with eitherAgrimonia eupatoria(AE) extract or the clinically used antibiotic rifampicin (RIF). Commercial polypropylene (PP) meshes were employed as control groups in bothin vitroandin vivoevaluations. Mechanical testing demonstrated that the developed biopatches exhibited tensile strengths within a clinically relevant range, with values of 5.13 MPa and 2.49 MPa for the 3D-printed RIF-loaded and AE-loaded electrospun-coated scaffolds, respectively. Both AE- and RIF-loaded groups showed pronounced antibacterial activity againstS. aureus, a predominant pathogen associated with surgical site infections. Sustained and controlled release profiles were observed over 160 h, with cumulative release values of approximately 30%-35%.In vivoevaluation using a rat incisional hernia model revealed that AE exhibits strong potential as an alternative to conventional antibiotics, attributable to its phenolic-rich composition and associated anti-inflammatory and tissue-remodeling properties. Overall, these findings demonstrate that the proposed dual-layer biopatch, which integrates mechanical reinforcement with sustained antimicrobial activity, represents a promising and effective strategy for infection-resistant incisional hernia repair.

Background: Obstructive sleep apnea (OSA) syndrome is a common sleep-related breathing disorder characterized by recurrent upper airway collapse during sleep and is closely associated with metabolic dysregulation, including insulin resistance, adipose tissue dysfunction, and impaired lipid metabolism. Perilipin-2 (PLIN-2), a lipid droplet-associated protein involved in triglyceride storage and regulation of lipolysis, may reflect alterations in lipid homeostasis associated with OSA. Objective: This study aimed to evaluate the association between serum PLIN-2 levels and OSA and to assess the relationship between PLIN-2 concentrations and disease severity. Methods: A total of 231 participants were included in this study, comprising 70 healthy controls and 161 patients with OSA. Patients were classified according to apnea-hypopnea index (AHI) as having mild (n = 60), moderate (n = 52), or severe OSA (n = 49). All participants underwent overnight polysomnography (PSG). Results: Serum PLIN-2 levels were significantly higher in patients with OSA and increased progressively with disease severity. PLIN-2 levels were positively correlated with polysomnographic indices of OSA severity, including AHI and oxygen desaturation index. ROC analysis demonstrated good discriminative performance of PLIN-2 for OSA presence and for distinguishing mild from severe OSA. Conclusions: This study is the first to demonstrate an association between serum PLIN-2 levels and OSA. Our findings suggest that PLIN-2 may serve as a novel biomarker reflecting metabolic and lipid-related disturbances in OSA and may provide new insights into the pathophysiological link between intermittent hypoxia and altered lipid metabolism.

Background: Palpitations are common in adolescents and often occur without structural heart disease. Although anxiety and autonomic dysregulation have been implicated, the role of micronutrient status remains unclear. This study aimed to investigate the association between palpitations, micronutrient levels, and anxiety in adolescents and to evaluate the independent associations between selected micronutrients and palpitations using multivariable regression models. Methods: This case-control study included 52 adolescents with palpitations and 52 frequency-matched healthy controls. Structural heart disease was excluded by electrocardiography, echocardiography, and 24-h Holter monitoring. Results: Adolescents with palpitations had significantly lower serum magnesium, selenium, and ferritin levels and higher anxiety scores than controls, despite most values remaining within reference ranges. In age- and sex-adjusted analyses, lower magnesium and selenium levels were independently associated with palpitations. Conclusions: Subclinical differences in micronutrient status, particularly magnesium and selenium, together with increased anxiety, may contribute to non-structural palpitations in adolescents. These findings support a more integrative evaluation that includes micronutrient and psychological assessment alongside standard cardiac investigations.

Applying deep learning models to RNA-Seq data poses substantial challenges, primarily due to the high dimensionality of the data and the limited sample sizes. To address these issues, this study introduces an advanced deep learning pipeline that integrates feature engineering with data augmentation. The engineering application focuses on biomedical engineering, specifically the classification of RNA-Seq datasets for disease diagnosis. The proposed framework was initially validated on synthetic datasets generated from Naive Bayes, where MLP-based augmentation yielded a notable improvement in predictive performance. Building on this foundation, we applied the approach to chromophobe renal cell carcinoma (KICH) RNA-Seq data from The Cancer Genome Atlas (TCGA). Following standard preprocessing steps normalization, transformation, and dimensionality reduction, the analysis concentrated on three main aspects: augmentation strategies, preprocessing methods, and explainable AI (XAI) techniques in relation to classification outcomes. Feature selection was performed through PCA, Boruta, and RF-based methods. Three augmentation strategies linear interpolation, SMOTE, and MixUp were evaluated. To maintain methodological rigor, augmentation was applied exclusively to the training set, while the test set was held out for unbiased evaluation. Within this framework, we conducted a comparative assessment of multiple deep learning architectures, including MLP, GNN, and the recently proposed Kolmogorov-Arnold networks (KAN). The GNN achieved the highest classification accuracy (99.47%) when trained with MixUp augmentation combined with RF feature selection, and achieved the best F1 score (0.9948). Consequently, the GNN-based XAI framework was applied to the RF dataset enriched with MixUp. XAI analyses identified the top 20 most influential genes, such as HNF4A, DACH2, MAPK15, and NAT2, which played the greatest role in classification, thereby confirming the biological plausibility of the model outputs. To further validate model robustness, cervical cancer and Alzheimer's RNA-Seq datasets were also tested, yielding consistent and reliable results. Overall, the findings highlight the value of incorporating data augmentation into deep learning models for RNA-Seq analysis, not only to improve predictive performance but also to enhance biological interpretability through explainable AI approaches.

Abstract Accurate classification of brain tumors is often limited by the scarcity of labeled medical images, particularly for complex tumor subtypes. This study proposes an auxiliary classifier generative adversarial network (ACGAN) to generate class-conditioned synthetic MRI images and to enhance the performance of tumor-classification models. The ACGAN was trained using the publicly available Figshare Brain MRI dataset, which contains four categories: glioma, meningioma, pituitary tumor, and healthy tissue. After training, the model produced high-fidelity synthetic MRI images that were quantitatively evaluated using the Fréchet inception distance (FID) as a distribution-level proxy metric, achieving a score of 4.6 and indicating close alignment between the real and synthetic data distributions. In addition to generative quality, the classification network trained with ACGAN-augmented data achieved 94.8% accuracy, 95.3% sensitivity, 93.6% specificity, and 94.1% F1-score, outperforming models trained solely on real data. Comparative analysis demonstrates that the proposed framework provides superior class separability and lower FID values than commonly used generative methods, including DCGAN and standard cGAN approaches. These findings highlight the potential of ACGAN-based synthetic augmentation to support medical-imaging tasks under limited-data conditions and to improve the reliability of brain-tumor classification systems.