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Correction to : The Psychometric Properties of Autism Mental Status Examination (AMSE) in Turkish Sample
(2025) Meral, Yavuz; Bıkmazer, Alperen; Örengül, Abdurrahman Cahid; Çakıroğlu, Süleyman; Altınbilek, Esra; Bakır, Fulya; Bıkmazer, Bilgihan; Saleh, Ayman; Görmez, Vahdet
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Identification of common genes associated with development of resistance against tamoxifen and doxorubicin in MCF7 cells
(Springer Nature, 2025) Karabay, Arzu Zeynep; Koç, Aslı; Hekmatshoar, Yalda
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Probabilistic Risk Framework for Nuclear- and Fossil-Powered Vessels: Analyzing Casualty Event Severity and Sub-Causes
(MDPI, 2025) Tanyıldızı-Kökkülünk, Handan; Kökkülünk, Görkem; Settles, John
Maritime activities pose significant safety risks, particularly with the growing presence of nuclear-powered vessels (NPVs) alongside traditional fossil-powered vessels (FPVs). This study employs a probabilistic risk assessment (PRA) approach to evaluate and compare accident hazards involving NPVs and FPVs. By analyzing historical data from 1960 to 2024, this study identifies risk patterns, accident frequency (probability), and severity levels. The methodology focuses on incidents such as marine incidents, marine casualties, and very serious cases with sub-causes. Key findings reveal that Russia exhibits the highest risk for very serious incidents involving both NPVs and FPVs, with a significant 100% risk for NPVs. China has the highest FPV risk, while France and the USA show above-average risks, particularly for marine casualties and very serious incidents. Moreover, collision is the most significant global risk, with a 26% risk for NPVs and 34% for FPVs, followed by fire hazards, which also pose a major concern, with a 17% risk for NPVs and 16% for FPVs, highlighting the need for enhanced safety and fire-prevention measures. In conclusion, comparative analysis highlights the need for enhanced stability improvements, fire prevention, and maintenance practices, particularly in the UK, France, Russia, and China. This study underscores the importance of targeted safety measures to mitigate risks, improve ship design, and promote safer maritime operations for both nuclear- and fossil-fueled vessels.
A machine learning assisted designing and chemical space generation of benzophenone based organic semiconductors with low lying LUMO energies
(Elsevier Ltd, 2025) Güleryüz, Cihat; Hassan, Abrar U.; Güleryüz, Hasan; Kyhoiesh, Hussein A.K.; Mahmoud, Mohamed H.H.
Current study presents a machine learning (ML) approach to design benzophenone-based organic chromophore with their lowest possible LUMO energy (ELUMO). A dataset of their 1142 donors is collected from literature and their molecular descriptors are designed by using RDKit. Among various models, the Random Forest regression model produces accurate results to predict their ELUMO values. Based on these predictions, their 5000 new donors are designed with their Synthetic Accessibility Likelihood Index (SALI) scores. Their SHAP value analysis reveals that their electro topological state indices are the most critical descriptors to lowering ELUMOs. The top- performing donor are further extended with acceptors and their photovoltaic (PV) properties by density functional theory (DFT). Their results show their maximum open-circuit voltage (Voc) of 2.30 V, a short-circuit current (Jsc) of 47.19 mA/cm2, and a light-harvesting efficiency (LHE) of 93 %. This study demonstrates the potential of ML assisted design to design new organic chromophores.
OPTIMIZING ROAD SAFETY: THE ROLE OF GEOGRAPHIC INFORMATION SYSTEMS (GIS) IN TRAFFIC ACCIDENT ANALYSIS AND PREDICTION
(Faculty of Engineering, University of Kragujevac, 2025) Alfaras, Mohammed Shukur; Karan, Oğuz; Kurnaz, Sefer
This study investigates the application of Geographic Information Systems (GIS) in traffic accident analysis and prediction. By integrating GIS with deep learning techniques, the research highlights how spatial data management and analysis can enhance road safety. Key objectives include identifying accident hotspots, optimizing traffic control systems, and improving emergency response. The methodology involves a comprehensive review of existing literature, emphasizing GIS's role in data integration, spatial analysis, and predictive modeling. Findings demonstrate that GIS significantly contributes to understanding traffic patterns, predicting accidents, and formulating targeted safety interventions. Challenges such as data complexity, real-time processing, and model interpretability are addressed, offering future directions for leveraging GIS in road safety management. The study concludes that GIS, combined with advanced analytics, presents a powerful tool for reducing traffic accidents and enhancing overall traffic safety.
A graph neural network assisted reverse polymers engineering to design low bandgap benzothiophene polymers for light harvesting applications
(Elsevier Ltd, 2025) Hassan, Abrar U.; Güleryüz, Cihat; El Azab, Islam H.; Elnaggar, Ashraf Y.; Mahmoud, Mohamed H.H.
In this study, we present a novel approach to reverse polymer engineering utilizing a Graph Neural Network (GNN) framework to design low bandgap benzothiophene (BT) polymers for light harvesting applications. We have curated an extensive dataset comprising 57,556 structure-property pairs of BT-based compounds, leveraging expert knowledge to enhance the quality and relevance of the data. Our Transformer-Assisted Oriented pretrained model for on-demand polymer generation (TAO) demonstrates exceptional performance, achieving a chemical validity rate of 99.27 % in top-1 generation mode across a test set of 6000 generated polymers, marking the highest success rate reported among polymer generative models to date. Throughout the training process, the loss steadily decreased with each epoch, indicating that the model was learning effectively from the data. The model predictive accuracy is further validated by an impressive average R2 value of 0.96 for 15 defined properties, highlighting the TAO with its robust capabilities in polymer design. The newly designed polymers exhibit a bandgap range of 1.5–3.40 eV, making them promising candidates for light harvesting applications. Additionally, their highest Synthetic Accessibility Likelihood Index (SALI) scores reach up to 17 and also indicates that the majority of these polymers are amenable to synthesis. This work not only advances the field of polymer design but also provides a powerful tool for the targeted development of materials with specific electronic properties.
A machine learning analysis to predict the stability driven structural correlations of selenium-based compounds as surface enhanced materials
(Elsevier Ltd, 2025) Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Elnaggar, Ashraf Y.; Noreen, Sadaf
The selenium-based compounds are gaining significance for their surface-enhanced properties. In order to accelerate their discovery, a machine learning (ML) approach has been employed to predict their structural correlations. For this a dataset of 618 compounds is collected from literature and is trained by using Support Vector Machine (SVM) with its Linear Kernal. Among ten ML evaluated models, three top-performing models are selected to make predictions for their stability energy. A Convex Hull Distribution (CHD) is constructed to elucidate the relationship for their stability and structural correlations. The main finding of this study reveals its strong correlation between stability and its related structural descriptors, particularly Bertz Branching Index" corrected for the number of Terminal atoms (BertzCT), Partial Equalization of Orbital Electronegativities-Van der Waals Surface Area with 14 bins (PEOE_VSA14), and First-Order Connectivity Index (chi 1). The analysis demon strates that the current ML models can effectively predict the stability of such materials to enable their rapid screening. Their calculations can provide a framework to understand their complex relationships between their material properties, structure, and stability.
Online simulation versus traditional classroom learnings in clinical pharmacy education: effect on students' knowledge, satisfaction and self-confidence
(BioMed Central, 2025) Selçuk, Aysu; Öztürk, Nur; Önal, Nurbanu; Bozkır, Asuman; Aksoy, Nilay
Background: Over the course of the past few years, the area of medical education has experienced a substantial movement towards the establishment of online learning platforms and resources. This study aimed to to evaluate the efficacy of an online simulation learning intervention, MyDispense®, compared to traditional classroom learning in terms of enhancing knowledge, satisfaction, and self-confidence among participants. Methods: A multicentre randomized controlled study was conducted among pharmacy students who were assigned either intervention MyDispense® or control traditional classroom learning groups. They were eligible if they previously had experience with online simulation learning. A previously validated questioner were used to measure the outcome of knowledge, satisfaction and self-confidence. Results: Both the intervention and control groups revealed significant improvement in knowledge, the P value for pre-post knowledge scores for each group was < 0,001. Despite these internal improvements, this study's findings showed no statistically significant differences (p > 0.05) between the intervention and control groups on knowledge gain, satisfaction, or self-confidence. This represents comparable outcomes irrespective of the group's exposure to intervention. Conclusion: The study evaluated the efficacy of online simulation learning intervention MyDispense® in comparison to traditional classroom learning. While both strategies effectively improved knowledge, satisfaction, and self-confidence, the findings demonstrated that the online simulation yielded equivalent learning benefits. MyDispense® could be an alternative to traditional education in situations where face to face learning is not feasible, with comparable learning outcomes. Clinical trial number: not applicable.
3D modelling and x-ray depth analysis map of the pulp with computer software via digital periapical radiography and cone beam computed tomography
(2025) Felek, Turgut; Şatır, Samed; Özel, Şelale
Objective: Periapical radiographs (PAR) offer information about the pulp and periodontal health of teeth. However, intraoral radiographs are insufficient for diagnosing buccolingual anomalies and variations such as bifid canals due to their two-dimensional nature. Cone beam computed tomography (CBCT) is the gold standard for 3D imaging in the clinic but requires additional radiation. The aim of this study was to create a software (XPAR) which obtains x-ray depth analysis and 3D modelling of the pulps of single-rooted teeth by converting the grey values in the original radiographs into numerical data. Materials and methods: Two single-rooted teeth were included in the experimental part of the study. Chicken fibula bone was preferred for alveolar bone simulation because it could simulate cortical and trabecular structures due to similarity. A total of four images (60kVp & 70kVp; single alveolar bone & double alveolar bone) were obtained. The aim of this experimental part is to test the repeatability and realism of the algorithm to be created for pulp modelling. Retrospectively, 31 single-rooted teeth with both periapical radiography and cone-beam computed tomography imaging were included in the retrospective part of the study. According to XPAR, depth increase areas were interpreted as root resorption and accessory canal. Depth decrease areas were evaluated as the transformation of the pulp from an elliptical to an oval form, pulp stone, bifid canal formation and the presence of thick alveolar bone. The diagnostic accuracy of XPAR application on pathological and morphological changes was evaluated by comparing the obtained results with CBCT. Results: 80% of the analyses diagnosed as bifurcation by XPAR application were supported by CBCT. This rate decreased to 27% in the diagnosis of transitions from elliptical to oval form. A total of 5 and 19 linear formations observed in the form of depth decrease and increase, respectively, were accepted as image errors in XPAR. Conclusion: Buccolingual bifid canal formations and pulp obliterations can be diagnosed with a rate of nearly 50% with the depth decrease finding obtained in XPAR application. Imaging errors caused by deformed detectors are typically observed as linear formations.
Evaluation of Subclinical Left Ventricular Dysfunction in HIV Patients Receiving Abacavir, Dolutegravir, and Lamivudine Therapy with Novel Tissue Doppler Imaging Techniques
(MDPI, 2025) Okşen, Doğaç; Aslan, Muzaffer; Serin, Ebru; Geçit, Muhammed Heja; Yavuz, Yunus Emre; Yerlikaya Zerdali, Esra; Oktay, Veysel
Background/Objectives: Highly active antiretroviral therapy (HAART) effectively suppresses viral load and aids immunological recovery in HIV patients, but may still lead to subclinical myocardial dysfunction. This study assesses left and right ventricular functions in patients on HAART containing abacavir, dolutegravir, and lamivudine using Tissue Doppler Imaging (TDI). Methods: This observational cross-sectional study involved 118 HIV-positive adults on HAART and 80 age- and gender-matched healthy controls. Comprehensive echocardiographic assessments, including TDI, were conducted to evaluate myocardial performance index (MPI) and isovolumic acceleration (IVA). Results: Conventional echocardiographic parameters showed no significant differences; however, TDI indicated significant impairments in ventricular functions in the HAART group, with increased MPI and decreased IVA (p < 0.001). Pulmonary artery pressures were also higher in the HIV group (p = 0.012). There was a strong positive correlation between MPI and HAART duration (r = 0.675, p = 0.002), and a negative correlation with CD4 count (r = −0.545, p = 0.006). Conclusions: TDI reveals significant subclinical ventricular dysfunction in HIV patients on HAART, correlating with therapy duration and immune status. These findings underscore the utility of TDI in detecting myocardial deterioration before clinical symptoms appear.
The Effect of Maternal Attitudes and Depression on Bonding During the Postpartum Period
(Dove Medical Press, 2025) Meterelliyoz, Kumru Şenyaşar; Çağlar Mengi, Kıymet; Yazar, Menekşe Sıla; Akbay Kısa, Ayşe Sevim
Purpose: Bonding refers to the development of an emotional relationship between a mother and her baby, which forms a strong and continuous bond that provides the baby with a sense of security and plays an important role in its mental well-being throughout life. The objective of this study was to assess the relationship between cognitive distortions, attitudes towards motherhood and postpartum depression, which have not been studied before, as well as to elucidate their impact on the mother-infant bonding process. Patients and Methods: The sample of the study was created between November 2018-June 2019 using the non-discriminatory multiplicity snowball sampling technique through social media. Women with infants aged 0–1 year residing in Turkey were asked to participate in the online survey. A sociodemographic data form, the Edinburgh Postpartum Depression Scale, Attitudes Towards Motherhood Scale (AToM), Postpartum Bonding Questionnaire (PBQ), and Cognitive Distortions Scale (CDS) were applied to the sample via social media. Results: The study sample consisted of 387 women with infants aged 0–1 years, and the rate of impairment bonding was found to be 11.4%. CDS, ATOM and depression scores were significantly higher in the impaired attached group (p < 0.05). The findings indicated that an individual with a psychiatric diagnosis was 2.653 times more likely to exhibit impaired bonding (OR: 2.653, 95% CI: [1.08–6.517]; p = 0.033), and those with a higher AToM score were 1.044 times more likely to display impaired bonding (OR: 1.044, 95% CI: [1.013–1.075]; p = 0.004). Conclusion: The cognitive structure of the mother is associated with impaired mother-baby bonding. Eliminating the mentioned cognitive elements with psychotherapy interventions will be protective in terms of impaired bonding related to psychopathologies and/ or interpersonal relationship problems.
Recent advances of structure, function, and engineering of carboxylesterases for the pharmaceutical industry: A minireview
(IPC Science and Technology Press, 2025) Sürmeli, Yusuf; Vardar-Yel, Nurcan; Tütüncü, Havva Esra
Carboxylesterases have a wide range of applications due to their catalytic efficiency, robust structure, and broad substrate specificity. These enzymes, which can hydrolyze carboxylic acid esters, amides, and thioesters, stand out with their regio- and enantioselective properties. They play a crucial role in synthesizing pharmaceutical intermediates, including secondary and tertiary alcohols, α-hydroxy acids, and various bioactive compounds. However, in some cases, the enantioselectivity of carboxylesterases may be insufficient to achieve conversions with the purity required by the pharmaceutical industry. This review summarizes the crucial role of carboxylesterases, particularly in the pharmaceutical field, focusing on the classification, structure, and engineering approaches. After introducing the main families of carboxylesterases, the structural studies are presented to give a comprehensive insight into the active site architecture and related key determinants for enantioselectivity. The protein engineering studies to improve the enantioselectivity of carboxylesterases are discussed along with solvent engineering and immobilization applications.
HAFMAB-Net: hierarchical adaptive fusion based on multilevel attention-enhanced bottleneck neural network for breast histopathological cancer classification
(SPRINGER LONDON, 2025) Abdulwahhab, Ali H.; Bayat, Oğuz; Ibrahim, Abdullahi Abdu
Histological images play a crucial role in diagnosing diseases, especially breast cancer, which remains a major health concern for women worldwide. Computer-aided diagnosis tools significantly assist physicians in early detection and treatment planning, helping reduce mortality rates. Convolutional neural networks (CNNs) based on deep learning have proven effective in distinguishing benign from malignant breast cancers. In this context, HAFMAB-Net: Hierarchical Adaptive Fusion based on Multilevel Attention-Enhanced Bottleneck Neural Network, is proposed. The network comprises two pathways utilizing an enhanced Bottleneck architecture with attention mechanisms to extract both global and spatial features. It incorporates a Deeper Spatial Attention Aggregator Module to boost the representation of locative features by focusing on key spatial regions, improving the discriminative power of aggregated features. Additionally, a modified Adaptive Fusion Module combines the enhanced global and boosted spatial features into a comprehensive and enriched feature representation, which is subsequently used for classification. The proposed HAFMAB-Net was evaluated on the BACH dataset and further tested on the BreaKHis and LC25000 datasets to validate its robustness. The model achieved 99% accuracy on the BACH dataset, 98.99% accuracy on BreaKHis, 100% accuracy on each Colon, Lung, LC25000 datasets, respectively. These results highlight the HAFMAB-Net's efficiency, accuracy, and effectiveness in both multi-class and binary classification tasks, demonstrating its potential for broader applications in medical image analysis.
3D Face Anti-Spoofing With Dense Squeeze and Excitation Network and Neighborhood-Aware Kernel Adaptation Scheme
(IEEE, 2025) Hussein, Mohammed Kareem Hussein; Uçan, Osman Nuri
Face anti-spoofing is a critical challenge in biometric security systems, where sophisticated spoofing techniques pose significant threats. To enhance the effectiveness and efficiency of the methods employed for face anti-spoofing, this paper presents a new lightweight 3D face anti-spoofing framework characterized by several advanced mechanisms. To this purpose, the proposed architecture introduces DenseNet, a Squeeze and Excitation mechanism, and a new computational component called Neighborhood-Aware Kernel Adaptation (NAKA) that adaptively modifies 3D convolution kernels according to spatial proximity. Initially, an adaptive thresholding-based wavelet decomposition is employed for image denoising, followed by cross-channel attention to improve feature learning. Finally, Multiple Instance Learning (MIL) is used to address face anti-spoofing for the first time by modeling the spatial and temporal variations across facial areas. We validate our framework on two publicly available datasets: CelebA-Spoof and CASIA-SURF. We compared the performance of our proposed framework with several state-of-the-art methods using Classification accuracy, Precision, Recall, F1-score, ACER, APCER, and BPCER. Our model realizes 99.62% on the CelebA-Spoof and 99.86% on CASIA-SURF datasets. The proposed approach realized superior results in terms of high classification accuracy (99.62% and 99.86%), precision (99.85% and 99.83%), recall (99.39% and 99.84%), F-score (99.62% and 99.84%), ACER (0.0038/ 0.0014), FPR (0.0015/ 0.0013), APCER (0.0015/0.0016), and BPCER (0.0061/0.0013). These results are compared with 10 state-of-the-art methods to show the effectiveness of our approach in outperforming existing methods. The global comparative results reveal that the proposed approach is relatively effective in determining masked and true face scans.
Optimal feature tuning model by variants of convolutional neural network with LSTM for driver distract detection in IoT platform
(SPRINGER LONDON, 2025) Farhan, Hameed Mutlag; Kurnaz Türkben, Ayça; Naseri, Raghda Awad Shaban
Nowadays, traffic accidents are caused due to the distracted behaviors of drivers that have been noticed with the emergence of smartphones. Due to distracted drivers, more accidents have been reported in recent years. Therefore, there is a need to recognize whether the driver is in a distracted driving state, so essential alerts can be given to the driver to avoid possible safety risks. For supporting safe driving, several approaches for identifying distraction have been suggested based on specific gaze behavior and driving contexts. Thus, in this paper, a new Internet of Things (IoT)-assisted driver distraction detection model is suggested. Initially, the images from IoT devices are gathered for feature tuning. The set of convolutional neural network (CNN) methods like ResNet, LeNet, VGG 16, AlexNet GoogleNet, Inception-ResNet, DenseNet, Xception, and mobilenet are used, in which the best model is selected using Self Adaptive Grass Fibrous Root Optimization (SA-GFRO) algorithm. The optimal feature tuning CNN model processes the input images for obtaining the optimal features. These optimal features are fed into the long short-term memory (LSTM) for getting the classified distraction behaviors of the drivers. From the validation of the outcomes, the accuracy of the proposed technique is 95.89%. Accordingly, the accuracy of the existing techniques like SMO-LSTM, PSO-LSTM, JA-LSTM, and GFRO-LSTM is attained as 92.62%, 91.08%, 90.99%, and 89.87%, respectively, for dataset 1. Thus, the suggested model achieves better classification accuracy while detecting distracted behaviors of drivers and this model can support the drivers to continue with safe driving habits.
Vacuum-assisted closure in secondary wound healing after pilonidal sinus surgery
(2025) Akyol, Hüseyin; Berrin, Erok
Objective: This study evaluated the utility of vacuum-assisted closure (VAC) in comparison to standard open wound care in patients operated for pilonidal sinus disease (PSD). Method: Patients with PSD who underwent standard pilonidal sinus excision-lay open technique/surgery in the Altinbas University School of Medicine Bahcelievler Medical Park Hospital, Istanbul, Turkey, between May 2015 and May 2018, were included in this study. A retrospective analysis of prospectively collected data was performed. The patients were divided into two groups according to the type of wound care, including the vacuum-assisted closure group (n=30, postoperative vacuum-assisted closure application) and the control group (n=30, standard open wound care). Wound size, postoperative infection rates and wound healing times were compared between study groups. Results: The experimental cohort included 60 patients. There was no statistically significant difference between vacuum-assisted closure and the control groups in terms of preoperative and postoperative infection rates (p>0.05). The total recovery time (time to complete wound healing) was significantly shorter in the vacuum-assisted closure group compared with the control group (21.47±4.38 days versus 67.60±7.83 days, p=0.001). Conclusion: The findings of this study emphasise that the use of vacuum-assisted closure in PSD patients treated with the lay-open technique seems notable in terms of its potential to shorten the otherwise longer secondary recovery time and thus enables the consideration of the lay-open technique once again among the most preferable methods. However, there is a need for larger scale prospective studies addressing the utility of vacuum-assisted closure in patients with PSD to validate these findings.
Dual-Functioning Metal-Organic Frameworks: Methotrexate-Loaded Gadolinium MOFs as Drug Carriers and Radiosensitizers
(2025) Karaca, Burcu; Sakarya, Deniz; Siyah, Pınar; Şenışık, Ahmet Murat; Kaptan, Yasemin; Çavuşoğlu, Ferda C.; Mansuroğlu, Demet S.; Öztürk, Sadullah; Bayazıt, Şahika S.; Barlas, Fırat
Cancer remains a critical global health challenge, necessitating advanced drug delivery systems through innovations in materials science and nanotechnology. This study evaluates gadolinium metal-organic frameworks (Gd-MOFs) as potential drug delivery systems for anticancer therapy, particularly when combined with radiotherapy. Gd-MOFs were synthesized using terephthalic acid and gadolinium (III) chloride hexahydrate and then loaded with methotrexate (MTX). Characterization via fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), magnetic resonance imaging (MRI), and X-ray diffraction (XRD) confirmed their correct structure and stability. Effective MTX loading and controlled release were demonstrated. Anticancer effects were assessed on human healthy bronchial epithelial cells (BEAS-2B) and human lung cancer cells (A549) using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay under in vitro radiation therapy. MTX/Gd-MOF combined with radiotherapy showed a greater reduction in cancer cell viability (41.89% ± 2.75 for A549) compared to healthy cells (56.80% ± 1.97 for BEAS-2B), indicating selective cytotoxicity. These findings highlight the potential of Gd-MOFs not only as drug delivery vehicles but also as radiosensitizers, enhancing radiotherapy efficacy and offering promising evidence for their use in combinatory cancer therapies to improve treatment outcomes.
Combating chronic kidney disease-associated cachexia: A literature review of recent therapeutic approaches
(2025) Saadat, Yalda Rahbar; Abbasi, Amin; Hejazian, Seyyed Sina; Hekmatshoar, Yalda; Ardalan, Mohammadreza; Farnood, Farahnoosh; Zahed, Sepideh Zununi
In 2008, the Society on Sarcopenia, Cachexia, and Wasting Disorders introduced a generic definition for all types of cachexia: "a complex metabolic syndrome associated with the underlying illness characterized by a loss of muscle, with or without fat loss". It is well-known that the presence of inflammatory burden in end-stage renal disease (ESRD) patients may lead to the evolution of cachexia. Since the etiology of cachexia in chronic kidney disease (CKD) is multifactorial, thus the successful treatment must involve several concomitant measures (nutritional interventions, appetite stimulants, and anti-inflammatory pharmacologic agents) to provide integrated effective therapeutic modalities to combat causative factors and alleviate the outcomes of patients. Given the high mortality rate associated with cachexia, developing new therapeutic modalities are prerequisite for ameliorating patients with CKD worldwide. The present review aims to discuss some therapeutic strategies and provide an update on advances in nutritional approaches to counteract cachexia.
Brushing motion caused no microcracks: a micro-computed tomography study
(2025) Yanık, Deniz; Özel, Şelale; Dağlı Taşman Cömert, Fügen
Objective: We evaluated the effect of brushing motion on microcrack formation in round distal canals after using multi-file rotary(MFR), single-file rotary(SFR), and single-file reciprocation(SFRc) systems via micro-computed tomography(micro-CT). Materials and methods: Thirty-six mandibular molars were used. Samples were allocated according to files and preparation patterns (n = 12); pecking (P) and brushing (B): Group-MFR-P, Group-MFR-B, Group-SFRc-P, Group-SFRc-B, Group-SFR-P, Group-SFR-B. MFR was ProTaper Next, SFR was TruNatomy, and SFRc was WaveOne Gold. Mesial and distal were prepared using pecking motion, and additional brushing motion. Brushing motions were performed after the pecking motions with 6 strokes. Pre-and-post-instrumentation scans were obtained. Wilcoxon, Krukal-Wallis, and Mann-Whitney-U were performed. Results: No differences were between pre-and-post-instrumentation scans (p > 0.05). Post-instrumentation microcracks were not different in Group MFR-P and Group MFR-B, Group SFRc-P and Group SFRc-B, Group SFR-P and Group SFR-B (p > 0.05). Conclusion: The brushing motion followed by the pecking motion did not cause microcracks. None of the file systems examined in the study induced microcracks.
Chemotherapy-related cognitive impairment and kidney dysfunction
(2025) Simeoni, Mariadelina; Mulholland, Michele M.; Workeneh, Biruh T.; Capasso, Anna; Capasso, Anna; Hafez, Gaye; Liabeuf, Sophie; Malyszko, Jolanta; Mani, Laila-Yasmin; Trevisani, Francesco; De, Ananya; Wagner, Carsten A.; Massy, Ziad A.; Unwin, Robert; Capasso, Giovambattista; CONNECT Action (Cognitive Decline in Nephro-Neurology European Cooperative Target) collaborators
Cancer and kidney diseases (KD) intersect in many ways resulting in worse outcomes. Both conditions are correlated with cognitive impairment, which can be exacerbated in cancer patients by known effects of many antineoplastic drugs on cognition, leading to a phenomenon known as chemotherapy-related cognitive impairment (CRCI). This manifests as poor attention span, disturbed short-term memory, and general mental sluggishness. This literature review explores CRCI and investigates the potential impact of KD on this phenomenon. Additionally, we highlight the shared pathogenetic mechanisms (including neurotoxicity, neuroinflammation, oxidative stress, vascular disease, electrolyte, and acid-base imbalances), clinical presentation and imaging findings between cognitive impairment in KD and CRCI. The disruption of the blood-brain barrier might be a key mechanism for increased brain permeability to anticancer drugs in nephropathic patients with cancer. Based on existing knowledge, we found a potential for heightened neurotoxicity of antineoplastic drugs and a synergistic potentiation of cognitive impairment in cancer patients with KD. However, further translational research is urgently required to validate this hypothesis.

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