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Vacuum-assisted closure in secondary wound healing after pilonidal sinus surgery
No full textObjective: 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
Tailored Callosotomy in Third Ventricle Colloid Cyst Resection via Anterior Interhemispheric Transcallosal Approach
Get PDFBackground: The colloid cyst represents a relatively uncommon intracranial lesion. It garners significant attention from neurosurgeons due to its benign nature, deep-seated location, and promising prognosis when identified early and surgically removed. A variety of surgical methods are used to treat these cysts, each with their strengths and weaknesses.
Objectives: The aim of this study to introduce and assess a precise microsurgical technique for managing colloid cysts using the anterior interhemispheric transcallosal approach.
Methods: The research involved a retrospective analysis of 14 cases between 2021 and 2023 treated with the anterior interhemispheric transcallosal approach by two experienced skull base surgeons. The evaluation encompassed demographic, clinical, radiological, histological, and surgical data. Additionally, the Colloid Cyst Risk Score (CCRS) was used to assess the risk of obstructive hydrocephalus. The procedure incorporated neuronavigation and ultrasound to determine the precise entry point and to plan the trajectory.
Results: The minimally invasive microsurgical technique was effectively employed in all 14 cases, with no reported postoperative complications. Post-surgery MRI scans confirmed complete cyst removal, with an average callosotomy measurement of 5.4 ± 2.5 mm. Importantly, none of the patients experienced disconnection syndrome associated with callosotomy.
Conclusions: The adapted microsurgical approach via the anterior interhemispheric transcallosal method emerges as a secure and efficient way to address colloid cysts. It ensures comprehensive cyst removal while minimizing complications, boasting advantages such as reduced invasiveness, enhanced visibility, and minimal tissue disturbance, thereby confirming its role in colloid cyst surgical interventions
Optimizing Deep Learning Models for Fire Detection, Classification, and Segmentation Using Satellite Images
Get PDFEarth observation (EO) satellites offer significant potential in wildfire detection and assessment due to their ability to provide fine spatial, temporal, and spectral resolutions. Over the past decade, satellite data have been systematically utilized to monitor wildfire dynamics and evaluate their impacts, leading to substantial advancements in wildfire management strategies. The present study contributes to this field by enhancing the frequency and accuracy of wildfire detection through advanced techniques for detecting, classifying, and segmenting wildfires using satellite imagery. Publicly available multi-sensor satellite data, such as Landsat, Sentinel-1, and Sentinel-2, from 2018 to 2020 were employed, providing temporal observation frequencies of up to five days, which represents a 25% increase compared to traditional monitoring approaches. Sophisticated algorithms were developed and implemented to improve the accuracy of fire detection while minimizing false alarms. The study evaluated the performance of three distinct models: an autoencoder, a U-Net, and a convolutional neural network (CNN), comparing their effectiveness in predicting wildfire occurrences. The results indicated that the CNN model demonstrated superior performance, achieving a fire detection accuracy of 82%, which is approximately 10% higher than the best-performing model in similar studies. This accuracy, coupled with the model's ability to balance various performance metrics and learnable weights, positions it as a promising tool for real-time wildfire detection. The findings underscore the significant potential of optimized machine learning approaches in predicting extreme events, such as wildfires, and improving fire management strategies. Achieving 82% detection accuracy in real-world applications could drastically reduce response times, minimize the damage caused by wildfires, and enhance resource allocation for firefighting efforts, emphasizing the importance of continued research in this domain
Correction to: Genetic Evaluation of the Patients with Clinically Diagnosed Inborn Errors of Immunity by Whole Exome Sequencing: Results from a Specialized Research Center for Immunodeficiency in Türkiye (Journal of Clinical Immunology, (2024), 44, 7, (157), 10.1007/s10875-024-01759-w)
No full textSince the publication of this article we have noticed several errors within the main Table 1 of the manuscript. Four variants were given with different transcript IDs of the same gene. There are also 2 nomenclature errors in the variants of P58 and P117. The necessary corrections have been made in the table below. The errors do not affect the causality of the variants, the results or conclusions reported in the manuscript. The authors apologize for the error, and regret any inconvenience this may have caused. The original version has been corrected. (Table presented.) Patient no Clinical diagnosis (IUIS) Age Gender Consan Gene Variant Transcript ID Zygosity Consequence Novelty P18 CID 20 F + c.214G>A p.Gly72Ser NM_001199917.1 The true RefseqID should be NM_001199919.1 Hom Missense Novel P29 SCID 6 m F + c.551_555del p.Glu184Glyfs*2 c.241G>A p.Gly81Arg NM_000022.4 NM_000022.4 The true RefseqID should be NM_001322050 for these variants Comp. Het Out of frame/Deletion Missense Novel rs2065384316 P34 SCID 1 M + c.779A>G p.Glu260Gly NM_001322050 The true RefseqID should be NM_000022.4 for this variant Hom Missense rs1354071013 P58 SCID 2 M + c.1633delT p.Glu545AsnfsTer The correct nomenclature of this variant is c.1633del p.Glu545Asnfs*58 NM_001350965.2 Hom Out of frame/Deletion Novel P113 PAD/CVID 7 F + c.919C>T p.Arg307Trp NM_001372051.1 The true RefseqID should be NM_001080125.1 Hom Missense rs17860424 P117 SCID 1 F + c.2322G>A p.Arg737His The correct nomenclature of this variant is c.2210G>A p.Arg737His The nucleotide position 2322 refers an old transcript NM_000448.3 Hom Missense rs10489428
Anatomy exam model for the circulatory and respiratory systems using GPT-4: a medical school study
No full textArticle Number : 158Purpose: The study aimed to evaluate the effectiveness of anatomy multiple-choice questions (MCQs) generated by GPT-4, focused on their methodological appropriateness and alignment with the cognitive levels defined by Bloom's revised taxonomy to enhance assessment.
Methods: The assessment questions developed for medical students were created utilizing GPT-4, comprising 240 MCQs organized into subcategories consistent with Bloom's revised taxonomy. When designing prompts to create MCQs, details about the lesson's purpose, learning objectives, and students' prior experiences were included to ensure the questions were contextually appropriate. A set of 30 MCQs was randomly selected from the generated questions for testing. A total of 280 students participated in the examination, which assessed the difficulty index of the MCQs, the item discrimination index, and the overall test difficulty level. Expert anatomists examined the taxonomy accuracy of GPT-4's questions.
Results: Students achieved a median score of 50 (range, 36.67-60) points on the test. The test's internal consistency, assessed by KR-20, was 0.737. The average difficulty of the test was 0.5012. Results show difficulty and discrimination indices for each AI-generated question. Expert anatomists' taxonomy-based classifications matched GPT-4's 26.6%. Meanwhile, 80.9% of students found the questions were clear, and 85.8% showed interest in retaking the assessment exam.
Conclusion: This study demonstrates GPT-4's significant potential for generating medical education exam questions. While it effectively assesses basic knowledge recall, it fails to sufficiently evaluate higher-order cognitive processes outlined in Bloom's revised taxonomy. Future research should consider alternative methods that combine AI with expert evaluation and specialized multimodal models
Effects of Caffeic Acid on Human Health: Pharmacological and Therapeutic Effects, Biological Activity and Toxicity
No full textPhenolic compounds are bioactive compounds found in many natural products. Natural products exhibit biological activities because of their bioactive compounds. This review presents an overview of the general characteristics of caffeic acid, including its derivatives and biosynthesis, pharmacological and therapeutic effects, and biological activities. According to the literature research conducted, it has been reported that there are medical and pharmacological effects such as atherosclerotic, cardioprotective, immunomodulatory, hypertension, radiotherapy, neurodegeneration, neuroprotective, anxiety, vasoactive, dyslipidemia, and obesity. Furthermore, it has been observed that the substance possesses biological activities such as antioxidant, antihyperglycemic, antimicrobial, anticancer, cytotoxic, anti-inflammatory, anticoagulatory, antidiabetic, and antiviral properties. Within this scope, it is believed that caffeic acid could serve as a significant natural resource in pharmacological designs
Advanced Anomaly Detection Framework Using CNN-Grid Autoencoder Integration and Recursive Fuzzy Feature Selection Approach
No full textConference name : 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, ICHORA 2025
Conference city : Ankara
Conference date : 23 May 2025 - 24 May 2025
Conference code : 209351Network intrusion poses a significant threat to data privacy and security in shared networks, often leading to cyber-attacks that compromise both system integrity and user data. Today's digital environment is seeing an increase in unauthorized actions, such as credential theft, illegal access, and data tampering. Despite the availability of various intrusion detection methodologies, accurately identifying and mitigating such threats remains a critical challenge. To address this issue, this paper proposes an automated attack classification model designed to enhance classification accuracy while minimizing errors based on input parameters. The proposed approach presents an innovative system for detecting network intrusions based on deep learning that integrates a fuzzy optimization method. The methodology begins with data pre-processing, including data cleansing and temporal smoothing, followed by feature extraction using Grid Convolutional Neural Networks (Grid-CNN). Optimal features are selected through a recursive multi-level fuzzy optimization algorithm. Finally, attack classification is performed using the SoftMax layer of the Grid-CNN architecture. The model is evaluated on the UNSW-NB15 dataset, with performance metrics including accuracy, precision, recall, and F1 score. Experimental results demonstrate that the proposed model achieves an accuracy of 96.02%, outperforming existing models in terms of both accuracy and robustness. This study highlights the potential of deep learning and fuzzy optimization in enhancing network intrusion
Forensic Image Enhancement and Forgery Detection Using Advanced Image Processing Techniques and Convolutional Neural Networks
No full textVolume editors : Wibowo F.W.
Conference name : 2025 International Conference on Computer Sciences, Engineering, and Technology Innovation, ICoCSETI 2025.
Conference city : Jakarta.
Conference date : 21 January 2025.
Conference code : 209376.Nowadays, there is a wide use of modern techniques and technology in general, which constitutes an important innovation. Manipulating, changing and modifying digital images has become very popular and relatively easy, which has created a great challenge and concern, especially in cases that rely on digital images as the main evidence for issuing judgments. In order to improve the quality of images and accurately identify fraud cases, a talented system has been invented to conduct forensic investigations by combining modern image processing techniques and Convolutional Neural Networks (CNN). In this experiment, Google Colab was used as an experimental platform to apply a pixel-based approach to extract features from three separate experiments. An approach to forgery detection using pixel base algorithm was tested in relation to multiple conditions within separate experiments involving CNNs integration. Experiment1, which had a rudimentary CNN architecture, achieved an accuracy of 94.53% and validation accuracy of 94.54%. Experiment 2, with a sophisticated CNN structure, resulted in an accuracy of 94.91%, and a validation accuracy of 94.92%
Wind farm sites selection using a machine learning approach and geographical information systems in Türkiye
Get PDFThis research highlights the importance of integrating machine learning algorithms with Geographical Information Systems (GIS) applications in the field of renewable energy by finding a suitable site for wind farms due to their importance in preserving the environment to achieve efficiency and cost-effectiveness and reduce the environmental impact of fossil fuel energy sources. Using GIS various factors affecting wind energy localization were processed and analyzed including natural, socio-economic and environmental criteria. Ensemble learning of four supervised machine learning algorithms (Random Forest, K-Nearest Neighbor, Support Vector Machines, Naive Bayes) was used to classify suitable and unsuitable data representing geo-referenced points on the ground with three criteria for each site (wind speed, elevation and slope). The results of the algorithms varied in terms of accuracy and variance, then the results were collected, and the intersection between them was found so that the location classification would be agreed upon in the results of the algorithms used. The aim of using this technique is to reduce the error, increase the accuracy and avoid the bias or variance present in individual models. Accuracy of the algorithms result was respectively (K-Nearest Neighbor, Random Forest, Support Vector Machines, Naive Bayes) (93.022%, 93.018%, 95.095%, 89.553%). The final result is a map using GIS showing the suitable and unsuitable sites of wind farms in the study area (Türkiye) has been chosen as a study area in the research due to several factors that make it suitable for wind energy projects, including its geographical location, which gives it great climatic and terrain diversity, as it is surrounded by seas (Black Sea, Aegean Sea, and Mediterranean Sea), which leads to the activity of seasonal and continuous winds, which contributes to the activity of seasonal and permanent winds. Its drive to develop investment in renewable energy due to economic and population growth has increased the demand for energy and consequently the development of renewable and sustainable energy sources. This research contributes to supporting the global transition to sustainable energy by providing a new methodology for integrating multiple technologies to support a sustainable energy future
Machine learning prediction of effective radiation doses in various computed tomography applications: a virtual human phantom study
Get PDFObjectives: In this work, it was aimed to employ machine learning (ML) algorithms to accurately forecast the radiation doses for phantoms while accounting for the most popular CT protocols.
Methods: A cloud-based software was utilized to calculate the effective doses from different CT protocols. To simulate a range of adult patients with different weights, eight entire body mesh-based computational phantom sets were used. The head, neck, and chest-abdomen-pelvis CT scan characteristics were combined to create a dataset with 33 rows for each phantom and 792 rows total. At the ML stage, linear (LR), random forest (RF) and support vector regression (SVR) were used. Mean absolute error, mean squared error and accuracy were used to evaluate the performances.
Results: The female phantoms received higher doses (7.8 %) than males. Furthermore, an average of 11 % more dose was taken to the normal weight phantom than to the overweight, the overweight in comparison to the obese I, and the obese I in comparison to the obese II. Among the ML algorithms, the LR showed 0 error rate and 100 % accuracy in predicting CT doses.
Conclusions: The LR was shown to be the best approach out of those used in the ML estimation of CT-induced doses