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Can Posttransplant Cyclophosphamide Reduce the Risk of Graft-Versus-Host Disease for Pediatric Fully Matched Nonrelated Stem Cell Transplant?
No full textObjectives: Allogeneic stem cell transplant is the only curative strategy for a variety of pediatric malignant/nonmalignant diseases. The introduction of posttransplant cyclophosphamide therapy as prophylaxis for graft-versus-host disease in haploi-dentical bone marrow transplant setting has been a game changer and is also being investigated for its benefits in matched hematopoietic stem cell transplants. The effect of posttransplant cyclophosphamide therapy for prophylaxis of graft-versus-host disease for fully matched hematopoietic stem cell transplants remains unclear for pediatric patients. As a part of conditioning regimen, antithymocyte globulin allows in vivo T-cell depletion for graft-versus-host disease prophylaxis and is known to reduce graft-versus-host disease incidence without increasing relapse. We investigated whether posttransplant cyclophosphamide therapy has an additive effect with antithymocyte globulin to reduce acute graft-versus-host disease and chronic graft-versus-host disease in pediatric cases of peripheral blood stem cell transplant.
Materials and methods: We conducted a retrospective clinical study of 110 pediatric patients who underwent pediatric blood stem cell transplant from matched unrelated donors. Fifty randomly selected patients received 50 mg/kg/d cyclophosphamide on post-transplant days 3 and 4 (group 1); the remaining 60 patients (group 2) did not receive cyclophosphamide. All patients were given a regimen of standard immunosuppression as graft-versus-host disease prophylaxis.
Results: Patients were followed for a median of 2 years. The incidence of grade 2-4 and grade 3-4 acute graft-versus-host disease was 28% and 18%, respectively, in group 2, and 20% and 10%, respectively, in group 1. The incidence of chronic graft-versus-host disease was 8.3% in group 2 and 4% in group 1. Differences between groups were not significant.
Conclusions: Our findings suggested no effect of post-transplant cyclophosphamide therapy for pediatric patients undergoing blood stem cell transplant with matched unrelated donors. Larger studies are needed to investigate the reasons for differences between pediatric and adult patients
Real-world outcomes of autologous whole blood clot therapy for venous leg ulcers
No full textObjective: Venous leg ulcers (VLUs) are hard-to-heal wounds primarily caused by venous insufficiency and venous hypertension. These wounds pose significant clinical and economic burdens, often failing to heal with standard compression therapy alone. Autologous whole blood clot (AWBC) therapy has emerged as a potential treatment for hard-to-heal wounds, complementing the body's natural wound healing mechanisms. This study aims to evaluate the outcomes of AWBC in a real-world setting for treating VLUs that have not responded to conventional therapies.
Method: A multicentre observational registry study was conducted between August 2021 and December 2024 (NCT04699305) across multiple countries. Patients with hard-to-heal VLUs were included to receive AWBC application. Median wound duration at baseline was 13.5 months (interquartile range: 5.25, 36.0). AWBC applications were used alongside compression therapy, and outcomes were assessed in terms of percentage area reduction (PAR) and complete wound healing.
Results: There were 56 patients in the study cohort. AWBC treatment resulted in a mean wound area reduction of 71.3%. Complete healing was achieved in 45% of patients, while 54% exhibited a PAR >90%. Among the wounds treated, 44% that had persisted for >1 year achieved complete healing. Treatment duration varied, with some patients requiring extended therapy (12-20 weeks) to achieve significant wound progression. Adverse events were minimal and unrelated to treatment.
Conclusion: In this study, AWBC therapy demonstrated high levels of effectiveness in treating hard-to-heal VLUs, particularly in patients whose wounds had failed to heal with standard compression therapy. AWBC therapy provides a supportive extracellular matrix, modulating inflammation and enhancing wound healing, demonstrating its valuable conjunction treatment to existing compression treatment protocols
Association of sarcopenia and pancreatic exocrine insufficiency in older adults type 2 diabetes mellitus patients
Get PDFBackground and aimAn association between type 2 diabetes mellitus (T2DM) and pancreatic exocrine insufficiency (PEI) has been increasingly recognized. Sarcopenia is also common among older adults with T2DM, contributing to poor functional outcomes and increased frailty. Recent findings indicate a potential link between PEI and sarcopenia. The present study aimed to investigate the relationship between PEI and sarcopenia in older adults patients with T2DM, independent of other contributing factors.MethodsA total of 120 patients aged >= 65 years with T2DM were enrolled in the study. Demographic characteristics, diabetes-related clinical data, and malnutrition risk were assessed. Muscle strength, muscle mass, physical performance, and fecal elastase-1 levels were measured. Sarcopenia was diagnosed according to the criteria defined by the European Working Group on Sarcopenia in Older People 2 (EWGSOP2).ResultsPEI was present in 22.5% of patients, including 10% with severe and 12.5% with mild-to-moderate insufficiency Low muscle mass was observed in 15% of participants, while confirmed sarcopenia was identified in 10%. Patients with reduced hand grip strength exhibited significantly lower Mini Nutritional Assessment - Short Form (MNA-SF) scores and serum vitamin D levels; however, in male patients, only MNA-SF was significantly lower. The prevalence of PEI was significantly higher in males with probable sarcopenia compared to those without (34.8% vs. 10%, p = 0.05). In multivariate analysis among men, obesity showed a borderline significant association with probable sarcopenia, whereas PEI demonstrated a nonsignificant trend toward association.Discussion and conclusionPEI may contribute to sarcopenia in older adults males with T2DM and should be considered in the clinical evaluation of sarcopenic patients.Funding agency : Bilimsel Araştırma Projeleri Birimi, İstanbul Üniversitesi.
Grant Number : TTU-2022-38661
Artificial Intelligence Techniques for Renewable Energy Based Smart Grid Management in Smart Cities
No full textIn the last decade, there has been an increase in environmental awareness and the responsibility of the energy industries to minimize environmental changes has been clear. Consequently, production levels using renewable energy sources have increased. The incessant growth of energy and environmental awareness leads to the search for a safe and stable electrical network, with high quality and reliability in the service provided to consumers. Renewable Energy Sources (RES) have been a common objective in the development of electrical energy at a global level. This study analyzes the issue of maximizing the production of active power in an electrical energy network. Specifically, it aims to determine which fuzzy rules can potentially maximize the value of this power. An algorithm known as Fuzzy-Particle Swarm Optimization (FL-PSO) was modified and encoded to apply to the proposed scenario across a variety of networks. Our experimental results, demonstrated in the attached figures, show significant improvements when comparing FL-PSO with state-of-the-art algorithms such as Greedy, FID, and traditional fuzzy logic applications (FuzzyN). For instance, after optimization, the total improvement across all households was striking, as illustrated by the reduction in annual average costs from €785 to €760, highlighting the efficiency of FL-PSO. Additionally, the ‘Best Setting’ figure clearly demonstrates a peak in performance at the third setting of our algorithm, emphasizing its optimal configuration, making this study a pioneering approach to integrating fuzzy logic with swarm intelligence for power optimization in electrical networks. Future work will focus on refining these fuzzy decision rules and expanding the adaptability of FL-PSO to further enhance its application to more diverse network configurations
A Lightweight Multimodal Deep Fusion Network for Face Antis Poofing with Cross-Axial Attention and Deep Reinforcement Learning Technique
Get PDFFace antispoofing has received a lot of attention because it plays a role in strengthening the security of face recognition systems. Face recognition is commonly used for authentication in surveillance applications. However, attackers try to compromise these systems by using spoofing techniques such as using photos or videos of users to gain access to services or information. Many existing methods for face spoofing face difficulties when dealing with new scenarios, especially when there are variations in background, lighting, and other environmental factors. Recent advancements in deep learning with multi-modality methods have shown their effectiveness in face antispoofing, surpassing single-modal methods. However, these approaches often generate several features that can lead to issues with data dimensionality. In this study, we introduce a multimodal deep fusion network for face anti-spoofing that incorporates cross-axial attention and deep reinforcement learning techniques. This network operates at three patch levels and analyzes images from modalities (RGB, IR, and depth). Initially, our design includes an axial attention network (XANet) model that extracts deeply hidden features from multimodal images. Further, we use a bidirectional fusion technique that pays attention to both directions to combine features from each mode effectively. We further improve feature optimization by using the Enhanced Pity Beetle Optimization (EPBO) algorithm, which selects the features to address data dimensionality problems. Moreover, our proposed model employs a hybrid federated reinforcement learning (FDDRL) approach to detect and classify face anti-spoofing, achieving a more optimal tradeoff between detection rates and false positive rates. We evaluated the proposed approach on publicly available datasets, including CASIA-SURF and GREATFASD-S, and realized 98.985% and 97.956% classification accuracy, respectively. In addition, the current method outperforms other state-of-the-art methods in terms of precision, recall, and F-measures. Overall, the developed methodology boosts the effectiveness of our model in detecting various types of spoofing attempts
A machine learning approach for early-stage prediction of chronic kidney disease
No full textChronic Kidney Disease (CKD) is a serious, lifelong condition that imposes a substantial financial burden, especially with costly end-stage treatments. Timely prediction and early interventions can decelerate the advancement of this chronic condition, preserving the patient's life. This study investigates the effectiveness of employing sustainable machine-learning methodologies to predict chronic kidney disease at an early stage. While machine learning has been integrated into the diagnosis of this chronic condition with a major focus on performance optimization, this study proposes a novel majority voting approach for feature selection to leverage diverse perspectives from various feature selection methods. This mechanism is designed to promote consensus and generate a holistic, well-rounded assessment of features' importance using various methods including filters and wrappers, ultimately reducing 75% of features from the original dataset. A benchmarking analysis of the eight well-known machine-learning algorithms in such sustainable methodology settings indicated that random forest algorithm got the best prediction performance with an Area Under the Curve (AUC) of 0.934, accuracy of 0.916, and recall of 1. The experimental procedure concludes that advances in such sustainable machine learning methodologies, particularly with a majority voting approach, seem to be one of the promising frameworks in early prediction of chronic kidney disease and perhaps beyond
Melatoninin MCF-7 Hücre Kültüründeki Apoptoz Aktivasyonunun ve Sitotoksisitesinin Polimeraz Zincir Reaksiyonu (PCR), MTT Hücre Canlılık Testi ve İmmunsitokimya Yöntemleriyle Araştırılması
No full textAmaç: Dünyada ve Türkiye’de en sık görülen kanser türlerinden biri olan meme kanseri, kanser nedenli ölüm oranları arasında ilk sıralarda yer almaktadır. Bu nedenle bu hastalığın tedavisine yönelik yeni araştırmalar yapılmaktadır. Bu çalışmada, güçlü bir antioksidan olan melatoninin kanserle olan ilişkisinin ve kanser hastalığının tedavisi yönündeki katkısının araştırılması amaçlanmıştır. Gereç ve Yöntemler: Çalışmamızda, melatoninin sitotoksik dozlarını ve IC50 değerini belirleyebilmek için MCF-7 hücre hattına 10 nM–100.000 nM aralığında melatonin konsantrasyonları uygulandı. Melatonin uygulamasının ardından hücre canlılığı analizi yapıldı ve melatonin için etkin dozlar belirlendi. MCF-7 hücreleri, 5 konsantrasyon (10, 100, 1000, 10.000 ve 100.000 nM) melatonin dozu ile 24 saat inkübasyon işlemine tabi tutuldu. Tüm gruplarda melatoninin sitotoksisitesi, toplam antioksidan kapasite ve toplam oksidan düzeyi, apoptotik aktivite (Bax ve p53 immünopozitifliği) ve p53 gen ekspresyon düzeyleri incelendi. Bulgular: Yirmi dört saatlik inkübasyon sonunda MCF-7 hücrelerine uygulanan melatoninin hücre proliferasyonunu inhibe ederek sitotoksik etki yaptığı (p<0.05), p53 gen ekspresyonunu ve Bax protein sentezini artırdığı tespit edildi. İmmünsitokimyasal boyamada Bax ve p53 immünpozitifliğinin arttığı belirlendi. Ayrıca melatonin tedavisi TAS’ı artırıp TOS’u azalttı. Sonuç: Bu etkiler, melatoninin p53 gen ekspresyonu ve Bax proteinindeki artışa bağlı olarak apoptoz aktivasyonunu indüklediğini ve dolayısıyla kanser tedavisinde kullanılabilecek yardımcı bir ajan olabileceğini göstermektedir
A novel quad directional RNN model for cyber attack detection and prevention
Get PDFIn an era marked by escalating cyber threats, traditional security measures struggle to
contend with the surging prevalence of cyber-attacks. To address this challenge, we present
a groundbreaking solution in the form of the Quad Directional Recurrent Neural Network
(Quad-RNN). This novel architecture, featuring four directions of input and output,
amalgamates the strengths of Bidirectional RNNs (BRNNs) and Simple RNNs. Our
evaluation, conducted on the NSL-KDD and DDoS datasets, establishes the superiority of
Quad-RNN over BRNN and Simple RNN counterparts. Demonstrating enhanced accuracy,
precision, recall, and F1 score, the Quad-RNN architecture notably diminishes false
positives. This research heralds a pivotal advancement in the realm of cyber-attack
detection and prevention, addressing the imperative for resilient and adaptive security
measures in the face of evolving threats
Analyzing effects on anterior open bite in twins by PLS-SEM and sobel test
Get PDFObjective: This study aimed to assess the different pathways between predictor factors such as zygosity, atypical swallowing, mouth breathing, breastfeeding and bottle feeding related to anterior open bite (AOB) in twins.
Methods: The study was conducted in monozygotic (MZ) and dizygotic (DZ) twin children aged 3-15 years. AOB, atypical swallowing, mouth breathing, feeding type, duration of bottle use, and mouth opening status during sleep were recorded during oral examination. Partial least squares structural equation model (PLS-SEM) and sobel tests were performed to assess the total and indirect effects among the variables on AOB.
Results: A total of 404 children (29.2% MZ;70.8% DZ) participated in this study. The effect of zygosity on mouth breathing in the PLS-SEM model was statistically significant. Conversely, it was determined that mouth breathing effected that atypical swallowing (p = 0.001). Atypical swallowing triggered AOB (p = 0.001). The atypical swallowing has a mediation effect between AOB and mouth breathing (p = 0.020). Mouth breathing causes atypical swallowing and therefore indirectly increases the likelihood of AOB. While breastfeeding decreases AOB incidence (p = 0.023), bottle feeding increases AOB incidence (p = 0.046). The sobel tests show that the fully mediator variable feature of mouth breathing is statistically significant in the negative relation between zygosity and atypical swallowing.
Conclusion: The PLS-SEM model showed that mouth breathing triggers atypical swallowing and atypical swallowing triggers AOB. As a result of this chain of relationships, an indirect effect of zygosity on AOB was observed. According to sobel tests, zygosity has an indirect effect on atypical swallowing through mouth breathing, while mouth breathing has a positive indirect effect on AOB through atypical swallowing.
Clinical relevance: This study identified the relationships between different factors and the presence of AOB. The findings of this study demonstrate in detail the relationships between AOB and zygosity, atypical swallowing, mouth breathing, breastfeeding and bottle feeding. Brestfeeding has a reducing effect on the frequency of AOB. Among the nutritional forms, breastfeeding ensures the proper development of the stomatognathic system by working the oro-facial muscles
Explainable AI for predicting user behavior in digital advertising
No full textOnline advertising has ushered in a new era of digital communication and business transformation. However, the inundation of digital content necessitates a deeper understanding of user behavior to ensure meaningful engagement. This paper investigates the potential of machine learning in predicting and analyzing user behavior in the realm of online advertising. Utilizing a dataset encompassing user interactions with advertisements, we deployed three machine learning models: Random Forest, Logistic Regression, and Gradient Boosting. Our findings highlight that the Random Forest model outperformed with an accuracy of 97.67%, followed closely by Logistic Regression and Gradient Boosting. Furthermore, recognizing the opaque nature of machine learning models, our research leverages SHAP and LIME, tools of explainable AI, ensuring that our models’ decisions remain interpretable. This study under-scores the power of a data-driven approach in online advertising, emphasizing the necessity for both precision and transparency in this digital age