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The unseen struggle-depression and associated factors in geriatric cancer patients
(Frontiers Media, 2025) Doğan, Özlem; Şahinli, Hayriye; Yazılıtaş, Doğan; Kantarcı, Selen
Background: The objective of this study was to investigate the frequency of depression and its associations, rather than causal relationships, in patients aged 65 years and older receiving chemotherapy, using the Geriatric Depression Scale (GDS). Methods: This prospective study was conducted between January 2023 and December 2023 at Ankara Etlik City Hospital, including 501 chemotherapy patients aged 65 years and older. Patients receiving only oral therapy, those under palliative care, those with brain metastases, or those with insufficient cognitive functionality were excluded. Demographic and clinical data were collected from medical records. Depression was assessed using the 15-item Yesavage Geriatric Depression Scale (GDS), with scores ≥5 indicating high depression symptoms. Results: Among the 501 patients included in the study, 204 (40.7%) were female, with a median age of 69 years (range: 65–84 years). A total of 214 patients (42.7%) had high depressive symptom scores (GDS ≥ 5). A multivariable logistic regression analysis identified the following as independent predictors of depression: being female (odds ratio (OR): 1.481, 95% confidence interval (CI): 1.011–2.168, p = 0.04), body mass index (BMI) ≥ 21 (OR: 1.665, 95% CI: 1.081–2.564, p = 0.02), higher pain scores (OR: 1.269, 95% CI: 1.122–1.436, p < 0.001), insomnia (OR: 1.626, 95% CI: 1.109–2.384, p = 0.01), and weak social support (OR: 2.004, 95% CI: 1.046–3.839, p = 0.03). Conclusion: Our study highlights the high prevalence of depressive symptoms among geriatric cancer patients. In this population, early diagnosis and management of depression, with particular attention to independent risk factors such as pain and insomnia, as well as strengthening social support mechanisms, may be crucial for enhancing quality of life and improving treatment adherence.
Integrative machine learning approaches for enhanced cardiovascular disease prediction: a comparative analysis of XGBoost and ANFIS algorithms
(Springer, 2025) Muhyi, Diyar Fadhil; Ata, Oğuz
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the urgent need for advanced diagnostic tools to improve early detection and patient outcomes. This study evaluates the predictive performance of two machine learning models-Extreme Gradient Boosting (XGBoost) and the Adaptive Neuro-Fuzzy Inference System (ANFIS)-across five datasets from the UCI Machine Learning Repository: Cleveland, Hungary, Switzerland, Long Beach VA, and Statlog Heart. Comprehensive preprocessing steps-including imputation, standardization, one-hot encoding, and SMOTEENN-were applied to ensure data consistency and address class imbalance. XGBoost achieved perfect accuracy (100%) on the Switzerland and Statlog datasets, reflecting its strength in structured data environments and consistent predictive performance. Conversely, ANFIS outperformed XGBoost on the Cleveland dataset, demonstrating its effectiveness in modeling complex, nonlinear relationships. Performance evaluation metrics included accuracy, precision, recall, F1 score, F2 score, and ROC-AUC. XGBoost consistently delivered high precision and recall, which are essential for minimizing false positives and negatives in clinical settings. ANFIS yielded high F2 scores, indicating a stronger emphasis on reducing false negatives-a critical concern in CVD diagnosis. This comparative analysis suggests that while XGBoost is well suited for scalable, high-throughput diagnostic applications, ANFIS offers greater interpretability and is more effective in nuanced clinical scenarios. These findings underscore the potential of integrating advanced machine learning models into cardiovascular disease prediction frameworks to enhance diagnostic accuracy and support real-world healthcare decision-making.
An intelligent atrous convolution-based cascaded deep learning framework for enhanced privacy preservation performance in edge computing
(IOS Press, 2025) Siryeh, Fatima Abu; Ibrahim, Abdullahi Abdu
A system without any communication delays, called edge computing, has been introduced for nearer and faster services. The major concern in the edge computing scenario is its privacy risks. A user, as well as a cloud data preservation scheme, is the main aim of this paperwork. Test data is given by the user to access the cloud-based data processing framework. The training of the suitable model is carried out by utilizing the data stored in the cloud. The suggested model divides the entire model into two sections, namely, the untrusted cloud and the trusted edge. On the trusted edge side the data is directly provided to the developed advanced deep learning model called the Atrous Convolution based Cascaded Deep Temporal Convolution Network (ACC-DTCN) for the data analysis process. However, instead of giving the whole data directly to the untrusted cloud side, the test data is protected on the cloud side by utilizing a hybrid encryption technique called the Optimal Hybrid Encryption Model (OHEM). Both Attribute-Based Encryption (ABE) and Homomorphic Encryption (HE) are utilized in the recommended OHEM scheme. The OHEM variables are tuned with the help of an advanced algorithm called the Enhanced Ladybug Beetle Optimization algorithm (ELBOA). The confidence score vector among the testing and training data is predicted by the implemented ACC-DTCN model by utilizing the encrypted data on the cloud side. The suggested privacy preservation scheme provides higher prediction accuracy and prevents interference attacks while contrasting it against conventional methods during extensive experimentations.
Donor impact on allogeneic transplant outcomes with PTCy for severe aplastic anemia: a study of the SAAWP EBMT
(Scientific & Medical Division, 2025) Montoro, Juan; Eikema, Dirk-Jan; Piepenbroek, Brian; Tuffnell, Joe; Halahleh, Khalid; Kulagin, Alexander; AlAhmari, Ali; Adaklı Aksoy, Başak; Remenyi, Peter; Itala-Remes, Maija; Gülbaş, Zafer; McDonald, Andrew; Apte, Shashikant; Kwon, Mi; Rovira, Montserrat; Kharya, Gaurav; Potter, Victoria; Gambella, Massimilano; Schroeder, Thomas; Giammarco, Sabrina; Bazarbachi, Ali; Aljurf, Mahmoud; Ho, Aloysius; Dalle, Jean-Hugues; Vydra, Jan; Sanz, Jaime; Perez-Simon, Jose Antonio; Colita, Anca; Collin, Matthew; Tanase, Alina; Halkes, Constantijn; Kulasekararaj, Austin; Risitano, Antonio; de Latour, Regis Peffault
The use of post-transplant cyclophosphamide (PTCy) for graft-versus-host disease (GVHD) prophylaxis in severe aplastic anemia (SAA) remains understudied, particularly beyond haploidentical transplants. We analyzed outcomes of SAA patients who underwent stem cell transplantation (SCT) with PTCy from haploidentical donors (n = 209), HLA-matched sibling donors (MSD, n = 70), and unrelated donors (UD, n = 69) using EBMT data from 2010 to 2022. Median age was 22 years, and median time to transplantation was 8.6 months. For haploidentical, MSD, and UD cohorts, the 100-day cumulative incidence of grade II-IV acute GVHD was 19%, 11%, and 14% (p = 0.15), while grade III-IV was 6%, 3%, and 2% (p = 0.1). Two-year chronic and extensive chronic GVHD were 14%, 13%, and 14% (p = 0.1) and 5%, 6%, and 2% (p = 0.5), respectively. Non-relapse mortality at two years was 24% for haploidentical, 7% for MSD, and 10% for UD (p = 0.003). Two-year overall survival (OS) and GVHD- and relapse-free survival were 66% and 54% for haploidentical, 92% and 70% for MSD, and 81% and 66% for UD (p < 0.001, p = 0.06). In multivariable analysis, MSD and UD were associated with superior OS and GRFS compared to haploidentical. PTCy is safe and effective in SAA patients, though haploidentical SCT had higher NRM, leading to lower survival.
An enhanced attention and dilated convolution-based ensemble model for network intrusion detection system against adversarial evasion attacks
(Springer, 2025) Awad, Omer Fawzi; Çevik, Mesut; Farhan, Hameed Mutlag
Network Intrusion Detection System (NIDS) is a system for recognizing suspicious activities in the network traffic. Numerous machines learning and deep learning-aided IDSs have been implemented in the past, however, most of these techniques face challenges based on class imbalance issues and high false positive rates. Other primary problems of the conventional techniques are their vulnerability to adversarial attacks and also there is no analysis done on how NIDS sustain their performance over various attacks. Moreover, recent studies have demonstrated that while handling the attackers in real-time, the deep learning-based IDS shows slight variations in accuracy. To defend against adversarial evasion attacks, an enhanced deep learning-based NIDS model is designed in this work. For this purpose, at first, the required data is collected from available websites. From the collected data, effective features are extracted to improve the accuracy of the process. To select the optimal features, this work employed the Improved Cheetah Optimizer (ICO) that eliminates the unwanted features efficiently. Further, an Attention and Dilated Convolution based Ensemble Network (ADCEN) is implemented to detect the intrusions from the optimal features. The Deep Temporal Convolutional Neural Network (DTCN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) models are integrated to develop the ADCEN. The outcomes from each technique are considered for the fuzzy ranking mechanism to generate the final detected outcome. Thus, recognized intrusion is attained as the outcome and to demonstrate how well the recommended deep learning-based NIDS defends against adversarial evasion assaults, experiments are conducted against conventional models. The accuracy and the FPR values of the recommended model are 95 and 4.9 when considering the first dataset which is superior to the conventional techniques. Thus, the findings indicated that the implemented NIDS against adversarial evasion attacks attained more effective solutions than the baseline approaches.
SCEN-SCADA Security: An Enhanced Osprey Optimization-Based Cyber Attack Detection Model in Supervisory Control and Data Acquisition System Using Serial Cascaded Ensemble Network
(John Wiley and Sons Ltd, 2025) Alzubaidi, Fatimah Yaseen Hashim; Kurnaz, Sefer; Naseri, Raghda Awad Shaban; Farhan, Hameed Mutlag
A significant role of Supervisory Control and Data Acquisition (SCADA) systems is to support the operation of the energy system, where Information and Communication Technology (ICT) is utilized to interconnect devices, and this increases the system complexity. The interconnection of SCADA systems increases complexity and the potential for cybersecurity vulnerabilities. In addition, the SCADA networks with legacy devices are affected by inherent cybersecurity deliberation that has provided severe cybersecurity vulnerable points. With the adoption of local-area networks and Internet Protocol (IP)-driven proprietary, malicious or unauthorized user accesses the information from outside sources, and hence, the SCADA systems are weakened by the elaborate attacks. SCADA systems need to deliberate the Denial of Service (DoS) and catastrophic failure and maloperation, which may subsequently compromise the safety and stability of the operations in the power system. Therefore, the pertinent priority in SCADA is to strengthen cybersecurity to guarantee reliable operation, and also, the system stability is governed concerning communications integrity. The smart grid features are used in the conventional machine learning approaches for identifying cyber attacks. Hence, implementing an efficient and accurate cyber attack detection approach with less computational overhead is still a crucial research problem in SCADA. So, a novel and secure model for cyber attack detection in the SCADA system using advanced deep learning techniques together with the heuristic algorithm is executed in this research work. The SCADA data are collected from various power grids. The features from these data are optimally selected and fused with the optimal weights to obtain the weighted optimal features. The weighted optimal feature selection is done using the Enhanced Osprey Optimization Algorithm (EOOA). These optimally selected weighted features are given to the Serial Cascaded Ensemble Network (SCEN) to obtain the final detection output. The developed SCEN is made with the cascading of Autoencoder, Dilated Bidirectional Long Short Term Memory (Bi-LSTM), and Bayesian classifier. The parameters in the SCEN are tuned using the executed IOOA. The final detection of the presence or absence of a cyber attack is evaluated by this SCEN. The performance and the efficiency of the developed framework are confirmed and contrasted by conducting various experiments.
Effects of Caffeic Acid on Human Health: Pharmacological and Therapeutic Effects, Biological Activity and Toxicity
(Springer, 2025) Yazar, Memet; Sevindik, Mustafa; Uysal, Imran; Polat, Abdullah Ozan
Phenolic 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.
A Cross-sectional Analysis of Immunological and Hematological Parameters in Patients With Chronic Opioid Use
(Lippincott Williams & Wilkins, 2025) Ergelen, Mine; Usta Sağlam, Nazife Gamze; Arpacıoğlu, Mahmut Selim; Yalçın, Murat; İzci, Filiz
Background and Aim: Previous research has recognized the dual role of opioids [agonists at μ-opioid receptors (MOP-r agonists)] in modulating immunity and neuroinflammation in individuals with opioid use disorder (OUD). This cross-sectional study investigates the interplay between chronic use of MOP-r agonists and inflammatory parameters in individuals with OUD, with the goal of providing insights into the relationship between immunological responses and OUD. Materials and Methods: A cohort of 129 patients with OUD seeking treatment at an addiction detoxification center underwent detailed clinical assessments. Blood samples were collected for analyses of serum alanine aminotransferase, aspartate aminotransferase, and C-reactive protein levels, and a complete blood count. Participants were categorized into inflammation and noninflammation groups based on C-reactive protein levels. Hematological and inflammation indices, along with pain severity, were compared between these groups. Results: Significant differences were observed between the inflammation and noninflammation groups on variables such as duration of MOP-r agonist intake, daily buprenorphine/naloxone dose, consumption route, severity of withdrawal symptoms, and level of self-reported pain. The inflammation group exhibited higher neutrophil counts and an increased neutrophil-to-lymphocyte ratio. The binary logistic regression models revealed that self-reported pain level, daily buprenorphine/naloxone dosage, Beck Depression Inventory scores, and age were significant predictors of inflammation. Conclusions: This study contributes to our understanding of OUD as a chronic inflammatory condition, shedding light on the intricate relationships between MOP-r agonist addiction, inflammatory responses, and withdrawal-related parameters. The findings offer valuable perspectives on effective management, emphasizing the need for further research in diverse populations to enhance understanding of this complex condition.
Structural Changes in the Temporomandibular Joint After Botulinum Toxin Injection Into the Masseter Muscle in Experimentally Induced Osteoarthritis in Rats
(Blackwell Scientific Publications, 2025) Coşkun, Ümmügülsüm; Yılmaz Altıntaş, Nuray
Background: Botulinum neurotoxin (BoNT) injections into the masticatory muscles have been used as a treatment to improve symptoms related to temporomandibular joint (TMJ) disorders. However, its safety and long-term effects on TMJ structures remain inconclusive and are still under discussion. Objective: The purpose of this study is to evaluate whether the effects of BoNT injection into the masseter affect the mandibular condyle in a rat model of TMJ osteoarthritis (TMJ-OA). Methods: Sixteen male Wistar albino rats were used. The 32 TMJ joints were divided into four groups: (1) TMJ-OA with BoNT (OA + BTX), (2) TMJ-OA without BoNT (OA), (3) BoNT without TMJ-OA (BTX) and (4) control. TMJ-OA was induced by CFA injections. One week later, BoNT was administered to the masseter in the OA + BTX and BTX groups. Micro-CT imaging was performed 8 weeks later to assess the TMJ condyle. Results: The analysis revealed significant differences in bone mineral density and microarchitectural changes between the BTX/control and the OA/OA-BTX groups, except for trabecular separation (p < 0.05). The OA and OA + BTX groups exhibited lower bone volume fraction and bone mineral density compared to the BTX and control groups. No significant differences were observed between the BTX and the groups without BoNT, suggesting that BoNT did not result in bone loss in healthy TMJs or in TMJ-OA cases. Conclusions: BoNT does not have a significant effect on healthy or existing degenerative conditions in the TMJ. Long-term experimental studies and clinical trials are needed to validate the safety of BoNT in managing TMJ-OA.
Protocol for an umbrella review of systematic reviews evaluating the efficacy of digital health solutions in supporting adult cancer survivorship care
(Public Library of Science, 2025) Keane, Danielle; Calbimonte, Jean-Paul; Pawlowska, Ewa; Kassianos, Angelos P.; Medina, Joan C.; Gregorio, Joao; Serra-Blasco, Maria; Celebic, Aleksandar; Meglio, Antonio Di; Asadi-Azarbaijani, Babak; Foster, Claire; Donohoe, Claire L.; Mafra, Allini; Backes, Claudine; Ochoa-Arnedo, Cristian; Gezer, Derya; Bozkul, Gamze; Taşvuran Horata, Emel; Özkan, Esra; Prue, Gillian; İşcan, Gökçe; Dural, Gül; Bahçecioğlu, Gülcan; Ersöğütçü, Filiz; Berzina, Guna; Bektaş, Hicran; Vaz-Luis, Ines; Mlakar, Izidor; Rocha-Gomes, Joao; O'Connor, Mairead; Clara, Maria Ines; Karekla, Maria; Hagen, Marte Hoff; İmançer, Merve Saniye; Çöme, Oğulcan; Mevsim, Vildan; Aksoy, Nilay; Martins, Rui Miguel; Yokuş, Sıdıka Ece; Bayram, Şule Bıyık; Akçakaya Can, Aysun; Brandao, Tania; Saab, Mohamad M.; Bayar Muluk, Nuray; Yıldırım, Zeynep; Podina, Ioana R.; Karadağ, Songül; Erden, Sevilay; Semerci, Remziye; Aydın, Aydanur; Frountzas, Maximos; Üzen Cura, Şengül; Ruveyde, Aydın; Billis, Antonios; Calleja-Agius, Jean; Vojvodic, Katarina; Jaswal, Poonam; Şahin, Eda; Ilgaz, Ayşegül; Pilleron, Sophie; Hegarty, Josephine
Introduction The growing number of people living with, through and beyond cancer poses a new challenge for sustainable survivorship care solutions. Digital health solutions which incorporate various information and communication technologies are reshaping healthcare; offering huge potential to facilitate health promotion, support healthcare efficiencies, improve access to healthcare and positively impact health outcomes. Digital health solutions include websites and mobile applications, health information technologies, telehealth solutions, wearable devices, AI-supported chatbots and other technologically assisted provision of health information, communication and services. The breadth and scope of digital health solutions necessitate a synthesis of evidence on their use in supportive care in cancer. This umbrella review will identify, synthesise, and compare systematic reviews which have evaluated the efficacy or effectiveness of digital solutions for adult cancer survivorship care with a particular focus on surveillance and management of physical effects, psychosocial effects, new cancer/ recurring cancers and supporting health promotion and disease prevention. Methods and analysis An umbrella review of published systematic reviews will be undertaken to explore the types of digital health solutions used, their efficacy or effectiveness as a form of supportive care, and the barriers and enablers associated with their implementation. The umbrella review will be reported according to the Preferred Reporting Items for Overviews of Reviews (PRIOR) checklist. A search will be conducted across key databases. Records will be assessed independently by two review authors for eligibility against predefined criteria and will undergo two stage title, abstract and full text screening. All systematic reviews that meet the inclusion criteria will be assessed for quality using the AMSTAR 2 checklist with quality assessment and data extraction by two reviewers. The degree of publication overlap of primary studies across the included reviews will also be calculated and a mapping of the evidence will also be presented.
Efficacy and safety of first-line maintenance therapy with lurbinectedin plus atezolizumab in extensive-stage small-cell lung cancer (IMforte): a randomised, multicentre, open-label, phase 3 trial
(Elsevier, 2025) Paz-Ares, Luis; Borghaei, Hossein; Liu, Stephen V.; Peters, Solange; Herbst, Roy S.; Stencel, Katarzyna; Majem, Margarita; Şendur, Mehmet Ali Nahit; Czyzewicz, Grzegorz; Caro, Reyes Bernabe; Lee, Ki Hyeong; Johnson, Melissa L.; Karadurmuş, Nuri; Grohe, Christian; Baka, Sofia; Csoszi, Tibor; Ahn, Jin Seok; Califano, Raffaele; Yang, Tsung-Ying; Kemal, Yasemin; Ballinger, Marcus; Cuchelkar, Vaikunth; Graupner, Vilma; Lin, Ya-Chen; Chakrabarti, Debasis; Bhatt, Kamalnayan; Cai, George; Iannone, Robert; Reck, Martin; IMforte investigators
Background: Despite improved efficacy with first-line immune checkpoint inhibitors plus platinum-based chemotherapy for extensive-stage small-cell lung cancer (ES-SCLC), survival remains poor. In this study, we aimed to compare lurbinectedin plus atezolizumab and atezolizumab alone as maintenance therapies in patients with ES-SCLC without progression after induction therapy with atezolizumab, carboplatin, and etoposide. Methods: IMforte was a randomised, open-label, phase 3 trial done at 96 hospitals and medical centres in 13 countries (Belgium, Germany, Greece, Hungary, Italy, Mexico, Poland, South Korea, Spain, Taiwan, Türkiye, the UK, and the USA). Eligible patients were aged 18 years or older with treatment-naive ES-SCLC. Patients received four 21-day cycles of induction treatment (atezolizumab, carboplatin, and etoposide). After completing induction treatment, eligible patients without disease progression were randomly assigned (1:1) using permuted blocks (Interactive Voice/Web Response System) to receive maintenance treatment intravenously every 3 weeks with lurbinectedin (3·2 mg/m2; with granulocyte colony-stimulating factor prophylaxis) plus atezolizumab (1200 mg) or atezolizumab (1200 mg). The two primary endpoints were independent review facility-assessed (IRF) progression-free survival and overall survival, measured from randomisation into the maintenance phase. Efficacy endpoints were assessed in the full analysis set, which included all patients who were randomly assigned to maintenance phase treatment, regardless of whether they received their assigned study treatment. Safety was assessed in all patients who received at least one dose of lurbinectedin or atezolizumab, and was analysed according to the treatment received. This study is registered with ClinicalTrials.gov, NCT05091567, and is closed for recruitment. Findings: Between Nov 17, 2021, and Jan 11, 2024, 895 patients were screened for enrolment, of whom 660 (74%) were enrolled into the induction phase. Between May 24, 2022, and April 30, 2024, 483 (73%) of 660 patients entered the maintenance phase and were randomly assigned to lurbinectedin plus atezolizumab (n=242) or atezolizumab (n=241). At the data cutoff (July 29, 2024), IRF progression-free survival was longer in the lurbinectedin plus atezolizumab group than the atezolizumab group (stratified hazard ratio [HR] 0·54 [95% CI 0·43–0·67]; p<0·0001), as was overall survival (stratified HR 0·73 [0·57–0·95]; p=0·017). 92 (38%) of 242 patients in the lurbinectedin plus atezolizumab group and 53 (22%) of 240 patients in the atezolizumab group had grade 3–4 adverse events. The most common grade 3–4 events in the lurbinectedin plus atezolizumab group were anaemia (20 [8%] of 242 patients), decreased neutrophil count (18 [7%] patients), and decreased platelet count (18 [7%] patients) and the most common events in the atezolizumab group were hyponatremia (five [2%] of 240 patients), dyspnoea (four [2%] patients), and pneumonia (four [2%] patients). Grade 5 adverse events occurred in 12 (5%) of 242 patients in the lurbinectedin plus atezolizumab group and six (3%) of 240 patients in the atezolizumab group. The incidence of myelosuppressive toxicities (eg, neutropenia and leukopenia) was higher in the lurbinectedin plus atezolizumab group than the atezolizumab group. Interpretation: IRF progression-free survival and overall survival were longer in the lurbinectedin plus atezolizumab group than the atezolizumab group for patients with ES-SCLC, albeit with a higher incidence of adverse events. Lurbinectedin plus atezolizumab represents a novel therapeutic option for first-line maintenance treatment in this setting. Funding: F Hoffmann-La Roche and Jazz Pharmaceuticals.
A Novel Pancreatic Tumor Detection and Diagnosis Using Adaptive TransResUnet Aided Segmentation and ASPP with Multi-Scale EfficientNet-Based Classification
(Taylor and Francis Ltd., 2025) Athab, Naama Methab; Ibrahim, Abdullahi Abdu; Naseri, Raghda Awad Shaban; Farhan, Hameed Mutlag
A deadly disease with poor prognosis procedure available at present is the pancreatic tumor. Efficient detection is done using a Computer-Aided Diagnosis (CAD) system. The early detection of pancreatic tumors can enhance the survival rate. However, no sufficient works are dedicated to detect pancreatic tumors at its beginning stages. Hence, an advanced deep learning-oriented segmentation process to assist in the detection of pancreatic tumor is developed in this work. The necessary CT and MRI images are gathered from the utilization of IoT-based devices. Once the input image is gathered, the segmentation is carried out. An Adaptive TransResUnet (ATResUNet) is utilized for the segmentation procedure. The variables in the ATResUNet are tuned with the help of Improved African Vultures Optimization Algorithm (IAVOA). The segmented image is further considered to crop the Region of Interest (ROI). The cropped ROI is finally given as input to the suggested Atrous Spatial Pyramid Pooling-based Multi-scale EfficientNet with Attention Mechanism (ASPP-MENetAM) model. The detection of the pancreatic tumor is carried out using the ASPP-MENetAM framework. The detection outcome from the implemented ASPP-MENetAM is then compared with the results from other conventional pancreatic tumor detection models to assess the efficacy of the implemented detection system.
Association between clinical findings and 3T MRI features in temporomandibular joint disorders
(BioMed Central, 2025) Özel, Şelale; Tunç, Selmi; Şenol, Abdullah Utku
Background: Temporomandibular joint disorders (TMD) commonly cause restricted mouth opening and pain, significantly affecting patients’ quality of life. This study aims to explore the relationship between common clinical symptoms—clicking and limited mouth opening—and MRI findings in patients diagnosed with TMD. Methods: A total of 46 patients, with either clicking sounds or limited mouth opening, were examined using a 3T MRI scanner. The study evaluated disc position, disc deformity, and signs of osteoarthrosis, comparing MRI findings with clinical symptoms. Results: Results revealed that disc deformation was positively correlated with clicking. In contrast, limited mouth opening was significantly associated with anterior disc displacement without reduction and osteoarthrosis, indicating joint degeneration. Conclusions: The findings highlight that limited mouth opening is a more reliable clinical indicator of TMD than joint clicking, which may not always reflect underlying disc displacement. Although clicking was observed in discs with and without displacement, limited mouth opening showed a strong correlation with degenerative changes in the temporomandibular joint. The study underscores the reliability of clinical symptoms of TMD, which play a crucial role in treatment planing. Clinical trial number: Not applicable.
Detection Lung Nodules Using Medical CT Images Based on Deep Learning Techniques
(2025) Mohammed, Ali Abdulwahhab; Abdulwahhab, Ali H.; Ibraheem, Ibraheem Kasim
Lung nodule cancer detection is a critical and complex medical challenge. Accuracy in detecting lung nodules can significantly improve patient prognosis and care. The main challenge is to develop a detection method that can accurately distinguish between benign and malignant nodules and perform effectively under various imaging conditions. The development of technology and investment in deep learning techniques in the medical field make it easy to use Positron Emission Tomography (PET) and Computed Tomography (CT). Thus, this paper presents lung cancer detection by filtering the PET-CT image, obtaining the lung region of interest (ROI), and training using Convolution neural network (CNN)-Deep learning models for defending the nodules' location. The limitation dataset composed of 220 cases with 560 nodules with fixed Hounsfield Units (HU) is used to increase the training's speed and save data. The trained models involve CNN, DCNN, 3DCNN, VGG 19, ResNet 18, Inception V1, and Inception-ResNet to detect the lung nodules. The experiment shows high-speed training with VGG 19 outperforming the rest of deep learning, it achieves accuracy, Precision, Specificity, Sensitivity, F1-Score, IoU, FP rate with standard division; 98.65 f 0.22, 98.80 f 0.15, 98.70 f 0.20, 98.55 f 0.18, 98.60 f 0.16, 0.94 f 0.03, 1.05 f 0.22, respectively. Moreover, the experiment results show an overall error rate and a standard division between f 0.04 to f 0.54 distributed over the calculation terms.
A Questionnaire-Based Study on Use of Plants in Diabetic Patients
(Marmara Üniversitesi, 2025) Gürdal, Bahar; Toprak, Behiye
Objective: Among patients diagnosed with diabetes mellitus, we aimed to investigate the rates of use of medicinal plants, detailed information on use and purchase, socio-demographic characteristics associated with the tendency to use plants, and the status of reporting to health care professionals. Methods: The study was conducted in family health centers where patients diagnosed with diabetes mellitus visited. A questionnaire form of objective questions was filled for patients. The questionnaire included socio-demographic characteristics (age, gender, education) of the patients, disease condition, medicines used, and whether or not they used plants. If so, more information was obtained on the plants that are the name, used part, preparation method, frequency of administration, the place from which the plants was obtained, the person who advised the product, the knowledge of the physician and his/her attitude about herbals. Results: 100 people (66% women, 34% men) participated in the study between the ages of 24-80, 13% of them have Type 1 and 87% have Type 2 diabetes. Only 11% of patients use plants. Eight plants have been identified. The most commonly used plant (36.36%) is cinnamon. Among the usage of the plants, decoction is placed on the top, with 78%. 62.5% of the patients obtain the plants from herbal shops. Friends or relatives are the primary sources of information regarding medicinal plant use (73%). Conclusion: By increasing the knowledge of physicians about plants, it has been seen that patients can share their usage of plants more easily with physicians.
Anatomy exam model for the circulatory and respiratory systems using GPT-4: a medical school study
(Springer International, 2025) Tekin, Ayla; Karamus, Nizameddin Fatih; Çolak, Tuncay
Purpose: 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.
Naples Prognostic Score Predicts New-Onset Atrial Fibrillation in Patients with ST-Elevated Myocardial Infarction Undergoing Primary Angioplasty
(Sociedad Brasileira De Cardiologia, 2025) Okşen, Doğaç; Arslan, Şükrü; Heja Geçit, Muhammed; Ertürk Tekin, Esra; Oktay ,Veysel; Abacı, Okay
Background: New-onset atrial fibrillation (NOAF) is a typical complication in patients with ST-segment elevated myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (pPCI). Previous studies have investigated inflammation as a NOAF predictor. The Naples prognostic score (NPS) is a novel marker of inflammation and nutritional status. Objective: The objective of this study was to evaluate the predictive power of the NPS for NOAF. Methods: This study enrolled 1537 consecutive STEMI who underwent pPCI. The patients who presented NOAF during hospital admission and those who remained in sinus rhythm (RSR) were compared in terms of baseline characteristics. Univariate and multivariate analyses were carried out to identify variables predicting NOAF development, and p< 0.05 was considered statistically significant. Results: NOAF was detected in 7.74% (n: 119) of the participants. The mean age (67.03±13.48 vs 57.84±11.31; p <0.001) and NPS (2.53±1.17 vs 2.25±1.10, p=0.008) were significantly higher in the NOAF group. Multivariate analysis revealed age (Odds ratio [OR]: 1.045 for a year, 95% confidence interval [CI]: 1.019–1.071, p=0.001), NPS (OR: 1.645, 95% CI: 0.984–2.748, p=0.037) and left atrial dimensions (OR: 2.542 for cm, 95% CI: 1.488–4.342, p=0.001) as independent predictors of NOAF. Conclusions: The NPS was an independent predictor of NOAF in STEMI patients, in addition to classical factors such as age and left atrial dimensions. This score, mostly related to an inflammatory burden, may help to predict NOAF incidence and select better potential therapies aimed at abating inflammation after myocardial infarction.
Advances in vaccine adjuvant development and future perspectives
(Taylor and Francis Ltd., 2025) Sinani, Genada; Şenel, Sevda
Use of highly purified antigens to improve vaccine safety has led to reduced immunogenicity and efficacy, resulting in the need for adjuvants to increase and/or modulate the immunogenicity of the vaccine. Despite the need for potent and safe vaccine adjuvants, currently, there are still very few adjuvants in licensed human vaccines. Advances in immunology and molecular biology, especially in the last decade, have allowed researchers to understand better how the adjuvants work and enhance immune responses. While aluminum salts are still the most widely used adjuvants, research has shifted toward the rational design of adjuvant systems containing immunostimulatory molecules. Application of systems biology, which is based on high-throughput technologies using mathematical and computational modeling, has provided a deeper understanding of the biological events elicited by vaccination as well as the influence of other factors such as sex, age, microbiota, genetics and metabolism on the immune response. By this means, it became possible to tailor potential vaccine adjuvants more precisely for a successful vaccine with enhanced efficacy, safety and protection. In this review, after describing the mechanism of action of the adjuvants, current adjuvants in licensed vaccines, as well as those under clinical development will be mentioned in detail. Finally, new approaches in vaccine adjuvant development using systems biology and artificial intelligence will be reviewed, and future directions in vaccine research in regard to efficacy, safety and quality aspects will be discussed.
Optimizing Smart Grid Load Forecasting via a Hybrid Long Short-Term Memory-XGBoost Framework: Enhancing Accuracy, Robustness, and Energy Management
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Dakheel, Falah; Çevik, Mesut
As renewable energy sources and distributed generation become more integrated into modern power systems, accurate short-term electricity load forecasting is increasingly critical for effective smart grid management. Most statistical techniques used in the analysis of time series models, conventional statistical models, often fail to account for temporal dependencies and inherent non-linear patterns found in real-world energy time series. Methods: To this end, merging the power of both the ML approaches, namely Long Short-Term Memory (LSTM) networks and XGBoost, into hybrid frameworks has become a powerful solution. This work aims to develop a new compound model of LSTM for time series pattern extraction from the temporal data and XGBoost for outstanding predictive performance. To assess the performance of the proposed model, we used the Elia Grid dataset from Belgium, which includes load data recorded every 15 min throughout 2022. Results: When compared to individual models, this hybrid approach outperformed them, achieving a Root Mean Square Error (RMSE) of 106.54 MW, a Mean Absolute Percentage Error (MAPE) of 1.18%, and a coefficient of determination (R2) of 0.994. Discussion: In addition, this study implements an ensemble learning strategy by combining LSTM and XGBoost to improve prediction accuracy and robustness. An experimental attempt to integrate attention mechanisms was also conducted, but it did not enhance the performance and was therefore excluded from the final model. The results extend the literature on the development of fusion-based machine learning models for time series forecasting, and the future work of energy consumption analysis, anomaly detection, and resource allocation in SM grids.
Thin endometrium restricts peri-ovulatory physiological transition between anti-adhesive and adhesive receptivity modulators
(Reproductive Healthcare Ltd., 2025) Çelik, Önder; Erşahin, Aynur; Güngör, Nur D.; Uluğ, Ulun; Çelik, Nilüfer; Yardım, Meltem; Tektemur, Ehmet; Dalkılıç, Semih; Kuloğlu, Tuncay; Erşahin, Suat Süphan; Çelik, Sudenaz; Akkoç, Ramazan F.
Research question: Does endometrial thinning affect the physiological transition of the endometrium from a non-receptive to a receptive state? Design: Fifty-eight women who underwent total embryo freezing were divided into three groups according to their endometrial thickness (EMT): group 1, EMT ≤ 7 mm; group 2, 7 mm < EMT ≤ 10 mm; and group 3, EMT > 10 mm. Group 1 was considered as having a thin EMT and groups 2 and 3 as a normal EMT. Endometrial sampling was performed at the time of oocyte retrieval. Anti-adhesive podocalyxin (PDX), adhesive homeobox A10 and A11 (HOXA10, HOXA11) and leukaemia inhibitory factor (LIF) mRNA, proinflammatory cytokines, oxidative stress markers and collagen deposition were determined. Results: Compared with groups 2 and 3, the relative expression of HOXA10, HOXA11 and LIF mRNA was down-regulated in group 1 (all P < 0.001), while PDX levels significantly increased (P < 0.001). The nuclear factor-κB, long pentraxin 3 (PTX3), tumour necrosis factor-α and total oxidant status of the thin endometrium group were significantly higher than those of participants with normal endometrium (all P < 0.001), while total antioxidant status was significantly lower (P < 0.001). The histoscore values for PTX3, PDX and Masson's trichrome were significantly higher in the thin endometrium group (P < 0.001 for each). Each millimetre increase in EMT decreased the risk of down-regulation of adhesive receptivity genes. The adjusted odds ratio for PDX was 1.69, representing a 69% increase in PDX expression. Conclusion: Endometrial thinning causes defective expression of anti-adhesive and adhesive receptivity modulators, restricting the transition of the endometrium from a non-receptive to a receptive state.

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