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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.
BCI-DRONE CONTROL BASED ON THE CONCENTRATION LEVEL AND EYE BLINK SIGNALS USING A NEUROSKY HEADSET
(University of Kufa, 2025) Mohammed, Ali Abdulwahhab; Abdulwahhab, Ali H.; Abdulaal, Alaa Hussein; Mahmood, Musaria Karim; Myderrizi, Indrit; Yassin, Riyam Ali; Abdulridha, Taha Talib; Valizadeh, Morteza
Brain neurons activate Human movements by producing electrical bio-signals. Neuron activity is used in several technologies by operating their applications based on mind waves. The Brain-Computer Interface (BCI) technology enables a processor to connect with the brain using a signal received from the brain. This study proposes a drone controlled using EEG signals acquired by a Neurosky device based on the BCI system. Two active signals are adapted for controlling the drone motions: concentration brain signals portrayed by attention level and the eye blinks as an integer value. A dynamic classification method is implemented via a Linear Regression algorithm for attention-level code. The eye blinking generates a binary code to control the drone's motions. The accuracy of this code is improved through Artificial Neural Networks and Machine Learning techniques. These codes (attention level and eye blink codes) drive two controlling layers and manipulate nine possible drone movements. The experiment was evaluated with several users and showed high performance for the classification methods and developed algorithm. The experiment shows a 90.37% accuracy control that outperforms most existing experiments. Also, the experiment can support 16 commands, making the algorithm appropriate for various applications.
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.
Boosting Multiverse Optimizer by Simulated Annealing for Dimensionality Reduction
(Airlangga University, 2025) Mutlag, Wamidh K.; Mazher, Wamidh Jalil; Ibrahim, Hadeel Tariq; Uçan, Osman Nuri
Background: Because of The Multi-Verse Optimizer (MVO) has gained popularity in feature selection due to its strong global and local search capabilities. However, its effectiveness diminishes when tackling high-dimensional datasets due to the exponential growth of the search space and a tendency for premature convergence. Objective: This study aims to enhance MVO’s performance by integrating it with the Simulated Annealing Algorithm (SAA), creating a hybrid model that improves search convergence and optimizes feature selection efficiency. Methods: A High-level Relay Hybrid (HRH) architecture is proposed, where MVO identifies promising regions of the feature space and passes them to SAA for local refinement. The resulting MVOSA-FS model was evaluated on ten high-dimensional benchmark datasets from the Arizona State University (ASU) repository. Support Vector Machine (SVM) classifiers were used to assess the classification accuracy. MVOSA-FS achieved superior performance compared to six state-of-the-art feature selection algorithms: Atom Search Optimization (ASO), Equilibrium Optimizer (EO), Emperor Penguin Optimizer (EPO), Monarch Butterfly Optimization (MBO), Satin Bowerbird Optimizer (SBO), and Sine Cosine Algorithm (SCA). Results: The proposed model yielded the lowest average classification error rate (1.45%), smallest standard deviation (0.008), and most compact feature subset (0.91%). The hybrid MVOSA-FS model effectively balances exploration and exploitation, delivering robust and scalable performance in feature selection for high-dimensional data. Conclusion: This hybridization approach demonstrates improved classification accuracy and reduced computational burden.
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.
Liquefaction Behavior of Suction Bucket Foundations for Wind Turbines Under Soil Variability
(Springer Science and Business Media Deutschland GmbH, 2025) Jasim Khammas Issa, Mohammed; Abdelsalam, Marwan; Hashim Khudhair, Liqaa; Seyedi, Mohsen; Anwar Khan, Mohammad Shouib
This paper investigates the dynamic performance of suction bucket foundations supporting wind turbines in fully and partially saturated soils, a topic that has received limited attention in geotechnical research. Numerical analyses using PLAXIS2D were conducted to study the effects of relative density and degree of saturation on the dynamic behavior of suction bucket foundations installed in loose to dense sandy soils under fully and partially saturated conditions. The models were subjected to strong ground motion and cyclic wind loads, with responses such as excess pore pressure ratio, settlement, rotation, and spectral acceleration of the suction bucket foundation evaluated. The results demonstrate that partial saturation significantly reduces the settlement and rotation of suction bucket foundations compared to fully saturated sands, particularly in dense soils. However, partially saturated soils also transfer higher accelerations to the structure. These findings suggest that partial saturation can be a viable liquefaction mitigation technique for wind turbines, offering improved performance without disrupting turbine operation.
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.
An Efficient IoT Intrusion Detection System Based on Machine Learning Approaches
(American Institute of Physics, 2025) Jasim, Abdulrahman Ahmed; Hazim, Layth Rafea; Ata, Oğuz; Ilyas, Muhammad
The Internet of Things (IoT) has advanced quickly and has been integrated into many different fields. With the use of this technology, gadgets have the ability for sending, receiving, and processing data automatically. IoT was rapidly accepted in many important fields since it makes life easier and boosts service quality, but privacy and security concerns are still significant problems. Intrusion Detection System (IDS) could be used as a security feature to protect IoT networks from a variety of cyber-attacks, which is a relief. This study suggests the utilization of the IDS for defending against various cyber-attacks in IoT systems. The suggested approach makes use of Random Forest (RF), Multi-layer Perceptron (MLP) to increase the detection rate, we use the pipeline to put together some processes that may be cross-validated against one another. A contemporary dataset was utilized for assessing and analyzing the performance results for validating the effectiveness of the suggested IDS approach. The evaluation findings show that the suggested IDS method may greatly increase detection performance results concerning accuracy rate while also improving detection efficiency. F1-Score, Recall, and Precision, The performance metrics demonstrate that the suggested approach produces significant outcomes, particularly when employing the pipeline with all dataset features, where the model achieved a very high result of 95.13 %.
Fractal Geometry in Hospital Design: Enhancing Healing Environments Using Fractal Fluency
(American Institute of Physics, 2025) Hasan, Saja; Uzun, Can; Sönmez, Elif
This research explores the impact of fractal geometry on hospital design, with a focus on "fractal fluency." Fractals, defined as intricate self-replicating patterns, which are commonly found in both nature and art, have fascinated mathematicians, scientists, architects, and psychologists for generations. This study thus investigates how these fractal patterns, particularly those of moderate complexity, can influence human perceptions, well-being, and cognition, using a multidisciplinary approach that combines mathematics, neuroscience, and architecture to examine the integration of fractal fluency principles in hospital design. Through a detailed analysis of architectural elements such as the material, space, light, form, and perspective in hospitals including Jacobs Medical Center and Friendship Hospital, the study assesses the implementation of design principles based on such patterns and assesses their impact on the healing environment. The results show that the design of Friendship Hospital is in good alignment with fractal fluency principles, with all architectural elements falling within the specified range, suggesting the use of a deliberate design approach that can potentially create a visually engaging and stress-reducing environment. In contrast, Jacobs Medical Center does not align with fractal fluency principles, thus failing to realize the potential benefits of mid-range fractals as used in design. The impact on occupants of these varying approaches appears to be significant: Friendship Hospital's alignment with fractal fluency principles appears to contribute to enhanced comfort, positive psychological effects, and a sense of positive energy, while Jacobs Medical Center, while not necessarily having a negative environment, displays none of the advantages of intentional fractal fluency in design. This research thus emphasizes the importance of fractal fluency in hospital design based on its potential to create healing environments that promote physical and emotional well-being. Incorporating mid-range fractals can enhance patient experiences, reduce stress levels, and contribute to the development of positive energy in healthcare spaces. By combining mathematics, neuroscience, and architecture, this study also opens the way to the development of more innovative approaches in healthcare design that prioritize the language of fractals.
The comorbidities of hidradenitis suppurativa
(J.B. Lippincott, 2025) Aşkın, Özge; Ferhatoğlu, Özge Altan; Özkoca, Defne; Küçükoğlu Cesur, Seher; Tüzün, Yalçın
Hidradenitis suppurativa is a chronic inflammatory disease that dramatically decreases the quality of life of afflicted patients. A number of factors may coexist with hidradenitis suppurativa, including stigmatization, social isolation, tobacco use, alcohol abuse, suicidal ideation, depression, other psychiatric disorders, and medical comorbidities such as obesity, diabetes mellitus, hypertension, dyslipidemia, metabolic syndrome, coronary artery disease, and polycystic ovarian syndrome. These comorbidities should be kept in mind while planning the treatment. A rare but important long-term complication of hidradenitis suppurativa is squamous cell cancer; men with perianal, gluteal, or perineal lesions are at increased risk, and multiple biopsies should be taken in case of any suspicious lesions.
Employing Innovative Energy-Saving and Optimization Techniques for A Zero-Energy Consumption Building: A Case Study in Turkey
(American Institute of Physics, 2025) Mwafaq, Baraa; Alaiwi, Yaser
This research was guided to identify various vital contributions and beneficial impacts of innovative energy-saving methods and modern energy-efficient approaches to minimize excessive energy consumption in facilities based on other evaluation methods that were not employed greatly or addressed broadly in the current literature, namely MATLAB and Python Simulation processes. To achieve the goal of this article, a case study was considered and analyzed, representing a building in Turkey (240 m2) with significant cooling demands requiring to be optimized and mitigated, helping provide a zero-energy consumption facility that relies only on passive cooling techniques and thermal insulation materials. Simulations and optimization procedures were adopted to explore critical gains of energy-saving mechanisms using Python and MATLAB. According to the numerical research outcomes and simulation work, the research revealed that utilizing modern and practical energy-saving approaches could reduce harmful relative humidity values from 60.4% to 25.1%, corresponding to active prevention of mold, mildew, and microbes. Also, the overall internal temperature declined from 29.8 ºC to 21.3 ºC, and cooling load reduction from 216.8 kW to 99.5 kW, before and after implementing functional energy efficiency solutions, respectively. Furthermore, it was determined that deploying those novel energy-saving technologies, passive cooling and heating, and functional insulation materials would reduce the overall annual budget needed for cooling requirements from 103.1 USD to 49.7 USD. These aspects, in turn, could fulfill beneficial thermal comfort and alleviate the yearly generation of Greenhouse Gas (GHG) emissions and make this facility more ecologically friendly and sustainable.
THE COMPARISON OF MICRORNAS INVOLVED IN SMALL CELL LUNG CANCER AND NON-SMALL CELL LUNG CANCER THROUGH BIOINFORMATICS ANALYSES
(University of Ankara, 2025) Hekmatshoar, Yalda; Karadağ Gürel, Aynur; Aydoğan, Adem
Objective: Lung cancer is a major cause of cancer-related deaths worldwide. There are two main types of lung cancer, small cell and non-small cell. Finding new methods for achieving a good prognosis, developing targeted therapy and identifying potential biomarkers is crucial for improving the clinical efficacy of lung cancer. The aim of this study was to investigate the pathogenesis and potential molecular markers by finding differentially expressed miRNAs in 2 subtypes of lung cancer. Materials and Methods: The datasets GSE19945 and GSE135918 containing miRNA data were downloaded from the GEO database. Analyzed with GEO2R online analysis tool with P<0.05 and log fold change |(FC)|≥ 1. Target genes of differentially expressed miRNAs have been identified. Network visualisation and module identification were performed using Cytoscape PPI. Three of the miRNA target genes were selected and validation of the genes was performed in the non-small cell lung cancer cell line A549. Results and discussion: 17 common miRNAs with decreased expression and 2 with increased expression include hsa-miR-1249, hsa-miR-326, hsa-let-7c, hsa-miR-199a-5p, hsa-miR-940, hsamiR-139-3p, hsa-miR-142-3p, hsa-miR-142-5p, hsa-miR-455-5p, hsa-miR-146b-5p,hsa-miR-152hsa-miR-133b, hsa-miR-498, hsa-miR-199b-5p, hsa-miR-140-3p, hsa-miR-203 and hsa-miR-139-5p. Defining the molecular functions and signaling pathways of miRNAs may deepen the current understanding of the molecular mechanisms of the 2 cancer types and contribute to the development of treatment options.

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