Search Results (5,092)

Background: Disease trajectories in leukemia are often unpredictable and recurrent, and patients’ experiences can impact their quality of life. Studies in the literature often do not explore gender-related illness experiences from an intersectional approach and throughout the illness trajectory. This comprehensive study aims to explore the full spectrum of experiences lived by women with leukemia throughout the disease trajectory, from diagnosis to treatments and post-stem cell transplant follow-up. Method: A grounded theory approach was meticulously developed to analyze semi-structured interviews with 13 women with leukemia in the post-transplant follow-up phase at a hospital in Northern Italy. The data analysis was an iterative process, conducted concurrently using a constant comparative method. Data collection concluded when data saturation was reached. Results: The core category identified is women’s inner strength during the disease trajectory, which was identified for its recurrence and cross-cutting nature, according to the women. This core category interconnects with five main categories: 1. Facing the disease: Between resistance and surrender. 2. Living for today and moving forward. 3. Unexpected elements in relationships. 4. Changes that shape women. 5. Demystifying the body and embracing ‘diminished beauty’. Conclusions: An explanatory model of the disease trajectory of women with leukemia was defined as: ‘Women with leukemia, between inner strength and fighting prejudice’. An in-depth analysis of the disease experiences revealed aspects that are not easily understood through a purely biological perspective of gender differences, often overlooking the psycho-social and relational peculiarities of women. Full article

This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which is the total amount of energy generated as an input. The MLR implementation involves multiple input variables being taken from various energy sources, including gas, coal, geothermal, wind, water, biomass, oil, etc. All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. By analyzing energy production data from previous periods of time, a prediction of energy demand can be made. Compared to the SLR implementation, the MLR implementation is found to perform better because it is able to achieve a smaller error value. Furthermore, the forecasting pattern is carried out sequentially based on a periodic pattern, so this paper calls this method the recurrence multilinear regression (RMLR) method. This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. Other models such as exponential GPR, sequential XGBoost, and seq2seq LSTM are used for comparison. The prediction results are evaluated by calculating the MAE, RMSE, MAPE, MAPA, and time execution for all models. The simulation results show that the fastest and best model that obtains the smallest error (3.4%) is the RMLR clustered using a weekly pattern period. Full article

Hypermobile Ehlers-Danlos syndrome (hEDS) is a connective tissue disorder marked by joint hypermobility, skin hyperextensibility, and tissue fragility. Recent studies have linked hEDS with mast cell activation syndrome (MCAS), suggesting a genetic interplay affecting immune regulation and infection susceptibility. This study aims to decode the genetic basis of mast cell hypersensitivity and increased infection risk in hEDS by identifying specific genetic variants associated with these conditions. We conducted whole-genome sequencing (WGS) on 18 hEDS participants and 7 first-degree relatives as controls, focusing on identifying genetic variants associated with mast cell dysregulation. Participants underwent clinical assessments to document hEDS symptoms and mast cell hypersensitivity, with particular attention to past infections and antihistamine response. Our analysis identified specific genetic variants in MT-CYB, HTT, MUC3A, HLA-B and HLA-DRB1, which are implicated in hEDS and MCAS. Protein–protein interaction (PPI) network analysis revealed significant interactions among identified variants, highlighting their involvement in pathways related to antigen processing, mucosal protection, and collagen synthesis. Notably, 61.1% of the hEDS cohort reported recurrent infections compared to 28.5% in controls, and 72.2% had documented mast cell hypersensitivity versus 14.2% in controls. These findings provide a plausible explanation for the complex interplay between connective tissue abnormalities and immune dysregulation in hEDS. The identified genetic variants offer insights into potential therapeutic targets for modulating mast cell activity and improving patient outcomes. Future research should validate these findings in larger cohorts and explore the functional implications of these variants to develop effective treatment strategies for hEDS and related mast cell disorders. Full article

Background: The goal of this study was to evaluate whether the medical recommendation of Angocin^®Anti-Infekt N (heretofore referenced as Angocin^®) on the day of diagnosis of acute bronchitis is negatively associated with the recurrence of acute bronchitis diagnosis, antibiotic prescriptions, incidence of chronic bronchitis, and duration of sick leave. Methods: This study included patients in general practices in Germany with a first documented diagnosis of acute bronchitis between 2005 and 2022 (index date) and a prescription of Angocin^®, thyme products, essential oils, mucolytics or antibiotics on the index date. The association between Angocin^® prescription and the risks of a relapse of acute bronchitis, development of chronic bronchitis, or subsequent antibiotic prescription were evaluated using Cox regression models. Univariable conditional logistic regression models were used to investigate the association between Angocin^® prescription and duration of sick leave. Results: After a 1:5 propensity score matching, 598 Angocin^® patients and 2990 patients in each of the four comparison cohorts were available for analysis. Angocin^® prescription was associated with significantly lower incidence of a renewed confirmed diagnosis of acute bronchitis as compared to essential oils (Hazard ratio (HR): 0.61; 95% Confidence Interval (CI): 0.46–0.80), thyme products (HR: 0.70; 95% CI: 0.53–0.91), mucolytics (HR: 0.65; 95% CI: 0.49–0.85) or antibiotics (HR: 0.64; 95% CI: 0.49–0.84). Also, there were significantly lower incidences of subsequent re-prescriptions of antibiotics when compared to mucolytics (HR: 0.73; 95% CI: 0.53–0.99) or antibiotics (HR: 0.53; 95% CI: 0.39–0.72) and a significantly lower risk of chronic bronchitis as compared to essential oils (HR: 0.60; 95% CI: 0.46–0.78), thyme products (HR: 0.53; 95% CI: 0.41–0.69), mucolytics (HR: 0.49; 95% CI: 0.38–0.63) or antibiotics (HR: 0.59; 95% CI: 0.45–0.76). Conclusions: Considering the limitations of the study, the results shed light on the sustaining effectiveness of Angocin^® prescription in the management of acute bronchitis and the associated outcomes when compared to several other treatments commonly used for this condition. Full article

Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques, including thresholding, edge-based methods, region-based approaches, clustering, and graph-based segmentation. While these methods are computationally efficient and interpretable, they often face significant challenges when applied to complex, noisy, or variable medical images. The central focus of this review is the transformative impact of deep learning on medical image segmentation. We delve into prominent deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, Recurrent Neural Networks (RNNs), Adversarial Networks (GANs), and Autoencoders (AEs). Each architecture is analyzed in terms of its structural foundation and specific application to medical image segmentation, illustrating how these models have enhanced segmentation accuracy across various clinical contexts. Finally, the review examines the integration of deep learning with traditional segmentation methods, addressing the limitations of both approaches. These hybrid strategies offer improved segmentation performance, particularly in challenging scenarios involving weak edges, noise, or inconsistent intensities. By synthesizing recent advancements, this review provides a detailed resource for researchers and practitioners, offering valuable insights into the current landscape and future directions of medical image segmentation. Full article

The increasing complexity of modern mechanical systems, especially rotating machinery, demands effective condition monitoring techniques, particularly deep learning, to predict potential failures in a timely manner and enable preventative maintenance strategies. Health monitoring data analysis, a widely used approach, faces challenges due to data randomness and interpretation difficulties, highlighting the importance of robust data quality analysis for reliable monitoring. This paper presents a two-part approach to address these challenges. The first part focuses on comprehensive data preprocessing using only feature scaling and selection via random forest (RF) algorithm, streamlining the process by minimizing human intervention while managing data complexity. The second part introduces a Recurrent Expansion Network (RexNet) composed of multiple layers built on recursive expansion theories from multi-model deep learning. Unlike traditional Rex architectures, this unified framework allows fine tuning of RexNet hyperparameters, simplifying their application. By combining data quality analysis with RexNet, this methodology explores multi-model behaviors and deeper interactions between dependent (e.g., health and condition indicators) and independent variables (e.g., Remaining Useful Life (RUL)), offering richer insights than conventional methods. Both RF and RexNet undergo hyperparameter optimization using Bayesian methods under variability reduction (i.e., standard deviation) of residuals, allowing the algorithms to reach optimal solutions and enabling fair comparisons with state-of-the-art approaches. Applied to high-speed bearings using a large wind turbine dataset, this approach achieves a coefficient of determination of 0.9504, enhancing RUL prediction. This allows for more precise maintenance scheduling from imperfect predictions, reducing downtime and operational costs while improving system reliability under varying conditions. Full article

The Bir el-Umdan cistern, a prominent archaeological site in the Judean Desert, is one of the largest and best preserved water systems in the region. Hewn in chalk, the cistern area measures 114 m² and has a ~700 m³ volume. Two massive columns, each with a base diameter of 2.5 m, support the ceiling within the cistern’s interior. This impressive structure is estimated to date back to the Hellenistic to Late Antiquity periods based on its architectural characteristics. Historical records indicate that the cistern was documented on 19th-century maps but disappeared from the 1935 and 1943 British Mandate maps. Its reappearance on the 1967 Survey of Israel map includes an upstream road disconnecting the cistern from its natural drainage basin. Despite its renovation in the 2010s, the cistern’s water supply remains limited due to its reduced catchment area, which now constitutes only 25% of its original size. Runoff coefficients calculated for the cistern’s drainage basin are relatively low (1.4% to 8.1%) compared to other desert regions. We analyzed the 21st-century runoff coefficient and recurrence interval over the original drainage basin (0.12 km²) to estimate the water volumes in antiquity. Our analysis suggests that using an 8.1% runoff coefficient, the estimated water volume is 806 m³, implying a cistern overflow every 6–7 years. A more conservative estimate using a 5% runoff coefficient yields a water volume of 500 m³ and a 15-year recurrence interval. Sediment analysis reveals that silt particles dominate the sediment accumulated in the cistern and its upstream sedimentation basins. The consistent grain size distribution throughout the system indicates rapid water flow during flood events. Reconstructing the sedimentation history is challenging due to potential maintenance and possible dredging and cleaning operations. Full article

This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R² values of 0.8416 and 0.9939. The model’s predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model’s high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas. Full article

Cancer cohorts are now known to be associated with increased rates of clonal hematopoiesis (CH). We sort to characterize the hematopoietic compartment of patients with melanoma and non-small cell lung cancer (NSCLC) given our recent population level analysis reporting evolving rates of secondary leukemias. The advent of immune checkpoint blockade (ICB) has dramatically changed our understanding of cancer biology and has altered the standards of care for patients. However, the impact of ICB on hematopoietic myeloid clonal expansion remains to be determined. We studied if exposure to ICB therapy affects hematopoietic clonal architecture and if their evolution contributed to altered hematopoiesis. Blood samples from patients with melanoma and NSCLC (n = 142) demonstrated a high prevalence of CH. Serial samples (or post ICB exposure samples; n = 25) were evaluated in melanoma and NSCLC patients. Error-corrected sequencing of a targeted panel of genes recurrently mutated in CH was performed on peripheral blood genomic DNA. In serial sample analysis, we observed that mutations in DNMT3A and TET2 increased in size with longer ICB exposures in the melanoma cohort. We also noted that patients with larger size DNMT3A mutations with further post ICB clone size expansion had longer durations of ICB exposure. All serial samples in this cohort showed a statistically significant change in VAF from baseline. In the serial sample analysis of NSCLC patients, we observed similar epigenetic expansion, although not statistically significant. Our study generates a hypothesis for two important questions: (a) Can DNMT3A or TET2 CH serve as predictors of a response to ICB therapy and serve as a novel biomarker of response to ICB therapy? (b) As ICB-exposed patients continue to live longer, the myeloid clonal expansion may portend an increased risk for subsequent myeloid malignancy development. Until now, the selective pressure of ICB/T-cell activating therapies on hematopoietic stem cells were less known and we report preliminary evidence of clonal expansion in epigenetic modifier genes (also referred to as inflammatory CH genes). Full article

With the advancement of deep learning (DL) technology, DL-based intrusion detection models have emerged as a focal point of research within the domain of cybersecurity. This paper provides an overview of the datasets frequently utilized in the research. This article presents an overview of the widely utilized datasets in the research, establishing a basis for future investigation and analysis. The text subsequently summarizes the prevalent data preprocessing methods and feature engineering techniques utilized in intrusion detection. Following this, it provides a review of seven deep learning-based intrusion detection models, namely, deep autoencoders, deep belief networks, deep neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformers. Each model is examined from various dimensions, highlighting their unique architectures and applications within the context of cybersecurity. Furthermore, this paper broadens its scope to include intrusion detection techniques facilitated by the following two large-scale predictive models: the BERT series and the GPT series. These models, leveraging the power of transformers and attention mechanisms, have demonstrated remarkable capabilities in understanding and processing sequential data. In light of these findings, this paper concludes with a prospective outlook on future research directions. Four key areas have been identified for further research. By addressing these issues and advancing research in the aforementioned areas, this paper envisions a future in which DL-based intrusion detection systems are not only more accurate and efficient but also better aligned with the dynamic and evolving landscape of cybersecurity threats. Full article

Toxoplasma gondii, an obligate intracellular parasite, is a globally prevalent pathogen capable of infecting a wide range of warm-blooded animals, including humans. Ocular toxoplasmosis (OT), a severe manifestation of T. gondii infection, can lead to potentially blinding complications. This comprehensive review delves into the current understanding of T. gondii biology, exploring its complex life cycle, diverse transmission routes, and strain diversity. This article provides an in-depth analysis of the clinical manifestations of OT, which can result from both congenital and acquired infections, presenting a spectrum of signs and symptoms. The review examines various diagnostic strategies employed for OT, including clinical examination, multimodal imaging techniques such as fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), optical coherence tomography (OCT), and optical coherence tomography angiography (OCTA), as well as laboratory tests including serology and molecular methods. Despite extensive research, the specific mechanisms underlying ocular involvement in T. gondii infection remain elusive, and current diagnostic options have limitations. Moreover, the treatment of active and recurrent OT remains a challenge. While existing therapies, such as antimicrobial agents and immunosuppressants, can control active infections, they do not offer a definitive cure or completely prevent recurrence. The clinical endpoints for the management of active and recurrent OT are also not yet well-established, and the available treatment methods carry the potential for adverse effects. This article highlights the need for future research to elucidate the pathogenesis of OT, investigate genetic factors influencing susceptibility to infection, and develop more sensitive and specific diagnostic tools. Enhancing global surveillance, implementing robust prevention strategies, and fostering multidisciplinary collaborations will be crucial in reducing the burden of OT and improving patient outcomes. This comprehensive review aims to provide a valuable resource for clinicians, researchers, and policymakers, contributing to a better understanding of T. gondii infection and its impact on ocular health. Full article

Background/Objectives: Luteolin (LUT) is a natural flavonoid with known anti-inflammatory, antioxidant, and anti-cancer properties. Cervical cancer, particularly prevalent in certain regions, remains a significant health challenge due to its high recurrence and poor response to treatment. This study aimed to investigate the anti-tumor effects of LUT on human cervical epidermoid carcinoma cells (Ca Ski), focusing on cell growth inhibition, apoptosis induction, and regulation of mitochondrial membrane potential. Methods: Ca Ski cells were treated with varying concentrations of LUT (0, 25, 50, 100 µM) for different time periods (24, 48, 72 hours). Cell viability was measured using the MTT assay, apoptosis was assessed by flow cytometry with annexin V-FITC/PI staining, and changes in mitochondrial membrane potential were evaluated using JC-1 staining. Caspase-3 activation was examined by flow cytometry, and expression of apoptosis-related proteins (caspase-3, -8, -9, AIF) was analyzed via Western blotting. Results: LUT significantly inhibited the growth of Ca Ski cells in a dose- and time-dependent manner, with the most pronounced effects observed at 100 µM over 72 hours. Flow cytometry confirmed that LUT induced apoptosis without causing necrosis. Mitochondrial membrane potential was reduced after LUT treatment, coinciding with increased caspase-3 activation. Western blot analysis revealed the upregulation of pro-apoptotic proteins caspase-3, -8, -9, and AIF, indicating that LUT induces apoptosis through the intrinsic mitochondrial pathway. Conclusions: Luteolin effectively inhibits cervical cancer cell proliferation and induces apoptosis by disrupting mitochondrial membrane potential and activating caspases. These findings suggest that LUT holds potential as a therapeutic agent for cervical cancer, with further studies needed to explore its in vivo efficacy and broader clinical applications. Full article

Background: This study highlighted the differences between the biochemical compositions of urinary stones from patients with type 1 diabetes versus those with type 2 diabetes. Materials and Methods: This study included patients diagnosed with kidney stones and diabetes who were referred to the Urological Clinic of the Dr. C. I. Parhon Hospital in Iasi from April 2017 to April 2024. We analyzed the spectroscopic stone composition from 128 lithiasis patients treated in our Clinic. In the current study, the distribution of the biochemical composition of stones varied significantly between diabetic patients with type 2 diabetes, who formed primarily mixed uric acid stones, and diabetic patients with type 1 diabetes, who mainly developed pure uric acid stones (p < 0.001). Patients with uric acid stones had significantly higher mean creatinine values than the other stone types (p < 0.001). Urinary pH levels were abnormal for all biochemical subtypes of stones, indicating acidic urine. However, patients with uric acid stones had lower pH values than the group average. From the Kaplan–Mayer analysis, patients with pure uric acid stones had a shorter time to stone recurrence compared to patients with other biochemical types identified. Conclusions: These findings, which highlight the prevalence of pure uric acid stones in patients with type 1 diabetes and the impact of this on the strategy for dissolving pure stones, represent a significant advancement in understanding urinary lithiasis in diabetic patients. Full article

Background/Objectives: the vaginal microbiota is known to confer protection in the genital ecosystem, due to the predominance of different Lactobacillus species, playing a crucial role in women’s health; alterations in the composition of the microbial communities in the vagina can be associated with the development of bacterial vaginosis (BV). Current therapy for BV involves oral or intravaginal administration of metronidazole or clindamycin, albeit the high recurrence rates suggest a need for alternative therapeutic tools, such as probiotics. Herein, the diversity and composition of vaginal microbiota in women with asymptomatic BV was investigated before and after the oral administration of a multi-strain probiotic formulation. Methods: a prospective observational pilot study with pre–post design was carried out from 1 June 2022, to 31 December 2022, on reproductive-age women with asymptomatic BV, as diagnosed via Nugent score, and matched healthy controls. The probiotic was administered to all study participants as acid-resistant oral capsules (twice daily), and a vaginal swab was collected at baseline and after 2 months of treatment, for the metagenomic analysis of 16s rDNA. Results: the diversity and richness of the vaginal microbiota in women with BV were significantly reduced after 2 months of supplementation with the oral probiotic, as evidenced by measures of α-diversity. Interestingly, some bacterial genera typically associated with dysbiosis, such as Megasphaera spp., were significantly decreased; whereas, at the same time, Lactobacillus spp. Doubled. Conclusions: our preliminary results suggest that the multi-strain oral probiotic is a beneficial treatment specifically targeting the dysbiotic vaginal microenvironment. Full article

Malware remains a major threat to computer systems, with a vast number of new samples being identified and documented regularly. Windows systems are particularly vulnerable to malicious programs like viruses, worms, and trojans. Dynamic analysis, which involves observing malware behavior during execution in a controlled environment, has emerged as a powerful technique for detection. This approach often focuses on analyzing Application Programming Interface (API) calls, which represent the interactions between the malware and the operating system. Recent advances in deep learning have shown promise in improving malware detection accuracy using API call sequence data. However, the potential of Generative Adversarial Networks (GANs) for this purpose remains largely unexplored. This paper proposes a novel hybrid deep learning model combining Gated Recurrent Units (GRUs) and GANs to enhance malware detection based on API call sequences from Windows portable executable files. We evaluate our GRU–GAN model against other approaches like Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) on multiple datasets. Results demonstrated the superior performance of our hybrid model, achieving 98.9% accuracy on the most challenging dataset. It outperformed existing models in resource utilization, with faster training and testing times and low memory usage. Full article