Search Results (8,104)

This study aims to enhance access to historical records by improving the efficiency of record retrieval in generative AI, which is increasingly utilized across various fields for generating visual content and gaining inspiration due to its ease of use. Currently, most generative AIs, such as Dall-E and Midjourney, employ conversational user interfaces (CUIs) for content creation and record retrieval. While CUIs facilitate natural interactions between complex AI models and users by making the creation process straightforward, they have limitations when it comes to navigating past records. Specifically, CUIs require numerous interactions, and users must sift through unnecessary information to find desired records, a challenge that intensifies as the volume of information grows. To address these limitations, we propose an automatic hierarchy method. This method, considering the modality characteristics of text-to-image applications, is implemented with two approaches: vision-based (output images) and prompt-based (input text) approaches. To validate the effectiveness of the automatic hierarchy method and assess the impact of these two approaches on users, we conducted a user study with 12 participants. The results indicated that the automatic hierarchy method enables more efficient record retrieval than traditional CUIs, and user preferences between the two approaches varied depending on their work patterns. This study contributes to overcoming the limitations of linear record retrieval in existing CUI systems through the development of an automatic hierarchy method. It also enhances record retrieval accessibility, which is essential for generative AI to function as an effective tool, and suggests future directions for research in this area. Full article

The COVID-19 pandemic has significantly disrupted the lives of pre-primary school children in Singapore where increased infection rates prompted lockdown measures that altered children’s daily routines. This study aimed to evaluate the impact of the pandemic on the lifestyle behaviours and health quality of 3134 children aged 5 to 6 years across three periods: pre-COVID, COVID-19 lockdown, and COVID-19 endemicity. Data were collected using the Surveillance of Digital Media Habits in Early Childhood Questionnaire (SMALLQ^®) to measure on- and off-screen media habits of children and the Pediatric Quality of Life Inventory (PaedQL) to assess children’s health quality. Adherence to physical activity (PA) guidelines dropped from 32.7% pre-COVID to 27.4% during lockdown but improved to 34.4% in endemicity (p < 0.05). Sleep (SL) adherence followed a similar pattern, decreasing from 33.4% to 27.9% before rising to 40.6% (p < 0.05). Screen time (ST) adherence significantly declined during lockdown (16.7% to 10.8%, p < 0.001). Weak positive correlations with all PaedQL metrics were observed across periods, except during endemicity (p < 0.05). Concerted efforts involving key stakeholders must be made to mitigate the negative effects of the pandemic on children’s lifestyle behaviours and QoL, ensuring they are better prepared for the transition to primary school. Full article

Introduction: Cardiovascular diseases, particularly myocardial infarction (MI), significantly impact patients’ lives, causing stress and prompting varied responses to illness. Aim and methods: We conducted a narrative review concerning the acceptance of illness and quality of life in post-MI patients. Based on an extensive search of the available literature, this review consolidates current evidence on the proposed topic. Conclusions and implications: While some patients struggle with acceptance and face emotional distress, others who accept their condition are more likely to engage in treatment and lifestyle changes, leading to an improved health-related quality of life (HRQoL). Following an MI, patients often experience depression, anxiety, and stress, complicating their acceptance of the illness. Risk factors, such as hypertension, diabetes, and smoking, play a significant role in influencing HRQoL in post-MI patients. An accurate assessment of HRQoL is crucial for tailoring effective treatments and support strategies to enhance patient outcomes and identify those most at risk of developing post-MI depression or anxiety. Effective physician–patient and nurse–patient communication and support from family might be helpful in recovery. Cardiac rehabilitation improves patients’ outcomes and HRQoL. This review underscores the importance of integrating psychological support with optimal medical care to improve patient prognosis and enhance the HRQoL of individuals recovering from MI. The healthcare system could implement routine psychological assessments for MI patients at admission and discharge to establish a baseline for follow-up. Future research should explore effective psychological interventions, the interplay of CVD risk factors and psychosocial aspects, the emerging role of artificial intelligence in personalized care, and the cost-effectiveness of integrated treatment models. Full article

Candida species, normally part of the healthy human flora, can cause severe opportunistic infections when their population increases. This risk is even greater in immunocompromised individuals. Women using intrauterine contraceptive devices (IUDs) are at higher risk for IUD-associated vulvovaginal candidiasis (VVC) because the device provides a surface for biofilm formation. This biofilm formation allows the normal flora to become opportunistic pathogens, leading to symptoms of VVC such as hemorrhage, pelvic pain, inflammation, itching and discharge. VVC is often linked to IUD use, requiring the prompt removal of these devices for effective treatment. This study evaluated the activity of the arginine-rich peptide “protamine” against Candida albicans, Candida tropicalis and Candida krusei isolated from IUD users who had signs of VVC. The antimicrobial activity was measured using the agar disk diffusion and microbroth dilution methods to determine the minimum inhibitory concentration (MIC). The MIC values of protamine against C. albicans, C. tropicalis and C. krusei are 32 μg mL⁻¹, 64 μg mL⁻¹ and 256 μg mL⁻¹, respectively. The determined MIC of protamine was used for a biofilm inhibition assay by crystal violet staining. Protamine inhibited the biofilm formation of the VVC isolates, and its mechanisms were studied through scanning electron microscopy (SEM) and a reactive oxygen species (ROS) assay. The disruption of cell membranes and the induction of oxidative stress appear to be key mechanisms underlying its anti-candidal effects. The results from an in vitro assay support the potential use of protamine as an antibiofilm agent to coat IUDs in the future for protective purposes. Full article

High-quality Earth Observation (EO) imagery is essential for accurate analysis and informed decision making across sectors. However, data scarcity caused by atmospheric conditions, seasonal variations, and limited geographical coverage hinders the effective application of Artificial Intelligence (AI) in EO. Traditional data augmentation techniques, which rely on basic parameterized image transformations, often fail to introduce sufficient diversity across key semantic axes. These axes include natural changes such as snow and floods, human impacts like urbanization and roads, and disasters such as wildfires and storms, which limits the accuracy of AI models in EO applications. To address this, we propose a four-stage data augmentation approach that integrates diffusion models to enhance semantic diversity. Our method employs meta-prompts for instruction generation, vision–language models for rich captioning, EO-specific diffusion model fine-tuning, and iterative data augmentation. Extensive experiments using four augmentation techniques demonstrate that our approach consistently outperforms established methods, generating semantically diverse EO images and improving AI model performance. Full article

Tacinga inamoena (K. Schum.) N.P. Taylor & Stuppy (Cactaceae, Opuntioideae) is a native Brazilian species found in the Caatinga phytogeographic domain. Although its flowers are adapted for bird pollination (ornithophily), few birds visit these plants in the ex situ collection at the Rio de Janeiro Botanical Garden. Despite this, fruit production occurs, prompting an investigation into the floral visitors and other animals interacting with T. inamoena flowers. This study aimed to identify floral visitors and quantify florivory damage to flowers in the Cacti and Succulents thematic collection. During the study, 79 flowers were monitored, along with their floral visitors and 26 instances of florivory, totaling 110 observation hours during the anthesis period. Despite recording only five hummingbird visits, a high fruit set was observed, with 72 fruits formed. Results indicated that bees of the genus Trigona sp. were the main floral visitors. Florivory damage, primarily caused by lizards of the species Tropidurus torquatus (Wied-Neuwied, 1820), did not negatively impact fruit formation in this cactus species under cultivation. To fully understand the reproductive success of this species, further studies are needed to assess the viability of seeds formed under these conditions, as the species may be self-compatible and autogamous. Full article

Percutaneous coronary intervention (PCI) for thrombotic and heavily calcified coronary artery lesions and occlusions is often hampered by difficulty in wiring the occlusions, restoring antegrade flow, and proceeding to successful stent implantation. Characterization of dynamic anatomical features such as thrombi and the calcium distribution is key to prevent periprocedural complications and long-term adverse events, which are mainly driven by stent underexpansion and malapposition and may prompt in-stent restenosis or stent thrombosis. Therefore, multimodal imaging is a critical step during PCI to better characterize these high-risk lesions and select those in which careful preparation with debulking devices is needed or to guide stent optimization with the aim of improving procedural and long-term clinical outcomes. Hence, obtaining a better understanding of the underlying cause of thrombus formation, imaging the calcium distribution, and thorough planning remain crucial steps in selecting the optimal revascularization strategy for an individual patient. In this review, we summarize current evidence about the prevalence, predictors, and clinical outcomes of “hard-rock” thrombotic lesions treated by PCI, focusing on the value of imaging and physiological assessments performed to guide interventions. Furthermore, we provide an overview of cutting-edge technologies with the aim of facilitating the use of such devices according to specific procedural features. Full article

Tramadol is a widely used pain medication detected in wastewater treatment plants, prompting concerns about its impact on the environment and the effectiveness of wastewater treatment. Nitrogen-doped carbon quantum dots (NCQDs) can be used to remove pollutants from the contaminated water sources. However, NCQDs can hardly be recovered after applications, leading to high regeneration costs. Thus, this study aims to explore the use of magnetite nitrogen-doped carbon quantum dots (magnetite NCQDs) fabricated from empty fruit bunches (EFBs) to remove tramadol from wastewater treatment. Various analytical methods were conducted to characterize the magnetite NCQDs. Magnetite NCQDs showed excellent separation and aggregate-free properties. This study investigated the effect of the initial concentration of tramadol, the dosage of magnetite NCQD adsorbent, and the contact time while keeping other parameters constant. Tramadol was efficiently adsorbed within 40 min with an adsorption efficiency of over 85.9% and further photodegraded by 4.5% after being exposed to UV light after undergoing photocatalysis for 50 min. Magnetite NCQDs exhibited outstanding properties in removing tramadol after undergoing five cycles. This research provides a promising approach for developing a highly efficient adsorbent for treating tramadol-contaminated wastewater. Full article

Infertility remains a complex clinical challenge, with intrauterine pathologies contributing to a significant percentage of in vitro fertilization (IVF) failures. Chronic endometritis (CE) has gained attention due to its potential association with unexplained infertility and recurrent miscarriage. This review explores the role of hysteroscopy in diagnosing and treating CE. The endometrium undergoes dynamic changes orchestrated by ovarian steroids, and disturbances may lead to CE, characterized by plasma cell infiltration. Diagnosis traditionally relies on histopathologic examination, but hysteroscopy offers real-time imaging, revealing the specific macroscopic alterations associated with CE. However, diagnostic accuracy varies, prompting the need for standardized criteria. CE has been linked to poor reproductive outcomes, emphasizing the importance of effective treatment. Antibiotic therapy is a common approach, with doxycycline as the first-line regimen. Hysteroscopic polypectomy, targeting non-infectious CE, emerges as a promising treatment, demonstrating fertility benefits. The review underscores the significance of hysteroscopy in diagnosing and treating CE, providing insights into its impact on reproductive outcomes in infertile women. Further prospective studies are needed to validate these findings and establish unified diagnostic criteria. Full article

We describe the changing research interests and goals of the responsible investigators of the Italian Rural Areas (IRA) of the Seven Countries Study of cardiovascular diseases (CVD) during a period of 60 years, dealing with a cohort of middle-aged men. Our initial interest was to discover the basic risk factors of coronary heart disease (CHD). Subsequently, the same problem was tackled regarding stroke and heart diseases of uncertain etiology. Later on, cancer deaths also became an end-point for which risk factors were investigated. The long duration of the study and the fact that CVD and cancer fatalities already cover 70% of all-cause mortality prompted the idea to focus on all-cause mortality, and particularly on age-at-death when the follow-up period reached 61 years together with the extinction of the cohort. At that point, a larger number of risk factors measured at baseline, including those which were unable to predict CVD, became the determinants of all-cause mortality and age-at-death, a metric that summarizes the life-span of health and disease. This study is supported by the presentation of data derived from published papers. Full article

The Enteric Nervous System (ENS) is a dynamic field of study where researchers devise sophisticated methodologies to comprehend the impact of chronic degenerative diseases on Enteric Neuron Cells (ENCs). These investigations demand labor-intensive effort, requiring manual selection and segmentation of each well-defined cell to conduct morphometric and quantitative analyses. However, the scarcity of labeled data and the unique characteristics of such data limit the applicability of existing solutions in the literature. To address this, we introduce a novel dataset featuring expert-labeled ENC called ENSeg, which comprises 187 images and 9709 individually annotated cells. We also introduce an approach that combines automatic instance segmentation models with Segment Anything Model (SAM) architectures, enabling human interaction while maintaining high efficiency. We employed YOLOv8, YOLOv9, and YOLOv11 models to generate segmentation candidates, which were then integrated with SAM architectures through a fusion protocol. Our best result achieved a mean DICE score (mDICE) of 0.7877, using YOLOv8 (candidate selection), SAM, and a fusion protocol that enhanced the input point prompts. The resulting combination protocols, demonstrated after our work, exhibit superior segmentation performance compared to the standalone segmentation models. The dataset comes as a contribution to this work and is available to the research community. Full article

Background: Phenylketonuria (PKU) is an autosomal recessive disorder caused by mutations in the phenylalanine hydroxylase (PAH) gene, leading to impaired amino acid metabolism. Early diagnosis through newborn screening (NBS) enables prompt treatment, preventing neurological complications. This study aims to describe the genetic and phenotypic spectrum of PKU and mild hyperphenylalaninemia (m-HPA) in patients diagnosed at the Department of Inborn Errors of Metabolism and Newborn Screening, Hospital G. Rodolico-S. Marco, Catania, over four decades (1987–2023). Materials and Methods: The retrospective analysis included 102 patients with elevated blood phenylalanine (Phe) levels born in Sicily and followed at the Institute. The phenotype evaluation comprised the Phe levels at birth/diagnosis, dietary tolerance, and sapropterin dihydrochloride responsiveness. The dietary compliance and Phe/Tyr ratios were assessed and compared across phenotypic classes and age groups. Results: Of 102 patients, 34 were classified as having classic PKU, 9 as having moderate PKU, 26 as having mild PKU, and 33 as having m-HPA, with a median age of 21.72 years. Common PAH variants included c.1066-11G>A (26/204 alleles), c.782G>A (18/204 alleles), and c.165delT (13/204 alleles). The phenotypes sometimes diverged from the genotype predictions, emphasizing dietary tolerance over the initial Phe levels for classification: m-HPA was statistically associated with a higher dietary tolerance (p < 0.001) compared to the classic, moderate, or mild forms of PKU. Conclusions: This study highlights the importance of large databases (e.g., BioPKU) for phenotype prediction and treatment optimization. Regular assessment of Phe/Tyr ratios is crucial for monitoring adherence and health. Phenotype determination, dietary management, and emerging therapies (Pegvaliase and gene therapy) are key to improving outcomes for PKU patients. Full article

The automation of educational and instructional assessment plays a crucial role in enhancing the quality of teaching management. In physics education, calculation problems with intricate problem-solving ideas pose challenges to the intelligent grading of tests. This study explores the automatic grading of physics problems through a combination of large language models and prompt engineering. By comparing the performance of four prompt strategies (one-shot, few-shot, chain of thought, tree of thought) within two large model frameworks, namely ERNIEBot-4-turbo and GPT-4o. This study finds that the tree of thought prompt can better assess calculation problems with complex ideas (N = 100, ACC ≥ 0.9, kappa > 0.8) and reduce the performance gap between different models. This research provides valuable insights for the automation of assessments in physics education. Full article

Text-to-image generation aims to create visually compelling images aligned with input prompts, but challenges such as subject mixing and subject neglect, often caused by semantic leakage during the generation process, remain, particularly in multi-subject scenarios. To mitigate this, existing methods optimize attention maps in diffusion models, using static loss functions at each time step, often leading to suboptimal results due to insufficient consideration of varying characteristics across diffusion stages. To address this problem, we propose a novel framework that adaptively guides the attention maps by dividing the diffusion process into four intervals: initial, layout, shape, and refinement. We adaptively optimize attention maps using interval-specific strategies and a dynamic loss function. Additionally, we introduce a seed filtering method based on the self-attention map analysis to detect and address the semantic leakage by restarting the generation process with new noise seeds when necessary. Extensive experiments on various datasets demonstrate that our method achieves significant improvements in generating images aligned with input prompts, outperforming previous approaches both quantitatively and qualitatively. Full article

This study examines educators’ perceptions of artificial intelligence (AI) in educational settings, focusing on their familiarity with AI tools, integration into teaching practices, professional development needs, the influence of institutional policies, and impacts on mental health. Survey responses from 353 educators across various levels and countries revealed that 92% of respondents are familiar with AI, utilizing it to enhance teaching efficiency and streamline administrative tasks. Notably, many educators reported students using AI tools like ChatGPT for assignments, prompting adaptations in teaching methods to promote critical thinking and reduce dependency. Some educators saw AI’s potential to reduce stress through automation but others raised concerns about increased anxiety and social isolation from reduced interpersonal interactions. This study highlights a gap in institutional AI policies, leading some educators to establish their own guidelines, particularly for matters such as data privacy and plagiarism. Furthermore, respondents identified a significant need for professional development focused on AI literacy and ethical considerations. This study’s findings suggest the necessity for longitudinal studies to explore the long-term effects of AI on educational outcomes and mental health and underscore the importance of incorporating student perspectives for a thorough understanding of AI’s role in education. Full article