bluuebunny/crossref_metadata_2025 huggingface dataset:
Current Status of Neoadjuvant Treatment Before Surgery in High-Risk Localized Prostate Cancer
Authors: Gómez Rivas Juan , Ortega Polledo Luis Enrique , De La Parra Sánchez Irene , Gutiérrez Hidalgo Beatriz , Martín Monterrubio Javier , Marugán Álvarez María Jesús , Somani Bhaskar K. , Enikeev Dmitry , Puente Vázquez Javier , Sanmamed Salgado Noelia , Galante Romo María Isabel , Moreno Sierra Jesús
Published: January 2025

Localized high-risk (HR) prostate cancer (PCa) is a heterogeneous disease whose likelihood of a biochemical recurrence, metastatic progression and cancer-related mortality after initial treatment is higher when compared with patients with low (LR) or intermediate-risk (IR) disease. In the past, neoadjuvant therapy has shown an improvement in postoperative oncological variables but failed to demonstrate any survival advantages. With the promising results from novel treatments in metastatic and non-metastatic castration resistant PCa settings, new evidence has appeared in the literature in the neoadjuvant setting. Background/Objectives: To describe the current evidence for different neoadjuvant treatments before a radical prostatectomy in high-risk prostate cancer. Methods: We performed a comprehensive English literature search for original and review articles through January–August 2024, using Pubmed, Medline and ClinicalTrials.gov databases, as well as a comprehensive review of different international guidelines, searching the following terms: “neoadjuvant ADT prostate cancer”, “neoadjuvant ADT”, “prostate cancer surgery” and “neoadjuvant high-risk prostate cancer”. We included 61 papers for the final review. Results and Discussion: Neoadjuvant therapy is not recommended in daily practice by any international guideline. The National Comprehensive Cancer Network (NCCN) guidelines strongly discourage the use of ADT as a neoadjuvant therapy outside of clinical trials. ADT + ARTAs show promising data in phase-II trials, including favorable pCR, MRD, PSA relapse and salvage therapy rates. Clinical trials on chemotherapy, 177Lu-PSMA, genomic-targeted therapies and markers of response leave room for further evidence acquisition due to their encouraging results. Conclusions: Currently, no phase III data supports systemic neoadjuvant therapy before RP. Phase II studies show promising data for ADT with second-generation agents, including favorable pCR, MRD, PSA relapse and salvage therapy rates.
Novel Computational and Artificial Intelligence Models in Cancer Research
Authors: Liu Li , Li Fuhai , Liu Xiaoming , Wang Kai , Zhao Zhongming
Published: January 2025

The ICIBM 2023 marked the 11th annual conference of its kind, with the ICIBM recently becoming the official conference of the International Association for Intelligent Biology and Medicine (IAIBM), showcasing cutting-edge advancements at the intersection of computation and biomedical research [...]
Emerging Trends in the Management of Gastric Malignancy with Peritoneal Dissemination: Same Disease, Heterogeneous Prognosis
Authors: Mela Evgenia , Theodorou Andreas Panagiotis , Kimpizi Despina , Konstantinou Kyriaki , Belimezakis Nektarios , Schizas Dimitrios , Theodorou Dimitrios , Triantafyllou Tania
Published: January 2025

Gastric cancer is a significant global contributor to cancer-related mortality. Stage IV gastric cancer represents a significant percentage of patients in Western countries, with peritoneal dissemination being the most prevalent site. Peritoneal disease comprises two distinct entities, macroscopic (P1) and microscopic (P0CY1), which are associated with poor long-term survival rates. Although the present standard of treatment is palliative chemotherapy, a global controversy has arisen concerning specific patients with limited disease burden or conversion to negative lavage cytology following chemotherapy. Available approaches include systemic or intraperitoneal chemotherapy, upfront gastrectomy, and conversion surgery. This review consolidated the current evidence regarding multimodal management, indicating prolonged survival for this distinct subgroup of patients. Considering the complexity of peritoneal metastases, the potential of the multimodal approach unveils promising prospects for identifying the optimal treatment for this particular subset of stage IV patients and thus enhancing their survival outcomes.
Treatment Quality of Rectal Cancer Patients in Certified Colorectal Cancer Centers Versus Non-Certified Hospitals: A Comparative Analysis
Authors: Mroczkowski Paweł , Kusian Henry , Jannasch Olof , Lippert Hans , Zajdel Radosław , Zajdel Karolina , Sadowski Arkadiusz , Merecz-Sadowska Anna
Published: January 2025

Background/Objectives: The certification of hospitals as colorectal cancer centers aims to improve treatment quality, but evidence supporting its effectiveness remains limited. This study evaluated the impact of certification on treatment outcomes for rectal cancer patients in Germany. Methods: We conducted a retrospective analysis of 14,905 patients with primary rectal cancer (UICC Stages I-III) treated at 271 hospitals. Treatment outcomes were compared between certified colorectal cancer centers (3624 patients in 55 hospitals) and non-certified hospitals (11,281 patients in 216 hospitals). Additionally, a subset analysis examined outcomes before and after certification within the same institutions. Results: Certified centers demonstrated higher utilization of preoperative imaging (endorectal ultrasound: 70.7% vs. 58.2%, p < 0.001; pelvic MRI: 39.1% vs. 28.5%, p < 0.001) and lower rates of intraoperative complications (4.6% vs. 6.2%, p < 0.001). Surgical quality indicators, including M.E.R.C.U.R.Y. classification (Grade 1: 86.5% both groups, p = 0.620) and anastomotic leakage rates (11.3% vs. 11.9%, p = 0.407), were comparable between certified and non-certified hospitals. Despite treating patients with more favorable tumor stages, certified centers showed no significant advantage in 5-year overall survival (82.8% vs. 82.0%, p = 0.880) or 30-day mortality (2.6% both groups, p = 0.869). Hospital stays were marginally shorter in certified centers (19.46 vs. 20.24 days, p < 0.001). Conclusions: While certification was associated with improved adherence to diagnostic protocols and reduced intraoperative complications, it did not significantly impact surgical quality or long-term survival outcomes. These findings suggest that certification alone may not guarantee superior treatment quality, as hospitals participating in quality assurance programs achieved comparable results without formal certification.
Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models
Authors: Disci Rukiye , Gurcan Fatih , Soylu Ahmet
Published: January 2025

Background/Objectives: Brain tumor classification is a crucial task in medical diagnostics, as early and accurate detection can significantly improve patient outcomes. This study investigates the effectiveness of pre-trained deep learning models in classifying brain MRI images into four categories: Glioma, Meningioma, Pituitary, and No Tumor, aiming to enhance the diagnostic process through automation. Methods: A publicly available Brain Tumor MRI dataset containing 7023 images was used in this research. The study employs state-of-the-art pre-trained models, including Xception, MobileNetV2, InceptionV3, ResNet50, VGG16, and DenseNet121, which are fine-tuned using transfer learning, in combination with advanced preprocessing and data augmentation techniques. Transfer learning was applied to fine-tune the models and optimize classification accuracy while minimizing computational requirements, ensuring efficiency in real-world applications. Results: Among the tested models, Xception emerged as the top performer, achieving a weighted accuracy of 98.73% and a weighted F1 score of 95.29%, demonstrating exceptional generalization capabilities. These models proved particularly effective in addressing class imbalances and delivering consistent performance across various evaluation metrics, thus demonstrating their suitability for clinical adoption. However, challenges persist in improving recall for the Glioma and Meningioma categories, and the black-box nature of deep learning models requires further attention to enhance interpretability and trust in medical settings. Conclusions: The findings underscore the transformative potential of deep learning in medical imaging, offering a pathway toward more reliable, scalable, and efficient diagnostic tools. Future research will focus on expanding dataset diversity, improving model explainability, and validating model performance in real-world clinical settings to support the widespread adoption of AI-driven systems in healthcare and ensure their integration into clinical workflows.
Oxidative Stress and the NLRP3 Inflammasome: Focus on Female Fertility and Reproductive Health
Authors: Moustakli Efthalia , Stavros Sofoklis , Katopodis Periklis , Skentou Charikleia , Potiris Anastasios , Panagopoulos Periklis , Domali Ekaterini , Arkoulis Ioannis , Karampitsakos Theodoros , Sarafi Eleftheria , Michaelidis Theologos M. , Zachariou Athanasios , Zikopoulos Athanasios
Published: January 2025

Chronic inflammation is increasingly recognized as a critical factor in female reproductive health; influencing natural conception and the outcomes of assisted reproductive technologies such as in vitro fertilization (IVF). An essential component of innate immunity, the NLR family pyrin domain-containing 3 (NLRP3) inflammasome is one of the major mediators of inflammatory responses, and its activation is closely linked to oxidative stress. This interaction contributes to a decline in oocyte quality, reduced fertilization potential, and impaired embryo development. In the ovarian milieu, oxidative stress and NLRP3 inflammasome activation interact intricately, and their combined effects on oocyte competence and reproductive outcomes are significant. The aims of this review are to examine these molecular mechanisms and to explore therapeutic strategies targeting oxidative stress and NLRP3 inflammasome activity, with the goal of enhancing female fertility and improving clinical outcomes in reproductive health.
Relations Between Parental Emotion Talk and Preschoolers’ Emotion Expressions in Low-Income Chinese American and Mexican American Families
Authors: Chan Megan , Taw Michelle , Eisenberg Nancy , Zhou Qing
Published: January 2025

Background/Objectives: Preschool children learn to express emotions in accordance with sociocultural norms. Parental emotion talk (ET) has been theorized to shape these processes. Limited research has examined preschoolers’ observed emotion expressions and emotion-related behaviors in culturally diverse samples. We sought to explore the following: (1) cultural group differences and similarities in observed emotion expressions (anger, sadness, and positive emotions) and emotion-related behaviors between Chinese American and Mexican American preschoolers, and (2) the concurrent links between parental ET and children’s emotion expressions. Methods: In a sample of 86 children (age range = 38 to 70 months, 62% girls) from low-income immigrant families (Mexican Americans/MA = 43 and Chinese Americans/CA = 43), the observed children’s emotion expressions and emotion-related behaviors were coded based on a frustration-eliciting task. Parental ET quality and quantity were coded from transcripts of a parent–child shared reading task. Results: MA children expressed more anger and sadness, but the two groups did not differ on positive emotions or emotion-related behaviors. Multiple regressions showed that children whose parents engaged in more ET expressed higher levels of anger and sadness and used more non-feeling state languages than children whose parents engaged in less ET. Conclusions: The results revealed cultural variations in preschool-age children’s emotion expressions and provided support for associations between parental ET and children’s emotion expressions.
Body Composition in Preterm Infants: Current Insights and Emerging Perspectives
Authors: Viswanathan Sreekanth , Thoene Melissa , Alja’nini Zaineh , Alur Pradeep , McNelis Kera
Published: January 2025

In recent years, significant advancements in respiratory and nutritional care have markedly improved the survival rates of preterm infants and enhanced long-term health outcomes. Despite these improvements, emerging research highlights the lasting impacts of early growth patterns on an individual’s health trajectory. Adults born prematurely face a higher incidence of health issues related to their early birth. The American Academy of Pediatrics recommends that preterm infants should achieve growth rates similar to those of fetuses, with clinicians emphasizing nutrition delivery to help these infants reach their expected weight for gestational age. However, this approach often results in altered body composition, characterized by increased fat mass and decreased fat-free mass compared to full-term infants. Air displacement plethysmography stands out as a highly reliable method for measuring preterm body composition, while DEXA scans, despite their reliability, tend to overestimate body fat. Other methods include bioelectric impedance, isotope dilution, MRI, ultrasound, and skinfold thickness, each with its own strengths and limitations. In this paper, we aim to raise awareness among neonatal clinicians about the importance of achieving acceptable neonatal body composition. We discuss the pros and cons of different body composition measurement methods, the impact of nutrition and other factors on body composition in preterm infants, long-term follow-up data, and the potential use of body composition data to tailor nutritional interventions in NICU and post-discharge settings. This comprehensive approach is designed to optimize health outcomes for preterm newborns by focusing on their body composition from an early stage.
Assessing the Understanding of Primary School Teachers in the Rural Communities of Limpopo Province, South Africa—Are We Trauma Informed?
Authors: Munyadziwa Muimeleli , Makhado Lufuno , Maphula Angelina
Published: January 2025

Background/Objectives: To effectively support children’s learning and well-being, primary educators must thoroughly understand child trauma. Being ‘trauma informed’ means recognizing the impact of trauma and responding supportively, which can help mitigate its adverse effects on learners. This study explored the understanding of childhood trauma among primary school teachers in Limpopo province, focusing on the circuits of Mvudi and Dzindi due to their high prevalence of childhood traumatic experiences. Methods: An exploratory descriptive and contextual design was used in this study. Data were collected through semi-structured interviews with 26 teachers, utilizing total population sampling for schools and purposive sampling for respondents. Results: The findings revealed that teachers typically define trauma in terms of domestic issues and abuse and identify symptoms in learners’ behavior, emotions, and academic performance. Teachers employ strategies to assist affected learners, such as building rapport and involving social workers or school authorities when needed. Conclusions: This study highlighted teachers’ critical role in identifying and assisting traumatized children and underlined the necessity for effective training programs and school-based support systems. A comprehensive strategy is necessary, including advocacy for equitable support services and standardized training on trauma-informed practices.
Elucidating the Mechanisms of Acquired Palbociclib Resistance via Comprehensive Metabolomics Profiling
Authors: Yang Lulu , Yue Yajun , Wang Zhendong , Jiang You , Xue Zhichao , Zhang Yongzhuo
Published: January 2025

Palbociclib is a cyclin-dependent kinase 4/6 inhibitor and a commonly used antitumor drug. Many cancers are susceptible to palbociclib resistance, however, the underlying metabolism mechanism and extent of resistance to palbociclib are unknown. In this study, LC-MS metabolomics was used to investigate the metabolite changes of colorectal cancer SW620 cells that were resistant to palbociclib. The study indicated that there were 76 metabolite expression differences between SW620 cells with palbociclib resistance and the parental SW620 cells involving amino acids, glutathione, ABC transporters, and so on. MetaboAnalyst 6.0 metabolic pathway analysis showed that arginine synthesis, β-alanine metabolism, and purine metabolism were disrupted. These results may provide potential clues to the metabolism mechanism of drug resistance in cancer cells that are resistant to palbociclib. Our study has the potential to contribute to the study of anti-palbociclib resistance.
Shoreline Change of Western Long Island, New York, from Satellite-Derived Shorelines
Authors: Janda Catherine N. , Warrick Jonathan A. , Buscombe Daniel , Batiste Sharon
Published: January 2025

Shoreline measurement techniques using satellite-derived imagery can provide decades of observations of shoreline change. Here we apply these techniques to the western south shore of Long Island, New York, which has three distinct beaches, Rockaway Peninsula, Long Beach, and Jones Beach Island, which are 18, 15, and 24 km in length, respectively. These beaches are recreation areas for millions of regional residents and include several groin fields, sediment dredging and nourishment operations, and a coastal wave climate that includes winter northeasterly storms and summer hurricanes. The shorelines along the western ends of these three beaches have been accreting at ~4 m/yr during the observation record (1984–2022) resulting from net westward longshore drift. The central 10–12 km of the beaches have lower shoreline change rates, and these rates are generally lowest within the groin fields (0.5–1.5 m/yr). Shoreline change observations also provide evidence for westward propagating accretion and erosion sediment waves that have durations of several years. Beach nourishment projects are shown to significantly influence rates of shoreline accretion, and this is commonly followed by significant shoreline retreat during the subsequent years.
Exploring the Role of Nanoparticles in Dental Materials: A Comprehensive Review
Authors: Bourgi Rim , Doumandji Zahra , Cuevas-Suárez Carlos Enrique , Ben Ammar Teissir , Laporte Chloé , Kharouf Naji , Haikel Youssef
Published: January 2025

In recent decades, the integration of nanotechnology into dentistry has led to groundbreaking advancements in dental materials and applications. This article explores the role of nanoparticles (NPs) in modern dentistry, highlighting their definitions, unique properties, and various applications. The introduction establishes the significance of nanotechnology in dental health care, emphasizing the potential of NPs to transform traditional practices. The overview includes a discussion of the fundamental properties of NPs, which contribute to their effectiveness in dental applications. The article categorizes NPs into three main groups: antimicrobial, therapeutic, and material property-improving NPs, detailing their clinical uses and mechanisms of action. Furthermore, it addresses current innovations in dental products incorporating NPs and examines emerging trends in the field. The research for this review was conducted using high-quality, peer-reviewed scientific databases, including PubMed, Scopus, Web of Science, and Google Scholar, with no time restriction as an inclusion criterion. These databases were selected for their credibility and comprehensive collections of relevant studies. In conclusion, NPs represent a promising avenue for innovation in dental materials and therapeutics. Their unique properties enable the development of enhanced antimicrobial agents, effective drug delivery systems, and improved material performance. However, the risks associated with cytotoxicity and stability must be carefully managed to ensure safe and effective use. Ongoing research is essential to fully understand and optimize the applications of NPs in dentistry, balancing their benefits against potential health risks. As the field advances, the integration of NPs into clinical practice will likely revolutionize approaches to dental care and treatment.
The Role of Signal Waveforms in Plasma Electrolytic Oxidation
Authors: Dogadkin Dmitriy , Sagidugumar Amangeldi , Kaliyev Daniyar , Dmitriev Nikolay , Kozhakhmetov Yernat
Published: January 2025

Plasma electrolytic oxidation (PEO) is often used to improve the physical and mechanical properties of valve metals. This method allows for the formation of thicker and denser metal oxide coatings, which helps to improve physical and mechanical properties, especially the wear and corrosion resistance of the surface. The PEO process is widely used in areas such as mechanical engineering, aerospace, biomedical, and others. This review aims to summarize and explain the fundamental principles of the PEO process, with a focus on the influence of waveform types and their parameters on the properties of PEO coatings. This study found that a sinusoidal waveform promotes the generation of more stable discharges compared to a rectangular waveform, thereby enhancing the corrosion resistance of the coatings. Furthermore, it was demonstrated that using a rectangular waveform with adjustable parameters enables the production of thicker and more wear-resistant coatings. Meanwhile, the application of sawtooth and trapezoidal waveforms reduces sharp current spikes during the onset of discharges, minimizing defect formation and positively influencing the coating formation process. In addition, bipolar and unipolar modes are analyzed, and the promising future directions are discussed.
Biosynthetic Gene Clusters and Liquid Chromatography Coupled to Mass Spectrometry Analysis of Aryl Polyene Pigments from Chryseobacterium sp. kr6 and Lysobacter sp. A03
Authors: Pailliè-Jiménez Maria Elisa , Pereira Jamile Queiroz , Rodrigues Eliseu , Brandelli Adriano
Published: January 2025

Aryl polyene (APE) are bacterial pigments which show great biotechnological potential because of their biological activities. In this study, the presence of gene clusters associated with APE synthesis was investigated in the genome of Chryseobacterium sp. kr6 and Lysobacter sp. A03. The pigments extracted from strains kr6 and A03 were further characterized by liquid chromatography coupled to a high-resolution mass spectrometer (LC-DAD-MS). These bacteria harbor the relevant genes for APE biosynthesis; while kr6 may produce flexirubin pigments and have a 75% similarity with the flexirubin cluster from Flavobacterium johnsoniae UW101, Lysobacter sp. A03 showed a 50% similarity with the xanthomonadin I gene cluster from Xanthomonas oryzae pv. oryzae. A comparison with the gene clusters of APE-producing bacteria revealed that kr6 and A03 harbor genes for key proteins that participate in APE biosynthesis, such as acyl carrier proteins, acyl dehydratases and acyl reductases. The LC-DAD-MS analysis revealed that kr6 produces a possible mixture of flexirubins, whereas the yellow pigment from A03 is proposed to be a xanthomonadin-like pigment. Although the fine molecular structure of these pigments are not yet fully elucidated, strains kr6 and A03 present great potential for the production of natural bioactive pigments.
A Review on Metal–Organic Frameworks as Technological Excipients: Synthesis, Characterization, Toxicity, and Application in Drug Delivery Systems
Authors: Sampaio Pedrita A. , Pereira Emanuella C. V. , Sá Pedro G. S. , Alencar Filho José Marcos T. , Ferraz Leslie R. M. , Nishimura Rodolfo H. V. , Ferreira Aline S. , Rolim Neto Pedro J. , Araújo Evando S. , Rolim Larissa A.
Published: January 2025

Metal–organic frameworks (MOFs) are also known as porous coordination polymers. This kind of material is constructed with inorganic nodes (metal ions or clusters) with organic linkers and has emerged as a promising class of materials with several unique properties. Well-known applications of MOFs include their use as gas storage and in separation, catalysis, carbon dioxide capture, sensing, slender film gadgets, photodynamic therapy, malignancy biomarkers, treatment, and biomedical imaging. Over the past 15 years, an increasing amount of research has been directed to MOFs due to their advanced applications in fuel cells, supercapacitors, catalytic conversions, and drug delivery systems. Various synthesis methods have been proposed to achieve MOFs with nanometric size and increased surface area, controlled surface topology, and chemical activity for industrial use. In this context, the pharmaceutical industry has been watching the accelerated development of these materials with great attention. Thus, the objective of this work is to study the synthesis, characterization, and toxicity of MOFs as potential technological excipients for the development of drug carriers. This work highlights the use of MOFs not only as delivery systems (DDSs) but also in advanced diagnostics and therapies, such as photodynamic therapy and targeted delivery to tumors. Bibliometric analyses showed a growing interest in the topic, emphasizing its contemporary relevance.
Soft-Label Supervised Meta-Model with Adversarial Samples for Uncertainty Quantification
Authors: Lucke Kyle , Vakanski Aleksandar , Xian Min
Published: January 2025

Despite the recent success of deep-learning models, traditional models are overconfident and poorly calibrated. This poses a serious problem when applied to high-stakes applications. To solve this issue, uncertainty quantification (UQ) models have been developed to allow the detection of misclassifications. Meta-model-based UQ methods are promising due to the lack of predictive model re-training and low resource requirement. However, there are still several issues present in the training process. (1) Most current meta-models are trained using hard labels that do not allow quantification of the uncertainty associated with a given data sample; and (2) in most cases, the base model has a high test accuracy. Therefore, the samples used to train the meta-model primarily consist of correctly classified samples. This leads the meta-model to learn a poor approximation of the true decision boundary. To address these problems, we propose a novel soft-label formulation that better differentiates between correct and incorrect classifications, thereby allowing the meta-model to distinguish between correct and incorrect classifications with high uncertainty (i.e., low confidence). In addition, a novel training framework using adversarial samples is proposed to explore the decision boundary of the base model and mitigate issues related to training datasets with label imbalance. To validate the effectiveness of our approach, we use two predictive models trained on SVHN and CIFAR10 and evaluate performance according to sensitivity, specificity, an F1-score-style metric, average precision, and the Area Under the Receiver Operating Characteristic curve. We find the soft-label approach can significantly increase the model’s sensitivity and specificity, while the training with adversarial samples can noticeably improve the balance between sensitivity and specificity. We also compare our method against four state-of-the-art meta-model-based UQ methods, where we achieve significantly better performance than most models.
Application of NerveCheck Master in the Diagnosis of Diabetic Peripheral Neuropathy
Authors: García de la Peña Raquel , Ortiz Romero Mercedes , Juárez Jiménez José María , Rayo Pérez Ana María
Published: January 2025

Backgraund/Objetive: Diabetic peripheral neuropathy is a condition that affects the motor, sensory, and autonomic fibers of the peripheral nervous system, with distal polyneuropathy being its most common form. Traditional methods for diagnosing sensory loss, such as tactile assessment, temperature evaluation, and vibratory perception threshold testing, are labor intensive and time consuming. Results: To effectively assess thermal and vibratory sensitivity, NerveCheck Master is an affordable and portable device that uses standardized stimuli to measure sensory response. Conclusions: Compared to traditional methods like the infrared laser thermometer, the Rydel–Seiffer tuning fork, and the Semmes–Weinstein monofilament, this device provides definitive results regarding the severity of DPN.
A Comparative Study on the Paradoxical Relationship Between Heavy Metal Exposure and Kidney Function
Authors: Rho Jee Hyun , Lee Seungho , Kwon Jung-Yeon , Hong Young-Seoub
Published: January 2025

Introduction: Korea has higher levels of heavy metals compared to other countries, raising the need to study the health impacts on vulnerable populations. This study examined the effects of heavy metal exposure—lead, mercury, and cadmium—on kidney function in residents of environmentally vulnerable areas compared to the general population in Korea. Methods: Epidemiological studies in vulnerable areas and official data from the Fourth Korean National Environmental Health Survey were analyzed to assess blood levels of lead and mercury and urinary cadmium. An integrated heavy metal concentration was calculated, combining the levels of these metals. Kidney function was evaluated using the estimated glomerular filtration rate (eGFR), classified into normal, mildly reduced, and impaired. Correlation and logistic regression analyses were used to examine relationships between heavy metal levels and eGFR. Results: The integrated heavy metal concentration in vulnerable areas was higher than in the general population. In the general population, increased heavy metal levels were associated with a decrease in eGFR, whereas in vulnerable areas, eGFR increased with higher heavy metal levels. In the general population, a rise in urinary cadmium increased the risk of eGFR decline by 19.9%, while in vulnerable areas, higher urinary cadmium reduced this risk by 23.3%. Conclusions: Contrasting relationships between heavy metal exposure and eGFR in vulnerable areas versus the general population may be due to long-term exposure and reduced renal excretion. This study underscores the need for continued monitoring in vulnerable areas, and future research should identify eGFR thresholds that correlate with heavy metal level shifts.
Knowledge, Practices, and Attitudes Towards Silver Diamine Fluoride Therapy Among Dentists and Students in Southeastern Spain
Authors: Serna-Muñoz Clara , Lucas-Porras Marina , Martínez-Beneyto Yolanda , Pérez-Silva Amparo , Ibañez-López Francisco Javier , Ortiz-Ruiz Antonio José
Published: January 2025

Background/Objectives: The use of silver diamine fluoride (SDF) has increased in recent years for the management of caries lesions in children and adults. The aim of this study is to determine the level of knowledge and the attitude of Spanish dentists (GDPs) and final-year dental students (DSs) regarding the use of SDF. Methods: A cross-sectional survey (questionnaire) was carried out aimed at final-year dental students (DSs) (n = 43) and registered dentists (GDPs) (n = 1050) in the autonomous community of the region of Murcia (Spain). Results: the response rates were GDPs 7.7% (n = 81) and DSs 84.5% (n = 38). Only 20.98% of GDP respondents reported having been trained on SDF versus 100% of DSs. Significant differences were observed between the groups (p < 0.05). While 94.7% of the students were aware of the indications for the use of SDF, only 56.8% of the general dentists reported it. Similarly, for hypersensitivity treatment, 71.1% of the students were informed versus 40.7% of the general dentists, and indications for paediatric patients, 100% for the DS group and 59.3% in GDPs. In adult patients, indications vary from GDPs’ (50%) to DSs’ (25.9%) responses. About 94.7% of DSs know the advantages of use and only 50.6% of GDPs. Both groups showed reluctance to use SDF in esthetic zones, with greater acceptance in non-esthetic areas (p < 0.05). In practice, fewer GDPs (27.16%) and DSs (23.68%) had applied SDF, reflecting a gap between knowledge and implementation. Conclusions: Dental students had a significantly higher level of knowledge, a situation that evidences the high level of education and training in the curricular guides of the universities.
Relationship Between Coefficients in Parametric Survival Models for Exponentially Distributed Survival Time—Registered Unemployment in Poland
Authors: Bieszk-Stolorz Beata
Published: January 2025

Survival analysis is a popular research tool in medicine and demography. It has been used for many years to study the duration of socio-economic phenomena. The aim of this article is to evaluate the relationship between the coefficients of the proportional hazards model (PH) and the accelerated failure time model (AFT), assuming an exponential distribution of survival time. The coefficients of the PH and AFT exponential models have the same magnitude but have opposite signs. It follows that there is a symmetric relation between the coefficients. In the case of exponential PH and AFT models, there is a relation of equality between the parameters describing the quality and fit of the model, as well as between the standard errors of the parameters of both models. In this case also, we can talk about a symmetric relation. The exponential PH model is valid if the exponential AFT model is valid. The study showed that the intensity of starting work was higher in the case of men, people with work experience, people with higher education and young people. The job search time was longer for women, people with no work experience, and people aged 60+, but shorter for people with higher education.
3D Object Detection System in Scattering Medium Environment
Authors: Ono Seiya , Kim Hyun-Woo , Cho Myungjin , Lee Min-Chul
Published: January 2025

Peplography is a technology for removing scattering media such as fog and smoke. However, Peplography only removes scattering media, and decisions about the images are made by humans. Therefore, there are still many improvements to be made in terms of system automation. In this paper, we combine Peplography with You Only Look Once (YOLO) to attempt object detection under scattering medium conditions. In addition, images reconstructed by Peplography have different characteristics from normal images. Therefore, by applying Peplography to the training images, we attempt to learn the image characteristics of Peplography and improve the detection accuracy. Also, when considering autonomous driving in foggy conditions or rescue systems at the scene of a fire, three-dimensional (3D) information such as the distance to the vehicle in front and the person in need of rescue is also necessary. Furthermore, we apply a stereo camera to this algorithm to achieve 3D object position and distance detection under scattering media conditions. In addition, when estimating the scattering medium in Peplography, it is important to specify the processing area, otherwise the scattering medium will not be removed properly. Therefore, we construct a system that continuously improves processing by estimating the size of the object in object detection and successively changing the area range using the estimated value. As a result, the PSNR result by our proposed method is better than the PSNR by the conventional Peplography process. The distance estimation and the object detection are also verified to be accurate, recording values of 0.989 for precision and 0.573 for recall. When the proposed system is applied, it is expected to have a significant impact on the stability of autonomous driving technology and the safety of life rescue at fire scenes.
A Tetris-Based Task Allocation Strategy for Real-Time Operating Systems
Authors: Chen Yumeng , Liu Songlin , He Zongmiao , Ling Xiang
Published: January 2025

Real-time constrained multiprocessor systems have been widely applied across various domains. In this paper, we focus on the scheduling algorithm for directed acyclic graph (DAG) tasks under partitioned scheduling on multiprocessor systems. Effective real-time task scheduling algorithms significantly enhance the performance and stability of multiprocessor systems. Traditional real-time task scheduling algorithms commonly rely on a single-heuristic parameter as the reference for task allocation, which typically results in suboptimal performance. Inspired by the Tetris algorithm, we propose a novel heuristic scheduling algorithm, named Tetris game scoring scheduling algorithm (TGSSA), that integrates multiple-heuristic parameters. The process of real-time DAG task scheduling on a multiprocessor system is modeled as a Tetris game. Through simulations of the worst-case response time (WCRT) analysis and observed average response times in RT-Linux, which is a frequently-used real-time operating system, our algorithm demonstrates superior performance, effectively improving the efficiency and stability of real-time operating systems.
Wearable AR System for Real-Time Pedestrian Conflict Alerts Using Live Roadside Data
Authors: Lin Adrian , Xu Hao , Chen Zhihui
Published: January 2025

Pedestrian safety is increasingly becoming a major concern within and around intersections. This paper outlines a novel approach to enhancing pedestrian safety using a wearable augmented reality (AR) system integrated with live roadside light detection and range (LiDAR) sensor data. The proposed system aims to provide real-time and spatially located warnings to pedestrians, thereby helping them proactively evade potential accidents. The system architecture is built to lessen the burden on edge devices, such as the AR headset itself, and ensure that most of the processor-heavy computations are performed within the server. The effectiveness of this system has been demonstrated and evaluated through various tests, including latency measurements. Point cloud accuracy measurements were recorded with an average offset of approximately 0.73 inches between a conflicting vehicle’s actual location and the visualized object location through AR. This paper also discusses the potential of this system with respect to vehicle-to-vehicle (V2V) systems and other societal benefits.
Cyberthreats and Security Measures in Drone-Assisted Agriculture
Authors: Tychola Kyriaki A. , Rantos Konstantinos
Published: January 2025

Nowadays, the use of Unmanned Aerial Vehicles (UAVs), or drones in agriculture for crop assessment and monitoring is a timely and important issue that concerns both researchers and farmers. Mapping agricultural land is imperative for making appropriate management decisions. As a result, the necessity of this technology is increasing, given its numerous benefits. However, as with any modern and automated technology, security concerns arise from various aspects. In this paper, we discuss cyberthreats to drones, as this technology is vulnerable to attackers during data collection, storage, and usage. Although various techniques and methods have been developed to address attacks on drones, this field remains in its infancy in many respects. This paper provides a comprehensive review of the security challenges associated with the use of agricultural drones. The security issues were thoroughly analyzed, with a particular focus on cybersecurity, categorized into four distinct levels: emerging threats, sensor vulnerabilities, hardware and software attacks, and communication-related threats. Additionally, we examined the limitations and challenges posed by cyberthreats to drone systems.
Multi-Objective Site Selection and Capacity Determination of Distribution Network Considering New Energy Uncertainties and Shared Energy Storage of Electric Vehicles
Authors: Wang Guodong , Li Haiyang , Yang Xiao , Lu Huayong , Song Xiao , Li Zheng , Wang Yi
Published: January 2025

In recent years, the share of renewable energy in the distribution network has been increasing. To deal with high renewable energy penetration, it is important to improve the energy efficiency and stability of the distribution network. In this paper, the optimal configuration of a distribution network with a high proportion of new energy and electric vehicles is investigated. Firstly, based on the copula theory, the clustered new energy data are obtained by optimizing the wind and solar output scenarios. Secondly, the uncertainty of renewable energy output is fully considered in the planning stage of the distribution network. Subsequently, an improved multi-objective particle swarm optimization algorithm is adopted to determine the optimal capacity and location of charging stations. Finally, the IEEE 33-node distribution network is used for case analysis. Through the comparison of network loss, voltage change, and other related parameters, the advantages of shared energy storage characteristics of electric vehicles in smoothing the uncertainty of the high proportion of new energy are verified.
Autonomous Forklifts: State of the Art—Exploring Perception, Scanning Technologies and Functional Systems—A Comprehensive Review
Authors: Fraifer Muftah A , Coleman Joseph , Maguire James , Trslić Petar , Dooly Gerard , Toal Daniel
Published: January 2025

This paper presents a comprehensive overview of cutting-edge autonomous forklifts, with a strong emphasis on sensors, object detection and system functionality. It aims to explore how this technology is evolving and where it is likely headed in both the near and long-term future, while also highlighting the latest developments in both academic research and industrial applications. Given the critical importance of object detection and recognition in machine vision and autonomous vehicles, this area receives particular attention. The article provides an in-depth summary of both commercial and prototype forklifts, discussing key aspects such as design features, capabilities and benefits, and offers a detailed technical comparison. Specifically, it clarifies that all available data pertains to commercially available forklifts. To obtain a better understanding of the current state-of-the-art and its limitations, the analysis also reviews commercially available autonomous forklifts. Finally, this paper includes a comprehensive bibliography of research findings in this field.
KELLM: Knowledge-Enhanced Label-Wise Large Language Model for Safe and Interpretable Drug Recommendation
Authors: Xu Tianhan , Li Bin
Published: January 2025

The proliferation of electronic health records (EHRs) and advances in deep learning have enabled personalized drug combination recommendations. However, traditional deep learning models often lack the contextual understanding and medical knowledge integration necessary for accurate predictions. While large language model (LLM)-based approaches address some of these challenges, they still fall short in incorporating critical medical knowledge, addressing comprehensive safety constraints such as multi-disease drug contraindications (MDCs), and providing sufficient interpretability of the causal mechanisms behind their outputs. To overcome these limitations, we propose KELLM, a knowledge-enhanced LLM framework for drug recommendations. By linking medical entities in EHRs to an external medical knowledge graph, inputs are enriched with causal chains, enhancing both prediction accuracy and interpretability. Additionally, we introduce a fine-tuned label-wise LLaMA model designed for multi-label classification, which incorporates safety considerations such as drug-drug interactions (DDIs) and MDCs to ensure clinically accurate and safe recommendations. Experimental results show that KELLM achieves state-of-the-art performance in effectiveness and safety metrics, while also providing evidence-based insights through causal chains that clarify its reasoning process. This establishes a new benchmark for trustworthy, interpretable drug combination recommendations.
Optimization of Traffic Signal Cooperative Control with Sparse Deep Reinforcement Learning Based on Knowledge Sharing
Authors: Fan Lingling , Yang Yusong , Ji Honghai , Xiong Shuangshuang
Published: January 2025

Urban traffic management is highly complex, and inefficient control strategies often worsen congestion and increase energy consumption. This paper introduces a collaborative multi-agent reinforcement learning method tailored for sparse control scenarios, IKS-SAC (Improved Knowledge Sharing Soft Actor–Critic), which enhances coordination between traffic signals to optimize traffic flow. IKS-SAC incorporates a communication protocol for knowledge sharing among agents, enabling each agent to access and utilize traffic environment data collected by other agents, effectively addressing the challenge of data processing in asynchronous updates, thereby achieving a comprehensive understanding of the traffic environment within a sparse control framework. Validation of the synthetic data demonstrates that IKS-SAC exhibits superior adaptability and efficiency in managing traffic flow fluctuations and uncertainties, significantly outperforming existing reinforcement learning-based and traditional traffic control methods. The proposed method demonstrates significant advantages in reducing traffic congestion, lowering energy consumption, and mitigating environmental pollution.
Energy Inefficiency in IoT Networks: Causes, Impact, and a Strategic Framework for Sustainable Optimisation
Authors: Almudayni Ziyad , Soh Ben , Samra Halima , Li Alice
Published: January 2025

The Internet of Things (IoT) has vast potential to drive connectivity and automation across various sectors, yet energy inefficiency remains a critical barrier to achieving sustainable, high-performing networks. This study aims to identify and address the primary causes of energy wastage in IoT systems, proposing a framework to optimise energy consumption and improve overall system performance. A comprehensive literature review was conducted, focusing on studies from 2010 onwards across major databases, resulting in the identification of eleven key factors driving energy inefficiency: offloading, scheduling, latency, changing topology, load balancing, node deployment, resource management, congestion, clustering, routing, and limited bandwidth. The impact of each factor on energy usage was analysed, leading to a proposed framework that incorporates optimised communication protocols (such as CoAP and MQTT), adaptive fuzzy logic systems, and bio-inspired algorithms to streamline resource management and enhance network stability. This framework presents actionable strategies to improve IoT energy efficiency, extend device lifespan, and reduce operational costs. By addressing these energy inefficiency challenges, this study provides a path forward for more sustainable IoT systems, emphasising the need for continued research into experimental validations, context-aware solutions, and AI-driven energy management to ensure scalable and resilient IoT deployment.
Fusing Essential Text for Question Answering over Incomplete Knowledge Base
Authors: Li Huiying , Feng Yuxi , Liu Liheng
Published: January 2025

Knowledge base question answering (KBQA) aims to answer a question using a knowledge base (KB). However, a knowledge base is naturally incomplete, and it cannot cover all the knowledge needed to answer the question. Therefore, obtaining accurate and comprehensive answers to complex questions is difficult when KBs are missing relations and entities. To mitigate this challenge, we propose an incomplete KBQA approach based on Relation-Aware Interactive Network and Text Fusion (RAIN-TF). Specifically, we provide essential textual knowledge by finely filtering the question-related text to compensate for the missing relations and entities in the KB. We propose a question-related subgraph construction method that fuses the knowledge from the text and KB and enhances the interactions among questions, entities, and relations. On this basis, we propose a relation-aware interactive network, which is a relation-aware multi-head attention graph neural network (GNN) model, to promote the deep semantic integration of unstructured texts and structured KBs, thus effectively compensating for the lack of knowledge. Comprehensive experiments on three mainstream incomplete KBQA datasets verify the effectiveness of the proposed approach.