bluuebunny/crossref_metadata_2025 huggingface dataset:
Revealing the Next Word and Character in Arabic: An Effective Blend of Long Short-Term Memory Networks and ARABERT
Authors: Al-Anzi Fawaz S. , Shalini S. T. Bibin
Published: November 2024

Arabic raw audio datasets were initially gathered to produce a corresponding signal spectrum, which was further used to extract the Mel-Frequency Cepstral Coefficients (MFCCs). The pronunciation dictionary, language model, and acoustic model were further derived from the MFCCs’ features. These output data were processed into Baidu’s Deep Speech model (ASR system) to attain the text corpus. Baidu’s Deep Speech model was implemented to precisely identify the global optimal value rapidly while preserving a low word and character discrepancy rate by attaining an excellent performance in isolated and end-to-end speech recognition. The desired outcome in this work is to forecast the next word and character in a sequential and systematic order that applies under natural language processing (NLP). This work combines the trained Arabic language model ARABERT with the potential of Long Short-Term Memory (LSTM) networks to predict the next word and character in an Arabic text. We used the pre-trained ARABERT embedding to improve the model’s capacity and, to capture semantic relationships within the language, we educated LSTM + CNN and Markov models on Arabic text data to assess the efficacy of this model. Python libraries such as TensorFlow, Pickle, Keras, and NumPy were used to effectively design our development model. We extensively assessed the model’s performance using new Arabic text, focusing on evaluation metrics like accuracy, word error rate, character error rate, BLEU score, and perplexity. The results show how well the combined LSTM + ARABERT and Markov models have outperformed the baseline models in envisaging the next word or character in the Arabic text. The accuracy rates of 64.9% for LSTM, 74.6% for ARABERT + LSTM, and 78% for Markov chain models were achieved in predicting the next word, and the accuracy rates of 72% for LSTM, 72.22% for LSTM + CNN, and 73% for ARABERET + LSTM models were achieved for the next-character prediction. This work unveils a novelty in Arabic natural language processing tasks, estimating a potential future expansion in deriving a precise next-word and next-character forecasting, which can be an efficient utility for text generation and machine translation applications.
A Study on the Vehicle Routing Planning Method for Fresh Food Distribution
Authors: Wang Yuxuan , Wang Yajun , Leng Junyu
Published: November 2024

Aimed at the high cost of cold chain distribution of fresh agricultural products within a specified time window, a joint optimization method based on a bi-level programming model for cold chain logistics is proposed for the location of front warehouses and distribution path planning. At the upper level of the bi-level programming model, k-means clustering analysis is used to obtain all accurate information about alternative locations for the front warehouse for site selection, thereby providing the corresponding foundation for the lower level algorithm. At the lower level of the model, a fusion algorithm of particle swarm optimization (PSO) and a genetic algorithm (GA) is used for solving. To accelerate the convergence speed of the population and lower the running time of the algorithm, the parameter values in the algorithm are determined adaptively. An adaptive hybrid algorithm combining the particle swarm optimization algorithm and the genetic algorithm (APSOGA) is used to reallocate the location information on backup points for the front-end warehouse, ultimately determining the facility location of the front-end warehouse and planning the end path from the front-end warehouse to the customer point, achieving joint optimization of the front-end warehouse’s location and path. A comparative analysis of algorithm optimization shows that using the APSOGA hybrid algorithm can reduce the total cost of the logistics network by 14.57% compared to a traditional single-algorithm PSO solution and reduce it by 5.21% compared to using a single GA. This proves the effectiveness of the APSOGA hybrid algorithm in solving location and path planning problems for cold chain logistics distribution companies.
Optimal Configuration of Soft Open Point and Energy Storage Based on Snowflake-Shaped Grid Characteristics and Sensitivity Analysis
Authors: Wang Zhe , Zhang Zhang , Luo Fengzhang , Qiu Xiaoyu , Zhang Xuefei , Duan Jiali
Published: November 2024

With the continuous penetration of flexible resources, the distribution network is gradually forming a two-way interactive supply and demand relationship with the transmission network and users. The deployment of soft open point (SOP) and energy storage represents a crucial strategy for voltage regulation and power flow control in distribution networks. This article puts forth a methodology for optimizing the configuration of SOP and energy storage based on the characteristics of the snowflake-shaped grid and sensitivity analysis. Firstly, the location of the SOP is determined based on the characteristics of the interconnection nodes between snowflake websites. Secondly, the voltage sensitivity analysis is employed to identify nodes that have a significant impact on the system voltage distribution, thereby enabling the selection of an optimal energy storage site. Subsequently, a multi-objective optimization configuration model for SOP and energy storage is established, taking into account the economic efficiency and load balancing of the power grid. Finally, the method is verified using a snowflake-shaped grid in Tianjin. In comparison with the plan that solely considers the economic aspects of the power grid, the method proposed in this article can reduce the degree of load balancing by 50.93% while simultaneously increasing the annual comprehensive cost by only 24.35%.
Accuracy of Dynamic Modulus Models of Asphalt Mixtures Containing Reclaimed Asphalt (RA)
Authors: Belhaj Majda , Valentin Jan , Baldo Nicola
Published: November 2024

The dynamic modulus (∣E*∣) is a fundamental mechanical parameter for studying the performance of hot mix asphalt and simulating its viscoelastic behaviour under different loading and thermal conditions. It is a primary tool to replicate road surface behaviour under vehicle traffic loading and temperature variations. Though, laboratory testing to determine this parameter is time-consuming and costly. Several predictive models have been developed to estimate the dynamic modulus, ranging from rheological to empirical regression models. This research was dedicated to studying two predictive models for determining the master curve of the dynamic modulus of hot mix asphalt used in a regular pavement binder course containing different reclaimed asphalt contents (0%, 30%, 40%, and 50%). Laboratory experiments were conducted to assess their accuracy. The results show that Witczak’s sigmoid function provided the best accuracy for the master curves, while the Generalized Huet-Sayegh (2S2P1D) model showed less accurate predictions, particularly at the range of low and high frequencies.
Statistical Analysis of AC Breakdown Performance of Epoxy/Al2O3 Micro-Composites for High-Voltage Applications
Authors: Cheon Changyeong , Seo Dongmin , Kim Myungchin
Published: November 2024

Thanks to the performance improvement introduced by micro sized functional fillers, application of epoxy composites for electrical insulation purposes has become popular. This paper investigates the dielectric properties of epoxy micro-composites filled with alumina (Al2O3). In particular, measurements of relative permittivity, dissipation factor, and electrical breakdown are performed, and a comprehensive statistical analysis on dielectric properties was conducted. AC breakdown strength (AC-BDS) was analyzed for normal distribution using four methods (Anderson–Darling, Shapiro–Wilk, Ryan–Joiner, and Kolmogorov–Smirnov). In addition, the AC-BDS was analyzed at risk probabilities of 1%, 5%, 10%, and 50% using Weibull distribution functions. Both normal and Weibull distributions were evaluated using the Anderson–Darling (A-D) statistic and p-value. Additionally, the log-normal, gamma, and exponential distributions of AC-BDS were examined by A-D goodness-of-fit test. The hypothesis test results of AC-BDS were fit by normal and Weibull distributions, and the compliance was evaluated by p-value and each method statistics. In addition, the experimental results of AC-BDS were fit by log-normal and gamma distributions, and the goodness-of-fit was evaluated by p-value and A-D testing. On the other hand, exponential distribution was not suitable for p-value and A-D testing. The results showed that the distributions of AC-BDS were the best using log-normal distribution. Meanwhile, statistical analysis results verified the apparent effect of temperature on dielectric properties using a paired t-test. The analysis results of this paper not only contribute to better characterization of epoxy/Al2O3 micro-composites but also introduce a comprehensive approach for performing statistical analysis for electrical insulation materials.
Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
Authors: Elbasi Ersin , Topcu Ahmet E. , Cina Elda , Zreikat Aymen I. , Shdefat Ahmed , Zaki Chamseddine , Abdelbaki Wiem
Published: November 2024

In botany and agriculture, classifying leaves is a crucial process that yields vital information for studies on biodiversity, ecological studies, and the identification of plant species. The Cope Leaf Dataset offers a comprehensive collection of leaf images from various plant species, enabling the development and evaluation of advanced classification algorithms. This study presents a robust methodology for classifying leaf images within the Cope Leaf Dataset by enhancing the feature extraction and selection process. Cope Leaf Dataset has 99 classes and 64 features with 1584 records. Features are extracted based on the margin, texture, and shape of the leaves. It is challenging to classify a large number of labels because of class imbalance, feature complexity, overfitting, and label noise. Our approach combines advanced feature selection techniques with robust preprocessing methods, including normalization, imputation, and noise reduction. By systematically integrating these techniques, we aim to reduce dimensionality, eliminate irrelevant or redundant features, and improve data quality. Increasing accuracy in classification, especially when dealing with large datasets and many classes, involves a combination of data preprocessing, model selection, regularization techniques, and fine-tuning. The results indicate that the Multilayer Perception algorithm gives 89.48%, the Naïve Bayes Classifier gives 89.63%, Convolutional Neural Networks has 88.72%, and the Hoeffding Tree algorithm gives 89.92% accuracy for the classification of 99 label plant leaf classification problems.
Development of an Optimized Non-Linear Model for Precise Dew Point Estimation in Variable Environmental Conditions
Authors: Hernandez-Torres José Antonio , Torreglosa Juan P. , Sanchez-Herrera Reyes , Bischi Aldo , Baccioli Andrea
Published: November 2024

Accurate dew point estimation is crucial for measuring water condensation in various fields such as environmental studies, agronomy, or water harvesting, among others. Despite the numerous models and equations developed over time, including empirical and machine learning approaches, they often involve trade-offs between accuracy, simplicity, and computational cost. A major limitation of the current approaches is the lack of balance among these three factors, limiting their practical applications under diverse conditions. This research addresses these key challenges by developing a new, streamlined equation for dew point estimation. Using the Magnus–Tetens equation, deemed as the most reliable equation, as a benchmark, and by applying a process of non-linear regression fitting and parametric optimization, a new equation was derived. The results demonstrate high accuracy with a streamlined implementation, validated through extensive data and computational simulations. This study highlights the importance of accurate dew point modeling, especially under variable environmental conditions, provides a reliable solution to existing limitations, paving the way for enhanced efficiency in related processes and research endeavors, and offers researchers and practitioners a practical tool for more effective modeling of water condensation phenomena.
Human Operator Mental Fatigue Assessment Based on Video: ML-Driven Approach and Its Application to HFAVD Dataset
Authors: Othman Walaa , Hamoud Batol , Shilov Nikolay , Kashevnik Alexey
Published: November 2024

The detection of the human mental fatigue state holds immense significance due to its direct impact on work efficiency, specifically in system operation control. Numerous approaches have been proposed to address the challenge of fatigue detection, aiming to identify signs of fatigue and alert the individual. This paper introduces an approach to human mental fatigue assessment based on the application of machine learning techniques to the video of a working operator. For validation purposes, the approach was applied to a dataset, “Human Fatigue Assessment Based on Video Data” (HFAVD) integrating video data with features computed by using our computer vision deep learning models. The incorporated features encompass head movements represented by Euler angles (roll, pitch, and yaw), vital signs (blood pressure, heart rate, oxygen saturation, and respiratory rate), and eye and mouth states (blinking and yawning). The integration of these features eliminates the need for the manual calculation or detection of these parameters, and it obviates the requirement for sensors and external devices, which are commonly employed in existing datasets. The main objective of our work is to advance research in fatigue detection, particularly in work and academic settings. For this reason, we conducted a series of experiments by utilizing machine learning techniques to analyze the dataset and assess the fatigue state based on the features predicted by our models. The results reveal that the random forest technique consistently achieved the highest accuracy and F1-score across all experiments, predominantly exceeding 90%. These findings suggest that random forest is a highly promising technique for this task and prove the strong connection and association among the predicted features used to annotate the videos and the state of fatigue.
Vulnerability and Attack Repository for IoT: Addressing Challenges and Opportunities in Internet of Things Vulnerability Databases
Authors: Felkner Anna , Adamski Jan , Koman Jakub , Rytel Marcin , Janiszewski Marek , Lewandowski Piotr , Pachnia Rafał , Nowakowski Wojciech
Published: November 2024

The article’s primary purpose is to highlight the importance of cybersecurity for Internet of Things (IoT) devices. Due to the widespread use of such devices in everyone’s daily and professional lives, taking care of their security is essential. This security can be strengthened by raising awareness about the vulnerabilities and risks of these devices among their manufacturers and users. Therefore, this paper shows the results of several years of work regarding building vulnerabilities and exploiting databases, with a particular focus on IoT devices. We highlight multiple unique features of our solution, such as aggregation, correlation, and enrichment of known vulnerabilities and exploits collected from 12 different sources, presentation of a timeline of threats, and combining vulnerability information with exploits. Our databases have more than 300,000 entries, which are the result of aggregating and correlating more than 1,300,000 entries from 12 different databases simultaneously, enriched with information from unstructured sources. We cover the innovative utilization of Artificial Intelligence (AI) to support data enrichment, examining the usage of the Light Gradient-Boosting Machine (LGBM) model to automatically predict vulnerability severity and Mistral7B to categorize vulnerable products, which, especially in the case of IoT devices, is critical due to the diversity of nomenclature. Social media and various unstructured sources are prominent places for gathering information. Retrieving data from them is much more complex than from structured databases, but the information there is normally supplied at a faster rate. Thus, we added Mastodon monitoring to enhance our threat timelines.
Modeling Directional Monotonicity in Sequence with Copulas
Authors: Quesada-Molina José Juan , Úbeda-Flores Manuel
Published: November 2024

In this paper, we present the concept of being monotonic in sequence according to a specific direction for a collection of random variables. This concept broadens the existing notions of multivariate dependence, such as sequential left-tail and right-tail dependence. Furthermore, we explore connections with other multivariate dependence concepts, highlight key properties, and analyze the new concept within the framework of copulas. Several examples are provided to demonstrate our findings.
Autonomous Second-Order ODEs: A Geometric Approach
Authors: Pan-Collantes Antonio J. , Álvarez-García José Antonio
Published: November 2024

Given an autonomous second-order ordinary differential equation (ODE), we define a Riemannian metric on an open subset of the first-order jet bundle. A relationship is established between the solutions of the ODE and the geodesic curves with respect to the defined metric. We introduce the notion of energy foliation for autonomous ODEs and highlight its connection to the classical energy concept. Additionally, we explore the geometry of the leaves of the foliation. Finally, the results are applied to the analysis of Lagrangian mechanical systems. In particular, we provide an autonomous Lagrangian for a damped harmonic oscillator.
Listening to Patients: Advanced Arabic Aspect-Based Sentiment Analysis Using Transformer Models Towards Better Healthcare
Authors: AlNasser Seba , AlMuhaideb Sarab
Published: November 2024

Patient satisfaction is a key measure of the quality of healthcare, directly impacting the success and competitiveness of healthcare providers in an increasingly demanding market. Traditional feedback collection methods often fall short of capturing the full spectrum of patient experiences, leading to skewed satisfaction reports due to patients’ reluctance to criticize services and the inherent limitations of survey designs. To address these issues, advanced Natural Language Processing (NLP) techniques such as aspect-based sentiment analysis are emerging as essential tools. Aspect-based sentiment analysis breaks down the feedback text into specific aspects and evaluates the sentiment for each aspect, offering a more nuanced and actionable understanding of patient opinions. Despite its potential, aspect-based sentiment analysis is under-explored in the healthcare sector, particularly in the Arabic literature. This study addresses this gap by performing an Arabic aspect-based sentiment analysis on patient experience data, introducing the newly constructed Hospital Experiences Arabic Reviews (HEAR) dataset, and conducting a comparative study using Bidirectional Embedding Representations from Transformers (BERT) combined with machine learning classifiers, as well as fine-tuning BERT models, including MARBERT, ArabicBERT, AraBERT, QARiB, and CAMeLBERT. Additionally, the performance of GPT-4 via OpenAI’s ChatGPT is evaluated in this context, making a significant contribution to the comparative study of BERT with traditional classifiers and the assessment of GPT-4 for aspect-based sentiment analysis in healthcare, ultimately offering valuable insights for enhancing patient experiences through the use of AI-driven approaches. The results show that the joint model leveraging MARBERT and SVM achieves the highest accuracy of 92.14%, surpassing other models, including GPT-4, in both aspect category detection and polarity tasks.
Mitochondrial Genome Instability in W303-SK1 Yeast Cytoplasmic Hybrids
Authors: Epremyan Khoren K. , Burlaka Arteom A. , Markova Olga V. , Galkina Kseniia V. , Knorre Dmitry A.
Published: November 2024

Unlike most animals, some fungi, including baker’s yeast, inherit mitochondrial DNA (mtDNA) from both parents. When haploid yeast cells fuse, they form a heteroplasmic zygote, whose offspring retain one or the other variant of mtDNA. Meanwhile, some mutant mtDNA (rho−), with large deletions in the nucleotide sequence, can displace wild-type (rho+) mtDNA. Consequently, offspring of zygotes with such rho− mtDNA predominantly carry the mutant variant. This phenomenon is called suppressivity. In this study, we investigated how the suppressivity of rho− mtDNA depends on the mitochondrial and nuclear genomes of the rho+ strain during crossing. Comparing two diverged laboratory strains, SK1 and W303, we measured suppressivity in crosses with four rho− strains. One rho− strain showed significantly higher suppressivity when crossed with SK1 than with W303. We then created cytoplasmic hybrids by swapping mtDNAs between these strains. Surprisingly, we found that the mtDNA of the rho+ strain, rather than its nuclear DNA, determines high suppressivity in crosses of SK1 rho+ with the rho− strain. Additionally, mtDNA replacement reduced respiration rate and growth rate on non-fermentable substrates while increasing the likelihood of functional mtDNA loss. Our data demonstrate that a mutant mtDNA variant’s ability to displace another mitochondrial DNA variant in a heteroplasmic cell depends more on mtDNA sequences than on the biochemical and structural context created by the nuclear genome background.
Targeting PDGF/PDGFR Signaling Pathway by microRNA, lncRNA, and circRNA for Therapy of Vascular Diseases: A Narrow Review
Authors: Ma Chao-Nan , Shi Shan-Rui , Zhang Xue-Ying , Xin Guo-Song , Zou Xiang , Li Wen-Lan , Guo Shou-Dong
Published: November 2024

Despite the significant progress in diagnostic and therapeutic strategies, vascular diseases, such as cardiovascular diseases (CVDs) and respiratory diseases, still cannot be successfully eliminated. Vascular cells play a key role in maintaining vascular homeostasis. Notably, a variety of cells produce and secrete platelet-derived growth factors (PDGFs), which promote mitosis and induce the division, proliferation, and migration of vascular cells including vascular smooth muscle cells (SMCs), aortic SMCs, endothelial cells, and airway SMCs. Therefore, PDGF/PDGR receptor signaling pathways play vital roles in regulating the homeostasis of blood vessels and the onset and development of CVDs, such as atherosclerosis, and respiratory diseases including asthma and pulmonary arterial hypertension. Recently, accumulating evidence has demonstrated that microRNA, long-chain non-coding RNA, and circular RNA are involved in the regulation of PDGF/PDGFR signaling pathways through competitive interactions with target mRNAs, contributing to the occurrence and development of the above-mentioned diseases. These novel findings are useful for laboratory research and clinical studies. The aim of this article is to conclude the recent progresses in this field, particular the mechanisms of action of these non-coding RNAs in regulating vascular remodeling, providing potential strategies for the diagnosis, prevention, and treatment of vascular-dysfunction-related diseases, particularly CVDs and respiratory diseases.
Visualization Methods for DNA Sequences: A Review and Prospects
Authors: Li Tan , Li Mengshan , Wu Yan , Li Yelin
Published: November 2024

The efficient analysis and interpretation of biological sequence data remain major challenges in bioinformatics. Graphical representation, as an emerging and effective visualization technique, offers a more intuitive method for analyzing DNA sequences. However, many visualization approaches are dispersed across research databases, requiring urgent organization, integration, and analysis. Additionally, no single visualization method excels in all aspects. To advance these methods, knowledge graphs and advanced machine learning techniques have become key areas of exploration. This paper reviews the current 2D and 3D DNA sequence visualization methods and proposes a new research direction focused on constructing knowledge graphs for biological sequence visualization, explaining the relevant theories, techniques, and models involved. Additionally, we summarize machine learning techniques applicable to sequence visualization, such as graph embedding methods and the use of convolutional neural networks (CNNs) for processing graphical representations. These machine learning techniques and knowledge graphs aim to provide valuable insights into computational biology, bioinformatics, genomic computing, and evolutionary analysis. The study serves as an important reference for improving intelligent search systems, enriching knowledge bases, and enhancing query systems related to biological sequence visualization, offering a comprehensive framework for future research.
deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction
Authors: Kryś Justyna D. , Głowacki  Maksymilian , Śmieja  Piotr , Gront Dominik
Published: November 2024

Coarse-grained models have provided researchers with greatly improved computational efficiency in modeling structures and dynamics of biomacromolecules, but, to be practically useful, they need fast and accurate conversion methods back to the all-atom representation. Reconstruction of atomic details may also be required in the case of some experimental methods, like electron microscopy, which may provide Cα-only structures. In this contribution, we present a new method for recovery of all backbone atom positions from just the Cα coordinates. Our approach, called deepBBQ, uses a deep convolutional neural network to predict a single internal coordinate per peptide plate, based on Cα trace geometric features, and then proceeds to recalculate the cartesian coordinates based on the assumption that the peptide plate atoms lie in the same plane. Extensive comparison with similar programs shows that our solution is accurate and cost-efficient. The deepBBQ program is available as part of the open-source bioinformatics toolkit Bioshell and is free for download and the documentation is available online.
C11orf58 (Hero20) Gene Polymorphism: Contribution to Ischemic Stroke Risk and Interactions with Other Heat-Resistant Obscure Chaperones
Authors: Shilenok Irina , Kobzeva Ksenia , Soldatov Vladislav , Deykin Alexey , Bushueva Olga
Published: November 2024

Background: Recently identified Hero proteins, which possess chaperone-like functions, are promising candidates for research into atherosclerosis-related diseases, including ischemic stroke (IS). Methods: 2204 Russian subjects (917 IS patients and 1287 controls) were genotyped for fifteen common SNPs in Hero20 gene C11orf58 using probe-based PCR and the MassArray-4 system. Results: Six C11orf58 SNPs were significantly associated with an increased risk of IS in the overall group (OG) and significantly modified by smoking (SMK) and low fruit/vegetable intake (LFVI): rs10766342 (effect allele (EA) A; P(OG = 0.02; SMK = 0.009; LFVI = 0.04)), rs11024032 (EA T; P(OG = 0.01; SMK = 0.01; LFVI = 0.036)), rs11826990 (EA G; P(OG = 0.007; SMK = 0.004; LFVI = 0.03)), rs3203295 (EA C; P(OG = 0.016; SMK = 0.01; LFVI = 0.04)), rs10832676 (EA G; P(OG = 0.006; SMK = 0.002; LFVI = 0.01)), rs4757429 (EA T; P(OG = 0.02; SMK = 0.04; LFVI = 0.04)). The top ten intergenic interactions of Hero genes (two-, three-, and four-locus models) involved exclusively polymorphic loci of C11orf58 and C19orf53 and were characterized by synergic and additive (independent) effects between SNPs. Conclusions: Thus, C11orf58 gene polymorphism represents a major risk factor for IS. Bioinformatic analysis showed the involvement of C11orf58 SNPs in molecular mechanisms of IS mediated by their role in the regulation of redox homeostasis, inflammation, vascular remodeling, apoptosis, vasculogenesis, neurogenesis, lipid metabolism, proteostasis, hypoxia, cell signaling, and stress response. In terms of intergenic interactions, C11orf58 interacts most closely with C19orf53.
Alterations in the Levels of Urinary Exosomal MicroRNA-183-5p and MicroRNA-125a-5p in Individuals with Type 2 Diabetes Mellitus
Authors: Fang Yixuan , Sun Shiyi , Wu Jing , Liu Guanjian , Wu Qinqin , Ran Xingwu
Published: November 2024

Background: Type 2 diabetes mellitus (T2DM) is a metabolic disorder, and urinary exosomal microRNAs (miRNAs) were utilized as potential disease prediction or diagnostic biomarkers in numerous studies. This study investigated the differential expression of urinary exosomal miRNAs between non-diabetes mellitus (NDM) individuals and those with T2DM. Aim: To elucidate the association between urinary exosomal miRNAs and T2DM. Methods: We recruited patients diagnosed with T2DM and NDM individuals in West China Hospital, Sichuan University, from November 2023 to February 2024. Subsequently, we performed sequencing of urinary exosomal microRNAs in both groups. The obtained sequencing results were further validated using RT-qPCR in both the training set and the validation set. Additionally, we conducted logistic regression analysis and Spearman correlation analysis on miRNAs with significant differential expression, as well as analysis of their biological functions. Results: A total of 118 urine samples were collected, 59 from individuals diagnosed with T2DM and 59 from NDM. There were differentially expressed miR-183-5p (p = 0.034) and miR-125a-5p (p = 0.008) between the two groups. Furthermore, multivariate regression analysis demonstrated that higher miR-125a-5p levels were negatively associated with the risk of T2DM (p = 0.044; OR: 0.046; 95% CI: 0.002, 0.922). Bioinformatics analysis indicated that the target genes of miR-183-5p were predominantly involved in insulin signaling and glucose transport processes, while those target genes of miR-125a-5p primarily mediated autophagy. Conclusions: miR-183-5p and miR-125a-5p might be involved in the pathogenesis of T2DM, while higher urinary exosomal miR-125a-5p was negatively associated with the risk of T2DM.
Study of the Impact on Zygomatic Bone Using Numerical Simulation
Authors: Ruiz-de-León Gonzalo , Baus-Domínguez María , González-Martín Maribel , Gutiérrez-Corrales Aida , Torres-Carranza Eusebio , Martínez-González Álvaro-José , Torres-Lagares Daniel , López-Millan José-Manuel , Ambrosiani-Fernández Jesús
Published: November 2024

The zygomatic bone, a fundamental structure in facial anatomy, is exposed to fractures in impact situations, such as traffic accidents or contact sports. The installation of zygomatic implants can also alter the distribution of forces in this region, increasing the risk of fractures. To evaluate this situation, the first step is to develop a complex anatomical model from the stomatognathic point of view so that simulations in this sense can be validated. This study uses numerical simulation using a finite-element method (FEM) to analyze the behavior of the zygomatic bone under impacts of different velocities, offering a more realistic approach than previous studies by including the mandible, cervical spine, and masticatory muscles. Methods: An FEM model was developed based on 3D scans of actual bones, and simulations were performed using Abaqus Explicit 2023 software (Dassault Systemes, Vélizy-Villacoublay, France). The impact was evaluated using a steel cylinder (200 mm length, 40 mm diameter, 2 kg weight) impacted at speeds of 5, 10, 15, and 20 km/h. Zygomatic, maxillary, and mandibular bone properties were based on dynamic stiffness parameters, and bone damage was analyzed using ductile fracture and fracture energy criteria. Results: The results show that at impact velocities of 15 and 20 km/h, the zygomatic bone suffered crush fractures, with impact forces up to 400 kg. At 10 km/h, a combination of crushing and bending was observed, while at 5 km/h, only local damage without complete fracture was detected. The maximum stresses were concentrated at the zygoma–jaw junction, with values above 100 MPa at some critical points. Conclusion: The FEM model developed offers a detailed representation of the mechanical behavior, integrating the main structures of the stomatognathic apparatus of the zygomatic bone under impact, providing valuable information to, for example, advance injury prevention and zygomatic implant design. Higher impact velocities result in severe fractures, underscoring the need for protective measures in clinical and sports settings.
The Use of Blood-Based Biomarkers in the Prediction of Colorectal Neoplasia at the Time of Primary Screening Colonoscopy Among Average-Risk Patients: A Systematic Literature Review
Authors: Sutherland R. Liam , O’Sullivan Dylan E. , Ruan Yibing , Chow Kristian , Mah Brittany , Kim Dayoung , Basmadjian Robert B. , Forbes Nauzer , Cheung Winson Y. , Hilsden Robert J. , Brenner Darren R.
Published: November 2024

Background/Objectives: Risk prediction models (RPMs) for colorectal cancer (CRC) could facilitate risk-based screening. Models incorporating biomarkers may improve the utility of current RPMs. We performed a systematic review of studies reporting RPMs for CRC that evaluated the impact of blood-based biomarkers on clinical outcome prediction at the time of screening colonoscopy in average-risk populations. Methods: We conducted a search of MEDLINE, Web of Science, and PubMed databases from inception through April 2024. Studies that developed or validated a model to predict risk of CRC or its precursors were included. Studies were limited to those including patients undergoing average-risk CRC screening. Results: Sixteen studies published between 2015 and 2024 were included. Outcomes included CRC (16 studies) and high-risk adenomas (1 study). Using a complete blood count was the most common biomarker and was able to achieve an AUC of 0.82 and a specificity of 0.88. Other blood-based biomarkers included were various serum proteins/metabolites/enzymes, plasma metabolites, insulin-related factors, and anemia markers. The highest-performing model, with an AUC of 0.99, involved the use of a plasma metabolite panel. Conclusions: The evidence base of RPMs for CRC screening is expanding and incorporating biomarkers, which remain a prominent aspect of model discovery. Most RPMs included a lack of internal/external validation or discussion as to how the model could be implemented clinically. As biomarkers improve the discriminatory potential of RPMs, more research is needed for the evaluation and implementation of RPMs within existing CRC screening frameworks.
Validation of the Scottish Inflammatory Prognostic Score (SIPS) in NSCLC Patients Treated with First-Line Pembrolizumab
Authors: Gomez-Randulfe Igor , Gomes Fabio , MacKean Melanie , Phillips Iain , Stares Mark
Published: November 2024

Background: The Scottish Inflammatory Prognostic Score (SIPS), combining albumin (≥/<35 g/L) and neutrophil count (≤/>7.5 × 109/L), has been identified as a prognostic biomarker for patients with non-small cell lung cancer (NSCLC) undergoing treatment with pembrolizumab monotherapy. We sought to validate this biomarker of systemic inflammation in an external cohort. Methods: Patients treated with first-line pembrolizumab for advanced NSCLC with programmed death-ligand 1 (PD-L1) expression ≥ 50% at an English cancer centre were identified. Pre-treatment clinicopathological characteristics and the SIPS were recorded. The relationship between these and progression-free survival (PFS) and overall survival (OS) was examined. Results: Among 257 patients evaluated, 56% (n = 144) were classified as SIPS 0, 36% (n = 93) as SIPS 1, and 8% (n = 20) as SIPS 2. Factors such as age, performance status (PS) and brain metastases presence were significantly correlated with SIPS categories. Multivariate analysis revealed that both SIPS and PD-L1 status were independently associated with PFS and OS. The combination of SIPS with either PS or PD-L1 expression enhanced the ability to detect patients with the most favourable or poorest survival. Conclusions: Our study confirms the prognostic significance of the SIPS in patients with advanced NSCLC treated with pembrolizumab in the context of high PD-L1 expression. SIPS offers a straightforward, clinically applicable approach to patient stratification, potentially guiding therapeutic decisions and enhancing outcomes in advanced NSCLC. Future research should focus on validating these findings in prospective studies and exploring the integration of SIPS into clinical practice, alongside other prognostic markers, to optimize treatment strategies.
On the Prognostic Implication of Delays in the Definitive Treatment of Uveal Melanoma
Authors: Stålhammar Gustav , Grisanti Salvatore , Finger Paul T.
Published: November 2024

Background: Recent studies suggest that delays in the definitive treatment of uveal melanoma may increase the risk of metastatic disease. This topic has been the subject of considerable debate. Methods: In this study, we combine and contrast medical evidence from several recent publications seeking to clarify the association between treatment delays and prognosis. Results: Emerging evidence indicates that metastatic seeding may continue until the primary tumor is effectively treated. Metastases that arise later in the disease course may carry additional genetic aberrations, enhancing their capacity to establish fatal macrometastases. Importantly, previous reports of shared mutations between primary tumors and metastases should not be interpreted as evidence that all metastases are seeded early. On the contrary, some tumors acquire additional driver mutations in the later stages, which are subsequently shared between the primary tumors and metastases. The increased risk of metastasis in patients with local tumor recurrence further highlights the importance of timely treatment. Additionally, new data on circulating tumor cells and treatment timing challenge the traditional practice of observing small melanomas. Conclusions: Observation is still warranted for indeterminate lesions to confirm malignancy. However, once a melanoma diagnosis has been established, further observation is harmful, and treatment should be administered as soon as reasonably possible.
Synergistic Activation of VDR-RXR Heterodimers by Vitamin D and Rexinoids in Human Kidney and Brain Cells
Authors: Doost Mobin Emran , Hong Jennifer , Broatch Jennifer E. , Applegate Michael T. , Wagner Carl E. , Marshall Pamela A. , Jurutka Peter W.
Published: November 2024

The active form of vitamin D, 1,25-dihydroxyvitamin D (1,25D), binds to the vitamin D receptor (VDR) with high affinity. The VDR then heterodimerizes with the retinoid X receptor (RXR) and associates with vitamin D response elements (VDREs) to regulate the transcription of target genes. Bexarotene (Bex) is an RXR ligand (rexinoid) developed to treat cutaneous T-cell lymphoma and is a putative therapeutic for other diseases. We postulate that VDR ligands (1,25D) and RXR ligands (Bex/analogs) can “synergize” to “super-activate” the VDR-RXR heterodimer. This “cross-talk” could allow disorders treated with high-dose Bex therapy (leading to significant adverse side effects) to instead be treated using both low-dose Bex and vitamin D. Thus, we designed experiments to examine the effect of both VDR and RXR ligands, alone and in combination, to activate VDR-RXR-mediated transcription. The goal was to determine if selected RXR-specific ligands can synergize with vitamin D to amplify RXR-VDR activity. The results demonstrate a synergistic effect with both Bex and 1,25D which could be further modulated by (1) the protein levels (or polymorphic version) of VDR present in the cell, (2) the concentration of the ligands, (3) the cellular “background” (e.g., brain cells versus kidney cells), (4) the nature of the VDRE platform, or (5) the type of rexinoid (Bex analogs). Our findings suggest that diseases that respond to treatment with either vitamin D, or with rexinoids, may be amenable to enhanced therapeutic potential by employing multi-ligand dosing via combinatorial therapy.
Mechanisms of Insulin Signaling as a Potential Therapeutic Method in Intestinal Diseases
Authors: Jarmakiewicz-Czaja Sara , Sokal-Dembowska Aneta , Ferenc Katarzyna , Filip Rafał
Published: November 2024

Gastrointestinal diseases are becoming a growing public health problem. One of them is inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD). The incidence of IBD is increasing in developing countries and declining in developed countries, affecting people of all ages. Researchers have been exploring new treatment options including insulin signaling pathways in the inflammation of the gastrointestinal tract. It seems that a better understanding of the mechanism of IGF-1, GLP-1 and TL1A on the gut microbiota and inflammation may provide new advances in future therapeutic strategies for patients with IBD, but also other intestinal diseases. This review aims to synthesize insights into the effects of GLP, IGF and anti-TL1A on inflammation and the gut microbiota, which may enable their future use in therapy for people with intestinal diseases.
Economic Impact of Droughts in Southern Brazil, a Duration Analysis
Authors: Tonetto Jorge Luis , Pique Josep Miquel , Fochezatto Adelar , Rapetti Carina
Published: November 2024

Hydrometeorological hazards are currently a cause for great concern worldwide. Droughts are among the most recurrent events, causing significant losses. This article presents a study on the duration of droughts in the southernmost state of Brazil, which has a large agricultural sector and experiences frequent drought events. The approach focuses on the economic recovery time of municipalities affected by the drought in 2020, 2022 and 2023, using the total value of invoices issued within each municipality between companies and from companies to consumers. The Kaplan–Meier estimator and Cox regression models are applied, incorporating covariates such as the size of the municipality, geographic location, and primary economic activity sector. The results show that the longest recovery period is concentrated in small cities, particularly in those where agriculture or livestock is the primary economic activity. The greatest resilience is observed in cities within the metropolitan region, where economic activity is more concentrated in services and industry and where populations are generally larger. The study identifies that after each drought event, at least 75% of municipalities achieve economic recovery within 3 months. These findings support better planning for both drought prevention and impact reduction and they are relevant for the development of economic and social policies.
Oral Diadochokinesis, Tongue Pressure, and Lip-Seal Strength Among Japanese Male Workers in the Taxi Industry: A Cross-Sectional Study
Authors: Minoura Akira , Ihara Yoshiaki , Kato Hirotaka , Murakami Kouzou , Watanabe Yoshio , Hirano Kojiro , Ito Yoshinori , Kokaze Akatsuki
Published: November 2024

Background/Objectives: Health management in workers in the taxi industry is particularly challenging due to irregular working hours and the need to prevent fatal accidents. In addition, drivers in Japan are aging, and the early prevention of age-related deterioration in oral health is an increasingly important issue. The aim of this cross-sectional study was to investigate the relationships between oral diadochokinesis (OD), tongue pressure, and lip-seal strength in Japanese male taxi workers. Methods: Measurements of tongue pressure and lip-seal strength were performed by dentists using specialized equipment. OD was measured using the number of consecutive “Pa”, “Ta”, and “Ka” vocalizations that could be produced in 5 s. We performed multiple regression analysis to examine the effects of lip-seal strength and tongue pressure on OD. Results: The study included 437 participants, excluding 17 who could not complete all oral cavity measurements. Tongue pressure showed a significant positive correlation with “Pa”, “Ta”, and “Ka” (correlation coefficients: 0.527–0.680). Lip-seal strength was not significantly correlated with OD. Tongue pressure showed a significant positive correlation with “Pa”, “Ta”, and “Ka”. In the results of multiple regression analyses without the elderly participants, tongue pressure was associated with “Pa” (β[95% confidence interval]: 0.574[0.304, 0.843]), “Ta” (0.436[0.231, 0.640]), and “Ka” (0.424[0.210, 0.639]), and lip-seal strength was associated with “Pa” (0.128[0.032, 0.224]) and “Ka” (0.083[0.006, 0.160]). Conclusions: OD may be associated with lip-seal strength and tongue pressure even without including elderly workers. Regardless of age, maintaining good OD may help maintain lip-seal strength and tongue pressure, which may play a role in reducing the risk of age-related oral disorders.
Artificial Intelligence Applied in Early Prediction of Lower Limb Fracture Complications
Authors: Anghele Aurelian-Dumitrache , Marina Virginia , Dragomir Liliana , Moscu Cosmina Alina , Fulga Iuliu , Anghele Mihaela , Popescu Cristina-Mihaela
Published: November 2024

Background: Artificial intelligence has become a valuable tool for diagnosing and detecting postoperative complications early. Through imaging and biochemical markers, clinicians can anticipate the clinical progression of patients and the risk of long-term complications that could impact the quality of life or even be life-threatening. In this context, artificial intelligence is crucial for identifying early signs of complications and enabling clinicians to take preventive measures before problems worsen. Materials and methods: This observational study analyzed medical charts from the electronic archive of the Clinical Emergency Hospital in Galați, Romania, covering a four-year period from 2018 to 2022. A neural network model was developed to analyze various socio-demographic and paraclinical data. Key features included patient demographics, laboratory investigations, and clinical outcomes. Statistical analyses were performed to identify significant risk factors associated with deep venous thrombosis (DVT). Results: The analysis revealed a higher prevalence of female patients (60.78%) compared to male patients, indicating a potential gender-related risk factor for DVT. The incidence of DVT was highest among patients aged 71 to 90 years, affecting 56.86% of individuals in this age group, suggesting that advanced age significantly contributes to the risk of developing DVT. Additionally, among the DVT patients, 15.69% had a body mass index (BMI) greater than 30, categorizing them as obese, which is known to increase the risk of thrombotic events. Furthermore, this study highlighted that the highest frequency of DVT was associated with femur fractures, occurring in 52% of patients with this type of injury. The neural network analysis indicated that elevated levels of direct bilirubin (≥1.5 mg/dL) and prothrombin activity (≤60%) were strong predictors of fracture-related complications, with sensitivity and specificity rates of 78% and 82%, respectively. These findings underscore the importance of monitoring these laboratory markers in at-risk populations for early intervention. Conclusions: This study identified critical risk factors for developing DVT, including advanced age, high BMI, and femur fractures, which necessitate longer recovery periods. Additionally, the findings indicate that elevated direct bilirubin and prothrombin activity play a significant role in predicting DVT development. These results suggest that AI can effectively enhance the anticipation of clinical evolution in patients, aiding in early intervention and management strategies.