bluuebunny/crossref_metadata_2025 huggingface dataset:
EMERGING TRENDS IN INTERNET TECHNOLOGY: SYSTEMATIC REVIEW OF THE LATEST INNOVATIONS AND THEIR IMPACT ON SOCIETY
Authors:   Department of ITE AAMUSTED, Kumasi, Ghana, Baffour Henry , Asiedu William ,   Department of ITE AAMUSTED, Kumasi, Ghana, Dankwah Daniel ,   Department of ITE AAMUSTED, Kumasi, Ghana
Published: October 2024

Emerging internet technologies such as 5G, the Internet of Things (IoT), edge computing, and quantum internet are driving significant advancements across various sectors. However, a systematic understanding of their impact, challenges, and future directions is necessary. This study systematically reviewed 120 peer-reviewed articles related to the themes to provide a comprehensive analysis of these technologies. The literature was sourced from Google Scholar, Scopus, and IEEE Xplore, focusing on publications from the last decade. The review identified that while 5G is set to revolutionize connectivity and IoT is transforming industries, both face challenges such as high infrastructure costs and cybersecurity risks. Edge computing was found to be crucial for real-time data processing, yet scalability issues persist. Quantum internet, though promising unprecedented security, is still in its infancy with significant technical hurdles. This study contributes to the field by highlighting the transformative potential of these technologies while also addressing the challenges that must be managed for their successful implementation. The findings provide valuable insights for policymakers, industry leaders, and researchers, emphasizing the need for coordinated efforts to harness these technologies' benefits responsibly.
MODEL IMPLEMENTATION AND COMPREHENSIVE STUDY ON VISUAL AIR TYPING SYSTEMS
Authors:   Department of Computational Intelligence SRM Institute of Science and Technology, Chennai, India, Jha Adarsh , Narendra Chaudhari Bhavisha ,   Department of Computational Intelligence SRM Institute of Science and Technology, Chennai, India
Published: October 2024

This paper explores the implementation and applications of visual air typing systems, focusing on fingertip detection and text recognition to provide an alternative to the traditional input methods. Although models with similar functionality exist, our study emphasizes how such systems can offer practical solutions for users with visual impairments and enhance user experience in virtual reality (VR) environments. By detecting fingertip movements and translating them into written words, such models present a seamless and intuitive user experience. For individuals with visual impairments, traditional keyboards can be challenging, and sign language lacks uniformity across different regions. An air typing system addresses these issues by offering a consistent text input method. Additionally, in VR applications where traditional keyboards are impractical, it enables more natural and efficient text entry. This paper provides a comprehensive review of existing air typing technologies, detailing their strengths and limitations. We discuss the potential future directions for air typing systems, highlighting the need for more inclusive and versatile input methods. Our findings indicate that air typing can significantly enhance user interaction in diverse environments, making it a valuable alternative to traditional keyboards. Overall, this study underscores the practical applications and benefits of air typing systems, particularly for visually impaired users and VR contexts, contributing to advancing more inclusive technological solutions.
TOMATO PLANT DISEASE DETECTION AND PREDICTION USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES: A COMPREHENSIVE REVIEW
Authors:   Department of Computer Science Don Bosco College (Co-Ed),Yelagiri Hils, Affiliated to Thiruvalluvar University Vellore, Tamil Nadu, India, M Baskar , A Dr. Naveen ,   Department of Computer Science Don Bosco College (Co-Ed),Yelagiri Hils, Affiliated to Thiruvalluvar University Vellore, Tamil Nadu, India
Published: October 2024

Tomatoes (Solanum lycopersicum) are critical to global agriculture, but their cultivation is threatened by diseases caused by fungi, bacteria, and viruses. Traditional disease detection methods are often manual and inefficient, resulting in delayed diagnosis and increased crop loss. Recent advances in machine learning (ML) and deep learning (DL) technologies have revolutionized disease detection by automating this process. These technologies utilize extensive image datasets and environmental data to train algorithms that are capable of identifying disease symptoms with high accuracy. This review explores various ML and DL techniques for tomato disease detection, including support vector machines, artificial neural networks, and convolution neural networks. This review also examines the integration of these technologies into practical tools, such as mobile applications for realtime diagnostics. The findings indicate that deep learning models offer superior accuracy compared to traditional methods, and that incorporating environmental data enhances prediction reliability. Challenges, such as data scarcity, real-world variability, and the need for userfriendly applications, remain. Future research should focus on integrating Internet of Things (IoT) technologies for real-time monitoring, fostering collaborative datasharing, and developing intuitive tools for farmers. By harnessing ML and DL, the agricultural sector could advance disease management, improve crop productivity, and promote sustainability.
Human Anthropometric Indicators for the Preparation of Simulation Models
Authors: Mammadov R. , Abdullayeva S. , Zeynalova A. , Abdullayeva S.
Published: October 2024

Normal human anthropometric parameters are used in the development of medical rehabilitation techniques, in the performance of surgical techniques used for normal human functioning. In order to perform these operations successfully, it is necessary to model normal human physical characteristics and, based on these models, perform the operations correctly. Normal anatomical characteristics have a positive effect on the physiological state of the person. The relevance of the research stimulates the development of the science of valeology and increases the relevance and practical significance of the topic. Prosthetics of organs lost in wars and accidents are also performed on the basis of computer models. The science of digital anthropology is modeling human anthropometrics, and practically important predictions are being made based on this modeling. Digital anthropology is based on the research of modern technology to compile a database of the most diverse anthropological indicators of people. Anthropology occupies a special place in the system of scientific knowledge about the world, because it is a field of science that studies the origin, development and role of humans in nature and society. In modern medicine, mathematical and graphic modeling of human anatomical and anthropological indicators is required in all fields. Anthropometric indicators of humans are taken as the basis in surgery, military science, sports. The structure of shoulders, diaphragm, hips, face, and the age of a human being can also be defined by these anthropometric indicators.
Indicators of Microbiological Examination of Periodontal Pockets in Children with Autism Spectrum Disorders Based on Materials from the National Center for Maternal and Child Health
Authors: Dzhumalieva M.
Published: October 2024

An analysis of the microbial flora in the periodontal spaces of children with autism spectrum disorders was carried out based on the materials of the Department of Maxillofacial Surgery of the National Center for Maternal and Child Health for the period 2020-2022. A comparative analysis of the microbial flora of periodontal pockets shows positive dynamics of the proposed algorithm for providing dental care to children with autism spectrum disorders compared to the generally accepted traditional method (p<0.05).
Comparative Characteristics of Short-term and Long-term Adaptation by Blood Analysis of Foreign Female Students (India) to the Educational Process System
Authors: Niyazalieva A. , Karaeva R. , Tashmetova V. , Stolyarova Ya.
Published: October 2024

The article is devoted to the study of the comparative characteristics of the analysis of the leukocyte formula indicators of foreign female students from India during short-term and long-term sociocultural and other demographic factors of adaptation to educational process systems. We found little difference in the blood analysis of foreign Indian female students with short-term, long-term and social adaptation to the educational environment. Based on the evidence of the studied material, it is determined that the body reacts to each environmental requirement with a special effect. If, in one case, as at the initial stage of training, a student is faced with a situation of non-variability, then this requires the mobilization of many resources of the body, while in another case, at a longer and already established stage of training, the student’s body successfully copes with various influencing factors.
Digitalization of Mining Enterprise: A Key Factor in Ensuring Economic Security
Authors: Mazirbaev K.
Published: October 2024

The level of competitiveness of mining companies is primarily determined by productivity and operational excellence. Digitalization is becoming a key factor that enables companies in this industry to remain competitive in the future. In the mining industry, the transition to new and better ways of working has been slow due to the scale and complexity of production processes, as well as the significant costs of modernization. As a result, some enterprises are not actively implementing digital technologies. However, leading global and Kyrgyz mining companies are actively investing in the development of modern technologies in the field of energy and automation in order to increase production volumes, improve environmental efficiency, reduce the use of manual labor, costs and energy consumption. It is obvious that digital technologies open new opportunities for a significant increase in labor productivity and profit growth. The article discusses the main directions of digital transformation of mining enterprises, reveals the key advantages and potential threats. The author analyses both foreign and Kyrgyz examples of effective digitalization in this industry.
Organizational and Methodological Analysis of the Conditions of the Enterprise in Terms of Finding Opportunities for the Use of New Accounting and Calculation Systems
Authors: Mametova B.
Published: October 2024

The article discusses the main methods for identifying the advantages and disadvantages of regulations governing the composition of costs included in the cost of production. We will consider methods for including costs in the cost of production by conducting a comparative analysis in order to identify current trends and highlight unresolved problems.
The Impact of Regional Socio-Economic Policy on Housing Accessibility for Different Population Groups
Authors: Imankulova S.
Published: October 2024

The article examines the impact of regional socio-economic policy on housing accessibility for various population groups. It analyzes how economic, social, and political factors shape housing conditions in different regions. Special attention is given to how high housing prices and a shortage of affordable housing options affect the quality of life and social mobility of the population, as well as the vulnerability of specific social groups such as young families, low-income individuals, and retirees. The consequences of limited housing accessibility, including increased social tension and inequality, are discussed. Recommendations for improving regional policy are provided, with an emphasis on the need for a comprehensive approach to achieve sustainable improvements in housing conditions.
Issues of Criminal Liability of Legal Entities in the Criminal Legislation of the Kyrgyz Republic
Authors: Smanaliev K. , Salybekova T.
Published: October 2024

The growth of offenses committed by legal entities or in their interests currently indicates that a qualitatively new type of crime has been formed in many countries of the world — the crime of legal entities (“corporate crime” or “corporate crime”). In this regard, the experience of countries in combating the crime of legal entities, the established standards and criteria reflected in international normative legal acts, as well as the experience of introducing into the criminal legislation of the Kyrgyz Republic in 2017 (valid from January 1, 2019, to November 30, 2021) provisions on the legal norms regulating criminal activity deserve attention liability of legal entities. which were subsequently excluded by the new Criminal Code and the CPC, which entered into force on December 1, 2021. The analysis of the development and theoretical design of such an institution of criminal legislation of Kyrgyzstan as bringing legal entities to criminal responsibility is of great scientific interest. Therefore, the questions of what specific socially dangerous acts a legal entity can be prosecuted for, whether it is a subject of criminal liability, the grounds for criminal liability of legal entities and many others are very fundamental.
The Application of Flipped Classroom Teaching Model in Foreign Language Teaching
Authors: Tursun R.
Published: October 2024

The application of the flipped classroom approach in foreign language instruction has drawn significant attention from recent educational studies. This essay provides a comprehensive examination of the advantages, disadvantages, and methods for implementing the flipped classroom concept in foreign language teaching. It analyzes how the flipped classroom model redefines the traditional teaching paradigm by reorganizing the learning environment to favor student-centered and active learning experiences. By synthesizing existing content, it highlights how teachers can maximize in-class time for group projects, conversational activities, and personalized feedback by moving instructional content delivery outside the classroom through pre-recorded lectures, multimedia materials, and interactive online activities. This approach encourages deeper learner engagement and language acquisition. Additionally, this article analyzes the potential advantages of the flipped classroom approach, including improved language proficiency, increased student autonomy in learning, and enhanced critical thinking skills. However, the author also identifies some disadvantages of the flipped classroom model in practical application. For instance, the implementation of the flipped classroom presents a significant challenge to teachers’ professional skills, and issues such as unequal access to resources and technological limitations pose substantial obstacles to the widespread use of the flipped classroom in foreign language instruction.
Linguistic Country Studies Semantization of Troika and Bogatyr Symbolic Concepts in Russian Language and Literature Lessons
Authors: Shalygina N.
Published: October 2024

This paper describes the process of teaching the Russian language in classes with the Kyrgyz language of instruction, where the development of effective teaching methods taking into account the latest achievement of linguistic science plays an important role. In this regard, this study sets the goal of identifying mental-linguistic differences and semantization of culturally- marked concepts, taking into account the national and cultural characteristics of the linguistic consciousness of Kyrgyz schoolchildren.
New Spain through the Lens of Spatial Humanities: The Case of the Bishopric of Michoacán
Authors: Vázquez Mariana Favila , Lefebvre Karine , Torres Pedro Sergio Urquijo , Chávez Carlos Ernesto Rangel , Pérez Estefanía Santoyo
Published: October 2024

This article provides an overview of the development of spatial humanities in Mexico, focusing specifically on the Bishopric of Michoacán. This ecclesiastical and jurisdictional unit, established during the Viceroyalty of New Spain, has recently been examined through the lens of spatial humanities. Our review identifies that this approach, when combined with traditional historical methods, can be highly effective in advancing research on territorial reconfiguration processes involving both Spanish and Indigenous actors. Furthermore, we present two examples of geographical text analysis (GTA) applied to the Relaciones Geográficas of Michoacán. Building on this review and the examples, we aim to encourage researchers interested in Mexico's past to enhance the potential of early historical sources by extracting valuable information intertwined with the spatial data contained in these documents.
Intertwining Narrative and Technology: Reinterpreting European Cultural Heritage through the Voices of Latin American Women Writers
Authors: Comino Alba
Published: October 2024

Travel literature not only provides relevant information about cultural heritage, such as authorship, chronology or location, but also reveals the impressions evoked by its reception. This article describes the methodology of the ongoing REWIND project, which explores European cultural heritage through the perspectives of Latin American women writers who travelled to Europe in the early twentieth century. The project seeks to amplify the voices of non-European women committed to feminism and sociocultural diversity to foster decolonial, inclusive and pluralistic historical narratives. On the one hand, we address the configuration of the project corpus, the semantic annotation process, and the design of the ontological modelling according to the deep data concept. On the other hand, we explain the analysis of the relationships between cultural heritage, time, space and emotion. The project uses travel literature in digital format to retrieve, study and interpret data on cultural heritage, creating a workflow that integrates information from various historical sources to raise further questions and provide new insights. The proposed methodology follows the FAIR principles and prioritizes using open-source software to ensure reproducibility and facilitate the use of deliverables in other studies.
Part-of-Speech Features in Bob Dylan's Song Lyrics: A Stylometric Analysis
Authors: Dai Zheyuan , Liu Haitao
Published: October 2024

Honoured as a Nobel Laureate in 2016, Bob Dylan's song lyrics have garnered well-deserved recognition and appreciation for their themes, content and artistic performances. Part-of-speech characteristics are effective in denoting stylistic features of texts in stylometric studies. The present study carried out a quantitative observation of stylistic features of Dylan's lyrics. Specifically, the study focuses on parts of speech like verbs, adjectives, adverbs, nouns and pronouns. Results of the present study reveal that: (1) Based on the distribution of verbs and adjectives, Dylan's lyrics are significantly active texts, and the activity sequences (Q-sequences) have validated the result in a dynamic way; (2) Dylan's lyrics tend to present the characteristics of a written body; (3) Individualism is prominent in Dylan's lyrics accompanied by the wide use of the first-person singular pronouns.
Successful and unsuccessful remembering and imagining: Editorial introduction
Authors: Lin Ying-Tung , McCarroll Christopher Jude , Michaelian Kourken , Stuart Mike
Published: October 2024

The relationship between memory and imagination has long intrigued philosophers. One focus of recent debate in this area has been the question whether memory and imagination differ in kind or merely in degree, with discontinuists holding that remembering indeed differs in kind from imagining, while continuists hold that even successful remembering differs from imagining only in degree. Another recent focus has been the need to approach memory and imagination from a broadly normative perspective, in an attempt to explain what it is for remembering and imagining to succeed or fail. The goal of this special issue, which builds on an online workshop organized in 2022 by the Institute of Philosophy of Mind and Cognition at the National Yang Ming Chiao Tung University and the Centre for Philosophy of Memory at the Université Grenoble Alpes, is to explore memory, imagination, and the relation between them from this normative perspective.
Physiological and behavioral variation by urbanization and climate in an urban-tolerant toad
Authors: Monroe Dillon J. , Bókony Veronika , Gabor Caitlin R.
Published: October 2024

The distribution of a species is best understood by examining the organism-environment interaction. Climate and anthropogenic habitat degradation, including urbanization, are salient features of the environment that can limit species distributions, especially for ectotherms. Comparative studies of the capacity of individuals to cope with rapid environmental change can help us understand the future success or failure of local populations or even the species. Studies of the glucocorticoid stress response are commonly used to understand how species cope with environmental stressors. Glucocorticoids modulate many aspects of physiological homeostasis including changes in energetic allocation and behavior. In a time of global amphibian decline the Gulf Coast Toad (Incilius nebulifer) is increasing its distribution and abundance. To understand how this species deals with thermal and urban stressors, we studied glucocorticoid regulation, hop performance, and lipid storage in I. nebulifer juveniles across nine populations that differed in average annual temperature and level of imperviousness (as an indication of urbanization). We measured corticosterone release rates at baseline, during agitation stress, and during recovery; then measured locomotor performance and whole-body lipids. We tested if I. nebulifer in hotter temperatures and more urbanized habitats exhibits elevated baseline corticosterone levels and either a reduced corticosterone stress response (“stress resistance” hypothesis) or quick post-stress recovery by negative feedback (“on again, off again” hypothesis). We also tested whether they exhibit reduced fat stores and decreased locomotor performance as costs of dealing with thermal and urban stressors. We found that I. nebulifer showed elevated baseline and agitation (stressed-induced) corticosterone release rates, and higher lipid storage with increasing urbanization. Climate had quadratic effects on these traits, such that populations living at the lowest and highest temperatures had the lowest corticosterone release rates and lipid stores, and the highest hop performance was observed in the least urbanized site at the warmest climate. Additionally, the rate of glucocorticoid recovery after agitation (negative feedback) decreased with increasing temperature and increased with increasing urbanization. These results indicate that I. nebulifer follows the “on again, off again” hypothesis in an adaptive pattern, which may help them cope with environmental change in terms of urbanization and climatic differences.
RGDSP-functionalized peptide hydrogel stimulates growth factor secretion via integrin αv/PI3K/AKT axis for improved wound healing by human amniotic mesenchymal stem cells
Authors: Wei Wei , Huang Lei , Chen Luoying , He Huanhuan , Liu Yanfei , Feng Yuan , Lin Fengqin , Chen Hui , He Qing , Zhao Junhong , Li Haihong
Published: October 2024

The wound healing process involves communication among growth factors, cytokines, signaling pathways, and cells in the extracellular matrix, with growth factors acting as key regulators. Although stem cells can promote wound healing by secreting diverse growth factors, their therapeutic potential is hindered by poor survival and engraftment. Mimicking the stem cell-matrix interactions can improve stem cell survival, regulate their fate, and even enhance their paracrine effects. This study investigated the use of composite RGDmix hydrogel, which can support the survival and proliferation of human amniotic mesenchymal stem cells (hAMSCs), and effectively increase the expression of various growth factors, thereby promoting wound re-epithelialization, angiogenesis, and epidermal maturation. At last, the specific role of integrin αv and PI3K/AKT signaling pathways in the secretion of growth factors were examined by silencing them in vitro and in vivo. Results suggested that the RGDmix hydrogel improved the secretion of growth factors by hAMSCs through the RGDSP/integrin αv/PI3K/AKT axis, thereby enhancing the therapeutic effect in wound healing.
VP-net: an end-to-end deep learning network for elastic wave velocity prediction in human skin in vivo using optical coherence elastography
Authors: Zhang Yilong , Liao Jinpeng , Feng Zhengshuyi , Yang Wenyue , Perelli Alessandro , Wang Zhiqiong , Li Chunhui , Huang Zhihong
Published: October 2024

Introduction

Acne vulgaris, one of the most common skin conditions, affects up to 85% of late adolescents, currently no universally accepted assessment system. The biomechanical properties of skin provide valuable information for the assessment and management of skin conditions. Wave-based optical coherence elastography (OCE) quantitatively assesses these properties of tissues by analyzing induced elastic wave velocities. However, velocity estimation methods require significant expertise and lengthy image processing times, limiting the clinical translation of OCE technology. Recent advances in machine learning offer promising solutions to simplify velocity estimation process.

Methods

In this study, we proposed a novel end-to-end deep-learning model, named velocity prediction network (VP-Net), aiming to accurately predict elastic wave velocity from raw OCE data of in vivo healthy and abnormal human skin. A total of 16,424 raw phase slices from 1% to 5% agar-based tissue-mimicking phantoms, 28,270 slices from in vivo human skin sites including the palm, forearm, back of the hand from 16 participants, and 580 slices of facial closed comedones were acquired to train, validate, and test VP-Net.

Results

VP-Net demonstrated highly accurate velocity prediction performance compared to other deep-learning-based methods, as evidenced by small evaluation metrics. Furthermore, VP-Net exhibited low model complexity and parameter requirements, enabling end-to-end velocity prediction from a single raw phase slice in 1.32 ms, enhancing processing speed by a factor of ∼100 compared to a conventional wave velocity estimation method. Additionally, we employed gradient-weighted class activation maps to showcase VP-Net’s proficiency in discerning wave propagation patterns from raw phase slices. VP-Net predicted wave velocities that were consistent with the ground truth velocities in agar phantom, two age groups (20s and 30s) of multiple human skin sites and closed comedones datasets.

Discussion

This study indicates that VP-Net could rapidly and accurately predict elastic wave velocities related to biomechanical properties of in vivo healthy and abnormal skin, offering potential clinical applications in characterizing skin aging, as well as assessing and managing the treatment of acne vulgaris.
Biocatalytic degradation of environmental endocrine disruptor chlorobenzene via surfactant-optimized laccase-mediator system
Authors: Wang Dan , Huang Guifang , Yu Chunming , Wang Yawen , Baek Nawon , Zhu Ruofei
Published: October 2024

The emergence of environmental endocrine disruptor chlorobenzene (CB) in surface water and its potential environmental impacts have attracted serious global attention. It is still very difficult to achieve effective degradation of it by catalytic oxidation process under mild conditions. Here, an optimized method for degrading CB in aqueous solution using Trametes versicolor laccase and surfactant-assisted laccase-mediator (SALM) system was investigated. The use of a Tween 80 surfactant enhanced the solubility of CB and promoted its efficient degradation. Under favorable conditions, the SALM system yielded a degradation efficiency of 43.5% and a dechlorination efficiency of 41.55% for CB (25 mg/L) within 24 h. The possible degradation pathway of CB by this system was speculated by detecting the intermediates produced during the reaction. The outcome of the proliferation assays on MCF-7 human breast cancer cells demonstrated a reduction in the estrogenic activity of the CB solution following treatment with the SALM system. Furthermore, the influence of the quantity and positional variation of chlorine substituents on the degradation process was methodically investigated. Moreover, molecular analyses were employed to study the detailed interaction mechanism between laccase and CB, which revealed that the hydrophobic interaction contributed dominantly to binding process. These findings provide an efficient and environmentally friendly degradation system for the development of purification strategies for halogenated pollutants.
Evaluation of four machine learning methods in predicting orthodontic extraction decision from clinical examination data and analysis of feature contribution
Authors: Huang Jialiang , Chan Ian-Tong , Wang Zhixian , Ding Xiaoyi , Jin Ying , Yang Congchong , Pan Yichen
Published: October 2024

Introduction

The study aims to predict tooth extraction decision based on four machine learning methods and analyze the feature contribution, so as to shed light on the important basis for experts of tooth extraction planning, providing reference for orthodontic treatment planning.

Methods

This study collected clinical information of 192 patients with malocclusion diagnosis and treatment plans. This study used four machine learning strategies, including decision tree, random forest, support vector machine (SVM) and multilayer perceptron (MLP) to predict orthodontic extraction decisions on clinical examination data acquired during initial consultant containing Angle classification, skeletal classification, maxillary and mandibular crowding, overjet, overbite, upper and lower incisor inclination, vertical growth pattern, lateral facial profile. Among them, 30% of the samples were randomly selected as testing sets. We used five-fold cross-validation to evaluate the generalization performance of the model and avoid over-fitting. The accuracy of the four models was calculated for the training set and cross-validation set. The confusion matrix was plotted for the testing set, and 6 indicators were calculated to evaluate the performance of the model. For the decision tree and random forest models, we observed the feature contribution.

Results

The accuracy of the four models in the training set ranges from 82% to 90%, and in the cross-validation set, the decision tree and random forest had higher accuracy. In the confusion matrix analysis, decision tree tops the four models with highest accuracy, specificity, precision and F1-score and the other three models tended to classify too many samples as extraction cases. In the feature contribution analysis, crowding, lateral facial profile, and lower incisor inclination ranked at the top in the decision tree model.

Conclusion

Among the machine learning models that only use clinical data for tooth extraction prediction, decision tree has the best overall performance. For tooth extraction decisions, specifically, crowding, lateral facial profile, and lower incisor inclination have the greatest contribution.
Development of a transformer-based deep learning algorithm for diabetic peripheral neuropathy classification using corneal confocal microscopy images
Authors: Chen Wenqu , Liao Danling , Deng Yuyang , Hu Jianzhang
Published: October 2024

Background

Diabetic peripheral neuropathy (DPN) is common and can go unnoticed until it is firmly developed. This study aims to establish a transformer-based deep learning algorithm (DLA) to classify corneal confocal microscopy (CCM) images, identifying DPN in diabetic patients.

Methods

Our classification model differs from traditional convolutional neural networks (CNNs) using a Swin transformer network with a hierarchical architecture backbone. Participants included those with (DPN+, n = 57) or without (DPN−, n = 37) DPN as determined by the updated Toronto consensus criteria. The CCM image dataset (consisting of 570 DPN+ and 370 DPN− images, with five images selected from each participant’s left and right eyes) was randomly divided into training, validation, and test subsets at a 7:1:2 ratio, considering individual participants. The effectiveness of the algorithm was assessed using diagnostic accuracy measures, such as sensitivity, specificity, and accuracy, in conjunction with Grad-CAM visualization techniques to interpret the model’s decisions.

Results

In the DPN + group (n = 12), the transformer model successfully predicted all participants, while in the DPN− group (n = 7), one participant was misclassified as DPN+, with an area under the curve (AUC) of 0.9405 (95% CI 0.8166, 1.0000). Among the DPN + images (n = 120), 117 were correctly classified, and among the DPN− images (n = 70), 49 were correctly classified, with an AUC of 0.8996 (95% CI 0.8502, 0.9491). For single-image predictions, the transformer model achieved a superior AUC relative to the ResNet50 model (0.8761, 95% CI 0.8155, 0.9366), the Inception_v3 model (0.8802, 95% CI 0.8231, 0.9374), and the DenseNet121 model (0.8965, 95% CI 0.8438, 0.9491).

Conclusion

Transformer-based networks outperform CNN-based networks in rapid binary DPN classification. Transformer-based DLAs have clinical DPN screening potential.
Trypanosoma cruzi-derived exovesicles contribute to parasite infection, tissue damage, and apoptotic cell death during ex vivo infection of human placental explants
Authors: Fernández-Moya Alejandro , Oviedo Bielca , Liempi Ana , Guerrero-Muñoz Jesús , Rivas Cristian , Arregui Rocío , Araneda Sebastian , Cornet-Gomez Alberto , Maya Juan Diego , Müller Marioly , Osuna Antonio , Castillo Christian , Kemmerling Ulrike
Published: October 2024

Trypanosoma cruzi, the causative agent of Chagas disease, can be congenitally transmitted by crossing the placental barrier. This study investigates the role of T. cruzi-derived exovesicles (TcEVs) in facilitating parasite infection and the consequent tissue damage and apoptotic cell death in human placental explants (HPEs). Our findings demonstrate that TcEVs significantly enhance the parasite load and induce tissue damage in HPEs, both in the presence and absence of the parasite. Through histopathological and immunohistochemical analyses, we show that TcEVs alone can disrupt the placental barrier, affecting the basal membrane and villous stroma. The induction of apoptotic cell death is evidenced by DNA fragmentation, caspase 8 and 3, and p18 fragment immunodetection. This damage is exacerbated when TcEVs are combined with T. cruzi infection. These findings suggest that TcEVs play a critical role in the pathogenesis of congenital Chagas disease by disrupting the placental barrier and facilitating parasite transmission to the fetus. This study provides new insights into the mechanisms of transplacental transmission of T. cruzi and highlights the potential of targeting TcEVs as a therapeutic strategy against congenital Chagas disease.
A systematic review of the nexus between climate change and social media: present status, trends, and future challenges
Authors: Sultana Bebe Chand , Prodhan Md. Tabiur Rahman , Alam Edris , Sohel Md. Salman , Bari A. B. M. Mainul , Pal Subodh Chandra , Islam Md. Kamrul , Islam Abu Reza Md. Towfiqul
Published: October 2024

Social media and climate change are some of the most controversial issues of the 21st century. Despite numerous studies, our understanding of current social media trends, popular hot topics, and future challenges related to climate change remains significantly limited. This research presents a systematic review of climate change and social media for the first time. Review the studies published between 2009 and 2022 in places like Google Scholar, Science Direct, Web-of-Science, Scopus, ResearchGate, and others. For this systematic review, we found 1,057 articles. Forty-five articles were the most relevant according to our goals and study design, which followed the PRISMA framework. The results of this review demonstrate that Twitter is the most popular platform. Every year, we identify rising trends in the number of publications. Past studies often focused on just one social media site, like Twitter (n = 26) or Facebook (n = 5). Although most studies focus on the United States, the study area is primarily “all over the world.” This study offers a theoretical framework by examining the relationship between social media platforms and the discourse surrounding climate change. It looked into how social media trends influence public perception, raise awareness, and spur action on climate change. In practical terms, the study focuses on important and trending topics like nonbelievers and climate change. The contribution consists of synthesizing the body of research, providing insights into the state of the digital world, and suggesting future lines of inquiry for the field of social media and climate change studies. We highlighted the studies’ quality assessment result of “moderate quality.” This systematic review provides information about how climate change is now portrayed on social media and lays the groundwork for further study in this area.
The impact of physician communication skills on glycemic control in patients with diabetes mellitus
Authors: Alotaibi Faisal Nahes , Aburisheh Khaled Hani , Alrasheed Abdulelah Ibrahim , Alazmi Alwaleed Khalaf , Alamri Khalid Dhaifallah , Alotaibi Nawaf Nahes , Alotaibi Anas Nahes , Alhoqail Abdulaziz Abdulkarem
Published: October 2024

Objectives

The purpose of the study was to evaluate the impact of physicians’ communication skills on glycemic control in type I and II diabetes patients.

Method

This cross-sectional study was conducted at the diabetic center at King Saud University Medical City, Riyadh, Saudi Arabia. The Arabic version of the communication assessment tool was used to evaluate the patient’s perception of the physicians’ communication skills.

Results

A total of 348 patients were invited to participate in the study in which 332 filled the questionnaire completely and hence participation rate was 95.4%. The average age of the patients was 44.2 (±19.7) years with an almost equal gender ratio. The percentage of excellent ratings for most of the questions was varying between 70 and 80%. The overall percentage of excellent ratings was 75.3%. Glycated hemoglobin and other metabolic features except for diastolic blood pressure did not have any significant association with the overall mean communication assessment tool score and the percentage of excellent rating.

Conclusion

Study findings provided high patient satisfaction with their physicians’ communication skills. However, diabetes and metabolic control were not affected by this satisfaction. Hence, improvement in the communication skills of the physicians is recommended.
Machine learning-based prediction of mortality in acute myocardial infarction with cardiogenic shock
Authors: Zhang Qitian , Xu Lizhen , Xie Zhiyi , He Weibin , Huang Xiaohong
Published: October 2024

Background

In the ICU, patients with acute myocardial infarction and cardiogenic shock (AMI-CS) often face high mortality rates, making timely and precise mortality risk prediction crucial for clinical decision-making. Despite existing models, machine learning algorithms hold the potential for improved predictive accuracy.

Methods

In this study, a predictive model was developed using the MIMIC-IV database, with external validation performed on the eICU-CRD database. We included ICU patients diagnosed with AMI-CS. Feature selection was conducted using the Boruta algorithm, followed by the construction and comparison of four machine learning models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Gaussian Naive Bayes (GNB). Model performance was evaluated based on metrics such as AUC (Area Under the Curve), accuracy, sensitivity, specificity, and so on. The SHAP method was employed to visualize and interpret the importance of model features. Finally, we constructed an online prediction model and conducted external validation in the eICU-CRD database.

Results

In this study, a total of 570 and 391 patients with AMI-CS were included from the MIMIC-IV and eICU-CRD databases, respectively. Among all machine learning algorithms evaluated, LR exhibited the best performance with a validation set AUC of 0.841(XGBoost: 0.835, AdaBoost: 0.839, GNB: 0.826). The model incorporated five variables: prothrombin time, blood urea nitrogen, age, beta-blockers and Angiotensin-Converting Enzyme Inhibitors or Angiotensin II Receptor Blockers. SHAP plots are employed to visualize the importance of model features and to interpret the results. An online prediction tool was developed, externally validated with the eICU-CRD database, achieving an AUC of 0.755.

Conclusion

Employing the LR algorithm, we developed a predictive model for assessing the mortality risk among AMI-CS patients in the ICU setting. Through model predictions, this facilitates early detection of high-risk individuals, ensures judicious allocation of healthcare resources.
Imminent risk of LVEF decline in asymptomatic patients with primary mitral regurgitation
Authors: Zheng Jingyi , Huang Shao-wei , Ahmed Mustafa I. , Pat Betty , Lloyd Steven G. , Sharifov Oleg F. , Denney Thomas S. , Dell’Italia Louis J.
Published: October 2024

Background

2020 American College of Cardiology/American Heart Association (ACC/AHA) Guidelines state that the ideal time for mitral valve surgery in primary mitral regurgitation (PMR) is when the LV approaches but has not yet reached echocardiographic LV ejection fraction (EF) < 60% or LV end-systolic dimension (ESD) > 40 mm. However, it is difficult to know the imminent risk of crossing this threshold when the surgical outcome is less optimal.

Objective

Using machine learning and statistical models, we have shown that cardiac magnetic resonance (CMR) LV sphericity index (SI) and LV mid circumferential strain rate (SRcirc) added to LVEF and LVESD predict LVEF < 50% after mitral valve surgery. Here we test the hypothesis that these CMR features predict LVEF < 60% in asymptomatic PMR patients at 18 months.

Methods

33 asymptomatic PMR patients with moderate to severe mitral regurgitation had CMR with tissue tagging at baseline and every 6 months for 18 months. Two types of models were employed to predict LVEF < 60% at 18 months: a model using CMR features at a single time point (e.g., baseline) and a model utilizing repeated measurements over time.

Results

CMR LVEF decreased below 60% in 13 patients over 18 months. LVEF varied over time with an inverse relation to mean arterial pressure and mean end-systolic wall stress. Random Forest models utilizing LV SI, LV mid SRcirc, LVESD, and LVEF at a single time point (baseline) had a predictive accuracy of 64%. LV SI, LV mid SRcirc, LVESD and LVEF at baseline, 6, and 12 months achieved a higher predictive accuracy of 79%, improved sensitivity from 57% to 85% than baseline alone and identified a threshold of CMR LVEF 63%–64% signaling LVEF < 60%.

Conclusion

The variability of LVEF due to blood pressure dependence may require a longitudinal study that incorporates LVEF, LVESD, SRcirc at multiple time points to identify the threshold at which LVEF is at risk for decline to less than 60%.
Outcomes of HeartMate 3 in pediatric patients with end-stage heart failure: a single-center preliminary experience from Turkey
Authors: Doğan Eser , Ulger Tutar Zulal , Tuncer Osman Nuri , Levent Reşit E. , Engin Çağatay , Yağdı Tahir , Atay Yüksel , Özbaran Mustafa
Published: October 2024

Objectives

We aim to evaluate our initial experience with the HeartMate 3 (HM3) device (Abbott, USA) for palliating pediatric patients with end−stage heart failure (ESHF).

Methods

We conducted a retrospective review of clinical data from pediatric patients (aged 7–18 years) who underwent HM3 implantation for ESHF at our institution between 2022 and 2024. Patient demographics and follow−up data were comprehensively analyzed.

Results

We identified 11 patients (45% males) with a median age of 14 years (IQR 11–17), a median weight of 47 kg (IQR 28–50), a median height of 159 cm (IQR 135–165), and a median body surface area of 1.36 m2 (IQR 1.07–1.53) at the time of the intervention. All patients were diagnosed with dilated cardiomyopathy and categorized with PEDIMACS profiles ranging from one to three. The median ICU stay was 14 days (IQR 6–32), with 11 patients receiving inotropic support for a median of four postoperative days (IQR 3–8). The median follow−up period was 150 days (IQR 90–210). Early complications included two cases of pleural effusion, 1 case of cardiac tamponade, 3 cases of polyuria, and one instance of positive blood cultures. One patient, who was non−compliant with warfarin therapy, developed a thrombus in the right atrium that was resolved with a revision of anticoagulant therapy, and did not experience pump thrombosis. During follow−up, one patient died after 28 days from sepsis, one underwent heart transplantation after 10 days, and nine patients remained alive on the device. Notably, there were no reported cases of pump thrombosis, ischemia, or stroke post− implantation.

Conclusions

The HM3 device appears to be a safe and effective palliative option for pediatric patients with ESHF.
The impact of inflammatory and oxidative stress biomarkers on the sympathetic nervous system in severe coronary atherosclerosis
Authors: Boieriu Alexandra Maria , Dumitrel Luca Cezar , Neculoiu Carmen Daniela , Ţînţ Diana
Published: October 2024

Objective

The present study aimed at evaluating the association between sympathetic nervous system activation (SNS) and the severity of coronary artery disease (CAD). In addition, we tested the hypothesis that inflammation and oxidative stress influence the SNS activation.

Methods

Adult patients with severe CAD scheduled for coronary artery bypass graft (CABG) surgery were enrolled. SYNTAX I score was calculated based on coronary angiography. Systemic activation of the SNS was estimated through circulating levels of norepinephrine (NE). Plasma levels of pro-inflammatory cytokines (IL 1β, IL 6 and HIF 1α) and oxidative stress molecules (SOD-1 and LOX-1) were obtained prior to surgery.

Results

Circulating NE levels were significantly correlated with the severity of CAD, as assessed by the SYNTAX I score (p 0.002; r 0.329). Elevated levels of circulating pro-inflammatory markers were significantly correlated with increased NE concentrations (for IL-1β: p < 0.001, r = 0.49; for IL-6 and NE: p = 0.003, r = 0.32; for HIF-1α and NE: p = 0.049, r = 0.21). Additionally, oxidative stress molecules were associated with circulating NE levels (for SOD-1 and NE: p = 0.016, r = 0.26; for LOX-1 and NE: p = 0.004, r = 0.31).

Conclusion

In patients with CAD referred for CABG, SNS activation, indicated by plasma NE levels, was correlated with disease severity as assessed by the SYNTAX I score, as well as with markers of inflammation and oxidative stress. This suggests that inflammation, oxidative stress, and SNS activation form an interconnected network, with each component influencing the others. It might be of interest to develop a scoring system including inflammation and oxidative stress markers to identify patients that require a more aggressive approach to lower inflammation, oxidative stress and modulate the sympathetic nervous system. This could be of use especially in the setting of a scheduled intervention -such as CABG surgery.