bluuebunny/crossref_metadata_2025 huggingface dataset:
Microbiological Characteristics and Antibiotic Resistance of Non-Lactational Mastitis
Authors: Xiong Xin
Published: November 2024

Abstract. Non-lactational mastitis is a common breast disease with a high incidence worldwide. In recent years, with changes in lifestyle and increasing environmental pollution, the incidence of non-lactational mastitis has been on the rise, drawing widespread attention. In terms of microbiological characteristics, the pathogens of non-lactational mastitis are diverse, mainly including Staphylococcus aureus, Streptococcus, among others. The detection rates of these pathogens vary across different regions and populations, and they exhibit distinct biological properties. At the same time, antibiotic resistance in non-lactational mastitis has become increasingly severe. According to statistics, the antibiotic resistance rate of pathogens associated with non-lactational mastitis has shown an upward trend year by year, posing significant challenges to clinical treatment. Studies on resistance mechanisms suggest that gene mutations and the spread of resistance genes are the main causes of antibiotic resistance. In response to this situation, it is necessary to conduct an in-depth analysis of antibiotic resistance to provide scientific evidence for clinical treatment. This study aims to briefly introduce the epidemiology, microbiological characteristics, and current status of antibiotic resistance in non-lactational mastitis, providing a reference for clinical treatment, prevention, and control. Through a review of relevant domestic and international studies, it has been found that the prevention and treatment of non-lactational mastitis still face many challenges, such as pathogen variation and increasing antibiotic resistance. Therefore, this study holds important practical significance and application value.
The influence of emotion on memory process
Authors: Zhang Wenqi
Published: November 2024

Abstract. This paper shows how emotions, from an intuitive point of view, have an essential and profound impact on the cognitive process across a variety of different environments, such as natural, social, and academic settings. Our skills in learning and memorizing are significantly adjusted and influenced by our emotional reactions to challenges arising from education, including exams, homework assignments, and strict deadlines. The present research is deeply concerned with the specific roles that anxiety, happiness, and sadness play in modulating memory processes, particularly within the context of educational technologies like adaptive learning systems and Massive Open Online Courses. These innovative technologies are currently revolutionizing traditional educational methodologies by introducing new emotional dynamics in learners that affect cognitive engagement, motivation, and memory retention differently than conventional face-to-face learning environments. These findings are essential in optimizing the thoughtful design and effective implementation of educational technologies to better support students affective needs and cognitive development.
Early Detection of Alzheimers Disease by Using Deep Learning Models: A Summary and Review
Authors: Miao Ryan
Published: November 2024

Abstract. Alzheimer's disease (AD) is a relentless neurodegenerative disorder that causes severe cognitive decline and devastating memory loss, afflicting millions of people around the world. Early detection is critical for effective intervention and potentially slowing progression of AD and improving patients quality of life. Traditional imaging diagnostic methods often miss the subtle changes of early-stage AD. This study reviews the recent advancements in deep learning techniques for early AD detection, particularly models analyzing neuroimaging data such as MRI, CT scans and PET scans. The study summarized the progression from Convolutional Neural Networks (CNNs) to advanced Transformer models and hybrid approaches; compared the strengths and limitations of both architectures and associated requirements of specific characteristics of the neuroimaging data and the Alzheimer's disease detection task. The integration of these models has led to significant improvements in diagnostic accuracy and early warning capabilities, addressing limitations of conventional methods. This Review aims to contribute to the rapid growing knowledge and application in this field. Additionally, the paper explores the challenges of data heterogeneity and the application of federated learning to enhance model robustness across diverse datasets, offering insights into future research directions and clinical implications.
The Reasons and Consequences of Disney's Acquisition of Marvel
Authors: Wang Yueze
Published: November 2024

The buyout of Marvel Entertainment by The Walt Disney Company in 2009 can be considered an important occurrence in the entertainment industry as it redefined the possibilities for content producers and consumers. This research uses the qualitative analysis method to discuss the key strategic motives for the merger and possible financial consequences and issues concerning the incorporation of Marvel IPs into Disneys vast library. Thus, the analysis is centered on the Disneys motivation to acquire Marvel, the financial implications of this integration and the brand influence in terms of culture. The study helps to explain large scale mergers and acquisitions of corporations and their impact on the entertainment business. Thus, the paper is relevant because it provides a historical analysis of the Disney-Marvel merger and considers possible future developments on the topic. The research provides information on the significance of integration and gives recommendations for policies to the investors and the heads of industries.
Enhancing Real-Time Vision Systems: Integrating Dynamic Vision Sensors with Vision Transformers to Increase Computational Efficiency
Authors: Yao Annabelle , Su Oliver , Kumar Sumedha
Published: November 2024

Abstract. Optimizing and understanding vision systems' computational accuracy is crucial as they become increasingly integrated into daily life. Dynamic Vision Sensors (DVS) and Vision Transformers (ViTs) lead computer vision technology with efficient object recognition and image processing. However, DVS data's high computational complexity poses a problem for its real-time implementations. Merging these technologies can enhance vision system performance in dynamic environments and optimize real-time DVS processing. In our work, we use a ViT architecture to classify the DVS 128 dataset and compare our results with existing works using SNNs. We analyze how our method affects accuracy and loss, experimenting with different DVS-to-ViT input patch sizes. Our results show that the large patch size of 32x32 pixels has better accuracy and smaller loss than the 4x4 pixel patches as epochs increase. Our method also achieved a high 98.4% accuracy and low 0.22 loss within five epochs, significantly outperforming previous works averaging 93.13% accuracy over more epochs. These results highlight ViTs' large potential for real-time DVS data classification in applications that require high accuracy, like autonomous vehicles and surveillance systems.
Applications and Challenges of Meta-Learning in Natural Language Processing
Authors: Luo Shengyao
Published: November 2024

Abstract. With the emergence and development of meta-learning algorithms, their remarkable advantages in few-shot learning (FSL) scenarios have led to the increasing application of meta-learning in natural language processing (NLP). However, meta-learning is not without limitations, and various challenges may arise during its practical application. This paper begins by providing a brief introduction to the concept of meta-learning and delves into its complex applications in NLP, such as federated learning (FL), cross-task generalization, and few-shot text classification. The research demonstrates that the advantages of meta-learning in few-shot learning can significantly aid these applications, particularly in scenarios where data scarcity necessitates rapid adaptation. Nevertheless, throughout these studies, meta-learning also exhibits limitations in various contexts. Finally, this paper summarizes the content, analyzes the interrelationships and potential connections between the topics, and highlights current challenges in the field as well as possible future directions for development.
The Analysis of the Heartbeat Detection & Sensory Temperature Detection
Authors: Luo Wenyu
Published: November 2024

Abstract. With the maturity of medical technology, the information emitted by human body parts can be detected by doctors and used as critical indicators. Usually, doctors conduct basic tests on patients by measuring body temperature and heart rate. Currently, hospitals still measure these values separately, which leads to low measurement efficiency. This paper analyzes the principles of the two measurement methods and summarizes the temperature and heart rate detection sensor to test the values and achieve simultaneous measurement of both values, thus optimizing medical testing. It also evaluates the accuracy and limitations of the instrument by measuring and plotting the values during human exercise and drawing a bar-line graph based on the image to prove a linear relationship between body temperature and heart rate.
Advances and Challenges in Machine Learning for Diabetes Prediction: A Comprehensive Review
Authors: Ding Yueheng
Published: November 2024

Abstract. Diabetes mellitus is a prevalent and severe metabolic disorder disease that poses significant health risks globally, leading to substantial healthcare burdens. Recent days, advancements in artificial intelligence (AI) have markedly enhanced the accuracy and efficiency of diabetes outcome predicted by machine learning (ML), offering a promising approach for early intervention and treatment. This paper evaluates several advanced ML models, including Random Forest (RF), Support Vector Machine (SVM), and Neural Networks techniques based on neural networks. Each model's strengths and limitations are discussed, highlighting the improvements in predictive performance and diagnostic precision. Despite these advancements, the field faces ongoing challenges related to ethical considerations and data scale, which impact ML application in healthcare from both technical and moral aspects. Future efforts should focus on these challenges by promoting data sharing and integration while safeguarding privacy. Through these endeavors, we aim to advance the field of diabetes prediction and improve patient care.
A Comprehensive Analysis of Game Success: Synergy Between Quality, Brand, and Market
Authors: Yu Haoying
Published: November 2024

This study scrutinizes the important factors behind the success of video games,Targeting a comprehensive paradigm which merges game quality, brand influence, and market strategies. By investigating the top 100 best-selling titles on Steam, the research looks into the interconnections between creative gameplay, the depth of storytelling, audiovisual quality, and sales results. Analysis of quantitative regression shows that although game quality is important, it is not the main factor for success. A qualitative assessment of high-priced, low-rated games including Overwatch 2 and NBA 2K25, and high-rated, low-selling games like Stardew Valley and Euro Truck Simulator 2, shows how significant brand loyalty, multiplayer interaction, and market segmentation are. This study demonstrates the value of original gameplay, successful marketing approaches, and technological innovations, like VR and superior graphics, in increasing sales and supporting player retention. The results provide key understanding for game developers and marketers who want to improve game performance in a cutthroat and rapidly evolving market.
Energy Transmission Optimization in Multilayer Glass Models Using Particle Swarm Optimization
Authors: Cui Yinghan
Published: November 2024

The intensification of global climate change and rising temperatures have underscored the need for energy-efficient building designs, particularly in southern China. This study proposes a particle swarm optimization (PSO) approach to optimize the thickness of triple-glazed windows (L1, L2, L3) to minimize sunlight transmission energy within the wavelength range of 300-2000 nm. A multilayer glass model was constructed, accounting for light transmission, reflection, and absorption properties. By simulating various thickness combinations, the transmitted energy was calculated across the wavelength spectrum. The PSO algorithm was then employed to search for the optimal thickness configuration that minimizes the total transmitted sunlight energy. Experimental results indicate that the proposed method significantly reduces indoor sunlight transmission compared to traditional design methods, enhancing both energy efficiency and occupant comfort. The findings highlight the importance of optimizing glass thickness and material selection in window design, balancing light transmission and shading to suit specific architectural needs. Additionally, this study underscores the impact of PSO parameters on convergence and solution diversity, suggesting further refinements for practical applications. The approach provides a scientific basis for building energy-saving designs and can be adapted to various climatic conditions and material specifications. Future work should focus on incorporating weather variations and expanding model complexity to enhance the algorithm's applicability in real-world scenarios.
Enhancing Facial Recognition: A Comprehensive Review of Deep Learning Approaches and Future Perspectives
Authors: Zhu Songze
Published: November 2024

This paper presents a comprehensive review of the application of deep learning in facial recognition, covering fundamental aspects from neural network architectures to advanced training methods. It starts with an introduction to the basic architectures such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), including variations like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. The review extends to sophisticated architectures like AlexNet, GoogleNet, VGGNet, and ResNet, which have significantly pushed the boundaries of accuracy in face recognition tasks. The paper details the process of data preprocessing in face recognition, which involves critical steps such as face detection, alignment, and normalization to ensure uniformity in the input data, enhancing the accuracy of feature extraction. Various feature extraction methods are discussed, including CNN-based and Generative Adversarial Network (GAN)-based techniques, which have shown considerable promise in deriving complex facial features from raw images. Loss functions such as Euclidean distance loss, angular/cosine margin loss, and softmax loss variants are explored to understand their impact on enhancing the discriminative power of the facial recognition systems. The paper highlights the evolution from traditional models using eigenfaces and feature descriptors like Local Binary Patterns (LBP) to cutting-edge deep learning models that utilize deep identity features (DeepID) and triplet loss functions to improve recognition accuracy.
The Application of VR Technology in the Field of Gaming
Authors: Ying Taoze
Published: November 2024

With the rapid advancement of Virtual Reality (VR) technology, its application in the gaming sector has introduced an unprecedented level of immersion for players. This paper aims to explore the integration of VR technology in game design and assess its potential and challenges in enhancing player experiences. The motivation for this research stems from the fundamental differences between VR and traditional games in terms of immersion, interactivity, and multisensory feedback. The paper provides an overview of the basic concepts and principles of VR technology, compares it with traditional gaming technologies, and discusses its role in delivering immersive gaming experiences, enhancing player interaction, and providing multisensory feedback, particularly in multiplayer online gaming contexts. Despite the rapid growth of the VR gaming market, high hardware costs, lack of content, and technical challenges remain significant barriers to widespread adoption. The conclusion of this paper underscores the significance and potential for future development of VR technology in the gaming field, while also identifying key areas for improvement necessary for mainstream acceptance.
Scene Modeling in Game Development Based on Generative Adversarial Networks
Authors: Liu Haotian
Published: November 2024

Due to booming advance on generative models, people have great interest on designing model structure to produce wonderful pictures, or even 3d shapes. The motivation of this work is that the 3d modeling manufacturing in game development is still challenging and time-consuming, and 3d shape produced from generative models may be powerful tools for the problem. The work tried to build game scenes, such as a city and a cave, the typical scene that requires many random but similar objects. This paper aims to explore a complete workflow for applying the GANs to the game development. The structure of the paper is introducing the background of the game development and the progress of the generative model, giving interpretation about the principle of Generative Adversarial Networks, and propose the process on how to utilize it to improve the productivity in developing game scene. Finally, this work found that it could considerably reduce the repetitive work on making massive and similar objects.
Advances in Bio-inspired Swimming Robots for Underwater Exploration: A Review
Authors: Zhang Mingxu
Published: November 2024

Bio-inspired swimming robots have become a focal point in underwater robotics due to their superior energy efficiency, enhanced maneuverability, and adaptability compared to traditional robots. This paper reviews the latest progress in bio-inspired fish robots, highlighting their advantages, the challenges faced by researchers, material selection, design methodologies, and real-world applications. The comparison between bio-inspired and traditional robots demonstrates how biomimetic designs can improve the performance and efficiency of underwater systems. Furthermore, advancements in materials and control mechanisms have enabled new applications, from environmental monitoring to military operations, offering promising prospects for future underwater exploration technologies.
Data Analysis in Game Design: Enhancing the Player Experience
Authors: Su Mengyuan
Published: November 2024

Data science plays an important role in many fields, and games have grown by leaps and bounds in recent years to occupy an important place in the entertainment industry. This paper explores the various applications of statistical analysis in game design, highlighting how developers can use data to optimize game mechanics, improve player engagement and overall user satisfaction. The results of the study show that by exploring and analyzing player behavioral patterns, market trends, and feedback, a science-based method is utilized to assist in decisions during the whole game development process. This method enables the developer to create games that are more relevant to the players preferences, providing a better gaming experience. Additionally, developers can use the data feedback to constantly optimize their games and make improvements based on player feedback in time after release. However, due to the rapid advancement of the gaming industry and the heterogeneity of the player community, data science is merely a supplementary instrument that assists developers in enhancing the player's gaming experience.
A Brief Analysis of the Progress and Trends in Software Defect Prediction Methods
Authors: Jia Ruxi
Published: November 2024

Software defect detection is particularly important for modern society, as it is a crucial step in ensuring the quality and reliability of software systems. With the emergence of artificial intelligence (AI), research in software defect detection has evolved from traditional methods to more complex approaches that utilize deep learning and large language models (LLMs). The advent of LLMs has fundamentally changed the paradigm of software development and defect detection, bringing new challenges and confusion to the field of software defect prediction research. To address these issues, we compare software defect detection methods based on traditional techniques, deep learning approaches, and LLMs through a literature review. We analyze the changes brought about by the introduction of LLMs to software development and propose new insights. Additionally, we examine the progress and trends in software defect prediction to provide inspiration for subsequent research.
Review of Language Structures and NLP Techniques for Chinese, Japanese, and English
Authors: Du Jingxuan
Published: November 2024

The increasing demand for cross-lingual communication in a globally interconnected world has spurred significant advancements in Natural Language Processing (NLP), a branch of artificial intelligence aimed at enabling computers to comprehend, generate, and process human language. Over the past few years, NLP has transitioned from traditional rule-based and statistical approaches to deep learning-based methods, with pre-trained models like BERT and GPT demonstrating remarkable performance across a variety of tasks, including text classification and machine translation. However, the linguistic diversity of the world's languages presents ongoing challenges, as many NLP models are primarily developed for high-resource languages like English, leaving other languages underserved. This paper examines the linguistic features of three representative languagesChinese, Japanese, and Englishand explores NLP methodologies tailored to their distinct grammatical, lexical, and structural properties. Through comparative linguistic analysis, this study discusses the challenges of multilingual NLP and the potential for cross-lingual applications. The findings underscore the importance of adapting NLP models to different language characteristics to enhance the effectiveness and inclusivity of cross-lingual processing.
Machine Learning-based Prediction Analysis of Potential Factors in Traffic Accidents
Authors: Jiang Jiawei , Miao Yangyang , Wu Dehao
Published: November 2024

Every year, traffic accidents cause a great deal of death, serious injury, and financial damage, making it a major global problem. Predicting accident severity is crucial for implementing effective traffic management and safety strategies. This study employs Random Forest and Deep Neural Network (DNN) models and so on from machine learning algorithms to predict the traffic accidents severity, identifying related outcomes from a joint dataset collected by the Seattle Department of Transportation as well as UKs Department for Transport. The results showcase that snowy road surfaces, standing water and dusk or insufficient lighting conditions causes respectively an excess of 10.75%, 10.44% and 13.01% above the average number of causalities in traffic accidents. The DNN model achieved the highest accuracy (91.12%), outperforming other methods by 310 percentage, especially in severe crash detection performance. In contrast, Random Forest model offers better stability across multiple classification threshold, illustrating better performance on general tasks. By diagnosing the high-risk conditions, traffic authorities can implement tailored interventions, ranging from boosting road maintenance efforts during adverse weather, improving street lighting, and increasing safety measures during peak times, to boost the standard of protection. Future research should investigate the method to optimise the robustness and generalization of DNN model through techniques like threshold adjustment, or adopting ensemble learning mechanisms as well as devising new segments that are more relevant.
Predicting the Dry Density of Clay Soil Improved by Adding Glass Powder Using a Back Propagation Neural Network Model
Authors: Fathia Alnaas  , A'laa Khalid
Published: November 2024

Clay soil has undesirable engineering properties, which can compromise structural stability. This study aims to enhance the compaction properties of high-plasticity clay soil by adding glass powder using artificial intelligence (AI), specifically through the Back propagation Neural Network (BPNET), to accurately predict dry density. The model used influential factors, such as wet soil weight (Wnet), glass powder ratio (Wglass), and water content (ω %) as inputs, with dry density (γ) as the output. The model demonstrated high accuracy, achieving a Mean Squared Error (MSE) of 0.0000117 and a Mean Absolute Error (MAE) of 0.002849, reflecting its effectiveness in improving clay soil properties and supporting its stability.
CUIDADO INTEGRAL DESDE A INFÂNCIA: A LINHA DE ATENÇÃO À CRIANÇA NA SAÚDE PÚBLICA DE MANAUS
Authors: Albuquerque Júlia Cardeal de , Sampaio Diana Santos , Matos Luana Violeta Rodrigues de , Costa Chrystine Helena Silva , Perez Julia Evan Mendes , Souza Kátia Araújo de , Silva Marco Antônio Lemos e , Vilanova Kamilly Vitória de Oliveira , Costa Iago Levi Reis Paiva da , Martins Eduarda Pereira , Brito Diana Cascaes de , Oliveira Suzane Lima de , Evaristo Jucyette da Silva Gomes , Souza Celsa da Silva Moura
Published: November 2024

Introdução: A infância é uma fase crucial do desenvolvimento humano, marcada por uma série de transformações físicas, cognitivas, emocionais e sociais. O Sistema Único de Saúde (SUS) desempenha um papel fundamental na promoção da linha de cuidado infantil no Brasil. Por meio dele, são oferecidos serviços e programas que visam garantir a saúde e o bem-estar das crianças do nascimento até a adolescência. Metodologia: Através de uma pesquisa bibliográfica, o artigo consiste numa atividade desenvolvida com fins didáticos e pedagógicos no contexto da disciplina de Saúde Coletiva II, ofertada pelo departamento de Saúde Coletiva, da Faculdade de Medicina da Universidade Federal do Amazonas. Desenvolvimento: Aborda os aspectos históricos da formação do Sistema Único de Saúde, e sua divisão atual nos Níveis de Atenção e nas Linhas de Cuidado, detalhando os ítens básicos, aspectos e as prioridades sobre a Linha de Cuidado da Criança, que serão aprofundados ao decorrer do relatório. Ademais, serão expostos os aspectos epidemiológicos acerca da vacinação, mortalidade infantil, pré-natal e aleitamento materno. Por fim, ainda foi realizado o relatório das práticas realizadas na disciplina ao longo do semestre, comparando as realidades experienciadas ao que deve ser oferecido pelo SUS. Conclusão: Dessa forma, é necessário entender que o relatório exposto é importante para demonstrar a realidade do SUS, além de entender quais melhorias são necessárias para que ocorra um acompanhamento adequado das crianças em todo território nacional. Logo, compreende-se a importância do SUS como entidade pública no quesito da proteção e acompanhamento adequado na saúde da criança.
Tổng quan về phân tích tĩnh và phân tích động trong kiểm tra bảo mật ứng dụng
Authors: Nguyen Thanh Cong  , Le Huy Toan  , Ta Minh
Published: November 2024

Trong bối cảnh các hệ thống thông tin ngày càng trở nên phức tạp và đối mặt với nhiều mối đe dọa an ninh mạng, việc đánh giá an toàn thông tin (ATTT) trở nên vô cùng quan trọng. Bài báo này tập trung vào hai phương pháp đánh giá ATTT phổ biến là đánh giá tĩnh và đánh giá động. Đánh giá tĩnh phân tích mã nguồn hoặc mã nhị phân để phát hiện lỗ hổng bảo mật từ giai đoạn phát triển phần mềm. Đánh giá động kiểm tra bảo mật của hệ thống trong quá trình hoạt động, giúp phát hiện lỗ hổng trong thời gian chạy. Bài báo trình bày tổng quan các kỹ thuật và công cụ của cả hai phương pháp, đồng thời so sánh ưu nhược điểm của chúng. Đánh giá tĩnh giúp phát hiện lỗi sớm nhưng có thể bỏ sót các lỗi thời gian chạy, trong khi đánh giá động kiểm tra thực tế nhưng có thể gây gián đoạn hệ thống. Sự kết hợp của cả hai phương pháp mang lại hiệu quả tốt nhất trong việc đảm bảo ATTT.
Điều khiển quadrotor ứng dụng giải thuật backstepping
Authors: Lê Hoàng Anh  , Lê Hữu Toàn  , Trần Đức Thuận
Published: November 2024

Bài báo trình bày ứng dụng của bộ điều khiển backstepping để bám theo quỹ đạo của quadrotor. Bộ điều khiển backstepping được thiết kế để thích nghi phù hợp với tính phi tuyến của hệ thống. Kết quả cho thấy bộ điều khiển đã đạt được khả năng dẫn quadrotor đến vị trí mục tiêu với lỗi nhỏ và duy trì sự ổn định khi theo dõi quỹ đạo đã được xác định trước. Hơn nữa, bộ điều khiển cũng thể hiện hiệu suất tốt trong việc bám quỹ đạo khi có sự tác động của nhiễu.
Crimean-Congo hemorrhagic fever: Pathogenesis, transmission and public health challenges
Authors: Karanam Sita Kumari , Nagvishnu Kandra , Uppala Praveen Kumar , Edhi Sandhya , Varri Srinivasa Rao
Published: November 2024

The dangerous Crimean-Congo hemorrhagic fever virus (CCHFV), an encapsulated negative-sense RNA virus of the family Nairoviridae, is transmitted from person to person via ticks. With a case fatality rate between 10% to 40%, the most common ways that the disease may spread to humans are via tick bites or coming into touch with infected animals' blood or tissues. Furthermore, the transfer of bodily fluids between individuals is another potential route of infection. There is a wide range of symptoms experienced by patients throughout each stage, from myalgia and fever to extreme bruising and excess bleeding. Tick management measures include minimising the spread of ticks from one species to another and from people to animals via the use of protective clothing, repellents, and proper animal handling. In order to prevent the spread of illness, healthcare workers must adhere to stringent protocols. Despite the lack of an authorised vaccine, the main components of treatment now consist of preventative measures and supportive care, which may include the antiviral medicine ribavirin. We still don't know very much about the virus's mechanisms, even though advances in molecular virology and animal models have improved our understanding of the pathogenesis of CCHFV. A critical need for vaccination that is both safe and effective, as well as for quick diagnosis and efficient treatments to lessen the disease's impact in areas where it is most prevalent. Important steps towards lowering Crimean-Congo hemorrhagic fever mortality and morbidity rates were to anticipatethe future availability of immunoglobulin products.
Pathogenesis and clinical management of arboviral diseases
Authors: Cenci Dietrich Victoria , Costa Juan Marcos Caram , Oliveira Marina Maria Gomes Leite , Aguiar Carlos Eduardo Oliveira , Silva Luis Guilherme de Oliveira , Luz Marcel Silva , Lemos Fabian Fellipe Bueno , de Melo Fabrício Freire
Published: November 2024

Arboviral diseases are viral infections transmitted to humans through the bites of arthropods, such as mosquitoes, often causing a variety of pathologies associated with high levels of morbidity and mortality. Over the past decades, these infections have proven to be a significant challenge to health systems worldwide, particularly following the considerable geographic expansion of the dengue virus (DENV) and its most recent outbreak in Latin America as well as the difficult-to-control outbreaks of yellow fever virus (YFV), chikungunya virus (CHIKV), and Zika virus (ZIKV), leaving behind a substantial portion of the population with complications related to these infections. Currently, the world is experiencing a period of intense globalization, which, combined with global warming, directly contributes to wider dissemination of arbovirus vectors across the globe. Consequently, all continents remain on high alert for potential new outbreaks. Thus, this review aims to provide a comprehensive understanding of the pathogenesis of the four main arboviruses today (DENV, ZIKV, YFV, and CHIKV) discussing their viral characteristics, immune responses, and mechanisms of viral evasion, as well as important clinical aspects for patient management. This includes associated symptoms, laboratory tests, treatments, existing or developing vaccines and the main associated complications, thus integrating a broad historical, scientific and clinical approach.
Unveiling the impact: COVID-19's influence on bacterial resistance in the Kingdom of Bahrain
Authors: Saeed Nermin K , Almusawi Safiya K , Albalooshi Noor A , Al-Beltagi Mohammed
Published: November 2024

BACKGROUND

Antibiotic resistance is a growing global health threat, and understanding local trends in bacterial isolates and their susceptibility patterns is crucial for effective infection control and antimicrobial stewardship. The coronavirus disease 2019 (COVID-19) pandemic has introduced additional complexities, potentially influencing these patterns.

AIM

To analyze trends in bacterial isolates and their antibiotic susceptibility patterns at Salmaniya Medical Complex from 2018 to 2023, with a specific focus on the impact of the COVID-19 pandemic on these trends.

METHODS

A retrospective analysis of microbiological data was conducted, covering the period from 2018 to 2023. The study included key bacterial pathogens such as Escherichia coli (E. coli ), Klebsiella pneumoniae , Acinetobacter baumannii , Pseudomonas aeruginosa , and Staphylococcus aureus , among others. The antibiotic susceptibility profiles of these isolates were assessed using standard laboratory methods. To contextualize the findings, the findings were compared with similar studies from other regions, including China, India, Romania, Saudi Arabia, the United Arab Emirates, Malaysia, and United States.

RESULTS

The study revealed fluctuating trends in the prevalence of bacterial isolates, with notable changes during the COVID-19 pandemic. For example, a significant increase in the prevalence of Staphylococcus aureus was observed during the pandemic years, while the prevalence of E. coli showed a more variable pattern. Antibiotic resistance rates varied among the different pathogens, with a concerning rise in resistance to commonly used antibiotics, particularly among Klebsiella pneumoniae and E. coli . Additionally, the study identified an alarming increase in the prevalence of multidrug-resistant (MDR) strains, especially within Klebsiella pneumoniae and E. coli isolates. The impact of the COVID-19 pandemic on these trends was evident, with shifts in the frequency, resistance patterns, and the emergence of MDR bacteria among several key pathogens.

CONCLUSION

This study highlights the dynamic nature of bacterial isolates and their antibiotic susceptibility patterns at Salmaniya Medical Complex, particularly in the context of the COVID-19 pandemic. The findings underscore the need for continuous monitoring and effective anti-microbial stewardship programs to combat the evolving threat of antibiotic resistance. Further research and policy initiatives are required to address the identified challenges and improve patient outcomes in the face of these ongoing challenges.
Prevalence of transfusion transmissible infections among various donor groups: A comparative analysis
Authors: Thakur Sanjay K , Sinha Anil K , Sharma Santosh K , Jahan Aarzoo , Negi Dinesh K , Gupta Ruchika , Singh Sompal
Published: November 2024

BACKGROUND

Transfusion transmissible infections (TTIs) are illnesses spread through contaminated blood or blood products. In India, screening for TTIs such as hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV)-I/II, malaria, and syphilis is mandatory before blood transfusions. Worldwide, HCV, HBV, and HIV are the leading viruses causing mortality, affecting millions of people globally, including those with co-infections of HIV/HCV and HIV/HBV. Studies highlight the impact of TTIs on life expectancy and health risks, such as liver cirrhosis, cancer, and other diseases in individuals with chronic HBV. Globally, millions of blood donations take place annually, emphasizing the importance of maintaining blood safety.

AIM

To study the prevalence of TTIs, viz., HBV, HCV, HIV I/II, syphilis, and malaria parasite (MP), among different blood donor groups.

METHODS

The study assessed the prevalence of TTIs among different blood donor groups in Delhi, India. Groups included total donors, in-house donors, total camp donors, institutional camp donors, and community camp donors. Tests for HIV, HBV, and HCV were done using enzyme-linked immunosorbent assay, while syphilis was tested with rapid plasma reagins and MP rapid card methods. The prevalence of HBV, HCV, HIV, and syphilis, expressed as percentages. Differences in infection rates between the groups were analyzed using χ ² tests and P -values (less than 0.05).

RESULTS

The study evaluated TTIs among 42158 blood donors in Delhi. The overall cumulative frequency of TTIs in total blood donors was 2.071%, and the frequencies of HBV, HCV, HIV-I/II, venereal disease research laboratory, and MP were 1.048%, 0.425%, 0.221%, 0.377%, and 0.0024%, respectively. In-house donors, representing 37656 donors, had the highest transfusion transmissible infection (TTI) prevalence at 2.167%. Among total camp donors (4502 donors), TTIs were identified in 1.266% of donors, while community camp donors (2439 donors) exhibited a prevalence of 1.558%. Institutional camp donors (2063 donors) had the lowest TTI prevalence at 0.921%. Statistical analysis revealed significant differences in overall TTI prevalence, with total and in-house donors exhibiting higher rates compared to camp donors.

CONCLUSION

Ongoing monitoring and effective screening programs are essential for minimizing TTIs. Customizing blood safety measures for different donor groups and studying socio-economic-health factors is essential to improving blood safety.
Dexamethasone in coronavirus disease 2019 care: Dosage and utilization insights
Authors: Shamim Laiba , Musharaf Imshaal , Nashwan Abdulqadir J
Published: November 2024

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2. It was declared a global pandemic on March 11, 2020, by the World Health Organization. An excessive inflammatory response is a severe respiratory manifestation of COVID-19, which becomes predominant in later stages. Due to its immunosuppressive and anti-inflammatory properties, dexamethasone is the first systemic glucocorticoid to treat severe COVID-19 patients. This editorial reviews the efficacy and safety of high-dose vs low-dose dexamethasone in patients with COVID-19. Findings indicate that using low-dose dexamethasone is beneficial and emphasize the need for additional research on the use of high-dose dexamethasone. While the study provides a robust evidence base, it is limited by the lack of long-term data, focus on specific outcomes and heterogeneity of the included studies. Future research should focus on the long-term effects of dexamethasone and its impact across varying disease severities and patient populations to refine treatment strategies and improve patient care.
Supplemental material for 'Unravelling parallel conceptions of the Ordovician trilobite Flexicalymene senaria (Conrad, 1841) and description of Flexicalymene trentonensis, n. sp. (American Museum novitates, no. 4029)'
Authors: Hopkins Melanie J. , Martin Markus J.
Published: November 2024

Supplemental material for 'Unravelling parallel conceptions of the Ordovician trilobite Flexicalymene senaria (Conrad, 1841) and description of Flexicalymene trentonensis, n. sp. (American Museum novitates, no. 4029)' - https://hdl.handle.net/2246/7386
NEURAL NETWORK SYSTEM FOR RECOGNIZING HUMAN PSYCHOEMOTIONAL STATE BY FACIAL EXPRESSION
Authors:   Kazan National Research Technical University named after A.N. Tupolev, KATASEV A.S. , KATASEVA D.V. ,   Kazan National Research Technical University named after A.N. Tupolev, SMIRNOV YU.N. ,   Kazan State Power Engineering University, GRAFSKIY V.A. ,   Kazan National Research Technical University named after A.N. Tupolev
Published: November 2024

The article is devoted to solving the problem of recognizing a person’s psycho-emotional state by facial expression based on a neural network convolutional system. To solve such problems, AlexNet and VGG-16 convolutional neural network architectures are often used. At the same time, the VGG-16 architecture, taken as the basis in this work, is the most promising for use due to its simple implementation and high efficiency. The implementation of the system required solving the following problems: selecting and preparing a data set for training a neural network convolutional model, selecting the necessary libraries and training the model, as well as developing a user interface for a recognition system based on the trained model. Data set preparation required selecting a data set from a publicly available source, analyzing the selected set for suitability for the task at hand, and preprocessing the data from the set. Kaggle was chosen as a public data source. As a result of a search in this source, a Fer2013Plus dataset was found containing images corresponding to 7 classes of human psycho-emotional states: “anger”, “disgust”, “fear”, “happiness”, “sadness”, “surprise” and “neutral state”. Preprocessing of the source data required their cleaning and balancing by class. As a result, a high-quality set of 29,023 images was obtained, of which 23,251 were included in the training set, and the remaining 5,772 were included in the test set. To train the model, program code was written in the Python programming language using the TensorFlow, Keras, Numpy, OpenCV, Matplotlib, Python Imaging Library libraries. During the research, 3 convolutional neural network models with different numbers of layers were built. The structure of the 1st model is presented, as well as the distinctive features of the remaining models. The training results for each of the constructed models are presented. The accuracy of recognizing the best model was 85% on the training data set and 80% on the test set. Based on the constructed model, a neural network system was implemented in the Python programming language. Examples of the system's operation on preprocessed and original images are shown. In both cases, the system correctly classified the input images. Based on the obtained results of the functioning of the system for recognizing human psycho-emotional states, it was concluded that the implemented convolutional neural network model is adequate and suitable for practical use.
MODIFICATION OF POLYOLEFINS WITH CLAYS AND PETROLEUM POLYMER RESINS
Authors:   Kazan National Research Technological University, STAROSTINA I.A. , VERNIGOROV K.B. ,   Sibur Polylab LLC, BUSHKOV V.V. , TUTOV S.V. ,   SIBUR LLC, VERTEPA A.V. ,   Kazan College of Construction, Architecture and Urban Economy, PERELYGINA R.A. , KAZAKOV YU.M. , STOYANOV O.V. ,   Kazan National Research Technological University
Published: November 2024

In order to enhance the physical and mechanical characteristics of polymer composite materials, their modification with various clay powders is widespread. In this study, using the example of high-density polyethylene and ethylene-vinyl acetate copolymer, the problems arising during this modification are considered, the main one of which is insufficient exfoliation of clay in the polymer matrix. Replacing layered clays with fibrous ones (sepiolite) does not lead to a satisfactory increase in the strength of composite materials. Possible ways to solve these problems by increasing the functional activity of the polymer matrix are highlighted. This effect can be achieved by introducing various petroleum polymer resins into polyolefins, changing their acid-base and surface energy characteristics - components and parameters of free surface energy. All resins used were shown to increase the surface acidity of the ethylene-vinyl acetate copolymer. Based on the experimentally determined surface energy characteristics for systems modified with clay powders and petroleum polymer resins, the values of the thermodynamic work of adhesion were calculated. Calculations were carried out according to the Oss-Chaudhry-Good bath theory for two-phase systems. The dependences of the tensile tensile stress of modified composites and the values of the thermodynamic work of adhesion calculated for them were obtained.
CHEMICAL DEPOLYMERIZATION OF POLYCARBONATE IN THE MONOETHANOLAMINE AND ETHYLENEDIAMINE MEDIUM
Authors:   Kazan National Research Technological University, ALI A.A.G. , SAMUILOV A.YA. , SAMUILOV YA.D. ,   Kazan National Research Technological University
Published: November 2024

The production volume of polycarbonates is constantly growing due to their wide range of applications and high consumer properties. Polycarbonates are popular materials in various industries, construction, packaging and other fields, which contributes to an increase in demand and production. Research on the recycling of polycarbonates is important in the modern world, where the problem of environmental pollution is becoming more and more urgent. Polycarbonates are widely used in the manufacture of various products such as plastic tableware, packaging, automotive parts and many others. However, after use, these materials often turn into garbage, which can pollute nature. Research on the recycling of polycarbonates allows us to find ways to reuse these materials, which reduces the amount of waste and reduces the negative impact on the environment. In addition, the recycling of polycarbonates allows you to save natural resources and reduce the cost of producing new materials. This work is aimed at studying the process of polycarbonate depolymerization in the medium of monoethanolamine and diethanolamine. Compared with other polycarbonate depolymerization methods, aminolysis reactions have received relatively little attention in the past. In this paper, the process of chemical depolymerization of polycarbonate based on diphenylolpropane in the medium of monoethanolamine and ethylenediamine is studied. It is shown that in the case of polycarbonate depolymerization in a monoethanolamine medium, the main product is diphenylolpropane. The carbonate fragment is converted into oxazolidine-2-on. This process can be catalyzed by sodium hydroxide and accelerated when exposed to microwave radiation. In the case of polycarbonate depolymerization in an ethylenediamine medium, the main product is also diphenylolpropane. In this case, the carbonate fragment is converted into ethylene urea.