Research In Field Research Articles

Concomitant investigation of crustacean amphipods lipidome and metabolome during the molting cycle by Zeno SWATH data-independent acquisition coupled with electron activated dissociation and machine learning

BackgroundDIA (Data-Independent Acquisition) is a powerful technique in Liquid Chromatography coupled with high-resolution tandem Mass Spectrometry (LC-MS/MS) initially developed for proteomics studies and recently emerging in metabolomics and lipidomics. It provides a comprehensive and unbiased coverage of molecules with improved reproducibility and quantitative accuracy compared to Data-Dependent Acquisition (DDA). Combined with the Zeno trap and Electron-Activated Dissociation (EAD), DIA enhances data quality and structural elucidation compared to conventional fragmentation under CID. These tools were applied to study the lipidome and metabolome of the freshwater amphipod Gammarus fossarum, successfully discriminating stages and highlighting significant biological features. Despite being underused, DIA, along with the Zeno trap and EAD, holds great potential for advancing research in the omics field. ResultsDIA combined with the Zeno trap enhances detection reproducibility compared to conventional DDA, improving fragmentation spectra quality and putative identifications. LC coupled with Zeno-SWATH-DIA methods were used to characterize molecular changes in reproductive cycle of female gammarids. Multivariate data analysis including Principal Component Analysis and Partial Least Square Discriminant Analysis successfully identified significant features. EAD fragmentation helped to identify unknown features and to confirm their molecular structure using fragmentation spectra database annotation or machine learning. EAD database matching accurately annotated five glycerophospholipids, including the position of double bonds on fatty acid chain moieties. SIRIUS database predicted structures of unknown features based on experimental fragmentation spectra to compensate for database incompleteness. SignificanceReproducible detection of features and confident identification of putative compounds are pivotal stages within analytical pipelines. The DIA approach combined with Zeno pulsing enhances detection sensitivity and targeted fragmentation with EAD in positive polarity provides orthogonal fragmentation information. In our study, Zeno-DIA and EAD thereby facilitated a comprehensive and insightful exploration of pertinent biological molecules associated with the reproductive cycle of gammarids. The developed methodology holds great promises for identifying informative biomarkers on the health status of an environmental sentinel species.

긍정적행동지원 연구 동향에 대한 서지학적 분석*

In the 1980s, Positive Behavior Support (PBS) emerged in response to inappropriate or abusive outcomes applied to students with developmental disabilities such as intellectual disabilities and autism spectrum disorders. Throughout its half-century history, PBS has made a lot of progress. This study systematically reviewed research in the PBS field using bibliometric analysis. To perform this study, 530 papers on PBS were extracted from the Web of Science database (1996-2023) and visual analysis of PBS research trends was conducted using the “CiteSpace” tool. The results are as follows: Firstly, based on the number of published studies related to PBS, the timeline can be divided into four phases: (1) the initial period from 1996 to 2001, (2) the growth period from 2002 to 2015, (3) the maturity period from 2016 to 2020, and (4) the adjustment period from 2021 to the present. Secondly, based on the number of authors in PBS-related studies, researchers such as R. H. Horner, K. McIntosh, G. Sugai, T. J. Dishion, and G. Dunlap are taking a leading position. Thirdly, based on the keywords of PBS-related research, research hot-spots are concentrated in areas such as intervention, students, children, implementation, and challenging behaviors. Fourthly, based on the keyword cluster of PBS-related research, while there was initially an emphasis on individual student behavior problems, recent emphasis has shifted towards school-wide application. This study contributes to understanding the historical progress and future directions of PBS research, aiding in the systematic comprehension of PBS development and application trends.

Comparison of Conventional CNN Sequential API and Functional API for Microalgae Identification

Microalgae is widely known for its application in producing biodiesel and other health supplements. However, microalgae are also the leading cause of harmful algae blooms that may affect consumers and sea wildlife. The current microalgae identification method requires professionals, resources, budgets, technologies, and time. Therefore, a novel approach to identifying microalgae has been produced by implementing deep learning, specifically the Convolutional Neural Network (CNN). Due to the blooming of research in the deep learning field for microalgae identification, this research aims to compare application programming interface (API) use and study its effects on the accuracy and loss of a model. Using a light microscope, the microalgae images' datasets are self-collected from the AlBio laboratory at the Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia. The microalgae species were Acutodesmus obliquus, Monoraphidium sp, Spirullina sp, Tetradesmus deserticola, and Desmodesmus perforatus. The architecture used to identify the microalgae in this research was the conventional CNN with different APIs, functional and sequential. The functional API resulted in 0.85 accuracies and a loss score of 3.77. On the other hand, the sequential scored 0.89 and a loss of 0.32. This study concluded that the sequential API was better than the functional API for a linear convolutional neural network. However, further improvement to the model could be applied by applying better hyperparameters and parameters to prevent underfitting and improve the model’s accuracy.

Preface

It is a pleasure to welcome you to the 2023 8th International Seminar on Computer Technology, Mechanical and Electrical Engineering (ISCME 2023). At this dynamic and innovative time, we were honored to invite academic elites from all over the world to discuss the latest advances and cutting-edge research in computer technology, mechanical and electrical engineering.ISCME 2023 was held in Nanjing, China from 17th to 19th November 2023 (hybrid event). It focused on cutting-edge issues in computer technology, mechanical and electrical engineering, and provided a platform for academia to share knowledge, exchange views and promote collaboration.The Conference has invited renowned scholars and experts from around the world, who brought us a series of fascinating thematic presentations. These reports covered multiple aspects of computer technology, mechanical and electrical engineering, including but not limited to hot topics such as Artificial Intelligence, Big Data Analytics, Smart Grid, Electrical Traction Systems and Controls, Mechanical and Electrical Integration, etc. We believe that these reports have provided participants with insight and inspiration, and promoted research and development in related fields. In addition, it featured oral presentations and poster presentations, providing an opportunity for researchers to present their research findings. It was an important platform for participants to exchange and interact with each other, helping to share experiences, promote cooperation and build new partnerships.The aim of ISCME 2023 was to promote interdisciplinary collaboration and innovation in the fields of computer technology, mechanical and electrical engineering. We believe that through the interaction and cooperation of this Conference, we have deepened our understanding of these fields, promoted interdisciplinary integration, and provided new solutions to the complex problems facing the world today.Finally, we believe that the proceedings of this conference will be of great significance to the academic community. The proceedings features outstanding papers from ISCME 2023, which represents the latest research results and innovative ideas. By publishing and sharing these papers, we are able to further expand the impact of our research, promote communication and collaboration among the academic community, and provide valuable references for future research.We would like to acknowledge all those who supported and participated in ISCME 2023. We look forward to have your active participation in future series conference of ISCME!The Committee of ISCME 2023List of Committee Member is available in this pdf.

Exploratory study of a meditation intervention program on Portuguese breast cancer survivors

IntroductionCancer patients, namely breast cancer survivors, are highly vulnerable to psychological morbidity. Noninvasive interventions are incentivized to promote the mental health and quality of life of cancer survivors. Recent studies provided evidence supporting the use of meditation as a promising adjuvant tool for improving the mental health and quality of life of cancer survivors.Objectives The present study aims to carry out a clinical trial to evaluate the effects of an online group of meditation program of Kundalini Yoga on breast cancer women, through a longitudinal and randomized research design, in the following variables: psychological morbidity, self-compassion, spirituality, and quality of life.MethodsThis study had the participation of 35 participants distributed randomly for 3 equivalent groups (N=11 EG, N=13 ACG, N=11 PCG), with the diagnosis of breast cancer, aged between 34 and 78 years.The sample of women with breast cancer was randomly selected from a breast cancer support association.The protocol was applied online individually on pre-test, post-test, and 1-month follow-up moments, in 3 comparison groups: 1) the Experimental Group(EG), who practiced yoga Kundalini meditation; 2) the Active Control Group(ACG) that practiced relaxation; 3) the waiting list Passive Control Group(PCG). Intervention sessions were carried out for the EG and the ACG, in an online format, lasting about 30 minutes, weekly, for 8 weeks. Statistical analyses were considered at a 0.05 significance level. All analyses were performed with IBM SPSS, version 27.ResultsThe results showed that the group that did yoga kundalini meditation (EG) had benefits, unlike the control groups, in the variables of emotional functioning, global spiritual well-being, and personal well-being. There were statistically significant differences in the overall self-compassion score when comparing the 3-time points in all groups. The sub-scale of self-kindness and transcendental well-being shows an increase significantly between the 3 moments in the active control group. The passive control group performed significantly worse over time in the self-kindness.ConclusionsBased on preliminary results, the Experimental Group (EG) exhibited improvements in Self-Compassion, Spirituality, and Emotional Functioning (as evaluated by the QLQ C-30) following eight consecutive weeks of online Kundalini Yoga Meditation practice. These findings contribute to the growing body of evidence supporting meditation’s potential to enhance life quality and spiritual well-being in individuals with breast cancer. These preliminary findings suggest that further research in this promising field is warranted.Disclosure of InterestNone Declared

Estimating the carbon footprint of computational fluid dynamics

Computational resources have grown exponentially in the past few decades. These machines make possible research and design in fields as diverse as medicine, astronomy, and engineering. Despite ever-increasing computational capabilities, direct simulation of complex systems has remained challenging owing to the degrees of freedom involved. At the cusp of exascale computing, high-resolution simulation of practical problems with minimal model assumptions may soon experience a renaissance. However, growing reliance on modern computers comes at the cost of a growing carbon footprint. To illustrate this, we examine historic computations in fluid dynamics where larger computers have afforded the opportunity to simulate flows at increasingly relevant Reynolds numbers. Under a variety of flow configurations, the carbon footprint of such simulations is found to scale roughly with the fourth power of Reynolds number. This is primarily explained by the computation cost in core-hours, which is also described by similar scaling, though regional differences in renewable energy use also play a role. Using the established correlation, we examine a large database of simulations to develop estimates for the carbon footprint of computational fluid dynamics in a given year. Collectively, the analysis provides an additional benchmark for new computations where, in addition to balancing considerations of model fidelity, carbon footprint should also be considered.

Limiting Temperature Rise in Cochlear Implantation Bone Drilling: A Novel Approach.

In the process of cochlear implantation surgery, it is crucial to develop a method to control the temperature during the drilling of the implant channel since high temperatures can result in damage to bone and nerve tissue. This paper simplified the traditional point heat source temperature rise model and proposed a novel extreme peck drilling model to quantitatively calculate the maximum temperature rise value. It is also innovatively introduced a new method for calculating the best peck drilling duty cycle to strictly control the maximum temperature rise value. Besides, the neural network is trained with virtual data to identify two important thermal parameters in the temperature rise model. In the experiment of epoxy resin and temporal bone, the difference between predicted maximum temperature and actual maximum temperature was less than 1.5 °C, and the error rate was less than 10%. And the error source was analyzed by variational mode decomposition, along with discussion of potential solutions. In the temperature control experiment, the model successfully controlled the maximum temperature rise within 10 °C.For cochlear implantation surgery, we also divide the implantation channel into different stages based on the bone density in CT images to identify thermal parameters and calculate drilling strategies. This method provides a new strategy for accurate and effective control of borehole heat generation. These achievements provide new ideas and directions for research in cochlear implantation surgery and related fields, and are expected to have extensive application in medical practice.

The Relationship Between Overparenting and Social Avoidance Among Emerging Adults: Mediating Role of Peer Attachment and Gender Differences.

We aim to explore the relationship between overparenting and social avoidance among emerging adults, and the mediating effects of peer attachment and gender differences. A total of 1161 Chinese college students completed the questionnaire. The structural equation model was established to validate the main effect model and the mediation model. The results found that both paternal and maternal overparenting positively predicted social avoidance among emerging adults. Both attachment avoidance and attachment anxiety played a significant mediating role in the relationship between paternal overparenting and social avoidance. Attachment anxiety played a significant mediating role in the relationship between maternal overparenting and social avoidance, while, the mediating effect of attachment avoidance was not significant. In addition, gender differences were found in the mediating effects of peer attachment. The mediating effect of attachment anxiety in males' families had an opposite-gender matching effect, while in females' families, the mediating effect of attachment avoidance had an opposite-gender matching effect. This study contributes to the understanding of the effects of overparenting on emerging adults, enriching empirical research in related fields.

Semiconductor ionic Cu doped CeO2 membrane fuel cells

Operation of low-temperature solid oxide fuel cells (LT-SOFCs) in the range of 300–500 °C has emerged as a promising technology and desired operational temperature for clean and efficient energy conversion. However, the lack of suitable electrolyte materials for these temperatures presents a significant challenge. In this article, a novel approach is proposed to address the hurdle of operational temperature by tailoring copper-doped cerium oxide, Cu0.1Ce0.9O2 as a semiconductor ionic membrane (SIM) to act as an electrolyte in a cell. The Cu0.1Ce0.9O2 formed a single-phase cubic fluorite structure. As a SIM, it shows enhanced ionic conductivity and reduced electrolyte/electrode polarizations, therefore achieved good fuel cell performance and electro-kinetics, reaching a power density of 515 mW/cm2 at 500 °C, with an ionic conductivity exceeding 0.1 S/cm. The detailed investigation shows dominant protonic conduction (H+) behavior in the Cu0.1Ce0.9O2 membrane. Interesting phenomenon of coupling effect elaborated through the distribution of relaxation time (DRT) analysis depicted to understand the romance of protons and electrons conduction mechanism. These advancements lead to Cu0.1Ce0.9O2 as a new type of SIM in LT-SOFCs, emphasizing the need for further research and development in this promising field to unlock the SIM and fuel cell potential.

ANALYZING THE IMPACT OF FEATURE SELECTION USING INFORMATION GAIN FOR AIRLINES' CUSTOMER SATISFACTION

Feature selection has become a focus of research in many fields that deal with machine learning and data mining because it makes classifiers cost-effective, faster, and more accurate. In this paper, the impact of feature selection using filter methods such as Information Gain is shown. The impact of feature selection has been analyzed based on the accuracy of two classifiers: J48 and Naïve Bayes. The Airline Customer Satisfaction datasets have been used for comparing with and without applying Information Gain. As a result, J48 achieved 0.33% and 0.29% improvements in accuracy after applying Information Gain for 10-fold and 20-fold cross-validation, respectively compared to Naïve Bayes. Most of the precision and F1-score for J48 with Information Gain have also improved for both evaluation methods compared to Naïve Bayes. In conclusion, J48 seems to be the classifier that is most sensitive to feature selection and has shown improvements compared to Naïve Bayes.

GLADEnet: A progressive web app for multi-messenger cosmology and electromagnetic follow-ups of gravitational-wave sources

Multi-messenger astronomy is an emerging field of research aimed at unravelling the physics governing astrophysical transients. GW170817 stands out as the first multi-messenger observation of the coalescence of a binary system of neutron stars, detected by the LIGO and Virgo gravitational-wave interferometers, along with space- and ground-based electromagnetic telescopes. It is a striking example of how multi-messenger observations significantly enhance our understanding of the physics of compact objects, relativistic outflows, and nucleosynthesis. It shows a new way of making cosmology and has the potential to resolve the tension between different measurements of the expansion rate of the Universe. To optimise multi-messenger observational strategies, to evaluate the efficiency of the searches for counterparts, and to identify the host galaxy of the source in a large sky localisation, information about the volumes of galaxies within the gravitational-wave localisation is of paramount importance. This requires the use of galaxy catalogues and appropriate knowledge of their completeness. Here, we describe a new interactive web tool namedGLADEnetthat allows us to identify catalogued galaxies and to assess the incompleteness of the catalogue of galaxies in real time across the gravitational-wave sky localisation. This measure is of particular importance when using catalogues such as the GLADE catalogue (Galaxy List for the Advanced Detector Era), which includes a collection of various catalogues that make completeness differ across different regions of the sky. We discuss the analysis steps to defining a completeness coefficient and provide a comprehensive guide on how to use the web app, detailing its functionalities. The app is geared towards managing the vast collection of over 22 million objects in GLADE. The completeness coefficient and the GLADE galaxy list will be disseminated in real time viaGLADEnet, powered by the Virtual Observatory (VO) standard and tools.

Oxygen‐Releasing Hydrogels for Tissue Regeneration

Hydrogels have emerged as a focal point of research in the biomedical field due to their applications in tissue repair. However, the majority of hydrogels lack the capability to release oxygen, constraining their therapeutic outcomes in environments with hypoxic tissues. In recent years, oxygen‐releasing hydrogels have garnered extensive attention in the field of tissue engineering, owing to their ability to modulate oxygen release and meet the diverse oxygenation requirements of various tissues. These hydrogels can enhance repair efficiency and promote tissue regeneration in hypoxic tissue environments. The design of oxygen‐releasing hydrogels primarily involves the utilization of diverse oxygen sources, such as algae, perfluorocarbons, and peroxides, to achieve optimal tissue oxygenation. This review provides a comprehensive summary of the design and fabrication strategies of oxygen‐releasing hydrogels, discusses deeply into their underlying oxygen‐releasing mechanisms, and their myriad applications in tissue repair along with the prospective challenges.

Dark Matter: The Search for the Invisible Universe

Abstract: The quest to unravel the mysteries of dark matter, an elusive and invisible form of matter that pervades our Universe, has captivated the minds of physicists and cosmologists for decades. Despite its profound impact on the formation and evolution of galaxies and cosmic structures, the true nature of dark matter remains an enigma. This paper delves into the compelling evidence for the existence of dark matter, explores the various theoretical candidates and detection methods, and examines the ongoing search for this enigmatic substance. We discuss the observational data from galaxies, galaxy clusters, and cosmological observations that have led to the inference of dark matter's presence. Furthermore, we investigate the diverse range of proposed dark matter candidates, from weakly interacting massive particles (WIMPs) to more exotic possibilities like axions and primordial black holes. We also examine the various experimental approaches employed in the quest to detect and characterize dark matter, including direct detection experiments, indirect detection methods, and collider searches. Additionally, we explore the potential implications of dark matter for our understanding of particle physics and cosmology, and the challenges that lie ahead in unraveling this profound mystery. Ultimately, this paper aims to provide a comprehensive overview of the search for dark matter, highlighting the significant progress made thus far and the exciting prospects that lie ahead in this vital field of research.

Exploring Journalists’ Narratives on Challenges to Climate Journalism in Pakistan

Background and Study Aim: Pakistan has been classified among the five most vulnerable societies to climate change, yet its coverage in Pakistani media remains event-driven and limited. Given the absence of research in field, this qualitative research, based on focus group discussion, documents the narratives of Pakistani journalists about challenges to climate journalism in Pakistan. Methodology: This qualitative research was conducted through a focus group discussion, collecting data from 11 Islamabad-based Pakistani journalists who have been serving different print, electronic and digital news media organizations as news reporters and news editors. Findings: The findings of the study reveal event-based media coverage of climate change, lack of prioritization and desensitization of media on climate change, the influence of policy agenda on media coverage, and the lack of professional journalistic training and education. Conclusion/Policy Recommendations: The study concluded/recommended that addressing the challenges to climate journalism in Pakistan required specialized training and sensitization of the journalists, besides the establishment of dedicated climate beats and desks within the media organizations. Enhancing the utilization of technology and fostering journalists-media academics collaboration, independent editorial policies, and introduction of climate change education in academic curricula can improve the quality and consistency of climate change awareness and coverage in Pakistan.

Collaborative innovations in Artificial Intelligence (AI): Partnering with leading U.S. tech firms to combat human trafficking

This article reviews, integrates, and expands upon research initiatives that explore the development and implementation of advanced artificial intelligence (AI)–driven tools and methodologies. In exploring collaborations with leading U.S. AI technology firms, including Nvidia, Dataiku, DataRobot, and C3.ai, this study is specifically aimed at identifying, preventing, and combating human trafficking. This collaborative effort seeks to create a synergistic framework that capitalizes on the unique capabilities and resources of each firm. The overarching goal is to enhance the effectiveness of AI technologies in detecting trafficking activities, analyzing diverse data sources, and providing vital support to law enforcement and NGOs in rescue and prevention efforts. The comprehensive review delves into the multifaceted applications of AI, emphasizing its role in prevention through machine learning, data analytics, and natural language processing. It navigates through collaborative initiatives, presenting partnerships with prominent U.S. tech firms, accompanied by case studies that showcase successful collaborative projects. Challenges inherent in collaborative AI efforts are addressed, and the paper proposes strategic solutions to overcome these obstacles. The ethical and legal considerations section scrutinizes the implications of deploying AI in human trafficking prevention, exploring the delicate balance between innovation and safeguarding privacy and civil liberties. Furthermore, the final section anticipates future directions and recommendations, providing insights into emerging technologies that could enhance anti-trafficking efforts. Specific recommendations are offered for fortifying collaborations between AI developers and tech firms, and the paper discusses broader implications for policy, suggesting avenues for future research in the dynamic field of AI-driven human trafficking prevention. In essence, this comprehensive review not only synthesizes existing knowledge but also integrates a forward-looking research initiative, contributing significantly to the ongoing discourse on leveraging technology to combat human trafficking on a global scale.

Reviewing predictive analytics in supply chain management: Applications and benefits

Supply chain management (SCM) is a critical component of modern business operations, and the integration of predictive analytics has emerged as a transformative force in enhancing efficiency, decision-making, and overall performance. This paper presents a comprehensive review of the applications and benefits of predictive analytics in supply chain management, exploring its role in demand forecasting, inventory optimization, and supply chain visibility. The literature review provides a historical perspective on the evolution of predictive analytics in SCM, delving into key concepts and definitions. Drawing upon existing research, the paper analyzes real-world applications, case studies, and successful implementations in areas such as demand forecasting, inventory management, and supply chain visibility. Methodologically, the paper outlines the criteria for selecting relevant studies, details the search strategies employed, and highlights the sources contributing to the comprehensive understanding of predictive analytics in SCM. The exploration of applications focuses on how predictive analytics is revolutionizing demand forecasting, optimizing inventory levels, and enhancing supply chain visibility. Through case studies and examples, the paper illustrates the practical implications of implementing predictive analytics in these key areas. Real-world examples and data-driven insights underscore the transformative impact of predictive analytics on SCM processes. Despite its numerous advantages, challenges and limitations exist in the implementation of predictive analytics. This paper examines common hurdles and proposes strategies to overcome these challenges, offering a balanced perspective on the practical implications of integrating predictive analytics into supply chain management. Looking towards the future, the paper discusses emerging trends and technologies in predictive analytics, anticipating advancements that will further shape the landscape of SCM analytics. It summarizes key findings, outlines implications for practitioners and researchers, and suggests avenues for future research in the dynamic field of predictive analytics in supply chain management.

A study on the arch mechanism of pedestrian evacuation and congestion alleviation strategies at building exits

The pedestrian flow capacity of building exits has always been a focus of research in evacuation field. This paper focuses on the congestion mechanism of pedestrian flow at exit bottlenecks through simulation and crowd experiments, and tries to propose strategies to alleviate evacuation congestion. Firstly, pedestrian flow evacuation experiments were conducted by setting up scenarios with exits in the middle and corners, and corresponding exit structure models were established using Massmotion software to analyze the congestion mechanisms of pedestrian flow at the exits through simulations coupled with the social force model. Secondly, the severity of congestion in the exit structure was quantified using the example of the middle exit. The width range of the pedestrian evacuation trajectory within 1 m before the exit was defined as the average chord length of the arch structure, and the area enclosed by the semicircular arch with this chord length was defined as the area with a relatively high instantaneous density of pedestrian flow. The results indicate that corner exits are more conducive to evacuation as they reduce the expected velocity of pedestrians near the center of congested areas. Both building width and the inflow rate of pedestrian flow, along with the number of pedestrians, demonstrate significant effects on the average chord length of the arch. The research results can be used to further supplement and improve the dynamic theory of pedestrian flow evacuation in buildings and provide a scientific reference for crowd congestion control at building exits.

Exploring the development and application of LSTM variants

Long Short-Term Memory (LSTM) is receiving increasing attention as the development of deep learning technology. The gate structure of LSTM enhances long-term memory, forming its superior capacity to complete tasks that challenge traditional RNN. However, considering the wide variety of applications, a comprehensive understanding of the development and application of the model, which is vital for future research, is comparatively lacking. Therefore, this paper is produced with the hope of offering an overview of the development of LSTM. It shows the process of development from RNN to LSTM and explains the aim and necessity of LSTMs birth. After that it introduces the structure of LSTM, analyses its advantages over RNN, and discusses the application of some popular LSTM variants, such as peephole LSTM, bidirectional LSTM, and GRU. Hopefully, this work can provide a more profound knowledge of LSTM's benefits and potential, identifying worthwhile avenues or fields of future research.

Visual Analysis of Research Hotspots on Near Miss in the Nursing Field

Objective To analyze the current research status, hot spots and dynamic frontiers of literature related to near misses in nursing using visual knowledge mapping, in order to provide a reference for the future development of near misses in nursing. Methods The literature related to near misses in nursing was searched in the Web of Science core collection from its establishment to August 2022, and visualized and analyzed in terms of the number of articles, journals and keywords. Results A total of 289 papers were included, and the annual publication volume showed a trend of year-on-year growth, with the United States taking the first place in terms of publication volume and the initial formation of a core author team, and the hotspots of proximity research in the nursing field were mainly focused on patient safety, safety management, and identification reporting. Conclusion The research related to proximity errors in nursing started late, and the current research on proximity errors is not comprehensive enough. Since the occurrence of proximity errors can have adverse effects on patients, nursing staff and medical institutions, it is suggested that nursing managers should increase the importance of this issue, pay attention to the frontier development, and conduct in-depth related research.

Enteric Nervous System Alterations in Inflammatory Bowel Disease: Perspectives and Implications.

The enteric nervous system (ENS), consisting of neurons and glial cells, is situated along the gastrointestinal (GI) tract's wall and plays a crucial role in coordinating digestive processes. Recent research suggests that the optimal functioning of the GI system relies on intricate connections between the ENS, the intestinal epithelium, the immune system, the intestinal microbiome, and the central nervous system (CNS). Inflammatory bowel disease (IBD) encompasses a group of chronic inflammatory disorders, such as Crohn's disease (CD) and ulcerative colitis (UC), characterized by recurring inflammation and damage to the GI tract. This review explores emerging research in the dynamic field of IBD and sheds light on the potential role of ENS alterations in both the etiology and management of IBD. Specifically, we delve into IBD-induced enteric glial cell (EGC) activation and its implications for persistent enteric gliosis, elucidating how this activation disrupts GI function through alterations in the gut-brain axis (GBA). Additionally, we examine IBD-associated ENS alterations, focusing on EGC senescence and the acquisition of the senescence-associated secretory phenotype (SASP). We highlight the pivotal role of these changes in persistent GI inflammation and the recurrence of IBD. Finally, we discuss potential therapeutic interventions involving senotherapeutic agents, providing insights into potential avenues for managing IBD by targeting ENS-related mechanisms. This approach might represent a potential alternative to managing IBD and advance treatment of this multifaceted disease.