Topically administered bevacizumab had longer standing anti-angiogenic effect than subconjunctivally injected bevacizumab in rat corneal neovacularization

AIMTo compare the effect of topically administered and subconjunctivally injected bevacizumab on experimental corneal neovascularization in rats for two weeks after treatment.

Film-Forming Systems in Topically Administered Pharmaceutical Formulations

The skin is the most extensive and outermost organ in the body and can be greatly exploited both from the point of view of alternative routes of systemic drug delivery and treatment of dermatological diseases. Because of its main function as a barrier against harmful external agents, it also becomes a barrier to drug administration, but there are strategies to reduce this limitation of this promising route of administration. The development of polymer-based film-forming formulations is extensively studied for this purpose, since the formation of a film on the skin increases the contact time of the drug, for this being characterized as a controlled release reservoir system. There are a multitude of possible polymers to compose these formulations and their choice must be made according to the purpose of each application. This work, therefore, aims to study the state of the art of film forming systems for topical application of pharmaceutical formulations.

A bibliometric analysis using machine learning to track paradigm shifts and analytical advances in forest ecology and forestry journal publications from 2010 to 2022

Forest habitats are critical for biodiversity,ecosystem services,human livelihoods,and well-being.Capacity to conduct theoretical and applied forest ecology research addressing direct(e.g.,deforestation)and indirect(e.g.,climate change)anthropogenic pressures has benefited considerably from new field-and statistical-techniques.We used machine learning and bibliometric structural topic modelling to identify 20 latent topics comprising four principal fields from a corpus of 16,952 forest ecology/forestry articles published in eight ecology and five forestry journals between 2010 and 2022.Articles published per year increased from 820 in 2010 to 2,354 in 2021,shifting toward more applied topics.Publications from China and some countries in North America and Europe dominated,with relatively fewer articles from some countries in West and Central Africa and West Asia,despite globally important forest resources.Most study sites were in some countries in North America,Central Asia,and South America,and Australia.Articles utilizing R statistical software predominated,increasing from 29.5%in 2010 to 71.4%in 2022.The most frequently used packages included lme4,vegan,nlme,MuMIn,ggplot2,car,MASS,mgcv,multcomp and raster.R was more often used in forest ecology than applied forestry articles.R software offers advantages in script and workflow-sharing compared to other statistical packages.Our findings demonstrate that the disciplines of forest ecology/forestry are expanding both in number and scope,aided by more sophisticated statistical tools,to tackle the challenges of redressing forest habitat loss and the socio-economic impacts of deforestation.

Enhancing emerging technology discovery in nanomedicine by integrating innovative sentences using BERT and NLDA

Purpose:Nanomedicine has significant potential to revolutionize biomedicine and healthcare through innovations in diagnostics,therapeutics,and regenerative medicine.This study aims to develop a novel framework that integrates advanced natural language processing,noise-free topic modeling,and multidimensional bibliometrics to systematically identify emerging nanomedicine technology topics from scientific literature.Design/methodology/approach:The framework involves collecting full-text articles from PubMed Central and nanomedicine-related metrics from the Web of Science for the period 2013-2023.A fine-tuned BERT model is employed to extract key informative sentences.Noiseless Latent Dirichlet Allocation(NLDA)is applied to model interpretable topics from the cleaned corpus.Additionally,we develop and apply metrics for novelty,innovation,growth,impact,and intensity to quantify the emergence of novel technological topics.Findings:By applying this methodology to nanomedical publications,we identify an increasing emphasis on research aligned with global health priorities,particularly inflammation and biomaterial interactions in disease research.This methodology provides deeper insights through full-text analysis and leading to a more robust discovery of emerging technologies.Research limitations:One limitation of this study is its reliance on the existing scientific literature,which may introduce publication biases and language constraints.Additionally,manual annotation of the dataset,while thorough,is subject to subjectivity and can be time-consuming.Future research could address these limitations by incorporating more diverse data sources,and automating the annotation process.Practical implications:The methodology presented can be adapted to explore emerging technologies in other scientific domains.It allows for tailored assessment criteria based on specific contexts and objectives,enabling more precise analysis and decision-making in various fields.Originality/value:This study offers a comprehensive framework for identifying emerging technologies in nanomedicine,combining theoretical insights and practical applications.Its potential for adaptation across scientific disciplines enhances its value for future research and decision-making in technology discovery.

Requirements ranking based on crowd-sourcing high-end product USs

Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured feedback.The key challenge lies in transforming abstract user needs into specific ones,requiring integration and analysis.Therefore,we propose a topic mining-based approach to categorize,summarize,and rank product requirements from user stories.Specifically,after determining the number of story categories based on py LDAvis,we initially classify“I want to”phrases within user stories.Subsequently,classic topic models are applied to each category to generate their names,defining each post-classification user story category as a requirement.Furthermore,a weighted ranking function is devised to calculate the importance of each requirement.Finally,we validate the effectiveness and feasibility of the proposed method using 2966 crowd-sourced user stories related to smart home systems.

Research evolution of metal organic frameworks: A scientometric approach with human-in-the-loop

Purpose:This paper reports on a scientometric analysis bolstered by human-in-the-loop,domain experts,to examine the field of metal-organic frameworks(MOFs)research.Scientometric analyses reveal the intellectual landscape of a field.The study engaged MOF scientists in the design and review of our research workflow.MOF materials are an essential component in next-generation renewable energy storage and biomedical technologies.The research approach demonstrates how engaging experts,via human-in-the-loop processes,can help develop a comprehensive view of a field’s research trends,influential works,and specialized topics.Design/methodology/approach:Ascientometric analysis was conducted,integrating natural language processing(NLP),topic modeling,and network analysis methods.The analytical approach was enhanced through a human-in-the-loop iterative process involving MOF research scientists at selected intervals.MOF researcher feedback was incorporated into our method.The data sample included 65,209 MOF research articles.Python3 and software tool VOSviewer were used to perform the analysis.Findings:The findings demonstrate the value of including domain experts in research workflows,refinement,and interpretation of results.At each stage of the analysis,the MOF researchers contributed to interpreting the results and method refinements targeting our focus Research evolution of metal organic frameworks:A scientometric approach with human-in-the-loop on MOF research.This study identified influential works and their themes.Our findings also underscore four main MOF research directions and applications.Research limitations:This study is limited by the sample(articles identified and referenced by the Cambridge Structural Database)that informed our analysis.Practical implications:Our findings contribute to addressing the current gap in fully mapping out the comprehensive landscape of MOF research.Additionally,the results will help domain scientists target future research directions.Originality/value:To the best of our knowledge,the number of publications collected for analysis exceeds those of previous studies.This enabled us to explore a more extensive body of MOF research compared to previous studies.Another contribution of our work is the iterative engagement of domain scientists,who brought in-depth,expert interpretation to the data analysis,helping hone the study.

Analyzing topics in social media for improving digital twinning based product development

Digital twinning enables manufacturers to create digital representations of physical entities,thus implementing virtual simulations for product development.Previous efforts of digital twinning neglect the decisive consumer feedback in product development stages,failing to cover the gap between physical and digital spaces.This work mines real-world consumer feedbacks through social media topics,which is significant to product development.We specifically analyze the prevalent time of a product topic,giving an insight into both consumer attention and the widely-discussed time of a product.The primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset distribution.Therefore,these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse topics.To this end,this work combines deep learning and survival analysis to predict the prevalent time of topics.We propose a specialized deep survival model which consists of two modules.The first module enriches input covariates by incorporating latent features of the time-varying text,and the second module fully captures the temporal pattern of a rumor by a recurrent network structure.Moreover,a specific loss function different from regular survival models is proposed to achieve a more reasonable prediction.Extensive experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.

A Video Captioning Method by Semantic Topic-Guided Generation

In the video captioning methods based on an encoder-decoder,limited visual features are extracted by an encoder,and a natural sentence of the video content is generated using a decoder.However,this kind ofmethod is dependent on a single video input source and few visual labels,and there is a problem with semantic alignment between video contents and generated natural sentences,which are not suitable for accurately comprehending and describing the video contents.To address this issue,this paper proposes a video captioning method by semantic topic-guided generation.First,a 3D convolutional neural network is utilized to extract the spatiotemporal features of videos during the encoding.Then,the semantic topics of video data are extracted using the visual labels retrieved from similar video data.In the decoding,a decoder is constructed by combining a novel Enhance-TopK sampling algorithm with a Generative Pre-trained Transformer-2 deep neural network,which decreases the influence of“deviation”in the semantic mapping process between videos and texts by jointly decoding a baseline and semantic topics of video contents.During this process,the designed Enhance-TopK sampling algorithm can alleviate a long-tail problem by dynamically adjusting the probability distribution of the predicted words.Finally,the experiments are conducted on two publicly used Microsoft Research Video Description andMicrosoft Research-Video to Text datasets.The experimental results demonstrate that the proposed method outperforms several state-of-art approaches.Specifically,the performance indicators Bilingual Evaluation Understudy,Metric for Evaluation of Translation with Explicit Ordering,Recall Oriented Understudy for Gisting Evaluation-longest common subsequence,and Consensus-based Image Description Evaluation of the proposed method are improved by 1.2%,0.1%,0.3%,and 2.4% on the Microsoft Research Video Description dataset,and 0.1%,1.0%,0.1%,and 2.8% on the Microsoft Research-Video to Text dataset,respectively,compared with the existing video captioning methods.As a result,the proposed method can generate video captioning that is more closely aligned with human natural language expression habits.

Pain perception enhancement in consecutive secondeye phacoemulsification cataract surgeries under topical anesthesia

Cataract is the main cause of visual impairment and blindness worldwide while the only effective cure for cataract is still surgery.Consecutive phacoemulsification under topical anesthesia has been the routine procedure for cataract surgery.However,patients often grumbled that they felt more painful during the second-eye surgery compared to the first-eye surgery.The intraoperative pain experience has negative influence on satisfaction and willingness for second-eye cataract surgery of patients with bilateral cataracts.Intraoperative ocular pain is a complicated process induced by the nociceptors activation in the peripheral nervous system.Immunological,neuropsychological,and pharmacological factors work together in the enhancement of intraoperative pain.Accumulating published literatures have focused on the pain enhancement during the secondeye phacoemulsification surgeries.In this review,we searched PubMed database for articles associated with pain perception differences between consecutive cataract surgeries published up to Feb.1,2024.We summarized the recent research progress in mechanisms and interventions for pain perception enhancement in consecutive secondeye phacoemulsification cataract surgeries.This review aimed to provide novel insights into strategies for improving patients’intraoperative experience in second-eye cataract surgeries.

Intravitreal injection practice patterns among Chinese ophthalmologists

AIM:To describe the practice patterns of intravitreal injections(IVIs)among ophthalmologists in China.METHODS:This was a cross-sectional online survey.Ophthalmologists who had performed accumulated more than 100 injections were contacted by the Brightness Center,a hospital-based national network,to complete an anonymous,24-question,internet-based survey.They were surveyed on practices in injection techniques,pre-,and post-injections procedures.RESULTS:A total of 333 ophthalmologists from 28 provinces/municipalities/autonomous regions responded to the survey(50.68%response rate).The 91.29%of the respondents evaluated systemic risk factors by medical history,electrocardiogram(ECG)and blood test.All the respondents used pre-injection prophylactic antibiotics.Most checked intraocular pressure(IOP,99.1%)and blood pressure(96.1%)before injections.A majority of the respondents performed injections in the operating room(98.8%),wore masks(99.7%),gloves(99.4%)and sterile surgical clothing(96.1%),performed topical anesthetics(97.9%),and applied povidone-iodine(95.8%)pre-injection.The 61.26%of the respondents dilated pupil.About half of the respondents(51.05%)performed bilateral injections in the same setting.Superior temporal quadrant(40.54%)was the most frequent site of injection.Around three quarters used 30-gauge needles.Most respondents(97.9%)measured the site of injection from limbus.More than half(53.45%)performed conjunctiva displacement prior to injection.The 32.43%of the respondents checked IOP post-injection and 87.99%physicians checked hand motion(HM)or counting fingers(CF)after injection,while 36.94%observed optic nerve perfusion.All participants used topical antibiotics post-injections.Most physicians(91.89%)reviewed patients on the following day.CONCLUSION:This study provides a description of the real-world practice patterns in IVIs in China and offers critical information regarding education and training of ophthalmologists and amendment of local society guidelines.

Evolution and spatiotemporal analysis of earthquake public opinion based on social media data

As critical conduits for the dissemination of online public opinion,social media platforms offer a timely and effective means for managing emergencies during major disasters,such as earthquakes.This study focuses on the analysis of online public opinions following the Maduo M7.4 earthquake in Qinghai Province and the Yangbi M6.4 earthquake in Yunnan Province.By collecting,cleaning,and organizing post-earthquake Sina Weibo(short for Weibo)data,we employed the Latent Dirichlet Allocation(LDA)model to extract information pertinent to public opinion on these earthquakes.This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events.An emotion analysis,utilizing an emotion dictionary,categorized the emotional content of post-earthquake Weibo posts,facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes.The findings were visualized using Geographic Information System(GIS)techniques.The analysis revealed certain commonalities in online public opinion following both earthquakes.Notably,the peak of online engagement occurred within the first 24 hours post-earthquake,with a rapid decline observed between 24 to 48 hours thereafter.The variation in popularity of online public opinion was linked to aftershock occurrences.Adjusted for population factors,online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high.Initially dominated by feelings of“fear”and“surprise”,the public sentiment shifted towards a more positive outlook with the onset of rescue operations.However,distinctions in the online public response to each earthquake were also noted.Following the Yangbi earthquake,Yunnan Province reported the highest number of Weibo posts nationwide;in contrast,Qinghai Province ranked third post-Maduo earthquake,attributable to its smaller population size and extensive damage to communication infrastructure.This research offers a methodological approach for the analysis of online public opinion related to earthquakes,providing insights for the enhancement of post-disaster emergency management and public mental health support.

Analyzing COVID-19 Discourse on Twitter: Text Clustering and Classification Models for Public Health Surveillance

Social media has revolutionized the dissemination of real-life information,serving as a robust platform for sharing life events.Twitter,characterized by its brevity and continuous flow of posts,has emerged as a crucial source for public health surveillance,offering valuable insights into public reactions during the COVID-19 pandemic.This study aims to leverage a range of machine learning techniques to extract pivotal themes and facilitate text classification on a dataset of COVID-19 outbreak-related tweets.Diverse topic modeling approaches have been employed to extract pertinent themes and subsequently form a dataset for training text classification models.An assessment of coherence metrics revealed that the Gibbs Sampling Dirichlet Mixture Model(GSDMM),which utilizes trigram and bag-of-words(BOW)feature extraction,outperformed Non-negative Matrix Factorization(NMF),Latent Dirichlet Allocation(LDA),and a hybrid strategy involving Bidirectional Encoder Representations from Transformers(BERT)combined with LDA and K-means to pinpoint significant themes within the dataset.Among the models assessed for text clustering,the utilization of LDA,either as a clustering model or for feature extraction combined with BERT for K-means,resulted in higher coherence scores,consistent with human ratings,signifying their efficacy.In particular,LDA,notably in conjunction with trigram representation and BOW,demonstrated superior performance.This underscores the suitability of LDA for conducting topic modeling,given its proficiency in capturing intricate textual relationships.In the context of text classification,models such as Linear Support Vector Classification(LSVC),Long Short-Term Memory(LSTM),Bidirectional Long Short-Term Memory(BiLSTM),Convolutional Neural Network with BiLSTM(CNN-BiLSTM),and BERT have shown outstanding performance,achieving accuracy and weighted F1-Score scores exceeding 80%.These results significantly surpassed other models,such as Multinomial Naive Bayes(MNB),Linear Support Vector Machine(LSVM),and Logistic Regression(LR),which achieved scores in the range of 60 to 70 percent.

Dynamic evaluation of digital and green development policies based on text mining of the PMC framework

Aiming to identify policy topics and their evolutionary logic that enhance the digital and green development(dual development)of traditional manufacturing enterprises,address weaknesses in current policies,and provide resources for refining dual development policies,a total of 15954 dual development-related policies issued by national and various departmental authorities in China from January 2000 to August 2023 were analyzed.Based on topic modeling techniques and the policy modeling consistency(PMC)framework,the evolution of policy topics was visualized,and a dynamic assessment of the policies was conducted.The results show that the digital and green development policy framework is progressively refined,and the governance philosophy shifts from a“regulatory government”paradigm to a“service-oriented government”.The support pattern evolves from“dispersed matching”to“integrated symbiosis”.However,there are still significant deficiencies in departmental cooperation,balanced measures,coordinated links,and multi-stakeholder participation.Future policy improvements should,therefore,focus on guiding multi-stakeholder participation,enhancing public demand orientation,and addressing the entire value chain.These steps aim to create an open and shared digital industry ecosystem to promote the coordinated dual development of traditional manufacturing enterprises.

Potential prospects of Chinese medicine application in diabetic retinopathy

Current treatment strategies for diabetic retinopathy(DR),an eye condition that can lead to blindness,have mainly focused on proliferative DR,including vitreous injection,retinal photocoagulation,and vitrectomy.Vitreous injections mainly depend on anti-vascular endothelial growth factor therapy.In this editorial,we comment on the article by Sun et al.We focus specifically on the mechanisms of the protective effect of genipin on the retina.Genipin is a gardenia extract used in traditional Chinese medicine(TCM).In their study,the authors suggest that controlling advanced glycosylation by the intraocular injection of genipin may be a strategy for preventing retinopathy.The innovative use of a Chinese medicine extract injected into the eye to achieve a curative effect has attracted our attention.Although TCM is effective in treating DR,the topical application of DR,especially intraocular injections,is not yet feasible.Herein,we present a brief analysis of effective Chinese medicines for the treatment of DR.The effectiveness of local injections of TCM applied directly into the eyes holds promise as an effective treatment approach for DR.

基于Citation Topic的领域期刊主题演化研究——以行为医学为例

基于论文层面的Citation Topic分类体系,通过测度行为医学领域SCI及SSCI期刊引文主题变动情况,展现国际行为医学领域研究重点以及相关支撑学科的演化。

Review of Research on English Translation of Chinese Running Sentences

In order to convey complete meanings,there is a phenomenon in Chinese of using multiple running sentences.Xu Jingning(2023,p.66)states,“In communication,a complete expression of meaning often requires more than one clause,which is common in human languages.”Domestic research on running sentences includes discussions on defining the concept and structural features of running sentences,sentence properties,sentence pattern classifications and their criteria,as well as issues related to translating running sentences into English.This article primarily focuses on scholarly research into the English translation of running sentences in China,highlighting recent achievements and identifying existing issues in the study of running sentence translation.However,by reviewing literature on the translation of running sentences,it is found that current research in the academic community on non-core running sentences is limited.Therefore,this paper proposes relevant strategies to address this issue.

Engaging in sports via the metaverse? An examination through analysis of metaverse research trends in sports

In sports, virtual spaces are sometimes utilized to enhance performance or user experience. In this study, we conducted a frequency analysis, semantic network analysis, and topic modeling using 134 abstracts obtained through keyword searches focusing on “sport(s)” in combination with “metaverse”, “augmented reality”, “virtual reality”, “lifelogging”, and “mixed reality”. First, the top 20 words were extracted through frequency analysis, and then each type of extracted word was retained to select seven words. The analysis revealed the emergence of key themes such as “user(s)”, “game(s)”, “technolog(y/ies)”, “experience(d)”, “physical”, “training”, and “video”, with variations in intensity depending on the type of metaverse. Second, the relationships between the words were reconfirmed using semantic networks based on the seven selected words. Finally, topic modeling analysis was conducted to uncover themes specific to each type of metaverse. We also found that “performance/scoring” was a prominent word across all types of metaverses. This suggests that in addition to providing enjoyment through sports, there is a high possibility that all users (both general users and athletes) utilize the metaverse to achieve positive outcomes and success. The importance of “performance/scoring” in sports may seem obvious;however, it also provides significant insights for practitioners when combined with metaverse-related keywords. Ultimately, this study has managerial implications for enhancing the performance of specialized users in the sports industry.

Data-and management-driven metaverse research

The metaverse has become a very important phenomenon in society because of the emergence of new technologies. The widespread adoption of the metaverse has generated significant discussions about the challenges and opportunities it presents. We invited three panelists to present their personal viewpoints on the metaverse in the 2022 AIS-SIG-ISAP Workshop on Information Systems in Asia-Pacific (ISAP). The discussion indicated that metaverse research is being conducted. Furthermore, it highlighted new research directions and offered research topics related to the advantages or disadvantages of the metaverse. The proposed research topics will offer new insights to academics and practitioners.

Evaluation of the Effect of Assessment Method Reform in “Nursing Research” Course

Objective:This paper aims to evaluate the implementation effect of the diversified course assessment method reform.Methods:A diversified assessment method was implemented for 196 undergraduate nursing students.Students’mastery of key knowledge in“Nursing Research”was assessed through group reports on topic selection and literature retrieval,as well as the proposition level of the final examination.Results:81.6%of the students agreed with the course assessment method,and 97.9%believed studying“Nursing Research”would be helpful for future scientific research applications.Conclusion:Diversified assessment methods can help improve undergraduate nursing students’scientific research skills and comprehensive quality.

SemreX:一种基于语义相似度的P2P覆盖网络

对等(peer-to-peer)网络的非集中结构、良好的自治性及容错性等特征,使其可能成为Internet上有效的信息共享模型.然而,内容定位问题仍然是大规模P2P网络中信息共享所面临的挑战.SemreX系统是一种P2P网络环境下的文献检索系统.针对SemreX系统,提出一种基于语义相似度的P2P拓扑管理和查询路由算法.仿真实验结果表明,语义拓扑能够有效地提高系统的搜索效率.