Recently,Jayabalan et al published an important study.The authors defined the liver outcome score as a novel biomarker for predicting liver-related mortality in patients with autoimmune hepatitis-primary biliary chola...Recently,Jayabalan et al published an important study.The authors defined the liver outcome score as a novel biomarker for predicting liver-related mortality in patients with autoimmune hepatitis-primary biliary cholangitis overlap syndrome.After thoroughly reviewing their work,we offer insights that primarily relate to their study design to enhance the medical community’s understanding of this complex disease.展开更多
In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mo...In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.展开更多
Currently,treatment options for infant sensorineural hearing loss(SNHL)are limited.This article describes a novel case of SNHL in an infant successfully treated with foot reflexology,along with observed brain activity...Currently,treatment options for infant sensorineural hearing loss(SNHL)are limited.This article describes a novel case of SNHL in an infant successfully treated with foot reflexology,along with observed brain activity changes before and after treatment,as indicated by functional magnetic resonance imaging.Hence,this commentary discusses the case and our viewpoints regarding foot reflexology for treating SNHL.展开更多
BACKGROUND The risks associated with negative doctor-patient relationships have seriously hindered the healthy development of medical and healthcare and aroused wide-spread concern in society.The number of public comm...BACKGROUND The risks associated with negative doctor-patient relationships have seriously hindered the healthy development of medical and healthcare and aroused wide-spread concern in society.The number of public comments on doctor-patient relationship risk events reflects the degree to which the public pays attention to such events.Thirty incidents of doctor-patient disputes were collected from Weibo and TikTok,and 3655 related comments were extracted.The number of comment sentiment words was extracted,and the comment sentiment value was calculated.The Kruskal-Wallis H test was used to compare differences between each variable group at different levels of incidence.Spearman’s correlation analysis was used to examine associations between variables.Regression analysis was used to explore factors influencing scores of comments on incidents.RESULTS The study results showed that public comments on media reports of doctor-patient disputes at all levels are mainly dominated by“good”and“disgust”emotional states.There was a significant difference in the comment scores and the number of partial emotion words between comments on varying levels of severity of doctor-patient disputes.The comment score was positively correlated with the number of emotion words related to positive,good,and happy)and negatively correlated with the number of emotion words related to negative,anger,disgust,fear,and sadness.CONCLUSION The number of emotion words related to negative,anger,disgust,fear,and sadness directly influences comment scores,and the severity of the incident level indirectly influences comment scores.展开更多
Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech a...Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech and abusive language. The proposed system employed a multifaceted approach to comment filtering, incorporating the multi-level filter theory. This involved developing a comprehensive list of words representing various types of offensive language, from slang to explicit abuse. Machine learning models were trained to identify abusive messages through sentiment analysis and contextual understanding. The system categorized comments as positive, negative, or abusive using sentiment analysis algorithms. Employing AI technology, it created a dynamic filtering mechanism that adapted to evolving online language and abusive behavior. Integrated with Instagram while adhering to ethical data collection principles, the platform sought to promote a clean and positive user experience, encouraging users to focus on non-abusive communication. Our machine-learned models, trained on a cleaned Arabic language dataset, demonstrated promising accuracy (75.8%) in classifying Arabic comments, potentially reducing abusive content significantly. This advancement aimed to provide users with a clean and positive online experience.展开更多
Classroom evaluation plays a critical role in shaping students’learning experiences,influencing not only their academic performance but also their motivation and engagement.In the context of primary Chinese language ...Classroom evaluation plays a critical role in shaping students’learning experiences,influencing not only their academic performance but also their motivation and engagement.In the context of primary Chinese language learning,oral feedback and written comments are two prevalent evaluation methods.This paper explores how these different types of feedback impact students’motivation,learning outcomes,and participation.By comparing the immediacy of oral feedback with the systematic nature of written comments,this study seeks to provide insights into how educators can utilize classroom evaluations more effectively to foster motivation in Chinese language learners.The findings indicate that both feedback methods have unique strengths,and a balanced approach may optimize learning outcomes.展开更多
基金Supported by The Key Research and Development Project of the Science and Technology Department of Sichuan Province,China,No.2023YFS0280The High-Level Research Initiation Fund of The First Affiliated Hospital of Chengdu Medical College,China,No.CYFY-GQ43.
文摘Recently,Jayabalan et al published an important study.The authors defined the liver outcome score as a novel biomarker for predicting liver-related mortality in patients with autoimmune hepatitis-primary biliary cholangitis overlap syndrome.After thoroughly reviewing their work,we offer insights that primarily relate to their study design to enhance the medical community’s understanding of this complex disease.
文摘In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.
文摘Currently,treatment options for infant sensorineural hearing loss(SNHL)are limited.This article describes a novel case of SNHL in an infant successfully treated with foot reflexology,along with observed brain activity changes before and after treatment,as indicated by functional magnetic resonance imaging.Hence,this commentary discusses the case and our viewpoints regarding foot reflexology for treating SNHL.
基金Supported by the National Natural Science Foundation of China,No.72374005Natural Science Foundation for the Higher Education Institutions of Anhui Province of China,No.2023AH050561Cultivation Programme for Young and Middle-aged Excellent Teachers in Anhui Province,No.YQZD2023021.
文摘BACKGROUND The risks associated with negative doctor-patient relationships have seriously hindered the healthy development of medical and healthcare and aroused wide-spread concern in society.The number of public comments on doctor-patient relationship risk events reflects the degree to which the public pays attention to such events.Thirty incidents of doctor-patient disputes were collected from Weibo and TikTok,and 3655 related comments were extracted.The number of comment sentiment words was extracted,and the comment sentiment value was calculated.The Kruskal-Wallis H test was used to compare differences between each variable group at different levels of incidence.Spearman’s correlation analysis was used to examine associations between variables.Regression analysis was used to explore factors influencing scores of comments on incidents.RESULTS The study results showed that public comments on media reports of doctor-patient disputes at all levels are mainly dominated by“good”and“disgust”emotional states.There was a significant difference in the comment scores and the number of partial emotion words between comments on varying levels of severity of doctor-patient disputes.The comment score was positively correlated with the number of emotion words related to positive,good,and happy)and negatively correlated with the number of emotion words related to negative,anger,disgust,fear,and sadness.CONCLUSION The number of emotion words related to negative,anger,disgust,fear,and sadness directly influences comment scores,and the severity of the incident level indirectly influences comment scores.
文摘Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech and abusive language. The proposed system employed a multifaceted approach to comment filtering, incorporating the multi-level filter theory. This involved developing a comprehensive list of words representing various types of offensive language, from slang to explicit abuse. Machine learning models were trained to identify abusive messages through sentiment analysis and contextual understanding. The system categorized comments as positive, negative, or abusive using sentiment analysis algorithms. Employing AI technology, it created a dynamic filtering mechanism that adapted to evolving online language and abusive behavior. Integrated with Instagram while adhering to ethical data collection principles, the platform sought to promote a clean and positive user experience, encouraging users to focus on non-abusive communication. Our machine-learned models, trained on a cleaned Arabic language dataset, demonstrated promising accuracy (75.8%) in classifying Arabic comments, potentially reducing abusive content significantly. This advancement aimed to provide users with a clean and positive online experience.
文摘Classroom evaluation plays a critical role in shaping students’learning experiences,influencing not only their academic performance but also their motivation and engagement.In the context of primary Chinese language learning,oral feedback and written comments are two prevalent evaluation methods.This paper explores how these different types of feedback impact students’motivation,learning outcomes,and participation.By comparing the immediacy of oral feedback with the systematic nature of written comments,this study seeks to provide insights into how educators can utilize classroom evaluations more effectively to foster motivation in Chinese language learners.The findings indicate that both feedback methods have unique strengths,and a balanced approach may optimize learning outcomes.
