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Exploring the potential of technology in addressing Sustainable Development Goals (SDGs) within intricate rural contexts holds paramount significance. Sustainable rural development holds profound significance for both developed and developing nations. This study was conducted to develop a methodological framework for placing experts with strategically relevant competencies to meet the specific needs of villages, thus enabling the practical application of their expertise in generating innovative solutions to problems in a village. This study entails several pivotal phases. Firstly, it constructs a Community Standard of Living Index (CSLI) using Delphi and Rank Reciprocal (RR). Secondly, it establishes village clustering through a hybrid Fuzzy CMeans (FCM), Self-Organizing Map (SOM), and Xie-Beni (XB). Thirdly, a classification of village development levels is created using Tsukamoto and Smallest of Maximum (SM). Finally, recommendations for placing experts in villages, aligning their skills with identified needs using the Cosine Similarity (CS). The results obtained are compared with factual data of each village to obtain relevant conclusions, where an accuracy value of 0.95 indicates a high success rate in the test results of the proposed technique. This study has the potential to significantly enhance decision-making by introducing opportunities for the development of hybrid methodologies in expert mapping for rural issues. [ABSTRACT FROM AUTHOR]

With the rapid popularity of online social media, recommendation systems have increasingly harnessed social relations to enhance user-item interactions and mitigate the data sparsity issue. Beyond social connections, the semantic relatedness among items has emerged as a crucial factor in comprehending their inherent connections. In this work, we propose a novel Multi-view Contrastive learning framework for Social Recommendation, named MultiCSR. This framework adaptively incorporates user social networks and item knowledge graphs into modeling users preferences within recommendation systems. To facilitate the alignment of different views, we introduce a dedicated multi-view contrastive learning process that extracts rich information from each view and foster mutual enhancement. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of our framework over representative recommendation methods. Furthermore, ablation studies offer a deeper understanding of the mechanisms underlying our framework. [ABSTRACT FROM AUTHOR]

Prevalence of heart failure (HF) and diabetes are markedly increasing globally. In a population of HF patients, approximately 40% have diabetes which is associated with a more severe HF, poorer cardiovascular outcomes and higher hospitalization rates for HF than HF patients without diabetes. Similar trends were shown in HF patients with prediabetes. In addition, the association between HF and renal function decline was demonstrated in patients with or without diabetes. However, the exact prevalence of dysglycemia in HF patients requires further investigation aiming to clarify the most accurate test to detect dysglycemia in this population. The relationship between HF and diabetes is complex and probably bidirectional. In one way, patients with diabetes have a more than two-fold risk of developing incident HF with reduced or preserved ejection fraction than those without diabetes. In the other way, patients with HF, when compared with those without HF, show an increased risk for the onset of diabetes due to several mechanisms including insulin resistance (IR), which makes HF emerging as a precursor for diabetes development. This article provides epidemiological evidence of undetected dysglycemia (prediabetes or diabetes) in HF patients and reviews the pathophysiological mechanisms which favor the development of IR and the risks associated with these disorders in HF patients. This review also offers a discussion of various strategies for the prevention of diabetes in HF patients, based first on fasting plasma glucose and HbA1c measurement and if normal on an oral glucose tolerance test as diagnostic tools for prediabetes and unknown diabetes that should be performed more extensively in those patients. It discusses the implementation of diabetes prevention measures and well-structured management programs for HF patients who are generally overweight or obese, as well as current pharmacotherapeutic options for prediabetes, including sodium–glucose cotransporter 2 inhibitors which are among the pillars of HF treatment and which recently showed a benefit in the reduction of incident diabetes in HF patients. Thus, there is an urgent need of routine screening for dysglycemia in all HF patients, which should contribute to reduce the incidence of diabetes and to treat earlier diabetes when already present. [ABSTRACT FROM AUTHOR]

Ibrutinib, a highly effective inhibitor of the Bruton tyrosine kinase, has significantly transformed the therapeutic approach in chronic lymphocytic leukemia (CLL). Despite these advancements, the disease continues to be characterized by immune dysfunction and increased susceptibility to infections, with mortality rates from infections showing no significant improvement over the past few decades. Therefore, timely prevention, recognition, and treatment of infections remains an important aspect of the standard management of a patient with CLL. A panel of hematologists with expertise in CLL met to discuss existing literature and clinical insights for the management of infectious in CLL undergoing ibrutinib treatment. Despite not being a fully comprehensive review on the topic, this work provides a set of practical recommendations that can serve as a guide to healthcare professionals who manage these patients in their daily clinical practice. [ABSTRACT FROM AUTHOR]

Due to the fast-changing environments caused by artificial intelligence development, the socio-technical challenge in contemporary educational systems focuses on the need for more regulative measures guiding system stakeholders' behavior. In fulfilling the present legal gap, enacted soft law regulation has been laid out, and a detailed systematic literature review was conducted in the paper presented. The specific methodological approach was selected to deal with two crucial research tasks: to reveal and recommend fundamental governing mechanisms regarding the use and application of generative artificial intelligence; more precisely, large language models in educational systems. Three systematically guided layers of quantitative and qualitative content analysis of central policy, legislation, and regulatory mechanisms in governing AI in education were extracted from the 142 Scopus Database and Web of Science research papers analyzed and presented. These research findings benefit policymakers, regulatory and legislative bodies, and agencies in constructing governing frames for using and applying generative artificial intelligence in education. [ABSTRACT FROM AUTHOR]

Atopic dermatitis often begins in infancy and follows a chronic course of exacerbations and remissions. The etiology is complex and involves numerous factors that contribute to skin barrier defect and inflammation. In the Middle East, the burden of atopic dermatitis is understudied. Epidemiological data specific to the Gulf region are scarce but reveal a prevalence of up to about 40% in the United Arab Emirates. Region-specific factors, such as the climate and the frequency of consanguineous marriages, may affect atopic dermatitis incidence, prevalence, and evolution over time. A panel of experts predominantly from the United Arab Emirates analyzed the evidence from published guidelines, and considered expert guidance and local treatment practices to develop clear recommendations for the management of atopic dermatitis in the United Arab Emirates. They encourage a systematic approach for the diagnosis and treatment, using disease severity scores and quality-of-life measurement tools. Treatment recommendations take into consideration both established therapies and the approved systemic biologics dupilumab and tralokinumab, and the Janus kinase inhibitors baricitinib, upadacitinib, and abrocitinib. [ABSTRACT FROM AUTHOR]

Background: Recent publications underscore the need for updated recommendations addressing less radical surgery for <2 cm tumors, induction chemotherapy, or immunotherapy for locally advanced stages of cervical cancer, as well as for the systemic therapy for recurrent or metastatic cervical cancer. Aim: To summarize the current evidence for the diagnosis, treatment, and follow-up of cervical cancer and provide evidence-based clinical practice recommendations. Methods: Developed according to AGREE II standards, the guidelines classify scientific evidence based on the Agency for Health Technology Assessment and Tariff System criteria. Recommendations are graded by evidence strength and consensus level from the development group. Key Results: (1) Early-Stage Cancer: Stromal invasion and lymphovascular space involvement (LVSI) from pretreatment biopsy identify candidates for surgery, particularly for simple hysterectomy. (2) Surgical Approach: Minimally invasive surgery is not recommended, except for T1A, LVSI-negative tumors, due to a reduction in life expectancy. (3) Locally Advanced Cancer: concurrent chemoradiation (CCRT) followed by brachytherapy (BRT) is the cornerstone treatment. Low-risk patients (fewer than two metastatic nodes or FIGO IB2-II) may consider induction chemotherapy (ICT) followed by CCRT and BRT after 7 days. High-risk patients (two or more metastatic nodes or FIGO IIIA, IIIB, and IVA) benefit from pembrolizumab with CCRT and maintenance therapy. (4) Metastatic, Persistent, and Recurrent Cancer: A PD-L1 status from pretreatment biopsy identifies candidates for Pembrolizumab with available systemic treatment, while triplet therapy (Atezolizumab/Bevacizumab/chemotherapy) becomes a PD-L1-independent option. Conclusions: These evidence-based guidelines aim to improve clinical outcomes through precise treatment strategies based on individual risk factors, predictors, and disease stages. [ABSTRACT FROM AUTHOR]

We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-Computer Interaction we observe high potential, but also challenges. In this article, we explore how an increasing amount of online services and tools not only further facilitates our lives, but also shapes our lives and how we perceive our environments. For this purpose, we adopt the metaphor of personalized 'online layers' and show how these layers are and will be interwoven with the lives that we live in the 'human layer' of the real world. [ABSTRACT FROM AUTHOR]

Abstract With the rapid popularity of online social media, recommendation systems have increasingly harnessed social relations to enhance user-item interactions and mitigate the data sparsity issue. Beyond social connections, the semantic relatedness among items has emerged as a crucial factor in comprehending their inherent connections. In this work, we propose a novel Multi-view Contrastive learning framework for Social Recommendation, named MultiCSR. This framework adaptively incorporates user social networks and item knowledge graphs into modeling users preferences within recommendation systems. To facilitate the alignment of different views, we introduce a dedicated multi-view contrastive learning process that extracts rich information from each view and foster mutual enhancement. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of our framework over representative recommendation methods. Furthermore, ablation studies offer a deeper understanding of the mechanisms underlying our framework.

Objective: Technological advances have led to drastic changes in daily life, and particularly healthcare, while traditional diagnosis methods are being replaced by technology-oriented models and paper-based patient healthcare records with digital files. Using the latest technology and data mining techniques, we aimed to develop an automated clinical decision support system (CDSS), to improve patient prognoses and healthcare delivery. Our proposed approach placed a strong emphasis on improvements that meet patient, parent, and physician expectations. We developed a flexible framework to identify hepatitis, dermatological conditions, hepatic disease, and autism in adults and provide results to patients as recommendations. The novelty of this CDSS lies in its integration of rough set theory (RST) and machine learning (ML) techniques to improve clinical decision-making accuracy and effectiveness. Methods: Data were collected through various web-based resources. Standard preprocessing techniques were applied to encode categorical features, conduct min-max scaling, and remove null and duplicate entries. The most prevalent feature in the class and standard deviation were used to fill missing categorical and continuous feature values, respectively. A rough set approach was applied as feature selection, to remove highly redundant and irrelevant elements. Then, various ML techniques, including K nearest neighbors (KNN), linear support vector machine (LSVM), radial basis function support vector machine (RBF SVM), decision tree (DT), random forest (RF), and Naive Bayes (NB), were employed to analyze four publicly available benchmark medical datasets of different types from the UCI repository and Kaggle. The model was implemented in Python, and various validity metrics, including precision, recall, F1-score, and root mean square error (RMSE), applied to measure its performance. Results: Features were selected using an RST approach and examined by RF analysis and important features of hepatitis, dermatology conditions, hepatic disease, and autism determined by RST and RF exhibited 92.85 %, 90.90 %, 100 %, and 80 % similarity, respectively. Selected features were stored as electronic health records and various ML classifiers, such as KNN, LSVM, RBF SVM, DT, RF, and NB, applied to classify patients with hepatitis, dermatology conditions, hepatic disease, and autism. In the last phase, the performance of proposed classifiers was compared with that of existing state-of-the-art methods, using various validity measures. RF was found to be the best approach for adult screening of: hepatitis with accuracy 88.66 %, precision 74.46 %, recall 75.17 %, F1-score 74.81 %, and RMSE value 0.244; dermatology conditions with accuracy 97.29 %, precision 96.96 %, recall 96.96 %, F1-score 96.96 %, and RMSE value, 0.173; hepatic disease, with accuracy 91.58 %, precision 81.76 %, recall 81.82 %, F1-Score 81.79 %, and RMSE value 0.193; and autism, with accuracy 100 %, precision 100 %, recall 100 %, F1-score 100 %, and RMSE value 0.064. Conclusion: The overall performance of our proposed framework may suggest that it could assist medical experts in more accurately identifying and diagnosing patients with hepatitis, dermatology conditions, hepatic disease, and autism.

Abstract Atopic dermatitis often begins in infancy and follows a chronic course of exacerbations and remissions. The etiology is complex and involves numerous factors that contribute to skin barrier defect and inflammation. In the Middle East, the burden of atopic dermatitis is understudied. Epidemiological data specific to the Gulf region are scarce but reveal a prevalence of up to about 40% in the United Arab Emirates. Region-specific factors, such as the climate and the frequency of consanguineous marriages, may affect atopic dermatitis incidence, prevalence, and evolution over time. A panel of experts predominantly from the United Arab Emirates analyzed the evidence from published guidelines, and considered expert guidance and local treatment practices to develop clear recommendations for the management of atopic dermatitis in the United Arab Emirates. They encourage a systematic approach for the diagnosis and treatment, using disease severity scores and quality-of-life measurement tools. Treatment recommendations take into consideration both established therapies and the approved systemic biologics dupilumab and tralokinumab, and the Janus kinase inhibitors baricitinib, upadacitinib, and abrocitinib.

Despite the global implementation of COVID-19 mitigation measures, the disease continues to maintain transmission. Although mask wearing became one of the key measures for preventing the transmission of COVID-19 early in the pandemic period, many countries have relaxed the mandatory or recommended wearing of masks. The objective of the present study was to estimate the epidemiological impact of removing the mask-wearing recommendation in Japan. We developed a model to assess the consequences of declining mask-wearing coverage after the government revoked its recommendation in February 2023. The declining mask-wearing coverage was estimated using serial cross-sectional data, and a mathematical model was devised to determine the age-specific incidence of COVID-19 using the observed case count in Tokyo from week of October 3, 2022 to October 30, 2023. We explored model-based counterfactual scenarios to measure hypothetical situations in which the mask-wearing coverage decreases or increases relative to the observed coverage. The results show that mask-wearing coverage declined from 97% to 69% by the week of October 30, 2023, and that if the mask-wearing recommendation had continued, 427 lives could have been saved in Tokyo. If the mask-wearing coverage had declined to 25% of the observed level, the model suggests there might have been 1587 additional deaths. Thus, revoking the mask-wearing recommendation had a substantial epidemiological impact. In future pandemics, our proposed approach could provide a realtime quantification of the effects of relaxing countermeasures. [ABSTRACT FROM AUTHOR]

Abstract Learning the emotional tendency of travelers improves their interests and provide optimal traveling recommendations. This, however, requires large volumes of data such as travel plans, visit sites, personal interests, value for money, etc. for a detailed analysis. For ease of such analysis, in this article, an organized combinational control method (OC2M) is proposed. This method relies on conventional long short-term memory (LSTM) and fuzzy control (FC) to support such analysis. The first is responsible for filtering non-repeated data from the previous travel/ tour history reducing the data discreteness. This is organized non-recurrently to prevent outdated/ trivial data from influencing the consumer’s emotional learning. The FC process filters the adaptable data with the future tour/ travel plan for providing optimal recommendations that are liable to the consumer’s emotional tendency. This is identified based on the user's interest, preferences, and emotional connectivity with the place or plan from previous histories. Therefore, the proposed method improves the recommendation and validation tendencies of the consumer’s travel plan accordingly.

Continuous improvement of professional training was included in the monitoring of the progress of competencies by teachers, considering the importance of fulfilling students’ graduation profile at the end of their professional training. This study aims to evaluate students’ specific competencies in the Hospitality and Gastronomy programs at a private university located on the northern coast of Peru. The methodology is quantitative, applied to a population of 408 students during semesters 2020-1, 2020-2, and 2021-1; through the application of documentary review guides, the students’ competencies were evaluated, as well as the verification of the students’ satisfaction and recommendation regarding the teachers who evaluated such competencies. The results show that Hospitality students achieved 91% compliance with the graduation profile of the specific competencies and, in the case of Gastronomy – 81.25%. On the other hand, in 2020-1, Hospitality had a higher degree of satisfaction based on the teaching of the teachers who evaluated the competencies but a lower degree of recommendation, while Gastronomy evidenced a lower degree of satisfaction and a higher degree of recommendation. Likewise, in 2020-2, Gastronomy evidenced a higher degree of satisfaction and recommendation than Hospitality, and on the contrary, in 2021-1, Hospitality evidenced a higher degree of satisfaction and recommendation than that identified in Gastronomy. It is concluded that Hospitality students stand out with respect to the fulfillment of the graduation profile in semesters 2020-1 and 2021-1, and although there was a closeness in both programs in semester 2020-2, Hospitality always showed better results than Gastronomy.

Previous research on subscriptions has not focused on the characteristics of content where multiple content is consumed over a certain period, and evaluations of that are not easily associated. Therefore, considering these characteristics, this study theoretically and empirically examines the effects and interactions of conditions such as the ratio of good content, the content selection criterion, and the evaluation level of original content on consumer satisfaction and intention to extend. The results show that 1) the ratio of good content, i.e., the proportion of highly rated content, is the most influential factor for satisfaction and extension intention; 2) when the main criterion of content selection is the consumers themselves rather than recommendations by the streaming service, the difference in satisfaction based on the ratio of good content is significant; 3) the influence of original content exclusively planned and produced by the streaming service is relatively lower on satisfaction compared to the influence of the ratio of highly rated content, but similar on extension intention; and 4) the influence of original content on satisfaction is as high as that of the ratio of highly rated content, only when the content selection is done based on consumer preferences.

Chronic obstructive pulmonary disease (COPD) is a common disease that seriously harms human health, and it is also a leading cause of death, with a large number of cases being underdiagnosed. Can COPD screening enable early diagnosis of the disease and improve patient outcomes? In 2022, the U.S. Preventive Services Task Force (USPSTF) updated its "Chronic Obstructive Pulmonary Disease Screening Recommendation Statement" (hereinafter referred to as the "Recommendation") for asymptomatic adults by evaluating the latest research evidence regarding the benefits and harms of COPD screening. Based on limited evidence, the USPSTF issused a 'D' statement (there is moderate certainty that the screening has no net benefit) and recommends against COPD screening for asymptomatic adults, which is consistent with the 2016 version. The current article interprets the principles of this Recommendation in the context of Chinese general practice.

Graph convolutional neural networks have been successfully applied to collaborative filtering to capture high-quality user-item representations. Despite their remarkable performance, there are still limitations that hinder further improvement of recommender systems. Most existing recommendation systems utilize implicit feedback data for model training, but such data inevitably contains adversarial interaction noise. The conventional graph-based collaborative filtering method fails to effectively filter out this noise, and instead amplifies its impact, resulting in degraded model performance. To address this issue, we propose a robustness-enhanced collaborative filtering graph neural network model that does not rely on explicit noise filtering. Our approach involves simulating user-item interactions that do not exist in practice as adversarial interaction noise using random noise. To mitigate the impact of this noise in hidden feedback, we replace them with randomly selected partial nodes based on the principle of mutual information maximization. Our model has been extensively experimented on three public datasets (MovieLens-1 M, Yelp, and Ta-feng) and achieves performance improvements of about 5%, 10%, and 14%, respectively, compared to the state-of-the-art baseline model. In particular, in model robustness experiments, our model achieves significant performance improvements of about 13% and 17% in Yelp and Ta-feng. A comprehensive experimental study shows that our proposed method is reasonably effective and interpretable. [ABSTRACT FROM AUTHOR]

Most existing collaborative filtering-based recommender systems rely solely on available user–item interactions for user and item representation learning. Their performance often suffers significantly when interactions are sparse, as limited user and item interactions are insufficient for learning robust representations. To address this issue, recent research has explored additional information between users and items by leveraging the user–item bipartite graph. However, these methods have not fully exploited high-order neighborhood information, primarily using sampled interactions to enrich training data rather than integrating this information directly into representation learning. In this paper, we propose a novel model, EIR-GCN (Embedding Integration with Relational Graph Convolutional Network), which directly incorporates various types of collaborative relations, such as user–user and item–item interactions, into the embedding function for user preference modeling. Specifically, our model employs advanced graph convolutional network (GCN) techniques to integrate user–item, user–user, and item–item relations for comprehensive representation learning. EIR-GCN initially selects the most influential second-order neighbors from the user–item bipartite graph to form user–user and item–item connections. With these enriched connections, a message-passing method is adopted to learn node representations by aggregating messages from directly linked nodes, including first-order item neighbors and selected second-order user neighbors. Extensive experiments on several public datasets demonstrate that EIR-GCN outperforms strong baselines, including recent GCN-based models and those exploiting high-order information. Our results show that EIR-GCN achieves state-of-the-art performance and effectively addresses the sparsity issue, highlighting its robustness and efficacy in recommendation tasks. [ABSTRACT FROM AUTHOR]

Zinc, a vital trace element, holds significant importance in numerous physiological processes within the body. It participates in over 300 enzymatic reactions, metabolic functions, regulation of gene expression, apoptosis and immune modulation, thereby demonstrating its essential role in maintaining overall health and well-being. While zinc deficiency is associated with significant health risks, an excess of this trace element can also lead to harmful effects. According to the World Health Organization (WHO), 6.7 to 15 mg per day are referred to be the dietary reference value. An excess of the recommended daily intake may result in symptoms such as anemia, neutropenia and zincinduced copper deficiency. The European Food Safety Authority (EFSA) defines the tolerable upper intake level (UL) as 25 mg per day, whereas the Food and Drug Administration (FDA) allows 40 mg per day. This review will summarize the current knowledge regarding the calculation of UL and other health risks associated with zinc. For example, zinc intake is not limited to oral consumption; other routes, such as inhalation or topical application, may also pose risks of zinc intoxication. [ABSTRACT FROM AUTHOR]

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The topic of privacy-preserving collaborative filtering is gaining more and more attention. Nevertheless, privacy-preserving collaborative filtering techniques are vulnerable to shilling or profile injection assaults. Hence, it is crucial to identify counterfeit profiles in order to achieve total success. Various techniques have been devised to identify and prevent intrusion patterns from infiltrating the system. Nevertheless, these strategies are specifically designed for collaborative filtering algorithms that do not prioritize privacy. There is a scarcity of research on identifying shilling attacks in recommender systems that prioritize privacy. This work presents a novel technique for identifying shilling assaults in privacy-preserving collaborative filtering systems. We employ an ant colony clustering detection method to effectively identify and eliminate fake profiles that are created by six widely recognized shilling attacks on compromised data. The objective of the study is to categorize the fraudulent profiles into a specific cluster and separate this cluster from the system. Empirical experiments are conducted with actual data. The empirical findings demonstrate that the strategy derived from the study effectively eliminates fraudulent profiles in privacy-preserving collaborative filtering. [ABSTRACT FROM AUTHOR]

Model-based systems engineering (MBSE) is a modeling approach used in industry to support the formalization, analysis, design, checking and verification of systems. In MBSE modeling, domain knowledge is the basis of the modeling. However, modeling does not happen overnight; it requires systematic training and a significant investment of resources. Unfortunately, many domain experts lack the expertise required for modeling, even though they know the domain well. The question arises about how to provide system modelers with domain knowledge at the right time to support the efficient completion of modeling. Since MBSE research that integrates AI is just beginning to take off, no public dataset is available. In this paper, aerospace SysML models are constructed based on spacecraft-related domain knowledge to form SysML model data. The validation rules are studied to validate the SysML model data, and combined with the concept of the recommended system, a recommendation method for the MBSE modeling process based on the knowledge and SysML model is proposed. A GLOVE language model is pre-trained by using domain knowledge and general knowledge; the model data are also used to fine-tune the GLOVE language model combined with the pre-training to recommend some domain development processes. The recommendation list is manually quality-verified and fed into the pre-training phase, while new requirement texts are continuously added in the fine-tuning phase, resulting in a more relevant and accurate recommendation list. Experiments show that the incremental recommender system can not only effectively recommend SysML models, but also improve the quality and efficiency of MBSE development. [ABSTRACT FROM AUTHOR]

The role of artificial intelligence (AI) in healthcare is evolving, offering promising avenues for enhancing clinical decision making and patient management. Limited knowledge about lipedema often leads to patients being frequently misdiagnosed with conditions like lymphedema or obesity rather than correctly identifying lipedema. Furthermore, patients with lipedema often present with intricate and extensive medical histories, resulting in significant time consumption during consultations. AI could, therefore, improve the management of these patients. This research investigates the utilization of OpenAI's Generative Pre-Trained Transformer 4 (GPT-4), a sophisticated large language model (LLM), as an assistant in consultations for lipedema patients. Six simulated scenarios were designed to mirror typical patient consultations commonly encountered in a lipedema clinic. GPT-4 was tasked with conducting patient interviews to gather medical histories, presenting its findings, making preliminary diagnoses, and recommending further diagnostic and therapeutic actions. Advanced prompt engineering techniques were employed to refine the efficacy, relevance, and accuracy of GPT-4's responses. A panel of experts in lipedema treatment, using a Likert Scale, evaluated GPT-4's responses across six key criteria. Scoring ranged from 1 (lowest) to 5 (highest), with GPT-4 achieving an average score of 4.24, indicating good reliability and applicability in a clinical setting. This study is one of the initial forays into applying large language models like GPT-4 in specific clinical scenarios, such as lipedema consultations. It demonstrates the potential of AI in supporting clinical practices and emphasizes the continuing importance of human expertise in the medical field, despite ongoing technological advancements. [ABSTRACT FROM AUTHOR]

This paper tackles the prevalent challenges faced by existing tourism route recommendation methods, including data sparsity, cold start, and low accuracy. To address these issues, a novel intelligent tourism route recommendation method based on collaborative filtering is introduced. The proposed method incorporates a series of key steps. Firstly, it calculates the interest level of users by analyzing the item attribute rating values. By leveraging this information, the method can effectively capture the preferences and interests of users. Additionally, a user attribute rating matrix is constructed by extracting implicit user behavior preferences, providing a comprehensive understanding of user preferences. Recognizing that user interests can evolve over time, a weight function is introduced to account for the possibility of interest shifting during product use. This weight function enhances the accuracy of recommendations by adapting to the changing preferences of users, improving the overall quality of the suggested tourism routes. The results demonstrate the significant advantages of the approach. Specifically, the proposed method successfully alleviates the problem of data sparsity, enhances neighbor selection, and generates tourism route recommendations that exhibit higher accuracy compared to existing methods. [ABSTRACT FROM AUTHOR]

Human Papilloma Virus (HPV) infection and HPV-related cancers can be prevented through vaccinations and mass cervical screening programmes. The Ministry of Health in Italy provides recommendations on primary and secondary prevention of HPV-related diseases, but the 19 Italian regions and 2 autonomous provinces have organisational and decision-making autonomy, with differences in the strategies for offering prevention. The aim of this study is to describe the HPV vaccination and cervical screening offered in all Italian regions. Regional official documents up until 31 December 2021 were first identified. Subsequently, primary and secondary prevention experts from each region were interviewed to validate the previously collected data. The National Immunisation Plan (NIP) 2017–2019 recommends HPV vaccination from the age of 11 for both sexes, with a coverage target of 95%. HPV vaccination is offered free of charge or co-payment. All regions have screening programmes for cervical cancer, using PAP or HPV-DNA tests every three to five years. All regions have an electronic registry for vaccination and screening status. All regions have developed awareness-raising campaigns. It is important to harmonise regional policies with the implementation of information systems integration. The collected data could enhance both regional and national public health initiatives, bolstering the resilience of vaccination programs. [ABSTRACT FROM AUTHOR]

Ibrutinib, a highly effective inhibitor of the Bruton tyrosine kinase, has significantly transformed the therapeutic approach in chronic lymphocytic leukemia (CLL). Despite these advancements, the disease continues to be characterized by immune dysfunction and increased susceptibility to infections, with mortality rates from infections showing no significant improvement over the past few decades. Therefore, timely prevention, recognition, and treatment of infections remains an important aspect of the standard management of a patient with CLL. A panel of hematologists with expertise in CLL met to discuss existing literature and clinical insights for the management of infectious in CLL undergoing ibrutinib treatment. Despite not being a fully comprehensive review on the topic, this work provides a set of practical recommendations that can serve as a guide to healthcare professionals who manage these patients in their daily clinical practice.

Advancements in technology have revolutionized various sectors, including agriculture, which serves as the backbone of many economies, particularly in Asian countries. The integration of new technologies and research has consistently aimed to enhance cultivation rates and reduce reliance on manual labor. Two key technologies, Artificial Intelligence (AI) and the Internet of Things (IoT), have emerged as pivotal tools in automating processes, providing recommendations, and monitoring agricultural activities to optimize results. While traditional soil cultivation has been the preferred method, the increasing urbanization trend necessitates alternative approaches such as hydroponics, which replaces soil with water as the medium for crop cultivation. Having many significant advantages, hydroponics serves a crucial role in achieving efficient space utilization. To get a higher density of plants in a confined area hydroponic approach provides water, nutrients and other essential elements directly to the plant's root. To utilize the hydroponic system more effectively, our proposed method, integrating AI and IoT helps to provide suitable crop recommendations, monitor the parameters of the plants and also suggest the necessary changes required for gaining optimal parameters. To ensure optimal resource allocation and maximize yields we have used machine learning models and trained them to recommend suitable crops from the given parameters and also refer to the changes in parameters that are needed for better plant growth. We have used the crop recommendation dataset from the Indian Chamber of Food and Agriculture to train our proposed machine-learning model. Our selected machine learning algorithms to predict the best crops are Random forests, Decision trees, SVM, KNN, and XGBoost. Our research combines AI and IoT with hydroponic systems to streamline crop recommendations, automate monitoring processes, and provide real-time guidance for optimized cultivation. Among them, the Random forest algorithm outperformed other algorithms with an accuracy of 97.5%.

The current challenges of industrial agriculture focus on environmental safety. Water quality is an indicator of environmental sustainability. The cultivation system has an impact on water quality. The aim of this study is to assess the water quality of agricultural systems. Anthropogenically, agricultural activities have an impact on environmental aspects. Identification of agricultural systems is clustered into organic and non-organic agriculture. The agricultural system in Indonesia is regulated based on SNI 6729;2016. Water sampling was carried out at three points, including upstream, middle (irrigation), and downstream (river). Water sampling at each point was repeated three times. Assessment of the physical quality of water using the Combo Quality Meter. Water samples for chemical and biological analysis at the Environmental Laboratory, Perum. Jasa Tirta 1. Determination of water quality standards refers to Government Regulation No. 82 of 2001. In the upstream section, pollution can be seen in the biological oxygen demand (BOD) indicator, with an average of 15.03 mg L-1 for organic and conventional systems. The phosphate indicator averaged 1.96 mg L-1. In the middle section (irrigation), the pollution indicators for BOD, phosphate, and total Coliform parameters were 6.76 mg L-1 for the organic system, 7.37 mg L-1 for the non-organic system, and 1,290 CFU mL-1. In the downstream (river), pollution indicators consist of total suspended solids (TSS), BOD, chemical oxygen demand (COD), and total Coliform. Anthropogenic identification for clustering agricultural systems at the research location uses stratified disproportional sampling. The results of this research provide recommendations for water quality management for sustainable agricultural environmental management.

Attracting prospective students could be difficult and expensive. Candidates, when choosing a future school for their studies, consider a great number of factors. Information from official university websites don´t suffice to persuade. Among others, the word of mouth plays a significant role. The aim of this paper is to present findings about important factors influencing the overall satisfaction of current students with university life as well as their willingness to share positive references. The study took place in the Czech Republic with students attending business schools at universities. Primary data was collected via online questionnaire with students of bachelor’s master’s, and doctor degrees (n = 274) and in-depth interviews (n = 10). Data was processed with regression analysis and Spearman’s correlation coefficient. The main findings suggest that there is a close positive correlation between students’ satisfaction and the willingness to recommend the university. The quality of student life and the reputation of the school were identified as the most important factors influencing this satisfaction and willingness to recommend.

This is a consensus-based Canadian guideline whose primary purpose is to standardize and facilitate the management of chronic graft-versus-host disease (cGvHD) across the country. Creating uniform healthcare guidance in Canada is a challenge for a number of reasons including the differences in healthcare authority structure, funding and access to healthcare resources between provinces and territories, as well as the geographic size. These differences can lead to variable and unequal access to effective therapies for GvHD. This document will provide comprehensive and practical guidance that can be applied across Canada by healthcare professionals caring for patients with cGvHD. Hopefully, this guideline, based on input from GvHD treaters across the country, will aid in standardizing cGvHD care and facilitate access to much-needed novel therapies. This consensus paper aims to discuss the optimal approach to the initial assessment of cGvHD, review the severity scoring and global grading system, discuss systemic and topical treatments, as well as supportive therapies, and propose a therapeutic algorithm for frontline and subsequent lines of cGvHD treatment in adults and pediatric patients. Finally, we will make suggestions about the future direction of cGvHD treatment development such as (1) a mode-of-action-based cGvHD drug selection, according to the pathogenesis of cGvHD, (2) a combination strategy with the introduction of newer targeted drugs, (3) a steroid-free regimen, particularly for front line therapy for cGvHD treatment, and (4) a pre-emptive approach which can prevent the progression of cGvHD in high-risk patients destined to develop severe and highly morbid forms of cGvHD.

Abstract Background Over the past few decades, patient-reported outcomes (PROs) have been used to understand patient health conditions better. Therefore, numerous PRO measures (questionnaires) and guidelines or guidance have been developed. However, it is challenging to select target guidance from among the many available guidance and to understand the chosen guidance. This study comprehensively collected the existing PRO guidance for clinical trials or studies and practices to support novice PRO users in academia, industry, clinical practice, and regulatory and reimbursement decision-making. Methods For the scoping review, we searched the MEDLINE, Embase, Google Books, WorldCat, and the National Library of Medicine (NLM) Bookshelf databases from 2009 to 2023. The eligibility criteria were PRO guidance for clinical trials, clinical practice, or application such as health technology assessment. Those guidance cover aspects such as quality of life (QOL), PRO, health-related QOL, health state utilities, psychometric requirements, implementation methods, analysis and interpretation, or clinical practice applications. After the systematic search, three researchers individually reviewed the collected data, and the reviewed articles and books were scrutinized using the same criteria. Results We collected the PRO guidance published in articles and books between 2009 and 2023. From the database searches, 1,455 articles and 387 books were identified, of which one book and 33 articles were finally selected. The collected PRO guidance was categorized into the adoption of PRO measures, design and reporting of trials or studies using PROs, implementation of PRO evaluation in clinical trials or studies or clinical practice, analysis and interpretation of PROs, and application of PRO evaluation. Based on this categorization, we suggest the following for novices: When selecting guidance, novices should clarify the “place” and “purpose” where the guidance will be used. Additionally, they should know that the terminology related to PRO and the scope and expectations of PROs vary by “places” and “purposes”. Conclusions From this scoping review of existing PRO guidance, we provided summaries and caveats to assist novices in selecting guidance that fits their purpose and understanding it.

Abstract Objective The purpose of this study was to systematically evaluate the methodological quality of massage-related clinical practice guidelines (CPGs)/consensus on massage using the Appraisal of Guidelines Research and Evaluation (AGREE II) instrument and to summarize the current status of recommendations in the CPGs. Methods The Chinese National Knowledge Infrastructure (CNKI), WanFang Data, China Science and Technology Journal Database (VIP), China Biology Medicine disc (CBM), PubMed, Embase, and guideline websites (such as the Chinese Medical Ace Base, the China Association of Chinese Medicine, the World Health Organization, Guideline International Network, National Institute for Health and Care Excellence, Scottish Intercollegiate Guidelines Network) were searched from inception to October 31, 2022. In addition, the reference lists of relevant studies were reviewed to identify domestic and overseas massage CPGs/consensus. The search terms adopted a combination of subject words and free words, mainly including traditional Chinese medicine, complementary therapies, Tuina, massage, manipulation, chiropractic/osteopathic, spinal, acupressure, guideline, and consensus. Two researchers independently completed the eligible records and extracted the data. Before the formal research, calibrations were performed twice on AGREE II, and all reviewers completed the pilot test three times until they understood and reached an agreement on the assessment items. Three researchers appraised the methodological quality of the included guidelines using the AGREE II instrument and calculated the overall intraclass correlation coefficient (ICC) of agreement. Results The evaluation results showed that among the 49 eligible CPGs/consensus, 4 (8.2%) CPGs/consensus were considered “recommended”, 15 (30.6%) CPGs/consensus were considered “recommended with modifications”, and 30 (61.2%) CPGs/consensus were considered “not recommended”, while the consensus was considered “not recommended”. Generally, the scores in the six domains of the guidelines were all higher than the consensus. Evaluation results for the overall quality of 36 CPGs showed that 4 (11%) were “good quality”, 15 (42%) were “sufficient quality” and 17 (47%) were “lower quality”. The AGREE II quality scores of domains ranged from 0.30 to 0.75 ([ICC = 0.993, 95% CI (0.992, 0.995)]). The domain of scope and purpose (domain 1), with a median score of 0.75 (0.52~0.91), performed best in the guidelines with AGREE II, and stakeholder involvement (domain 2) [median 0.39 (0.31~0.56)] and application (domain 5) [median 0.30 (0.17~0.47] obtained lower scores. The consensus score of domain 1 was better at 26.0 (21.6~44.8), followed by rigor of development (domain 3) with a score of 18.0 (10.0~28.9). A total of 119 massage-related recommendations were extracted from 49 guidelines/consensuses, including “in favor” (102, 85.7%), “against” (9, 7.6%), and “did not make recommendations” (8, 6.7%). Conclusion The overall quality of the included guidelines was low, and most of the guidelines were not “recommended”. In future guideline updates, the existing evidence should be used, the professional composition of members of the expert group should be enriched, and patients’ values and preferences should be fully considered. It is necessary to clearly propose recognizable recommendations and strengthen the rigor and standardization of guideline formulation. Thus, clear standard guidelines can be formulated to better guide clinical practice.

ABSTRACTNext point-of-interest (POI) recommendation has been applied by many internet companies to enhance the user travel experience. Recent research advocates deep-learning methods to model long-term check-in sequences and mine mobility patterns of people to improve recommendation performance. Existing approaches model general user preferences based on historical check-ins and can be termed as preference pattern models. The preference pattern is different from the intention pattern, in that it does not emphasize the user mobility pattern of revisiting POIs, which is a common behavior and kind of intention for users. An effective module is needed to predict when and where users will repeat visits. In this paper, we propose a Spatio-Temporal Intention Learning Self-Attention Network (STILSAN) for next POI recommendation. STILSAN employs a preference-intention module to capture the user’s long-term preference and recognizes the user’s intention to revisit some specific POIs at a specific time. Meanwhile, we design a spatial encoder module as a pretrained model for learning POI spatial feature by simulating the spatial clustering phenomenon and the spatial proximity of the POIs. Experiments are conducted on two real-world check-in datasets. The experimental results demonstrate that all the proposed modules can effectively improve recommendation accuracy and STILSAN yields outstanding improvements over the state-of-the-art models.

Abstract In this era of information explosion, recommendation systems play a key role in helping users to uncover content of interest among massive amounts of information. Pursuing a breadth of recall while maintaining accuracy is a core challenge for current recommendation systems. In this paper, we propose a new recommendation algorithm model, the interactive higher-order dual tower (IHDT), which improves current models by adding interactivity and higher-order feature learning between the dual tower neural networks. A heterogeneous graph is constructed containing different types of nodes, such as users, items, and attributes, extracting richer feature representations through meta-paths. To achieve feature interaction, an interactive learning mechanism is introduced to inject relevant features between the user and project towers. Additionally, this method utilizes graph convolutional networks for higher-order feature learning, pooling the node embeddings of the twin towers to obtain enhanced end-user and item representations. IHDT was evaluated on the MovieLens dataset and outperformed multiple baseline methods. Ablation experiments verified the contribution of interactive learning and high-order GCN components.

The multi-armed bandit (MAB) problem is a typical problem of exploration and exploitation. As a classical MAB problem, the stochastic multi-armed bandit (SMAB) is the basis of reinforcement learning recommendation. However, most existing SMAB and MAB algorithms have two limitations: (1) they do not make full use of feedback from the environment or agent, such as the number of arms and rewards contained in user feedback; (2) they overlook the utilization of different action selections, which can affect the exploration and exploitation of the algorithm. These limitations motivate us to propose a novel dynamic grouping within the minimax optimal strategy in the stochastic case (DG-MOSS) algorithm for reinforcement learning recommendation for small and medium-sized data scenarios. DG-MOSS does not require additional contextual data and can be used for recommendation of various types of data. Specifically, we designed a new exploration calculation method based on dynamic grouping which uses the feedback information automatically in the selection process and adopts different action selections. During the thorough training of the algorithm, we designed an adaptive episode length to effectively improve the training efficiency. We also analyzed and proved the upper bound of DG-MOSS's regret. Our experimental results for different scales, densities, and field datasets show that DG-MOSS can yield greater rewards than nine baselines with sufficiently trained recommendation and demonstrate that it has better robustness. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF), a fundamental technique in personalized Recommender Systems, operates by leveraging user–item preference interactions. Matrix factorization remains one of the most prevalent CF-based methods. However, recent advancements in deep learning have spurred the development of hybrid models, which extend matrix factorization, particularly with autoencoders, to capture nonlinear item relationships. Despite these advancements, many proposed models often neglect dynamic changes in the rating process and overlook user features. This paper introduces IUAutoTimeSVD++, a novel hybrid model that builds upon autoTimeSVD++. By incorporating item–user features into the timeSVD++ framework, the proposed model aims to address the static nature and sparsity issues inherent in existing models. Our model utilizes a contractive autoencoder (CAE) to enhance the capacity to capture a robust and stable representation of user-specific and item-specific features, accommodating temporal variations in user preferences and leveraging item characteristics. Experimental results on two public datasets demonstrate IUAutoTimeSVD++'s superiority over baseline models, affirming its effectiveness in capturing and utilizing user and item features for temporally adaptive recommendations. [ABSTRACT FROM AUTHOR]

Non-specific symptoms and difficulty in collecting urine specimens make the diagnosis of urinary tract infection (UTI) challenging in children. However, timely diagnosis and initiation of therapy are essential to prevent complications. Children with recurrent UTIs require detailed evaluation and follow-up for optimal management. We report key updates from the revised evidence-based practice guidelines of the Indian Society of Pediatric Nephrology for UTIs and primary vesicoureteric reflux. [ABSTRACT FROM AUTHOR]

Next point-of-interest (POI) recommendation has been applied by many internet companies to enhance the user travel experience. Recent research advocates deep-learning methods to model long-term check-in sequences and mine mobility patterns of people to improve recommendation performance. Existing approaches model general user preferences based on historical check-ins and can be termed as preference pattern models. The preference pattern is different from the intention pattern, in that it does not emphasize the user mobility pattern of revisiting POIs, which is a common behavior and kind of intention for users. An effective module is needed to predict when and where users will repeat visits. In this paper, we propose a Spatio-Temporal Intention Learning Self-Attention Network (STILSAN) for next POI recommendation. STILSAN employs a preference-intention module to capture the user's long-term preference and recognizes the user's intention to revisit some specific POIs at a specific time. Meanwhile, we design a spatial encoder module as a pretrained model for learning POI spatial feature by simulating the spatial clustering phenomenon and the spatial proximity of the POIs. Experiments are conducted on two real-world check-in datasets. The experimental results demonstrate that all the proposed modules can effectively improve recommendation accuracy and STILSAN yields outstanding improvements over the state-of-the-art models. [ABSTRACT FROM AUTHOR]

The current challenges of industrial agriculture focus on environmental safety. Water quality is an indicator of environmental sustainability. The cultivation system has an impact on water quality. The aim of this study is to assess the water quality of agricultural systems. Anthropogenically, agricultural activities have an impact on environmental aspects. Identification of agricultural systems is clustered into organic and nonorganic agriculture. The agricultural system in Indonesia is regulated based on SNI 6729;2016. Water sampling was carried out at three points, including upstream, middle (irrigation), and downstream (river). Water sampling at each point was repeated three times. Assessment of the physical quality of water using the Combo Quality Meter. Water samples for chemical and biological analysis at the Environmental Laboratory, Perum. Jasa Tirta 1. Determination of water quality standards refers to Government Regulation No. 82 of 2001. In the upstream section, pollution can be seen in the biological oxygen demand (BOD) indicator, with an average of 15.03 mg L-1 for organic and conventional systems. The phosphate indicator averaged 1.96 mg L-1. In the middle section (irrigation), the pollution indicators for BOD, phosphate, and total Coliform parameters were 6.76 mg L-1 for the organic system, 7.37 mg L-1 for the non-organic system, and 1,290 CFU mL-1. In the downstream (river), pollution indicators consist of total suspended solids (TSS), BOD, chemical oxygen demand (COD), and total Coliform. Anthropogenic identification for clustering agricultural systems at the research location uses stratified disproportional sampling. The results of this research provide recommendations for water quality management for sustainable agricultural environmental management. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Over the past few decades, patient-reported outcomes (PROs) have been used to understand patient health conditions better. Therefore, numerous PRO measures (questionnaires) and guidelines or guidance have been developed. However, it is challenging to select target guidance from among the many available guidance and to understand the chosen guidance. This study comprehensively collected the existing PRO guidance for clinical trials or studies and practices to support novice PRO users in academia, industry, clinical practice, and regulatory and reimbursement decision-making. Methods: For the scoping review, we searched the MEDLINE, Embase, Google Books, WorldCat, and the National Library of Medicine (NLM) Bookshelf databases from 2009 to 2023. The eligibility criteria were PRO guidance for clinical trials, clinical practice, or application such as health technology assessment. Those guidance cover aspects such as quality of life (QOL), PRO, health-related QOL, health state utilities, psychometric requirements, implementation methods, analysis and interpretation, or clinical practice applications. After the systematic search, three researchers individually reviewed the collected data, and the reviewed articles and books were scrutinized using the same criteria. Results: We collected the PRO guidance published in articles and books between 2009 and 2023. From the database searches, 1,455 articles and 387 books were identified, of which one book and 33 articles were finally selected. The collected PRO guidance was categorized into the adoption of PRO measures, design and reporting of trials or studies using PROs, implementation of PRO evaluation in clinical trials or studies or clinical practice, analysis and interpretation of PROs, and application of PRO evaluation. Based on this categorization, we suggest the following for novices: When selecting guidance, novices should clarify the "place" and "purpose" where the guidance will be used. Additionally, they should know that the terminology related to PRO and the scope and expectations of PROs vary by "places" and "purposes". Conclusions: From this scoping review of existing PRO guidance, we provided summaries and caveats to assist novices in selecting guidance that fits their purpose and understanding it. [ABSTRACT FROM AUTHOR]

Understanding digital menu choices in limited-option environments, such as university cafés, is crucial for promoting healthier and more sustainable food choices. We are, therefore, looking at two possible interventions or nudges—recommendation and position—and how they interact with, for example, price. In the first smartphone-based study (N = 517), participants were presented with two menu options, while the factors “recommendation”, “position”, and “price” were manipulated. We only found effects in relation to the choice of the more popular menu option. Specifically, when the popular meal was the expensive option, the recommendation had a negative effect on choice, but not when the popular meal was the cheaper option. The aim of the second smartphone-based study (N = 916) was to shed more light on the role of popularity or personal preference in relation to recommendations. We manipulated the differences in personal preference (small or large) using a ranking task presented before the menu choice. In Study 2, the interaction effect between recommendation and price for the more popular menu option could not be replicated. Instead, we found that the greater the difference in preference, the less pronounced the price effect was. Overall, some effects of the recommendations have been identified, but further research is needed to clarify the exact circumstances under which they arise. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to systematically evaluate the methodological quality of massage-related clinical practice guidelines (CPGs)/consensus on massage using the Appraisal of Guidelines Research and Evaluation (AGREE II) instrument and to summarize the current status of recommendations in the CPGs. Methods: The Chinese National Knowledge Infrastructure (CNKI), WanFang Data, China Science and Technology Journal Database (VIP), China Biology Medicine disc (CBM), PubMed, Embase, and guideline websites (such as the Chinese Medical Ace Base, the China Association of Chinese Medicine, the World Health Organization, Guideline International Network, National Institute for Health and Care Excellence, Scottish Intercollegiate Guidelines Network) were searched from inception to October 31, 2022. In addition, the reference lists of relevant studies were reviewed to identify domestic and overseas massage CPGs/consensus. The search terms adopted a combination of subject words and free words, mainly including traditional Chinese medicine, complementary therapies, Tuina, massage, manipulation, chiropractic/osteopathic, spinal, acupressure, guideline, and consensus. Two researchers independently completed the eligible records and extracted the data. Before the formal research, calibrations were performed twice on AGREE II, and all reviewers completed the pilot test three times until they understood and reached an agreement on the assessment items. Three researchers appraised the methodological quality of the included guidelines using the AGREE II instrument and calculated the overall intraclass correlation coefficient (ICC) of agreement. Results: The evaluation results showed that among the 49 eligible CPGs/consensus, 4 (8.2%) CPGs/consensus were considered "recommended", 15 (30.6%) CPGs/consensus were considered "recommended with modifications", and 30 (61.2%) CPGs/consensus were considered "not recommended", while the consensus was considered "not recommended". Generally, the scores in the six domains of the guidelines were all higher than the consensus. Evaluation results for the overall quality of 36 CPGs showed that 4 (11%) were "good quality", 15 (42%) were "sufficient quality" and 17 (47%) were "lower quality". The AGREE II quality scores of domains ranged from 0.30 to 0.75 ([ICC = 0.993, 95% CI (0.992, 0.995)]). The domain of scope and purpose (domain 1), with a median score of 0.75 (0.52~0.91), performed best in the guidelines with AGREE II, and stakeholder involvement (domain 2) [median 0.39 (0.31~0.56)] and application (domain 5) [median 0.30 (0.17~0.47] obtained lower scores. The consensus score of domain 1 was better at 26.0 (21.6~44.8), followed by rigor of development (domain 3) with a score of 18.0 (10.0~28.9). A total of 119 massage-related recommendations were extracted from 49 guidelines/consensuses, including "in favor" (102, 85.7%), "against" (9, 7.6%), and "did not make recommendations" (8, 6.7%). Conclusion: The overall quality of the included guidelines was low, and most of the guidelines were not "recommended". In future guideline updates, the existing evidence should be used, the professional composition of members of the expert group should be enriched, and patients' values and preferences should be fully considered. It is necessary to clearly propose recognizable recommendations and strengthen the rigor and standardization of guideline formulation. Thus, clear standard guidelines can be formulated to better guide clinical practice. [ABSTRACT FROM AUTHOR]

This is a consensus-based Canadian guideline whose primary purpose is to standardize and facilitate the management of chronic graft-versus-host disease (cGvHD) across the country. Creating uniform healthcare guidance in Canada is a challenge for a number of reasons including the differences in healthcare authority structure, funding and access to healthcare resources between provinces and territories, as well as the geographic size. These differences can lead to variable and unequal access to effective therapies for GvHD. This document will provide comprehensive and practical guidance that can be applied across Canada by healthcare professionals caring for patients with cGvHD. Hopefully, this guideline, based on input from GvHD treaters across the country, will aid in standardizing cGvHD care and facilitate access to much-needed novel therapies. This consensus paper aims to discuss the optimal approach to the initial assessment of cGvHD, review the severity scoring and global grading system, discuss systemic and topical treatments, as well as supportive therapies, and propose a therapeutic algorithm for frontline and subsequent lines of cGvHD treatment in adults and pediatric patients. Finally, we will make suggestions about the future direction of cGvHD treatment development such as (1) a mode-of-action-based cGvHD drug selection, according to the pathogenesis of cGvHD, (2) a combination strategy with the introduction of newer targeted drugs, (3) a steroid-free regimen, particularly for front line therapy for cGvHD treatment, and (4) a pre-emptive approach which can prevent the progression of cGvHD in high-risk patients destined to develop severe and highly morbid forms of cGvHD. [ABSTRACT FROM AUTHOR]

Graph neural networks have proven their effectiveness for user-item interaction graph collaborative filtering. However, most of the existing recommendation models highly depended on abundant and high-quality datasets and neglected the alignment and uniformity of embedding representation. The noisy and skewed distribution of data from real-world applications is ubiquitous. The alignment and uniformity are crucial for representation learning. In this paper, we propose SHTAU (self-supervised hypergraph transformer with alignment and uniformity for the recommendation) to address the problem. Specifically, we first perform a graph neural network on the user-item interaction graph, then captures the global collaboration relationship between users and items through the hypergraph transformer structure, and use the extracted global information to generate self-supervised signals for data enhancement on the user-item interaction graph to enhance the robustness of the recommendation algorithm. Simultaneously, alignment and uniformity auxiliary task update embeddings from different angles to improve performance. Extensive experiments are conducted on Yelp, Gowalla, MovieLens, Amazon-book and Tmall datasets. The experimental results show that SHTAU has obvious advantages over the baseline methods. [ABSTRACT FROM AUTHOR]

• CAM offers a potentially effective treatment for CIPN patients. • CAM is now considered safe and feasible under the guidance of clinicians. • More rigorous protocols should be considered in future clinical trials of CAM. • Focus on emotional processing in cancer survivors when applying CAM therapy. To summarizes the available evidence on the effectiveness, safety, and feasibility of complementary and alternative medicine (CAM) in the management of chemotherapy-induced peripheral neuropathy (CIPN). We searched for systematic reviews, and meta-analyzes published up to April 2023 in the Pubmed and Web of Science databases. The latest original research on related topics was also reviewed. The search was restricted to English-language papers. Two independent reviewers performed a quality assessment of the identified literature. The results of 35 systematic reviews and meta-analyzes were included in this study. Preliminary evidence suggests that CAM, including acupuncture, physical activity (PA), herbal and nutritional supplements, mind-body therapies, touch therapy, and non-invasive neuromodulation techniques, have shown tremendous potential for the prevention and treatment of CIPN. Of these, there is strong evidence supporting acupuncture, PA, and herbal medicine. However, existing clinical studies are also limited by the heterogeneity of study methods, insufficient sample size, and poor study design. Further studies are needed to validate the efficacy of CAM in patients with CIPN and to elucidate potential therapeutic mechanisms. Current research has reached a preliminary conclusion suggesting the potential efficacy of certain CAMs in the management of CIPN. Future clinical trials should incorporate more robust study design protocols and larger sample sizes to enhance the validity of findings. [ABSTRACT FROM AUTHOR]

Objective: To estimate the proportion of healthy under-fives whose physical activity, sedentary behavior, sleep and screen time adhered to WHO 2019 recommendations and to identify risk factors for non-adherence. Methods: A cross-sectional study was conducted among 480 healthy children (6 mo - 4 years) who attended 20 selected urban anganwadis or immunization clinics in South Kerala, India. Sociodemographic, anthropometric and outcome variables (duration of physical activity, sedentary behavior, sleep and screen time) were collected for all participants. Results: Physical activity, sedentary behavior, sleep and screen time recommendations were adhered by 63.3%, 22.7%, 82.2% and 22.7% under-five children, respectively. Risk factors for inadequate physical activity were female sex, nuclear family, maternal education below college level, unskilled maternal occupation/housewife, unskilled paternal occupation and low monthly income. Risk factors for non-adherence to recommended sedentary behavior duration included joint family, paternal education college level/above, unskilled maternal occupation/housewife, unskilled paternal occupation and low monthly income. Conclusion: Under-fives should reduce sedentary behaviors and screen time and spend more time on physical activities. [ABSTRACT FROM AUTHOR]

The rise of graph neural networks has greatly contributed to the development of recommendation systems, and self-supervised learning has emerged as one of the most important approaches to address sparse interaction data. However, existing methods mostly focus on the recommendation's accuracy while neglecting the role of recommended item diversity in enhancing user interest and merchant benefits. The reason for this phenomenon is mainly due to the bias of popular items, which makes the long-tail items (account for a large proportion) be neglected. How to mitigate the bias caused by item popularity has become one of the hot topics in current research. To address the above problems, we propose a Popularity-Debiased Graph Self-Supervised for Recommendation (PDGS). Specifically, we apply a penalty constraint on item popularity during the data enhancement process on the user–item interaction graph to eliminate the inherent popularity bias. We generate item similarity graphs with the popularity bias removed to construct a self-supervised learning task under multiple views, and we design model optimization strategies from the perspectives of popular items and long-tail items to generate recommendation lists. We conduct a large number of comparison experiments, as well as ablation experiments, on three public datasets to verify the effectiveness and the superiority of the model in balancing recommendation accuracy and diversity. [ABSTRACT FROM AUTHOR]