In sub-Saharan Africa(SSA),63%of new human immunodeficiency virus(HIV)infections in 2021 were among women,particularly adolescent girls,and young women.There is a high incidence of HIV among pregnant and lactating wom...In sub-Saharan Africa(SSA),63%of new human immunodeficiency virus(HIV)infections in 2021 were among women,particularly adolescent girls,and young women.There is a high incidence of HIV among pregnant and lactating women(PLW)in SSA.It is estimated that the risk of HIV-acquisition during pregnancy and the postpartum period more than doubles.In this article,we discuss the safety and effectiveness of drugs used for oral HIV pre-exposure prophylaxis(PrEP),considerations for initiating PrEP in PLW,the barriers to initiating and adhering to PrEP among them and suggest recommendations to address these barriers.Tenofovir/emtricitabine,the most widely used combination in SSA,is safe,clinically effective,and cost-effective among PLW.Any PLW who requests PrEP and has no medical contraindications should receive it.PrEP users who are pregnant or lactating may experience barriers to starting and adhering for a variety of reasons,including personal,pill-related,and healthcare facility-related issues.To address the barriers,we recommend an increased provision of information on PrEP to the women and the communities,increasing and/or facilitating access to PrEP among the PLW,and developing strategies to increase adherence.展开更多
A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer prefe...A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences.Nowadays,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’preferences.On the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested items.However,the cost of these computations increases nonlinearly as the number of items and users increases.To triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two phases.In the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM approach.In the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple clusters.Hence the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation time.An experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures.展开更多
Solid organ transplant recipients face unique challenges in managing their im-munosuppressed status,making vaccination a critical consideration.This review aimed to comprehensively analyze current recommendations,eval...Solid organ transplant recipients face unique challenges in managing their im-munosuppressed status,making vaccination a critical consideration.This review aimed to comprehensively analyze current recommendations,evaluate the effi-cacy of vaccinations in this population,and assess safety concerns.We explored the latest evidence on vaccine types,timing,and potential benefits for transplant patients,highlighting the importance of individualized approaches for routinely used vaccines as well as coronavirus disease 2019 vaccines.By synthesizing avai-lable data,this review underscored the pressing need to optimize vaccination strategies,ensuring that transplant recipients can obtain the full protection against many pathogens while minimizing risks associated with their post-transplant immunosuppression.展开更多
Introduction: Chest radiography is the most frequently prescribed imaging test in general practice in France. We aimed to assess the extent to which general practitioners follow the recommendations of the French Natio...Introduction: Chest radiography is the most frequently prescribed imaging test in general practice in France. We aimed to assess the extent to which general practitioners follow the recommendations of the French National Authority for Health in prescribing chest radiography. Methodology: We conducted a retrospective analysis study, in two radiology centers belonging to the same group in Saint-Omer and Aire-sur-la-Lys, of requests for chest radiography sent by general practitioners over the winter period between December 22, 2013, and March 21, 2014, for patients aged over 18 years. Results: One hundred and seventy-seven requests for chest X-rays were analyzed, 71.75% of which complied with recommendations. The most frequent reason was the search for bronchopulmonary infection, accounting for 70.08% of prescriptions, followed by 11.2% for requests to rule out pulmonary neoplasia, whereas the latter reason did not comply with recommendations. Chest X-rays contributed to a positive diagnosis in 28.81% of cases. The positive diagnosis was given by 36.22% of the recommended chest X-rays, versus 10% for those not recommended. Conclusion: In most cases, general practitioners follow HAS recommendations for prescribing chest X-rays. Non-recommended chest X-rays do not appear to make a major contribution to diagnosis or patient management, confirming the value of following the recommendations of the French National Authority for Health.展开更多
A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually limited.To enrich the services in mobile communications,developers have combin...A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually limited.To enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a mashup.The emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of mashups.Such a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage data.Thus,the question of how to determine the most suitable mashups from big data has become a challenging problem.In this paper,we propose a mashup recommendation framework from big data in mobile networks and the IoT.The proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix factorization.We employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the mashups.We also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization algorithm.We then crawl through a real-world large mashup dataset and perform experiments.The experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.展开更多
文摘In sub-Saharan Africa(SSA),63%of new human immunodeficiency virus(HIV)infections in 2021 were among women,particularly adolescent girls,and young women.There is a high incidence of HIV among pregnant and lactating women(PLW)in SSA.It is estimated that the risk of HIV-acquisition during pregnancy and the postpartum period more than doubles.In this article,we discuss the safety and effectiveness of drugs used for oral HIV pre-exposure prophylaxis(PrEP),considerations for initiating PrEP in PLW,the barriers to initiating and adhering to PrEP among them and suggest recommendations to address these barriers.Tenofovir/emtricitabine,the most widely used combination in SSA,is safe,clinically effective,and cost-effective among PLW.Any PLW who requests PrEP and has no medical contraindications should receive it.PrEP users who are pregnant or lactating may experience barriers to starting and adhering for a variety of reasons,including personal,pill-related,and healthcare facility-related issues.To address the barriers,we recommend an increased provision of information on PrEP to the women and the communities,increasing and/or facilitating access to PrEP among the PLW,and developing strategies to increase adherence.
文摘A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences.Nowadays,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’preferences.On the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested items.However,the cost of these computations increases nonlinearly as the number of items and users increases.To triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two phases.In the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM approach.In the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple clusters.Hence the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation time.An experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures.
基金the European Union-Next Generation EU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,No.BG-RRP-2.004-0008.
文摘Solid organ transplant recipients face unique challenges in managing their im-munosuppressed status,making vaccination a critical consideration.This review aimed to comprehensively analyze current recommendations,evaluate the effi-cacy of vaccinations in this population,and assess safety concerns.We explored the latest evidence on vaccine types,timing,and potential benefits for transplant patients,highlighting the importance of individualized approaches for routinely used vaccines as well as coronavirus disease 2019 vaccines.By synthesizing avai-lable data,this review underscored the pressing need to optimize vaccination strategies,ensuring that transplant recipients can obtain the full protection against many pathogens while minimizing risks associated with their post-transplant immunosuppression.
文摘Introduction: Chest radiography is the most frequently prescribed imaging test in general practice in France. We aimed to assess the extent to which general practitioners follow the recommendations of the French National Authority for Health in prescribing chest radiography. Methodology: We conducted a retrospective analysis study, in two radiology centers belonging to the same group in Saint-Omer and Aire-sur-la-Lys, of requests for chest radiography sent by general practitioners over the winter period between December 22, 2013, and March 21, 2014, for patients aged over 18 years. Results: One hundred and seventy-seven requests for chest X-rays were analyzed, 71.75% of which complied with recommendations. The most frequent reason was the search for bronchopulmonary infection, accounting for 70.08% of prescriptions, followed by 11.2% for requests to rule out pulmonary neoplasia, whereas the latter reason did not comply with recommendations. Chest X-rays contributed to a positive diagnosis in 28.81% of cases. The positive diagnosis was given by 36.22% of the recommended chest X-rays, versus 10% for those not recommended. Conclusion: In most cases, general practitioners follow HAS recommendations for prescribing chest X-rays. Non-recommended chest X-rays do not appear to make a major contribution to diagnosis or patient management, confirming the value of following the recommendations of the French National Authority for Health.
基金supported by the National Key R&D Program of China (No.2021YFF0901002)the National Natural Science Foundation of China (No.61802291)+1 种基金Fundamental Research Funds for the Provincial Universities of Zhejiang (GK199900299012-025)Fundamental Research Funds for the Central Universities (No.JB210311).
文摘A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually limited.To enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a mashup.The emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of mashups.Such a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage data.Thus,the question of how to determine the most suitable mashups from big data has become a challenging problem.In this paper,we propose a mashup recommendation framework from big data in mobile networks and the IoT.The proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix factorization.We employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the mashups.We also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization algorithm.We then crawl through a real-world large mashup dataset and perform experiments.The experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.