As a ligand-dependent transcription factor,retinoid-associated orphan receptor gt(RORγt)that controls T helper(Th)17 cell differentiation and interleukin(IL)-17 expression plays a critical role in the progression of ...As a ligand-dependent transcription factor,retinoid-associated orphan receptor gt(RORγt)that controls T helper(Th)17 cell differentiation and interleukin(IL)-17 expression plays a critical role in the progression of several inflammatory and autoimmune conditions.An emerging novel approach to the therapy of these diseases thus involves controlling the transcriptional capacity of RORγt to decrease Th17 cell development and IL-17 production.Several RORγt inhibitors including both antagonists and inverse agonists have been discovered to regulate the transcriptional activity of RORγt by binding to orthosteric-or allosteric-binding sites in the ligand-binding domain.Some of small-molecule inhibitors have entered clinical evaluations.Therefore,in current review,the role of RORγt in Th17 regulation and Th17-related inflammatory and autoimmune diseases was highlighted.Notably,the recently developed RORγt inhibitors were summarized,with an emphasis on their optimization from lead compounds,efficacy,toxicity,mechanisms of action,and clinical trials.The limitations of current development in this area were also discussed to facilitate future research.展开更多
Collaborative filtering(CF)methods are widely adopted by existing medical recommendation systems,which can help clinicians perform their work by seeking and recommending appropriate medical advice.However,privacy issu...Collaborative filtering(CF)methods are widely adopted by existing medical recommendation systems,which can help clinicians perform their work by seeking and recommending appropriate medical advice.However,privacy issue arises in this process as sensitive patient private data are collected by the recommendation server.Recently proposed privacy-preserving collaborative filtering methods,using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service.The aim of this study is to address the privacy issues in the context of neighborhoodbased CF methods by proposing a Privacy Preserving Medical Recommendation(PPMR)algorithm,which can protect patients’treatment information and demographic information during online recommendation process without compromising recommendation accuracy and efficiency.The proposed algorithm includes two privacy preserving operations:Private Neighbor Selection and Neighborhood-based Differential Privacy Recommendation.Private Neighbor Selection is conducted on the basis of the notion of k-anonymity method,meaning that neighbors are privately selected for the target user according to his/her similarities with others.Neighborhood-based Differential Privacy Recommendation and a differential privacy mechanism are introduced in this operation to enhance the performance of recommendation.Our algorithm is evaluated using the real-world hospital EMRs dataset.Experimental results demonstrate that the proposed method achieves stable recommendation accuracy while providing comprehensive privacy for individual patients.展开更多
基金supported by the grants from the Sichuan Science and Technology Program,China(Grant Nos.:2023NSFSC0614 and 2022YFS0624)Southwest Medical University Science and Technology Program,China(Grant No.:2021ZKZD017)+2 种基金the Luzhou Science and Technology Program,China(Grant Nos.:2022-YJY-127,2022YFS0624-B1,2022YFS0624-C1,and 2022YFS0624-B3)the Open Research Project Program funded by the Science and Technology Development Fund(Grant No.:SKL-QRCM(UM)-2020-2022)the State Key Laboratory of Quality Research in Chinese Medicine(University of Macao,Macao,China)(Grant No.:SKL-QRCMOP21006).
文摘As a ligand-dependent transcription factor,retinoid-associated orphan receptor gt(RORγt)that controls T helper(Th)17 cell differentiation and interleukin(IL)-17 expression plays a critical role in the progression of several inflammatory and autoimmune conditions.An emerging novel approach to the therapy of these diseases thus involves controlling the transcriptional capacity of RORγt to decrease Th17 cell development and IL-17 production.Several RORγt inhibitors including both antagonists and inverse agonists have been discovered to regulate the transcriptional activity of RORγt by binding to orthosteric-or allosteric-binding sites in the ligand-binding domain.Some of small-molecule inhibitors have entered clinical evaluations.Therefore,in current review,the role of RORγt in Th17 regulation and Th17-related inflammatory and autoimmune diseases was highlighted.Notably,the recently developed RORγt inhibitors were summarized,with an emphasis on their optimization from lead compounds,efficacy,toxicity,mechanisms of action,and clinical trials.The limitations of current development in this area were also discussed to facilitate future research.
文摘Collaborative filtering(CF)methods are widely adopted by existing medical recommendation systems,which can help clinicians perform their work by seeking and recommending appropriate medical advice.However,privacy issue arises in this process as sensitive patient private data are collected by the recommendation server.Recently proposed privacy-preserving collaborative filtering methods,using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service.The aim of this study is to address the privacy issues in the context of neighborhoodbased CF methods by proposing a Privacy Preserving Medical Recommendation(PPMR)algorithm,which can protect patients’treatment information and demographic information during online recommendation process without compromising recommendation accuracy and efficiency.The proposed algorithm includes two privacy preserving operations:Private Neighbor Selection and Neighborhood-based Differential Privacy Recommendation.Private Neighbor Selection is conducted on the basis of the notion of k-anonymity method,meaning that neighbors are privately selected for the target user according to his/her similarities with others.Neighborhood-based Differential Privacy Recommendation and a differential privacy mechanism are introduced in this operation to enhance the performance of recommendation.Our algorithm is evaluated using the real-world hospital EMRs dataset.Experimental results demonstrate that the proposed method achieves stable recommendation accuracy while providing comprehensive privacy for individual patients.
