BackgroundBiomarker testing in oncology is fundamental for targeted therapy use and clinical trial participation. Factors contributing to previously identified racial disparities in biomarker testing remain unclear. This study investigated biomarker testing, clinical trial participation and targeted therapy by race among patients with metastatic lung cancer with Medicaid coverage in the U.S.MethodsThe Merative Marketscan Medicaid claims database was used for this study to identify patients diagnosed with metastatic lung cancer between 2017 and 2019 with at least 121 days of follow up. Racial differences in biomarker testing, clinical trial enrollment and targeted therapy use were analyzed using Chi-squared/t-tests followed by logistic regression for confounding covariates.ResultsA total of 3,845 patients were eligible. A total of 970 (25.2%) patients included in this study were Black. Biomarker testing was observed among 57.0%, targeted therapy among 4.6%, and 2.6% of the study cohort had evidence of clinical trial participation. No significant disparities between Black and White race were identified. Younger age and metastatic disease at initial diagnosis were the strongest independent factors associated with increased biomarker testing. Biomarker testing was positively associated with targeted therapy use (odds ratio: 1.69, p=0.005).ConclusionsPatients with metastatic lung cancer with Medicaid coverage demonstrate exceedingly low biomarker testing rates; only 57% had evidence of any biomarker testing. While no consistent differences between Black and White race were identified, this study calls attention to care experienced by socioeconomically disadvantaged patients with metastatic lung cancer in the U.S.

The recurrence of head and neck squamous cell carcinoma (HNSCC) after surgical resection continues to pose a major challenge to cancer treatment. Advanced HNSCC exhibits a low response rate to immune checkpoint blockade (ICB), while photothermal therapy (PTT) can increase the infiltration of immune cells to make tumors more susceptible to cancer immunotherapy. In this regard, we designed and constructed a novel multifunctional nanocomposite comprised of oxidized bacterial cellulose (OBC), thrombin (TB), and gold nanocages (AuNCs) containing anti-programmed death 1 (PD-1) antibody (αPD-1@AuNCs), which allows the combination of therapies with remarkable postoperative antitumor immunity to control local tumor recurrence. The αPD-1@AuNCs displayed high light-to-heat conversion efficiency and induced pyroptosis under near infrared (NIR) irradiation, which activated a potent antitumor immune response. More importantly, the therapeutic system could induce tumor pyroptosis and enhance antitumor immune response by increasing T-cell infiltration and reducing the immune suppressive cells, when combined with local ICB therapy, which effectively avoided the tumor recurrence in a HNSCC postoperative mice model. Overall, the newly developed multifunctional nanocomposites could be a promising candidate for the treatment of postoperative HNSCC.

围绕软件工程知识体系及其学习、构造、再造和应用等方面的特点,在建构主义学习理论的指导下,结合工程学科特点,讨论学好软件工程专业知识并铸造专业素质的价值观念,探讨突出和强化专业知识学习的教学方法,通过总结教学实践工作体会,提出亟待解决的一系列教学问题.

Purpose In MONARCH 1 (NCT02102490), single-agent abemaciclib demonstrated promising efficacy activity and tolerability in a population of heavily pretreated women with refractory HR+, HER2− metastatic breast cancer (MBC). To help interpret these results and put in clinical context, we compared overall survival (OS) and duration of therapy (DoT) between MONARCH 1 and a real-world single-agent chemotherapy cohort. Methods The real-world chemotherapy cohort was created from a Flatiron Health electronic health records-derived database based on key eligibility criteria from MONARCH 1. The chemotherapies included in the cohort were single-agent capecitabine, gemcitabine, eribulin, or vinorelbine. Results were adjusted for baseline demographics and clinical differences using Mahalanobis distance matching (primary analysis) and entropy balancing (sensitivity analysis). OS and DoT were analyzed using the Kaplan–Meier method and Cox proportional hazards regression. Results A real-world single-agent chemotherapy cohort ( n = 281) with eligibility criteria similar to the MONARCH 1 population ( n = 132) was identified. The MONARCH 1 ( n = 108) cohort was matched to the real-world chemotherapy cohort ( n = 108). Median OS was 22.3 months in the abemaciclib arm versus 13.6 months in the matched real-world chemotherapy cohort with an estimated hazard ratio (HR) of 0.54. The median DoT was 4.1 months in MONARCH 1 compared to 2.9 months in the real-world chemotherapy cohort with HR of 0.76. Conclusions This study demonstrates an approach to create a real-world chemotherapy cohort suitable to serve as a comparator for trial data. These exploratory results suggest a survival advantage and place the benefit of abemaciclib monotherapy in clinical context.

Background MiRNAs play significant roles in many fundamental and important biological processes, and predicting potential miRNA-disease associations makes contributions to understanding the molecular mechanism of human diseases. Existing state-of-the-art methods make use of miRNA-target associations, miRNA-family associations, miRNA functional similarity, disease semantic similarity and known miRNA-disease associations, but the known miRNA-disease associations are not well exploited. Results In this paper, a network embedding-based multiple information integration method (NEMII) is proposed for the miRNA-disease association prediction. First, known miRNA-disease associations are formulated as a bipartite network, and the network embedding method Structural Deep Network Embedding (SDNE) is adopted to learn embeddings of nodes in the bipartite network. Second, the embedding representations of miRNAs and diseases are combined with biological features about miRNAs and diseases (miRNA-family associations and disease semantic similarities) to represent miRNA-disease pairs. Third, the prediction models are constructed based on the miRNA-disease pairs by using the random forest. In computational experiments, NEMII achieves high-accuracy performances and outperforms other state-of-the-art methods: GRNMF, NTSMDA and PBMDA. The usefulness of NEMII is further validated by case studies. The studies demonstrate the great potential of network embedding method for the miRNA-disease association prediction, and SDNE outperforms other popular network embedding methods: DeepWalk, High-Order Proximity preserved Embedding (HOPE) and Laplacian Eigenmaps (LE). Conclusion We propose a new method, named NEMII, for predicting miRNA-disease associations, which has great potential to benefit the field of miRNA-disease association prediction.

Prostate cancer metastases primarily localize in the bone where they induce a unique osteoblastic response. Elevated Notch activity is associated with high-grade disease and metastasis. To address how Notch affects prostate cancer bone lesions, we manipulated Notch expression in mouse tibia xenografts and monitored tumor growth, lesion phenotype, and the bone microenvironment. Prostate cancer cell lines that induce mixed osteoblastic lesions in bone expressed 5–6 times more Notch3, than tumor cells that produce osteolytic lesions. Expression of active Notch3 (NICD3) in osteolytic tumors reduced osteolytic lesion area and enhanced osteoblastogenesis, while loss of Notch3 in osteoblastic tumors enhanced osteolytic lesion area and decreased osteoblastogensis. This was accompanied by a respective decrease and increase in the number of active osteoclasts and osteoblasts at the tumor–bone interface, without any effect on tumor proliferation. Conditioned medium from NICD3-expressing cells enhanced osteoblast differentiation and proliferation in vitro, while simultaneously inhibiting osteoclastogenesis. MMP-3 was specifically elevated and secreted by NICD3-expressing tumors, and inhibition of MMP-3 rescued the NICD3-induced osteoblastic phenotypes. Clinical osteoblastic bone metastasis samples had higher levels of Notch3 and MMP-3 compared with patient matched visceral metastases or osteolytic metastasis samples. We identified a Notch3–MMP-3 axis in human prostate cancer bone metastases that contributes to osteoblastic lesion formation by blocking osteoclast differentiation, while also contributing to osteoblastogenesis. These studies define a new role for Notch3 in manipulating the tumor microenvironment in bone metastases.

SummaryIn the last decade, the number of web‐based applications is increasing rapidly, which leads to high demand for user authentication protocol for multiserver environment. Many user‐authentication protocols have been proposed for different applications. Unfortunately, most of them either have some security weaknesses or suffer from unsatisfactory performance. Recently, Ali and Pal proposed a three‐factor user‐authentication protocol for multiserver environment. They claimed that their protocol can provide mutual authentication and is secure against many kinds of attacks. However, we find that Ali and Pal's protocol cannot provide user anonymity and is vulnerable to 4 kinds of attacks. To enhance security, we propose a new user‐authentication protocol for multiserver environment. Then, we provide a formal security analysis and a security discussion, which indicate our protocol is provably secure and can withstand various attacks. Besides, we present a performance analysis to show that our protocol is efficient and practical for real industrial environment.

Electronic prescription is increasingly popular in our society, particularly in technologically advanced countries. Due to strict legal requirements and privacy regulations, authorization and data confidentiality are two important features in electronic prescription system. By combining signature and encryption functions, signcryption is an efficient cryptographic primitive that can be used to provide these two features. While signcryption is a fairly established research area, most signcryption schemes proposed recently have several limitations (e.g., high communication costs, limited bandwidth, and insecurity), and designing secure and practical signcryption schemes remains challenging. In this paper, we propose an improved certificateless proxy signcryption (CLPSC) scheme, based on elliptic curve cryptography (ECC). We also demonstrate that the proposed CLPSC scheme is secure in the random oracle model and evaluate its performance with related schemes. The security and performance evaluations show that the proposed CLPSC scheme can potentially be implemented on resource-constrained low-computing mobile devices in an electronic prescription system.

Drug side effects are one of the major concerns in the drug discovery. A great number of machine learning-based computational methods have been proposed to predict drug side effects. Many methods combine diverse drug features for the side effect prediction, but complete features are not available for all drugs. Drug side effect prediction with limited information is challenging and meaningful. In this paper, we propose a novel computational method feature-derived graph regularized matrix factorization (FGRMF), which predicts unobserved side effects for approved drugs based on known drug-side effect associations and available drug features. FGRMF projects the drug-side effect association relationship into the low-dimensional space, which uncovers the latent features of drugs and side effects. A graph is constructed based on individual drug features, and the graph regularization which preserves the structure of the drug graph is incorporated into FGRMF. FGRMF is different from the traditional matrix factorization technique, and can take the biomedical context into account. In the computational experiments, FGRMF can produce satisfying results, and outperforms benchmark side effect prediction methods on the benchmark datasets. When complete features are available, we can extend FGRMF to integrate diverse features. We develop a web server to facilitate drug side effect prediction, available at http://www.bioinfotech.cn/FGRMF/.

Background Drug-drug interactions (DDIs) are one of the major concerns in drug discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions in the entire lifecycle of drugs, and are important for the drug safety surveillance. Results Since many DDIs are not detected or observed in clinical trials, this work is aimed to predict unobserved or undetected DDIs. In this paper, we collect a variety of drug data that may influence drug-drug interactions, i.e., drug substructure data, drug target data, drug enzyme data, drug transporter data, drug pathway data, drug indication data, drug side effect data, drug off side effect data and known drug-drug interactions. We adopt three representative methods: the neighbor recommender method, the random walk method and the matrix perturbation method to build prediction models based on different data. Thus, we evaluate the usefulness of different information sources for the DDI prediction. Further, we present flexible frames of integrating different models with suitable ensemble rules, including weighted average ensemble rule and classifier ensemble rule, and develop ensemble models to achieve better performances. Conclusions The experiments demonstrate that different data sources provide diverse information, and the DDI network based on known DDIs is one of most important information for DDI prediction. The ensemble methods can produce better performances than individual methods, and outperform existing state-of-the-art methods. The datasets and source codes are available at https://github.com/zw9977129/drug-drug-interaction/ .

Recent studies show that drug-disease associations provide important information for drug discovery and drug repositioning. Wet experimental identification of drug-disease associations is time-consuming and labor-intensive. Therefore, the development of computational methods that predict drug-disease associations is an urgent task. In this paper, we propose a novel computational method named NTSIM, which only uses known drug-disease associations to predict unobserved associations. First of all, known drug-disease associations are represented as a drug-disease bipartite network, and a novel similarity measure named linear neighborhood similarity (LNS) is proposed to calculate drug-drug similarity and disease-disease similarity based on the bipartite network. Then, we predict unobserved drug-disease associations in the similarity-based graph by using label propagation process. In the computational experiments, this proposed method achieves high-accuracy performances, and outperforms representative state-of-the-art methods: PREDICT, TL-HGBI and LRSSL. Our studies reveal that known drug-disease associations can provide enough information to build the high-accuracy prediction models; linear neighbor similarity (LNS) can lead to better performances than other similarity measures such as Jaccard similarity, Gauss similarity and cosine similarity; the bipartite network-derived features outperform the drug biological features and disease semantic features.

237 Background: A number of important advancements in the treatment of metastatic non-small cell lung cancer (mNSCLC) have increased and diversified options for improved patient care based on individual characteristics. The ability to tailor therapy increases the challenges related to appropriate treatment sequencing. This study was designed to describe these emerging treatment patterns following the approval of novel targeted agents. Methods: Flatiron Health OncoEMR, a nationally-representative electronic medical records database in the US, was used to evaluate treatment patterns by histology (squamous and nonsquamous). Eligible patients were 18+ years of age who initiated 2ndline therapy for mNSCLC from Dec 2014-Jul 2016. Descriptive statistics were used to characterize the clinical and demographic characteristics of the study population, treatments used by line of therapy, and treatment sequencing. Analyses were conducted using SAS version 9.2. Results: A total of 3498 eligible patients were included in this study: 51.3% male; mean age 66.6 years; 65% white; 25% squamous/70.7% nonsquamous (4.3% not specified); and 93% were treated at community practices. ALK testing was performed on 20.0%/74.8%, EGFR testing on 21.5%/79.8%, and PDL-1 on 8.6%/9.7% of patients with squamous/nonsquamous tumors, respectively. Single-agent PDL-1 inhibitors were used by 54.2% of squamous and 35.2% of nonsquamous patients in the 2nd-line setting; however, there were more than 35 (squamous) and 64 (nonsquamous) unique first-line regimens prior to single-agent PDL-1 treatment. Other 2nd-line regimens included pemetrexed (24.9% of nonsquamous patients) and gemcitabine (18.4% of squamous patients), which were preceded by 70 and 48 unique first-line regimens, respectively. Conclusions: There is interest in understanding treatment sequencing to identify the optimal sequence of care for patients with NSCLC; however, there was considerable heterogeneity in sequencing. Since few patients follow any similar trajectory of care, comparative effectiveness research will be challenged to appropriately balance groups due to insufficient patient numbers in any specific treatment sequence.

针对目前本科生普遍存在的系统分析能力欠缺、沟通表达能力不足、团队合作意识不强、文档素养低等问题,分析本科阶段培养目标和软件行业人才实际需求,阐述以能力培养为目标的软件工程教学模式,提出用“能力—培养环节”矩阵指导教师落实能力培养的方法,并给出在软件工程课程中的具体教学实践.

研究采用质性方法对5名“211工程”师范类院校的毕业生从学校到社会角色转换中的道德适应进行了探索.研究结果显示,道德适应并不是每一个毕业生必经的过程,只有遭遇真实的道德冲突才会经历道德适应.影响道德适应的因素有环境(真实的道德情境),理想,人格,利益(生存和发展资源)等.道德适应过程经历了理想激发期、道德冲突期、调整适应期、平衡稳定期.研究中还疑似发现了道德领域的“第三人效应”,但是否存在此现象还有待后继研究证实.

武汉大学的“思想政治教育活动超市”开辟了一种新的思想政治教育活动形式.在话语视角下,“思想政治教育活动超市”不仅仅是一个复杂的名词,同时也是“会话”和“话语建构”的过程.“思想政治教育活动超市”产生的时间、地点有它的特殊性.“思想政治教育活动超市”对我们完善对话机制、创新话语模式、建设话语生态提供了有益的启示.

The recognition performance of face recognition was affected by the facial expression,pose,scale,illumination and other environmental parameters,for which this paper proposed a novel stochastic optimization method.Firstly,it divided the original image into specific space within the block,and used the two order Volterra core for the nonlinear mapping function. Then,it used artificial bee colony optimization technique to obtain the optimal Volterra nucleus.Volterra nucleus could be op-timal in the feature space to maximize the between class distance and minimize the within class distance.Finally,it used voting strategy and nearest neighbor classifier to classify the blocks.It evaluated the application of the algorithm in the two common benchmark face recognition data sets,and compared face recognition in other statistical learning algorithm and the newly pro-posed several methods’performance.Experimental results show that the artificial bee colony optimization technique with Levy mutation is in optimizing the effectiveness of Volterra nucleus,and is significantly better than many existing algorithms.

Breast cancer (BCa) bone metastases cause osteolytic bone lesions, which result from the interactions of metastatic BCa cells with osteoclasts and osteoblasts. Osteoclasts differentiate from myeloid lineage cells. To understand the cell-specific role of transforming growth factor beta (TGF-β) in the myeloid lineage, in BCa bone metastases, MDA-MB-231 BCa cells were intra-tibially or intra-cardially injected into LysM Cre /Tgfbr2 floxE2/floxE2 knockout ( LysM Cre /Tgfbr2 KO) or Tgfbr2 floxE2/floxE2 mice. Metastatic bone lesion development was compared by analysis of both lesion number and area. We found that LysM Cre /Tgfbr2 knockout significantly decreased MDA-MB-231 bone lesion development in both the cardiac and tibial injection models. LysM Cre /Tgfbr2 knockout inhibited the tumor cell proliferation, angiogenesis and osteoclastogenesis of the metastatic bones. Cytokine array analysis showed that basic fibroblast growth factor (bFGF) was downregulated in MDA-MB-231-injected tibiae from the LysM Cre /Tgfbr2 KO group, and intravenous injection of the recombinant bFGF to LysM Cre /Tgfbr2 KO mice rescued the inhibited metastatic bone lesion development. The mechanism by which bFGF rescued the bone lesion development was by promotion of tumor cell proliferation through the downstream mitogen-activated protein kinase (MAPK)-extracellular signal–regulated kinase (ERK)-cFos pathway after binding to the FGF receptor 1 (FGFR1). Consistent with animal studies, we found that in human BCa bone metastatic tissues, TGF-β type II receptor (TβRII) and p-Smad2 were expressed in osteoclasts and tumor cells, and were correlated with the expression of FGFR1. Our studies suggest that myeloid-specific TGF-β signaling-mediated bFGF in the bone promotes BCa bone metastasis.

新媒体具有“超时空”、“双向互动”、“多人参与”的传播特性，这使得社会话语有了由“权威环境”向“博弈环境”转变的可能性。思想政治教育从某种意义上来说是一个话语表达过程。在新媒体创设的话语环境中，思想政治教育的话语关系、话语内容、话语输出理念和话语表达方式受到挑战，面临着在国与国之间、代与代之间、精英与大众之间、精英与精英之间内容的话语转换问题。