No residual disease (R0 resection) after debulking surgery is the most critical independent prognostic factor for advanced ovarian cancer (AOC). There is an unmet clinical need for selecting primary ...or interval debulking surgery in AOC patients using existing prediction models.
RNA sequencing of circulating small extracellular vesicles (sEVs) was used to discover the differential expression microRNAs (DEMs) profile between any residual disease (R0, n = 17) and no residual disease (non-R0, n = 20) in AOC patients. We further analyzed plasma samples of AOC patients collected before surgery or neoadjuvant chemotherapy via TaqMan qRT-PCR. The combined risk model of residual disease was developed by logistic regression analysis based on the discovery-validation sets.
Using a comprehensive plasma small extracellular vesicles (sEVs) microRNAs (miRNAs) profile in AOC, we identified and optimized a risk prediction model consisting of plasma sEVs-derived 4-miRNA and CA-125 with better performance in predicting R0 resection. Based on 360 clinical human samples, this model was constructed using least absolute shrinkage and selection operator (LASSO) and logistic regression analysis, and it has favorable calibration and discrimination ability (AUC:0.903; sensitivity:0.897; specificity:0.910; PPV:0.926; NPV:0.871). The quantitative evaluation of Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) suggested that the additional predictive power of the combined model was significantly improved contrasted with CA-125 or 4-miRNA alone (NRI = 0.471, IDI = 0.538, p < 0.001; NRI = 0.122, IDI = 0.185, p < 0.01).
Overall, we established a reliable, non-invasive, and objective detection method composed of circulating tumor-derived sEVs 4-miRNA plus CA-125 to preoperatively anticipate the high-risk AOC patients of residual disease to optimize clinical therapy.
The problem we aim to address is the optimization of cost management for executing multiple continuous queries on data streams, where each query is defined by several filters, each of which monitors ...certain status of the data stream. Specially, the filter can be shared by different queries and expensive to evaluate. The conventional objective for such a problem is to minimize the overall execution cost to solve all queries, by planning the order of filter evaluation in shared strategy. However, in the streaming scenario, the characteristics of data items may change in process, which can bring some uncertainty to the outcome of individual filter evaluation, and affect the plan of query execution as well as the overall execution cost. In our work, considering the influence of the uncertain variation of data characteristics, we propose a framework to deal with the dynamic adjustment of filter ordering for query execution on data stream, and focus on the issues of cost management. By incrementally monitoring and analyzing the results of filter evaluation, our proposed approach can be effectively adaptive to the varied stream behavior and adjust the optimal ordering of filter evaluation, so as to optimize the execution cost. In order to achieve satisfactory performance and efficiency, we also discuss the trade-off between the adaptivity of our framework and the overhead incurred by filter adaption. The experimental results on synthetic and two real data sets (traffic and multimedia) show that our framework can effectively reduce and balance the overall query execution cost and keep high adaptivity in streaming scenario.
Ovarian cancer (OC) is the most fatal gynecological malignancy because of its early asymptomatic nature and acquired resistance to chemotherapy. Small extracellular vesicles (sEVs) are a ...heterogeneous group of biological vesicles with a diameter <200 nm released by cells under physiological or pathological conditions. sEVs-derived non-coding RNAs (ncRNAs) are the essential effectors in the biological environment. sEVs-ncRNAs have critical roles in tumor progression via regulating mRNA expression of target cells to affect cell signaling. In addition, the status of parental cells can be disclosed via analyzing the composition of sEVs-ncRNAs, and their "cargoes" with specific changes can be used as key biomarkers for the diagnosis and prognosis of OC. Accumulating evidence has demonstrated that sEVs-ncRNAs are involved in multiple key processes that mediate the development of metastasis and chemotherapeutic resistance in OC: epithelial-mesenchymal transition; tumorigenicity of mesenchymal stem cells; immune evasion; angiogenesis. The nanomedicine delivery system based on engineering sEVs is expected to be a novel therapeutic strategy for OC. Insights into the biological roles of sEVs-ncRNAs in the invasion, metastasis, immune regulation, and chemoresistance of OC will contribute to discovery of novel biomarkers and molecular targets for early detection and innovative therapy. In this review, we highlight recent advances and applications of sEVs-ncRNAs in OC diagnosis and treatment. We also outline current challenges and knowledge gaps.
Image Attribute Adaptation Yahong Han; Yi Yang; Zhigang Ma ...
IEEE transactions on multimedia,
06/2014, Letnik:
16, Številka:
4
Journal Article
Recenzirano
Visual attributes can be considered as a middle-level semantic cue that bridges the gap between low-level image features and high-level object classes. Thus, attributes have the advantage of ...transcending specific semantic categories or describing objects across categories. Since attributes are often human-nameable and domain specific, much work constructs attribute annotations ad hoc or take them from an application-dependent ontology. To facilitate other applications with attributes, it is necessary to develop methods which can adapt a well-defined set of attributes to novel images. In this paper, we propose a framework for image attribute adaptation. The goal is to automatically adapt the knowledge of attributes from a well-defined auxiliary image set to a target image set, thus assisting in predicting appropriate attributes for target images. In the proposed framework, we use a non-linear mapping function corresponding to multiple base kernels to map each training images of both the auxiliary and the target sets to a Reproducing Kernel Hilbert Space (RKHS), where we reduce the mismatch of data distributions between auxiliary and target images. In order to make use of un-labeled images, we incorporate a semi-supervised learning process. We also introduce a robust loss function into our framework to remove the shared irrelevance and noise of training images. Experiments on two couples of auxiliary-target image sets demonstrate that the proposed framework has better performance of predicting attributes for target testing images, compared to three baselines and two state-of-the-art domain adaptation methods.
Data imputation aims at filling in missing attribute values in databases. Most existing imputation methods to string attribute values are inferring-based approaches, which usually fail to reach a ...high imputation recall by just inferring missing values from the complete part of the data set. Recently, some retrieving-based methods are proposed to retrieve missing values from external resources such as the World Wide Web, which tend to reach a much higher imputation recall, but inevitably bring a large overhead by issuing a large number of search queries. In this paper, we investigate the interaction between the inferring-based methods and the retrieving-based methods. We show that retrieving a small number of selected missing values can greatly improve the imputation recall of the inferring-based methods. With this intuition, we propose an inTeractive Retrieving-Inferring data imPutation approach (TRIP), which performs retrieving and inferring alternately in filling in missing attribute values in a data set. To ensure the high recall at the minimum cost, TRIP faces a challenge of selecting the least number of missing values for retrieving to maximize the number of inferable values. Our proposed solution is able to identify an optimal retrieving-inferring scheduling scheme in deterministic data imputation, and the optimality of the generated scheme is theoretically analyzed with proofs. We also analyze with an example that the optimal scheme is not feasible to be achieved in τ-constrained stochastic data imputation (τ-SDI), but still, our proposed solution identifies an expected-optimal scheme in τ-SDI. Extensive experiments on four data collections show that TRIP retrieves on average 20 percent missing values and achieves the same high recall that was reached by the retrieving-based approach.
BackgroundThe VELscope fluorescence method has been applied to the identification and detection of oral potentially malignant disorders, but autofluorescence visualization lacks objectivity and its ...diagnostic value varies greatly. The effectiveness of VELscope in detection of the cancer risk in oral potentially malignant disorders at different lesion sites remains unclear, given that only a few studies have investigated the value of VELscope for detecting high- and low-risk lesions in oral potentially malignant disorders. This study used the objective VELscope fluorescence method based on quantitative analysis to investigate its value in oral potentially malignant disorders for: (I) detecting oral cancer; (II) distinguishing high-risk lesions from low-risk lesions; and (III) measuring differences in oral cancer diagnostic accuracy between different sites. MethodsConventional oral examination and subjective and objective VELscope examinations were performed on 59 oral potentially malignant disorders; autofluorescence results were compared with histopathological diagnosis. ResultsThe sensitivity of subjective and objective VELscope fluorescence methods for detecting oral cancer in oral potentially malignant disorders was 76.9% and 65.4%, respectively; specificity for distinguishing high-risk from low-risk lesions in oral potentially malignant disorders was 50.0% and 82.1%, respectively; and sensitivity for detecting oral cancer in oral potentially malignant disorders of lining mucosa was 81.0%, higher than that of the masticatory mucosa. ConclusionsThe identification ability for low-risk lesions can be improved by combining it with objective autofluorescence. Autofluorescence has different screening abilities in different parts of the oral mucosa.
Interactive Top-k Spatial Keyword queries Kai Zheng; Han Su; Bolong Zheng ...
2015 IEEE 31st International Conference on Data Engineering,
2015-April
Conference Proceeding
Conventional top-k spatial keyword queries require users to explicitly specify their preferences between spatial proximity and keyword relevance. In this work we investigate how to eliminate this ...requirement by enhancing the conventional queries with interaction, resulting in Interactive Top-k Spatial Keyword (ITkSK) query. Having confirmed the feasibility by theoretical analysis, we propose a three-phase solution focusing on both effectiveness and efficiency. The first phase substantially narrows down the search space for subsequent phases by efficiently retrieving a set of geo-textual k-skyband objects as the initial candidates. In the second phase three practical strategies for selecting a subset of candidates are developed with the aim of maximizing the expected benefit for learning user preferences at each round of interaction. Finally we discuss how to determine the termination condition automatically and estimate the preference based on the user's feedback. Empirical study based on real PoI datasets verifies our theoretical observation that the quality of top-k results in spatial keyword queries can be greatly improved through only a few rounds of interactions.
Outsourcing database to clouds is a scalable and cost-effective way for large scale data storage, management, and query processing. Trajectory data contain rich spatio-temporal relationships and ...reveal many forms of individual sensitive information (e.g., home address, health condition), which necessitate them to be encrypted before being outsourced for privacy concerns. However, efficient query processing over encrypted trajectory data is a very challenging task. Though some achievements have been reported very recently for simple queries (e.g., SQL queries, kNN queries) on encrypted data, there is rather limited progress on secure evaluation of trajectory queries because they are more complex and need special treatment. In this paper, we focus on secure trajectory similarity computation that is the cornerstone of secure trajectory query processing. More specifically, we propose an efficient solution to securely compute the similarity between two encrypted trajectories, which reveals nothing about the trajectories, but the final result. We theoretically prove that our solution is secure against the semi-honest adversaries model as all the intermediate information in our protocols can be simulated in polynomial time. Finally we empirically study the efficiency of the proposed method, which demonstrates the feasibility of our solution.
•The TiO2 film exhibited excellent laser-induced voltage(LIV) signals.•The LIV signals are strongly affected by deposition temperature and substrates.•The rutile-structured TiO2 film is very stable ...under the action of intense laser.
TiO2 thin films were deposited on two different kinds of substrates (α-Al2O3 and LaAlO3) by pulsed laser deposition. The crystal structure and laser-induced voltage (LIV) signal in the films were analyzed through X-ray diffraction and LIV measurements respectively. It was established that the films on the α-Al2O3 substrate were found growing epitaxial to the 100 direction of the rutile structure and remained structure stabilization after intense laser operating, in contrast to the films produced on LaAlO3 were 001 oriented of the anatase phase and had weak structure stability. The magnitude of the LIV signals increases linearly with laser energy density developing in TiO2 thin films, and the films exhibited the best LIV performance of 1.37 V at 0.1 mJ/cm2 with a laser wavelength of 248 nm. The results demonstrate that the TiO2 film deposited on α-Al2O3 is a potential candidate for detecting laser radiations, especially in ultraviolet laser detection.
Multi system symptoms such as gastrointestinal tract and respiratory tract exist in coronavirus disease 2019 (COVID-19) patients. There is a lack of reliable evidence to prove that probiotics are ...effective in improving these symptoms. In this study, we aimed to evaluate the efficacy of probiotics in meta-analysis.
We systematically searched PubMed, Embase, Web of Science, and Cochrane Library up to February 15, 2023. Randomized controlled trials or high quality retrospective studies comparing the efficacy of probiotics as supplementation with non-probiotics in improving symptoms for patients with COVID-19 were included. This meta-analysis assessed endpoints using Review Manager 5.3.
Ten citations comprising 1198 patients with COVID-19 were included. The results showed that probiotics could increase the number of people with overall symptom improvement (RR = 1.62, 95% CI 1.10, 2.38,
= 0.01) and shorten the duration (days) of overall symptoms (MD = -1.26, 95% CI -2.36, -0.16,
= 0.02). For the duration (days) of specific symptoms, probiotics could improve diarrhea (MD = -2.12, 95% CI -2.41, -1.83,
< 0.00001), cough (MD = -2.21, 95% CI -4.56, 0.13,
= 0.06) and shortness of breath (MD = -1.37, 95% CI -2.22, -0.53, P = 0.001). Probiotics had no obvious effect on fever, headache and weakness. For inflammation, probiotics could effectively reduce C-reactive Protein (CRP) serum level (mg/L) (MD = -4.03, 95% CI -5.12, -2.93,
< 0.00001). Regarding hospital stay (days), probiotics group was shorter than non-probiotics group (MD = -0.98, 95% CI -1.95, -0.01,
= 0.05).
To some extent probiotics could improve the overall symptoms, inflammatory reaction and shorten hospital stay of patients with COVID-19. Probiotics may improve gastrointestinal symptoms (such as improving intestinal flora and reducing the duration of diarrhea) and further improve respiratory symptoms through the gut-lung axis.
https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=398309, identifier: CRD42023398309.
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