Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan

Translational analysis of moderate to severe asthma GWAS signals into candidate causal genes and their functional, tissue-dependent and disease-related associations

Asthma affects more than 300 million people globally and is both under diagnosed and under treated. The most recent and largest genome-wide association study investigating moderate to severe asthma to date was carried out in 2019 and identified 25 independent signals. However, as new and in-depth downstream databases become available, the translational analysis of these signals into target genes and pathways is timely. In this study, unique (U-BIOPRED) and publicly available datasets (HaploReg, Open Target Genetics and GTEx) were investigated for the 25 GWAS signals to identify 37 candidate causal genes. Additional traits associated with these signals were identified through PheWAS using the UK Biobank resource, with asthma and eosinophilic traits amongst the strongest associated. Gene expression omnibus dataset examination identified 13 candidate genes with altered expression profiles in the airways and blood of asthmatic subjects, including MUC5AC and STAT6. Gene expression analysis through publicly available datasets highlighted lung tissue cell specific expression, with both MUC5AC and SLC22A4 genes showing enriched expression in ciliated cells. Gene enrichment pathway and interaction analysis highlighted the dominance of the HLA-DQA1/A2/B1/B2 gene cluster across many immunological diseases including asthma, type I diabetes, and rheumatoid arthritis. Interaction and prediction analyses found IL33 and IL18R1 to be key co-localization partners for other genes, predicted that CD274 forms co-expression relationships with 13 other genes, including the HLA-DQA1/A2/B1/B2 gene cluster and that MUC5AC and IL37 are co-expressed. Drug interaction analysis revealed that 11 of the candidate genes have an interaction with available therapeutics. This study provides significant insight into these GWAS signals in the context of cell expression, function, and disease relationship with the view of informing future research and drug development efforts for moderate-severe asthma

A Multi-wavelength Study of the Sunyaev-Zel'dovich Effect in the Triple-Merger Cluster MACS J0717.5+3745 with MUSTANG and Bolocam

We present 90, 140, and 268GHz sub-arcminute resolution imaging of the Sunyaev-Zel'dovich effect (SZE) in MACSJ0717.5+3745. Our 90GHz SZE data result in a sensitive, 34uJy/bm map at 13" resolution using MUSTANG. Our 140 and 268GHz SZE imaging, with resolutions of 58" and 31" and sensitivities of 1.8 and 3.3mJy/beam respectively, was obtained using Bolocam. We compare these maps to a 2-dimensional pressure map derived from Chandra X-ray observations. Our MUSTANG data confirm previous indications from Chandra of a pressure enhancement due to shock-heated, >20keV gas immediately adjacent to extended radio emission seen in low-frequency radio maps. The MUSTANG data also detect pressure substructure that is not well-constrained by the X-ray data in the remnant core of a merging subcluster. We find that the small-scale pressure enhancements in the MUSTANG data amount to ~2% of the total pressure measured in the 140GHz Bolocam observations. The X-ray template also fails on larger scales to accurately describe the Bolocam data, particularly at the location of a subcluster known to have a high line of sight optical velocity (~3200km/s). Our Bolocam data are adequately described when we add an additional component - not described by a thermal SZE spectrum - coincident with this subcluster. Using flux densities extracted from our model fits, and marginalizing over the temperature constraints for the region, we fit a thermal+kinetic SZE spectrum to our data and find the subcluster has a best-fit line of sight proper velocity of 3600+3440/-2160km/s. This agrees with the optical velocity estimates for the subcluster. The probability of velocity<0 given our measurements is 2.1%. Repeating this analysis using flux densities measured non-parametrically results in a 3.4% probability of a velocity<=0. We note that this tantalizing result for the kinetic SZE is on resolved, subcluster scales.Comment: 10 Figures, 18 pages. this version corrects issues with the previous arXiv versio

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention

Background: Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. Methods: In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. Results: The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. Conclusion: The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan

GenomeRNAi: a database for cell-based RNAi phenotypes. 2009 update

The GenomeRNAi database (http://www.genomernai.org/) contains phenotypes from published cell-based RNA interference (RNAi) screens in Drosophila and Homo sapiens. The database connects observed phenotypes with annotations of targeted genes and information about the RNAi reagent used for the perturbation experiment. The availability of phenotypes from Drosophila and human screens also allows for phenotype searches across species. Besides reporting quantitative data from genome-scale screens, the new release of GenomeRNAi also enables reporting of data from microscopy experiments and curated phenotypes from published screens. In addition, the database provides an updated resource of RNAi reagents and their predicted quality that are available for the Drosophila and the human genome. The new version also facilitates the integration with other genomic data sets and contains expression profiling (RNA-Seq) data for several cell lines commonly used in RNAi experiments

The nurse educator role in the acute care setting in Australia: important but poorly described AUTHORS

ABSTRACT Objective The purpose of this paper is to describe the nurse educator role in the acute care setting in Australia

Finding related sentence pairs in MEDLINE

We explore the feasibility of automatically identifying sentences in different MEDLINE abstracts that are related in meaning. We compared traditional vector space models with machine learning methods for detecting relatedness, and found that machine learning was superior. The Huber method, a variant of Support Vector Machines which minimizes the modified Huber loss function, achieves 73% precision when the score cutoff is set high enough to identify about one related sentence per abstract on average. We illustrate how an abstract viewed in PubMed might be modified to present the related sentences found in other abstracts by this automatic procedure

Activation of BKCa Channels in Zoledronic Acid-Induced Apoptosis of MDA-MB-231 Breast Cancer Cells

BACKGROUND: Zoledronic acid, one of the most potent nitrogen-containing biphosphonates, has been demonstrated to have direct anti-tumor and anti-metastatic properties in breast cancer in vitro and in vivo. In particular, tumor-cell apoptosis has been recognized to play an important role in the treatment of metastatic breast cancer with zoledronic acid. However, the precise mechanisms remain less clear. In the present study, we investigated the specific role of large conductance Ca(2+)-activated potassium (BK(Ca)) channel in zoledronic acid-induced apoptosis of estrogen receptor (ER)-negative MDA-MB-231 breast cancer cells. METHODOLOGY/PRINCIPAL FINDINGS: The action of zoledronic acid on BK(Ca) channel was investigated by whole-cell and cell-attached patch clamp techniques. Cell apoptosis was assessed with immunocytochemistry, analysis of fragmented DNA by agarose gel electrophoresis, and flow cytometry assays. Cell proliferation was investigated by MTT test and immunocytochemistry. In addition, such findings were further confirmed with human embryonic kidney 293 (HEK293) cells which were transfected with functional BK(Ca) α-subunit (hSloα). Our results clearly indicated that zoledronic acid directly increased the activities of BK(Ca) channels, and then activation of BK(Ca) channel by zoledronic acid contributed to induce apoptosis in MDA-MB-231 cells. The possible mechanisms were associated with the elevated level of intracellular Ca(2+) and a concomitant depolarization of mitochondrial membrane potential (Δψm) in MDA-MB-231 cells. CONCLUSIONS: Activation of BK(Ca) channel was here shown to be a novel molecular pathway involved in zoledronic acid-induced apoptosis of MDA-MB-231 cells in vitro

A pilot survey of post-deployment health care needs in small community-based primary care clinics

<p>Abstract</p> <p>Background</p> <p>Relatively little is known regarding to what extent community-based primary care physicians are encountering post-deployment health care needs among veterans of the Afghanistan or Iraq conflicts and their family members.</p> <p>Methods</p> <p>This pilot study conducted a cross-sectional survey of 37 primary care physicians working at small urban and suburban clinics belonging to a practice-based research network in the south central region of Texas.</p> <p>Results</p> <p>Approximately 80% of the responding physicians reported caring for patients who have been deployed to the Afghanistan or Iraq war zones, or had a family member deployed. Although these physicians noted a variety of conditions related to physical trauma, mental illnesses and psychosocial disruptions such as marital, family, financial, and legal problems appeared to be even more prevalent among their previously deployed patients and were also noted among family members of deployed veterans.</p> <p>Conclusions</p> <p>Community-based primary care physicians should be aware of common post-deployment health conditions and the resources that are available to meet these needs.</p