Advances in lab-on-a-chip systems have strong potential for multiplexed detection of a wide range of analytes with reduced sample and reagent volume; lower costs and shorter analysis times. The completion of high-fidelity multiplexed and multiclass assays remains a challenge for the medical microdevice field; as it struggles to achieve and expand upon at the point-of-care the quality of results that are achieved now routinely in remote laboratory settings. This review article serves to explore for the first time the key intersection of multiplexed bead-based detection systems with integrated microfluidic structures alongside porous capture elements together with biomarker validation studies. These strategically important elements are evaluated here in the context of platform generation as suitable for near-patient testing. Essential issues related to the scalability of these modular sensor ensembles are explored as are attempts to move such multiplexed and multiclass platforms into large-scale clinical trials. Recent efforts in these bead sensors have shown advantages over planar microarrays in terms of their capacity to generate multiplexed test results with shorter analysis times. Through high surface-to-volume ratios and encoding capabilities; porous bead-based ensembles; when combined with microfluidic elements; allow for high-throughput testing for enzymatic assays; general chemistries; protein; antibody and oligonucleotide applications.

Clinical decision support systems (CDSSs) have the potential to save lives and reduce unnecessary costs through early detection and frequent monitoring of both traditional risk factors and novel biomarkers for cardiovascular disease (CVD). However, the widespread adoption of CDSSs for the identification of heart diseases has been limited, likely due to the poor interpretability of clinically relevant results and the lack of seamless integration between measurements and disease predictions. In this paper we present the Cardiac ScoreCard—a multivariate index assay system with the potential to assist in the diagnosis and prognosis of a spectrum of CVD. The Cardiac ScoreCard system is based on lasso logistic regression techniques which utilize both patient demographics and novel biomarker data for the prediction of heart failure (HF) and cardiac wellness. Lasso logistic regression models were trained on a merged clinical dataset comprising 579 patients with 6 traditional risk factors and 14 biomarker measurements. The prediction performance of the Cardiac ScoreCard was assessed with 5-fold cross-validation and compared with reference methods. The experimental results reveal that the ScoreCard models improved performance in discriminating disease versus non-case (AUC = 0.8403 and 0.9412 for cardiac wellness and HF, respectively), and the models exhibit good calibration. Clinical insights to the prediction of HF and cardiac wellness are provided in the form of logistic regression coefficients which suggest that augmenting the traditional risk factors with a multimarker panel spanning a diverse cardiovascular pathophysiology provides improved performance over reference methods. Additionally, a framework is provided for seamless integration with biomarker measurements from point-of-care medical microdevices, and a lasso-based feature selection process is described for the down-selection of biomarkers in multimarker panels.

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Background: The program "Chile grows with you" is a part of the (Rev Med Chile 2011; 139: 739-747).

from the mothers' stories, significant patterns were identified in their experiences, yielding insights into society from these women's perspectives. For humanized, comprehensive nursing care, this expertise directs interventions designed to overcome despair in women excluded because of their invisibility and poverty.