Lensless high-resolution on-chip optofluidic microscopes for Caenorhabditis elegans and cell imaging

Low-cost and high-resolution on-chip microscopes are vital for reducing cost and improving efficiency for modern biomedicine and bioscience. Despite the needs, the conventional microscope design has proven difficult to miniaturize. Here, we report the implementation and application of two high-resolution (≈0.9 μm for the first and ≈0.8 μm for the second), lensless, and fully on-chip microscopes based on the optofluidic microscopy (OFM) method. These systems abandon the conventional microscope design, which requires expensive lenses and large space to magnify images, and instead utilizes microfluidic flow to deliver specimens across array(s) of micrometer-size apertures defined on a metal-coated CMOS sensor to generate direct projection images. The first system utilizes a gravity-driven microfluidic flow for sample scanning and is suited for imaging elongate objects, such as Caenorhabditis elegans; and the second system employs an electrokinetic drive for flow control and is suited for imaging cells and other spherical/ellipsoidal objects. As a demonstration of the OFM for bioscience research, we show that the prototypes can be used to perform automated phenotype characterization of different Caenorhabditis elegans mutant strains, and to image spores and single cellular entities. The optofluidic microscope design, readily fabricable with existing semiconductor and microfluidic technologies, offers low-cost and highly compact imaging solutions. More functionalities, such as on-chip phase and fluorescence imaging, can also be readily adapted into OFM systems. We anticipate that the OFM can significantly address a range of biomedical and bioscience needs, and engender new microscope applications

Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

BACKGROUND: An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. RESULTS: We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. CONCLUSION: Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site

Role of galectin-3 combined with multi-detector contrast enhanced computed tomography in predicting disease recurrence in patients with ovarian cancer

Galectin-3 (Gal-3) is an endogenous β-galactoside-binding lectin, playing an important role in the pathogenesis of multiple malignancies. Aim of the study was to evaluate in a group of patients treated for ovarian cancer (EOC), the role of Gal-3 combined with multi-detector contrast-enhanced computed tomography (MDCT), as predictor of recurrence disease. Seventeen follow-up patients with recurrent ovarian cancer and 13 follow-up patients with stable ovarian disease, who performed MDCT at one-year follow-up after cytoreductive treatment, were enrolled. Serum Gal-3 concentrations were determined by using ELISA method. Twenty healthy controls were included in the analysis. Two radiologist blinded to patients status, reviewed MDCT exams, recording the following signs of disease recurrence: local tumor spread, enlarged lymph-nodes, carcinomatosis implants and metastases. We calculated the respective threshold values of Gal- 3 identified by ROC curve analysis for each imaging findings related to disease recurrence : lymphoadenopathies 92.45 ng/ml (AUC: 0.81, Se=91% Spe=73%), carcinomatosis 85.95 ng/ml (AUC:0.93 Se= 93.7%, Spe=92.8%), local tumor spread 99.05 (AUC:0.90, Se=100%, Spe=73% ) and metastasis 99.05ng/ml (AUC :0,78, Se=100% , Spe=70%). A significant correlation between high Gal-3 serum levels and presence of local tumor spread (n=11/17, p:0.001), carcinomatosis (n=16/17, p:0.00), lymphoadenopathies (n=15/17, p:0.00) and metastasis (n=11/17, p:0.003) related with recurrence disease was observed. Patients with recurrence of ovarian cancer presents higher Gal-3 values compared to women with stable diseases. Gal-3 combined to CECT should be used to improve the monitoring of EOC patients

Virtual biopsy in abdominal pathology: where do we stand?

In recent years, researchers have explored new ways to obtain information from pathological tissues, also exploring non-invasive techniques, such as virtual biopsy (VB). VB can be defined as a test that provides promising outcomes compared to traditional biopsy by extracting quantitative information from radiological images not accessible through traditional visual inspection. Data are processed in such a way that they can be correlated with the patient’s phenotypic expression, or with molecular patterns and mutations, creating a bridge between traditional radiology, pathology, genomics, and artificial intelligence (AI). Radiomics is the backbone of VB, since it allows the extraction and selection of features from radiological images, feeding them into AI models in order to derive lesions' pathological characteristics and molecular status. Presently, the output of VB provides only a gross approximation of the findings of tissue biopsy. However, in the future, with the improvement of imaging resolution and processing techniques, VB could partially substitute the classical surgical or percutaneous biopsy, with the advantage of being non-invasive, comprehensive, accounting for lesion heterogeneity, and low cost. In this review, we investigate the concept of VB in abdominal pathology, focusing on its pipeline development and potential benefits

Translational Oncogenomics and Human Cancer Interactome Networks

An overview of translational, human oncogenomics, transcriptomics and cancer interactomic networks is presented together with basic concepts and potential, new applications to Oncology and Integrative Cancer Biology. Novel translational oncogenomics research is rapidly expanding through the application of advanced technology, research findings and computational tools/models to both pharmaceutical and clinical problems. A self-contained presentation is adopted that covers both fundamental concepts and the most recent biomedical, as well as clinical, applications. Sample analyses in recent clinical studies have shown that gene expression data can be employed to distinguish between tumor types as well as to predict outcomes. Potentially important applications of such results are individualized human cancer therapies or, in general, ‘personalized medicine’. Several cancer detection techniques are currently under development both in the direction of improved detection sensitivity and increased time resolution of cellular events, with the limits of single molecule detection and picosecond time resolution already reached. The urgency for the complete mapping of a human cancer interactome with the help of such novel, high-efficiency / low-cost and ultra-sensitive techniques is also pointed out

Accomplishments and challenges in stem cell imaging in vivo.

Stem cell therapies have demonstrated promising preclinical results, but very few applications have reached the clinic owing to safety and efficacy concerns. Translation would benefit greatly if stem cell survival, distribution and function could be assessed in vivo post-transplantation, particularly in patients. Advances in molecular imaging have led to extraordinary progress, with several strategies being deployed to understand the fate of stem cells in vivo using magnetic resonance, scintigraphy, PET, ultrasound and optical imaging. Here, we review the recent advances, challenges and future perspectives and opportunities in stem cell tracking and functional assessment, as well as the advantages and challenges of each imaging approach

A knowledge engineering approach to the recognition of genomic coding regions

ได้ทุนอุดหนุนการวิจัยจากมหาวิทยาลัยเทคโนโลยีสุรนารี ปีงบประมาณ พ.ศ.2556-255

Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery

<p>Abstract</p> <p>Background</p> <p>Although high-throughput microarray based molecular diagnostic technologies show a great promise in cancer diagnosis, it is still far from a clinical application due to its low and instable sensitivities and specificities in cancer molecular pattern recognition. In fact, high-dimensional and heterogeneous tumor profiles challenge current machine learning methodologies for its small number of samples and large or even huge number of variables (genes). This naturally calls for the use of an effective feature selection in microarray data classification.</p> <p>Methods</p> <p>We propose a novel feature selection method: multi-resolution independent component analysis (MICA) for large-scale gene expression data. This method overcomes the weak points of the widely used transform-based feature selection methods such as principal component analysis (PCA), independent component analysis (ICA), and nonnegative matrix factorization (NMF) by avoiding their global feature-selection mechanism. In addition to demonstrating the effectiveness of the multi-resolution independent component analysis in meaningful biomarker discovery, we present a multi-resolution independent component analysis based support vector machines (MICA-SVM) and linear discriminant analysis (MICA-LDA) to attain high-performance classifications in low-dimensional spaces.</p> <p>Results</p> <p>We have demonstrated the superiority and stability of our algorithms by performing comprehensive experimental comparisons with nine state-of-the-art algorithms on six high-dimensional heterogeneous profiles under cross validations. Our classification algorithms, especially, MICA-SVM, not only accomplish clinical or near-clinical level sensitivities and specificities, but also show strong performance stability over its peers in classification. Software that implements the major algorithm and data sets on which this paper focuses are freely available at <url>https://sites.google.com/site/heyaumapbc2011/</url>.</p> <p>Conclusions</p> <p>This work suggests a new direction to accelerate microarray technologies into a clinical routine through building a high-performance classifier to attain clinical-level sensitivities and specificities by treating an input profile as a ‘profile-biomarker’. The multi-resolution data analysis based redundant global feature suppressing and effective local feature extraction also have a positive impact on large scale ‘omics’ data mining.</p