Suchergebnisse

Hollow organs such as the lungs pose a considerable challenge for post-mortem imaging in preclinical research owing to their extremely low contrast and high structural complexity. The aim of our study was to enhance the contrast of tuberculosis lesions for their stratification by 3D x-ray&-based virtual slicing. Organ samples were taken from five control and five tuberculosis-infected mice. Micro-Computed Tomography (CT) scans of the subjects were acquired in vivo (without contrast agent) and post-mortem (with contrast agent). The proposed contrast-enhancing technique consists of x-ray contrast agent uptake (silver nitrate and iodine) by immersion. To create the histology ground-truth, the CT scan of the paraffin block guided the sectioning towards specific planes of interest. The digitalized histological slides reveal the presence, extent, and appearance of the contrast agents in lung structures and organized aggregates of immune cells. These findings correlate with the contrast-enhanced micro-CT slice. The abnormal densities in the lungs due to tuberculosis disease are concentrated in the right tail of the lung intensity histograms. The increase in the width of the right tail (~376%) indicates a contrast enhancement of the details of the abnormal densities. Postmortem contrast agents enhance the x-ray attenuation in tuberculosis lesions to allow 3D visualization by polychromatic x-ray CT, providing an advantageous tool for virtual slicing of whole lungs. The proposed contrast-enhancing technique combined with computational methods and the diverse micro-CT modalities will open the doors to the stratification of lesion types associated with infectious diseases. ; The research leading to these results received funding from the Innovative Medicines Initiative (www.imi.europa.eu) Joint Undertaking under grant agreement no. 115337, whose resources comprise funding from the European Union Seventh Framework Programme (FP7/2007–2013) and EFPIA companies in kind contribution. This work was partially funded by projects RTC-2015-3772-1, TEC2015-73064-EXP and TEC2016-78052-R from the Spanish Ministry of Economy, TOPUS S2013/MIT-3024 project from the regional government of Madrid and by the Department of Health, UK. This study (was supported by the Instituto de Salud Carlos III (Plan Estatal de I + D + i 2013–2016) and co-financed by the European Social Fund (ESF) "ESF investing in your future". The authors thank Dr.Guembe from CIMA-Universidad de Navarra for preparing and staining the tissue sections, and to Dr. Guerrero-Aspizua and Prof. Conti of the Department of Bioengineering, Universidad Carlos III de Madrid for the pathology evaluation.

Purpose: Computed tomography (CT) images enable capturing specific manifestations of tuberculosis (TB) that are undetectable using common diagnostic tests, which suffer from limited specificity. In this study, we aimed to automatically quantify the burden of Mycobacterium tuberculosis (Mtb) using biomarkers extracted from x-ray CT images. Procedures: Nine macaques were aerosol-infected with Mtb and treated with various antibiotic cocktails. Chest CT scans were acquired in all animals at specific times independently of disease progression. First, a fully automatic segmentation of the healthy lungs from the acquired chest CT volumes was performed and air-like structures were extracted. Next, unsegmented pulmonary regions corresponding to damaged parenchymal tissue and TB lesions were included. CT biomarkers were extracted by classification of the probability distribution of the intensity of the segmented images into three tissue types: (1) Healthy tissue, parenchyma free from infection; (2) soft diseased tissue, and (3) hard diseased tissue. The probability distribution of tissue intensities was assumed to follow a Gaussian mixture model. The thresholds identifying each region were automatically computed using an expectation-maximization algorithm. Results: The estimated longitudinal course of TB infection shows that subjects that have followed the same antibiotic treatment present a similar response (relative change in the diseased volume) with respect to baseline. More interestingly, the correlation between the diseased volume (soft tissue + hard tissue), which was manually delineated by an expert, and the automatically extracted volume with the proposed method was very strong (R2 ≈ 0.8). ; The research leading to these results received funding from the Innovative Medicines Initiative (www.imi.europa.eu) Joint Undertaking under grant agreement no. 115337, whose resources comprise funding from EU FP7/2007-2013 and EFPIA companies in-kind contribution. This study was partially funded by projects TEC2013-48552-C2-1-R, RTC-2015-3772-1, TEC2015-73064-EXP and TEC2016-78052-R from the Spanish Ministry of Economy, Industry and Competitiveness (MEIC), TOPUS S2013/MIT-3024 project from the regional government of Madrid and by the Department of Health, UK. LEV is funded by the Intramural Research Program of NIAID, NIH. The CNIC is supported by the MEIC and the Pro CNIC Foundation and is a Severo Ochoa Center of Excellence (SEV-2015-0505)

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that produces pulmonary damage. Radiological imaging is the preferred technique for the assessment of TB longitudinal course. Computer-assisted identification of biomarkers eases the work of the radiologist by providing a quantitative assessment of disease. Lung segmentation is the step before biomarker extraction. In this study, we present an automatic procedure that enables robust segmentation of damaged lungs that have lesions attached to the parenchyma and are affected by respiratory movement artifacts in a Mycobacterium Tuberculosis infection model. Its main steps are the extraction of the healthy lung tissue and the airway tree followed by elimination of the fuzzy boundaries. Its performance was compared with respect to a segmentation obtained using: (1) a semi-automatic tool and (2) an approach based on fuzzy connectedness. A consensus segmentation resulting from the majority voting of three experts' annotations was considered our ground truth. The proposed approach improves the overlap indicators (Dice similarity coefficient, 94% ± 4%) and the surface similarity coefficients (Hausdorff distance, 8.64 mm ± 7.36 mm) in the majority of the most difficult-to-segment slices. Results indicate that the refined lung segmentations generated could facilitate the extraction of meaningful quantitative data on disease burden. ; The research leading to these results received funding from the Innovative Medicines Initiative (www.imi.europa.eu) Joint Undertaking under grant agreement no. 115337, whose resources comprise funding from the European Union's Seventh Framework Programme (FP7/2007–2013) and EFPIA companies' in kind contribution. This work was partially funded by projects TEC2013-48552-C2-1-R, RTC-2015-3772-1, TEC2015-73064-EXP and TEC2016-78052-R from the Spanish Ministerio de Economía, Industria y Competitividad, TOPUS S2013/MIT-3024 project from the regional government of Madrid and by the Department of Health, UK.

This review focuses on novel applications based on multifunctional materials to actuate biological processes. The first section of the work revisits the current knowledge on mechanically dependent biological processes across several scales from subcellular and cellular level to the cellcollective scale (continuum approaches). This analysis presents a wide variety of mechanically dependent biological processes on nervous system behaviour; bone development and healing; collective cell migration. In the second section, this review presents recent advances in smart materials suitable for use as cell substrates or scaffolds, with a special focus on magneto-active polymers (MAPs). Throughout the manuscript, both experimental and computational methodologies applied to the different treated topics are reviewed. Finally, the use of smart polymeric materials in bioengineering applications is discussed. ; This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of "Fostering Young Doctors Research" (BIOMASKIN-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation, and support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 947723). DGG acknowledges support from the Talent Attraction grant (CM 2018-2018-T2/IND-9992) from the Comunidad de Madrid. ; Publicado

Developing more efficient methods for antibiotic susceptibility testing is a pressing issue in novel drug development as bacterial resistance to antibiotics becomes increasingly common. Microfluidic devices have been demonstrated to be powerful platforms that allow researchers to perform multiplexed antibiotic testing. However, the level of multiplexing within microdevices is limited, evidencing the need of creating simple, low-cost and high-resolution imaging systems that can be integrated in antibiotic development pipelines. This paper describes the design and development of an epifluorescence inverted microscope that enables long-term monitoring of bacteria inside multiplexed microfluidic devices. The goal of this work is to provide a simple microscope powerful enough to allow single-cell analysis of bacteria at a reduced cost. This facilitates increasing the number of microscopes that are simultaneously used for antibiotic testing. We prove that the designed system is able to accurately detect fluorescent beads of 100 nm, demonstrating comparable features to high-end commercial microscopes and effectively achieving the resolution required for single-cell analysis of bacteria. The proposed microscope could thus increase the efficiency in antibiotic testing while reducing cost, size, weight, and power requirements, contributing to the successful development of new antibiotic drugs. ; This work was partially funded by project TEC2016-78052-R from the Spanish Ministry of Economy and PID2019-109820RB-I00 from the Spanish Ministry of Science and Innovation. The research leading to these results received funding from the Innovative Medicines Innitiative 2 Joint Undertaking (JU) under grant agreement No 853989. The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and Global Alliance for TB Drug Development non profit organisation, Bill & Melinda Gates Foundation and University of Dundee.