tracking-quantifying-developmental-processes-c-elegans-using-open

Tracking and Quantifying Developmental Processes in <em>C. elegans</em> Using Open-source Tools

Quantitatively capturing developmental processes is crucial to derive mechanistic models and key to identify and describe mutant phenotypes. Here ...

Goethe University

A key challenge in tissue engineering is overcoming cell death in the scaffold interior due to the limited diffusion of oxygen and nutrients therein. We here hypothesize that immobilizing a gradient of a growth/survival factor from the periphery to the center of a porous scaffold would guide endothelial cells into the interior of the scaffold, thus overcoming a necrotic core. Proteins were immobilized by one of three methods on porous collagen scaffolds for cardiovascular tissue engineering. The proteins were first activated with 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide/sulfo N-hydroxysuccinimide and then applied to the scaffold by one of three methods to establish the gradient: perfusion (the flow method), use of a source and a sink (the source-sink method) or by injecting 5 μl of the solution at the center of the scaffold (point source method). Due to the high reproducibility and ease of application of the point source method it was further used for VEGF-165 gradient formation, where an ~2 ng ml(-1) mm(-1) gradient was formed in a radial direction across a scaffold, 12 mm in diameter and 2.5mm thick. More endothelial cells were guided by the VEGF-165 gradient deep into the center of the scaffold compared with both uniformly immobilized VEGF-165 (with the same total VEGF concentration) and VEGF-free controls. All scaffolds (including the controls) yielded the same number of cells, but notably the VEGF-165 gradient scaffolds demonstrated a higher cell density in the centre. Thus we concluded that the VEGF-165 gradient promoted the migration, but not proliferation, of cells into the scaffold. These gradient scaffolds provide the foundation for future in vivo tissue engineering studies.

Endothelial cells guided by immobilized gradients of vascular endothelial growth factor on porous collagen scaffolds.

The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.

Computational intelligence in early diabetes diagnosis: a review.

The concept of classification of clinical data can be utilized in the development of an effective diagnosis system by taking the advantage of computational intelligence. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important problem in neural networks. Unfortunately, although several classification studies have been carried out with significant performance, many of the current methods often fail to reach out to patients. Graphical user interface-enabled tools need to be developed through which medical practitioners can simply enter the health profiles of their patients and receive an instant diabetes prediction with an acceptable degree of confidence.

Java-based diabetes type 2 prediction tool for better diagnosis.

The purpose of this article is to determine the influence of chest wall composition on image quality in cardiac CT.

The influence of chest wall tissue composition in determining image noise during cardiac CT.

We previously showed that the sequential, but not simultaneous, culture of endothelial cells (ECs), fibroblasts (FBs), and cardiomyocytes (CMs) resulted in elongated, beating cardiac organoids. We hypothesized that the expression of Cx43 and contractile function are mediated by vascular endothelial growth factor (VEGF) released by nonmyocytes during the preculture period. Cardiac organoids (~200 μm diameter) were cultivated in microchannels to enable rapid screening. Three experimental groups were formed: (i) Simultaneous Preculture (ECs+FBs for 48 h, followed by CMs), (ii) Sequential Preculture (ECs for 24 h, FBs for 24 h, followed by CMs), and (iii) Simultaneous Triculture (ECs+FBs+CMs). Controls included CMs only, FBs only, and ECs only groups, and preculture with ECs only or FBs only. The highest VEGF levels were found in the Preculture groups [Simultaneous Preculture, 8.9±2.7 ng/(mL·h(-1)); Sequential Preculture, 16.6±3.4 ng/(mL·h(-1))], as compared with Simultaneous Triculture where VEGF was not detectable, as shown by enzyme-linked immunosorbent assay. Analytical flow cytometry showed that VEGFR2 was expressed by ECs (86%±2 VEGFR2+), FBs (44%±1 VEGFR2+), and CMs (49%±2 VEGFR2+), showing that all three cell types were capable of responding to changes in VEGF. Addition of anti-VEGF neutralizing IgG (0.4 μg/mL) to Simultaneous Preculture resulted in 3-fold decrease in Cx43 mRNA and 1.5-fold decrease in Cx43 protein, while Simultaneous Triculture supplemented with VEGF ligand (30 ng/mL) had a threefold increase in Cx43 mRNA and a twofold increase in Cx43 protein. Addition of a small molecule inhibitor of the VEGFR2 receptor (19.4 nM) to Sequential Preculture caused a 1.4-fold decrease in Cx43 mRNA and a 4.1-fold decrease in Cx43 protein. Cx43 was localized within CMs, and not within FBs or ECs. Enriched CM organoids and Sequential Preculture organoids grown in the presence of VEGFR2 inhibitor displayed low levels of Cx43 and poor functional properties. Taken together, these results suggest that endogenous VEGF-VEGFR2 signaling enhanced Cx43 expression and cardiac function in engineered cardiac organoids.

Vascular endothelial growth factor secretion by nonmyocytes modulates Connexin-43 levels in cardiac organoids.

The incidence of diabetes is increasing rapidly across the globe. India has the highest proportion of diabetic patients, earning it the doubtful distinction of the 'diabetes capital of the world'. Early detection of diabetes could help to prevent or postpone its onset by taking appropriate preventive measures, including the initiation of lifestyle changes. To date, early identification of prediabetes or type 2 diabetes has proven problematic, such that there is an urgent requirement for tools enabling easy, quick, and accurate diagnosis.

Computational intelligence-based diagnosis tool for the detection of prediabetes and type 2 diabetes in India.

Global spread of multi-drug resistant bacteria like Klebsiella pneumoniae and Escherichia coli have raised the alarm for researchers and doctors throughout the world. This new mechanism of resistance and the ability of ndm-1 gene to be transferred between the species may end the era of antibiotics treatment. Carbapenems are reliable drugs against many multi-resistant gram-negative pathogens. A 3-D homology model of NDM-1 was built and analysed for elucidation of functional site and binding interactions. This study revealed that meropenem has good interaction with the active sites of the receptor that could retard the spread of antibiotic resistant bacteria.

Meropenem: a potent drug against superbug as unveiled through bioinformatics approaches.

To compare the low-contrast detectability and image quality of computed tomography (CT) at different radiation dose levels reconstructed with iterative reconstruction (IR) and filtered back projection (FBP).

Iterative reconstruction algorithm for CT: can radiation dose be decreased while low-contrast detectability is preserved?

To compare the image quality and radiation dose using image-noise (IN)-based determination of X-ray tube settings compared with a body mass index (BMI)-based protocol during CT coronary angiography (CTCA).

Image noise-based dose adaptation in dynamic volume CT of the heart: dose and image quality optimisation in comparison with BMI-based dose adaptation.

The purpose of this study was to assess the impact of a noise reduction technique on image quality, radiation dose, and low-contrast detectability in abdominal CT for obese patients.

Hybrid iterative reconstruction technique for abdominal CT protocols in obese patients: assessment of image quality, radiation dose, and low-contrast detectability in a phantom.

Devang Odedra

Buchmann Institute for Molecular Life Sciences and Institute of Biochemistry II,
School of Medicine,
Buchmann Institute for Molecular Life Sciences and Institute of Biochemistry II, School of Medicine

Goethe University

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Devang Odedra

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Buchmann Institute for Molecular Life Sciences and Institute of Biochemistry II,School of Medicine,Buchmann Institute for Molecular Life Sciences and Institute of Biochemistry II, School of Medicine

Goethe University

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Buchmann Institute for Molecular Life Sciences and Institute of Biochemistry II,
School of Medicine,
Buchmann Institute for Molecular Life Sciences and Institute of Biochemistry II, School of Medicine