Assessment of Atrial Electromechanical Delay by Tissue Doppler Echocardiography in Obese Subjects

Our aim was to evaluate whether atrial electromechanical delay measured by tissue Doppler imaging (TDI), which is an early predictor of atrial fibrillation (AF) development, is prolonged in obese subjects. A total of 40 obese and 40 normal‐weight subjects with normal coronary angiograms were included in this study. P‐wave dispersion (PWD) was calculated on the 12‐lead electrocardiogram (ECG). Systolic and diastolic left ventricular (LV) functions, inter‐ and intra‐atrial electromechanical delay were measured by TDI and conventional echocardiography. Inter‐ and intra‐atrial electromechanical delay were significantly longer in the obese subjects compared with the controls (44.08 ± 10.06 vs. 19.35 ± 5.94 ms and 23.63 ± 6.41 vs. 5.13 ± 2.67 ms, P < 0.0001 for both, respectively). PWD was higher in obese subjects (53.40 ± 5.49 vs. 35.95 ± 5.93 ms, P < 0.0001). Left atrial (LA) diameter, LA volume index and LV diastolic parameters were significantly different between the groups. Interatrial electromechanical delay was correlated with PWD (r = 0.409, P = 0.009), high‐sensitivity C‐reactive protein (hsCRP) levels (r = 0.588, P < 0.0001). Interatrial electromechanical delay was positively correlated with LA diameter, LA volume index, and LV diastolic function parameters consisting of mitral early wave (E) deceleration time (DT) and isovolumetric relaxation time (IVRT; r = 0.323, P = 0.042; r = 0.387, P = 0.014; r = 0.339, P = 0.033; r = 0.325, P = 0.041; respectively) and, negatively correlated with mitral early (E) to late (A) wave ratio (E/A) (r = ?0.380, P = 0.016) and myocardial early‐to‐late diastolic wave ratio (E_m/A_m) (r = ?0.326, P = 0.040). This study showed that atrial electromechanical delay is prolonged in obese subjects. Prolonged atrial electromechanical delay is due to provoked low‐grade inflammation as well as LA enlargement and early LV diastolic dysfunction in obese subjects.     

Subretinal electronic chips allow blind patients to read letters and combine them to words

A light-sensitive, externally powered microchip was surgically implanted subretinally near the macular region of volunteers blind from hereditary retinal dystrophy. The implant contains an array of 1500 active microphotodiodes ('chip'), each with its own amplifier and local stimulation electrode. At the implant's tip, another array of 16 wire-connected electrodes allows light-independent direct stimulation and testing of the neuron-electrode interface. Visual scenes are projected naturally through the eye's lens onto the chip under the transparent retina. The chip generates a corresponding pattern of 38 × 40 pixels, each releasing light-intensity-dependent electric stimulation pulses. Subsequently, three previously blind persons could locate bright objects on a dark table, two of whom could discern grating patterns. One of these patients was able to correctly describe and name objects like a fork or knife on a table, geometric patterns, different kinds of fruit and discern shades of grey with only 15 per cent contrast. Without a training period, the regained visual functions enabled him to localize and approach persons in a room freely and to read large letters as complete words after several years of blindness. These results demonstrate for the first time that subretinal micro-electrode arrays with 1500 photodiodes can create detailed meaningful visual perception in previously blind individuals.     

CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations

Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of the available data are produced using different technologies and scattered across individual computational resources without any explicit connections to each other, which hinders extensive and integrative multi-omics-based analysis. We aimed to address this issue by developing a new data integration/representation methodology and its application by constructing a biological data resource. CROssBAR is a comprehensive system that integrates large-scale biological/biomedical data from various resources and stores them in a NoSQL database. CROssBAR is enriched with the deep-learning-based prediction of relationships between numerous data entries, which is followed by the rigorous analysis of the enriched data to obtain biologically meaningful modules. These complex sets of entities and relationships are displayed to users via easy-to-interpret, interactive knowledge graphs within an open-access service. CROssBAR knowledge graphs incorporate relevant genes-proteins, molecular interactions, pathways, phenotypes, diseases, as well as known/predicted drugs and bioactive compounds, and they are constructed on-the-fly based on simple non-programmatic user queries. These intensely processed heterogeneous networks are expected to aid systems-level research, especially to infer biological mechanisms in relation to genes, proteins, their ligands, and diseases.     

The effects of Pseudomonas putida biotype B on Tetranychus urticae (Acari: Tetranychidae)

This study investigated Pseudomonas putida biotype B as a potential biological control agent of Tetranychus urticae. The bacteria were isolated from greenhouse soil from Carsamba, Turkey. The experiment was carried out in a completely randomized plot design under laboratory conditions. For this purpose, spraying and dipping applications of a suspension of P. putida biotype B (10(8)-10(9) colony forming units/ml) were applied to newly emerged, copulated females. Dead mite and egg counts were started on the 3rd day after treatments, and observations were continued daily until all the mites had died and egg hatching had finished. Both types of bacterial application significantly reduced total egg numbers and egg hatching, compared to their respective controls. Bacterial spraying was significantly more effective than dipping-the spray application demonstrated 100% efficacy and resulted in the fewest viable eggs. The results of this study indicated that P. putida biotype B has a strong efficacy in causing mortality in T. urticae.     

Genomic Pathogen Typing Using Solid-State Nanopores

In clinical settings, rapid and accurate characterization of pathogens is essential for effective treatment of patients; however, subtle genetic changes in pathogens which elude traditional phenotypic typing may confer dangerous pathogenic properties such as toxicity, antibiotic resistance, or virulence. Existing options for molecular typing techniques characterize the critical genomic changes that distinguish harmful and benign strains, yet the well-established approaches, in particular those that rely on electrophoretic separation of nucleic acid fragments on a gel, have room for only incremental future improvements in speed, cost, and complexity. Solid-state nanopores are an emerging class of single-molecule sensors that can electrophoretically characterize charged biopolymers, and which offer significant advantages in terms of sample and reagent requirements, readout speed, parallelization, and automation. We present here the first application of nanopores for single-molecule molecular typing using length based “fingerprints” of critical sites in bacterial genomes. This technique is highly adaptable for detection of different types of genetic variation; as we illustrate using prototypical examples including Mycobacterium tuberculosis and methicillin-resistant Streptococcus aureus, the solid-state nanopore diagnostic platform may be used to detect large insertions or deletions, small insertions or deletions, and even single-nucleotide variations in bacterial DNA. We further show that Bayesian classification of test samples can provide highly confident pathogen typing results based on only a few tens of independent single-molecule events, making this method extremely sensitive and statistically robust.     

Early Life Stress Differentially Modulates Distinct Forms of Brain Plasticity in Young and Adult Mice

Background

Early life trauma is an important risk factor for many psychiatric and somatic disorders in adulthood. As a growing body of evidence suggests that brain plasticity is disturbed in affective disorders, we examined the short-term and remote effects of early life stress on different forms of brain plasticity.

Methodology/Principal Findings

Mice were subjected to early deprivation by individually separating pups from their dam in the first two weeks after birth. Distinct forms of brain plasticity were assessed in the hippocampus by longitudinal MR volumetry, immunohistochemistry of neurogenesis, and whole-cell patch-clamp measurements of synaptic plasticity. Depression-related behavior was assessed by the forced swimming test in adult animals. Neuropeptides and their receptors were determined by real-time PCR and immunoassay. Early maternal deprivation caused a loss of hippocampal volume, which returned to normal in adulthood. Adult neurogenesis was unaffected by early life stress. Long-term synaptic potentiation, however, was normal immediately after the end of the stress protocol but was impaired in adult animals. In the forced swimming test, adult animals that had been subjected to early life stress showed increased immobility time. Levels of substance P were increased both in young and adult animals after early deprivation.

Conclusion

Hippocampal volume was affected by early life stress but recovered in adulthood which corresponded to normal adult neurogenesis. Synaptic plasticity, however, exhibited a delayed impairment. The modulation of synaptic plasticity by early life stress might contribute to affective dysfunction in adulthood.     

Determination of calcium, sodium, potassium and magnesium concentrations in human senile cataractous lenses

Cataractous lenses have been found to have a distribution of the intracellular ionic environment, the concentrations of potassium and magnesium decreasing and the concentrations of sodium and calcium increasing relative to the cytosol of most cells. This arises as a result of changes to lens membrane characteristics causing an increase in lens membrane permeability. These changes have been found to be initiated as a result of normal ageing of the human lens. In this study, total Ca2+, K+, Na+ and Mg2+ contents have been determined in human normal and cataractous lenses using atomic absorption and flame emission spectroscopy. The normal human lens Ca2+ is between 0.15 and 0.5 miromol g(-1) fresh lens weight; in senile cataracts the value increased up to 9.31 micromol g(-1) ( p < 0.0001). The normal levels of Na+, Mg2+ and K+ are 20, 5.5 and 60 micromol g(-1) respectively; these changed to 136.10, 3.60 and 9.33 micro mol g(-1), respectively in cataractous senile human lenses ( p < 0.002, p < 0.002 and p < 0.01). The remarkable differences in these elements may play some role in cataractogenesis.     

Protein domain-based prediction of drug/compound–target interactions and experimental validation on LIM kinases

Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins’ structure/function, and bias in system training datasets. Here, we propose a new method “DRUIDom” (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying their interactions. As such, other proteins containing the same mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including those mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting drug/compound–target pairs (~2.9M data points), and used as training data for calculating parameters of compound–domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of ~5 million new compound–protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound–domain relationships. Datasets, results, and the source code of DRUIDom are fully-available at: https://github.com/cansyl/DRUIDom.     

Towards a Complete In Silico Assessment of the Outcome of Cochlear Implantation Surgery

Cochlear implantation (CI) surgery is a very successful technique, performed on more than 300,000 people worldwide. However, since the challenge resides in obtaining an accurate surgical planning, computational models are considered to provide such accurate tools. They allow us to plan and simulate beforehand surgical procedures in order to maximally optimize surgery outcomes, and consequently provide valuable information to guide pre-operative decisions. The aim of this work is to develop and validate computational tools to completely assess the patient-specific functional outcome of the CI surgery. A complete automatic framework was developed to create and assess computationally CI models, focusing on the neural response of the auditory nerve fibers (ANF) induced by the electrical stimulation of the implant. The framework was applied to evaluate the effects of ANF degeneration and electrode intra-cochlear position on nerve activation. Results indicate that the intra-cochlear positioning of the electrode has a strong effect on the global performance of the CI. Lateral insertion provides better neural responses in case of peripheral process degeneration, and it is recommended, together with optimized intensity levels, in order to preserve the internal structures. Overall, the developed automatic framework provides an insight into the global performance of the implant in a patient-specific way. This enables to further optimize the functional performance and helps to select the best CI configuration and treatment strategy for a given patient.