Notes

Blood pressure levels variation as well as analysis in aneurysmal subarachnoid lose blood: a systematic review as well as meta-analysis.
Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug-disease or drug-target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.With the development of high-throughput sequencing technology, the genomic sequences increased exponentially over the last decade. In order to decode these new genomic data, machine learning methods were introduced for genome annotation and analysis. Ezatiostat purchase to the requirement of most machines learning methods, the biological sequences must be represented as fixed-length digital vectors. In this representation procedure, the physicochemical properties of k-tuple nucleotides are important information. However, the values of the physicochemical properties of k-tuple nucleotides are scattered in different resources. To facilitate the studies on genomic sequences, we developed the first comprehensive database, namely KNIndex (https//knindex.pufengdu.org), for depositing and visualizing physicochemical properties of k-tuple nucleotides. Currently, the KNIndex database contains 182 properties including one for mononucleotide (DNA), 169 for dinucleotide (147 for DNA and 22 for RNA) and 12 for trinucleotide (DNA). KNIndex database also provides a user-friendly web-based interface for the users to browse, query, visualize and download the physicochemical properties of k-tuple nucleotides. With the built-in conversion and visualization functions, users are allowed to display DNA/RNA sequences as curves of multiple physicochemical properties. We wish that the KNIndex will facilitate the related studies in computational biology.Parkinson's disease is clinically defined by bradykinesia, along with rigidity and tremor. However, the severity of these motor signs is greatly variable between individuals, particularly the presence or absence of tremor. This variability in tremor relates to variation in cognitive/motivational impairment, as well as the spatial distribution of neurodegeneration in the midbrain and dopamine depletion in the striatum. Here we ask whether interindividual heterogeneity in tremor symptoms could account for the puzzlingly large variability in the effects of dopaminergic medication on reinforcement learning, a fundamental cognitive function known to rely on dopamine. Given that tremor-dominant and non-tremor Parkinson's disease patients have different dopaminergic phenotypes, we hypothesized that effects of dopaminergic medication on reinforcement learning differ between tremor-dominant and non-tremor patients. Forty-three tremor-dominant and 20 non-tremor patients with Parkinson's disease were recruited to be tespe in future work.
Computational methods accelerate drug discovery and play an important role in biomedicine, such as molecular property prediction and compound-protein interaction (CPI) identification. A key challenge is to learn useful molecular representation. #link# In the early years, molecular properties are mainly calculated by quantum mechanics or predicted by traditional machine learning methods, which requires expert knowledge and is often labor-intensive. Nowadays, graph neural networks have received significant attention because of the powerful ability to learn representation from graph data. Nevertheless, current graph-based methods have some limitations that need to be addressed, such as large-scale parameters and insufficient bond information extraction.

In this study, we proposed a graph-based approach and employed a novel triplet message mechanism to learn molecular representation efficiently, named triplet message networks (TrimNet). We show that TrimNet can accurately complete multiple molecular representation learning tasks with significant parameter reduction, including the quantum properties, bioactivity, physiology and CPI prediction. In the experiments, TrimNet outperforms the previous state-of-the-art method by a significant margin on various datasets. Besides the few parameters and high prediction accuracy, TrimNet could focus on the atoms essential to the target properties, providing a clear interpretation of the prediction tasks. These advantages have established TrimNet as a powerful and useful computational tool in solving the challenging problem of molecular representation learning.

The quantum and drug datasets are available on the website of MoleculeNet http//moleculenet.ai. The source code is available in GitHub https//github.com/yvquanli/trimnet.

[email protected], [email protected].
[email protected], [email protected] simple screening analysis of cyanide in blood has been developed, using 4-(4,6-dimethoxy-1,3,5-triazin-2-yl)-4-methylmorpholinium chloride (DMTMM). DMTMM, a convenient reagent for dehydrocondensation, converted cyanide to 2-cyano-4,6-dimethoxy-1,3,5-triazine, the dimethoxytriazinyl derivative of cyanide. This reaction proceeded in whole blood samples after treatment with trichloroacetic acid, and in basic aqueous solution samples. Sufficient sensitivity was observed by the method using gas chromatography/mass spectrometry. Intra- and inter-day repeated analyses (0.05, 0.1, 0.25, 1 and 5 μg/mL, n = 5) were performed and the accuracy and precision were within 20% for the lower limit of quantification (LLOQ) and within 15% for other concentrations. LLOQs for the aqueous solution and blood were 0.05 and 0.1 μg/mL, respectively, which are suitable for detecting cyanide poisoning. The limits of detection (signal-to-noise ratio ≥ 3) for aqueous solution and blood were 0.01 and 0.05 μg/mL, respectively. Interference from 13 other anions was tested and no false positive response was obtained, even in the case of thiocyanate, nitrite and nitrate, which are known to yield cyanide by acid treatment of blood.
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