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1.
Bioinformatics ; 2022 Jan 18.
Artigo em Inglês | MEDLINE | ID: mdl-35040932

RESUMO

MOTIVATION: Alzheimer's disease (AD) is a complex brain disorder with risk genes incompletely identified. The candidate genes are dominantly obtained by computational approaches. In order to obtain biological insights of candidate genes or screen genes for experimental testing, it is essential to assess their relevance to AD. A platform that integrates different types of omics data and approaches would facilitate the analysis of candidate genes and is in great need. RESULTS: We report AlzCode, a platform for multiview analysis of genes related to AD. First, this platform integrates a rich collection of functional genomic data, including expression data of AD samples (gene expression, single-cell RNA-seq data, and protein expression), AD-specific biological networks (co-expression networks and functional gene networks), neuropathological and clinical traits (CERAD score, Braak staging score, Clinical Dementia Rating, cognitive function, and clinical severity), as well as general data such as protein-protein interaction, regulatory networks, sequence similarity and miRNA-target interactions. These data provide basis for analyzing genes from different views. Second, the platform integrates multiple approaches designed for the various types of data. We implement functions to analyze both individual genes and gene sets. We also compare AlzCode with two existing platforms for AD analysis, which are Agora and AD Altas. We pinpoint the features of each platform and highlight their differences. This platform would be valuable to the understanding of AD genetics and pathological mechanisms. AVAILABILITY AND IMPLEMENTATION: AlzCode is freely available at: http://www.alzcode.xyz. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

2.
IEEE Trans Med Imaging ; PP2022 Jan 12.
Artigo em Inglês | MEDLINE | ID: mdl-35020590

RESUMO

The accurate prediction of isocitrate dehydrogenase (IDH) mutation and glioma segmentation are important tasks for computer-aided diagnosis using preoperative multimodal magnetic resonance imaging (MRI). The two tasks are ongoing challenges due to the significant inter-tumor and intra-tumor heterogeneity. The existing methods to address them are mostly based on single-task approaches without considering the correlation between the two tasks. In addition, the acquisition of IDH genetic labels is expensive and costly, resulting in a limited number of IDH mutation data for modeling. To comprehensively address these problems, we propose a fully automated multimodal MRI-based multi-task learning framework for simultaneous glioma segmentation and IDH genotyping. Specifically, the task correlation and heterogeneity are tackled with a hybrid CNN-Transformer encoder that consists of a convolutional neural network and a transformer to extract the shared spatial and global information learned from a decoder for glioma segmentation and a multi-scale classifier for IDH genotyping. Then, a multi-task learning loss is designed to balance the two tasks by combining the segmentation and classification loss functions with uncertain weights. Finally, an uncertainty-aware pseudo-label selection is proposed to generate IDH pseudo-labels from larger unlabeled data for improving the accuracy of IDH genotyping by using semi-supervised learning. We evaluate our method on a multi-institutional public dataset. Experimental results show that our proposed multi-task network achieves promising performance and outperforms the single-task learning counterparts and other existing state-of-the-art methods. With the introduction of unlabeled data, the semi-supervised multi-task learning framework further improves the performance of glioma segmentation and IDH genotyping. The source codes of our framework are publicly available at https://github.com/miacsu/MTTU-Net.git.

3.
Bioact Mater ; 11: 240-253, 2022 May.
Artigo em Inglês | MEDLINE | ID: mdl-34977429

RESUMO

So far, how to achieve the optimal regenerative repair of large load-bearing bone defects using artificial bone grafts is a huge challenge in clinic. In this study, a strategy of combining osteoinductive biphasic calcium phosphate (BCP) bioceramic scaffolds with intramedullary nail fixation for creating stable osteogenic microenvironment was applied to repair large segmental bone defects (3.0 cm in length) in goat femur model. The material characterization results showed that the BCP scaffold had the initial compressive strength of over 2.0 MPa, and total porosity of 84%. The cell culture experiments demonstrated that the scaffold had the excellent ability to promote the proliferation and osteogenic differentiation of rat bone marrow-derived mesenchymal stem cells (BMSCs). The in vivo results showed that the intramedullary nail fixation maintained the initial stability and structural integrity of the implants at early stage, promoting the osteogenic process both guided and induced by the BCP scaffolds. At 9 months postoperatively, good integration between the implants and host bone was observed, and a large amount of newborn bones formed, accompanying with the degradation of the material. At 18 months postoperatively, almost the complete new bone substitution in the defect area was achieved. The maximum bending strength of the repaired bone defects reached to the 100% of normal femur at 18 months post-surgery. Our results demonstrated the good potential of osteoinductive BCP bioceramics in the regenerative repair of large load-bearing bone defects. The current study could provide an effective method to treat the clinical large segmental bone defects.

4.
NPJ Digit Med ; 5(1): 5, 2022 Jan 14.
Artigo em Inglês | MEDLINE | ID: mdl-35031687

RESUMO

While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital's image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform.

5.
Eur Radiol ; 32(1): 205-212, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34223954

RESUMO

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.


Assuntos
COVID-19 , Inteligência Artificial , Humanos , Estudos Retrospectivos , SARS-CoV-2 , Tomografia Computadorizada por Raios X
6.
Brief Bioinform ; 23(1)2022 Jan 17.
Artigo em Inglês | MEDLINE | ID: mdl-34953465

RESUMO

Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer's Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer's brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.

7.
Brief Bioinform ; 2021 Dec 07.
Artigo em Inglês | MEDLINE | ID: mdl-34874989

RESUMO

Investigating differentially methylated regions (DMRs) presented in different tissues or cell types can help to reveal the mechanisms behind the tissue-specific gene expression. The identified tissue-/disease-specific DMRs also can be used as feature markers for spotting the tissues-of-origins of cell-free DNA (cfDNA) in noninvasive diagnosis. In recent years, many methods have been proposed to detect DMRs. However, due to the lack of benchmark DMRs, it is difficult for researchers to choose proper methods and select desirable DMR sets for downstream studies. The application of DMRs, used as feature markers, can be benefited by the longer length of DMRs containing more CpG sites when a threshold is given for the methylation differences of DMRs. According to this, two metrics ($Qn$ and $Ql$), in which the CpG numbers and lengths of DMRs with different methylation differences are weighted differently, are proposed in this paper to evaluate the DMR sets predicted by different methods on BS-seq data. DMR sets predicted by eight methods on both simulated datasets and real BS-seq datasets are evaluated by the proposed metrics, the benchmark-based metrics, and the enrichment analysis of biological data, including genomic features, transcription factors and histones. The rank correlation analysis shows that the $Qn$ and $Ql$ are highly correlated to the benchmark metrics for simulated datasets and the biological data enrichment analysis for real BS-seq data. Therefore, with no need for additional biological data, the proposed metrics can help researchers selecting a more suitable DMR set on a certain BS-seq dataset.

8.
Nucleic Acids Res ; 2021 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-34850956

RESUMO

Repeats are prevalent in the genomes of all bacteria, plants and animals, and they cover nearly half of the Human genome, which play indispensable roles in the evolution, inheritance, variation and genomic instability, and serve as substrates for chromosomal rearrangements that include disease-causing deletions, inversions, and translocations. Comprehensive identification, classification and annotation of repeats in genomes can provide accurate and targeted solutions towards understanding and diagnosis of complex diseases, optimization of plant properties and development of new drugs. RepBase and Dfam are two most frequently used repeat databases, but they are not sufficiently complete. Due to the lack of a comprehensive repeat database of multiple species, the current research in this field is far from being satisfactory. LongRepMarker is a new framework developed recently by our group for comprehensive identification of genomic repeats. We here propose msRepDB based on LongRepMarker, which is currently the most comprehensive multi-species repeat database, covering >80 000 species. Comprehensive evaluations show that msRepDB contains more species, and more complete repeats and families than RepBase and Dfam databases. (https://msrepdb.cbrc.kaust.edu.sa/pages/msRepDB/index.html).

9.
J Immunother Cancer ; 9(12)2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34862254

RESUMO

BACKGROUND: Induction of CD8+ T cells that recognize immunogenic, mutated protein fragments in the context of major histocompatibility class I (MHC-I) is a pressing challenge for cancer vaccine development. METHODS: Using the commonly used murine renal adenocarcinoma RENCA cancer model, MHC-I restricted neoepitopes are predicted following next-generation sequencing. Candidate neoepitopes are screened in mice using a potent cancer vaccine adjuvant system that converts short peptides into immunogenic nanoparticles. An identified functional neoepitope vaccine is then tested in various therapeutic experimental tumor settings. RESULTS: Conversion of 20 short MHC-I restricted neoepitope candidates into immunogenic nanoparticles results in antitumor responses with multivalent vaccination. Only a single neoepitope candidate, Nesprin-2 L4492R (Nes2LR), induced functional responses but still did so when included within 20-plex or 60-plex particles. Immunization with the short Nes2LR neoepitope with the immunogenic particle-inducing vaccine adjuvant prevented tumor growth at doses multiple orders of magnitude less than with other vaccine adjuvants, which were ineffective. Nes2LR vaccination inhibited or eradicated disease in subcutaneous, experimental lung metastasis and orthotopic tumor models, synergizing with immune checkpoint blockade. CONCLUSION: These findings establish the feasibility of using short, MHC-I-restricted neoepitopes for straightforward immunization with multivalent or validated neoepitopes to induce cytotoxic CD8+ T cells. Furthermore, the Nes2LR neoepitope could be useful for preclinical studies involving renal cell carcinoma immunotherapy.

10.
Nucleic Acids Res ; 2021 Nov 26.
Artigo em Inglês | MEDLINE | ID: mdl-34850130

RESUMO

Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.

11.
Front Microbiol ; 12: 723818, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34925252

RESUMO

COVID-19 is a severe disease in humans, as highlighted by the current global pandemic. Several studies about the metabolome of COVID-19 patients have revealed metabolic disorders and some potential diagnostic markers during disease progression. However, the longitudinal changes of metabolomics in COVID-19 patients, especially their association with disease progression, are still unclear. Here, we systematically analyzed the dynamic changes of the serum metabolome of COVID-19 patients, demonstrating that most of the metabolites did not recover by 1-3 days before discharge. A prominent signature in COVID-19 patients comprised metabolites of amino acids, peptides, and analogs, involving nine essential amino acids, 10 dipeptides, and four N-acetylated amino acids. The levels of 12 metabolites in amino acid metabolism, especially three metabolites of the ornithine cycle, were significantly higher in severe patients than in mild ones, mainly on days 1-3 or 4-6 since onset. Integrating blood metabolomic, biochemical, and cytokine data, we uncovered a highly correlated network, including 6 cytokines, 13 biochemical parameters, and 49 metabolites. Significantly, five ornithine cycle-related metabolites (ornithine, N-acetylornithine, 3-amino-2-piperidone, aspartic acid, and asparagine) highly correlated with "cytokine storms" and coagulation index. We discovered that the ornithine cycle dysregulation significantly correlated with inflammation and coagulation in severe patients, which may be a potential mechanism of COVID-19 pathogenicity. Our study provided a valuable resource for detailed exploration of metabolic factors in COVID-19 patients, guiding metabolic recovery, understanding the pathogenic mechanisms, and creating drugs against SARS-CoV-2 infection.

12.
Artigo em Inglês | MEDLINE | ID: mdl-34951851

RESUMO

Nuclei segmentation is an essential step in DNA ploidy analysis by image-based cytometry (DNA-ICM) which is widely used in cytopathology and allows an objective measurement of DNA content (ploidy). The routine fully supervised learning-based method requires often tedious and expensive pixel-wise labels. In this paper, we propose a novel weakly supervised nuclei segmentation framework which exploits only sparsely annotated bounding boxes, without any segmentation labels. The key is to integrate the traditional image segmentation and self-training into fully supervised instance segmentation. We first leverage the traditional segmentation to generate coarse masks for each box-annotated nucleus to supervise the training of a teacher model, which is then responsible for both the refinement of these coarse masks and pseudo labels generation of unlabeled nuclei. These pseudo labels and refined masks along with the original manually annotated bounding boxes jointly supervise the training of student model. Both teacher and student share the same architecture and especially the student is initialized by the teacher. We have extensively evaluated our method with both our DNA-ICM dataset and public cytopathological dataset. Without bells and whistles, our method outperforms all existing weakly supervised entries on both datasets.

13.
PLoS One ; 16(12): e0259985, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34905540

RESUMO

Situated at a geographic crossroads, the eastern Tianshan Mountain region in northwest China is crucial to understanding various economic, social, and cultural developments on the Eurasian Steppes. One promising way to gain a better knowledge of ancient subsistence economy, craft production, and social change in the eastern Tianshan Mountain region is to study the artifact assemblages from archaeological contexts. Here, we present an analysis of 488 worked animal bones from the large site of Shirenzigou (ca. 1300-1 BCE), to date the largest assemblage of this kind uncovered in the eastern Tianshan Mountain region. We classified these worked bones into six categories, including "ritual objects", "ornaments", "tools", "worked astragali", "warfare and mobility", and "indeterminate". The identification of animal species and skeletal elements indicates that worked bones from Shirenzigou are characterized by a predominance of caprine products, particularly worked astragali, which is consistent with the large proportion of caprine fragments found in animal remains associated with food consumption. This demonstrates the contribution of caprine pastoralism to bone working activities at Shirenzigou. The making of most worked bones does not appear to have required advanced or specialized skills. Considering the absence of dedicated bone working space, alongside the variability in raw material selection and in dimensions of certain types of artifacts, we infer that worked bone production at Shirenzigou was not standardized. In terms of raw material selection and mode of production, Shirenzigou differed from their settled, farming counterparts in the Yellow River valley of northern China. In addition, along with the evidence for violence and horseback riding, the increasing use of bone artifacts associated with warfare and mobility during the late occupation phase of Shirenzigou reflects growing social instability and implies the likely emergence of single mounted horsemen, equipped with light armors, in the region during the late first millennium BCE. Our results provide new insights into animal resource exploitation and changing lifeways of early pastoral societies in the eastern Tianshan Mountain region, expanding our knowledge of the economic, social, and political milieu of late Bronze Age and early Iron Age eastern Eurasia.


Assuntos
Agricultura/história , Criação de Animais Domésticos/história , Dieta Paleolítica/história , Guerra/história , Animais , Arqueologia/métodos , Osso e Ossos/anatomia & histologia , Comportamento Ritualístico , China , Cabras , História Antiga , Humanos
14.
PLoS One ; 16(11): e0255547, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34735446

RESUMO

In this study, the complete mitogenome of Lysmata vittata (Crustacea: Decapoda: Hippolytidae) has been determined. The genome sequence was 22003 base pairs (bp) and it included thirteen protein-coding genes (PCGs), twenty-two transfer RNA genes (tRNAs), two ribosomal RNA genes (rRNAs) and three putative control regions (CRs). The nucleotide composition of AT was 71.50%, with a slightly negative AT skewness (-0.04). Usually the standard start codon of the PCGs was ATN, while cox1, nad4L and cox3 began with TTG, TTG and GTG. The canonical termination codon was TAA, while nad5 and nad4 ended with incomplete stop codon T, and cox1 ended with TAG. The mitochondrial gene arrangement of eight species of the Hippolytidae were compared with the order of genes of Decapoda ancestors, finding that the gene arrangement order of the Lebbeus groenlandicus had not changed, but the gene arrangement order of other species changed to varying degrees. The positions of the two tRNAs genes (trnA and trnR) of the L. vittata had translocations, which also showed that the Hippolytidae species were relatively unconserved in evolution. Phylogenetic analysis of 50 shrimp showed that L. vittata formed a monophyletic clade with Lysmata/Exhippolysmata species. This study should be helpful to better understand the evolutionary status, and population genetic diversity of L. vittata and related species.

15.
J Xray Sci Technol ; 2021 Oct 29.
Artigo em Inglês | MEDLINE | ID: mdl-34719471

RESUMO

High-energy, high-dose, microfocus X-ray computed tomography (HHM CT) is one of the most effective methods for high-resolution X-ray radiography inspection of high-density samples with fine structures. Minimizing the effective focal spot size of the X-ray source can significantly improve the spatial resolution and the quality of the sample images, which is critical and important for the performance of HHM CT. The objective of this study is to present a 9 MeV HHM CT prototype based on a high-average-current photo-injector in which X-rays with about 70µm focal spot size are produced via using tightly focused electron beams with 65/66µm beam size to hit an optimized tungsten target. In digital radiography (DR) experiment using this HHM CT, clear imaging of a standard 0.1 mm lead DR resolution phantom reveals a resolution of 6 lp/mm (line pairs per mm), while a 5 lp/mm resolution is obtained in CT mode using another resolution phantom made of 10 mm ferrum. Moreover, comparing with the common CT systems, a better turbine blade prototype image was obtained with this HHM CT system, which also indicates the promising application potentials of HHM CT in non-destructive inspection or testing for high-density fine-structure samples.

16.
Radiother Oncol ; 166: 44-50, 2021 Nov 11.
Artigo em Inglês | MEDLINE | ID: mdl-34774651

RESUMO

PURPOSE: This study aimed to evaluate whether high-energy X-rays (HEXs) of the PARTER (platform for advanced radiotherapy research) platform built on CTFEL (Chengdu THz Free Electron Laser facility) can produce ultrahigh dose rate (FLASH) X-rays and trigger the FLASH effect. MATERIALS AND METHODS: EBT3 radiochromic film and fast current transformer (FCT) devices were used to measure absolute dose and pulsed beam current of HEXs. Subcutaneous tumor-bearing mice and healthy mice were treated with sham, FLASH, and conventional dose rate radiotherapy (CONV), respectively to observe the tumor control efficiency and normal tissue damage. RESULTS: The maximum dose rate of HEXs of PARTER was up to over 1000 Gy/s. Tumor-bearing mice experiment showed a good result on tumor control (p < 0.0001) and significant difference in survival curves (p < 0.005) among the three groups. In the thorax-irradiated healthy mice experiment, there was a significant difference (p = 0.038) in survival among the three groups, with the risk of death decreased by 81% in the FLASH group compared to that in the CONV group. The survival time of healthy mice irradiated in the abdomen in the FLASH group was undoubtedly higher (62.5% of mice were still alive when we stopped observation) than that in the CONV group (7 days). CONCLUSION: This study confirmed that HEXs of the PARTER system can produce ultrahigh dose rate X-rays and trigger a FLASH effect, which provides a basis for future scientific research and clinical application of HEX in FLASH radiotherapy.

17.
J Basic Microbiol ; 2021 Nov 19.
Artigo em Inglês | MEDLINE | ID: mdl-34796543

RESUMO

A strain was isolated from an activated sludge system and identified as Halomonas piezotolerans HN2 in this study, which is the first strain in H. piezotolerans with the capability of heterotrophic nitrification and aerobic denitrification. Strain HN2 showed the maximum nitrogen removal rate of 9.10 mg/L/h by utilizing ammonium at the salinity of 3.0%. Under saline environment, HN2 could remove nitrogen efficiently in neutral and slightly alkaline environments, with the carbon sources of sodium succinate and sodium citrate and the C/N ratio of 15-20, and the maximum removal efficiencies of ammonium, nitrite, and nitrate were 100%, 96.35%, and 99.7%, respectively. The genomic information revealed the presence of amoA, napA, and nosZ genes in strain HN2, and the target bands of nirS were obtained via a polymerase chain reaction. Therefore, we inferred that ammonium was mainly utilized for the growth of strain HN2 through assimilation, and another part of the initial ammonium was converted into nitrate through nitrification, and then into gaseous nitrogen through denitrification. This report indicated the potential application of strain HN2 and other nitrifying and denitrifying Halomonas strains in the removal of nitrogen pollution in marine-related environments and also implies the important role of Halomonas in the nitrogen cycle process of the ocean.

18.
Anal Chem ; 93(46): 15474-15481, 2021 11 23.
Artigo em Inglês | MEDLINE | ID: mdl-34775758

RESUMO

We demonstrate the practicability of cavity-enhanced Raman spectroscopy (CERS) with a folded multipass cavity as a unique tool for the detection of hazardous gases in the atmosphere. A four-mirror Z-sharped multipass cavity results in a greatly extended laser-gas interaction length to improve the Raman signal intensity of gases. For Raman intensity maximization, the optimal number of intracavity beams of a single reflection cycle is calculated and then the cavity parameters are designed. A total of 360 intracavity beams are realized, which are circulated four times in the cavity based on the polarization. ppb-Level Raman gas sensing at atmospheric pressure for several typical explosive gases and toxic gases in ambient air, including hydrogen (H2), methane (CH4), carbon monoxide (CO), hydrogen sulfide (H2S), and chlorine (Cl2), is achieved at 300 s exposure time. Our CERS apparatus, which can detect multiple gases simultaneously with ultrahigh sensitivity and high selectivity, is powerful for detecting hazardous gases in the atmosphere, and it has excellent potential for environmental safety monitoring.


Assuntos
Gases , Análise Espectral Raman , Monóxido de Carbono , Hidrogênio , Metano
19.
Artigo em Inglês | MEDLINE | ID: mdl-34777534

RESUMO

To investigate the antiatherosclerotic effects of flavonoids extracted from Apocynum venetum (AVF) leaves in atherosclerotic rats and the underlying mechanisms, a total of 72 male Wistar rats were randomly divided into six groups: control group, model group, simvastatin group, low-dose AVF group, medium-dose AVF group, and high-dose AVF group. Atherosclerosis in rats was induced with a high-fat diet and an intraperitoneal injection of VD3 once daily for three contiguous days at a total injection dose of 70 U/kg. At the end of the 13th week, total serum cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) contents were measured. The hematoxylin-eosin (HE) staining was applied to evaluate the morphological changes. The ELISA method was used to detect related inflammatory factors and oxidative stress indicators. The corresponding protein expression and the mRNA level were detected by western blot analysis and reverse transcriptase PCR. HE staining showed that the thoracic aorta wall was thickened, and the aortic subendothelial foam cells and lipid vacuoles were reduced in the medium/high-AVF groups. Similarly, the TC, TG, LDL-C, and malondialdehyde (MDA) levels in the model group were significantly higher, but the HDL-C level and superoxide dismutase (SOD) activity were lower than those of the control group, and these effects were ameliorated by treatment with simvastatin or AVF. ELISA results showed that compared with the control group, the model group C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) results were significantly increased, and the medium AVF and high AVF could significantly reduce the expression of related inflammatory factors. The AVF inhibited intercellular cell adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), and E-selectin mRNA and related protein expression in the aorta in atherosclerotic rats. Western blot analysis also showed that AVF can significantly reduce the protein expression of fractalkine (FKN), spleen tyrosine kinase (SYK), and p38 mitogen-activated protein kinase (p38) in the rat aorta. We believe that the AVF can effectively reduce blood lipid levels in rats with atherosclerosis and delay atherosclerotic progression by inhibiting excessive inflammatory factors and inhibiting related adhesion factors. The underlying mechanism may be related to the FKN/SYK/p38 signaling pathway activity. Our results contribute to validating the traditional use of the Apocynum leaf extract in the treatment of atherosclerosis.

20.
Methods ; 2021 Oct 24.
Artigo em Inglês | MEDLINE | ID: mdl-34700014

RESUMO

Similar diseases are usually caused by molecular origins or similar phenotypes. Confirming the relationship between diseases can help researchers gain a deep insight of the pathogenic mechanisms of emerging complex diseases, and improve the corresponding diagnoses and treatment. Therefore, similar diseases are considerably important in biology and pathology. However, the insufficient number of labelled similar disease pairs cannot support the optimal training of the models. In this paper, we propose a Multi-Task Graph Neural Network (MTGNN) framework to measure disease similarity by few-shot learning. To tackle the problem of insufficient number of labelled similar disease pairs, we design the multi-task optimization strategy to train the graph neural network for disease similarity task (lack of labelled training data) by introducing link prediction task (sufficient labelled training data). The similarity between diseases can then be obtained by measuring the distance between disease embeddings in high-dimensional space learning from the double tasks. The experiment results evaluate the performance of MTGNN and illustrate its advantages over previous methods on few labeled training dataset.

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