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1.
Glob Chang Biol ; 2021 Mar 05.
Artigo em Inglês | MEDLINE | ID: mdl-33666308

RESUMO

A quantitative understanding of physiological thermal responses is vital for forecasting species distributional shifts in response to climate change. Many studies have focused on metabolic rate as a global metric for analyzing the sublethal effects of changing environments on physiology. Thermal performance curves (TPCs) have been suggested as a viable analytical framework, but standard TPCs may not fully capture physiological responses, due in part to failure to consider the process of metabolic depression. We derived a model based on the nonlinear regression of biological temperature-dependent rate processes and built a heart rate data set for 26 species of intertidal molluscs distributed from 33°S to ~40°N. We then calculated physiological thermal performance limits with continuous heating using T 1 / 2 H , the temperature at which heart rate is decreased to 50% of the maximal rate, as a more realistic measure of upper thermal limits. Results indicate that heat-induced metabolic depression of cardiac performance is a common adaptive response that allows tolerance of harsh environments. Furthermore, our model accounted for the high inter-individual variability in the shape of cardiac TPCs. We then used these TPCs to calculate physiological thermal safety margins (pTSM), the difference between the maximal operative temperature (95th percentile of field temperatures) and T 1 / 2 H of each individual. Using pTSMs, we developed a physiological species distribution model (pSDM) to forecast future geographic distributions. pSDM results indicate that climate-induced species range shifts are potentially less severe than predicted by a simple correlative SDM. Species with metabolic depression below the optimum temperature will be more thermal resistant at their warm trailing edges. High intraspecific variability further suggests that models based on species-level vulnerability to environmental change may be problematic. This multi-scale, mechanistic understanding that incorporates metabolic depression and inter-individual variability in thermal response enables better predictions about the relationship between thermal stress and species distributions.

2.
Cardiovasc Ultrasound ; 19(1): 8, 2021 Jan 14.
Artigo em Inglês | MEDLINE | ID: mdl-33446185

RESUMO

BACKGROUND: The purpose of this study was to explore echocardiographic views and methods of aortic arch anomalies in infants, so as to improve the screening sensitivity and diagnostic accuracy. METHODS: 140 children with abnormal aortic arch diagnosed by ultrasound in Children's Hospital of Hebei Province from January 2014 to December 2019 were selected for retrospective analysis. All were confirmed by surgery or/and computerized tomography angiography. Series of views for aortic arch (the three-vessel and tracheal view, aortic arch short axis view, left aortic arch long axis view, aortic arch long axis continuous scan views) were performed in all cases on the basis of the routine views of echocardiography. The screening sensitivity and diagnostic coincidence rate of different echocardiographic views for aortic arch anomalies were analyzed. RESULTS: Among the 140 infants, right aortic arch were 21 cases (6/21 were accompanied by mirror branch and 15/21 were with aberrant left subclavian artery). Left aortic arch with aberrant right subclavian artery were 2 cases, and double aortic arch with both arches open were 20 cases. Double aortic arch with left arch atresia were 2 cases, and atresia of the proximal aorta with aortic arch dysplasia was 1 case. Coarctation of the aorta were 67 cases, and interruption of aortic arch were 27 cases. All the patients were correctly diagnosed except that 2 infants with interruption of aortic arch were incorrectly diagnosed as coarctation of the aorta, and 1 infant with coarctation of the aorta was misdiagnosed as interruption of aortic arch by echocardiography. The screening sensitivities of four views and four-view combination for abnormal aortic arch were 99.3, 73.6, 87.1, 99.3, and 100%; the diagnostic coincidence rates were 85.7, 27.1,66.4, 95.0%, and 97.9% respectively. On the basis of traditional left aortic long axis view, other three views had their own advantages. The screening sensitivity and diagnostic coincidence rate of four-view combination were significantly improved. CONCLUSIONS: The three-vessel trachea view is simple and feasible, which is suitable for screening abnormal aortic arch. The combination of four views conduces to improving screening sensitivity and diagnostic accuracy of aortic arch abnormalities.

3.
Commun Biol ; 4(1): 99, 2021 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-33483602

RESUMO

The clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

4.
Brief Bioinform ; 2020 Dec 16.
Artigo em Inglês | MEDLINE | ID: mdl-33320930

RESUMO

CyanoPATH is a database that curates and analyzes the common genomic functional repertoire for cyanobacteria harmful algal blooms (CyanoHABs) in eutrophic waters. Based on the literature of empirical studies and genome/protein databases, it summarizes four types of information: common biological functions (pathways) driving CyanoHABs, customized pathway maps, classification of blooming type based on databases and the genomes of cyanobacteria. A total of 19 pathways are reconstructed, which are involved in the utilization of macronutrients (e.g. carbon, nitrogen, phosphorus and sulfur), micronutrients (e.g. zinc, magnesium, iron, etc.) and other resources (e.g. light and vitamins) and in stress resistance (e.g. lead and copper). These pathways, comprised of both transport and biochemical reactions, are reconstructed with proteins from NCBI and reactions from KEGG and visualized with self-created transport/reaction maps. The pathways are hierarchical and consist of subpathways, protein/enzyme complexes and constituent proteins. New cyanobacterial genomes can be annotated and visualized for these pathways and compared with existing species. This set of genomic functional repertoire is useful in analyzing aquatic metagenomes and metatranscriptomes in CyanoHAB research. Most importantly, it establishes a link between genome and ecology. All these reference proteins, pathways and maps and genomes are free to download at http://www.csbg-jlu.info/CyanoPATH.

5.
6.
Sci Adv ; 6(44)2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-33127686

RESUMO

Efficient single-cell assignment without prior marker gene annotations is essential for single-cell sequencing data analysis. Current methods, however, have limited effectiveness for distinct single-cell assignment. They failed to achieve a well-generalized performance in different tasks because of the inherent heterogeneity of different single-cell sequencing datasets and different single-cell types. Furthermore, current methods are inefficient to identify novel cell types that are absent in the reference datasets. To this end, we present scLearn, a learning-based framework that automatically infers quantitative measurement/similarity and threshold that can be used for different single-cell assignment tasks, achieving a well-generalized assignment performance on different single-cell types. We evaluated scLearn on a comprehensive set of publicly available benchmark datasets. We proved that scLearn outperformed the comparable existing methods for single-cell assignment from various aspects, demonstrating state-of-the-art effectiveness with a reliable and generalized single-cell type identification and categorizing ability.

7.
Am J Hum Genet ; 107(5): 977-988, 2020 11 05.
Artigo em Inglês | MEDLINE | ID: mdl-33058759

RESUMO

PRKACA and PRKACB code for two catalytic subunits (Cα and Cß) of cAMP-dependent protein kinase (PKA), a pleiotropic holoenzyme that regulates numerous fundamental biological processes such as metabolism, development, memory, and immune response. We report seven unrelated individuals presenting with a multiple congenital malformation syndrome in whom we identified heterozygous germline or mosaic missense variants in PRKACA or PRKACB. Three affected individuals were found with the same PRKACA variant, and the other four had different PRKACB mutations. In most cases, the mutations arose de novo, and two individuals had offspring with the same condition. Nearly all affected individuals and their affected offspring shared an atrioventricular septal defect or a common atrium along with postaxial polydactyly. Additional features included skeletal abnormalities and ectodermal defects of variable severity in five individuals, cognitive deficit in two individuals, and various unusual tumors in one individual. We investigated the structural and functional consequences of the variants identified in PRKACA and PRKACB through the use of several computational and experimental approaches, and we found that they lead to PKA holoenzymes which are more sensitive to activation by cAMP than are the wild-type proteins. Furthermore, expression of PRKACA or PRKACB variants detected in the affected individuals inhibited hedgehog signaling in NIH 3T3 fibroblasts, thereby providing an underlying mechanism for the developmental defects observed in these cases. Our findings highlight the importance of both Cα and Cß subunits of PKA during human development.

8.
J Biomech ; 111: 110002, 2020 Oct 09.
Artigo em Inglês | MEDLINE | ID: mdl-32898825

RESUMO

Lumped parameter model (LPM) is a common numerical model for hemodynamic simulation of human's blood circulatory system. The numerical simulation of enhanced external counterpulsation (EECP) is a typical biomechanical simulation process based on the LPM of blood circulatory system. In order to simulate patient-specific hemodynamic effects of EECP and develop best treatment strategy for each individual, this study developed an optimization algorithm to individualize LPM elements. Physiological data from 30 volunteers including approximate aortic pressure, cardiac output, ankle pressure and carotid artery flow were clinically collected as optimization objectives. A closed-loop LPM was established for the simulation of blood circulatory system. Aiming at clinical data, a sensitivity analysis for each element was conducted to identify the significant ones. We improved the traditional simulated annealing algorithm to iteratively optimize the sensitive elements. To verify the accuracy of the patient-specific model, 30 samples of simulated data were compared with clinical measurements. In addition, an EECP experiment was conducted on a volunteer to verify the applicability of the optimized model for the simulation of EECP. For these 30 samples, the optimization results show a slight difference between clinical data and simulated data. The average relative root mean square error is lower than 5%. For the subject of EECP experiment, the relative error of hemodynamic responses during EECP is lower than 10%. This slight error demonstrated a good state of optimization. The optimized modeling algorithm can effectively individualize the LPM for blood circulatory system, which is significant to the numerical simulation of patient-specific hemodynamics.

9.
Scand J Immunol ; 92(5): e12956, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32767795

RESUMO

In a healthy person, metabolically quiescent T lymphocytes (T cells) circulate between lymph nodes and peripheral tissues in search of antigens. Upon infection, some T cells will encounter cognate antigens followed by proliferation and clonal expansion in a context-dependent manner, to become effector T cells. These events are accompanied by changes in cellular metabolism, known as metabolic reprogramming. The magnitude and variation of metabolic reprogramming are, in addition to antigens, dependent on factors such as nutrients and oxygen to ensure host survival during various diseases. Herein, we describe how metabolic programmes define T cell subset identity and effector functions. In addition, we will discuss how metabolic programs can be modulated and affect T cell activity in health and disease using cancer and autoimmunity as examples.


Assuntos
Autoimunidade/imunologia , Metabolismo Energético/imunologia , Ativação Linfocitária/imunologia , Neoplasias/imunologia , Subpopulações de Linfócitos T/imunologia , Linfócitos T/imunologia , Animais , Microambiente Celular/imunologia , Humanos , Modelos Imunológicos , Neoplasias/metabolismo , Subpopulações de Linfócitos T/metabolismo , Linfócitos T/metabolismo
10.
Genes (Basel) ; 11(8)2020 Aug 04.
Artigo em Inglês | MEDLINE | ID: mdl-32759821

RESUMO

With the high prevalence of breast cancer, it is urgent to find out the intrinsic difference between various subtypes, so as to infer the underlying mechanisms. Given the available multi-omics data, their proper integration can improve the accuracy of breast cancer subtype recognition. In this study, DeepMO, a model using deep neural networks based on multi-omics data, was employed for classifying breast cancer subtypes. Three types of omics data including mRNA data, DNA methylation data, and copy number variation (CNV) data were collected from The Cancer Genome Atlas (TCGA). After data preprocessing and feature selection, each type of omics data was input into the deep neural network, which consists of an encoding subnetwork and a classification subnetwork. The results of DeepMO based on multi-omics on binary classification are better than other methods in terms of accuracy and area under the curve (AUC). Moreover, compared with other methods using single omics data and multi-omics data, DeepMO also had a higher prediction accuracy on multi-classification. We also validated the effect of feature selection on DeepMO. Finally, we analyzed the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which were discovered during the feature selection process. We believe that the proposed model is useful for multi-omics data analysis.

11.
Sensors (Basel) ; 20(13)2020 Jun 29.
Artigo em Inglês | MEDLINE | ID: mdl-32610685

RESUMO

To solve the problems of low teaching programming efficiency and poor flexibility in robot welding of complex box girder structures, a method of seam trajectory recognition based on laser scanning displacement sensing was proposed for automated guidance of a welding torch in the skip welding of a spatially intermittent welding seam. Firstly, a laser scanning displacement sensing system for measuring angles adaptively is developed to detect corner features of complex structures. Secondly, a weld trajectory recognition algorithm based on Euclidean distance discrimination is proposed. The algorithm extracts the shape features by constructing the characteristic triangle of the weld trajectory, and then processes the set of shape features by discrete Fourier analysis to solve the feature vector used to describe the shape. Finally, based on the Euclidean distance between the feature vector of the test sample and the class matching library, the class to which the sample belongs is identified to distinguish the weld trajectory. The experimental results show that the classification accuracy rate of four typical spatial discontinuous welds in complex box girder structure is 100%. The overall processing time for weld trajectory detection and classification does not exceed 65 ms. Based on this method, the field test was completed in the folding special container production line. The results show that the system proposed in this paper can accurately identify discontinuous welds during high-speed metal active gas arc welding (MAG) welding with a welding speed of 1.2 m/min, and guide the welding torch to automatically complete the skip welding, which greatly improves the welding manufacturing efficiency and quality stability in the processing of complex box girder components. This method does not require a time-consuming pre-welding teaching programming and visual inspection system calibration, and provides a new technical approach for highly efficient and flexible welding manufacturing of discontinuous welding seams of complex structures, which is expected to be applied to the welding manufacturing of core components in heavy and large industries such as port cranes, large logistics transportation equipment, and rail transit.

12.
J Bioinform Comput Biol ; 18(3): 2050017, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32576054

RESUMO

Membrane proteins play essential roles in modern medicine. In recent studies, some membrane proteins involved in ectodomain shedding events have been reported as the potential drug targets and biomarkers of some serious diseases. However, there are few effective tools for identifying the shedding event of membrane proteins. So, it is necessary to design an effective tool for predicting shedding event of membrane proteins. In this study, we design an end-to-end prediction model using deep neural networks with long short-term memory (LSTM) units and attention mechanism, to predict the ectodomain shedding events of membrane proteins only by sequence information. Firstly, the evolutional profiles are encoded from original sequences of these proteins by Position-Specific Iterated BLAST (PSI-BLAST) on Uniref50 database. Then, the LSTM units which contain memory cells are used to hold information from past inputs to the network and the attention mechanism is applied to detect sorting signals in proteins regardless of their position in the sequence. Finally, a fully connected dense layer and a softmax layer are used to obtain the final prediction results. Additionally, we also try to reduce overfitting of the model by using dropout, L2 regularization, and bagging ensemble learning in the model training process. In order to ensure the fairness of performance comparison, firstly we use cross validation process on training dataset obtained from an existing paper. The average accuracy and area under a receiver operating characteristic curve (AUC) of five-fold cross-validation are 81.19% and 0.835 using our proposed model, compared to 75% and 0.78 by a previously published tool, respectively. To better validate the performance of the proposed model, we also evaluate the performance of the proposed model on independent test dataset. The accuracy, sensitivity, and specificity are 83.14%, 84.08%, and 81.63% using our proposed model, compared to 70.20%, 71.97%, and 67.35% by the existing model. The experimental results validate that the proposed model can be regarded as a general tool for predicting ectodomain shedding events of membrane proteins. The pipeline of the model and prediction results can be accessed at the following URL: http://www.csbg-jlu.info/DeepSMP/.

13.
Bioprocess Biosyst Eng ; 43(11): 2017-2026, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32572568

RESUMO

Herein, we reported a green biosynthesis method of copper nanoparticles (CuNPs) at microwave irradiation condition by using pectin as a stabilizer and ascorbic acid as a reducing agent. Under the optimum conditions, CuNPs1 and 2 were synthesized under microwave times 0 and 3 min, respectively. Transmission electron microscope and scanning electron microscope (SEM) tests showed that CuNPs1 and 2 had irregular polygon particles with average diameters of 61.9 ± 19.4 and 40.9 ± 13.6 nm, respectively. Zeta potentials of CuNPs1 and 2 were -45.2 and -48.7 mV, respectively. X-ray diffraction, Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and X-ray photoelectron spectroscopy techniques were used to characterize the properties of CuNPs. Furthermore, inhibition zone tests showed that CuNPs2 exhibited higher antimicrobial activities against Escherichia coli, Staphylococcus aureus, and Aspergillus japonicus than CuNPs1. The antibacterial activities were also studied by the bacterial growth kinetics in broth media, and CuNPs2 exhibited lower minimum bactericidal concentrations than CuNPs1.

14.
BMC Bioinformatics ; 21(1): 237, 2020 Jun 09.
Artigo em Inglês | MEDLINE | ID: mdl-32517646

RESUMO

BACKGROUND: Compared with disease biomarkers in blood and urine, biomarkers in saliva have distinct advantages in clinical tests, as they can be conveniently examined through noninvasive sample collection. Therefore, identifying human saliva-secretory proteins and further detecting protein biomarkers in saliva have significant value in clinical medicine. There are only a few methods for predicting saliva-secretory proteins based on conventional machine learning algorithms, and all are highly dependent on annotated protein features. Unlike conventional machine learning algorithms, deep learning algorithms can automatically learn feature representations from input data and thus hold promise for predicting saliva-secretory proteins. RESULTS: We present a novel end-to-end deep learning model based on multilane capsule network (CapsNet) with differently sized convolution kernels to identify saliva-secretory proteins only from sequence information. The proposed model CapsNet-SSP outperforms existing methods based on conventional machine learning algorithms. Furthermore, the model performs better than other state-of-the-art deep learning architectures mostly used to analyze biological sequences. In addition, we further validate the effectiveness of CapsNet-SSP by comparison with human saliva-secretory proteins from existing studies and known salivary protein biomarkers of cancer. CONCLUSIONS: The main contributions of this study are as follows: (1) an end-to-end model based on CapsNet is proposed to identify saliva-secretory proteins from the sequence information; (2) the proposed model achieves better performance and outperforms existing models; and (3) the saliva-secretory proteins predicted by our model are statistically significant compared with existing cancer biomarkers in saliva. In addition, a web server of CapsNet-SSP is developed for saliva-secretory protein identification, and it can be accessed at the following URL: http://www.csbg-jlu.info/CapsNet-SSP/. We believe that our model and web server will be useful for biomedical researchers who are interested in finding salivary protein biomarkers, especially when they have identified candidate proteins for analyzing diseased tissues near or distal to salivary glands using transcriptome or proteomics.


Assuntos
Proteínas/química , Saliva/química , Humanos
15.
Front Physiol ; 11: 323, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32425805

RESUMO

The abnormal diameter of the coronary artery is twice or more than the normal diameter, which is a coronary artery aneurysm (CAA). According to the clinical statistics, CAA shows high occurrence on right coronary artery (RCA). The most common cause of CAA in adults is atherosclerosis, which destroys the elastic fibers in the middle layer of the blood vessel. Under the intravascular pressure, the weak wall bulges outward and form CAA. This article aims to explain the hemodynamic mechanism of coronary artery aneurysm shows high occurrence on RCA. Occurrence of CAA was simulated by the volume growth of coronary artery. Firstly, a 0-3D multi-scale model of normal coronary artery was constructed to obtain the hemodynamic environments of coronary artery. Then, fluid-structure interaction of normal and atherosclerotic blood vessel was performed to obtain volume growth rate of the coronary artery. Atherosclerosis was simulated by modifying Young's modulus in middle layer of the blood vessel. Finally, creep simulation was performed to compare the deformation of the blood vessels under the accumulation of time. Under normal condition, the volume growth rate of the RCA is 2.28 times and 1.55 times of the LAD and the LCX. After atherosclerosis, the volume growth rate of the RCA was 2.69 times and 2.12 times of the LAD and the LCX. And the volume growth rate of the RCA was 3.85 times and 3.45 times of the LAD and the LCX after further deepening of atherosclerosis. The expansion time above the average volume growth rate of the RCA, the LAD and the LCX respectively were 0.194, 0.168 and 0.179 s. The RCA is 2.06 times the original, the LAD and LCX are 1.53 times and 1.56 times after 10 years in creep simulation. It can be concluded that the RCA is more prone to aneurysms originated from the larger expansion of the RCA under normal physiological condition, and the larger expansion is magnified under atherosclerosis condition with destroyed vessel elasticity, and further magnified during the time accumulated viscoelastic creep to develop to aneurysm eventually.

16.
Int J Biol Macromol ; 148: 956-968, 2020 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-31972200

RESUMO

Starch extracted from lily bulb (Lilium brownii var. Viridulum Baker) was modified via heat-moisture treatment (HMT) at different moisture levels (15-35%) and acid treatment (AT) with hydrochloric acid at five different concentrations (0.25-2.0 M). The effects of HMT and AT on the physicochemical properties and in vitro digestibility of lily starch were investigated. HMT and AT led to the clustering of the starch granules, whose surface became rougher, thereby increasing the particle size. X-ray diffraction results showed that HMT increased the relative crystallinity and transformed the crystalline structure from B- to A-type. The relative crystallinity and X-ray patterns of the AT starch significantly increased. The swelling power of HMT and AT starch was significantly reduced, whereas the solubility of HMT starch decreased. The solubility of AT starch was significantly higher than that of native starch (NS) (p < 0.05). Differential scanning calorimetry revealed that the gelatinization temperature of lily starch was higher than that of NS after two modifications, whereas the gelatinization enthalpy of the NS was lower than that of the modified samples. The starch with HMT at 25% showed the highest resistant starch content of 44.15% in cooked samples.


Assuntos
Lilium/química , Extratos Vegetais/química , Amido/química , Cristalização , Digestão , Temperatura Alta , Ácido Clorídrico/química , Hidrogéis/química , Estrutura Molecular , Tamanho da Partícula , Transição de Fase , Solubilidade , Propriedades de Superfície , Termodinâmica , Temperatura de Transição , Água
17.
Biomolecules ; 9(12)2019 12 03.
Artigo em Inglês | MEDLINE | ID: mdl-31816983

RESUMO

This study aimed to compare the flavonoid accumulation between ozone-treated and untreated Satsuma mandarin (Citrus unshiu Marc.) fruits. The fruits exposed to gaseous ozone were found to have higher antioxidant activities and content of flavonoid during the storage period by ultra-high performance liquid chromatography (UPLC). To reveal the molecular regulation of flavonoid accumulation by ozone, chalcone synthase (CHS), chalcone isomerase (CHI), ß-1,3-glucanase (GLU), chitinase (CHT), phenylalanine ammonia-lyase (PAL), and peroxidase (POD) were identified and their expression was examined by quantitative real-time polymerase chain reaction (q-PCR). These results support the promising application of ozone treatment as a safe food preservation technique for controlling postharvest disease and extending shelf-life of harvested Satsuma mandarin.


Assuntos
Citrus/química , Flavonoides/análise , Ozônio/farmacologia , Antioxidantes/análise , Vias Biossintéticas/efeitos dos fármacos , Cromatografia Líquida de Alta Pressão , Citrus/efeitos dos fármacos , Citrus/genética , Flavonoides/biossíntese , Armazenamento de Alimentos , Regulação da Expressão Gênica de Plantas/efeitos dos fármacos , Extratos Vegetais/química , Proteínas de Plantas/genética
18.
Echocardiography ; 36(12): 2274-2277, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31742745

RESUMO

Double aortic arch with atretic left arch distal to the origin of left subclavian artery is a rare type of vascular ring, and it can be easily confused with the right aortic arch with mirror branching. We provided a rare case of a 10-month-old infant with dyspnea. Echocardiography showed a suspicious double aortic arch with atretic left arch distal to the origin of left subclavian artery, which was confirmed intra-operatively. We summarize ultrasonic image characteristics of the disease and combine it with computed tomography angiography, bronchoscopy, and clinical symptoms in order to improve the detection rate and treatment strategy.


Assuntos
Aorta Torácica/anormalidades , Doenças da Aorta/complicações , Dispneia/etiologia , Artéria Subclávia/diagnóstico por imagem , Angiografia , Aorta Torácica/diagnóstico por imagem , Doenças da Aorta/congênito , Doenças da Aorta/diagnóstico , Broncoscopia , Diagnóstico Diferencial , Dispneia/diagnóstico , Ecocardiografia , Humanos , Lactente , Masculino , Tomografia Computadorizada por Raios X
19.
Sci Rep ; 9(1): 14930, 2019 10 17.
Artigo em Inglês | MEDLINE | ID: mdl-31624300

RESUMO

Owing to the diversity of pulse-wave morphology, pulse-based diagnosis is difficult, especially pulse-wave-pattern classification (PWPC). A powerful method for PWPC is a convolutional neural network (CNN). It outperforms conventional methods in pattern classification due to extracting informative abstraction and features. For previous PWPC criteria, the relationship between pulse and disease types is not clear. In order to improve the clinical practicability, there is a need for a CNN model to find the one-to-one correspondence between pulse pattern and disease categories. In this study, five cardiovascular diseases (CVD) and complications were extracted from medical records as classification criteria to build pulse data set 1. Four physiological parameters closely related to the selected diseases were also extracted as classification criteria to build data set 2. An optimized CNN model with stronger feature extraction capability for pulse signals was proposed, which achieved PWPC with 95% accuracy in data set 1 and 89% accuracy in data set 2. It demonstrated that pulse waves are the result of multiple physiological parameters. There are limitations when using a single physiological parameter to characterise the overall pulse pattern. The proposed CNN model can achieve high accuracy of PWPC while using CVD and complication categories as classification criteria.


Assuntos
Doenças Cardiovasculares/diagnóstico , Frequência Cardíaca/fisiologia , Redes Neurais de Computação , Diagnóstico Tradicional pelo Pulso/métodos , Doenças Cardiovasculares/fisiopatologia , Conjuntos de Dados como Assunto , Estudos de Viabilidade , Humanos
20.
Molecules ; 24(17)2019 Aug 22.
Artigo em Inglês | MEDLINE | ID: mdl-31443455

RESUMO

The effects of two different processing methods on the volatile components of candied kumquats were investigated via headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). The characteristic volatile fingerprints of fresh kumquats (FKs), vacuum sugaring osmosis combined with hot-air drying kumquats (VS-ADKs), and atmospheric pressure sugaring osmosis combined with hot-air drying kumquats (AS-ADKs) were established using 3D topographic plots. From the fingerprints, 40 signal peaks for 22 compounds were confirmed and quantified in all types of kumquats, namely, two terpenes, four esters, seven aldehydes, three ketones, and six alcohols. 3-Pentanone was identified as the major component of FKs; followed by 1-hexanol and the Z-3-hexen-1-ol dimer. The hexanal dimer, 2-hexen-1-ol, and the ethyl acetate dimer were the major markers of VS-ADKs. Benzaldehyde and furfurol were the prominent constituent parts of AS-ADKs. Compared with that in FKs, the pentanal and dimethyl ketone contents of VS-ADKs and AS-ADKs exhibited a dramatic increase (p < 0.05). By contrast, the change in ethanol dimer tended to decrease (p < 0.05). Principal component analysis (PCA) clearly showed that the samples, which were distributed in a separate space could be well-distinguished. Furthermore, the similarity of different processed kumquats and their corresponding volatile components was demonstrated via heat map clustering analysis. The results confirmed the potential of HS-GC-IMS-based approaches to evaluate processed kumquats with various volatile profiles.


Assuntos
Cromatografia Gasosa-Espectrometria de Massas , Espectrometria de Mobilidade Iônica , Rutaceae/química , Compostos Orgânicos Voláteis/análise , Análise por Conglomerados , Análise de Componente Principal
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