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OBJECTIVE: Kawasaki syndrome (KS) is an acute vasculitis that affects children < 5 years of age and leads to coronary artery lesions (CAL) in about 20-25% of untreated cases. Machine learning (ML) is a branch of artificial intelligence (AI) that integrates complex data sets on a large scale and uses huge data to predict future events. The purpose of the present study was to use ML to present the model for early risk assessment of CAL in children with KS by different algorithms. METHODS: A total of 158 children were enrolled from Women and Children's Hospital, Qingdao University, and divided into 70-30% as the training sets and the test sets for modeling and validation studies. There are several classifiers are constructed for models including the random forest (RF), the logistic regression (LR), and the eXtreme Gradient Boosting (XGBoost). Data preprocessing is analyzed before applying the classifiers to modeling. To avoid the problem of overfitting, the 5-fold cross validation method was used throughout all the data. RESULTS: The area under the curve (AUC) of the RF model was 0.925 according to the validation of the test set. The average accuracy was 0.930 (95% CI, 0.905 to 0.956). The AUC of the LG model was 0.888 and the average accuracy was 0.893 (95% CI, 0,837 to 0.950). The AUC of the XGBoost model was 0.879 and the average accuracy was 0.935 (95% CI, 0.891 to 0.980). CONCLUSION: The RF algorithm was used in the present study to construct a prediction model for CAL effectively, with an accuracy of 0.930 and AUC of 0.925. The novel model established by ML may help guide clinicians in the initial decision to make a more aggressive initial anti-inflammatory therapy. Due to the limitations of external validation and regional population characteristics, additional research is required to initiate a further application in the clinic.
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Síndrome de Linfonodos Mucocutâneos , Criança , Feminino , Humanos , Síndrome de Linfonodos Mucocutâneos/complicações , Síndrome de Linfonodos Mucocutâneos/diagnóstico , Inteligência Artificial , Vasos Coronários/diagnóstico por imagem , Aprendizado de Máquina , AgressãoRESUMO
Protein adenosine diphosphate-ribosylation (ADPr) is caused by the covalent binding of one or more ADP-ribose moieties to a target protein and regulates the biological functions of the target protein. To fully understand the regulatory mechanism of ADP-ribosylation, the essential step is the identification of the ADPr sites from the proteome. As the experimental approaches are costly and time-consuming, it is necessary to develop a computational tool to predict ADPr sites. Recently, serine has been found to be the major residue type for ADP-ribosylation but no predictor is available. In this study, we collected thousands of experimentally validated human ADPr sites on serine residue and constructed several different machine-learning classifiers. We found that the hybrid model, dubbed DeepSADPr, which integrated the one-dimensional convolutional neural network (CNN) with the One-Hot encoding approach and the word-embedding approach, compared favourably to other models in terms of both ten-fold cross-validation and independent test. Its AUC values reached 0.935 for ten-fold cross-validation. Its values of sensitivity, accuracy and Matthews's correlation coefficient reached 0.933, 0.867 and 0.740, respectively, with the fixed specificity value of 0.80. Overall, DeepSADPr is the first classifier for predicting Serine ADPr sites, which is available at http://www.bioinfogo.org/DeepSADPr.
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Processamento de Proteína Pós-Traducional , Serina , ADP-Ribosilação , Adenosina Difosfato Ribose/química , Adenosina Difosfato Ribose/metabolismo , Humanos , Proteoma , Serina/metabolismoRESUMO
With the increasing prevalence of depression among children and adolescents, understanding the role of peer contagion in the spread of emotional distress is a critical area of research. The aim of this study is to examine the effect of classmates' depression on a rural child's own depression in China (aged 9-17, N = 1777). The study controls for possible endogeneity of peer effects through the instrumental variable method (the Wald F statistic is significant at the 1% level) and random class assignment data (all students and teachers are randomly assigned to classes).The results indicate that when the average depression score of a rural child's classmates increases by 1 point, that child's own depression score is likely to increase by 0.345 points (p value < .01).This study further finds that the contagion of depression among classmates is more severe for girls and children who play online games, and less severe for children who are cheerful and good-humored. Online games may be an important mechanism through which peer effects operate. Children's classmates' negative emotions are found to increase the occurrence of the children's internalizing behavior in online games, in turn increasing the occurrence of negative emotions in these children themselves.
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Necroptosis, a form of inflammation-related programmed cell death, is a major mechanism of proximal tubular cell injury in acute kidney injury (AKI). Blockade of necroptosis signalling represents a promising strategy for clinical therapy of AKI. Previously, we identified a small molecular receptor-interacting protein kinases (RIPK)1 inhibitor Cpd-71 with nephroprotective activities. To discover more nephroprotective agents, in this study, 20 chalcone derivatives were synthesized and evaluated for their anti-necroptosis and nephroprotective activities. Among the chalcone derivatives, Cpd-2 exhibited the most potent anti-necroptosis activity (IC50 = 1.08 µM) and protective activity (EC50 = 1.49 µM) through directly binding to RIPK1 and blocking RIPK1-RIPK3-mixed-lineage kinase domain-like protein (MLKL) signalling pathway. Furthermore, Cpd-2 effectively attenuated cisplatin or hypoxia/reoxygenation (H/R)-induced injury and necroptotic inflammation in renal cell models. Moreover, in cisplatin- or ischemia/reperfusion (I/R) induced AKI mouse model, detection of creatinine and urea nitrogen in blood showed that Cpd-2 improved kidney function. Periodic acid-Schiff (PAS) staining and immunofluorescence analysis indicated that Cpd-2 also reduced pathological damage and inhibited inflammatory development in kidney tissues. In summary, although some chalcone derivatives have been reported to prevent kidney injury previously, our present study not only discovered a promising leading compound Cpd-2, but also provided a novel and successful practice for the development of necroptosis inhibitors from natural products derivatives as AKI therapeutic agents.
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Injúria Renal Aguda , Chalcona , Chalconas , Injúria Renal Aguda/metabolismo , Animais , Apoptose , Chalcona/efeitos adversos , Chalconas/farmacologia , Chalconas/uso terapêutico , Cisplatino/efeitos adversos , Inflamação , Camundongos , Camundongos Endogâmicos C57BLRESUMO
A quantum spin Hall insulating state that arises from spontaneous symmetry breaking has remarkable properties: skyrmion textures of the SO(3) order parameter carry charge 2e. Doping this state of matter opens a new route to superconductivity via the condensation of skyrmions. We define a model amenable to large-scale negative sign free quantum Monte Carlo simulations that allows us to study this transition. Our results support a direct and continuous doping-induced transition between the quantum spin Hall insulator and an s-wave superconductor. We can resolve dopings away from half-filling down to δ=0.0017. Such routes to superconductivity have been put forward in the realm of twisted bilayer graphene.
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BACKGROUND: Using random forest to predict arrhythmia after intervention in children with atrial septal defect. METHODS: We constructed a prediction model of complications after interventional closure for children with atrial septal defect. The model was based on random forest, and it solved the need for postoperative arrhythmia risk prediction and assisted clinicians and patients' families to make preoperative decisions. RESULTS: Available risk prediction models provided patients with specific risk factor assessments, we used Synthetic Minority Oversampling Technique algorithm and random forest machine learning to propose a prediction model, and got a prediction accuracy of 94.65 % and an Area Under Curve value of 0.8956. CONCLUSIONS: Our study was based on the model constructed by random forest, which can effectively predict the complications of arrhythmia after interventional closure in children with atrial septal defect.
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Comunicação Interatrial , Arritmias Cardíacas/diagnóstico , Arritmias Cardíacas/etiologia , Criança , Comunicação Interatrial/cirurgia , Humanos , Período Pós-OperatórioRESUMO
Breast carcinoma is one of the most commonly diagnosed tumors and also one of the deadliest cancers in the female. Long noncoding RNAs (lncRNAs) are emerging as novel targets and biomarkers for breast cancer diagnosis and treatment. In this study, we aimed to study the lncRNAs associated with the outcomes in patients using the breast invasive carcinoma datasets from The Cancer Genome Atlas. The Cox proportional hazards regression model was fitted to each lncRNA. Hierarchy clustering was carried out using these survival-related lncRNAs and the log-rank test was carried out for the clustered groups. DNA methylation status was utilized to identify the lncRNAs regulated by epigenetics. Finally, the coexpressed messenger RNA with the potential lncRNAs were utilized to study the possible functions and mechanisms of lncRNAs. In total, 182 lncRNAs had an impact on the survival time of the patients with a cutoff <0.01. The patients were clustered into three groups using these survival-related genes, which performed significantly different prognosis. Two lncRNAs, which were significantly correlated with the outcomes of breast cancer and were regulated by methylation status, were obtained. These two lncRNAs were TP53TG1 and RP5-1061H20.4. We proposed that TP53TG1 was activated by the wild-type TP53 and performed an impact on the PI3Ks family by binding YBX2 in breast cancer.
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Neoplasias da Mama/genética , Neoplasias da Mama/mortalidade , Proteínas de Ligação a DNA/genética , RNA Longo não Codificante/genética , Biomarcadores Tumorais/genética , Neoplasias da Mama/patologia , Metilação de DNA/genética , Epigênese Genética/genética , Feminino , Humanos , Prognóstico , RNA Mensageiro/genética , Análise de SobrevidaRESUMO
To improve the bioavailability of orally administered drugs, we synthesized a pH-sensitive polymer (poly(ethylene glycol)-poly(2-methyl-2-carboxyl-propylene carbonate)-vitamin E, mPEG-PCC-VE) attempting to integrate the advantages of enteric coating and P-glycoprotein (P-gp) inhibition. The aliphatic polycarbonate chain was functionalized with carboxyl groups and vitamin E via postpolymerization modification. Optimized by comparison and central composite design, mPEG113-PCC32-VE4 exhibited low critical micelle concentration of 1.7 × 10(-6) mg/mL and high drug loading ability for tacrolimus (21.2% ± 2.7%, w/w). The pH-responsive profile was demonstrated by pH-dependent swelling and in vitro drug release. Less than 4.0% tacrolimus was released under simulated gastric fluid after 2.5 h, whereas an immediate release was observed under simulated intestinal fluid. The mPEG113-PCC32-VE4 micelles significantly increased the absorption of P-gp substrate tacrolimus in the whole intestine. The oral bioavailability of tacrolimus micelles was 6-fold higher than that of tacrolimus solution in rats. This enteric polymer therefore has the potential to become a useful nanoscale carrier for oral delivery of drugs.
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Portadores de Fármacos/síntese química , Micelas , Cimento de Policarboxilato/química , Polietilenoglicóis/química , Tacrolimo/administração & dosagem , Vitamina E/química , Administração Oral , Animais , Portadores de Fármacos/farmacocinética , Concentração de Íons de Hidrogênio , Absorção Intestinal , Ratos , Ratos Sprague-Dawley , Tacrolimo/farmacocinética , Distribuição TecidualRESUMO
Tb(2-FBA)3 x 2H2O and Tb(2-FBA)3 phen were synthesized using o-fluoro-benzoic acid (2-FBA) as the first ligand, and 1,10-phenanthroline (phen) as the second ligand. Elemental analysis and IR spectra were employed to characterize the molecular composition of the two kinds of lanthanide complexes. The UV absorption spectra with same concentration show that the second ligand phen of Tb(2-FBA)3 phen absorbs the portion of the UV light instead of the first ligand 2-FBA. Liquid fluorescence spectra with same concentration show that the fluorescence intensity of Tb(2-FBA)3 x 2H2O is higher than that of Tb (2-FBA)3 phen. The analytical results show that the energy level of 2-FBA matches the lowest excited state energy level of Tb3+ (5D4) better than that of phen. The O-H oscillation of the crystal water in Tb(2-FBA)3 x 2H2O will greatly consume the absorbed energy by ligands, and cause the fluorescence intensity of Tb(2-FBA)3 x 2H2O significantly decline. The energy level of triplet state of the first ligand 2-FBA corresponding to the absorption peak 273 nm has poor matching degree with the 5D4 energy level of Tb3+. In this case, the emission intensity of Tb(2-FBA)3 x 2H2O is still stronger than that of Tb(2-FBA)3 phen. It illustrates that the energy level of the triplet state of the first ligand 2-FBA corresponding to 252 nm has much better matching degree with the lowest excited state of 5D4 energy level of Tb3+ than that of phen. It is the only way to compensate for energy loss by thermal vibration of water molecules and low energy transfer efficiency for poor matching degree between the energy level of corresponding to 273 nm of the first ligand 2-FBA and 5D4 energy level of Tb3+. By combining UV absorption spectra with fluorescence spectra of lanthanide complexes to qualitatively analyze energy level of ligands, the contribution of different types of ligands to the fluorescence properties can be preliminarily understood.
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Maximizing the utilization efficiency of monatomic Fe sites in Fe-N-C catalysts poses a significant challenge for their commercial applications. Herein, a structural and electronic dual-modulation is achieved on a Fe-N-C catalyst to substantially enhance its catalytic performance. We develop a facile multi-component ice-templating co-assembly (MIC) strategy to construct two-dimensional (2D) arrays of monatomic Fe-anchored hollow carbon nanoboxes (Fe-HCBA) via a novel dual-outward interfacial contraction hollowing mechanism. The pore engineering not only enlarges the physical surface area and pore volume but also doubles the electrochemically active specific surface area. Additionally, the unique 2D carbon array structure reduces interfacial resistance and promotes electron/mass transfer. Consequently, the Fe-HCBA catalysts exhibit superior oxygen reduction performance with a six-fold enhancement in both mass activity (1.84 A cm-2) and turnover frequency (0.048 e- site-1 s-1), compared to microporous Fe-N-C catalysts. Moreover, the incorporation of phosphorus further enhances the total electrocatalytic performance by three times by regulating the electron structure of Fe-N4 sites. Benefitting from these outstanding characteristics, the optimal 2D P/Fe-HCBA catalyst exhibits great applicability in rechargeable liquid- and solid-state zinc-air batteries with peak power densities of 186 and 44.5 mW cm-2, respectively.
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Despite the fact that tropomyosin (TM) has highly stable structural characteristics, thermal processing can adversely influence its immunodetection, and the mechanism involved has not been elucidated. Purified TM was heated at various temperatures, and then the IgG/IgE-binding capacity and immunodetection recovery were determined; meanwhile, the structural alterations were analyzed via spectroscopic and molecular dynamics simulation techniques. The obtained results demonstrated that heat-treated TM showed significantly increased IgG/IgE reactivity, confirmed by indirect ELISA and immunoblotting analysis, which might be attributed to the increased structural flexibility, and thus allowed TM to be recognized IgG/IgE easily. However, these structural alterations during thermal processing would contribute to the masking of some epitopes located in TM's surface due to the presence of curled or folded conformation with a considerable reduction of the solvent-accessible surface and radius of gyration, which primarily caused immunodetection recovery reduction in the sandwich ELISA (sELISA) test. The number of antigen binding sites might play a crucial role in a sandwich immunodetection system for sensitive and precise analysis in processed foods.
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αs1-Casein (αs1-CN) is a major cow milk allergen, while the tertiary structure of αs1-CN and conformational epitopes of αs1-CN have not been clarified. Here, a reasonable three-dimensional structure of αs1-CN was established using ab initio methods, and hot-spot residues and epitopes were investigated by combining molecular dynamics simulation, peptides synthesis, and ELISA. Obtained results demonstrated that the binding mechanism between αs1-CN and IgG was located on three main regions: a helical structure zone (E77-Q97), the flexible loop zone (Y154-T174), and a flexible C-terminal (N190-L198), mainly connecting via hydrogen bond and ionic bonds. The hydrolysates produced by papain with lowest antigenicity (12.43%), which could considerably destroy the essential epitopes of αs1-CN confirmed by epitope synthesis, and LC-MS/MS. The results reported herein would provide novel insights into the interface interactions between αs1-CN and IgG, and prove valuable for developing hypoallergenic infant-formula and peptide vaccines for allergen-specific immunotherapy.
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Caseínas , Espectrometria de Massas em Tandem , Alérgenos , Animais , Caseínas/química , Bovinos , Cromatografia Líquida , Epitopos , Feminino , Humanos , Imunoglobulina G/análise , Leite/químicaRESUMO
This paper introduces a new approach called double asynchronous orthogonal sample design (DAOSD) to probe intermolecular interactions. A specifically designed concentration series is selected according to the mathematical analysis to generate useful 2D correlated spectra. As a result, the interfering portions are completely removed and a pair of complementary sub-2D asynchronous spectra can be obtained. A computer simulation is applied on a model system with two solutes to study the spectral behavior of cross peaks in 2D asynchronous spectra generated by using the DAOSD approach. Variations on different spectral parameters, such as peak position, bandwidth, and absorptivity, caused by intermolecular interactions can be estimated by the characteristic spectral patterns of cross peaks in the pair of complementary sub-2D asynchronous spectra. Intermolecular interactions between benzene and iodine in CCl(4) solutions were investigated using the DAOSD approach to prove the applicability of the DAOSD method in real chemical system.
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Simulação por Computador , Benzeno/química , Tetracloreto de Carbono/química , Iodo/química , Espectroscopia de Infravermelho com Transformada de FourierRESUMO
A new azobenzene-based symmetric amphiphile was synthesized and characterized using 1H NMR spectroscopy. Its self-assembly behavior as well as photo-responsive behavior in its solution and gel states were investigated. Such a compound can self-assemble into fiber mesophases in water solvent. After irradiation of the gels with UV light, the trans isomer of the compound rapidly photoisomerized to the cis isomer, which resulted in a rapid destruction of the gel. High temperature also caused a rapid drop in viscosity. To verify the antimicrobial activity of the hydrogel, live and death assays of human fibroblasts L929 properties were used for in vitro cell viability studies. The compound was converted to the terminal tertiary amine in a quaternary ammonium salt molecule by using hydrochloric acid. This azobenzene quaternary ammonium salt has a relatively better antimicrobial effect biocidal activity that was demonstrated when challenged against Escherichia coli on in vitro conditions.
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Lysine SUMOylation plays an essential role in various biological functions. Several approaches integrating various algorithms have been developed for predicting SUMOylation sites based on a limited dataset. Recently, the number of identified SUMOylation sites has significantly increased due to investigation at the proteomics scale. We collected modification data and found the reported approaches had poor performance using our collected data. Therefore, it is essential to explore the characteristics of this modification and construct prediction models with improved performance based on an enlarged dataset. In this study, we constructed and compared 16 classifiers by integrating four different algorithms and four encoding features selected from 11 sequence-based or physicochemical features. We found that the convolution neural network (CNN) model integrated with residue structure, dubbed ResSUMO, performed favorably when compared with the traditional machine learning and CNN models in both cross-validation and independent tests. The area under the receiver operating characteristic (ROC) curve for ResSUMO was around 0.80, superior to that of the reported predictors. We also found that increasing the depth of neural networks in the CNN models did not improve prediction performance due to the degradation problem, but the residual structure could be included to optimize the neural networks and improve performance. This indicates that residual neural networks have the potential to be broadly applied in the prediction of other types of modification sites with great effectiveness and robustness. Furthermore, the online ResSUMO service is freely accessible.
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Aprendizado Profundo , Lisina , Lisina/metabolismo , Aprendizado de Máquina , Redes Neurais de Computação , SumoilaçãoRESUMO
Nowadays, tourists increasingly prefer to check the reviews of attractions before traveling to decide whether to visit them or not. To respond to the change in the way tourists choose attractions, it is important to classify the reviews of attractions with high precision. In addition, more and more tourists like to use emojis to express their satisfaction or dissatisfaction with the attractions. In this paper, we built a dataset for Chinese attraction evaluation incorporating emojis (CAEIE) and proposed an explicitly n-gram masking method to enhance the integration of coarse-grained information into a pre-training (ERNIE-Gram) and Text Graph Convolutional Network (textGCN) (E2G) model to classify the dataset with a high accuracy. The E2G preprocesses the text and feeds it to ERNIE-Gram and TextGCN. ERNIE-Gram was trained using its unique mask mechanism to obtain the final probabilities. TextGCN used the dataset to construct heterogeneous graphs with comment text and words, which were trained to obtain a representation of the document output category probabilities. The two probabilities were calculated to obtain the final results. To demonstrate the validity of the E2G model, this paper was compared with advanced models. After experiments, it was shown that E2G had a good classification effect on the CAEIE dataset, and the accuracy of classification was up to 97.37%. Furthermore, the accuracy of E2G was 1.37% and 1.35% ahead of ERNIE-Gram and TextGCN, respectively. In addition, two sets of comparison experiments were conducted to verify the performance of TextGCN and TextGAT on the CAEIE dataset. The final results showed that ERNIE and ERNIE-Gram combined TextGCN and TextGAT, respectively, and TextGCN performed 1.6% and 2.15% ahead. This paper compared the effects of eight activation functions on the second layer of the TextGCN and the activation-function-rectified linear unit 6 (RELU6) with the best results based on experiments.
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Análise de Sentimentos , Turismo , Coleta de Dados , ChinaRESUMO
In the last decade, deep neural networks have been widely applied to medical image segmentation, achieving good results in computer-aided diagnosis tasks etc. However, the task of segmenting highly complex, low-contrast images of organs and tissues with high accuracy still faces great challenges. To better address this challenge, this paper proposes a novel model SWTRU (Star-shaped Window Transformer Reinforced U-Net) by combining the U-Net network which plays well in the image segmentation field, and the Transformer which possesses a powerful ability to capture global contexts. Unlike the previous methods that import the Transformer into U-Net, an improved Star-shaped Window Transformer is introduced into the decoder of the SWTRU to enhance the decision-making capability of the whole method. The SWTRU uses a redesigned multi-scale skip-connection model, which retains the inductive bias of the original FCN structure for images while obtaining fine-grained features and coarse-grained semantic information. Our method also presents the FFIM (Filtering Feature Integration Mechanism) to integration and dimensionality reduction of the fused multi-layered features, which reduces the computation. Our SWTRU yields 0.972 DICE on CHLISC for liver and tumor segmentation, 0.897 DICE on LGG for glioma segmentation, and 0.904 DICE on ISIC2018 for skin diseases' segmentation, achieves substantial improvements over the current SoTA across 9 different medical image segment methods. SWTRU can combine feature mapping from different scales, high-level semantics, and global contextual relationships, this architecture is effective in the medical image segmentation. The experimental findings indicate that SWTRU produces superior performance on the medical image segmentation tasks.
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Diagnóstico por Computador , Glioma , Humanos , Fígado , Redes Neurais de Computação , Semântica , Processamento de Imagem Assistida por ComputadorRESUMO
In the present study, sarcoplasmic calcium-binding protein (SCP) was first expressed in E. coli BL21 (DE3), and then identified based on immunoblotting and SCP amino acid sequencing of shrimp (Litopenaeus vannamei) using mass spectrometry (MS). The recombinant SCP (rSCP) was treated with different temperature conditions to investigate its immunological properties, in vitro digestibility and structural changes with enzyme-linked immunosorbent assay (ELISA), immunoblotting, spectrophotometry and molecular dynamics simulation techniques. The immunoglobulin (Ig) E-binding activity of the rSCP could remain stable until 80 °C, whereas the higher thermal processing temperatures resulted in a significant decrease in IgG/IgE-binding capacity coupled with alterations in the secondary and tertiary structures. Notably, the maximum reduction of IgG/IgE reactivity and in vitro digestibility were observed in the autoclaved rSCP. The decrease in the potential allergenicity of rSCP not only correlated well with the decreasing of α-helix, epitopes masking and exposure of more protease cleavage sites, but also with the destruction of Ca2+ binding sites due to the unfolding of the rSCP with heating treatments, which was supported by the thermal-induced changes of the secondary and tertiary structures. These findings indicate that autoclaved treatment may be an effective and promising approach for producing hypoallergenic seafood.
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Hipersensibilidade Alimentar , Penaeidae , Alérgenos , Animais , Proteínas de Ligação ao Cálcio/genética , Escherichia coli/metabolismo , Hipersensibilidade Alimentar/prevenção & controle , Imunoglobulina E/metabolismo , Imunoglobulina G , Simulação de Dinâmica Molecular , Penaeidae/química , Alimentos Marinhos , Análise EspectralRESUMO
Shrimps were first subjected to various thermal processing, then tropomyosin (TM) was purified and their structure, IgG/IgE-binding ability and detectability were evaluated for elucidating the mechanisms responsible for thermal-induced TM immunodetection recovery alterations. According to CD and FT-IR analysis, heat-treated shrimp TM had significantly reduced α-helix and ß-sheet contents with elevated random coil contents, contributing to an increase of 24.42%-62.22% in IgG/IgE reactivity as compared with raw shrimp TM. The exposure of hydrophobic residues and glycosylation occurred in various heated shrimps TM were confirmed by UV, intrinsic/extrinsic fluorescence spectrum and free amino group analysis, which caused some epitopes masking or modification, thereby inducing considerable TM recovery reduction (48.48%-90.44%). These results demonstrated that thermal-treated TMs with higher structural flexibility facilitated IgG/IgE recognition, however the lower number of epitopes within the thermal-treated TMs might cause considerable underestimation of recovery. The number of antigen binding sites might play a critical role in sandwich immunodetection.
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Hipersensibilidade Alimentar , Penaeidae , Alérgenos , Animais , Epitopos , Imunoglobulina E , Imunoglobulina G/metabolismo , Penaeidae/química , Espectroscopia de Infravermelho com Transformada de Fourier , TropomiosinaRESUMO
BACKGROUND: Pneumonia is a common infection of the lung parenchyma in children, and early and accurate diagnosis of childhood pneumonia (CP) is important for implementing appropriate preventive and treatment strategies. This study aimed to evaluate the diagnostic value of the combination of long non-coding RNA (lncRNA) RP11-248E9.5, RP11-456D7.1, c-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) in CP. PATIENTS AND METHODS: A total of 50 healthy children (HC) and 100 CP patients were enrolled. The serum expression of RP11-248e9.5 and RP11-456d7.1 was detected by qRT-PCR. The white blood cell (WBC), hemoglobin (HB), platelet (PLT), neutrophil, and lymphocyte were analyzed by automated hematology analyzer. The serum levels of CRP and procalcitonin (PCT) were analyzed by automatic biochemical analyzer. The receiver operating characteristic (ROC) curves were applied to evaluate the diagnostic value in CP. RESULTS: The NLR and PLR, expression of RP11-248E9.5 and RP11-456D7.1, and serum levels of CRP and PCT were significantly higher in the CP group than those in the HC group. Both RP11-248E9.5 (AUC, 0.86; sensitivity, 84%; specificity, 78%) and RP11-456D7.1 (AUC, 0.89; sensitivity, 79%; specificity, 92%) exhibited certain diagnostic value in CP. The diagnostic values of PCT, CRP, NLR and PLR in CP were limited by low sensitivity (≤ 71%). The combination of multiple indicators improved the diagnostic value. The combination of RP11-248E9.5, RP11-456D7.1, CRP, NLR, and PLR had the best diagnostic value in CP (AUC, 0.992; Sensitivity, 0.97; Specificity, 0.99). CONCLUSION: The combination of RP11-248E9.5, RP11-456D7.1, CRP, NLR, and PLR was a potential diagnostic strategy for CP.