CDEMI: Characterizing differences in microbial composition and function in microbiome data.

Microbial communities influence host phenotypes through microbiota-derived metabolites and interactions between exogenous active substances (EASs) and the microbiota. Owing to the high dynamics of microbial community composition and difficulty in microbial functional analysis, the identification of mechanistic links between individual microbes and host phenotypes is complex. Thus, it is important to characterize variations in microbial composition across various conditions (for example, topographical locations, times, physiological and pathological conditions, and populations of different ethnicities) in microbiome studies. However, no web server is currently available to facilitate such characterization. Moreover, accurately annotating the functions of microbes and investigating the possible factors that shape microbial function are critical for discovering links between microbes and host phenotypes. Herein, an online tool, CDEMI, is introduced to discover microbial composition variations across different conditions, and five types of microbe libraries are provided to comprehensively characterize the functionality of microbes from different perspectives. These collective microbe libraries include (1) microbial functional pathways, (2) disease associations with microbes, (3) EASs associations with microbes, (4) bioactive microbial metabolites, and (5) human body habitats. In summary, CDEMI is unique in that it can reveal microbial patterns in distributions/compositions across different conditions and facilitate biological interpretations based on diverse microbe libraries. CDEMI is accessible at http://rdblab.cn/cdemi/.

A critical assessment of clustering algorithms to improve cell clustering and identification in single-cell transcriptome study.

Cell clustering is typically the initial step in single-cell RNA sequencing (scRNA-seq) analyses. The performance of clustering considerably impacts the validity and reproducibility of cell identification. A variety of clustering algorithms have been developed for scRNA-seq data. These algorithms generate cell label sets that assign each cell to a cluster. However, different algorithms usually yield different label sets, which can introduce variations in cell-type identification based on the generated label sets. Currently, the performance of these algorithms has not been systematically evaluated in single-cell transcriptome studies. Herein, we performed a critical assessment of seven state-of-the-art clustering algorithms including four deep learning-based clustering algorithms and commonly used methods Seurat, Cosine-based Tanimoto similarity-refined graph for community detection using Leiden's algorithm (CosTaL) and Single-cell consensus clustering (SC3). We used diverse evaluation indices based on 10 different scRNA-seq benchmarks to systematically evaluate their clustering performance. Our results show that CosTaL, Seurat, Deep Embedding for Single-cell Clustering (DESC) and SC3 consistently outperformed Single-Cell Clustering Assessment Framework and scDeepCluster based on nine effectiveness scores. Notably, CosTaL and DESC demonstrated superior performance in clustering specific cell types. The performance of the single-cell Variational Inference tools varied across different datasets, suggesting its sensitivity to certain dataset characteristics. Notably, DESC exhibited promising results for cell subtype identification and capturing cellular heterogeneity. In addition, SC3 requires more memory and exhibits slower computation speed compared to other algorithms for the same dataset. In sum, this study provides useful guidance for selecting appropriate clustering methods in scRNA-seq data analysis.

MIAOME: Human microbiome affect the host epigenome.

Besides the genetic factors having tremendous influences on the regulations of the epigenome, the microenvironmental factors have recently gained extensive attention for their roles in affecting the host epigenome. There are three major types of microenvironmental factors: microbiota-derived metabolites (MDM), microbiota-derived components (MDC) and microbiota-secreted proteins (MSP). These factors can regulate host physiology by modifying host gene expression through the three highly interconnected epigenetic mechanisms (e.g. histone modifications, DNA modifications, and non-coding RNAs). However, no database was available to provide the comprehensive factors of these types. Herein, a database entitled 'Human Microbiome Affect The Host Epigenome (MIAOME)' was constructed. Based on the types of epigenetic modifications confirmed in the literature review, the MIAOME database captures 1068 (63 genus, 281 species, 707 strains, etc.) human microbes, 91 unique microbiota-derived metabolites & components (16 fatty acids, 10 bile acids, 10 phenolic compounds, 10 vitamins, 9 tryptophan metabolites, etc.) derived from 967 microbes; 50 microbes that secreted 40 proteins; 98 microbes that directly influence the host epigenetic modification, and provides 3 classifications of the epigenome, including (1) 4 types of DNA modifications, (2) 20 histone modifications and (3) 490 ncRNAs regulations, involved in 160 human diseases. All in all, MIAOME has compiled the information on the microenvironmental factors influence host epigenome through the scientific literature and biochemical databases, and allows the collective considerations among the different types of factors. It can be freely assessed without login requirement by all users at: http://miaome.idrblab.net/ttd/.

GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification.

BACKGROUND: Autism spectrum disorders (ASD) imply a spectrum of symptoms rather than a single phenotype. ASD could affect brain connectivity at different degree based on the severity of the symptom. Given their excellent learning capability, graph neural networks (GNN) methods have recently been used to uncover functional connectivity patterns and biological mechanisms in neuropsychiatric disorders, such as ASD. However, there remain challenges to develop an accurate GNN learning model and understand how specific decisions of these graph models are made in brain network analysis. RESULTS: In this paper, we propose a graph attention network based learning and interpreting method, namely GAT-LI, which learns to classify functional brain networks of ASD individuals versus healthy controls (HC), and interprets the learned graph model with feature importance. Specifically, GAT-LI includes a graph learning stage and an interpreting stage. First, in the graph learning stage, a new graph attention network model, namely GAT2, uses graph attention layers to learn the node representation, and a novel attention pooling layer to obtain the graph representation for functional brain network classification. We experimentally compared GAT2 model's performance on the ABIDE I database from 1035 subjects against the classification performances of other well-known models, and the results showed that the GAT2 model achieved the best classification performance. We experimentally compared the influence of different construction methods of brain networks in GAT2 model. We also used a larger synthetic graph dataset with 4000 samples to validate the utility and power of GAT2 model. Second, in the interpreting stage, we used GNNExplainer to interpret learned GAT2 model with feature importance. We experimentally compared GNNExplainer with two well-known interpretation methods including Saliency Map and DeepLIFT to interpret the learned model, and the results showed GNNExplainer achieved the best interpretation performance. We further used the interpretation method to identify the features that contributed most in classifying ASD versus HC. CONCLUSION: We propose a two-stage learning and interpreting method GAT-LI to classify functional brain networks and interpret the feature importance in the graph model. The method should also be useful in the classification and interpretation tasks for graph data from other biomedical scenarios.

The RNA-Binding Protein CELF2 Inhibits Ovarian Cancer Progression by Stabilizing FAM198B.

An increasing number of studies have clarified the functional roles of RNA-binding proteins (RBPs) in driving post-transcriptional mechanisms of cancer progression. In this study, we integrated data from the RBP database and Gene Expression Omnibus (GEO) data with RNA sequencing (RNA-seq) data from 10 ovarian cancer tissues and 8 normal ovarian tissues and identified an RBP, CUGBP- and ETR-3-like family 2 (CELF2). We found that CELF2 expression was downregulated in ovarian cancer and positively correlated with the overall survival (OS) and progression-free survival (PFS) of patients with ovarian cancer. Altered CELF2 expression led to changes in the proliferation, migration, and invasion of ovarian cancer cells in vitro and in vivo. CELF2 expression increased the stability of its target, FAM198B, by binding to AU/U-rich elements (AREs) in the 3' untranslated region (3' UTR). FAM198B knockdown restored the CELF2-mediated suppression of proliferation and migration. We also found that CELF2/FAM198B may repress ovarian cancer progression by inhibiting the mitogen-activated protein kinase/extracellular-regulated protein kinase (MAPK/ERK) signaling pathway. Finally, a curcumin-induced increase in CELF2 expression resulted in increased ovarian cancer cell sensitivity to cisplatin. Our study elucidated a novel mechanism by which the CELF2/FAM198B axis regulates proliferation and metastasis in ovarian cancer, providing novel, potential therapeutic targets for ovarian cancer.

Role of adjuvant therapy after radical hysterectomy in intermediate-risk, early-stage cervical cancer.

OBJECTIVE: Adjuvant treatment remains a controversial issue for intermediate-risk cervical cancer. The aim of this study was to compare the prognosis of patients who underwent no adjuvant treatment, pelvic radiotherapy alone, or concurrent chemoradiotherapy after radical hysterectomy for intermediate-risk, early-stage cervical cancer. METHODS: Patients with stage IB1-IIA2 (FIGO 2009) cervical squamous cell carcinoma treated with radical hysterectomy and pelvic lymph node dissection, with negative lymph nodes, surgical margins, or parametria, who had combined intermediate risk factors as defined in the Gynecologic Oncology Group trial (GOG-92; Sedlis criteria) were included in the study. Recurrence-free survival and disease-specific survival were compared. RESULTS: Of 861 patients included in the analysis, 85 patients received no adjuvant treatment, 283 patients were treated with radiotherapy, and 493 patients with concurrent chemoradiotherapy. After a median follow-up of 63 months (IQR 45 to 84), adjuvant radiotherapy or concurrent chemoradiotherapy was not associated with a survival benefit compared with no adjuvant treatment. The 5-year recurrence-free survival and corresponding disease-specific survival were 87.1%, 84.2%, 89.6% (p=0.27) and 92.3%, 87.7%, 91.4% (p=0.20) in the no adjuvant treatment, radiotherapy alone, and concurrent chemoradiotherapy groups, respectively. Lymphovascular space invasion was the only independent prognostic factor for both recurrence-free survival and disease-specific survival. Additionally, significant heterogeneity exists in Sedlis criteria: higher risk of relapse (HR=1.88; 95% CI 1.19 to 2.97; p=0.007) and death (HR=2.36; 95% CI 1.41 to 3.95; p=0.001) occurred in patients with lymphovascular space invasion and deep 1/3 stromal invasion compared with no lymphovascular space invasion, middle or deep 1/3 stromal invasion, and tumor diameter ≥4 cm. CONCLUSIONS: Radical hysterectomy alone without adjuvant treatment may achieve a favorable survival for patients with intermediate-risk cervical cancer as defined by Sedlis criteria. Criteria for adjuvant treatment in patients without high risk factors need to be further evaluated.

The clinical and prognostic implication of deep stromal invasion in cervical cancer patients undergoing radical hysterectomy.

Background: To evaluate the patterns of recurrence and survival related to deep stromal invasion (DSI) in cervical cancer patients who underwent the radical surgery. Methods: Patients with International Federation of Gynaecology and Obstetrics (FIGO) 2009 stage IB and IIA and definite pathology-confirmed deep stromal invasion between 03/2006 and 06/2014 were collected. A subcategorization of deep stromal invasion (inner full-thickness, full-thickness and outer full-thickness) were performed. Disease-free survival (DFS) and overall survival (OS) were compared by Kaplan-Meier analysis and independent predictors were identified using Cox regression analysis. Results: A total of 3,298 cervical cancer patients were included. The proportion of patients with outer 1/3 to full-thickness invasion, full-thickness invasion and outer-full-thickness invasion were 60.6%, 33.5% and 5.9%, respectively. Deep stromal invasion strongly correlated with patients' age, stage, menopause status, tumor diameter, lymphovascular space invasion (LVSI), nodal metastasis, parametrial and vaginal involvement, as well as the site of recurrence. However, no connection was found between the DSI and tumor histologic type. Upon further analysis, patients with full- and outer-full-thickness invasion exhibited significantly higher recurrence rates compared to inner full-thickness group. Both DFS and OS was independently associated with the depth of deep stromal invasion. By subgroup analysis, multivariate analysis revealed that only adjuvant radiotherapy was independent risk factors for both DFS and OS in isolated full-thickness invasion patients. Conclusions: This study indicated that the depth of deep stromal invasion is an important prognostic factor in patients with cervical cancer. Patients with full-thickness invasion should receive customized adjuvant treatment.

Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder.

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.

Distinctive clinicopathologic characteristics and prognosis for different histologic subtypes of early cervical cancer.

OBJECTIVES: To compare clinicopathologic characteristics and prognosis for different histologic subtypes in early cervical cancer. METHODS: Patients who underwent radical surgery for stage IA2-IIA2 cervical cancer with squamous cell carcinoma, adenocarcinoma, or adenosquamous carcinoma between March 2006 and February 2014 at our institution were retrospectively evaluated. The two-sample t-test was used to compare the mean values of continuous variables. The Chi-square test was used to assess differences in the distribution of categorical variables. Survival curves were generated by the Kaplan-Meier method using log-rank test. Univariable and multivariable analyses were performed using Cox regression analysis. RESULTS: Of 5181 patients evaluated, 4510 had squamous cell carcinoma, 488 had adenocarcinoma, and 183 had adenosquamous carcinoma. Compared with squamous cell carcinoma, adenocarcinoma was associated with earlier stage, smaller tumor size, less lymphovascular space invasion (26.7% vs 37.9%), less deep (>2/3 depth) stromal invasion (30.4% vs 36.2%), and more ovarian metastasis (4.2% vs 0.7%) (all p<0.001). Compared with adenosquamous carcinoma, adenocarcinoma was associated with earlier stage (p=0.011), smaller tumor size (p<0.001), less lymphovascular space invasion (26.7% vs 41.5%, p<0.001), and less peripheral nerve infiltration (5.7% vs 15.4%, p<0.001). Except for more peripheral nerve infiltration in adenosquamous carcinoma (15.4% vs 8.4%, p=0.002), no significant differences in other clinicopathologic characteristics were noted between squamous cell carcinoma and adenosquamous carcinoma. Five-year recurrence-free survival was 85.1%, 78.2%, and 72.3% for squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma, respectively (p<0.001). Corresponding 5-year overall survival was 89.7%, 83.1%, and 79.6%, respectively (p<0.001). In multivariable analysis, adenocarcinoma and adenosquamous carcinoma were independent prognostic factors for worse recurrence-free survival for adenocarcinoma versus squamous cell carcinoma (HR 2.594 (95% CI 2.030 to 3.316), p<0.001) and for adenosquamous carcinoma versus squamous cell carcinoma (HR 2.105 (95% CI 1.517 to 2.920), p<0.001), and overall survival for adenocarcinoma versus squamous cell carcinoma (HR 2.976 (95% CI 2.226 to 3.977), p<0.001) and for adenosquamous carcinoma versus squamous cell carcinoma (HR 2.295 (95% CI 1.579 to 3.338), p<0.001). CONCLUSION: Squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma carried distinctive patterns of clinicopathologic characteristics. Adenocarcinoma and adenosquamous carcinoma had worse survival outcomes than squamous cell carcinoma.

Long non-coding RNA SNHG6 promotes cell proliferation and migration through sponging miR-4465 in ovarian clear cell carcinoma.

Dysregulation of small nucleolar RNA host gene 6 (SNHG6) exerts critical oncogenic effects and facilitates tumourigenesis in human cancers. However, little information about the expression pattern of SNHG6 in ovarian clear cell carcinoma (OCCC) is available, and the contributions of this long non-coding RNA to the tumourigenesis and progression of OCCC are unclear. In the present study, we showed via quantitative real-time PCR that SNHG6 expression was abnormally up-regulated in OCCC tissues relative to that in unpaired normal ovarian tissues. High SNHG6 expression was correlated with vascular invasion, distant metastasis and poor survival. Further functional experiments demonstrated that knockdown of SNHG6 in OCCC cells inhibited cell proliferation, migration and invasion in vitro as well as tumour growth in vivo. Moreover, SNHG6 functioned as a competing endogenous RNA (ceRNA), effectively acting as a sponge for miR-4465 and thereby modulating the expression of enhancer of zeste homolog 2 (EZH2). Taken together, our data suggest that SNHG6 is a novel molecule involved in OCCC progression and that targeting the ceRNA network involving SNHG6 may be a treatment strategy in OCCC.

A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification.

Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely used models including SVM, 1D CNN, 2D CNN, 3D CNN, and 3D separable CNN with respect to their performance in classifying task-based fMRI data. We tested M2D CNN against six models as benchmarks to classify a large number of time-series whole-brain imaging data based on a motor task in the Human Connectome Project (HCP). The results of our experiments demonstrate the following: (i) convolution operations in the CNN models are advantageous for high-dimensional whole-brain imaging data classification, as all CNN models outperform SVM; (ii) 3D CNN models achieve higher accuracy than 2D CNN and 1D CNN model, but 3D CNN models are computationally costly as any extra dimension is added in the input; (iii) the M2D CNN model proposed in this study achieves the highest accuracy and alleviates data overfitting given its smaller number of parameters as compared with 3D CNN.

The increase of O-acetylation and N-deacetylation in cell wall promotes acid resistance and nisin production through improving cell wall integrity in Lactococcus lactis.

Cell wall is closely related to bacterial robustness and adsorption capacity, playing crucial roles in nisin production in Lactococcus lactis. Peptidoglycan (PG), the essential component of cell wall, is usually modified with MurNAc O-acetylation and GlcNAc N-deacetylation, catalyzed by YvhB and XynD, respectively. In this study, increasing the two modifications in L. lactis F44 improved autolysis resistance by decreasing the susceptibility to PG hydrolases. Furthermore, both modifications were positively associated with overall cross-linkage, contributing to cell wall integrity. The robust cell wall rendered the yvhB/xynD-overexpression strains more acid resistant, leading to the increase of nisin production in fed-batch fermentations by 63.7 and 62.9%, respectively. Importantly, the structural alterations also reduced nisin adsorption capacity, resulting in reduction of nisin loss. More strikingly, the co-overexpression strain displayed the highest nisin production (76.3% higher than F44). Our work provides a novel approach for achieving nisin overproduction via extensive cell wall remodeling.

[Efficacy of early treatment via fiber bronchoscope in children with Mycoplasma pneumoniae pneumonia complicated by airway mucus obstruction].

OBJECTIVE: To study the efficacy of early treatment via fiber bronchoscope in children with Mycoplasma pneumoniae pneumonia (MPP) complicated by airway mucus obstruction. METHODS: According to the time from admission to the treatment via fiber bronchoscope, the children with MPP who were found to have airway mucus obstruction under a fiber bronchoscope were randomly divided into early intervention group (≤3 days; n=40) and late intervention group (>3 days; n=56). The two groups were compared in terms of clinical data and imaging recovery.The children were followed for 1-3 months. RESULTS: Of the 96 children, 38 were found to have the formation of plastic bronchial tree, among whom 10 were in the early intervention group and 28 were in the late intervention group (P=0.01). Compared with the late intervention group, the early intervention group had a shorter duration of fever, length of hospital stay, and time to the recovery of white blood cell count and C-reactive protein (P<0.05), as well as a higher atelectasis resolution rate (P<0.05). Compared with the late intervention group, the early intervention group had a higher percentage of children with a ≥ 60% absorbed area of pulmonary consolidation at discharge. After 3 months of follow-up, the early intervention group had a higher percentage of children with a ≥ 90% absorbed area of pulmonary consolidation than the late intervention group (80% vs 55%; P=0.01), and the early intervention group had a lower incidence rate of atelectasis than the late intervention group (P<0.05). CONCLUSIONS: Early treatment via fiber bronchoscope can shorten the course of the disease and reduce complications and sequelae in MPP children with airway mucus obstruction.

The utility of "low current" stimulation threshold of intraoperative electromyography monitoring in predicting facial nerve function outcome after vestibular schwannoma surgery: a prospective cohort study of 103 large tumors.

To investigate the predictive utility of stimulation threshold (ST) of intraoperative electromyography monitoring for facial nerve (FN) outcomes among vestibular schwannoma (VS) patients postoperatively. The authors enrolled 103 unilateral VS patients who underwent surgical resection into a prospective cohort observational study from January 2013 to April 2015 in our hospital. ST values were used to categorize 81 patients into the "low current" (ST ≤ 0.05 mA) group and 22 patients into the control (ST > 0.05 mA) group. The FN function outcomes were summarized and correlated with these two groups at 1, 3, 6, and 12 months after surgery. Binary regression analysis revealed that the percentage of "good" FN outcome, defined by House-Brackmann (HB) classification of facial function (I-II), in the "low current" group was significantly higher than that of the control group (42.0 vs. 4.5% at 1 month, P = 0.015; 64.2 vs. 31.8% at 3 months, P = 0.024; 72.8 vs. 40.9% at 6 months, P = 0.021; 84.0 vs. 45.5% at 12 months, P = 0.002). Ordinal regression analysis showed that the distribution of HB scores was shifted in a favorable direction in the "low current" group at 1, 3, 6, and 12 months postoperatively. For patients with HB IV at the first month postoperative period, the recovery rate of the "low current" group was significantly higher than that of control group (P = 0.003). "Low current" can predict FN function outcomes better and has faster recovery rates than that of the control group.

Promoting acid resistance and nisin yield of Lactococcus lactis F44 by genetically increasing D-Asp amidation level inside cell wall.

Nisin fermentation by Lactococcus lactis requires a low pH to maintain a relatively higher nisin activity. However, the acidic environment will result in cell arrest, and eventually decrease the relative nisin production. Hence, constructing an acid-resistant L. lactis is crucial for nisin harvest in acidic nisin fermentation. In this paper, the first discovery of the relationship between D-Asp amidation-associated gene (asnH) and acid resistance was reported. Overexpression of asnH in L. lactis F44 (F44A) resulted in a sevenfold increase in survival capacity during acid shift (pH 3) and enhanced nisin desorption capacity compared to F44 (wild type), which subsequently contributed to higher nisin production, reaching 5346 IU/mL, 57.0% more than that of F44 in the fed-batch fermentation. Furthermore, the engineered F44A showed a moderate increase in D-Asp amidation level (from 82 to 92%) compared to F44. The concomitant decrease of the negative charge inside the cell wall was detected by a newly developed method based on the nisin adsorption amount onto cell surface. Meanwhile, peptidoglycan cross-linkage increased from 36.8% (F44) to 41.9% (F44A), and intracellular pH can be better maintained by blocking extracellular H+ due to the maintenance of peptidoglycan integrity, which probably resulted from the action of inhibiting hydrolases activity. The inference was further supported by the acmC-overexpression strain F44C, which was characterized by uncontrolled peptidoglycan hydrolase activity. Our results provided a novel strategy for enhancing nisin yield through cell wall remodeling, which contributed to both continuous nisin synthesis and less nisin adsorption in acidic fermentation (dual enhancement).

The early examination of combined serum and imaging data under flexible fiberoptic bronchoscopy as a novel predictor for refractory Mycoplasma pneumoniae pneumonia diagnosis.

The treatment role of flexible bronchoscopy (FOB) for pediatric refractory Mycoplasma pneumoniae pneumonia (RMPP) has been well documented. Besides, the application indication of FOB is also studied in patients with general MPP (GMPP), especially in those with large pulmonary lesions. This study was designed to examine the diagnostic value of bronchoscopic features for RMPP.The FOB and bronchoalveolar lavage (BAL) were adopted for pediatric patients who showed clinical and radiograph indications. On the basis of the final diagnosis on discharge, patients were divided into general and refractory MPP groups. The clinical, laboratory, and bronchoscopic imaging features were retrospectively investigated between these 2 groups.From June 2012 to May 2014, a total of 62 RMPP and 101 GMPP patients were treated with therapeutic bronchoscopy. The comparison analysis showed that the CRP, HBDH, LDH were significantly different between RMPP and GMPP groups (all P < .001). In the bronchoscopic imaging, the mucus plug was significantly more commonly seen in the RMPP group (P < .001). Receiver operating characteristic (ROC) analysis revealed that the combined serum, clinical, and FOB imaging data possessed greater specificity and sensitivity than serum and clinical data alone.Our data suggest that the combined serum, clinical, and bronchoscopic imaging data might serve as a promising predictor for early RMPP diagnosis for pediatric patients with large pulmonary lesions.

Simple colorimetric DNA detection based on hairpin assembly reaction and target-catalytic circuits for signal amplification.

A simple strategy of colorimetric DNA detection is presented based on a hairpin assembly reaction and target-catalytic DNA circuits to achieve enzyme-free signal amplification. The method employed two hairpin species (H1 and H2), which were stable and unable to hybridize in the absence of target. In the presence of target, the target hybridized with hairpin H1 and the opened hairpin H1 hybridized with hairpin H2, allowing the target to be displaced. H1 and H2 were respectively attached to gold nanoparticles, allowing the duplex formed from H1 and H2 to be visualized with the naked eye. The displaced target again triggered the next round of strand exchange reaction to achieve signal amplification. The method may have a wide range of sensor applications because it is enzyme-free and simple to perform.

The real-time PCR for sensitive protein detection by target-induced intermolecular hybridization.

An approach was developed to sensitively and specially detect protein in buffer and serum based on target-induced intermolecular hybridization and the significant difference of duplex stability in intramolecular versus intermolecular.

Cocaine detection via rolling circle amplification of short DNA strand separated by magnetic beads.

A novel and sensitive fluorescence biosensor based on aptamer and rolling circle amplification for the determination of cocaine was developed in the present work. Here cocaine aptamers immobilized onto Au nanoparticles modified magnetic beads hybridized with short DNA strand. In the presence of cocaine, the short DNA strand was displaced from aptamer owing to cocaine specially binding with aptamer. Next, the short DNA strand was separated by magnetic beads and used to originate rolling circle amplification as primer. The end products of rolling circle amplification were detected by fluorescence signal generation upon molecular beacons hybridizing with the end products of rolling circle amplification. With rolling circle amplification and the separation by magnetic beads reducing the background signal, the new strategy was suitable for the detection of as low as 0.48 nM cocaine. Compared with reported cocaine sensors, our method exhibited excellent sensitivity. Our new strategy may provide a platform for numerous proteins and low molecular weight analytes to highly sensitively detect by DNA amplification.