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Morinda officinalis is a traditional Chinese tonic herb, and have been used in the treatment of multiple diseases. Here, three iridoid glycosides isolated from M. officinalis were evaluated for their roles in the autophagy-lysosomal pathway. All three iridoid glycosides could induce TFEB/TFE3-mediated lysosomal biogenesis and trigger autophagy. Interestingly, they promoted the nuclear import of TFEB/TFE3 without affecting their nuclear export, suggesting their role in the maintenance of lysosomal homeostasis. The results from this study shed light on the identification of autophagy activators from M. officinalis and provide a basis for developing them in the treatment of oxidative stress-involved diseases.

Objectives: To develop a predictive model for patent ductus arteriosus (PDA) in preterm infants at seven days postpartum. The model employs ultrasound measurements of the ductus arteriosus (DA) intimal thickness (IT) obtained within 24 h after birth., Methods: One hundred and five preterm infants with gestational ages ranging from 27.0 to 36.7 weeks admitted within 24 h following birth were prospectively enrolled. Echocardiographic assessments were performed to measure DA IT within 24 h after birth, and DA status was evaluated through echocardiography on the seventh day postpartum. Potential predictors were considered, including traditional clinical risk factors, M-mode ultrasound parameters, lumen diameter of the DA (LD), and DA flow metrics. A final prediction model was formulated through bidirectional stepwise regression analysis and subsequently subjected to internal validation. The model's discriminative ability, calibration, and clinical applicability were also assessed., Results: The final predictive model included birth weight, application of mechanical ventilation, left ventricular end-diastolic diameter (LVEDd), LD, and the logarithm of IT (logIT). The receiver operating characteristic (ROC) curve for the model, predicated on logIT, exhibited excellent discriminative power with an area under the curve (AUC) of 0.985 (95% CI: 0.966-1.000), sensitivity of 1.000, and specificity of 0.909. Moreover, the model demonstrated robust calibration and goodness-of-fit ( χ 2 value = 0.560, p  > 0.05), as well as strong reproducibility (accuracy: 0.935, Kappa: 0.773), as evidenced by 10-fold cross-validation. A decision curve analysis confirmed the model's broad clinical utility., Conclusions: Our study successfully establishes a predictive model for PDA in preterm infants at seven days postpartum, leveraging the measurement of DA IT. This model enables identifying, within the first 24 h of life, infants who are likely to benefit from timely DA closure, thereby informing treatment decisions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Hu, Zhu, Wang, Hou, Ni, Zhang, Li, Peng, Li, Sun and Xu.)

Background: Early identification of abnormal left ventricular function in children with obstructive sleep apnea (OSA) is difficult using conventional echocardiographic indices and commonly used clinical markers of myocardial damage. We sought to investigate the value of automatic function imaging and myocardial work parameters in predicting early cardiac impairment in children having OSA with preserved left heart function and thereby identifying an optimal index for assessment., Patients and Methods: Fifty-two children who presented with symptoms of nocturnal sleep snoring and open-mouth breathing and 34 healthy controls were enrolled in this study. Clinical characteristics and conventional echocardiographic data were collected, and image analysis was performed using two-dimensional speckle-tracking echocardiography to obtain left ventricular global longitudinal strain (GLS), post-systolic index, peak strain dispersion, global work index (GWI), global constructive work (GCW), global wasted work, and global work efficiency., Results: Children with OSA had significantly lower GLS, GWI, and GCW than those without (P < 0.05). Additionally, GWI (β = -32.87, 95% CI: -53.47 to -12.27), and GCW (β = -35.09, 95% CI: -55.35 to -14.84) were found to correlate with the disease severity in the multiple linear regression mode, with worsening values observed as the severity of the disease increased. ROC curve analysis revealed that GCW was the best predictor of myocardial dysfunction, with an AUC of 0.809 (P < 0.001), and the best cutoff point for diagnosing myocardial damage in children with OSA was 1965.5 mmHg%, with a sensitivity of 92.5% and a specificity of 58.7%., Conclusions: GLS, GWI, and GCW were identified as predictors of myocardial dysfunction in children with OSA, with GCW being the best predictor., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

To assess the value of parameters of myocardial work for dynamic monitoring of myocardial injury after neonatal asphyxia. Fifty-three neonates with asphyxia admitted within 24 h after delivery were divided into a mild asphyxia group (n = 40) and severe asphyxia group (n = 13). Echocardiography was performed within 24 h post-birth, within 72 h post-birth (48 h after first echo), and during recovery. The left ventricular ejection fraction on M-mode echocardiography and by Simpson's biplane method (LVEF and Bi-EF, respectively), stroke volume (SV), cardiac output (CO), cardiac index (CI), global longitudinal strain (GLS), global work index (GWI), global constructive work (GCW), and other parameters were measured. Echocardiographic indicators were compared between groups and over time. GWI was significantly increased at 72 h in the mild asphyxia group (P < 0.05) but showed no significant change over time in the severe asphyxia group (P > 0.05). While GCW increased significantly over time in both groups (P < 0.05), it increased earlier in the mild asphyxia group. Time and grouping factors had independent effects on GWI and GCW (P > 0.05). The characteristics of differences in GWI and GCW between the two groups were different from those for LVEF, Bi-EF, SV, CO, CI, and GLS and their change characteristics with improvement from treatment. GWI and GCW changed significantly during recovery from neonatal asphyxia, and their change characteristics differed between mild and severe asphyxia cases. Myocardial work parameters can be used as valuable supplements to traditional indicators of left ventricular function to dynamically monitor the recovery from myocardial injury after neonatal asphyxia., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Single-cell RNA sequencing (scRNA-seq) has rapidly emerged as a powerful technique for analyzing cellular heterogeneity at the individual cell level. In the analysis of scRNA-seq data, cell clustering is a critical step in downstream analysis, as it enables the identification of cell types and the discovery of novel cell subtypes. However, the characteristics of scRNA-seq data, such as high dimensionality and sparsity, dropout events and batch effects, present significant computational challenges for clustering analysis. In this study, we propose scGCC, a novel graph self-supervised contrastive learning model, to address the challenges faced in scRNA-seq data analysis. scGCC comprises two main components: a representation learning module and a clustering module. The scRNA-seq data is first fed into a representation learning module for training, which is then used for data classification through a clustering module. scGCC can learn low-dimensional denoised embeddings, which is advantageous for our clustering task. We introduce Graph Attention Networks (GAT) for cell representation learning, which enables better feature extraction and improved clustering accuracy. Additionally, we propose five data augmentation methods to improve clustering performance by increasing data diversity and reducing overfitting. These methods enhance the robustness of clustering results. Our experimental study on 14 real-world datasets has demonstrated that our model achieves extraordinary accuracy and robustness. We also perform downstream tasks, including batch effect removal, trajectory inference, and marker genes analysis, to verify the biological effectiveness of our model.

It is a challenging task to utilize efficient electrocatalytic metal hydroxide-based materials for the oxygen evolution reaction (OER) in order to produce clean hydrogen energy through water splitting, primarily due to the restricted availability of active sites and the undesirably high adsorption energies of oxygenated species. To address these challenges simultaneously, we intentionally engineer a hollow star-shaped Ag/CoMo-LDH heterostructure as a highly efficient electrocatalytic system. This design incorporates a considerable number of heterointerfaces between evenly dispersed Ag nanoparticles and CoMo-LDH nanosheets. The heterojunction materials have been prepared using self-assembly, in situ transformation, and spontaneous redox processes. The nanosheet-integrated hollow architecture can prevent active entities from agglomeration and facilitate mass transportation, enabling the constant exposure of active sites. Specifically, the powerful electronic interaction within the heterojunction can successfully regulate the Co 3+ /Co 2+ ratio and the d-band center, resulting in rational optimization of the adsorption and desorption of the intermediates on the site. Benefiting from its well-defined multifunctional structures, the Ag0.4/CoMo-LDH with optimal Ag loading exhibits impressive OER activity, the overpotential being 290 mV to reach a 10 mA cm -2 current density. The present study sheds some new insights into the electron structure modulation of hollow heterostructures toward rationally designing electrocatalytic materials for the OER.

Accomplishing a green hydrogen economy in reality through water spitting ultimately relies upon earth-abundant efficient electrocatalysts that can simultaneously accelerate the oxygen and hydrogen evolution reactions (OER and HER). The perspective of electronic structure modulation via interface engineering is of great significance to optimize electrocatalytic output but remains a tremendous challenge. Herein, an efficient tactic has been explored to prepare nanosheet-assembly tumbleweed-like CoFeCe-containing precursors with time-/energy-saving and easy-operating features. Subsequently, the final metal phosphide materials containing multiple interfaces, denoted CoP/FeP/CeO x , have been synthesized via the phosphorization process. Through the optimization of the Co/Fe ratio and the content of the rare-earth Ce element, the electrocatalytic activity has been regulated. As a result, bifunctional Co3Fe/Ce0.025 reaches the top of the volcano for both OER and HER simultaneously, with the smallest overpotentials of 285 mV (OER) and 178 mV (HER) at 10 mA cm -2 current density in an alkaline environment. Multicomponent heterostructure interface engineering would lead to more exposed active sites, feasible charge transport, and strong interfacial electronic interaction. More importantly, the appropriate Co/Fe ratio and Ce content can synergistically tailor the d-band center with a downshift to enhance the per-site intrinsic activity. This work would provide valuable insights to regulate the electronic structure of superior electrocatalysts toward water splitting by constructing rare-earth compounds containing multiple heterointerfaces.

CircRNAs have a stable structure, which gives them a higher tolerance to nucleases. Therefore, the properties of circular RNAs are beneficial in disease diagnosis. However, there are few known associations between circRNAs and disease. Biological experiments identify new associations is time-consuming and high-cost. As a result, there is a need of building efficient and achievable computation models to predict potential circRNA-disease associations. In this paper, we design a novel convolution neural networks framework(DMFCNNCD) to learn features from deep matrix factorization to predict circRNA-disease associations. Firstly, we decompose the circRNA-disease association matrix to obtain the original features of the disease and circRNA, and use the mapping module to extract potential nonlinear features. Then, we integrate it with the similarity information to form a training set. Finally, we apply convolution neural networks to predict the unknown association between circRNAs and diseases. The five-fold cross-validation on various experiments shows that our method can predict circRNA-disease association and outperforms state of the art methods.

The oxygen evolution reaction (OER) is kinetically sluggish due to the limitation of the four-electron transfer pathway, so it is imperative to explore advanced catalysts with a superior structure and catalytic output under facile synthetic conditions. In the present work, an easily accessible strategy was proposed to implement the plant-polyphenol-involved coordination assembly on Co(OH) 2 nanosheets. A TA-Fe (TA = tannic acid) coordination assembly growing on Co(OH) 2 resulted in the heterostructure of Co(OH) 2 @TA-Fe as an electrocatalyst for OER. It could significantly decrease the overpotential to 297 mV at a current density of 10 mA cm -2 . The heterostructure Co(OH) 2 @TA-Fe also possessed favorable reaction kinetics with a low Tafel slope of 64.8 mV dec -1 and facilitated a charge-transfer ability. The enhanced electrocatalytic performance was further unraveled to be related to the confined growth of the coordination assembly on Co(OH) 2 to expose more active sites, the modulated surface properties and their synergistic effect. This study demonstrated a simple and feasible strategy to utilize inexpensive biomass-derived substances as novel modifiers to enhance the performance of energy-conversion electrocatalysis.

Recently, as a growing number of associations between microRNAs (miRNAs) and diseases are discovered, researchers gradually realize that miRNAs are closely related to several complicated biological processes and human diseases. Hence, it is especially important to construct availably models to infer associations between miRNAs and diseases. In this study, we presented Improved Graph Regression for miRNA-Disease Association Prediction (IGRMDA) to observe potential relationship between miRNAs and diseases. In order to reduce the inherent noise existing in the acquired biological datasets, we utilized matrix decomposition algorithm to process miRNA functional similarity and disease semantic similarity and then combining them with existing similarity information to obtain final miRNA similarity data and disease similarity data. Then, we applied miRNA-disease association data, miRNA similarity data and disease similarity data to form corresponding latent spaces. Furthermore, we performed improved graph regression algorithm in latent spaces, which included miRNA-disease association space, miRNA similarity space and disease similarity space. Non-negative matrix factorization and partial least squares were used in the graph regression process to obtain important related attributes. The cross validation experiments and case studies were also implemented to prove the effectiveness of IGRMDA, which showed that IGRMDA could predict potential associations between miRNAs and diseases.

Lots of experimental studies have revealed the significant associations between lncRNAs and diseases. Identifying accurate associations will provide a new perspective for disease therapy. Calculation-based methods have been developed to solve these problems, but these methods have some limitations. In this paper, we proposed an accurate method, named MLGCNET, to discover potential lncRNA-disease associations. Firstly, we reconstructed similarity networks for both lncRNAs and diseases using top k similar information, and constructed a lncRNA-disease heterogeneous network (LDN). Then, we applied Multi-Layer Graph Convolutional Network on LDN to obtain latent feature representations of nodes. Finally, the Extra Trees was used to calculate the probability of association between disease and lncRNA. The results of extensive 5-fold cross-validation experiments show that MLGCNET has superior prediction performance compared to the state-of-the-art methods. Case studies confirm the performance of our model on specific diseases. All the experiment results prove the effectiveness and practicality of MLGCNET in predicting potential lncRNA-disease associations.

The modulation of the electronic structure is the effective access to achieve highly active electrocatalysts for the hydrogen evolution reaction (HER). Transition-metal phosphide-based heterostructures are very promising in enhancing HER performance but the facile fabrication and an in-depth study of the catalytic mechanisms still remain a challenge. In this work, the catalytically inactive n-type CeO x is successfully combined with p-type CoP to form the CoP/CeO x heterojunction. The crystalline-amorphous CoP/CeO x heterojunction is fabricated by the phosphorization of predesigned Co(OH) 2 /CeO x via the as-developed reduction-hydrolysis strategy. The p-n CoP/CeO x heterojunction with a strong built-in potential of 1.38 V enables the regulation of the electronic structure of active CoP within the space-charge region to enhance its intrinsic activity and facilitate the electron transfer. The functional CeO x entity and the negatively charged CoP can promote the water dissociation and optimize H adsorption, synergistically boosting the electrocatalytic HER output. As expected, the heterostructured CoP/CeO x -20:1 with the optimal ratio of Co/Ce shows significantly improved HER activity and favorable kinetics (overpotential of 118 mV at a current density of 10 mA cm -2 and Tafel slope of 77.26 mV dec -1 ). The present study may provide new insight into the integration of crystalline and amorphous entities into the p-n heterojunction as a highly efficient electrocatalyst for energy storage and conversion.

The addition of an extra metal source induces the transformation from crystalline α-Ni(OH) 2 to an amorphous NiCoFeCrMo-based high-entropy hydroxide (HEH) and maximizes the high-valence Ni 3+ content in HEH. For OER electrocatalysis, the quinary HEH possesses an overpotential of 292 mV at 10 mA cm -2 , a Tafel slope of 54.31 mV dec -1 and the boosted intrinsic activity, surpassing other subsystems.

Background: Factors that are associated with the short-term rehospitalization have been investigated previously in numerous studies. However, the majority of these studies have not produced any conclusive results because of their smaller sample sizes, differences in the definition of pneumonia, joint pooling of the in-hospital and post-discharge deaths and lower generalizability., Aim: To estimate the effect of various risk factors on the rate of hospital readmissions in patients with pneumonia., Methods: Systematic search was conducted in PubMed Central, EMBASE, MEDLINE, Cochrane library, ScienceDirect and Google Scholar databases and search engines from inception until July 2021. We used the Newcastle Ottawa (NO) scale to assess the quality of published studies. A meta-analysis was carried out with random-effects model and reported pooled odds ratio (OR) with 95% confidence interval (CI)., Results: In total, 17 studies with over 3 million participants were included. Majority of the studies had good to satisfactory quality as per NO scale. Male gender (pooled OR = 1.22; 95%CI: 1.16-1.27), cancer (pooled OR = 1.94; 95%CI: 1.61-2.34), heart failure (pooled OR = 1.28; 95%CI: 1.20-1.37), chronic respiratory disease (pooled OR = 1.37; 95%CI: 1.19-1.58), chronic kidney disease (pooled OR = 1.38; 95%CI: 1.23-1.54) and diabetes mellitus (pooled OR = 1.18; 95%CI: 1.08-1.28) had statistically significant association with the hospital readmission rate among pneumonia patients. Sensitivity analysis showed that there was no significant variation in the magnitude or direction of outcome, indicating lack of influence of a single study on the overall pooled estimate., Conclusion: Male gender and specific chronic comorbid conditions were found to be significant risk factors for hospital readmission among pneumonia patients. These results may allow clinicians and policymakers to develop better intervention strategies for the patients., Competing Interests: Conflict-of-interest statement: The authors deny any conflict of interest., (©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.)

Numerous studies have shown that microRNAs are associated with the occurrence and development of human diseases. Thus, studying disease-associated miRNAs is significantly valuable to the prevention, diagnosis and treatment of diseases. In this paper, we proposed a novel method based on matrix completion and non-negative matrix factorization (MCNMF)for predicting disease-associated miRNAs. Due to the information inadequacy on miRNA similarities and disease similarities, we calculated the latter via two models, and introduced the Gaussian interaction profile kernel similarity. In addition, the matrix completion (MC)was employed to further replenish the miRNA and disease similarities to improve the prediction performance. And to reduce the sparsity of miRNA-disease association matrix, the method of weighted K nearest neighbor (WKNKN)was used, which is a pre-processing step. We also utilized non-negative matrix factorization (NMF)using dual L 2,1 -norm, graph Laplacian regularization, and Tikhonov regularization to effectively avoid the overfitting during the prediction. Finally, several experiments and a case study were implemented to evaluate the effectiveness and performance of the proposed MCNMF model. The results indicated that our method could reliably and effectively predict disease-associated miRNAs.

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA). In the proposed model, we utilized miRNA-miRNA similarities, disease-disease similarities and verified miRNA-disease associations to construct a heterogeneous network, which is applied to learn the embeddings of miRNAs and diseases. In addition, we separately constructed miRNA-based and disease-based sub-networks. Combining the embeddings of miRNAs and diseases, graph convolutional autoencoder (GCAE) was utilized to calculate association scores of miRNA-disease on two sub-networks, respectively. Furthermore, we obtained final prediction scores between miRNAs and diseases by adopting an average ensemble way to integrate the prediction scores from two types of subnetworks. To indicate the accuracy of GCAEMDA, we applied different cross validation methods to evaluate our model whose performances were better than the state-of-the-art models. Case studies on a common human diseases were also implemented to prove the effectiveness of GCAEMDA. The results demonstrated that GCAEMDA was beneficial to infer potential associations of miRNA-disease., Competing Interests: The authors have declared that no competing interests exist.

MicroRNAs (miRNAs) are small non-coding RNAs that have been demonstrated to be related to numerous complex human diseases. Considerable studies have suggested that miRNAs affect many complicated bioprocesses. Hence, the investigation of disease-related miRNAs by utilizing computational methods is warranted. In this study, we presented an improved label propagation for miRNA-disease association prediction (ILPMDA) method to observe disease-related miRNAs. First, we utilized similarity kernel fusion to integrate different types of biological information for generating miRNA and disease similarity networks. Second, we applied the weighted k-nearest known neighbor algorithm to update verified miRNA-disease association data. Third, we utilized improved label propagation in disease and miRNA similarity networks to make association prediction. Furthermore, we obtained final prediction scores by adopting an average ensemble method to integrate the two kinds of prediction results. To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness of ILPMDA. All results demonstrated that ILPMDA had the ability to discover potential miRNA-disease associations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Wang, Li, Ji, Zheng and Ni.)

Predicting disease-related long non-coding RNAs (lncRNAs) is beneficial to finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. In this paper, we proposed a machine learning techniques-based classification approach to identify disease-related lncRNAs by graph auto-encoder (GAE) and random forest (RF) (GAERF). First, we combined the relationship of lncRNA, miRNA and disease into a heterogeneous network. Then, low-dimensional representation vectors of nodes were learned from the network by GAE, which reduce the dimension and heterogeneity of biological data. Taking these feature vectors as input, we trained a RF classifier to predict new lncRNA-disease associations (LDAs). Related experiment results show that the proposed method for the representation of lncRNA-disease characterizes them accurately. GAERF achieves superior performance owing to the ensemble learning method, outperforming other methods significantly. Moreover, case studies further demonstrated that GAERF is an effective method to predict LDAs., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases., Competing Interests: The authors have declared that no competing interests exist.

Background: MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time-consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations., Methods: This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score., Result: Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies., Conclusion: MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA-disease association predication.

MicroRNAs (miRNAs) that belong to non-coding RNAs are verified to be closely associated with several complicated biological processes and human diseases. In this study, we proposed a novel model that was Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction (SNFIMCMDA). We applied inductive matrix completion (IMC) method to acquire possible associations between miRNAs and diseases, which also could obtain corresponding correlation scores. IMC was performed based on the verified connections of miRNA-disease, miRNA similarity, and disease similarity. In addition, miRNA similarity and disease similarity were calculated by similarity network fusion, which could masterly integrate multiple data types to obtain target data. We integrated miRNA functional similarity and Gaussian interaction profile kernel similarity by similarity network fusion to obtain miRNA similarity. Similarly, disease similarity was integrated in this way. To indicate the utility and effectiveness of SNFIMCMDA, we both applied global leave-one-out cross-validation and five-fold cross-validation to validate our model. Furthermore, case studies on three significant human diseases were also implemented to prove the effectiveness of SNFIMCMDA. The results demonstrated that SNFIMCMDA was effective for prediction of possible associations of miRNA-disease., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Li, Gao, Zheng, Wang, Wang and Ni.)

Phytochemistry investigations on Ailanthus altissima (Mill.) Swingle, a Simaroubaceae plant that is recognized as a traditional herbal medicine, have afforded various natural products, among which C 20 quassinoid is the most attractive for their significant and diverse pharmacological and biological activities. Our continuous study has led to the isolation of two novel quassinoid glycosides, named chuglycosides J and K, together with fourteen known lignans from the samara of A. altissima . The new structures were elucidated based on comprehensive spectra data analysis. All of the compounds were evaluated for their anti-tobacco mosaic virus activity, among which chuglycosides J and K exhibited inhibitory effects against the virus multiplication with half maximal inhibitory concentration (IC 50 ) values of 56.21 ± 1.86 and 137.74 ± 3.57 μM, respectively.

Background: The aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers., Results: Here, we present a computational framework based on graph Laplacian regularized L 2, 1 -nonnegative matrix factorization (GRL 2, 1 -NMF) for inferring possible human disease-connected miRNAs. First, manually validated disease-connected microRNAs were integrated, and microRNA functional similarity information along with two kinds of disease semantic similarities were calculated. Next, we measured Gaussian interaction profile (GIP) kernel similarities for both diseases and microRNAs. Then, we adopted a preprocessing step, namely, weighted K nearest known neighbours (WKNKN), to decrease the sparsity of the miRNA-disease association matrix network. Finally, the GRL 2,1 -NMF framework was used to predict links between microRNAs and diseases., Conclusions: The new method (GRL 2, 1 -NMF) achieved AUC values of 0.9280 and 0.9276 in global leave-one-out cross validation (global LOOCV) and five-fold cross validation (5-CV), respectively, showing that GRL 2, 1 -NMF can powerfully discover potential disease-related miRNAs, even if there is no known associated disease.

Purpose: To demonstrate the feasibility and evaluate the performance of a deep-learning convolutional neural network (CNN) classification model for automated identification of different types of inferior vena cava (IVC) filters on radiographs., Materials and Methods: In total, 1,375 cropped radiographic images of 14 types of IVC filters were collected from patients enrolled in a single-center IVC filter registry, with 139 images withheld as a test set and the remainder used to train and validate the classification model. Image brightness, contrast, intensity, and rotation were varied to augment the training set. A 50-layer ResNet architecture with fixed pre-trained weights was trained using a soft margin loss over 50 epochs. The final model was evaluated on the test set., Results: The CNN classification model achieved a F1 score of 0.97 (0.92-0.99) for the test set overall and of 1.00 for 10 of 14 individual filter types. Of the 139 test set images, 4 (2.9%) were misidentified, all mistaken for other filter types that appear highly similar. Heat maps elucidated salient features for each filter type that the model used for class prediction., Conclusions: A CNN classification model was successfully developed to identify 14 types of IVC filters on radiographs and demonstrated high performance. Further refinement and testing of the model is necessary before potential real-world application., (Copyright © 2019 SIR. All rights reserved.)

A new phenolic derivative, 4-hydroxyphenol-1-O-[6-O-(E)-feruloyl-β-d-glucopyranosyl]-(1→6)-β-d-glucopyranoside (1), and a new terpenylated coumarin, named altissimacoumarin H (2) were identified from the fruit of Ailanthus altissima (Mill.) Swingle (Simaroubaceae), together with ten known compounds (3-12), including two coumarins and eight phenylpropanoids. Their structures were determined on the basis of chemical method and spectroscopic data. Antiviral effect against Tobacco mosaic virus (TMV) of all the compounds obtained were evaluated using leaf-disc method.

Quassinoids are bitter constituents characteristic of the family Simaroubaceae. A total of 18 C 20 quassinoids, including nine new quassinoid glycosides, named chuglycosides A-I (1-6 and 8-10), were identified from the samara of Ailanthus altissima (Mill.) Swingle. All of the quassinoids showed potent anti-tobacco mosaic virus (TMV) activity. A preliminary structure-anti-TMV activity relationship of quassinoids was discussed. The effects of three quassinoids, including chaparrinone (12), glaucarubinone (15), and ailanthone (16), on the accumulation of TMV coat protein (CP) were studied by western blot analysis. Ailanthone (16) was further investigated for its influence on TMV spread in the Nicotiana benthamiana plant.

Four novel compounds-two phenylpropionamides, one piperidine, and one phenolic derivatives-were isolated and identified from the fruit of a medicinal plant, Ailanthus altissima (Mill.) Swingle (Simaroubaceae), together with one known phenylpropionamide, 13 known phenols, and 10 flavonoids. The structures of the new compounds were elucidated as 2-hydroxy- N -[(2- O -β-d-glucopyranosyl)phenyl]propionamide ( 1 ), 2-hydroxy- N -[(2- O -β-d-glucopyranosyl-(1→6)-β-d-glucopyranosyl)phenyl]propionamide ( 2 ), 2β-carboxyl-piperidine-4β-acetic acid methyl ester ( 4 ), and 4-hydroxyphenyl-1- O -[6-(hydrogen-3-hydroxy-3-methylpentanedioate)]-β-d-glucopyranoside ( 5 ) based on spectroscopic analysis. All the isolated compounds were evaluated for their inhibitory activity against Tobacco mosaic virus (TMV) using the leaf-disc method. Among the compounds isolated, arbutin ( 6 ), β-d-glucopyranosyl-(1→6)-arbutin ( 7 ), 4-methoxyphenylacetic acid ( 10 ), and corilagin ( 18 ) showed moderate inhibition against TMV with IC 50 values of 0.49, 0.51, 0.27, and 0.45 mM, respectively., Competing Interests: The authors declare no conflict of interest.

The fermentation and isolation of metabolites produced by an endophytic fungus, which was identified as Phomopsis sp. FJBR-11, based on phylogenetic analysis, led to the identification of six compounds, including dothiorelones A-C, and H, and cytosporones C and U. Among these compounds, cytosporone U exhibited potent inhibitory activity against Tobacco mosaic virus (TMV). Moreover, the crude and a purified exopolysaccharide were proved to possess strong inhibitory effects against the virus infection., Competing Interests: The authors declare no conflict of interest.

A new Erythrina alkaloid glycoside, named erythraline-11β- O -glucopyranoside, was isolated from the seeds of Erythrina crista-galli L., together with five known Erythrina alkaloids and an indole alkaloid. The structure of the new alkaloid glycoside was elucidated by spectroscopic methods, and all of the compounds were evaluated for their antiviral activity against tobacco mosaic virus., Competing Interests: The authors declare no conflict of interest.

Long microchannels with thin walls, small width, and nanoholes or irregular shaped microgaps, which are similar to capillaries or cancerous vessels, are urgently needed to simulate the physiological activities in human body. However, the fabrication of such channels remains challenging. Here, microchannels with designable holes are manufactured by combining laser printing with line-contact capillary-force assembly. Two microwalls are first printed by femtosecond laser direct-writing, and subsequently driven to collapse into a channel by the capillary force that arises in the evaporation of developer. The channel can remain stable in solvent due to the enhanced Van der Waals' force caused by the line-contact of microwalls. Microchannels with controllable nanoholes and almost arbitrary patterns can be fabricated without any bonding or multistep processes. As-prepared microchannels, with wall thicknesses less than 1 µm, widths less than 3 µm, lengths more than 1 mm, are comparable with human capillaries. In addition, the prepared channels also exhibit the ability to steer the flow of liquid without any external pump., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

High efficiency fabrication and integration of three-dimension (3D) functional devices in Lab-on-a-chip systems are crucial for microfluidic applications. Here, a spatial light modulator (SLM)-based multifoci parallel femtosecond laser scanning technology was proposed to integrate microstructures inside a given 'Y' shape microchannel. The key novelty of our approach lies on rapidly integrating 3D microdevices inside a microchip for the first time, which significantly reduces the fabrication time. The high quality integration of various 2D-3D microstructures was ensured by quantitatively optimizing the experimental conditions including prebaking time, laser power and developing time. To verify the designable and versatile capability of this method for integrating functional 3D microdevices in microchannel, a series of microfilters with adjustable pore sizes from 12.2 μm to 6.7 μm were fabricated to demonstrate selective filtering of the polystyrene (PS) particles and cancer cells with different sizes. The filter can be cleaned by reversing the flow and reused for many times. This technology will advance the fabrication technique of 3D integrated microfluidic and optofluidic chips.

The adsorption ability of duckweed (Lemna minor) powders for removing inorganic and organic mercury (methyl and ethyl mercury) has been studied using cold vapour atomic absorption spectrometry. The optimal adsorption conditions were: (a) the pH value of the solution 7.0 for inorganic and ethyl mercury, 9.0 for methyl mercury, and (b) equilibrium adsorption time 10, 20, and 40 min for inorganic mercury, methyl mercury, and ethyl mercury, respectively. After adsorption by L. minor powder for 40 min, when the initial concentrations of inorganic and organic mercury were under 12.0 μg L(-1) and 50.0 μg L(-1), respectively, the residual concentrations of mercury could meet the criterion of drinking water (1.0 μg L(-1)) and the permitted discharge limit of wastewater (10.0 μg L(-1)) set by China and USEPA, respectively. Thorough removal of both inorganic and organic mercury from aqueous solutions was reported for the first time. The significant adsorption sites were C-O-P and phosphate groups by the surface electrostatic interactions with aqueous inorganic and organic mercury cations, and then the selective adsorption was resulted from the strong chelating interaction between amine groups and mercury on the surface of L. minor cells., (Copyright © 2010 Elsevier B.V. All rights reserved.)