Xiaoer-Feire-Qing granules alleviate pyretic pulmonary syndrome induced by Streptococcus pneumoniae in young rats by affecting the lungs and intestines: An in vivo study based on network pharmacology.

ETHNOPHARMACOLOGICAL RELEVANCE: The traditional Chinese medicine (TCM) Xiaoer-Feire-Qing granules (XEFRQ) has been used to treat pyretic pulmonary syndrome (PPS) in children for many years. The function of the lungs is considered to be closely related to the large intestine in TCM. PURPOSE: We aimed to investigate the effects of XEFRQ on PPS and the underlying mechanisms via network pharmacology and animal experiments. METHODS: The TCMSP platform was used to identify the ingredients and potential targets of XEFRQ. The GeneCards, OMIM, and TTD databases were used to predict PPS-associated targets. Cytoscape 3.9.1 was employed to construct the protein-protein interaction network, and target prediction was performed by GO and KEGG analyses. For the animal experiment, a PPS model was constructed by three cycles of nasal drip of Streptococcus pneumoniae (STP; 0.5 mL/kg). The animals were randomly divided into the following four groups according to their weight (n = 10 rats per group): the blank group, the model group, the XEFRQ-L (16.3 g/kg) group, and the XEFRQ-H (56.6 g/kg) group. Rats in the blank group and the model group were given 0.5% CMC-Na by gavage. The general conditions of the rats were observed, and their food-intake, body weight, and body temperature were recorded for 14 days. After the intervention of 14 days, serum was collected to detect inflammatory cytokines (TNF-α, IL-1ß, and PGE2) and neurotransmitters (5-HT, SP, and VIP). H&E staining was used to observe the pathological morphology of lung and colon tissue. AQP3 expression was detected by Western blot. In addition, the gut microbiota in cecal content samples were analyzed by 16S rDNA high-throughput sequencing. RESULTS: Our network analysis revealed that XEFRQ may alleviate PPS injury by affecting the levels of inflammatory cytokines and neurotransmitters and mitigating STP-induced PPS.In vivo validation experiments revealed that XEFRQ improved STP-induced PPS and reduced the expression of inflammatory cytokines and neurotransmitters. Notably, XEFRQ significantly decreased the protein expression levels of AQP3, which was associated with dry stool. Our gut microbiota analysis revealed that the relative abundance of [Eubacterium]_ruminantium_group, Colidextribacter, Romboutsia, and Oscillibacter was decreased, which means XEFRQ exerts therapeutic effects against PPS associated with these bacteria. CONCLUSION: Our results demonstrate that XEFRQ alleviates PPS by affecting the lungs and intestines, further guiding its clinical application.

Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction.

MOTIVATION: The diverse structures and functions inherent in RNAs present a wealth of potential drug targets. Some small molecules are anticipated to serve as leading compounds, providing guidance for the development of novel RNA-targeted therapeutics. Consequently, the determination of RNA-small molecule binding affinity is a critical undertaking in the landscape of RNA-targeted drug discovery and development. Nevertheless, to date, only one computational method for RNA-small molecule binding affinity prediction has been proposed. The prediction of RNA-small molecule binding affinity remains a significant challenge. The development of a computational model is deemed essential to effectively extract relevant features and predict RNA-small molecule binding affinity accurately. RESULTS: In this study, we introduced RLaffinity, a novel deep learning model designed for the prediction of RNA-small molecule binding affinity based on 3D structures. RLaffinity integrated information from RNA pockets and small molecules, utilizing a 3D convolutional neural network (3D-CNN) coupled with a contrastive learning-based self-supervised pre-training model. To the best of our knowledge, RLaffinity was the first deep learning based method for the prediction of RNA-small molecule binding affinity. Our experimental results exhibited RLaffinity's superior performance compared to baseline methods, revealed by all metrics. The efficacy of RLaffinity underscores the capability of 3D-CNN to accurately extract both global pocket information and local neighbor nucleotide information within RNAs. Notably, the integration of a self-supervised pre-training model significantly enhanced predictive performance. Ultimately, RLaffinity was also proved as a potential tool for RNA-targeted drugs virtual screening. AVAILABILITY AND IMPLEMENTATION: https://github.com/SaisaiSun/RLaffinity.

Examining the Inverted U-Shaped Relationship Between Benevolent Leadership and Employees' Work Initiative: The Role of Work Engagement and Growth Need Strength.

Benevolent leadership is generally considered to be beneficial for work initiative. However, based on social exchange theory, this paper explores an inverted U-shaped relationship between benevolent leadership and work initiative. Using a multilevel structural equation model that analyzed the data from 596 employees and 139 supervisors in multiple technology companies, our findings show that benevolent leadership had an indirect, negative curvilinear relationship with work initiative via work engagement at both the individual and team levels. Furthermore, we also indicate that growth need strength moderates the positive relationship between benevolent leadership and work engagement at the individual level. Theoretical and practical implications and future research directions are discussed.

RNALigands: a database and web server for RNA-ligand interactions.

RNA molecules can fold into complex and stable 3D structures, allowing them to carry out important genetic, structural, and regulatory roles inside the cell. These complex structures often contain 3D pockets made up of secondary structural motifs that can be potentially targeted by small molecule ligands. Indeed, many RNA structures in PDB contain bound small molecules, and high-throughput experimental studies have generated a large number of interacting RNA and ligand pairs. There is considerable interest in developing small molecule lead compounds targeting viral RNAs or those RNAs implicated in neurological diseases or cancer. We hypothesize that RNAs that have similar secondary structural motifs may bind to similar small molecule ligands. Toward this goal, we established a database collecting RNA secondary structural motifs and bound small molecule ligands. We further developed a computational pipeline, which takes as input an RNA sequence, predicts its secondary structure, extracts structural motifs, and searches the database for similar secondary structure motifs and interacting small molecule. We demonstrated the utility of the server by querying α-synuclein mRNA 5' UTR sequence and finding potential matches which were validated as correct. The server is publicly available at http://RNALigands.ccbr.utoronto.ca The source code can also be downloaded at https://github.com/SaisaiSun/RNALigands.

Quantifying seasonal variations in microphytobenthos biomass on estuarine tidal flats using Sentinel-1/2 data.

Quantifying temporal and spatial changes in microphytobenthos (MPB) biomass is critical for understanding its ecological function in estuarine food web networks and carbon flows. However, tidal fluctuations and the complex composition of coastal sediment limit remote sensing applications for estimating MPB biomass seasonal variations in estuarine tidal flats. We integrated radar Sentinel-1 (S1) and optical Sentinel-2 (S2) remote sensing data to quantify the temporal and spatial variability in MPB biomass in the Changjiang estuary, China. Pixels of water bodies on the tidal flats were removed by dynamic threshold segmentation of the water index with the combined S1 and S2 data, and salt marsh pixels were masked with the first red-edge band in the S2 data. We used the continuum-removed spectral absorption depth feature to construct a regression model for estimating MPB biomass with a regression coefficient of 0.81. The results showed that spectral absorption continuum removal methods using broadband multispectral data for MPB estimation are a promising alternative to hyperspectral narrowband ratio operation. Compared with the widely used normalized difference vegetation index (NDVI), the scaled absorption depth feature was more stable for MPB estimation under a changeable sediment background. The produced seasonal map showed that the high biomass levels of the MPB in the study area are not limited to one season and one site, with an annual mean biomass of 14.39 mg chlorophyll a (Chl-a)·m-2 and 71% confirmed accuracy. The highest biomass levels occurred in summer in the supratidal zone (19.51 mg Chl-a·m-2) and in spring in the intertidal zone (17.10 mg Chl-a·m-2) in the Changjiang estuary. The relative shore height, derived from the tidal range here, is an important variable that shapes the MPB spatial distribution. This study demonstrates the potential of integrating high-spatial-resolution (10 m) S1 and S2 data for future large-scale estimation of intertidal MPB.

Esophageal cancer patients of heavier weight have more nutritional risk of inadequate calorie intake immediately after esophagectomy: a retrospective study.

BACKGROUND: Perioperative malnutrition is common in patients undergoing esophagectomy, and nutritional support is critical for postoperative recovery in these patients. But few studies reported which characteristics of these patients were associated with post-esophagectomy inadequate calorie intake. This study aimed to explore which patients were more likely to have inadequate calories immediately after esophagectomy and the impact on clinical outcomes. METHODS: From January 2018 to June 2019, patients undergoing esophagectomy were retrospectively divided into the "adequate calorie group" and the "inadequate calorie group" according to whether they met daily calorie requirements in a week after esophagectomy. Caloric requirements met rate and clinical outcomes were compared between patients with and without complications, and with weight > 70 kg or ≤ 70 kg. RESULTS: Patients in the inadequate calorie group (n = 104) had significantly higher weight (p < 0.001), lean body mass (p = 0.028), and BMI (p = 0.001) than the adequate calorie group (n = 46). Weight loss after esophagectomy was reduced (p = 0.043) in the adequate calorie group. Patients with complications had lower rate of adequate calorie intake (72.8% vs. 63.8%). The caloric requirements met rate in patients with weigh ≤ 70 kg was significantly higher than those weight > 70 kg (80.2% vs. 43.2%, p < 0.001). CONCLUSION: The weights of patients having inadequate calories in a week after esophagectomy were significantly heavier than those having adequate calories. Heavier patients after esophagectomy should attract more attention to their nutrition support. TRIAL REGISTRATION: This trial was registered ( ChiCTR1900025557 ).

RNA inter-nucleotide 3D closeness prediction by deep residual neural networks.

MOTIVATION: Recent years have witnessed that the inter-residue contact/distance in proteins could be accurately predicted by deep neural networks, which significantly improve the accuracy of predicted protein structure models. In contrast, fewer studies have been done for the prediction of RNA inter-nucleotide 3D closeness. RESULTS: We proposed a new algorithm named RNAcontact for the prediction of RNA inter-nucleotide 3D closeness. RNAcontact was built based on the deep residual neural networks. The covariance information from multiple sequence alignments and the predicted secondary structure were used as the input features of the networks. Experiments show that RNAcontact achieves the respective precisions of 0.8 and 0.6 for the top L/10 and L (where L is the length of an RNA) predictions on an independent test set, significantly higher than other evolutionary coupling methods. Analysis shows that about 1/3 of the correctly predicted 3D closenesses are not base pairings of secondary structure, which are critical to the determination of RNA structure. In addition, we demonstrated that the predicted 3D closeness could be used as distance restraints to guide RNA structure folding by the 3dRNA package. More accurate models could be built by using the predicted 3D closeness than the models without using 3D closeness. AVAILABILITY AND IMPLEMENTATION: The webserver and a standalone package are available at: http://yanglab.nankai.edu.cn/RNAcontact/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Application of Modified Tailed Microcoil in Preoperative Localization of Small Pulmonary Nodules: A Retrospective Study.

BACKGROUND: The localization of small pulmonary nodules (SPNs) during video-assisted thoracoscopic surgery (VATS) is challenging thoracic surgeon, especially in patients with severe pleural adhesion or visceral pleura pigmentation due to low success rate and future conversion to thoracotomy. This study aims to compare the efficacy and safety between modified microcoil and methylene blue in preoperative localization of small nodules, particularly patients with severe pleural adhesion or visceral pleura pigmentation. MATERIALS AND METHODS: From January 2018 to February 2019 in our institute, 342 patients who underwent computed tomography-guided localization of SPN were recruited in this retrospective cohort study and divided into the modified microcoil group (n = 239) and the methylene blue group (n = 103) according to the localization method. Clinical characteristics and perioperative complications were collected to analyze. RESULTS: All SPNs were successfully marked in both groups. Location-related complications, the duration of localization procedure, and the length of hospital stay were not different between the two groups. The operation time of modified microcoil and the duration of removal of nodule in operation were both shorter than the methylene blue (p = 0.014 and p = 0.047). The analysis stratified by gender showed that similar results were found in male patients (p = 0.01 and p = 0.00), while in female patients, no significant difference was found. Additionally, in senior patients (older than 60 years), the operation time in modified microcoil groups was less than methylene blue group (p = 0.024). CONCLUSION: Compared with methylene blue, modified microcoil achieved a shorter operation time of removal of nodule in VATS, especially for patients with pleural adhesion and the pigmentation of the lung surface as well as the male patients and the patients older than 60 years.

A diaminomaleonitrile-appended BODIPY chemosensor for the selective detection of Cu2+ via oxidative cyclization and imaging in SiHa cells and zebrafish.

A specific Cu2+ sensor, 2-amino-3-(BODIPYmethyleneamino)maleonitrile (BDM), was established by a simple dehydration between BODIPY and diaminomaleonitrile. Cu2+ could be recognized by BDM over other competing metal ions in acetonitrile with distinct fluorescence emission signal response. Upon the addition of Cu2+ to BDM in acetonitrile, the maximum absorption at approximately 530 nm on the longer wavelength side was quenched, and the emission at 530 nm was ignited simultaneously. The fluorescence intensity enhancement could reach a maximum of 204 times the intensity of the BDM blank solution. The fluorescence "off-on" effect is established according to the Cu2+-induced fast intramolecular oxidative cyclization reaction, which could be deduced from the formation of an imidazole ring appended to the cyclization product (2-BODIPY-1H-imidazole-4,5-dicarbonitrile, BMC). Single-crystal structure analysis of the sensor BDM and cyclization product BMC further demonstrated this oxidative cyclization. Finally, the Cu2+ recognition property of BDM was validated in SiHa cells and living zebrafish. Additionally, the blood-brain barrier of the zebrafish can be penetrated by the BDM dye and the neuron cells in the brain were stained.

A Strategy for the Synthesis of Sulfonamides on DNA.

An efficient method is reported to synthesize sulfonamides on DNA from sulfinic acids or sodium sulfinates and amines in the presence of iodine under mild conditions. This method demonstrates a major expansion of scope of sulfonamide formation on DNA through the utilization of a novel sodium carbonate-sodium sulfinate bifunctional reagent class.

Improving the prediction of protein-nucleic acids binding residues via multiple sequence profiles and the consensus of complementary methods.

MOTIVATION: The interactions between protein and nucleic acids play a key role in various biological processes. Accurate recognition of the residues that bind nucleic acids can facilitate the study of uncharacterized protein-nucleic acids interactions. The accuracy of existing nucleic acids-binding residues prediction methods is relatively low. RESULTS: In this work, we introduce NucBind, a novel method for the prediction of nucleic acids-binding residues. NucBind combines the predictions from a support vector machine-based ab-initio method SVMnuc and a template-based method COACH-D. SVMnuc was trained with features from three complementary sequence profiles. COACH-D predicts the binding residues based on homologous templates identified from a nucleic acids-binding library. The proposed methods were assessed and compared with other peering methods on three benchmark datasets. Experimental results show that NucBind consistently outperforms other state-of-the-art methods. Though with higher accuracy, similar to many other ab-initio methods, cross prediction between DNA and RNA-binding residues was also observed in SVMnuc and NucBind. We attribute the success of NucBind to two folds. The first is the utilization of improved features extracted from three complementary sequence profiles in SVMnuc. The second is the combination of two complementary methods: the ab-initio method SVMnuc and the template-based method COACH-D. AVAILABILITY AND IMPLEMENTATION: http://yanglab.nankai.edu.cn/NucBind. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Enhanced prediction of RNA solvent accessibility with long short-term memory neural networks and improved sequence profiles.

MOTIVATION: The de novo prediction of RNA tertiary structure remains a grand challenge. Predicted RNA solvent accessibility provides an opportunity to address this challenge. To the best of our knowledge, there is only one method (RNAsnap) available for RNA solvent accessibility prediction. However, its performance is unsatisfactory for protein-free RNAs. RESULTS: We developed RNAsol, a new algorithm to predict RNA solvent accessibility. RNAsol was built based on improved sequence profiles from the covariance models and trained with the long short-term memory (LSTM) neural networks. Independent tests on the same datasets from RNAsnap show that RNAsol achieves the mean Pearson's correlation coefficient (PCC) of 0.43/0.26 for the protein-bound/protein-free RNA molecules, which is 26.5%/136.4% higher than that of RNAsnap. When the training set is enlarged to include both types of RNAs, the PCCs increase to 0.49 and 0.46 for protein-bound and protein-free RNAs, respectively. The success of RNAsol is attributed to two aspects, including the improved sequence profiles constructed by the sequence-profile alignment and the enhanced training by the LSTM neural networks. AVAILABILITY AND IMPLEMENTATION: http://yanglab.nankai.edu.cn/RNAsol/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PotentialNet for Molecular Property Prediction.

The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales. The key parameters range from solubility (angstroms) to protein-ligand binding (nanometers) to in vivo toxicity (meters). Through feature learning-instead of feature engineering-deep neural networks promise to outperform both traditional physics-based and knowledge-based machine learning models for predicting molecular properties pertinent to drug discovery. To this end, we present the PotentialNet family of graph convolutions. These models are specifically designed for and achieve state-of-the-art performance for protein-ligand binding affinity. We further validate these deep neural networks by setting new standards of performance in several ligand-based tasks. In parallel, we introduce a new metric, the Regression Enrichment Factor EFχ (R), to measure the early enrichment of computational models for chemical data. Finally, we introduce a cross-validation strategy based on structural homology clustering that can more accurately measure model generalizability, which crucially distinguishes the aims of machine learning for drug discovery from standard machine learning tasks.