A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks.

MOTIVATION: A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy data. RESULTS: In this paper, we proposed JSNDCMI, which combines the multi-structure feature extraction framework and Denoising Autoencoder (DAE) to meet the challenge of CMI prediction in sparse networks. In detail, JSNDCMI integrates functional similarity and local topological structure similarity in the CMI network through the multi-structure feature extraction framework, then forces the neural network to learn the robust representation of features through DAE and finally uses the Gradient Boosting Decision Tree classifier to predict the potential CMIs. JSNDCMI produces the best performance in the 5-fold cross-validation of all data sets. In the case study, seven of the top 10 CMIs with the highest score were verified in PubMed. AVAILABILITY: The data and source code can be found at https://github.com/1axin/JSNDCMI.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.

Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.

Thiostrepton suppresses triple-negative breast cancer through downregulating c-FLIP/SMAD2/3 signaling pathway.

Triple-negative breast cancer (TNBC) is an aggressive subtype with poor prognosis of breast cancer. Thiostrepton exerts anti-tumor activities against several cancers including TNBC. Herein we discussed the new molecular mechanisms of thiostrepton in TNBC. Thiostrepton inhibited MDA-MB-231 cell viability, accompanied by a decrease of c-FLIP and p-SMAD2/3. c-FLIP overexpression reduced the sensitivity of MDA-MB-231 cells to thiostrepton, while SMAD2/3 knockdown increased the sensitivity of MDA-MB-231 cells to thiostrepton. Moreover, c-FLIP overexpression significantly increased the expression and phosphorylation of SMAD2/3 proteins and vice versa. In conclusion, our study reveals c-FLIP/SMAD2/3 signaling pathway as a novel mechanism of antitumor activity of thiostrepton.

Identification of a circulating three-miRNA panel for the diagnosis of primary open angle glaucoma.

PURPOSE: Conventional diagnosis of primary open angle glaucoma (POAG) needs a combination of ophthalmic examinations. An efficient assay is urgently needed for a timely POAG diagnosis. We aim to explore differential expressions of circulating microRNAs (miRNA) and provide novel miRNA biomarkers for POAG diagnosis. METHODS: A total of 180 POAG patients and 210 age-related cataract (ARC) patients were enrolled. We collected aqueous humor (AH) and plasma samples from the recruited patients. The expressions of candidate miRNAs were measured using quantitative real time polymerase chain reaction. The diagnostic ability of candidate miRNAs was analyzed by receiver operating characteristic curve. RESULTS: The expressions of miR-21-5p and miR-29b-3p were downregulated significantly in AH and plasma of POAG and miR-24-3p expression was significantly increased in AH and plasma of POAG, comparing with those of ARC. A three-miRNA panel was constructed by a binary logistic regression. And the panel could differentiate between POAG and ARC with an area under the curve of 0.8867 (sensitivity = 78.0%, specificity = 83.3%) in aqueous humor and 0.7547 (sensitivity = 73.8%, specificity = 81.2%) in plasma. Next, we verified the three-miRNA panel working as a potential diagnostic biomarker stable and reliable. At last, we identified related function and regulation pathways in vitro. CONCLUSIONS: In conclusion, we built and identified a circulating three-miRNA panel as a potential diagnostic biomarker for POAG. It may be developed into an efficient assay and help improve the POAG diagnosis in the future.

BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path.

More and more evidence suggests that circRNA plays a vital role in generating and treating diseases by interacting with miRNA. Therefore, accurate prediction of potential circRNA-miRNA interaction (CMI) has become urgent. However, traditional wet experiments are time-consuming and costly, and the results will be affected by objective factors. In this paper, we propose a computational model BCMCMI, which combines three features to predict CMI. Specifically, BCMCMI utilizes the bidirectional encoding capability of the BERT algorithm to extract sequence features from the semantic information of circRNA and miRNA. Then, a heterogeneous network is constructed based on cosine similarity and known CMI information. The Metapath2vec is employed to conduct random walks following meta-paths in the network to capture topological features, including similarity features. Finally, potential CMIs are predicted using the XGBoost classifier. BCMCMI achieves superior results compared to other state-of-the-art models on two benchmark datasets for CMI prediction. We also utilize t-SNE to visually observe the distribution of the extracted features on a randomly selected dataset. The remarkable prediction results show that BCMCMI can serve as a valuable complement to the wet experiment process.

mTORC2 acts as a gatekeeper for mTORC1 deficiency-mediated impairments in ILC3 development.

Group 3 innate lymphoid cells (ILC3s) are mediators of intestinal immunity and barrier function. Recent studies have investigated the role of the mammalian target of rapamycin complex (mTOR) in ILC3s, whereas the mTORC1-related mechanisms and crosstalk between mTORC1 and mTORC2 involved in regulating ILC3 homeostasis remain unknown. In this study, we found that mTORC1 but not mTORC2 was critical in ILC3 development, IL-22 production, and ILC3-mediated intestinal homeostasis. Single-cell RNA sequencing revealed that mTORC1 deficiency led to disruption of ILC3 heterogeneity, showing an increase in differentiation into ILC1-like phenotypes. Mechanistically, mTORC1 deficiency decreased the expression of NFIL3, which is a critical transcription factor responsible for ILC3 development. The activities of both mTORC1 and mTORC2 were increased in wild-type ILC3s after activation by IL-23, whereas inhibition of mTORC1 by Raptor deletion or rapamycin treatment resulted in increased mTORC2 activity. Previous studies have demonstrated that S6K, the main downstream target of mTORC1, can directly phosphorylate Rictor to dampen mTORC2 activity. Our data found that inhibition of mTORC1 activity by rapamycin reduced Rictor phosphorylation in ILC3s. Reversing the increased mTORC2 activity via heterozygous or homozygous knockout of Rictor in Raptor-deleted ILC3s resulted in severe ILC3 loss and complete susceptibility to intestinal infection in mice with mTORC1 deficiency (100% mortality). Thus, mTORC1 acts as a rheostat of ILC3 heterogeneity, and mTORC2 protects ILC3s from severe loss of cells and immune activity against intestinal infection when mTORC1 activity is diminished.

Deciphering the photolysis products and biological concerns of triclosan under UVC and UVA.

Triclosan (TCS) is omnipresent in the environment and has drawn increasing attention due to its potential adverse effects on human health. Direct photolysis of TCS readily occurs, especially in the surface layers of waters that receive abundant ultraviolet radiation during the daytime. However, biological concerns and the identification of toxic products during TCS photolysis have been explored limitedly. Therefore, in the present work, the structural characterization of the photolysis products by UVC and UVA were performed based on the mass spectra and fragmental ions. The results displayed that TCS was more readily eliminated by UVC than UVA, and the product species were completely different when TCS was degraded by UVC and UVA, respectively. Two products, m/z 235 and m/z 252, were produced via reductive dechlorination and nucleophilic substitution with UVC, while three dioxin-like isomer products were generated by dechlorination, cyclization and hydroxylation. Furthermore, the results of biological concerns suggested that the elimination of TCS did not represent the disappearance of biological risks. Specifically, more hazardous and photolysis products were formed during TCS photolysis with ultraviolets. For instance, the dioxin-like isomer products were highly microtoxic and genotoxic, and mildly antiestrogenic. The positive findings highlighted the biological concerns of TCS photolysis by ultraviolet radiation in the aquatic environment.

DeepWalk based method to predict lncRNA-miRNA associations via lncRNA-miRNA-disease-protein-drug graph.

BACKGROUND: Long non-coding RNAs (lncRNAs) play a crucial role in diverse biological processes and have been confirmed to be concerned with various diseases. Largely uncharacterized of the physiological role and functions of lncRNA remains. MicroRNAs (miRNAs), which are usually 20-24 nucleotides, have several critical regulatory parts in cells. LncRNA can be regarded as a sponge to adsorb miRNA and indirectly regulate transcription and translation. Thus, the identification of lncRNA-miRNA associations is essential and valuable. RESULTS: In our work, we present DWLMI to infer the potential associations between lncRNAs and miRNAs by representing them as vectors via a lncRNA-miRNA-disease-protein-drug graph. Specifically, DeepWalk can be used to learn the behavior representation of vertices. The methods of fingerprint, k-mer and MeSH descriptors were mainly used to learn the attribute representation of vertices. By combining the above two kinds of information, unknown lncRNA-miRNA associations can be predicted by the random forest classifier. Under the five-fold cross-validation, the proposed DWLMI model obtained an average prediction accuracy of 95.22% with a sensitivity of 94.35% at the AUC of 98.56%. CONCLUSIONS: The experimental results demonstrated that DWLMI can effectively predict the potential lncRNA-miRNA associated pairs, and the results can provide a new insight for related non-coding RNA researchers in the field of combing biology big data with deep learning.

Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier.

BACKGROUND: Self-interacting proteins (SIPs), two or more copies of the protein that can interact with each other expressed by one gene, play a central role in the regulation of most living cells and cellular functions. Although numerous SIPs data can be provided by using high-throughput experimental techniques, there are still several shortcomings such as in time-consuming, costly, inefficient, and inherently high in false-positive rates, for the experimental identification of SIPs even nowadays. Therefore, it is more and more significant how to develop efficient and accurate automatic approaches as a supplement of experimental methods for assisting and accelerating the study of predicting SIPs from protein sequence information. RESULTS: In this paper, we present a novel framework, termed GLCM-WSRC (gray level co-occurrence matrix-weighted sparse representation based classification), for predicting SIPs automatically based on protein evolutionary information from protein primary sequences. More specifically, we firstly convert the protein sequence into Position Specific Scoring Matrix (PSSM) containing protein sequence evolutionary information, exploiting the Position Specific Iterated BLAST (PSI-BLAST) tool. Secondly, using an efficient feature extraction approach, i.e., GLCM, we extract abstract salient and invariant feature vectors from the PSSM, and then perform a pre-processing operation, the adaptive synthetic (ADASYN) technique, to balance the SIPs dataset to generate new feature vectors for classification. Finally, we employ an efficient and reliable WSRC model to identify SIPs according to the known information of self-interacting and non-interacting proteins. CONCLUSIONS: Extensive experimental results show that the proposed approach exhibits high prediction performance with 98.10% accuracy on the yeast dataset, and 91.51% accuracy on the human dataset, which further reveals that the proposed model could be a useful tool for large-scale self-interacting protein prediction and other bioinformatics tasks detection in the future.

Switching the Photoreactions of Ir(III) Diamine Complexes between C-N Coupling and Dehydrogenation under Visible Light Irradiation.

The selective photoreactions under mild conditions play an important role in synthetic chemistry. Herein, efficient and mild protocols for switching the photoreactions of Ir(III)-diamine complexes between the interligand C-N coupling and dehydrogenation are developed in the presence of O2 in EtOH solution. The photoreactions of achiral diamine complexes rac-[Ir(L)2(dm)](PF6) (L is 2-phenylquinoline or 2-(2,4-difluorophenyl)quinoline, dm is 1,2-ethylenediamine, 1,2-diaminopropane, 2-methyl-1,2-diamino-propane, or N,N'-dimethyl-1,2-ethylenediamine) are competitive in the oxidative C-N coupling and dehydrogenation at room temperature, which can be switched into the interligand C-N coupling reaction at 60 °C, affording hexadentate complexes in good to excellent yields, or the dehydrogenative reaction in the presence of a catalytic amount of TEMPO as an additive, affording imine complexes. Mechanism studies reveal that 1O2 is the major reactive oxygen species, and metal aminyl is the key intermediate in the formation of the oxidative C-N coupling and imine products in the photoreaction processes. These will provide a new and practical protocol for the synthesis of multidentate and imine ligands in situ via the postcoordinated strategy under mild conditions.

In Vitro Inhibitory Effects of Maqian Essential Oil against Ectopic Endometrial Stromal Cells and LPS-Induced Endometrial Epithelial Cells.

Previous studies revealed that MQEO (Maqian fruits essential oil), which is extracted from the fruit of Maqian (Zanthoxylum myriacanthum var. Pubescens), had a good anti-inflammatory effect, but the effect on endometriosis in vitro remains unknown. In the present study, the inhibitory effects of MQEO against the EESCs (ectopic endometrial stromal cells) were investigated. Cells were treated with a concentration gradient (from 0.025 % to 0.15 %) of MQEO for 24 h and cell viability was detected by CCK-8. In addition, apoptotic rates were investigated using flow cytometry. The effect of MQEO on cell migration was determined by wound-healing and transwell assay. The expression of apoptosis-associated and cell adhesion-related proteins was assessed by western blotting. The transcriptional levels of IL-1, IL-6 and TNF-α were determined by Real-time qPCR. RNA-seq was used to identify the DEGs (differentially expressed genes) in MQEO-pretreated EESCs. We found that the MQEO condition dosage-dependently reduced the cell viability of EESCs. Based on flow cytometry results, the number of apoptotic cells increased significantly with dosage. The wound-healing and transwell results showed that MQEO group exhibited a significantly decreased cell motility and migration ability in comparison with the normal group. Western blotting results showed that MQEO down-regulated the expression of Bcl-2, ICAM-1 (intercellular adhesion molecule 1) and CD44, but up-regulated the cleaved caspase-3 expression in EESCs. What's more, MQEO also inhibited the LPS-induced inflammation in human EECs (endometrial epithelial cells). RNA-seq revealed that 221 DEGs were up-regulated genes and 284 DEGs were down-regulated in MQEO-pretreated EESCs. Our data uncovered the beneficial effects of MQEO in endometriosis and provided new insights into the mechanism of the effect of MQEO on EESCs, suggesting MQEO could be a promising new therapeutic agent for endometriosis.

Visible-Light-Induced Amination of Quinoline at the C8 Position via a Postcoordinated Interligand-Coupling Strategy under Mild Conditions.

The postcoordinated interligand-coupling strategy provides a useful and complementary protocol for synthesizing polydentate ligands. Herein, diastereoselective photoreactions of Λ-[Ir(pq)2(d-AA)] (Λ-d) and Λ-[Ir(pq)2(l-AA)] (Λ-l, where pq is 2-phenylquinoline and AA is an amino acid) are reported in the presence of O2 under mild conditions. Diastereomer Λ-d is dehydrogenatively oxidized into an imino acid complex, while diastereomer Λ-l mainly occurs via interligand C-N cross-dehydrogenative coupling between quinoline at the C8 position and AA ligands at room temperature, affording Λ-[Ir(pq)(l-pq-AA)]. Furthermore, the photoreaction of diastereomer Λ-l is temperature-dependent. Mechanistic experiments reveal the ligand-radical intermediates may be involved in the reaction. Density functional theory calculations were used to eluciate the origin of diastereoselectivity and temperature dependence. This will provide a new protocol for the amination of quinoline at the C8 position via the postcoordinated interligand C-N cross-coupling strategy under mild conditions.

Selective stabilization of multiple promoter G-quadruplex DNA by using 2-phenyl-1H-imidazole-based tanshinone IIA derivatives and their potential suppressing function in the metastatic breast cancer.

The G-quadruplex (G4) DNA, which has been developed as a potential anticancer target in drug screening and design, plays a crucial role in the oncogene transcription and translation. Tanshinone IIA derivatives with a planar heterocycle structure may function as G4 stabilizers. We present an innovative case of imidazole-based tanshinone IIA derivatives (1-8) especially compound 4 that improve the selectivity and the binding affinity with G4 DNA and enhance the target tumor inhibition. Cellular and in vivo experiments indicate that the tanshinone IIA derivative 4 inhibits the growth, metastasis, and angiogenesis of triple-negative breast cancer cells possibly through the stabilization of multiple G4 DNAs (e.g., c-myc, K-ras, and VEGF) to induce DNA damage. Further investigation of the intermolecular interaction and the molecular docking indicates that tanshinone IIA derivatives have better selective binding capability to various G4 DNAs than to double-stranded DNA. These findings provide guidance in modifying the molecular structures of tanshinone IIA derivatives and reveal their potential to function as specific G4 stabilizers.

ITRAQ-based proteomics analysis of tanshinone IIA on human ectopic endometrial stromal cells of adenomyosis.

PURPOSE: Adenomyosis is a diffuse or localized disease. Our previous study has indicated that tanshinone IIA (TSIIA) inhibits the proliferation, migration, and induces apoptosis of ectopic endometrial stromal cells (EESCs) of adenomyosis. However, the complex molecular mechanism of TSIIA in adenomyosis remains unclear. The objective of this study was to explore the complex molecular mechanism of TSIIA on EESCs. METHODS: In our present study, we used the proteomics approach iTRAQ (isobaric tags for relative and absolute quantitation) combined with LC-MS/MS (liquid chromatography-mass spectrometry) to investigate changes in the protein profile of EESCs treated with TSIIA. Differential proteins were analyzed by employing bioinformatics tools and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. In TSIIA treated EESCs, the protein expression levels of TNFRSF10D, PLEKHM1, FECH, and TPM1A were detected by western blotting. RESULTS: Quantitative results revealed 267 significantly differential proteins in TSIIA pretreated EESCs. Gene Ontology (GO) analysis presented an overview of dysregulated proteins in the biological process (BP), cell component (CC), and molecular function (MF) categories. Interestingly, we observed that differential proteins in the extracellular matrix (ECM)-receptor interaction pathway and estrogen signaling pathway were all involved in the focal adhesion pathway, which plays essential roles in the TSIIA-mediated inhibition of EESC proliferation and migration. Furthermore, some significantly differential proteins, which may be potential targets for the treatment of adenomyosis in the future, were validated by western blotting. CONCLUSIONS: Our study provides a useful method to detect the detailed mechanism underlying the efficacy of TSIIA on EESCs.

Gait analysis in the elderly patients with lumbar spinal stenosis.

PURPOSE: This study aims to analyze the gait characteristics of the elderly patients with lumbar spinal stenosis by an intelligent device for energy expenditure and activity (IDEEA) to assist clinical work. METHODS: A total of 98 subjects were included in this study from January 2017 to December 2018. A total of 49 elderly outpatients with symptomatic lumbar spinal stenosis in unilateral lower extremity were included as the experimental group, and another 49 healthy subjects matched with gender, age, and body mass index (BMI) were analyzed as the control group. The gait data of the subjects (including single support, double support, SLS/DLS, swing duration, step duration, cycle duration, pulling accel, swing power, ground impact, foot fall, foot off, push off, speed, cadence, step length, and stride length) were collected to compare between the experience group and control group, the affected leg and the healthy leg in experimental group. RESULTS: The results of this study presented that small intermittent claudication occurred in all patients. The time of single support was significantly increased (p < 0.05). Double support, step duration, and pulling accel were increased (p < 0.05), and the Push off, speed, step length, and Stride length were decreased (p < 0.05) in the experimental group compared with the control group. CONCLUSION: Small intermittent claudication was the basic gait composition of the elderly patients with lumbar spinal stenosis that can reflect the abnormal gait characteristics by IDEEA.

Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method.

Identification of drug-target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop novel and effective computational methods to predict DTIs in order to shorten the development cycles of new drugs. In this study, we present a novel computational approach to identify DTIs, which uses protein sequence information and the dual-tree complex wavelet transform (DTCWT). More specifically, a position-specific scoring matrix (PSSM) was performed on the target protein sequence to obtain its evolutionary information. Then, DTCWT was used to extract representative features from the PSSM, which were then combined with the drug fingerprint features to form the feature descriptors. Finally, these descriptors were sent to the Rotation Forest (RoF) model for classification. A 5-fold cross validation (CV) was adopted on four datasets (Enzyme, Ion Channel, GPCRs (G-protein-coupled receptors), and NRs (Nuclear Receptors)) to validate the proposed model; our method yielded high average accuracies of 89.21%, 85.49%, 81.02%, and 74.44%, respectively. To further verify the performance of our model, we compared the RoF classifier with two state-of-the-art algorithms: the support vector machine (SVM) and the k-nearest neighbor (KNN) classifier. We also compared it with some other published methods. Moreover, the prediction results for the independent dataset further indicated that our method is effective for predicting potential DTIs. Thus, we believe that our method is suitable for facilitating drug discovery and development.

Anesthesia Management at Fuwai Hospital:Practice, Evidence and Outcomes.

Fuwai Hospital was established in 1956 and the Anesthesia Department of Fuwai Hospital was one of the earliest anesthesia departments then in China. Under the leadership of several department directors and with the concerted efforts of all generations of colleagues, the Anesthesia Department of Fuwai Hospital has dramatically transformed, upgraded and modernized. For more than six decades, the Anesthesia Department has been providing high-quality peri-operative anesthesia care for cardiovascular surgeries, conducting innovative experimental and clinical researches, and offering comprehensive training on cardiovascular anesthesiology for professionals across China. Currently, Fuwai Hospital is the National Center for Cardiovascular Diseases of China and one of the largest cardiovascular centers in the world. The present review introduces the Anesthesia Department of Fuwai Hospital, summarizes its current practice of anesthesia management, the outcomes of cardiovascular surgeries at Fuwai Hospital, accumulates relevant evidence, and provides prospects for future development of cardiovascular anesthesiology.

New Triazole NT-a9 Has Potent Antifungal Efficacy against Cryptococcus neoformans In Vitro and In Vivo.

In the past decades, the incidence of cryptococcosis has increased dramatically, which poses a new threat to human health. However, only a few drugs are available for the treatment of cryptococcosis. Here, we described a leading compound, NT-a9, an analogue of isavuconazole, that showed strong antifungal activities in vitro and in vivo NT-a9 showed a wide range of activities against several pathogenic fungi in vitro, including Cryptococcus neoformans, Cryptococcus gattii, Candida albicans, Candida krusei, Candida tropicalis, Candida glabrata, and Candida parapsilosis, with MICs ranging from 0.002 to 1 µg/ml. In particular, NT-a9 exhibited excellent efficacy against C. neoformans, with a MIC as low as 0.002 µg/ml. NT-a9 treatment resulted in changes in the sterol contents in C. neoformans, similarly to fluconazole. In addition, NT-a9 possessed relatively low cytotoxicity and a high selectivity index. The in vivo efficacy of NT-a9 was assessed using a murine disseminated-cryptococcosis model. Mice were infected intravenously with 1.8 × 106 CFU of C. neoformans strain H99. In the survival study, NT-a9 significantly prolonged the survival times of mice compared with the survival times of the control group or the isavuconazole-, fluconazole-, or amphotericin B-treated groups. Of note, 4 and 8 mg/kg of body weight of NT-a9 rescued all the mice, with a survival rate of 100%. In the fungal-burden study, NT-a9 also significantly reduced the fungal burdens in brains and lungs, while fluconazole and amphotericin B only reduced the fungal burden in lungs. Taken together, these data suggested that NT-a9 is a promising antifungal candidate for the treatment of cryptococcosis infection.