scGAAC: A graph attention autoencoder for clustering single-cell RNA-sequencing data.

Single-cell RNA-sequencing (scRNA-seq) enables the investigation of intricate mechanisms governing cell heterogeneity and diversity. Clustering analysis remains a pivotal tool in scRNA-seq for discerning cell types. However, persistent challenges arise from noise, high dimensionality, and dropout in single-cell data. Despite the proliferation of scRNA-seq clustering methods, these often focus on extracting representations from individual cell expression data, neglecting potential intercellular relationships. To overcome this limitation, we introduce scGAAC, a novel clustering method based on an attention-based graph convolutional autoencoder. By leveraging structural information between cells through a graph attention autoencoder, scGAAC uncovers latent relationships while extracting representation information from single-cell gene expression patterns. An attention fusion module amalgamates the learned features of the graph attention autoencoder and the autoencoder through attention weights. Ultimately, a self-supervised learning policy guides model optimization. scGAAC, a hypothesis-free framework, performs better on four real scRNA-seq datasets than most state-of-the-art methods. The scGAAC implementation is publicly available on Github at: https://github.com/labiip/scGAAC.

Two-dimensional chromatography in screening of bioactive components from natural products.

INTRODUCTION: Screening and analysis of bioactive components from natural products is a fundamental part of new drug development and innovation. Two-dimensional (2D) chromatography has been demonstrated to be an effective method for screening and preparation of specific bioactive components from complex natural products. OBJECTIVE: To collect details of application of 2D chromatography in screening of natural product bioactive components and to outline the research progress of different separation mechanisms and strategies. METHODOLOGY: Three screening strategies based on 2D chromatography are reviewed, including traditional separation-based screening, bioactivity-guided screening and affinity chromatography-based screening. Meanwhile, in order to cover these aspects, selections of different separation mechanisms and modes are also presented. RESULTS: Compared with traditional one-dimensional (1D) chromatography, 2D chromatography has unique advantages in terms of peak capacity and resolution, and it is more effective for screening and identifying bioactive components of complex natural products. CONCLUSION: Screening of natural bioactive components using 2D chromatography helps separation and analysis of complex samples with greater targeting and relevance, which is very important for development of innovative drug leads.

Interferon-Induced Transmembrane Protein 3 Genetic Variant rs12252-C Associated With Disease Severity in Coronavirus Disease 2019.

A major unanswered question in the current global coronavirus disease 2019 (COVID-19) outbreak is why severe disease develops in a small minority of infected individuals. In the current article, we report that homozygosity for the C allele of rs12252 in the interferon-induced transmembrane protein 3 (IFITM3) gene is associated with more severe disease in an age-dependent manner. This supports a role for IFITM3 in disease pathogenesis and the opportunity for early targeted intervention in at-risk individuals.

Application of two-dimensional reversed phase countercurrent chromatography × high-performance liquid chromatography to bioactivity-guided screening and isolation of α-glucosidase inhibitors from Rheum palmatum L.

In the present work, comprehensive two-dimensional reversed-phase countercurrent chromatography × reversed-phase liquid chromatography combined (2D RPCCC × RPLC) with 2D microfraction bioactive evaluation was employed to screen and isolate α-glucosidase inhibitors from Rheum palmatum L. Countercurrent chromatography was employed to improve 2D analysis and preparative separation. A selected biphasic solvent system composed of petroleum ether/ethyl acetate/methanol/water with gradient elution mode was used for the first dimension RPCCC separation (1D RPCCC). Solid-phase extraction was applied to eliminate interfering polar compounds before the second dimension analysis (2D RPLC). 76 components were shown in 2D contour plot in UV 280 nm. 11 Candidates were separated by a scaled-up CCC and identified by 1H NMR and 13C NMR, including anthraquinones, flavonoids, stilbenes, phenols, and glucoside derivatives. In addition, it was found that two components, resveratrol-4'-O-(6″-galloyl)glucoside (36) and lyciumaside (43) were identified as natural α-glucosidase inhibitors in Rheum palmatum L. for the first time.

Screening and identification of antibacterial components in Artemisia argyi essential oil by TLC-direct bioautography combined with comprehensive 2D GC × GC-TOFMS.

One of the primary components that contribute to Artemisia argyi 's effectiveness is essential oil, which has an exceptional antibacterial effect that has been well documented. The actual cause of its antibacterial activity and associated mechanism, however, are still not completely understood. For the first time, comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (2D GC × GC-TOFMS) and thin-layer chromatography-direct bioautography (TLC-DB) were employed to investigate its antibacterial components. The antibacterial properties of A. argyi essential oil were investigated, and the antibacterial activity of six compounds was evaluated, using Staphylococcus aureus (S. aureus) and Escherichia coli (E. coil) as test microorganisms. TLC-direct bioautography was used to screen two bioactive clusters. Following 2D GC × GC-TOFMS identification of bioactive clusters, six compounds were evaluated for in vitro antibacterial activity verification. All the components tested displayed antibacterial action. Results showed that α-terpineol and eugenol had high potent antibacterial activity (MIC＜0.62 mg/mL, IC50＜2.00 mg/mL). For complex essential oils from traditional Chinese medicine, this method is efficient for quick screening and identifying antibacterial compounds.

Liver transplantation in acute-on-chronic liver failure patients with high model for end-stage liver disease (MELD) scores: a single center experience of 100 consecutive cases.

BACKGROUND: Acute-on-chronic liver failure (ACLF) is a severe clinical condition for which liver transplantation (LT) is the only curative option. However, there are little published data on risk factors and outcomes of LT for ACLF. METHODS: The objective of this study was to analyze preoperative, intraoperative, postoperative, and overall survival data on 100 consecutive cases with ACLF in order to try to determine for which patients LT are futile. RESULTS: One hundred consecutive patients with pathology-confirmed ACLF who underwent LT from June 2004 to September 2012 were enrolled. The preoperative data showed that all patients were in a serious condition with a median high model for end-stage liver disease (MELD) score of 32, total bilirubin of 440.20 umol/L, international normalized ratio (INR) of 3.012, and at least one organ dysfunction as assessed by a Sequential Organ Failure Assessment (SOFA) score of ≥9. The patients had either deceased or a living donor LT with an overall mortality of 20%. The 1-, 3-, and 5-year cumulative survival rates were 76.8%, 75.6%, and 74.1%, respectively, and graft 1-, 3-, and 5-y accumulative survival rates were 73.3%, 72.1%, and 70.6%, respectively. However, the area under receiver operating characteristic of SOFA score, MELD score, as well as Child-Pugh score were 0.552, 0.547, and 0.547, respectively. CONCLUSIONS: Both deceased and living donor LT are effective therapeutic options for patients with ACLF and the short- and long-term survival rates are encouraging. It is important to conduct more prospective and multi-center studies to define preoperatively which patients would benefit from LT.

Enantioseparation of two antifungal azole drugs by analytical countercurrent chromatography using sulfobutyl ether-ß-cyclodextrin as chiral selector.

This study reports a successful enantioseparation of two antifungal drugs, Ketoconazole and Voriconazole, using countercurrent chromatography (CCC) with synthesized sulfobutyl ether-ß-cyclodextrin (SBE-ß-CD) as chiral selector. Two biphasic solvent systems composed of dichloromethane: 0.1 mol L-1 of phosphate buffer solution (pH 3.0) (1:1, v/v) and n-hexane: ethyl acetate: 0.1 mol L-1 phosphate buffer solution (pH 3.0) (1.5:0.5:2, v/v/v) were selected. Influence factors were investigated, including degree of substitution of SBE-ß-CD, concentration of SBE-ß-CD, equilibrium temperature, and pH of aqueous phase. Under optimized separation conditions, a large enantioseparation factor of α ≥ 3.26 and a high peak resolution Rs= 1.82, was achieved for enantioseparation of Voriconazole by countercurrent chromatography, and purity of two azole stereoisomers collected from CCC separation reached 98.5%, as determined by HPLC. Molecular docking was employed to investigate the formation of inclusion complex.

Comprehensive two-dimensional countercurrent chromatography × gas chromatography characterization of Artemisia argyi essential oil.

A comprehensive two-dimensional (2D) countercurrent chromatography (CCC) × gas chromatography (GC) was investigated for characterization of chemical constituents of Artemisia argyi essential oil, and orthogonality for the 2D chromatographic system was evaluated. A solvent system composed of n-hexane/acetonitrile/methanol (2:2:1, v/v/v) was selected for first dimensional separation of Artemisia argyi essential oil. Then all CCC fractions were analyzed by GC, which provided a wealth of information regarding the composition of the essential oil. Visualization of chemical compositions obtained from the comprehensive 2D CCC × GC separation was achieved by creation of a 2D contour plot map. Total peak capacity was evaluated and approximately 1392 peaks were obtained through a comprehensive 2D CCC × GC separation. A high spatial coverage and a low linear correlation coefficient were achieved. Meanwhile, all compounds were identified by GC-MS. The obtained 2D contour plot could be divided into six zones to show the characteristic chemical compositions. Six zones could be divided into different component groups, including monoterpenes, sesquiterpenes, monoterpene alcohols, phenols, aldehydes, ketones and esters, which could be used to identify compounds that have not been reported, and to predict the structure of unknown compounds in Artemisia argyi essential oil and comprehensively characterize fingerprint peak.

Computational analysis of single nucleotide polymorphisms (SNPs) in PPAR gamma associated with obesity, diabetes and cancer.

The single nucleotide polymorphisms (SNPs) are the common genetic variations in human genomes and act as markers for molecular susceptibility of complex traits and diseases in humans. Amino acid variations in the non-synonymous SNPs (nsSNPs) in coding and non-coding regions affect the function/structure of the proteins. The Peroxisome proliferator-activated receptor gamma (PPARγ or PPARG) is a nuclear receptor that plays a significant role in lipid metabolism and insulin production and is associated with diabetes, obesity, and cancer. In this study, the PPARG sequence was retrieved from the NCBI database (dbSNP: NP_619726.2), and an analysis was done to predict the damaged/harmful mutated amino acids. We identified five mutated variants (C162S, R166W, Q286P, or Q314P and P467L), which were mostly expressed in cancer tissues and associated with insulin resistance and partial lipodystrophy. The identified mutations were induced, and the analysis of molecular dynamics simulation was established to determine the dynamic stability/flexibility of PPARG. The dynamic trajectories were analyzed by RMSD, RMSF, and Radius of Gyration (Rg) analysis; a vast difference was noticed in each of the protein structure when compared with the PPARG wild-type, and the mutations in PPARG impaired its functions, leading to more significant problems in humans.Communicated by Ramaswamy H. Sarma.

A Novel Scoring System for Prediction of Disease Severity in COVID-19.

Background: A novel enveloped RNA beta coronavirus, Corona Virus Disease 2019 (COVID-19) caused severe and even fetal pneumonia in China and other countries from December 2019. Early detection of severe patients with COVID-19 is of great significance to shorten the disease course and reduce mortality. Methods: We assembled a retrospective cohort of 80 patients (including 56 mild and 24 severe) with COVID-19 infection treated at Beijing You'an Hospital. We used univariable and multivariable logistic regression analyses to select the risk factors of severe and even fetal pneumonia and build scoring system for prediction, which was validated later on in a group of 22 COVID-19 patients. Results: Age, white blood cell count, neutrophil, glomerular filtration rate, and myoglobin were selected by multivariate analysis as candidates of scoring system for prediction of disease severity in COVID-19. The scoring system was applied to calculate the predictive value and found that the percentage of ICU admission (20%, 6/30) and ventilation (16.7%, 5/30) in patients with high risk was much higher than those (2%, 1/50; 2%, 1/50) in patients with low risk (p = 0.009; p = 0.026). The AUC of scoring system was 0.906, sensitivity of prediction is 70.8%, and the specificity is 89.3%. According to scoring system, the probability of patients in high risk group developing severe disease was 20.24 times than that in low risk group. Conclusions: The possibility of severity in COVID-19 infection predicted by scoring system could help patients to receiving different therapy strategies at a very early stage. Topic: COVID-19, severe and fetal pneumonia, logistic regression, scoring system, prediction.

Pathological features of COVID-19-associated lung injury: a preliminary proteomics report based on clinical samples.

The COVID-19 pandemic has emerged as a global health emergency due to its association with severe pneumonia and relative high mortality. However, the molecular characteristics and pathological features underlying COVID-19 pneumonia remain largely unknown. To characterize molecular mechanisms underlying COVID-19 pathogenesis in the lung tissue using a proteomic approach, fresh lung tissues were obtained from newly deceased patients with COVID-19 pneumonia. After virus inactivation, a quantitative proteomic approach combined with bioinformatics analysis was used to detect proteomic changes in the SARS-CoV-2-infected lung tissues. We identified significant differentially expressed proteins involved in a variety of fundamental biological processes including cellular metabolism, blood coagulation, immune response, angiogenesis, and cell microenvironment regulation. Several inflammatory factors were upregulated, which was possibly caused by the activation of NF-κB signaling. Extensive dysregulation of the lung proteome in response to SARS-CoV-2 infection was discovered. Our results systematically outlined the molecular pathological features in terms of the lung response to SARS-CoV-2 infection, and provided the scientific basis for the therapeutic target that is urgently needed to control the COVID-19 pandemic.

Longitudinal COVID-19 profiling associates IL-1RA and IL-10 with disease severity and RANTES with mild disease.

BACKGROUND: Identifying immune correlates of COVID-19 disease severity is an urgent need for clinical management, vaccine evaluation, and drug development. Here, we present a temporal analysis of key immune mediators, cytokines, and chemokines in blood of hospitalized COVID-19 patients from serial sampling and follow-up over 4 weeks. METHODS: A total of 71 patients with laboratory-confirmed COVID-19 admitted to Beijing You'an Hospital in China with either mild (53 patients) or severe (18 patients) disease were enrolled with 18 healthy volunteers. We measured 34 immune mediators, cytokines, and chemokines in peripheral blood every 4-7 days over 1 month per patient using a bioplex multiplex immunoassay. RESULTS: We found that the chemokine RANTES (CCL5) was significantly elevated, from an early stage of the infection, in patients with mild but not severe disease. We also found that early production of inhibitory mediators including IL-10 and IL-1RA were significantly associated with disease severity, and a combination of CCL5, IL-1 receptor antagonist (IL-1RA), and IL-10 at week 1 may predict patient outcomes. The majority of cytokines that are known to be associated with the cytokine storm in virus infections such as IL-6 and IFN-γ were only significantly elevated in the late stage of severe COVID-19 illness. TNF-α and GM-CSF showed no significant differences between severe and mild cases. CONCLUSION: Together, our data suggest that early intervention to increase expression of CCL5 may prevent patients from developing severe illness. Our data also suggest that measurement of levels of CCL5, as well as IL-1RA and IL-10 in blood individually and in combination, might be useful prognostic biomarkers to guide treatment strategies.