Dynamic expression of 27.8R in FG (A) and HINAE (B) cells post LCDV infection detected by sandwich ELISA.

<p>The cells were infected with LCDV at a MOI of 3.0 and sampled at different time points post infection. Error bars represented SD. Data represented the absorbance value at 405 nm (mean ± SD; n = 3) and were compared by Student’s <i>t</i> test. Un-infected cells (0 h) represented the negative control. The asterisk represented the statistical significance (<i>p</i> < 0.05) as compared with the negative control.</p

SDS-PAGE and western blotting of the LCDV cellular receptor-27.8kDa protein.

<p>Lane 1: Molecular mass marker; Lane 2, 3: SDS-PAGE of FG and HINAE cell membrane protein, stained with coomassie bule; Lane 4, 5: SDS-PAGE of FG and HINAE cell cytoplasm protein, stained with coomassie bule; Lane 6, 7: reaction with anti-27.8R MAbs showed only one 27.8 kDa in FG and HINAE cell membrane protein; Lane 8, 9: reaction with anti-27.8R MAbs showed no band in FG and HINAE cell cytoplasm protein; Lane 10, 11: anti-WSSV MAb 1D5 instead of anti-27.8R MAbs served as negative controls.</p

Co-localization of LCDV and 27.8R in FG and HINAE cell surface.

<p>FG cells (A) and HINAE cells (B) were exposed to LCDV at 22°C for 2 h and stained with mouse anti-27.8R MAbs and rabbit anti-LCDV serum for detection of 27.8R (green) and LCDV (red) simultaneously. The merged yellow signals (arrows) indicated the co-localization of LCDV and 27.8R protein on cell surface. Cell nuclei were counterstained in blue by DAPI. Scale bar = 20 μm. (a) and (b) were the higher magnification view of the co-localized area in FG and HINAE cells, respectively, scale bar = 5 μm.</p

Table_1_Application of multiple machine learning approaches to determine key pyroptosis molecules in type 2 diabetes mellitus.xlsx

ObjectivePyroptosis, a lytic and inflammatory programmed cell death, has been implicated in type 2 diabetes mellitus (T2DM) and its complications. Nonetheless, it remains elusive exactly which pyroptosis molecule exerts an essential role in T2DM, and this study aims to solve such issue.MethodsTranscriptional profiling datasets of T2DM, i.e., GSE20966, GSE95849, and GSE26168, were acquired. Four machine learning models, namely, random forest, support vector machine, extreme gradient boosting, and generalized linear modeling, were built based on pyroptosis genes. A nomogram of key pyroptosis genes was also generated, and the clinical value was appraised via calibration curves and decision curve analysis. Immune infiltration was inferred utilizing CIBERSORT. Drug–druggable target relationships were acquired from the Drug Gene Interaction Database. Through WGCNA, key pyroptosis-relevant genes were selected.ResultsMost pyroptosis genes exhibited upregulation in T2DM relative to controls, indicating the activity of pyroptosis in T2DM. The SVM model composed of BAK1, CHMP2B, NLRP6, PLCG1, and TIRAP exhibited the best performance in T2DM diagnosis, with AUC = 1. The nomogram can predict the risk of T2DM for clinical practice. NK cells resting exhibited a lower abundance in T2DM versus normal specimens, with a higher abundance of neutrophils. NLRP6 was positively linked with neutrophils. Drugs (keracyanin, 9,10-phenanthrenequinone, diclofenac, phosphomethylphosphonic acid adenosyl ester, acetaminophen, cefixime, aspirin, ustekinumab) potentially targeted the key pyroptosis genes. Additionally, CHMP2B-relevant genes were determined.ConclusionAltogether, this work proposes the key pyroptosis genes in T2DM, which might become possible molecules for the management and treatment of T2DM and its complications.</p

Blocking effect of anti-27.8R MAbs on 27.8R expression.

<p>The cells were pre-incubated with different concentration of anti-27.8R MAbs. Experimental groups were challenged by LCDV at a MOI of 3.0, and the cells without LCDV infection served as control groups. The cells were sampled at 48 h post infection. Error bars represented SD. Data represented the absorbance value at 405 nm (mean ± SD; n = 3) and were compared by Student’s <i>t</i> test. The asterisk represented the statistical significance (<i>p</i> < 0.05) as compared with the control group.</p

Table_2_Application of multiple machine learning approaches to determine key pyroptosis molecules in type 2 diabetes mellitus.xlsx

ObjectivePyroptosis, a lytic and inflammatory programmed cell death, has been implicated in type 2 diabetes mellitus (T2DM) and its complications. Nonetheless, it remains elusive exactly which pyroptosis molecule exerts an essential role in T2DM, and this study aims to solve such issue.MethodsTranscriptional profiling datasets of T2DM, i.e., GSE20966, GSE95849, and GSE26168, were acquired. Four machine learning models, namely, random forest, support vector machine, extreme gradient boosting, and generalized linear modeling, were built based on pyroptosis genes. A nomogram of key pyroptosis genes was also generated, and the clinical value was appraised via calibration curves and decision curve analysis. Immune infiltration was inferred utilizing CIBERSORT. Drug–druggable target relationships were acquired from the Drug Gene Interaction Database. Through WGCNA, key pyroptosis-relevant genes were selected.ResultsMost pyroptosis genes exhibited upregulation in T2DM relative to controls, indicating the activity of pyroptosis in T2DM. The SVM model composed of BAK1, CHMP2B, NLRP6, PLCG1, and TIRAP exhibited the best performance in T2DM diagnosis, with AUC = 1. The nomogram can predict the risk of T2DM for clinical practice. NK cells resting exhibited a lower abundance in T2DM versus normal specimens, with a higher abundance of neutrophils. NLRP6 was positively linked with neutrophils. Drugs (keracyanin, 9,10-phenanthrenequinone, diclofenac, phosphomethylphosphonic acid adenosyl ester, acetaminophen, cefixime, aspirin, ustekinumab) potentially targeted the key pyroptosis genes. Additionally, CHMP2B-relevant genes were determined.ConclusionAltogether, this work proposes the key pyroptosis genes in T2DM, which might become possible molecules for the management and treatment of T2DM and its complications.</p

Table_3_Application of multiple machine learning approaches to determine key pyroptosis molecules in type 2 diabetes mellitus.xlsx

ObjectivePyroptosis, a lytic and inflammatory programmed cell death, has been implicated in type 2 diabetes mellitus (T2DM) and its complications. Nonetheless, it remains elusive exactly which pyroptosis molecule exerts an essential role in T2DM, and this study aims to solve such issue.MethodsTranscriptional profiling datasets of T2DM, i.e., GSE20966, GSE95849, and GSE26168, were acquired. Four machine learning models, namely, random forest, support vector machine, extreme gradient boosting, and generalized linear modeling, were built based on pyroptosis genes. A nomogram of key pyroptosis genes was also generated, and the clinical value was appraised via calibration curves and decision curve analysis. Immune infiltration was inferred utilizing CIBERSORT. Drug–druggable target relationships were acquired from the Drug Gene Interaction Database. Through WGCNA, key pyroptosis-relevant genes were selected.ResultsMost pyroptosis genes exhibited upregulation in T2DM relative to controls, indicating the activity of pyroptosis in T2DM. The SVM model composed of BAK1, CHMP2B, NLRP6, PLCG1, and TIRAP exhibited the best performance in T2DM diagnosis, with AUC = 1. The nomogram can predict the risk of T2DM for clinical practice. NK cells resting exhibited a lower abundance in T2DM versus normal specimens, with a higher abundance of neutrophils. NLRP6 was positively linked with neutrophils. Drugs (keracyanin, 9,10-phenanthrenequinone, diclofenac, phosphomethylphosphonic acid adenosyl ester, acetaminophen, cefixime, aspirin, ustekinumab) potentially targeted the key pyroptosis genes. Additionally, CHMP2B-relevant genes were determined.ConclusionAltogether, this work proposes the key pyroptosis genes in T2DM, which might become possible molecules for the management and treatment of T2DM and its complications.</p

Dynamics of LCDV copies in FG and HINAE cells post LCDV infection investigated by qPCR.

<p>(A) Standard curve of LCDV MCP qPCR assays. The X-axis showed the positive control plasmid copy number in Log 10 value, and the Y-axis indicated the corresponding cycle threshold (Ct) value. R²: coefficient of determination. (B) Changes of LCDV copies in FG and HINAE cells post LCDV infection. 0 h represented un-infected cells. Error bars represented SD. Data represented the number of LCDV copies per microgram of total DNA in the cell samples (mean ± SD; n = 3).</p

A Recyclable Organocatalyst for Asymmetric Michael Addition of Acetone to Nitroolefins

Based on different chiral diamine skeletons, a series of bifunctional primary amine-thiophosphoramides were synthesized and screened as the catalysts for the asymmetric Michael addition of acetone to both aromatic and aliphatic nitroolefins. Under the catalysis of a thiophosphoramide derived from 1,2-diphenylethane-1,2-diamine, the corresponding adducts were obtained in high yields (up to >99%) with excellent enantioselectivities (97−99% ee) under mild reaction conditions. Moreover, the catalyst could be recovered via simple phase separation and reused at least five times without any loss of both catalytic activity and stereocontrol

Data_Sheet_1_Differential Circular RNA Expression Profiles Following Spinal Cord Injury in Rats: A Temporal and Experimental Analysis.zip

Spinal cord injury (SCI), one of the most severe types of neurological damage, results in persistent motor and sensory dysfunction and involves complex gene alterations. Circular RNAs (circRNAs) are a recently discovered class of regulatory molecules, and their roles in SCI still need to be addressed. This study comprehensively investigated circRNA alterations in rats across a set time course (days 0, 1, 3, 7, 14, 21, and 28) after hemisection SCI at the right T9 site. A total of 360 differentially expressed circRNAs were identified using RNA sequencing. From these, the functions of the exonic circRNA_01477 were further explored in cultured spinal cord astrocytes. Knockdown of circRNA_01477 significantly inhibited astrocyte proliferation and migration. The circRNA_01477/microRNAs (miRNA)/messenger RNA (mRNA) interaction network was visualized following microarray assay. Among the downregulated differentially expressed mRNAs, four of the seven validated genes were controlled by miRNA-423-5p. We then demonstrated that miRNA-423-5p is significantly upregulated after circRNA_01477 depletion. In summary, this study provides, for the first time, a systematic evaluation of circRNA alterations following SCI and an insight into the transcriptional regulation of the genes involved. It further reveals that circRNA_01477/miR-423-5p could be a key regulator involved in regulating the changeable regeneration environment that occurs during recovery from SCI.</p