Common bunt disease of wheat in China is caused mainly by Tilletia foetida. However, reliable approaches for determining disease-associated biochemical markers are rarely reported. Here, a headspace-solid-phase microextraction coupled with headspace GC-tandem mass spectrometry (HS-SPME-GC-MS) was used to screen volatile substances in infected wheat, and an optimal chemical marker was selected to establish analytical methods for disease determination. Non-targeted screening of 13 volatile compounds unique to diseased wheat allowed a metabolite with rotten fish-like smell,...

Additional file 7: Figure S7. Results of the Cochrane Risk of Bias assessment tool 2 for randomized controlled trials

Supplemental material, sj-tif-1-tct-10.1177_15330338221123634 for Ixazomib Combined With Autologous Stem Cell Transplantation for POEMS Syndrome: A Case Report and Meta-Analysis by Ji Ma, Xiaoyue Wang, Hui Lu, Zhihao Liang and Liang Wang in Technology in Cancer Research & Treatment

The aim of this study was to evaluate the association between recurrence-free survival (RFS) and perirenal fat thickness (PFT) in a cohort of Chinese population with unilateral nephrolithiasis. We retrospectively reviewed the medical records of 81 patients with unilateral nephrolithiasis in our center from January 2019 to June 2019. PFT measured on computed tomography (CT) scans was evaluated. Kaplan-Meier curves and log-rank tests were used to assess significant differences in RSF between high-PFT and low-PFT...

Additional file 1: Fig. S1. TMT10-plex proteomic data processing and protein interaction networks. A Characteristics of the detected peptides. The left panel represents the distribution of peptide length, and the right panel represents the distribution of missed cleavages. B Number of peptides in each sample. C Normalized data distribution before and after normalization. D Protein interaction network of differentially expressed proteins in eight modules.

Additional file 2: Fig. S2. Data processing for weighted correlation network analysis (WGCNA) and Gene ontology enrichment categories for transcripts in the most significantly correlated module #7EC563. A Analysis of network topology for 1-20 soft-thresholding powers. B Clustering dendrogram of transcripts, with dissimilarity based on the topological overlap, and assigned merged and original module colors. C Gene ontology enrichment categories for transcripts in the most significantly correlated module #7EC563.

Additional file 3: Fig. S3. Differentially expressed transcripts were not enriched in any molecular function annotations. A MA plot of the quantified transcripts, and Venn diagram displaying differentially expressed transcripts. B Heatmap of hierarchical clustering based on the differentially expressed transcripts. C Gene ontology enrichment categories of the differentially expressed transcripts.

Additional file 5: Fig. S5. Bacteria in the duck cecum exhibited similar patterns in the CL and TH groups. A Rarefaction curve of the number of observed operational taxonomic units (OTUs) from 16S rRNA sequence data. B Histogram showing the distribution of the phyla. C Principal coordinates analysis (PCoA) with phylogeny-based (UniFrac) weighted distances of bacterial communities present in the duck cecum.

Additional file 3: Fig S2. Origin and evolution of LSSL. (A) The phylogenetic tree for the LSSL family based on the neighbor-joining (NJ) method. An NJ tree was constructed using the amino acid sequences of the LSSL proteins. All species represented by abbreviations are shown in Table S2. The bar (0.020) indicates the genetic distance. Different color lines correspond to different species groups. (B) The motif composition of the LSSL family proteins. (C) Model diagram...

Additional file 2: Fig. S2. The number and rough ratio of incidence, prevalence, death and dalys in men and women worlwide from 1990 TO 2019.

Additional file 5: Fig. S5. Trends in the 5 risk factors attributed to AF/AFL in each of the 5 SDI regions, from 1990 to 2019.

Additional file 1.

Additional file 2: Supplementary Figure S2. Correlation analysis of the 76 NRGs for CESC (A), OV (B), UCEC (C), and UCS D. Red indicates a positive correlation; blue indicates a negative correlation.

Additional file 2: Supplementary Figure S2. Correlation analysis of the 76 NRGs for CESC (A), OV (B), UCEC (C), and UCS D. Red indicates a positive correlation; blue indicates a negative correlation.

Additional file 6: Supplementary Figure S6. Expression values of NRGs in prognostic signature of OV. A-D Expression values of NRGs in TCGA for BACH2 (A), MLKL (B), MYC (C), MYCN (D), and SIRT2 (E). F-J Expression values of NRGs in our cohort for BACH2 (F), MLKL (I), MYC (G), MYCN (H), and SIRT2 (J).

Additional file 7: Supplementary Figure S7. Clinical value of risk score by independent prognostic analysis. A-H The Univariate Cox regression analysis and Multivariate Cox regression analysis for CESC (A-B), OV (C-D), UCEC (E-F), and UCS G-H.

Supplementary Material 18

Additional file 2: Fig. S1. The correlation between the lactate metabolism-related gene signatures and GSVA scores of 50 cancer hallmark pathways.

Additional file 12: Figure S4. Representative immunohistochemical staining images for GSTT1 in bone marrows of Control, Model, S+Rg, S, and Rg groups.

Additional file 1: Figure S1. HPLC analysis of SMI from different batches. (A) HPLC analysis of SMI (Lot No. 1907018). (B) HPLC analysis of SMI (Lot No. 1907229). (C) HPLC analysis of SMI (Lot No. 1907277).

Additional file 1.

Additional file 6: Fig S5. Elimination of MASP-1 from serum to detect the deposition of LSSL and C3 on the surface of Hela cells. (A) Quantitative analysis of proteins on HeLa cell membranes analyzed by Alexa-488 staining followed by flow cytometry. (B, C) The histogram shows the fluorescence intensity and cell proportions of the above flow cytometry results, respectively. The data are presented as the means ± SDs. (D) Western blotting analysis of depleted and...

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