Data_Sheet_1_A model based on meta-analysis to evaluate poor prognosis of patients with severe fever with thrombocytopenia syndrome.docx

BackgroundEarly identification of risk factors associated with poor prognosis in Severe fever with thrombocytopenia syndrome (SFTS) patients is crucial to improving patient survival.MethodRetrieve literature related to fatal risk factors in SFTS patients in the database, extract the risk factors and corresponding RRs and 95% CIs, and merge them. Statistically significant factors were included in the model, and stratified and assigned a corresponding score. Finally, a validation cohort from Yantai Qishan Hospital in 2021 was used to verify its predictive ability.ResultA total of 24 articles were included in the meta-analysis. The model includes six risk factors: age, hemorrhagic manifestations, encephalopathy, Scr and BUN. The analysis of lasso regression and multivariate logistic regression shows that model score is an independent risk factor (OR = 1.032, 95% CI 1.002–1.063, p = 0.034). The model had an area under the curve (AUC) of 0.779 (95% CI 0.669–0.889, PConclusionThe prediction model for the fatal outcome of SFTS patients has shown positive outcomes.Systematic review registration:https://www.crd.york.ac.uk/prospero/ (CRD42023453157).</p

Data_Sheet_3_A model based on meta-analysis to evaluate poor prognosis of patients with severe fever with thrombocytopenia syndrome.docx

BackgroundEarly identification of risk factors associated with poor prognosis in Severe fever with thrombocytopenia syndrome (SFTS) patients is crucial to improving patient survival.MethodRetrieve literature related to fatal risk factors in SFTS patients in the database, extract the risk factors and corresponding RRs and 95% CIs, and merge them. Statistically significant factors were included in the model, and stratified and assigned a corresponding score. Finally, a validation cohort from Yantai Qishan Hospital in 2021 was used to verify its predictive ability.ResultA total of 24 articles were included in the meta-analysis. The model includes six risk factors: age, hemorrhagic manifestations, encephalopathy, Scr and BUN. The analysis of lasso regression and multivariate logistic regression shows that model score is an independent risk factor (OR = 1.032, 95% CI 1.002–1.063, p = 0.034). The model had an area under the curve (AUC) of 0.779 (95% CI 0.669–0.889, PConclusionThe prediction model for the fatal outcome of SFTS patients has shown positive outcomes.Systematic review registration:https://www.crd.york.ac.uk/prospero/ (CRD42023453157).</p

Data_Sheet_2_A model based on meta-analysis to evaluate poor prognosis of patients with severe fever with thrombocytopenia syndrome.docx

BackgroundEarly identification of risk factors associated with poor prognosis in Severe fever with thrombocytopenia syndrome (SFTS) patients is crucial to improving patient survival.MethodRetrieve literature related to fatal risk factors in SFTS patients in the database, extract the risk factors and corresponding RRs and 95% CIs, and merge them. Statistically significant factors were included in the model, and stratified and assigned a corresponding score. Finally, a validation cohort from Yantai Qishan Hospital in 2021 was used to verify its predictive ability.ResultA total of 24 articles were included in the meta-analysis. The model includes six risk factors: age, hemorrhagic manifestations, encephalopathy, Scr and BUN. The analysis of lasso regression and multivariate logistic regression shows that model score is an independent risk factor (OR = 1.032, 95% CI 1.002–1.063, p = 0.034). The model had an area under the curve (AUC) of 0.779 (95% CI 0.669–0.889, PConclusionThe prediction model for the fatal outcome of SFTS patients has shown positive outcomes.Systematic review registration:https://www.crd.york.ac.uk/prospero/ (CRD42023453157).</p

RFLP Analysis of Different S. suis 2 Isolates

<p> S. suis S10: a highly virulent strain from China; <i>S</i>. <i>suis</i> 9801: swine isolate from Jiangsu Province in 1998; S. suis Habb: human isolate from Jiangsu Province in 1998; S. suis ZYS3: swine isolate from Sichuan Province in 2005; S. suis ZYH13: human isolate from Sichuan Province in 2005; M: 1 kb DNA Ladder (MBI Ferments, Gdansk, Poland). </p

Microscopic Characterization of Sectioned Liver Tissue from Patients Who Had Died

<div><p>(A) Light image of a liver tissue section (×100). The central vein is indicated with an arrow.</p> <p>(B) Light image of a liver tissue section (×200).</p> <p>(C) The convergent zone is indicated with an arrow (×100).</p> <p>(D) TEM image of a liver tissue section (×20,000). A bacterium found in the tissue is highlighted with an arrow.</p></div

Phylogenetic Trees of Six Representative Isolates Based on Comparison of 16S rDNA and Five Putative Virulence-Associated-Factor Genes with Known Sequences

<p>Swine isolates from Sichuan ( S. suis ZYS3 and S. suis ZYS8) labeled in green, human isolates ( S. suis ZYH13 and S. suis ZYH14) from Sichuan labeled in red, Jiangsu isolates from 1998 ( S. suis 9801 and S. suis Habb) labeled in blue, and the standard highly virulent strain S. suis P1/7 labeled in pink. All representative strains from other streptococcus species or isolates of S. suis 2 are as indicated in the tree. </p

Immunohistochemical Analysis of Liver Tissue Sections from Dead Patients Incubated with Normal and S. suis 2-Infected Swine Serum

<div><p>(A) Staining with normal swine serum detected no S. suis 2 antigen. </p> <p>(B) Staining with the serum from the infected swine indicated the presence of S. suis 2 antigen. </p></div

Detection of the Pathogenic SS2 and Identification of Its Specific Genes

<div><p>(A) Light microscopy image of the isolates cultured from autopsy specimens.</p> <p>GP⁺ cocci (pointed to with black arrows) are arranged in various short chains (×100). </p> <p>(B) Qualitative PCR detection of isolates from the liver of fatal human cases with a set of primers specific for <i>S</i>. <i>suis</i> 2. M: 100bp DNA marker (Fermentas, Vilnius, Lithuania). CK: 16S rDNA PCR product from the R 735 standard strain of S. suis 2. Multi-PCR: performed with a set of unique primers specific for <i>mrp, epf, suilysin,</i> and <i>cps-2J,</i> respectively. </p></div