SIRS, SOFA, qSOFA, and NEWS in the diagnosis of sepsis and prediction of adverse outcomes: a systematic review and meta-analysis

We compared Systemic Inflammatory Response Syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), Quick Sepsis-related Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS) for sepsis diagnosis and adverse outcomes prediction. Clinical studies that used SIRS, SOFA, qSOFA, and NEWS for sepsis diagnosis and prognosis assessment were included. Data were extracted, and meta-analysis was performed for outcome measures, including sepsis diagnosis, in-hospital mortality, 7/10/14-day mortality, 28/30-day mortality, and ICU admission. Fifty-seven included studies showed good overall quality. Regarding sepsis prediction, SIRS demonstrated high sensitivity (0.85) but low specificity (0.41), qSOFA showed low sensitivity (0.42) but high specificity (0.98), and NEWS exhibited high sensitivity (0.71) and specificity (0.85). For predicting in-hospital mortality, SOFA demonstrated the highest sensitivity (0.89) and specificity (0.69). In terms of predicting 7/10/14-day mortality, SIRS exhibited high sensitivity (0.87), while qSOFA had high specificity (0.75). For predicting 28/30-day mortality, SOFA showed high sensitivity (0.97) but low specificity (0.14), whereas qSOFA displayed low sensitivity (0.41) but high specificity (0.88). NEWS independently demonstrates good diagnostic capability for sepsis, especially in high-income countries. SOFA emerges as the optimal choice for predicting in-hospital mortality and can be employed as a screening tool for 28/30-day mortality in low-income countries.</p

The pooled sensitivity and specificity of unstimulated CSF IFN-γ for diagnosing tuberculous meningitis.

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Diagnostic performance of IFN-γ in the individual studies.

Diagnostic performance of IFN-γ in the individual studies.</p

Hierarchical summary receiver operating characteristic (HSROC) plot to summarize diagnostic accuracy for CSF IGRA in diagnosing tuberculous meningitis.

Hierarchical summary receiver operating characteristic (HSROC) plot to summarize diagnostic accuracy for CSF IGRA in diagnosing tuberculous meningitis.</p

Articles’ selection process.

Articles’ selection process.</p

The pooled sensitivity and specificity of CSF IGRA for diagnosing tuberculous meningitis.

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Quality for the included articles.

Quality for the included articles.</p

Additional file 1: of Transcriptomic insights into citrus segment membrane’s cell wall components relating to fruit sensory texture

Figure S1. Heatmap of differentially expressing genes. Figure S2. Verification of RNA-seq by qRT-PCT. Figure S3. Top 30 significantly enriched pathways based on up-expressed genes in ‘Kiyomi’ compared with in ‘Shiranui’ at the first-stage. Figure S4. Top 30 significantly enriched pathways based on up-expressed genes in ‘Shiranui’ compared with in ‘Kiyomi’ at the first-stage. Figure S5. Top 30 significantly enriched pathways based on up-expressed genes in ‘Kiyomi’ compared with in ‘Shiranui’ at the second-stage. Figure S6. Top 30 significantly enriched pathways based on up-expressed genes in ‘Shiranui’ compared with in ‘Kiyomi’ at the second-stage. Table S1. Primers used in quantitative qRT-PCR experiments. Table S2. Top 30 significantly enriched pathways based on up-regulated genes. Table S3. Genes involved in pectin metabolism. Table S4. Genes involved in cellulose metabolism. Table S5. Genes involved in hemicellulose (xyloglucans and xylan) metabolism. (PDF 808 kb

Table_1_Detection of citrus diseases in complex backgrounds based on image–text multimodal fusion and knowledge assistance.docx

Diseases pose a significant threat to the citrus industry, and the accurate detection of these diseases represent key factors for their early diagnosis and precise control. Existing diagnostic methods primarily rely on image models trained on vast datasets and limited their applicability due to singular backgrounds. To devise a more accurate, robust, and versatile model for citrus disease classification, this study focused on data diversity, knowledge assistance, and modal fusion. Leaves from healthy plants and plants infected with 10 prevalent diseases (citrus greening, citrus canker, anthracnose, scab, greasy spot, melanose, sooty mold, nitrogen deficiency, magnesium deficiency, and iron deficiency) were used as materials. Initially, three datasets with white, natural, and mixed backgrounds were constructed to analyze their effects on the training accuracy, test generalization ability, and classification balance. This diversification of data significantly improved the model’s adaptability to natural settings. Subsequently, by leveraging agricultural domain knowledge, a structured citrus disease features glossary was developed to enhance the efficiency of data preparation and the credibility of identification results. To address the underutilization of multimodal data in existing models, this study explored semantic embedding methods for disease images and structured descriptive texts. Convolutional networks with different depths (VGG16, ResNet50, MobileNetV2, and ShuffleNetV2) were used to extract the visual features of leaves. Concurrently, TextCNN and fastText were used to extract textual features and semantic relationships. By integrating the complementary nature of the image and text information, a joint learning model for citrus disease features was achieved. ShuffleNetV2 + TextCNN, the optimal multimodal model, achieved a classification accuracy of 98.33% on the mixed dataset, which represented improvements of 9.78% and 21.11% over the single-image and single-text models, respectively. This model also exhibited faster convergence, superior classification balance, and enhanced generalization capability, compared with the other methods. The image-text multimodal feature fusion network proposed in this study, which integrates text and image features with domain knowledge, can identify and classify citrus diseases in scenarios with limited samples and multiple background noise. The proposed model provides a more reliable decision-making basis for the precise application of biological and chemical control strategies for citrus production.</p

Predicted CitXET active sites.

<p>Predicted CitXET active sites.</p