Journal of Fungi

To address the “fungus-forest conflict” in the edible mushroom industry and the challenge of resource utilization for bamboo substrate waste, this study focused on white Auricularia cornea, and cultivation systems were established with bamboo substrate replacing wood chips at ratios of 0%, 18%, 38%, 58%, and 78%. By integrating liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis with agronomic trait measurements, the study elucidated the metabolic adaptation mechanisms of the substrate. Results indicated that white A. cornea could grow normally in all bamboo substrates, with the 58% bamboo substrate replacement group (D_58) demonstrating the most optimal overall performance. The mycelial growth rate reached 3.55 ± 0.24 mm/d, and the growth period was the shortest (86.2 d), balancing growth efficiency with cost advantages. Metabolomics detected 3779 metabolites, primarily amino acids and their derivatives (42.2%) and organic acids (35.54%). Compared to the control group, each treatment group exhibited 104–528 upregulated and 192–630 downregulated differential metabolites, with 93 shared differential metabolites and numerous unique markers. KEGG pathway enrichment analysis revealed that varying bamboo substrate ratios shaped growth adaptation strategies by regulating core pathways such as nucleotide metabolism and ABC transporters. This study established the feasibility and optimal formulation of bamboo substrate substitution, elucidated the substrate–metabolite–phenotype linkage mechanism, and provided theoretical foundations and practical references for high-quality cultivation of white A. cornea and sustainable development through “substituting bamboo for wood” to reduce carbon emissions.

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The early diagnosis of invasive candidiasis remains challenging in immunocompromised and other high-risk patients, prompting interest in artificial intelligence models for assisting clinical decision-making. We conducted a PROSPERO-registered systematic review and meta-analysis of artificial intelligence-based predictive models for the early identification of invasive Candida infections. We searched multiple databases for studies reporting model performance in hospitalized immuno-compromised patients. Data on study characteristics, model details, validation strategy, and diagnostic accuracy were extracted. A bivariate random-effects meta-analysis was performed for candidemia prediction models with compatible data. Eight studies met inclusion criteria. Models were typically developed using retrospective hospital data with heterogeneous populations and predictors. Five candidemia studies provided threshold-based performance data for meta-analysis. Pooled sensitivity and specificity for candidemia prediction were 81.3% (95% confidence interval (CI) 72.9–87.6%) and 81.6% (95% CI 68.4–90.1%), respectively. Most models achieved high negative predictive values, whereas positive predictive values were modest, reflecting low event prevalence. The risk of bias was generally moderate to high (PROBAST), and the certainty of evidence was low (GRADE) due to study limitations and indirectness. AI models show promise for early candidemia identification with moderate diagnostic accuracy. They may be useful as decision-support tools, but further multicenter prospective validation is needed before routine clinical adoption.