Background: Bloodstream infections (BSIs) pose a great challenge to treating patients, especially those with underlying diseases, such as immunodeficiency diseases. Early diagnosis helps to direct precise empirical antibiotic administration and proper clinical management. This study carried out a serum metabolomic analysis using blood specimens sampled from patients with a suspected infection whose routine culture results were later demonstrated to be positive. Methods: A liquid chromatograph-mass spectrometry-based metabolomic analysis was carried out to profile the BSI serum samples. The serum metabolomics data could be used to successfully differentiate BSIs from non-BSIs. Results: The major classes of the isolated pathogens (e.g., Gram-positive and Gram-negative bacteria) could be differentiated using our optimized statistical algorithms. In addition, by using different machine-learning algorithms, the isolated pathogens could also be classified at the species levels (e.g., Escherichia coli and Klebsiella pneumoniae) or according to their specific antibiotic-resistant phenotypes (e.g., extended-spectrum β-lactamase-producing and non-producing phenotypes) if needed. Conclusions: This study provides an early diagnosis method that could be an alternative to the traditional time-consuming culture process to identify BSIs. Moreover, this metabolomics strategy was less affected by several risk factors (e.g., antibiotics administration) that could produce false culture results.

Background: Transfer-order heterogeneity may affect live-birth prediction after freeze-all FET cycles, but existing prediction studies have rarely modeled first- and second-transfer records separately. Methods: We developed and compared logistic regression (LR), support vector machine, random forest, XGBoost, LightGBM, and CatBoost models in three related single-center analytical cohorts: an overall cohort comprising 990 eligible cycles, a first-transfer subgroup comprising 576 cycles, and a second retained transfer-record subgroup comprising 238 cycles. Feature templates denoted as T6, T8, and T10 refer to retained predictor sets containing 6, 8, and 10 variables, respectively. Model performance was evaluated using stratified 10-fold cross-validation, and binary metrics were summarized at receiver operating characteristic (ROC)-derived Youden thresholds. Results: CatBoost + T6 was retained for the overall cohort (area under the curve [AUC]=0.704) and the first-transfer subgroup (AUC = 0.812), whereas CatBoost + T10 was retained for the second retained transfer-record subgroup (AUC = 0.825). Parallel LR-based interpretive nomograms were used to support transparent presentation. Shapley additive explanations (SHAP) analysis showed recurrent contributions from female age and ovarian-reserve-related indicators, while the second retained transfer-record subgroup additionally highlighted basal progesterone and interval days. Conclusions: Transfer-order subgroup modeling improved model-population matching and provided more clinically interpretable structures for pre-transfer live-birth assessment after freeze-all FET cycles.