The aims of this study were to evaluate the safety and probiotic characteristics of the newly isolated Enterococcus lactis strain JDM1. Safety assessment of E. lactis JDM1 was accomplished by the combination of whole genome sequence information analysis and phenotypic assays, including antimicrobial susceptibility test, haemolysis assay, biogenic amine production assay, cytotoxicity assay. The bacteriostatic experiment and gastrointestinal tolerance experiment were also conducted to evaluate its applicability. E. lactis JDM1 possesses good gastrointestinal tolerance and can inhibit the growth of the pathogenic bacteria Clostridioides difficile and Listeria monocytogenes. The chromosome size of JDM1 was 2,570,998 bp with a GC content of 38.46%, which contained a plasmid. One intact prophage, 13 genomic islands and 19 IS elements were predicted in the JDM1 chromosome. Five resistance-related genes and seven virulence-related genes were predicted in the genome. Most resistance genes were conserved, and virulence factors were not related to functional pathogenicity. Antimicrobial susceptibility tests showed that JDM1 was sensitive to tedizolid, ciprofloxacin, levofloxacin, penicillin, ampicillin, vancomycin, linezolid, tetracycline, high-level gentamicin and high-level streptomycin. Genes encoding putative enzymes responsible for adverse metabolites were not found and JDM1 was unable to produce the six main biogenic amines. Cytotoxicity test showed that the JDM1 supernatant had no toxic effect. E. lactis JDM1 is expected to be developed as a probiotic, and its probiotic properties are worthy of further exploration.

Background: Schizophrenia is frequently underdiagnosed or diagnosed late due to the lack of objective diagnostic markers. This study aimed to develop and validate a machine learning model using routine hematological parameters for the auxiliary diagnosis of schizophrenia. Methods: A total of 150 first-episode drug-naïve schizophrenia patients and 150 healthy controls were enrolled. Study parameters included routine hematological parameters and its derived inflammatory markers. Feature selection was performed using Elastic Net regression, followed by the construction of an L2-regularized logistic regression model. Model discriminative performance, calibration, and clinical utility were assessed through internal validation, area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. An independent cohort of 50 schizophrenia patients and 50 patients with major depressive disorder (MDD) was used for exploratory differential diagnostic evaluation. Results: The final model incorporated 12 features: neutrophil count (NEUT), eosinophil count (EO), mean platelet volume (MPV), hematocrit (HCT), hemoglobin (HGB), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), neutrophil-to-platelet ratio (NPR), along with age and sex. The model achieved an AUC of 0.859 (95% CI: 0.780–0.937) on the test set, with an accuracy of 0.767, sensitivity of 0.711, and specificity of 0.822. Calibration curves confirmed good calibration, and decision curve analysis further verified its clinical utility. In the exploratory differential diagnostic analysis, the model showed limited performance in distinguishing schizophrenia from MDD. Conclusions: The model based on routine hematological parameters and L2-regularized logistic regression can effectively differentiate first-episode drug-naïve schizophrenia patients from healthy controls, providing a low-cost and easily accessible auxiliary diagnostic tool for clinical practice.