Optowares, Inc., Woburn, Massachusetts, United States of America.
PLoS One. 2022 May 5;17(5):e0267945. doi: 10.1371/journal.pone.0267945. eCollection 2022.
US military service members experiencing combat-related wounds have higher risk of infection by multidrug-resistant bacteria. The gold standard culture-based antimicrobial susceptibility testing (AST) is not feasible in the battlefield environment. Thus, a rapid deployable system for bacteria identification and AST directly from wound sample is urgently needed. We report the potential of a Rapid, Label-free Pathogen Identification (RAPID) diagnostic system based on ATR-FTIR method to detect and distinguish multi-drug resistant strains for six different species in the ESKAPEE group. Our RAPID system combines sample processing on-broad to isolate and enrich bacteria cells from wound sample, ATR-FTIR measurement to detect antimicrobial-induced bacterial cell spectral changes, and machine learning model for automated, objective, and quantitative spectral analysis and unknown sample classification. Based on experimental results, our RAPID system is a promising technology for label-free, sensitive (104 cfu/mL from mixture), species-specific (> 95% accuracy), rapid (< 10 min for identification, ~ 4 hours for AST) bacteria detection directly from wound samples.
美国经历与战斗相关的创伤的军人有更高的感染多药耐药菌的风险。基于培养的金标准抗菌药物敏感性测试（AST）在战场环境中不可行。因此，迫切需要一种从伤口样本中直接进行细菌鉴定和 AST 的快速可部署系统。我们报告了一种基于 ATR-FTIR 方法的快速、无标记病原体识别（RAPID）诊断系统的潜力，该系统可用于检测和区分 ESKAPEE 组中的六种不同物种的多药耐药株。我们的 RAPID 系统结合了在现场进行的样本处理，从伤口样本中分离和富集细菌细胞，ATR-FTIR 测量以检测抗菌药物诱导的细菌细胞光谱变化，以及用于自动、客观和定量光谱分析和未知样本分类的机器学习模型。基于实验结果，我们的 RAPID 系统是一种很有前途的无标记技术，可用于直接从伤口样本中进行敏感（混合物中为 104 cfu/mL）、种特异性（>95%准确率）、快速（<10 分钟用于鉴定，~4 小时用于 AST）的细菌检测。
