Scholarlink

<p>This study introduces a reflection-enhanced laser-induced fluorescence (RELIF) technique for label-free classification of inflammation severity across three levels in blood serum. RELIF overcomes key limitations of conventional laser-induced fluorescence (LIF), particularly low fluorescence sensitivity and the prozone effect, which are frequently encountered in traditional immunoassays. In RELIF, a cuvette with a reflective surface facing the incident laser beam is used to amplify the diode-laser-pumped fluorescence signal by a factor substantially greater than in standard LIF. In parallel, complementary laser-induced breakdown spectroscopy (LIBS) measurements were performed on dried serum samples using a Q-switched Nd:YAG laser to verify elemental differences among the inflammation classes. RELIF delivered enhanced spectral resolution and sensitivity, yielding approximately 1.30× to 1.53× higher fluorescence signals than conventional LIF. While standard LIF showed reduced performance, especially for samples with severe inflammation, RELIF clearly distinguished all inflammation classes, capturing subtle spectral variations and achieving higher signal-to-noise ratios. Notably, RELIF correctly identified severe inflammation as the class with the weakest fluorescence signal, thereby avoiding the false negatives associated with the prozone phenomenon without the need for sample dilution. LIBS analysis further supported class separation through differences in Ca atomic and CN molecular emissions. Multivariate analysis using partial least squares regression (PLSR) and partial least squares discriminant analysis (PLS-DA) demonstrated high predictive accuracy and robust discrimination. Model performance was further assessed using receiver operating characteristic (ROC) curves; the RELIF-based model outperformed LIF, achieving an area under the curve (AUC) of 0.94 compared with 0.84 for LIF, indicating strong classification performance. Overall, the combined RELIF–LIBS strategy offers a rapid, cost-effective, and non-destructive tool for inflammation classification.</p> Graphical abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Reflection-enhanced LIF for improved label-free classification of blood inflammation with complementary LIBS validation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月30日

查看全文
  • Rania M. Abdelazeem,
  • Z. Abdel-Salam,
  • M. Abdel-Harith

摘要

This study introduces a reflection-enhanced laser-induced fluorescence (RELIF) technique for label-free classification of inflammation severity across three levels in blood serum. RELIF overcomes key limitations of conventional laser-induced fluorescence (LIF), particularly low fluorescence sensitivity and the prozone effect, which are frequently encountered in traditional immunoassays. In RELIF, a cuvette with a reflective surface facing the incident laser beam is used to amplify the diode-laser-pumped fluorescence signal by a factor substantially greater than in standard LIF. In parallel, complementary laser-induced breakdown spectroscopy (LIBS) measurements were performed on dried serum samples using a Q-switched Nd:YAG laser to verify elemental differences among the inflammation classes. RELIF delivered enhanced spectral resolution and sensitivity, yielding approximately 1.30× to 1.53× higher fluorescence signals than conventional LIF. While standard LIF showed reduced performance, especially for samples with severe inflammation, RELIF clearly distinguished all inflammation classes, capturing subtle spectral variations and achieving higher signal-to-noise ratios. Notably, RELIF correctly identified severe inflammation as the class with the weakest fluorescence signal, thereby avoiding the false negatives associated with the prozone phenomenon without the need for sample dilution. LIBS analysis further supported class separation through differences in Ca atomic and CN molecular emissions. Multivariate analysis using partial least squares regression (PLSR) and partial least squares discriminant analysis (PLS-DA) demonstrated high predictive accuracy and robust discrimination. Model performance was further assessed using receiver operating characteristic (ROC) curves; the RELIF-based model outperformed LIF, achieving an area under the curve (AUC) of 0.94 compared with 0.84 for LIF, indicating strong classification performance. Overall, the combined RELIF–LIBS strategy offers a rapid, cost-effective, and non-destructive tool for inflammation classification.

Graphical abstract