Xiaohui Li

Screening and diagnosis of major subtypes and stages of non-Hodgkin lymphomas are achieved using serum laser-induced breakdown spectroscopy and chemometrics, providing a minimally invasive, rapid and cost-effective technique for NHL diagnosis.

The NELIBS plasma has more upper-level emitters, leading to a longer lifetime and stronger intensity, causing spectral enhancement.

Serum contains rich elemental information, and the elemental analysis of the serum using laser-induced breakdown spectroscopy (LIBS) can help with the diagnosis and treatment of cancer. In this work, serum specimens from breast cancer patients were used as samples, and the elements Mg, Ca, and K were analyzed using LIBS. Silver nanoparticles (Ag NPs) were introduced to enhance spectral emission intensities, and this technique is known as nanoparticle-enhanced LIBS (NELIBS). An Ag NP-coated filter paper (Ag NP paper) substrate was prepared and was compared with silicon and aluminum substrates for NELIBS analysis. The Ag NP paper substrate showed lower spectral intensity variances and better elemental distribution uniformity due to the reduced coffee-ring effect compared with silicon and aluminum substrates. The enhanced effect of the Ag NP paper was optimized by adjusting serum volume and Ag NPs concentration. It was found that Mg, Ca, and K required different optimal conditions for spectral enhancement. The highest enhancement factors (EF) for Mg, Ca, and K were 1.85, 1.76, and 3.10, respectively. The electron temperature (T_e) and electron number density (N_e) of NELIBS plasmas were measured using the Boltzmann plot and Stark broadening methods and compared with those of the normal LIBS plasmas. The T_e and N_e of the NELIBS and normal LIBS samples were generally comparable with each other. Higher Ag NP concentrations led to lower T_e values for NELIBS plasmas.

In this study, mineral-element migration upon pyrolysis and quantitative prediction of volatiles were investigated by laser induced breakdown spectroscopy (LIBS) for three characteristic types of coal. KBr was used to polymerize coal or char into a pellet. High percentage of KBr provided a good environment for forming plasmas of stable emissions with relatively high electron excitation temperature and electron density, due to the suppression of the laser-ablation induced pyrolysis of volatile matter. With the increasing pyrolysis temperature, char atomic emissions were greatly increased, which is thought to be related to mineral-element reloading in the char surface upon pyrolysis. Divergence of intensity ratios I_char/I_coal of Fe/Ca/Mg between high-volatile and low-volatile coal indicates that the Fe/Ca/Mg-containing minerals from volatiles may change their existing forms upon pyrolysis, which can easily trigger more intensive emissions. These findings indicate that LIBS can offer a powerful tool for studying mineral-element migration and contribute to improving numerical simulations of coal pyrolysis and combustion. Finally, multiple linear regression (MLR) and artificial neural network (ANN) methods were applied for quantification of volatile content. MLR models with R² > 0.977 for single-rank coal samples and ANN models with R² = 0.991 for all samples were achieved, which provide an accurate and reliable method for industrial coal quality evaluations.

Diagnosis and staging of multiple myeloma (MM) have been achieved using serum-based laser-induced breakdown spectroscopy (LIBS) in combination with machine learning methods. 130 cases of serum samples collected from registered MM patients in different progressive stages and healthy controls were deposited onto standard quantitative filter papers and ablated with a Q-switched Nd:YAG laser. Emissions of Ca, Na, K, Mg, C, H, O, N and CN were selected for malignancy diagnosis and staging. Multivariate statistics and machine learning methods, including principal component analysis (PCA), k-nearest neighbor (kNN), support vector machine (SVM) and artificial neural network (ANN) classifiers, were used to build the discrimination models. The performances of the classifiers were optimized via 10-fold cross-validation and evaluated in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves (AUC). The kNN, SVM and ANN classifiers achieved comparable discrimination performances with accuracies of over 90% for both diagnosis and staging of MM. For diagnosis of MM, the classifiers achieved performances with AUC of ∼0.970, sensitivity of ∼0.930 and specificity of ∼0.910; for staging of MM, the corresponding values were AUC of ∼0.970, sensitivity of ∼0.910 and specificity of ∼0.930. These results show that the serum-based LIBS in combination with machine learning methods can serve as a fast, less invasive, cost-effective, and robust technique for diagnosis and staging of human malignancies.