Detection of Benzo[a]pyrene Diol Epoxide Adducts to Histidine and Lysine in Serum Albumin In Vivo by High-Resolution-Tandem Mass Spectrometry

A novel fluorescence and colorimetric dual sensing system for rapid and sensitive detection of histidine based on TSPP-CA

Histidine (His) plays an important role in human health and life activities. It will harm human health when His intake is insufficient or excessive. Its residue also will pollute the natural environment. Therefore, establishing a reliable and sensitive method for detecting histidine is particularly important. However, most of the existing detection methods of His rely on the single change of single signal, which are susceptible to interference from testing environmental factors and prone to generating false positive results. In contrast, the fluorescence and colorimetric dual-signal sensing system can not only effectively improve the reliability of detection, but also significantly reduce the risk of false positives. Therefore, the dual-signal sensing system has gradually become the research hotspot. Based on this, 5,10,15,20-tetra-(4-sulfophenyl) porphyrin (TSPP) was selected as the fluorescence and colorimetry dual-signal probe for the rapid detection of His in this study. Since TSPP could interact with many substances due to the specificity of molecular structure, it is the best choice to build an "ON-OFF-ON" sensing system in order to improve the specificity of the sensing system. Therefore, citric acid (CA) as an intermediate based on TSPP probe was successfully developed fluorescence and colorimetric dual-sensing system for the quantitative detection of histidine in real samples. The signal of the dual sensing system was "turned on" when the red TSPP solution obtained the fluorescence emission wavelength of 642 nm at the 514 nm optimal excitation wavelength and the UV-Vis absorption at 413 nm, respectively. The fluorescence intensity and absorbance of TSPP gradually decreased with the introduction of CA. At this time, the signal of the dual-sensing system became "turned off", and the color of the solution changed from light pink to light green. The quenched fluorescence intensity and absorbance, however, was restored upon the introduction of histidine into the system. Simultaneously, the color of the solution changed from light green to light pink, and the dual-sensing system became "turned on". Therefore, a novel fluorescence and colorimetric dual-signal sensing system based on TSPP was proposed for histidine detection. The results indicated that the linear ranges of the fluorescence sensing system were 8.34 × 10-6 M - 1.51 × 10-4 M and 1.85 × 10-4 M - 1.4 × 10-3 M with detection limits of 0.282 μM and 10.91 μM (LOD, S/N = 3), respectively. The linear range of the colorimetric sensing system was 2.04 × 10-4 M-4.35 × 10-4 M with a detection limit of 11.97 μM (LOD, S/N = 3). Meanwhile, the dual-sensing system provided a promising platform for practical samples sensing applications.

A sensitive GC-MS/MS method for the quantification of benzo[a]pyrene tetrol in urine

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous pollutants formed during the incomplete combustion of organic matter such as tobacco. Among these, benzo[a]pyrene (BaP) has been classified as a known carcinogen to humans. It unfolds its effect through metabolic activation to BaP-(7R,8S)-diol-(9S,10R)-epoxide (BPDE), the ultimate carcinogen of BaP. In this article, we describe a simple and highly sensitive GC-NICI-MS/MS method for the quantification of urinary BaP-(7R,8S,9R,10S)-tetrol (( +)-BPT I-1), the hydrolysis product of BPDE. The method was validated and showed excellent results in terms of accuracy, precision, and sensitivity (lower limit of quantification (LLOQ): 50 pg/L). In urine samples derived from users of tobacco/nicotine products and non-users, only consumption of combustible cigarettes was associated with a significant increase in BPT I-1 concentrations (0.023 ± 0.016 nmol/mol creatinine, p < 0.001). Levels of users of potentially reduced-risk products as well as non-users were all below the LLOQ. In addition, the urine levels of six occupationally exposed workers were analyzed and showed the highest overall concentrations of BPT I-1 (844.2 ± 336.7 pg/L). Moreover, comparison with concentrations of 3-hydroxybenzo[a]pyrene (3-OH-BaP), the major detoxification product of BaP oxidation, revealed higher levels of 3-OH-BaP than BPT I-1 in almost all study subjects. Despite the lower levels, BPT I-1 can provide more relevant information on an individual's cancers susceptibility since BPDE is generated by the metabolic activation of BaP. In conclusion, BPT I-1 is a suitable biomarker to distinguish not only cigarette smokers from non-smokers but also from users of potentially reduced-risk products.

Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats

Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high-performance optimization method (SCBA) and added the Sobol sequence, vertical crossover and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA-KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA-KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2 and CYP1A1/2 have the most impact on rats. The SCBA-KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment.