Evaluating the toxic mechanism of 1,2-diacetylbenzene in neural cells/tissues: The favorable impact of silibinin.

1,2-diacetylbenzene (1,2-DAB) is a neurotoxic component of aromatic solvents commonly used in industrial applications that induces neuropathological changes in animals. This study unraveled the toxic impact of 1,2-DAB in nerve tissues, explant cultures, and neuron-glial cultures, and explored whether herbal products can mitigate its toxicity. The effects of DAB on axonal transport were studied in retinal explant cultures grown in a micro-patterned dish. The mitochondrial movement in the axons was captured using time-lapse video recordings. The results showed that 1,2-DAB, but not 1,3-DAB inhibited axonal outgrowth and mitochondrial movement in a dose-dependent manner. The toxicity of 1,2-DAB was further studied in spinal cord tissues and cultures. 1,2-DAB selectively induced modifications of microtubules and neurofilaments in spinal cord tissues. 1,2-DAB also potently induced cell damage in both neuronal and glial cultures. Further, 1,2-DAB-induced cellular ATP depletion precedes cell damage in glial cells. Interestingly, treatment with the herbal products silibinin or silymarin effectively mitigated 1,2-DAB-induced toxicity in spinal cord tissues and neuronal/glial cultures. Collectively, the molecular toxicity of 1,2-DAB in neural tissues involves protein modification, ATP depletion, and axonal transport defects, leading to cell death. Silibinin and silymarin show promising neuroprotective effects against 2-DAB-induced toxicity.

Rapid detection of nicotine and benzoic acid in e-liquids with surface-enhanced Raman scattering and artificial intelligence-assisted spectrum interpretation.

Electronic cigarettes have rapidly gained acceptance recently. Nicotine-containing electronic cigarette liquids (e-liquids) are prohibited in some countries, but are permitted and simply available online in others. A rapid detection method is therefore required for on-site inspection or screening of a large amount of samples. Our previous study demonstrated a surface-enhanced Raman scattering (SERS)-based approach to identify nicotine-containing e-liquids; without any pre-treatment, e-liquid can be directly tested on our solid-phase SERS substrates, made of silver nanoparticle arrays embedded in anodic aluminium oxide nanochannels (Ag/AAO). However, this approach required manual determination of spectral signatures and negative samples should be validated in the second round detection. Here, after examining 406 commercial e-liquids, we refined this approach by developing artificial intelligence (AI)-assisted spectrum interpretations. We also found that nicotine and benzoic acid can be simultaneously detected in our platform. This increased test sensitivity because benzoic acid is usually used in nicotine salts. Around 64% of nicotine-positive samples in this study showed both signatures. Using either cutoffs of nicotine and benzoic acid peak intensities or a machine learning model based on the CatBoost algorithm, over 90% of tested samples can be correctly discriminated with only one round of SERS measurement. False negative and false positive rates were 2.5-4.4% and 4.4-8.9%, respectively, depending on the interpretation method and thresholds applied. The new approach takes only 1 microliter of sample and can be performed in 1-2 min, suitable for on-site inspection with portable Raman detectors. It could also be a complementary platform to reduce samples that need to be analyzed in the central labs and has the potential to identify other prohibited additives.

Rapid identification of nicotine in electronic cigarette liquids based on surface-enhanced Raman scattering.

Nicotine-containing electronic cigarette liquid (e-liquid) is prohibited in many countries, creating requirements for rapid detection approaches for on-site inspection or screening for large amounts of samples. Here, we demonstrate a simple way to identify nicotine using surface-enhanced Raman scattering (SERS) with substrates made of silver nanoparticle arrays imbedded in anodic aluminum oxide nanochannels (Ag/AAO). Compared with the reported colloidal nanoparticle-based SERS, that required serial dilutions to enable colloid aggregation in the viscous e-liquid, a small amount of undiluted e-liquid sample can be directly added onto our solid-phase Ag/AAO substrate without any pre-treatment. The sensitivity of our SERS measurements is 2-3 orders of magnitude higher than that required for identification of nicotine in e-liquid, which is typically around 1000-18,000 ppm. Using such nanoparticle array-based SERS, we have tested 22 commercially available e-liquid products, using the corresponding gas chromatography-mass spectrometry (GC-MS) reports as the reference. The SERS measurements were done within one hour and successfully identified 20 samples. Only 2 samples showed SERS interference from ingredients that were not suitable for SERS analysis.