Enhanced detection of acrylamide using a versatile solid-state upconversion sensor through spectral and visual analysis.

Acrylamide (AM) generally forms in high-temperature processes and has been classified as a potential carcinogen. In this study, we put forward a maneuverable solid-state luminescence sensor using polydimethylsiloxane (PDMS) as the matrix coupled with upconversion nanoparticles as the indicator. The core-shell upconversion nanoparticles emitting cyan light were uniformly encapsulated in PDMS. Then it was further modified with complementary DNA of AM aptamer. The nanocrystalline fluorescein isothiocyanate isomer (FITC), coupled with AM aptamer, was attached to the surface of PDMS. FITC effectively quenched the upconversion luminescence through fluorescence resonance energy transfer (FRET). The introduction of AM resulted in preferentially bound to aptamer caused the separation of the quencher and the donor, and led to luminescence recovery. The developed sensor was applied for both spectral and visual monitoring, demonstrating a detection limit (LOD) of 1.00 nM and 1.07 nM, respectively. Importantly, in the actual foodstuffs detection, there is no obvious difference between the results of this study and the standard method, which indicates the developed method has good accuracy. Therefore, this solid-state sensor has the potential for on-site detection using a smartphone device and an Android application.

Label-free detection of trace level zearalenone in corn oil by surface-enhanced Raman spectroscopy (SERS) coupled with deep learning models.

Surface-enhanced Raman spectroscopy (SERS) and deep learning models were adopted for detecting zearalenone (ZEN) in corn oil. First, gold nanorods were synthesized as a SERS substrate. Second, the collected SERS spectra were augmented to improve the generalization ability of regression models. Third, five regression models, including partial least squares regression (PLSR), random forest regression (RFR), Gaussian progress regression (GPR), one-dimensional convolutional neural networks (1D CNN), and two-dimensional convolutional neural networks (2D CNN), were developed. The results showed that 1D CNN and 2D CNN models possessed the best prediction performance, i.e., determination of prediction set (RP2) = 0.9863 and 0.9872, root mean squared error of prediction set (RMSEP) = 0.2267 and 0.2341, ratio of performance to deviation (RPD) = 6.548 and 6.827, limit of detection (LOD) = 6.81 × 10-4 and 7.24 × 10-4 µg/mL. Therefore, the proposed method offers an ultrasensitive and effective strategy for detecting ZEN in corn oil.

Mesoporous silica supported orderly-spaced gold nanoparticles SERS-based sensor for pesticides detection in food.

In this study, a novel sensor fabricated with compactly arranged gold nanoparticles (AuNPs) templated from mesoporous silica film (MSF) via air-water interface has been confirmed as a promising surface-enhanced Raman scattering (SERS) substrate for detecting trace levels of 2,4-dichlorophenoxyacetic acid (2,4-D), pymetrozine and thiamethoxam. The densely arranged AuNPs@MSF had an average AuNPs size of 5.15 nm with small nanogaps (<2nm) between AuNPs, and exhibited a high SERS performance. SERS spectra of pesticides were collected after their adsorption on the AuNPs@MSF. The results showed that the concentration of 2,4-D, pymetrozine and thiamethoxam gave a good linear relationship with SERS intensity. Moreover, the designed SERS-based sensor (AuNPs@MSF) was stable for 3 months with ca. 3% relative standard deviation (RSD) and was applied successfully for the analysis of 2,4-D extraction from both environmental and food samples. The proposed SERS-based sensor was further validated by HPLC and showed satisfactory result (p > 0.05).

[Clinical analysis of 38 patients with cephalic and cervical adenoid cystic carcinoma].

OBJECTIVE: To assess the clinical features and the treatment result of cephalic and cervical adenoid cystic carcinoma (ACC). METHOD: A retrospective analysis was performed on 38 patients with cephalic and cervical ACC by their clinical manifestation, the therapy and follow-up results. RESULT: We had 38 patients of operation. All patients followed up from 3 years to 13 years. The overall 3-year survival rate of patients was 71.1%, the overall 5-year survival rate of patients was 63.2%. CONCLUSION: Cephalic and cervical ACC is not easy to exact diagnosis in early stage. It progresses slowly. It also has the tendency of recurrence and cancerous metastasis. Thus it, is necessary to do both thorough surgery.