Machine-Learning-Assisted Aggregation-Induced Emissive Nanosilicon-Based Sensor Array for Point-of-Care Identification of Multiple Foodborne Pathogens.

How timely identification and determination of pathogen species in pathogen-contaminated foods are responsible for rapid and accurate treatments for food safety accidents. Herein, we synthesize four aggregation-induced emissive nanosilicons with different surface potentials and hydrophobicities by encapsulating four tetraphenylethylene derivatives differing in functional groups. The prepared nanosilicons are utilized as receptors to develop a nanosensor array according to their distinctive interactions with pathogens for the rapid and simultaneous discrimination of pathogens. By coupling with machine-learning algorithms, the proposed nanosensor array achieves high performance in identifying eight pathogens within 1 h with high overall accuracy (93.75-100%). Meanwhile, Cronobacter sakazakii and Listeria monocytogenes are taken as model bacteria for the quantitative evaluation of the developed nanosensor array, which can successfully distinguish the concentration of C. sakazakii and L. monocytogenes at more than 103 and 102 CFU mL-1, respectively, and their mixed samples at 105 CFU mL-1 through the artificial neural network. Moreover, eight pathogens at 1 × 104 CFU mL-1 in milk can be successfully identified by the developed nanosensor array, indicating its feasibility in monitoring food hazards.

Dual-Readout Ultrasensitive Lateral Flow Immunosensing of Salmonella typhimurium in Dairy Products by Doping Engineering-Powered Nanoheterostructure with Enhanced Photothermal Performance.

The photothermal lateral flow immunoassay (LFIA) is of great significance to suitable for on-site semiquantitative detection, which has the upper hand in further constructing detection methods for low-concentration targets. Herein, we presented a doping engineering-powered nanoheterostructure with an enhanced photothermal performance strategy, employing bimetallic nanocuboid Pt3Sn (PSNCs) as a proof of concept. With the help of finite element simulation analysis, the contrast of direct temperature experiment, and the evaluation of photothermal conversion efficiency (η), the distinguished and enthusiastic photothermal feedback of PSNCs is proved. Based on steady bright black of colorimetric and superior photothermal performance, the PSNCs were employed to construct an ultrasensitive model LIFA for detecting Salmonella typhimurium (S. typhimurium), which achieved the double-signal semiquantitative detection, the detection limit reached 103 cfu mL-1 (colorimetric mode) and 102 cfu mL-1 (photothermal mode), which is 100 times higher than that of the traditional colloidal gold method. In addition, the method was effective for the detection of targets in dairy samples only through a simple dilution treatment, which was completed within 15 min. Meanwhile, this PSNCs dual-signal LFIA demonstrated the sensitive detection of S. typhimurium due to the excellent colorimetric signal and significant photothermal performance, which provides a broad spectrum for the future detection of foodborne pathogens.

Perovskite Nanocrystals: Superior Luminogens for Food Quality Detection Analysis.

With the global limited food resources receiving grievous damage from frequent climate changes and ascending global food demand resulting from increasing population growth, perovskite nanocrystals with distinctive photoelectric properties have emerged as attractive and prospective luminogens for the exploitation of rapid, easy operation, low cost, highly accurate, excellently sensitive, and good selective biosensors to detect foodborne hazards in food practices. Perovskite nanocrystals have demonstrated supreme advantages in luminescent biosensing for food products due to their high photoluminescence (PL) quantum yield, narrow full width at half-maximum PL, tunable PL in the entire visible spectrum, easy preparation, and various modification strategies compared with conventional semiconductors. Herein, we have carried out a comprehensive discussion concerning perovskite nanocrystals as luminogens in the application of high-performance biosensing of foodborne hazards for food products, including a brief introduction of perovskite nanocrystals, perovskite nanocrystal-based biosensors, and their application in different categories of food products. Finally, the challenges and opportunities faced by perovskite nanocrystals as superior luminogens were proposed to promote their practicality in the future food supply.

"Potential Scalpel": A Bioassisted Ultrafast Staining Lateral Flow Immunoassay from De Novo to Results.

It is of great importance to overcome potential incompatibility problems between dyestuffs and antibodies (mAbs) for extensive commercial application of a dyestuff-chemistry-based ultrafast colorimetric lateral flow immunoassay (cLFIA). Herein, inspired by traditional staining technologies, a basic dyestuff gallocyanine (GC)-assisted biogenic "potential scalpel"-based cLFIA (GC-ABPS-based cLFIA) by employing clenbuterol (CLE) as proof-of-concept was proposed to solve a high degree of incompatibility between the same potential dyestuffs and mAbs. Goat antimouse immunoglobulin (Ab2) could serve as the "potential scalpel" to form the positive potential value biomolecular network self-assemblers (BNSA) with anti-CLE mAbs (AbCLE) by noncovalent force. The cLFIA completed the entire detection process from de novo to detection results within 30 min thanks to the easy availability and ideal marking efficiency (≤1 min, saving 0.4-10 h) of GC. Encouragingly, the proposed ultrafast GC-ABPS-based cLFIA has also exhibited high sensitivity (0.411 ng mL-1) and low cost (300 times) compared with other cLFIAs. Also, the feasibility of the proposed cLFIA was demonstrated by detecting CLE in beef, pork ham, and skim milk. Finally, the proposed GC-ABPS-based cLFIA has broadened the application range of dyestuffs and provided an effective reference strategy for the application of dyestuffs in food safety monitoring.

Characteristics of Sleep Structure Assessed by Objective Measurements in Patients With Amnestic Mild Cognitive Impairment: A Meta-Analysis.

Objectives: This study aims to explore the differences of sleep structure between patients with amnestic mild cognitive impairment (aMCI) and elderly people with normal cognition, which will help to provide evidence for the relationship between sleep disturbances and cognitive impairment. Methods: A systematic review and meta-analysis were conducted on the literature on sleep parameters obtained by polysomnography or actigraphy in patients with aMCI. The PubMed and EMBASE databases were searched up to April 2020. Inclusion and exclusion criteria were established according to evidence-based medicine methods, and data of all eligible studies were meta-analyzed using the Review Manager 5.3 software. Results: Among the 1,171 literature articles on sleep structure of patients with MCI, eight case-control studies met the inclusion criteria and were included in this meta-analysis. A total of 278 subjects were included, of which 103 were patients with aMCI and 175 were elderly people with normal cognition. The results showed that sleep efficiency (SE) and slow wave sleep (SWS) of patients with aMCI were significantly lower than those of healthy elderly people. Compared with the control group, the percentage of stage 1 of non-rapid eye movement (N1%) in the aMCI patients group increased, and the percentage of stage 2 of non-rapid eye movement (N2%) decreased. Conclusions: Patients with aMCI may experience more severe sleep disturbances than normal cognitive elderly people. There were specific changes, especially in SE and SWS, in the sleep structure of patients with aMCI when compared to those with normal cognition.