Cronobacter sakazakii (C. sakazakii) is a foodborne opportunistic pathogen that can cause life-threatening invasive diseases, such as necrotizing enterocolitis, meningitis, and sepsis in infants. The potential risk of C. sakazakii contamination of powdered infant formula (PIF) is an issue that has attracted considerable attention from manufacturers, regulators, and consumers. C. sakazakii biofilms on the surfaces of equipment and in diverse food-production environments constitute a mode of cell growth that protects the pathogen from hostile environments, and are an important source of persistent contamination of food products. Bacterial biofilms are difficult to remove due to their resistant properties. Conventional cleaning and sterilizing procedures may be insufficient for biofilm control, and may lead to further biofilm development and dispersal. Consequently, novel control strategies are being developed, such as nanotechnology-based delivery systems, interspecies interactions, antimicrobial molecules of microbial origin, natural extracts, and phages. This review focuses on describing the mechanisms underlying the biofilm formation and behavior of C. sakazakii and discussing potential control strategies.
Human milk fat (HMF) is an important source of nutrients and energy for infants. Triacylglycerols (TAGs) account for about 98% of HMF and have a unique molecular structure. HMF is highly enriched in palmitic acid (PA) at the sn-2 position of the glycerol backbone (more than 70%) and in unsaturated fatty acids at the sn-1,3 position. The specific TAG structure in HMF plays a valuable function in infant growth. Sn-2 palmitate (mainly 1,3-dioleoyl-2-palmitoyl-glycerol) is one of the structured TAGs that is commonly supplemented into infant formula in order to enable it to present a similar structure to HMF. In this review, the development of the lipase-catalyzed synthesis of sn-2 palmitate over the last 25 years are summarized, with a focus on reaction schemes in a laboratory setting. Particular attention is also paid to the commercialized sn-1,3 regioselective lipases that are used in structured TAGs synthesis, to general methods of TAG analysis, and to successfully developed sn-2 palmitate products on the market. Prospects for the lipase-catalyzed synthesis of sn-2 palmitate are discussed.
It is well known that the gut microbiota plays an extremely important role in modulating host physiological functions such as immunity and metabolic homeostasis. In recent years, accumulated evidence has revealed that the gut microbiota can regulate the functions of the central nervous system (CNS) through the gut–brain axis, which provides a novel insight into the interactions between the gut and brain. This review focuses on the molecular mechanism of the crosstalk between the gut microbiota and the brain via the gut–brain axis, and on the onset and development of neurological disorders triggered by gut microbiota dysbiosis. These topics are followed by a critical analysis of potential intervention strategies targeting gut microbiota dysbiosis, including the use of probiotics, prebiotics, synbiotics, and diets. While research on the microbiome–gut–brain axis is still in its relative infancy, clarifying the molecular mechanism that underlies how the gut microbiota regulates neurological functions not only holds the promise of revealing potentially novel pathogeneses of neurological disorders, but also may lead to the development of potential diagnosis biomarkers and intervention strategies targeting microbiota dysbiosis for neurological disorders.
The normal development and maintenance of central neural functions are highly correlated with the amount of docosahexaenoic acid (DHA; Ω-3 fatty acid) accumulated in the brain. DHA incorporated at the sn-2 position of lipids is well absorbed by intestinal mucosa and utilized efficiently in vivo. However, modern consumers have a reduced direct intake of DHA and increased intake of saturated fats or Ω-6 fatty acid oils, resulting in behavioral and neurophysiological deficits. To provide an understanding of the integrated beneficial effects of DHA on the human brain, this review introduces the positional difference (sn-2 and sn-1,3 positions) of DHA on a glycerol skeleton in natural fats and oils, and further discusses the possible functional mechanism regarding DHA supplementation and the gut-brain axis. The multiple bidirectional routes in this axis offer a novel insight into the interaction between DHA supplementation, the gut microbiota, and brain health. To achieve high sn-2 DHA in diets, it is suggested that sn-2 DHA lipids be enzymatically produced in more efficient and economical ways by improving the specific activities of lipases and optimizing the purification procedures. These types of diets will benefit individuals with strong needs for sn-2 Ω-3 lipids such as infants, children, and pregnant and lactating women.
In this paper, we report the construction of two accurate mass databases and the development of a combination detection method that simultaneously screens for 733 pesticide and chemical contaminant multi-residues via high-throughput liquid chromatography (LC)– and gas chromatography (GC)–quadru pole-time-of-flight mass spectrometry (Q-TOFMS). This work demonstrates that electronic mass spectral standards may replace chemical-source standard materials as references through one sample preparation and the combination of GC/LC–Q-TOFMS screening. This cutting-edge technique has also replaced multiresidue determination using targeted detection with non-targeted screening. The pesticide residue types, sensitivity, recovery, and reproducibility of this combination technique are evaluated in eight fruit and vegetable matrices. This technique shows three advantages: ① In comparison with the discovery capability of a single technique, the combination technique shows an improvement of 51.1% (GC–QTOFMS) and 39.6% (LC–Q-TOFMS), respectively; ② the combination technique can satisfy a screening limit lower than 10 μg·kg−1 and meet the requirements of ″uniform standards,″ although some of the pesticide residues could be optimized to further improve screening sensitivity; ③ over 488 pesticides with recoveries between 60%–120% and relative standard deviation (RSD) < 20% at a spiked level of 10 μg·kg−1 were detected with the combination technique in eight different matrices. From 2012 to 2017, this combination technique was applied in an investigation to screen pesticide residues from 1384 sampling locations for 38 138 batched samples covering 18 categories and 134 types of fruits and vegetables obtained from across the mainland of China. After statistical analysis, 533 pesticides in 115 891 determinations were detected, and the regularity of pesticides in the fruits and vegetables sold on the Chinese market was shown.
Human noroviruses (HuNoVs) are major foodborne pathogens that cause nonbacterial acute gastroenteritis worldwide. As the tissue-culture system for HuNoVs is not mature enough for routine detection of the virus, detection is mainly dependent on molecular approaches such as reverse transcription polymerase chain reaction (RT-PCR) and reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR). The widely used primers and probes for RT-qPCR were established in the early 2000s. As HuNoVs are highly variant viruses, viral genome mutations result in previously designed primers and/or probes that were perfectly matched working less efficiently over time. In this study, a new duplex RT-qPCR (ND-RT-qPCR) was designed for the detection of genogroup I (GI) and genogroup II (GII) HuNoVs based on an analysis of viral sequences added in the database after 2010. Using long transcribed viral RNAs, the results demonstrate that the sensitivity of ND-RT-qPCR is as low as one genomic copy for both GI and GII HuNoVs. The performance of ND-RT-qPCR was further evaluated by a comparison with the commonly used Kageyama primer-probe sets for RT-qPCR (Kageyama RT-qPCR) for 23 HuNoV-positive clinical samples. All five GI samples were registered as positive by ND-RT-qPCR, whereas only two samples were registered as positive by Kageyama RT-qPCR. All 18 GII samples were registered as positive by ND-RT-qPCR, while 17 samples were registered as positive by Kageyama RT-qPCR. The sensitivity reflected by the Cq value was lower in ND-RT-qPCR than in Kageyama RT-qPCR. Our data suggest that ND-RT-qPCR could be a good fit for the detection of current strains of HuNoVs.
A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion, while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body. Incoming visual information can be processed by the brain in millisecond intervals. The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation. Thus, the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike. Closed-loop computation in a neuroprosthesis includes two stages: encoding a stimulus as a neuronal signal, and decoding it back into a stimulus. In this paper, we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos. We hypothesize that in order to obtain a better understanding of the computational principles in the retina, a hypercircuit view of the retina is necessary, in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina. The different building blocks of the retina, which include a diversity of cell types and synaptic connections—both chemical synapses and electrical synapses (gap junctions)—make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes. An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.
A number of brain research projects have recently been carried out to study the etiology and mechanisms of psychiatric disorders. Although psychiatric disorders are part of the brain sciences, psychiatrists still diagnose them based on subjective experience rather than by gaining insights into the pathophysiology of the diseases. Therefore, it is urgent to have a clear understanding of the etiology and pathogenesis of major psychiatric diseases, which can help in the development of effective treatments and interventions. Artificial intelligence (AI)-based applications are being quickly developed for psychiatric research and diagnosis, but there is no systematic review that summarizes their applications. For this reason, this study briefly reviews three main brain observation techniques used to study psychiatric disorders—namely, magnetic resonance imaging (MRI), electroencephalography (EEG), and kinesics-based diagnoses—along with related AI applications and algorithms. Finally, we discuss the challenges, opportunities, and future study directions of AI-based applications.
In recent years, Pickering emulsions and their applications have attracted a great deal of attention due to their special features, which include easy preparation and enhanced stability. In contrast to classical emulsions, in Pickering emulsions, solid microparticles or nanoparticles that localize at the interface between liquids are used as stabilizers, instead of surfactants, to enhance the droplet lifetime. Furthermore, Pickering emulsions show higher stability, lower toxicity, and stimuli-responsiveness, compared with emulsions that are stabilized by surfactants. Therefore, they can be considered attractive components for various uses, such as photocatalysis and the preparation of new materials. Moreover, the nanoparticle morphology strongly influences Pickering emulsion stability as well as the potential utilization of such emulsions. Here, we review recent findings concerning Pickering emulsions, with a particular focus on how the nanoparticles morphology (i.e., cube, ellipsoid, nanosheet, sphere, cylinder, rod, peanut) influences the type and stability of such emulsions, and their current applications in different fields such as antibacterial activity, protein recognition, catalysis, photocatalysis, and water purification.