Electrospinning of nanofibers incorporated with essential oils: Applications in food

Nowadays, modern food preservation techniques have emerged in the last decade. Recently, a combination of nanotechnology and active packaging has allowed the incorporation of bioactive compounds, such as essential oils, into nanoscale electrospun fibers. This phenomenon provides a new horizon in food safety and preservation. The incorporation of essential oils into electrospun nanofibers can extend the duration of antimicrobial and antioxidant activity of essential oils, which subsequently leads to longer shelf life, better preservation, and superior quality of food. In the current paper, the essential oils incorporated into nanofibers have been reviewed. The fabrication of nanofibers is usually carried out using different substances by applying various manufacturing methods, including needleless and needle-based electrospinning techniques. In this study, an emphasis on the antioxidant and antibacterial effects of electrospun nanofibers loaded with essential oils and their application in food models has been laid. Nevertheless, other challenges associated with using nanofibers incorporated with essential oils, such as their impact on organoleptic properties, cytotoxicity, and durability, have been discussed to achieve a holistic view of applying the electrospinning techniques in the food industry.

WITHDRAWN: Electrospinning of Nanofibers Incorporated with Essential Oils: Applications in Food

The article has been withdrawn at the request of the author of the journal Current Pharmaceutical Biotechnology.

Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused.

Bentham Science Disclaimer

It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

The role of the gut microbiota and nutrition on spatial learning and spatial memory: a mini review based on animal studies

The gut-brain axis is believed to constitute a bidirectional communication mechanism that affects both mental and digestive processes. Recently, the role of the gut microbiota in cognitive performance has been the focus of much research. In this paper, we discuss the effects of gut microbiota and nutrition on spatial memory and learning. Studies have shown the influence of diet on cognitive capabilities such as spatial learning and memory. It has been reported that a high-fat diet can alter gut microbiota which subsequently leads to changes in spatial learning and memory. Some microorganisms in the gut that can significantly affect spatial learning and memory are Akkermansia muciniphila, Bifidobacterium, Lactobacillus, Firmicutes, Bacteroidetes, and Helicobacter pylori. For example, a reduction in the amount of A. muciniphila in the gut leads to increased intestinal permeability and induces immune response in the brain which then negatively affects cognitive performances. We suggest that more studies should be carried out regarding the indirect effects of nutrition on cognitive activities via alteration in gut microbiota.

Low-contrast Pattern-reversal Visual Evoked Potential in Different Spatial Frequencies

Purpose

To evaluate the pattern-reversal visual evoked potential (PRVEP) in low-contrast, spatial frequencies in time, frequency, and time-frequency domains.

Methods

PRVEP was performed in 31 normal eyes, according to the International Society of Electrophysiology of Vision (ISCEV) protocol. Test stimuli had checkerboard of 5% contrast with spatial frequencies of 1, 2, and 4 cycles per degree (cpd). For each VEP waveform, the time domain (TD) analysis, Fast Fourier Transform(FFT), and discrete wavelet transform (DWT) were performed using MATLAB software. The VEP component changes as a function of spatial frequency (SF) were compared among time, frequency, and time–frequency dimensions.

Results

As a consequence of increased SF, a significant attenuation of the P100 amplitude and prolongation of P100 latency were seen, while there was no significant difference in frequency components. In the wavelet domain, an increase in SF at a contrast level of 5% enhanced DWT coefficients. However, this increase had no meaningful effect on the 7P descriptor.

Conclusion

At a low contrast level of 5%, SF-dependent changes in PRVEP parameters can be better identified with the TD and DWT approaches compared to the Fourier approach. However, specific visual processing may be seen with the wavelet transform.

Analysis of pattern electroretinogram signals of early primary open-angle glaucoma in discrete wavelet transform coefficients domain

PURPOSE

To evaluate discrete wavelet transform coefficients and identify descriptors of pattern electroretinogram (PERG) waveforms in order to determine PERG characteristics for optimizing the diagnosis of early primary open-angle glaucoma (POAG).

METHODS

Pattern electroretinogram was performed in 30 normal eyes and 30 eyes with primary open-angle glaucoma according to the ISCEV protocol. The check size was 0.8° and 16°, and the color was black/white in both groups. The results were analyzed in time domain (TD) and discrete wavelet transform (DWT) using the MATLAB software. The mean value, standard deviation, and relative energy of level 6 and 7 detail coefficients (d6, d7) and level 7 approximation coefficients (a7) of Daubechies 4 (db4), Daubechies 8 (db8), Symlet 5 (sym5), Symlet 7 (sym7), and Coiflet 5 (coif5) wavelets were calculated. In all the mentioned wavelets, DWT descriptors were extracted. Signals were reconstructed by inverse DWT. All data obtained by TD and DWT analyses were compared between the two groups.

RESULTS

In both check sizes, a significant attenuation of N95 amplitude was seen in the patient group. The relative energy of a7 of db8 increased significantly in the POAG group in the 0.8° check size. In larger check stimuli, the relative energy of d7 of coif5 decreased significantly and the standard deviation of d7 of sym7 increased markedly in glaucomatous patients (P < 0.05). In small stimuli, N95 descriptor (7N) of db8 had the highest value and showed a significant increase as compared to the POAG group. In the 16° check size, there was no significant difference. A strong correlation was seen between reconstructed signals and originals (r = 0.99).

CONCLUSION

The DWT can quantify PERG responses more accurately. In agreement with TD and wavelet coefficients domain results, 7N of db8 decomposition can be used as a good indicator for early detection of POAG.

Driving Drowsiness Detection Using Fusion of Electroencephalography, Electrooculography, and Driving Quality Signals

This study investigates the detection of the drowsiness state (DS) for future application such as in the reduction of the road traffic accidents. The electroencephalography, electrooculography, driving quality, and Karolinska sleepiness scale data of 7 males during approximately 20 h of sleep deprivation were recorded. To reduce the eye blink artifact, an automatic mechanism based on the independent component analysis method and Higuchi's fractal dimension has been applied. After recordings, for selecting the best subset of features, a new combined method, called class separability feature selection-sequential feature selection, has been developed. This method reduces the time of calculations from 6807 to 2096 s (by 69.21%) while the classification accuracy remains relatively unchanged. For diagnosis of the DS and classification of the state, a new approach based on a self-organized map network is used. First, using the data obtained from two classes of awareness state (AS) and DS, the network achieved an accuracy of 76.51 ± 3.43%. Using data from three classes of AS, AS/DS (passing from awareness to drowsiness), and DS to the network, an accuracy of 62.70 ± 3.65% was achieved. It is suggested that the DS during driving is detectable with an unsupervised network.