Protein and Polysaccharide Recovery from Shrimp Wastes by Natural Deep Eutectic Solvent Mediated Subcritical Water Hydrolysis for Biodegradable Film.

Environmental pollution is a significant problem due to the improper disposal of plastics and shrimp shells outdoors. Therefore, the synthesis of biodegradable film from waste materials is highly important. The novelty of this research lies in the extraction of protein hydrolysates and chitosan from shrimp shells, as well as the fabrication of biodegradable film from these materials. In this study, the composite films were produced using the solution casting method. Moreover, the combined effect of ultrasound pretreatments (UPT) and natural deep eutectic solvents (NADES) was investigated as extraction media, to determine their potential impact on shrimp waste subcritical water hydrolysis (SWH). Shrimp shells were submitted to UPT in NADES solution, followed by SWH at different temperatures ranging from 150 to 230 °C under 3 MPa for 20 min. Then, the physiochemical properties and bioactivities of the hydrolysates were assessed to determine their suitability for use in biodegradable packaging films. Additionally, the physiochemical properties and bioactivities of the resulting hydrolysates were also analyzed. The highest amount of protein (391.96 ± 0.48 mg BSA/g) was obtained at 190 °C/UPT/NADES, and the average molecular size of the protein molecules was less than 1000 Da with different kinds of peptide. Overall, combined UPT and SWH treatments yielded higher antioxidant activity levels than individual treatments. Finally, the application of composite films was evaluated by wrapping fish samples and assessing their lipid oxidation. The use of higher concentrations of protein hydrolysates significantly delayed changes in the samples, thereby demonstrating the film's applicability.

Subcritical water-assisted fish gelatin hydrolysis for astaxanthin-loaded fish oil emulsion stability.

Though gelatin emulsifying properties have been intensively studied, how low-molecular-weight (LMW) fish gelatin affects astaxanthin (AST)-loaded fish oil emulsion stability remains elusive. In this study, subcritical water hydrolysis (SWH)-modified LMW fish gelatin (SWHG) was produced from 110 °C to 180 °C and used to enhance the AST steadiness in oil/water emulsions in the presence of an emulsifier, lecithin. In the prepared emulsions, the surface charge increased while droplet size decreased with the decrease in gelatin MW due to the reduced thickness of the adsorbed gelatin membrane. LMW gelatin and lecithin could form a firm-absorbed layer on the droplet surface by electrostatic interaction between amide groups of gelatin molecules and phosphate groups of lecithin, thus stabilizing the emulsions. SWHG improved the creaming stability of the emulsions and hindered the oxygen- and light-induced AST degradation for 11 months compared to high MW gelatin. Whereas, the control emulsion showed noticeable phase separation after two weeks of storage. These findings prove the advantage of the SWH approach and propose the use of SWHG in oil-in-water emulsions for AST stabilization.

Effects of thermal and salt water soaking pre-treatment on the physicochemical and nutritional properties of sundried tilapia fish (Oreocromis niloticus) products.

The effects of saltwater soaking (10-30 %, w/v) and thermal (60°C-90 °C) pre-treatment on the physicochemical and nutritional quality of sundried tilapia fish (Oreocromis niloticus) products were assessed. The wet reduction was 14.47 % in the sample treated with a 30 % salt solution at 90 °C, whereas the wet reduction of 21.23 % was observed in the sample without treatment (control). Protein, lipid, and ash content were increased significantly (P < 0.05) with higher pre-treatment salt concentration and temperature, while the moisture content showed the opposite trend. The content of essential and non-essential amino acids in the treated samples ranged from 7149.97 mg/100 g to 8063.42 mg/100 g and 10530.66 mg/100 g to 11365.59 mg/100 g, respectively, whereas the values were 7018.55 mg/100 g and 10400.84 mg/100 g, respectively in the control. The fatty acids composition, particularly ω-3 polyunsaturated fatty acids, was higher in pretreated samples (6.14-7.08 %) compared to the control. Mineral content was found to improve with saltwater and thermal pre-treatment, and the levels of heavy metals, including Ni and Cu, were significantly lower in the sundried tilapia fish. The sample pretreated with 10 % salt solution and 75 °C showed the highest rehydration capacity of 66.63 %. These findings suggest that saltwater and thermal pre-treatment can effectively enhance the physicochemical and nutritional properties of sundried tilapia fish products.

Development of fish gelatin/carrageenan/zein bio-nanocomposite active-films incorporated with turmeric essential oil and their application in chicken meat preservation.

Gelatin/carrageenan (Ge/Car) active packaging films incorporated with turmeric essential oil (TEO) encapsulated in zein nanoparticles (ZNP) were developed. The efficacy of these active packaging films and their antimicrobial properties were also investigated to ensure their practical application. Three different types of nanocomposite films (Ge/Car, Ge/Car/TEO, and Ge/Car/ZNP) were prepared. The characterization of the films was elucidated using Fourier transform infrared (FTIR), X-ray diffraction analyses (XRD), and scanning electron microscope (SEM). Physicochemical and mechanical properties of the films were enhanced, owing to the application of TEO-containing nanocomposites. Supercritical-CO2 extracted TEO showed excellent biological activities, alongside GC-MS analysis identified that TEO contained 33 bioactive compounds where the major constituent was Zingiberene. ZNP proved an excellent carrier of TEO. The nanocomposite film sustainably released TEO, improving the shelf life of the chicken meat by reducing bacterial colonies from 3.08 log CFU/g to 2.81 log CFU/g after 14 days incubation against Salmonella enterica compared with 6.66 log CFU/g observed in the control film. The overall results of this study suggest that the nanocomposite active film is an excellent candidate for food packaging to ensure a better world.

A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection.

Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body's normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with treatment in the early stage. It must also divide cancer patients into high- and low-risk groups. While realizing efficient detection of cancer is frequently a time-taking and exhausting task with the high possibility of pathologist errors and previous studies employed data mining and machine learning (ML) techniques to identify cancer, these strategies rely on handcrafted feature extraction techniques that result in incorrect classification. On the contrary, deep learning (DL) is robust in feature extraction and has recently been widely used for classification and detection purposes. This research implemented a novel hybrid AlexNet-gated recurrent unit (AlexNet-GRU) model for the lymph node (LN) breast cancer detection and classification. We have used a well-known Kaggle (PCam) data set to classify LN cancer samples. This study is tested and compared among three models: convolutional neural network GRU (CNN-GRU), CNN long short-term memory (CNN-LSTM), and the proposed AlexNet-GRU. The experimental results indicated that the performance metrics accuracy, precision, sensitivity, and specificity (99.50%, 98.10%, 98.90%, and 97.50) of the proposed model can reduce the pathologist errors that occur during the diagnosis process of incorrect classification and significantly better performance than CNN-GRU and CNN-LSTM models. The proposed model is compared with other recent ML/DL algorithms to analyze the model's efficiency, which reveals that the proposed AlexNet-GRU model is computationally efficient. Also, the proposed model presents its superiority over state-of-the-art methods for LN breast cancer detection and classification.

On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer's Disease Using Neuroimaging Modalities and Data Augmentation Methods.

Alzheimer's disease (AD) is an irreversible illness of the brain impacting the functional and daily activities of elderly population worldwide. Neuroimaging sensory systems such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) measure the pathological changes in the brain associated with this disorder especially in its early stages. Deep learning (DL) architectures such as Convolutional Neural Networks (CNNs) are successfully used in recognition, classification, segmentation, detection, and other domains for data interpretation. Data augmentation schemes work alongside DL techniques and may impact the final task performance positively or negatively. In this work, we have studied and compared the impact of three data augmentation techniques on the final performances of CNN architectures in the 3D domain for the early diagnosis of AD. We have studied both binary and multiclass classification problems using MRI and PET neuroimaging modalities. We have found the performance of random zoomed in/out augmentation to be the best among all the augmentation methods. It is also observed that combining different augmentation methods may result in deteriorating performances on the classification tasks. Furthermore, we have seen that architecture engineering has less impact on the final classification performance in comparison to the data manipulation schemes. We have also observed that deeper architectures may not provide performance advantages in comparison to their shallower counterparts. We have further observed that these augmentation schemes do not alleviate the class imbalance issue.