Atlantic salmon (Salmo salar) waste as a unique source of biofunctional protein hydrolysates: Emerging productions, promising applications, and challenges mitigation.

The Atlantic salmon is an extremely popular fish for its nutritional value and unique taste among several fish species. Researchers are focusing on the utilization of Atlantic salmon waste for generating protein hydrolysates rich in peptides and amino acids and investigating their health benefits. Several technological approaches, including enzymatic, chemical, and the recently developed subcritical water hydrolysis, are currently used for the production of Atlantic salmon waste protein hydrolysates.

Advances of microplastics ingestion on the morphological and behavioral conditions of model zebrafish: A review.

Concerns have been conveyed regarding the availability and hazards of microplastics (MPs) in aquatic biota due to their widespread presence in aquatic habitats. Zebrafish (Danio rerio) are widely used as a model organism to study the adverse impacts of MPs due to their several compelling advantages, such as their small size, ease of breeding, inexpensive maintenance, short life cycle, year-round spawning, high fecundity, fewer legal restrictions, and genetic resemblances to humans. Exposure of organisms to MPs produces physical and chemical toxic effects, including abnormal behavior, oxidative stress, neurotoxicity, genotoxicity, immune toxicity, reproductive imbalance, and histopathological effects.

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.

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.

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

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.

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.

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.