Next-Generation Food Drying: Specialized and Smart Approaches to Boost Efficiency and Quality.

Drying is a critical unit operation in food processing, essential for extending shelf life, ensuring microbial safety, and preserving the nutritional and sensory attributes of food products. However, conventional convective drying techniques are often energy-intensive and lead to undesirable changes such as texture degradation, loss of bioactive compounds, and reduced product quality, thereby raising concerns regarding their sustainability and efficiency. In response, recent advancements have focused on the development of innovative drying technologies that offer energy-efficient, rapid, and quality-preserving alternatives.

Exploring the multilayered response of TB bacterium Mycobacterial tuberculosis to lysosomal injury.

Mtb subverts host immune surveillance by damaging phagolysosomal membranes, exploiting them as replication niches. In response, host cells initiate a coordinated LDR, integrating membrane repair, selective autophagy, and de novo biogenesis. This review delineates a systems-level model of lysosomal quality control governed by three critical regulatory axes: LGALS3/8/9, TRIM E3 ubiquitin ligases, and the AMPK-TFEB signaling pathway.

Advancements in cyberthreat intelligence through resource exhaustion attack detection using hybrid deep learning with heuristic search algorithms.

The Distributed Denial of Service (DDoS) attack is uncontrollable and appears in different patterns and shapes; accordingly, it is not easily detected and solved with preceding solutions. A DDoS attack is the most serious threat on the Internet. These attacks became a preferred weapon for cyber extortionists, terrorists, and hackers.

Toxicity and antiproliferative activity assessment of a novel compound Zr I isolated from Zingiber roseum Rosc.

Secondary metabolites from the Zingiberaceae family are enormously active compounds used especially in traditional medicine practices and can be effective drugs in cancer treatment. 8-Isopropyl-5,11-dimethyl-dodecane-4,5-diol (Zr I) was isolated from chloroform extract of Zingiber roseum rhizome and analyzed for anticancer activity against MCF-7 breast cancer cell line. The structure was elucidated based on advanced IR, GC MS, and 2D NMR spectral data.

Ag-decorated 2D CuO nanoflakes: a dual-functional material for solar-driven photocatalysis and antimicrobial applications.

Developing effective strategies to reduce and prevent water pollution due to excessive contamination by harmful pollutants is crucial. Consequently, there is a requirement to design new catalyst materials to enhance the efficiency of the oxidation processes for the wastewater management plant, ensuring the mineralization of trace organic pollutants. Here, we wisely modified the surfaces along with the morphology of copper oxide (CuO) nanostructures with silver (Ag) nanoparticles (~12-20 nm), a variety of Ag-decorated (~7-16%) CuO two-dimensional (2D) nanoflakes (length ~400 nm and width ~70 nm) with enhanced photocatalytic and antibacterial properties, that can provide sustainable solutions to present environmental remediation.

Retinal image-based disease classification using hybrid deep architecture with improved image features.

Objective: Ophthalmologists use retinal fundus imaging as a useful tool to diagnose retinal issues. Recently, research on machine learning has concentrated on disease diagnosis. However, disease detection is less accurate, more likely to be misidentified, and often takes a long time to get the right conclusions.

Design, synthesis, crystal structure of novel hydrazone analogues as SARS-CoV-2 potent inhibitors: MD simulations, MM-GBSA, docking and ADMET studies.

In this study, we have synthesized novel thiohydrazone and hydrazone analogues by one-pot methodology with good to excellent yields (85%-91%). molecular dynamics (MD) simulations, docking, and absorption, distribution, metabolism, excretion and toxicity (ADMET) were evaluated as inhibitor activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (PDB ID: 5N5O) main protease. The dynamics simulations studies and docking were conducted by GROMACS and AutoDock Vina.

A perspective on the synthetic methodologies and biological attributes of thiazole-hydrazone compounds: a medicinal chemistry-based investigation.

Thiazole-hydrazone compounds serve as an essential bis-heterocyclic scaffold in drug discovery, combining the features of a thiazole ring and a hydrazone linkage. This combination offers enhanced biological activity and versatility due to the distinct characteristics of each component. The thiazole ring provides electron density through its sulfur and nitrogen atoms, contributing to lipophilicity and improved membrane permeability, which enhances the drug's ability to reach intracellular targets.

Blockchain framework with IoT device using federated learning for sustainable healthcare systems.

The Internet of Medical Things (IoMT) sector has advanced rapidly in recent years, and security and privacy are essential considerations in the IoMT due to the extensive scope and implementation of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have dramatically improved the functionalities and services of Healthcare 5.0, giving rise to a new domain termed Smart Healthcare.

Hybrid deep learning model for image de-noising and de-mosaicking with adaptive Gannet optimization algorithm.

Image reconstruction is a critical step in various applications, such as art restoration, medical image processing, and agriculture, but it faces challenges due to noise and mosaic artefacts. In this research, a novel approach is introduced for de-noising and de-mosaicking images to enhance image reconstruction quality. The proposed model consists of three main steps: detail layer extraction, image de-noising using an Efficient Generative Adversarial Network (E-GAN), and de-mosaicking using an Adaptive Gannet-based Residual DenseNet (AG_DenseResNet).

Attention-driven hybrid deep learning and SVM model for early Alzheimer's diagnosis using neuroimaging fusion.

Alzheimer's Disease (AD) poses a significant global health challenge, necessitating early and accurate diagnosis to enable timely interventions. AD is a progressive neurodegenerative disorder that affects millions worldwide and is one of the leading causes of cognitive impairment in older adults. Early diagnosis is critical for enabling effective treatment strategies, slowing disease progression, and improving the quality of life for patients.

Kaempferide and Norbergenin avert aluminium chloride-induced amyloid β accumulation and neurocognitive shutdown via oxidative and apoptotic mechanisms.

Objective: To investigate the involvement of oxidative and apoptotic mechanisms in the possible neuroprotective effect of Kaempferide (KPD) and Norbergenin (NRG) against AlCl-induced cognitive shutdown in rats.

Introduction: Aluminium chloride (AlCl) is widely known as a neurotoxic agent that induces memory and cognitive shutdown via induction of oxidative stress and apoptosis. KPD is an O-methylated flavonol that possesses anti-oxidant, anti-inflammatory, anti-dementia and anti-depression properties, whereas NRG, a demethylated compound derived from bergenin, possesses an anti-oxidant property and has neuroprotective effects.

Pyrazole in drug development: a medicinal-chemistry based analysis of USFDA-approved drugs in last decade.

Among the diversity of existing heterocycles, nitrogen-containing heterocycles, i.e., azaheterocycles, are the most popular entity in drug discovery.

Dietary fiber modification: A comparative study of physical, chemical, biological, combined technologies and bioactive impact on food applications.

Daily consumption of a vast variety of plants leads to the direct disposal of a huge number of by-products, one of which is dietary fiber (DF), which are potentially advantageous bioactive compounds, in the ashcan. Nutritionists regard DF as the seventh most significant nutrient for humans. Among its many health advantages are enhanced gut flora, decreased risk of obesity and cardiovascular disorder and many more.

Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization.

Breast cancer diagnosis remains a crucial challenge in medical research, necessitating accurate and automated detection methods. This study introduces an advanced deep learning framework for histopathological image classification, integrating AlexNet and Gated Recurrent Unit (GRU) networks, optimized using the Hippopotamus Optimization Algorithm (HOA). Initially, DenseNet-41 extracts intricate spatial features from histopathological images.

Evaluation of Cranberry as a Novel Therapeutic Strategy for Intracerebroventricular (ICV) Quinolinic Acid-induced Cognitive Impairment in Rats.

Background: Cranberry (Vaccinium macrocarpon) is a small, red fruit that has been widely recognized for its potential health benefits. The cranberry is rich in antioxidant-rich bioactive chemicals and nutritious components like essential vitamins, minerals, and antioxidants; for example, vitamin C, vitamin E, magnesium, copper, potassium, anthocyanins, flavonoids, phenolic acid, etc. Cranberries are thought to offer a variety of health advantages because they are high in Polyphenols (PPs), which have significant antioxidant activity.

Design, Synthesis and Alpha-glucosidase Inhibitory Effect of Pyrazole-1,2,3-Triazole Hybrids.

A novel pyrazole-1,2,3-triazole hybrids were developed and evaluated for its glucosidase inhibitory effects. The targeted 1,2,3-triazole hybrids were obtained from the copper-catalyzed reaction between azide and pyrazole alkyne in dichloromethane at room temperature. Compounds with 4-nitro and 4-chloro groups, respectively, proved the most significant, promising α-glucosidase inhibition activity with IC values of 3.

Zinc-encapsulating covalent organic frameworks for enhanced chemiresistive NH sensing at room temperature.

Ammonia (NH) is a hazardous gas used in industry, agriculture, and biomedical applications, and the development of efficient room-temperature and low-concentration ammonia detection sensors is essential. However, conventional sensors, including metal oxides, nanocomposites, and MOFs, require highly elevated temperatures (200-500 °C), leading to high energy consumption and less durability. To overcome these challenges, we developed functionalized zinc-encapsulated covalent organic frameworks (Zn@COFs) using a facile metal-doping approach.

Enhancing sparse data recommendations with self-inspected adaptive SMOTE and hybrid neural networks.

Personalized recommendation systems are vital for enhancing user satisfaction and reducing information overload, especially in data-sparse environments like e-commerce platforms. This paper introduces a novel hybrid framework that combines Long Short-Term Memory (LSTM) with a modified Split-Convolution (SC) neural network (LSTM-SC) and an advanced sampling technique-Self-Inspected Adaptive SMOTE (SASMOTE). Unlike traditional SMOTE, SASMOTE adaptively selects "visible" nearest neighbors and incorporates a self-inspection strategy to filter out uncertain synthetic samples, ensuring high-quality data generation.

Constructing chiral bicyclo[3.2.1]octanes low-loading organocatalysis.

Multi-centric ambident 2-hydroxy-3,5-dialkyl--quinones selectively reacted with α-alkylnitroethylenes under a low-loading of (1.0 mol% to 500 ppm) Rawal's quinine-squaramide-catalyst, followed by acid-catalysed -hydroxy group acetylation to construct high-yielding chiral bicyclo[3.2.

Hybrid AI models for predicting heat distribution in complex tissue structures with bioheat transfer simulation.

The accurate prediction of thermal behaviour in biological tissues is critical for various medical treatments, including hyperthermia, thermal ablation, and tissue engineering. This paper presents a novel deep learning-enhanced bioheat transfer model that integrates a Fractional Legendre wavelet approach to predict thermal effects in engineered tissue constructs precisely. The model incorporates a multi-phase analysis considering key properties such as blood perfusion, thermal conductivity, and metabolic heat generation.

Hybrid optimization driven fake news detection using reinforced transformer models.

The large-scale production of multimodal fake news, combining text and images, presents significant detection challenges due to distribution discrepancies. Traditional detectors struggle with open-world scenarios, while Large Vision-Language Models (LVLMs) lack specificity in identifying local forgeries. Existing methods often overestimate public opinion's impact, failing to curb misinformation at early stages.

Sorption Studies of Eu Ions Using YPO and YPO:20% Ce Nanoparticles, Optical Properties, and in Conjunction with Instrumental Neutron Activation Analysis.

The contamination of rare earth (RE) ions in an aqueous medium causes severe health hazards due to their toxicity. Hence, the removal of RE ions is necessary. In this work, we have used a concept of host-guest interaction which assists in the removal of RE ions by choosing the host as YPO nanomaterial and the Eu as foreign/guest ions.

Optimized machine learning mechanism for big data healthcare system to predict disease risk factor.

Heart disease is becoming more and more common in modern society because of factors like stress, inadequate diets, etc. Early identification of heart disease risk factors is essential as it allows for treatment plans that may reduce the risk of severe consequences and enhance patient outcomes. Predictive methods have been used to estimate the risk factor, but they often have drawbacks such as improper feature selection, overfitting, etc.

Metaparameter optimized hybrid deep learning model for next generation cybersecurity in software defined networking environment.

The Software Defined Networking (SDN) method has evolved to project future systems and collect novel application needs for several years. SDN delivers sources for enhancing management and system control by splitting data and control plane, and the control logic is federal in a controller. Conversely, the central logical control is a perfect objective for malicious assaults, chiefly Distributed Denial of Service (DDoS) threats.