Polymeric Nanodiscs Comprising 5-Fluorouracil for Inhibition of Protein Aggregation and Their Anti-Alzheimer's Activity in the Model.

Nanoconjugates are promising for therapeutic drug delivery and targeted applications due to the numerous opportunities to functionalize their surface. The present study reports the synthesis of 5-fluorouracil (5-FU)-entrapped polyvinylpyrrolidone (PVP) nanoconjugates, precisely 5-FU-PVP and 5-FU-PVP-Au, and the evaluation of protein aggregation inhibition efficiency. The 5-FU-loaded polymer nanoconjugates were functionalized with gold nanoparticles and analyzed using characterization techniques like dynamic light scattering, UV-visible spectroscopy, Fourier-transform infrared spectroscopy, and zeta potential analysis.

Gold nanoparticle encapsulated hybrid MOF: synthesis, characterization, and co-drug delivery of 5-fluorouracil and curcumin.

The unique features of Metal-Organic Frameworks (MOFs), including structural flexibility, high surface area, and variable pore size, have drawn attention in cancer therapy. However, despite advances in surface functionalization, engineering structural features, and porosity, achieving controlled release, stability, scalability, and toxicity remains a challenge. The current study reports gold nanoparticle (AuNP) encapsulated dual metal-organic frameworks (MOFs) comprising zeolitic imidazolate (ZIF8) and cobalt-imidazole (ZIF67) by a simple precipitation method for dual drug delivery applications.

AlO/ZrO dual-dielectric Gr/CNT nanoribbon vertical tunnel FET based biosensor for genomic classification and S-protein detection in SARS-CoV-2.

The ongoing genetic mutation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) possesses the capacity to inadvertently lead to an increase in both the rates of transmission and mortality. In this study, we showcase the use of an Al2O3/ZrO2 Dual-Dielectric Gr/CNT Nanoribbon vertical tunnel field-effect transistor biosensor for the purpose of detecting spike proteins of SARS-CoV-2 in clinical samples. The proteins mentioned above are situated within the protein capsids of the virus.

Multifunctional characteristics of biosynthesized CoFeO@Ag nanocomposite by photocatalytic, antibacterial and cytotoxic applications.

Carissa carandas, a traditional medicinal herb with a high concentration of antioxidant phytochemicals, has been used for thousands of years in the Ayurveda, Unani, and homoeopathic schools of medicine. By employing Carissa carandas bark extract as a reducing and capping agent in green biosynthesis, we extend this conventional application to produce CoFeO and CoFeO@Ag nanocomposite. A variety of techniques have been used to characterize the synthesised nanocomposite, including UV-Vis, FTIR, XRD, FESEM, EDX, and BET.

Biosensor nanoarchitectonics of Cu-Fe-nanoparticles/Zeolite-A/Graphene nanocomposite for enhanced electrooxidation and dopamine detection.

Cu-Fe NPs/ZEA/Gr electrochemical biosensor is developed by sol-gel spin coating technique, where copper-iron nanoparticles (Cu-Fe NPs) is synthesized using a chemical reduction method and modified with Zeolite & Graphene to develop a hybrid nanocomposite - Cu-Fe NPs/ZEA/Gr. The synthesized nanocomposite is then mixed with poly (vinyl alcohol) as a binding agent and coated on to the glass substrate to produce thin film electrode. Then the electrode was analyzed for structural and morphological studies using XRD, SEM, TEM, UV-VIS, absorption, and emission spectra.

X-ray photoelectron spectroscopy and tunable photoluminescence study of gold nanoparticles embedded in PVA films.

This article reports the systematic photoluminescence study of the various contents of gold nanocomposites in polyvinyl alcohol (PVA) films. The variations in the gold content in PVA film were 0.2, 0.

Sustainable developments in near-dry electrical discharge machining process using sunflower oil-mist dielectric fluid.

In this study, a near-dry electrical discharge machining (NDEDM) process has been conducted using compressed air mixed with a low quantity of biodegradable refined sunflower oil (called oil-mist) to investigate the machining characteristics. The Box-Behnken method looks at how oil flow rate (OR), air pressure (AR), spark current (SC), and pulse width (PW) affect gas emission concentration (GEC), material removal rate (MRR), and surface roughness (SR). The TOPSIS (The Technique for Order of Preference by Similarity to the Ideal Solution) technique estimates the parameter optimal set for the best machining characteristics.

Toxicity, Pharmacokinetic Profile, and Compound-Protein Interaction Study of Polygonum minus Huds Extract.

Several phytochemicals with potential for bioactivity can be found in Polygonum minus (PM). The goal of this investigation was to establish the minimally toxic dose of PM for pharmaceutical use. To explain the stability and reactivity of the compounds under study, the lowest unoccupied molecular orbital (LUMO), the highest occupied molecular orbital (HOMO), and the natural bond orbital were all combined.

Impact of reactivity controlled compression ignition (RCCI) mode engine operation in diesel engine powered with B20 blend of waste cooking oil biodiesel.

The purpose of this study is to conduct an experimental assessment of the impact of RCCI (reactivity regulated compression ignition) on the performance, emissions, and combustion of a CRDI engine. A fuel mix (20% biodiesel, 80% diesel, and a NaOH catalyst) is generated. The produced combination is evaluated for attributes using standards established by the American Society for Testing and Materials (ASTM).

Cost-effective and eco-friendly copper recovery from waste printed circuit boards using organic chemical leaching.

Electronic waste generation is indeed a global concern; therefore, appropriate management and recycling are becoming highly significant. Printed circuit boards (PCBs) are significant portion of e-waste; contains a large number of valuable metals, rendering this material an important recovery resource. Among all other metals, the high Copper concentration of PCB residues, which is often ten times higher than that of rich-content rocks, makes these residues an appealing secondary source of Cu recovery.

Hybrid Silicon Substrate FinFET-Metal Insulator Metal (MIM) Memristor Based Sense Amplifier Design for the Non-Volatile SRAM Cell.

Maintaining power consumption has become a critical hurdle in the manufacturing process as CMOS technologies continue to be downscaled. The longevity of portable gadgets is reduced as power usage increases. As a result, less-cost, high-density, less-power, and better-performance memory devices are in great demand in the electronics industry for a wide range of applications, including Internet of Things (IoT) and electronic devices like laptops and smartphones.

Recent developments in wireless capsule endoscopy imaging: Compression and summarization techniques.

Wireless capsule endoscopy (WCE) can be viewed as an innovative technology introduced in the medical domain to directly visualize the digestive system using a battery-powered electronic capsule. It is considered a desirable substitute for conventional digestive tract diagnostic methods for a comfortable and painless inspection. Despite many benefits, WCE results in poor video quality due to low frame resolution and diagnostic accuracy.

An ultra-sensitive laccase/polyaziridine-bismuth selenide nanoplates modified GCE for detection of atenolol in pharmaceuticals and urine samples.

The analysis of β-blockers in pharmaceutical, biological and environmental samples has gained much interest due to their wide applications. The aim of this study was to develop an enzyme-based biosensor using hexagonal-shaped low-dimensional BiSe NPs decorated with laccase through polyaziridine (PAZ) modified glassy carbon electrode (Lac/PAZ-BiSe NPs/GCE). Surface properties were examined using SEM, TEM, EDX, XRD, XPS, FTIR, UV-Visible, and zeta potential.

A multi-modal assessment of sleep stages using adaptive Fourier decomposition and machine learning.

Healthy sleep is essential for the rejuvenation of the body and helps in maintaining good health. Many people suffer from sleep disorders that are characterized by abnormal sleep patterns. Automated assessment of such disorders using biomedical signals has been an active subject of research.

Deep Learning and Transfer Learning for Malaria Detection.

Infectious disease malaria is a devastating infectious disease that claims the lives of more than 500,000 people worldwide every year. Most of these deaths occur as a result of a delayed or incorrect diagnosis. At the moment, the manual microscope is considered to be the most effective equipment for diagnosing malaria.

A facile route to synthesize n-SnO/p-CuFeO to rapidly degrade toxic methylene blue dye under natural sunlight.

In the present study, the n-SnO/p-CuFeO (p-CFO) complex was prepared by a two-step process. p-CFO synthesized by the molten salt method was coated with SnO synthesized by a facile chemical precipitation method. The formation of n-SnO/p-CFO was confirmed by powder X-ray diffraction (PXRD).

Mathematical approach for segmenting chromosome clusters in metaspread images.

Karyotyping is an examination that helps in detecting chromosomal abnormalities. Chromosome analysis is a very challenging task which requires various steps to obtain a karyotype. The challenges associated with chromosome analysis are overlapping and touching of chromosomes.

Performance Evaluation of SeisTutor Using Cognitive Intelligence-Based "Kirkpatrick Model".

The classroom learning environment facilitates human tutors to interact with every learner and get the opportunity to understand the learner's psychology and then provide learning material (access learner prior knowledge and well align the learning material as per learner requirement) to them accordingly. Implementing this cognitive intelligence in intelligent tutoring system is quite tricky. This research has focused on mimicking human tutor cognitive intelligence in the computer-aided system of offering an exclusive curriculum to the learners.

Speech as a Biomarker for COVID-19 Detection Using Machine Learning.

The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not.

A comparative study for predictive monitoring of COVID-19 pandemic.

COVID-19 pandemic caused by novel coronavirus (SARS-CoV-2) crippled the world economy and engendered irreparable damages to the lives and health of millions. To control the spread of the disease, it is important to make appropriate policy decisions at the right time. This can be facilitated by a robust mathematical model that can forecast the prevalence and incidence of COVID-19 with greater accuracy.

A 2D FSI mathematical model of blood flow to analyze the hyper-viscous effects in atherosclerotic COVID patients.

Recent researches on COVID 19 has been extended to analyze the various morphological and anatomical changes in a patient's body due to the invasion of the virus. These latest studies have concluded that there happens a high rise in the viscosity of the blood in a COVID 19 patient, supported by the extensive analysis of the clinical data. In the present paper, a mathematical model in the form of a differential equation system has been proposed to disclose the various changes that occur in the flow across the stenosis of an arterial segment.

COVID-19 image classification using deep learning: Advances, challenges and opportunities.

Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors.

Disposable biosensors based on metal nanoparticles.

The coronavirus disease2019 (COVID-19) pandemic has highlighted the need for disposable biosensors that can detect viruses in infected patients quickly due to fast response and also at a low cost.The present review provides an overview of the applications of disposable biosensors based on metal nanoparticles in enzymatic and non-enzymatic sensors with special reference to glucose and HO, immunosensors as well as genosensors (DNA biosensors in which the recognized event consists of the hybridization reaction)for point-of-care diagnostics. The disposable biosensors for COVID19 have also been discussed.