Micro and Nanosystems - Volume 17, Issue 1, 2025

Delivering hydrophobic or poorly soluble drugs has become increasingly challenging, with issues like stability and bioavailability complicating the process. Among various strategies devised to address these problems, nanoemulgels have proven effective. Nanoemulgels combine a gel base and an emulsion at the nanoscale, making them excellent for drug delivery. The nanoemulsion component protects the active ingredient from degradative reactions like hydrolysis and enzymatic degradation. Meanwhile, the gel base enhances the emulsion's thermodynamic stability by increasing the viscosity of the aqueous phase and reducing surface and interfacial tension.

The primary objective of this review was to explore nanoemulgels as a drug delivery system in the pharmaceutical industry. It delves into the advantages and applications of nanoemulgels in various medical fields, compares them with conventional emulgel, and examines formulation strategies, preparation methods, patent trends, future prospects, and evaluation methods in detail.

An exhaustive literature survey was conducted keeping in view the various aspects of nanoemulgel. Information from various resources, such as books, review articles, scientific reports, research articles, and patents, were searched, read, analyzed, and summarized.

This review article thoroughly examines nanoemulgels, discussing their formulation strategies, characterization techniques, and applications in various fields. It highlights their benefits, such as enhanced drug solubility, controlled release, improved stability, and targeted delivery. The article also covers patents related to nanoemulgel technology and explores its future prospects, emphasizing potential applications in pharmaceuticals, cosmetics, dermatology, and other industries.

Prostate cancer is the second most often occurring cancer in males and the fourth most common cancer overall. Beta-sitosterol (β-Sit), the most prevalent plant phytosterol found in several plant species, has been reported to have inhibitory effects against several malignancies. Even though β-Sit has considerable potential, its therapeutic uses are limited due to its poor aqueous solubility (<0.1 mg/ml), low bioavailability (0.41%), and poor absorption from GIT. Nanosuspension is one of the most innovative approaches to address problems linked to low solubility and poor absorption.

In the present research work, β-Sit nanosuspension has been fabricated by nanoprecipitation-ultrasonication, followed by high-pressure homogenization (Panda plus 2000) employing HPMC E5 and poloxamer (188 and 407) as stabilizers, optimized using a Box-Behnken technique. Subsequently, in situ nano gel was prepared by dispersion method using gellan gum as an ion-sensitive polymer by incorporating optimized nanosuspension.

The optimized nanosuspension was evaluated for various parameters and has been found to have an average particle size (137 ± 5.07 nm), zeta potential (-24 ± 4.99), PDI (0.207), and improved solubility up to 5 folds, being suitable for systemic absorption through the nasal route. The optimized in situ gel was characterized and showed the desired viscosity, good spreadability, acceptable gelation property, and sufficient mucoadhesive strength to adhere to nasal mucosa after ionic interaction. The in vitro release of pure drug, nanosuspension, and the optimized gel was compared, and optimized in situ gel showed a maximum release of 91.41 ± 1.32% up to 8 hours.

It was concluded that the in situ nasal nanogel could be the best possible approach to delivering β-Sit into systemic circulation for the treatment of prostate cancer.

The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing.

To implement the bitonic sorting circuit, 2x2 switch, is used. The overarching objective of implementing a reversible sorting circuit is to mitigate power consumption while promising secure communication. The successful implementation of the bitonic sorting circuit utilizing the proposed 2x2 switch is validated through simulations conducted in IBM Qiskit.

This study begins by employing the control swap gate to implement a 2x2 switch, aimed at enabling concurrent computing operations within multiprocessor systems to enhance throughput. Subsequently, following the successful implementation of the 2x2 switch, the study proceeds to design a bitonic sorting circuit.

Bitonic sort using a 2x2 switch on the IBM Quantum Experience (IBMQ) platform via the QISKit SDK in Python 3.6. Specifically, Qiskit v0.29.0 is utilized, with OpenQASM. OpenQASM serves as the quantum assembly and instruction language.

This work presents a methodology for constructing a 2x2 switch, utilizing a reversible control swap gate as its core component. Additionally, it demonstrates the intricate development and implementation of a bitonic sorting circuit, capitalizing on this innovative switch architecture.

Tremendous developments in multimedia technology have promoted a massive amount of research in image and video processing. As imaging technologies are rapidly increasing, it is becoming essential to use images in almost every application in our day-to-day life.

This paper presents a comparative analysis of various image restoration approaches, ranging from fundamental methods to advanced techniques. These approaches aim to improve the quality of images that have been degraded during acquisition or transmission. A brief overview of the image restoration approaches is mentioned in the paper, which are as follows:

(i) Wiener Filter: The Wiener filter is a classical approach used for image restoration. It is a linear filter that minimizes the mean square error between the original image and the restored image. (ii) Inverse Filter: The inverse filter is another traditional restoration technique. It attempts to invert the degradation process to recover the original image. However, inverse filtering is highly sensitive to noise and tends to amplify noise artifacts. (iii) Linear and Nonlinear Filtering: These methods involve applying linear or nonlinear filters to the degraded image to enhance its quality. Linear filters, such as Gaussian filters, can effectively reduce noise but may blur the image. Nonlinear filters, such as median filters, can preserve edges while reducing noise. (iv) Compressive Sensing and Restoration Approaches: Compressive sensing is a signal processing technique that exploits the sparsity of signals or images to reconstruct them from fewer measurements. CS-based restoration methods aim to recover high-quality images from compressed or incomplete measurements. (v) Neural Networks Approaches: With the advancements in deep learning, neural networks have been widely used for image restoration tasks. Convolutional neural networks (CNNs) and generative adversarial networks (GANs) have shown promising results in restoring degraded images by learning from large datasets.

The paper likely provides a detailed analysis and comparison of these approaches, highlighting their strengths, weaknesses, and performance in different scenarios.

This paper aims to improve the quality of restored images. Several image restoration approaches have been compared, and they have exhibited enhanced performance compared to several existing image restoration approaches.