Review of Biosensors Based on Plasmonic-Enhanced Processes in the Metallic and Meta-Material-Supported Nanostructures.

Surface plasmons, continuous and cumulative electron vibrations confined to metal-dielectric interfaces, play a pivotal role in aggregating optical fields and energies on nanostructures. This confinement exploits the intrinsic subwavelength nature of their spatial profile, significantly enhancing light-matter interactions. Metals, semiconductors, and 2D materials exhibit plasmonic resonances at diverse wavelengths, spanning from ultraviolet (UV) to far infrared, dictated by their unique properties and structures. Surface plasmons offer a platform for various light-matter interaction mechanisms, capitalizing on the orders-of-magnitude enhancement of the electromagnetic field within plasmonic structures. This enhancement has been substantiated through theoretical, computational, and experimental studies. In this comprehensive review, we delve into the plasmon-enhanced processes on metallic and metamaterial-based sensors, considering factors such as geometrical influences, resonating wavelengths, chemical properties, and computational methods. Our exploration extends to practical applications, encompassing localized surface plasmon resonance (LSPR)-based planar waveguides, polymer-based biochip sensors, and LSPR-based fiber sensors. Ultimately, we aim to provide insights and guidelines for the development of next-generation, high-performance plasmonic technological devices.

Uncertainty quantification of spectral predictions using deep neural networks.

We investigate the performance of uncertainty quantification methods, namely deep ensembles and bootstrap resampling, for deep neural network (DNN) predictions of transition metal K-edge X-ray absorption near-edge structure (XANES) spectra. Bootstrap resampling combined with our multi-layer perceptron (MLP) model provides an accurate assessment of uncertainty with >90% of all predicted spectral intensities falling within ±3σ of the true values for held-out data across the nine first-row transition metal K-edge XANES spectra.

Computational Investigation of Advanced Refractive Index Sensor Using 3-Dimensional Metamaterial Based Nanoantenna Array.

Photonic researchers are increasingly exploiting nanotechnology due to the development of numerous prevalent nanosized manufacturing technologies, which has enabled novel shape-optimized nanostructures to be manufactured and investigated. Hybrid nanostructures that integrate dielectric resonators with plasmonic nanostructures are also offering new opportunities. In this work, we have explored a hybrid coupled nano-structured antenna with stacked multilayer lithium tantalate (LiTaO3) and Aluminum oxide (Al2O3), operating at wavelength ranging from 400 nm to 2000 nm. Here, the sensitivity response has been explored of these nano-structured hybrid arrays. It shows a strong electromagnetic confinement in the separation gap (g) of the dimers due to strong surface plasmon resonance (SPR). The influences of the structural dimensions have been investigated to optimize the sensitivity. The designed hybrid coupled nanostructure with the combination of 10 layers of gold (Au) and Lithium tantalate (LiTaO3) or Aluminum oxide (Al2O3) (five layers each) having height, h1 = h2 = 10 nm exhibits 730 and 660 nm/RIU sensitivity, respectively. The sensitivity of the proposed hybrid nanostructure has been compared with a single metallic (only gold) elliptical paired nanostructure. Depending on these findings, we demonstrated that a roughly two-fold increase in the sensitivity (S) can be obtained by utilizing a hybrid coupled nanostructure compared to an identical nanostructure, which competes with traditional sensors of the same height, (h). Our innovative novel plasmonic hybrid nanostructures provide a framework for developing plasmonic nanostructures for use in various sensing applications.

A comprehensive deep learning method for empirical spectral prediction and its quantitative validation of nano-structured dimers.

Nanophotonics exploits the best of photonics and nanotechnology which has transformed optics in recent years by allowing subwavelength structures to enhance light-matter interactions. Despite these breakthroughs, design, fabrication, and characterization of such exotic devices have remained through iterative processes which are often computationally costly, memory-intensive, and time-consuming. In contrast, deep learning approaches have recently shown excellent performance as practical computational tools, providing an alternate avenue for speeding up such nanophotonics simulations. This study presents a DNN framework for transmission, reflection, and absorption spectra predictions by grasping the hidden correlation between the independent nanostructure properties and their corresponding optical responses. The proposed DNN framework is shown to require a sufficient amount of training data to achieve an accurate approximation of the optical performance derived from computational models. The fully trained framework can outperform a traditional EM solution using on the COMSOL Multiphysics approach in terms of computational cost by three orders of magnitude. Furthermore, employing deep learning methodologies, the proposed DNN framework makes an effort to optimise design elements that influence the geometrical dimensions of the nanostructure, offering insight into the universal transmission, reflection, and absorption spectra predictions at the nanoscale. This paradigm improves the viability of complicated nanostructure design and analysis, and it has a lot of potential applications involving exotic light-matter interactions between nanostructures and electromagnetic fields. In terms of computational times, the designed algorithm is more than 700 times faster as compared to conventional FEM method (when manual meshing is used). Hence, this approach paves the way for fast yet universal methods for the characterization and analysis of the optical response of nanophotonic systems.

Rheumatoid arthritis: advances in treatment strategies.

Rheumatoid arthritis (RA) is characterised by severe joint and bone damage due to heightened autoimmune response at the articular sites. Worldwide annual incidence and prevalence rate of RA is 3 cases per 10,000 population and 1%, respectively. Several genetic and environmental (microbiota, smoking, infectious agents) factors contribute to its pathogenesis. Although convention treatment strategies, predominantly Disease Modifying Anti Rheumatic Drugs (DMARDs) and Glucocorticoids (GC), are unchanged as the primary line of treatment; novel strategies consisting of biological DMARDs, are being developed and explored. Personalized approaches using biologicals targetspecific pathways associated with disease progression. However, considering the economic burden and side-effects associated with these, there is an unmet need on strategies for early stratification of the inadequate responders with cDMARDs. As RA is a complex disease with a variable remission rate, it is important not only to evaluate the current status of drugs in clinical practice but also those with the potential of personalised therapeutics. Here, we provide comprehensive data on the treatment strategies in RA, including studies exploring various combination strategies in clinical trials. Our systematic analysis of current literature found that conventional DMARDs along with glucocorticoid may be best suited for early RA cases and a combination of conventional and targeted DMARDs could be effective for treating seronegative patients with moderate to high RA activity. Clinical trials with insufficient responders to Methotrexate suggest that adding biologicals may help in such cases. However, certain adverse events associated with the current therapy advocate exploring novel therapeutic approaches such as gene therapy, mesenchymal stem cell therapy in future.

Synthesis and Biological Evaluation of 2-(Halophenyl)benzoxazole-5-Carboxylic Acids as Potential Anti-Inflammatory and Cytotoxic Agent with Molecular Docking Studies.

2-Halogenatedphenyl benzoxazole-5-carboxylic acids with mono-halogen (chloro, bromo and fluoro) substituted at ortho-, meta- and para-positions on the phenyl ring were designed and synthesized based on significance of presence of halogen in increasing number of marketed halogenated drugs and importance of benzoxazoles. These 2-alogenatedphenylbenzoxazole-5-carboxylic acids and their methyl esters were screened for anti-inflammatory activity, and cytotoxicity. 2-(3-Chlorophenyl)benzoxaole-5-carboxylic acid (6b) exhibited significant anti-inflammatory activity with IC50 values of 0.103 mM almost equivalent to the standard drug ibuprofen (0.101 mM). 2-(4-Chlorophenyl)benzoxaole-5-carboxylic acid (6c) showed excellent cytotoxic activity against 22Rv1 cells (human prostate carcinoma epithelial cell lines) with IC50 value of 1.54 µM better than that of standard drug doxorubicin having IC50 value of 2.32 µM. More importantly, the selectivity index of this potential molecule was found to be 57.74. Molecular docking analysis resulted in good binding interactions of these compounds with their respective biochemical targets viz. Cyclooxygenase-2 and aldo-keto reductase IC3.

Optical Fiber, Nanomaterial, and THz-Metasurface-Mediated Nano-Biosensors: A Review.

The increasing use of nanomaterials and scalable, high-yield nanofabrication process are revolutionizing the development of novel biosensors. Over the past decades, researches on nanotechnology-mediated biosensing have been on the forefront due to their potential application in healthcare, pharmaceutical, cell diagnosis, drug delivery, and water and air quality monitoring. The advancement of nanoscale science relies on a better understanding of theory, manufacturing and fabrication practices, and the application specific methods. The topology and tunable properties of nanoparticles, a part of nanoscale science, can be changed by different manufacturing processes, which separate them from their bulk counterparts. In the recent past, different nanostructures, such as nanosphere, nanorods, nanofiber, core-shell nanoparticles, nanotubes, and thin films, have been exploited to enhance the detectability of labelled or label-free biological molecules with a high accuracy. Furthermore, these engineered-materials-associated transducing devices, e.g., optical waveguides and metasurface-based scattering media, widened the horizon of biosensors over a broad wavelength range from deep-ultraviolet to far-infrared. This review provides a comprehensive overview of the major scientific achievements in nano-biosensors based on optical fiber, nanomaterials and terahertz-domain metasurface-based refractometric, labelled and label-free nano-biosensors.

Artificial Neural Network Modelling for Optimizing the Optical Parameters of Plasmonic Paired Nanostructures.

The Artificial Neural Network (ANN) has become an attractive approach in Machine Learning (ML) to analyze a complex data-driven problem. Due to its time efficient findings, it has became popular in many scientific fields such as physics, optics, and material science. This paper presents a new approach to design and optimize the electromagnetic plasmonic nanostructures using a computationally efficient method based on the ANN. In this work, the nanostructures have been simulated by using a Finite Element Method (FEM), then Artificial Intelligence (AI) is used for making predictions of associated sensitivity (S), Full Width Half Maximum (FWHM), Figure of Merit (FOM), and Plasmonic Wavelength (PW) for different paired nanostructures. At first, the computational model is developed by using a Finite Element Method (FEM) to prepare the dataset. The input parameters were considered as the Major axis, a, the Minor axis, b, and the separation gap, g, which have been used to calculate the corresponding sensitivity (nm/RIU), FWHM (nm), FOM, and plasmonic wavelength (nm) to prepare the dataset. Secondly, the neural network has been designed where the number of hidden layers and neurons were optimized as part of a comprehensive analysis to improve the efficiency of ML model. After successfully optimizing the neural network, this model is used to make predictions for specific inputs and its corresponding outputs. This article also compares the error between the predicted and simulated results. This approach outperforms the direct numerical simulation methods for predicting output for various input device parameters.

All-Opto Plasmonic-Controlled Bulk and Surface Sensitivity Analysis of a Paired Nano-Structured Antenna with a Label-Free Detection Approach.

Gold nanoantennas have been used in a variety of biomedical applications due to their attractive electronic and optical properties, which are shape- and size-dependent. Here, a periodic paired gold nanostructure exploiting surface plasmon resonance is proposed, which shows promising results for Refractive Index (RI) detection due to its high electric field confinement and diffraction limit. Here, single and paired gold nanostructured sensors were designed for real-time RI detection. The Full-Width at Half-Maximum (FWHM) and Figure-Of-Merit (FOM) were also calculated, which relate the sensitivity to the sharpness of the peak. The effect of different possible structural shapes and dimensions were studied to optimise the sensitivity response of nanosensing structures and identify an optimised elliptical nanoantenna with the major axis a, minor axis b, gap between the pair g, and heights h being 100 nm, 10 nm, 10 nm, and 40 nm, respectively. In this work, we investigated the bulk sensitivity, which is the spectral shift per refractive index unit due to the change in the surrounding material, and this value was calculated as 526-530 nm/RIU, while the FWHM was calculated around 110 nm with a FOM of 8.1. On the other hand, the surface sensing was related to the spectral shift due to the refractive index variation of the surface layer near the paired nanoantenna surface, and this value for the same antenna pair was calculated as 250 nm/RIU for a surface layer thickness of 4.5 nm.

Vitamin D deficiency: concern for rheumatoid arthritis and COVID-19?

Vitamin D is an immunomodulatory hormone with an established role in calcium and phosphate metabolism and skeletal mineralization. Evidence showing its immunological benefits by regulating essential components of the innate and adaptive immune system is prevalent. Vitamin D deficiency is reported worldwide and is thereby found to be associated with various immune-related diseases. Rheumatoid Arthritis and COVID-19 are two such diseases, sharing a similar hyperinflammatory response. Various studies have found an association of lower Vitamin D levels to be associated with both these diseases. However, contrasting data is also reported. We review here the available scientific data on risk factor association and supplementation benefits of Vitamin D in Rheumatoid Arthritis and COVID-19, intending to critically evaluate the literature.

All-circular hole microstructured fiber with high birefringence and low confinement loss: retraction.

The referenced paper [Appl. Opt.58, 3767 (2019)APOPAI0003-693510.1364/AO.58.003767] has been retracted.

All-circular hole microstructured fiber with high birefringence and low confinement loss.

An air-silica microstructure optical fiber of ultrahigh birefringence of the order ∼10-2 and low confinement loss is proposed. The birefringence is achieved by axial anisotropy introduced in the fiber geometry by the preferred arrangement of circular air holes of different sizes in the cladding structure. Vital properties of the microstructure fiber, e.g., birefringence, confinement loss, dispersion, and walk-off, have been studied by employing numerical solutions through the finite element method. These findings should be useful for the fabrication of the proposed microstructure optical fiber.

Oral bioavailability enhancement of agomelatine by loading into nanostructured lipid carriers: Peyer's patch targeting approach.

Agomelatine (AGM) is a new antidepressant drug with a novel mechanism of action and fewer side effects compared with older antidepressants. AGM is a melatonin receptor (MT1 and MT2) agonist and 5-hydroxytryptamine receptor (5-HT2C) antagonist. In the present study, the enhancement of the oral bioavailability of AGM was formulated and loaded into nanostructured lipid carriers (NLCs), using ultrasonication method. In vitro and ex vivo drug release was performed using a dialysis bag and rat duodenum, respectively. Our pharmacodynamic study showed that AGM-NLCs are more efficacious than a pure drug and marketed product, and confocal microscopy revealed lymphatic uptake of AGM-NLCs. The present study concluded that the NLCs enhanced the oral bioavailability of AGM (6.5-fold) by avoiding its first-pass metabolism by way of lymphatic uptake.