Self-emulsifying drug delivery systems (SEDDS) possess unparalleled potential in improving oral bioavailability of poorly water-soluble drugs. Following their oral administration, these systems rapidly disperse in gastrointestinal fluids, yielding micro- or nanoemulsions containing the solubilized drug. Owing to its miniscule globule size, the micro/nanoemulsifed drug can easily be absorbed through lymphatic pathways, bypassing the hepatic first-pass effect. We present an exhaustive and updated account of numerous literature reports and patents on diverse types of self-emulsifying drug formulations, with emphasis on their formulation, characterization, and systematic optimization strategies. Recent advancements in various methodologies employed to characterize their globule size and shape, ability to encapsulate the drug, gastrointestinal and thermodynamic stability, rheological characteristics, and so forth, are discussed comprehensively to guide the formula-tor in preparing an effective and robust SEDDS formulation. Also, this exhaustive review offers an explicit discussion on vital applications of the SEDDS in bioavailability enhancement of various drugs, outlining an overview on myriad in vitro, in situ, and ex vivo techniques to assess the absorption and/ or permeation potential of drugs incorporated in the SEDDS in animal and cell line models, and the subsequent absorption pathways followed by them. In short, the current article furnishes an updated compilation of wide-ranging information on all the requisite vistas of the self-emulsifying formulations, thus paving the way for accelerated progress into the SEDDS application in pharmaceutical research.

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, caused by the severe acute respiratory syndrome corona virus 2 (SARS-CoV-2), was declared a pandemic by the World Health Organization on March 9, 2020. Hematopoietic stem-cell transplantation (HSCT) recipients may be highly susceptible to infection and related pulmonary complications due to nascent immune systems or organ damage from treatment-related toxicities.Poor outcomes in such group of patients were linked to older age, steroid therapy at the time of COVID-19 infection, and COVID-19 infection within a year of HSCT. We studied a cohort of 28 hematopoietic stem cell transplant recipients (male 17, M:F ratio of 1.5) with COVID-19 infection from 1st June 2020, through 31st December 2020 for outcome. Fever was the most common symptom at the time of presentation in 22 (78.5%) patients. Mortality rate at Day 28 and Day 42 was found to be 4/28 (14.3%) and 7/28 (25%) respectively. Patients within one year of HSCT and severe infection had higher day 28 mortality (with p values = 0.038)''. There was no relation of mortality with type of transplant.

Quantitative comparative evaluation provides details of exposure and surgical ease with both techniques. We promote hybrid/EDAC technique for vascular pathologies because of better anatomic orientation. Extradural clinoidectomy is the preferred technique for midline cranial neoplasia. An awareness of different variations of clinoidectomy can prevent dependency on any particular approach and facilitate flexibility.

Topological states in photonics offer novel prospects for guiding and manipulating photons and facilitate the development of modern optical components for a variety of applications. Over the past few years, photonic topology physics has evolved and unveiled various unconventional optical properties in these topological materials, such as silicon photonic crystals. However, the design of such topological states still poses a significant challenge. Conventional optimization schemes often fail to capture their complex high dimensional design space. In this manuscript, we develop a deep learning framework to map the design space of topological states in the photonic crystals. This framework overcomes the limitations of existing deep learning implementations. Specifically, it reconciles the dimension mismatch between the input (topological properties) and output (design parameters) vector spaces and the non-uniqueness that arises from one-to-many function mappings. We use a fully connected deep neural network (DNN) architecture for the forward model and a cyclic convolutional neural network (cCNN) for the inverse model. The inverse architecture contains the pre-trained forward model in tandem, thereby reducing the prediction error significantly.

We demonstrate the chemical characterization of aerosol particles with on-chip spectroscopy using a photonic cavity enhanced silicon nitride (Si3N4) racetrack resonatorbased sensor. The sensor operates over a broad and continuous wavelength range, showing cavity enhanced sensitivity at specific resonant wavelengths. Analysis of the relative change in the quality factor of the cavity resonances successfully yields the absorption spectrum of the aerosol particles deposited on the resonators. Detection of N-methyl aniline based aerosol detection in the Near InfraRed (NIR) range of 1500 nm to 1600 nm is demonstrated. Our aerosol sensor spectral data compares favorably with that from a commercial spectrometer, indicating good accuracy. The small size of the device is advantageous in remote, environmental, medical and body-wearable sensing applications.

We propose an on-chip mid-infrared (MIR) photonic spectroscopy platform for aerosol characterization to obtain highly discriminatory information on the chemistry of aerosol particles. Sensing of aerosols is crucial for various environmental, climactic, warfare threat detection, and pulmonary healthcare applications. Currently, chemical characterization of aerosols is performed using FTIR spectroscopy yielding chemical fingerprinting because most of the vibrational and rotational transitions of chemical molecules fall in the MIR range; and Raman spectroscopy. Both techniques use free space bench-top geometries.Here, we propose miniaturized on-chip MIR photonics-based aerosol spectroscopy consisting of a broadband spiral-waveguide sensor that significantly enhances particle-light interaction to improve sensitivity. The spiral waveguides are made of a chalcogenide glass material (Ge23Sb7S70) which shows a broad transparency over IR range. We demonstrate the sensing of Nmethyl aniline-based aerosol particles with the device. We anticipate that the sensor will readily complement existing photonic resonator-based particle sizing and counting techniques to develop a unified framework for on-chip integrated photonic aerosol spectroscopy.

Nanophotonics is a rapidly emerging field in which complex on-chip components are required to manipulate light waves. The design space of on-chip nanophotonic components, such as an optical meta surface which uses sub-wavelength meta-atoms, is often a high dimensional one. As such conventional optimization methods fail to capture the global optimum within the feasible search space. In this manuscript, we explore a Machine Learning (ML)-based method for the inverse design of the meta-optical structure. We present a data-driven approach for modeling a grating meta-structure which performs photonic beam engineering. On-chip planar photonic waveguide-based beam engineering offers the potential to efficiently manipulate photons to create excitation beams (Gaussian, focused and collimated) for lab-on-chip applications of Infrared, Raman and fluorescence spectroscopic analysis. Inverse modeling predicts meta surface design parameters based on a desired electromagnetic field outcome. Starting with the desired diffraction beam profile, we apply an inverse model to evaluate the optimal design parameters of the meta surface. Parameters such as the repetition period (in 2D axis), height and size of scatterers are calculated using a feedforward deep neural network (DNN) and convolutional neural network (CNN) architecture. A qualitative analysis of the trained neural network, working in tandem with the forward model, predicts the diffraction profile with a correlation coefficient as high as 0.996. The developed model allows us to rapidly estimate the desired design parameters, in contrast to conventional (gradient descent based or genetic optimization) time-intensive optimization approaches.