Composite nanocarriers for nucleic acid delivery

Supported with a strong literature background, this thesis elaborately describes the perspectives of an efficient, biocompatible delivery system capable of transfecting both in vitro and in vivo with minimal toxicity. A detailed study of the lipopolyplexes was performed to evaluate its efficacy and capabilities yielding consistent results.The thesis deals with aspects such as gene delivery, RNA interference and vectors used for the delivery. Non-viral vectors, especially polymer and liposomal based gene delivery vehicles are reviewed. These formed the basis for the composite nanocarrier system, lipopolyplex used in this study. Advantages and disadvantages of liposomal and polymer based gene delivery systems are reviewed. Composition, structural characteristics and physicochemical properties of lipopolyplexes are discussed. Physical methods for enhancing the gene transfer using lipopolyplexes via photochemical internalisation and ultrasound enhanced gene transfer are described. A therapeutic anti-inflammatory model to evaluate the efficacy of the lipopolyplexes has been described. The necessity of toxicity and haemocompatibility studies for the evaluation of delivery vehicles have been summarised. Chorioallantoic membrane model has been described with the aim to prove the biocompatibility and efficacy of the lipopolyplexes in vivo

Representation of Cyclotomic Fields and Their Subfields

Let \K be a finite extension of a characteristic zero field \F. We say that the pair of $n\times n$ matrices $(A,B)$ over \F represents \K if \K \cong \F[A]/ where \F[A] denotes the smallest subalgebra of M_n(\F) containing $A$ and $$ is an ideal in \F[A] generated by $B$. In particular, $A$ is said to represent the field \K if there exists an irreducible polynomial q(x)\in \F[x] which divides the minimal polynomial of $A$ and \K \cong \F[A]/. In this paper, we identify the smallest circulant-matrix representation for any subfield of a cyclotomic field. Furthermore, if $p$ is any prime and \K is a subfield of the $p$-th cyclotomic field, then we obtain a zero-one circulant matrix $A$ of size $p\times p$ such that (A,\J) represents \K, where \J is the matrix with all entries 1. In case, the integer $n$ has at most two distinct prime factors, we find the smallest 0-1 companion-matrix that represents the $n$-th cyclotomic field. We also find bounds on the size of such companion matrices when $n$ has more than two prime factors.Comment: 17 page

An Efficient and Improved Algorithm for a Recommender System to Detect & Recognize Communities in Social Networks

Social Network is a communicative platform which is a part of social media, useful for interaction of information among people i.e. users. There will be millions of users over online Social Networks, they might or might not have similar interests. People with similar interests / mindset would like to have friendly relationship among themselves. Connections with many similar mindset people forms groups or communities. These Communities will be helpful for gaining knowledge/information transmission. In this paper, we will observe efficient methods for recommending groups or communities to users based on their similarities with their friend's or user’s similar to them and groups followed by their friend's, using Hybrid Recommendation Filtering System combined with Singular Value Decomposition

LowDINO -- A Low Parameter Self Supervised Learning Model

This research aims to explore the possibility of designing a neural network architecture that allows for small networks to adopt the properties of huge networks, which have shown success in self-supervised learning (SSL), for all the downstream tasks like image classification, segmentation, etc. Previous studies have shown that using convolutional neural networks (ConvNets) can provide inherent inductive bias, which is crucial for learning representations in deep learning models. To reduce the number of parameters, attention mechanisms are utilized through the usage of MobileViT blocks, resulting in a model with less than 5 million parameters. The model is trained using self-distillation with momentum encoder and a student-teacher architecture is also employed, where the teacher weights use vision transformers (ViTs) from recent SOTA SSL models. The model is trained on the ImageNet1k dataset. This research provides an approach for designing smaller, more efficient neural network architectures that can perform SSL tasks comparable to heavy model

Driving The Last Mile: Characterizing and Understanding Distracted Driving Posts on Social Networks

In 2015, 391,000 people were injured due to distracted driving in the US. One of the major reasons behind distracted driving is the use of cell-phones, accounting for 14% of fatal crashes. Social media applications have enabled users to stay connected, however, the use of such applications while driving could have serious repercussions -- often leading the user to be distracted from the road and ending up in an accident. In the context of impression management, it has been discovered that individuals often take a risk (such as teens smoking cigarettes, indulging in narcotics, and participating in unsafe sex) to improve their social standing. Therefore, viewing the phenomena of posting distracted driving posts under the lens of self-presentation, it can be hypothesized that users often indulge in risk-taking behavior on social media to improve their impression among their peers. In this paper, we first try to understand the severity of such social-media-based distractions by analyzing the content posted on a popular social media site where the user is driving and is also simultaneously creating content. To this end, we build a deep learning classifier to identify publicly posted content on social media that involves the user driving. Furthermore, a framework proposed to understand factors behind voluntary risk-taking activity observes that younger individuals are more willing to perform such activities, and men (as opposed to women) are more inclined to take risks. Grounding our observations in this framework, we test these hypotheses on 173 cities across the world. We conduct spatial and temporal analysis on a city-level and understand how distracted driving content posting behavior changes due to varied demographics. We discover that the factors put forth by the framework are significant in estimating the extent of such behavior.Comment: Accepted at International Conference on Web and Social Media (ICWSM) 2020; 12 page

High fidelity simulations of binary collisions of liquid drops

The phenomena of binary droplet collision is seen in dispersed phase systems such as sprays in internal combustion engines, gas turbines, etc. The present work aims at understanding off-center collision dynamics of two droplets of unequal sizes. High fidelity simulations are performed using three dimensional, two phase- finite volume based solver in OpenFOAM platform. Volume of fluid (VOF) method is used for interface capturing. Navier-Stokes equations are solved using a projection algorithm. Simulations are done with MPI parallelization to meet the computational demand. The solver is validated against droplet splashing benchmark test case. The present numerical results study the effect of impact parameter and diameter ratio on droplet collision dynamics

Ameliorating the antitumor activity of lenalidomide using PLGA nanoparticles for the treatment of multiple myeloma

Abstract Lenalidomide (LND) is an anti-cancer drug and an effective derivative of thalidomide used for multiple myeloma therapy. Because of its poor solubility in water, LND is known to cause low oral bioavailability (below 33%), and as a direct consequence of this, the dosing frequency is extended thus increasing risk of toxicity. To improve its bioavailability and sustained release, the present study aims to formulate polymeric nanoparticles (NPs) for LND using [Poly (lactic-co-glycolic acid)] (PLGA) as a polymer. The polymeric NPs were evaluated for particle size, SEM, XRD, drug content, entrapment efficiency (EE), in vitro release studies and in vivo bioavailability studies in rats. The formulated NPs possessed a size of 179±0.9 nm and a zeta potential of -24.4 ± 0.2 mV. The drug loading and EE of the optimized formulation was 32 ± 0.37 % and 78 ± 0.92% respectively. After oral administration of LND PLGA-NPs, the relative bioavailability was enhanced about 3.67-fold compared to LND. This study demonstrates the novel drug delivery for LND with PLGA-NPs as effective drug delivery system for sustained delivery of LND

Ameliorating the antitumor activity of lenalidomide using PLGA nanoparticles for the treatment of multiple myeloma

Abstract Lenalidomide (LND) is an anti-cancer drug and an effective derivative of thalidomide used for multiple myeloma therapy. Because of its poor solubility in water, LND is known to cause low oral bioavailability (below 33%), and as a direct consequence of this, the dosing frequency is extended thus increasing risk of toxicity. To improve its bioavailability and sustained release, the present study aims to formulate polymeric nanoparticles (NPs) for LND using [Poly (lactic-co-glycolic acid)] (PLGA) as a polymer. The polymeric NPs were evaluated for particle size, SEM, XRD, drug content, entrapment efficiency (EE), in vitro release studies and in vivo bioavailability studies in rats. The formulated NPs possessed a size of 179±0.9 nm and a zeta potential of -24.4 ± 0.2 mV. The drug loading and EE of the optimized formulation was 32 ± 0.37 % and 78 ± 0.92% respectively. After oral administration of LND PLGA-NPs, the relative bioavailability was enhanced about 3.67-fold compared to LND. This study demonstrates the novel drug delivery for LND with PLGA-NPs as effective drug delivery system for sustained delivery of LND