Synthesis and Characterization of Nanoparticulated Rifampicin

Rifampicin is a naturally made, non-peptide antibiotic. It is bactericidal, killing by disabling the protein expression system universally conserved by all bacteria. Specifically, it inhibits the RNA polymerase protein, which is responsible for binding to a strand of DNA as a template and using it to construct a strand of mRNA. The reason rifampicin works so well is that it is a rigid molecule, and sits tightly in the pocket where it binds, allowing the bonds to be very strong. However, this also means if an amino acid with the edge of the channel with a small side chain is replaced by an amino acid with a large side chain, rifampicin may not be able to bind, simply because it cannot fit in the space. Rifampicin is mainly useful in the treatment of tuberculosis and meningococcal infections. Coming to the pharmacokinetics of the drug, rifampicin is easily absorbed from the gastro intestinal tract. After about six hours almost the entire drug is deacetylated by esterases even in this deacytylated form rifampicin is a potent antibiotic. However, it can no longer be absorbed by the intestines and it is subsequently eliminated from the body. About 7% of the administered drug will be excreted unchanged through the urine, and urinary elimination accounts for about 30% of the dose of the drug that is excreted. About 60% - 65% is excreted through feces. As a result of this, the drug dose has to be increased or the drug must be given at regular intervals to maintain the minimum effective concentration. Rifampicin shows polymorphism and therefore, it is necessary to select a suitable crystal form to ensure optimum solubility and dissolution rates. An increase in amorphous content significantly reduces the dissolution rate of the powders in water. Because of the disadvantages both in physical properties and biologically rifampicin has to be synthesized in the form of nano particles which have low surface area and show good solubility and dissolution properties. Nano particles can also be designed to allow sustained drug release from the matrix. This property of nano particles enables improvement of drug bio-availability and reduction of the dosing frequency and may resolve the problem of drug resistance, which is one of the major obstacles in the control of tuberculosis epidemics. In this method, equal amounts of PEG200 and tween 60(polysorbate 60) and calculated amount of rifampicin was dissolved in that mixture. After that, the solution was stirred with the help of high speed magnetic stirrer. After dissolving the compound completely, this solution was added to the water drop wise while the water was stirred with the magnetic stirrer. Then the nano particles are formed. If the nano particles are not formed after this procedure, homogenizer can be used. After the formation of nanoparticles, characterization using scanning electron microscope is done to confirm the surface properties of the nanoparticle

Design and Evaluation of Temporal Summarization Systems

Temporal Summarization (TS) is a new track introduced as part of the Text REtrieval Conference (TREC) in 2013. This track aims to develop systems which can return important updates related to an event over time. In TREC 2013, the TS track specifically used disaster related events such as earthquake, hurricane, bombing, etc. This thesis mainly focuses on building an effective TS system by using a combination of Information Retrieval techniques. The developed TS system returns updates related to disaster related events in a timely manner. By participating in TREC 2013 and with experiments conducted after TREC, we examine the effectiveness of techniques such as distributional similarity for term expansion, which can be employed in building TS systems. Also, this thesis describes the effectiveness of other techniques such as stemming, adaptive sentence selection over time and de-duplication in our system, by comparing it with other baseline systems. The second part of the thesis examines the current methodology used for evaluating TS systems. We propose a modified evaluation method which could reduce the manual effort of assessors, and also correlates well with the official track’s evaluation. We also propose a supervised learning based evaluation method, which correlates well with the official track’s evaluation of systems and could save the assessor’s time by as much as 80%

A LITERARY REVIEW ON MERCURY W.S.R. TO ITS MEDICINAL ASPECT

Rasasastra is a branch of Ayurvedic medicine dealing with metals and minerals having higher therapeutic efficacy. Recent world wide discussions on hazardous nature of Mercury have led to the global ban on Mercury through Minamata Convention. This is a crisis situation for Ayurveda as Parada (Mercury) is the heart of Rasasastra. An attempt has been made to educate the readers regarding the medicinal aspects of Mercury. Rasasastra texts starting from Rasendra mangalam to Rasatrangini have been analyzed for relevant verses regarding Mercurial formulations with respect to its disease curing property. Mercury has qualities similar to ambrosia. Mercurial medications prepared from purified Mercury can readily treat even Asadhya rogas (incurable diseases). Purified Mercury samples were tested for their therapeutic efficacy through alchemy. A systematic classification regarding types of Mercury, their impurities and purification procedures are mentioned after extensive research in Rasasastra. Method of intake, Pathya (wholesome), Apathya (unwholesome), complications during Mercurial medication have been cited in classical verses. Paribhasha (definitions) regarding basics of Rasasastra have been described for better understanding of Mercurial processes. Procedures like Jarana (digestion), Astadasa samskara (eighteen purification procedures), Dhruti (stable liquid metal), Satwapatana (extracting the essence), Bandhana, etc have been elaborately described in the texts, but their techniques are still unclear in practical aspect. A special clause should be created for the use of Mercury in Ayurvedic medicines with respect to its health benefits and disease curing properties. Also there is a need to intensify research in the field of Rasasastra.

Individual copper nanowire decorated by gold nanoparticles for Surface enhanced Raman scattering

In this Thesis, I discuss the theory, implementation and applications of Surface enhanced Raman scattering (SERS). Surface enhanced Raman scattering has been used to detect 4 mercaptopyridine molecules. On a Silicon wafer, Gold nanoparticles are deposited onto Copper nanowires. Hotspots occur at the small gap (less than 10nm) between the nanowire and nanoparticle. The interaction of the electromagnetic field of the incident laser and the surface plasmon resonances of the metal nanoparticles at the hot spots enhances the Raman scattering signal of the adsorbed pyridine molecule (10 -3 M ). The dependence of SERS signal on the polarization angle of the incident laser is observed. When compared to other SERS substrates (gold, silver) this one is very economical

Granular cell ameloblastoma of jaw: report of a case with an emphasis on its characterization

Ameloblastoma is a neoplasm of odontogenic epithelium, especially of enamel organ-type tissue that has not under gone differentiation to the point of hard tissue formation. It accounts for approximately 10% of all tumors originating from gnathic bones. It exhibits diverse microscopic patterns which occurs either singly or in combination with other patterns. Granular cell ameloblastoma is a rare condition, accounting for 3.5% of all ameloblastoma cases that shows marked transformation in the cytoplasm of tumor cells, which are usually stellate reticulum like cells. The transformed cells possess very coarse, granular, eosinophilic cytoplasm. The 'granular change' is thought to be due to a dysfunctional status of neoplastic cells, and the pathogenesis of this tumour seems to be age-related. Ultrastructural, histochemical, and immunohitochemical studies have revealed that cytoplasmic granularity is caused by overload; however the mechanism ivolved remains poorly understood. This article describes a case of granular cell variant of ameloblastoma affecting a 55-year old femal

Scheduling schemes for throughput optimization in wireless adhoc networks using directional antennas

Title from PDF of title page, viewed on June 9, 2011Thesis advisor: Cory BeardVitaIncludes bibliographical references (p. 49-51)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2011This thesis presents a scheme for scheduling of wireless adhoc networks by using directional antennas using the concept of frequency reuse. The idea of wireless adhoc network communication is taken one step further by adding directional antennas for both wider coverage and can also be used for maximizing the throughput by using the concept of frequency reuse. This system can be applied to any adhoc network and can be scaled for dense networks. Most wireless adhoc networks use the same channel and try to send information by using an old protocol of CSMA/CA. Throughput remains a major hindrance in the case of adhoc networks which have to compete for the same channel. This thesis modeled an adhoc network in which there is traffic to be sent in pairs from every node to every other node and the advantages of using the frequency reuse scheduling scheme is observed. A routing scheme in which the node which sends data first is given precedence and also a shortest first routing scheme where all the data transfers which are closest are sent first so that they do not interfere with others and provide maximum throughput are also analyzed. This thesis seeks to extend the benefits of mesh networks by taking advantage of the directional antennas to allow simultaneous conversations to happen at the same time. We show how much the capacity can be increased and then develop scheduling schemes to balance capacity and fairness. Those connections which are lower priority might not be given fair access otherwise.Overview of wireless MESH networks --- Background study -- Design -- Simulation -- Scheduling schemes -- Future wor

Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model

In autonomous navigation settings, several quantities can be subject to variations. Terrain properties such as friction coefficients may vary over time depending on the location of the robot. Also, the dynamics of the robot may change due to, e.g., different payloads, changing the system's mass, or wear and tear, changing actuator gains or joint friction. An autonomous agent should thus be able to adapt to such variations. In this paper, we develop a novel probabilistic, terrain- and robot-aware forward dynamics model, termed TRADYN, which is able to adapt to the above-mentioned variations. It builds on recent advances in meta-learning forward dynamics models based on Neural Processes. We evaluate our method in a simulated 2D navigation setting with a unicycle-like robot and different terrain layouts with spatially varying friction coefficients. In our experiments, the proposed model exhibits lower prediction error for the task of long-horizon trajectory prediction, compared to non-adaptive ablation models. We also evaluate our model on the downstream task of navigation planning, which demonstrates improved performance in planning control-efficient paths by taking robot and terrain properties into account.Comment: \copyright 2023 IEEE. Accepted for publication in European Conference on Mobile Robots (ECMR), 2023. Updated copyright statemen

Similarity score of two images using different measures.

In the field of computer vision and image processing, image similarity has been a central concern for decades. If you compare two pictures, Image Similarity returns a value that tells you how physically they are close. A quantitative measure of the degree of correspondence between the images concerned is given by this test. The score of the similarity between images varies from 0 to 1. In this paper, ORB (Oriented Fast Rotated Brief) algorithm is used to measure the similarity and other types of similarity measures like Structural Similarity Index (SSIM), pixel similarity, Earth mover's Distance are used to obtain the score. When two images are compared, it shows how much identical (common) objects are there in the two images. So, the accuracy or similarity score is about 87 percent when the two images are compared

Detecting image similarity using SIFT.

Manually identifying similarity between any images is a difficult task. This study proposes an image similarity detection model. The scale-invariant feature transform (SIFT) algorithm is used to detect similarity between input images, and also to calculate the similarity score that defines the extent to which the images are similar. SIFT detects the keypoints and computes its descriptors. A FLANN-based algorithm is used to find the best matches of the descriptors, taking the descriptor of first image and comparing it with the second image. The model achieved 60% accuracy in translational image similarity and 90% in rotational image similarity; feature-matching similarity differed depending upon the given inputs

Real-Time Diagnostic Integrity Meets Efficiency: A Novel Platform-Agnostic Architecture for Physiological Signal Compression

Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable systems will play a pivotal role in clinical diagnosis, monitoring, and treatment of important brain disorder diseases. However, the real-time transmission of the significant corpus physiological signals over extended periods consumes substantial power and time, limiting the viability of battery-dependent physiological monitoring wearables. This paper presents a novel deep-learning framework employing a variational autoencoder (VAE) for physiological signal compression to reduce wearables' computational complexity and energy consumption. Our approach achieves an impressive compression ratio of 1:293 specifically for spectrogram data, surpassing state-of-the-art compression techniques such as JPEG2000, H.264, Direct Cosine Transform (DCT), and Huffman Encoding, which do not excel in handling physiological signals. We validate the efficacy of the compressed algorithms using collected physiological signals from real patients in the Hospital and deploy the solution on commonly used embedded AI chips (i.e., ARM Cortex V8 and Jetson Nano). The proposed framework achieves a 91% seizure detection accuracy using XGBoost, confirming the approach's reliability, practicality, and scalability