Various forms of tobacco usage and its associated oral mucosal lesions

Background: To study the various forms of tobacco usage and its associated oral mucosal lesions among the patients attending Vishnu Dental College Bhimavaram. Material and Methods: An observational cross-sectional study was conducted in a total of 450 patients who were divided into three groups based upon type of tobacco use, as Group-1 Reverse smoking, Group-2 Conventional smoking, Group-3 Smokeless tobacco group and each group consists of 150 subjects. Results: Reverse smoking was observed to be more prevalent among old females with smoker’s palate and carcinomatous lesions being the most common. Conventional smoking was observed more in male patients with maximum occurrence of leukoplakia and tobacco associated melanosis. Smokeless tobacco habit was predominantly seen in younger males. Habit specific lesions like tobacco pouch keratosis, Oral Submucous Fibrosis (OSMF), Quid induced lichenoid reaction were noticed in smokeless tobacco habit group except for erythroplakia which was noticed only in conventional smoking group and it was not significant statistically . Conclusions: In the present study it was found that the usage of reverse smoking habit was most commonly seen in females and this habit is practiced in and surrounding areas of Bhimavaram with more occurrence of carcinoma compared to conventional smoking and smokeless tobacco

Effects of MS-153 on chronic ethanol consumption and GLT1 modulation of glutamate levels in male alcohol-preferring rats

We have recently shown that upregulation of glutamate transporter 1 (GLT1) in the brain is associated in part with reduction in ethanol intake in alcohol-preferring (P) male rats. In this study, we investigated the effects of a synthetic compound, (R)-(−)-5-methyl-1-nicotinoyl-2-pyrazoline (MS-153), known to activate GLT1 on ethanol consumption as well as GLT1 expression and certain signaling pathways in P rats. P rats were given 24-hour concurrent access to 15% and 30% ethanol, water and food for five weeks. On week 6, P rats received MS-153 at a dose of 50 mg/kg (i.p.) or a vehicle (i.p.) for five consecutive days. We also tested the effect of MS-153 on daily sucrose (10%) intake. Our studies revealed a significant decrease in ethanol intake at the dose of 50 mg/kg MS-153 from Day 1 through Day 14. In addition, MS-153 at dose of 50 mg/kg did not induce any significant effect on sucrose intake. Importantly, we found that MS-153 upregulated the GLT1 level in the nucleus accumbens (NAc) but not in the prefrontal cortex (PFC). In accordance, we found upregulation of nuclear NFkB-65 level in NAc in MS-153-treated group, however, IkB was downregulated in MS-153-treated group in NAc. We did not find any changes in NFkB-65 and IkB levels in PFC. Interestingly, we revealed that p-Akt was downregulated in ethanol vehicle treated groups in the NAc; this downregulation was reversed by MS-153 treatment. We did not observe any significant differences in glutamate aspartate transporter (GLAST) expression among all groups. These findings reveal MS-153 as a GLT1 modulator that may have potential as a therapeutic drug for the treatment of alcohol dependence

Discrete element modeling of hydrogel extrusion

Hydrogels are widely used in extrusion bioprinting as bioinks. Understanding how the hydrogel microstructure affects the bioprinting process aids researchers in predicting the behavior of biological components. Current experimental tools are unable to measure internal forces and microstructure variations during the bioprinting process. In this work, discrete element modeling was used to study the internal interactions and the elastic deformation of the molecular chains within hydrogel networks during the extrusion process. Two-dimensional models of hydrogel extrusions were created in Particle Flow Code (PFC; Itasca Co., Minneapolis, MN). For our model\u27s calibration, hydrogel compression testing was used in which a cluster of particles is pushed in the vertical direction with a confined load similar to the uniaxial compression test. The parameter sensitivity study was performed using a set of parameters, e.g., coefficient of friction, restitution coefficient, and stiffness. Force distribution among the particles during the extrusion process was then predicted using the results of the study. Using this model, we analyzed the distribution of internal forces

Trends of metals (Pb, Zn, Cd, Cr, Mn, Fe, Hg and Ni) concentration in the New Jersey air environment

The atmospheric concentrations and relative trends of ten trace elements (Co, Cd, Fe, Hg, Pb, Ni, Zn, Cu, Cr and Mn) were studied at Elizabeth and Carteret, New Jersey from July 1987 to September 1989. The specific sites are Industrial and Residential Interface areas. The analytical procedure involved collection of the airborne particulates on a quartz microfiber filter using a high volume sampler. The samples were digested and then analyzed using the atomic absorption spectrometer. Elizabeth usually showed lower values for the metals than Carteret. Average concentrations (ng/m3) for the respective metals over the entire project are: Metal: Co Cd Fe Hg Pb Elizabeth: 9.56 1.78 495.45 0.48 29.66 Carteret: 12.78 3.32 788.64 0.58 49.18 Metal: Ni Zn Cu Cr Mn Elizabeth: 18.78 103.56 44.60 18.96 16.40 Carteret: 33.21 106.81 89.20 27.46 24.28 It was determined that there were significant variations (more than 25%) in average levels of the metals Cadmium, Iron, Lead, Nickel, Copper, Chromium and Manganese, between the Elizabeth and the Carteret sites. In addition there are variations of over 50% in levels of quarterly averages for Iron, Copper, Nickel at the Carteret site and Copper, Nickel and Iron at the Elizabeth site. The results were compared with those of a previous NJIT study from 1981 and 1982 at Elizabeth and Newark, New Jersey where the levels of Lead, Iron, Cadmium, Manganese, Copper, and Nickel were quantitated. For. Pb the following illustrates the very significant decrease observed. Comparison Site % Pb decrease Elizabeth 88/89 - Elizabeth 81/82 91.3 Elizabeth 88/89 - Newark 81/82 92.0 Carteret 88/89 - Elizabeth 81/82 85.5 Carteret 88/89 - Newark 81/82 86.8 These decreases in Lead concentrations are assigned to decreased use of lead in Vehicular fuels. Zinc and Cadmium also showed similar trends to that of Lead with a decrease of 15.55 in 88/89 for Elizabeth when compared with Elizabeth 81/82 and 61.3% decrease when compared with the Newark site. Manganese, Copper and Nickel showed increases in concentration for this 1988/89 study by 50.5%, 68.1% and 65.4% respectively relative to the Elizabeth 1881/82 site. Statistical Analysis on metal concentration as a function at the 3-hour average wind direction reported by the US Meteorological Service has also been performed to determine trends in metal concentration vs wind direction

Physical design of USB1.1

In earlier days, interfacing peripheral devices to host computer has a big problematic. There existed so many different kinds’ ports like serial port, parallel port, PS/2 etc. And their use restricts many situations, Such as no hot-pluggability and involuntary configuration. There are very less number of methods to connect the peripheral devices to host computer. The main reason that Universal Serial Bus was implemented to provide an additional benefits compared to earlier interfacing ports. USB is designed to allow many peripheral be connecting using single standardize interface. It provides an expandable fast, cost effective, hot-pluggable plug and play serial hardware interface that makes life of computer user easier allowing them to plug different devices to into USB port and have them configured automatically. In this thesis demonstrated the USB v1.1 architecture part in briefly and generated gate level net list form RTL code by applying the different constraints like timing, area and power. By applying the various types design constraints so that the performance was improved by 30%. And then it implemented in physically by using SoC encounter EDI system, estimation of chip size, power analysis and routing the clock signal to all flip-flops presented in the design. To reduce the clock switching power implemented register clustering algorithm (DBSCAN). In this design implementation TSMC 180nm technology library is used

UV SPECTROPHOTOMETRIC METHOD FOR SIMULTANEOUS ESTIMATION OF TRAMADOL HYDROCHLORIDE AND ACECLOFENAC IN BULK AND TABLET DOSAGE FORM

Objective: To develop a simple, economic and validated UV spectrophotometric method for the simultaneous estimation of aceclofenac and tramadol hydrochloride in bulk and tablet dosage form.Methods: The UV spectrophotometric method for simultaneous estimation of aceclofenac and tramadol hydrochloride has been developed. Methanol and water in the ratio 60:40 was used as the solvent. Tramadol hydrochloride and aceclofenac showed maximum absorbance at wavelength 214.8 nm and 275.6 nm respectively.Results: The developed method showed tramadol hydrochloride and aceclofenac to be linear in the concentration range of 5–30 µg/ml with a correlation coefficient of 0.998 and 0.999 respectively. The result of recovery studies for the tablet was found to be in the range of 98.125%-101.0% and 98.01%-98.36% for tramadol hydrochloride and aceclofenac respectively.Conclusion: The results show that the developed UV spectrophotometric method is simple, economical, accurate, precise and repeatable.Keywords: UV spectrophotometric method, Tramadol Hydrochloride, Aceclofenac, Simultaneous estimation, Tablet dosage form, Validatio

Novel Heuristic Recurrent Neural Network Framework to Handle Automatic Telugu Text Categorization from Handwritten Text Image

In the near future, the digitization and processing of the current paper documents describe efficient role in the creation of a paperless environment. Deep learning techniques for handwritten recognition have been extensively studied by various researchers. Deep neural networks can be trained quickly thanks to a lot of data and other algorithmic advancements. Various methods for extracting text from handwritten manuscripts have been developed in literature. To extract features from written Telugu Text image having some other neural network approaches like convolution neural network (CNN), recurrent neural networks (RNN), long short-term memory (LSTM). Different deep learning related approaches are widely used to identification of handwritten Telugu Text; various techniques are used in literature for the identification of Telugu Text from documents. For automatic identification of Telugu written script efficiently to eliminate noise and other semantic features present in Telugu Text, in this paper, proposes Novel Heuristic Advanced Neural Network based Telugu Text Categorization Model (NHANNTCM) based on sequence-to-sequence feature extraction procedure. Proposed approach extracts the features using RNN and then represents Telugu Text in sequence-to-sequence format for the identification advanced neural network performs both encoding and decoding to identify and explore visual features from sequence of Telugu Text in input data. The classification accuracy rates for Telugu words, Telugu numerals, Telugu characters, Telugu sentences, and the corresponding Telugu sentences were 99.66%, 93.63%, 91.36%, 99.05%, and 97.73% consequently. Experimental evaluation describe extracted with revealed which are textured i.e. TENG shown considerable operations in applications such as private information protection, security defense, and personal handwriting signature identification

Financial Fraud Detection using Improved Artificial Humming Bird Algorithm with Modified Extreme Learning Machine

More and more industries, including the financial sector, are moving their operations online as internet usage continues to rise at an exponential rate. As a result, financial fraud is on the rise in all its guises and in all parts of the world, causing enormous economic damage. The purpose of financial fraud detection systems is to identify potential dangers, such as unauthorised access or unusual attacks. In recent years, this problem has been attacked using a variety of machine learning and data mining methods. Aalgorithms, on the other hand, are better able to deal with only a small quantity of labelled data and a large amount of unlabeled data, making them useful in situations where it would be impractical to rely solely on supervised learning algorithms to train a good-performing classifier. In this research, we propose a Semi-supervised Extreme Learning Machine (SKELM) built on top of the weighted kernel, which we call SELMWK. For the purpose of detecting financial fraud, this research proposes an enhanced artificial hummingbird algorithm (IAHA). The algorithm combines two essential techniques to enhance its capacity for optimisation. To begin, the Chebyshev chaotic map is used to seed the first population of artificial hummingbirds, which boosts the population's overall ability to do global searches. Second, the guided foraging phase incorporates the Levy flight to enlarge the search field and forestall early convergence. The experimental results demonstration that the suggested technique recovers the Internet monetary fraud detections