Prevalence of carpal tunnel syndrome among dairy parlor workers

2011 Summer.Includes bibliographical references.BACKGROUND The purpose of this study was to determine the prevalence of carpal tunnel syndrome (CTS) and median mononeuropathy among dairy workers. METHODS Sixty-six dairy parlor workers and 58 non-parlor workers at dairies in Texas, New Mexico and Colorado participated in structured interviews regarding hand symptoms and nerve conduction studies (NCS). A case definition of CTS was based on the presence of characteristic CTS symptoms and an abnormal median mononeuropathy. RESULTS The prevalence of CTS among the dairy parlor workers was 16.9% (n=11) and 3.6% (n=2) among non-parlor workers. The difference was found to be statistically significant (p<0.05) with an odds ratio of 5.3, CI (1.1-25.5). CONCLUSIONS Dairy parlor workers are exposed to highly repetitive and excessive hand and wrist postures combined with high muscle forces increasing the risk of developing CTS. Work tasks in dairy parlor need additional study to identify engineering as well as administrative controls to reduce CTS risk

Tracking and Recognition: A Unified Approach on Tracking and Recognition

This paper proposes a unified approach on tracking and recognition .Object tracking is done at low level and recognition is done at high level. Traditional tracking methods give importance to low level image correspondences between frames. High level image correspondences are used for reliable tracking. Online and Offline models are used for both tracking and recognition which is done simultaneously. Thus high level offline model is combined with low level online model to increase the tracking performance. Onine model used for tracking is given to the video based recognition and at same time offline model plays important role to recognize the category of the object. This method is useful to handle difficult scenarios like abrupt change, background clutter, pose variations, occlusion and morphable objects. This is based on study of different IEEE papers

Survey of Noise Estimation Algorithms for Speech Enhancement Using Spectral Subtraction

Speech enhancement means speech improvement. Actually the speech enhancement is performed by using various techniques and different algorithms. Over the past several years there has been attention focused on the problem of enhancement of speech degraded by additive background noise. For many applications background suppression is required. The spectral - subtractive algorithm is one of the first algorithm proposed for additive background noise and it has gone through many modifications with time. For spectral subtraction method noise estimation is important for that there are various noise estimation algorithms. All these noise estimation algorithms are important for removing background noise

Upgradation of Manual Magnetic Core Drill Machine

This paper deals with the upgradation of existing manual core drill machine. The existing manual core drill machine requires a labor operator every time to drill the hole till the drilling gets completed. So more time is required and accuracy is reduced. So the main aim is to make core drill machine fully automatic. So the automatic part can be developed by developing some of the part related to current sensing and controlling, depth sensing using encoder, no load position sensing , enrolling the required depth through the keypad and operating it with the stepper motor

Artificial Neural Network Based Automatic Number Plate Recognition System

This paper deals with Automatic Number Plate Recognition (ANPR) using Artificial Neural Network. The ANPR system includes steps like pre-processing, localization, character segmentation and character recognition. The developed system first detects the vehicle and then captures the vehicle image. The captured image is pre-processed in order to enhance it for further processing. In localization the license plate region is located and cropped from the complete image. In character segmentation images of individual alpha-numeric characters are extracted from the localized plate. In this paper, we proposed Neural Network based character recognition. Scaled Conjugate Gradient Backpropogation algorithm is used for training the neural network. This system is implemented in MATLAB R2014b

Access Control Using 3 Level Authentications For E-Banking

E-banking is used by a number of users in their day to day lives, still a lot of people refrain to use it considering its security threats. This study evaluates the potential of biometric authentication for online banking as a way of improving and making online banking more secure. Biometric verification is any means by which a person can be uniquely evaluated one or more distinguishing biological traits. Unique identifiers include fingerprints, hand geometry, iris, retina, voice waves, dna, signatures.These biometrics features can be used to make computer systems more secure for authentication purpose in computer based security systems. The ID can be stolen; passwords can be forgotten or cracked but the physical characteristics of a person cannot be stolen or hacked. The biometric identification overcomes all the above. The process that we are using includes face detection from the biometric domain

Efecto de Punica granatum en ratas con leucocitosis y eosinofilia inducida por leche

Punica granatum Linn. (Punicaceae) comúnmente conocida como granada es un pequeño árbol o arbusto grande de hoja caduca, de unos 50-10 m de altura y de color verde oscuro. Los botones florales se utilizan tradicionalmente en el tratamiento del asma y la alergia. El objetivo de este estudio fue validar la propiedad tradicional antialérgica utilizando ratas con leucocitosis y eosinofilia inducida por leche. Los botones florales de la planta se extrajeron sucesivamente con varios solventes para obtener los extractos respectivos. Estos extractos se administraron a los ratones a dosis de 50 y 100 mg / kg, por vía oral. Sólo el extracto obtenido con etanol mostró una reducción significativa en el recuento de leucocitos y eosinófilos, estos resultados son una validación del uso del extracto de compuestos polares de los botones florales de P. granatum como un agente antialérgico

Role of Drug Repurposing in Cancer Treatment and Liposomal Approach of Drug Targeting

Cancer is the leading cause of death, and incidences are increasing significantly and patients suffering from it desperately need a complete cure from it. The science of using an already-invented drug that has been approved by the FDA for a new application is known as “drug repurposing.” Currently, scientists are drawn to drug repositioning science in order to investigate existing drugs for newer therapeutic uses and cancer treatment. Because of their unique ability to target cancer cells, recently repurposed drugs and the liposomal approach are effective in the treatment of cancer. Liposomes are nanovesicles that are drastically flexible, rapidly penetrate deeper layers of cells, and enhance intracellular uptake. More importantly, liposomes are biocompatible, biodegradable; entrap both hydrophobic and hydrophilic drugs. This chapter summarizes various approaches to drug repurposing, as well as drug repurposing methods, advantages and limitations of drug repurposing, and a liposomal approach to using repurposed drugs in cancer targeting. This chapter also summarizes liposomal structure, drug loading, and the mechanism of liposomes in targeted cancer treatment. The lipid-based liposomal approach is emerging as a powerful technique for improving drug solubility, bioavailability, reducing side effects, and improving the therapeutic efficacy of repurposed drugs for cancer treatment

Comparison and Consensus Guidelines for Delineation of Clinical Target Volume for CT- and MR-Based Brachytherapy in Locally Advanced Cervical Cancer

To create and compare consensus clinical target volume (CTV) contours for computed tomography (CT) and 3 Tesla (3T) magnetic resonance (MR) image-based cervical-cancer brachytherap

Individualised prediction of drug resistance and seizure recurrence after medication withdrawal in people with juvenile myoclonic epilepsy: A systematic review and individual participant data meta-analysis

Summary Background A third of people with juvenile myoclonic epilepsy (JME) are drug-resistant. Three-quarters have a seizure relapse when attempting to withdraw anti-seizure medication (ASM) after achieving seizure-freedom. It is currently impossible to predict who is likely to become drug-resistant and safely withdraw treatment. We aimed to identify predictors of drug resistance and seizure recurrence to allow for individualised prediction of treatment outcomes in people with JME. Methods We performed an individual participant data (IPD) meta-analysis based on a systematic search in EMBASE and PubMed – last updated on March 11, 2021 – including prospective and retrospective observational studies reporting on treatment outcomes of people diagnosed with JME and available seizure outcome data after a minimum one-year follow-up. We invited authors to share standardised IPD to identify predictors of drug resistance using multivariable logistic regression. We excluded pseudo-resistant individuals. A subset who attempted to withdraw ASM was included in a multivariable proportional hazards analysis on seizure recurrence after ASM withdrawal. The study was registered at the Open Science Framework (OSF; https://osf.io/b9zjc/). Findings  368) was predicted by an earlier age at the start of withdrawal, shorter seizure-free interval and more currently used ASMs, resulting in an average internal-external cross-validation concordance-statistic of 0·70 (95%CI 0·68–0·73). Interpretation We were able to predict and validate clinically relevant personalised treatment outcomes for people with JME. Individualised predictions are accessible as nomograms and web-based tools. Funding MING fonds