Knowledge, attitude and behaviour of women towards abnormal menstrual bleeding and its impact on quality of life

Background: Abnormal menstrual bleeding has a major impact on a woman's quality of life. Any intervention therefore should aim to improve this rather than focusing on the amount of menstrual blood loss. The aim of this study was to gain an in depth understanding of women’s current knowledge, attitudes and beliefs around abnormal menstrual bleeding. Also, to understand how it affects their lives, while identifying current misconceptions and gaps in knowledge and their expectations and attitudes towards the treatment so that treatment can address their most bothersome complaint.Methods: A total of 200 women, between 18-55 years of age, not on any hormonal treatment attending the gynaecology OPD at a tertiary care centre between Jan 2017 to May 2017 were interviewed by a questionnaire. Women who had undergone hysterectomy were excluded from the study.Results: Of the 200 women interviewed, 49 women felt that their bleeding was heavy and formed the heavy menstrual bleeding group (HMB) while 151 women who perceived their bleeding to be normal formed the normal bleeding group. In the (HMB) group, a significantly greater proportion of women identified their menstrual bleeding as being an inconvenience and a disruption to their lives. HMB also significantly affected their relationships, social life and productivity at work place.Conclusions: Despite its common occurrence, awareness and understanding of HMB and its treatment within the survey population was poor

A Study of Stress among Students of Professional Colleges from an Urban area in India

Objectives: Various studies across the globe have emphasised that students undertaking professional courses, such as medical and dental studies, are subjected to higher stress. Excessive stress could lead to psychological problems like depression and anxiety. The objective of the current study was to assess stress among students of various professional colleges and its association with various academic, social and health-related factors.Methods: This cross-sectional study was conducted from September 2011 to February 2012 among students of medical, dental and engineering colleges from the urban area of Sangli district, Maharashtra, India, using a convenience sampling technique. The calculated total sample size was 1,200. A pretested self-administered questionnaire was used for the data collection. Analysis was done using percentage, the chi-square test, binary logistic regression and multinomial logistic regression. Results: Out of the 1,224 respondents, 299 (24.4%) experienced stress. Among them 115 (38.5%), 102 (34.1%) and 82 (27.4%) were dental, medical and engineering students, respectively. There was a statistically significant association between stress and the field of education. Stress was observed in 187 (27.7%) females and 112 (20.4%) males; the association with gender was statistically significant. By applying binary logistic regression, medical studies, health and lifestyle factors, and academic factors were the significant predictors for stress. Conclusion: Students from all the three fields studied were exposed to stress. Academic factors were one of the most important stressors. The introduction of stress management education into the curriculum could prove useful in combatting this problem

Transfer and Ensemble Approach for Breast Cancer Detection and Classification Using Deep Learning

Breast cancer is a serious disease that can cause significant health problems for women worldwide. It is crucial to detect and classify breast cancer early stage so that doctors can promptly treat it and aid patients in their recovery. Many investigators have used various deep learning (DL) strategies to detect and classify breast cancer. However, due to the complexity of the problem, relying on a single DL model may not suffice to achieve high accuracy. Therefore, this study suggests a transfer and ensemble deep model for breast cancer detection and classification. The suggested model involves using pre-trained models such as Sequential, Xception, DenseNet201, VGG16, and InceptionResNetV2. The top three models are selected to collaborate and deliver the most accurate results. The proposed DL model was tested on publicly available breast BUSI datasets, demonstrating its superiority over single DL models, achieving an accuracy of 87.9% on the BUSI dataset. Additionally, the model proved to be adapTABLE to different amounts of data, making it potentially valuable in hospitals and clinics

Surface Science of DNA Adsorption onto Citrate-Capped Gold Nanoparticles

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Langmuir copyright © American Chemical Society after peer review and technical editing by publisher. To access the final edited and published work see Zhang, X., Servos, M. R., & Liu, J. (2012). Surface Science of DNA Adsorption onto Citrate-Capped Gold Nanoparticles. Langmuir, 28(8), 3896–3902. https://doi.org/10.1021/la205036pSingle-stranded DNA can be adsorbed by citrate capped gold nanoparticles (AuNPs), resulting in increased AuNP stability, which forms the basis of a number of biochemical and analytical applications, but the fundamental interaction of this adsorption reaction remains unclear. In this study, we measured DNA adsorption kinetics, capacity, and isotherms, demonstrating that the adsorption process is governed by electrostatic forces. The charge repulsion among DNA strands and between DNA and AuNPs can be reduced by adding salt, reducing pH or by using noncharged peptide nucleic acid (PNA). Langmuir adsorption isotherms are obtained, indicating the presence of both adsorption and desorption of DNA from AuNPs. While increasing salt concentration facilitates DNA adsorption, the desorption rate is also enhanced in higher salt due to DNA compaction. DNA adsorption capacity is determined by DNA oligomer length, DNA concentration, and salt. Previous studies indicated faster adsorption of short DNA oligomers by AuNPs, we find that once adsorbed, longer DNAs are much more effective in protecting AuNPs from aggregation. DNA adsorption is also facilitated by using low pH buffers and high alcohol concentrations. A model based on electrostatic repulsion on AuNPs is proposed to rationalize the DNA adsorption/desorption behavior.University of Waterloo || Canadian Foundation for Innovation || Ontario Ministry of Research & Innovation || Canadian Institutes of Health Research || Natural Sciences and Engineering Research Council |

Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions

The Industry 4.0 revolution is insisting strongly for use of machine learning-based processes and condition monitoring. In this paper, emphasis is given on machine learning-based approach for condition monitoring of shaft misalignment. This work highlights combined approach of artificial neural network and support vector machine for identification and measure of shaft misalignment. The measure of misalignment requires more features to be extracted under variable load conditions. Hence, primary objective is to measure misalignment with a minimum number of extracted features. This is achieved through normalization of vibration signal. An experimental setup is prepared to collect the required vibration signals. The normalized time domain nonstationary signals are given to discrete wavelet transform for features extraction. The extracted features such as detailed coefficient is considered for feature selection viz. Skewness, Kurtosis, Max, Min, Root mean square, and Entropy. The ReliefF algorithm is used to decide best feature on rank basis. The ratio of maximum energy to Shannon entropy is used in wavelet selection. The best feature is used to train machine learning algorithm. The rank-based feature selection has improved classification accuracy of support vector machine. The result obtained with the combined approach are discussed for different misalignment conditions

A cross-sectional study of correlation of body image anxiety with social phobia and their association with depression in the adolescents from a rural area of Sangli district in India

Background: Prevailing socio-cultural influences lead females to desire a thin body and males a muscular body, especially in adolescents. This results in body image anxiety which may lead to social phobia. Together they can develop depression. The aim was to study the correlation of body image anxiety with social phobia and their association with depression, among adolescents. Methods: This was a cross-sectional study conducted in randomly selected colleges from a rural area of Sangli district Maharashtra, India. Stratified random sampling technique used with sample size 805. Pretested self-administered questionnaire used. Percentage, Chi-square test, binary logistic regression model was used to estimate odds ratio (OR) and its 95% confidence intervals. Results: Of 997 study subjects body image anxiety, social phobia and depression were observed in 232 (23.3%), 193 (19.4%) and 326 (32.7%) participants, respectively. Binary logistic regression showed that body image anxiety (OR = 1.849 [1.22, 2.804]; P = 0.004) and social phobia (OR = 4.575 [2.952-7.09]; P < 0.001) were significant predictors for depression. Conclusions: Body image anxiety and social phobia are linked with the development of depression. This impresses the need for timely counseling and education among adolescents

Parental Use of Antibiotics as Self Medication to Their School Going Children: A Cross Sectional Study

Background: Self-medication with antibiotics constitutes a major form of irrational use of medicine and it is associated with risk of developing antibiotic resistance. Parents many times use antibiotics as self medication for their children which is hazardous. There is a need to find out such practices if they are happening. Aim and Objectives: to find out the prevalence and practices of antibiotics use as self medication in children amongst the parents and reasons for the same. Material and Methods: It was a cross-sectional study. Schools from Sangli-Miraj-Kupwad Corporation area were the study place. Parents of primary and secondary school students were the study subjects. The sample size was 1407. Cluster sampling method was used. The study duration was of 6 months. The questionnaire was used as a study tool. Results: The prevalence of non prescriptional parental use of antibiotic for school going children was 22.8%. Most common symptom treated was runny nose (41.43%). Majority parents (35.51%) selected antibiotic by using their own experience. Commonest reason was 'convenience' (27.72%). Gender and type of family are the strong predictors for self-medication with antibiotic for children. Conclusion: Parental use of non prescribed antibiotic was high. Risk for parental use of self medication was high with nuclear families and mothers

Primary endobronchial Hodgkin's disease

We report a case of primary pulmonary Hodgkin's disease presenting as an endobronchial mass. Tissue diagnosis was made by microscopic examination following open thoracotomy and excision biopsy of the mass. The patient responded well to the chemotherapy regimen