Introduction: Grandparents usually played an important role in the care of their grandchildren and often provide care and assistance to their grandchildren. Across the world, grandparents are principal caregivers or supplement other forms of extra family care and this experience may have an effect on their psychological well-being. Objective: The objective of this study was to assess the extent of psychological well- being of urban grandparents caring grandchildren. Methods: This cross-sectional study was conducted among grandparents over the period of one year from January to December 2018 among 171 participants. Participants were selected by purposive sampling; data were collected by face-to-face interview with a semi-structured questionnaire to observe socio-demographic characteristics, caregiving information and PSS, 3-item UCLA Loneliness scale, HADS scale for psychological well-being. Data was analyzed by IBM software-SPSS 25 version. Results: Study revealed that, 71% were grandmother and 49% were grandfather. There was statistical significant association between psychological well-being and gender (p<0.01). There was significant association between age group and psychological well-being and age was negatively correlated with stress (p< 0.01), loneliness (p< 0.05) and anxiety (0.01). In this study 75% were married and 25% were widowed/widower. Loneliness was significantly associated with marital status (p<0.01). Monthly family income was negatively correlated with loneliness (p<0.01) and depression (p<0.05). There was significant association between psychological well-being and duration of caregiving (p<0.01) and duration of caregiving was positively correlated with loneliness (p<0.01), anxiety (p<0.01), depression (p<0.01). In this study, 64% grandparents experienced problem during caring and 36% didnt which was significantly associated with psychological well-being (p<0.01). Psychological well-being attributes (stress, loneliness, anxiety, depression) were significantly correlated with each other (p<0.01). Conclusion: The findings of this study revealed that there are some adverse consequences on grandparents psychological well-being. Effective measure should be taken for caregiver grandparents.

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Background: Although glaucoma is the second leading cause of irreversible blindness globally, the condition shows no signs or symptoms until later stages. Knowledge about the disease is known to influence utilization of eye screening services. This study aimed at understanding knowledge and awareness of glaucoma and its associated risk factors among residents of urban community of Rajshahi district, Bangladesh. Methods: This was a crosssectional study with the use of a semi-structured questionnaire. Descriptive statistics were used to describe the socio-demographic characteristics, knowledge and awareness of glaucoma and associated risk factors. Results: Out of a total of 185 respondents, 52.3 % were females and 0.5 % were aware of glaucoma. Majority (99.5 %) of the respondents were unaware of glaucoma and 24.3% (n=45) of the respondents didn't know that the disease can result in blindness. Only (16.2%) affirming that blindness from glaucoma is irreversible. 9.2 % of the respondents perceived themselves to be at risk of developing glaucoma. The results showed that age, sex, level of education, employment status, occupation, residential status, marital status and monthly income of the respondents' (p<0.05) were statistically significant with glaucoma knowledge level. In addition, the result also showed that age, level of education, employment status, occupation, residential status, marital status and monthly income of the respondents' (p<0.05) were statistically significant with glaucoma awareness. Conclusion:Glaucoma awareness was not satisfactory and the findings also display inadequate knowledge about glaucoma. So there is a need to effectively inform and educate people about the disease.

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Breast cancer is the most common cancer among women but in can occur in both the genders. It is accountable for an appalling number of deaths worldwide. In a particularly low-resource developing country like Bangladesh, there is a lack of awareness and facilities mostly in rural areas and high rate of instances of breast cancer that is diagnosed in the last stages. However, the early detection of breast cancer can lead to help increase the odds of survival. Nowadays, with the increasing number of patients, manual analysis of medical images becomes tedious, time consuming and unfeasible. With the advancement in the field of machine learning, it is now possible to create an automated and accurate Computer Aided Diagnosis (CAD) system in order to make the entire process of detecting a malignant tumor more resource efficient and time saving through proper utilization. This paper presents the comparative analysis of different machine learning algorithms and their results in predicting cancerous tumors. The proposed model uses supervised machine learning algorithms such as Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes and Logistic Regression with and without PCA on a dataset with 30 features extracted from a digitized image of a fine needle aspirate (FNA) of a breast mass. Deep learning models like Artificial Neural Network and Convolutional Neural Network are used and their performances are compared. From the comparative analysis, it is observed that the deep learning models outperform all other classifiers and achieves impressive scores across multiple performance metrics such as Accuracy of 98.83%, Precision of 98.44% and Recall of 100%.