Evaluating the toxicity of capecitabine-cisplatin versus gemcitabine-cisplatin regimens for palliative chemotherapy in advanced biliary tract carcinoma

Background: Advanced biliary tract carcinoma is a malignancy associated with poor prognosis and limited treatment options. This study aimed to compare the treatment effects in terms of toxicities of Capecitabine-Cisplatin and Gemcitabine-Cisplatin regimens as palliative chemotherapy for ABTC in Bangladesh. Methods: This quasi-experimental study was conducted at the Department of Oncology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh, involving 78 patients with histopathologically confirmed ABTC (AJCC Stage IV). Participants were divided into two groups: Arm-A received Capecitabine-Cisplatin, and Arm-B received Gemcitabine-Cisplatin. Treatment response, hematological and non-hematological toxicities were assessed and compared between the two groups. Results: No significant differences in baseline demographic and clinical characteristics were observed between the two groups. Arm-A demonstrated a higher rate of partial response in the final assessment (51.28% vs. 41.03%, p=0.029). Acute hematological toxicities were more frequent in Arm-B, with a higher incidence of Grade 2 and 3 anemia, neutropenia, and leukopenia (p<0.05). Non-hematological toxicities were comparable, except for Hand-Foot Syndrome, which was significantly higher in Arm-A (p=0.03). Conclusions: The Capecitabine-Cisplatin regimen exhibited a different toxicity profile compared to the Gemcitabine-Cisplatin regimen for palliative chemotherapy in advanced biliary tract carcinoma. While both regimens were generally well-tolerated, the Capecitabine-Cisplatin regimen demonstrated lower incidences of hematological toxicities. These findings emphasize the importance of considering toxicity profiles when selecting treatment options for patients with advanced biliary tract carcinoma

Domestic violence and decision-making power of married women in Myanmar: analysis of a nationally representative sample

BACKGROUND: Women in Myanmar are not considered decision makers in the community and the physical and psychological effect of violence makes them more vulnerable. There is a strong negative reaction, usually violent, to any economic activity generated by women among poorer and middle-class families in Myanmar because a woman's income is not considered necessary for basic survival. OBJECTIVE: Explore the relationship between domestic violence on the decision-making power of married women in Myanmar. DESIGN: Cross-sectional. SETTING: National, both urban and rural areas of Myanmar. PATIENTS AND METHODS: Data from the Myanmar Demographic and Health Survey 2015-16 were used in this analysis. In that survey, married women aged between 15 to 49 years were selected for interview using a multistage cluster sampling technique. The dependent variables were domestic violence and the decision-making power of women. Independent variables were age of the respondents, educational level, place of residence, employment status, number of children younger than 5 years of age and wealth index. MAIN OUTCOME MEASURES: Domestic violence and decision-making power of women. SAMPLE SIZE: 7870 currently married women. RESULTS: About 50% respondents were 35 to 49 years of age and the mean (SD) age was 35 (8.4) years. Women's place of residence and employment status had a significant impact on decision-making power whereas age group and decision-making power of women had a relationship with domestic violence. CONCLUSION: Giving women decision making power will be indispensable for the achievement of sustainable development goals. Government and other stakeholders should emphasize this to eliminate violence against women. LIMITATIONS: Use of secondary data analysis of cross-sectional study design and cross-sectional studies are not suitable design to assess this causality. Secondly the self-reported data on violence may be subject to recall bias. CONFLICT OF INTEREST: None

Risk factors for COVID-19 mortality among telehealth patients in Bangladesh: A prospective cohort study.

Background and objectiveEstimating the contribution of risk factors of mortality due to COVID-19 is particularly important in settings with low vaccination coverage and limited public health and clinical resources. Very few studies of risk factors of COVID-19 mortality used high-quality data at an individual level from low- and middle-income countries (LMICs). We examined the contribution of demographic, socioeconomic and clinical risk factors of COVID-19 mortality in Bangladesh, a lower middle-income country in South Asia.MethodsWe used data from 290,488 lab-confirmed COVID-19 patients who participated in a telehealth service in Bangladesh between May 2020 and June 2021, linked with COVID-19 death data from a national database to study the risk factors associated with mortality. Multivariable logistic regression models were used to estimate the association between risk factors and mortality. We used classification and regression trees to identify the risk factors that are the most important for clinical decision-making.FindingsThis study is one of the largest prospective cohort studies of COVID-19 mortality in a LMIC, covering 36% of all lab-confirmed COVID-19 cases in the country during the study period. We found that being male, being very young or elderly, having low socioeconomic status, chronic kidney and liver disease, and being infected during the latter pandemic period were significantly associated with a higher risk of mortality from COVID-19. Males had 1.15 times higher odds (95% Confidence Interval, CI: 1.09, 1.22) of death compared to females. Compared to the reference age group (20-24 years olds), the odds ratio of mortality increased monotonically with age, ranging from an odds ratio of 1.35 (95% CI: 1.05, 1.73) for ages 30-34 to an odds ratio of 21.6 (95% CI: 17.08, 27.38) for ages 75-79 year group. For children 0-4 years old the odds of mortality were 3.93 (95% CI: 2.74, 5.64) times higher than 20-24 years olds. Other significant predictors were severe symptoms of COVID-19 such as breathing difficulty, fever, and diarrhea. Patients who were assessed by a physician as having a severe episode of COVID-19 based on the telehealth interview had 12.43 (95% CI: 11.04, 13.99) times higher odds of mortality compared to those assessed to have a mild episode. The finding that the telehealth doctors' assessment of disease severity was highly predictive of subsequent COVID-19 mortality, underscores the feasibility and value of the telehealth services.ConclusionsOur findings confirm the universality of certain COVID-19 risk factors-such as gender and age-while highlighting other risk factors that appear to be more (or less) relevant in the context of Bangladesh. These findings on the demographic, socioeconomic, and clinical risk factors for COVID-19 mortality can help guide public health and clinical decision-making. Harnessing the benefits of the telehealth system and optimizing care for those most at risk of mortality, particularly in the context of a LMIC, are the key takeaways from this study

Web search engine misinformation notifier extension (SEMiNExt):a machine learning based approach during COVID-19 pandemic

Abstract Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues