Through Thick and Thin: Exposure to Instagram Advertisements and Willingness to Engage in Appearance-Altering Practices.

Instagram is increasingly used in advertising, yet little is known about the unintended consequences of Instagram advertising on women's and girls' body image. Also largely unexplored is if and how curvy models (large breasts and buttocks, wide hips, and small waist) used in this advertising affect women's and girls' body image. We drew on social comparison and cultivation theories to explore if exposure to thin and curvy models through Instagram advertising is associated with late-adolescent girls' willingness to take action to be thinner or curvier, respectively. Two mediation models examined the mechanisms through which any such effects occur. A sample of 284 17-19 year old girls completed self-administered online questionnaires. Results revealed that exposure to thin and curvy models was positively associated with willingness to take action to be thinner and curvier, respectively. These associations were mediated by thin/curvy body preference (model 1), and by thin/curvy body preference, upward physical appearance comparisons, and body dissatisfaction (model 2). Results suggest that although exposure to different body types may be associated with different types of unhealthy (body-altering) actions, the processes underlying these effects are similar. This research highlights possible cultural shifts toward more diverse body ideals and informs tailored body concern interventions and media literacy programs.

Predicting changes in land use/land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh.

Land use/land cover (LULC) variations are accelerated by rapid urbanization and significantly impacted global Land Surface Temperature (LST). The dynamic increase in LST results in the Urban Heat Island (UHI) effect. In this study, future LULC change scenarios, seasonal (summer & winter) LST variations, and LST distribution over different LULC classes were predicted using Landsat satellite images for 1999, 2009, and 2019 in Rajshahi District, Bangladesh. Cellular Automata (CA) and Artificial Neural Network (ANN) procedures were used to predict the LULC changes and seasonal LST variations for 2029 and 2039. In addition, Focus Group Discussions (FGDs) and Key Informants Interviews (KIIs) were conducted to identify the possible impacts of LULC change, LST shifts, and climate change on agricultural productivity and developed a sustainable land use management plan for the study area. Validation of the CA model demonstrated an excellent accuracy with a kappa value of 0.82. Similarly, the ANN model's validation using Mean Square Error (0.523 and 0.796 for summer) and Correlation coefficient (0.6023 and 0.831 for winter) values demonstrated a good prediction accuracy. The LULC prediction result indicated that the built-up area will be expanded by 58.03 km2 and 79.90 km2, respectively, from 2019 to 2029 and 2039. The predicted seasonal LST indicated that in 2029 and 2039, more than 23.30 % and 50.46 % of the summer and 3.02 % and 13.02 % of the winter seasons will likely be experienced LSTs greater than 35 °C. The results of public participation exposed that changes in LULC classes, variations in LST, and climate change significantly impact the regional biodiversity (loss of farmland and water bodies), reduce agricultural productivity, and increase extreme weather events (flood, heavy rainfall, and cold/warm temperature). This study provides the useful guidelines for agricultural officers, urban planners, and environmental engineers to understand the spatial configurations of built-up area enlargement and provide effective policy measures to conserve farming lands to ensure environmental sustainability.

Previously published ethno-pharmacological reports reveal the potentiality of plants and plant-derived products used as traditional home remedies by Bangladeshi COVID-19 patients to combat SARS-CoV-2.

Several plants have traditionally been used since antiquity to treat various gastroenteritis and respiratory symptoms similar to COVID-19 outcomes. The common symptoms of COVID-19 include fever or chills, cold, cough, flu, headache, diarrhoea, tiredness/fatigue, sore throat, loss of taste or smell, asthma, shortness of breath, or difficulty breathing, etc. This study aims to find out the plants and plant-derived products which are being used by the COVID-19 infected patients in Bangladesh and how those plants are being used for the management of COVID-19 symptoms. In this study, online and partially in-person survey interviews were carried out among Bangladeshi respondents. We selected Bangladeshi COVID-19 patients who were detected Coronavirus positive (+) by RT-PCR nucleic acid test and later recovered. Furthermore, identified plant species from the surveys were thoroughly investigated for safety and efficacy based on the previous ethnomedicinal usage reports. Based on the published data, they were also reviewed for their significant potentialities as antiviral, anti-inflammatory, and immunomodulatory agents. We explored comprehensive information about a total of 26 plant species, belonging to 23 genera and 17 different botanical families, used in COVID-19 treatment as home remedies by the respondents. Most of the plants and plant-derived products were collected directly from the local marketplace. According to our survey results, greatly top 5 cited plant species measured as per the highest RFC value are Camellia sinensis (1.0) > Allium sativum (0.984) > Azadirachta indica (0.966) > Zingiber officinale (0.966) > Syzygium aromaticum (0.943). Previously published ethnomedicinal usage reports, antiviral, anti-inflammatory, and immunomodulatory activity of the concerned plant species also support our results. Thus, the survey and review analysis simultaneously reveals that these reported plants and plant-derived products might be promising candidates for the treatment of COVID-19. Moreover, this study clarifies the reported plants for their safety during COVID-19 management and thereby supporting them to include in any future pre-clinical and clinical investigation for developing herbal COVID-19 therapeutics.

Impact of human papillomavirus genotype on response to treatment and survival in patients receiving radiotherapy for squamous cell carcinoma of the cervix.

To determine the clinical implications and prognostic value of the human papillomavirus (HPV) genotype, we evaluated the various HPV types in patients receiving radiotherapy for squamous cell carcinoma of the cervix. The study population included 113 invasive squamous cell carcinoma patients treated with radiation or chemoradiation between 1993 and 2002. The median age of the patients was 61 years. Tumors were classified by the International Federation of Gynecology and Obstetrics staging as stage IB in 11 patients, stage II in 39, stage III in 57 and stage IVA in 6 patients. To investigate HPV infection and its genotypes in the tumor specimens, L1 consensus PCR was performed followed by the direct nucleotide sequencing of the PCR products. Ninety-five samples (84.1%) were positive for HPV DNA. The most prevalent type was HPV-16 (34.7%). Poorer response to radiotherapy was observed in the patients with the HPV-16 genotype, in which 7 of the 33 patients had persistent disease. Only 1 of the 10 patients with HPV-58, 1 of the 5 with HPV-31 and 5 of the 10 patients with HPV-33 had a recurrence. The 5-year survival rate was 90, 80, 69.4 and 39% in the HPV-58, HPV-31, HPV-16 and HPV-33 type groups, respectively. Patients with HPV-31 and HPV-58 types were found to have better survival, whereas patients with the HPV-33 type experienced a higher risk of death. HPV genotyping may serve as a potential biomarker of response to radiation and prognosis in cervical carcinoma patients undergoing radio- or chemoradiotherapy.