Association of Chronic Toxoplasma gondii Infection with Pro-Inflamatory Cytokine Interleukin (IL)-12 Responses in Type-2 Diabetes Mellitus Patients of Bangladesh.

Toxoplasma gondii is an intracellular protozoan parasite that causes toxoplasmosis in around one-third of the world population, particularly in pregnant women and immunocompromised individuals. Diabetes mellitus (DM) is one of the most severe global health challenges in the 21st century, and especially, type-2 diabetes mellitus (T2DM) accounts for 90% of the diabetes cases diagnosed globally. In Bangladesh, the rate of T2DM is rising gradually with the improvement in living standards. The aim of this study is to find out the correlation between latent toxoplasmosis and T2DM, emphasizing the pro-inflammatory cytokine immunity. For this, 100 (N = 100) patients with T2DM and 100 (N = 100) healthy controls were enrolled to determine the seroprevalence of toxoplasmosis using enzyme-linked immunosorbent assay (ELISA). In addition, ELISA was also performed to determine the level of pro-inflammatory cytokine, interleukin (IL)-12, to understand its role in the development of toxoplasmosis. In our study, 39.39% of the T2DM patients were positive with anti-T. gondii Immunoglobulin G by ELISA, whereas the rate of seropositivity in healthy controls was 39.73%. We did not find significant association between T. gondii infection and T2DM, but our data confirmed a high prevalence of chronic toxoplasmosis in Bangladeshi population. From hematology tests, it was found that the T2DM patients had significantly lower levels of total white blood cells (P = 0.0015), circulating eosinophils (P = 0.0026), and neutrophils (P = 0.0128) than the healthy controls. On the other hand, the levels of lymphocytes (P = 0.0204) and monocytes (P = 0.0067) were significantly higher in patients. Furthermore, T. gondii infected T2DM patients had significantly higher levels of IL-12 than the healthy controls (P = 0.026), suggesting a link between parasitic infection and IL-12 secretion. Further studies are to be performed to find out the exact cause of high prevalence of chronic T. gondii infection in Bangladeshi population.

Mapping and evaluating sustainable and unsustainable urban areas for ecological management towards achieving low-carbon city: an empirical study of Asir Region, Saudi Arabia.

Urbanisation can cause a variety of environmental and health issues, which has prompted experts to evaluate degraded areas and develop management strategies aimed at promoting urban sustainability and reducing carbon emissions. In low-carbon cities, sustainable urban areas have low carbon emission and prioritised carbon reduction by implementing sustainable transportation, green infrastructure, and energy-efficient buildings. On the other hand, unsustainable urban areas tend to lack these priorities and rely heavily on non-renewable energy sources and have high carbon emission. Therefore, this study aims to identify the most sustainable and unsustainable regions in the Abha-Khamis Mushayet Twin City region of Saudi Arabia in respect to urbanisation and carbon emission during the period between 1990 and 2020. To do so, we used Landsat datasets to create land use land cover (LULC) maps and then calculated carbon storage, emission, and absorption using InVest software. Additionally, the study examined micro-climatic conditions by calculating the urban heat island (UHI) effect, which allowed determining sustainable and unsustainable regions by comparing the UHI model and carbon similarity and mismatch model using coupling coordination degree model (CCDM). The study found that during the last three decades, the LULC pattern of the region underwent significant alterations, resulting in a substantial decline in carbon storage from 710,425 Mg C/hm2 in 1990 to approximately 527,012.9 Mg C/hm2 in 2020. Conversely, carbon emissions were observed to be very high in areas with high built-up density, with emission levels exceeding 20 tons per annum. Whilst the areas of excess carbon have decreased significantly, the areas of excess carbon emission have increased over time, resulting in the UHI effect due to high greenhouse gases. By comparing the UHI and carbon similarity and mismatch model, the researchers found that over 280 km2 of the study area is unsustainable and has increased since 1990. In contrast, only about 410 km2 of the study area is currently sustainable. To promote sustainability, the study recommends several strategies such as carbon capture, utilisation, and storage; green infrastructure; and the use of renewable energy to manage carbon emissions.

Long-term care use among people living with dementia: a retrospective register-based study from Sweden.

BACKGROUND: Although many people with dementia need progressive support during their last years of life little is known to what extent they use formal long-term care (LTC). This study investigates the use of LTC, including residential care and homecare, in the month preceding death, as well as the number of months spent in residential care, among Swedish older decedents with a dementia diagnosis, compared with those without a dementia diagnosis. METHODOLOGY: This retrospective cohort study identified all people who died in November 2019 in Sweden aged 70 years and older (n = 6294). Dementia diagnoses were collected from the National Patient Register (before death) and the National Cause of Death Register (death certificate). The use of LTC was based on the Social Services Register and sociodemographic factors were provided by Statistics Sweden. We performed regression models (multinomial and linear logistic regression models) to examine the association between the utilization of LTC and the independent variables. RESULTS: Not only dementia diagnosis but also time spent with the diagnosis was crucial for the use of LTC in the month preceding death, in particular residential care. Three out of four of the decedents with dementia and one fourth of those without dementia lived in a residential care facility in the month preceding death. People who were diagnosed more recently were more likely to use homecare (e.g., diagnosis for 1 year or less: home care 29%, residential care 56%), while the predicted proportion of using residential care increased substantially for those who had lived longer with a diagnosis (e.g., diagnosis for 7 + years: home care 11%, residential care 85%). On average, people with a dementia diagnosis stayed six months longer in residential care, compared with people without a diagnosis. CONCLUSIONS: People living with dementia use more LTC and spend longer time in residential care than those without dementia. The use of LTC is primarily influenced by the time with a dementia diagnosis. Our study suggests conducting more research to investigate differences between people living with different dementia diagnoses with co-morbidities.

Trend analysis and forecasting of streamflow using random forest in the Punarbhaba River basin.

Streamflow rate changes due to damming are hydro-ecologically sensitive in present and future times. Very less studies have done an investigation of the damming effect on the streamflow along with future forecasting, which can be the solution for the existing problems. Therefore, this study aims to use the Pettitt test as well as standard normal homogeneity test (SNHT) to discover trends in streamflow with the future situation in the Punarbhaba River in Indo-Bangladesh from 1978 to 2017. Trend was spotted using Mann-Kendall test, Spearman's rank correlation approach, innovative trend analysis, and a linear regression model. The current work additionally uses advanced machine learning techniques like random forest (RF) to estimate flow regimes using historical time series data. 1992 appears to be a yard mark in this continuum of time series datasets, indicating a significant transformation in the streamflow regime. The MK test as well as Spearman's rho was used to find a significant negative trend for the average (-0.57), maximum (-0.62), and minimum (-0.48) flow regimes. The consistency of the flow regime has been losing consistency, and the variability of flow regime has increased from 2.1 to 6.7% of the average water level, 1.5 to 6.5% of the maximum streamflow, and 3.1 to 5.8% of the minimum streamflow in the post-change point phase. The forecast trend using random forest for streamflow up to 2030 are negative for all four seasons with a flow volume likely to be reduced by 0.67% to-5.23%. Annual and monthly streamflows revealed very negative tendencies, according to the conclusions of unique trend analysis. Flow declination of this magnitude impacts downstream habitat and environment. According to future estimates, the seasonal flow will decrease. Furthermore, the outcome of this research will give a wealth of data for river management and other places with comparable environment.

Improving Dose-Optimization Processes Used in Oncology Drug Development to Minimize Toxicity and Maximize Benefit to Patients.

This review highlights strategies to integrate dose optimization into premarketing drug development and discusses the underlying statistical principles. Poor dose optimization can have negative consequences for patients, most commonly because of toxicity, including poor quality of life, reduced effectiveness because of inability of patients to stay on current therapy or receive subsequent therapy because of toxicities, and difficulty in developing combination regimens. We reviewed US Food and Drug Administration initial approvals (2019-2021) of small molecules and antibody-drug conjugates for oncologic indications to determine the proportion with a recommended dosage at the maximum tolerated dose or the maximal administered dose, to characterize the use of randomized evaluations of multiple dosages in dose selection, to describe the frequency of dose modifications at the recommended dosage, and to identify case examples that highlight key principles for premarket dose optimization during drug development. Herein, we highlight major principles for dose optimization and review examples of recent US Food and Drug Administration approvals that illustrate how investigation of dose- and exposure-response relationships and use of randomized dose trials can support dose optimization. Although there has been some progress, dose optimization through randomized dose evaluation in oncology trials is not routinely conducted. Dose optimization is essential to ensure that patients receive therapies which maximize efficacy while minimizing toxicity.

Modeling the spatiotemporal heterogeneity of land surface temperature and its relationship with land use land cover using geo-statistical techniques and machine learning algorithms.

Rapid changes in land use and land cover (LULC) have ecological and environmental effects in metropolitan areas. Since the 1990s, Saudi Arabia's cities have undergone tremendous urban growth, causing urban heat islands, groundwater depletion, air pollution, loss of ecosystem services, etc. This study evaluates the variance and heterogeneity in land surface temperature (LST) because of LULC changes in Abha-Khamis Mushyet, Saudi Arabia, from 1990 to 2020. The research aims to determine the impact of urban biophysical parameters on the High-High (H-H) LST cluster using geospatial, statistical, and machine learning techniques. The support vector machine (SVM) was used to map LULC. The land surface temperature (LST) has been derived using the mono-window algorithm (MWA). The local indicator of spatial associations (LISA) model was implemented on the spatiotemporal LST maps to identify LST clusters. Also, the parallel coordinate plot (PCP) approach was employed to examine the relationship between LST clusters and urban biophysical variables as a proxy of LULC. LULC maps show that urban areas rose by > 330% between 1990 and 2020. Built-up areas had an 83.6% transitional probability between 1990 and 2020. In addition, vegetation and agricultural land have been transformed into built-up areas by 17.9% and 21.8% respectively between 1990 and 2020. Uneven LULC changes in terms of built-up areas lead to increased LST hotspots. High normalized difference built-up index (NDBI) was linked to LST hotspots but not normalized difference water index (NDWI) or normalized difference vegetation index (NDVI). This research could help policymakers develop mitigation strategies for urban heat islands.

Application of advanced trend analysis techniques with clustering approach for analysing rainfall trend and identification of homogenous rainfall regions in Delhi metropolitan city.

In the era of global urbanization, the cities across the world are experiencing significant change in the climate pattern. However, analysing the trend and pattern of rainfall over the urban areas has a number of challenges such as availability of long-term data as well as the uneven distribution of rain-gauge stations. In this research, the rainfall regionalization approach has been applied along with the advanced statistical techniques for analysing the trend and pattern of rainfall in the Delhi metropolitan city. Fuzzy C-means and K-means clustering techniques have been applied for the identification of homogeneous rainfall regions while innovative trend analysis (ITA) along with the family of Mann-Kendall (MK) tests has been applied for the trend analysis of rainfall. The result shows that in all rain-gauge stations of Delhi, an increasing trend in rainfall has been recorded during 1991-2018. But the rate of increase was low as the trend slope of ITA and Sen's slope in MK tests are low, which varies between 0.03 and 0.05 and 0.01 and 0.16, respectively. Furthermore, none of the rain-gauge stations have experienced a monotonic trend in rainfall as the null hypothesis has not been rejected (p value > 0.05) for any stations. Furthermore, the study shows that ITA has a better performance than the family of MK tests. The findings of this study may be utilized for the urban flood mitigation and solving other issues related to water resources in Delhi and other cities.

People Living with a Dementia Diagnosis with No Eldercare at all: Who Are They?

Dementia is a non-curable disease that progressively affects people's ability to handle their everyday life. Still, previous Swedish research found that many people living with dementia (PlwD) do not use any eldercare. In this study, we investigated the association between not using eldercare services and key social background factors: years with dementia, hospital care, and country of birth for PlwD. We identified all people aged 65+ diagnosed with dementia between January 2006 and March 2015 (n = 43,372) using secondary data analysis of information taken from multiple Swedish nationwide registers. Results showed that not using eldercare was more common among younger age groups and men. The likelihood of having no eldercare was three times higher for those cohabiting; notably, almost a fifth of the PlwD who had the diagnosis for four years or more did not have eldercare at all. Finally, people born outside Europe used eldercare less than persons born in Sweden. Since the public dementia policy in Sweden is basically geared toward PlwD in a later stage and primarily in residential care, there has been almost no policy development around the needs and rights of PlwD in their homes. This study provides a ground for reconsidering dementia policy regardless of the ethnic and cultural backgrounds of PlwD.

Curcumin improves D-galactose and normal-aging associated memory impairment in mice: In vivo and in silico-based studies.

Aging-induced memory impairment is closely associated with oxidative stress. D-Galactose (D-gal) evokes severe oxidative stress and mimics normal aging in animals. Curcumin, a natural flavonoid, has potent antioxidant and anti-aging properties. There are several proteins like glutathione S-transferase A1 (GSTA1), glutathione S-transferase omega-1 (GSTO1), kelch-like ECH-associated protein 1 (KEAP1), beta-secretase 1 (BACE1), and amine oxidase [flavin-containing] A (MAOA) are commonly involved in oxidative stress and aging. This study aimed to investigate the interaction of curcumin to these proteins and their subsequent effect on aging-associated memory impairment in two robust animal models: D-Gal and normal aged (NA) mice. The aging mice model was developed by administering D-gal intraperitoneally (i.p). Mice (n = 64) were divided into the eight groups (8 mice in each group): Vehicle, Curcumin-Control, D-gal (100mg/kg; i.p), Curcumin + D-gal, Astaxanthin (Ast) + D-gal, Normal Aged (NA), Curcumin (30mg/kg Orally) + NA, Ast (20mg/kg Orally) + NA. Retention and freezing memories were assessed by passive avoidance (PA) and contextual fear conditioning (CFC). Molecular docking was performed to predict curcumin binding with potential molecular targets. Curcumin significantly increased retention time (p < 0.05) and freezing response (p < 0.05) in PA and CFC, respectively. Curcumin profoundly ameliorated the levels of glutathione, superoxide dismutase, catalase, advanced oxidation protein products, nitric oxide, and lipid peroxidation in mice hippocampi. In silico studies revealed favorable binding energies of curcumin with GSTA1, GSTO1, KEAP1, BACE1, and MAOA. Curcumin improves retention and freezing memory in D-gal and nature-induced aging mice. Curcumin ameliorates the levels of oxidative stress biomarkers in mice. Anti-aging effects of curcumin could be attributed to, at least partially, the upregulation of antioxidant enzymes through binding with GSTA1, GSTO1, KEAP1, and inhibition of oxidative damage through binding with BACE1 and MAOA.

Monitoring drought pattern for pre- and post-monsoon seasons in a semi-arid region of western part of India.

Drought has become a regular phenomenon in the western semi-arid regions of India, where severe drought occurs once in 8-9 years. Therefore, two drought indices, namely temperature condition index (TCI) and vegetation condition index (VCI), were prepared from using Landsat datasets to appraise and monitor of drought pattern for the pre- and post-monsoon seasons between 1996 and 2016 in the Latur district, the north-western part of India. Additionally, the average frequency layers (AFL) of all drought and land use indices were prepared to analyse the correlation between them. The results show a substantial increase in the area under high, very high and severe drought classes both pre- and post-monsoon seasons during the study period. The highest increase was noticed from the high drought class from 2532.45 to 4792.49 sq. km and 1559.84 to 3342.32 sq. km for pre- and post-monsoon season, respectively, based on the TCI and 1269.81 to 1787.77 sq. km in very high drought class for the post-monsoon season using the VCI. The correlation analysis showed that there exists a significant relationship between the land use indices and drought indices. However, the spatial pattern of correlation was heterogeneous for both pre- and post-monsoon seasons. The results of this research can help in the drought management and mitigation planning in the study area. In addition, a similar approach may be applied to analyse drought patterns in other places with similar geographic characteristics as both VCI and TCI are cost-effective and less time-consuming methods and produce reliable outcomes.

Mechanistic insights into the interplays between neutrophils and other immune cells in cancer development and progression.

Cancer is considered a major public health concern worldwide and is characterized by an uncontrolled division of abnormal cells. The human immune system recognizes cancerous cells and induces innate immunity to destroy those cells. However, sustained tumors may protect themselves by developing immune escape mechanisms through multiple soluble and cellular mediators. Neutrophils are the most plenteous leukocytes in the human blood and are crucial for immune defense in infection and inflammation. Besides, neutrophils emancipate the antimicrobial contents, secrete different cytokines or chemokines, and interact with other immune cells to combat and successfully kill cancerous cells. Conversely, many clinical and experimental studies signpost that being a polarized and heterogeneous population with plasticity, neutrophils, particularly their subpopulations, act as a modulator of cancer development by promoting tumor metastasis, angiogenesis, and immunosuppression. Studies also suggest that tumor infiltrating macrophages, neutrophils, and other innate immune cells support tumor growth and survival. Additionally, neutrophils promote tumor cell invasion, migration and intravasation, epithelial to mesenchymal transition, survival of cancer cells in the circulation, seeding, and extravasation of tumor cells, and advanced growth and development of cancer cells to form metastases. In this manuscript, we describe and review recent studies on the mechanisms for neutrophil recruitment, activation, and their interplay with different immune cells to promote their pro-tumorigenic functions. Understanding the detailed mechanisms of neutrophil-tumor cell interactions and the concomitant roles of other immune cells will substantially improve the clinical utility of neutrophils in cancer and eventually may aid in the identification of biomarkers for cancer prognosis and the development of novel therapeutic approaches for cancer treatment.

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.

One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.

Analysis of peri-urban land use/land cover change and its drivers using geospatial techniques and geographically weighted regression.

The rate of transformation of natural land use land cover (LULC) to the built-up areas is very high in the peri-urban areas of Indian metropolitan cities. Delhi National Capital Region (Delhi NCR) is an inter-state planning region, located in the central part of India. The region has attracted a larger chunk of population by providing better economic opportunities during last few decades. This has resulted in large-scale transformation of the LULC pattern in the region. Thus, this study is intended to analyze and quantify the LULC change and its drivers in the peri-urban areas of Delhi NCR using Landsat datasets. Based on an extensive literature survey, several potential drivers of the LULC change have been analyzed using ordinary least squares (OLS) and geographical weighted regression (GWR) for the Delhi NCR. The results from LULC classification showed that the built-up area has increased from 1.67 to 7.12% of the total area of Delhi NCR during 1990-2018 while other LULC types have declined significantly. The OLS results showed that migration and employment in the tertiary sector are the most important drivers of built-up expansion in the study area. The standard residuals and local R2 results from GWR showed spatial heterogeneity among the coefficients of the explanatory variables throughout the study area. This study can be helpful for the urban policy makers and planners for making better master plan of Delhi NCR and other cities of developing countries.

FDA Approval Summary: Sotorasib for KRAS G12C-Mutated Metastatic NSCLC.

On May 28, 2021, the FDA granted accelerated approval to sotorasib (Lumakras, Amgen) for the treatment of adults with advanced non-small cell lung cancer (NSCLC) with a Kirsten rat sarcoma proto-oncogene (KRAS) G12C mutation who have received at least one prior systemic therapy. The approval was based on CodeBreaK 100 (Study 20170543), a dose-escalation and dose-expansion trial in patients with an advanced, KRAS G12C-mutated, solid tumor. The overall response rate (ORR) observed in patients with KRAS G12C-mutated NSCLC treated with sotorasib (n = 124) was 36% [95% confidence interval (CI), 28-45]. The median duration of response was 10.0 months (95% CI, 6.9-not estimable). The most common adverse reactions (≥20%) were diarrhea, musculoskeletal pain, nausea, fatigue, hepatotoxicity, and cough. This is the first approval of a targeted therapy for KRAS G12C-mutated NSCLC. Because of pharmacokinetic data and ORRs of patient cohorts who took sotorasib at lower doses in the dose-escalation portion of CodeBreaK 100, a dose comparison study is being conducted as a post-marketing requirement.

Attitudes and Views Concerning Human Milk Banking Among Mothers Residing in a Rural Region of Bangladesh.

BACKGROUND: Human milk banks play an essential role by providing human milk to infants who would otherwise not be able to receive mother's milk. STUDY AIM: To determine the opinions and attitudes among possible donor mothers regarding human milk banks in one rural region in Bangladesh. METHODS: A prospective, cross-sectional study following a probability type of stratified cluster sampling technique was used. Included in the study (N = 121) were mothers aged 20-49 years, with at least one child, who was currently lactating or had breastfed her child, and who resided in the rural community of Bangladesh where the study was conducted. Data were collected through a 21-item, close-ended questionnaire and a face-to-face interview conducted by the researcher at each participant's home. RESULTS: Among the participants, 98.3% (n = 119) said that they had not heard about human milk banks before speaking with the researchers. Most participants (71.9%, n = 87) said would obtain human milk from milk banks if necessary, but 28% (n =34) of mothers indicated that they would not receive milk from a milk bank, even if it was necessary for their children. Only 8.3% (n = 10) said human milk banks were not appropriate according to Islam and 99.2% (n = 120) did not know about the acceptance of human milk banking practices in Bangladesh. CONCLUSIONS: For those with religious concerns, a framework for both the donors and recipients can be established. It can be recommended that health education through healthcare personnel (midwives, nurses, gynecologists, pediatricians, and other health professionals) and religious leaders may strengthen the belief and increase awareness among family members about milk banking practices.

Effect of neutralizers and silicone softeners on phenolic yellowing phenomenon of OBA treated cotton knitted fabric.

Phenolic yellowing is a complex physicochemical phenomenon of cotton, especially for white and pastel-colored fabrics. This study tried to analyze the effect of neutralizers and silicone softeners on this. Three types of neutralizers of different chemical characteristics (acetic acid, citric acid, and commercial complex acid) were used to achieve core neutralization whereas two unlike ionic nature of silicone softeners (cationic & non-ionic) for the finishing process used. The acetic acid and nonionic softener treated fabric gives the maximum CIE (International Commission on Illumination) whiteness index value of 151 while citric acid and cationic softener have the least rating of 140.93 with a similar chemical dosage of 1.5 g/l by immediate testing. However, after repeated testing for one-month conditioning in atmospheric conditions, complex acid, and nonionic softener treated samples exhibited the maximum whiteness index of 145.86; on the other hand, citric acid and cationic softener treated one had the least rating of 125.85. Moreover, fabric core pH, reading was found to be 6.68, 6.15 & 5.13 for acetic acid, citric acid, and complex acid through immediate testing. After one month of conditioning, the values were 7.2, 6.61, and 5.25, respectively. Finally, a maximum phenolic yellowing rating of 4-5 was found with complex acid & nonionic softener in contrast poor rating of 2 with acetic acid & cationic softener for immediate testing. Storage of the samples has a significant impact on phenolic yellowing for all types of chemical concentrations. Lightfastness rating was found identical for all samples while bursting strength had a very negligible impact, Fourier transform infrared (FT-IR) spectroscopy also revealed the presence of noticeable chemical functional groups.

Dementia in Media Coverage: A Comparative Analysis of Two Online Newspapers across Time.

The news media, specifically online newspapers, is one of the powerful transmitters of discourse due to its rapid accessibility that contributes to social beliefs and attitudes that often shape our perceptions on dementia and Alzheimer's disease. The media portrayal of dementia is largely heterogeneous, but there is certainly an association between the influence of online news coverage and the social perceptions of dementia that need to be understood more broadly. In this study, we examined the portrayal of dementia in two online newspapers (The New York Times and The Guardian) that might have an influence on dementia discourse by comparing the content and form of the news coverage on dementia across time. This study was guided by three interconnected theoretical understandings: cultivation theory, agenda-setting theory, and spiral of silence theory. A total of 291 published articles featuring dementia from 2014 to 2019 were included in this study and a content analysis of the articles provided insight into the dementia-related news coverage. Our results showed that both newspapers have a decreasing trend in publishing articles related to dementia over time. In addition, dementia-related (modifiable) risk factors as principal news content was significantly associated with the year of publication. Despite a weak association between story categories and newspapers, the majority of articles reported preventive measures as the main story category. Although both newspapers featured more articles with a less negative tone across time when reporting on dementia, derogative wording, as discourse, was commonly used to address the illness. We have provided some insight into understanding how online newspapers potentially affect subjective representations of dementia as well as perpetuate dementia discourse. Finally, we suggest that future study may benefit from establishing a linkage between the depiction of dementia in online newspapers and the contextualization of dementia within cultures.