Summary In this study we analysed the effects of age on T and B lymphocytes in human lymph nodes by comparing lymphocyte subsets in paraffin sections from lymph node tissue taken from healthy young and elderly people. We demonstrate that the relative number of CD8+ T cells decreases with age but that the relative number of CD4+ T cells does not. There is also a very pronounced age‐dependent loss of CD45RA+ naïve T cells. The number and size of follicles and the relative number of CD20+ B cells are similar in young and elderly donors. For polymerase chain reaction analysis of the T‐cell receptor (TCR) repertoire the TCR‐γ gene rearrangements were used as a marker of clonality. This is a reliable tool to detect not only clonal TCR‐γδ populations but also TCR‐αβ populations. Young donors with clonal T‐cell expansions in their lymph node tissue do, however, have a higher number of CD20+ B cells, a higher relative size of germinal centres compared to the follicle mantles and a higher number of immunoglobulin M‐expressing cells than young donors without evidence of clonal T‐cell expansions. Corresponding changes are not observed in elderly donors with clonal T‐cell expansions in their lymph node tissue. In summary our findings demonstrate characteristic effects of aging on human lymph node tissue, the most striking feature being the depletion of naïve T cells and the apparent dysregulation of T‐cell/B‐cell interactions in old age.
BackgroundThe healthcare sector is a significant contributor to global carbon emissions, in part due to extensive travelling by patients and health workers.ObjectivesTo evaluate the potential of telemedicine services based on videoconferencing technology to reduce travelling and thus carbon emissions in the healthcare sector.MethodsA life cycle inventory was performed to evaluate the carbon reduction potential of telemedicine activities beyond a reduction in travel related emissions. The study included two rehabilitation units at Umeå University Hospital in Sweden. Carbon emissions generated during telemedicine appointments were compared with care-as-usual scenarios. Upper and lower bound emissions scenarios were created based on different teleconferencing solutions and thresholds for when telemedicine becomes favorable were estimated. Sensitivity analyses were performed to pinpoint the most important contributors to emissions for different set-ups and use cases.ResultsReplacing physical visits with telemedicine appointments resulted in a significant 40–70 times decrease in carbon emissions. Factors such as meeting duration, bandwidth and use rates influence emissions to various extents. According to the lower bound scenario, telemedicine becomes a greener choice at a distance of a few kilometers when the alternative is transport by car.ConclusionsTelemedicine is a potent carbon reduction strategy in the health sector. But to contribute significantly to climate change mitigation, a paradigm shift might be required where telemedicine is regarded as an essential component of ordinary health care activities and not only considered to be a service to the few who lack access to care due to geography, isolation or other constraints.
Background: Digital traces are rapidly used for health monitoring purposes in recent years. This approach is growing as the consequence of increased use of mobile phone, Internet, and machine learning. Many studies reported the use of Google Trends data as a potential data source to assist traditional surveillance systems. The rise of Internet penetration (54.7%) and the huge utilization of Google (98%) indicate the potential use of Google Trends in Indonesia. No study was performed to measure the correlation between country wide official dengue reports and Google Trends data in Indonesia. Objective: This study aims to measure the correlation between Google Trends data on dengue fever and the Indonesian national surveillance report. Methods: This research was a quantitative study using time series data (2012–2016). Two sets of data were analyzed using Moving Average analysis in Microsoft Excel. Pearson and Time lag correlations were also used to measure the correlation between those data. Results: Moving Average analysis showed that Google Trends data have a linear time series pattern with official dengue report. Pearson correlation indicated high correlation for three defined search terms with R-value range from 0.921 to 0.937 (p ≤ 0.05, overall period) which showed increasing trend in epidemic periods (2015–2016). Time lag correlation also indicated that Google Trends data can potentially be used for an early warning system and novel tool to monitor public reaction before the increase of dengue cases and during the outbreak. Conclusions: Google Trends data have a linear time series pattern and statistically correlated with annual official dengue reports. Identification of information-seeking behavior is needed to support the use of Google Trends for disease surveillance in Indonesia.
One of the most striking changes in the primary lymphoid organs during human aging is the progressive involution of the thymus. As a consequence, the rate of naïve T cell output dramatically declines with age and the peripheral T cell pool shrinks. These changes lead to increased incidence of severe infections and decreased protective effect of vaccinations in the elderly. Little is, however, known of the composition and function of the residual naïve T cell repertoire in elderly persons. To evaluate the impact of aging on the naïve T cell pool, we investigated the quantity, phenotype, function, composition, and senescence status of CD45RA(+)CD28(+) human T cells--a phenotype generally considered as naïve cells--from both young and old healthy donors. We found a significant decrease in the number of CD45RA(+)CD28(+) T cells in the elderly, whereas the proliferative response of these cells is still unimpaired. In addition to their reduced number, CD45RA(+)CD28(+) T cells from old donors display significantly shorter telomeres and have a restricted TCR repertoire in nearly all 24 Vbeta families. These findings let us conclude that naïve T cells cannot be classified with conventional markers in old age.
Research is needed to create early warnings of dengue outbreaks to inform stakeholders and control the disease. This analysis composes of a comparative set of prediction models including only meteorological variables; only lag variables of disease surveillance; as well as combinations of meteorological and lag disease surveillance variables. Generalized linear regression models were used to fit relationships between the predictor variables and the dengue surveillance data as outcome variable on the basis of data from 2001 to 2010. Data from 2011 to 2013 were used for external validation purposed of prediction accuracy of the model. Model fit were evaluated based on prediction performance in terms of detecting epidemics, and for number of predicted cases according to RMSE and SRMSE, as well as AIC. An optimal combination of meteorology and autoregressive lag terms of dengue counts in the past were identified best in predicting dengue incidence and the occurrence of dengue epidemics. Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods. A combination of surveillance and meteorological data including lag patterns up to a few years in the past showed most predictive of dengue incidence and occurrence in Yogyakarta, Indonesia. The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead. Prior studies support the fact that past meteorology and surveillance data can be predictive of dengue. However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population.
Effort to control dengue transmission requires community participation to ensure its sustainability. We carried out a knowledge attitude and practice (KAP) survey of dengue prevention to inform the design of a vector control intervention. A cross-sectional survey was conducted in June–August 2014 among 521 households in two villages of Yogyakarta, Indonesia. Demographic characteristics and KAP questions were asked using a self-managed questionnaire. Knowledge, attitudes and practice scores were summarized for the population according to sex, age, occupation and education. The average knowledge score was rather poor—3.7 out of 8—although both attitude and practice scores were good: 25.5 out of 32 and 9.2 out of 11 respectively. The best knowledge within the different groups were found among women, the age group 30–44 years, people with a university degree and government employees. Best practice scores were found among retired people and housewives. There were several significant gaps in knowledge with respect to basic dengue symptoms, preventive practices and biting and breeding habits of the Aedes mosquito. In contrast, people’s practices were considered good, although many respondents failed to recognize outdoor containers as mosquito breeding sites. Accordingly, we developed a vector control card to support people’s container cleaning practices. The card was assessed for eight consecutive weeks in 2015, with pre-post larvae positive houses and containers as primary outcome measures. The use of control cards reached a low engagement of the community. Despite ongoing campaigns aiming to engage the community in dengue prevention, knowledge levels were meagre and adherence to taught routines poor in many societal groups. To increase motivation levels, bottom-up strategies are needed to involve all community members in dengue control, not only those that already comply with best practices.
Background. Stroke remains one of the most common noncommunicable diseases among Indonesian populations. This study aimed to identify the prevalence of stroke and its associated risk factors in the Sleman District of Yogyakarta Special Region, Indonesia. Method. This study was a secondary analysis of community-based data collected by the Sleman Health and Demographic Surveillance System (HDSS) in 2016. Basic demographic and socioeconomic data were collected. Additional questions about history of stroke and other chronic diseases were interviewed as a self-reported diagnosis. History of hormonal contraceptives use and dietary patterns were also collected. We examined the association between the prevalence of stroke and risk factors, namely, age, gender, self-reported history of chronic diseases, hormonal contraceptives use, and high-risk dietary patterns. Results. The survey included 4,996 households composed of 20,465 individuals. Data regarding stroke incidents were available from 13,605 subjects aged ≥20 years old. Among them, a total of 4,884 subjects also have data regarding stroke risk factors. The overall prevalence of stroke in Sleman District was 1.4% (0.5% men and 0.90% women). The prevalence increased with additional decades of age (p<0.001). In a multivariable model, increasing age, self-reported history of hypertension (OR=8.37, 95%CI: 4.76 to 14.69), and self-reported history of diabetes mellitus (OR=2.87, 95%CI: 1.54 to 5.35) were significantly associated with stroke. Conclusions. A community-based survey in Indonesia showed a high prevalence of stroke which was associated with increasing age, hypertension, and diabetes mellitus. These findings suggest that preventive actions against the aforementioned modifiable risk factors should be prioritized.
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