Rotavirus seasonality and age effects in a birth cohort study of southern India

Introduction: Understanding the temporal patterns in disease occurrence is valuable for formulating effective disease preventive programs. Cohort studies present a unique opportunity to explore complex interactions associated with emergence of seasonal patterns of infectious diseases. Methods: We used data from 452 children participating in a birth cohort study to assess the seasonal patterns of rotavirus diarrhea by creating a weekly time series of rotavirus incidence and fitting a Poisson harmonic regression with biannual peaks. Age and cohort effects were adjusted for by including the weekly counts of number of children in the study and the median age of cohort in a given week. Weekly average temperature, humidity and an interaction term to reflect the joint effect of temperature and humidity were included to consider the effects of meteorological variables. Results: In the overall rotavirus time series, two significant peaks within a single year were observed – one in winter and the other in summer. The effect of age was found to be the most significant contributor for rotavirus incidence, showing a strong negative association. Seasonality remained a significant factor, even after adjusting for meteorological parameters, and the age and cohort effects. Conclusions: The methodology for assessing seasonality in cohort studies is not yet developed. This is the first attempt to explore seasonal patterns in a cohort study with a dynamic denominator and rapidly changing immune response on individual and group levels, and provides a highly promising approach for a better understanding of the seasonal patterns of infectious diseases, tracking emergence of pathogenic strains and evaluating the efficacy of intervention programs

Seasonal Synchronization of Influenza in the United States Older Adult Population

In temperate regions, influenza epidemics occur annually with the highest activity occurring during the winter months. While seasonal dynamics of the influenza virus, such as time of onset and circulating strains, are well documented by the Centers for Disease Control and Prevention Influenza Surveillance System, an accurate prediction of timing, magnitude, and composition of circulating strains of seasonal influenza remains elusive. To facilitate public health preparedness for seasonal influenza and to obtain better insights into the spatiotemporal behavior of emerging strains, it is important to develop measurable characteristics of seasonal oscillation and to quantify the relationships between those parameters on a spatial scale. The objectives of our research were to examine the seasonality of influenza on a national and state level as well as the relationship between peak timing and intensity of influenza in the United States older adult population.A total of 248,889 hospitalization records were extracted from the Centers for Medicare and Medicaid Services for the influenza seasons 1991-2004. Harmonic regression models were used to quantify the peak timing and absolute intensity for each of the 48 contiguous states and Washington, DC. We found that individual influenza seasons showed spatial synchrony with consistent late or early timing occurring across all 48 states during each influenza season in comparison to the overall average. On a national level, seasons that had an earlier peak also had higher rates of influenza (r(s) = -0.5). We demonstrated a spatial trend in peak timing of influenza; western states such as Nevada, Utah, and California peaked earlier and New England States such as Rhode Island, Maine, and New Hampshire peaked later.Our findings suggest that a systematic description of influenza seasonal patterns is a valuable tool for disease surveillance and can facilitate strategies for prevention of severe disease in the vulnerable, older adult population

Intonation of Russian Declarative Sentence: Methodology for Teaching Foreign Students

The article deals with teaching Russian intonation of declarative sentences to foreign students. The emphasis is placed on the way teaching materials are presented. In particular, the variable rows for intonation patterns in declarative sentences have been developed, as well as the teaching of syntagmatic segmentation and intonation patterns in complex utterances. This method of working with foreigners is efficient for teaching foreign students the Russian declarative intonation which manifests in verbal communication and reading

Spatiotemporal modeling of schistosomiasis in Ghana: linking remote sensing data to infectious disease

More than 90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. The use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. The transmission of schistosomiasis, a disease acquired from contact with contaminated surface water, requires specific environmental conditions to sustain freshwater snails. If a connection between schistosomiasis and remotely sensed environmental variables can be established, then cost effective and current disease risk predictions can be made available. Schistosomiasis transmission has unknown seasonality, and the disease is difficult to study due to a long lag between infection and clinical symptoms. To overcome these challenges, we employed a comprehensive 15-year time-series built from remote sensing feeds, which is the longest environmental dataset to be used in the application of remote sensing to schistosomiasis. The following environmental variables will be used in the model: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique. This technique, improves upon the conventional Köppen-Geiger method, which has been the primary climate classification system in use the past 100 years. These predictor variables will be regressed against 8 years of national health data in Ghana, the largest health dataset of its kind to be used in this context, and acquired from freely available satellite imagery data. A benefit of remote sensing processing is that it only requires training and time in terms of resources. The results of a fixed effects model can be used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.Published versio

Influence of an internal picture of illness, coping-strategies and the self-relation of the patients after myocardial infarction on adherence to long treatment of coronary artery disease and its regularity

Patients after myocardial infarction have been interrogated by the questionnaires, allowing to establish prevailing coping-strategy in a difficult reality situation (an illness situation), the internal picture of illness, level of the general internal conflictness, feature of the self-relation, and also model of the doctor-patient relationships from a position of the patient. Telephone contact to patients has been carried out after 12 months from discharging from a hospital and adherence of the therapy recommended in a hospital and its regularity was found out. Adherence to long treatment in patients after myocardial infarction is higher in case of anxieting and sensitive internal picture of illness, the positive self-relation as a whole and constant close cooperation with the attending physician

Interdisciplinary cooperation between dentists and endocrinologists for identification and management of diabetes mellitus

Background: DM mellitus (DM) leads to worsening periodontal diseases, and in turn the inflammatory diseases of maxillofacial region adversely affect the glycemic control and exacerbate the severity of DM, thereby engendering a vicious cycle that compromises the DM management in patients. Taking account of the bidirectional relationship between DM and periodontal disease, interdisciplinary examination of patients with both DM and periodontal diseases is warranted to improve the health outcomes in patients. Aims: This study aims to evaluate the perceptions of dentists and endocrinologists on the interdisciplinary cooperation for identification and management of patients with DM. Materials and methods: Studying patients’ knowledge about DM and their compliance in providing endocrinological recommendations, dental screening survey to identify DM’ risk and signs The research was done in 2015-2016 years using clinical survey (dental status survey), statistical analysis. 432 patients from different dental organizations and 433 doctors (371 – dentists and 62 – endocrinologists) took part in the research. The research was approved by Regional research ethics committee. The written informed consent was taken from each participant. Results: There was insufficient interdisciplinary collaboration for identification and management of patients with diabetes, and lack of motivation among dental patients to endocrinological survey. Hence, it is important to incorporate definitive screening for risk of DM for patients with inflammatory periodontal disease and include dentists in consultation for patients with DM. The feasibility of statutory determination of collaboration between specialists in identification and management of patients with DM was found, dental lectures are necessary in DM school. Conclusions: Our findings suggest the necessity of including dentists in the standard of medical management of patients with DM and incorporating DM screening by a questionnaire upon dental examination

Elasticity Maps of Living Neurons Measured by Combined Fluorescence and Atomic Force Microscopy

Detailed knowledge of mechanical parameters such as cell elasticity, stiffness of the growth substrate, or traction stresses generated during axonal extensions is essential for understanding the mechanisms that control neuronal growth. Here we combine Atomic Force Microscopy based force spectroscopy with Fluorescence Microscopy to produce systematic, high-resolution elasticity maps for three different types of live neuronal cells: cortical (embryonic rat), embryonic chick dorsal root ganglion, and P-19 (mouse embryonic carcinoma stem cells) neurons. We measure how the stiffness of neurons changes both during neurite outgrowth and upon disruption of microtubules of the cell. We find reversible local stiffening of the cell during growth, and show that the increase in local elastic modulus is primarily due to the formation of microtubules. We also report that cortical and P-19 neurons have similar elasticity maps, with elastic moduli in the range 0.1-2 kPa, with typical average values of 0.4 kPa (P-19) and 0.2 kPa (cortical). In contrast, DRG neurons are stiffer than P-19 and cortical cells, yielding elastic moduli in the range 0.1-8 kPa, with typical average values of 0.9 kPa. Finally, we report no measurable influence of substrate protein coating on cell body elasticity for the three types of neurons

The use of remotely sensed environmental parameters for spatial and temporal schistosomiasis prediction across climate zones in Ghana

Schistosomiasis control in sub-Saharan Africa is enacted primarily through preventive chemotherapy. Predictive models can play an important role in filling knowledge gaps in the distribution of the disease and help guide the allocation of limited resources. Previous modeling approaches have used localized cross-sectional survey data and environmental data typically collected at a discrete point in time. In this analysis, 8 years (2008-2015) of monthly schistosomiasis cases reported into Ghana's national surveillance system were used to assess temporal and spatial relationships between disease rates and three remotely sensed environmental variables: land surface temperature (LST), normalized difference vegetation index (NDVI), and accumulated precipitation (AP). Furthermore, the analysis was stratified by three major and nine minor climate zones, defined using a new climate classification method. Results showed a downward trend in reported disease rates (~ 1% per month) for all climate zones. Seasonality was present in the north with two peaks (March and September), and in the middle of the country with a single peak (July). Lowest disease rates were observed in December/January across climate zones. Seasonal patterns in the environmental variables and their associations with reported schistosomiasis infection rates varied across climate zones. Precipitation consistently demonstrated a positive association with disease outcome, with a 1-cm increase in rainfall contributing a 0.3-1.6% increase in monthly reported schistosomiasis infection rates. Generally, surveillance of neglected tropical diseases (NTDs) in low-income countries continues to suffer from data quality issues. However, with systematic improvements, our approach demonstrates a way for health departments to use routine surveillance data in combination with publicly available remote sensing data to analyze disease patterns with wide geographic coverage and varying levels of spatial and temporal aggregation.Accepted manuscrip