Association of vitamin D receptor gene polymorphisms with type 2 diabetes mellitus in Taif population: a case-control study

Several reasons may underlie the dramatic increase in type2 diabetes mellitus. One of these reasons is the genetic basis and variations. Vitamin D receptor polymorphisms are associated with different diseases such as rheumatoid arthritis and diabetes. The aim of this study is to investigate the possible association of two identified mutations ApaI (rs7975232) and TaqI (rs731236). Eighty-nine healthy individuals and Fifty-six Type 2 Diabetic (T2D) patients were investigated using RFLP technique for genotyping and haplotyping as well. The distribution of Apal genotypes was not statistically significant among the control (P=0.65) as well as for diabetic patients (P=0.58). For Taql allele frequencies of T allele was 0.61 where of G allele was 0.39. The frequency distribution of Taql genotypes was not statistically significant among the control (P=0.26) as well as diabetic patients (P=0.17). Relative risk of the allele T of Apa1 gene is 1.28 and the odds ratio of the same allele is 1.53, while both estimates were < 1.0 of the allele G. Similarly, with the Taq1 gene the relative risk and the odds ratio values for the allele T are 1.09 and 1.27 respectively and both estimates of the allele C were 0.86 for the relative risk and 0.79 for the odds ratio. The pairwise linkage disequilibrium between the two SNPs Taq1/apa1 was statistically significant in control group (D = 0.218, D' = 0.925 and P value < 0.001) and similar data in diabetic groups (D = 0.2, D' = 0.875 and P value < 0.001). These data suggest that the T allele of both genes Apa1 and Taq1 is associated with the increased risk of type 2 diabetes. We think that we need a larger number of volunteers to reach a more accurate conclusion.

The statistical analysis of daily data associated with different parameters of the New Coronavirus COVID-19 pandemic in Georgia and their monthly interval prediction from September 1, 2021 to December 31, 2021

The lockdown introduced in Georgia on November 28, 2020 contributed to positive trends in the spread of COVID-19 until February - the first half of March 2021. Then, in April-May 2021, the epidemiological situation worsened significantly, and from June to the end of December COVID - situation in Georgia was very difficult. In this work results of the next statistical analysis of the daily data associated with New Coronavirus COVID-19 infection of confirmed (C), recovered (R), deaths (D) and infection rate (I) cases of the population of Georgia in the period from September 01, 2021 to December 31, 2021 are presented. It also presents the results of the analysis of monthly forecasting of the values of C, D and I. As earlier, the information was regularly sent to the National Center for Disease Control & Public Health of Georgia and posted on the Facebook page https://www.facebook.com/Avtandil1948/. The analysis of data is carried out with the use of the standard statistical analysis methods of random events and methods of mathematical statistics for the non-accidental time-series of observations. In particular, the following results were obtained. Georgia's ranking in the world for Covid-19 monthly mean values of infection and deaths cases in investigation period (per 1 million population) was determined. Among 157 countries with population ≥ 1 million inhabitants in October 2021 Georgia was in the 4 place on new infection cases, and in September - in the 1 place on death. Georgia took the best place in terms of confirmed cases of diseases (thirteenth) in December, and in mortality (fifth) - in October. A comparison between the daily mortality from Covid-19 in Georgia from September 01, 2021 to December 31, 2021with the average daily mortality rate in 2015-2019 shows, that the largest share value of D from mean death in 2015-2019 was 76.8 % (September 03, 2021), the smallest 18.7 % (November 10, 2021). As in previous work [9,10] the statistical analysis of the daily and decade data associated with coronavirus COVID-19 pandemic of confirmed, recovered, deaths cases and infection rate of the population of Georgia are carried out. Maximum daily values of investigation parameters are following: C = 6024 (November 3, 2021), R = 6017 (November 15, 2021), D = 86 (September 3, 2021), I = 12.04 % (November 24, 2021). Maximum mean decade values of investigation parameters are following: C = 4757 (1 Decade of November 2021), R = 4427 (3 Decade of November 2021), D = 76 (2 Decade of November 2021), I = 10.55 % (1 Decade of November 2021). It was found that as in spring and summer 2021 [9,10], from September to December 2021 the regression equations for the time variability of the daily values of C, R, D and I have the form of a tenth order polynomial. Mean values of speed of change of confirmed -V(C), recovered - V(R), deaths - V(D) and infection rate V(I) coronavirus-related cases in different decades of months for the indicated period of time were determined. Maximum mean decade values of investigation parameters are following: V(C) = +139 cases/day (1 Decade of October 2021), V(R) = +124 cases/day (3 Decade of October 2021), V(D) = +1.7 cases/day (3 Decade of October 2021), V(I) = + 0.20 %/ day (1 decades of October 2021). Cross-correlations analysis between confirmed COVID-19 cases with recovered and deaths cases shows, that from September 1, 2021 to November 30, 2021 the maximum effect of recovery is observed on 12 and 14 days after infection (CR=0.77 and 0.78 respectively), and deaths - after 7, 9, 11, 13 and 14 days (0.70≤CR≤0.72); from October 1, 2021 to December 31, 2021 - the maximum effect of recovery is observed on 14 days after infection (RC=0.71), and deaths - after 9 days (CR=0.43). In Georgia from September 1, 2021 to November 30, 2021 the duration of the impact of the delta variant of the coronavirus on people (recovery, mortality) could be up to 28 and 35 days respectively; from October 1, 2021 to December 31, 2021 - up to 21 and 29 days respectively. Comparison of daily real and calculated monthly predictions data of C, D and I in Georgia are carried out. It was found that in investigation period of time daily and mean monthly real values of C, D and I practically fall into the 67% - 99.99% confidence interval of these predicted values. Traditionally, the comparison of data about C and D in Georgia (GEO) with similar data in Armenia (ARM), Azerbaijan (AZE), Russia (RUS), Turkey (TUR) and in the World (WRL) is also carried out.

Nationwide COVID-19-EII Study: Incidence, Environmental Risk Factors and Long-Term Follow-Up of Patients with Inflammatory Bowel Disease and COVID-19 of the ENEIDA Registry

We aim to describe the incidence and source of contagion of COVID-19 in patients with IBD, as well as the risk factors for a severe course and long-term sequelae. This is a prospective observational study of IBD and COVID-19 included in the ENEIDA registry (53,682 from 73 centres) between March–July 2020 followed-up for 12 months. Results were compared with data of the general population (National Centre of Epidemiology and Catalonia). A total of 482 patients with COVID-19 were identified. Twenty-eight percent were infected in the work environment, and 48% were infected by intrafamilial transmission, despite having good adherence to lockdown. Thirty-five percent required hospitalization, 7.9% had severe COVID-19 and 3.7% died. Similar data were reported in the general population (hospitalisation 19.5%, ICU 2.1% and mortality 4.6%). Factors related to death and severe COVID-19 were being aged ≥ 60 years (OR 7.1, 95% CI: 1.8–27 and 4.5, 95% CI: 1.3–15.9), while having ≥2 comorbidities increased mortality (OR 3.9, 95% CI: 1.3–11.6). None of the drugs for IBD were related to severe COVID-19. Immunosuppression was definitively stopped in 1% of patients at 12 months. The prognosis of COVID-19 in IBD, even in immunosuppressed patients, is similar to that in the general population. Thus, there is no need for more strict protection measures in IBD.

Parental Disease Specific Knowledge and Its Impact on Health-Related Quality of Life

Objective: Structured education programs have been shown to improve somatic outcome and health-related quality of life (HRQOL) in a variety of chronic childhood diseases. Similar data are scarce in paediatric liver transplantation (pLTx). The purpose of this study was to examine the relationship of parental disease-specific knowledge and psychosocial disease outcome in patients after pLTx. Methods: Parents of 113 children (chronic liver disease n = 25, after pLTx n = 88) completed the transplant module of the HRQOL questionnaire PedsQL, the “Ulm quality of life inventory for parents of children with chronic diseases” ULQUI, and a tailor-made questionnaire to test disease-specific knowledge. Results: Parental knowledge was highest on the topic of “liver transplantation” and lowest in “basic background knowledge” (76% and 56% correct answers respectively). Knowledge performance was only marginally associated with HRQOL scores, with better knowledge being related to worse HRQOL outcomes. In contrast, self-estimation of knowledge performance showed significant positive correlations with both PedsQL and ULQUI results. Conclusion: Patient HRQOL and parental emotional wellbeing after pLTx are associated with positive self-estimation of parental disease-specific knowledge. Objective disease-specific knowledge has little impact on HRQOL. Parental education programs need to overcome language barriers and address self-efficacy in order to improve HRQOL after pLTx.

Sørensen-Dice Similarity Indexing based Weighted Iterative Clustering for Big Data Analytics

Big data is a collection of large volume of data and extract similar data points from large dataset. Clustering is an essential data mining technique for examining large volume of data. Several techniques have been developed for handling big dataset. However, with much time consumption and space complexity, accuracy is said to be compromised. In order to improve clustering accuracy with less complexity, Sørensen-Dice Indexing based Weighted Iterative X-means Clustering (SDI-WIXC) technique is introduced. SDI-WIXC technique is used for grouping the similar data points with higher clustering accuracy and minimal time. First, number of data points is collected from big dataset. Then, along with the weight value, the given dataset is partitioned into ‘X’ number of clusters. Next, based on the similarity measure, Weighted Iterated X-means Clustering (WIXC) is applied for clustering data points. Sørensen-Dice Indexing Process is used for measuring similarity between cluster weight value and data points. Upon similarity found between weight value of cluster and data point, data points are grouped into a specific cluster. Besides, the WIXC method also improves the cluster assignments through repeated subdivision using Bayesian probability criterion. This in turn helps to group all data points and hence, improving the clustering accuracy. Experimental evaluation is carried out with number of factors such as clustering accuracy, clustering time and space complexity with respect to the number of data points. The experimental results reported that the proposed SDI-WIXC technique obtains high clustering accuracy with minimum time as well as space complexity.

Prevalence of osteopenia and osteoporosis among the population of southern Kyrgyzstan - A cross-sectional observational study

Introduction and Aim: Osteoporosis is a skeletal disorder characterized by diminished bone strength that increases the risk of fracture in instances of trivial trauma. The objective was to conduct ultrasound bone densitometry in different age groups (18-60 years and older) in southern Kyrgyzstan, to identify and study the prevalence of osteopenia and osteoporosis. Materials and Methods: In this cross-sectional observational study a total of 1200 participants were included, where 580 men and 620 women were aged between 18-60 years and older. Based on the age, the participants were divided into three groups. Bone mineral density in participants was measured using a SONOST-3000 densitometer model. The study was conducted among the population of the Osh and Jalal-Abad regions. Results: Among the population of Osh state in the first group, normal values were found in 65.0%, osteopenia in 26.0%, and osteoporosis in 9.0% of participants. In the second group, values were significantly higher than in the first group. In the third age group, values exceeded significantly compared to the first and second groups. Similar data were obtained from the population of Jalal-Abad state, but a significant difference was found in the elderly people group with a higher percentage of osteopenia and osteoporosis. Conclusion: The results showed the prevalence of osteopenia and osteoporosis in participants of different age categories of Osh and Jalal-Abad states, and especially in the elderly. Depending on the gender distribution, the prevalence of osteopenia and osteoporosis in our study is significantly higher in women than in men.

MI3: Machine-initiated Intelligent Interaction for Interactive Classification and Data Reconstruction

In many applications, while machine learning (ML) can be used to derive algorithmic models to aid decision processes, it is often difficult to learn a precise model when the number of similar data points is limited. One example of such applications is data reconstruction from historical visualizations, many of which encode precious data, but their numerical records are lost. On the one hand, there is not enough similar data for training an ML model. On the other hand, manual reconstruction of the data is both tedious and arduous. Hence, a desirable approach is to train an ML model dynamically using interactive classification, and hopefully, after some training, the model can complete the data reconstruction tasks with less human interference. For this approach to be effective, the number of annotated data objects used for training the ML model should be as small as possible, while the number of data objects to be reconstructed automatically should be as large as possible. In this article, we present a novel technique for the machine to initiate intelligent interactions to reduce the user’s interaction cost in interactive classification tasks. The technique of machine-initiated intelligent interaction (MI3) builds on a generic framework featuring active sampling and default labeling. To demonstrate the MI3 approach, we use the well-known cholera map visualization by John Snow as an example, as it features three instances of MI3 pipelines. The experiment has confirmed the merits of the MI3 approach.

Modelos da Produtividade Primária Bruta em área de floresta tropical em sazonalmente seca, usando dados reflectância da vegetação de caatinga

R E S U M OArtigo recebido em XX/XX/2021 e aceito em XX/XX/2021 Os estudos da troca de energia nos ecossistemas fornecem informações importantes para a compreensão da Produtividade nos ecossistemas. A vegetação é um dos principais elementos da biosfera terrestre sendo responsável pela avaliação e funcionamento da atividade fotossintética bem como para as trocas de carbono entre os ecossistemas e a atmosfera. Neste contexto, a PPB é utilizada para avaliar, planejar e gerenciar os recursos ambientais frente as mudanças climáticas globais. Esse estudo tem por objetivo avaliar a Produção Primária Bruta no Bioma da Caatinga em Pernambuco. O estudo foi realizado na área de Floresta Tropical Sazonalmente Seca, a Caatinga no Estado de Pernambuco. Utilizou-se a refletância da superfície do produto (MOD09) a partir do MODIS/TERRA satélite do sensor, a refletância de superfície (SR) Landsat-8 e a reflectancia a superficie do fieldspec. Foram adquiridas nove cenas para o produto (MOD09), seis cenas para a refletância de superfície (SR) Landsat-8 e as mesmas datas das imagens foram utilizados os espectros de campo (filedspec). Foi realizada a seleção de amostras espectrais na imagem (espectros de referência), considerando o ponto espectral do local de coleta. Os modelos foram construídos a partir das combinações das bandas (ρ_350, ρ_351, ρ_352, ..., ρ_2500) suas transformações (ρ, 1/ρ, ln⁡(ρ), log_10⁡(ρ), √ρ, ρ^2, e^ρ). Os desempenhos dos modelos foram avaliados utilizando dois índices estatísticos, um de tendência (coeficiente de Pearson– r) e outro de desvio (Erro médio quadrático (RMSE– RMSE), e o PBIAS. Os resultados apontaram que os modelos calibrados demostraram bom desempenho na previsão com o uso das bandas do sensor OLI/Landsat 8 e do MODIS/Terra (MOD09GA). Models of Gross Primary Productivity in a seasonally dry tropical forest area using reflectance data from the Caatinga vegetationA B S T R A C TThe studies of energy exchange in ecosystems provide important information for the understanding of Productivity in ecosystems. Vegetation is one of the main elements of the terrestrial biosphere and is responsible for the evaluation and functioning of photosynthetic activity as well as for carbon exchanges between ecosystems and the atmosphere. In this context, a PPB is used to assess, plan and manage environmental resources in the face of global climate change. This study aims to evaluate a Gross Primary Production in the Caatinga Biome in Pernambuco. The study was carried out in the Seasonally Dry Tropical Forest, a Caatinga in the State of Pernambuco. Use the product's surface reflectance (MOD09) from the sensor's MODIS / TERRA satellite and the Landsat-8 surface reflectance (SR), nine scenes for the product (MOD09), six scenes for surface reflectance (SR) Landsat-8 and similar data with fieldspec. A selection of spectral members in the image (reference spectra) was carried out, considering the spectral point of the collection site. The models were built from the combinations of the bands (ρ_350, ρ_351, ρ_352, ..., ρ_2500) their transformations (ρ, 1/ρ, ln⁡(ρ), log_10⁡(ρ), √ρ, ρ^2, e^ρ). The performances of the models were taken using two statistical indices, one of trend (Pearson's coefficient - r) and another of deviation (Mean square error (RMSE - RMSE), and PBIAS. The results showed that the calibrated models showed good performance in prediction using the OLI / Landsat 8 and MODIS / Terra (MOD09GA) bands.Keyword: Remote sensing, FieldSpec®3, Caatinga

Analisis Standar Belanja (ASB) Pada Pemerintah Kabupaten Padang Pariaman

In general, regional financial management goes through the following stages: budget preparation, activity implementation and financial accountability or financial report preparation. Based on the above, Permendagri No. 21 of 2011 concerning Guidelines for Regional Financial Management, states the need for Standard Expenditure Analysis (ASB) as a main research tool in conducting performance-based budgeting. Expenditure Standard Analysis (ASB) is an assessment of the fairness of the costs and workloads used to carry out an activity at each Regional Apparatus Organization (OPD). This study aims to determine the reasonableness of spending in carrying out an activity so as to minimize unclear expenses that cause budget inefficiency. This research was conducted at the Regional Government of Padang Pariaman Regency, West Sumatra. The stages of preparing this Standard Expenditure Analysis (ASB) are to input data and group similar data, determine the Cost Driver, create regression equations, determine the average, upper and lower limits, and analyze the fairness of costs and workloads of the ASB model. has been compiled. The preparation of a Standard Expenditure Analysis (ASB) at the District Government of Padang Pariaman which is discussed in this is for the types of training activities for personnel and training for the community.

A comprehensive high cost drugs dataset from the NHS in England - An OpenSAFELY-TPP Short Data Report

Background: At the outset of the COVID-19 pandemic, there was no routine comprehensive hospital medicines data from the UK available to researchers. These records can be important for many analyses including the effect of certain medicines on the risk of severe COVID-19 outcomes. With the approval of NHS England, we set out to obtain data on one specific group of medicines, “high-cost drugs” (HCD) which are typically specialist medicines for the management of long-term conditions, prescribed by hospitals to patients. Additionally, we aimed to make these data available to all approved researchers in OpenSAFELY-TPP. This report is intended to support all studies carried out in OpenSAFELY-TPP, and those elsewhere, working with this dataset or similar data. Methods: Working with the North East Commissioning Support Unit and NHS Digital, we arranged for collation of a single national HCD dataset to help inform responses to the COVID-19 pandemic. The dataset was developed from payment submissions from hospitals to commissioners. Results: In the financial year (FY) 2018/19 there were 2.8 million submissions for 1.1 million unique patient IDs recorded in the HCD. The average number of submissions per patient over the year was 2.6. In FY 2019/20 there were 4.0 million submissions for 1.3 million unique patient IDs. The average number of submissions per patient over the year was 3.1. Of the 21 variables in the dataset, three are now available for analysis in OpenSafely-TPP: Financial year and month of drug being dispensed; drug name; and a description of the drug dispensed. Conclusions: We have described the process for sourcing a national HCD dataset, making these data available for COVID-19-related analysis through OpenSAFELY-TPP and provided information on the variables included in the dataset, data coverage and an initial descriptive analysis.