Estimation of illuminance on the south facing surfaces for clear skies in Iran

Background: Daylight availability data are essential for designing effectively day lighted buildings. In respect to no available daylight availability data in Iran, illuminance data on the south facing vertical surfaces were estimated using a proper method. Methods: An illuminance measuring set was designed for measuring vertical illuminances for standard times over 15 days at one hour intervals from 9 a.m. to 3 p.m. at three measuring stations (Hamadan, Eshtehard and Kerman). Measuring data were used to confirm predicted by the IESNA method. Results: Measurement of respective illuminances on the south vertical surfaces resulted in minimum values of 10.5 KLx, mean values of 33.59 KLx and maximum values of 79.6 KLx. Conclusion: In this study was developed a regression model between measured and calculated data of south facing vertical illuminance. This model, have a good linear correlation between measured and calculated values (r= 0.892)

New Insight of microRNAs & short interfering RNA in Treatment of COVID-19; a Narrative Review

Since 31 December 2019, the coronavirus disease 2019 (COVID-19) resulted in a state of hyperinflammation syndrome and multiorgan failure. In areas with pandemic outbreaks, despite several emerging vaccines, supportive treatments to mitigate fatality rates were required. Growing evidence suggests that several small RNAs such as microRNAs (miRNAs) and short interfering RNA (siRNA) could be candidates for the treatment of COVID-19 by inhibiting the expression of crucial virus genes. small RNAs by binding to the 3′-untranslated region (UTR) or 5′-UTR of viral RNA play an important role in COVID-19-host interplay and viral replication. In this review, the authors sought to specify the efficacy and safety of miRNAs and siRNA expressions of patients with COVID-19, which has an axial role in the pathogenesis of human diseases

Supervised wavelet method to predict patient survival from gene expression data.

In microarray studies, the number of samples is relatively small compared to the number of genes per sample. An important aspect of microarray studies is the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on combining wavelet approximation coefficients and Cox regression was presented. The proposed method was compared with supervised principal component and supervised partial least squares methods. The different fitted Cox models based on supervised wavelet approximation coefficients, the top number of supervised principal components, and partial least squares components were applied to the data. The results showed that the prediction performance of the Cox model based on supervised wavelet feature extraction was superior to the supervised principal components and partial least squares components. The results suggested the possibility of developing new tools based on wavelets for the dimensionally reduction of microarray data sets in the context of survival analysis

Modeling the trajectory of CD4 cell count and its effect on the risk of AIDS progression and TB infection among HIV-infected patients using a joint model of competing risks and longitudinal ordinal data

Background: This study was conducted to better understand the influence of prognostic factors and the trend of CD4 cell count on the risk of progression to acquired immunodeficiency syndrome (AIDS) and tuberculosis (TB) infection among patients with human immunodeficiency virus (HIV) in a developing country.  Methods: The information of 1530 HIV-infected patients admitted in Behavioral Diseases Counseling Centers, Tehran, Iran, (2004-2014) was analyzed in this study. A joint model of ordinal longitudinal outcome and competing events is used to model longitudinal measurements of CD4 cell count and the risk of TB-infection and AIDS-progression among HIV patients, simultaneously.  Results: The results revealed that the trend of CD4 cell count had a significant association with the risk of TB-infection and AIDS-progression (p<0.001). Higher ages (p<0.001), the history of being in prison (p=0.013), receiving antiretroviral therapy (ART) (p<0.001) and isoniazid preventive therapy (IPT) (p<0.001) were associated with the positive trend of CD4 cell count. Higher ages were also associated with higher risks of TB (p<0.001) and AIDS-progression (p<0.001). Furthermore, ART (p=.0009) and IPT (p<0.001) were associated with a lower risk of TB-infection. In addition, ART (p<0.001) was associated with a lower risk of AIDS-progression. Moreover, individuals being imprisoned (p=0.001) and abusing alcohol (p=0.012) were more likely to have TB-co-infection.  Conclusions: The used joint model provided a flexible framework for simultaneous studying of the effects of covariates on the level of CD4 cell count and the risk of progression to TB and AIDS. This model also assessed the effect of CD4 trajectory on the hazards of competing events.&nbsp

Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross- sectional study

OBJECTIVES Diabetes is increasing in worldwide prevalence, toward epidemic levels. Diabetic neuropathy, one of the most common complications of diabetes mellitus, is a serious condition that can lead to amputation. This study used a multicategory support vector machine (MSVM) to predict diabetic peripheral neuropathy severity classified into four categories using patients’ demographic characteristics and clinical features. METHODS In this study, the data were collected at the Diabetes Center of Hamadan in Iran. Patients were enrolled by the convenience sampling method. Six hundred patients were recruited. After obtaining informed consent, a questionnaire collecting general information and a neuropathy disability score (NDS) questionnaire were administered. The NDS was used to classify the severity of the disease. We used MSVM with both one-against-all and one-against-one methods and three kernel functions, radial basis function (RBF), linear, and polynomial, to predict the class of disease with an unbalanced dataset. The synthetic minority class oversampling technique algorithm was used to improve model performance. To compare the performance of the models, the mean of accuracy was used. RESULTS For predicting diabetic neuropathy, a classifier built from a balanced dataset and the RBF kernel function with a one-against-one strategy predicted the class to which a patient belonged with about 76% accuracy. CONCLUSIONS The results of this study indicate that, in terms of overall classification accuracy, the MSVM model based on a balanced dataset can be useful for predicting the severity of diabetic neuropathy, and it should be further investigated for the prediction of other diseases

Meta-analysis of case-referent studies of specific environmental or occupational pollutants on lung cancer

BACKGROUND: Meta-analysis is a statistical tool for combining and integrating the results of independent studies of a given scientific issue. The present investigation was initiated to investigate case-referent studies of lung cancer risk from specific environmental and occupational pollutants, using detailed individual exposure data. MATERIALS AND METHODS: To examine the risk of lung cancer associated with environmental and occupational pollutants, a meta-analysis of published case-control studies was undertaken using a random effects model. For this study, the papers were selected for review from electronic search of PubMed, Medline and Google Scholar during 1990-2006. The principal outcome measure was the odds ratio for the risk of lung cancer. Twelve study reports detailing the relationship between the lung cancer and the type of exposure were identified. RESULTS: The odds ratio of asbestos, cooking fuel, cooking fumes, motor and diesel exhaust related to lung cancer were 1.67, 1.99, 2.52 and 1.42 ( P < 0.001), respectively. The odds ratio of metal fumes related to lung cancer was 1.28 (0.001< P < 0.01). The combined odds ratio for the environmental and occupational exposure related to lung cancer was 1.67 ( P< 0.001). CONCLUSIONS: The meta-analysis of the present study shows the magnitude association between asbestos, cooking fumes, cooking fuels, motor and diesel exhaust, with lung cancer risk. Lung cancer risk may be reduced by controlling exposure levels

SCREENING FOR A MULTIVARIATE MIXTURE NORMAL DISTRIBUTION

Abstract. The screening problem has been studied by many authors mostly focused on individual multivariate normal model with screening for normal distribution when all or part of the parameters are known, or the performance variable is dichotomous. In this paper a screening method is presented when the screening variable is a mixture of two multivariate normal distributions, meanwhile the performance variable is dichotomous. The method is used for the case when the parameters are known or estimated from separate samples. To reduce the dimensionality of the problem and therefore the scale of the computation, a Fisher's linear discrimination function is applied to find coefficients of a standard linear combination of the variables used in the proposed method. A comparison of methods is made for Conn's syndrome date. The results of the study are equivalent to the predictive screening approach

Effects of Small Group Education on Interdialytic Weight Gain, and Blood Pressures in Hemodialysis Patients

Background: One of the most common problems in patients undergoing hemodialysis is interdialytic weight gain due to high liquid intake. Many patients are not fully aware of the fluid restriction. Group educations, such as small-group education, are among powerful methods to enable patients correct their behaviors, and enhance their capabilities, knowledge, and awareness. Objectives: The purpose of this study was to determine the effect of small-group education on interdialytic weight gain, and blood pressures in patients undergoing hemodialysis. Patients and Methods: This is a quasi-experimental study. Data collected from 42 patients undergoing hemodialysis. Before education, the mean of interdialytic weight gain during one week, and blood pressure were recorded. Then small-group education performed in 4 sessions. One week, and one month after the education, the mentioned parameters were recorded again. Repeated measure analysis of variances was conducted and pair-wise comparison was done using the Bonferroni test. Descriptive statistics were calculated for demographic variables. Results: The mean, and standard deviation of interdialytic weight gain of participants was 3.64 ± 0.88 kg, before the education, and significantly decreased to 1.34 ± 0.61 kg, and 1.71 ± 0.72 kg one week, and one month after the education, respectively (P = 0.001). Also, the mean and standard deviation of participants' systolic blood pressure was 139.7 ± 16.45 mmHg before the education, and significantly decreased to 129.6 ± 12.16, and 129.5 ± 11.51 mmHg one week, and one month after the education, respectively (P = 0.001). But, the mean and standard deviation of diastolic blood pressure of the participants was 81.4 ± 6.07 mmHg before the education, and decreased to 79.7 ± 5.51 and 81.7 ± 5.27 mmHg one week, and one month after the education respectively. However, no statistically significant difference was observed between the diastolic blood pressure in the three phases (P = 0.061). Conclusions: Small-group education in patients undergoing hemodialysis leads to a decrease in interdialytic weight gain, and systolic blood pressure, but has no effect on diastolic blood pressure

Longitudinal Machine Learning Model for Predicting Systolic Blood Pressure in Patients with Heart Failure

Objective: Systolic blood pressure (SBP) is a powerful prognostic factor in heart failure (HF) patients, which is associated with death and readmission. Therefore, control of blood pressure is an important element for managing these patients. The goal of this study was to compare the performance of classical and machine learning models for predicting SBP and identify important variables related to SBP changes over time. Methods: The information of 483 HF patients was analyzed in this retrospective cohort study. These patients were hospitalized at least twice in Farshchian Heart Center Hamadan province, the west of Iran, between October 2015 and July 2019. We applied a linear mixed-effects model (LMM) and mixed-effects least-square support vector regression (MLS-SVR) for predicting SBP. The performance of both models was assessed by mean absolute error, and root mean squared error. Results: Based on LMM results, there was a significant association between sex, body mass index (BMI), sodium, time, and history of hypertension with SBP changes over time (P-value <0.05). Also, MLS-SVR indicated that the four most important variables were history of hypertension, sodium, BMI, and triglyceride. The performance of MLS-SVR compared to LMM was better in both training and testing datasets. Conclusions: According to our results, BMI, sodium, and history of hypertension were the important variables on SBP changes in both LMM and MLS-SVR models. Also, it seems that MLS-SVR can be used as an alternative for classical longitudinal models for predicting SBP in HF patients