Developing a Valid and Reliable Instrument to Predict the Protective Sexual Behaviors in Women at Risk of Human Immunodeficiency Virus

Background: One much needed tool to assist with the monitoring and evaluation of Human Immunodeficiency Virus (HIV) prevention programs is to provide a valid instrument to measure protective sexual behavior and related factors. Objectives: The current study aimed to design a valid and reliable instrument to predict the protective sexual behaviors of women at risk of HIV in Iran. Patients and Methods: The current study was a sequential mixed cross-sectional and methodological research. Initially, via a qualitative research, constructs and factors associated with sexual protective behavior of women at risk were identified through 25 indepth interviews. The questionnaire on predictors of protective sexual behaviors in women at risk of HIV (PSPB) was designed based on a qualitative study, and then its qualitative validity, content, and construct validity were evaluated. Exploratory factor analysis was performed and 200 women at risk participated. Results: Seven concepts emerged after exploratory factor analysis of the 48 items. The content validity ratio (CVR) of the questionnaire constructs were 0.55 to 0.76, and content validity index (CVI) structure was 0.86 to 0.95. Cronbach's alpha coefficient for the entire questionnaire was 0.78, and correlation coefficient of the test-retest reliability for the constructs was from 0.73 to 0.89. Conclusions: The current study proved the capability of the predictors of sexual protective behavior in women at risk for HIV questionnaire as a valid and reliable instrument for the Iranian community

Evaluating the Serum Levels of CCL17, CCL22, and CCL28 Chemokines and the Gene Expression of α4β1 and α4β7 Integrins in Patients With Allergic Rhinitis

Allergic rhinitis (AR) is a chronic inflammatory disease involving the nasal mucosa. Leukocytes recruitment to the inflammation sites is controlled by chemokines, cytokines, and adhesion molecules. Retinoic acid (RA), a vitamin A metabolite, plays an essential role in mucosal immunity and the production of inflammatory cytokines and chemokines. This study intended to evaluate the serum levels of RA, CCL17, CCL22, CCL28, and the mRNA expression levels of α4, β1, and β7 integrins in AR patients compared to healthy subjects. Peripheral blood was collected from 37 patients with AR and 30 age- and gender-matched healthy individuals. Serum levels of RA, CCL17, CCL22, and CCL28 were measured by the enzyme-linked immunosorbent assay (ELISA) technique, and the mRNA expression levels for α4, β1, and β7 integrins were assessed using the quantitative real-time PCR method. Our results showed that the serum levels of CCL22 and CCL28 chemokines are significantly higher in the AR group compared to the healthy controls (P<0.01). However, the gene expression of the β1 integrin in the AR group was significantly lower than that of the control group (P<0.001). Besides, there was a positive association between serum RA and CCL17 levels in patients (P<0.0001, r=0.6). In conclusion, increased serum levels of CCL22 and CCL28 chemokines, as well as decreased gene expression of β1 integrin in AR patients, may contribute to the pathogenesis and/or exacerbation of AR

The evaluation of a virtual education system based on the DeLone and McLean model: A path analysis [version 2; referees: 3 approved]

Background: The Internet has dramatically influenced the introduction of virtual education. Virtual education is a term that involves online education and e-learning. This study was conducted to evaluate a virtual education system based on the DeLone and McLean model. Methods: This descriptive analytical study was conducted using the census method on all the students of the Nursing and Midwifery Department of Alborz University of Medical Sciences who had taken at least one online course in 2016-2017. Data were collected using a researcher-made questionnaire based on the DeLone and McLean model in six domains and then analyzed in SPSS-16 and LISREL-8.8 using the path analysis. Results: The goodness of fit indices (GFI) of the model represent the desirability and good fit of the model, and the rational nature of the adjusted relationships between the variables based on a conceptual model (GFI = 0.98; RMSEA = 0.014).The results showed that system quality has the greatest impact on the net benefits of the system through both direct and indirect paths (β=0.52), service quality through the indirect path (β=0.03) and user satisfaction through the direct path (β=0.73). Conclusions: According to the results, system quality has the greatest overall impact on the net benefits of the system, both directly and indirectly by affecting user satisfaction and the intention to use. System quality should therefore be further emphasized, to use these systems more efficiently

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

The examination of quality of pregnancy care based on the World Health Organization’s “Responsiveness” model of selected pregnant women in Tehran

Introduction: The World Health Organization (WHO) Responsiveness model showing the ability of health systems in fulfilling people’s expectations in connection with nonclinical aspects is an appropriate pattern to assess healthcare. The purpose of this study was to determine the status of pregnancy care provisions based on the responsiveness model. Methods: This was a cross-sectional study conducted by randomly sampling 130 women visiting selected hospitals in Tehran in 2015. A researcher-made questionnaire based on the responsiveness model of WHO was used to collect data. We determined the face validity and content validity of the questionnaire, and its reliability was confirmed by Cronbach’s alpha coefficient (0.94) and test-retest analysis (0.96). The obtained data were analyzed by SPSS version 20 descriptive statistics, t-test, one-way ANOVA, Pearson product-moment correlation coefficient, and Spearman correlation. Results: Total responsiveness from the perspective of service recipients was 69.46±14.65 from 100. The obtained scores showed that, in the range of 0 to 100, 73.02 were about basic amenities (the most score), 72.93 about dignity, 70.91 about communication, 70.76 about confidentiality, 66.30 about provision social needs, 65.96 about choice of provider, 65.92 about autonomy, and 52.65 about prompt attention (the lowest score), which are representing the average level of service quality. There were significant relationships between participating in preparation class of labor and dignity (p<0.001), autonomy (p=0.01), provision social needs (p=0.01), and overall responsiveness (p=0.03). It was obtained that there is a significant linear relationship between scores given to hospitals and dimensions of responsiveness (p=0.05). Findings indicated a significant relationship between insurance type and dimensions of choice of provider (p=0.03) and communication (p=0.03). Conclusion: The mean score of service quality in the present investigation illustrated that nonclinical dimensions have been disregarded and it has potential to be better. So some grand plans are needed

Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield

Applying the optimum rate of fertilizer nitrogen (N) is a critical factor for field management. Multispectral information collected by active canopy sensors can potentially indicate the leaf N status and aid in predicting grain yield. Crop Circle multispectral data were acquired with the purpose of measuring the reflectance data to calculate vegetation indices (VIs) at different growth stages. Applying the optimum rate of fertilizer N can have a considerable impact on grain yield and profitability. The objectives of this study were to evaluate the reliability of a handheld Crop Circle ACS-430, to estimate corn leaf N concentration and predict grain yield of corn using machine learning (ML) models. The analysis was conducted using four ML models to identify the best prediction model for measurements acquired with a Crop Circle ACS-430 field sensor at three growth stages. Four fertilizer N levels from deficient to excessive in 50/50 spilt were applied to corn at 1–2 leaves, with visible leaf collars (V1–V2 stage) and at the V6–V7 stage to establish widely varying N nutritional status. Crop Circle spectral observations were used to derive 25 VIs for different growth stages (V4, V6, and VT) of corn at the W. B. Andrews Agricultural Systems farm of Mississippi State University. Multispectral raw data, along with Vis, were used to quantify leaf N status and predict the yield of corn. In addition, the accuracy of wavelength-based and VI-based models were compared to examine the best model inputs. Due to limited observed data, the stratification approach was used to split data to train and test set to obtain balanced data for each stage. Repeated cross validation (RCV) was then used to train the models. Results showed that the Simplified Canopy Chlorophyll Content Index (SCCCI) and Red-edge ratio vegetation index (RERVI) were the most effective VIs for estimating leaf N% and that SCCCI, Red-edge chlorophyll index (CIRE), RERVI, Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index (NDVI) were the most effective VIs for predicting corn grain yield. Additionally, among the four ML models utilized in this research, support vector regression (SVR) achieved the most accurate results for estimating leaf N concentration using either spectral bands or VIs as the model inputs

Relation of attachment styles and cognitive emotion regulation strategies to depression in patients with chronic skin diseases

Skin is a vital organ for communication throughout the life cycle, so that skin disease can cause a significant psychological distress. This study aimed to assessment the relation of attachment styles and cognitive emotion regulation strategies to depression in patients with skin diseases. The 200 participants were selected using purposeful sampling among patients diagnosed with psoriasis, atopic dermatitis, and chronic idiopathic urticaria and who referred to dermatology clinics or phototherapy units of the hospitals in Mashhad. Patients who had inclusion criteria participated in the study after giving the informed consent. The participants filled out the scales of cognitive emotion regulation strategies, Collins and Read attachment styles, and hospital anxiety and depression. The results of path analysis showed a direct relation of secure attachment style to adaptive cognitive emotion regulation strategies and depression, cognitive emotion regulation strategies to depression, insecure attachment styles to maladaptive cognitive emotion regulation strategies, insecure attachment to depression, and maladaptive cognitive emotion regulation strategies to depression. Secure attachment had indirect effect on depression and insecure attachment had indirect relation to depression. These results imply that attachment styles and cognitive emotion regulation strategies in patients with skin diseases have multiple relations with depression

Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn

Changes in spatial and temporal variability in yield estimation are detectable through plant biophysical characteristics observed at different phenological development stages of corn. A multispectral red-edge sensor mounted on an Unmanned Aerial Systems (UAS) can provide spatial and temporal information with high resolution. Spectral analysis of UAS acquired spatiotemporal images can be used to develop a statistical model to predict yield based on different phenological stages. Identifying critical vegetation indices (VIs) and significant spectral information could lead to increased yield prediction accuracy. The objective of this study was to develop a yield prediction model at specific phenological stages using spectral data obtained from a corn field. The available spectral bands (red, blue, green, near infrared (NIR), and red-edge) were used to analyze 26 different VIs. The spectral information was collected from a cornfield at Mississippi State University using a MicaSense multispectral red-edge sensor, mounted on a UAS. In this research, a new empirical method used to reduce the effects of bare soil pixels in acquired images was introduced. The experimental design was a randomized complete block that consisted of 16 blocks with 12 rows of corn planted in each block. Four treatments of nitrogen (N) including 0, 90, 180, and 270 kg/ha were applied randomly. Random forest was utilized as a feature selection method to choose the best combination of variables for different stages. Multiple linear regression and gradient boosting decision trees were used to develop yield prediction models for each specific phenological stage by utilizing the most effective variables at each stage. At the V3 (3 leaves with visible leaf collar) and V4-5 (4-5 leaves with visible leaf collar) stages, the Optimized Soil Adjusted Vegetation Index (OSAVI) and Simplified Canopy Chlorophyll Content Index (SCCCI) were the single dominant variables in the yield predicting models, respectively. A combination of the Green Atmospherically Resistant Index (GARI), Normalized Difference Red-Edge (NDRE), and green Normalized Difference Vegetation Index (GNDVI) at V6-7, SCCCI, and Soil-Adjusted Vegetation Index (SAVI) at V10,11, and SCCCI, Green Leaf Index (GLI), and Visible Atmospherically Resistant Index (VARIgreen) at tasseling stage (VT) were the best indices for predicting grain yield of corn. The prediction models at V10 and VT had the greatest accuracy with a coefficient of determination of 0.90 and 0.93, respectively. Moreover, the SCCCI as a combined index seemed to be the most proper index for predicting yield at most of the phenological stages. As corn development progressed, the models predicted final grain yield more accurately

The effect of omega-3 supplementation on androgen profile and menstrual status in women with polycystic ovary syndrome: A randomized clinical trial

Background: There is some evidence regarding the effect of poly unsaturated fatty acid intake on androgen levels and gonadal function in polycystic ovary syndrome (PCOS). Objective: This study was conducted to determine the effect of omega-3 supplementation on sex hormone-binding protein (SHBG), testosterone, free androgen index (FAI) and menstrual status in women with PCOS. Materials and Methods: This double-blind randomized clinical trial was conducted on 78 overweight/obese women with PCOS. Participants were randomized to receive omega-3 (3gr/day) or placebo for 8 weeks. Data about weight, height and nutrient intake as well as blood samples were collected before and after intervention. Serum concentrations of testosterone (nmol/L) and SHBG (nmol/L) were measured. FAI was also calculated as the ratio of testosterone to SHBG. Results: Seventy eight patients (age: 26.92±5.46 yrs, Body Mass Index: 31.69±4.84 Kg/m2) completed the study. There was no significant difference in mean age, weight, height, Body Mass Index and intake of energy, and macronutrients between 2 study groups before and after treatment. All the participants had irregular periods. After the trial the percentage of regular menstruation in the omega-3 group was more than the placebo group (47.2% vs. 22.9%, p=0.049). Furthermore, testosterone concentration was significantly lower in the omega-3 group compared with placebo, after supplementation (p=0.04). SHBG and FAI did not change in either group. Conclusion: Omega-3 supplementation could reduce serum concentrations of testosterone and regulate menstrual cycle without significant effect on SHBG and FAI. Future studies with longer period of supplementation are warranted