Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank.

The availability of protein measurements and whole exome sequence data in the UK Biobank enables investigation of potential observational and genetic protein-cancer risk associations. We investigated associations of 1463 plasma proteins with incidence of 19 cancers and 9 cancer subsites in UK Biobank participants (average 12 years follow-up). Emerging protein-cancer associations were further explored using two genetic approaches, cis-pQTL and exome-wide protein genetic scores (exGS). We identify 618 protein-cancer associations, of which 107 persist for cases diagnosed more than seven years after blood draw, 29 of 618 were associated in genetic analyses, and four had support from long time-to-diagnosis ( > 7 years) and both cis-pQTL and exGS analyses: CD74 and TNFRSF1B with NHL, ADAM8 with leukemia, and SFTPA2 with lung cancer. We present multiple blood protein-cancer risk associations, including many detectable more than seven years before cancer diagnosis and that had concordant evidence from genetic analyses, suggesting a possible role in cancer development.

CFSSynergy: Combining Feature-Based and Similarity-Based Methods for Drug Synergy Prediction.

Drug synergy prediction plays a vital role in cancer treatment. Because experimental approaches are labor-intensive and expensive, computational-based approaches get more attention. There are two types of computational methods for drug synergy prediction: feature-based and similarity-based. In feature-based methods, the main focus is to extract more discriminative features from drug pairs and cell lines to pass to the task predictor. In similarity-based methods, the similarities among all drugs and cell lines are utilized as features and fed into the task predictor. In this work, a novel approach, called CFSSynergy, that combines these two viewpoints is proposed. First, a discriminative representation is extracted for paired drugs and cell lines as input. We have utilized transformer-based architecture for drugs. For cell lines, we have created a similarity matrix between proteins using the Node2Vec algorithm. Then, the new cell line representation is computed by multiplying the protein-protein similarity matrix and the initial cell line representation. Next, we compute the similarity between unique drugs and unique cells using the learned representation for paired drugs and cell lines. Then, we compute a new representation for paired drugs and cell lines based on the similarity-based features and the learned features. Finally, these features are fed to XGBoost as a task predictor. Two well-known data sets were used to evaluate the performance of our proposed method: DrugCombDB and OncologyScreen. The CFSSynergy approach consistently outperformed existing methods in comparative evaluations. This substantiates the efficacy of our approach in capturing complex synergistic interactions between drugs and cell lines, setting it apart from conventional similarity-based or feature-based methods.

Iran Quality of Care in Medicine Program (IQCAMP): Design and Outcomes.

BACKGROUND: Assessment of quality and cost of medical care has become a core health policy concern. We conducted a nationwide survey to assess these measures in Iran as a developing country. To present the protocol for the Iran Quality of Care in Medicine Program (IQCAMP) study, which estimates the quality, cost, and utilization of health services for seven diseases in Iran. METHODS: We selected eight provinces for this nationally representative short longitudinal survey. Interviewers from each province were trained comprehensively. The standard definition of seven high-burden conditions (acute myocardial infarction [MI], heart failure [HF], diabetes mellitus [DM], stroke, chronic obstructive pulmonary (COPD) disease, major depression, and end-stage renal disease [ESRD]) helped customize a protocol for disease identification. With a 3-month follow-up window, the participants answered pre-specified questions four times. The expert panels developed a questionnaire in four modules (demographics, health status, utilization, cost, and quality). The expert panel chose an inclusive set of quality indicators from the current literature for each condition. The design team specified the necessary elements in the survey to calculate the cost of care for each condition. The utilization assessment included various services, including hospital admissions, outpatient visits, and medication. RESULTS: Totally, 156 specialists and 78 trained nurses assisted with patient identification, recruitment, and interviewing. A total of 1666 patients participated in the study, and 1291 patients completed all four visits. CONCLUSION: The IQCAMP study was the first healthcare utilization, cost, and quality survey in Iran with a longitudinal data collection to represent the pattern, quantity, and quality of medical care provided for high-burden conditions.

DeepTraSynergy: drug combinations using multimodal deep learning with transformers.

MOTIVATION: Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. RESULTS: Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug-target interaction, protein-protein interaction, and cell-target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug-target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synergy loss, toxic loss, and drug-protein interaction loss. The last two loss functions are designed as auxiliary losses to help learn a better solution. DeepTraSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the two latest drug combination datasets. The DeepTraSynergy algorithm achieves accuracy values of 0.7715 and 0.8052 (an improvement over other approaches) on the DrugCombDB and Oncology-Screen datasets, respectively. Also, we evaluate the contribution of each component of DeepTraSynergy to show its effectiveness in the proposed method. The introduction of the relation between proteins (PPI networks) and drug-protein interaction significantly improves the prediction of synergistic drug combinations. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/fatemeh-rafiei/DeepTraSynergy.

The levels and trends of diabetes prevalence at national and sub-national levels in Iran (1990 - 2016).

Background: The increasing trends in Diabetes prevalence and its attributed burden emphasized as an important issue that needs serious and urgent attention, all over the word. We estimated the mean Fasting Plasma Glucose (FPG) and the prevalence of Diabetes in aged 25 years or older Iranian adults, by sex, age, province, and year through the time period of 1990 to 2016. Methods: In order to access the most comprehensive relevant data at the same time the systematic data searched added to the data of 5 national surveys and 7 sub-national population based investigations. Two round of modeling, including the Age-Spatio-Temporal and Gaussian Process Regression were used for estimation of mean FPG trend and uncertainties. To estimate Diabetes estimations in target groups, a crosswalk model was applies to the FPG estimates. The model reiterated separately for women and men. All of estimations standardized based on the Iran national census population of 2016 by year, age groups and sexes at national and sub-national levels. Results: In 2016, the number of the diabetic population was 4.43 (3.93-4.99) million (2.38 million women). Between 1990 and 2016, the age-standardized mean of FPG increased from 84.69 mg/dl (79.8-89.8) to 100.5 mg/dl (97.9-103.3) in women and from 82.7 mg/dl (78.3-87.5) to 98.8 mg/dl (96.2-101.4) in men. Simultaneously, with considerable difference, the Diabetes prevalence, has increased from 6.1% (4.7-7.8) to 9.8% (8.7-11.1) in women and from 5.0% 18 (3.8-6.3) to 8.1% (7.2-9.2) in men (75% attributed to population growth). Considering the geographical patterns, the greatest increment in the prevalence of Diabetes detected in the northwestern and the central provinces. Conclusion: Significant increasing trends of Diabetes led to alarming threat, which can make the strategies and goals of our prevention programs out of control. We should plan for more effective communicative interventions for prevention and management of Diabetes, to be designed, implemented and monitored based on the updated scientific evidence. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-023-01197-2.

Inequality in prevalence, awareness, treatment, and control of hypertension in Iran: the analysis of national households' data.

BACKGROUND: Providing an equitable Universal Health Coverage (UHC) is key for progressing towards the sustainable development goals in the health systems. To help policymakers make hypertension services more equitable with existing (limited) resources in Iran, we examined the inequality of the prevalence, awareness, treatment, and control (PATC) of hypertension as the four indicators of hypertension UHC in Iran.  METHODS: This research was a cross-sectional study of inequality of PATC of hypertension using a representative sample of Iranians aged ≥ 25 years from the Iran 2016 STEP wise approach to Surveillance study (STEPS). Outcome variables consisted of PATC of hypertension. Covariates were demographic (age, sex, and marital status) and living standard (area of residence, wealth status, education, and health insurance) indicators. We drew concentration curves (CC) and estimated concentration indices (C). We also conducted normalized Erreygers decomposition analysis for binary outcomes to identify covariates that explain the wealth-related inequality in the outcomes. Analysis was conducted in STATA 14.1. RESULTS: The normalized concentration index of hypertension prevalence and control was -0.066 (p < .001) and 0.082 (p < .001), respectively. The C of awareness and treatment showed nonsignificant difference between the richest and poorest. Inequality in the hypertension prevalence of females was significantly higher than males (C = -0.103 vs. male C = -0.023, p < .001). Our analyses explained 33% of variation in the C of hypertension prevalence and 99.7% of variation in the C of control. Education, wealth index, and complementary insurance explained most inequality in the prevalence. Area of residence, education, wealth status, and complementary insurance had the largest contribution to C of control by 30%, 28%, 26%, and 21%, respectively. CONCLUSIONS: This study showed a pro-rich inequality in the prevalence and control of hypertension in Iran. We call for expanding the coverage of complementary insurance to reduce inequality of hypertension prevalence and control as compared with other factors it can be manipulated in short run. We furthermore advocate for interventions to reduce the inequality of hypertension control between rural and urban areas.

Trends in dental caries of deciduous teeth in Iran: a systematic analysis of the national and sub-national data from 1990 to 2017.

BACKGROUND: Dental caries is the most prevalent child affliction in the world and can be reduced through effective preventive interventions. To plan cost-effective interventions, clear and integrated data are needed. This study has been designed to overcome the lack of national trend in deciduous dental caries in Iran. OBJECTIVE: To estimate the dental caries trend in deciduous teeth in the Iranian population at different ages from 1990 to 2017. METHODS: From 1990 to 2017 a literature search about dmf and its components (decayed, missed, and filled tooth, abbreviated as dt, mt, and ft) as well as dental caries was done in the Iranian population in three English (PubMed, Web of Science, and Scopus) and three national databases (in Persian). All eligible national oral health surveys in these 28 years were included. National dmft data were categorised based on age (1-4, 5-9, and 10-14), sex, province and year. The final trends were estimated using an age-spatio-temporal hierarchical model. We used the bootstrap method in multilevel models to predict the uncertainty interval (UI) of the modelled results. Finally, the estimations of dmft, dt, mt, and ft with a 95% UI were reported from 1990 to 2017. RESULTS: Almost 22% of the Iranian deciduous teeth were involved with dental caries in 1990 [dmft = 4.37; (95% UI 2.23, 6.62)] which more than 83% of it was dt [3.64 (1.53, 5.88)] and less than 7% was ft [0.30 (0.06, 0.65)]. During 1990-2017, dmft increased by more than 15% [in 2017, dmft = 5.03 (2.82, 7.29)]. The highest increase was seen in dt which was more than 17% [in 2017, dt = 4.27 (1.96, 6.57)]. CONCLUSION: Increasing dental caries among Iranian children over 28 years shows that oral health policies in Iran need critical evaluation. We need cost-effective nationwide interventions (e.g., supervised tooth brushing and improving dietary habits) and training well-experienced intermediate manpower (e.g., dental hygienists) to reduce dental caries.

The Impact of Cancer Incidence on Catastrophic Health Expenditure in Iran with a Bayesian Spatio-Temporal Analysis.

Background: We investigated the impact of cancer incidence on CHE in Iran by considering spatial variation across provinces as well as temporal trends. Methods: Data from Household Income-Expenditure Survey were merged with cancer incidence rates during 2011-2016. We developed a Bayesian hierarchical model to explore the spatial and temporal patterns of CHE and its associated factors at provincial level. We used a Besag-York-Mollie2 prior and a random walk prior for spatial and temporal random effects respectively. All statistical analysis was carried out in R software. Results: All-type cancer incidence (OR per SD (95% CrI) = 1.16 (1.02, 1.32)), unemployment rate (1.08 (1.01, 1.15)) and income equity (0.88 (0.81, 0.97)) have important association with CHE. Percentage of urbanization and percentage of poverty were not statistically significant. Conclusion: The results suggest the development of new policies to protect cancer patients against financial hardship, narrow the gap in income inequality and solve the problem of high unemployment rate to reduce the level of CHE at provincial level.

National and sub-national trends of salt intake in Iranians from 2000 to 2016: a systematic analysis.

BACKGROUND: One fifth of the global burden of cardiovascular diseases (CVDs) in 2017 was attributable to excessive salt intake. As a member of the World Health Organization (WHO), Iran has committed itself to a 30% reduction in salt intake by 2025. Evidence on the amount and trend of salt intake among the Iranian population at national and sub-national levels is scarce. This study aimed to estimate the Iranian population's salt intake during 2000-2016 at the national and sub-national levels, by sex and age groups. METHODS: Data on national and sub-national mean salt intake was obtained through systematically searching the literature and contacting the research studies' principal investigators. Data collected through various methods were harmonized using the cross-walk method. Bayesian hierarchical and spatio-temporal-age regression models and simulation analysis were used to estimate the mean salt intake and its uncertainty interval across sex, age, year, and province. RESULTS: National age-sex standardized mean salt intake decreased from 10·53 g/day (95% uncertainty interval [UI]: 10·2 to 10·9) in 2000 to 9·41 (9·2 to 10·6) in 2016 (percent change: - 9·8% [- 21·1-3·1]). The age-standardized mean salt intake in women had decreased from 9·8 g/day (95% UI: 9·0-10·6) in 2000 to 9·1 g/day (8·6-9·7) in 2016 (percent change: - 6·6% [- 19·0-7·9]). The same measure in men was 11·1 g/day in 2000 (95% UI: 10·3-11·8) and 9·7 g/day (9·1-10·2) in 2016 (percent change: - 12·7% [- 23·0 - -0·9]). Age-sex standardized mean salt intake at the sub-national level in 2016 varied from 8·0 (95% UI: 7·0-9·0) to 10·5 (10·0-11·1). The difference between the provinces with the highest and the lowest levels of salt intake in 2016 was 31·3%. CONCLUSION: Salt intake decreased in Iran from 2000 to 2016, while persistently exceeding the recommended values. This declining trend was more pronounced between 2010 and 2016, which might be attributed to Iran's compliance to WHO's Action Plan for reducing NCDs.

Prevalence, awareness, treatment, and control of hypertension based on ACC/AHA versus JNC7 guidelines in the PERSIAN cohort study.

In this cross-sectional population-based study, we used the baseline data of the Prospective Epidemiologic Research Studies in IrAN cohort study collected in Iran from 2014 to 2020. The main outcomes were the prevalence of hypertension and proportion of awareness, treatment, and control based on the 2017 ACC/AHA guideline compared to the seventh report of the Joint National Committee (JNC7). Of the total of 163,770 participants, aged 35-70 years, 55.2% were female. The sex-age standardized prevalence of hypertension was 22.3% (95% CI 20.6, 24.1) based on the JNC7 guideline and 36.5% (31.1, 41.8) based on the ACC/AHA guideline. A total of 24,312 participants [14.1% (10.1, 18.1)] were newly diagnosed based on the ACC/AHA guideline. Compared to adults diagnosed with hypertension based on the JNC7 guideline, the newly diagnosed participants were mainly young literate males who had low levels of risk factors and were free from conventional comorbidities of hypertension. About 30.7% (25.9, 35.4) of them (4.3% of the entire population) were eligible for pharmacologic intervention based on the ACC/AHA guideline. Implementation of the new guideline may impose additional burden on health systems. However, early detection and management of elevated blood pressure may reduce the ultimate burden of hypertension in Iran.

Diabetes mellitus risk prediction in the presence of class imbalance using flexible machine learning methods.

BACKGROUND: Early detection and prediction of type two diabetes mellitus incidence by baseline measurements could reduce associated complications in the future. The low incidence rate of diabetes in comparison with non-diabetes makes accurate prediction of minority diabetes class more challenging. METHODS: Deep neural network (DNN), extremely gradient boosting (XGBoost), and random forest (RF) performance is compared in predicting minority diabetes class in Tehran Lipid and Glucose Study (TLGS) cohort data. The impact of changing threshold, cost-sensitive learning, over and under-sampling strategies as solutions to class imbalance have been compared in improving algorithms performance. RESULTS: DNN with the highest accuracy in predicting diabetes, 54.8%, outperformed XGBoost and RF in terms of AUROC, g-mean, and f1-measure in original imbalanced data. Changing threshold based on the maximum of f1-measure improved performance in g-mean, and f1-measure in three algorithms. Repeated edited nearest neighbors (RENN) under-sampling in DNN and cost-sensitive learning in tree-based algorithms were the best solutions to tackle the imbalance issue. RENN increased ROC and Precision-Recall AUCs, g-mean and f1-measure from 0.857, 0.603, 0.713, 0.575 to 0.862, 0.608, 0.773, 0.583, respectively in DNN. Weighing improved g-mean and f1-measure from 0.667, 0.554 to 0.776, 0.588 in XGBoost, and from 0.659, 0.543 to 0.775, 0.566 in RF, respectively. Also, ROC and Precision-Recall AUCs in RF increased from 0.840, 0.578 to 0.846, 0.591, respectively. CONCLUSION: G-mean experienced the most increase by all imbalance solutions. Weighing and changing threshold as efficient strategies, in comparison with resampling methods are faster solutions to handle class imbalance. Among sampling strategies, under-sampling methods had better performance than others.

Validating the "Treatment Satisfaction Questionnaire for Medication" in Persian and Evaluating Treatment Satisfaction Among Patients With Psoriasis.

OBJECTIVES: Psoriasis is a dermatological condition often associated with systemic comorbidities such as arthritis. Because of the vital role of treatment compliance in improvement in patients' condition and the scarcity of studies on this subject in Iran, we decided to measure and compare treatment satisfaction (as a predictor of compliance) of patients with psoriasis by using the Treatment Satisfaction Questionnaire for Medication (TSQM). METHODS: We administered the TSQM version II to adult patients with psoriasis, referring to the clinics and wards of the Razi hospital. First, we translated and investigated the validity and reliability of the TSQM in a group of 34 patients; then, we measured the satisfaction of 100 patients with psoriasis who were receiving topical, phototherapy, or systemic or biologic medications. RESULTS: Content validity was established by experts' review of the translation and by comparing the results to those of previous studies. Then, reliability was confirmed by calculating reliability and agreement measures (Cronbach's alpha = 0.864, intraclass correlation coefficient = 0.984, and Pearson's correlation = 0.969). Biologic medications showed the highest satisfaction score in "effectiveness," "convenience," "global satisfaction," and "total score" (P = .000). Topical treatments demonstrated the highest "side effects" score (P = .006). Patients older than 50 years were significantly more satisfied than younger patients (P = .029). Patients with a Psoriasis Area Severity Index of 5 or more and patients with psoriatic arthritis reported lower satisfaction (P = .012, P = .000). Treatment satisfaction of patients with arthritis was higher with biologic medications than with traditional systemic medications (P = .000). CONCLUSIONS: TSQM, which had not been used in Persian before, is valid and reliable in Persian and provides reproducible results. Patients reported the highest satisfaction with the use of biologic agents, which was associated with age, Psoriasis Area Severity Index, and arthritis.

Identifying associations between health services operational factors and health experience for patients with type 2 diabetes in Iran.

BACKGROUND: Facing limited health resources, healthcare providers need to rely on health service delivery models that produce the best clinical outcomes and patient experience. We aimed to contribute to developing a patient experience-based type 2 diabetes service delivery model by identifying operational structures and processes of care that were associated with clinical outcome, health experience, and service experience. METHODS: We conducted a cross-sectional survey of type 2 diabetes patients between January 2019 to February 2020. Having adjusted for demand variables, we examined relationships between independent variables (behaviours, services/processes, and structures) and three categories of dependent variables; clinical outcomes (HbA1c and fasting blood glucose), health experience (EuroQol quality of life (EQ-5D), evaluation of quality of life (visual analgene scale of EQ-5D), and satisfaction with overall health status), and service experience (evaluation of diabetes services in comparison with worst and best imaginable diabetes services and satisfaction with diabetes services). We analysed data using multivariate linear regression models using Stata software. RESULTS: After adjusting for demand variables; structures, diabetes-specific health behaviours, and processes explained up to 22, 12, and 9% of the variance in the outcomes, respectively. Based on significant associations between the diabetes service operations and outcomes, the components of an experience-based service delivery model included the structural elements (continuity of care, redistribution of task to low-cost resources, and improved access to provider), behaviours (improved patient awareness and adherence), and process elements (reduced variation in service utilization, increased responsiveness, caring, comprehensiveness of care, and shared decision-making). CONCLUSIONS: Based on the extent of explained variance and identified significant variables, health services operational factors that determine patient-reported outcomes for patients with type 2 diabetes in Iran were identified, which focus on improving continuity of care and access to providers at the first place, improving adherence to care at the second, and various operational process variables at the third place.

Social Awareness of Whole Grains and the Feasibility of Replacement with Refined Grains: A Qualitative Study.

BACKGROUND: A correlation between type 2 diabetes and refined carbohydrates has been proven, while several studies have indicated that Iranian daily diets are poor in term of proper carbohydrates. It was thus considered absolutely critical to conduct a qualitative study in terms of people's attitudes toward whole grains, and the feasibility of their replacing existing refined carbohydrates in their diets. The aim of this study is to probe Iranian awareness of whole grains, to explore barriers to refined-grain substitution with whole grains and legumes, and to assess whole-grain sensory perceptions. METHODS: Focus group discussions (FGDs) and taste tests conducted between July 2016 and March 2017 in urban and rural areas of Kurdistan, Yazd, and Tehran provinces in Iran. A total of 96 healthy men and women (aged 40-65, BMI ≥25 kg/m2) were selected through purposive sampling with maximum variation. FGDs were categorized by content analysis method. As for taste test, ANOVA analysis with Bonferroni post-hoc was used to determine significant differences (P < 0.05). RESULTS: Four themes and 11 sub-themes emerged. Cultural beliefs, traditional eating patterns, sensory properties, and familial acceptance were the most influential factors in choosing the type of bread and rice. Simultaneously they are the most prominent barriers to consuming whole grains and legumes. Plain cooked brown rice had the lowest mean sensory attribute score and traditional whole-wheat flatbread was the highest. CONCLUSIONS: There was a higher acceptance tendency toward using traditional whole-wheat flatbread rather than refined grains, as it was consistent with preference and priority. However, low availability was the largest substitution problem.

Introducing an efficient sampling method for national surveys with limited sample sizes: application to a national study to determine quality and cost of healthcare.

BACKGROUND: Sampling a small number of participants from an entire country is not straightforward. In this case, researchers reluctantly sample from a single setting or few settings, which limits the generalizability of findings. Therefore, there is a need to design efficient sampling method for small sample size surveys that can produce generalizable results at the country level. METHODS: Data comprised of twenty proxy variables to measure health services demands, structures, and outcomes of 413 districts of Iran. We used two data mining methods (hierarchical clustering method (HCM) and model-based clustering method (MCM)) to create homogenous groups of districts, i.e., strata based on these variables. We compared the internal and stability validity of the methods by statistical indices. An expert group checked the face validity of the methods, particularly regarding the total number of strata and the combination of districts in each stratum. The efficiency of selected method, which is measured by the inverse of variance, was compared with a simple random sampling (SRS) through simulation. The sampling design was tested in a national study in Iran, which aimed to evaluate the quality and costs of medical care for eight selected diseases by only recruiting 300 participants per disease at the country level. RESULTS: MCM and HCM divided the districts into eight and two clusters, respectively. The measures of internal and stability validity showed that clusters created by MCM were more separated, compact, and stable, thus forming our optimum strata. The probability of death from stroke, chronic obstructive pulmonary disease, and in-hospital mortality rate were the most important indicators that distinguished the eight strata. Based on the simulation results, MCM increased the efficiency of the sampling design up to 1.7 times compared to SRS. CONCLUSIONS: The use of data mining improved the efficiency of sampling up to 1.7 times greater than SRS and markedly reduced the number of strata to eight in the entire country. The proposed sampling design also identified key variables that could be used to classify districts in Iran for sampling from these target populations in the future studies.

Evaluation of Hippocampal Function in Temporal Lobe Epilepsy: Spatial Bayesian Variable Selection and Grouping the Regression Coefficient in Multilevel Functional Magnetic Resonance Imaging Data Analysis.

Background: A pre-surgical evaluation of cognitive functions in patients with mesial temporal lobe epilepsy (mTLE) is critical. The limitations of the usual brain analysis model were resolved by the spatial Bayesian variable selection (SBVS) method. An Ising and Dirichlet Process (Ising-DP) model considers SBVS and the grouping of a large number of voxels. The present study aimed to identify brain areas involved in episodic memory in patients with right mTLE and controls via the Ising-DP model. The model was extended to include between-subject factors (BSFs), and the results were compared with other classical methods. Methods: The present cross-sectional study was conducted on 15 patients with right mTLE and 20 controls in Tehran, Iran, in 2018. During functional magnetic resonance imaging, the subjects were tested with the face-encoding memory task, followed by a recognition memory test. The participants demographic factors such as age, sex, marital status, area of residence, and years of schooling were considered to comprise BSFs. The independent t test, the chi-square test, and the correlation test were conducted using the SPSS software (version 20.0). The image processing was carried out using SPM (version 12.0) and MATLAB (version R2014a). Results: The Ising-DP model appropriately (R2=0.642) detected activated hippocampal areas. The model adjusted for BSFs indicated a better fit by the significant effect of age (P((γ]>0.91), sex (P[γ]>0.87), and years of schooling (P[γ]>0.89). The heat maps exhibited decreased activation in the right hippocampal region in the patients compared with the controls (P<0.0001). Right hippocampal activity had a significant positive correlation with the recognition memory test in the mTLE group (r=0.665) and the control group (r=0.593). Conclusion: The Ising-DP model was sufficiently sensitive to detect activated areas in our patients with right mTLE during the face-encoding memory task. Since the model adjusted for BSFs improved sensitivity, we recommend the use of more detailed BSFs such as seizure history in future research.

Mental Health Problems and the Associated Family and School Factors in Adolescents: A Multilevel Analysis.

Objective: Mental health is one of the most important issues in adolescents' life. Adolescents' health is highly important, because of their role in the future. This study was conducted using multilevel analysis to investigate the risk factors at student and school levels. Method : This was a cross sectional study for which 1740 students and 53 schools were selected between February and March 2018 in Qazvin, Iran. Multistage stratified cluster sampling was used for data collection. Mental health problems were measured by the Strengths and Difficulties Questionnaire (SDQ). Emotional symptom, conduct problem, hyperactivity, peer relationship problem, and prosocial behavior were the subscales. This study used multilevel analysis to determine the association between each of the questionnaire scales and students and schools variables. Results: The prevalence of the mental health problems was 16.2%. Conduct problem was more prevalent than others (21.1%). Overall, the score of mental health problems was significantly lower in boys' schools, in adolescents with physical activity, and in families with high socioeconomic status. Hyperactivity and emotional symptoms were significantly higher in girls' schools. While prosocial behavior and peer relationship problems were significantly higher in boys' schools. The association between variables and the scales of mental health problems was different.Conclusion: Results indicated desirable physical activity and socioeconomic status are effective components in the adolescents' mental health, and, mostly girls' schools were more vulnerable than boys' schools. Therefore, the educational authorities and health policymakers should consider this diversity to design interventional programs and pay more attention to the high-risk adolescents in different schools.

Malaria intermittent preventive treatment in Nigeria: a qualitative study to explore barriers.

BACKGROUND: While the use of sulphadoxine pyrimethamine (SP) is effective in preventing malaria infection during pregnancy, there are challenges limiting its uptake in Nigeria. This study aimed at exploring the barriers to IPTp usage among pregnant women in Kano state - Nigeria. METHODS: This is a qualitative study. The purposive sampling strategy was used for identification and selection of 14 key informants for interviews. In addition, six focus group discussions (FGDs) were conducted with pregnant women (3 FGDs) and married men (3 FGDs). The conventional content analysis method was used to interpret meaning from the content of the data. MAXQDA 10 software was used for data management and analysis. RESULTS: Poor policy implementation, poor antenatal care attendance, inadequate access to intermittent preventive treatment at the community levels, lack of sustainable funding, and poor community engagement emerged as major barriers to IPTp use in Nigeria. CONCLUSION: While the political will to allocate sufficient financial resources could help improve service delivery and IPTp usage among pregnant women, community participation is critical to sustain the gains.

Hepatocellular carcinoma incidence at national and provincial levels in Iran from 2000 to 2016: A meta-regression analysis.

BACKGROUND: The incidence of Hepatocellular carcinoma (HCC), the most common primary liver cancer with high mortality, is undergoing global change due to evolving risk factor profiles. We aimed to describe the epidemiologic incidence of HCC in Iran by sex, age, and geographical distribution from 2000 to 2016. METHODS: We used the Iran Cancer Registry to extract cancer incidence data and applied several statistical procedures to overcome the dataset's incompleteness and misclassifications. Using Spatio-temporal and random intercept mixed effect models, we imputed missing values for cancer incidence by sex, age, province, and year. Besides, we addressed case duplicates and geographical misalignments in the data. RESULTS: Age-standardized incidence rate (ASIR) increased 1.17 times from 0.57 (95% UI: 0.37-0.78) per 100,000 population in 2000 to 0.67 (0.50-0.85) in 2016. It had a 21.8% total percentage change increase during this time, with a 1.28 annual percentage change in both sexes. Male to female ASIR ratio was 1.51 in 2000 and 1.57 in 2016. Overall, after the age of 50 years, HCC incidence increased dramatically with age and increased from 1.19 (0.98-1.40) in the 50-55 age group to 6.65 (5.45-7.78) in the >85 age group. The geographical distribution of this cancer was higher in the central, southern, and southwestern regions of Iran. CONCLUSION: The HCC incidence rate increased from 2000 to 2016, with a more significant increase in subgroups such as men, individuals over 50 years of age, and the central, southern, and southwestern regions of the country. We recommend health planners and policymakers to adopt more preventive and screening strategies for high-risk populations and provinces in Iran.

Spatio-temporal analysis of misaligned burden of disease data using a geo-statistical approach.

Data used to estimate the burden of diseases (BOD) are usually sparse, noisy, and heterogeneous. These data are collected from surveys, registries, and systematic reviews that have different areal units, are conducted at different times, and are reported for different age groups. In this study, we developed a Bayesian geo-statistical model to combine aggregated sparse, noisy BOD data from different sources with misaligned areal units. Our model incorporates the correlation of space, time, and age to estimate health indicators for areas with no data or a small number of observations. The model also considers the heterogeneity of data sources and the measurement errors of input data in the final estimates and uncertainty intervals. We applied the model to combine data from nine different sources of body mass index in a national and sub-national BOD study. The cross-validation results confirmed a high out-of-sample predictive ability in sparse and noisy data. The proposed model can be used by other BOD studies especially at the sub-national level when the areal units are subject to misalignment.