Effectiveness of theory-based educational interventions of promoting oral health among elementary school students.

BACKGROUND: The aim of the present study was to determine the effect of oral health education programs on the oral health of primary school students. METHODS: In this randomized controlled trial study, 190 elementary fifth-grade female students were chosen using the multistage cluster sampling method. In this study, the Plaque Index (PI), Simplified Oral Hygiene Index (OHI-S), Community Periodontal Index (CPI), tooth brushing using fluoride toothpaste, dental flossing frequency and factors affecting them were determined according to social cognitive theory (SCT). Interventions were implemented using the play method and with the help of three pamphlets, five posters, a celebration of oral health, and the creation of a Telegram group. Data were analyzed using descriptive statistics indexes, t tests, paired sample t tests, chi-square tests, and Pearson correlation tests. RESULTS: The results showed that 3 months after the intervention, compared to before the intervention, the percentage of participants in the intervention group who brushed their teeth twice or more per day increased by 48.5%, and the percentage of participants who used dental floss at least once per day increased by 64.2%. The rate of gum bleeding decreased by 6.3%. The good OHI-S rate increased by 44.4%. Dental plaque decreased by 38.1%. CONCLUSION: The results demonstrated that a gamification design can be effective and useful in promoting the oral health of students. TRIAL REGISTRATION: registration timing: retrospective, registration date: 18/10/2022, registration number: IRCT20141128020129N2.

Diabetic foot ulcers risk prediction in patients with type 2 diabetes using classifier based on associations rule mining.

Identifying diabetic patients at risk of developing foot ulcers, as one of the most significant complications of diabetes, is a crucial healthcare concern. This study aimed to develop an associative classification model (CBA) using the Apriori algorithm to predict diabetic foot ulcers (DFU). This retrospective cohort study included 666 patients with type 2 diabetes referred to Shahid Beheshti Hospital in Iran between April 2020 and August 2022, of which 279 (42%) had DFU. Data on 29 specific baseline features were collected, which were preprocessed by discretizing numerical variables based on medical cutoffs. The target variable was the occurrence of DFU, and the minimum support, confidence, and lift thresholds were set to 0.01, 0.7, and 1, respectively. After data preparation and cleaning, a CBA model was created using the Apriori algorithm, with 80% of the data used as a training set and 20% as a testing set. The accuracy and AUC (area under the roc curve) measure were used to evaluate the performance of the model. The CBA model discovered a total of 146 rules for two patient groups. Several factors, such as longer duration of diabetes over 10 years, insulin therapy, male sex, older age, smoking, addiction to other drugs, family history of diabetes, higher body mass index, physical inactivity, and diabetes complications such as proliferative and non-proliferative retinopathy and nephropathy, were identified as major risk factors contributing to the development of DFU. The CBA model achieved an overall accuracy of 96%. Also, the AUC value was 0.962 (95%CI 0.924, 1.000). The developed model has a high accuracy in predicting the risk of DFU in patients with type 2 diabetes. The creation of accurate predictive models for DFU has the potential to significantly reduce the burden of managing recurring ulcers and the need for amputation, which are significant health concerns associated with diabetes.

Association of MGLL Intronic C>T Single Nucleotide Polymorphism (rs782440) with Borderline Personality Disorder: A Case-Control Study.

OBJECTIVE: From the perspective of etiology, borderline personality disorder (BPD) is a multifactorial and complex disorder, hence our understanding about the molecular basis and signaling of this disorder is extremely limited. The purpose of this study was evaluating the relationship between BPD and the Monoacylglycerol lipase (MGLL) polymorphism rs782440 in the population of Hamadan, Iran. MATERIALS AND METHODS: In this case-control study, 106 participants including 53 patients with BPD and 53 healthy control subjects were selected by psychiatrists in the Department of Psychiatry at Farshchian Sina Hospital in Hamadan. The BPD patients were selected based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) form for diagnosing BPD patients. For genotyping, polymerase chain reaction (PCR) was used to amplify the desired region including the (MGLL) intronic C>T single nucleotide polymorphism (SNP) (rs782440) and afterward the amplicon was sequenced using the Sanger sequencing method. To determine the genotype of these patients, their sequences were aligned with the reference sequence of MGLL through the CLC genomic workbench software. RESULTS: The results indicated that the frequency of TT in comparison to the CC genotype was significantly different (P=0.003) and the risk of BPD in change from the TT genotype to CC genotype was increased by 6.679%. Regarding the frequency of allele in this group, no significant difference was observed. CONCLUSION: This paper, has studied and reports for the first time, the association between MGLL SNP (rs782440) with BPD. The findings of the current research revealed that the TT genotype increases the risk of BPD compared to the CC genotype. Considering the lack of a suitable diagnostic biomarker for BPD, using this potential biomarker in the near future can be promising.

Validity and reliability of the Persian version of the Suboptimal Health Status Questionnaire among university staff in Iran.

Background: Suboptimal Health Status Questionnaire-25 (SHSQ-25) is an established tool for measuring a precision health state between health and illness. The present study aims to assess the validity and reliability of a Persian version of SHSQ-25 (P-SHSQ-25) in a university staff Iranian population. Methods: A sample of 316 academic and supporting staff (163 males, age range from 23 to 64 years old) from Hamadan University of Medical Sciences, Hamadan, Iran was recruited in this population-based cross-sectional study with a questionnaire validation from Apri1 to October 2022. Forward-backward translation method was performed for the SHSQ-25 translation from English to Persian. Internal reliability, content, convergence, discriminative and construct validity of the P-SHSQ-25 were examined. The factorial structure of the P-SHSQ-25 across groups was examined using measurement invariant test. Results: In the translation process, the conceptual equivalence of the P-SHSQ-25 with the English version was confirmed. The item-content validity index and content validity ratio of all P-SHSQ-25 items were higher than the cut-off values of 0.70 and 0.62, respectively. Cronbach's α was higher than 0.70 for all P-SHSQ-25 domains. The confirmatory factor analysis (CFA) showed the fitness of five factors on the data set (comparative fit index = 0.88, and root mean square error of approximation = 0.07). The CFA model fit did not change substantially across sex, age, occupation, economic status, and body mass index (Δ comparative fit index (CFI)<0.01). Conclusions: The P-SHSQ-25 can be used as a reliable and valid tool to measure health status for screening pre-chronic disease conditions in a primary care setting among Iranian population.

Circular RNAs in tuberculosis: From mechanism of action to potential diagnostic biomarker.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (M. tuberculosis), continues to be a major global health concern. Understanding the molecular intricacies of TB pathogenesis is crucial for developing effective diagnostic and therapeutic approaches. Circular RNAs (circRNAs), a class of single-stranded RNA molecules characterized by covalently closed loops, have recently emerged as potential diagnostic biomarkers in various diseases. CircRNAs have been demonstrated to modulate the host's immunological responses against TB, specifically by reducing monocyte apoptosis, augmenting autophagy, and facilitating macrophage polarization. This review comprehensively explores the roles and mechanisms of circRNAs in TB pathogenesis. We also discuss the growing body of evidence supporting their utility as promising diagnostic biomarkers for TB. By bridging the gap between fundamental circRNA biology and TB diagnostics, this review offers insights into the exciting potential of circRNAs in combatting this infectious disease.

Evaluating the Potential of a New Low-Profile Urinary Catheter in Preventing Catheter-Associated Urinary Tract Infections: A Prospective Randomized Blinded Clinical Trial.

Aim: To investigate the efficacy of a new low-profile catheter on incidence of the catheter-associated urinary tract infections (CAUTI) in comatose patients admitted to the intensive care unit. Background: Catheter-induced urothelial injury is a key component in the development of urinary tract infections in catheterized patients. Methods: In this prospective randomized blinded clinical trial, 80 patients requiring indwelling urinary catheterization were equally randomized to either the standard Foley catheter (control) or the low-profile catheter (experimental) group. The signs of urinary tract infection for comatose patients were considered (ie, ≥105 of colony-forming unit/milliliter of urine, hematuria, serum leukocytes, and body temperature) and recorded at baseline and on days 3 and 5 after catheterization. The analysis of covariance was applied by the SPSS-20 software at a 95% confidence level. Results: An increasing proportion of patients with elevated urinary colony counts were seen in the Foley catheter group compared with the low-profile catheter group (12.5% vs 5%). However, there were no between-group differences in the urinary colony counts and body temperature after controlling for antibiotic doses and fluid intake. Patients in the low-profile catheter group had significantly lower rates of hematuria and serum leukocytes than those in the Foley catheter group. Conclusion: A newly designed low-profile urinary catheter has demonstrated a trend toward reducing the incidence of CAUTI in patients with indwelling urinary catheters. Further studies with larger sample sizes and follow-up are needed to confirm the benefits.

Impact of COVID-19 Disease Control Committee (CDCC) policies on prevention of the disease using Bayes network inference in west of Iran.

BACKGROUND: The start of the COVID-19 pandemic was an emergency situation that led each country to adopt specific regional strategies to control it. Given the spread of COVID-19 disease, it is crucial to evaluate which policy is more effective in reducing disease transmission. The purpose of this study was to determine the impact of policies made by COVID-19 Disease Control Committee (CDCC) to reduce the risk of the disease in Hamadan province. METHODS: In the observational study, the data were extracted from three sources in Hamadan, west of Iran; first, the session reports of CDCC; second, information on periodic evaluations conducted by the primary health care directory in Hamadan from April to August 2021 and third, expert panel opinion. Bayes network analysis was used to determine the effect of each policy on mortality rate by GeNIe software version 2.2. RESULTS: Among the policies adopted by CDCC in Hamadan, seven policies, i.e., vaccination, limiting gatherings, social distancing, wearing a mask, job closure, travel restriction, and personal hygiene had the most impact to prevent the spread of COVID-19, respectively. In this study, the prevalence of the disease was 17.64% with the implementation of these policies. Now, if all these policies are observed 30% more, the prevalence will decrease to 14.18%. CONCLUSION: This study showed that if the seven policies (i.e., vaccination, limiting gatherings, social distancing, wearing a mask, job closure, travel restriction, and personal hygiene) are followed simultaneously in the community, the risk of contracting the disease will be greatly reduced. Therefore, in the pandemic of infectious diseases, such policies can help prevent the spread of the disease.

The Performance Evaluation of The Random Forest Algorithm for A Gene Selection in Identifying Genes Associated with Resectable Pancreatic Cancer in Microarray Dataset: A Retrospective Study.

OBJECTIVE: In microarray datasets, hundreds and thousands of genes are measured in a small number of samples, and sometimes due to problems that occur during the experiment, the expression value of some genes is recorded as missing. It is a difficult task to determine the genes that cause disease or cancer from a large number of genes. This study aimed to find effective genes in pancreatic cancer (PC). First, the K-nearest neighbor (KNN) imputation method was used to solve the problem of missing values (MVs) of gene expression. Then, the random forest algorithm was used to identify the genes associated with PC. MATERIALS AND METHODS: In this retrospective study, 24 samples from the GSE14245 dataset were examined. Twelve samples were from patients with PC, and 12 samples were from healthy control. After preprocessing and applying the fold-change technique, 29482 genes were used. We used the KNN imputation method to impute when a particular gene had MVs. Then, the genes most strongly associated with PC were selected using the random forest algorithm. We classified the dataset using support vector machine (SVM) and naïve bayes (NB) classifiers, and F-score and Jaccard indices were reported. RESULTS: Out of the 29482 genes, 1185 genes with fold-changes greater than 3 were selected. After selecting the most associated genes, 21 genes with the most important value were identified. S100P and GPX3 had the highest and lowest importance values, respectively. The F-score and Jaccard value of the SVM and NB classifiers were 95.5, 93, 92, and 92 percent, respectively. CONCLUSION: This study is based on the application of the fold change technique, imputation method, and random forest algorithm and could find the most associated genes that were not identified in many studies. We therefore suggest researchers use the random forest algorithm to detect the related genes within the disease of interest.

The environmental risk factors associated with ectopic pregnancy: An umbrella review.

INTRODUCTION: Ectopic pregnancy(EP) is the implantation of a fertilized ovum outside of the uterine cavity. The incidence of EP has steadily increased around the world. The present umbrella review evaluated risk factors prior to conception associated with EP based on meta-analyses and systematic reviews. METHODS: We searched PubMed, Scopus, and Web of Science until June 25, 2021. All meta-analyses that had focused on assessing the risk factors associated with EP were included. We calculated summary effect estimates, 95% CI, heterogeneity I², 95% prediction interval, small-study effects, excess significance biases, and sensitive analysis. The quality of the meta-analyses was evaluated with A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2). RESULTS: Two risk factors including chlamydia trachomatis (OR: 3.03) and smoking (OR: 1·77) were graded as suggestive evidence (class III). IUD with pregnant control (OR: 10.63) and endometriosis for case-control studies (OR: 2·66) and tubal ligation with pregnant control (OR: 9.3) were graded as risk factors with weak evidence (class IV). Tubal ligation with non-pregnant control was a protective factor (class IV). IUD with non-pregnant control and endometriosis for cohort studies were not as risk factors for EP. CONCLUSION: Two risk factors including chlamydia trachomatis and smoking were graded as suggestive evidence. IUD with pregnant control and endometriosis for case-control studies and tubal ligation with pregnant control were graded as risk factors with weak evidence. Strong evidence for risk factors of EP was not achieved, indicating the degree of uncertainty and bias, which bring an emergency to conduct further no-bias studies. SYSTEMATIC REVIEW REGISTRATION: PROSPERO (CRD42021281632).

Nurses' healing presence: A panacea for the comfort of acute coronary syndrome patients in CCU, A qualitative study.

AIM: The study aimed to explain the acute coronary syndrome (ACS) patients' perception of the nurse's healing presence in their comfort in the critical care unit. DESIGN: This descriptive qualitative study was conducted from December 2020 to September 2021. METHODS: Twenty-seven ACS patients were purposively selected from a cardiovascular university Hospital, Iran. Data were collected through semi-structured interviews (45-60 min). Data analysis was performed based on the contractual content analysis method of Graneheim and Lundman. RESULTS: In the data analysis, the main theme 'nurses' healing presence' includes two categories: 'Nurse-patient communication' with two subcategories and the category 'Compassionate care' with three subcategories.

Effects of Air Pollutant Exposure on Acute Myocardial Infarction.

BACKGROUND: Air pollution is a consequence of industrial development that is exacerbated as a result of population growth, and urbanisation. AIM: The goal of the study is to investigate the effects of air pollution on the number of cases of acute myocardial infarction (AMI) according to gender using the Zero-inflated Poisson Regression model in Hamadan, Iran. METHODS: The study used an ecological design, and data collected from March 2016 to September 2020 in Hamadan were included. The intended response was the number of cases of AMI recorded in the investigated period. The time lag of the pollutants was used to investigate the effect of air pollution on the number of AMIs. RESULTS: The number of AMI recorded for men and women was 1,195 and 553, respectively. The average age (±SD) for men and women was 64.60 (±12.27) and 70.98 (±11.79) years, respectively. According to the air quality index in Hamadan, the values of particulate matter < 2.5 µm (PM2.5), SO2, O3, and CO were below moderate levels. Also, according to NO2 and particulate matter between 25 µm-10 µm (PM10), the air quality index of Hamadan was in the very unhealthy mode just for 2 and 3 days, respectively. The O3 and NO2 are significant positive effects on AMI among men. But, PM2.5, PM10, and SO2 are negative impacts on hospitalisation in men due to AMI. For women, PM2.5 and O3 had positive effects on AMI. But, NO2 and PM10 had a significant negative impact on hospitalisation in women during different time lags. CONCLUSIONS: The results of the study showed that if the analyses are based on gender, the responses to pollutants are different and hence the stratified analysis is important.

Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran.

BACKGROUND: The high number of COVID-19 deaths is a serious threat to the world. Demographic and clinical biomarkers are significantly associated with the mortality risk of this disease. This study aimed to implement Generalized Neural Additive Model (GNAM) as an interpretable machine learning method to predict the COVID-19 mortality of patients. METHODS: This cohort study included 2181 COVID-19 patients admitted from February 2020 to July 2021 in Sina and Besat hospitals in Hamadan, west of Iran. A total of 22 baseline features including patients' demographic information and clinical biomarkers were collected. Four strategies including removing missing values, mean, K-Nearest Neighbor (KNN), and Multivariate Imputation by Chained Equations (MICE) imputation methods were used to deal with missing data. Firstly, the important features for predicting binary outcome (1: death, 0: recovery) were selected using the Random Forest (RF) method. Also, synthetic minority over-sampling technique (SMOTE) method was used for handling imbalanced data. Next, considering the selected features, the predictive performance of GNAM for predicting mortality outcome was compared with logistic regression, RF, generalized additive model (GAMs), gradient boosting decision tree (GBDT), and deep neural networks (DNNs) classification models. Each model trained on fifty different subsets of a train-test dataset to ensure a model performance. The average accuracy, F1-score and area under the curve (AUC) evaluation indices were used for comparison of the predictive performance of the models. RESULTS: Out of the 2181 COVID-19 patients, 624 died during hospitalization and 1557 recovered. The missing rate was 3 percent for each patient. The mean age of dead patients (71.17 ± 14.44 years) was statistically significant higher than recovered patients (58.25 ± 16.52 years). Based on RF, 10 features with the highest relative importance were selected as the best influential features; including blood urea nitrogen (BUN), lymphocytes (Lym), age, blood sugar (BS), serum glutamic-oxaloacetic transaminase (SGOT), monocytes (Mono), blood creatinine (CR), neutrophils (NUT), alkaline phosphatase (ALP) and hematocrit (HCT). The results of predictive performance comparisons showed GNAM with the mean accuracy, F1-score, and mean AUC in the test dataset of 0.847, 0.691, and 0.774, respectively, had the best performance. The smooth function graphs learned from the GNAM were descending for the Lym and ascending for the other important features. CONCLUSIONS: Interpretable GNAM can perform well in predicting the mortality of COVID-19 patients. Therefore, the use of such a reliable model can help physicians to prioritize some important demographic and clinical biomarkers by identifying the effective features and the type of predictive trend in disease progression.

Predictors of COVID-19 Mortality in Hemodialysis Patients, Hamadan Province, Iran.

BACKGROUND: Identification of the predictors of coronavirus disease 2019 (COVID-19)-related death in hemodialysis patients plays a key role in the management of these patients. In this regard, the present study aimed to evaluate the predictors of death among COVID-19 infected hemodialysis patients in Hamadan province, Iran. STUDY DESIGN: A cross-sectional study. METHODS: This cross-sectional study investigated 50 COVID-19 infected hemodialysis patients who were confirmed by polymerase chain reaction (PCR) test and referred to hemodialysis wards of hospitals located in Hamadan province, Iran, from March 2019 and January 2020. In order to compare the demographic characteristics and clinical variables between survived and deceased patients, the independent student t test and chi-square test were applied. RESULTS: Out of 50 confirmed COVID-19 hemodialysis patients, 27 (54%) cases were male, 38 (76%) subjects were urban residents, and 4 (8%) individuals were smokers. A significant relationship was observed between patients' gender, age, acute respiratory distress syndrome (ARDS) status, and body mass index (BMI) with the treatment outcome (P < 0.05). A significantly higher level of serum albumin was observed in the survived patients (3.49 ±â€…0.37 vs. 3.17 ±â€…0.42, P =  0.030). Moreover, in terms of lactate dehydrogenase (LDH) level, a significantly higher level of LDH was observed in the patients who died (1471.1 ±â€…1484.89 vs. 670.86 ±â€…268.85, P =  0.005). CONCLUSIONS: It can be concluded that some demographic characteristics of the patients, including age, gender, ARDS status, BMI, co-morbidities, and laboratory signs and symptoms are associated with disease outcomes in COVID-19 infected hemodialysis patients. Therefore, awareness about the predictors of death in these patients can help make better and direct clinical decisions and inform health officials about the risk of COVID-19 mortality among hemodialysis patients.

Zero-augmented beta-prime model for multilevel semi-continuous data: a Bayesian inference.

Semi-continuous data characterized by an excessive proportion of zeros and right-skewed continuous positive values appear frequently in medical research. One example would be the pharmaceutical expenditure (PE) data for which a substantial proportion of subjects investigated may report zero. Two-part mixed-effects models have been developed to analyse clustered measures of semi-continuous data from multilevel studies. In this study, we propose a new flexible two-part mixed-effects model with skew distributions for nested semi-continuous cost data under the framework of a Bayesian approach. The proposed model specification consists of two mixed-effects models linked by the correlated random effects: Part I) a model on the occurrence of positive values using a generalized logistic mixed model; and Part II) a model on the magnitude of positive values using a linear mixed model where the model errors follow skew distributions including beta-prime (BP). The proposed method is illustrated with pharmaceutical expenditure data from a multilevel observational study and the analytic results are reported by comparing potential models under different skew distributions. Simulation studies are conducted to assess the performance of the proposed model. The DIC3, LPML, WAIC, and LOO as the Bayesian model selection criteria and measures of divergence used to compare the models.

Identification of Risk Factors for COVID-19-related Death using Machine Learning Methods.

Background: Unknown cases of pneumonia appeared in late 2019 in Wuhan, China. Following the worldwide spread of the disease, the World Health Organization declared it a pandemic on March 11, 2020. The total number of infected people worldwide as of December 16, 2020, was more than 74 million, more than one million and six hundred thousand of whom died from Coronavirus Disease 2019 (COVID-19). This study aimed to identify the risk factors for the mortality of COVID-19 in Hamadan, west of Iran. Materials and Methods: This cross-sectional study used the information of all patients with COVID-19 admitted to Shahid Beheshti and Sina hospitals in Hamadan during January 2020-November 2020. Logistic regression model, decision tree, and random forest were used to assess risk factors for death due to COVID-19. Results: This study was conducted on 1853 people with COVID-19. Blood urea nitrogen change, SPO2 at admission, the duration of hospitalization, age, neutrophil count, lymphocyte count, number of breaths, complete blood count, systolic blood pressure, hemoglobin, and sodium were effective predictors in both methods of decision tree and random forest. Conclusion: The risk factors identified in the present study may serve as surrogate indicators to identify the risk of death due to COVID-19. The proper model to predict COVID-19-related mortality is random forest based on sensitivity.

Role of EFNA1 SNP (rs12904) in Tumorigenesis and Metastasis of Colorectal Cancer: A Bioinformatic Analysis and HRM SNP Genotyping Verification.

OBJECTIVE: Colorectal cancer is a prevalent disease with a poor prognosis and is known as a heterogeneous disease with many differences in clinical Symptoms and molecular profiles. The present study aimed to systematically evaluate the association of SNPs in miRNA binding sites of target genes that are involved in CRC angiogenesis, epithelial to mesenchymal transition, and cytoskeleton organization with tumorigenesis and metastasis of CRC. METHODS: A case-control study was performed on 146 samples of CRC patients and 132 healthy samples. After that, the DNA of all samples was isolated by the salting-out method. Finally, the genotypes for EFNA1 SNP (rs12904) were identified by HRM (High-resolution melting analysis) method. In order to evaluate the results of genotyping, two samples from each genotype were sequenced using the sanger sequencing method. RESULT: The frequency of AA genotype and the frequency of GG for rs12904 in satge4 and other stages are different from each other (P-value <0.0001) (P-value = 0.008). Also, the frequency of AA genotype in patients with different grades is different from each other (P-value = 0.035), while the frequency of AG   genotype and the frequency of GG   genotype is not significantly different in patients with different grades (P-value = 0.377) (P-value = 0.284). CONCLUSION: Results of this study indicated that patients carrying the GA and GG genotypes reduced the risk of disease progression compared to the AA genotype. As a result, this polymorphism plays a key role in CRC pathogenesis and metastasis and could be used as a biomarker in molecular diagnosis and metastatic state prediction in the near future after further study of its signaling pathways and molecular mechanism.

Evaluation of the Relationship Between Serum and Urine Ferritin Level of Low Birth Weight Infants.

Background & Objective: Iron deficiency before birth or in infancy can cause long-term behavioral and neurological disorders. Measuring serum ferritin is an effective way to diagnose iron deficiency but requires significant blood volume from a low birth weight infant. Therefore, the present study was performed to investigate the relationship between serum and urinary ferritin levels in low birth weight infants. Methods: In this cross-sectional study, 76 infants weighing less than 2500 g were studied. To measure serum ferritin level, 1.5 mL of blood and to measure urinary ferritin level, at least 1 mL of urine was collected from each infant. Then the results were compared. Data analysis was performed using SPSS software version 16, and the significance level was considered less than 0.05. Results: Out of 76 neonates studied, 51.3% were boys, and 80.3% were premature infants. The mean birth weight of infants was 2056.31±318.74 g, and the mean serum and urinary ferritin levels were 134.77±72.35 and 85.55±70.97 ng, respectively. There was a statistically significant relationship between serum and urinary ferritin levels. Also, serum ferritin and urinary ferritin levels had a statistically significant relationship with birth weight and gestational age. The higher the birth weight as well as the age at birth, the higher the serum ferritin and urinary ferritin. Conclusion: According to the findings of this study, measurement of urinary ferritin level can be used as a noninvasive tool for iron deficiency screening in low birth weight infants instead of serum ferritin level.

Which patients bring the most costs for hospital? A study on the cost determinants among COVID-19 patients in Iran.

BACKGROUND: Accurate information on the cost determinants in the COVID-19 patients could provide policymakers a valuable planning tool for dealing with the future COVID-19 crises especially in the health systems with limited resources. OBJECTIVES: This study aimed to determine the factors affecting direct medical cost of COVID-19 patients in Hamadan, the west of Iran. METHODS: This study considered 909 confirmed COVID-19 patients with positive real-time reverse-transcriptase polymerase-chain-reaction test which were hospitalized from 1 March to 31 January 2021 in Farshchian (Sina) hospital in Hamadan, Iran. A checklist was utilized to assess the relationship of demographic characteristics, clinical presentation, medical laboratory findings and the length of hospitalization to the direct hospitalization costs in two groups of patients (patients with hospitalization ≤ 9 days and > 9 days). Statistical analysis was performed using chi-square, median test and multivariable quantile regression model at 0.05 significance levels with Stata 14 software program. RESULTS: The median cost of hospitalization in patients was totally 134.48 dollars (Range: 19.19-2397.54) and respectively 95.87 (Range: 19.19-856.63) and 507.30 dollars (Range: 68.94-2397.54) in patients with hospitalization ≤ 9 days and > 9 days. The adjusted estimates presented that in patients with 9 or less hospitalization days history of cardiovascular disease, wheezing pulmonary lung, SPO2 lower than 90%, positive CRP, LDH higher than 942 U/L, NA lower than 136 mEq/L, lymphosite lower than 20% and patients with ICU experience had significantly positive relationship to the median of cost. Moreover, in patients with more than 9 hospitalization days, history of cardiovascular disease and ICU experience was statistically positive association and age older than 60 years and WBC lower than 4.5 mg/dL had statistically negative relationship to the median of hospitalization cost. CONCLUSION: As the length of hospital stay, which can be associated with the severity of the disease, increases, health systems become more vulnerable in terms of resource utilization, which in turn can challenge their responsiveness and readiness to meet the specialized treatment needs of individuals.

Human Error Probability Determination in Blasting Process of Ore Mine Using a Hybrid of HEART and Best-Worst Methods.

Background: One of the important actions for enhancing human reliability in any industry is assessing human error probability (HEP). The HEART technique is a robust tool for calculating HEP in various industries. The traditional HEART has some weaknesses due to expert judgment. For these reasons, a hybrid model is presented in this study to integrate HEART with Best-Worst Method. Materials Method: In this study, the blasting process in an iron ore mine was investigated as a case study. The proposed HEART-BWM was used to increase the sensitivity of APOA calculation. Then the HEP was calculated using conventional HEART formula. A consistency ratio was calculated using BWM. Finally, for verification of the HEART-BWM, HEP calculation was done by traditional HEART and HEART-BWM. Results: In the view of determined HEPs, the results showed that the mean of HEP in the blasting of the iron ore process was 2.57E-01. Checking the full blast of all the holes after the blasting sub-task was the most dangerous task due to the highest HEP value, and it was found 9.646E-01. On the other side, obtaining a permit to receive and transport materials was the most reliable task, and the HEP was 8.54E-04. Conclusion: The results showed a good consistency for the proposed technique. Comparing the two techniques confirmed that the BWM makes the traditional HEART faster and more reliable by performing the basic comparisons.

Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran.

BACKGROUND: Due to the high mortality of COVID-19 patients, the use of a high-precision classification model of patient's mortality that is also interpretable, could help reduce mortality and take appropriate action urgently. In this study, the random forest method was used to select the effective features in COVID-19 mortality and the classification was performed using logistic model tree (LMT), classification and regression tree (CART), C4.5, and C5.0 tree based on important features. METHODS: In this retrospective study, the data of 2470 COVID-19 patients admitted to hospitals in Hamadan, west Iran, were used, of which 75.02% recovered and 24.98% died. To classify, at first among the 25 demographic, clinical, and laboratory findings, features with a relative importance more than 6% were selected by random forest. Then LMT, C4.5, C5.0, and CART trees were developed and the accuracy of classification performance was evaluated with recall, accuracy, and F1-score criteria for training, test, and total datasets. At last, the best tree was developed and the receiver operating characteristic curve and area under the curve (AUC) value were reported. RESULTS: The results of this study showed that among demographic and clinical features gender and age, and among laboratory findings blood urea nitrogen, partial thromboplastin time, serum glutamic-oxaloacetic transaminase, and erythrocyte sedimentation rate had more than 6% relative importance. Developing the trees using the above features revealed that the CART with the values of F1-score, Accuracy, and Recall, 0.8681, 0.7824, and 0.955, respectively, for the test dataset and 0.8667, 0.7834, and 0.9385, respectively, for the total dataset had the best performance. The AUC value obtained for the CART was 79.5%. CONCLUSIONS: Finding a highly accurate and qualified model for interpreting the classification of a response that is considered clinically consequential is critical at all stages, including treatment and immediate decision making. In this study, the CART with its high accuracy for diagnosing and classifying mortality of COVID-19 patients as well as prioritizing important demographic, clinical, and laboratory findings in an interpretable format, risk factors for prognosis of COVID-19 patients mortality identify and enable immediate and appropriate decisions for health professionals and physicians.