Machine learning-based classifiers to predict metastasis in colorectal cancer patients.

Background: The increasing prevalence of colorectal cancer (CRC) in Iran over the past three decades has made it a key public health burden. This study aimed to predict metastasis in CRC patients using machine learning (ML) approaches in terms of demographic and clinical factors. Methods: This study focuses on 1,127 CRC patients who underwent appropriate treatments at Taleghani Hospital, a tertiary care facility. The patients were divided into training and test datasets in an 80:20 ratio. Various ML methods, including Naive Bayes (NB), random rorest (RF), support vector machine (SVM), neural network (NN), decision tree (DT), and logistic regression (LR), were used for predicting metastasis in CRC patients. Model performance was evaluated using 5-fold cross-validation, reporting sensitivity, specificity, the area under the curve (AUC), and other indexes. Results: Among the 1,127 patients, 183 (16%) had experienced metastasis. In the predictionof metastasis, both the NN and RF algorithms had the highest AUC, while SVM ranked third in both the original and balanced datasets. The NN and RF algorithms achieved the highest AUC (100%), sensitivity (100% and 100%, respectively), and accuracy (99.2% and 99.3%, respectively) on the balanced dataset, followed by the SVM with an AUC of 98.8%, a sensitivity of 97.5%, and an accuracy of 97%. Moreover, lower false negative rate (FNR), false positive rate (FPR), and higher negative predictive value (NPV) can be confirmed by these two methods. The results also showed that all methods exhibited good performance in the test datasets, and the balanced dataset improved the performance of most ML methods. The most important variables for predicting metastasis were the tumor stage, the number of involved lymph nodes, and the treatment type. In a separate analysis of patients with tumor stages I-III, it was identified that tumor grade, tumor size, and tumor stage are the most important features. Conclusion: This study indicated that NN and RF were the best among ML-based approaches for predicting metastasis in CRC patients. Both the tumor stage and the number of involved lymph nodes were considered the most important features.

Atorvastatin Effect on COVID-19 Outcomes: A Propensity Score Matched Study on Hospitalized Patients.

BACKGROUND: This study investigated the association of atorvastatin use on survival, need for intensive care unit (ICU) admission, and length of hospital stay (LOS) among COVID-19 inpatients. MATERIALS AND METHODS: A retrospective study was conducted between March 20th, 2020, and March 18th, 2021, on patients with confirmed COVID-19 admitted to three hospitals in Tehran, Iran. The unadjusted and adjusted effects of atorvastatin on COVID-19 prognosis were investigated. Propensity score matching (PSM) was used to achieve a 1:1 balanced dataset with a caliper distance less than 0.1 and the nearest neighbor method without replacement. RESULTS: Of 4322 COVID-19 patients, 2136 (49.42%) were treated with atorvastatin. After PSM, 1245 atorvastatin inpatients and 1245 controls were included with a median age of 62.0 (interquartile range [IQR]: 51.0, 76.0) and 63.0 (IQR: 51.0, 75.0) years, respectively. The standardized mean differences were less than 0.1 for all confounders, suggesting a good covariate balance. The use of atorvastatin was associated with decreased COVID-19 mortality (HR: 0.80; 95% CI: 0.68-0.95), whereas no relationship was found between atorvastatin and the need for ICU admission (HR: 1.21; 95% CI: 0.99-1.47). LOS was significantly higher in the atorvastatin cohort than controls (Atorvastatin vs. others: 7 [5, 11] vs. 6 [4, 10] days; p = 0.003). The survival rate was higher in combination therapy of atorvastatin plus enoxaparin than in those who received atorvastatin alone (p-value=0.001). CONCLUSION: Atorvastatin may reduce the risk of COVID-19 in-hospital mortality and could be a beneficial option for an add-on therapy. Randomized trials are warranted to confirm the results of the current observational studies.

Prevalence of depressive symptoms and suicidal behaviors among Iranian high school students: A systematic review and meta-analysis.

AIM: The objective of this meta-analysis was to determine the prevalence of depressive symptoms and suicidal behaviors among high-school students in Iran. MATERIALS AND METHODS: A comprehensive search strategy was conducted in the following original databases: PubMed, Web of Science (ISI), Scopus, Psycinfo, and national databases, including the Scientific Information Database (SID) and MagIran, from January 1988 through January 2023. Studies that reported the prevalence of depressive symptoms and suicidal behaviors among high school students in Iran were included. Two investigators extracted all relevant data independently. For deriving mean prevalence rates, random-effects meta-analyses were used. We assessed the quality of studies by the Joanna Briggs Institute (JBI). RESULTS: Total of 67 studies (45,798 participants) were included in the syntheses on depressive symptoms and suicidal ideation. For depressive symptoms, the mean prevalence rate was 48 % (40 %, 55 %). 2804 participants were enrolled for assessing of the prevalence of suicide ideation, and the mean prevalence rate was 21 % (6 %, 36 %). The meta-analysis pooling of the prevalence estimates of suicide attempts was 18 % (15 %, 20 %). CONCLUSIONS: Approximately half of the high school students experience depressive symptoms and, astonishingly, one in five high school students experiences suicidal ideation, so to identify strategies for preventing and treating depressive symptoms and suicidal behaviors in this special population, further research and policymaking are urgently needed.

Validity and reliability of artificial intelligence chatbots as public sources of information on endodontics.

AIM: This study aimed to evaluate and compare the validity and reliability of responses provided by GPT-3.5, Google Bard, and Bing to frequently asked questions (FAQs) in the field of endodontics. METHODOLOGY: FAQs were formulated by expert endodontists (n = 10) and collected through GPT-3.5 queries (n = 10), with every question posed to each chatbot three times. Responses (N = 180) were independently evaluated by two board-certified endodontists using a modified Global Quality Score (GQS) on a 5-point Likert scale (5: strongly agree; 4: agree; 3: neutral; 2: disagree; 1: strongly disagree). Disagreements on scoring were resolved through evidence-based discussions. The validity of responses was analysed by categorizing scores into valid or invalid at two thresholds: The low threshold was set at score ≥4 for all three responses whilst the high threshold was set at score 5 for all three responses. Fisher's exact test was conducted to compare the validity of responses between chatbots. Cronbach's alpha was calculated to assess the reliability by assessing the consistency of repeated responses for each chatbot. RESULTS: All three chatbots provided answers to all questions. Using the low-threshold validity test (GPT-3.5: 95%; Google Bard: 85%; Bing: 75%), there was no significant difference between the platforms (p > .05). When using the high-threshold validity test, the chatbot scores were substantially lower (GPT-3.5: 60%; Google Bard: 15%; Bing: 15%). The validity of GPT-3.5 responses was significantly higher than Google Bard and Bing (p = .008). All three chatbots achieved an acceptable level of reliability (Cronbach's alpha >0.7). CONCLUSIONS: GPT-3.5 provided more credible information on topics related to endodontics compared to Google Bard and Bing.

Comparison of the efficacy of diltiazem versus fluoxetine in the treatment of distal esophageal spasm: A randomized-controlled-trial.

BACKGROUND AND STUDY AIM: Distal esophageal spasm is an uncommon esophageal motility disorder presenting with non-cardiac chest pain and dysphagia. The main goal of therapy is symptom relief with pharmacologic, endoscopic, and surgical therapies. Pharmacologic treatment is less invasive and is the preferred method of choice. The purpose of this study was to compare the effectiveness of diltiazem versus fluoxetine in the treatment of distal esophageal spasm. PATIENTS AND METHODS: A total of 125 patients with distal esophageal spasm diagnosed using endoscopy, barium esophagogram, and manometry were evaluated. Patients were divided into diltiazem and fluoxetine groups and received a 2-month trial of diltiazem + omeprazole or fluoxetine + omeprazole, respectively. Of 125 patients, 55 were lost to follow up and 70 were eligible for final analysis. Clinical signs and symptoms were assessed before and after therapy using four validated questionnaires: Eckardt score, short form-36, heartburn score, and the hospital anxiety and depression scale. RESULTS: Both regimens significantly relieved symptoms (a decrease in mean Eckardt score of 2.57 and 3.18 for diltiazem and fluoxetine groups, respectively; and a decrease in mean heartburn score by 0.89 and 1.03 for diltiazem and fluoxetine groups, respectively). Patients' quality of life improved based on short form-36 (an increase in mean score of 2.37 and 3.95 for fluoxetine and diltiazem groups, respectively). There was no relationship between patients' improvement and severity of symptoms. Psychological findings based on the hospital anxiety and depression scale were inconsistent (a decrease in mean of 0.143 and 0.57 for fluoxetine and diltiazem groups, respectively; p > 0.05). CONCLUSION: Fluoxetine and diltiazem were effective for clinical symptom relief in patients with distal esophageal spasm, but were not promising for improving psychological symptoms. Neither regimen was superior in terms of efficacy. Consequently, it is key to consider side effects and comorbidities when choosing a therapy.

Effect of interferon-α on COVID-19 in-hospital mortality: a large-scale propensity score-matched study.

BACKGROUND:  Coronavirus infection can induce the production of inflammatory cytokines leading to acute respiratory distress syndrome (ARDS) and death. It is well-established that interferons (IFNs) are essential in regulating the immune response, thus their effects of IFNs on COVID-19 patients should be subject to investigation. This study aimed to investigate the effects of IFN-α alone or in combination with remdesivir in hospitalized COVID-19 patients. MATERIAL AND METHODS:  A multicentre, retrospective study was conducted on COVID-19 patients admitted to three hospitals in Tehran, Iran, from March 20, 2020, to March 18, 2021. The unadjusted and adjusted effects of IFN-α on COVID-19 outcomes were investigated through propensity score matching (PSM) to achieve a 1:1 balanced dataset. RESULTS: Among 4,782 patients, 3,764 were eligible for the study, including 1,704 patients (45.27%) receiving at least one treatment with IFN-α and 2,060 controls not receiving IFN-α. After PSM, 851 IFN-α patients and 851 controls were recruited in the PSM analysis with a median age of 60.8 (standard deviation [SD]: 16.2 and 60.9 [SD: 17.4]), respectively. The PSM results showed no significant difference between the survival curves of the IFN-α group and the control group (p=0.340). However, the unadjusted impact of IFN-α on the risk of mortality was statistically significant (p=0.043, hazard-ratio: 0.86; 95% confidence interval [CI]: 0.75-0.99). Also, the combination of IFN-α and remdesivir had no significant benefit (HR: 89, 95% CI: 0.74-1.34). CONCLUSION: Our findings indicate that subcutaneous administration of IFN-α, with or without remdesivir, does not have any significant impact on COVID-19 mortality and ICU admission. Future clinical trials considering the time, subtype, and form of IFN-α administration are warranted to investigate the potential therapeutic effects of IFN-α on COVID-19.

Accuracy of the Discriminatory Ability of Combined Fecal Microbiota Panel in the Early Detection of Patients with Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) screening and detecting it at an early stage is an effective way to decrease mortality from CRC. Colonoscopy, considered the gold standard (GS) for diagnosing the disease in many countries, has several limitations. Therefore, the main focus of this literature is to investigate the ability of combining candidate gut microbiota for early diagnosis of CRC, both in the presence and absence of GS test outcomes. METHODS: We analyzed the data derived from a case-control study, including 83 screening colonoscopies conducted on subjects aged 18-92 years in Tehran, Iran. The candidate gut microbiota including, ETBF, Enterococcus faecalis, and Porphyromonas gingivalis were quantified in samples using absolute qRT PCR. The Bayesian latent class model (LCM) was employed to combine the values from the multiple bacterial markers in order to optimize the discriminatory ability compared with a single marker. RESULTS: Based on Bayesian logistic regression, we discovered that family history of CRC, physical activity, cigarette smoking, and food diet were all significantly associated with an increased risk of CRC. When comparing ETBF and E. faecalis to P. gingivalis, we have observed that P. gingivalis exhibited greater predictive power in detecting high-risk individuals with CRC. As such, the sensitivity, specificity, and the area under the receiver-operating characteristics curve of combining ETBF, E. faecalis, and P. gingivalis were 98%, 96%, and 0.97, respectively. CONCLUSIONS: This study suggests that the combined use of the three markers markedly improves classification performance compared to pairwise combinations, as well as individual markers, both with and without GS test outcomes. Noticeably, the triple composition of the fecal markers may serve as a reliable non-invasive indicator for the early prediction of CRC.

Relationship between anxiety and quality of life in the presence of other factors in adult celiac patients; a nationwide study.

Aim: This study aimed to detect relationships among quality of life (QoL) and anxiety and demographic factors in patients with celiac disease (CD). Background: CD is a type of autoimmune small intestine diseases caused by gluten ingestion. In Iran, the prevalence of CD is considered to be 1% in the general population. As physical problems and behavioral disorders of CD can lead to a reduction in QoL. Methods: This cross-sectional study was performed on 533 patients with Celiac Disease from 9 cities of Iran. Data collected were analyzed by SPSS version 22. Quality of life and anxiety respectively evaluated by (GHQ-28) and SAS questionnaires. Predictors of quality of life (sex, age, age of diagnosis, city of life, education level, family history of celiac, occupation and anxiety) were tested by multiple linear regression. Results: Our results showed a significant relationship between poor quality of life and anxiety (correlation= -0.143, P=0.001). The mean of the quality of life index in celiac diseases was 126.2±30.4 and women had a lower quality of life than men (P=0.003) importantly in emotions and worries scores. There was no significant difference between male and female in terms of anxiety level. Conclusion: According to the results, both quality of life and anxiety correlated together and women seem to suffer more than men from celiac disease. Therefore, greater attention to women who have celiac disease are suggested.

Translation and cross-cultural adaptation of the Persian version of inflammatory bowel disease-fatigue (IBD-F) self-assessment questionnaire.

BACKGROUND: This study appraises the psychometrics properties of the Inflammatory bowel disease-fatigue (IBD-F) Persian version questionnaire. METHODS: The original IBD-F questionnaire was translated into the Persian version in a standard forward-back manner. The validation was performed through the face, content, and construct validity. Fifteen experts scored each item's necessity on an ordinal Likert scale of three; then, the content validity ratio was calculated using the Lawshe formula. Eight judges from pre-defined panel rated each item on an ordinal 4-point Likert scale concerning its relevancy, clarity, and simplicity for individual-CVI calculation. The mean individual-CVI was considered as the Scale-CVI for each domain. Twenty lay experts (selected from the target population) were asked to express their opinion on each item's importance by scoring on a 5-point Likert scale; subsequently, face validity was determined by the impact score formula. The questions that had minimum values of CVR, CVI, and impact score were retained in the final version of the questionnaire for reliability evaluation. Construct validity was evaluated via Confirmatory Factor Analysis. Internal consistency and test-retest reliability were checked using Cronbach's α and intraclass correlation coefficients (ICC). Fifty-four patients recruited based on inclusion and exclusion criteria to participate in the reliability analysis. RESULTS: All the questions received the qualified values of CVR (exceeding 0.49 points) and impact score (more than 1.5 points) and were retained in the questionnaire; however, revisions were made for questions with a CVI 0.7-0.9 for clarity and simplicity. The result demonstrated relative goodness CFA and proper internal consistency, as Cronbach's α coefficient was 0.964 for the test (0.845 and 0.963 for the first and second part of the questionnaire, respectively (and 0.888 for the re-test (0.793 and 0.876 for the first and second section of the questionnaire, respectively). The ICC values between test and re-test for the first and second part and the whole questionnaire were obtained as 0.904, 0.922, and 0.921, respectively. CONCLUSION: The Persian version of the IBD-F questionnaire was valid and reliable; thus, an appropriate scale was deemed to measure fatigue (severity, frequency, and impact on daily activities).

Global long-term trends and spatial cluster analysis of pancreatic cancer incidence and mortality over a 30-year period using the global burden of disease study 2019 data.

INTRODUCTION: Pancreatic cancer (PC) is one of the most fatal malignancies, and its incidence and mortality rates are growing annually throughout the world. In this research, we aimed to investigate the time trends and identify the spatial clusters of incidence and mortality on a global scale over the last 30 years, using the Global Burden of Disease (GBD) study 2019 data. METHODS: Age-standardized incidence and mortality data due to PC were extracted from the GBD study, which was carried out from 1990 to 2019. A Joinpoint regression analysis was utilized to examine trends in the incidence and mortality of PC over the past three decades. As such, spatial analyses were undertaken to detect the spatial distribution and clustering of the metrics globally. RESULTS: It was observed that both the incidence and mortality rates were higher in males than in females worldwide. The global mortality and incidence rates significantly increased by 0.8% per year over the time of follow-up period (p<0.05). By spatial cluster analysis for mortality, European and North African countries, as well as Greenland were explored as hot spots; while South African and Southeast Asian countries were explored as cold spots. Regarding incidence, hot spots were found in European countries, Southern America, and Greenland; whilst cold spots were determined in Southern Africa and Madagascar. CONCLUSIONS: Collectively, the temporal trends disclosed a gradual rise in PC incidence and mortality rates over the period 1990-2019, reflecting the global health concern. We further found geographical variations in the patterns and identified high- and low-risk areas for incidence and mortality. These findings facilitate the design and implementation of more resource-efficient and geographically targeted treatments. Given the results of the current study, a practical approach to minimizing the future PC burden involves planned population-wide interventions, as well as primary prevention through healthier lifestyles.

Predicting the COVID-19 Patients Status Using Chest CT Scan Findings: A Risk Assessment Model Based on Decision Tree Analysis.

BACKGROUND: The role of chest computed tomography (CT) to diagnose coronavirus disease 2019 (COVID-19) is still an open field to be explored. The aim of this study was to apply the decision tree (DT) model to predict critical or non-critical status of patients infected with COVID-19 based on available information on non-contrast CT scans. METHODS: This retrospective study was performed on patients with COVID-19 who underwent chest CT scans. Medical records of 1078 patients with COVID-19 were evaluated. The classification and regression tree (CART) of decision tree model and k-fold cross-validation were used to predict the status of patients using sensitivity, specificity, and area under the curve (AUC) assessments. RESULTS: The subjects comprised of 169 critical cases and 909 non-critical cases. The bilateral distribution and multifocal lung involvement were 165 (97.6%) and 766 (84.3%) in critical patients, respectively. According to the DT model, total opacity score, age, lesion types, and gender were statistically significant predictors for critical outcomes. Moreover, the results showed that the accuracy, sensitivity and specificity of the DT model were 93.3%, 72.8%, and 97.1%, respectively. CONCLUSIONS: The presented algorithm demonstrates the factors affecting health conditions in COVID-19 disease patients. This model has the potential characteristics for clinical applications and can identify high-risk subpopulations that need specific prevention. Further developments including integration of blood biomarkers are underway to increase the performance of the model.

On-admission and dynamic trend of laboratory profiles as prognostic biomarkers in COVID-19 inpatients.

This large-scale study aimed to investigate the trend of laboratory tests of patients with COVID-19. Hospitalized confirmed and probable COVID-19 patients in three general hospitals were examined from March 20, 2020, to June 18, 2021. The confirmed and probable COVID-19 patients with known outcomes and valid laboratory results were included. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to select admittance prognostic features. Parallel Pairwise Comparison of mortality versus survival was used to examine the trend of markers. In the final cohort, 11,944 patients were enrolled, with an in-hospital mortality rate of 21.8%, mean age of 59.4 ± 18.0, and a male-to-female ratio of 1.3. Abnormal admittance level of white blood cells, neutrophils, lymphocytes, mean cellular volume, urea, creatinine, bilirubin, creatine kinase-myoglobin binding, lactate dehydrogenase (LDH), Troponin, c-reactive protein (CRP), potassium, and creatinine phosphokinase reduced the survival of COVID-19 inpatients. Moreover, the trend analysis showed lymphocytes, platelet, urea, CRP, alanine transaminase (ALT), and LDH have a dissimilar trend in non-survivors compared to survived patients. This study proposed a novel approach to find serial laboratory markers. Serial examination of platelet count, creatinine, CRP, LDH, and ALT can guide healthcare professionals in finding patients at risk of deterioration.

Predicting metastasis in gastric cancer patients: machine learning-based approaches.

Gastric cancer (GC), with a 5-year survival rate of less than 40%, is known as the fourth principal reason of cancer-related mortality over the world. This study aims to develop predictive models using different machine learning (ML) classifiers based on both demographic and clinical variables to predict metastasis status of patients with GC. The data applied in this study including 733 of GC patients, divided into a train and test groups at a ratio of 8:2, diagnosed at Taleghani tertiary hospital. In order to predict metastasis in GC, ML-based algorithms, including Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (RT) and Logistic Regression (LR), with 5-fold cross validation were performed. To assess the model performance, F1 score, precision, sensitivity, specificity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and precision-recall AUC (PR-AUC) were obtained. 262 (36%) experienced metastasis among 733 patients with GC. Although all models have optimal performance, the indices of SVM model seems to be more appropiate (training set: AUC: 0.94, Sensitivity: 0.94; testing set: AUC: 0.85, Sensitivity: 0.92). Then, NN has the higher AUC among ML approaches (training set: AUC: 0.98; testing set: AUC: 0.86). The RF of ML-based models, which determine size of tumor and age as two essential variables, is considered as the third efficient model, because of higher specificity and AUC (84% and 87%). Based on the demographic and clinical characteristics, ML approaches can predict the metastasis status in GC patients. According to AUC, sensitivity and specificity in both SVM and NN can be regarded as better algorithms among 6 applied ML-based methods.

Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features.

We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system.

Defective 3-parameter Gompertz model with frailty term for estimating cure fraction in survival data.

In several survival data, a proportion of units is not susceptible to the event of interest, even if, accompanied by a sufficiently large time, which is so-called immune or cured. In this paper, the defective Gamma 3-parameter Gompertz model with a frailty term is proposed for estimating the cure fraction. This model does not require adding extra parameters for modeling the cure rate, and it accommodates unimodal hazard shapes as well as monotone hazards. A simulation study has been carried out to assess the performance of the maximum likelihood estimators. The model was applied to two real data sets.

Efficacy of Hemoperfusion in Severe and Critical Cases of COVID-19.

INTRODUCTION: Uncontrolled overproduction of inflammatory mediators is predominantly observed in patients with severe COVID-19. The excessive immune response gives rise to multiple organ dysfunction. Implementing extracorporeal therapies may be useful in omitting inflammatory mediators and supporting different organ systems. We aimed to investigate the effectiveness of hemoperfusion in combination with standard therapy in critically ill COVID-19 patients. METHOD: We conducted a single-center, matched control retrospective study on patients with confirmed SARS-CoV-2 infection. Patients were treated with hemoperfusion in combination with standard therapy (hemoperfusion group) or standard treatment (matched group). Hemoperfusion or hemoperfusion and continuous renal replacement therapies were initiated in the hemoperfusion group. The patients in the matched group were matched one by one with the hemoperfusion group for age, sex, oxygen saturation (SPO2) at the admission, and the frequency of using invasive mechanical ventilation during hospitalization. Two types of hemoperfusion cartridges used in this study were Jafron© (HA330) and CytoSorb® 300. RESULT: A total of 128 COVID-19-confirmed patients were enrolled in this study; 73 patients were allotted to the matched group and 55 patients received hemoperfusion. The median SPO2 at the admission day in the control and hemoperfusion groups was 80% and 75%, respectively (p value = 0.113). The mortality rate was significantly lower in the hemoperfusion group compared to the matched group (67.3% vs. 89%; p value = 0.002). The median length of ICU stay was statistically different in studied groups (median, 12 days for hemoperfusion group vs. 8 days for the matched group; p < 0.001). The median final SPO2 was statistically higher in the hemoperfusion group than in the matched group, and the median PaCO2 was lower. CONCLUSION: Among critically ill COVID-19 patients, based on our study, the use of hemoperfusion may reduce the mortality rate and improve SPO2 and PaCO2.

Knowledge, Attitude, and Practice Regarding Cardiovascular Disease among Coronary Artery Disease and Premature Coronary Artery Disease Patients Referred to Imam Hossein Hospital, Tehran, Iran.

Background: Knowledge, attitudes, and practices (KAP) studies are widely used in public health. This study aimed to investigate and compare KAP among patients with coronary artery disease (CAD) and premature coronary artery disease (PCAD) regarding cardiovascular disease (CVD). Methods: This cross-sectional study was conducted on 100 PCAD patients and 100 CAD patients in a general hospital in Tehran, Iran, between April and October 2022. A valid questionnaire was utilized to gather information, and descriptive analysis and logistic regression were employed for analysis using SPSS, version 23. Results: This study involved 100 CAD and 100 PCAD patients with average ages of 68.09±7.20 and 50.20±7.65 years, respectively. Men accounted for 58% of the PCAD group and 73% of the CAD group. The PCAD group had a higher level of knowledge and exhibited a more positive attitude toward CVD than the CAD group (P=0.007 and P<0.001, respectively). The PCAD patients and those with a family history of chronic diseases had a higher level of knowledge (P=0.045 and P=0.27, respectively) and showed a more positive attitude (P=0.030 and P<0.001, respectively). However, participants with a self-reported history of chronic diseases and those who were employed exhibited a less positive attitude. Occupation was associated with nutritional and smoking status (P=0.037). Conclusion: Higher levels of knowledge and more positive attitudes regarding CVD were observed; still, the study population's behaviors were unsatisfactory. Educational interventions are needed to promote positive health behaviors, emphasizing the link between knowledge and risk reduction and decreased CVD and mortality rates.

A Bayesian approach to correct the under-count of cancer registry statistics before population-based cancer registry program.

Aim: This study aims to correct undercounts in cancer data before initiating a population-based cancer registry program, employing an innovative Bayesian methodology. Background: Underestimation is a widespread issue in cancer registries within developing countries. Methods: This secondary study utilized cancer registry data. We employed the Bayesian approach to correct undercounting in cancer data from 2005 to 2010, using the ratio of pathology to population-based data in the Golestan province as the initial value. Results: The results of this study showed that the lowest percentage of undercounting belonged to Khorasan Razavi province with an average of 21% and the highest percentage belonged to Sistan and Baluchestan province with an average of 38%.The average age-standardized incidence rate (ASR) for all provinces of the country except Golestan province was equal to 105.72 (Confidence interval (CI) 95% 105.35-106.09) per 100,000 and after Bayesian correction was 137.17 (CI 95% 136.74-137.60) per 100,000. In 2010 the amount of ASR before Bayesian correction was 100.28 (CI 95% 124.39-127.09) per 100,000 for women and 136.49 (CI 95% 171.20-174.38) per 100,000 for men. Also, after implementing the Bayesian correction, ASR increased to 125.74 per 100,000 for women and 172.79 per 100,000 for men. Conclusion: The study demonstrates the effectiveness of the Bayesian approach in correcting undercounting in cancer registries. By utilizing the Bayesian method, the average ASR after Bayesian correction with a 29.74 percent change was 137.17 per 100,000. These corrected estimates provide more accurate information on cancer burden and can contribute to improved public health programs and policy evaluation. Furthermore, this research emphasizes the suitability of the Bayesian method for addressing underestimation in cancer registries. It also underscores its pivotal role in shaping the trajectory of future investigations in this field.