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.

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.

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 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.

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.

Evaluating the predictive performance of gut microbiota for the early-stage colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) has been regarded as one of the most frequently diagnosed malignancies among the leading causes of cancer-related morbidity and mortality globally. Diagnosis of CRC at the early-stages of tumour might improve the survival rate of patients. The current study sought to determine the performance of fecal Fusobacterium nucleatum (F. nucleatum) and Streptococcus bovis (S. bovis) for timely predicting CRC. METHODS: Through a case-control study, the fecal sample information of 83 individuals (38 females, 45 males) referring to a hospital in Tehran, Iran was used. All patients underwent a complete colonoscopy, regarded as a gold standard test. Bacterial species including S. bovis and F. nucleatum were measured by absolute quantitative real-time PCR. The Bayesian univariate and bivariate latent class models (LCMs) were applied to estimate the ability of the candidate bacterial markers in order to early detection of patients with CRC. RESULTS: Bayesian univariate LCMs demonstrated that the sensitivities of S. bovis and F. nucleatum were estimated to be 86% [95% credible interval (CrI) 0.82-0.91] and 82% (95% CrI 0.75-0.88); while specificities were 84% (95% CrI 0.78-0.89) and 80% (95% CrI 0.73-0.87), respectively. Moreover, the area under the receiver operating characteristic curves (AUCs) were 0.88 (95% CrI 0.83-0.94) and 0.80 (95% CrI 0.73-0.85) respectively for S. bovis and F. nucleatum. Based on the Bayesian bivariate LCMs, the sensitivities of S. bovis and F. nucleatum were calculated as 93% (95% CrI 0.84-0.98) and 90% (95% CrI 0.85-0.97), the specificities were 88% (95% CrI 0.78-0.93) and 87% (95% CrI 0.79-0.94); and the AUCs were 0.91 (95% CrI 0.83-0.99) and 0.88(95% CrI 0.81-0.96), respectively. CONCLUSIONS: Our data has identified that according to the Bayesian bivariate LCM, S. bovis and F. nucleatum had a more significant predictive accuracy compared with the univariate model. In summary, these intestinal bacteria have been highlighted as novel tools for early-stage CRC diagnosis.

Palliative care nurse: A quantitative study of caring for neonates at end-of-life stage.

Objective: This study aimed to evaluate the quality of the care provided to newborns at End-of-Life (EOL) stages and compare the care which is already being given to the infants admitted to the NICUs of the selected hospital in (XXX) with the existing standards. Knowing how End-of-Life nursing care is provided, compared with the standard in the terminal stage of neonates' life can provide accurate information for policies, research, and educational practices. Methods: In this descriptive study, 100 nursing care services, provided to neonates at the EOL stages and their parents, were observed and compared to the standard checklist which had been developed based on the literature review and existing standards in 2015. The study setting included the NICUs of the four hospitals affiliated to (XXX) The Content and Face Validity of the checklist were determined based on specialists' comments. The Content Validity Index was 94.85%, and its reliability was evaluated too through the inter-rater correlation coefficient (ICC = 0.715). Results: Only 11.49% of EOL nursing care services were given properly and 77.92% were not given at all. Results showed that 10.59% of EOL nursing care services were performed improperly. Conclusion: The overall rate of accordance with the EOL nursing care standards was poor (16.78%). Nursing care given to infants and their parents at EOL stages is far from the standards. This can be due to various reasons, such as inadequate training and the lack of comprehensive guidelines.

Cause-specific hazard regression estimation for modified Weibull distribution under a class of non-informative priors.

In time to event analysis, the situation of competing risks arises when the individual (or subject) may experience p mutually exclusive causes of death (failure), where cause-specific hazard function is of great importance in this framework. For instance, in malignancy-related death, colorectal cancer is one of the leading causes of the death in the world and death due to other causes considered as competing causes. We include prognostic variables in the model through parametric Cox proportional hazards model. Mostly, in literature exponential, Weibull, etc. distributions have been used for parametric modelling of cause-specific hazard function but they are incapable to accommodate non-monotone failure rate. Therefore, in this article, we consider a modified Weibull distribution which is capable to model survival data with non-monotonic behaviour of hazard rate. For estimating the cumulative cause-specific hazard function, we utilized maximum likelihood and Bayesian methods. A class of non-informative types of prior (uniform, Jeffrey's and half-t) is introduced for Bayes estimation under squared error (symmetric) as well as LINEX (asymmetric) loss functions. A simulation study is performed for a comprehensive comparison of Bayes and maximum likelihood estimators of cumulative cause-specific hazard function. Real data on colorectal cancer is used to demonstrate the proposed model.

Predictive factors of inadequate bowel preparation for elective colonoscopy.

Aim: This study aimed to evaluate the effects of factors like demographic items, comorbidities, and drug history on the inadequacy of colonic preparation before colonoscopy. Background: Inadequate bowel preparation can lead to lower polyp detection rates, longer procedure times, and lower cecal intubation rates. Methods: This population-based study was conducted on 2476 Iranian adults who were referred to two tertiary centers for elective colonoscopy between 2017 and 2018. Bowel preparation quality was scored by the Boston bowel preparation scale (BBPS). Univariate and multivariate logistic regressions were used to find the independent predictors of bowel preparation inadequacy. Results: The results showed that 31.8% of patients had inadequate bowel preparation before their colonoscopy. Higher age, BMI>25, abdominal circumference>95 cm, low fruit consumption, and history of smoking were independently correlated with bowel preparation inadequacy. Additionally, using NSAIDs and SSRIs were correlated with bowel preparation adequacy in multivariate regression analysis. Finally, age, gender, ethnicity, BMI, abdominal circumference, fruit consumption, smoking, NSAIDs, SSRIs, education, constipation, physical activity, and diabetes entered the predictive model of this study. The area under the curve (AUC) reached 0.70 in the final step. Conclusion: The independent risk factors associated with colonic preparation inadequacy were identified, and herein, a predictive model is suggested for identifying patients with a high risk of bowel preparation inadequacy before a colonoscopy so that alternative preparation techniques can be employed among high-risk groups to yield optimal preparation quality.

Global pattern of trends in incidence, mortality, and mortality-to-incidence ratio rates related to liver cancer, 1990-2019: a longitudinal analysis based on the global burden of disease study.

BACKGROUND: Liver cancer (LC) is considered as one of the most dominant malignant tumors which ranked 4th and in terms of global mortality and incidence, respectively. This work aimed to investigate the global temporal trends in LC mortality-to-incidence ratio (MIR) and its components, with a particular focus on examining long-term effect of human development index (HDI) on these metrics in a 30-year follow-up. METHODS: The age-standardized LC incidence and mortality data were derived from the global burden of disease (GBD) study 2019. We first leveraged joinpoint piecewise linear regression analysis to ascertain time trends in LC incidence, mortality, and MIR complement [1-MIR] and the average annual percentage change (AAPC) of the rates over the period 1990-2019. Then, the association between the metrics and HDI was explored through longitudinal multilevel models (LMMs). RESULTS: The incidence rates paralleled the mortality rates worldwide and they had similar significant monotonic decrementing trends with AAPC values of - 1.10% (95% confidence interval (CI): - 1.40, - 0.90%) and - 1.40% (- 1.50, - 1.30%), respectively from 1990 to 2019. The [1-MIR] rates were around 0 and showed an increasing pattern from 1.70 to 8.10 per 100,000 people (AAPC, 4.90%) at the same period of time. Results from the LMMs displayed that the majority of the variation lies at the country level accounted for about 88% of the total variance. Moreover, our analysis supported that the HDI was negatively associated with either incidence or mortality over time (p < 0.05). CONCLUSIONS: Our findings highlighted that the global long-term temporal trends of LC incidence and mortality decreased slightly during 1990-2019 which may reflect improved therapeutic strategies and public health interventions. Besides, the low rates of [1-MIR] revealed the five-year relative survival rate was poor implying LC is diagnosed late in its development. Thereby, the policymakers' focus must be on early screening and detection of liver cancer.

Development of web-based dynamic nomogram to predict survival in patients with gastric cancer: a population-based study.

Gastric cancer (GC) is the fifth most frequent malignancy worldwide and the third leading cause of cancer-associated mortality. The study's goal was to construct a predictive model and nomograms to predict the survival of GC patients. This historical cohort study assessed 733 patients who underwent treatments for GC. The univariate and multivariable Cox proportional hazard (CPH) survival analyses were applied to identify the factors related to overall survival (OS). A dynamic nomogram was developed as a graphical representation of the CPH regression model. The internal validation of the nomogram was evaluated by Harrell's concordance index (C-index) and time-dependent AUC. The results of the multivariable Cox model revealed that the age of patients, body mass index (BMI), grade of tumor, and depth of tumor elevate the mortality hazard of gastric cancer patients (P < 0.05). The built nomogram had a discriminatory performance, with a C-index of 0.64 (CI 0.61, 0.67). We constructed and validated an original predictive nomogram for OS in patients with GC. Furthermore, nomograms may help predict the individual risk of OS in patients treated for GC.

A Systematic Review and Meta-Analysis on the Association between Inflammatory Bowel Disease Family History and Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) and inflammatory bowel disease (IBD) are closely interrelated. However, the effect of having a family history of one disease on the risk of another remains undetermined. AIM: The purpose of this meta-analysis was to estimate the prevalence of a family history of CRC among patients with IBD, as well as the prevalence of a family history of IBD among patients with CRC. METHODS: PubMed, Scopus, Embase, Web of Science, and Google Scholar were searched to identify studies reporting the prevalence of family history of IBD among patients with CRC, in addition to the prevalence of family history of CRC among IBD patients. Criteria for study inclusion consisted of the following: (1) studies that evaluated either IBD or CRC and dysplasia, (2) included all age groups, and (3) evaluated the family history effects for IBD or CRC. The total number of IBD patients and IBD patients with a family history of CRC and the total number of CRC patients and CRC patients with a family history of IBD were reviewed. The pooled prevalence of diseases was also estimated according to degree of relatives and geographical area. Random-effects models were used for estimating pooled prevalence. RESULTS: A total of 27 studies were included with 26,576 IBD and 9,181 CRC or dysplasia patients. Eligible studies included 13 case-control, 10 cohort, and 4 cross-sectional types. The pooled prevalence of a family history of CRC among patients with IBD was 6% (95% CI: 4-9%). The pooled prevalence for first- and second-degree relatives (11%, 95% CI: 0-37%) was more than that for the other relative subgroups of relatedness degree. The prevalence in the American regions (8% (95% CI: 5-13%)) was higher than that in the others. The pooled prevalence for a family history of IBD among CRC or dysplasia patients was 11% (95% CI: 6-16%). The pooled prevalence for first-degree relatives (13% (95% CI: 3-28%) was higher than that for the other relative subgroups of relatedness degree; it was also greater in American countries (15%, 95% CI: 8-23%). CONCLUSION: This study emphasizes the relationship between a family history of IBD and CRC development. Additionally, there was notable prevalence for a family history of CRC among IBD patients. American countries and first-degree relatives were identified to have a higher prevalence for both disease processes.

Evaluation of the Factors Affecting the Cure Rate of Cervical Intra-Epithelial Neoplasia Recurrence Using Defective Models.

BACKGROUND: Treatment of cervical intraepithelial neoplasia is very important since if it remains untreated, it may progress to cervical cancer. It is usually treated with excisional surgery. This study aimed to find the factors affecting the cure rate of cervical intraepithelial neoplasia recurrence after surgery using defective models. STUDY DESIGN: A retrospective cohort study. METHODS: Excisional surgery was performed on 307 patients with high-grade cervical intraepithelial neoplasia, from 2009 to 2017. The patients were followed up until recurrence based on histopathology report. Hematologic factors were measured before surgery. The cure rates were estimated using defective models with a Gamma frailty term and the results were compared. RESULTS: Neutrophil-to-lymphocyte ratio (NLR) (P<0.001) and excised mass size (P<0.001) had significant impacts on cure rates, and their cut-off values were 1.9 (P<0.001) and 15 mm2 (P<0.001), respectively. Patients with lower neutrophil-to-lymphocyte ratios and larger excised tissues had higher cure rates. Defective 3-parameter Gompertz distribution with gamma frailty term had the best fit to the data, and its estimated cure rates were 98% among patients with an excised mass size of > 15 mm2 and NLR of <1.9, 84% among patients with an excised mass size of >15 mm2 and NLR of >1.9, 79% among patients with an excised mass size of <15 mm2 and NLR of <1.9, and 30% among patients with an excised mass size of <15 mm2 and NLR of >1.9. CONCLUSION: Cervical intraepithelial neoplasia must be identified and treated before its progress. Excision of more tissues during excisional surgery, especially when the NLR of the patient is high, can help to prevent cervical intraepithelial neoplasia recurrence.

Nomogram to Predict the Overall Survival of Colorectal Cancer Patients: A Multicenter National Study.

BACKGROUND: Colorectal cancer (CRC) is the third foremost cause of cancer-related death and the fourth most commonly diagnosed cancer globally. The study aimed to evaluate the survival predictors using the Cox Proportional Hazards (CPH) and established a novel nomogram to predict the Overall Survival (OS) of the CRC patients. MATERIALS AND METHODS: A historical cohort study, included 1868 patients with CRC, was performed using medical records gathered from Iran's three tertiary colorectal referral centers from 2006 to 2019. Two datasets were considered as train set and one set as the test set. First, the most significant prognostic risk factors on survival were selected using univariable CPH. Then, independent prognostic factors were identified to construct a nomogram using the multivariable CPH regression model. The nomogram performance was assessed by the concordance index (C-index) and the time-dependent area under the ROC curve. RESULTS: The age of patients, body mass index (BMI), family history, tumor grading, tumor stage, primary site, diabetes history, T stage, N stage, and type of treatment were considered as significant predictors of CRC patients in univariable CPH model (p < 0.2). The multivariable CPH model revealed that BMI, family history, grade and tumor stage were significant (p < 0.05). The C-index in the train data was 0.692 (95% CI, 0.650-0.734), as well as 0.627 (0.670, 0.686) in the test data. CONCLUSION: We improved a novel nomogram diagram according to factors for predicting OS in CRC patients, which could assist clinical decision-making and prognosis predictions in patients with CRC.

The potential of endoscopic ultrasound sonography (EUS)-elastography in determining the stage of pancreatic tumor.

AIM: The current study was designed to evaluate the role of semi-quantitative EUS- elastography (strain ratio) in staging malignant pancreatic lesions. BACKGROUND: Pancreatic cancer is considered one of the most lethal malignancies with a survival rate of only 5% worldwide. Pancreatic lesions include a wide range of diagnoses from benign to malignant forms. Biopsy and pathological study are the gold standard for the differentiation of malignant lesions and staging of tumors. Recently, endoscopic ultrasound sonography (EUS) elastography has been noticed as a non-invasive diagnosis modality. Nevertheless, no evidence of its potential to determine different stages of malignant tumors is available. METHODS: This prospective study included 81 adult patients with a confirmed diagnosis of malignant pancreatic lesion in different clarified stages. All diagnoses were confirmed after endoscopic ultrasound sonography via pathological investigation of surgical specimens or needle biopsies. The results of EUS-elastography based on tumor size (T staging), involved lymph nodes (N staging), and metastasis (M staging) were compared with the gold standard. RESULTS: The mean age of patients was 60.11±13.57 years. The mean SR elastography value was 52.78±48.97. Elastography could not significantly discriminate T stage, N stage, or M stage of tumors (p=0.57, p=0.92, p=0.11, respectively). Moreover, the Spearman rank correlation coefficients for the correlation between T staging, N staging, M staging and SR elastography were not significant (p=0.40, p=0.94, p=0.39, respectively). CONCLUSION: The non-invasive modality EUS-elastography cannot replace the gold standard in staging tumors; however, EUS-elastography seemed to differentiate benign lesions from malignant ones.

Assessment of prognostic factors in long-term survival of male and female patients with colorectal cancer using non-mixture cure model based on the Weibull distribution.

OBJECTIVE: Colorectal cancer (CRC) is known as one of the malignant form of cells growing in the inner lining of colon and rectum which could seriously affect the cure rate of patients. We aimed to evaluate the effect of prognostic factors on cure fraction of CRC patients. METHODS: A total of 1043 CRC patients were included to the study from December 2001 to January 2007 at the Research Center of Gastroenterology and Liver Disease in Shahid Beheshti University of Medical Sciences, Tehran, Iran. Patients' information was extracted from their medical records, then they were followed to identify their death status via phone-call. Weibull non-mixture cure model was used to evaluate the effect of the risk factors on cure fraction of CRC patients. RESULTS: The five-years survival rate was 0.66 (males: 0.64 and female: 0.69). The median survival time for non-cured CRC patients were 3.45 years (males: 3.46; females = 3.45 years). In the single Weibull model, BMI≥30 (OR = 4.61, p-value = 0.033), poorly differentiated tumor grade (OR = 0.36, p-value = 0.036), tumor size≥25 mm (OR = 0.22, p-value = 0.046), and N1-stage (OR = 0.42, p-value = 0.005) had significant effect on females' cure fraction. Also, cure fraction of male CRC patients significantly affected by BMI (levels:25.0-29.9-OR = 12.13-p-value<0.001; ≥30-OR = 7.00-p-value = 0.017), T1-stage (OR = 0.52, p-value = 0.021), M1-stage (OR = 0.45, p-value = 0.007), IV-staging (OR = 0.36, p-value = 0.041) and IBD (OR = 0.26, p-value = 0.017). In multiple Weibull model, females were associated with tumor size≥25 mm (OR = 0.20, p-value = 0.044) and N1-stage (OR = 0.45, p-value = 0.013) and males were affected by M1-stage (OR = 0.41, p-value = 0.011) and IBD (OR = 0.20, p-value = 0.022).The cure fraction of males and females CRC patients was 64% and 69%, respectively. CONCLUSIONS: The prognostic factors for cure fraction of patients with CRC may be different among males and females. Further multicenter studies are required to assess the effect of common prognostic factors between males and females.

Gender Susceptibility to COVID-19 Mortality: Androgens as the Usual Suspects?

Identification of the causal risk factors of COVID-19 would allow better risk stratification and designing effective therapies. Epidemiological data have shown a higher incidence and mortality of COVID-19 in males compared to females. Here, we have used logistic regression analysis modeling to determine the association between gender and COVID-19 mortality in the Iranian population. The records of 2293 patients with COVID-19 infection were analyzed. The odds of death due to COVID-19 were 1.7 times higher in males compared to females after adjustment for age and background diseases. The gender difference was mainly observed at higher ages, suggesting an adjusted 2.32-fold higher risk of mortality in males aged >59.5 years old compared to females within the same age group. This finding suggests the male gender is a potential predisposing factor for mortality due to COVID-19 infection. The potential role of male hormones, particularly testosterone, as therapeutic targets deserves further investigation.

Effect of the Specific Training Course for Competency in Doing Arterial Blood Gas Sampling in the Intensive Care Unit: Developing a Standardized Learning Curve according to the Procedure's Time and Socioprofessional Predictors.

BACKGROUND: Standardization of clinical practices is an essential part of continuing education of newly registered nurses in the intensive care unit (ICU). The development of educational standards based on evidence can help improve the quality of educational programs and ultimately clinical skills and practices. OBJECTIVES: The objectives of the study were to develop a standardized learning curve of arterial blood gas (ABG) sampling competency, to design a checklist for the assessment of competency, to assess the relative importance of predictors and learning patterns of competency, and to determine how many times it is essential to reach a specific level of ABG sampling competency according to the learning curve. DESIGN: A quasi-experimental, nonrandomized, single-group trial with time series design. Participants. All newly registered nurses in the ICU of a teaching hospital of Tehran University of Medical Sciences were selected from July 2016 to April 2018. Altogether, 65 nurses participated in the study; however, at the end, only nine nurses had dropped out due to shift displacement. METHODS: At first, the primary checklist was prepared to assess the nurses' ABG sampling practices and it was finalized after three sessions of the expert panel. The checklist had three domains, including presampling, during sampling, and postsampling of ABG competency. Then, 56 nurses practiced ABG sampling step by step under the supervision of three observers who controlled the processes and they filled the checklists. The endpoint was considered reaching a 95 score on the learning curve. The Poisson regression model was used in order to verify the effective factors of ABG sampling competency. The importance of variables in the prediction of practice scores had been calculated in a linear regression of R software by using the relaimpo package. RESULTS: According to the results, in order to reach a skill level of 55, 65, 75, 85, and 95, nurses, respectively, would need average ABG practice times of 6, 6, 7, 7, and 7. In the linear regression model, demographic variables predict 47.65 percent of changes related to scores in practices but the extent of prediction of these variables totally decreased till 7 practice times, and in each practice, nurses who had the higher primary skill levels gained 1 to 2 skill scores more than those with low primary skills. CONCLUSIONS: Utilization of the learning curve could be helpful in the standardization of clinical practices in nursing training and optimization of the frequency of skills training, thus improving the training quality in this field. This trial is registered with NCT02830971.