Author Response.

How to cite this article: Rahmatinejad Z, Hoseini B, Pourmand A, Reihani H, Rahmatinejad F, Eslami S, et al. Author Response. Indian J Crit Care Med 2024;28(2):183-184.

A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department.

This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision-recall curve (AUCPR), Hosmer-Lemeshow (H-L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital's ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802-0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787-0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality.

Spatio-temporal modeling of human leptospirosis prevalence using the maximum entropy model.

BACKGROUND: Leptospirosis, a zoonotic disease, stands as one of the prevailing health issues in some tropical areas of Iran. Over a decade, its incidence rate has been estimated at approximately 2.33 cases per 10,000 individuals. Our research focused on analyzing the spatiotemporal clustering of Leptospirosis and developing a disease prevalence model as an essential focal point for public health policymakers, urging targeted interventions and strategies. METHODS: The SaTScan and Maximum Entropy (MaxEnt) modeling methods were used to find the spatiotemporal clusters of the Leptospirosis and model the disease prevalence in Iran. We incorporated nine environmental covariates by employing a spatial resolution of 1 km x 1 km, the finest resolution ever implemented for modeling Human Leptospirosis in Iran. These covariates encompassed the Digital Elevation Model (DEM), slope, displacement areas, water bodies, and land cover, monthly recorded Normalized Difference Vegetation Index (NDVI), monthly recorded precipitation, monthly recorded mean and maximum temperature, contributing significantly to our disease modeling approach. The analysis using MaxEnt yielded the Area Under the Receiver Operating Characteristic Curve (AUC) metrics for the training and test data, to evaluate the accuracy of the implemented model. RESULTS: The findings reveal a highly significant primary cluster (p-value < 0.05) located in the western regions of the Gilan province, spanning from July 2013 to July 2015 (p-value < 0.05). Moreover, there were four more clusters (p-value < 0.05) identified near Someh Sara, Neka, Gorgan and Rudbar. Furthermore, the risk mapping effectively illustrates the potential expansion of the disease into the western and northwestern regions. The AUC metrics of 0.956 and 0.952 for the training and test data, respectively, underscoring the robust accuracy of the implemented model. Interestingly, among the variables considered, the influence of slope and distance from water bodies appears to be minimal. However, altitude and precipitation stand out as the primary determinants that significantly contribute to the prevalence of the disease. CONCLUSIONS: The risk map generated through this study carries significant potential to enhance public awareness and inform the formulation of impactful policies to combat Leptospirosis. These maps also play a crucial role in tracking disease incidents and strategically directing interventions toward the regions most susceptible.

Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles.

Liposome nanoparticles have emerged as promising drug delivery systems due to their unique properties. Assessing particle size and polydispersity index (PDI) is critical for evaluating the quality of these liposomal nanoparticles. However, optimizing these parameters in a laboratory setting is both costly and time-consuming. This study aimed to apply a machine learning technique to assess the impact of specific factors, including sonication time, extrusion temperature, and compositions, on the size and PDI of liposomal nanoparticles. Liposomal solutions were prepared and subjected to sonication with varying values for these parameters. Two compositions: (A) HSPC:DPPG:Chol:DSPE-mPEG2000 at 55:5:35:5 molar ratio and (B) HSPC:Chol:DSPE-mPEG2000 at 55:40:5 molar ratio, were made using remote loading method. Ensemble learning (EL), a machine learning technique, was employed using the Least-squares boosting (LSBoost) algorithm to accurately model the data. The dataset was randomly split into training and testing sets, with 70% allocated for training. The LSBoost algorithm achieved mean absolute errors of 1.652 and 0.0105 for modeling the size and PDI, respectively. Under conditions where the temperature was set at approximately 60 °C, our EL model predicted a minimum particle size of 116.53 nm for composition (A) with a sonication time of approximately 30 min. Similarly, for composition (B), the model predicted a minimum particle size of 129.97 nm with sonication times of approximately 30 or 55 min. In most instances, a PDI of less than 0.2 was achieved. These results highlight the significant impact of optimizing independent factors on the characteristics of liposomal nanoparticles and demonstrate the potential of EL as a decision support system for identifying the best liposomal formulation. We recommend further studies to explore the effects of other independent factors, such as lipid composition and surfactants, on liposomal nanoparticle characteristics.

Optimizing nanoliposomal formulations: Assessing factors affecting entrapment efficiency of curcumin-loaded liposomes using machine learning.

BACKGROUND: Curcumin faces challenges in clinical applications due to its low bioavailability and poor water solubility. Liposomes have emerged as a promising delivery system for curcumin. This study aims to apply ensemble learning, a machine learning technique, to determine the most effective experimental conditions for formulating stable curcumin-loaded liposomes with a high entrapment efficiency (EE). METHODS: Two liposomal formulations composed of HSPC:DPPG:Chol:DSPE-mPEG2000 and HSPC:Chol:DSPE-mPEG2000 at 55:5:35:5 and 55:40:5 M ratios, respectively, were prepared using the remote loading method, and their particle size and polydispersity index (PDI) were determined using Dynamic Light Scattering. To model the impact of five factors (molar ratios, particle size, sonication time, pH, and PDI) on EE%, the Least-squares boosting (LSBoost) ensemble learning algorithm was employed due to its capability to effectively handle nonlinear and non-stationary problems. The implementation and optimization of LSBoost were performed using MATLAB R2020a. The dataset was randomly split into training and testing sets, with 70% allocated for training. The mean absolute error (MAE) was used as the cost function to evaluate model performance. Additionally, a novel approach was employed to visualize the results using 3D plots, facilitating practical interpretation. RESULTS: The optimal model exhibited an MAE of 3.61, indicating its robust predictive capability. The study identified several optimal conditions for achieving the highest EE value of 100%. However, to ensure both the highest EE value and a suitable particle size, it is recommended to set the following conditions: a molar ratio of 55:5:35:5, a PDI within the range of 0.09-0.13, a particle size of approximately 130 nm, a sonication time of 30 min, and a pH within the range of 7.2-8. It is worth mentioning that adjusting the molar ratio to 55:40:5 resulted in a maximum EE of 88.38%. CONCLUSION: These findings underscore the high performance of ensemble learning in accurately predicting and optimizing the EE of the curcumin-loaded liposomes. The application of this technique provides valuable insights and holds promise for the development of efficient drug delivery systems.

Socioeconomic and environmental determinants of asthma prevalence: a cross-sectional study at the U.S. County level using geographically weighted random forests.

BACKGROUND: Some studies have established associations between the prevalence of new-onset asthma and asthma exacerbation and socioeconomic and environmental determinants. However, research remains limited concerning the shape of these associations, the importance of the risk factors, and how these factors vary geographically. OBJECTIVE: We aimed (1) to examine ecological associations between asthma prevalence and multiple socio-physical determinants in the United States; and (2) to assess geographic variations in their relative importance. METHODS: Our study design is cross sectional based on county-level data for 2020 across the United States. We obtained self-reported asthma prevalence data of adults aged 18 years or older for each county. We applied conventional and geographically weighted random forest (GWRF) to investigate the associations between asthma prevalence and socioeconomic (e.g., poverty) and environmental determinants (e.g., air pollution and green space). To enhance the interpretability of the GWRF, we (1) assessed the shape of the associations through partial dependence plots, (2) ranked the determinants according to their global importance scores, and (3) mapped the local variable importance spatially. RESULTS: Of the 3059 counties, the average asthma prevalence was 9.9 (standard deviation ± 0.99). The GWRF outperformed the conventional random forest. We found an indication, for example, that temperature was inversely associated with asthma prevalence, while poverty showed positive associations. The partial dependence plots showed that these associations had a non-linear shape. Ranking the socio-physical environmental factors concerning their global importance showed that smoking prevalence and depression prevalence were most relevant, while green space and limited language were of minor relevance. The local variable importance measures showed striking geographical differences. CONCLUSION: Our findings strengthen the evidence that socio-physical environments play a role in explaining asthma prevalence, but their relevance seems to vary geographically. The results are vital for implementing future asthma prevention programs that should be tailor-made for specific areas.

Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling.

Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity. In our analysis, we employed global models, namely ordinary least squares (OLS), spatial error model (SEM), and spatial lag model (SLM), as well as local models, including geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MGWR model yielded the highest adjusted [Formula: see text] of 90%, outperforming the other local and global models. Using local models to map the effects of environmental determinants (such as the percentage of sheep and precipitation) on the spatial variability of FMD incidence provides decision-makers with helpful information for targeted interventions. Our findings advocate for multiscale and multidisciplinary policies to reduce FMD incidence.

Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department.

Background: The study aimed to compare the prognostic accuracy of six different severity-of-illness scoring systems for predicting in-hospital mortality among patients with confirmed SARS-COV2 who presented to the emergency department (ED). The scoring systems assessed were worthing physiological score (WPS), early warning score (EWS), rapid acute physiology score (RAPS), rapid emergency medicine score (REMS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA). Materials and methods: A cohort study was conducted using data obtained from electronic medical records of 6,429 confirmed SARS-COV2 patients presenting to the ED. Logistic regression models were fitted on the original severity-of-illness scores to assess the models' performance using the Area Under the Curve for ROC (AUC-ROC) and Precision-Recall curves (AUC-PR), Brier Score (BS), and calibration plots were used to assess the models' performance. Bootstrap samples with multiple imputations were used for internal validation. Results: The mean age of the patients was 64 years (IQR:50-76) and 57.5% were male. The WPS, REMS, and NEWS models had AUROC of 0.714, 0.705, and 0.701, respectively. The poorest performance was observed in the RAPS model, with an AUROC of 0.601. The BS for the NEWS, qSOFA, EWS, WPS, RAPS, and REMS was 0.18, 0.09, 0.03, 0.14, 0.15, and 0.11 respectively. Excellent calibration was obtained for the NEWS, while the other models had proper calibration. Conclusion: The WPS, REMS, and NEWS have a fair discriminatory performance and may assist in risk stratification for SARS-COV2 patients presenting to the ED. Generally, underlying diseases and most vital signs are positively associated with mortality and were different between the survivors and non-survivors. How to cite this article: Rahmatinejad Z, Hoseini B, Reihani H, Hanna AA, Pourmand A, Tabatabaei SM, et al. Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department. Indian J Crit Care Med 2023;27(6):416-425.

Comparing In-Hospital Mortality Prediction by Senior Emergency Resident's Judgment and Prognostic Models in the Emergency Department.

Background: A comparison of emergency residents' judgments and two derivatives of the Sequential Organ Failure Assessment (SOFA), namely, the mSOFA and the qSOFA, was conducted to determine the accuracy of predicting in-hospital mortality among critically ill patients in the emergency department (ED). Methods: A prospective cohort research was performed on patients over 18 years of age presented to the ED. We used logistic regression to develop a model for predicting in-hospital mortality by using qSOFA, mSOFA, and residents' judgment scores. We compared the accuracy of prognostic models and residents' judgment in terms of the overall accuracy of the predicted probabilities (Brier score), discrimination (area under the ROC curve), and calibration (calibration graph). Analyses were carried out using R software version R-4.2.0. Results: In the study, 2,205 patients with median age of 64 (IQR: 50-77) years were included. There were no significant differences between the qSOFA (AUC 0.70; 95% CI: 0.67-0.73) and physician's judgment (AUC 0.68; 0.65-0.71). Despite this, the discrimination of mSOFA (AUC 0.74; 0.71-0.77) was significantly higher than that of the qSOFA and residents' judgments. Additionally, the AUC-PR of mSOFA, qSOFA, and emergency resident's judgments was 0.45 (0.43-0.47), 0.38 (0.36-0.40), and 0.35 (0.33-0.37), respectively. The mSOFA appears stronger in terms of overall performance: 0.13 vs. 0.14 and 0.15. All three models showed good calibration. Conclusion: The performance of emergency residents' judgment and the qSOFA was the same in predicting in-hospital mortality. However, the mSOFA predicted better-calibrated mortality risk. Large-scale studies should be conducted to determine the utility of these models.

PTEN hamartoma tumour syndrome: case report based on data from the Iranian hereditary colorectal cancer registry and literature review.

BACKGROUND: PTEN hamartoma tumour syndrome (PHTS) is a rare hereditary disorder caused by germline pathogenic mutations in the PTEN gene. This study presents a case of PHTS referred for genetic evaluation due to multiple polyps in the rectosigmoid area, and provides a literature review of PHTS case reports published between March 2010 and March 2022. CASE PRESENTATION: A 39-year-old Iranian female with a family history of gastric cancer in a first-degree relative presented with minimal bright red blood per rectum and resistant dyspepsia. Colonoscopy revealed the presence of over 20 polyps in the rectosigmoid area, while the rest of the colon appeared normal. Further upper endoscopy showed multiple small polyps in the stomach and duodenum, leading to a referral for genetic evaluation of hereditary colorectal polyposis. Whole-exome sequencing led to a PHTS diagnosis, even though the patient displayed no clinical or skin symptoms of the condition. Further screenings identified early-stage breast cancer and benign thyroid nodules through mammography and thyroid ultrasound. METHOD AND RESULTS OF LITERATURE REVIEW: A search of PubMed using the search terms "Hamartoma syndrome, Multiple" [Mesh] AND "case report" OR "case series" yielded 43 case reports, predominantly in women with a median age of 39 years. The literature suggests that patients with PHTS often have a family history of breast, thyroid and endometrial neoplasms along with pathogenic variants in the PTEN/MMAC1 gene. Gastrointestinal polyps are one of the most common signs reported in the literature, and the presence of acral keratosis, trichilemmomas and mucocutaneous papillomas are pathognomonic characteristics of PHTS. CONCLUSION: When a patient presents with more than 20 rectosigmoid polyps, PHTS should be considered. In such cases, it is recommended to conduct further investigations to identify other potential manifestations and the phenotype of PHTS. Women with PHTS should undergo annual mammography and magnetic resonance testing for breast cancer screening from the age of 30, in addition to annual transvaginal ultrasounds and blind suction endometrial biopsies.

Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran.

Background: Prioritizing the patients requiring intensive care may decrease the fatality of coronavirus disease-2019 (COVID-19). Aims and objectives: To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care. Materials and methods: In 2021, 506 suspected COVID-19 patients, with clinical presentations along with radiographic findings, were laboratory confirmed and included in the study. The primary end-point was patients with COVID-19 requiring intensive care, defined as actual admission to the intensive care unit (ICU). The data were randomly partitioned into training and testing sets (70% and 30%, respectively) without overlapping. A decision-tree algorithm and multivariate logistic regression were performed to develop the models for predicting the cases based on their first 24 hours data. The predictive performance of the models was compared based on the area under the receiver operating characteristic curve (AUC), sensitivity, and accuracy of the models. Results: A 10-fold cross-validation decision-tree model predicted cases requiring intensive care with the AUC, accuracy, and sensitivity of 97%, 98%, and 94.74%, respectively. The same values in the machine-learning logistic regression model were 75%, 85.62%, and 55.26%, respectively. Creatinine, smoking, neutrophil/lymphocyte ratio, temperature, respiratory rate, partial thromboplastin time, white blood cell, Glasgow Coma Scale (GCS), dizziness, international normalized ratio, O2 saturation, C-reactive protein, diastolic blood pressure (DBP), and dry cough were the most important predictors. Conclusion: In an Iranian population, our decision-based machine-learning method offered an advantage over logistic regression for predicting patients requiring intensive care. This method can support clinicians in decision-making, using patients' early data, particularly in low- and middle-income countries where their resources are as limited as Iran. How to cite this article: Sabetian G, Azimi A, Kazemi A, Hoseini B, Asmarian N, Khaloo V, et al. Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study based on Machine-learning Approach from Iran. Indian J Crit Care Med 2022;26(6):688-695. Ethics approval: This study was approved by the Ethical Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.018).

Internal Validation of the Predictive Performance of Models Based on Three ED and ICU Scoring Systems to Predict Inhospital Mortality for Intensive Care Patients Referred from the Emergency Department.

Background: A variety of scoring systems have been introduced for use in both the emergency department (ED) such as WPS, REMS, and MEWS and the intensive care unit (ICU) such as APACHE II, SAPS II, and SOFA for risk stratification and mortality prediction. However, the performance of these models in the ICU remains unclear and we aimed to evaluate and compare their performance in the ICU. Methods: This multicenter retrospective cohort study was conducted on severely ill patients admitted to the ICU directly from the ED in seven tertiary hospitals in Iran from August 2018 to August 2020. We evaluated all models in terms of discrimination (AUROC), the balance between positive predictive value and sensitivity (AUPRC), calibration (Hosmer-Lemeshow test and calibration plots), and overall performance using the Brier score (BS). The endpoint was considered inhospital mortality. Results: Among the 3,455 patients included in the study, 54.4% of individuals were male (N = 1,879) and 26.5% deceased (N = 916). The BS for the WPS, REMS, MEWS, APACHE II, SAPS II, and SOFA were 0.178, 0.165, 0.183, 0.157, 0.170, and 0.182, respectively. The AUROC of these models were 0.728 (0.71-0.75), 0.761 (0.74-0.78), 0.682 (0.66-0.70), 0.810 (0.79-0.83), 0.767 (0.75-0.79), and 0.785 (0.77-0.80), respectively. The AUPRC was 0.517 (0.50-0.53) for WPS, 0.547 (0.53-0.56) for REMS, 0.445 (0.42-0.46) for MEWS, 0.630 (0.61-0.65) for APACHE II, 0.559 (0.54-0.58) for SAPS II, and 0.564 (0.54-0.57) for SOFA. All models except the MEWS and SOFA had good calibration. The most accurate model belonged to APACHE II with lowest BS. Conclusion: The APACHE II outperformed all the ED and ICU models and was found to be the most appropriate model in predicting inhospital mortality of patients in the ICU in terms of discrimination, calibration, and accuracy of predicted probability. Except for MEWS, the rest of the models had fair discrimination and partially good calibration. Interestingly, although the REMS is less complicated than the SAPS II, both models exhibited similar performance. Clinicians can utilize the REMS as part of a larger clinical assessment to manage patients more effectively.

Colorectal Cancer in North-Eastern Iran: a retrospective, comparative study of early-onset and late-onset cases based on data from the Iranian hereditary colorectal cancer registry.

BACKGROUND: The incidence rate of colorectal cancer (CRC) is increasing among patients below 50 years of age. The reason for this is unclear, but could have to do with the fact that indicative variables, such as tumour location, gender preference and genetic preponderance have not been followed up in a consistent mann er. The current study was primarily conducted to improve the hereditary CRC screening programme by assessing the demographic and clinicopathological characteristics of early-onset CRC compared to late-onset CRC in northeast Iran. METHODS: This retrospective study, carried out over a three-year follow-up period (2014-2017), included 562 consecutive CRCs diagnosed in three Mashhad city hospital laboratories in north-eastern Iran. We applied comparative analysis of pathological and hereditary features together with information on the presence of mismatch repair (MMR) gene deficiency with respect to recovery versus mortality. Patients with mutations resulting in absence of the MMR gene MLH1 protein product and normal BRAF status were considered to be at high risk of Lynch syndrome (LS). Analyses using R studio software were performed on early-onset CRC (n = 222) and late-onset CRC (n = 340), corresponding to patients ≤50 years of age and patients > 50 years. RESULTS: From an age-of-onset point of view, the distribution between the genders differed with females showing a higher proportion of early-onset CRC than men (56% vs. 44%), while the late-onset CRC disparity was less pronounced (48% vs. 52%). The mean age of all participants was 55.6 ± 14.8 years, with 40.3 ± 7.3 years for early-onset CRC and 65.1 ± 9.3 years for late-onset CRC. With respect to anatomical tumour location (distal, rectal and proximal), the frequencies were 61, 28 and 11%, respectively, but the variation did not reach statistical significance. However, there was a dramatic difference with regard to the history of CRC in second-degree relatives between two age categories, with much higher numbers of family-related CRCs in the early-onset group. Expression of the MLH1 and PMS2 genes were significantly different between recovered and deceased, while this finding was not observed with regard to the MSH6 and the MSH2 genes. Mortality was significantly higher in those at high risk of LS. CONCLUSION: The variation of demographic, pathological and genetic characteristics between early-onset and late-onset CRC emphasizes the need for a well-defined algorithm to identify high-risk patients.

Characteristics of gastric precancerous conditions and Helicobacter pylori infection among dyspeptic patients in north-eastern Iran: is endoscopic biopsy and histopathological assessment necessary?

BACKGROUND: Early detection and appropriate treatment of precancerous, mucosal changes could significantly decrease the prevalence of life-threatening gastric cancer. Biopsy of the normal-appearing mucosa to detect Helicobacter pylori and these conditions is not routinely obtained. This study assesses the prevalence and characteristics of H. pylori infection and precancerous conditions in a group of patients suffering from chronic dyspepsia who were subjected to gastric endoscopy and biopsy mapping. METHODS: This cross-sectional study included dyspeptic patients, not previously treated for H. pylori, undergoing esophagogastroduodenoscopy (EGD) with their gastric endoscopic biopsies obtained for examination for evidence of H. pylori infection and precancerous conditions. Demographic and clinical data on the gender, smoking, opium addiction, alcohol consumption, medication with aspirin, corticosteroids and non-steroidal anti-inflammatory drugs (NSAIDs) and family history of cancer were collected by interviewing the patients and evaluating their health records. The cohort examined consisted of 585 patients with a mean (SD) age of 48.0 (14.46) years, 397 (67.9%) of whom were women. RESULTS: H. pylori infection was identified in 469 patients (80.2%) with the highest prevalence (84.2%) in those aged 40-60 years. Opium addiction correlated with a higher a H. pylori infection rate, while alcohol consumption was associated with a lower rate by Odds Ratio 1.98 (95% CI 1.11-3.52) and 0.49 (95% CI 0.26-0.92), respectively. The prevalence of intestinal metaplasia, gastric atrophy and gastric dysplasia was 15.2, 12.6 and 7.9%, respectively. Increased age, positive H. pylori infection, endoscopic abnormal findings and opium addiction showed a statistically significant association with all precancerous conditions, while NSAID consumption was negatively associated with precancerous conditions. For 121 patients (20.7% of all), the EGD examination revealed normal gastric mucosa, however, for more than half (68/121, 56.2%) of these patients, the histological evaluation showed H. pylori infection, and also signs of atrophic mucosa, intestinal metaplasia and dysplasia in 1.7, 4.1 and 1.7%, respectively. CONCLUSION: EGD with gastric biopsy mapping should be performed even in the presence of normal-appearing mucosa, especially in dyspeptic patients older than 40 years with opium addiction in north-eastern Iran. Owing to the high prevalence of precancerous conditions and H. pylori infection among patients with dyspepsia in parts of Iran, large-scale national screening in this country should be beneficial.

Telehealth-Based Services During the COVID-19 Pandemic: A Systematic Review of Features and Challenges.

Background: As an ever-growing popular service, telehealth catered for better access to high-quality healthcare services. It is more valuable and cost-effective, particularly in the middle of the current COVID-19 pandemic. Accordingly, this study aimed to systematically review the features and challenges of telehealth-based services developed to support COVID-19 patients and healthcare providers. Methods: A comprehensive search was done for the English language and peer-reviewed articles published until November 2020 using PubMed and Scopus electronic databases. In this review paper, only studies focusing on the telehealth-based service to support COVID-19 patients and healthcare providers were included. The first author's name, publication year, country of the research, study objectives, outcomes, function type including screening, triage, prevention, diagnosis, treatment or follow-up, target population, media, communication type, guideline-based design, main findings, and challenges were extracted, classified, and tabulated. Results: Of the 5,005 studies identified initially, 64 met the eligibility criteria. The studies came from 18 countries. Most of them were conducted in the United States and China. Phone calls, mobile applications, videoconferencing or video calls, emails, websites, text messages, mixed-reality, and teleradiology software were used as the media for communication. The majority of studies used a synchronous communication. The articles addressed the prevention, screening, triage, diagnosis, treatment, and follow-up aspects of COVID-19 which the most common purpose was the patients' follow-up (34/64, 53%). Thirteen group barriers were identified in the literature, which technology acceptance and user adoption, concerns about the adequacy and accuracy of subjective patient assessment, and technical issues were the most frequent ones. Conclusion: This review revealed the usefulness of telehealth-based services during the COVID-19 outbreak and beyond. The features and challenges identified through the literature can be helpful for a better understanding of current telehealth approaches and pointed out the need for clear guidelines, scientific evidence, and innovative policies to implement successful telehealth projects.

Demographic and clinical characteristics of severe Covid-19 infections: a cross-sectional study from Mashhad University of Medical Sciences, Iran.

BACKGROUND: Coronavirus Disease 2019 (Covid-19) is expanding worldwide. The characteristics of this infection in patients varies from country to country. To move forward, clinical data on infected patients are needed. Here, we report a comparison between fatalities and recovery of patients with severe Covid-19, based on demographic and clinical characteristics. METHODS: Between 5 March and 12 May 2020 in Mashhad, Iran, 1278 of 4000 suspected Covid-19 patients were confirmed positive by real-time reverse-transcriptase-polymerase-chain-reaction assay of upper respiratory specimens. We compared the demographic, exposure history and clinical symptoms of 925 survivors and 353 fatal cases with confirmed disease. RESULTS: Mean (SD) age for all confirmed patients was 56.9 (18.7) years, 67.1 (15.9) years in fatal cases and 53.0 (18.3) years in survivors. Multivariate logistic regression analysis showed that the outcome of patients was associated with age (odds ratio = 1.049, P = 0.0001, 95% CI = 1.040-1.057). Despite a high burden of Covid-19 infections in the 30-39 and 40-49 year age groups, most of these (89.6 and 87.2%, respectively) recovered. The median (IQR) duration of hospitalization was 9.0 (6.0-14.0) days. The most prevalent co-morbidities were cardiovascular disorders (21%) and diabetes (16.3%). Dyspnoea (72.7%), cough (68.1%) and fever (63.8%) were the most frequent clinical symptoms. Healthcare workers, of whom two (3%) died, comprised 5.2% of infected cases. Combination antiviral and antibiotic therapy was used in 43.0% of cases. CONCLUSIONS: The characteristics of severe Covid-19 varied substantially between fatal cases and survivors, with diabetes and cardiovascular disorders the most prevalent co-morbidities. In contrast to other studies, there were a higher number of fatalities in younger patients in our setting.

Genetic Aspects and Immune Responses in Covid-19: Important Organ Involvement.

In the last two decades, the world has experienced outbreaks of three major coronaviruses with high morbidity and mortality rates. The most recent of these started in the form of an unusual viral pneumonia in Wuhan, China, and now the world is facing a serious pandemic. This new disease has been called COVID-19 and is caused by the SARS-CoV-2 virus. Understanding the specific genetic and phenotypic structure of SARS-CoV-2 in COVID-19 pathogenesis is vital in finding appropriate drugs and vaccines. With this in mind, this review sheds light on the virology, genetics, immune-responses, and mechanism of action of this virus.

Association between heavy metals and colon cancer: an ecological study based on geographical information systems in North-Eastern Iran.

BACKGROUND: Colorectal cancer has increased in Middle Eastern countries and exposure to environmental pollutants such as heavy metals has been implicated. However, data linking them to this disease are generally lacking. This study aimed to explore the spatial pattern of age-standardized incidence rate (ASR) of colon cancer and its potential association with the exposure level of the amount of heavy metals existing in rice produced in north-eastern Iran. METHODS: Cancer data were drawn from the Iranian population-based cancer registry of Golestan Province, north-eastern Iran. Samples of 69 rice milling factories were analysed for the concentration levels of cadmium, nickel, cobalt, copper, selenium, lead and zinc. The inverse distance weighting (IDW) algorithm was used to interpolate the concentration of this kind of heavy metals on the surface of the study area. Exploratory regression analysis was conducted to build ordinary least squares (OLS) models including every possible combination of the candidate explanatory variables and chose the most useful ones to show the association between heavy metals and the ASR of colon cancer. RESULTS: The highest concentrations of heavy metals were found in the central part of the province and particularly counties with higher amount of cobalt were shown to be associated with higher ASR of men with colon cancer. In contrast, selenium concentrations were higher in areas with lower ASR of colon cancer in men. A significant regression equation for men with colon cancer was found (F(4,137) = 38.304, P < .000) with an adjusted R2 of 0.77. The predicted ASR of men colon cancer was - 58.36 with the coefficients for cobalt = 120.33; cadmium = 80.60; selenium = - 6.07; nickel = - 3.09; and zinc = - 0.41. The association of copper and lead with colon cancer in men was not significant. We did not find a significant outcome for colon cancer in women. CONCLUSION: Increased amounts of heavy metals in consumed rice may impact colon cancer incidence, both positively and negatively. While there were indications of an association between high cobalt concentrations and an increased risk for colon cancer, we found that high selenium concentrations might instead decrease the risk. Further investigations are needed to clarify if there are ecological or other reasons for these discrepancies. Regular monitoring of the amount of heavy metals in consumed rice is recommended.

Prevalence of Mismatch Repair-Deficient Colorectal Adenoma/Polyp in Early-Onset, Advanced Cases: a Cross-Sectional Study Based on Iranian Hereditary Colorectal Cancer Registry.

BACKGROUND: Lynch syndrome (LS) increases the risk of many types of cancer, mainly colorectal cancer (CRC). The purpose of this study was to assess the prevalence of mismatch repair (MMR) deficiency in patients under the age of 50 with advanced adenomatous polyps, aiming at an early diagnosis of LS. METHODS: This retrospective, cross-sectional study included eligible patients with advanced adenomas diagnosed ≤ 50 years of age registered between April 2014 and February 2017 at three pathology centers in Mashhad. Pathological records were reviewed, and colon tissue specimens were analyzed by immunohistochemistry (IHC) staining to identify proteins which serve as markers for LS as they are related to loss of MMR gene (MLH1, MSH2, MSH6, and PMS2) expression. RESULTS: Of 862 consecutive patients, a total of 50 adenomas (54% males, 46% females of mean age 41.24 ± 6.5) met the eligibility criteria. Of the adenomas examined, 20 (40%) had a tubulovillous component, 34 (68%) had high-grade dysplasia, and 30 (60%) had were larger than 10 mm protrusions. None of the patients had loss of MMR protein expression. CONCLUSION: No individual with MMR genetic disorder was identified by IHC screening of early-onset advanced colorectal adenomas. This strategy is therefore not an effective strategy for detecting MMR mutation carriers.

Spatio-temporal mapping of breast and prostate cancers in South Iran from 2014 to 2017.

BACKGROUND: The most common gender-specific malignancies are cancers of the breast and the prostate. In developing countries, cancer screening of all at risk is impractical because of healthcare resource limitations. Thus, determining high-risk areas might be an important first screening step. This study explores incidence patterns of potential high-risk clusters of breast and prostate cancers in southern Iran. METHODS: This cross-sectional study was conducted in the province of Kerman, South Iran. Patient data were aggregated at the county and district levels calculating the incidence rate per 100,000 people both for cancers of the breast and the prostate. We used the natural-break classification with five classes to produce descriptive maps. A spatial clustering analysis (Anselin Local Moran's I) was used to identify potential clusters and outliers in the pattern of these cancers from 2014 to 2017. RESULTS: There were 1350 breast cancer patients (including, 42 male cases) and 478 prostate cancer patients in the province of Kerman, Iran during the study period. After 45 years of age, the number of men with diagnosed prostate cancer increased similarly to that of breast cancer for women after 25 years of age. The age-standardised incidence rate of breast cancer for women showed an increase from 29.93 to 32.27 cases per 100,000 people and that of prostate cancer from 13.93 to 15.47 cases per 100,000 during 2014-2017. Cluster analysis at the county level identified high-high clusters of breast cancer in the north-western part of the province for all years studied, but the analysis at the district level showed high-high clusters for only two of the years. With regard to prostate cancer, cluster analysis at the county and district levels identified high-high clusters in this area of the province for two of the study years. CONCLUSIONS: North-western Kerman had a significantly higher incidence rate of both breast and prostate cancer than the average, which should help in designing tailored screening and surveillance systems. Furthermore, this study generates new hypotheses regarding the potential relationship between increased incidence of cancers in certain geographical areas and environmental risk factors.