Background: Cystic Echinococcosis (CE) or hydatid cyst is one of the most common diseases between humans and livestock that is caused by the larval stage of Echinococcus granulosus. The disease is a major health and also economic concern in Iran. Most of the studies on CE in different areas of Iran have been conducted in adults and there is little information about the prevalence of the disease in children. Methods: This cross-sectional study was conducted to find out the seroprevalence of hydatid cyst in children in a rural community in Fars province, southern Iran. Children sera samples were evaluated for anti-hydatid cyst antibodies; using a recombinant B8/1 antigen of E. graunlosus in an ELISA system. Subjects: Blood samples were taken from 578 children living in three villages in Sarmashhad district in Fars province, southern Iran. Results: Of the 578 recruited children, 298 (51.6%) were boys and 280 (48.4%) were girls. The mean age of the children studied was 6.8 (± 3.7) years. Anti-hydatid cyst antibodies were detected in sera of 39 out of 578 children, corresponding to a seroprevalence rate of 6.7%. While age, educational level, keeping dogs in the household, and residential areas had no significant influence on the risk of CE infection (p > 0.05), the correlation between sex and seropositivity to CE was significant (p < 0.05). Conclusion: Findings of this study showed that the rate of seroprevalence of hydatid cyst in children in the rural community is high. The study suggests that preventive measurements including the education of the population should begin in early childhood as a large percentage of the adult CE patients are resulted from childhood infection.

Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.

Background Complementary and Integrative Medicine (CIM) is often taken up by individuals seeking relief from different diseases. This study investigates the prevalence and associated factors of CIM use in patients with COVID-19. Methods In this telephone-based, cross-sectional study, data on CIM usage were collected from COVID-19 patients from February till June 2020 in Fars province, Iran using a researcher-made checklist. Additionally, we asked about the patients’ attitudes toward these treatments. Results Out of 453 patients diagnosed with COVID-19, 400 (88.30%) responded to our calls and agreed to participate in the study. Among them, 276 patients reported using CIM to treat COVID-19 [prevalence: 69% (95% CI: 64.2 to 73.5)]. The most frequently used herbal medicine among COVID-19 patients was ginger (n = 273, 98.9%), thyme (n = 263, 95.3%), and black cumin (n = 205, 74.3%). Most of these patients were recommended to use herbal medicine by their families and friends (n = 96, 34.8%). Univariable logistic regression revealed that age under 50 years old, residency in urban areas (including the capital of the province and small cities), employment, academic education, and being an outpatient were statistically significant factors resulting in CIM usage. Multivariable logistic regression revealed that CIM use among outpatients was 3.65 times more than among inpatients. In addition, patients under 50 years old used CIM 85% more than older patients. Ultimately, only 9 (3.3%) patients consulted with their doctors regarding these medications. No side effects due to CIM use were reported. Conclusion Many patients with COVID-19 used CIM, but few consulted with their physicians in this regard. Therefore, physicians should ask their patients about CIM usage, and patients should also report their use of CIM therapies during their medical visits. Furthermore, age and hospitalization status affected CIM use among patients with COVID-19.

A bstract 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, O 2 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).

Introduction: Even though over a year has passed since the coronavirus 2019 (COVID-19) outbreak, our information regarding certain aspects of the disease, such as post-infection immunity is still very limited. This study aimed to evaluate post-infection protection and COVID-19 features among healthcare workers (HCWs), during three subsequent surges.Method: The study population consisted of all HCWs in either public or private hospitals in Fars province, Southern Iran from 20 April 2020 up to 20th February 2021. We calculated the rate of infection as the number of individuals with positive PCR tests divided by the cumulative number of person-days at risk. Poisson regression was utilized to calculate the adjusted rate ratio and estimated protection. Results: During the study period, a total of 30,546 PCR tests were performed among HCWs, of which 13,749 HCWs were positive. Among a total of 141 diagnosed cases who experienced a second episode of COVID-19, 44 (31.2%) cases of reactivation and relapse, and 97 (68.8% of infected and 1.81% of total HCWs) cases of reinfection was observed. The daily rate of infection was 4.72 for previously infected HCWs, while 2.20 for HCWs without previous infection. The estimated protection against repeat infection after a previous SARS-CoV-2 infection was 94.8% (95% CI: 93.6-95.7).Conclusion: Re-positivity, relapse, and reinfection of SARS-CoV-2 are quite rare in the population of HCWs. Also, after a first episode of infection, estimated protection of 94.8% was achieved against repeat infections.