International Journal of Case Reports and Images (IJCRI) is an international, peer reviewed, monthly, open access, online journal, publishing high-quality, articles in all areas of basic medical sciences and clinical specialties.Aim of IJCRI is to encourage the publication of new information by providing a platform for reporting of unique, unusual and rare cases which enhance understanding of disease process, its diagnosis, management and clinico-pathologic correlations. Case Report: A case of 20-year-old female, primigravida, in 22nd week of pregnancy who presented to our hospital with a three-day history of colicky abdominal pain, few episodes of vomiting and obstipation. Ultrasound showed a single live intrauterine fetus and a dilated gut loop extending from left hypochondrium to the left iliac fossa. T2-weighted TRUFI and HASTE MR Images in axial and coronal plane showed radiological signs highly suggestive of sigmoid volvulus. The radiological findings were confirmed on laparotomy and detorsion of the sigmoid loop with decompression followed by sigmoidopexy was performed. No maternal or fetal complications occurred in the perioperative period. Conclusion: Sigmoid volvulus is a rare non-obstetric complication of pregnancy which requires an early diagnosis and prompt intervention to prevent maternal and fetal complications. Magnetic resonance imaging scan can provide an accurate diagnosis of sigmoid volvulus and its use is safe in pregnancy with respect to the risks of radiation exposure in pregnancy.
IJCRI publishes
Landslides are critical natural disasters characterized by a downward movement of land masses. As one of the deadliest types of disasters worldwide, they have a high death toll every year and cause a large amount of economic damage. The transition between urban and rural areas is characterized by highways, which, in rugged Himalayan terrain, have to be constructed by cutting into the mountains, thereby destabilizing them and making them prone to landslides. This study was conducted in one most landslide-prone regions of the entire Himalayan belt, i.e., National Highway NH-44 (the Jammu–Srinagar stretch). The main objectives of this study are to understand the causes behind the regular recurrence of the landslides in this region and propose a landslide early warning system (LEWS) based on the most suitable machine learning algorithms among the four selected, i.e., multiple linear regression, adaptive neuro-fuzzy inference system (ANFIS), random forest, and decision tree. It was found that ANFIS and random forest outperformed the other proposed methods with a substantial increase in overall accuracy. The LEWS model was developed using the land system parameters that govern landslide occurrence, such as rainfall, soil moisture, distance to the road and river, slope, land surface temperature (LST), and the built-up area (BUA) near the landslide site. The developed LEWS was validated using various statistical error assessment tools such as the root mean square error (RMSE), mean square error (MSE), confusion matrix, out-of-bag (OOB) error estimation, and area under the receiver operating characteristic (ROC) curve (AUC). The outcomes of this study can help to manage landslide hazards in the Himalayan urban–rural transition zones and serve as a sample study for similar mountainous regions of the world.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.