Enterobius vermicularis in tubo-ovarian abscess: A rare and interesting incidental finding - A case Report

Enterobius vermicularis is a common intestinal nematode; however, rare extraintestinal Enterobius infections have been reported from different parts of the world. Here, we present a case of tubo-ovarian abscess in an otherwise healthy young sexually active female with no known comorbids with history of on and off lower abdominal pain for one year and high grade fever for one month. On the basis of further workup and radiological evaluation, a preoperative diagnosis of right sided tubo-ovarian abscess was made and salpingo-oophorectomy was performed laproscopically in July 2015. Histopathology of the resected tissue revealed necrosis and in one area Enterobius vermicularis was identified surrounded by neutrophils and eosinophil rich abscess. A final diagnosis of severe acute and chronic salpingo-oophoritis with abscess formation, secondary to Enterobius vermicularis was made. Signs and symptoms of parasitic involvement in tubo-ovarian abscesses are not much different than usual presentations of pelvic inflammatory diseases and identification of a parasite in a tubo-ovarian tissue sample is a rare clinical finding. A high index of suspicion on the part of histopathologist as well as clinician is important for timely diagnosis and effective management of such cases

Fracture Pattern Analysis of the Upper Cretaceous-Eocene Carbonates along with the Ghumawan Dome, Hazara Basin

Deformational history of the Hazara basin indicates a primitive collision of the two landmasses that undergoes an episodic deformation with NE-SW structural trend. Panjal Thrust (PT) and Main Boundary Thrust (MBT) demarcate the northern and southern extremities of the basin, respectively. The area bounded between these two thrusts is the core consideration of the present research. Different stratigraphic units juxtapose along the Hazara Kashmir Syntaxes (HKS), while the strike-slip component is indicated by imbrication due to thrusts. The study is amied to analyze the paleo-stresses along with developed fracture patterns. Field data were collected via Circle Inventory Method from various localities of the Ghumawan dome, Hazara basin. The zones of upper Cretaceous to Eocene carbonates were mainly targeted during the data collection. Win-Tensor was the key software that helps to analyze the paleo-stresses and fracture pattern of the study area. NW-trending fracture pattern was observed with a highly non-symmetric to dense fracture pattern. The local thrust system lead to severely de-shape the study area. N-S oriented σ1 indicated the compressional tectonic condition that prevailed during deformation of this area. Some segments also show extensional features i.e. normal faulting

The Role of Deep Learning in Parking Space Identification and Prediction Systems

In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future

Structural and Economic Analysis of Meyal Oil Field in the Northern Potwar Deformed Zone, Upper Indus Basin, Pakistan

Potwar sub-basin is famous for its structural style, hydrocarbon exploration and production activities from Cambrian to Pliocene rocks. Foreland basin related subsurface structures, in the presence of source and seal rocks offer a variety of traps to host hydrocarbons. Meyal Oil field, situated in the NW Potwar sub-basin, is a hydrocarbon resource for the country. Subsurface structures of Meyal area were outlined by interpreting two strike and four dip lines in IHS Kingdom suite. Borehole data of MYL-10, MYL-12 and MYL-13 exploratory wells were incorporated to improve the subsurface understanding. A total five prominent reflectors of Permian, Triassic, Jurassic, Paleocene and Eocene rocks were marked on the seismic sections. The seismic interpretation shows a post Eocene pop-up structure flanked by a back thrust and a fore thrust. Moreover, the time structure maps for Meyal area display a doubly plunging and faulted anticline as a result of south directed compression. Four isochron maps show thickness variation in Permian to Eocene sediments in the study area. The results of interpretation show favorable structural trap for economic hydrocarbon exploration

Enteric Fever as an Antecedent to Development of Miller-Fisher Syndrome and Possible Role of COVID-19 Vaccination

Summary: Guillain-Barre Syndrome is an immune-mediated demyelinating disorder. Miller-Fisher Syndrome is an uncommon subtype of GBS. It is characterized by findings of ophthalmoplegia, ataxia, and areflexia. Here we present the case of Miller-Fisher Syndrome following an episode of typhoidal diarrhea. The presentation was of rapidly progressing weakness beginning in the lower extremity with diplopia. Examination revealed diminished reflexes. CSF testing revealed albuminocytologic dissociation which was later supported by neurophysiological testing. The patient was treated with intravenous immunoglobulins (IVIG). We conclude that Miller-Fisher syndrome should be considered in the diagnostic workup of patients presenting with new sensorimotor deficits following diarrheal illnesses and/or COVID-19 mRNA vaccination. Early recognition is essential given the propensity of GBS to cause life-threatening respiratory failure and prompt IVIG administration is associated with a better prognosis. Keywords: Enteric Fever, Miller-Fisher Syndrome, COVID-19, Vaccinatio

Predicting Divorce Prospect Using Ensemble Learning:Support Vector Machine, Linear Model, and Neural Network

A divorce is a legal step taken by married people to end their marriage. It occurs after a couple decides to no longer live together as husband and wife. Globally, the divorce rate has more than doubled from 1970 until 2008, with divorces per 1,000 married people rising from 2.6 to 5.5. Divorce occurs at a rate of 16.9 per 1,000 married women. According to the experts, over half of all marriages ends in divorce or separation in the United States. A novel ensemble learning technique based on advanced machine learning algorithms is proposed in this study. The support vector machine (SVM), passive aggressive classifier, and neural network (MLP) are applied in the context of divorce prediction. A question-based dataset is created by the field specialist. The responses to the questions provide important information about whether a marriage is likely to turn into divorce in the future. The cross-validation is applied in 5 folds, and the performance results of the evaluation metrics are examined. The accuracy score is 100%, and Receiver Operating Characteristic (ROC) curve accuracy score, recall score, the precision score, and the F1 accuracy score are close to 97% confidently. Our findings examined the key indicators for divorce and the factors that are most significant when predicting the divorce