A rare complication in a Covid-19 positive patient with sigmoid colon cancer-hemoperitoneum due to gallbladder necrosis following micro-thrombosis

Covid-19, also known as acute respiratory syndrome 2019-nCoV, severe acute respiratory syndrome (SARS) 2, and Wuhan pneumonia, is a viral respiratory disease caused by a SARS-associated coronavirus (SARS-CoV-2). The most serious complications of Covid-19 are due to the development of micro-thrombosis in various organs and systems as a result of the high levels of pro-inflammatory cytokines (tumor necrosis factor alpha, interleukin 1 and 6) which initiate the activation of coagulation and the generation of thrombin. Several studies demonstrated the poor outcome of Covid-19-infected patients who underwent surgery, suggesting that surgery may accelerate and exacerbate Covid-19 progression. We report the case of an 81-year-old patient admitted as an emergency with Covid-19 pneumonia, hemoperitoneum, ischemic acute cholecystitis and obstructive sigmoid cancer. Cholecystectomy, pneumoperitoneal lavage, and Hartmann operation were performed under combined epidural-spinal anesthesia. This technique has some advantages compared to spinal and epidural techniques, such as: rapid onset of analgesia and the possibility of obtaining the desired sensory level, control of the anesthetic block, and ensuring postoperative analgesia. The unfavorable outcome of this case is due to the occurrence of the cytokine storm and coagulation disorders, with the change in the related biological constants, both from a biochemical and systemic point of view

The surgical management of pancreatic pseudocysts – outcomes on a group of seven patients

The pancreatic pseudocyst represents the main complication that occurred 3 to 6 weeks after an outbreak of acute or chronic pancreatitis represented by a collection containing pancreatic enzymes without their own epithelial wall. In the present paper, we present a study performed on 7 patients admitted to the Surgical Department of Sibiu County Emergency Clinical Hospital who were diagnosed with pancreatic pseudocyst between 2016 and 2020, and the drainage of the mini-invasive pancreatic pseudocyst by an incision in the right lumbar area, in the case of a 53-year-old patient known to have a history of multiple cardiac defects, the pancreatic pseudocyst being discovered approximately 6 months before, for which the patient underwent conservative treatment, and who had 5 resuscitated cardio-respiratory arrests throughout the evolution

DR-KNN: A hybrid Approach for Dimensionality Reduction of EO Image Datasets

The two Sentinel-2 satellites provide, since March 2017, high-resolution worldwide images every five days, freely distributed, generating terabytes of high-dimensional data. An intuitive manner to summarize the main characteristics of the data and gather knowledge is visual exploratory analysis, which is often based on dimensionality reduction methods to represent high-dimensional data. From previous research and the state-of-the-art literature, turned out that t-distributed Stochastic Neighbour Embedding is one of the most appropriate technique to reduce the dimensionality of a dataset, but it requires very high computational power. To overcome this inconvenience, we proposed two hybrid DR algorithms, which combine the DR with the nearest neighbour technique or random forest regression. The main conclusion is that our approaches reduce computational power without compromising the representation quality

Sentinel-2 60-m Band Super-Resolution Using Hybrid CNN-GPR Model

Sentinel-2 image super-resolution (SR) has proven advantageous in multiple data analysis pipelines, leading to a more comprehensive assessment of different environment-related metrics. This research aims to provide a method for super-resolving the 60-m bands provided by Sentinel-2 up to 10-m spatial resolution, using Gaussian process regression (GPR). While common GPR methods directly operate on raw data using carefully designed kernels, we propose a convolutional neural network (CNN)-based feature extraction kernel to directly process the input 10-m patches, applied in constructing the elements of the integrated covariance matrices. For each scene, a small number of training patches are sampled to optimize the CNN parameters and to construct the predictive mean function, the latter being further used for predicting super-resolved pixels for new input areas. We prove that our method is a reliable SR mechanism by assessing its performance both quantitatively, using metrics against other methods from literature, and qualitatively, through visual analysis of the results

Clinical and biological factors with prognostic value in acute pancreatitis

Acute pancreatitis is an acute inflammatory process of the pancreas, which can remain localized at the level of the gland or can extend to the peripancreatic and retroperitoneal tissues. The use and interpretation of paraclinical examinations at the onset can predict the form of evolution of acute pancreatitis (mild or severe). Depending on the evolution, these data are useful in determining the type of surgical intervention that might be necessary based on severity. We present a retrospective study consisting of 118 patients diagnosed and hospitalized with acute pancreatitis during 2016-2020 in the Surgery I section of the Sibiu County Emergency Clinical Hospital. Several parameters were taken into account at hospitalization such as age, sex, the environment of origin, etiology of pancreatitis, biochemical parameters with their repetition at 24, 72 hours, and at discharge, and clinical signs at hospitalization. surgeries performed depending on the severity of pancreatitis specifying their complications

Tips and tricks for laparoscopic cholecystectomy in the patient with ventriculoperitoneal shunt; a case report

Laparoscopic surgery in patients with ventriculo-peritoneal shunt is challenging in terms of technical approach. The severity of possible complications and the lack of studies on this association increase the surgeon's discomfort with such surgery. The main complications that may occur are increased intracranial pressure, secondary pneumo-peritoneum pneumocephalus, encephalitis and the risk of catheter injury during laparoscopic procedures. We present the case of a 56-year-old patient operated in 2004 for a basilar artery top aneurysm with subarachnoid hemorrhage and secondary hydrocephalus, for which a ventriculo- peritoneal shunt was fitted. This patient presented in our clinic with diffuse abdominal pain, more accentuated in the right hypochondrium, nausea, postprandial biliary vomiting, inappetence, asthenia, fatigability, symptoms with onset about 6 months, but accentuated in the last 48 hours. The patient underwent surgery and the evolution was favorable, being discharged without postoperative complications

Laparoscopic versus open surgical treatment of umbilical hernia

Umbilical hernia is one of the types of ventral hernias of the abdominal wall and it represents the externalization of a part of the abdominal contents through a defect of the anterior abdominal wall located in the umbilical region. It is estimated that more than 20 million abdominal wall hernia surgeries are performed worldwide each year. The paper presents a retrospective study on the patients diagnosed with umbilical hernia and admitted to the First and Second Surgery Departments of the Sibiu County Emergency Clinical Hospital. The study includes 82 cases diagnosed with umbilical hernias over a period of 4 years, between 01.01.2017 and 31.12.2020. Open and laparoscopic surgical techniques are compared in terms of outcomes and postoperative complications. Most cases of umbilical hernia were within the age group 51-70 years, with a slightly higher incidence in males. Arterial hypertension and obesity were the most frequent comorbidities. The alloplastic, classic or laparoscopic procedure became the most widely used due to benefits such as: rapid socio-professional reintegration, short-term hospitalization and low incidence of relapses and postoperative complications. The current trend is for the IPOM laparoscopic procedure to become the gold standard in the treatment of umbilical hernias

A Data Mining Approach to Post Disaster Assessment of 2008 Floods in Romania

This paper demonstrates the performance of a rapid mapping kind of approach considering knowledge discovery from Earth Observation images, to provide information support during response and immediate post-response by delivering products emphasizing the extent and impact of the event, by event understanding any type of natural or man made disaster. Knowledge discovery from Earth Observation images implies mapping low level descriptors (primitive features) extracted from the image into semantic classes in order to provide an interactive method for effective image information mining. In the frame of information theory a communication channel is considered between remote sensing imagery and the user who receive existing information in the data sources, coded as image semantic content. This channel has three components - Data Source Model Generation, Query and Data Mining. Data Source Model Generation uses image content analysis to generate a set of scene’s content descriptors. Further, the Query component involves the user and performs an image retrieval based on image content as query parameter. The query component relies on the Support Vector Machine classifier which is able to group descriptors into relevant semantic classes. The classifier supports rapid mapping scenarios and interactive mapping. The proposed concept is illustrated analyzing Earth Observation images acquired post (SPOT 4 and TerraSAR-X) floods disaster in Romania at the end of July 2008. Hundreds of towns and villages were affected and more than 20,000 people evacuated. The northeastern region of Romania was declared back then national disaster area. The results includes potentially flood affected areas detected on 28 of July, detailed semantic classes for rapid mapping and a quantitative evaluation of damages. A validation procedure is considered, taking into account rapid mapping products delivered by Romanian Space Agency (ROSA) and SERTIT (SErvice Régional de Traitementd'Image et de Télédétection)

Facilitating the use of high-resolution EO data to support the development of mapping products for natural disasters

Earth observation capabilities are used to respond to major disasters around the world, for humanitarian aid and security. Satellite derived information needs to be used in combination with additional data to be presented in a proper geospatial context for the work of civil protection agencies and relief organizations. This paper aims to reveal a methodology developed to quantitatively evaluate the impact of a natural disaster over a region. The proposed approach was initiated in the frame of GEODIM Project (http://geodim.meteoromania.ro) whose goal is to develop a Romanian downstream emergency response service in order to contribute to current disaster and risk management approach based on Earth observation data. The project is focused on developing experimental processing algorithms and mapping products for natural disasters (floods, earthquakes, landslides) damage assessment in urban areas based on very high resolution optical and SAR satellite imagery acquired worldwide. The usefulness of remote sensing data for natural disasters damage assessment clearly rely on the number of available images, their type and quality and last, but not least, the timeliness of the data sets, or how delayed are the available post disaster images relative to the damaging event. Previous work demonstrated the use of a semi-automated data processing method in order to reveal and determine the area affected by the disaster, considering both qualitative and quantitative approaches. The proposed scenarios consider knowledge discovery from pre and post event EO images by mapping the extracted data features into semantic classes and symbolic representations like ”buildings”, ”vegetation”, ”streets”, ”bare land”, and ”damaged buildings”, etc. In order to fully exploit the high-resolution EO data a method based on patches is proposed to extract relevant contextual information to be further used to build the situation maps

A rapid mapping approach to quantify damages caused by the 2003 Bam earthquake using high resolution multitemporal optical images

This paper aims to reveal a methodology used to quantitatively evaluate the impact of an earthquake on a region, considering multi temporal high resolution optical images. The proposed approach was initiated in the frame of GEODIM Project whose goal is to develop a Romanian downstream emergency response service in order to contribute to current disaster and risk management approach based on Earth observation data. The project is focused on developing experimental processing algorithms and mapping products for natural disasters (floods, earthquakes, landslides) damage assessment in urban areas based on very high resolution optical and SAR satellite imagery acquired worldwide. The prospective scenario considers knowledge discovery from pre and post event satellite images by mapping the extracted data features into semantic classes and symbolic representations like “buildings”, “vegetation”, “streets”, “bare land” and “damaged buildings”, etc