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Ulcerated intussuscepted jejunal lipoma-uncommon cause of obscure gastrointestinal bleeding: A case report

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World Journal of Clinical Cases 2019年第22期7卷 3765-3771页

作者： tudor Cuciureanu Laura Huiban stefan Chiriac Ana-Maria Singeap Mihai Danciu Florin Mihai Carol Stanciu Anca Trifan Nutu Vlad Department of Gastroenterology “Grigore T.Popa”University of Medicine and Pharmacy“St.Spiridon”Emergency HospitalIasi 700115Romania Department of Pathology “Grigore T.Popa”University of Medicine and Pharmacy“St.Spiridon”Emergency HospitalIasi 700115Romania Department of Radiology “Grigore T.Popa”University of Medicine and Pharmacy“St.Spiridon”Emergency HospitalIasi 700115Romania Department of General Surgery “Grigore T.Popa”University of Medicine and Pharmacy“St.Spiridon”Emergency HospitalIasi 700115Romania

BACKGROUND Intestinal lipomas are rare benign gastrointestinal(GI)tumors,usually asymptomatic,but may become symptomatic as the result of some complications such as intussusception,intestinal obstruction,volvulus or *** can occur at any site along the entire GI tract,more frequent in colon and rarely in small *** patient reported here is a very rare case of jejunal lipoma,ulcerated and intussuscepted,diagnosed in an adult investigated for a chronic iron deficiency anemia(IDA),and successfully managed by segmental jejunal *** SUMMARY A 63-year-old male was referred to“***”Hospital,Institute of Gastroenterology and Hepatology,Iasi,to investigate an obscure GI bleeding with an *** upper GI endoscopy and colonoscopy were performed,excluding potentially bleeding lesions,videocapsule endoscopy was then carried out,revealing fresh blood and a protruding lesion in proximal jejunum,findings confirmed by a single-balloon *** biopsies were taken from the lesion,but histological results were ***,contrast-enhanced computed tomography was performed showing jejunal polypoid mass with homogenous fat density,suggestive for lipoma.A week later a laparotomy was performed revealing the intussuscepted jejunal segment which was resected enbloc,and sent for further histopathologic *** patient made an uneventful recovery and was discharged seven days later,and at six months follow-up he had no complains and his hemoglobin returned to normal *** Lipomas are very rarely located in the jejunum,usually asymptomatic,but they may lead to complications such as intussusception and *** resection remains the treatment of choice.

关键词： Lipoma Intussusception Computed tomography Video capsule endoscopy Gastrointestinal bleeding Case report

Role of ammonia in predicting the outcome of patients with acuteon- chronic liver failure

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World Journal of Clinical Cases 2021年第3期9卷 552-564页

作者： stefan Chiriac Carol Stanciu Camelia Cojocariu Ana-Maria Singeap Catalin Sfarti tudor Cuciureanu Irina Girleanu Razvan Alexandru Igna Anca Trifan Department of Gastroenterology “Grigore T.Popa”University of Medicine and PharmacyIasi 700115Romania Institute of Gastroenterology and Hepatology "St.Spiridon"Emergency HospitalIasi 700111Romania Intensive Care “Grigore T.Popa”University of Medicine and PharmacyIasi 700115Romania

BACKGROUND High venous ammonia(VA)values have been proven to be a part of the mechanism of hepatic encephalopathy in patients with liver cirrhosis(LC)as well as acute ***,VA has been associated with poor prognosis and high mortality in these clinical ***,the role of ammonia in acuteon-chronic liver failure(ACLF)has not yet been clearly *** To assess the role of VA in predicting the outcome of cirrhotic patients with ACLF in a tertiary care *** We performed a retrospective observational study including consecutive patients with LC hospitalized for acute non-elective indications such as ascites,hepatic encephalopathy(HE),upper gastrointestinal bleeding,or bacterial infections that fulfilled the Asian Pacific Association for the Study of the Liver(APASL)criteria for *** study was conducted in“***”University Hospital,Iasi,Romania,a tertiary care center,between January 2017 and January *** APASL ACLF Research Consortium(AARC)score was calculated and ACLF grade was established ***-haven classification was used for *** analysis was performed using IBM SPSS version *** Four hundred and forty-six patients were included,aged 59(50-65)years,57.4%***-Pugh,model for end-stage liver disease(MELD)and AARC scores were 11(10-12),19.13±6.79,and 7(6-8),respectively.66.4%had ACLF grade I,31.2%ACLF grade II,and 2.5%ACLF grade *** was diagnosed in 83.9%,34%grade I,37.2%grade II,23.5%grade III,and 5.3%grade *** mortality was 7.8%.VA was 103(78-148)μmol/*** operating characteristic analysis showed good accuracy for the prediction of in-hospital mortality for the AARC score[Area under the curve(AUC)=0.886],MELD score(AUC=0.816),VA(AUC=0.812)and a fair accuracy for the Child-Pugh score(AUC=0.799).Subsequently,a cut-off value for the prediction of mortality was identified for VA(152.5μmol/L,sensitivity=0.706,1-specificity=0.190).Univariate analysis found acute kidney in

关键词： Venous ammonia Hepatic encephalopathy Acute-on-chronic liver failure Asian Pacific Association for the Study of the Liver Acute-on-chronic Liver Failure Research Consortium score Cirrhosis Mortality

Post-operative computed tomography scan – reliable tool for quality assessment of complete mesocolic excision

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World Journal of Gastrointestinal Oncology 2019年第3期11卷 208-226页

作者： Cristian Livadaru stefan Morarasu tudor Cristian Frunza Florina A Ghitun Elena Florina Paiu-Spiridon Florina Sava Cristina Terinte Dan Ferariu Sorinel Lunca Gabriel Mihail Dimofte "Grigore T Popa" University of Medicine and Pharmacy 2~(nd) Clinic of Surgical Oncology Regional Oncology Institute "Grigore T Popa" University of Medicine and Pharmacy Department of Radiology Regional Oncology Institute "Grigore T Popa" University of Medicine and Pharmacy Department of Pathology Regional Oncology Institute "Grigore T Popa" University of Medicine and Pharmacy

BACKGROUND Quality control in colon cancer surgery is an ongoing debate ever since standardization proved to be highly efficient in improving survival in rectal cancer. Complete mesocolic excision(CME) is widely acclaimed as the new goldstandard in colon cancer resections, thus it is imperative to establish quality criteria of CME in order to make it easily understood and verified by surgeons worldwide. One simple and reproducible tool could be the measurement of arterial stumps postoperatively and a straightforward way to test its reliability is to test it in a comparative study between CME and non-CME *** To validate arterial stump measurement as a surgical quality tool by comparing CME with conventional radical *** This was a retrospective study, carried out on a prospective database. We collected data from two groups of patients, divided according to standard CME with D2 central vascular ligation(group A) and non-standardized surgery(group B). The two groups were compared with regard to the arterial stump length after right-and left-sided colectomies for colon cancer. The actual stump lengths of the ileocolic artery(ICA) and inferior mesenteric artery(IMA) were compared with their theoretical best D2 position of predicted ligation levels(D2 PLLs) for calculating the potential for improvement. Measurements on follow-up computed tomography scans were carried out by three observers. Pathological data were recorded(specimen length, lymph node yield) and correlated with stump *** We analysed 58 colectomies. The stump lengths(mean ± SD) in group A were16.97 ± 4.77 mm for ICA and 31.70 ± 15.71 mm for IMA, whereas group B had 49.93 ± 20.29 mm for ICA and 67.24 ± 28.71 mm for IMA. Shorter lengths were obtained in group A, by a mean difference of 35.66 mm(χ~2 = 27.38, P < 0.001),which was significant for all types of colectomies. Except for a 5.85 ± 4.71 mm difference for right colectomies, all the ligations from group A significantly re

关键词： Complete mesocolic excision Central vascular ligation Colon surgery Arterial stump measurement Computed tomography

Did the severe acute respiratory syndrome-coronavirus 2 pandemic cause an endemic Clostridium difficile infection?

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World Journal of Clinical Cases 2021年第33期9卷 10180-10188页

作者： Camelia Cojocariu Irina Girleanu Anca Trifan Andrei Olteanu Cristina Maria Muzica Laura Huiban stefan Chiriac Ana Maria Singeap tudor Cuciureanu Catalin Sfarti Carol Stanciu Department of Gastroenterology “Grigore T.Popa”University of Medicine and Pharmacy“St.Spiridon”University HospitalInstitute of Gastroenterology and HepatologyIasi 700115Romania

BACKGROUND Clostridium difficile infection(CDI)has increased in prevalence during the last *** coronavirus disease 2019(COVID-19)pandemic has negatively influenced patient *** majority of the severe acute respiratory syndrome-coronavirus 2(SARS-CoV-2)-infected patients received antibiotics during *** To analyze the factors that influenced CDI development after SARS-CoV-2 *** Between March 2020 to December 2020,we performed a prospective observational study including 447 patients diagnosed with CDI who were admitted to our tertiary referral university *** diagnosis of CDI was based on the presence of diarrhea(≥3 watery stools within 24 h)associated with Clostridium difficile toxins A or *** excluded patients with other etiology of acute *** Among the total 447(12.5%)patients with CDI,most were male(54.3%)and mean age was 59.7±10.8 ***-six(17.0%)had history of COVID-19,most being elderly(COVID-19:62.6±14.6 years vs non-COVID-19:56.8±17.6 years,P=0.007),with history of alcohol consumption(43.4%vs 29.4%,P=0.017),previous hospitalizations(81.6%vs 54.9%,P<0.001)and antibiotic treatments(60.5%vs 35.5%,P<0.001),requiring higher doses of vancomycin and prone to recurrent disease(25.0%vs 13.1%,P=0.011).Age over 60 years[odds ratio(OR):2.591,95%confidence interval(CI):1.452-4.624,P=0.001],urban residence(OR:2.330,95%CI:1.286-4.221,P=0.005),previous antibiotic treatments(OR:1.909,95%CI:1.083-3.365,P=0.025),previous hospitalizations(OR:2.509,95%CI:1.263-4.986,P=0.009)and alcohol consumption(OR:2.550,95%CI:1.459-4.459,P=0.001)were risk factors of CDI in *** CDI risk is unrelated to history of SARS-CoV-2 ***,previous COVID-19 may necessitate higher doses of vancomycin for CDI.

关键词： COVID-19 infection Clostridium difficile infection Risk factors Antibiotic use Pandemic Recurrence

Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

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Journal of Computer and Communications 2021年第7期9卷 8-20页

作者： Andreea Roxana Luca tudor Florin Ursuleanu Liliana Gheorghe Roxana Grigorovici stefan Iancu Maria Hlusneac Cristina Preda Alexandru Grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania Departament Obstetrics and Gynecology Integrated Ambulatory of Hospital “Sf. Spiridon” Iasi Romania Department of Surgery VI “Sf. Spiridon” Hospital Iasi Romania Iasi Departament of Surgery I Regional Institute of Oncology Iasi Romania Department of Radiology “Sf. Spiridon” Hospital Iasi Romania Department of Endocrinology “Sf. Spiridon” Hospital Iasi Romania

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimiz

关键词： Combined Model of U-Net-Based Architectures Medical Image Segmentation 2D/3D/CT/RMN Images

Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

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E-Health Telecommunication Systems and Networks 2021年第2期10卷 41-74页

作者： tudor Florin Ursuleanu Andreea Roxana Luca Liliana Gheorghe Roxana Grigorovici stefan Iancu Maria Hlusneac Cristina Preda Alexandru Grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania Department of Surgery VI “Sf. Spiridon” Hospital Iasi Romania Department of Surgery I Regional Institute of Oncology Iasi Romania Department of Obstetrics and Gynecology Integrated Ambulatory of Hospital “Sf. Spiridon” Iasi Romania Department of Radiology “Sf. Spiridon” Hospital Iasi Romania Department of Endocrinology “Sf. Spiridon” Hospital Iasi Romania

Deep learning (DL) has seen an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image. The purpose of the work converges in determining the importance of each component, describing the specificity and correlations of these elements involved in achieving the precision of interpretation of medical images using DL. The major contribution of this work is primarily to the updated characterisation of the characteristics of the constituent elements of the deep learning process, scientific data, methods of knowledge incorporation, DL models according to the objectives for which they were designed and the presentation of medical applications in accordance with these tasks. Secondly, it describes the specific correlations between the quality, type and volume of data, the deep learning patterns used in the interpretation of diagnostic medical images and their applications in medicine. Finally presents problems and directions of future research. Data quality and volume, annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform image analysis tasks. Moreover, the development of models capable of extracting unattended features and easily incorporated into the architecture of DL networks and the development of techniques to search for a certain network architecture according to the objectives set lead to performance in the interpretation of medical images.

关键词： Medical Image Analysis Data Types Labels Deep Learning Models

The Use of Artificial Intelligence on Segmental Volumes, Constructed from MRI and CT Images, in the Diagnosis and Staging of Cervical Cancers and Thyroid Cancers: A Study Protocol for a Randomized Controlled Trial

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Journal of Biomedical Science and Engineering 2021年第6期14卷 300-304页

作者： tudor Florin Ursuleanu Andreea Roxana Luca Liliana Gheorghe Roxana Grigorovici stefan Iancu Maria Hlusneac Cristina Preda Alexandru Grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania “Sf. Spiridon” Hospital Department of Surgery VI Iasi Romania Regional Institute of Oncology Department of Surgery I Iasi Romania Integrated Ambulatory of Hospital “Sf. Spiridon” Department of Obstetrics and Gynecology Iasi Romania “Sf. Spiridon” Hospital Department of Radiology Iasi Romania “Sf. Spiridon” Hospital Department of Endocrinology Iasi Romania

Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accurate and effective therapeutic approach. For accurate diagnosis and staging of cervical and thyroid cancers, we aim to create a diagnostic method, optimized by the algorithms of artificial intelligence and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation computer tomography (CT) scan, magnetic resonance imaging (MRI) of the doctor and improve therapeutic planning. Materials and Methods: The optimization of the computer assisted diagnosis (CAD) method will consist in the development and formation of artificial intelligence models, using algorithms and tools used in segmental volumetric constructions to generate 3D images from MRI/CT. We propose a comparative study of current developments in “DICOM” image processing by volume rendering technique, the use of the transfer function for opacity and color, shades of gray from “DICOM” images projected in a three-dimensional space. We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven to be effective in representing complex data distributions, as we do in this study. Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical and thyroid cancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the CAD method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks o

关键词： Artificial Intelligence Cervical Cancer Thyroid Cancer MRI Images CT Images

The Use of Artificial Intelligence on Colposcopy Images, in the Diagnosis and Staging of Cervical Precancers: A Study Protocol for a Randomized Controlled Trial

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Journal of Biomedical Science and Engineering 2021年第6期14卷 266-270页

作者： Andreea Roxana Luca tudor Florin Ursuleanu Liliana Gheorghe Roxana Grigorovici stefan Iancu Maria Hlusneac Cristina Preda Alexandru Grigorovici Faculty of General Medicine “Grigore T. Popa” University of Medicine and Pharmacy Iasi Romania Integrated Ambulatory of Hospital “Sf. Spiridon” Department of Obstetrics and Gynecology Iasi Romania “Sf. Spiridon” Hospital Department of Surgery VI Iasi Romania Regional Institute of Oncology Iasi Department of Surgery I Iasi Romania “Sf. Spiridon” Hospital Department of Radiology Iasi Romania “Sf. Spiridon” Hospital Department of Endocrinology Iasi Romania

Rationale and Objectives: Accurate diagnosis and staging of cervical precancers is essential for practical medicine in determining the extent of the lesion extension and determines the most correct and effective therapeutic approach. For accurate diagnosis and staging of cervical precancers, we aim to create a diagnostic method optimized by artificial intelligence (AI) algorithms and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation of colposcopy images and improve therapeutic planning. Materials and Methods: The optimization of the method will consist in the development and formation of artificial intelligence models, using complicated convolutional neural networks (CNN) to identify precancers and cancers on colposcopic images. We will use topologies that have performed well in similar image recognition projects, such as Visual Geometry Group Network (VGG16), Inception deep neural network with an architectural design that consists of repeating components referred to as Inception modules (Inception), deeply separable convolutions that significantly reduce the number of parameters (MobileNet) that is a class of Convolutional Neural Network (CNN), Return of investment for machine Learning (ROI), Fully Convolutional Network (U-Net) and Overcomplete Convolutional Network Kite-Net (KiU-Net). Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical precancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the computer assisted diagnostic (CAD) method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising re

关键词： Artificial Intelligence Colposcopy Images Cervical Precancer