Lower respiratory tract infection and rapid expansion of an abdominal aortic aneurysm: a case report

<p>Abstract</p> <p>Introduction</p> <p>The rate of abdominal aortic aneurysm expansion is related to multiple factors. There is some evidence that inflammation can accelerate aneurysm expansion. However, the association between pulmonary sepsis and rapid abdominal aortic aneurysm expansion is rarely reported.</p> <p>Case presentation</p> <p>Here we present a case of a rapidly expanding abdominal aortic aneurysm in a 68-year-old Caucasian man with a concomitant lower respiratory tract infection and systemic sepsis requiring intensive monitoring and urgent endovascular intervention. Our patient had an uncomplicated post-operative recovery and a follow-up computed tomography scan at one month demonstrated no evidence of an endoleak.</p> <p>Conclusion</p> <p>This case highlights the potential association between pulmonary sepsis and rapid abdominal aortic aneurysm expansion. In such cases, a policy of frequent monitoring should be adopted to identify those patients requiring definitive management.</p

Prognostic imaging biomarkers for diabetic kidney disease (iBEAt):study protocol

Background: Diabetic kidney disease (DKD) remains one of the leading causes of premature death in diabetes. DKD is classified on albuminuria and reduced kidney function (estimated glomerular filtration rate (eGFR)) but these have modest value for predicting future renal status. There is an unmet need for biomarkers that can be used in clinical settings which also improve prediction of renal decline on top of routinely available data, particularly in the early stages. The iBEAt study of the BEAt-DKD project aims to determine whether renal imaging biomarkers (magnetic resonance imaging (MRI) and ultrasound (US)) provide insight into the pathogenesis and heterogeneity of DKD (primary aim) and whether they have potential as prognostic biomarkers in DKD (secondary aim). Methods: iBEAt is a prospective multi-centre observational cohort study recruiting 500 patients with type 2 diabetes (T2D) and eGFR ≥30 ml/min/1.73m2. At baseline, blood and urine will be collected, clinical examinations will be performed, and medical history will be obtained. These assessments will be repeated annually for 3 years. At baseline each participant will also undergo quantitative renal MRI and US with central processing of MRI images. Biological samples will be stored in a central laboratory for biomarker and validation studies, and data in a central data depository. Data analysis will explore the potential associations between imaging biomarkers and renal function, and whether the imaging biomarkers improve the prediction of DKD progression. Ancillary substudies will: (1) validate imaging biomarkers against renal histopathology; (2) validate MRI based renal blood flow measurements against H2O15 positron-emission tomography (PET); (3) validate methods for (semi-)automated processing of renal MRI; (4) examine longitudinal changes in imaging biomarkers; (5) examine whether glycocalyx and microvascular measures are associated with imaging biomarkers and eGFR decline; (6) explore whether the findings in T2D can be extrapolated to type 1 diabetes. Discussion: iBEAt is the largest DKD imaging study to date and will provide valuable insights into the progression and heterogeneity of DKD. The results may contribute to a more personalised approach to DKD management in patients with T2D. Trial registration: Clinicaltrials.gov (NCT03716401)

Technical update on transcatheter arterial chemoembolization

Transcatheter arterial chemoembolization has become an established drug delivery system for palliative or bridging treatment of hepatocellular carcinoma. Over the last two decades, various research and developments have taken place to improve the transcatheter arterial chemoembolization procedure from both a clinical and a technical perspective. This review article aims to provide an update on the technical developments over the last decade

Metalliteollisuuden yritysten resurssitarvekartoitus

Tämän opinnäytetyön toimeksiantaja oli Kainuun Etu Oy. Opinnäytetyön tarkoituksena oli selvittää Kainuun ja lähialueiden metalliteollisuuden yritysten resurssitarpeita. Pääasiassa selvityksen alla oli yritysten erityiskoneistustarpeet ja suorittavan tason henkilökunnan koulutustarpeet. Resurssitarvekartoitus tehtiin osana Kajaanin Otanmäkeen suunnitteilla olleen koulutustehtaan perustamisselvitystä. Opinnäytetyön tavoite oli saada tietoa potentiaalisten asiakasyritysten tarpeista, että perustettavassa tehtaassa päätöksiä tekevät henkilöt saavat lisätietoa tai varmistavaa tietoa päätöksenteon tueksi. Tiedon pääasiallinen käyttötarkoitus oli tehtaan alkutuotannon suunnittelu asiakkaiden tarpeita varten. Kyselyn piiriin kuuluvilta Kainuulaisilta yrityksiltä tiedusteltiin myös yrityksen tai yrittäjän halukkuudesta lähteä osakkaaksi tehtaaseen. Resurssitarvekartoitus suoritettiin kvalitatiivisena tutkimuksena. Suunniteltu kyselylomake lähetettiin sähköpostilla ennakkoon päätettyihin yrityksiin, ja siten aineisto kerättiin kyselyyn vastanneiden yritysten vastausten pohjalta. Tutkimuksen tulokset heijastelevat koulutustarpeiden osalta toimialan työvoimapulan vaikutuksia. Tarvetta on etenkin joko suorittavan tason työntekijöistä, tai sitten halutaan tuotannon automaatioon liittyvää koulutusta. Koneistuspuolelta tarvetta löytyi lähinnä raskaasta aarporauksesta. Opinnäytetyön tuloksilla ei luultavasti ole myöhempiä käyttömahdollisuuksia muuten kuin opinnäytetyön toimeksiantajalle, tai vastaavanlaisen kartoituksen suunnittelijalle. Kaikki yritysten lähettämät vastaukset käsiteltiin opinnäytetyön raporttia tehdessä luottamuksellisesti ja nimettömänä.This thesis was commissioned by Kainuun Etu Oy. The purpose was to find out about the nature of resource demands at metal industry companies. The companies were mainly located in the Kainuu and Northern Ostrobothnia regions. The primary resource demands to be examined were the companies' special machining needs and training needs for the companies' executive personnel. The resource demand survey was made as a part of the foundation report for a training workshop that was planned to be founded in Otanmäki, Kajaani. The purpose was to gather information about the needs of the potential business clients, so that the workshop management would get information to support their decision making. The primary purpose of the information was the planning of the workshop production according to the clients' needs. The companies located in the Kainuu region were also asked about their interest in being a shareholder in the planned workshop. The resource demand survey was conducted as qualitative research. The questionnaire was e-mailed to the group of companies, which was decided beforehand. The data was gathered from the companies' answers to the questionnaire. The results of the survey seem to reflect the effects of the labor shortage in the metal industry, especially in the training needs. Companies seem to need either executive personnel or training associated with industrial automation. There were no major machining needs apart from reaming, especially when it comes to machining large and heavy objects. There are probably no later utilization possibilities for this thesis, apart from the client or someone who plans to conduct a similar survey. While writing this thesis report, all the companies' answers were reported with confidentiality and anonymously

Supplementary Material List from Computational tools for clinical support: a multi-scale compliant model for haemodynamic simulations in an aortic dissection based on multi-modal imaging data

Aortic dissection (AD) is a vascular condition with high morbidity and mortality rates. Computational fluid dynamics (CFD) can provide insight into the progression of AD and aid clinical decisions; however, oversimplified modelling assumptions and high computational cost compromise the accuracy of the information and impede clinical translation. To overcome these limitations, a patient-specific CFD multi-scale approach coupled to Windkessel boundary conditions and accounting for wall compliance was developed and used to study an AD patient. A new moving boundary algorithm was implemented to capture wall displacement and a rich <i>in vivo</i> clinical dataset was used to tune model parameters and for validation. Comparisons between <i>in silico</i> and <i>in vivo</i> data showed that this approach successfully captures flow and pressure waves for the patient-specific AD and is able to predict the pressure in the false lumen (FL), a critical variable for the clinical management of the condition. Results showed regions of low and oscillatory wall shear stress which, together with higher diastolic pressures predicted in the FL, may indicate risk of expansion. This study, at the interface of engineering and medicine, demonstrates a relatively simple and computationally efficient approach to account for arterial deformation and wave propagation phenomena in a three-dimensional model of AD, representing a step forward in the use of CFD as potential tool for AD management and clinical support

Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol

Abstract Background Diabetic kidney disease (DKD) remains one of the leading causes of premature death in diabetes. DKD is classified on albuminuria and reduced kidney function (estimated glomerular filtration rate (eGFR)) but these have modest value for predicting future renal status. There is an unmet need for biomarkers that can be used in clinical settings which also improve prediction of renal decline on top of routinely available data, particularly in the early stages. The iBEAt study of the BEAt-DKD project aims to determine whether renal imaging biomarkers (magnetic resonance imaging (MRI) and ultrasound (US)) provide insight into the pathogenesis and heterogeneity of DKD (primary aim) and whether they have potential as prognostic biomarkers in DKD (secondary aim). Methods iBEAt is a prospective multi-centre observational cohort study recruiting 500 patients with type 2 diabetes (T2D) and eGFR ≥30 ml/min/1.73m2. At baseline, blood and urine will be collected, clinical examinations will be performed, and medical history will be obtained. These assessments will be repeated annually for 3 years. At baseline each participant will also undergo quantitative renal MRI and US with central processing of MRI images. Biological samples will be stored in a central laboratory for biomarker and validation studies, and data in a central data depository. Data analysis will explore the potential associations between imaging biomarkers and renal function, and whether the imaging biomarkers improve the prediction of DKD progression. Ancillary substudies will: (1) validate imaging biomarkers against renal histopathology; (2) validate MRI based renal blood flow measurements against H2O15 positron-emission tomography (PET); (3) validate methods for (semi-)automated processing of renal MRI; (4) examine longitudinal changes in imaging biomarkers; (5) examine whether glycocalyx and microvascular measures are associated with imaging biomarkers and eGFR decline; (6) explore whether the findings in T2D can be extrapolated to type 1 diabetes. Discussion iBEAt is the largest DKD imaging study to date and will provide valuable insights into the progression and heterogeneity of DKD. The results may contribute to a more personalised approach to DKD management in patients with T2D. Trial registration Clinicaltrials.gov ( NCT03716401 ).http://deepblue.lib.umich.edu/bitstream/2027.42/173568/1/12882_2020_Article_1901.pd

Additional file 4 of Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol

Additional file 4: 4.0 Biopsy SOP. PDF file. Biopsy and pathology SOPs. Protocol for storing and capturing meta-data regarding the renal biopsy tissue for the iBEAt study. (EXE 543 kb

Additional file 1 of Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol

Additional file 1: 1.1 MRI biomarkers. File type: PDF file. Title: List of primary MRI biomarkers. Description: A table listing the biomarkers that will be derived from the MRI data to address the primary objectives. 1.2 MRI acquisition protocol. PDF file. MRI acquisition protocol (reference scanner). MRI sequence parameters for the iBEAt protocol on the reference scanner (Siemens 3 T). 1.3 Renal ultrasound SOP. PDF file. Ultrasound Standard Operating Procedures. Standard operating procedures for Ultrasound scanning in iBEAt

Additional file 2 of Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol

Additional file 2: 2.1 Biofluid collection SOPs. PDF file. Biofluid collection protocol. The protocol for the collection of blood and urine samples within iBEAt. 2.2 SOPs Biofluid processing. PDF file. Biofluid processing protocol. The protocol for processing blood and urine samples within iBEAt. 2.3 Biofluid schematics. PDF file. iBEAt kit contents and biofluid processing schematics. Schematics of iBEAt collection kits, and processing and storage protocols for collected blood and urine samples within iBEAt

Additional file 3 of Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol

Additional file 3: 3.0 CRF Screening. PDF file. Study recruitment – prescreening / screening. Clinical record form for prescreening / screening data. 3.1 CRF Adherence Checklist. PDF file. Baseline visit (V1) – adherence checklist. Clinical record form documenting participant adherence to guidance for the baseline visit. 3.2 CRF Limited Clinical Exam. PDF file. Limited Clinical Exam. Clinical record form for clinical examination data including, for example, blood pressure, height and weight. 3.3 CRF Medical and Family Hx. PDF file. Baseline (V1) – Medical and family history V2. Clinical record form for medical and family history (version 2). 3.4 CRF Local Study Labs. PDF file. Baseline (V1) – local study labs. Clinical record form for laboratory measurements performed at recruiting centre. 3.5 CRF Routine Labs. PDF file. Baseline visit (V1) – labs. Clinical record form for documenting all available laboratory values in the year prior to the baseline visit. 3.6 CRF Medications. PDF file. Medication log. Clinical record form documenting all current medications. 3.7 CRF Ultrasound. PDF file. Baseline visit (V1) – Ultrasound. Clinical record form for the renal ultrasound measurements. 3.8 CRF Biosamples. PDF file. Study biosamples. Clinical record form / checklist documenting what biofluid samples were collected and processed for the iBEAt study