| 1. |
- Jansson, Ida, et al.
(författare)
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Minoriteterna i minoriteten: Om nationalism, migration och modersmålsundervisning på Åland
- 2018
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Ingår i: Mänskliga rättigheter i samhället. - 9789186980702 ; , s. 103-125
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Kapitlet fokuserar på den potentiella motsättningen mellan minoriteters rättigheter och universella mänskliga rättigheter. Exemplet är det svenskspråkiga Åland, som idag har ett av världens mest omfattande nationalitetsskydd med snart hundra år på nacken – men det är ett skydd som inte alltid går ihop med nyare konventioner om mänskliga rättigheter. Kapitlet undersöker kritiken från Europarådets rådgivande kommitté (som övervakar implementeringen av Ramkonventionen om skydd för nationella minoriteter) att undervisningen i finska på Åland inte tillfredsställer behoven hos den finskspråkiga ”minoriteten i minoriteten”. Rådets kritik granskas i förhållande till Will Kymlickas teori om minoritetsnationalism. Analysen visar att den åländska nationalismen över tid har gått från att ha etniska inslag till att anta tydligt medborgerliga drag, men att det åländska inflytandet över ögruppens integrationspolitik samtidigt har använts för att stärka det svenska språkets ställning. Resultaten leder till följande paradox: att implementeringen av Ramkonventionen – som är tänkt att bidra till ökad tolerans – potentiellt sett kan resultera i det motsatta.
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| 2. |
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| 3. |
- Arvidsson, Ida
(författare)
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Applications of Deep Learning in Medical Image Analysis : Grading of Prostate Cancer and Detection of Coronary Artery Disease
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A wide range of medical examinations are using analysis of images from different types of equipment. Using artificial intelligence, the assessments could be done automatically. This can have multiple benefits for the healthcare; reduce workload for medical doctors, decrease variations in diagnoses and cut waiting times for the patient as well as improve the performance. The aim of this thesis has been to develop such solutions for two common diseases: prostate cancer and coronary artery disease. The methods used are mainly based on deep learning, where the model teaches itself by training on large datasets.Prostate cancer is one of the most common cancer diagnoses among men. The diagnosis is most commonly determined by visual assessment of prostate biopsies in a light microscope according to the Gleason scale. Deep learning methods to automatically detect and grade the cancer areas are presented in this thesis. The methods have been adapted to improve the generalisation performance on images from different hospitals, images which have inevitable variations in e.g.\ stain appearance. The methods include the usage of digital stain normalisation, training with extensive augmentation or using models such as a domain-adversarial neural network. One Gleason grading algorithm was evaluated on a small cohort with biopsies annotated in detail by two pathologists, to compare the performance with pathologists' inter-observer variability. Another cancer detection algorithm was evaluated on a large active surveillance cohort, containing patients with small areas of low-grade cancer. The results are promising towards a future tool to facilitate grading of prostate cancer.Cardiovascular disease is the leading cause of death world-wide, whereof coronary artery disease is one of the most common diseases. One way to diagnose coronary artery disease is by using myocardial perfusion imaging, where disease in the three main arteries supplying the heart with blood can be detected. Methods based on deep learning to perform the detection automatically are presented in this thesis. Furthermore, an algorithm developed to predict the degree of coronary artery stenosis from myocardial perfusion imaging, by means of quantitative coronary angiography, has also been developed. This assessment is normally done using invasive coronary angiography. Making the prediction automatically from myocardial perfusion imaging could save suffering for patients and free resources within the healthcare system.
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| 4. |
- Arvidsson, Ida, et al.
(författare)
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Apyrase decreases phage induction and Shiga toxin release from E. coli O157:H7 and has a protective effect during infection
- 2022
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Ingår i: Gut microbes. - : Informa UK Limited. - 1949-0976 .- 1949-0984. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Shiga toxin (Stx)-producing enterohemorrhagic Escherichia coli (EHEC) cause gastrointestinal infection and, in severe cases, hemolytic uremic syndrome which may lead to death. There is, to-date, no therapy for this infection. Stx induces ATP release from host cells and ATP signaling mediates its cytotoxic effects. Apyrase cleaves and neutralizes ATP and its effect on Stx and EHEC infection was therefore investigated. Apyrase decreased bacterial RecA and dose-dependently decreased toxin release from E. coli O157:H7 in vitro, demonstrated by reduced phage DNA and protein levels. The effect was investigated in a mouse model of E. coli O157:H7 infection. BALB/c mice infected with Stx2-producing E. coli O157:H7 were treated with apyrase intraperitoneally, on days 0 and 2 post-infection, and monitored for 11 days. Apyrase-treated mice developed disease two days later than untreated mice. Untreated infected mice lost significantly more weight than those treated with apyrase. Apyrase-treated mice exhibited less colonic goblet cell depletion and apoptotic cells, as well as lower fecal ATP and Stx2, compared to untreated mice. Apyrase also decreased platelet aggregation induced by co-incubation of human platelet-rich-plasma with Stx2 and E. coli O157 lipopolysaccharide in the presence of collagen. Thus, apyrase had multiple protective effects, reducing RecA levels, stx2 and toxin release from EHEC, reducing fecal Stx2 and protecting mouse intestinal cells, as well as decreasing platelet activation, and could thereby delay the development of disease.
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| 5. |
- Arvidsson, Ida, et al.
(författare)
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Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer’s disease in patients with mild cognitive symptoms
- 2024
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Ingår i: Alzheimer's Research and Therapy. - 1758-9193. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Predicting future Alzheimer’s disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions. Methods: A cohort of 332 individuals with SCD/MCI were included from the Swedish BioFINDER-1 study. The goal was to predict longitudinal SCD/MCI-to-AD dementia progression and change in Mini-Mental State Examination (MMSE) over four years. Four models were evaluated using different predictors: (1) clinical data only, including demographics, cognitive tests and APOE ε4 status, (2) clinical data plus hippocampal volume, (3) clinical data plus all regional MRI gray matter volumes (N = 68) extracted using FreeSurfer software, (4) a DL model trained using multi-task learning with MRI images, Jacobian determinant images and baseline cognition as input. A double cross-validation scheme, with five test folds and for each of those ten validation folds, was used. External evaluation was performed on part of the ADNI dataset, including 108 patients. Mann-Whitney U-test was used to determine statistically significant differences in performance, with p-values less than 0.05 considered significant. Results: In the BioFINDER cohort, 109 patients (33%) progressed to AD dementia. The performance of the clinical data model for prediction of progression to AD dementia was area under the curve (AUC) = 0.85 and four-year cognitive decline was R2 = 0.14. The performance was improved for both outcomes when adding hippocampal volume (AUC = 0.86, R2 = 0.16). Adding FreeSurfer brain regions improved prediction of four-year cognitive decline but not progression to AD (AUC = 0.83, R2 = 0.17), while the DL model worsened the performance for both outcomes (AUC = 0.84, R2 = 0.08). A sensitivity analysis showed that the Jacobian determinant image was more informative than the MRI image, but that performance was maximized when both were included. In the external evaluation cohort from ADNI, 23 patients (21%) progressed to AD dementia. The results for predicted progression to AD dementia were similar to the results for the BioFINDER test data, while the performance for the cognitive decline was deteriorated. Conclusions: The DL model did not significantly improve the prediction of clinical disease progression in AD, compared to regression models with a single pre-defined brain region.
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| 6. |
- Arvidsson, Ida, et al.
(författare)
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Comparison of different augmentation techniques for improved generalization performance for gleason grading
- 2019
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Ingår i: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). - 9781538636411 ; , s. 923-927
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Konferensbidrag (refereegranskat)abstract
- The fact that deep learning based algorithms used for digital pathology tend to overfit to the site of the training data is well-known. Since an algorithm that does not generalize is not very useful, we have in this work studied how different data augmentation techniques can reduce this problem but also how data from different sites can be normalized to each other. For both of these approaches we have used cycle generative adversarial networks (GAN); either to generate more examples to train on or to transform images from one site to another. Furthermore, we have investigated to what extent standard augmentation techniques improve the generalization performance. We performed experiments on four datasets with slides from prostate biopsies, stained with HE, detailed annotated with Gleason grades. We obtained results similar to previous studies, with accuracies of 77% for Gleason grading for images from the same site as the training data and 59% for images from other sites. However, we also found out that the use of traditional augmentation techniques gave better performance compared to when using cycle GANs, either to augment the training data or to normalize the test data.
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| 7. |
- Arvidsson, Ida, et al.
(författare)
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Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera
- 2023
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Ingår i: Journal of Nuclear Cardiology. - : Springer Science and Business Media LLC. - 1071-3581 .- 1532-6551. ; 30:1, s. 116-126
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Evaluate the prediction of quantitative coronary angiography (QCA) values from MPI, by means of deep learning. Methods: 546 patients (67% men) undergoing stress 99mTc-tetrofosmin MPI in a CZT camera in the upright and supine position were included (1092 MPIs). Patients were divided into two groups: ICA group included 271 patients who performed an ICA within 6 months of MPI and a control group with 275 patients with low pre-test probability for CAD and a normal MPI. QCA analyses were performed using radiologic software and verified by an expert reader. Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. A deep learning model was trained using a double cross-validation scheme such that all data could be used as test data as well. Results: Area under the receiver-operating characteristic curve for the prediction of QCA, with > 50% narrowing of the artery, by deep learning for the external test cohort: per patient 85% [95% confidence interval (CI) 84%-87%] and per vessel; LAD 74% (CI 72%-76%), RCA 85% (CI 83%-86%), LCx 81% (CI 78%-84%), and average 80% (CI 77%-83%). Conclusion: Deep learning can predict the presence of different QCA percentages of coronary artery stenosis from MPIs.
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| 8. |
- Arvidsson, Ida, et al.
(författare)
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Detection of left bundle branch block and obstructive coronary artery disease from myocardial perfusion scintigraphy using deep neural networks
- 2021
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Ingår i: Medical Imaging 2021 : Computer-Aided Diagnosis - Computer-Aided Diagnosis. - : SPIE. - 1605-7422. - 9781510640238 ; 11597
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Konferensbidrag (refereegranskat)abstract
- Myocardial perfusion scintigraphy, which is a non-invasive imaging technique, is one of the most common cardiological examinations performed today, and is used for diagnosis of coronary artery disease. Currently the analysis is performed visually by physicians, but this is both a very time consuming and a subjective approach. These are two of the motivations for why an automatic tool to support the decisions would be useful. We have developed a deep neural network which predicts the occurrence of obstructive coronary artery disease in each of the three major arteries as well as left bundle branch block. Since multiple, or none, of these could have a defect, this is treated as a multi-label classification problem. Due to the highly imbalanced labels, the training loss is weighted accordingly. The prediction is based on two polar maps, captured during stress in upright and supine position, together with additional information such as BMI and angina symptoms. The polar maps are constructed from myocardial perfusion scintigraphy examinations conducted in a dedicated Cadmium-Zinc-Telluride cardio camera (D-SPECT Spectrum Dynamics). The study includes data from 759 patients. Using 5-fold cross-validation we achieve an area under the receiver operating characteristics curve of 0.89 as average on per-vessel level for the three major arteries, 0.94 on per-patient level and 0.82 for left bundle branch block.
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| 9. |
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| 10. |
- Arvidsson, Ida, et al.
(författare)
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Domain-adversarial neural network for improved generalization performance of gleason grade classification
- 2020
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Ingår i: Medical Imaging 2020 : Digital Pathology - Digital Pathology. - : SPIE. - 1605-7422. - 9781510634077 ; 11320
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Konferensbidrag (refereegranskat)abstract
- When training a deep learning model, the dataset used is of great importance to make sure that the model learns relevant features of the data and that it will be able to generalize to new data. However, it is typically difficult to produce a dataset without some bias toward any specific feature. Deep learning models used in histopathology have a tendency to overfit to the stain appearance of the training data - if the model is trained on data from one lab only, it will usually not be able to generalize to data from other labs. The standard technique to overcome this problem is to use color augmentation of the training data which, artificially, generates more variations for the network to learn. In this work we instead test the use of a so called domain-adversarial neural network, which is designed to prevent the model from being biased towards features that in reality are irrelevant such as the origin of an image. To test the technique, four datasets from different hospitals for Gleason grading of prostate cancer are used. We achieve state of the art results for these particular datasets, and furthermore for two of our three test datasets the approach outperforms the use of color augmentation.
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| 11. |
- Arvidsson, Ida, et al.
(författare)
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Early terminal complement blockade and C6 deficiency are protective in enterohemorrhagic Escherichia coli-infected mice
- 2016
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Ingår i: Journal of Immunology. - : The American Association of Immunologists. - 0022-1767 .- 1550-6606. ; 197:4, s. 1276-1286
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Tidskriftsartikel (refereegranskat)abstract
- Complement activation occurs during enterohemorrhagic Escherichia coli (EHEC) infection and may exacerbate renal manifestations. In this study, we show glomerular C5b-9 deposits in the renal biopsy of a child with EHEC-associated hemolytic uremic syndrome. The role of the terminal complement complex, and its blockade as a therapeutic modality, was investigated in a mouse model of E. coli O157:H7 infection. BALB/c mice were treated with monoclonal anti-C5 i.p. on day 3 or 6 after intragastric inoculation and monitored for clinical signs of disease and weight loss for 14 d. All infected untreated mice (15 of 15) or those treated with an irrelevant Ab (8 of 8) developed severe illness. In contrast, only few infected mice treated with anti-C5 on day 3 developed symptoms (three of eight, p < 0.01 compared with mice treated with the irrelevant Ab on day 3) whereas most mice treated with anti-C5 on day 6 developed symptoms (six of eight). C6-deficient C57BL/6 mice were also inoculated with E. coli O157:H7 and only 1 of 14 developed disease, whereas 10 of 16 wild-type mice developed weight loss and severe disease (p < 0.01). Complement activation via the terminal pathway is thus involved in the development of disease in murine EHEC infection. Early blockade of the terminal complement pathway, before the development of symptoms, was largely protective, whereas late blockade was not. Likewise, lack of C6, and thereby deficient terminal complement complex, was protective in murine E. coli O157:H7 infection.
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| 12. |
- Arvidsson, Ida, et al.
(författare)
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Generalization of prostate cancer classification for multiple sites using deep learning
- 2018
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Ingår i: 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. - 9781538636367 ; 2018-April, s. 191-194
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Konferensbidrag (refereegranskat)abstract
- Deep learning has the potential to drastically increase the accuracy and efficiency of prostate cancer diagnosis, which would be of uttermost use. Today the diagnosis is determined manually from H&E stained specimens using a light microscope. In this paper several different approaches based on convolutional neural networks for prostate cancer classification are presented and compared, using three different datasets with different origins. The issue that algorithms trained on a certain site might not generalize to other sites, due to for example inevitable stain variations, is highlighted. Two different techniques to overcome this complication are compared; by training the networks using color augmentation and by using digital stain separation. Furthermore, the potential of using an autoencoder to get a more efficient downsampling is investigated, which turned out to be the method giving the best generalization. We achieve accuracies of 95% for classification of benign versus malignant tissue and 81% for Gleason grading for data from the same site as the training data. The corresponding accuracies for images from other sites are in average 88% and 52% respectively.
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| 13. |
- Arvidsson, Ida, et al.
(författare)
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Prediction of Obstructive Coronary Artery Disease from Myocardial Perfusion Scintigraphy using Deep Neural Networks
- 2021
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Ingår i: 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR). - : IEEE COMPUTER SOC. - 1051-4651. - 9781728188089 ; , s. 4442-4449
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Konferensbidrag (refereegranskat)abstract
- For diagnosis and risk assessment in patients with stable ischemic heart disease, myocardial perfusion scintigraphy is one of the most common cardiological examinations performed today. There are however many motivations for why an artificial intelligence algorithm would provide useful input to this task. For example to reduce the subjectiveness and save time for the nuclear medicine physicians working with this time consuming task. In this work we have developed a deep learning algorithm for multi-label classification based on a convolutional neural network to estimate the probability of obstructive coronary artery disease in the left anterior artery, left circumflex artery and right coronary artery. The prediction is based on data from myocardial perfusion scintigraphy studies conducted in a dedicated Cadmium-Zinc-Telluride cardio camera (D-SPECT Spectrum Dynamics). Data from 588 patients was available, with stress images in both upright and supine position, as well as a number of auxiliary parameters such as angina symptoms and age. The data was used to train and evaluate the algorithm using 5-fold cross-validation. We achieve state-of-the-art results for this task with an area under the receiver operating characteristics curve of 0.89 as average on per-vessel level and 0.95 on per-patient level.
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| 14. |
- Arvidsson, Ida, et al.
(författare)
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Shiga toxin-induced complement-mediated hemolysis and release of complement-coated red blood cell-derived microvesicles in hemolytic uremic syndrome.
- 2015
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Ingår i: Journal of Immunology. - : The American Association of Immunologists. - 1550-6606 .- 0022-1767. ; 194:5, s. 2309-2318
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Tidskriftsartikel (refereegranskat)abstract
- Shiga toxin (Stx)-producing Escherichia coli (STEC) cause hemolytic uremic syndrome (HUS). This study investigated whether Stx2 induces hemolysis and whether complement is involved in the hemolytic process. RBCs and/or RBC-derived microvesicles from patients with STEC-HUS (n = 25) were investigated for the presence of C3 and C9 by flow cytometry. Patients exhibited increased C3 deposition on RBCs compared with controls (p < 0.001), as well as high levels of C3- and C9-bearing RBC-derived microvesicles during the acute phase, which decreased after recovery. Stx2 bound to P1 (k) and P2 (k) phenotype RBCs, expressing high levels of the P(k) Ag (globotriaosylceramide), the known Stx receptor. Stx2 induced the release of hemoglobin and lactate dehydrogenase in whole blood, indicating hemolysis. Stx2-induced hemolysis was not demonstrated in the absence of plasma and was inhibited by heat inactivation, as well as by the terminal complement pathway Ab eculizumab, the purinergic P2 receptor antagonist suramin, and EDTA. In the presence of whole blood or plasma/serum, Stx2 induced the release of RBC-derived microvesicles coated with C5b-9, a process that was inhibited by EDTA, in the absence of factor B, and by purinergic P2 receptor antagonists. Thus, complement-coated RBC-derived microvesicles are elevated in HUS patients and induced in vitro by incubation of RBCs with Stx2, which also induced hemolysis. The role of complement in Stx2-mediated hemolysis was demonstrated by its occurrence only in the presence of plasma and its abrogation by heat inactivation, EDTA, and eculizumab. Complement activation on RBCs could play a role in the hemolytic process occurring during STEC-HUS.
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| 15. |
- Arvidsson, Ida
(författare)
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Studies of the pathogenesis of Shiga toxin-producing Escherichia coli-induced hemolytic uremic syndrome
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Hemolytic uremic syndrome (HUS) is characterized by non-immune hemolytic anemia, thrombocytopenia and acute kidney failure. The most common form of HUS is associated with gastrointestinal Shiga toxin-producing enterohemorrhagic Escherichia coli (EHEC) infection. EHEC are non-invasive strains with Shiga toxin as the unique virulence factor. The aim of this study was to describe host-pathogen interactions during EHEC infection using patient samples, an established mouse model and blood cells in vitro. In the first study the mechanism by which hemolysis is induced was investigated. Red blood cells from HUS patients and from controls were utilized to demonstrate complement activation on red blood cells in patients and show that Shiga toxin could induce hemolysis mediated by complement activation. Shiga toxin also induced the release of complement-coated red blood cell-derived microvesicles. The toxin may thus contribute to the induction of hemolysis. After intestinal colonization Shiga toxin is released into the circulation but does not circulate in free form. In the second study the means by which toxin reaches the kidney was investigated showing that the toxin circulated within blood cell-derived microvesicles (in patients and in the mouse model). These toxin-containing microvesicles were internalized in renal endothelial and epithelial cells where they emptied their cargo. The study thereby identified a novel mechanism of bacterial virulence by which virulence factors evade the host response. In the third study the importance of the antimicrobial peptide cathelicidin in EHEC infection was investigated using cathelicidin-deficient mice. These mice exhibited a thinner colonic mucosa layer, increased intestinal colonization, severe symptoms accompanied by intestinal and renal damage, in comparison to the wild-type cathelicidin-producing mice, which remained asymptomatic. Thus cathelicidin was found to be protective in murine EHEC infection. In the fourth study the protective effect of complement blockade during murine EHEC infection was determined, showing that early blockade, or inherent deficiency, of the terminal complement complex was protective whereas late blockade was not. In summary, this thesis unraveled mechanisms of induction of hemolysis and of toxin transfer and as well as the protective role of the anti-microbial cathelicidin and the terminal complement cascade in EHEC-HUS.
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| 16. |
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| 17. |
- Arvidsson, Martin, 1973, et al.
(författare)
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A Product Robustness Process in a Medium-Sized Company
- 2012
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Ingår i: IIE Asia Conference 2012, 28-30 June, Singapore.
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Konferensbidrag (refereegranskat)abstract
- Robust Design Methodology (RDM) has been established as an approach to design products or processes that are reliable and have stable performance despite exposure to variation in uncontrollable factors. Reliable and robust products with stable performance reduce scrap, waste and re-work and thus contribute to increased sustainability. Research on RDM has traditionally focused on the application of various tools to support RDM. Less has been written on how RDM can be applied throughout a product development process. This paper presents a study of a medium-sized manufacturing company developing a Product Robustness Process (PRP) as a sub-process in their product development process; a process focusing on practices but also in some cases include indications of suitable tools. The resulting process shows how various practices of RDM can be made a part of an established product development process, from concept design to verification of reliability and robustness. It also shows the necessity to focus on practices, not merely tools or techniques, to make full use of RDM in all phases of product development.
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| 18. |
- Arvidsson, Martin, 1973, et al.
(författare)
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A Review of Robust Design
- 2005
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Ingår i: 8th QMOD International Conference, Palermo.
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Konferensbidrag (refereegranskat)
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| 19. |
- Arvidsson, Martin, 1973, et al.
(författare)
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A Robust Design Methodology Process
- 2014
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Ingår i: International Symposiu of Robust Design, Copenhagen, August 2014.
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Konferensbidrag (refereegranskat)abstract
- Robust Design Methodology (RDM) has been established as an approach to design products that are reliable and have stable performance despite exposure to variation in uncontrollable factors. Research on RDM has traditionally focused on the application of various tools to support RDM. Less has been written on how RDM practices can be applied throughout a product development (PD) process. This paper presents a study of a medium-sized manufacturing company working with a Product Robustness Process (PRP). PRP is a sub-process in their PD, and focuses mainly on practices supporting RDM and the outcome in terms of increased robustness. An example of a practice is to systematically identify factors that will vary under operating conditions and affect product performance and reliability. By knowledge of such factors it is possible to take robustness into account in early PD phases. In some phases of the PRP, indications of suitable tools are given, however not as a compulsory prescription. The PRP shows how practices of RDM can be made a part of an established PD process. It also aims to focus on practices, rather than tools or techniques, to maximise RDM application. Case studies on RDM are often focusing on tools such as Design of Experiments; albeit important it is of value to consider daily RDM practices without necessarily involving a prescribed tool. This paper aims at contributing to the latter by means of a case study at a company working with a PRP for about two years. The purpose of the paper is to describe and evaluate a process for RDM practices throughout PD. The most important outcome of the PRP is that the development teams have reliased a systematic way to address quality and reliability on the agenda throughout PD.
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| 20. |
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| 21. |
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| 22. |
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| 23. |
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| 24. |
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| 25. |
- Burlaka, Ievgeniia, et al.
(författare)
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Ouabain Protects against Shiga Toxin-Triggered Apoptosis by Reversing the Imbalance between Bax and Bcl-xL
- 2013
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Ingår i: Journal of the American Society of Nephrology. - 1046-6673 .- 1533-3450. ; 24:9, s. 1413-1423
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Tidskriftsartikel (refereegranskat)abstract
- Hemolytic uremic syndrome, a life-threatening disease often accompanied by acute renal failure, usually occurs after gastrointestinal infection with Shiga toxin 2 (Stx2)-producing Escherichia coli. Stx2 binds to the glycosphingolipid globotriaosylceramide receptor, expressed by renal epithelial cells, and triggers apoptosis by activating the apoptotic factor Bax. Signaling via the ouabain/Na,K-ATPase/IP3R/NF-B pathway increases expression of Bcl-xL, an inhibitor of Bax, suggesting that ouabain might protect renal cells from Stx2-triggered apoptosis. Here, exposing rat proximal tubular cells to Stx2 in vitro resulted in massive apoptosis, upregulation of the apoptotic factor Bax, increased cleaved caspase-3, and downregulation of the survival factor Bcl-xL; co-incubation with ouabain prevented all of these effects. Ouabain activated the NF-B antiapoptotic subunit p65, and the inhibition of p65 DNA binding abolished the antiapoptotic effect of ouabain in Stx2-exposed tubular cells. Furthermore, in vivo, administration of ouabain reversed the imbalance between Bax and Bcl-xL in Stx2-treated mice. Taken together, these results suggest that ouabain can protect the kidney from the apoptotic effects of Stx2.
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| 26. |
- Calderon Toledo, Carla, et al.
(författare)
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Cross-Reactive Protection against Enterohemorrhagic Escherichia coli Infection by Enteropathogenic Escherichia coli in a Mouse Model.
- 2011
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Ingår i: Infection and Immunity. - 1098-5522. ; 79:6, s. 2224-2233
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Tidskriftsartikel (refereegranskat)abstract
- Enteropathogenic (EPEC) and enterohemorrhagic (EHEC) Escherichia coli are related attaching and effacing (A/E) pathogens. The genes responsible for the A/E pathology are encoded in a chromosomal pathogenicity island termed the locus of enterocyte effacement (LEE). Both pathogens share a high degree of homology in the LEE and additional 'O' islands. EHEC prevalence is much lower in EPEC endemic areas. This may be due to the development of antibodies against common EPEC and EHEC antigens. This study investigated the hypothesis that EPEC infections may protect against EHEC infections. We used a mouse model to inoculate BALB/c mice intragastrically, first with EPEC, followed by EHEC (E. coli O157:H7). Four control groups received either a non-pathogenic E. coli (NPEC) strain followed by EHEC (NPEC/EHEC), alternatively PBS/EHEC, EPEC/PBS or PBS/PBS. Mice were monitored for weight loss and symptoms. EPEC colonized the intestine after challenge and mice developed serum antibodies to intimin and E. coli secreted protein B (encoded in the LEE). Prechallenge with an EPEC strain had a protective effect after EHEC infection as few mice developed mild symptoms, from which they recovered. These mice had an increase in body weight similar to control animals and tissue morphology exhibited mild intestinal changes and normal renal histology. All mice that were not pre-challenged with the EPEC strain developed mild to severe symptoms after EHEC infection, weight loss as well as intestinal and renal histopathological changes. These data suggest that EPEC may protect against EHEC infection in this mouse model.
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| 27. |
- Chromek, Milan, et al.
(författare)
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The Antimicrobial Peptide Cathelicidin Protects Mice from Escherichia coli O157:H7-Mediated Disease.
- 2012
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 7:10
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Tidskriftsartikel (refereegranskat)abstract
- This study investigated the role of the antimicrobial peptide cathelicidin in Escherichia coli O157:H7 infection and subsequent renal damage. Mouse and human cathelicidin, CRAMP and LL-37, respectively, killed E. coli O157:H7 in vitro. Intestines from healthy wild-type (129/SvJ) and cathelicidin-knock-out (Camp(-/-)) mice were investigated, showing that cathelicidin-deficient mice had a thinner colonic mucus layer compared with wild-type mice. Wild-type (n = 11) and cathelicidin-knock-out (n = 11) mice were inoculated with E. coli O157:H7. Cathelicidin-deficient animals exhibited higher fecal counts of E. coli O157:H7 and bacteria penetrated the mucus forming attaching-and-effacing lesions to a much higher extent than in wild-type animals. Cathelicidin knock-out mice developed symptoms (9/11) as well as anemia, thrombocytopenia and extensive renal tubular damage while all cathelicidin-producing mice remained asymptomatic with normal laboratory findings. When injected with Shiga toxin intraperitoneally, both murine strains developed the same degree of renal tubular damage and clinical disease indicating that differences in sensitivity to infection between the murine strains were related to the initial intestinal response. In conclusion, cathelicidin substantially influenced the antimicrobial barrier in the mouse colon mucosa. Cathelicidin deficiency lead to increased susceptibility to E. coli O157:H7 infection and subsequent renal damage. Administration of cathelicidin or stimulation of endogenous production may prove to be novel treatments for E. coli O157:H7-induced hemolytic uremic syndrome.
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| 28. |
- Elmsjö, Albert, et al.
(författare)
-
Method selectivity evaluation using the co-feature ratio in LC/MS metabolomics : Comparison of HILIC stationary phase performance for the analysis of plasma, urine and cell extracts.
- 2018
-
Ingår i: Journal of Chromatography A. - : Elsevier BV. - 0021-9673 .- 1873-3778. ; 1568, s. 49-56
-
Tidskriftsartikel (refereegranskat)abstract
- Evaluation of the chromatographic separation in metabolomics studies has primarily been done using preselected sets of standards or by counting the number of detected features. An alternative approach is to calculate each feature's co-feature ratio, which is a combined selectivity measurement for the separation (i.e. extent of co-elution) and the MS-signal (i.e. adduct formation and in-source fragmentation). The aim of this study was to demonstrate how the selectivity of different HILIC stationary phases can be evaluated using the co-feature ratio approach. The study was based on three sample types; plasma, urine and cell extracts. Samples were analyzed on an UHPLC-ESI-Q-ToF system using an amide, a bare silica and a sulfobetaine stationary phase. For each feature, a co-feature ratio was calculated and used for multivariate analysis of the selectivity differences between the three stationary phases. Unsupervised PCA models indicated that the co-feature ratios were highly dependent on type of stationary phase. For several metabolites a 15-30 fold difference in the co-feature ratio were observed between the stationary phases. Observed selectivity differences related primarily to the retention patterns of unwanted matrix components such as inorganic salts (detected as salt clusters), glycerophospholipids, and polyethylene glycols. These matrix components affected the signal intensity of co-eluting metabolites by interfering with the ionization efficiency and/or their adduct formation. Furthermore, the retention pattern of these matrix components had huge influence on the number of detected features. The co-feature ratio approach has successfully been applied for evaluation of the selectivity performance of three HILIC stationary phases. The co-feature ratio could therefore be used in metabolomics for developing selective methods fit for their purpose, thereby avoiding generic analytical approaches, which are often biased, as type and amount of interfering matrix components are metabolome dependent.
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| 29. |
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| 30. |
- Erngren, Ida, et al.
(författare)
-
Adduct formation in electrospray ionisation-mass spectrometry with hydrophilic interaction liquid chromatography is strongly affected by the inorganic ion concentration of the samples
- 2019
-
Ingår i: Journal of Chromatography A. - : ELSEVIER SCIENCE BV. - 0021-9673 .- 1873-3778. ; 1600, s. 174-182
-
Tidskriftsartikel (refereegranskat)abstract
- Hydrophilic interaction liquid chromatography (HILIC)/electrospray ionisation-mass spectrometry (ESI-MS) has gained interest for the analysis of polar analytes in bioanalytical applications in recent years. However, ESI-MS is prone to adduct formation of analytes. In contrast to reversed phase chromatography, small inorganic ions have retention in HILIC, i.e. analytes and inorganic ions may co-elute, which could influence the adduct formation. In the present paper, it was demonstrated that the co-elution of sodium ions or potassium ions and analytes in HILIC/ESI-MS affect the adduct formation and that different concentrations of sodium ions and potassium ions in biological samples could have an impact on the quantitative response of the respective adducts as well as the quantitative response of the protonated adduct. The co-elution also lead to cluster formation of analytes and sodium formate or potassium formate, causing extremely complicated spectra. In analytical applications using HILIC/ESI-MS where internal standards are rarely used or not properly matched, great care needs to be taken to ensure minimal variation of inorganic ion concentration between samples. Moreover, the use of alkali metal ion adducts as quantitative target ions in relative quantitative applications should be made with caution if proper internal standards are not used.
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| 31. |
- Erngren, Ida, 1989-
(författare)
-
Analytical method development in liquid chromatography- mass spectrometry based metabolomics
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Metabolomics is the analytical field which aims at analyzing all small molecules, metabolites, in a biological system simultaneously. Currently no analytical methods are able to capture the entire metabolome, therefore, the analytical methods are often developed to be as general as possible. However, as research within the metabolomics field is generally driven by biological questions method development is often overlooked. Moreover, method development in metabolomics is very challenging, as evaluation of the methods are difficult since they are not developed for any particular metabolites. Method development is very important though, data quality and accuracy of relative quantitations is paramount if metabolomics is to be used to answer the biological questions at hand.The articles included in the thesis focus around both analytical method development and applications of metabolomics. In the first paper, head and neck cancer cell lines with different sensitivity to ionizing radiation was investigated using LC-MS based metabolomics. A theory on how the radiation resistant (UM-SCC-74B) cell line could alter its metabolism to handle redox status, DNA repair and DNA methylation was formulated. In the second article the sampling of sponge samples (Geodia barretti) was investigated with regard to its effects on detected metabolite profiles and data quality. It was found that freezing the samples directly was the best alternative which allowed for analysis of most metabolite classes. Storing the samples in solvent lead to a substantial extraction of metabolites to the solvent. For metabolomics, the solvents were more useful than the actual sponge samples that had been stored in solvent. In article three the problems caused by high concentrations of inorganic ions in biological samples in HILIC-ESI-MS analyses was described. The inorganic ions can affect relative quantitation and lead to erroneous results and overly complicated datasets inflated by the extra signals caused by cluster formation. To mitigate the problems caused by the inorganic ions a sample preparation method was developed in article four. The method used cation exchange SPE to trap alkali metal ions which, resulted in less ion-suppression, higher signal intensities of relevant metabolites as well as reduced adduct and cluster formation.In conclusion, this thesis have described projects where metabolomics have been applied to answer biological questions as well as analytical method development in LC-MS based metabolomics. Limitations with current methods was described and possible solutions to improve the methods has been presented.
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| 32. |
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| 33. |
- Friberg Hed, Niklas, et al.
(författare)
-
Arginase release in hemolytic uremic syndrome affects the vasculature
- 2023
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Hemolysis is a cardinal feature of hemolytic uremic syndrome (HUS) and during hemolysis excess arginase 1 is released from erythrocytes. Increased arginase activity leads to reduced arginine, as it is converted to urea and ornithine, and thereby reduced nitric oxide bioavailability, with secondary vascular injury. In this study we investigated arginase release in HUS patients and laboratory models. Two separate cohorts of patients (n=47 in total) with HUS associated with Shiga toxin-producing enterohemorrhagic E. coli (EHEC) and pediatric controls (n=35) were investigated. HUS patients had excessively high arginase 1 levels and activity (conversion to urea and ornithine) in plasma/serum during the acute phase, compared to remission and controls. Arginase 1 levels correlated with lactate dehydrogenase activity, indicating hemolysis, as well as the need for dialysis treatment. Patients also exhibited high levels of plasma alpha-1-microglobulin, a heme scavenger. Two mouse models were used, mice were challenged intragastrically with E. coli O157:H7 or injected intraperitoneally with Shiga toxin 2. Both models exhibited significantly elevated plasma arginase 1 levels and activity. Arginase 1 levels correlated with lactate dehydrogenase activity and alpha-1-microglobulin in the plasma of EHEC-infected mice. In vitro perfusion of Shiga toxin 2- and E. coli O157-lipopolysaccharide-stimulated human blood cells combined with ADAMTS13-deficient plasma over glomerular endothelial cells induced hemolysis and the release of bioactive arginase 1. The high levels of arginase released during HUS could thereby contribute to microvascular injury during HUS.
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| 34. |
- Friberg, Niklas, et al.
(författare)
-
Red blood cell-derived arginase release in hemolytic uremic syndrome
- 2024
-
Ingår i: Journal of Translational Medicine. - 1479-5876. ; 22:1
-
Tidskriftsartikel (refereegranskat)abstract
- BackgroundHemolysis is a cardinal feature of hemolytic uremic syndrome (HUS) and during hemolysis excess arginase 1 is released from red blood cells. Increased arginase activity leads to reduced L-arginine, as it is converted to urea and L-ornithine, and thereby reduced nitric oxide bioavailability, with secondary vascular injury. The objective of this study was to investigate arginase release in HUS patients and laboratory models and correlate arginase levels to hemolysis and kidney injury.MethodsTwo separate cohorts of patients (n = 47 in total) with HUS associated with Shiga toxin-producing enterohemorrhagic E. coli (EHEC) and pediatric controls (n = 35) were investigated. Two mouse models were used, in which mice were either challenged intragastrically with E. coli O157:H7 or injected intraperitoneally with Shiga toxin 2. An in vitro model of thrombotic microangiopathy was developed in which Shiga toxin 2- and E. coli O157 lipopolysaccharide-stimulated human blood cells combined with ADAMTS13-deficient plasma were perfused over glomerular endothelial cells. Two group statistical comparisons were performed using the Mann–Whitney test, multiple groups were compared using the Kruskal–Wallis test followed by Dunn’s procedure, the Wilcoxon signed rank test was used for paired data, or linear regression for continuous variables.ResultsHUS patients had excessively high plasma arginase 1 levels and activity (conversion of L-arginine to urea and L-ornithine) during the acute phase, compared to remission and controls. Arginase 1 levels correlated with lactate dehydrogenase activity, indicating hemolysis, as well as the need for dialysis treatment. Patients also exhibited high levels of plasma alpha-1-microglobulin, a heme scavenger. Both mouse models exhibited significantly elevated plasma arginase 1 levels and activity. Plasma arginase 1 levels correlated with lactate dehydrogenase activity, alpha-1-microglobulin and urea levels, the latter indicative of kidney dysfunction. In the in vitro model of thrombotic microangiopathy, bioactive arginase 1 was released and levels correlated to the degree of hemolysis.ConclusionsElevated red blood cell-derived arginase was demonstrated in HUS patients and in relevant in vivo and in vitro models. The excessively high arginase levels correlated to the degree of hemolysis and kidney dysfunction. Thus, arginase inhibition should be investigated in HUS.
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| 35. |
- Gremyr, Ida, 1975, et al.
(författare)
-
Principles of Robust Design Metholology
- 2008
-
Ingår i: Quality and Reliability Engineering International. - : Wiley. - 1099-1638 .- 0748-8017. ; 24:1, s. 23-35
-
Tidskriftsartikel (refereegranskat)abstract
- The literature on robust design has focused chiefly on the development of methods for identifying robust design solutions. In this paper we present a literature review of conflicts and agreements on the principles of robust design. Through this review four central principles of robust design are identified: awareness of variation, insensitivity to noise factors, application of various methods, and application in all stages of a design process. These principles are comprised into the following definition of robust design methodology: Robust design methodology means systematic efforts to achieve insensitivity to noise factors. These efforts are founded on an awareness of variation and can be applied in all stages of product design.
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| 36. |
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| 37. |
- Gummeson, Anna, et al.
(författare)
-
Automatic Gleason grading of H&E stained microscopic prostate images using deep convolutional neural networks
- 2017
-
Ingår i: Medical Imaging 2017: Digital Pathology. - : SPIE. - 9781510607255 ; 10140
-
Konferensbidrag (refereegranskat)abstract
- Prostate cancer is the most diagnosed cancer in men. The diagnosis is confirmed by pathologists based on ocular inspection of prostate biopsies in order to classify them according to Gleason score. The main goal of this paper is to automate the classification using convolutional neural networks (CNNs). The introduction of CNNs has broadened the field of pattern recognition. It replaces the classical way of designing and extracting hand-made features used for classification with the substantially different strategy of letting the computer itself decide which features are of importance. For automated prostate cancer classification into the classes: Benign, Gleason grade 3, 4 and 5 we propose a CNN with small convolutional filters that has been trained from scratch using stochastic gradient descent with momentum. The input consists of microscopic images of haematoxylin and eosin stained tissue, the output is a coarse segmentation into regions of the four different classes. The dataset used consists of 213 images, each considered to be of one class only. Using four-fold cross-validation we obtained an error rate of 7.3%, which is significantly better than previous state of the art using the same dataset. Although the dataset was rather small, good results were obtained. From this we conclude that CNN is a promising method for this problem. Future work includes obtaining a larger dataset, which potentially could diminish the error margin.
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| 38. |
- Hasenkamp, Torben, 1977, et al.
(författare)
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A review of practices for robust design methodology
- 2009
-
Ingår i: Journal of Engineering Design. - : Informa UK Limited. - 1466-1837 .- 0954-4828. ; 20:6, s. 645-657
-
Tidskriftsartikel (refereegranskat)abstract
- Robust Design Methodology (RDM) comprises systematic efforts to achieve insensitivity of products or processes to sources of unwanted variation. In this paper we review the literature and identify practices that facilitate industrial use of RDM by providing concrete ideas about what to do in order to come up with more robust designs. So far the literature has focused mainly on statistical techniques useful for creating robust designs, i.e. solutions that are insensitive to sources of unwanted variation, while scope and overall framework have been less emphasized, causing an ambiguity in these respects. One practice identified for insensitivity to variation sources is to exploit nonlinearities (between response and control factors) and interactions (between noise and control factors), and suitable tools for accomplishing this can be design of experiments or simulation techniques. As systematic RDM efforts are based on an awareness of variation and are beneficial in all design stages, the review also focuses on these two aspects of RDM.
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| 39. |
- Isaksson, Johan, et al.
(författare)
-
Semantic segmentation of microscopic images of H&E stained prostatic tissue using CNN
- 2017
-
Ingår i: 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. - 9781509061815 ; 2017-May, s. 1252-1256
-
Konferensbidrag (refereegranskat)abstract
- There is a need for an automatic Gleason scoring system that can be used for prostate cancer diagnosis. Today the diagnoses are determined by pathologists manually, which is both a complex and a time-consuming task. To reduce the pathologists' workload, but also to reduce variations between different pathologists, an automatic classification system would be of great use. Some previous works have aimed for this, but still more work needs to be done. It is probable that such a tool would benefit from having access to individually segmented, pathologically relevant objects from the images. Therefore, we have developed an algorithm for semantic segmentation of the microscopic images of H&E stained prostate tissue into Background, Stroma, Epithelial Cytoplasm and Nuclei. This algorithm is based on deep learning, or more specifically a convolutional neural network. The network design is inspired by architectures that previously have been proved successful in different applications. It consists of a contracting and an expanding part, which are symmetrical. We have reached an accuracy of 80 %, as measured by the mean of the intersection over union, for segmentation into four classes. Previous works have only investigated nuclei segmentation, and our network performed similar but for the more challenging task of four class segmentation.
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| 40. |
- Johansson, Karl E., et al.
(författare)
-
Shiga toxin signals via ATP and its effect is blocked by purinergic receptor antagonism
- 2019
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1
-
Tidskriftsartikel (refereegranskat)abstract
- Shiga toxin (Stx) is the main virulence factor of enterohemorrhagic Escherichia coli (EHEC), that cause gastrointestinal infection leading to hemolytic uremic syndrome. The aim of this study was to investigate if Stx signals via ATP and if blockade of purinergic receptors could be protective. Stx induced ATP release from HeLa cells and in a mouse model. Toxin induced rapid calcium influx into HeLa cells, as well as platelets, and a P2X1 receptor antagonist, NF449, abolished this effect. Likewise, the P2X antagonist suramin blocked calcium influx in Hela cells. NF449 did not affect toxin intracellular retrograde transport, however, cells pre-treated with NF449 exhibited significantly higher viability after exposure to Stx for 24 hours, compared to untreated cells. NF449 protected HeLa cells from protein synthesis inhibition and from Stx-induced apoptosis, assayed by caspase 3/7 activity. The latter effect was confirmed by P2X1 receptor silencing. Stx induced the release of toxin-positive HeLa cell- and platelet-derived microvesicles, detected by flow cytometry, an effect significantly reduced by NF449 or suramin. Suramin decreased microvesicle levels in mice injected with Stx or inoculated with Stx-producing EHEC. Taken together, we describe a novel mechanism of Stx-mediated cellular injury associated with ATP signaling and inhibited by P2X receptor blockade.
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| 41. |
- Karlsson, Jennie, et al.
(författare)
-
Classification of point-of-care ultrasound in breast imaging using deep learning
- 2023
-
Ingår i: Medical Imaging 2023 : Computer-Aided Diagnosis - Computer-Aided Diagnosis. - 2410-9045 .- 1605-7422. - 9781510660359 ; 12465
-
Konferensbidrag (refereegranskat)abstract
- Early detection of breast cancer is important to reduce morbidity and mortality. Access to breast imaging is limited in low- and middle-income countries compared to high-income countries. This contributes to advance-stage breast cancer presentation with poor survival. Pocket-sized portable ultrasound device, also known as point-of-care ultrasound (POCUS), aided by decision support using deep learning-based algorithms for lesion classification could be a cost-effective way to enable access to breast imaging in low-resource settings. A previous study, where using convolutional neural networks (CNN) to classify breast cancer in conventional ultrasound (US) images, showed promising results. The aim of the present study is to classify POCUS breast images. A POCUS data set containing 1100 breast images was collected. To increase the size of the data set, a Cycle-Consistent Adversarial Network (CycleGAN) was trained on US images to generate synthetic POCUS images. A CNN was implemented, trained, validated and tested on POCUS images. To improve performance, the CNN was trained with different combinations of data consisting of POCUS images, US images, CycleGAN-generated POCUS images and spatial augmentation. The best result was achieved by a CNN trained on a combination of POCUS images and CycleGAN-generated POCUS images and augmentation. This combination achieved a 95% confidence interval for AUC between 93.5% - 96.6%.
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| 42. |
- Karlsson, Jennie, et al.
(författare)
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Machine learning algorithm for classification of breast ultrasound images
- 2022
-
Ingår i: Medical Imaging 2022 : Computer-Aided Diagnosis - Computer-Aided Diagnosis. - : SPIE. - 9781510649422 ; 12033
-
Konferensbidrag (refereegranskat)abstract
- Breast cancer is the most common type of cancer globally. Early detection is important for reducing the morbidity and mortality of breast cancer. The aim of this study was to evaluate the performance of different machine learning models to classify malignant or benign lesions on breast ultrasound images. Three different convolutional neural network approaches were implemented: (a) Simple convolutional neural network, (b) transfer learning using pre-trained InceptionV3, ResNet50V2, VGG19 and Xception and (c) deep feature networks based on combinations of the four transfer networks in (b). The data consisted of two breast ultrasound image data sets: (1) an open, single-vendor, data set collected by Cairo University at Baheya Hospital, Egypt, consisting of 437 benign lesions and 210 malignant lesions, where 10% was set to be a test set and the rest was used for training and validation (development) and (2) An in-house, multi-vendor data set collected at Unilabs Mammography Unit, Skåne University Hospital, Sweden, consisting of 13 benign lesions and 265 malignant lesions, was used as an external test set. Both test sets were used for evaluating the networks. The performance measures used were area under the receiver operating characteristic curve (AUC), sensitivity, specificity and weighted accuracy. Holdout, i.e. the splitting of the development data into training and validation data sets just once, was used to find a model with as good performance as possible. 10-fold cross-validation was also performed to provide uncertainty estimates. For the transfer networks which were obtained with holdout, Gradient-weighted Class Activation Mapping was used to generate heat maps indicating which part of the image contributed to the network’s decision. For 10-fold cross-validation it was possible to achieve a mean AUC of 92% and mean sensitivity of 95% for the transfer network based on Xception when testing on the first data set. When testing on the second data set it was possible to obtain a mean AUC of 75% and mean sensitivity of 86% for the combination of ResNet50V2 and Xception.
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| 43. |
- Karlsson, Linda, et al.
(författare)
-
Cerebrospinal fluid reference proteins increase accuracy and interpretability of biomarkers for brain diseases.
- 2024
-
Ingår i: Nature Communications. - 2041-1723. ; 15:1
-
Tidskriftsartikel (refereegranskat)abstract
- Cerebrospinal fluid (CSF) biomarkers reflect brain pathophysiology and are used extensively in translational research as well as in clinical practice for diagnosis of neurological diseases, e.g., Alzheimer's disease (AD). However, CSF biomarker concentrations may be influenced by non-disease related inter-individual variability. Here we use a data-driven approach to demonstrate the existence of inter-individual variability in mean standardized CSF protein levels. We show that these non-disease related differences cause many commonly reported CSF biomarkers to be highly correlated, thereby producing misleading results if not accounted for. To adjust for this inter-individual variability, we identified and evaluated high-performing reference proteins which improved the diagnostic accuracy of key CSF AD biomarkers. Our reference protein method attenuates the risk for false positive findings, and improves the sensitivity and specificity of CSF biomarkers, with broad implications for both research and clinical practice.
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| 44. |
- Karpman, Diana, et al.
(författare)
-
Complement Interactions with Blood Cells, Endothelial Cells and Microvesicles in Thrombotic and Inflammatory Conditions.
- 2015
-
Ingår i: Advances in Experimental Medicine and Biology. - Cham : Springer International Publishing. - 0065-2598. - 9783319186023 ; 865, s. 19-42
-
Konferensbidrag (refereegranskat)abstract
- The complement system is activated in the vasculature during thrombotic and inflammatory conditions. Activation may be associated with chronic inflammation on the endothelial surface leading to complement deposition. Complement mutations allow uninhibited complement activation to occur on platelets, neutrophils, monocytes, and aggregates thereof, as well as on red blood cells and endothelial cells. Furthermore, complement activation on the cells leads to the shedding of cell derived-microvesicles that may express complement and tissue factor thus promoting inflammation and thrombosis. Complement deposition on red blood cells triggers hemolysis and the release of red blood cell-derived microvesicles that are prothrombotic. Microvesicles are small membrane vesicles ranging from 0.1 to 1 μm, shed by cells during activation, injury and/or apoptosis that express components of the parent cell. Microvesicles are released during inflammatory and vascular conditions. The repertoire of inflammatory markers on endothelial cell-derived microvesicles shed during inflammation is large and includes complement. These circulating microvesicles may reflect the ongoing inflammatory process but may also contribute to its propagation. This overview will describe complement activation on blood and endothelial cells and the release of microvesicles from these cells during hemolytic uremic syndrome, thrombotic thrombocytopenic purpura and vasculitis, clinical conditions associated with enhanced thrombosis and inflammation.
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| 45. |
- Karpman, Diana, et al.
(författare)
-
Extracellular vesicles in renal disease
- 2017
-
Ingår i: Nature Reviews Nephrology. - : Springer Science and Business Media LLC. - 1759-5061 .- 1759-507X. ; 13:9, s. 545-562
-
Forskningsöversikt (refereegranskat)abstract
- Extracellular vesicles, such as exosomes and microvesicles, are host cell-derived packages of information that allow cell-cell communication and enable cells to rid themselves of unwanted substances. The release and uptake of extracellular vesicles has important physiological functions and may also contribute to the development and propagation of inflammatory, vascular, malignant, infectious and neurodegenerative diseases. This Review describes the different types of extracellular vesicles, how they are detected and the mechanisms by which they communicate with cells and transfer information. We also describe their physiological functions in cellular interactions, such as in thrombosis, immune modulation, cell proliferation, tissue regeneration and matrix modulation, with an emphasis on renal processes. We discuss how the detection of extracellular vesicles could be utilized as biomarkers of renal disease and how they might contribute to disease processes in the kidney, such as in acute kidney injury, chronic kidney disease, renal transplantation, thrombotic microangiopathies, vasculitides, IgA nephropathy, nephrotic syndrome, urinary tract infection, cystic kidney disease and tubulopathies. Finally, we consider how the release or uptake of extracellular vesicles can be blocked, as well as the associated benefits and risks, and how extracellular vesicles might be used to treat renal diseases by delivering therapeutics to specific cells.
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| 46. |
- Karpman, Diana, et al.
(författare)
-
Haemolytic uraemic syndrome
- 2017
-
Ingår i: Journal of Internal Medicine. - : Wiley. - 0954-6820. ; 281:2, s. 123-148
-
Forskningsöversikt (refereegranskat)abstract
- Haemolytic uraemic syndrome (HUS) is defined by the simultaneous occurrence of nonimmune haemolytic anaemia, thrombocytopenia and acute renal failure. This leads to the pathological lesion termed thrombotic microangiopathy, which mainly affects the kidney, as well as other organs. HUS is associated with endothelial cell injury and platelet activation, although the underlying cause may differ. Most cases of HUS are associated with gastrointestinal infection with Shiga toxin-producing enterohaemorrhagic Escherichia coli (EHEC) strains. Atypical HUS (aHUS) is associated with complement dysregulation due to mutations or autoantibodies. In this review, we will describe the causes of HUS. In addition, we will review the clinical, pathological, haematological and biochemical features, epidemiology and pathogenetic mechanisms as well as the biochemical, microbiological, immunological and genetic investigations leading to diagnosis. Understanding the underlying mechanisms of the different subtypes of HUS enables tailoring of appropriate treatment and management. To date, there is no specific treatment for EHEC-associated HUS but patients benefit from supportive care, whereas patients with aHUS are effectively treated with anti-C5 antibody to prevent recurrences, both before and after renal transplantation.
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| 47. |
- Lindell Jonsson, Eva, et al.
(författare)
-
Exploring Radiation Response in Two Head and Neck Squamous Carcinoma Cell Lines Through Metabolic Profiling
- 2019
-
Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 9
-
Tidskriftsartikel (refereegranskat)abstract
- Head and neck squamous cell carcinoma (HNSCC) is the sixth most common form of cancer worldwide. Radiotherapy, with or without surgery, represents the major approach to curative treatment. However, not all tumors are equally sensitive to irradiation. It is therefore of interest to apply newer system biology approaches (e.g., metabolic profiling) in squamous cancer cells with different radiosensitivities in order to provide new insights on the mechanisms of radiation response. In this study, two cultured HNSCC cell lines from the same donor, UM-SCC-74A and UM-SCC-74B, were first genotyped using Short Tandem Repeat (STR), and assessed for radiation response by the means of clonogenic survival and growth inhibition assays. Thereafter, cells were cultured, irradiated and collected for subsequent metabolic profiling analyses using liquid chromatography-mass spectrometry (LC-MS). STR verified the similarity of UM-SCC-74A and UM-SCC-74B cells, and three independent assays proved UM-SCC-74B to be clearly more radioresistant than UM-SCC-74A. The LC-MS metabolic profiling demonstrated significant differences in the intracellular metabolome of the two cell lines before irradiation, as well as significant alterations after irradiation. The most important differences between the two cell lines before irradiation were connected to nicotinic acid and nicotinamide metabolism and purine metabolism. In the more radiosensitive UM-SCC-74A cells, the most significant alterations after irradiation were linked to tryptophan metabolism. In the more radioresistant UM-SCC-74B cells, the major alterations after irradiation were connected to nicotinic acid and nicotinamide metabolism, purine metabolism, the methionine cycle as well as the serine, and glycine metabolism. The data suggest that the more radioresistant cell line UM-SCC-74B altered the metabolism to control redox-status, manage DNA-repair, and change DNA methylation after irradiation. This provides new insights on the mechanisms of radiation response, which may aid future identification of biomarkers associated with radioresistance of cancer cells.
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| 48. |
- Malm, Joakim, et al.
(författare)
-
UTVÄRDERING AV SI-VERKSAMHETEN VID LUNDS UNIVERSITET 2016/17
- 2017
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Samverkansinlärning/Supplemental Instruction (SI) är en pedagogisk metod som syftar till att öka genomströmningen i svåra kurser. Metoden uppstod i USA vid University of Missouri Kansas City i mitten på 1970-talet och har därefter spridits till mer än 1500 högre lärosäten i ett trettiotal länder (Martin, 2008). Kort kan SI sägas vara ett studentdrivet komplement till ordinarie undervisning vid högre utbildning. Material i en kurs bearbetas tillsammans i en studiegrupp genom diskussion och grupparbeten under ledning av en äldre student – den s.k. SI-ledaren. Samverkansinlärning kom till Lunds universitet 1994 och har fått stor spridning på universitetet. Lund universitet utgör också ett nav för SI både i Sverige (SI finns på ca 15 svenska högre lärosäten) och i Europa och är också platsen för det nordeuropeiska SI-centret som ansvarar för utbildning och uppföljning i området på uppdrag av det internationella SI-centret i USA. Denna rapport sammanfattar resultaten från en utvärdering av SI-verksamheten vid Lunds universitet under läsåret 2016/17. SI-programmet vid Lunds universitet är omfattande med verksamhet på sex av universitetets fakulteter. Totalt har vi årligen ca 230 verksamma SIledare i ett sjuttiotal kurser. Organisationen av SI varierar mellan de olika fakulteterna. En ytterlighet finns vid LTH och naturvetenskaplig fakultet där verksamheten är starkt centraliserad. Den andra ytterligheten är vid samhällsvetenskaplig fakultet där SI-verksamheten är decentraliserad till de olika ämnena. Det som förenar de olika verksamhetsansvariga är den entusiasm som finns för SI-konceptet samt de dedikerade studenter som är SI-ledare. Närvaron på SI-möten är relativt god. Totalt har vi över 4000 studenter vid Lunds universitet som nyttjar SI varje år och en närvaro kring 30 % av kursregistrerade i snitt. Antalet deltagare på ett SI-möte ligger kring 10 i medeltal med en standardavvikelse kring 5 studenter, vilket ger goda förutsättningar för produktiva SI-möten generellt sett. De utvärderingar som gjorts av sambandet mellan SI-närvaro och kursresultat antyder att aktivt deltagande på SI ökar chanserna till ett bra resultat på kursen oavsett ämne. En specialundersökning i en kurs i matematik antyder också att alla studenter verkar gynnas av att gå på SI oavsett förkunskapsnivå. Enkätutvärderingar bland deltagarna antyder att den främsta drivkraften att delta på SI är att förstå kursmaterialet oavsett ämne och fakultet. Strategiska motiv som att klara kursen är normalt underordnade även om detta varierar beroende på fakultet/ämne. Det är roligt att konstatera att studiesocial interaktion mestadels värderas högt som motiv att delta på SI. Arbetsmiljön på SI upplevs som bra (lätt att fråga, lättsam, positiv och stödjande atmosfär samt lagom tempo) och generellt verkar mötena följa SI-metodiken väl. Oavsett fakultet verkar majoriteten av deltagarna känna att SI-mötena hjälper dem i deras kursarbete. De får en bättre förståelse av vad som förväntas av dem i kursen, ett ökat intresse för ämnet samt stöd i kursarbetet. De allra flesta verkar känna att de får en betydligt djupare förståelse för det kursmaterial man går igenom på SI-mötena. Dessutom upplever många att de sannolikt kommer att förbättra sitt kursresultat genom deltagande på SI. Detta antagande stödjs också som nämnts ovan av SI-närvaro och examinationsstatistik från kurser och ju högre SI närvaro desto bättre verkar det gå oavsett ämnesområde. Att döma av deltagarnas enkätsvar är det inte bara kursen med tillhörande SI som gynnas av SI-deltagande. Flera generella färdigheter tränas som bör gynna studenter i studier i andra kurser utan SI. Många deltagare upplever att färdigheter som problemlösningsförmåga, kritiskt tänkande, lagsamverkan och samarbete, presentation inför andra och sätt att studera utvecklats genom att delta på SI. Dessutom får flera ett förbättrat självförtroende i sina studier samt ett ökat nätverk av studiekamrater. Tidigare studier vid LTH antyder att de som deltagit på SI också presterar bättre i andra kurser utan SI och att de är mindre benägna att göra studieavbrott och tar ut examen i högre utsträckning (Malm, Bryngfors, & Mörner, 2012; Malm, Bryngfors, & Mörner, 2015; Malm, Bryngfors, & Fredriksson, 2017). Deltagarnas svar på frågan med ”Vad tycker du är det bästa med SI-mötena?” betonar möjligheten till diskussion och social interaktion, en god studiemiljö samt förståelseinriktad studietid. Detta gäller oavsett ämnesområde. Vad kan då förbättras med SI-mötena? Några antyder att strukturen/effektiviteten på mötena kan vara bristande. Detta kan i sin tur bero av att stödet för SI-ledare i form av observationer och handledning inte alltid fungerar som det skall. Detta är något att titta närmare på inom universitetet. Annat som kan förbättras med SI-verksamheten är att man vill ha fler möten, mer tid till mötena eller bättre schematider. Internationellt anses det ofta att SI-ledarna är de största ”vinnarna” i SI-konceptet pga de ledarskaps- och grupphanteringsfärdigheter de erhåller. Detta antyds även i enkätutvärderingarna för SI-ledare vid Lunds universitet. SI-ledarna verkar dessutom få ett bättre självförtroende, djupare förståelse av kursämnet samt bli bättre på att prata inför andra. Både deltagare och SI-ledare är generellt nöjda med sina möten.
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- Marginean, Felicia, et al.
(författare)
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An Artificial Intelligence-based Support Tool for Automation and Standardisation of Gleason Grading in Prostate Biopsies
- 2021
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Ingår i: European Urology Focus. - : Elsevier BV. - 2405-4569. ; 7:5, s. 995-1001
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Gleason grading is the standard diagnostic method for prostate cancer and is essential for determining prognosis and treatment. The dearth of expert pathologists, the inter- and intraobserver variability, as well as the labour intensity of Gleason grading all necessitate the development of a user-friendly tool for robust standardisation.OBJECTIVE: To develop an artificial intelligence (AI) algorithm, based on machine learning and convolutional neural networks, as a tool for improved standardisation in Gleason grading in prostate cancer biopsies.DESIGN, SETTING, AND PARTICIPANTS: A total of 698 prostate biopsy sections from 174 patients were used for training. The training sections were annotated by two senior consultant pathologists. The final algorithm was tested on 37 biopsy sections from 21 patients, with digitised slide images from two different scanners.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Correlation, sensitivity, and specificity parameters were calculated.RESULTS AND LIMITATIONS: The algorithm shows high accuracy in detecting cancer areas (sensitivity: 100%, specificity: 68%). Compared with the pathologists, the algorithm also performed well in detecting cancer areas (intraclass correlation coefficient [ICC]: 0.99) and assigning the Gleason patterns correctly: Gleason patterns 3 and 4 (ICC: 0.96 and 0.94, respectively), and to a lesser extent, Gleason pattern 5 (ICC: 0.82). Similar results were obtained using two different scanners.CONCLUSIONS: Our AI-based algorithm can reliably detect prostate cancer and quantify the Gleason patterns in core needle biopsies, with similar accuracy as pathologists. The results are reproducible on images from different scanners with a proven low level of intraobserver variability. We believe that this AI tool could be regarded as an efficient and interactive tool for pathologists.PATIENT SUMMARY: We developed a sensitive artificial intelligence tool for prostate biopsies, which detects and grades cancer with similar accuracy to pathologists. This tool holds promise to improve the diagnosis of prostate cancer.
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| 50. |
- Niklison-Chirou, Maria Victoria, et al.
(författare)
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TAp73 is a marker of glutamine addiction in medulloblastoma
- 2017
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Ingår i: Genes & Development. - : COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT. - 0890-9369 .- 1549-5477. ; 31:17, s. 1738-1753
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Tidskriftsartikel (refereegranskat)abstract
- Medulloblastoma is the most common solid primary brain tumor in children. Remarkable advancements in the understanding of the genetic and epigenetic basis of these tumors have informed their recent molecular classification. However, the genotype/phenotype correlation of the subgroups remains largely uncharacterized. In particular, the metabolic phenotype is of great interest because of its druggability, which could lead to the development of novel and more tailored therapies for a subset of medulloblastoma. p73 plays a critical role in a range of cellular metabolic processes. We show overexpression of p73 in a proportion of non-WNT medulloblastoma. In these tumors, p73 sustains cell growth and proliferation via regulation of glutamine metabolism. We validated our results in a xenograft model in which we observed an increase in survival time in mice on a glutamine restriction diet. Notably, glutamine starvation has a synergistic effect with cisplatin, a component of the current medulloblastoma chemotherapy. These findings raise the possibility that glutamine depletion can be used as an adjuvant treatment for p73-expressing medulloblastoma.
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