| 1. |
- Hagberg, Eva, et al.
(författare)
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Semi-supervised learning with natural language processing for right ventricle classification in echocardiography—a scalable approach
- 2022
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Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 143
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Tidskriftsartikel (refereegranskat)abstract
- We created a deep learning model, trained on text classified by natural language processing (NLP), to assess right ventricular (RV) size and function from echocardiographic images. We included 12,684 examinations with corresponding written reports for text classification. After manual annotation of 1489 reports, we trained an NLP model to classify the remaining 10,651 reports. A view classifier was developed to select the 4-chamber or RV-focused view from an echocardiographic examination (n = 539). The final models were two image classification models trained on the predicted labels from the combined manual annotation and NLP models and the corresponding echocardiographic view to assess RV function (training set n = 11,008) and size (training set n = 9951. The text classifier identified impaired RV function with 99% sensitivity and 98% specificity and RV enlargement with 98% sensitivity and 98% specificity. The view classification model identified the 4-chamber view with 92% accuracy and the RV-focused view with 73% accuracy. The image classification models identified impaired RV function with 93% sensitivity and 72% specificity and an enlarged RV with 80% sensitivity and 85% specificity; agreement with the written reports was substantial (both κ = 0.65). Our findings show that models for automatic image assessment can be trained to classify RV size and function by using model-annotated data from written echocardiography reports. This pipeline for auto-annotation of the echocardiographic images, using a NLP model with medical reports as input, can be used to train an image-assessment model without manual annotation of images and enables fast and inexpensive expansion of the training dataset when needed. © 2022
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| 2. |
- Altay, Özlem, et al.
(författare)
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Combined Metabolic Activators Accelerates Recovery in Mild-to-Moderate COVID-19
- 2021
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Ingår i: Advanced Science. - : Wiley. - 2198-3844. ; 8:17
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Tidskriftsartikel (refereegranskat)abstract
- COVID-19 is associated with mitochondrial dysfunction and metabolic abnormalities, including the deficiencies in nicotinamide adenine dinucleotide (NAD+) and glutathione metabolism. Here it is investigated if administration of a mixture of combined metabolic activators (CMAs) consisting of glutathione and NAD+ precursors can restore metabolic function and thus aid the recovery of COVID-19 patients. CMAs include l-serine, N-acetyl-l-cysteine, nicotinamide riboside, and l-carnitine tartrate, salt form of l-carnitine. Placebo-controlled, open-label phase 2 study and double-blinded phase 3 clinical trials are conducted to investigate the time of symptom-free recovery on ambulatory patients using CMAs. The results of both studies show that the time to complete recovery is significantly shorter in the CMA group (6.6 vs 9.3 d) in phase 2 and (5.7 vs 9.2 d) in phase 3 trials compared to placebo group. A comprehensive analysis of the plasma metabolome and proteome reveals major metabolic changes. Plasma levels of proteins and metabolites associated with inflammation and antioxidant metabolism are significantly improved in patients treated with CMAs as compared to placebo. The results show that treating patients infected with COVID-19 with CMAs lead to a more rapid symptom-free recovery, suggesting a role for such a therapeutic regime in the treatment of infections leading to respiratory problems.
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| 3. |
- Fromentin, S., et al.
(författare)
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Microbiome and metabolome features of the cardiometabolic disease spectrum
- 2022
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Ingår i: Nature Medicine. - : Springer Science and Business Media LLC. - 1078-8956 .- 1546-170X. ; 28:2
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Tidskriftsartikel (refereegranskat)abstract
- By studying individuals along a spectrum of cardiometabolic disease and adjusting for effects of lifestyle and medication, this investigation identifies alterations of the metabolome and microbiome from dysmetabolic conditions, such as obesity and type 2 diabetes, to ischemic heart disease. Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages-acute coronary syndrome, chronic IHD and IHD with heart failure-and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.
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| 4. |
- Liu, Xixi, 1995, et al.
(författare)
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Deep Nearest Neighbors for Anomaly Detection in Chest X-Rays
- 2024
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 1611-3349 .- 0302-9743. ; 14349 LNCS, s. 293-302
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Konferensbidrag (refereegranskat)abstract
- Identifying medically abnormal images is crucial to the diagnosis procedure in medical imaging. Due to the scarcity of annotated abnormal images, most reconstruction-based approaches for anomaly detection are trained only with normal images. At test time, images with large reconstruction errors are declared abnormal. In this work, we propose a novel feature-based method for anomaly detection in chest x-rays in a setting where only normal images are provided during training. The model consists of lightweight adaptor and predictor networks on top of a pre-trained feature extractor. The parameters of the pre-trained feature extractor are frozen, and training only involves fine-tuning the proposed adaptor and predictor layers using Siamese representation learning. During inference, multiple augmentations are applied to the test image, and our proposed anomaly score is simply the geometric mean of the k-nearest neighbor distances between the augmented test image features and the training image features. Our method achieves state-of-the-art results on two challenging benchmark datasets, the RSNA Pneumonia Detection Challenge dataset, and the VinBigData Chest X-ray Abnormalities Detection dataset. Furthermore, we empirically show that our method is robust to different amounts of anomalies among the normal images in the training dataset. The code is available at: https://github.com/XixiLiu95/deep-kNN-anomaly-detection.
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| 5. |
- Belda, E., et al.
(författare)
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Impairment of gut microbial biotin metabolism and host biotin status in severe obesity: effect of biotin and prebiotic supplementation on improved metabolism
- 2022
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Ingår i: Gut. - : BMJ. - 0017-5749 .- 1468-3288. ; 71:12, s. 2463-2480.
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Tidskriftsartikel (refereegranskat)abstract
- Objectives Gut microbiota is a key component in obesity and type 2 diabetes, yet mechanisms and metabolites central to this interaction remain unclear. We examined the human gut microbiome's functional composition in healthy metabolic state and the most severe states of obesity and type 2 diabetes within the MetaCardis cohort. We focused on the role of B vitamins and B7/B8 biotin for regulation of host metabolic state, as these vitamins influence both microbial function and host metabolism and inflammation. Design We performed metagenomic analyses in 1545 subjects from the MetaCardis cohorts and different murine experiments, including germ-free and antibiotic treated animals, faecal microbiota transfer, bariatric surgery and supplementation with biotin and prebiotics in mice. Results Severe obesity is associated with an absolute deficiency in bacterial biotin producers and transporters, whose abundances correlate with host metabolic and inflammatory phenotypes. We found suboptimal circulating biotin levels in severe obesity and altered expression of biotin-associated genes in human adipose tissue. In mice, the absence or depletion of gut microbiota by antibiotics confirmed the microbial contribution to host biotin levels. Bariatric surgery, which improves metabolism and inflammation, associates with increased bacterial biotin producers and improved host systemic biotin in humans and mice. Finally, supplementing high-fat diet-fed mice with fructo-oligosaccharides and biotin improves not only the microbiome diversity, but also the potential of bacterial production of biotin and B vitamins, while limiting weight gain and glycaemic deterioration. Conclusion Strategies combining biotin and prebiotic supplementation could help prevent the deterioration of metabolic states in severe obesity.
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| 6. |
- Bryl-Górecka, Paulina, et al.
(författare)
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Bilberry Supplementation after Myocardial Infarction Decreases Microvesicles in Blood and Affects Endothelial Vesiculation
- 2020
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Ingår i: Molecular Nutrition & Food Research. - : Wiley-VCH Verlagsgesellschaft. - 1613-4125 .- 1613-4133. ; 64:20
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Tidskriftsartikel (refereegranskat)abstract
- Scope: Diet rich in bilberries is considered cardioprotective, but the mechanisms of action are poorly understood. Cardiovascular disease is characterized by increased proatherogenic status and high levels of circulating microvesicles (MVs). In an open-label study patients with myocardial infarction receive an 8 week dietary supplementation with bilberry extract (BE). The effect of BE on patient MV levels and its influence on endothelial vesiculation in vitro is investigated.Methods and results: MVs are captured with acoustic trapping and platelet-derived MVs (PMVs), as well as endothelial-derived MVs (EMVs) are quantified with flow cytometry. The in vitro effect of BE on endothelial extracellular vesicle (EV) release is examined using endothelial cells and calcein staining. The mechanisms of BE influence on vesiculation pathways are studied by Western blot and qRT-PCR. Supplementation with BE decreased both PMVs and EMVs. Furthermore, BE reduced endothelial EV release, Akt phosphorylation, and vesiculation-related gene transcription. It also protects the cells from P2X(7)-induced EV release and increase in vesiculation-related gene expression.Conclusion: BE supplementation improves the MV profile in patient blood and reduces endothelial vesiculation through several molecular mechanisms related to the P2X(7)receptor. The findings provide new insight into the cardioprotective effects of bilberries.
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| 7. |
- Bondesson, Johan, 1991
(författare)
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Geometric Modeling of Thoracic Aortic Surface Morphology - Implications for Pathophysiology and Clinical Interventions
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Vascular disease risk factors such as hypertension, hyperlipidemia and old age are all results of modern-day lifestyle, and these diseases are getting more and more common. One treatment option for vascular diseases such as aneurysms and dissections is endovascular aortic repair introduced in the early 1990s. This treatment uses tubular fabric covered metallic structures (endografts) that are implanted using a minimally invasive approach and placed to serve as an articial vessel in a damaged portion of the vasculature. To ensure that the interventions are successful, the endograft must be placed in the correct location, and designed to sustain the hostile biological, chemical, and mechanical conditions in the body for many years. This is an interaction that goes both ways, and keeping in mind that the endograft is a foreign object placed in the sensitive vascular system, it is also important that it does not disrupt the native conditions more than necessary. This thesis presents a segmentation and quantication methodology to accurately describe the complex morphology and motion of diseased blood vessels in vivo through a natural and intuitive description of their luminal surfaces. After methodology validation, a series of important clinical applications are performed, all based on non-invasive imaging. Firstly, it is shown that explicit surface curvature quantication is necessary when compared to relying solely on centerline curvature and estimation methods. Secondly, it is shown that endograft malapposition severity can be predicted from preoperative geometric analysis of thoracic aortic surfaces. Thirdly, a multiaxial dynamics analysis of cardiac induced thoracic aortic surface motion shows how thoracic endovascular aortic repair affects the deformations of the dierent portions of the thoracic aorta. Fourthly, the helical propagation pattern of type B aortic dissection is determined, and two distinct modes of chirality are revealed, i.e., achiral and right-handed chiral groups. Finally, the effects of thoracic endovascular aortic repair on helical and cross-sectional morphology of type B dissections are investigated revealing how acuity and chirality affects the alteration due to intraluminal lining with endografts. Thus, the work presented in this thesis contributes by adding knowledge about pathology and pathophysiology through better geometric description of surface conditions of diseased thoracic aortas. This gives clinicians insights to use in their treatment planning and provides more nuanced boundary conditions for endograft manufacturers. Comprehensive knowledge about diseases, better treatment planning, and better devices are all crucial in order to improve the outcomes of performed interventions and ultimately the quality of life for the treated patients.
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| 8. |
- Polymeri, Erini, et al.
(författare)
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Deep learning-based quantification of PET/CT prostate gland uptake : association with overall survival
- 2020
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Ingår i: Clinical Physiology and Functional Imaging. - Chichester : Blackwell Publishing. - 1475-0961 .- 1475-097X. ; 40:2, s. 106-113
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Tidskriftsartikel (refereegranskat)abstract
- Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-naïve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18F-choline PET images above a standardized uptake value (SUV) of 2·65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results: The SDI between the automated and the manual volume segmentations was 0·78 and 0·79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0·02), whereas age, PSA, and Gleason score were not. Conclusion: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival. © 2019 The Authors. Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and Nuclear Medicine
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| 9. |
- Håkansson, Samuel, 1996, et al.
(författare)
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Potential for improved retention rate by personalized antiseizure medication selection: A register-based analysis
- 2021
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Ingår i: Epilepsia. - : Wiley. - 0013-9580 .- 1528-1167. ; 62:9, s. 2123-2132
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Tidskriftsartikel (refereegranskat)abstract
- Objective The first antiseizure medication (ASM) is ineffective or intolerable in 50% of epilepsy cases. Selection between more than 25 available ASMs is guided by epilepsy factors, but also age and comorbidities. Randomized evidence for particular patient subgroups is seldom available. We asked whether register data could be used for retention rate calculations based on demographics, comorbidities, and ASM history, and quantified the potential improvement in retention rates of the first ASM in several large epilepsy cohorts. We also describe retention rates in patients with epilepsy after traumatic brain injury and dementia, patient groups with little available evidence. Methods We used medical, demographic, and drug prescription data from epilepsy cohorts from comprehensive Swedish registers, containing 6380 observations. By analyzing 381 840 prescriptions, we studied retention rates of first- and second-line ASMs for patients with epilepsy in multiple sclerosis (MS), brain infection, dementia, traumatic brain injury, or stroke. The rank of retention rates of ASMs was validated by comparison to published randomized control trials. We identified the optimal stratification for each brain disease, and quantified the potential improvement if all patients had received the optimal ASM. Results Using optimal stratification for each brain disease, the potential improvement in retention rate (percentage points) was MS, 20%; brain infection, 21%; dementia, 14%; trauma, 21%; and stroke, 14%. In epilepsy after trauma, levetiracetam had the highest retention rate at 80% (95% confidence interval [CI] = 65-89), exceeding that of the most commonly prescribed ASM, carbamazepine (p = .04). In epilepsy after dementia, lamotrigine (77%, 95% CI = 68-84) and levetiracetam (74%, 95% CI = 68-79) had higher retention rates than carbamazepine (p = .006 and p = .01, respectively). Significance We conclude that personalized ASM selection could improve retention rates and that national registers have potential as big data sources for personalized medicine in epilepsy.
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| 10. |
- Björnson, Elias, 1988, et al.
(författare)
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Personalized Cardiovascular Disease Prediction and Treatment-A Review of Existing Strategies and Novel Systems Medicine Tools
- 2016
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Ingår i: Frontiers in Physiology. - : Frontiers Media SA. - 1664-042X. ; 7:JAN
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Forskningsöversikt (refereegranskat)abstract
- Cardiovascular disease (CVD) continues to constitute the leading cause of death globally. CVD risk stratification is an essential tool to sort through heterogeneous populations and identify individuals at risk of developing CVD. However, applications of current risk scores have recently been shown to result in considerable misclassification of high-risk subjects. In addition, despite long standing beneficial effects in secondary prevention, current CVD medications have in a primary prevention setting shown modest benefit in terms of increasing life expectancy. A systems biology approach to CVD risk stratification may be employed for improving risk-estimating algorithms through addition of high-throughput derived omics biomarkers. In addition, modeling of personalized benefit-of-treatment may help in guiding choice of intervention. In the area of medicine, realizing that CVD involves perturbations of large complex biological networks, future directions in drug development may involve moving away from a reductionist approach toward a system level approach. Here, we review current CVD risk scores and explore how novel algorithms could help to improve the identification of risk and maximize personalized treatment benefit. We also discuss possible future directions in the development of effective treatment strategies for CVD through the use of genome-scale metabolic models (GEMs) as well as other biological network-based approaches.
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