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- Goyal, Gaurav, 1983, et al.
(författare)
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CRISPR/CAS9 BASED DNA-COMBING ASSAY FOR DETECTING ANTIMICROBIAL RESISTANCE GENES ON PLASMIDS
- 2021
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Ingår i: MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. ; , s. 801-802
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Konferensbidrag (refereegranskat)abstract
- We present a method based on CRISPR/Cas9 excision and DNA combing to detect anti-microbial resistance (AMR) genes on bacterial plasmids. The assay is inexpensive, simple, fast, and also provides information on the number and size of plasmids in a sample. We demonstrate detection of the gene encoding for the New Delhi metallobeta-lactamase 1 (blaNDM-1) enzyme, known to make bacteria resistant to a broad range of beta-lactam antibiotics.
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| 4. |
- Nyblom, My, 1995, et al.
(författare)
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Bacterial identification by optical mapping of genomic DNA in nanofluidic channels
- 2019
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Ingår i: 23rd International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019. - 9781733419000 ; , s. 821-822
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Konferensbidrag (refereegranskat)abstract
- A variety of pathogenic bacteria can infect humans and the increase in bacteria resistant to common antibiotics is a large threat to human health worldwide. This work presents a method, based on optical DNA mapping (ODM) in nanofluidic channels, that can detect the type of bacterial present in a sample by matching the obtained maps of large DNA molecules to a database of fully assembled bacterial genomes. The extraction and labelling protocol has been designed to work for both Gram-positive and Gram-negative bacteria, not requiring any prior knowledge about the sample content.
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- Wallman, Mikael, 1979, et al.
(författare)
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A Spatially Extended Model of the Human Atrioventricular Node
- 2017
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Ingår i: 2017 COMPUTING IN CARDIOLOGY (CINC). - 0276-6574. ; 44
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Konferensbidrag (refereegranskat)abstract
- The atrioventricular (AV) node plays a crucial role during many supraventricular tachycardias (SVT). To better understand its function under these complex conditions, mathematical modelling has emerged as a valuable tool. The model presented here builds on a recently published 1D model of the human AV-node, consisting of a series of interacting nodes, each with separate dynamics in refractory time and conduction delay. Here, we extend the formulation to 2D and demonstrate its ability to reproduce clinical data. Subsequently, we use it to study how AV-nodal properties for clinically assessed single and dual AV-node physiology affect activation for regular and stochastic input. In particular we study the effect of functional gradients within the AV node on ventricular response during atrial pacing and atrial fibrillation. Simulation results display important emergent features such as pathway switching and concealed conduction, and show differences in AF response that are not present in response to pacing. Simulation of a single impulse takes around 30 ms, admitting interactive use on clinical time scales as well as parameter estimation and uncertainty quantification. To our knowledge, the presented model is the first spatially extended human AV-node model, and as such represents a novel tool for understanding the human AV-nodal function in both healthy and diseased individuals, thereby paving the way for improved SVT diagnosis and therapy.
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| 7. |
- af Klinteberg, C, et al.
(författare)
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Laser-induced fluorescence diagnostics of basal cell carcinomas of the skin following topical ALA application
- 1996
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Ingår i: Optical Biopsies and Microscopic Techniques, Proceedings of. - : SPIE. - 0819423289 ; 2926, s. 32-40
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Konferensbidrag (refereegranskat)abstract
- Fourteen patients with superficial basal cell carcinomas (BCCs) and fifteen patients with nodular BCCs were investigated by means of laser-induced fluorescence (LIF) in connection with photodynamic therapy (PDT). Topical application of delta-amino levulinic acid (ALA) was performed six hours prior to the treatment session. Fluorescence spectra were recorded, using a point-monitoring system with an excitation wavelength of 405 nm. The measurements were performed in scans over the lesion and the surrounding normal skin before application of ALA, and immediately before and after the laser treatment. The selective uptake of the photosensitiser resulted in a fluorescence intensity ratio of 2.4:1 for superficial BCCs and 2.5:1 for nodular BCCs. If the fluorescence intensity was divided by the autofluorescence, this resulted in a contrast enhancement of about a factor 6 for tumour tissue. In seven patients (five with nodular BCC and two with superficial BCC), additional fluorescence measurements were performed two and four hours following the ALA application, and two hours after the PDT procedure. Thus, the kinetics of the transformation of ACA to protoporphyrin IX (PpIX) could be followed, which indicated that the synthesis of PpIX was more rapid in the tumour than in the normal tissue. After four hours, the PpIX level inside the tumour was saturated, while there still was an accumulation in the surrounding skin. The highest contrast between tumour and normal skin was reached within two hours after the ALA application.
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- Alvén, Jennifer, 1989, et al.
(författare)
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A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images
- 2019
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Ingår i: Medical Image Computing and Computer Assisted Intervention : MICCAI 2019 - 22nd International Conference, Proceedings - MICCAI 2019 - 22nd International Conference, Proceedings. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783030322458 - 9783030322441 ; 11765 LNCS, s. 355-363
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Konferensbidrag (refereegranskat)abstract
- The procedure of aligning a positron emission tomography (PET) image with a common coordinate system, spatial normalization, typically demands a corresponding structural magnetic resonance (MR) image. However, MR imaging is not always available or feasible for the subject, which calls for enabling spatial normalization without MR, MR-less spatial normalization. In this work, we propose a template-free approach to MR-less spatial normalization for [18F]flortaucipir tau PET images. We use a deep neural network that estimates an aligning transformation from the PET input image, and outputs the spatially normalized image as well as the parameterized transformation. In order to do so, the proposed network iteratively estimates a set of rigid and affine transformations by means of convolutional neural network regressors as well as spatial transformer layers. The network is trained and validated on 199 tau PET volumes with corresponding ground truth transformations, and tested on two different datasets. The proposed method shows competitive performance in terms of registration accuracy as well as speed, and compares favourably to previously published results.
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