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| 2. |
- Liu, Kui, et al.
(författare)
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Kallikrein genes are associated with lupus and glomerular basement membrane-specific antibody-induced nephritis in mice and humans
- 2009
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Ingår i: Journal of Clinical Investigation. - 0021-9738 .- 1558-8238. ; 119:4, s. 911-923
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Tidskriftsartikel (refereegranskat)abstract
- Immune-mediated nephritis contributes to disease in systemic lupus erythematosus, Goodpasture syndrome (caused by antibodies specific for glomerular basement membrane [anti-GBM antibodies]), and spontaneous lupus nephritis. Inbred mouse strains differ in susceptibility to anti-GBM antibody-induced and spontaneous lupus nephritis. This study sought to clarify the genetic and molecular factors that maybe responsible for enhanced immune-mediated renal disease in these models. When the kidneys of 3 mouse strains sensitive to anti-GBM antibody-induced nephritis were compared with those of 2 control strains using microarray analysis, one-fifth of the underexpressed genes belonged to the kallikrein gene family,which encodes serine esterases. Mouse strains that upregulated renal and urinary kallikreins exhibited less evidence of disease. Antagonizing the kallikrein pathway augmented disease, while agonists dampened the severity of anti-GBM antibody-induced nephritis. In addition, nephritis-sensitive mouse strains had kallikrein haplotypes that were distinct from those of control strains, including several regulatory polymorphisms,some of which were associated with functional consequences. Indeed, increased susceptibility to anti-GBM antibody-induced nephritis and spontaneous lupus nephritis was achieved by breeding mice with a genetic interval harboring the kallikrein genes onto a disease-resistant background. Finally, both human SLE and spontaneous lupus nephritis were found to be associated with kallikrein genes, particularly KLK1 and the KLK3 promoter, when DNA SNPs from independent cohorts of SLE patients and controls were compared. Collectively, these studies suggest that kallikreins are protective disease-associated genes in anti-GBM antibody-induced nephritis and lupus.
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| 3. |
- Manni, F, et al.
(författare)
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Hyperspectral Imaging for Glioblastoma Surgery: Improving Tumor Identification Using a Deep Spectral-Spatial Approach
- 2020
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Ingår i: Sensors (Basel, Switzerland). - : MDPI AG. - 1424-8220. ; 20:23
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Tidskriftsartikel (refereegranskat)abstract
- The primary treatment for malignant brain tumors is surgical resection. While gross total resection improves the prognosis, a supratotal resection may result in neurological deficits. On the other hand, accurate intraoperative identification of the tumor boundaries may be very difficult, resulting in subtotal resections. Histological examination of biopsies can be used repeatedly to help achieve gross total resection but this is not practically feasible due to the turn-around time of the tissue analysis. Therefore, intraoperative techniques to recognize tissue types are investigated to expedite the clinical workflow for tumor resection and improve outcome by aiding in the identification and removal of the malignant lesion. Hyperspectral imaging (HSI) is an optical imaging technique with the power of extracting additional information from the imaged tissue. Because HSI images cannot be visually assessed by human observers, we instead exploit artificial intelligence techniques and leverage a Convolutional Neural Network (CNN) to investigate the potential of HSI in twelve in vivo specimens. The proposed framework consists of a 3D–2D hybrid CNN-based approach to create a joint extraction of spectral and spatial information from hyperspectral images. A comparison study was conducted exploiting a 2D CNN, a 1D DNN and two conventional classification methods (SVM, and the SVM classifier combined with the 3D–2D hybrid CNN) to validate the proposed network. An overall accuracy of 80% was found when tumor, healthy tissue and blood vessels were classified, clearly outperforming the state-of-the-art approaches. These results can serve as a basis for brain tumor classification using HSI, and may open future avenues for image-guided neurosurgical applications.
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| 4. |
- Manni, F, et al.
(författare)
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Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery
- 2020
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Ingår i: APPLIED SCIENCES-BASEL. - : MDPI AG. - 2076-3417. ; 10:12
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of the patient. However, the reference frame can be dislodged or obscured during the surgical procedure, resulting in loss of navigation. Hyperspectral imaging (HSI) captures a large number of spectral information bands across the electromagnetic spectrum, providing image information unseen by the human eye. We aim to exploit HSI to detect skin features in a novel methodology to track patient position in navigated spinal surgery. In our approach, we adopt two local feature detection methods, namely a conventional handcrafted local feature and a deep learning-based feature detection method, which are compared to estimate the feature displacement between different frames due to motion. To demonstrate the ability of the system in tracking skin features, we acquire hyperspectral images of the skin of 17 healthy volunteers. Deep-learned skin features are detected and localized with an average error of only 0.25 mm, outperforming the handcrafted local features with respect to the ground truth based on the use of optical markers.
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| 5. |
- Manni, F, et al.
(författare)
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Towards Optical Imaging for Spine Tracking without Markers in Navigated Spine Surgery
- 2020
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Ingår i: Sensors (Basel, Switzerland). - : MDPI AG. - 1424-8220. ; 20:13
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Tidskriftsartikel (refereegranskat)abstract
- Surgical navigation systems are increasingly used for complex spine procedures to avoid neurovascular injuries and minimize the risk for reoperations. Accurate patient tracking is one of the prerequisites for optimal motion compensation and navigation. Most current optical tracking systems use dynamic reference frames (DRFs) attached to the spine, for patient movement tracking. However, the spine itself is subject to intrinsic movements which can impact the accuracy of the navigation system. In this study, we aimed to detect the actual patient spine features in different image views captured by optical cameras, in an augmented reality surgical navigation (ARSN) system. Using optical images from open spinal surgery cases, acquired by two gray-scale cameras, spinal landmarks were identified and matched in different camera views. A computer vision framework was created for preprocessing of the spine images, detecting and matching local invariant image regions. We compared four feature detection algorithms, Speeded Up Robust Feature (SURF), Maximal Stable Extremal Region (MSER), Features from Accelerated Segment Test (FAST), and Oriented FAST and Rotated BRIEF (ORB) to elucidate the best approach. The framework was validated in 23 patients and the 3D triangulation error of the matched features was < 0.5 mm. Thus, the findings indicate that spine feature detection can be used for accurate tracking in navigated surgery.
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| 6. |
- Manni, Giovanni Li, et al.
(författare)
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The OpenMolcas Web : A Community-Driven Approach to Advancing Computational Chemistry
- 2023
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Ingår i: Journal of Chemical Theory and Computation. - : American Chemical Society (ACS). - 1549-9618 .- 1549-9626. ; 19:20, s. 6933-6991
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Tidskriftsartikel (refereegranskat)abstract
- The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations.
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| 10. |
- Aquilante, Francesco, et al.
(författare)
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Molcas 8 : New capabilities for multiconfigurational quantum chemical calculations across the periodic table
- 2016
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Ingår i: Journal of Computational Chemistry. - : Wiley. - 0192-8651 .- 1096-987X. ; 37:5, s. 506-541
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Tidskriftsartikel (refereegranskat)abstract
- In this report, we summarize and describe the recent unique updates and additions to the Molcas quantum chemistry program suite as contained in release version 8. These updates include natural and spin orbitals for studies of magnetic properties, local and linear scaling methods for the Douglas-Kroll-Hess transformation, the generalized active space concept in MCSCF methods, a combination of multiconfigurational wave functions with density functional theory in the MC-PDFT method, additional methods for computation of magnetic properties, methods for diabatization, analytical gradients of state average complete active space SCF in association with density fitting, methods for constrained fragment optimization, large-scale parallel multireference configuration interaction including analytic gradients via the interface to the Columbus package, and approximations of the CASPT2 method to be used for computations of large systems. In addition, the report includes the description of a computational machinery for nonlinear optical spectroscopy through an interface to the QM/MM package Cobramm. Further, a module to run molecular dynamics simulations is added, two surface hopping algorithms are included to enable nonadiabatic calculations, and the DQ method for diabatization is added. Finally, we report on the subject of improvements with respects to alternative file options and parallelization.
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