WSGR Docket No. 53242—701301
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`WHAT IS CLAIMED IS:
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`CLAIMS
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`1. A method for determining a disorder state of brain tissue in a brain of a subject,
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`comprising:
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`(a) obtaining magnetic resonance imaging (MRI) data comprising at least one MRI
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`image of the brain, the MRI image comprising a plurality of voxels, a voxel of the
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`plurality of voxels being associated with the brain tissue of the brain of the subject
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`and comprising one or more measured MRI parameters in the MRI data;
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`(b) for the voxel of the plurality of voxels, using one or more computer processors to
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`process the one or more measured MRI parameters with one or more simulated
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`MRI parameters for the voxel, the one or more simulated MRI parameters being
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`generated from one or more microstructural models at the voxel;
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`(c) for the voxel of the plurality of voxels, selecting a diagnostic model from the one
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`or more microstructural models, the diagnostic model meeting a threshold
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`congruence between the one or more measured MRI parameters and the one or
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`more simulated MRI parameters associated with the diagnostic model; and
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`(d) for the voxel of the plurality of voxels, using the diagnostic model to determine
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`the disorder state of the brain tissue associated with the voxel.
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`2. The method of claim 1, wherein each voxel comprises a plurality of measured MRI
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`parameters.
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`3. The method of claim 2, wherein the one or more measured MRI parameters are a plurality
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`of measured MRI parameters.
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`4. The method of claim 3, wherein the one or more simulated MRI parameters are a plurality
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`of simulated MRI parameters.
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`5. The method of claim 4, further comprising repeating (b)-(d) one or more times for
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`additional voxels of the plurality of voxels.
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`6. The method of claim 5, further comprising repeating (b)-(d) for all other voxels of the
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`plurality of voxels.
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`7. The method of claim 5, further comprising repeating (b)-(d) for all voxels associated with
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`a specified region of the brain.
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`8. The method of claim 5, further comprising repeating (b)-(d) for all voxels associated with
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`an entirety of the brain.
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`9. The method of claim 5, further comprising repeating (a)-(d) for a plurality of MRI
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`images, each MRI image of the plurality of MRI images associated with a brain selected
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`WSGR Docket No. 53242—701301
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`from a plurality of brains, each brain of the plurality of brains associated with a subject
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`selected from a plurality of subjects.
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`The method of any of claims 6-9, wherein the MRI image is selected from the group
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`consisting of: a longitudinal relaxation time (T1)-weighted MRI image, a transverse
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`relaxation time (T2)-weighted MRI image, and a diffusion-weighted MRI image.
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`The method of claim 10, wherein the measured MRI parameter is selected from the group
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`consisting of: a longitudinal relaxation time (T1), a transverse relaxation time (T2), and a
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`diffusion coefficient.
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`The method of claim 11, wherein the simulated MRI parameter is selected from the group
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`consisting of: a longitudinal relaxation time (T1), a transverse relaxation time (T2), and a
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`diffusion coefficient.
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`The method of claim 12, wherein the one or more microstructural models comprise
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`information regarding a parameter selected from the group consisting of: intracellular
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`content, extracellular content, distribution of extracellular content within interstitial space,
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`distribution of intracellular content within intracellular space, and tissue geometry.
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`The method of claim 13, wherein the one or more microstructural models comprise
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`measured or predicted values of a parameter selected from the group consisting of: cell
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`density, cell shape, cell geometry, cell size, cell distribution, intercellular spacing,
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`extracellular matrix homogeneity, interstitial tortuosity, water to protein ratio, water to
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`lipid ratio, water to carbohydrate ratio, protein to lipid ratio, protein to carbohydrate ratio,
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`and lipid to carbohydrate ratio.
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`The method of claim 14, wherein the one or more microstructural models are selected
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`from a microstructural model library.
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`The method of claim 15, wherein the microstructural model library comprises at least 100
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`microstructural models.
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`The method of claim 16, wherein the microstructural model library is constructed by:
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`(a) creating a first microstructural model corresponding to a brain state that is not
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`associated with a disorder; and
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`(b) iteratively subjecting the first microstructural model to a perturbation, each
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`iteration producing an additional perturbed microstructural model.
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`The method of claim 17, wherein (b) comprises subjecting the first microstructural model
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`to at least 100 iterations to generate at least 100 perturbed microstructural models.
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`The method of claim 18, wherein the first microstructural model is selected based on
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`10.
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`11.
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`12.
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`13.
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`14.
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`15.
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`16.
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`17.
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`18.
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`19.
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`knowledge of the brain region associated with the voxel.
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`WSGR Docket No. 53242—701301
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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.
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`26.
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`27.
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`28.
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`29.
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`30.
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`31.
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`32.
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`33.
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`The method of claim 19, wherein the perturbation comprises an operation selected from
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`the group consisting of: depleting cells, altering cellular morphology or distribution,
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`altering intracellular or interstitial physico-chemical composition or distribution, altering
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`extracellular matrix composition or distribution, and altering intercellular spacing.
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`The method of claim 20, wherein the perturbation comprises a stochastic procedure.
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`The method of claim 21, wherein the threshold congruence is determined by computing
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`an objective function between the one or more measured MRI parameters and the one or
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`more simulated MRI parameters.
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`The method of claim 22, wherein the objective function comprises an L1 norm or an L2
`norm.
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`The method of claim 23, wherein determining the disorder state of the brain tissue
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`associated with the voxel is achieved at an accuracy of at least 90%.
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`The method of claim 23, wherein determining the disorder state across the brain tissue
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`associated with the specified region of the brain is achieved at an accuracy of at least
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`90%.
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`The method of claim 23, wherein determining the disorder state of the brain tissue
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`associated with the whole brain of the subject is achieved at an accuracy of at least 90%.
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`The method of claim 23, wherein determining the disorder state of the brain tissue
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`associated the plurality of subjects is achieved at an accuracy of at least 90%.
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`The method of claim 23, wherein the disorder is a non-neurodegenerative disorder.
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`The method of claim 28, wherein the disorder is selected from the group consisting of: a
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`primary neoplasm, a metastatic neoplasm, a motor neuron disease, a seizure disorder, a
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`seizure disorder with focal cortical dysplasia, multiple sclerosis, a non-neurodegenerative
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`encephalopathy, and a psychological disorder.
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`The method of any one of claim 23, wherein the disorder is a neurodegenerative disorder.
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`The method of claim 30, wherein the method enables diagnosis of a neurodegenerative
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`disorder more than 5 years prior to the development of symptoms associated with the
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`neurodegenerative disorder.
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`The method of claim 30, wherein the method enables monitoring of the
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`neurodegenerative disorder at a plurality of time points, the plurality of time points
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`separated by a plurality of time intervals.
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`The method of any one of claim 30, wherein the neurodegenerative disorder is selected
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`from the group consisting of: Alzheimer’s disease, a non-Alzheimer’s dementia disorder,
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`Parkinson’s disease, a Parkinsonism disorder, a motor neuron disease, Huntington’s
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`WSGR Docket No. 53242—701301
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`disease, a Huntington’s disease-like syndrome, transmissible spongiform encephalopathy,
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`chronic traumatic encephalopathy, and a tauopathy.
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`The method of claim 27, further comprising constructing a brain map that, for each voxel
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`of the plurality of voxels, indicates the disorder state of the brain tissue associated with
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`the voxel.
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`The method of claim 34, further comprising displaying the brain map on a graphical user
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`interface of an electronic device of a user.
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`The method of claim 34, wherein the brain map comprises a qualitative abnormality map.
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`The method of claim 34, wherein the brain map comprises a binary abnormality map.
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`The method of claim 34, wherein the brain map comprises a quantitative abnormality
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`map.
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`The method of claim 34, wherein the brain map comprises a percent abnormality map.
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`A method for determining a disorder state of a tissue in a portion of a body of a subject,
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`34.
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`35.
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`36.
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`37.
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`38.
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`39.
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`40.
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`comprising:
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`(a) obtaining magnetic resonance imaging (MRI) data comprising at least one MRI
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`image of the tissue, the MRI image comprising a plurality of voxels, a voxel of the
`
`plurality of voxels being associated with the tissue of the subject and comprising
`
`one or more measured MRI parameters in the MRI data;
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`(b) for the voxel of the plurality of voxels, using one or more computer processors to
`
`process the one or more measured MRI parameters with one or more simulated
`
`MRI parameters for the voxel, the one or more simulated MRI parameters being
`
`generated from one or more microstructural models at the voxel;
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`(c) for the voxel of the plurality of voxels, selecting a diagnostic model from the one
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`or more microstructural models, the diagnostic model meeting a threshold
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`congruence between the one or more measured MRI parameters and the one or
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`more simulated MRI parameters associated with the diagnostic model; and
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`(d) for the voxel of the plurality of voxels, using the diagnostic model to determine
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`the disorder state of the tissue associated with the voxel.
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`41.
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`The method of claim 40, wherein the tissue is selected from the group consisting of:
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`spinal cord tissue, heart tissue, vascular tissue, lung tissue, liver tissue, kidney tissue,
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`esophageal tissue, stomach tissue, intestinal tissue, pancreatic tissue, thyroid tissue,
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`adrenal tissue, spleen tissue, lymphatic tissue, appendix tissue, breast tissue, bladder
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`tissue, vaginal tissue, ovarian tissue, uterine tissue, penile tissue, testicular tissue,
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`prostatic tissue, skeletal muscle tissue, skin, and non-brain tissue of the head and neck.
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