Unraveling a rare cause of spinal stenosis: Coexistent AL and ATTR amyloidosis involving the ligamentum flavum

Background: Amyloidosis is a protein misfolding disorder that leads to the deposition of beta-pleated sheets of a fibrillar derivative of various protein precursors. Identification of the type of precursor protein is integral in treatment decision-making. The presence of two different types of amyloid in the same patient is unusually rare, and there are no previous reports of two different types of amyloid deposition in the ligamentum flavum (LF) in the same patient. Case Description: Here, we describe two patients with spinal stenosis who underwent laminectomies and were found to have AL and ATTR amyloid deposits in the LF. Conclusion: As the spine is becoming recognized as a site for ATTRwt amyloid deposition, patients undergoing spinal decompression surgery may potentially benefit from evaluation for amyloidosis in the LF.

Amyloid Positive Hydrocephalus: A Hydrocephalic Variant of Alzheimer’s Disease?

Background: Alzheimer’s disease (AD) and normal pressure hydrocephalus (NPH) commonly coexist. Objective: We aimed to characterize an overlapping syndrome of AD and NPH that presents with gait disturbance, ventriculomegaly on magnetic resonance imaging, and significant amyloid deposition on positron emission tomography (PET). Methods: Of 114 patients who underwent cerebrospinal fluid (CSF) drainage for a possible diagnosis of NPH between 2015 and 2020 in Samsung Medical Center, we identified 24 patients (21.1%) with the NPH patients with amyloid deposition on PET, which we referred to as hydrocephalic AD in this study. We compared their clinical and imaging findings with those of 123 typical AD without hydrocephalic signs/symptoms. We also investigated the frequency and potential predictors of the tap test response in hydrocephalic AD. Results: Evans’ index was 0.36±0.03, and a disproportionately enlarged subarachnoid space was present in 54.2% of the hydrocephalic AD patients. The mean age (75.2±7.3 years) and the APOE4 frequency (68.2%) did not differ from those of AD controls. However, the hydrocephalic AD patients showed better memory and language performance, and a thinner cingulate cortex. About 42% of the hydrocephalic AD patients responded to the tap test, of whom seven underwent shunt surgery. Cognition did not improve, whereas gait improved after shunt surgery in all. Conclusion: Hydrocephalic AD has different neuropsychological and imaging characteristics from typical AD. Future studies are warranted to further investigate the effect of CSF removal on their clinical course and to elucidate the pathophysiological interaction between amyloid and NPH.

Molecular Mechanisms of Cardiac Amyloidosis

Cardiac involvement has a profound effect on the prognosis of patients with systemic amyloidosis. Therapeutic methods for suppressing the production of causative proteins have been developed for ATTR amyloidosis and AL amyloidosis, which show cardiac involvement, and the prognosis has been improved. However, a method for removing deposited amyloid has not been established. Methods for reducing cytotoxicity caused by amyloid deposition and amyloid precursor protein to protect cardiovascular cells are also needed. In this review, we outline the molecular mechanisms and treatments of cardiac amyloidosis.

Mangostanaxanthone IV Ameliorates Streptozotocin-Induced Neuro-Inflammation, Amyloid Deposition, and Tau Hyperphosphorylation via Modulating PI3K/Akt/GSK-3β Pathway

Alzheimer’s disease (AD), a progressive neurodegenerative disorder, is characterized by amyloid deposition and neurofibrillary tangles formation owing to tau protein hyperphosphorylation. Intra-cerebroventricular (ICV) administration of streptozotocin (STZ) has been widely used as a model of sporadic AD as it mimics many neuro-pathological changes witnessed in this form of AD. In the present study, mangostanaxanthone IV (MX-IV)-induced neuro-protective effects in the ICV-STZ mouse model were investigated. STZ (3 mg/kg, ICV) was injected once, followed by either MX-IV (30 mg/kg/day, oral) or donepezil (2.5 mg/kg/day, oral) for 21 days. Treatment with MX-IV diminished ICV-STZ-induced oxidative stress, neuro-inflammation, and apoptosis which was reflected by a significant reduction in malondialdehyde (MDA), hydrogen peroxide (H2O2), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) brain contents contrary to increased glutathione (GSH) content. Moreover, nicotinamide adenine dinucleotide phosphate (NADPH) oxidase content and cleaved caspase-3 activity were reduced together with a marked decrement in amyloid plaques number and phosphorylated tau expression via PI3K/Akt/GSK-3β pathway modulation, leading to obvious enhancement in neuronal survival and cognition. Therefore, MX-IV is deemed as a prosperous nominee for AD management with obvious neuro-protective effects that were comparable to the standard drug donepezil.

Machine learning quantification of amyloid deposits in histological images of ligamentum flavum

ABSTRACTBackgroundWild-type transthyretin amyloidosis (ATTRwt) is an underdiagnosed and potentially fatal disease. Interestingly, ATTRwt deposits have been found to deposit in the ligamentum flavum (LF) of patients with lumbar spinal stenosis prior to the development of systemic and cardiac amyloidosis. In order to study this phenomenon and its possible relationship with LF thickening and systemic amyloidosis, a precise method of quantifying amyloid deposits in histological slides of LF is critical. However, such a method is currently unavailable. Here, we present a machine learning quantification method with Trainable Weka Segmentation (TWS) to assess amyloid deposition in histological slides of LF.MethodsImages of ligamentum flavum specimens stained with Congo red are obtained from spinal stenosis patients undergoing laminectomies and confirmed to be positive for ATTRwt. Amyloid deposits in these specimens are classified and quantified by TWS through training the algorithm via user-directed annotations on images of LF. TWS can also be automated through exposure to a set of training images with user-directed annotations, and then application to a set of new images without additional annotations. Additional methods of color thresholding and manual segmentation are also used on these images for comparison to TWS.ResultsWe develop the use of TWS in images of LF and demonstrate its potential for automated quantification. TWS is strongly correlated with manual segmentation in the training set of images with user-directed annotations (R = 0.98; p = 0.0033) as well as in the application set of images where TWS was automated (R = 0.94; p = 0.016). Color thresholding was weakly correlated with manual segmentation in the training set of images (R = 0.78; p = 0.12) and in the application set of images (R = 0.65; p = 0.23).ConclusionTWS machine learning closely correlates with the gold standard comparator of manual segmentation and outperforms the color thresholding method. This novel machine learning method to quantify amyloid deposition in histological slides of ligamentum flavum is a precise, objective, accessible, high throughput, and powerful tool that will hopefully pave the way towards future research and clinical applications.