Cardiac Amyloidosis: A Review of Current Imaging Techniques

Systemic amyloidosis is a rare, heterogenous group of diseases characterized by extracellular infiltration and deposition of amyloid fibrils. Cardiac amyloidosis (CA) occurs when these fibrils deposit within the myocardium. Untreated, this inevitably leads to progressive heart failure and fatality. Historically, treatment has remained supportive, however, there are now targeted disease-modifying therapeutics available to patients with CA. Advances in echocardiography, cardiac magnetic resonance (CMR) and repurposed bone scintigraphy have led to a surge in diagnoses of CA and diagnosis at an earlier stage of the disease natural history. CMR has inherent advantages in tissue characterization which has allowed us to better understand the pathological disease process behind CA. Combined with specialist assessment and repurposed bone scintigraphy, diagnosis of CA can be made without the need for invasive histology in a significant proportion of patients. With existing targeted therapeutics, and novel agents being developed, understanding these imaging modalities is crucial to achieving early diagnosis for patients with CA. This will allow for early treatment intervention, accurate monitoring of disease course over time, and thereby improve the length and quality of life of patients with a disease that historically had an extremely poor prognosis. In this review, we discuss key radiological features of CA, focusing on the two most common types; immunoglobulin light chain (AL) and transthyretin (ATTR) CA. We highlight recent advances in imaging techniques particularly in respect of their clinical application and utility in diagnosis of CA as well as for tracking disease change over time.

Preclinical and Clinical Studies of p38α MAP kinase inhibition to Treat Basal Forebrain Cholinergic Dysfunction and Degeneration

The endosome-associated protein Rab5 is a central player in the molecular mechanisms leading to degeneration of basal forebrain cholinergic neurons (BFCN), a long-standing target for drug development. As p38α kinase is a Rab-5 activator, we hypothesized that inhibition of this kinase held potential as an approach to treat diseases associated with BFCN loss. Herein we report that treatment with an oral small molecule p38α kinase inhibitor reversed pathological disease progression in the basal forebrain in a mouse model that develops BFCN degeneration. Further, the preclinical results were successfully translated to the clinic, with improvement of clinical outcomes associated with cholinergic function in a clinical study in dementia with Lewy bodies (DLB), a disease in which BFCN dysfunction and degeneration is the primary driver of disease expression. The findings both advances a novel approach to treating DLB and validates the translational platform that provided the mechanistic rationale for advancing that approach.

Nebulized delivery of a broadly neutralizing SARS-CoV-2 RBD-specific nanobody prevents clinical, virological and pathological disease in a Syrian hamster model of COVID-19

There remains an unmet need for globally deployable, low-cost therapeutics for the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Previously, we reported on the isolation and in vitro characterization of a potent single-domain nanobody, NIH-CoVnb-112, specific for the receptor binding domain (RBD) of SARS-CoV-2. Here, we report on the molecular basis for the observed broad in vitro neutralization capability of NIH-CoVnb-112 against variant SARS-CoV-2 pseudoviruses, including the currently dominant Delta variant. The structure of NIH-CoVnb-112 bound to SARS-CoV-2 RBD reveals a large contact surface area overlapping the angiotensin converting enzyme 2 (ACE2) binding site, which is largely unencumbered by the common RBD mutations. In an in vivo pilot study, we demonstrate effective reductions in weight loss, viral burden, and lung pathology in a Syrian hamster model of COVID-19 following nebulized delivery of NIH-CoVnb-112. These findings support the further development of NIH-CoVnb-112 as a potential adjunct preventative therapeutic for the treatment of SARS-CoV-2 infection.

The Adverse Impact of the COVID-19 Pandemic on Abdominal Emergencies: A Retrospective Clinico-Pathological Analysis

The COVID-19 pandemic has caused a worldwide significant drop of admissions to the emergency department (ED). The aim of the study was to retrospectively investigate the pandemic impact on ED admissions, management, and severity of three abdominal emergencies (appendicitis, diverticulitis, and cholecystitis) during the COVID-19 pandemic using 2017–2019 data as a control. The difference in clinical and pathological disease severity was the primary outcome measure while differences in (i) ED admissions, (ii) triage urgency codes, and (iii) surgical rates were the second ones. Overall, ED admissions for the selected conditions decreased by 34.9% during the pandemic (control: 996, 2020: 648) and lower triage urgency codes were assigned for cholecystitis (control: 170/556, 2020: 66/356, p < 0.001) and appendicitis (control: 40/178, 2020: 21/157, p = 0.031). Less surgical procedures were performed in 2020 (control: 447, 2020: 309), but the surgical rate was stable (47.7% in 2020 vs. 44.8% in 2017–2019). Considering the clinical and pathological assessments, a higher percentage of severe cases was observed in the four pandemic peak months of 2020 (control: 98/192, 2020: 87/109; p < 0.001 and control: 105/192, 2020: 87/109; p < 0.001). For the first time in this study, pathological findings objectively demonstrated an increased disease severity of the analyzed conditions during the early COVID-19 pandemic.

A Whole Slide Image Managing Library Based on Fastai for Deep Learning in the Context of Histopathology: Two Use-Cases Explained

Background: Processing whole-slide images (WSI) to train neural networks can be intricate and laborious. We developed an open-source library covering recurrent tasks in processing of WSI and in evaluating the performance of the trained networks for classification tasks. Methods: Two histopathology use-cases were selected. First we aimed to train a CNN to distinguish H&amp;amp;E-stained slides obtained from neuropathologically classified low-grade epilepsy-associated dysembryoplastic neuroepithelial tumor (DNET) and ganglioglioma (GG). The second project we trained a convolutional neural network (CNN) to predict the hormone expression of pituitary adenoms only from hematoxylin and eosin (H&amp;amp;E) stained slides. In the same approach, we addressed the issue to also predict clinically silent corticotroph adenoma. We included four clinico-pathological disease conditions in a multilabel approach. Results: Our best performing CNN achieved an area under the curve (AUC) of 0.97 for the receiver operating characteristic (ROC) for corticotroph adenoma, 0.86 for silent corticotroph adenoma and 0.98 for gonadotroph adenoma. Our DNET-GG classifier achieved an AUC of 1.00 for the ROC curve. All scores were calculated with the help of our library on predictions on a case basis. Conclusions: Our comprehensive library is most helpful to standardize the work-flow and minimize the work-burden in training CNN. It is also compatible with fastai. Indeed, our new CNNs reliably extracted neuropathologically relevant information from the H&amp;amp;E staining only. This approach will supplement the clinico-pathological diagnosis of brain tumors, which is currently based on cost-intense microscopic examination and variable panels of immunohistochemical stainings.

A Whole Slide Image Managing Library Based on Fastai for Deep Learning in the Context of Histopathology: Two Use-Cases Explained

Background: Processing whole-slide images (WSI) to train neural networks can be intricate and laborious. We developed an open-source library covering recurrent tasks in processing of WSI and in evaluating the performance of the trained networks for classification tasks. Methods: Two histopathology use-cases were selected. First we aimed to train a CNN to distinguish H&amp;amp;E-stained slides obtained from neuropathologically classified low-grade epilepsy-associated dysembryoplastic neuroepithelial tumor (DNET) and ganglioglioma (GG). The second project we trained a convolutional neural network (CNN) to predict the hormone expression of pituitary adenoms only from hematoxylin and eosin (H&amp;amp;E) stained slides. In the same approach, we addressed the issue to also predict clinically silent corticotroph adenoma. We included four clinico-pathological disease conditions in a multilabel approach. Results: Our best performing CNN achieved an area under the curve (AUC) of 0.97 for the receiver operating characteristic (ROC) for corticotroph adenoma, 0.86 for silent corticotroph adenoma and 0.98 for gonadotroph adenoma. Our DNET-GG classifier achieved an AUC of 1.00 for the ROC curve. All scores were calculated with the help of our library on predictions on a case basis. Conclusions: Our comprehensive library is most helpful to standardize the work-flow and minimize the work-burden in training CNN. It is also compatible with fastai. Indeed, our new CNNs reliably extracted neuropathologically relevant information from the H&amp;amp;E staining only. This approach will supplement the clinico-pathological diagnosis of brain tumors, which is currently based on cost-intense microscopic examination and variable panels of immunohistochemical stainings.

A six-metabolite panel as potential blood-based biomarkers for Parkinson’s disease

Characterisation and diagnosis of idiopathic Parkinson’s disease (iPD) is a current challenge that hampers both clinical assessment and clinical trial development with the potential inclusion of non-PD cases. Here, we used a targeted mass spectrometry approach to quantify 38 metabolites extracted from the serum of 231 individuals. This cohort is currently one of the largest metabolomic studies including iPD patients, drug-naïve iPD, healthy controls and patients with Alzheimer’s disease as a disease-specific control group. We identified six metabolites (3-hydroxykynurenine, aspartate, beta-alanine, homoserine, ornithine (Orn) and tyrosine) that are significantly altered between iPD patients and control participants. A multivariate model to predict iPD from controls had an area under the curve (AUC) of 0.905, with an accuracy of 86.2%. This panel of metabolites may serve as a potential prognostic or diagnostic assay for clinical trial prescreening, or for aiding in diagnosing pathological disease in the clinic.

Therapeutically leveraging GABAA receptors in cancer

γ-aminobutyric acid or GABA is an amino acid that functionally acts as a neurotransmitter and is critical to neurotransmission. GABA is also a metabolite in the Krebs cycle. It is therefore unsurprising that GABA and its receptors are also present outside of the central nervous system, including in immune cells. This observation suggests that GABAergic signaling impacts events beyond brain function and possibly human health beyond neurological disorders. Indeed, GABA receptor subunits are expressed in pathological disease states, including in disparate cancers. The role that GABA and its receptors may play in cancer development and progression remains unclear. If, however, those cancers have functional GABA receptors that participate in GABAergic signaling, it raises an important question whether these signaling pathways might be targetable for therapeutic benefit. Herein we summarize the effects of modulating Type-A GABA receptor signaling in various cancers and highlight how Type-A GABA receptors could emerge as a novel therapeutic target in cancer.