Modulation of γ-Secretase Reduces β-Amyloid Deposition in a Transgenic Mouse Model of Alzheimer's Disease

SummaryAlzheimer's disease (AD) is characterized pathologically by the abundance of senile plaques and neurofibrillary tangles in the brain. We synthesized over 1200 novel gamma-secretase modulator (GSM) compounds that reduced Aβ42 levels without inhibiting epsilon-site cleavage of APP and Notch, the generation of the APP and Notch intracellular domains, respectively. These compounds also reduced Aβ40 levels while concomitantly elevating levels of Aβ38 and Aβ37. Immobilization of a potent GSM onto an agarose matrix quantitatively recovered Pen-2 and to a lesser degree PS-1 NTFs from cellular extracts. Moreover, oral administration (once daily) of another potent GSM to Tg 2576 transgenic AD mice displayed dose-responsive lowering of plasma and brain Aβ42; chronic daily administration led to significant reductions in both diffuse and neuritic plaques. These effects were observed in the absence of Notch-related changes (e.g., intestinal proliferation of goblet cells), which are commonly associated with repeated exposure to functional gamma-secretase inhibitors (GSIs)

Glycosphingolipid synthesis inhibition limits osteoclast activation and myeloma bone disease

Glycosphingolipids (GSLs) are essential constituents of cell membranes and lipid rafts and can modulate signal transduction events. The contribution of GSLs in osteoclast (OC) activation and osteolytic bone diseases in malignancies such as the plasma cell dyscrasia multiple myeloma (MM) is not known. Here, we tested the hypothesis that pathological activation of OCs in MM requires de novo GSL synthesis and is further enhanced by myeloma cell-derived GSLs. Glucosylceramide synthase (GCS) inhibitors, including the clinically approved agent N-butyl-deoxynojirimycin (NB-DNJ), prevented OC development and activation by disrupting RANKL-induced localization of TRAF6 and c-SRC into lipid rafts and preventing nuclear accumulation of transcriptional activator NFATc1. GM3 was the prevailing GSL produced by patient-derived myeloma cells and MM cell lines, and exogenous addition of GM3 synergistically enhanced the ability of the pro-osteoclastogenic factors RANKL and insulin-like growth factor 1 (IGF-1) to induce osteoclastogenesis in precursors. In WT mice, administration of GM3 increased OC numbers and activity, an effect that was reversed by treatment with NB-DNJ. In a murine MM model, treatment with NB-DNJ markedly improved osteolytic bone disease symptoms. Together, these data demonstrate that both tumor-derived and de novo synthesized GSLs influence osteoclastogenesis and suggest that NB-DNJ may reduce pathological OC activation and bone destruction associated with MM

A case of hyperzincemia with functional zinc depletion: a new disorder?

We report the case of an 11-y-old boy with a plasma Zn concentration greater than 200 μmol/L, but with symptoms consistent with Zn deficiency. He has had hepatosplenomegaly, rashes, stunted growth (<3rd centile), anemia, and impaired immune function since infancy. He also has vasculitis and osteoporosis. A plasma Zn-binding protein has been separated and characterized by a combination of size exclusion and ion exchange chromatography and electrophoretic studies and by immunologic methods. Antibodies to the partially purified protein have been raised in rabbits. Size exclusion chromatography shows that Zn is bound to a protein with a mass 110 000-300 000 kD. Electrophoretic and mass spectrometry studies suggest that the protein may be composed of several subunits. One component of the isolated protein reacts with antiserum to α2-macroglobulin; immunoprecipitation studies confirm that the protein is not α2-macroglobulin or a histidine-rich glycoprotein. Kinetic studies of zinc metabolism in the patient and his mother with stable Zn isotopes show the presence of increased exchangeable Zn, with a rapid flux from plasma to a stable pool. Liver and muscle Zn and Cu concentrations are raised, but with no abnormal liver histology. Immunoreactive metallothionein in the liver is increased. We suggest that this boy may suffer from a previously unrecognized inborn error of Zn metabolism causing symptomatic zinc deficiency

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings