Evaluating Response to High-Dose 13.3 mg/24 h Rivastigmine Patch in Patients with Severe Alzheimer's Disease

AIMS: To identify factors predicting improvement/stabilization on the Alzheimer's Disease Cooperative Study-Clinical Global Impression of Change (ADCS-CGIC) and investigate whether early treatment responses can predict long-term outcomes, during a trial of 13.3 mg/24 h versus 4.6 mg/24 h rivastigmine patch in patients with severe Alzheimer's disease (AD). METHODS: Logistic regression was used to relate Week 24 ADCS-CGIC score to potential baseline predictors. Additional analyses based on receiver-operating characteristic curves were performed using Week 8/16 ADCS-CGIC scores to predict response (13.3 mg/24 h patch) at Week 24. ADCS-CGIC score of (1) 1-3 = "improvement," (2) 1-4 = "improvement or no change". RESULTS: "Treatment" (13.3 mg/24 h patch) and increased age were significant predictors of "improvement" (P = 0.01 and P = 0.003, respectively), and "treatment" (P = 0.001), increased age (P = 0.002), and prior AD treatment (P = 0.03) for "improvement or no change". At Week 8 and 16, ADCS-CGIC scores of 4 and 5 were optimal thresholds in predicting "improvement," and "improvement or no change," respectively, at Week 24. CONCLUSIONS: A significant therapeutic effect of high-dose rivastigmine patch on ADCS-CGIC response was observed. The 13.3 mg/24 h patch was identified as a predictor of "improvement" or "improvement or no change". Patients with minimal worsening/improvement/no change after treatment initiation may be more likely to respond following long-term therapy

A 24-week, randomized, controlled trial of rivastigmine patch 13.3 mg/24 h versus 4.6 mg/24 h in severe Alzheimer's dementia

AIMS: The 24-week, prospective, randomized, double-blind ACTION study investigated the efficacy, safety, and tolerability of 13.3 versus 4.6 mg/24 h rivastigmine patch in patients with severe Alzheimer's disease (AD). METHODS: Patients had probable AD and Mini-Mental State Examination scores ≥3-≤12. Primary outcome measures were as follows: Severe Impairment Battery (SIB) and AD Cooperative Study-Activities of Daily Living scale-Severe Impairment Version (ADCS-ADL-SIV). Secondary outcomes were as follows: ADCS-Clinical Global Impression of Change (ADCS-CGIC), 12-item Neuropsychiatric Inventory (NPI-12), and safety/tolerability. RESULTS: Of 1014 patients screened, 716 were randomized to 13.3 mg/24 h (N = 356) or 4.6 mg/24 h (N = 360) patch. Baseline characteristics/demographics were comparable. Completion rates were as follows: 64.3% (N = 229) with 13.3 mg/24 h and 65.0% (N = 234) with 4.6 mg/24 h patch. The 13.3 mg/24 h patch was significantly superior to 4.6 mg/24 h patch on cognition (SIB) and function (ADCS-ADL-SIV) at Week 16 (P < 0.0001 and P = 0.049, respectively) and 24 (primary endpoint; P < 0.0001 and P = 0.025). Significant between-group differences (Week 24) were observed on the ADCS-CGIC (P = 0.0023), not NPI-12 (P = 0.1437). A similar proportion of the 13.3 mg/24 h and 4.6 mg/24 h patch groups reported adverse events (AEs; 74.6% and 73.3%, respectively) and serious AEs (14.9% and 13.6%). CONCLUSIONS: The 13.3 mg/24 h patch demonstrated superior efficacy to 4.6 mg/24 h patch on SIB and ADCS-ADL-SIV, without marked increase in AEs, suggesting higher-dose patch has a favorable benefit-to-risk profile in severe AD

A 24-Week, Open-Label Extension Study to Investigate the Long-term Safety, Tolerability, and Efficacy of 13.3 mg/24 h Rivastigmine Patch in Patients With Severe Alzheimer Disease

The long-term safety, tolerability, and efficacy of high-dose 13.3 mg/24 h rivastigmine patch in severe Alzheimer disease was evaluated in a 24-week, open-label extension to the double-blind ACTION study. Safety and tolerability, and efficacy on the Alzheimer’s Disease Cooperative Study-Activities of Daily Living scale-Severe Impairment Version (ADCS-ADL-SIV), Severe Impairment Battery (SIB), and ADCS-Clinical Global Impression of Change (ADCS-CGIC) were assessed. Overall, 197 patients continued on 13.3 mg/24 h patch; 199 uptitrated from 4.6 mg/24 h to 13.3 mg/24 h patch. The incidence of adverse events (AEs), serious AEs and discontinuations due to AEs was similar in patients who continued on, and patients who uptitrated to, 13.3 mg/24 h patch (AEs: 57.9% and 59.8%; serious AEs: 16.2% and 16.1%; discontinuations: 11.2% and 12.1%, respectively). Larger mean changes from double-blind baseline were observed in patients uptitrated on the ADCS-ADL-SIV (−4.6; SD=8.7) and SIB (−7.0; SD=16.6), than those who continued on 13.3 mg/24 h patch (−3.9; SD=8.0 and −4.7; SD=16.8, respectively). ADCS-CGIC scores were comparable. There were no clinically relevant between-group differences in safety and tolerability. Greater decline was observed in patients with delayed uptitration to high-dose 13.3 mg/24 h patch than patients who continued on high-dose patch

Denitrifying Bacteria Active in Woodchip Bioreactors at Low-Temperature Conditions

Woodchip bioreactor technology removes nitrate from agricultural subsurface drainage by using denitrifying microorganisms. Although woodchip bioreactors have demonstrated success in many field locations, low water temperature can significantly limit bioreactor efficiency and performance. To improve bioreactor performance, it is important to identify the microbes responsible for nitrate removal at low temperature conditions. Therefore, in this study, we identified and characterized denitrifiers active at low-temperature conditions by using culture-independent and -dependent approaches. By comparative 16S rRNA (gene) analysis and culture isolation technique, Pseudomonas spp., Polaromonas spp., and Cellulomonas spp. were identified as being important bacteria responsible for denitrification in woodchip bioreactor microcosms at relatively low temperature conditions (15°C). Genome analysis of Cellulomonas sp. strain WB94 confirmed the presence of nitrite reductase gene nirK. Transcription levels of this nirK were significantly higher in the denitrifying microcosms than in the non-denitrifying microcosms. Strain WB94 was also capable of degrading cellulose and other complex polysaccharides. Taken together, our results suggest that Cellulomonas sp. denitrifiers could degrade woodchips to provide carbon source and electron donors to themselves and other denitrifiers in woodchip bioreactors at low-temperature conditions. By inoculating these denitrifiers (i.e., bioaugmentation), it might be possible to increase the nitrate removal rate of woodchip bioreactors at low-temperature conditions

A Clinical Trial to Validate Event-Related Potential Markers of Alzheimer\u27s Disease in Outpatient Settings

INTRODUCTION: We investigated whether event-related potentials (ERP) collected in outpatient settings and analyzed with standardized methods can provide a sensitive and reliable measure of the cognitive deficits associated with early Alzheimer\u27s disease (AD). METHODS: A total of 103 subjects with probable mild AD and 101 healthy controls were recruited at seven clinical study sites. Subjects were tested using an auditory oddball ERP paradigm. RESULTS: Subjects with mild AD showed lower amplitude and increased latency for ERP features associated with attention, working memory, and executive function. These subjects also had decreased accuracy and longer reaction time in the target detection task associated with the ERP test. DISCUSSION: Analysis of ERP data showed significant changes in subjects with mild AD that are consistent with the cognitive deficits found in this population. The use of an integrated hardware/software system for data acquisition and automated data analysis methods make administration of ERP tests practical in outpatient settings

Florbetapir F 18 amyloid PET and 36-month cognitive decline:a prospective multicenter study

This study was designed to evaluate whether subjects with amyloid beta (Aβ) pathology, detected using florbetapir positron emission tomorgraphy (PET), demonstrated greater cognitive decline than subjects without Aβ pathology. Sixty-nine cognitively normal (CN) controls, 52 with recently diagnosed mild cognitive impairment (MCI) and 31 with probable Alzheimer's disease (AD) dementia were included in the study. PET images obtained in these subjects were visually rated as positive (Aβ+) or negative (Aβ−), blind to diagnosis. Fourteen percent (10/69) of CN, 37% (19/52) of MCI and 68% (21/31) of AD were Aβ+. The primary outcome was change in ADAS-Cog score in MCI subjects after 36 months; however, additional outcomes included change on measures of cognition, function and diagnostic status. Aβ+ MCI subjects demonstrated greater worsening compared with Aβ− subjects on the ADAS-Cog over 36 months (5.66±1.47 vs −0.71±1.09, P=0.0014) as well as on the mini-mental state exam (MMSE), digit symbol substitution (DSS) test, and a verbal fluency test (P<0.05). Similar to MCI subjects, Aβ+ CN subjects showed greater decline on the ADAS-Cog, digit-symbol-substitution test and verbal fluency (P<0.05), whereas Aβ+ AD patients showed greater declines in verbal fluency and the MMSE (P<0.05). Aβ+ subjects in all diagnostic groups also showed greater decline on the CDR-SB (P<0.04), a global clinical assessment. Aβ+ subjects did not show significantly greater declines on the ADCS-ADL or Wechsler Memory Scale. Overall, these findings suggest that in CN, MCI and AD subjects, florbetapir PET Aβ+ subjects show greater cognitive and global deterioration over a 3-year follow-up than Aβ− subjects do

Study of Leading Hadrons in Gluon and Quark Fragmentation

The study of quark jets in e+e- reactions at LEP has demonstrated that the hadronisation process is reproduced well by the Lund string model. However, our understanding of gluon fragmentation is less complete. In this study enriched quark and gluon jet samples of different purities are selected in three-jet events from hadronic decays of the Z collected by the DELPHI experiment in the LEP runs during 1994 and 1995. The leading systems of the two kinds of jets are defined by requiring a rapidity gap and their sum of charges is studied. An excess of leading systems with total charge zero is found for gluon jets in all cases, when compared to Monte Carlo Simulations with JETSET (with and without Bose-Einstein correlations included) and ARIADNE. The corresponding leading systems of quark jets do not exhibit such an excess. The influence of the gap size and of the gluon purity on the effect is studied and a concentration of the excess of neutral leading systems at low invariant masses (<~ 2 GeV/c^2) is observed, indicating that gluon jets might have an additional hitherto undetected fragmentation mode via a two-gluon system. This could be an indication of a possible production of gluonic states as predicted by QCD.Comment: 19 pages, 6 figures, Accepted by Phys. Lett.

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states

Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer’s disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, ‘shape connections’ between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus

Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1–3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature