A Two-Study Comparison of Clinical and MRI Markers of Transition from Mild Cognitive Impairment to Alzheimer's Disease

A published predictor model in a single-site cohort study (questionable dementia, QD) that contained episodic verbal memory (SRT total recall), informant report of function (FAQ), and MRI measures was tested using logistic regression and ROC analyses with comparable measures in a second multisite cohort study (Alzheimer's Disease Neuroimaging Initiative, ADNI). There were 126 patients in QD and 282 patients in ADNI with MCI followed for 3 years. Within each sample, the differences in AUCs between the statistical models were very similar. Adding hippocampal and entorhinal cortex volumes to the model containing AVLT/SRT, FAQ, age and MMSE increased the area under the curve (AUC) in ADNI but not QD, with sensitivity increasing by 2% in ADNI and 2% in QD for a fixed specificity of 80%. Conversely, adding episodic verbal memory (SRT/AVLT) and FAQ to the model containing age, Mini Mental State Exam (MMSE), hippocampal and entorhinal cortex volumes increased the AUC in ADNI and QD, with sensitivity increasing by 17% in ADNI and 10% in QD for 80% specificity. The predictor models showed similar differences from each other in both studies, supporting independent validation. MRI hippocampal and entorhinal cortex volumes showed limited added predictive utility to memory and function measures

Understanding depletion forces beyond entropy

The effective interaction energy of a colloidal sphere in a suspension containing small amounts of non-ionic polymers and a flat glass surface has been measured and calculated using total internal reflection microscopy (TIRM) and a novel approach within density functional theory (DFT), respectively. Quantitative agreement between experiment and theory demonstrates that the resulting repulsive part of the depletion forces cannot be interpreted entirely in terms of entropic arguments but that particularly at small distances ($\lesssim$ 100 nm) attractive dispersion forces have to be taken into account

Polymer Induced Bundling of F-actin and the Depletion Force

The inert polymer polyethylene glycol (PEG) induces a "bundling" phenomenon in F-actin solutions when its concentration exceeds a critical onset value C_o. Over a limited range of PEG molecular weight and ionic strength, C_o can be expressed as a function of these two variables. The process is reversible, but hysteresis is also observed in the dissolution of the bundles, with ionic strength having a large influence. Additional actin filaments are able to join previously formed bundles. Little, if any, polymer is associated with the bundle structure. Continuum estimates of the Asakura-Oosawa depletion force, Coulomb repulsion, and van der Waals potential are combined for a partial explanation of the bundling effect and hysteresis. Conjectures are presented concerning the apparent limit in bundle size

PET Network Abnormalities and Cognitive Decline in Patients with Mild Cognitive Impairment

Temporoparietal and posterior cingulate metabolism deficits characterize patients with Alzheimer's disease (AD). A H(2)(15)O resting PET scan covariance pattern, derived by using multivariate techniques, was previously shown to discriminate 17 mild AD patients from 16 healthy controls. This AD covariance pattern revealed hypoperfusion in bilateral inferior parietal lobule and cingulate; and left middle frontal, inferior frontal, precentral, and supramarginal gyri. The AD pattern also revealed hyperperfusion in bilateral insula, lingual gyri, and cuneus; left fusiform and superior occipital gyri; and right parahippocampal gyrus and pulvinar. In an independent sample of 23 outpatients with mild cognitive impairment (MCI) followed at 6-month intervals, the AD pattern score was evaluated as a predictor of cognitive decline. In this MCI sample, an H2(15)O resting PET scan was carried out at baseline. Mean duration of follow-up was 48.8 (SD 15.5) months, during which time six of 23 MCI patients converted to AD. In generalized estimating equations (GEE) analyses, controlling for age, sex, education, and baseline neuropsychological scores, increased AD pattern score was associated with greater decline in each neuropsychological test score over time (Mini Mental State Exam, Selective Reminding Test delayed recall, Animal Naming, WAIS-R digit symbol; Ps<0.01-0.001). In summary, a resting PET covariance pattern previously reported to discriminate AD patients from control subjects was applied prospectively to an independent sample of MCI patients and found to predict cognitive decline. Independent replication in larger samples is needed before clinical application can be considere

An exploratory study of factors that affect the performance and usage of rapid diagnostic tests for malaria in the Limpopo Province, South Africa

<p>Abstract</p> <p>Background</p> <p>Malaria rapid diagnostic tests (RDTs) are relatively simple to perform and provide results quickly for making treatment decisions. However, the accuracy and application of RDT results depends on several factors such as quality of the RDT, storage, transport and end user performance. A cross sectional survey to explore factors that affect the performance and use of RDTs was conducted in the primary care facilities in South Africa.</p> <p>Methods</p> <p>This study was conducted in three malaria risk sub-districts of the Limpopo Province, in South Africa. Twenty nurses were randomly selected from 17 primary health care facilities, three nurses from hospitals serving the study area and 10 other key informants, representing the managers of the malaria control programmes, routine and research laboratories, were interviewed, using semi-structured questionnaires.</p> <p>Results</p> <p>There was a high degree of efficiency in ordering and distribution of RDTs, however only 13/20 (65%) of the health facilities had appropriate air-conditioning and monitoring of room temperatures. Sixty percent (12/20) of the nurses did not receive any external training on conducting and interpreting RDT. Fifty percent of nurses (10/20) reported RDT stock-outs. Only 3/20 nurses mentioned that they periodically checked quality of RDT. Fifteen percent of nurses reported giving antimalarial drugs even if the RDT was negative.</p> <p>Conclusion</p> <p>Storage, quality assurance, end user training and use of RDT results for clinical decision making in primary care facilities in South Africa need to be improved. Further studies of the factors influencing the quality control of RDTs, their performance of RDTs and the ways to improve their use of RDTs are needed.</p

Polymer depletion interaction between two parallel repulsive walls

The depletion interaction between two parallel repulsive walls confining a dilute solution of long and flexible polymer chains is studied by field-theoretic methods. Special attention is paid to self-avoidance between chain monomers relevant for polymers in a good solvent. Our direct approach avoids the mapping of the actual polymer chains on effective hard or soft spheres. We compare our results with recent Monte Carlo simulations [A. Milchev and K. Binder, Eur. Phys. J. B 3, 477 (1998)] and with experimental results for the depletion interaction between a spherical colloidal particle and a planar wall in a dilute solution of nonionic polymers [D. Rudhardt, C. Bechinger, and P. Leiderer, Phys. Rev. Lett. 81, 1330 (1998)].Comment: 17 pages, 3 figures. Final version as publishe

A 10-item smell identification scale related to risk for Alzheimer's disease

University of Pennsylvania Smell Identification Test data from control subjects (n = 63), patients with mild cognitive impairment (n = 147), and patients with Alzheimer's disease (n = 100) were analyzed to derive an optimal subset of items related to risk for Alzheimer's disease (ie, healthy through mild cognitive impairment to early and moderate disease stages). The derived 10-item scale performed comparably with the University of Pennsylvania Smell Identification Test in classifying subjects, and it strongly predicted conversion to Alzheimer's disease on follow-up evaluation in patients with mild cognitive impairment. Independent replication is needed to validate these findings

Odor Identification Impairment and Change with Cholinesterase Inhibitor Treatment in Mild Cognitive Impairment

Background: Anticholinergic challenge can induce odor identification impairment that indicates Alzheimer’s disease (AD) pathology, and short-term change in odor identification impairment with cholinesterase inhibitor (CheI) treatment may predict longer term cognitive outcomes. Objective: In patients with mild cognitive impairment (MCI) treated prospectively with donepezil, a CheI, for 52 weeks, to determine if 1) acute decline in odor identification ability with anticholinergic challenge can predict cognitive improvement, and 2) change in odor identification over 8 weeks can predict cognitive improvement. Methods: MCI was diagnosed clinically without AD biomarkers. At baseline, the University of Pennsylvania Smell identification Test (UPSIT) was administered before and after an anticholinergic atropine nasal spray challenge. Donepezil was started at 5 mg daily, increased to 10 mg daily if tolerated, and this dose was maintained for 52 weeks. Main outcomes were ADAS-Cog total score and Selective Reminding Test (SRT) total immediate recall score measured at baseline, 26 and 52 weeks. Results: In 100 study participants, mean age 70.14 (SD 9.35) years, atropine-induced decrease in UPSIT score at baseline was not associated with change in ADAS-Cog or SRT scores over 52 weeks. Change in UPSIT score from 0 to 8 weeks did not show a significant association with change in the ADAS-Cog or SRT measures over 52 weeks. Conclusion: These negative findings in a relatively large sample of patients with MCI did not replicate results in much smaller samples. Change in odor identification with anticholinergic challenge, and over 8 weeks, may not be useful predictors of cognitive improvement with CheI in patients with MCI

A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and tabular clinical data (demographics, biomarkers) are fused in a single neural network. The network is invariant to shape transformations and avoids the need to identify point correspondences between shapes. To account for right censored time-to-event data, i.e., when it is only known that a patient did not develop Alzheimer's disease up to a particular time point, we employ a loss commonly used in survival analysis. Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers. Our experiments on data from the Alzheimer's Disease Neuroimaging Initiative demonstrate that our proposed model is able to learn a shape descriptor that augments clinical biomarkers and outperforms a deep neural network on shape alone and a linear model on common clinical biomarkers.Comment: Data and Machine Learning Advances with Multiple Views Workshop, ECML-PKDD 201