Failure of A Novel, Rapid Antigen and Antibody Combination Test to Detect Antigen-Positive HIV Infection in African Adults with Early HIV Infection

BACKGROUND: Acute HIV infection (prior to antibody seroconversion) represents a high-risk window for HIV transmission. Development of a test to detect acute infection at the point-of-care is urgent. METHODS: Volunteers enrolled in a prospective study of HIV incidence in four African cities, Kigali in Rwanda and Ndola, Kitwe and Lusaka in Zambia, were tested regularly for HIV by rapid antibody test and p24 antigen ELISA. Five subgroups of samples were also tested by the Determine Ag/Ab Combo test 1) Antigen positive, antibody negative (acute infection); 2) Antigen positive, antibody positive; 3) Antigen negative, antibody positive; 4) Antigen negative, antibody negative; and 5) Antigen false positive, antibody negative (HIV uninfected). A sixth group included serial dilutions from a p24 antigen-positive control sample. Combo test results were reported as antigen positive, antibody positive, or both. RESULTS: Of 34 group 1 samples with VL between 5x105 and >1.5x107 copies/mL (median 3.5x106), 1 (2.9%) was detected by the Combo antigen component, 7 (20.6%) others were positive by the Combo antibody component. No group 2 samples were antigen positive by the Combo test (0/18). Sensitivity of the Combo antigen test was therefore 1.9% (1/52, 95% CI 0.0, 9.9). One false positive Combo antibody result (1/30, 3.3%) was observed in group 4. No false-positive Combo antigen results were observed. The Combo antigen test was positive in group 6 at concentrations of 80 pg/mL, faintly positive at 40 and 20 pg/mL, and negative thereafter. The p24 ELISA antigen test remained positive at 5 pg/mL. CONCLUSIONS: Although the antibody component of the Combo test detected antibodies to HIV earlier than the comparison antibody tests used, less than 2% of the cases of antigen-positive HIV infection were detected by the Combo antigen component. The development of a rapid point-of-care test to diagnose acute HIV infection remains an urgent goal

Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients

In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. The proposed technique employs multi-spectrum networks to characterize the complex yet subtle blood oxygenation level dependent (BOLD) signal changes caused by pathological attacks. The utilization of multi-spectrum networks in identifying MCI individuals is motivated by the inherent frequency-specific properties of BOLD spectrum. It is believed that frequency specific information extracted from different spectra may delineate the complex yet subtle variations of BOLD signals more effectively. In the proposed technique, regional mean time series of each region-of-interest (ROI) is band-pass filtered ( Hz) before it is decomposed into five frequency sub-bands. Five connectivity networks are constructed, one from each frequency sub-band. Clustering coefficient of each ROI in relation to the other ROIs are extracted as features for classification. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy obtained by this approach is 86.5%, which is an increase of at least 18.9% from the conventional full-spectrum methods. A cross-validation estimation of the generalization performance shows an area of 0.863 under the receiver operating characteristic (ROC) curve, indicating good diagnostic power. It was also found that, based on the selected features, portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe regions provided the most discriminant information for classification, in line with results reported in previous studies. Analysis on individual frequency sub-bands demonstrated that different sub-bands contribute differently to classification, providing extra evidence regarding frequency-specific distribution of BOLD signals. Our MCI classification framework, which allows accurate early detection of functional brain abnormalities, makes an important positive contribution to the treatment management of potential AD patients

Early and reversible changes to the hippocampal proteome in mice on a high-fat diet

Funding LMW, FMC, CG, ACM and C-DM were funded by the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS). FHM was supported by an EASTBIO DTP BBSRC studentship. DS was supported by a SULSA studentship.Peer reviewedPublisher PD

Alzheimer’s disease: diagnostics, prognostics and the road to prevention

Alzheimer’s disease (AD) presents one of the leading healthcare challenges of the 21st century, with a projected worldwide prevalence of >107 million cases by 2025. While biomarkers have been identified, which may correlate with disease progression or subtype for the purpose of disease monitoring or differential diagnosis, a biomarker for reliable prediction of late onset disease risk has not been available until now. This deficiency in reliable predictive biomarkers, coupled with the devastating nature of the disease, places AD at a high priority for focus by predictive, preventive and personalized medicine. Recent data, discovered using phylogenetic analysis, suggest that a variable length poly-T sequence polymorphism in the TOMM40 gene, adjacent to the APOE gene, is predictive of risk of AD age-of-onset when coupled with a subject’s current age. This finding offers hope for reliable assignment of disease risk within a 5-7 year window, and is expected to guide enrichment of clinical trials in order to speed development of preventative medicines

Predicting rate of cognitive decline in probable Alzheimer's disease.

Recent attempts to identify predictors of rate of decline in Alzheimer's disease (AD) have been extremely variable in choice of outcome variables, predictor variables tested, timing of assessments, and statistical approaches. In this study, a random effects regression model was applied to seek predictors of decline on the Mini-Mental State Exam in 132 patients with probable AD reassessed every 6 months for up to 7.5 years. Potential predictor variables at baseline were of three types: patients characteristics, clinical variables, and cognitive performances. The final multivariate analysis indicated that the following characteristics predicted more rapid cognitive decline: more education, history of dementia in a first degree relative, non-right handedness, better performances of Boston Naming Test, Gollin Incomplete Figures Test, and Benton Visual Retention Test-Delay, and worse performances on Responsive Naming Test, WAIS-R Block Design, and Benton Visual Retention Test-Copy

Predicting rate of cognitive decline in probable Alzheimer's disease.

Recent attempts to identify predictors of rate of decline in Alzheimer's disease (AD) have been extremely variable in choice of outcome variables, predictor variables tested, timing of assessments, and statistical approaches. In this study, a random effects regression model was applied to seek predictors of decline on the Mini-Mental State Exam in 132 patients with probable AD reassessed every 6 months for up to 7.5 years. Potential predictor variables at baseline were of three types: patients characteristics, clinical variables, and cognitive performances. The final multivariate analysis indicated that the following characteristics predicted more rapid cognitive decline: more education, history of dementia in a first degree relative, non-right handedness, better performances of Boston Naming Test, Gollin Incomplete Figures Test, and Benton Visual Retention Test-Delay, and worse performances on Responsive Naming Test, WAIS-R Block Design, and Benton Visual Retention Test-Copy

APOE genotype and survival in men and women with Alzheimer's disease.

BackgroundThe epsilon 4 allele of the APOE gene (APOE) is more frequent in patients with AD than in the general population, but studies are inconclusive as to whether it affects rate of progression or survival. Because survival in AD is generally longer in women than in men, the authors investigated whether APOE affects 10-year survival equally in men and women.MethodsAPOE testing was performed on 125 patients with probable AD enrolled in the Johns Hopkins AD Research Center between November 1984 and March 1987. The 39 men and 86 women were followed at 6-month intervals until censoring (by death or withdrawal from the study) or March 1997. Patients were dichotomized into those with and those without at least one epsilon 4 allele. For each sex, a Cox proportional hazards regression, allowing for delayed entry and covarying for age at onset, was used to examine the effect of epsilon 4 on survival.ResultsAll patients who died during the study period and had autopsy (n = 92) were found to have definite AD. Average survival from disease onset did not differ by sex (12.1 years in men; 12.3 years in women). In neither sex were differences found between epsilon 4-positive and epsilon 4-negative subgroups in education, duration of AD at entry, or severity of dementia. However, in both sexes the epsilon 4-positive subgroup was approximately 3 years older at onset of AD and at entry to the study than the epsilon 4-negative subgroup. Adjusting for age at onset, the presence of an epsilon 4 allele significantly increased the relative risk of death only for men (RR = 2.69; 95% CI = 1.23 to 5.87).ConclusionsIn this sample of mostly white, well-educated research participants with AD, the APOE epsilon 4 allele was associated with shorter survival in men but not in women