Cancer in Punjab: evidence from cancer atlas

Cancer in Punjab has been a news item in the recent past. It was thought that cases in Punjab exceeded the national average and felt that “Punjab the country’s food bowl was in throes of cancer” (1). This presumption was perhaps incorrect. In order to have clarity on the issue, we aimed to review the report of Cancer Atlas in Punjab state for the year 2012-13, recently released by Indian Council of Medical Research (ICMR). The main idea of generating data through Cancer Atlas approach is to assess patterns of cancer in various parts of Punjab state and to estimate cancer incidence at various districts in Punjab. The sources of data collection in the state are all medical colleges, pathology labs, civil hospitals and individual oncologist throughout the state. These data collection sources are considered important as over 80-85% of registered cases of cancer are generally with a microscopic diagnosis (2). Patient data details in the Atlas approach included are Cancer site and morphology of the cancer as per guidelines for collecting information on all malignant cases. The similar approach that adopted in Cancer Atlas in India such as internet approach is used in entering core patient data for Punjab Atlas by standardized procedures.&nbsp

A multicountry evaluation of careHPV testing, visual inspection with acetic acid, and papanicolaou testing for the detection of cervical cancer.

OBJECTIVE: This study evaluates the feasibility and performance of careHPV, a novel human papillomavirus (HPV) DNA test, when used for screening women for cervical cancer in low-resource settings. METHODS AND MATERIALS: Clinician-collected (cervical) and self-collected (vaginal) careHPV specimens, visual inspection with acetic acid (VIA), and Papanicolaou test were evaluated among 16,951 eligible women in India, Nicaragua, and Uganda. Women with positive screening results received colposcopy and histologic follow-up as indicated. The positivity of each screening method was calculated overall, by site, and age. In addition, the clinical performance of each screening test was determined for detection of cervical intraepithelial neoplasia (CIN) grade 2 (CIN2+) and CIN grade 3. RESULTS: Moderate or severe dysplasia or cancer (taken together as CIN2+) was diagnosed in 286 women. The positivity rate ranged between 2.4% to 19.6% for vaginal careHPV, 2.9% to 20.2% for cervical careHPV, 5.5% to 34.4% for VIA, and 2.8% to 51.8% for Papanicolaou test. Cervical careHPV was the most sensitive for CIN2+ (81.5%; 95% confidence interval [CI], 76.5-85.8) and CIN grade 3 (85.3%; 95% CI, 78.6-90.6) at all sites, followed by vaginal careHPV (69.6% and 71.3%, respectively). The sensitivity of VIA ranged from 21.9% to 73.6% and Papanicolaou test from 40.7% to 73.7%. The pooled specificities of cervical careHPV, vaginal careHPV, VIA, and Papanicolaou test were 91.6%, 90.6%, 84.2%, and 87.7%, respectively. CONCLUSIONS: careHPV performed well in large multicountry demonstration studies conducted in resource-limited settings that have not previously been conducted this type of testing; its sensitivity using cervical samples or vaginal self-collected samples was better than VIA or Papanicolaou test. The feasibility of using careHPV in self-collected vaginal samples opens the possibility of increasing coverage and early detection in resource-constrained areas

Prevalence and factors associated with tuberculosis infection in India

Background: The risk of tuberculosis (TB) disease is higher in individuals with TB infection. In a TB endemic country like India, it is essential to understand the current burden of TB infection at the population level. The objective of the present analysis is to estimate the prevalence of TB infection in India and to explore the factors associated with TB infection. Methods: Individuals aged > 15 years in the recently completed National TB prevalence survey in India who were tested for TB infection by QuantiFERON-TB Gold Plus (QFT-Plus) assay were considered for this sub- analysis. TB infection was defined as positive by QFT-Plus (value > 0.35 IU/ml). The estimates for prevalence, prevalence ratio (PR) and adjusted risk ratio (aRR) estimates with 95% confidence intervals (CIs) were calculated. Results: Of the 16864 individuals analysed, the prevalence of TB infection was 22.6% (95% CI:19.4 −25.8). Factors more likely to be associated with TB infection include age > 30 years (aRR:1.49;95% CI:1.29–1.73), being male (aRR:1.26; 95%CI: 1.18–1.34), residing in urban location (aRR:1.58; 95%CI: 1.03–2.43) and past history of TB (aRR:1.49; 95%CI: 1.26–1.76). Conclusion: About one fourth (22.6%) of the individuals were infected with TB in India. Individuals aged > 30 years, males, residing in urban location, and those with past history of TB were more likely to have TB infection. Targeted interventions for prevention of TB and close monitoring are essential to reduce the burden of TB in India

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

The impact of PICALM genetic variations on reserve capacity of posterior cingulate in AD continuum

Phosphatidylinositolbinding clathrin assembly protein (PICALM) gene is one novel genetic player associated with late-onset Alzheimer’s disease (LOAD), based on recent genome wide association studies (GWAS). However, how it affects AD occurrence is still unknown. Brain reserve hypothesis highlights the tolerant capacities of brain as a passive means to fight against neurodegenerations. Here, we took the baseline volume and/or thickness of LOAD-associated brain regions as proxies of brain reserve capacities and investigated whether PICALM genetic variations can influence the baseline reserve capacities and the longitudinal atrophy rate of these specific regions using data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In mixed population, we found that brain region significantly affected by PICALM genetic variations was majorly restricted to posterior cingulate. In sub-population analysis, we found that one PICALM variation (C allele of rs642949) was associated with larger baseline thickness of posterior cingulate in health. We found seven variations in health and two variations (rs543293 and rs592297) in individuals with mild cognitive impairment were associated with slower atrophy rate of posterior cingulate. Our study provided preliminary evidences supporting that PICALM variations render protections by facilitating reserve capacities of posterior cingulate in non-demented elderly

Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease