Task shifting to frontline community health workers for improved Diabetes care in low-resource settings in India : A phase II Non-randomized controlled clinical trial

Acknowledgments: We are indebted to the our research team who worked passionately to complete the study, health workers who were willing to function as patient navigators to improve diabetes management, and to all the participants who responded to our screening invitations and structured care Funding: We acknowledge the funding received from Friends of Vellore, UK and NHS Grampian Endowment fund, University of Aberdeen- Approval Number: EA0852Peer reviewedPublisher PD

Electronic Health Records in Primary Healthcare in India

Systematic Review of EHR in Primary healthcare in India -Lancet Commission_CMC Vellor

AUTOMATED DETECTION OF MITOTIC FIGURES IN BREAST CANCER HISTOPATHOLOGY IMAGES USING GABOR FEATURES AND DEEP NEURAL NETWORKS

The count of mitotic figures in Breast cancer histopathology slides is the most significant independent prognostic factor enabling determination of the proliferative activity of the tumor. In spite of the strict protocols followed, the mitotic counting activity suffers from subjectivity and considerable amount of observer variability despite being a laborious task. Interest in automated detection of mitotic figures has been rekindled with the advent of Whole Slide Scanners. Subsequently mitotic detection grand challenge contests have been held in recent years and several research methodologies developed by their participants. This paper proposes an efficient mitotic detection methodology for Hematoxylin and Eosin stained Breast cancer Histopathology Images using Gabor features and a Deep Belief Network- Deep Neural Network architecture (DBN-DNN). The proposed method has been evaluated on breast histopathology images from the publicly available dataset from MITOS contest held at the ICPR 2012 conference. It contains 226 mitoses annotated on 35 HPFs by several pathologists and 15 testing HPFs, yielding an F-measure of 0.74. In addition the said methodology was also tested on 3 slides from the MITOSIS- ATYPIA grand challenge held at the ICPR 2014 conference, an extension of MITOS containing 749 mitoses annotated on 1200 HPFs, by pathologists worldwide. This study has employed 3 slides (294 HPFs) from the MITOS-ATYPIA training dataset in its evaluation and the results showed F-measures 0.65, 0.72and 0.74 for each slide. The proposed method is fast and computationally simple yet its accuracy and specificity is comparable to the best winning methods of the aforementioned grand challenge

Stability and utility of flow cytometric platelet activation tests: A modality to bridge the gap between diagnostic demand and supply

Light transmission aggregometry (LTA) is the gold standard for the diagnosis of platelet function disorders (PFDs). The requirement of customized aggregometer, large blood volume, normal platelet count and processing within 4 hours of venipuncture for LTA makes platelet function testing inaccessible to wider population. Flow cytometric platelet activation test (PACT) may overcome these limitations. This study compares the performance of PACT with LTA, characterizes diagnostic patterns of PFDs on PACT and assesses the stability of PACT beyond 4 hours of venipuncture in controls (n = 5) at different temperature conditions. LTA and PACT were performed in 121 healthy controls and 66 patients with suspected PFD. PACT had excellent agreement (kappa = 0.93) with LTA and 94.1% sensitivity, 98.5% specificity. PACT had distinct patterns in Bernard Soulier Syndrome (n = 10), Glanzmann Thrombasthenia (n = 24), δ-granule disorder (n = 7), and other PFDs (n = 12). PACT could assess platelet function in patients (14%) with thrombocytopenia/lipemia wherein LTA was inconclusive. PACT was stable up to 24 hours in samples stored/transported at 2–8◦C. The results of utility and stability are only valid for the specific markers, agonist concentrations, and conditions investigated in this paper. PACT is a useful modality for the diagnosis of PFD, especially in children, thrombocytopenia cases or in the setup where an aggregometer is not readily available

Automated Segmentation of Nuclei in Breast Cancer Histopathology Images - Fig 3

<p>a) A Sub Image showing an overlapping nuclei cluster, b) pre-processing: enhanced grayscale image which is obtained by applying principal component analysis to image (a), c) Graphical illustration of stick tokens oriented perpendicular to gradient direction. d) Graphical illustration of stick tokens oriented parallel to gradient directions. e) Nuclei seed points plotted in red–found using non regional maximal suppression of the parallel saliency map f) result of parallel voting g) result of perpendicular voting, h) combined nuclei saliency map obtained by subtracting (g) from (f).</p

Flowchart for the proposed nuclei segmentation technique.

<p>Flowchart for the proposed nuclei segmentation technique.</p

Results of boundary detection.

<p>Results of boundary detection.</p