Correction: Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology.

[This corrects the article DOI: 10.1371/journal.pone.

Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology.

Robust semantic segmentation of tumour micro-environment is one of the major open challenges in machine learning enabled computational pathology. Though deep learning based systems have made significant progress, their task agnostic data driven approach often lacks the contextual grounding necessary in biomedical applications. We present a novel fuzzy water flow scheme that takes the coarse segmentation output of a base deep learning framework to then provide a more fine-grained and instance level robust segmentation output.

Instrument-free single-step direct estimation of the plasma glucose level from one drop of blood using smartphone-interfaced analytics on a paper strip.

We demonstrated an instrument-free miniaturized adaptation of the laboratory gold standard methodology for the direct estimation of plasma glucose from a drop of whole blood using a low-cost single-user-step paper-strip sensor interfaced with a smartphone. Unlike a majority of the existing glucose meters that use whole blood-based indirect sensing technologies, our direct adaptation of the gold-standard laboratory benchmark could eliminate the possibilities of cross interference with other analytes present in the whole blood by facilitating an plasma separation, capillary flow and colorimetric reaction occurring concomitantly, without incurring additional device complexity or embodiment. The test reagents were dispensed in lyophilized form, and the resulting paper strips were found to be stable over three months stored in a normal freezer, rendering easy adaptability commensurate with the constrained supply chains in extreme resource-poor settings.

Smartphone-Enabled Paper-Based Hemoglobin Sensor for Extreme Point-of-Care Diagnostics.

We report a simple, affordable (∼0.02 US $/test), rapid (within 5 min), and quantitative paper-based sensor integrated with smartphone application for on-spot detection of hemoglobin (Hgb) concentration using approximately 10 μL of finger-pricked blood. Quantitative analytical colorimetry is achieved via an Android-based application (Sens-Hb), integrating key operational steps of image acquisition, real-time analysis, and result dissemination.

Evaluation of oxidative stress and the microenvironment in oral submucous fibrosis.

Background: Oral Submucous fibrosis (OSF) is a chronic inflammatory mucosal disease of unknown etiology. Statistics show cases of OSF which has a high rate of overall prevalence and increase the chance of malignant transformation. As we know malignant cells is situated in a very complex microenvironment with altered metabolic pathway including intermediates which participate in oxidative stress process which enhances metabolic rewiring and promotes tumor progression.

Delineating metabolic dysfunction in cellular metabolism of oral submucous fibrosis using H nuclear magnetic resonance spectroscopy.

Objective: To delineate the metabolism involved in oral submucous fibrosis progression towards carcinogenesis by H nuclear magnetic resonance spectroscopy.

Methods: The proposed study was designed using H-NMR by comparing the metabolites in the serum sample of oral submucous fibrosis (n = 20) compared to the normal group (n = 20) using H nuclear magnetic resonance spectroscopy. Various statistical analysis like multivariate statistical analysis, Principle component analysis, Partial least squares Discriminant Analysis, Hierarchical cluster analysis was applied to analyze potential serum metabolites.

2dSpAn: semiautomated 2-d segmentation, classification and analysis of hippocampal dendritic spine plasticity.

Motivation: Accurate and effective dendritic spine segmentation from the dendrites remains as a challenge for current neuroimaging research community. In this article, we present a new method (2dSpAn) for 2-d segmentation, classification and analysis of structural/plastic changes of hippocampal dendritic spines. A user interactive segmentation method with convolution kernels is designed to segment the spines from the dendrites.

CD-Based Microfluidics for Primary Care in Extreme Point-of-Care Settings.

We review the utility of centrifugal microfluidic technologies applied to point-of-care diagnosis in extremely under-resourced environments. The various challenges faced in these settings are showcased, using areas in India and Africa as examples. Measures for the ability of integrated devices to effectively address point-of-care challenges are highlighted, and centrifugal, often termed CD-based microfluidic technologies, technologies are presented as a promising platform to address these challenges.