Differentiating false positive lesions from clinically significant cancer and normal prostate tissue using VERDICT MRI and other diffusion models

False positives on multiparametric MRIs (mp-MRIs) result in many unnecessary invasive biopsies in men with clinically insignificant diseases. This study investigated whether quantitative diffusion MRI could differentiate between false positives, true positives and normal tissue non-invasively. Thirty-eight patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors (VERDICT) MRI, followed by transperineal biopsy. The patients were categorized into two groups following biopsy: (1) significant cancer—true positive, 19 patients; (2) atrophy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN)—false positive, 19 patients. The clinical apparent diffusion coefficient (ADC) values were obtained, and the intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted via deep learning. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) (p < 0.0001) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular–extravascular volume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were found for the VERDICT fIC (p = 0.004) and IVIM D. These results demonstrate that model-based diffusion MRI could reduce unnecessary biopsies occurring due to false positive prostate lesions and shows promising sensitivity to benign diseases

Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

SMART AGRICULTURE: A BLISS TO FARMERS

In developing nations like India, despite of technological advancement we have been less attentive towards our agriculture. Present condition of agriculture is not so satisfactory to produce maximum crop yield because of lack of technology awareness among farmers. As the literacy rates of farmers those involved in agricultural field is significantly low, applying and working with new technology is a major concern. If farmers can embrace new technologies properly, agriculture sector can be a major sector for generating employment as well as increasing GDP in developing countries like India. As of 2012, this sector contributes about 18% of the total G.D.P. of India but around 50% people are involved in this. IoT will help us to increase the productivity of this huge % of people involved in this sector. Application of IoT ecosystem can bring renaissance in agricultural field. IoT will aid in predicting crop yield, crop price, soil temperature, real time data about air quality, water level and proper timing of crop to be delivered to market, which will help to increase productivity. Study says we will have 9.6 billion people on Earth by 2050 which will increase demand for food and IoT in agriculture should be an important driver to meet this requirement. Therefore we need to develop such system which will enhance farming procedure. Objective of this paper is to present an idea how IoT ecosystem can enhance the overall farming output as well as increase GDP

Fibrillar disruption by AC electric field induced oscillation: a case study with human serum albumin

The effect of oscillation induced by a frequency-dependent alternating current (AC) electric field to dissociate preformed amyloid fibrils has been investigated. An electrowetting-on-dielectric type setup has been used to apply the AC field of varying frequencies on preformed fibrils of human serum albumin (HSA). The disintegration potency has been monitored by a combination of spectroscopic and microscopic techniques. The experimental results suggest that the frequency of the applied AC field plays a crucial role in the disruption of preformed HSA fibrils. The extent of stress generated inside the droplet due to the application of the AC field at different frequencies has been monitored as a function of the input frequency of the applied AC voltage. This has been accomplished by assessing the morphology deformation of the oscillating HSA fibril droplets. The shape deformation of the oscillating droplets is characterized using image analysis by measuring the dynamic changes in the shape dependent parameters such as contact angle and droplet footprint radius and the amplitude. It is suggested that the cumulative effects of the stress generated inside the HSA fibril droplets due to the shape deformation induced hydrodynamic flows and the torque induced by the intrinsic electric dipoles of protein due to their continuous periodic realignment in presence of the AC electric field results in the destruction of the fibrillar species

ssVERDICT: Self‐supervised VERDICT‐MRI for enhanced prostate tumor characterization

Purpose: Demonstrating and assessing self‐supervised machine‐learning fitting of the VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumors) model for prostate cancer. Methods: We derive a self‐supervised neural network for fitting VERDICT (ssVERDICT) that estimates parameter maps without training data. We compare the performance of ssVERDICT to two established baseline methods for fitting diffusion MRI models: conventional nonlinear least squares and supervised deep learning. We do this quantitatively on simulated data by comparing the Pearson's correlation coefficient, mean‐squared error, bias, and variance with respect to the simulated ground truth. We also calculate in vivo parameter maps on a cohort of 20 prostate cancer patients and compare the methods' performance in discriminating benign from cancerous tissue via Wilcoxon's signed‐rank test. Results: In simulations, ssVERDICT outperforms the baseline methods (nonlinear least squares and supervised deep learning) in estimating all the parameters from the VERDICT prostate model in terms of Pearson's correlation coefficient, bias, and mean‐squared error. In vivo, ssVERDICT shows stronger lesion conspicuity across all parameter maps, and improves discrimination between benign and cancerous tissue over the baseline methods. Conclusion: ssVERDICT significantly outperforms state‐of‐the‐art methods for VERDICT model fitting and shows, for the first time, fitting of a detailed multicompartment biophysical diffusion MRI model with machine learning without the requirement of explicit training labels

Metabolic regulation of CTCF expression and chromatin association dictates starvation response in mice and flies

Summary: Coordinated temporal control of gene expression is essential for physiological homeostasis, especially during metabolic transitions. However, the interplay between chromatin architectural proteins and metabolism in regulating transcription is less understood. Here, we demonstrate a conserved bidirectional interplay between CTCF (CCCTC-binding factor) expression/function and metabolic inputs during feed-fast cycles. Our results indicate that its loci-specific functional diversity is associated with physiological plasticity in mouse hepatocytes. CTCF differential expression and long non-coding RNA-Jpx mediated changes in chromatin occupancy, unraveled its paradoxical yet tuneable functions, which are governed by metabolic inputs. We illustrate the key role of CTCF in controlling temporal cascade of transcriptional response, with effects on hepatic mitochondrial energetics and lipidome. Underscoring the evolutionary conservation of CTCF-dependent metabolic homeostasis, CTCF knockdown in flies abrogated starvation resistance. In summary, we demonstrate the interplay between CTCF and metabolic inputs that highlights the coupled plasticity of physiological responses and chromatin function