Global variations in diabetes mellitus based on fasting glucose and haemogloblin A1c

Fasting plasma glucose (FPG) and haemoglobin A1c (HbA1c) are both used to diagnose diabetes, but may identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening had elevated FPG, HbA1c, or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardised proportion of diabetes that was previously undiagnosed, and detected in survey screening, ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the agestandardised proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global gap in diabetes diagnosis and surveillance.peer-reviewe

Plant-to-Plant Variability in Corn Production

Corn (Zea mays L.) grain yields are known to vary from plant to plant, but the extent of this variability across a range of environments has not been evaluated. This study was initiated to evaluate by-plant corn grain yield variability over a range of production environments and to establish the relationships among mean grain yield, standard deviation, coefficient of variation, and yield range. A total of forty-six 8- to 30-m corn transects were harvested by plant in Argentina, Mexico, Iowa, Nebraska, Ohio, Virginia, and Oklahoma from 2002 to 2004. By-plant corn grain yields were determined, and the average individual plant yields were calculated. Over all sites in all countries and states, plant-to-plant variation in corn grain yield averaged 2765 kg ha-1 (44.1 bu ac-1). At the sites with the highest average corn grain yield (11 478 and 14 383 kg ha-1, Parana Argentina, and Phillips, NE), average plant-to-plant variation in yield was 4211 kg ha-1 (67 bu ac-1) and 2926 kg ha-1 (47 bu ac-1), respectively. As average grain yields increased, so did the standard deviation of the yields obtained within each row. Furthermore, the yield range (maximum corn grain yield minus the minimum corn grain yield per row) was found to increase with increasing yield level. Regardless of yield level, plant-to-plant variability in corn grain yield can be expected and averaged more than 2765 kg ha-1 over sites and years. Averaging yield over distances \u3e0.5 m removed the extreme by-plant variability, and thus, the scale for treating other factors affecting yield should be less than 0.5 m. Methods that homogenize corn plant stands and emergence may decrease plant-to-plant variation and could lead to increased grain yields

Intra-operative brain tumor detection with deep learning-optimized hyperspectral imaging

Surgery for gliomas (intrinsic brain tumors), especially when low-grade, is challenging due to the infiltrative nature of the lesion. Currently, no real-Time, intra-operative, label-free and wide-field tool is available to assist and guide the surgeon to find the relevant demarcations for these tumors. While marker-based methods exist for the high-grade glioma case, there is no convenient solution available for the low-grade case; thus, marker-free optical techniques represent an attractive option. Although RGB imaging is a standard tool in surgical microscopes, it does not contain sufficient information for tissue differentiation. We leverage the richer information from hyperspectral imaging (HSI), acquired with a snapscan camera in the 468-787 nm range, coupled to a surgical microscope, to build a deep-learning-based diagnostic tool for cancer resection with potential for intra-operative guidance. However, the main limitation of the HSI snapscan camera is the image acquisition time, limiting its widespread deployment in the operation theater. Here, we investigate the effect of HSI channel reduction and pre-selection to scope the design space for the development of cheaper and faster sensors. Neural networks are used to identify the most important spectral channels for tumor tissue differentiation, optimizing the trade-off between the number of channels and precision to enable real-Time intra-surgical application. We evaluate the performance of our method on a clinical dataset that was acquired during surgery on five patients. By demonstrating the possibility to efficiently detect low-grade glioma, these results can lead to better cancer resection demarcations, potentially improving treatment effectiveness and patient outcome