Probabilistic spatial analysis in quantitative microscopy with uncertainty-aware cell detection using deep Bayesian regression

The investigation of biological systems with three-dimensional microscopy demands automatic cell identification methods that not only are accurate but also can imply the uncertainty in their predictions. The use of deep learning to regress density maps is a popular successful approach for extracting cell coordinates from local peaks in a postprocessing step, which then, however, hinders any meaningful probabilistic output. We propose a framework that can operate on large microscopy images and output probabilistic predictions (i) by integrating deep Bayesian learning for the regression of uncertainty-aware density maps, where peak detection algorithms generate cell proposals, and (ii) by learning a mapping from prediction proposals to a probabilistic space that accurately represents the chances of a successful prediction. Using these calibrated predictions, we propose a probabilistic spatial analysis with Monte Carlo sampling. We demonstrate this in a bone marrow dataset, where our proposed methods reveal spatial patterns that are otherwise undetectable

Artificial Intelligence Based Deep Bayesian Neural Network (DBNN) Toward Personalized Treatment of Leukemia with Stem Cells

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of Deep Bayesian Neural Network (DBNN) for the personalized treatment of leukemia cancer has shown a significant tested accuracy for the model. DBNNs used in this study was able to classify images with accuracy exceeding 98.73%. This study depicts that the DBNN can classify cell cultures only based on unstained light microscope images which allow their further use. Therefore, building a bayesian‐based model to great help during commercial cell culturing, and possibly a first step in the process of creating an automated/semiautomated neural network‐based model for classification of good and bad quality cultures when images of such will be available

Age-related relationships among peripheral B lymphocyte subpopulations

An immunological data-driven model is proposed, for age related changes in the network of relationships among cell quantities of eight peripheral B lymphocyte subpopulations, that is, cells exhibiting all combinations of three specific receptor clusters (CD27, CD23, CD5). The model is based on immunological data (quantities of cells exhibiting CD19, characterizing B lymphocytes) from about six thousands patients, having an age ranging between one day and ninety-five years, by means of a suitably combination of data analysis methods, such as piecewise linear regression models. With relaxed values for statistically significant models (coefficient p-values bounded by 0.05), we found a network holding for all ages, that likely represents the general assessment of adaptive immune system for healthy human beings. When statistical validation comes to be more restrictive, we found that some of these interactions are lost with aging, as widely observed in medical literature. Namely, interesting (inverse or directed) proportions are highlighted among mutual quantities of a partition of peripheral B lymphocytes

Effects of obesity and resistance exercise on bone health studied with modern imaging methods

Altered metabolic states, such as obesity and resistance exercise, may affect bone health either in a negative or positive manner. In clinical practice, bone health may be easily understood as a synonym for bone mineral density or osteoporosis, which is defined as a skeletal disorder resulting from decreased bone strength. In addition, bone glucose metabolism and bone marrow adiposity may contribute to bone overall health status. The aims of this thesis were to investigate the effects of obesity and resistance exercise on bone glucose metabolism, bone marrow adiposity and bone mineral density using modern imaging methods, such as positron emission tomography, magnetic resonance imaging and quantitative computed tomography, in various study settings. It was found that obesity did not alter bone glucose metabolism or bone marrow adiposity. However, resistance exercise resulted in improved bone glucose metabolism and bone mineral density. In conclusion, resistance exercise, but not obesity, had an impact on bone health studied with modern imaging methods. The obtained results may offer new insights into quantification and into the follow-up of bone health during altered metabolic conditions. In addition, more individualized and accurately allocated lifestyle interventions may be administered in the treatment or prevention of diseases associated with obesity or insulin resistance.Ylipainon ja lihasvoimaharjoittelun vaikutus luun terveyteen tutkittuna modernien kuvantamismenetelmien avulla Aineenvaihduntaa muuntelevat fysiologiset tilat kuten lihavuus ja lihasvoimaharjoittelu voivat mahdollisesti vaikuttaa luun terveyteen joko negatiivisesti tai positiivisesti. Luun terveys kuitenkin ymmärretään usein vain synonyymina luuntiheydelle tai osteoporoosille, joka on luun alentuneesta lujuudesta johtuva sairaus. Luun terveyteen voi kuitenkin olla osallisena muitakin vähemmän tunnettuja potentiaalisia tekijöitä kuten luun sokeriaineenvaihdunta ja luuytimen rasvoittuminen. Väitöskirjan tarkoituksena oli tutkia lihavuuden ja lihasvoimaharjoittelun vaikutuksia luun sokeriaineenvaihduntaan, luuytimen rasvoittumiseen sekä luuntiheyteen modernien kuvantamismenetelmien kuten positroniemissiotomografian, magneettikuvauksen ja kvantitatiivisen tietokonetomografian avulla erilaisissa tutkimusasetelmissa. Tulosten mukaan lihavuudella ei ollut vaikutusta luun sokeriaineenvaihduntaan tai luuytimen rasvoittumiseen. Sen sijaan lihasvoimaharjoittelu näytti parantavan luun sokeriaineenvaihduntaa ja luuntiheyttä. Yhteenvetona voidaan siis todeta, että lihasvoimaharjoittelulla on positiivinen vaikutus luun terveyteen modernien kuvantamismenetelmien avulla tutkittuna. Sen sijaan lihavuudella ei ole siihen vaikutusta. Väitöskirjatutkimuksen tulokset voivat tuottaa uusia oivalluksia aineenvaihdunnallisiin häiriötiloihin liittyvässä luun terveyden määrittämisessä ja seurannassa. Lisäksi tuloksia soveltaen voidaan suunnitella aiempaa yksilöllisempiä ja kohdistetumpia elämäntapainterventioita lihavuuteen ja insuliiniresistenssiin liittyvien luun häiriötilojen ehkäisyssä ja hoidossa.Siirretty Doriast

An Optimized Approach to Perform Bone Histomorphometry

Bone histomorphometry allows quantitative evaluation of bone micro-architecture, bone formation, and bone remodeling by providing an insight to cellular changes. Histomorphometry plays an important role in monitoring changes in bone properties because of systemic skeletal diseases like osteoporosis and osteomalacia. Besides, quantitative evaluation plays an important role in fracture healing studies to explore the effect of biomaterial or drug treatment. However, until today, to our knowledge, bone histomorphometry remain time-consuming and expensive. This incited us to set up an open-source freely available semi-automated solution to measure parameters like trabecular area, osteoid area, trabecular thickness, and osteoclast activity. Here in this study, the authors present the adaptation of Trainable Weka Segmentation plugin of ImageJ to allow fast evaluation of bone parameters (trabecular area, osteoid area) to diagnose bone related diseases. Also, ImageJ toolbox and plugins (BoneJ) were adapted to measure osteoclast activity, trabecular thickness, and trabecular separation. The optimized two different scripts are based on ImageJ, by providing simple user-interface and easy accessibility for biologists and clinicians. The scripts developed for bone histomorphometry can be optimized globally for other histological samples. The showed scripts will benefit the scientific community in histological evaluation