A Comparison of Arterial Input Function Interpolation Methods for Patlak Plot Analysis of 68Ga-PSMA-11 PET Prostate Cancer Studies.

Positron emission tomography (PET) imaging enables quantitative assessment of tissue physiology. Dynamic pharmacokinetic analysis of PET images requires accurate estimation of the radiotracer plasma input function to derive meaningful parameter estimates, and small discrepancies in parameter estimation can mimic subtle physiologic tissue variation. This study evaluates the impact of input function interpolation method on the accuracy of Patlak kinetic parameter estimation through simulations modeling the pharmacokinetic properties of [68Ga]-PSMA-11. This study evaluated both trained and untrained methods. Although the mean kinetic parameter accuracy was similar across all interpolation models, the trained node weighting interpolation model estimated accurate kinetic parameters with reduced overall variability relative to standard linear interpolation. Trained node weighting interpolation reduced kinetic parameter estimation variance by a magnitude approximating the underlying physiologic differences between normal and diseased prostatic tissue. Overall, this analysis suggests that trained node weighting improves the reliability of Patlak kinetic parameter estimation for [68Ga]-PSMA-11 PET.

The EXO70 inhibitor Endosidin2 alters plasma membrane protein composition in Arabidopsis roots.

To sustain normal growth and allow rapid responses to environmental cues, plants alter the plasma membrane protein composition under different conditions presumably by regulation of delivery, stability, and internalization. Exocytosis is a conserved cellular process that delivers proteins and lipids to the plasma membrane or extracellular space in eukaryotes. The octameric exocyst complex contributes to exocytosis by tethering secretory vesicles to the correct site for membrane fusion; however, whether the exocyst complex acts universally for all secretory vesicle cargo or just for specialized subsets used during polarized growth and trafficking is currently unknown. In addition to its role in exocytosis, the exocyst complex is also known to participate in membrane recycling and autophagy. Using a previously identified small molecule inhibitor of the plant exocyst complex subunit EXO70A1, Endosidin2 (ES2), combined with a plasma membrane enrichment method and quantitative proteomic analysis, we examined the composition of plasma membrane proteins in the root of Arabidopsis seedlings, after inhibition of the ES2-targetted exocyst complex, and verified our findings by live imaging of GFP-tagged plasma membrane proteins in root epidermal cells. The abundance of 145 plasma membrane proteins was significantly reduced following short-term ES2 treatments and these likely represent candidate cargo proteins of exocyst-mediated trafficking. Gene Ontology analysis showed that these proteins play diverse functions in cell growth, cell wall biosynthesis, hormone signaling, stress response, membrane transport, and nutrient uptake. Additionally, we quantified the effect of ES2 on the spatial distribution of EXO70A1 with live-cell imaging. Our results indicate that the plant exocyst complex mediates constitutive dynamic transport of subsets of plasma membrane proteins during normal root growth.

Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia.

Feature selection plays a crucial role in the development of machine learning algorithms. Understanding the impact of the features on a model, and their physiological relevance can improve the performance. This is particularly helpful in the healthcare domain wherein disease states need to be identified with relatively small quantities of data. Autonomic Dysreflexia (AD) is one such example, wherein mismanagement of this neurological condition could lead to severe consequences for individuals with spinal cord injuries. We explore different methods of feature selection needed to improve the performance of a machine learning model in the detection of the onset of AD. We present different techniques used as well as the ideal metrics using a dataset of thirty-six features extracted from electrocardiograms, skin nerve activity, blood pressure and temperature. The best performing algorithm was a 5-layer neural network with five relevant features, which resulted in 93.4% accuracy in the detection of AD. The techniques in this paper can be applied to a myriad of healthcare datasets allowing forays into deeper exploration and improved machine learning model development. Through critical feature selection, it is possible to design better machine learning algorithms for detection of niche disease states using smaller datasets.

Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data.

Automatic boundary detection of 4D ultrasound (4DUS) cardiac data is a promising yet challenging application at the intersection of machine learning and medicine. Using recently developed murine 4DUS cardiac imaging data, we demonstrate here a set of three machine learning models that predict left ventricular wall kinematics along both the endo- and epi-cardial boundaries. Each model is fundamentally built on three key features: (1) the projection of raw US data to a lower dimensional subspace, (2) a smoothing spline basis across time, and (3) a strategic parameterization of the left ventricular boundaries. Model 1 is constructed such that boundary predictions are based on individual short-axis images, regardless of their relative position in the ventricle. Model 2 simultaneously incorporates parallel short-axis image data into their predictions. Model 3 builds on the multi-slice approach of model 2, but assists predictions with a single ground-truth position at end-diastole. To assess the performance of each model, Monte Carlo cross validation was used to assess the performance of each model on unseen data. For predicting the radial distance of the endocardium, models 1, 2, and 3 yielded average R2 values of 0.41, 0.49, and 0.71, respectively. Monte Carlo simulations of the endocardial wall showed significantly closer predictions when using model 2 versus model 1 at a rate of 48.67%, and using model 3 versus model 2 at a rate of 83.50%. These finding suggest that a machine learning approach where multi-slice data are simultaneously used as input and predictions are aided by a single user input yields the most robust performance. Subsequently, we explore the how metrics of cardiac kinematics compare between ground-truth contours and predicted boundaries. We observed negligible deviations from ground-truth when using predicted boundaries alone, except in the case of early diastolic strain rate, providing confidence for the use of such machine learning models for rapid and reliable assessments of murine cardiac function. To our knowledge, this is the first application of machine learning to murine left ventricular 4DUS data. Future work will be needed to strengthen both model performance and applicability to different cardiac disease models.