Osteoclasts and their circulating precursors in rheumatoid arthritis: Relationships with disease activity and bone erosions.

Patients with rheumatoid arthritis (RA) have very different outcomes, particularly with regard to bone erosions. Since osteoclasts are responsible for bone destruction adjacent to rheumatoid synovium, profiling osteoclasts from circulating precursors in RA could help identify patients at risk for bone destruction. In this study, we sought to determine whether the functional characteristics of osteoclasts generated from their blood precursors were modified by RA activity or were intrinsic to osteoclasts and associated with the RA phenotype (erosive or not). Osteoclasts were generated in vitro from peripheral blood mononuclear cells (PBMCs) of subjects with RA (n = 140), as well as sex- and age-matched healthy controls (n = 101). Osteoclastic parameters were analyzed at baseline and during the follow-up for up to 4 years, with regular assessment of RA activity, bone erosions, and bone mineral density (BMD). As a validation cohort, we examined RA patients from the Early Undifferentiated PolyArthritis (EUPA) study (n = 163). The proportion of CD14+ PBMC was higher in RA than in control subjects, but inversely correlated with the 28-joint disease activity score (DAS28). Also surprisingly, in osteoclast cultures from PBMCs, active RA was associated with lower osteoclastogenic capacity, while in vitro bone resorption per osteoclast and resistance to apoptosis were similar in both active and quiescent RA. In a small subgroup analysis, osteoclasts from subjects with recent RA that had progressed at four years to an erosive RA exhibited at baseline greater resistance to apoptosis than those from patients remaining non-erosive. Our findings establish that when RA is active, circulating monocytes have a reduced potential to generate osteoclasts from PBMCs in vitro. In addition, osteoclasts associated with erosive disease had resistance to apoptosis from the start of RA.

Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

BACKGROUND AND PURPOSE: Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma. MATERIALS AND METHODS: Seventy-one adult patients with enhancing gliomas and 35 adult patients with primary central nervous system lymphomas were included. The tumors were manually contoured on contrast-enhanced T1WI, and the resulting volumes of interest were mined for textural features and subjected to a support vector machine-based machine-learning protocol. Three readers classified the tumors independently on contrast-enhanced T1WI. Areas under the receiver operating characteristic curves were estimated for each reader and for the support vector machine classifier. A noninferiority test for diagnostic accuracy based on paired areas under the receiver operating characteristic curve was performed with a noninferiority margin of 0.15. RESULTS: The mean areas under the receiver operating characteristic curve were 0.877 (95% CI, 0.798-0.955) for the support vector machine classifier; 0.878 (95% CI, 0.807-0.949) for reader 1; 0.899 (95% CI, 0.833-0.966) for reader 2; and 0.845 (95% CI, 0.757-0.933) for reader 3. The mean area under the receiver operating characteristic curve of the support vector machine classifier was significantly noninferior to the mean area under the curve of reader 1 (P = .021), reader 2 (P = .035), and reader 3 (P = .007). CONCLUSIONS: Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma.

Frontotemporal correlates of impulsivity and machine learning in retired professional athletes with a history of multiple concussions.

The frontotemporal cortical network is associated with behaviours such as impulsivity and aggression. The health of the uncinate fasciculus (UF) that connects the orbitofrontal cortex (OFC) with the anterior temporal lobe (ATL) may be a crucial determinant of behavioural regulation. Behavioural changes can emerge after repeated concussion and thus we used MRI to examine the UF and connected gray matter as it relates to impulsivity and aggression in retired professional football players who had sustained multiple concussions. Behaviourally, athletes had faster reaction times and an increased error rate on a go/no-go task, and increased aggression and mania compared to controls. MRI revealed that the athletes had (1) cortical thinning of the ATL, (2) negative correlations of OFC thickness with aggression and task errors, indicative of impulsivity, (3) negative correlations of UF axial diffusivity with error rates and aggression, and (4) elevated resting-state functional connectivity between the ATL and OFC. Using machine learning, we found that UF diffusion imaging differentiates athletes from healthy controls with significant classifiers based on UF mean and radial diffusivity showing 79-84 % sensitivity and specificity, and 0.8 areas under the ROC curves. The spatial pattern of classifier weights revealed hot spots at the orbitofrontal and temporal ends of the UF. These data implicate the UF system in the pathological outcomes of repeated concussion as they relate to impulsive behaviour. Furthermore, a support vector machine has potential utility in the general assessment and diagnosis of brain abnormalities following concussion.

How profilin/barbed-end synergy controls actin polymerization: a kinetic model of the ATP hydrolysis circuit.

The role of ATP hydrolysis in the regulation of the actin cytoskeleton continues to be a subject of controversy. Since actin polymerization can occur in the absence of ATP, the energy of hydrolysis is not needed for filament assembly. Recent work has instead suggested a regulatory role for ATP in cytoskeletal remodeling. In particular, both profilin and free filament barbed ends have been shown to play major roles in the processing of ATP by actin. We have developed a new integrated kinetic model to examine how the maintenance of the pool of unpolymerized actin and the flux of actin subunits through filaments are controlled by profilin and free filament barbed ends through their interaction with ATP. An analysis of the model's steady states predicts how two novel regulatory pathways may regulate the cytoskeleton in vivo. Coordinated changes in the availability of both profilin and free barbed ends mediate the following regulatory effects: (1) both the nucleotide composition and the absolute amount of free G-actin can be changed separately or together to substantially alter the total amount of F-actin; and (2) uncapping the barbed ends of only a modest fraction of filaments causes all filaments to begin slowly depolymerizing from their pointed ends, resulting in the total depolymerization of the remaining capped filaments. We report that the phenomenon of treadmilling, wherein the barbed end growth of each filament is exactly balanced by pointed end loss at steady state, is only possible in the limiting case when all barbed ends are uncapped. The capping of any fraction of barbed ends increases the critical concentration of ATP-G-actin, causing the remaining free barbed ends to grow faster than their pointed ends can shrink. On the basis of these findings we propose a major revision to the treadmilling model for actin-based motility, in which the rapidly growing filaments with free barbed ends are continuously severed toward their rear followed by capping of the newly exposed barbed ends. This revised model, herein referred to as "treadsevering," allows sustained and rapid barbed end growth to occur indefinitely at a steady state provided a continuous input of ATP.

High microfilament concentration results in barbed-end ADP caps.

Current theory and experiments describing actin polymerization suggest that site-specific cleavage of bound nucleotide following F-actin filament formation causes the barbed ends of microfilaments to be capped first with ATP subunits, then with ADP bound to inorganic phosphate (ADP.Pi) at steady-state. The barbed ends of depolymerizing filaments consist of ADP subunits. The decrease in stability of the barbed-end cap accompanying the transition from ADP.Pi to ADP allows nucleotide hydrolysis and subsequent loss of Pi to regulate F-actin filament dynamics. We describe a novel computational model of nucleotide capping that simulates both the spatial and temporal properties of actin polymerization. This model has been used to test the effects of high filament concentration on the behavior of the ATP hydrolysis cycle observed during polymerization. The model predicts that under conditions of high microfilament concentration an ADP cap can appear during steady-state at the barbed ends of filaments. We show that the presence of the cap can be accounted for by a kinetic model and predict the relationship between the nucleotide concentration ratio [ATP]/[ADP], the F-actin filament concentration, and the steady-state distribution of barbed-end ADP cap lengths. The possible consequences of this previously unreported phenomenon as a regulator of cytoskeletal behavior are discussed.

Cellular automaton model of the actin cytoskeleton.

We describe a cellular automaton model of the actin cytoskeleton. The model incorporates spatial and temporal behavior at the macromolecular level and is relevant to the viscous nonequilibrium conditions suspected to occur in vivo. The model includes cation and nucleotide binding to actin monomers, actin nucleation and polymerization into filaments, cross-linking with alpha-actinin, monomer sequestration with profilin, filament severing, capping and nucleation with gelsolin, binding of profilin and gelsolin to membrane-bound phosphatidylinositide biphosphate (PIP2), and regulation of cross-linking and severing by changing calcium levels. We derive 1) equations for the molecular translation and rotation probabilities required for the cellular automaton simulation in terms of molecular size, shape, cytoplasmic viscosity, and temperature; and 2) equations for the binding probabilities of adjacent molecules in terms of experimentally determined reaction rate constants. The model accurately captures the known characteristics of actin polymerization and subsequent ATP hydrolysis under different cation and nucleotide conditions. An examination of gelation and sol-gel transitions resulting from calcium regulation of alpha-actinin and gelsolin predicts an inhomogeneous distribution of bound alpha-actinin and F-actin. The double-bound alpha-actinin (both ends bound to F-actin) is tightly bunched, while single-bound alpha-actinin is moderately bunched and unbound alpha-actinin is homogeneously distributed. The spatial organization of the alpha-actinin is quantified using estimates of fractal dimension. The simulation results also suggest that actin/alpha-actinin gels may shift from an isotropic to an amorphous phase after shortening of filaments. The gel-sol transition of the model shows excellent agreement with the present theory of polymer gels. The close correspondence of the model's predictions with previous experimental and theoretical results suggests that the model may be pertinent to better understanding the spatial and temporal properties of complex cytoskeletal processes.

Color categorization and color constancy in a neural network model of V4.

We develop a neural network model that instantiates color constancy and color categorization in a single unified framework. Previous models achieve similar effects but ignore important biological constraints. Color constancy in this model is achieved by a new application of the double opponent cells found in the "blobs" of the visual cortex. Color categorization emerges naturally, as a consequence of processing chromatic stimuli as vectors in a four-dimensional color space. A computer simulation of this model is subjected to the classic psychophysical tests that first uncovered these phenomena, and its response matches psychophysical results very closely.