Widespread somatosensory sensitivity in naturally occurring canine model of osteoarthritis

Osteoarthritis (OA)-associated pain is a leading cause of disability. Central sensitization (CS), as a result of OA, is recognized as an important facet of human patients' chronic pain and has been measured in people using quantitative sensory testing (QST) testing. The spontaneous canine OA model has been suggested as a good translational model, but CS has not been explored in this model. In this study, QST was performed on dogs with and without spontaneous hip or stifle OA to determine whether OA is associated with CS in this model. Mechanical (von Frey and blunt pressure) and thermal (hot and cold) sensory thresholds obtained in dogs with chronic OA-associated pain (n = 31) were compared with those of normal dogs (n = 23). Dogs were phenotyped and joint-pain scored, and testing was performed at the OA-affected joint, cranial tibial muscle, and dorsal metatarsal region. QST summary data were evaluated using mixed-effect models to understand the influence of OA status and covariates, and dogs with OA and control dogs were compared. The presence of OA was strongly associated with hyperalgesia across all QST modalities at the index joint, cranial tibial muscle, and metatarsal site. Mechanical QST scores were significantly moderately negatively correlated with total joint-pain scores. The spontaneous canine OA model is associated with somatosensory sensitivity, likely indicative of CS. These data further validate the canine spontaneous OA model as an appropriate model of the human OA pain condition

Supervised machine learning algorithms used to predict post-surgical outcomes following anterior surgical fixation of odontoid fractures

Background: Odontoid fractures have a high mortality rate, and numerous classification systems have previously predicted surgical outcomes with mixed consensus. We generated a machine learning (ML) construct to predict post-operative adverse events following anterior (ORIF) of odontoid fractures. Methods: 266 patients from the American college of surgeons-national surgical quality improvement program (ACS-NSQIP) with anterior ORIF (CPT 22318) of odontoid fractures from 2008-2018 were analyzed using ML algorithms random forest classifier (RF), gradient boosting classifier (GB), support vector machine classifier (SVM), Gaussian Naive Bayes classifier (GNB), and multi-layer perceptron classifier (MLP), and were compared to logistic regression classifier (LR). Algorithms predicted increased length of stay (LOS), need for transfusion (Transf), non-home discharge (NHD), and any adverse event (AAE). Permutation feature importance (PFI) identified risk factors. Results: ML algorithms outperformed LR. The average AUC for predicting Transf was 0.635 (accuracy=77.4%), extended LOS=0.652 (accuracy 59.6%), NHD 0.788 (accuracy=71.9%) and AAE 0.649 (accuracy 68.1%). GB performed highest for Transf (AUC=0.861), identifying operative time (PFI 0.253, p=0.016). GB and RF performed equally for NHD (AUC=0.819), highlighting preoperative hematocrit (PFI=0.157, p<0.001). GB predicted AAE (AUC=0.720) also identifying preoperative hematocrit (PFI=0.112, p<0.001). RF predicted extended LOS (AUC=0.669) highlighting preoperative hematocrit (PFI=0.049, p<0.001). Conclusions: ML outperformed LR, successfully predicting Transf, extended LOS, NHD, and AAE for anterior ORIF of odontoid fractures. Our construct may complement conventional risk stratification to reduce adverse outcomes and excess cost

Bostonia: The Boston University Alumni Magazine. Volume 25

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs

The fundamental cycle of concept construction underlying various theoretical frameworks

In this paper, the development of mathematical concepts over time is considered. Particular reference is given to the shifting of attention from step-by-step procedures that are performed in time, to symbolism that can be manipulated as mental entities on paper and in the mind. The development is analysed using different theoretical perspectives, including the SOLO model and various theories of concept construction to reveal a fundamental cycle underlying the building of concepts that features widely in different ways of thinking that occurs throughout mathematical learning

Using a cognitive architecture to examine what develops

Different theories of development propose alternative mechanisms by which development occurs. Cognitive architectures can be used to examine the influence of each proposed mechanism of development while keeping all other mechanisms constant. An ACT-R computational model that matched adult behavior in solving a 21-block pyramid puzzle was created. The model was modified in three ways that corresponded to mechanisms of development proposed by developmental theories. The results showed that all the modifications (two of capacity and one of strategy choice) could approximate the behavior of 7-year-old children on the task. The strategy-choice modification provided the closest match on the two central measures of task behavior (time taken per layer, r = .99, and construction attempts per layer, r = .73). Modifying cognitive architectures is a fruitful way to compare and test potential developmental mechanisms, and can therefore help in specifying “what develops.

Nonlinear saturation of electrostatic waves: mobile ions modify trapping scaling

The amplitude equation for an unstable electrostatic wave in a multi-species Vlasov plasma has been derived. The dynamics of the mode amplitude $\rho(t)$ is studied using an expansion in $\rho$; in particular, in the limit $\gamma\rightarrow0^+$, the singularities in the expansion coefficients are analyzed to predict the asymptotic dependence of the electric field on the linear growth rate $\gamma$. Generically $|E_k|\sim \gamma^{5/2}$, as $\gamma\rightarrow0^+$, but in the limit of infinite ion mass or for instabilities in reflection-symmetric systems due to real eigenvalues the more familiar trapping scaling $|E_k|\sim \gamma^{2}$ is predicted.Comment: 13 pages (Latex/RevTex), 4 postscript encapsulated figures which are included using the utility "uufiles". They should be automatically included with the text when it is downloaded. Figures also available in hard copy from the authors ([email protected]

Employing machine learning to predict adverse acute post-surgical outcomes following elective ulnar collateral ligament reconstruction

Background: Ulnar collateral ligament reconstruction ameliorates valgus elbow instability in various patient populations, including overhead athletes, patients with acute UCL rupture following high energy trauma, and those with chronic, subclinical elbow laxity. This study aims to explore machine learning algorithms to identify risk factors in patients undergoing elective UCL reconstruction in the ambulatory setting to predict postoperative outcomes. Methods: RStudio was used to create a filtering code to identify adult patients who underwent elective UCL reconstruction from 2008 to 2018 in the American college of surgeons national surgical quality improvement program database. Patients were analyzed using six ML algorithms, which were trained to predict outcomes such as extended length of stay, non-home discharge, and adverse events based on various patient characteristics and surgical variables. Algorithmic performance was then assessed and top performing algorithms underwent further analysis to determine relative feature importance using a permutation feature importance method. Results: ML exhibited excellent performance in predicting LOS, with an average AUC of 0.953, similar to that of logistic regression. Regarding NHD, ML demonstrated a 60.8% increase in AUC compared to LR. In predicting AAE, ML achieved an average AUC that was 12.7% higher than LR. Conclusions: The highly predictive capability of ML indicates the possibility to represent a procedure-specific complementary tool for the preoperative risk stratification process. This study provides a comprehensive analysis of UCL reconstruction in the management and outcomes of any patient, regardless of age or activity level

Ozone exposure is associated with acute changes in inflammation, fibrinolysis, and endothelial cell function in coronary artery disease patients

Air pollution is a major risk factor for cardiovascular disease, of which ozone is a major contributor. Several studies have found associations between ozone and cardiovascular morbidity, but the results have been inconclusive. We investigated associations between ozone and changes across biological pathways associated with cardiovascular disease