Background Despite increased experience and technical developments in total knee arthroplasty (TKA), chronic postsurgical pain (CPSP) remains one of physicians’ biggest challenges. The aim of the present study was to evaluate the effectiveness of perineural injection therapy (PIT) in the management of CPSP after TKA. Material/Methods A total of 60 patients who had been surgically treated with TKA because of advanced knee osteoarthritis was included in the present study. The study included 2 groups. Group A consisted of patients who received 3 rounds of PIT combined with standard postoperative TKA protocol during the same period. Group B received standard postoperative TKA protocols (rehabilitation programs, oral and intravenous analgesics). Clinical effectiveness was evaluated via Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Visual Analog Scale (VAS) at baseline and 1-, 3-, and 6-month follow-ups. Results All repeated measures showed significant improvements ( P <0.001) in both groups for VAS and WOMAC scores. These scores were significantly better in group A in all follow-up periods compared with group B ( P <0.001). Twenty-nine patients (93.5%) in group A reported excellent or good outcomes compared with 26 patients (89.6%) in group B. Conclusions PIT is a promising approach in CPSP with minimal cost, simple and secure injection procedures, minimal side effects, and higher clinical efficacy.

This study aimed to evaluate the biochemical composition and biological activity of propolis samples from different regions of Türkiye to characterize and classify 24 Anatolian propolis samples according to their geographical origin. Chemometric techniques, namely, principal component analysis (PCA) and a hierarchical clustering algorithm (HCA), were applied for the first time to all data, including antioxidant capacity, individual phenolic constituents, and the antimicrobial activity of propolis to reveal the possible clustering of Anatolian propolis samples according to their geographical origin. As a result, the total phenolic content (TPC) of the propolis samples varied from 16.73 to 125.83 mg gallic acid equivalent per gram (GAE/g) sample, while the number of total flavonoids varied from 57.98 to 327.38 mg quercetin equivalent per gram (QE/g) sample. The identified constituents of propolis were phenolic/aromatic acids (chlorogenic acid, caffeic acid, p-coumaric acid, ferulic acid, and trans-cinnamic acid), phenolic aldehyde (vanillin), and flavonoids (pinocembrin, kaempferol, pinobanksin, and apigenin). This study has shown that the application of the PCA chemometric method to the biochemical composition and biological activity of propolis allows for the successful clustering of Anatolian propolis samples from different regions of Türkiye, except for samples from the Black Sea region.

Existing tools for bifurcation detection from signals of dynamical systems typically are either limited to a special class of systems or they require carefully chosen input parameters and a significant expertise to interpret the results. Therefore, we describe an alternative method based on persistent homology—a tool from topological data analysis—that utilizes Betti numbers and CROCKER plots. Betti numbers are topological invariants of topological spaces, while the CROCKER plot is a coarsened but easy to visualize data representation of a one-parameter varying family of persistence barcodes. The specific bifurcations we investigate are transitions from periodic to chaotic behavior or vice versa in a one-parameter collection of differential equations. We validate our methods using numerical experiments on ten dynamical systems and contrast the results with existing tools that use the maximum Lyapunov exponent. We further prove the relationship between the Wasserstein distance to the empty diagram and the norm of the Betti vector, which shows that an even more simplified version of the information has the potential to provide insight into the bifurcation parameter. The results show that our approach reveals more information about the shape of the periodic attractor than standard tools, and it has more favorable computational time in comparison with the Rösenstein algorithm for computing the maximum Lyapunov exponent.

SummaryIn this study, we demonstrate that engineered topological features can distinguish time series sampled from different stochastic processes with different noise characteristics, in both balanced and unbalanced sampling schemes. We compare our classification results against the results of the same classification on features coming from descriptive statistics and the wavelet transform. We conclude that machine learning models built on engineered topological features alone perform consistently better than those built on the standard statistical and wavelet features for time series classification tasks. We also apply dimension reduction techniques to our engineered features and compare the result of our classification models before and after dimensionality reduction. Finally, we also show that in our calculations of the engineered topological features, employing parallel computing methods does yield significant improvements in run time and memory footprint.