The Relationship between Kinesiophobia, Emotional State, Functional State and Chronic Pain in Subjects with/without Temporomandibular Disorders.

Although there is growing evidence that kinesiophobia is correlated with temporomandibular disorders (TMD), its relationship with other characteristic TMD comorbidities, such as depression, anxiety, functional limitations, and pain in the TMD population, has rarely been investigated. This study aims to evaluate the relationship between kinesiophobia, emotional state, functional state and chronic pain in subjects both with and without TMD. A total of 94 subjects participated in the study and were divided into two groups (47 subjects each)-TMD (subjects with temporomandibular disorders) and nTMD (asymptomatic controls)-on the basis of the RDC/TMD protocol. All measurements were taken with self-administered questionnaires: TSK-TMD for kinesiophobia, PHQ-9 and GAD-7 for psychoemotional state, JFLS-20 for jaw functional limitations, and GCPS for chronic pain. The prevalence of kinesiophobia in the TMD group was 38.3% for moderate risk, and 61.7% for high risk. The TMD group showed significantly higher scores in all categories (kinesiophobia, depression, jaw functional limitations and chronic pain), with the exception of anxiety which was right at the cut-off point. Moreover, a significant correlation was found between kinesiophobia (TSK-TMD) and jaw functional limitations (JFLS-20). Results of this study could provide new insight into the relationship between kinesiophobia and TMD, further improving the diagnosis process.

A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe.

Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adjacent populations of common origin. Machine learning (ML) techniques seem to be especially well suited for analyzing large datasets obtained using MPS. The Slavic populations constitute about a third of the population of Europe and inhabit a large area of the continent, while being relatively closely related in population genetics terms. In this proof-of-concept study, various ML techniques were used to classify DNA samples from Slavic and non-Slavic individuals. The primary objective of this study was to empirically evaluate the feasibility of discerning the genetic provenance of individuals of Slavic descent who exhibit genetic similarity, with the overarching goal of categorizing DNA specimens derived from diverse Slavic population representatives. Raw sequencing data were pre-processed, to obtain a 1200 character-long binary vector. A total of three classifiers were used-Random Forest, Support Vector Machine (SVM), and XGBoost. The most-promising results were obtained using SVM with a linear kernel, with 99.9% accuracy and F1-scores of 0.9846-1.000 for all classes.

Integrating glycolysis, citric acid cycle, pentose phosphate pathway, and fatty acid beta-oxidation into a single computational model.

The metabolic network of a living cell is highly intricate and involves complex interactions between various pathways. In this study, we propose a computational model that integrates glycolysis, the pentose phosphate pathway (PPP), the fatty acids beta-oxidation, and the tricarboxylic acid cycle (TCA cycle) using queueing theory. The model utilizes literature data on metabolite concentrations and enzyme kinetic constants to calculate the probabilities of individual reactions occurring on a microscopic scale, which can be viewed as the reaction rates on a macroscopic scale. However, it should be noted that the model has some limitations, including not accounting for all the reactions in which the metabolites are involved. Therefore, a genetic algorithm (GA) was used to estimate the impact of these external processes. Despite these limitations, our model achieved high accuracy and stability, providing real-time observation of changes in metabolite concentrations. This type of model can help in better understanding the mechanisms of biochemical reactions in cells, which can ultimately contribute to the prevention and treatment of aging, cancer, metabolic diseases, and neurodegenerative disorders.

Biometric Technologies Based on Optical Coherence Tomography.

Optical coherence tomography (OCT) is one of the newest and most important optical non-invasive methods for the investigation and testing of various materials (e [...].

Conversion of fat to cellular fuel-Fatty acids ß-oxidation model.

ß-oxidation of fatty acids plays a significant role in the energy metabolism of the cell. This paper presents a ß-oxidation model of fatty acids based on queueing theory. It uses Michaelis-Menten enzyme kinetics, and literature data on metabolites' concentration and enzymatic constants. A genetic algorithm was used to optimize the parameters for the pathway reactions. The model enables real-time tracking of changes in the concentrations of metabolites with different carbon chain lengths. Another application of the presented model is to predict the changes caused by system disturbance, such as altered enzyme activity or abnormal fatty acid concentration. The model has been validated against experimental data. There are diseases that change the metabolism of fatty acids and the presented model can be used to understand the cause of these changes, analyze metabolites abnormalities, and determine the initial target of treatment.

Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction.

The use of neural networks for retinal vessel segmentation has gained significant attention in recent years. Most of the research related to the segmentation of retinal blood vessels is based on fundus images. In this study, we examine five neural network architectures to accurately segment vessels in fundus images reconstructed from 3D OCT scan data. OCT-based fundus reconstructions are of much lower quality compared to color fundus photographs due to noise and lower and disproportionate resolutions. The fundus image reconstruction process was performed based on the segmentation of the retinal layers in B-scans. Three reconstruction variants were proposed, which were then used in the process of detecting blood vessels using neural networks. We evaluated performance using a custom dataset of 24 3D OCT scans (with manual annotations performed by an ophthalmologist) using 6-fold cross-validation and demonstrated segmentation accuracy up to 98%. Our results indicate that the use of neural networks is a promising approach to segmenting the retinal vessel from a properly reconstructed fundus.

Queueing theory model of mTOR complexes' impact on Akt-mediated adipocytes response to insulin.

A queueing theory based model of mTOR complexes impact on Akt-mediated cell response to insulin is presented in this paper. The model includes several aspects including the effect of insulin on the transport of glucose from the blood into the adipocytes with the participation of GLUT4, and the role of the GAPDH enzyme as a regulator of mTORC1 activity. A genetic algorithm was used to optimize the model parameters. It can be observed that mTORC1 activity is related to the amount of GLUT4 involved in glucose transport. The results show the relationship between the amount of GAPDH in the cell and mTORC1 activity. Moreover, obtained results suggest that mTORC1 inhibitors may be an effective agent in the fight against type 2 diabetes. However, these results are based on theoretical knowledge and appropriate experimental tests should be performed before making firm conclusions.

Repeatability of the Vibroarthrogram in the Temporomandibular Joints.

Current research concerning the repeatability of the joint's sounds examination in the temporomandibular joints (TMJ) is inconclusive; thus, the aim of this study was to investigate the repeatability of the specific features of the vibroarthrogram (VAG) in the TMJ using accelerometers. The joint sounds of both TMJs were measured with VAG accelerometers in two groups, study and control, each consisting of 47 participants (n = 94). Two VAG recording sessions consisted of 10 jaw open/close cycles guided by a metronome. The intraclass correlation coefficient (ICC) was calculated for seven VAG signal features. Additionally, a k-nearest-neighbors (KNN) classifier was defined and compared with a state-of-the-art method (joint vibration analysis (JVA) decision tree). ICC indicated excellent (for the integral below 300 Hz feature), good (total integral, integral above 300 Hz, and median frequency features), moderate (integral below to integral above 300 Hz ratio feature) and poor (peak amplitude feature) reliability. The accuracy scores for the KNN classifier (up to 0.81) were higher than those for the JVA decision tree (up to 0.60). The results of this study could open up a new field of research focused on the features of the vibroarthrogram in the context of the TMJ, further improving the diagnosing process.

Queueing theory model of pentose phosphate pathway.

Due to its role in maintaining the proper functioning of the cell, the pentose phosphate pathway (PPP) is one of the most important metabolic pathways. It is responsible for regulating the concentration of simple sugars and provides precursors for the synthesis of amino acids and nucleotides. In addition, it plays a critical role in maintaining an adequate level of NADPH, which is necessary for the cell to fight oxidative stress. These reasons prompted the authors to develop a computational model, based on queueing theory, capable of simulating changes in PPP metabolites' concentrations. The model has been validated with empirical data from tumor cells. The obtained results prove the stability and accuracy of the model. By applying queueing theory, this model can be further expanded to include successive metabolic pathways. The use of the model may accelerate research on new drugs, reduce drug costs, and reduce the reliance on laboratory animals necessary for this type of research on which new methods are tested.

Advances in Computer Recognition, Image Processing and Communications.

This Special Issue aimed to gather high-quality advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity [...].

Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks.

This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to 96.35%. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.

Queueing theory model of Krebs cycle.

MOTIVATION: Queueing theory can be effective in simulating biochemical reactions taking place in living cells, and the article paves a step toward development of a comprehensive model of cell metabolism. Such a model could help to accelerate and reduce costs for developing and testing investigational drugs reducing number of laboratory animals needed to evaluate drugs. RESULTS: The article presents a Krebs cycle model based on queueing theory. The model allows for tracking of metabolites concentration changes in real time. To validate the model, a drug-induced inhibition affecting activity of enzymes involved in Krebs cycle was simulated and compared with available experimental data. AVAILABILITYAND IMPLEMENTATION: The source code is freely available for download at https://github.com/UTP-WTIiE/KrebsCycleUsingQueueingTheory, implemented in C# supported in Linux or MS Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

IMAGING AND MEASUREMENT OF THE PRERETINAL SPACE IN VITREOMACULAR ADHESION AND VITREOMACULAR TRACTION BY A NEW SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY ANALYSIS.

PURPOSE: To evaluate a new method for volumetric imaging of the preretinal space (also known as the subhyaloid, subcortical, or retrocortical space) and investigate differences in preretinal space volume in vitreomacular adhesion (VMA) and vitreomacular traction (VMT). METHODS: Nine patients with VMA and 13 with VMT were prospectively evaluated. Automatic inner limiting membrane line segmentation, which exploits graph search theory implementation, and posterior cortical vitreous line segmentation were performed on 141 horizontal spectral domain optical coherence tomography B-scans per patient. Vertical distances (depths) between the posterior cortical vitreous and inner limiting membrane lines were calculated for each optical coherence tomography B-scan acquired. The derived distances were merged and visualized as a color depth map that represented the preretinal space between the posterior surface of the hyaloid and the anterior surface of the retina. The early treatment d retinopathy study macular map was overlaid onto final virtual maps, and preretinal space volumes were calculated for each early treatment diabetic retinopathy study map sector. RESULTS: Volumetric maps representing preretinal space volumes were created for each patient in the VMA and VMT groups. Preretinal space volumes were larger in all early treatment diabetic retinopathy study map macular regions in the VMT group compared with those in the VMA group. The differences reached statistical significance in all early treatment diabetic retinopathy study sectors, except for the superior outer macula and temporal outer macula where significance values were P = 0.05 and P = 0.08, respectively. Overall, the relative differences in preretinal space volumes between the VMT and VMA groups varied from 2.7 to 4.3 in inner regions and 1.8 to 2.9 in outer regions. CONCLUSION: Our study provides evidence of significant differences in preretinal space volume between eyes with VMA and those with VMT. This may be useful not only in the investigation of preretinal space properties in VMA and VMT, but also in other conditions, such as age-related macular degeneration, diabetic retinopathy, and central retinal vein occlusion.

Continuity of Y chromosome haplotypes in the population of Southern Poland before and after the Second World War.

The Polish population is reported to be very homogenous as far as Y chromosome polymorphism is concerned. One of the hypotheses that explains this phenomenon is based on the assumption that massive migrations that took place in Poland after the Second World War might have evoked such an effect. Thus, knowledge of the pre-war frequencies of Y chromosome haplotypes in different parts of the country would be a useful tool in testing such a hypothesis. We have collected 226 DNA samples, together with family history data, from males living in the rural area of Malopolska, Polish Southern border region. Based on donors' family histories we were able to reconstruct an 'ancestral' subpopulation of 108 males whose ancestors had inhabited the area before both World Wars. We have analyzed 12 Y-STR loci: DYS19, DYS385, DYS389I&II, DYS390, DYS391, DYS392, DYS393, DYS437, DYS438 and DYS439 in all the collected samples. Comparisons of our contemporary and 'ancestral' population samples with other Polish and Central European populations showed that the population of Southern Malopolska is very closely related to other Polish and Slavic populations. The above-mentioned observations suggest that the population of Southern Poland could have been highly homogenous even before the Second World War.