Search

The current study aimed to investigate the weight status and the level of kinesiophobia of university male students with non-specific lower back pain. 106 male students were enrolled voluntarily in the study. Kinesiophobia had assessed using the Tampa Scale (TSK), pain with 0-to-10 Pain Numerical Rating Scale (NRS). Subgroups was classified according to BMI and to kinesiophobia level. Beyond descriptive statistics, Pearson's rank correlation test (rho) was adopted for identifying the correlation between parameters. Percentage of overweight and obese students was above 61%, and near to half of the university students had high TSK score. All studied variables (weight, BMI, TSK, pain) showed significant (p

Objective: Our objective is to develop a robust emotion recognition system based on facial expressions, with a particularemphasis on two key regions: the eyes and the mouth. This paper presents a comprehensive analysis of emotion recognitionachieved through the examination of various facial regions. Facial expressions serve as invaluable indicators of humanemotions, with the eyes and mouth being particularly expressive areas. By focusing on these regions, we aim to accuratelycapture the nuances of emotional states. Methodology: The algorithm we devised not only detects facial features but also autonomously isolates the eyes andmouth regions. To enhance classification accuracy, we utilized various feature extraction and selection techniques. Subse-quently, we assessed the performance of multiple classifiers, including Support Vector Machine (SVM), Logistic Regression,Bayesian Regression, and Decision Trees, to identify the most effective approach Results: Our experimental methodology involved employing various classification techniques to assess performanceacross different models. Among these, SVM exhibited exceptional performance, boasting an impressive accuracy rate of99.2 %. This outstanding result surpassed the performance of all other methods examined in our study. Through meticu-lous examination and experimentation, we explore the effectiveness of different facial regions in conveying emotions. Ouranalysis encompasses two datasets and evaluation methodologies to ensure a comprehensive understanding of emotionrecognition capabilities. Conclusions: Our investigation presents compelling evidence that analyzing the eye region using a Support Vector Ma-chine (SVM) along with textural, HoG, and LBP features achieves an outstanding accuracy rate of 99.2 %. This remarkablefinding underscores the significant potential of prioritizing the eyes alone for precise emotion recognition. In doing so, itchallenges the conventional approach of including the entire facial area for analysis.

ABSTRACTVisual scene recognition is predominantly based on visual textures representing an object's material properties. However, the single material texture varies in scale and illumination angles due to mapping an object's shape. We present a comparative study of the colour histogram, Gabor, opponent Gabor, Local Binary Pattern (LBP), and wide-sense Markovian textural features concerning their sensitivity to simultaneous scale and illumination variations. Due to their application dominance, these textural features are selected from more than 50 published textural features. Markovian features are information preserving, and we demonstrate their superior performance for scale and illumination variable observation conditions over the standard alternative textural features. We bound the scale variation by double size, and illumination variation includes illumination spectra, acquisition devices, and 35 illumination directions spanned above a sample hemisphere. Recognition accuracy is tested on textile patterns from the University of East Anglia and wood veneers from UTIA BTF databases.

With the rapid development of video surveillance technology, face recognition has become an important security and surveillance tool. To improve the accuracy and applicability of face recognition in video surveillance, this study improved the Inertia Weight (IW) and Learning Factor (LF) based on the Particle Swarm Optimization (PSO) algorithm. Support Vector Machine (SVM) algorithm and Local Binary Mode (LBP) were used to optimize the processing. The results showed that the optimal solution could be obtained after 10 iterations, and the recognition accuracy reached 92.3%. When the number of iterations reached 40, the recognition accuracy inertia weight reached 99.7%. The average operating time of the original PSO algorithm and the optimized PSO algorithm was 26.3 s and 24.7 s, respectively. This shows that the optimization algorithm not only improves the recognition accuracy, but also shortens the operation time, and enhances the convergence performance and robustness to varying degrees. The improved model can improve the recognition rate of video surveillance system, indicating that the optimization algorithm has great application potential in the video surveillance face recognition.

Three frequent factors such as low classification accuracy, computational complexity, and resource consumption have an impact on malware evaluation methods. These challenges are exacerbated by elements such as unbalanced data environments and specific feature generation. To address these challenges, we aim to identify optimal texture features and classifiers for effective malware detection. The article outlines a method that consists of four stages: malware conversion to grayscale, feature extraction using (segmentation-based fractal texture analysis (SFTA), Local Binary Pattern (LBP), Haralick, Gabor, and Tamura), classification using (Gaussian Discriminant Analysis (GDA), k-Nearest Neighbor (KNN), Logistic, Support Vector Machines (SVM), Random Forest (RF), Extreme Learning Machine (Ensemble)), and finally the evaluation. Using the Malimg imbalanced and MaleVis balanced datasets, we assess classifier performance and feature effectiveness. Comparative analysis indicates that KNN outperforms other classifiers in terms of Accuracy, Error, F1, and Precision, while SVM and RF as runners-up. Gabor performs better in MaleVis, whereas the SFTA feature performs better under the Malimg dataset. The proposed SFTA-KNN and Gabor-KNN methods achieve 96.29% and 98.02% accuracy, respectively, surpassing current state-of-the-art approaches. Additionally, higher computing performance is achieved by using fewer dimensions when employing our feature extraction method.

Abstract Background COVID-19 is a disease that caused a contagious respiratory ailment that killed and infected hundreds of millions. It is necessary to develop a computer-based tool that is fast, precise, and inexpensive to detect COVID-19 efficiently. Recent studies revealed that machine learning and deep learning models accurately detect COVID-19 using chest X-ray (CXR) images. However, they exhibit notable limitations, such as a large amount of data to train, larger feature vector sizes, enormous trainable parameters, expensive computational resources (GPUs), and longer run-time. Results In this study, we proposed a new approach to address some of the above-mentioned limitations. The proposed model involves the following steps: First, we use contrast limited adaptive histogram equalization (CLAHE) to enhance the contrast of CXR images. The resulting images are converted from CLAHE to YCrCb color space. We estimate reflectance from chrominance using the Illumination–Reflectance model. Finally, we use a normalized local binary patterns histogram generated from reflectance (Cr) and YCb as the classification feature vector. Decision tree, Naive Bayes, support vector machine, K-nearest neighbor, and logistic regression were used as the classification algorithms. The performance evaluation on the test set indicates that the proposed approach is superior, with accuracy rates of 99.01%, 100%, and 98.46% across three different datasets, respectively. Naive Bayes, a probabilistic machine learning algorithm, emerged as the most resilient. Conclusion Our proposed method uses fewer handcrafted features, affordable computational resources, and less runtime than existing state-of-the-art approaches. Emerging nations where radiologists are in short supply can adopt this prototype. We made both coding materials and datasets accessible to the general public for further improvement. Check the manuscript’s availability of the data and materials under the declaration section for access.

The performance of the overhead squat may affect the golf swing mechanics associated with golf-related low back pain. This study investigates the difference in lumbar kinematics and joint loads during the golf downswing between golfers with different overhead squat abilities. Based on the performance of the overhead squat test, 21 golfers aged 18 to 30 years were divided into the highest-scoring group (HS, N = 10, 1.61 ± 0.05 cm, and 68.06 ± 13.67 kg) and lowest-scoring group (LS, N = 11, 1.68 ± 0.10 cm, and 75.00 ± 14.37 kg). For data collection, a motion analysis system, two force plates, and TrackMan were used. OpenSim 4.3 software was used to simulate the joint loads for each lumbar joint. An independent t-test was used for statistical analysis. Compared to golfers demonstrating limitations in the overhead squat test, golfers with better performance in the overhead squat test demonstrated significantly greater angular extension displacement on the sagittal plane, smaller lumbar extension angular velocity, and smaller L4-S1 joint shear force. Consequently, the overhead squat test is a useful index to reflect lumbar kinematics and joint loading patterns during the downswing and provides a good training guide reference for reducing the risk of a golf-related lower back injury. [ABSTRACT FROM AUTHOR]

Identifying and classifying tumors are critical in-patient care and treatment planning within the medical domain. Nevertheless, the conventional approach of manually examining tumor images is characterized by its lengthy duration and subjective nature. In response to this challenge, a novel method is proposed that integrates the capabilities of Gray-Level Co-Occurrence Matrix (GLCM) features and Local Binary Pattern (LBP) features to conduct a quantitative analysis of tumor images (Glioma, Meningioma, Pituitary Tumor). The key contribution of this study pertains to the development of interaction features, which are obtained through the outer product of the GLCM and LBP feature vectors. The utilization of this approach greatly enhances the discriminative capability of the extracted features. Furthermore, the methodology incorporates aggregated, statistical, and non-linear features in addition to the interaction features. The GLCM feature vectors are utilized to compute these values, encompassing a range of statistical characteristics and effectively modifying the feature space. The effectiveness of this methodology has been demonstrated on image datasets that include tumors. Integrating GLCM (Gray-Level Co-occurrence Matrix) and LBP (Local Binary Patterns) features offers a comprehensive representation of texture characteristics, enhancing tumor detection and classification precision. The introduced interaction features, a distinctive element of this methodology, provide enhanced discriminative capability, resulting in improved performance. Incorporating aggregated, statistical, and non-linear features enables a more precise representation of crucial tumor image characteristics. When utilized with a linear support vector machine classifier, the approach showcases a better accuracy rate of 99.84%, highlighting its efficacy and promising prospects. The proposed improvement in feature extraction techniques for brain tumor classification has the potential to enhance the precision of medical image processing significantly. The methodology exhibits substantial potential in facilitating clinicians to provide more accurate diagnoses and treatments for brain tumors in forthcoming times. [ABSTRACT FROM AUTHOR]

In the current trend, the image retrieval (IR) system has also been shifted from traditional text-based to content-based with the advancement in technology. Many issues have been resolved by content-based IR. In a particular case of information retrieval/image retrieval (IR) systems, the number of images on the web and the usage of images are growing exponentially. Therefore, IR system needs to be scalable, flexible, modularizable and promotes reusability so that it becomes easy to deploy, develop and maintain the system. In this paper, we have presented a CBIR model using Color Histogram and Local Binary Pattern (LBP) where both are built with Microservices architecture using docker platform. The framework used in this model is logic independent therefore any CBIR system can be run using this framework. The CBIR using Color Histogram uses chi-squared distance as a similarity measure while CBIR model using LBP is implemented using Linear Support Vector Machines for image classification. In our experiments, we have achieved the average recall, precision, and F-measure using Color Histogram 22.25%, 63.12%, and 32.67%, respectively. Though, we have achieved the average recall, precision, and F-measure using LBP 77.15%, 79.90%, and 76.17%, respectively. It has been observed that LBP model is more accurate than Color Histogram for detecting different weather conditions. It has also been found that the use of Microservices architecture leads to improve the non-functional qualities of a CBIR system as compared to traditional architecture styles by a great margin. [ABSTRACT FROM AUTHOR]

Background and Objectives: The success of combined antiretroviral therapy (cART) has led to a dramatic improvement in the life expectancy of people living with HIV (PLWH). However, there has been an observed increase in cardiometabolic, bone, renal, hepatic, and neurocognitive manifestations, as well as neoplasms, known as serious non-AIDS events/SNAEs, compared to the general population of corresponding age. This increase is linked to a harmful phenomenon called inflammaging/immunosenescence, which is driven by chronic immune activation and intestinal bacterial translocation. In this study, we examined immunological and metabolic parameters in individuals receiving current cART. Materials and Methods: The study was conducted at Laiko General Hospital in Athens, Greece. Plasma concentrations of sCD14, IL-6, SuPAR, I-FABP, and LBP were measured in virally suppressed PLWH under cART with at least 350 CD4 lymphocytes/μL. We compared these levels between PLWH receiving integrase strand transfer inhibitors (INSTIs) and protease inhibitors (PIs) and attempted to correlate them with chronic immune activation and metabolic parameters. Results: Data from 28 PLWH were analyzed, with a mean age of 52 and 93% being males. Among the two comparison groups, IL-6 levels were higher in the PIs group (5.65 vs. 7.11 pg/mL, p = 0.03). No statistically significant differences were found in the other measured parameters. A greater proportion of PLWH under INSTIs had normal-range LBP (33% vs. 0%, p = 0.04). When using inverse probability of treatment weighting, no statistically significant differences in the measured parameters were found between the two groups (sCD14 p = 0.511, IL-6 p = 0.383, SuPAR p = 0.793, I-FABP p = 0.868, and LBP p = 0.663). Glucose levels were found to increase after viral suppression in the entire sample (92 mg/dL vs. 98 mg/dL, p = 0.009). Total (191 mg/dL vs. 222 mg/dL, p = 0.005) and LDL cholesterol (104 mg/dL vs. 140 mg/dL, p = 0.002) levels were higher in the PIs group. No significant differences were observed in liver and renal function tests. Conclusions: Further investigation is warranted for PLWH on cART-containing INSTI regimens to explore potential reductions in chronic immune activation and intestinal bacterial translocation. [ABSTRACT FROM AUTHOR]

Recently, many existing automatic facial verification methods have focused on learning the optimal distance measurements between facials. Especially in the case of learning facial features by similarity which can make the proposed descriptors too weak. To justify filling this gap, we have proposed a new descriptor called Local Binary Gabor Quantization (LGBQ) for 3/2D face verification based on Gabor filters and uses tensor subspace transformation. Our main idea is to binarize the responses of eight Gabor filters based on eight orientations θ as a binary code which is converted into a decimal number and combines the advantage of three methods: Gabor, LBP, and LPQ. These descriptors provide more robustness to shape variations in face parts such as expression, pose, lighting, and scale. To do this, we have chosen to merge two techniques which are multilinear whitened principal component analysis (MWPCA) and tensor exponential discriminant analysis (TEDA). The experimentation is using two publicly available databases, namely, Bhosphorus, and CASIA 3D face database. The results show the supremacy of our method in terms of accuracy and execution time compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The traditional Local Binary Pattern (LBP) employs a 3x3 pixel window and examines the intensity differences between the center pixel and nearby neighbourhood pixels. However, LBP excludes the magnitude of difference information entirely, which highly enhances the discriminative performance between classes. In this work, we propose two new feature descriptors called Extended Transition-LBP (ETLBP) and Extended Radial Difference-LBP (ERDLBP) that include the mean of the magnitude difference of each neighbourhood pixel from the central pixel. The robustness of the proposed descriptors is investigated on four publicly available facial databases. The study has established the effectiveness of the feature descriptors. The experimental findings show that the suggested methods statistically outperformed the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Hand gesture recognition has become one of the most important directions in human-computer interaction (HCI) research. Despite recent advances in this area, the development of methods and techniques to correctly recognize gestures is still ongoing. In this paper, a hand gesture and sign recognition system (HGRS/SGRS) based on textural features is implemented using a local binary pattern (LBP), local directional pattern (LDP), local optimal-oriented pattern (LOOP) and local Gabor binary pattern histogram sequence (LGBPHS). In terms of feature extraction, we introduce Modifiedi-LOOP, a modified local texture descriptor for HGRS and SGRS to improve the efficiency of our system. The experiments are carried out on five datasets, for Arabic, American Alphabet Sign languages and dynamic gestures where the proposed Mi-LOOP as well as LGBPHS achieve satisfactory simulation results. [ABSTRACT FROM AUTHOR]

Background: COVID-19 is a disease that caused a contagious respiratory ailment that killed and infected hundreds of millions. It is necessary to develop a computer-based tool that is fast, precise, and inexpensive to detect COVID-19 efficiently. Recent studies revealed that machine learning and deep learning models accurately detect COVID-19 using chest X-ray (CXR) images. However, they exhibit notable limitations, such as a large amount of data to train, larger feature vector sizes, enormous trainable parameters, expensive computational resources (GPUs), and longer run-time. Results: In this study, we proposed a new approach to address some of the above-mentioned limitations. The proposed model involves the following steps: First, we use contrast limited adaptive histogram equalization (CLAHE) to enhance the contrast of CXR images. The resulting images are converted from CLAHE to YCrCb color space. We estimate reflectance from chrominance using the Illumination–Reflectance model. Finally, we use a normalized local binary patterns histogram generated from reflectance (Cr) and YCb as the classification feature vector. Decision tree, Naive Bayes, support vector machine, K-nearest neighbor, and logistic regression were used as the classification algorithms. The performance evaluation on the test set indicates that the proposed approach is superior, with accuracy rates of 99.01%, 100%, and 98.46% across three different datasets, respectively. Naive Bayes, a probabilistic machine learning algorithm, emerged as the most resilient. Conclusion: Our proposed method uses fewer handcrafted features, affordable computational resources, and less runtime than existing state-of-the-art approaches. Emerging nations where radiologists are in short supply can adopt this prototype. We made both coding materials and datasets accessible to the general public for further improvement. Check the manuscript's availability of the data and materials under the declaration section for access. [ABSTRACT FROM AUTHOR]

The automatic detection of image orientation is an important part of computer vision research. It is widely used in a variety of intelligent devices and application software. In the existing research on orientation detection, low-level features used in classification model cannot accurately express the high-level semantics of the image, and fine-tuning the existing deep learning network does not consider whether the extracted features can express the human visual perception of the orientation. As a result, the generalization ability of the model is not high. Based on the above shortcomings, we propose an automatic image orientation detection method based on the fusion of attention features (AF) and rotation features (RF). Firstly, the AF is obtained by fusing the attention mechanism features, which are extracted from the feature maps of different scales of ResNet50. It can quickly screen out high-value information from a large amount of information by using limited attention resources. Secondly, the "rotating LBP" features of different scales that can better reflect the direction attribute are extracted. The RF is obtained by residual dilated convolution combing with ResNet50. It can more accurately express the directional characteristics of the image and improve the generalization ability of the model. Finally, AF and RF are fused to realize the detection of four orientations of the image. The proposed method is verified on five different types of data sets. The results show that this method can more comprehensively express the directional semantics of images and improve the classification accuracy and wide application of the model. [ABSTRACT FROM AUTHOR]

Deep learning and Machine Learning Algorithms has become the most popular method for analyzing and extracting features especially in medical images. And feature extraction has made this task much easier. Our aim is to check which feature extraction technique works best for a classifier. We used Ophthalmic Images and applied feature extraction techniques such as Gabor, LBP (Local Binary Pattern), HOG (Histograms of Oriented Gradients), and SIFT (Scale-Invariant Feature Transform), where the obtained feature extraction techniques are passed through classifiers such as RFC (Random Forest Classifier), CNN (Convolutional Neural Network), SVM (Support Vector Machine), and KNN (K-Nearest Neighbors). Then, we compared the performance of each technique and selected which feature extraction technique gives the best performance for a specified classifier. We achieved an accuracy of 94% for Gabor Feature Extraction technique using CNN Classifier, 92% accuracy for HOG Feature Extraction technique using RFC Classifier, 90% accuracy for LBP Feature Extraction technique using RFC Classifier and we achieved 92% accuracy for SIFT Feature Extraction technique using RFC Classifier. [ABSTRACT FROM AUTHOR]

Live biotherapeutic products (LBPs) represent a new class of therapeutics indicated to prevent the recurrence of Clostridioides difficile infection (CDI) in adults. However, microbiota-based therapies have been used in CDI management before the Food and Drug Administration (FDA) designated this new drug class. The regulation of these microbiome-based therapies has varied, and several safety concerns have arisen over time. Requirements established by the FDA regarding the development of LBPs minimizes many of these prior concerns, and phase III trials have proven the safety and efficacy of 2 stool donor-derived LBPs: fecal microbiota, live-jslm (Rebyota™; formerly RBX2660) and fecal microbiota spores, live-brpk (Vowst™; formerly SER-109). Mild gastrointestinal side effects are common, but no severe drug-related adverse events have been reported with their use to date. A third LBP entering phase III clinical trials, VE303, follows a novel approach by sourcing bacterial strains from clonal cell banks and has demonstrated a similarly favorable safety profile. Live biotherapeutic products have inherent safety concerns associated with the administration of live microorganisms, but clinical trial data demonstrate their safety and efficacy when used to prevent the recurrence of Clostridioides difficile infection. [ABSTRACT FROM AUTHOR]

Veneer is the critical raw material for manufacturing man-made board products, therefore the quality of the veneer determines the level of the man-made board. However, defects in the veneer may significantly lower its grade. Currently, identifying veneer defects requires manual inspection and subsequent inpainting using a veneer digging machine. Unfortunately, this method only removes the defects of the veneer but ignore the consistency of its texture. To address this issue, we propose a feasible veneer defect reconstruction method that utilizes a texture-aware-multiscale-GAN architecture. Our method performs texture reconstruction of veneer defects to increase the texture information of the reconstructed image while improving the model efficiency, so that generates natural-looking textures in the reconstructed defect areas. The model is trained by end-to-end updating of four cascades of efficient generators and discriminators. We also employed a loss function based on local binary patterns (LBP) to ensure that the restored images contain sufficient texture information. Finally, region normalization is used in the model to enhance the accuracy of the model. Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) are used to evaluate the effectiveness of image restoration, the results show that PSNR of the method reacheds 35.32 and SSIM reaches 0.971. By minimizing the difference between the generated texture and that of the original image, our model produces high-quality results. [ABSTRACT FROM AUTHOR]

Diabetes Mellitus has turned out to be a complicated disease and as of 2016 one out of eleven humans suffer from this disease leading to Diabetic Foot Ulcers (DFU). When not treated, DFUs lead to amputation and in this work, a novel image processing method is proposed for the efficient assessment and classification of DFU images. Initially, pre-processing is done by cascaded fuzzy filter followed by nonlinear partial differential equation (NPDE) based segmentation that segments the foot ulcer regions. Consequently, the local binary pattern (LBP) is employed to extract the useful features. Then the proposed hybrid Grey Wolf Optimization-Convolutional Neural Network (GWO-CNN) model uses these features to identify the DFU regions. The performance evaluation is done by the estimation of the performance metrics and the results are compared with existing algorithms indicating the efficacy of the proposed technique. The obtained results reveal that the proposed work generates an accuracy of 98.5% with a reduced error percentage of 1.4%. [ABSTRACT FROM AUTHOR]

BACKGROUND: Work-related low back pain (LBP) increases the workforce disability and healthcare costs. This study evaluated the LBD risk level associated with handling the ACGIH TLVs in lifting tasks corresponding to various horizontal and vertical zones. OBJECTIVE: The aim of this study was to compare the low-risk ACGIH TLV to risk outcomes from various validated lifting assessment methods, including the OSU LBD Risk Model, NIOSH Lifting Equation, and LiFFT. METHODS: Twenty-four subjects were recruited for this study to perform various lifting conditions. The various ergonomic assessment methods were then used to obtain the risk assessment outcomes. RESULTS: The selected assessment methods showed that the ACGIH-defined TLVs are associated with less than high-risk for LBD for all the assessed tasks. The findings showed a moderate agreement (Kendall's W = 0.477) among the various assessment methods risk outcomes. The highest correlation (ρ= 0.886) was observed between the NIOSH Lifting Equation and LiFFT methods risk assessment outcomes. CONCLUSION: The findings showed that ACGIH-defined TLVs possesses less than high-risk for LBD. The outcomes of the selected ergonomic assessment methods moderately agree to each other. [ABSTRACT FROM AUTHOR]

BackgroundLow back pain (LBP) is one of the most common problems of public health and creates a burden globally. The aim was to assess the Polish population’s back pain prevention behaviors and beliefs and to examine how these health behaviors and beliefs vary across sociodemographic factors and physical activity.MethodsA cross-sectional survey was carried out among 208 randomly selected patients of the public general practitioner clinic. The differences in LBP-related beliefs and attitudes were determined due to participants’ status of requiring or non-requiring LBP treatment.ResultsMore than half of the respondents did not engage in behaviors that protect against back pain. Individuals with higher education levels and those who exercised at least once a week were significantly more likely to adopt behaviors to protect their backs. Less than half of the participants reported having a workplace that was adequately prepared to protect against back pain, and only 35.1% of the participants reported receiving instruction while taking up work on how to avoid back pain while working. According to respondents’ opinions, preventive actions are necessary to protect against back pain. Inappropriate exercises and stress can be contributors to back pain, with these opinions reported more often by women and participants with higher education levels. Participants who received treatment for LBP showed a significantly higher expression of behaviors to protect against back pain compared to participants who did not require treatment. However, there were no significant differences in participants’ beliefs about back pain prevention between the group requiring LBP treatment and the group not requiring LBP treatment.ConclusionThe study provides valuable insights into the association between LBP treatment, back pain prevention behaviors, and beliefs, suggesting potential avenues for future research and intervention development. By addressing workplace ergonomics and promoting a culture of back health, it may be possible to reduce the burden of LBP in Poland.

Cervical cancer is a significant concern for women, necessitating early detection and precise treatment. Conventional cytological methods often fall short in early diagnosis. The proposed innovative Heap Optimizer-based Self-Systematized Neural Fuzzy (HO-SsNF) method offers a viable solution. It utilizes HO-based segmentation, extracting features via Gray-Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). The proposed SsNF-based classifier achieves an impressive 99.6% accuracy in classifying cervical cancer cells, using the Herlev Pap Smear database. Comparative analyses underscore its superiority, establishing it as a valuable tool for precise cervical cancer detection. This algorithm has been seamlessly integrated into cervical cancer diagnosis centers, accessible through smartphone applications, with minimal resource demands. The resulting insights provide a foundation for advancing cancer prevention methods.

Abstract Objective Medication overuse headache (MOH) is a secondary headache that accompanies chronic migraine. Nonsteroidal anti-inflammatory drugs (NSAIDs) are the most frequently used analgesics worldwide and they are known to induce leaky gut. In this study, we aimed to investigate whether NSAID induced MOH is associated with altered circulating lipopolysaccharide binding protein (LBP) levels and inflammatory molecules. Materials and methods Piroxicam (10 mg/kg/day, po) for 5 weeks was used to induce MOH in female Sprague Dawley rats. Pain behavior was evaluated by periorbital withdrawal thresholds, head-face grooming, freezing, and head shake behavior. Serum samples and brain tissues were collected to measure circulating LBP, tight junction protein occludin, adherens junction protein vascular endothelial (VE)-cadherin, calcitonin gene-related peptide (CGRP), IL-6 levels and brain high mobility group box-1 (HMGB1) and IL-17 levels. Results Chronic piroxicam exposure resulted in decreased periorbital mechanical withdrawal thresholds, increased head-face grooming, freezing, and head shake behavior compared to vehicle administration. Serum LBP, CGRP, IL-6, IL-17, occludin, VE-cadherin levels and brain IL-17 and HMGB1 levels were significantly higher in piroxicam group compared to controls. Serum LBP was positively correlated with occludin (r = 0.611), VE-cadherin (r = 0.588), CGRP (r = 0.706), HMGB1 (r = 0.618) and head shakes (r = 0.921), and negatively correlated with periorbital mechanical withdrawal thresholds (r = -0.740). Conclusion Elevated serum LBP, VE-cadherin and occludin levels indicating disrupted intestinal barrier function and leakage of LPS into the systemic circulation were shown in female rats with MOH. LPS induced low-grade inflammation and elevated nociceptive and/or pro-inflammatory molecules such as HMGB1, IL-6, IL-17 and CGRP may play a role in the development and maintenance of MOH. Interference with leaky gut and pro-inflammatory nociceptive molecules could also be a target for sustained management of MOH.

Abstract Background Liver metastasis (LM) is one of the most common distant metastases of gastric cancer (GC). However, the mechanisms underlying the LM of GC (GC-LM) remain poorly understood. This study aimed to identify the tumour-secreted protein associated with GC-LM and to investigate the mechanisms by which this secreted protein remodels the liver microenvironment to promote GC-LM. Methods Data-independent acquisition mass spectrometry (DIA-MS), mRNA expression microarray, quantitative real-time PCR, enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC) were performed to identify and validate the GC-secreted proteins associated with GC-LM. A modified intrasplenic injection mouse model of LM was used to evaluate the progression and tumour burden of LM in vivo. Flow cytometry, immunofluorescence (IF), western blots (WB) and IHC were performed to validate the pre-metastatic niche (PMN) formation in the pre-modelling mouse models. mRNA sequencing of PMA-treated THP-1 cells with or without lipopolysaccharide binding protein (LBP) treatment was used to identify the functional target genes of LBP in macrophages. Co-immunoprecipitation (Co-IP), WB, ELISA, IF and Transwell assays were performed to explore the underlying mechanism of LBP in inducing intrahepatic PMN formation. Results LBP was identified as a critical secreted protein associated with GC-LM and correlated with a worse prognosis in patients with GC. LBP activated the TLR4/NF-κB pathway to promote TGF-β1 secretion in intrahepatic macrophages, which, in turn, activated hepatic satellite cells (HSCs) to direct intrahepatic fibrotic PMN formation. Additionally, TGF-β1 enhanced the migration and invasion of incoming metastatic GC cells in the liver. Consequently, selective targeting of the TGF-β/Smad signaling pathway with galunisertib demonstrated its efficacy in effectively preventing GC-LM in vivo. Conclusions The results of this study provide compelling evidence that serological LBP can serve as a valuable diagnostic biomarker for the early detection of GC-LM. Mechanistically, GC-derived LBP mediates the crosstalk between primary GC cells and the intrahepatic microenvironment by promoting TGF-β1 secretion in intrahepatic macrophages, which induces intrahepatic fibrotic PMN formation to promote GC-LM. Importantly, selectively targeting the TGF-β/Smad signaling pathway with galunisertib represents a promising preventive and therapeutic strategy for GC-LM.

Introduction: Epimuscular fat (EF) has rarely been studied in the context of low back pain (LBP). Research question: This study aims to assess the presence and extent of EF in the lumbar muscles and its association with vertebral level in patients with low back disorders and to explore correlations between EF, demographics, BMI, and LBP. Material and methods: T2 axial MRIs from L1 to L5 were manually segmented to analyze the cross-sectional area (CSA) of EF (mm2), and fat infiltration (FI,%) of 40 patients (23 females, 17 males; mean age:65.9 years) with lumbar degenerative pathologies awaiting a surgical procedure. COMI, LBP, demographic, and clinical data were extracted from the institutional registry. Statistical analyses included Wilcoxon and Mann-Whitney tests for differences in EF between sides and sexes, the Friedman test for EF size differences among lumbar levels, and Spearman’s correlation for associations, adjusted for BMI, age, and sex. Results: EF was found in 77.5% of subjects at L1, 92.5% at L2, 100% at L3 and L4, and 95.0% at L5. EF was significantly larger at L4 (253.1 ± 183.6 mm2) and L5 (220.2 ± 194.9 mm2) than at L1 (36.1 ± 37.8 mm2) and L2 (72.2 ± 84.4 mm2). No significant EF differences were found between sides and sexes. EF correlated strongly with BMI (rs = 0.65,p

Identifying and classifying tumors are critical in-patient care and treatment planning within the medical domain. Nevertheless, the conventional approach of manually examining tumor images is characterized by its lengthy duration and subjective nature. In response to this challenge, a novel method is proposed that integrates the capabilities of Gray-Level Co-Occurrence Matrix (GLCM) features and Local Binary Pattern (LBP) features to conduct a quantitative analysis of tumor images (Glioma, Meningioma, Pituitary Tumor). The key contribution of this study pertains to the development of interaction features, which are obtained through the outer product of the GLCM and LBP feature vectors. The utilization of this approach greatly enhances the discriminative capability of the extracted features. Furthermore, the methodology incorporates aggregated, statistical, and non-linear features in addition to the interaction features. The GLCM feature vectors are utilized to compute these values, encompassing a range of statistical characteristics and effectively modifying the feature space. The effectiveness of this methodology has been demonstrated on image datasets that include tumors. Integrating GLCM (Gray-Level Co-occurrence Matrix) and LBP (Local Binary Patterns) features offers a comprehensive representation of texture characteristics, enhancing tumor detection and classification precision. The introduced interaction features, a distinctive element of this methodology, provide enhanced discriminative capability, resulting in improved performance. Incorporating aggregated, statistical, and non-linear features enables a more precise representation of crucial tumor image characteristics. When utilized with a linear support vector machine classifier, the approach showcases a better accuracy rate of 99.84%, highlighting its efficacy and promising prospects. The proposed improvement in feature extraction techniques for brain tumor classification has the potential to enhance the precision of medical image processing significantly. The methodology exhibits substantial potential in facilitating clinicians to provide more accurate diagnoses and treatments for brain tumors in forthcoming times.

Objective: Medication overuse headache (MOH) is a secondary headache that accompanies chronic migraine. Nonsteroidal anti-inflammatory drugs (NSAIDs) are the most frequently used analgesics worldwide and they are known to induce leaky gut. In this study, we aimed to investigate whether NSAID induced MOH is associated with altered circulating lipopolysaccharide binding protein (LBP) levels and inflammatory molecules. Materials and methods: Piroxicam (10 mg/kg/day, po) for 5 weeks was used to induce MOH in female Sprague Dawley rats. Pain behavior was evaluated by periorbital withdrawal thresholds, head-face grooming, freezing, and head shake behavior. Serum samples and brain tissues were collected to measure circulating LBP, tight junction protein occludin, adherens junction protein vascular endothelial (VE)-cadherin, calcitonin gene-related peptide (CGRP), IL-6 levels and brain high mobility group box-1 (HMGB1) and IL-17 levels. Results: Chronic piroxicam exposure resulted in decreased periorbital mechanical withdrawal thresholds, increased head-face grooming, freezing, and head shake behavior compared to vehicle administration. Serum LBP, CGRP, IL-6, IL-17, occludin, VE-cadherin levels and brain IL-17 and HMGB1 levels were significantly higher in piroxicam group compared to controls. Serum LBP was positively correlated with occludin (r = 0.611), VE-cadherin (r = 0.588), CGRP (r = 0.706), HMGB1 (r = 0.618) and head shakes (r = 0.921), and negatively correlated with periorbital mechanical withdrawal thresholds (r = -0.740). Conclusion: Elevated serum LBP, VE-cadherin and occludin levels indicating disrupted intestinal barrier function and leakage of LPS into the systemic circulation were shown in female rats with MOH. LPS induced low-grade inflammation and elevated nociceptive and/or pro-inflammatory molecules such as HMGB1, IL-6, IL-17 and CGRP may play a role in the development and maintenance of MOH. Interference with leaky gut and pro-inflammatory nociceptive molecules could also be a target for sustained management of MOH. [ABSTRACT FROM AUTHOR]

Background: We examined the effect of the 2000-m ergometer test on gut injury in competitive elite rowers in two different training phases. Given that inflammatory markers during the competitive phase are higher, we hypothesise that markers of intestinal injury are also more elevated during that phase. Methods: We performed this study during the preparatory phase (Test I) and competitive phase (Test II) of annual training. We included 10 competitive elite rowers, members of the Polish Rowing Team, in the study after applying the inclusion/exclusion criteria. The participants performed a 2000-m ergometer test during both phases (Tests I and II). We collected blood samples before the test, immediately after the test and after 1 h of recovery. We measured the levels of interleukin 6 (IL-6), intestinal fatty acid binding protein (I-FABP), lipopolysaccharide (LPS), lipopolysaccharide-binding protein (LBP), and zonulin. Results: There were no significant changes over time in Test I and Test II in the gut integrity markers. There were significantly lower I-FABP and IL-6 levels after the test for Test II compared with Test I. The pre-test LPS level was significantly lower for Test II compared with Test I. The pre-test LBP and zonulin levels were numerically lower in Test II, but the differences were not significant. Conclusions: The 2000-m ergometer test showed no influence on gut integrity markers. However, there were differences in the response to exercise between Tests I and II. The lower level of gut injury markers after extreme exercise tests carried out during the preparation period may be the result of adaptive mechanisms and could indicate that rationally conducted training significantly decreases intestinal injury. [ABSTRACT FROM AUTHOR]

Recently, there had been a massive group of people, who were being rapidly affected by melanoma. Melanoma is a form of skin cancer that develops on the skin's surface layer. This is primarily caused due to excessive skin exposure to UV radiation and severe sunburns. Thus, the early detection of melanoma can aid us to cure it completely. This paper intends to introduce a new melanoma detection framework with four main phases viz. segmentation, feature extraction, optimal feature selection, as well as detection. Initially, the segmentation process takes place to the input skin image via Fuzzy C-Means Clustering (FCM) approach. From the segmented image ( Im seg ) , some of the features such as Gray Level Run Length Matrix (GLRM), Local Vector Pattern (LVP), Local Binary Pattern (LBP), Local Directional Pattern (LDP) and Local Tetra Pattern (LTrP) are extracted. As the extracted features (F) suffered from the issue of "curse of dimensionality", this paper utilizes optimization to select optimal features, which makes the detection more precise. As a novelty, a new hybrid algorithm Particle-Assisted Moth Search Algorithm (PA-MSA) is introduced that hybridizes the concept of Moth Search Algorithm (MSA) and Particle Swarm Optimization (PSO), respectively. For the classification process, the optimally chosen features (F opt ) are fed as input, where Deep Convolution Neural Network (DCNN) is used. Finally, a performance-based comparative analysis is conducted among the proposed PA-MSA as well as the existing models with respect to various measures. [ABSTRACT FROM AUTHOR]

Simple Summary: The research aimed to address the challenges in the early diagnosis of Oral Squamous Cell Carcinoma (OSCC), a critical concern given its high fatality rate and global prevalence. Through the development of hybrid methodologies, the study sought to improve early diagnosis, reduce the burden on pathologists, and enhance the accuracy of OSCC diagnosis. By employing transfer learning, a combination of CNN and SVMs, and a fusion of deep and texture-based features, the research achieved a significant overall accuracy of 97.00%, effectively addressing the critical problem of timely and accurate OSCC diagnosis. Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient's chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively. [ABSTRACT FROM AUTHOR]

Biometric recognition systems are frequently used in daily life although they are vulnerable to attacks. Today, especially the increasing use of face authentication systems has made these systems the target of face presentation attacks (FPA). This has increased the need for sensitive systems detecting the FPAs. Recently surgical masks, frequently used due to the pandemic, directly affect the performance of face recognition systems. Researchers design face recognition systems only from the eye region. This motivated us to evaluate the FPA detection performance of the eye region. Based on this, in cases where the whole face is not visible, the FPA detection performance of other parts of the face has also been examined. Therefore, in this study, FPA detection performances of facial regions of wide face, cropped face, eyes, nose, and mouth was investigated. For this purpose, the facial regions were determined and normalized, and texture features were extracted using powerful texture descriptor local binary patterns (LBP) due to its easy computability and low processing complexity. Multi-block LBP features are used to obtain more detailed texture information. Generally uniform LBP patterns are used for feature extraction in the literature. In this study, the FPA detection performances of both uniform LBP patterns and all LBP patterns were investigated. The size of feature vector is reduced by principal component analysis, and real/fake classification is performed with support vector machines. Experimental results on NUAA, CASIA, REPLAY-ATTACK and OULU-NPU datasets show that the use of all patterns increased the performance of FPA detection. [ABSTRACT FROM AUTHOR]

Background: Liver metastasis (LM) is one of the most common distant metastases of gastric cancer (GC). However, the mechanisms underlying the LM of GC (GC-LM) remain poorly understood. This study aimed to identify the tumour-secreted protein associated with GC-LM and to investigate the mechanisms by which this secreted protein remodels the liver microenvironment to promote GC-LM. Methods: Data-independent acquisition mass spectrometry (DIA-MS), mRNA expression microarray, quantitative real-time PCR, enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC) were performed to identify and validate the GC-secreted proteins associated with GC-LM. A modified intrasplenic injection mouse model of LM was used to evaluate the progression and tumour burden of LM in vivo. Flow cytometry, immunofluorescence (IF), western blots (WB) and IHC were performed to validate the pre-metastatic niche (PMN) formation in the pre-modelling mouse models. mRNA sequencing of PMA-treated THP-1 cells with or without lipopolysaccharide binding protein (LBP) treatment was used to identify the functional target genes of LBP in macrophages. Co-immunoprecipitation (Co-IP), WB, ELISA, IF and Transwell assays were performed to explore the underlying mechanism of LBP in inducing intrahepatic PMN formation. Results: LBP was identified as a critical secreted protein associated with GC-LM and correlated with a worse prognosis in patients with GC. LBP activated the TLR4/NF-κB pathway to promote TGF-β1 secretion in intrahepatic macrophages, which, in turn, activated hepatic satellite cells (HSCs) to direct intrahepatic fibrotic PMN formation. Additionally, TGF-β1 enhanced the migration and invasion of incoming metastatic GC cells in the liver. Consequently, selective targeting of the TGF-β/Smad signaling pathway with galunisertib demonstrated its efficacy in effectively preventing GC-LM in vivo. Conclusions: The results of this study provide compelling evidence that serological LBP can serve as a valuable diagnostic biomarker for the early detection of GC-LM. Mechanistically, GC-derived LBP mediates the crosstalk between primary GC cells and the intrahepatic microenvironment by promoting TGF-β1 secretion in intrahepatic macrophages, which induces intrahepatic fibrotic PMN formation to promote GC-LM. Importantly, selectively targeting the TGF-β/Smad signaling pathway with galunisertib represents a promising preventive and therapeutic strategy for GC-LM. [ABSTRACT FROM AUTHOR]

Aim: The gut–liver axis may contribute to pathophysiology of cholestatic liver disorders like biliary atresia (BA) by bacterial translocation (BT). Toll‐like receptors (TLR) are pattern recognition receptors known to activate innate immunity and secretion of inflammatory cytokines. Herein, we examined BT‐associated biomarkers and TLRs in relation to liver injury after successful portoenterostomy (SPE) in BA. Methods: Serum levels of lipopolysaccharide‐binding protein (LBP), CD14, LAL, TNF‐α, IL‐6 and FABP2 along with liver expression of TLRs (TLR1, TLR4, TLR7 and TLR9), LBP and CD14 were measured during median 4.9 (1.7–10.6) years follow‐up after SPE in 45 BA patients. Results: Serum LBP, CD14, TNF‐α and IL‐6 all increased after SPE whereas LAL and FABP‐2 remained unchanged. Serum LBP correlated positively with CD14 and markers of hepatocyte injury and cholestasis, but not with Metavir fibrosis stage, transcriptional markers for fibrosis (ACTA2) or ductular reaction. Serum CD14 concentration was significantly higher in patients with portal hypertension than without. While liver expression of TLR4 and LBP remained low, TLR7 and TLR1 showed marked BA‐specific increases, and TLR7 correlated with Metavir fibrosis stage and ACTA2. Conclusion: BT does not seem to play a significant role in liver injury after SPE in our series of BA patients. [ABSTRACT FROM AUTHOR]