Pattern Recognition Research Articles

Literature Review on Multi-Criteria Analysis and Application in Education Environment

Due to the increasing complexity of educational data, the use of decision analysis techniques such as Multi-Criteria Decision Making (MCDM) models has become more popular in the education system in recent years. Multi criteria decision making methods provide data analysis methods faster and more efficient for revealing unhidden patterns and other meaningful information from vast educational data those conventional analytics are unable to discover in a reasonable amount of time. Particularly, MCDM techniques have been demonstrated to be effective methods for pattern recognition in educational systems. Motivated by this consideration, the purpose of this paper is to investigate the MCDM approaches applied to education systems through a review of new architectures, applications, and educational trends. The primary objective of this paper is to provide extensive insight into the application of MCDM models to education solutions in order to bridge the gap between MCDM techniques and human-based education interpretability. Then, the application of MCDM to various aspects of education are categorized. Finally, we present the current open challenges and future directions.

Brief Study on Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision and pattern recognition, offering state-of-the-art performance in tasks such as image classification, object detection, and segmentation. This study explores the architecture, training strategies, and applications of CNNs, focusing on their ability to automatically learn spatial hierarchies of features through layers of convolutional filters. The research evaluates various CNN architectures, including Lent, Alex Net, and ResNet, highlighting their improvements in accuracy and efficiency. Additionally, the study investigates advanced techniques such as data augmentation, transfer learning, and regularization methods (e. g, dropout and batch normalization), which enhance the model's generalization capabilities. Through empirical experiments, we demonstrate the effectiveness of CNNs in real-world scenarios, including medical image analysis, autonomous driving, and facial recognition. The study concludes with a discussion of the future trends of CNNs, particularly in the context of deep learning optimization, hardware acceleration, and integration with emerging technologies like edge computing and quantum machine learning.

RIPK2 is crucial for the microglial inflammatory response to bacterial muramyl dipeptide but not to lipopolysaccharide.

Receptor-interacting serine/threonine protein kinase 2 (RIPK2) is a kinase that plays an essential role in the modulation of innate and adaptive immune responses. As a downstream signaling molecule for nucleotide-binding oligomerization domain 1 (NOD1), NOD2, and Toll-like receptors (TLRs), it is implicated in the signaling triggered by recognition of microbe-associated molecular patterns by NOD1/2 and TLRs. Upon activation of these innate immune receptors, RIPK2 mediates the release of pro-inflammatory factors by activating mitogen-activated protein kinases (MAPKs) and nuclear factor-kappa B (NF-κB). However, whether RIPK2 is essential for downstream inflammatory signaling following the activation of NOD1/2, TLRs, or both remains controversial. In this study, we examined the role of RIPK2 in NOD2- and TLR4-dependent signaling cascades following stimulation of microglial cells with bacterial muramyl dipeptide (MDP), a NOD2 agonist, or lipopolysaccharide (LPS), a TLR4 agonist. We utilized a highly specific proteolysis targeting chimera (PROTAC) molecule, GSK3728857A, and found dramatic degradation of RIPK2 in a concentration- and time-dependent manner. Importantly, the PROTAC completely abolished MDP-induced increases in iNOS and COX-2 protein levels and pro-inflammatory gene transcription of Nos2 , Ptgs2 , Il-1β , Tnfα , Il6 , Ccl2 , and Mmp9 . However, increases in iNOS and COX-2 proteins and pro-inflammatory gene transcription induced by the TLR4 agonist, LPS, were only slightly attenuated with the GSK3728857A pretreatment. Further findings revealed that the RIPK2 PROTAC completely blocked the phosphorylation and activation of p65 NF-κB and p38 MAPK induced by MDP, but it had no effects on the phosphorylation of these two mediators triggered by LPS. Collectively, our findings strongly suggest that RIPK2 plays an essential role in the inflammatory responses of microglia to bacterial MDP but not to LPS.

Transformer assisted by DSC and BiLSTM for bearing fault pattern recognition under strong noise interference

ABSTRACT Deep learning has greatly promoted intelligent fault diagnosis for bearings and has led to the development of many intelligent fault recognition methods based on deep neural networks. However, bearing fault signals in industrial applications are susceptible to noise interference, and intelligent recognition methods for bearing faults under strong noise interference are lacking. An intelligent fault pattern recognition model termed IFPR-MDL, which is based on a Transformer assisted by depthwise separable convolutional (DSC) and bidirectional long short-term memory (BiLSTM) networks, is designed to identify bearing faults under strong noise conditions. In this model, multiscale features are first extracted by a multiscale feature extraction module designed on the basis of DSC; then, contextual information is further extracted by a BiLSTM, and information from different scales is integrated by Transformer encoders. The DSC network facilitates model parameter reduction, the BiLSTM network has expertise in mining temporal features, and the Transformer network excels at capturing long-range feature correlations. This model fully utilises the advantages of these models and complements them to improve the anti-interference ability of bearing fault pattern recognition. Tests on several public datasets show that the proposed model has higher recognition accuracy, stronger generalisation ability, and noise resistance in strong noise situations.

Breaking the barriers: Overcoming cancer resistance by targeting the NLRP3 inflammasome.

Inflammation has a pivotal role in the initiation and progression of various cancers, contributing to crucial processes such as metastasis, angiogenesis, cell proliferation and invasion. Moreover, the release of cytokines mediated by inflammation within the tumour microenvironment (TME) has a crucial role in orchestrating these events. The activation of inflammatory caspases, facilitated by the recruitment of caspase-1, is initiated by the activation of pattern recognition receptors on the immune cell membrane. This activation results in the production of proinflammatory cytokines, including IL-1β and IL-18, and participates in diverse biological processes with significant implications. The NOD-Like Receptor Protein 3 (NLRP3) inflammasome holds a central role in innate immunity and regulates inflammation through releasing IL-1β and IL-18. Moreover, it interacts with various cellular compartments. Recently, the mechanisms underlying NLRP3 inflammasome activation have garnered considerable attention. Disruption in NLRP3 inflammasome activation has been associated with a spectrum of inflammatory diseases, encompassing diabetes, enteritis, neurodegenerative diseases, obesity and tumours. The NLRP3 impact on tumorigenesis varies across different cancer types, with contrasting roles observed. For example, colorectal cancer associated with colitis can be suppressed by NLRP3, whereas gastric and skin cancers may be promoted by its activity. This review provides comprehensive insights into the structure, biological characteristics and mechanisms of the NLRP3 inflammasome, with a specific focus on the relationship between NLRP3 and tumour-related immune responses, and TME. Furthermore, the review explores potential strategies for targeting cancers via NLRP3 inflammasome modulation. This encompasses innovative approaches, including NLRP3-based nanoparticles, gene-targeted therapy and immune checkpoint inhibitors.

An Effective Yak Behavior Classification Model with Improved YOLO-Pose Network Using Yak Skeleton Key Points Images

Yak behavior is a valuable indicator of their welfare and health. Information about important statuses, including fattening, reproductive health, and diseases, can be reflected and monitored through several indicative behavior patterns. In this study, an improved YOLOv7-pose model was developed to detect six yak behavior patterns in real time using labeled yak key-point images. The model was trained using labeled key-point image data of six behavior patterns including walking, feeding, standing, lying, mounting, and eliminative behaviors collected from seventeen 18-month-old yaks for two weeks. There were another four YOLOv7-pose series models trained as comparison methods for yak behavior pattern detection. The improved YOLOv7-pose model achieved the best detection performance with precision, recall, mAP0.5, and mAP0.5:0.95 of 89.9%, 87.7%, 90.4%, and 76.7%, respectively. The limitation of this study is that the YOLOv7-pose model detected behaviors under complex conditions, such as scene variation, subtle leg postures, and different light conditions, with relatively lower precision, which impacts its detection performance. Future developments in yak behavior pattern detection will amplify the simple size of the dataset and will utilize data streams like optical and video streams for real-time yak monitoring. Additionally, the model will be deployed on edge computing devices for large-scale agricultural applications.

Deep Learning-Based Network Security Situation Prediction for Sensor-Enabled Networks

Aims and background: Network security detection has become increasingly complex due to the proliferation of Internet nodes and the ever-changing nature of network architecture. To address this, a multi-layer feedforward neural-network has been employed to construct a model for security threat detection, which has enhanced network security protection. Objectives and Methods: Improving prediction accuracy and real-time performance, this research suggests an optimal strategy based on Clockwork Recurrent-Neural-Networks(CW-RNNs) to handle nonlinearity and temporal dynamics in network security circumstances. We get the model to pick up on both the short-term and long-term temporal aspects of network-security situations by using the clock-cycle RNN. To further improve the network security scenario prediction model, we tune the network hyperparameters using the Grey-Wolf-Optimization(GWO) technique. By incorporating a clock-cycle for hidden units, the model can improve its pattern recognition capabilities by learning short-term knowledge from high-frequency update modules and preserving long-term memory from low-frequency update modules. Results: The optimized clock-cycle RNN achieves better prediction accuracy than competing network models when it comes to extracting nonlinear and temporal characteristics of network security scenarios, according to the experimental data. Conclusions: In addition, our method is perfect for tracking massive amounts of data transmitted by sensor networks because of its minimal time complexity and outstanding real-time performance.

Multivariate Modelling Based on Isotopic, Elemental, and Fatty Acid Profiles to Distinguish the Backyard and Barn Eggs.

The ability to trace the origin of eggs from backyard-raised hens is important due to their higher market value compared to barn-raised eggs. This study aimed to differentiate eggs from these two rearing systems using isotopic, elemental, and fatty acid profiles of egg yolks. A total of 90 egg yolk samples were analyzed, analytical results being followed by statistical tests (Student's t-test) showing significant differences in δ18O, several elements (Mg, K, Sc, Mn, Fe, Ni, Cu, Zn, As, Cd, Ba, Pb), and fatty acids compositions (C23:0, C17:0, C18:0, C16:1n7, C18:1n9, C18:2n6, C20:1n7, C20:4n6, C20:5n3, C22:6n3), as well as in the ratios of SFA, PUFA, and UFA. The results indicated a nutritional advantage in backyard eggs due to their lower n-6 polyunsaturated fatty acid content and a more favorable n-6 to n-3 ratio, linked to differences in the hens' diet and rearing systems. To classify the production system (backyard vs. barn), three pattern recognition methods were applied: linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and multilayer perceptron artificial neural networks (MLP-ANN). LDA provided perfect initial separation, achieving 98.9% accuracy in cross-validation. k-NN yielded classification rates of 98.4% for the training set and 85.7% for the test set, while MLP-ANN achieved 100% accuracy in training and 92.3% in testing, with minor misclassification. These results demonstrate the effectiveness of fusion among isotopic, elemental, and fatty acid profiles in distinguishing backyard eggs from barn eggs and highlight the nutritional benefits of the backyard-rearing system.

Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert–Huang and wavelet transforms with explainable vision transformer and CNN models

Background and Objective:Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time. This study aims to develop a deep learning-based model for the early prediction of SCD using electrocardiography (ECG) signals. Methods:A multimodal explainable deep learning-based model is developed to analyze ECG signals at discrete intervals ranging from 5 to 30 min before SCD onset. The raw ECG signals, 2D scalograms generated through wavelet transform and 2D Hilbert spectrum generated through Hilbert–Huang transform (HHT) of ECG signals were applied to multiple deep learning algorithms. For raw ECG, a combination of 1D-convolutional neural networks (1D-CNN) and long short-term memory networks were employed for feature extraction and temporal pattern recognition. Besides, to extract and analyze features from scalograms and Hilbert spectra, Vision Transformer (ViT) and 2D-CNN have been used. Results:The developed model achieved high performance, with accuracy, precision, recall and F1-score of 98.81%, 98.83%, 98.81%, and 98.81% respectively to predict SCD onset 30 min in advance. Further, the proposed model can accurately classify SCD patients and normal controls with 100% accuracy. Thus, the proposed method outperforms the existing state-of-the-art methods. Conclusions:The developed model is capable of capturing diverse patterns on ECG signals recorded at multiple discrete time intervals (at 5-minute increments from 5 min to 30 min) prior to SCD onset that could discriminate for SCD. The proposed model significantly improves early SCD prediction, providing a valuable tool for continuous ECG monitoring in high-risk patients.

LPS binding caspase activation and recruitment domains (CARDs) are bipartite lipid binding modules.

Caspase-11 is an innate immune pattern recognition receptor (PRR) that detects cytosolic bacterial lipopolysaccharides (LPS) through its caspase activation and recruitment domain (CARD), triggering inflammatory cell death known as pyroptosis. Caspase-11 also detects eukaryotic ( i.e. self) lipids. This observation raises the question of whether common or distinct mechanisms govern the interactions with self and nonself lipids. In this study, using biochemical, computational, and cell-based assays, we report that the caspase-11 CARD functions as a bipartite lipid-binding module. Distinct regions within the CARD bind to phosphate groups and long acyl chains of self and nonself lipids. Self-lipid binding capability is conserved across numerous caspase-11 homologs and orthologs. The symmetry in self and nonself lipid detection mechanisms enabled us to engineer an LPS-binding domain de novo , using an ancestral CARD-like domain present in the fish Amphilophus citrinellus . These findings offer critical insights into the molecular basis of LPS recognition by caspase-11 and highlight the fundamental and likely inseparable relationship between self and nonself discrimination.

Effects of dietary Lactobacillus postbiotics and bacitracin on the modulation of mucosa-associated microbiota and pattern recognition receptors affecting immunocompetence of jejunal mucosa in pigs challenged with enterotoxigenic F18+ Escherichia coli

BackgroundEnterotoxigenic Escherichia coli (E. coli) is a threat to humans and animals that causes intestinal disorders. Antimicrobial resistance has urged alternatives, including Lactobacillus postbiotics, to mitigate the effects of enterotoxigenic E. coli.MethodsForty-eight newly weaned pigs were allotted to NC: no challenge/no supplement; PC: F18+ E. coli challenge/no supplement; ATB: F18+ E. coli challenge/bacitracin; and LPB: F18+ E. coli challenge/postbiotics and fed diets for 28 d. On d 7, pigs were orally inoculated with F18+ E. coli. At d 28, the mucosa-associated microbiota, immune and oxidative stress status, intestinal morphology, the gene expression of pattern recognition receptors (PRR), and intestinal barrier function were measured. Data were analyzed using the MIXED procedure in SAS 9.4.ResultsPC increased (P < 0.05) Helicobacter mastomyrinus whereas reduced (P < 0.05) Prevotella copri and P. stercorea compared to NC. The LPB increased (P < 0.05) P. stercorea and Dialister succinatiphilus compared with PC. The ATB increased (P < 0.05) Propionibacterium acnes, Corynebacterium glutamicum, and Sphingomonas pseudosanguinis compared to PC. The PC tended to reduce (P = 0.054) PGLYRP4 and increased (P < 0.05) TLR4, CD14, MDA, and crypt cell proliferation compared with NC. The ATB reduced (P < 0.05) NOD1 compared with PC. The LPB increased (P < 0.05) PGLYRP4, and interferon-γ and reduced (P < 0.05) NOD1 compared with PC. The ATB and LPB reduced (P < 0.05) TNF-α and MDA compared with PC.ConclusionsThe F18+ E. coli challenge compromised intestinal health. Bacitracin increased beneficial bacteria showing a trend towards increasing the intestinal barrier function, possibly by reducing the expression of PRR genes. Lactobacillus postbiotics enhanced the immunocompetence of nursery pigs by increasing the expression of interferon-γ and PGLYRP4, and by reducing TLR4, NOD1, and CD14.

Extracellular plant subtilases dampen cold-shock peptide elicitor levels.

Recognizing pathogen-associated molecular patterns on the cell surface is crucial for plant immunity. The proteinaceous nature of many of these patterns suggests that secreted proteases play important roles in their formation and stability. Here we demonstrate that the apoplastic subtilase SBT5.2a inactivates the immunogenicity of cold-shock proteins (CSPs) of the bacterial plant pathogen Pseudomonas syringae by cleaving within the immunogenic csp22 epitope. Consequently, mutant plants lacking SBT5.2a activity retain higher levels of csp22, leading to enhanced immune responses and reduced pathogen growth. SBT5.2 sensitivity is influenced by sequence variation surrounding the cleavage site and probably extends to CSPs from other bacterial species. These findings suggest that variations in csp22 stability among bacterial pathogens are a crucial factor in plant-bacteria interactions and that pathogens exploit plant proteases to avoid pattern recognition.

GATA1-deficient human pluripotent stem cells generate neutrophils with improved antifungal immunity that is mediated by the integrin CD18.

Neutrophils are critical for host defense against fungi. However, the short life span and lack of genetic tractability of primary human neutrophils has limited in vitro analysis of neutrophil-fungal interactions. Human induced pluripotent stem cell (iPSC)-derived neutrophils (iNeutrophils) are a genetically tractable alternative to primary human neutrophils. Here, we show that deletion of the transcription factor GATA1 from human iPSCs results in iNeutrophils with improved antifungal activity against Aspergillus fumigatus. GATA1 knockout (KO) iNeutrophils have increased maturation, antifungal pattern recognition receptor expression and more readily execute neutrophil effector functions compared to wild-type iNeutrophils. iNeutrophils also show a shift in their metabolism following stimulation with fungal β-glucan, including an upregulation of the pentose phosphate pathway (PPP), similar to primary human neutrophils in vitro. Furthermore, we show that deletion of the integrin CD18 attenuates the ability of GATA1-KO iNeutrophils to kill A. fumigatus but is not necessary for the upregulation of PPP. Collectively, these findings support iNeutrophils as a robust system to study human neutrophil antifungal immunity and has identified specific roles for CD18 in the defense response.

Semantic-aware frame-event fusion based pattern recognition via large vision–language models

Pattern recognition through the fusion of RGB frames and Event streams has emerged as a novel research area in recent years. Current methods typically employ backbone networks to individually extract the features of RGB frames and event streams, and subsequently fuse these features for pattern recognition. However, we posit that these methods may suffer from two key issues: (1). They attempt to directly learn a mapping from the input vision modality to the semantic labels. This approach often leads to sub-optimal results due to the disparity between the input and semantic labels; (2). They utilize small-scale backbone networks for the extraction of RGB and Event input features, thus these models fail to harness the recent performance advancements of large-scale visual-language models. In this study, we introduce a novel pattern recognition framework that consolidates the semantic labels, RGB frames, and event streams, leveraging pre-trained large-scale vision–language models. Specifically, given the input RGB frames, event streams, and all the predefined semantic labels, we employ a pre-trained large-scale vision model (CLIP vision encoder) to extract the RGB and event features. To handle the semantic labels, we initially convert them into language descriptions through prompt engineering and polish using ChatGPT, and then obtain the semantic features using the pre-trained large-scale language model (CLIP text encoder). Subsequently, we integrate the RGB/Event features and semantic features using multimodal Transformer networks. The resulting frame and event tokens are further amplified using self-attention layers. Concurrently, we propose to enhance the interactions between text tokens and RGB/Event tokens via cross-attention. Finally, we consolidate all three modalities using self-attention and feed-forward layers for recognition. Comprehensive experiments on the HARDVS and PokerEvent datasets fully substantiate the efficacy of our proposed SAFE model. The source code has been released at https://github.com/Event-AHU/SAFE_LargeVLM.

Enhancing Hopfield network performance for pattern retrieval using sparse recovery algorithm and Parzen estimator

An improved pattern recovery approach that integrates the Hopfield neural network (HNN) with the iterative signal reconstruction and Parzen window-based classification is proposed. The HNN is observed as a form of associative memory network, used for various pattern recognition and optimization tasks. However, when the input pattern is highly damaged with a very limited set of available samples, the Hopfield network fails to perform the retrieval. The convex optimization-based gradient descent algorithm is considered for pattern recovery of damaged inputs in order to provide an improved pattern approximation for further processing within the HNN, enabling successful network performance. Additionally, in the case of grayscale images, the Parzen window approach is used to classify the probability density functions (pdfs) of the training set and to choose those being comparable to the pdf of the input pattern, therefore refining the selection of patterns and providing better convergence to the exact retrieval. The theoretical considerations are verified experimentally, showing the high performance of the proposed approach when only 10 % of the pixels are available for binary patterns and 40 % of pixels for grayscale patterns.

Exploring the Role of Artificial Intelligence in Internet of Things Systems: A Systematic Mapping Study.

The use of Artificial Intelligence (AI) in Internet of Things (IoT) systems has gained significant attention due to its potential to improve efficiency, functionality and decision-making. To further advance research and practical implementation, it is crucial to better understand the specific roles of AI in IoT systems and identify the key application domains. In this article we aim to identify the different roles of AI in IoT systems and the application domains where AI is used most significantly. We have conducted a systematic mapping study using multiple databases, i.e., Scopus, ACM Digital Library, IEEE Xplore and Wiley Online. Eighty-one relevant survey articles were selected after applying the selection criteria and then analyzed to extract the key information. As a result, six general tasks of AI in IoT systems were identified: pattern recognition, decision support, decision-making and acting, prediction, data management and human interaction. Moreover, 15 subtasks were identified, as well as 13 application domains, where healthcare was the most frequent. We conclude that there are several important tasks that AI can perform in IoT systems, improving efficiency, security and functionality across many important application domains.

Comparison of sedation and general anesthesia protocols for 18F-FDG-PET/CT studies in dogs and cats: Musculoskeletal uptake and radiation dose to workers.

In veterinary medicine, PET/CT scans are generally performed with the patient under general anesthesia. The aim of this prospective crossover study was to compare the musculoskeletal uptake of 18F-FDG and radiation doses to workers during PET/CT studies of healthy dogs and cats between sedation and general anesthesia. Volume and maximal standard uptake values (SUVmax) values of abnormal 18F-FDG uptake in the skeletal musculature, presence of misregistration artifact, and radiation doses to workers for each PET/CT study were recorded. Sedation was associated with increased volume of 18F-FDG uptake in the musculature of the thoracic limbs (p=.01), cervical (p=.02), and thoracic (p=.03) spine. Increased volume and SUVmax of the musculature assessed altogether were associated with the lighter degree of sedation (p=.04 for both). A significant decrease in the odds of misregistration artifact was observed for anesthetized animals in comparison with sedated (OR: 0.0, 95% CI: 0.0-0.0, p=.01). Radiation doses to workers were significantly higher for sedation compared with general anesthesia (p=.01) and for the anesthesia technician compared with the nuclear medicine technologist (p=.01). Use of sedation for PET/CT studies in dogs and cats is feasible. However, it is associated with increased physiologic musculoskeletal uptake of 18F-FDG in the thoracic limbs, cervical, and thoracic spine, with increased frequency of misregistration artifact, and with increased radiation doses to workers. These limitations can be overcome by recognition of the uptake pattern and monitoring/rotation of the involved staff at institutions where a high caseload is expected.

MTC-NET: A Multi-Channel Independent Anomaly Detection Method for Network Traffic.

In recent years, deep learning-based approaches, particularly those leveraging the Transformer architecture, have garnered widespread attention for network traffic anomaly detection. However, when dealing with noisy data sets, directly inputting network traffic sequences into Transformer networks often significantly degrades detection performance due to interference and noise across dimensions. In this paper, we propose a novel multi-channel network traffic anomaly detection model, MTC-Net, which reduces computational complexity and enhances the model's ability to capture long-distance dependencies. This is achieved by decomposing network traffic sequences into multiple unidimensional time sequences and introducing a patch-based strategy that enables each sub-sequence to retain local semantic information. A backbone network combining Transformer and CNN is employed to capture complex patterns, with information from all channels being fused at the final classification header in order to achieve modelling and detection of complex network traffic patterns. The experimental results demonstrate that MTC-Net outperforms existing state-of-the-art methods in several evaluation metrics, including accuracy, precision, recall, and F1 score, on four publicly available data sets: KDD Cup 99, NSL-KDD, UNSW-NB15, and CIC-IDS2017.

G-protein–coupled estrogen receptor 30 regulation of signaling downstream of protein kinase Cε mediates sex dimorphism in hyaluronan-induced antihyperalgesia

Abstract High molecular weight hyaluronan (HMWH) inhibits hyperalgesia induced by diverse pronociceptive inflammatory mediators and their second messengers, in rats of both sexes. However, the hyperalgesia induced by ligands at 3 pattern recognition receptors, lipopolysaccharide (a toll-like receptor 4 agonist), lipoteichoic acid (a toll-like receptor 2/6 agonist), and nigericin (a NOD-like receptor family, pyrin domain containing 3 activator), and oxaliplatin and paclitaxel chemotherapy–induced peripheral neuropathy are only attenuated in males. After gonadectomy or intrathecal administration of an antisense to G-protein–coupled estrogen receptor 30 (GPER) mRNA, HMWH produces antihyperalgesia in females. In nociceptors cultured from rats that had been treated with oxaliplatin, HMWH reverses nociceptor sensitization from male and GPER antisense–treated female, but not from gonad intact females. G-protein–coupled estrogen receptor–dependent sex dimorphism for HMWH-induced antihyperalgesia was also observed for the prolongation of prostaglandin E2 (PGE2)-induced hyperalgesia in primed nociceptors. While in primed rats, HMWH inhibits early, protein kinase A-dependent hyperalgesia, 30 minutes post PGE2 injection, in both sexes; measured 4 hours post-PGE2, HMWH inhibits the protein kinase Cε (PKCε)-dependent prolongation of PGE2 hyperalgesia only in males and GPER antisense–treated females. In females, hyperalgesia induced by PKCε agonist, ψεRACK, in control but not in primed nociceptors, was inhibited by HMWH. Inhibitors of 2 GPER second messengers, extracellular-regulated kinase 1/2 and nonreceptor tyrosine kinase, also unmasked HMWH antihyperalgesia in females with oxaliplatin chemotherapy–induced peripheral neuropathy, a condition in which nociceptors are primed as well as sensitized. Our results support GPER-dependent sex dimorphism in HMWH-induced antihyperalgesia for pain induced by pattern recognition receptor agonists, and chronic inflammatory and neuropathic pain, mediated by changes in signaling downstream of PKCε in primed nociceptors.

Infrared spectroscopy as a new approach for early fabry disease screening: a pilot study

BackgroundFabry disease (FD) is a rare X-linked lysosomal storage disorder marked by alpha-galactosidase-A (α-Gal A) deficiency, caused by pathogenic mutations in the GLA gene, resulting in the accumulation of glycosphingolipids within lysosomes. The current screening test relies on measuring α-Gal A activity. However, this approach is limited to males. Infrared (IR) spectroscopy is a technique that can generate fingerprint spectra of a biofluid’s molecular composition and has been successfully applied to screen numerous diseases. Herein, we investigate the discriminating vibration profile of plasma chemical bonds in patients with FD using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy.ResultsThe Fabry disease group (n = 47) and the healthy control group (n = 52) recruited were age-matched (39.2 ± 16.9 and 36.7 ± 10.9 years, respectively), and females were predominant in both groups (59.6% and 65.4%, respectively). All patients had the classic phenotype (100%), and no late-onset phenotype was detected. A generated partial least squares discriminant analysis (PLS-DA) classification model, independent of gender, allowed differentiation of samples from FD vs. control groups, reaching 100% sensitivity, specificity and accuracy.ConclusionATR-FTIR spectroscopy harnessed to pattern recognition algorithms can distinguish between FD patients and healthy control participants, offering the potential of a fast and inexpensive screening test.