Search Results (391)

Osteoclastogenesis—the activation and differentiation of osteoclasts—is one of the pivotal processes of bone remodeling and is regulated by RANKL/RANK signaling, the decoy function of osteoprotegerin (OPG), and a cascade of pro- and anti-inflammatory cytokines. The disruption of this balance leads to pathological bone loss in diseases such as osteoporosis and rheumatoid arthritis. FFAR4 (Free Fatty Acid Receptor 4), a G protein-coupled receptor for long-chain omega-3 fatty acids, has been confirmed as a key mediator of metabolic and anti-inflammatory effects. This review focuses on how FFAR4 acts as the selective receptor for the omega-3 fatty acid docosahexaenoic acid (DHA). It activates two divergent signaling pathways. The Gαq-dependent cascade facilitates intracellular calcium mobilization and ERK1/2 activation. Meanwhile, β-arrestin-2 recruitment inhibits NF-κB. These collective actions reshape the cytokine environment. In macrophages, DHA–FFAR4 signaling lowers the levels of TNF-α, interleukin-6 (IL-6), and IL-1β while increasing IL-10 secretion. Consequently, the activation of NFATc1 and NF-κB p65 is profoundly suppressed under TNF-α or RANKL stimulation. Additionally, DHA modulates the RANKL/OPG axis in osteoblastic cells by suppressing RANKL expression, thereby reducing osteoclast differentiation in an inflammatory mouse model. Full article

Introduction: Celiac disease (CD) impairs bone development in children through inflammation and nutrient malabsorption. Osteoprotegerin (OPG), a decoy receptor for RANKL, plays a role in bone remodeling and is increasingly recognized as a potential biomarker of bone metabolism and inflammation. However, its clinical significance in pediatric CD remains unclear. Aim: To evaluate the relationship between OPG levels, growth parameters, and delayed bone age in children with CD, and to assess OPG’s potential as a biomarker of bone health and disease activity. Methods: This three-year case–control study included 146 children: 25 with newly diagnosed CD (Group A), 54 with established CD on a gluten-free diet (Group B), and 67 healthy controls (Group C). Participants underwent clinical, anthropometric, and laboratory assessments at baseline and after 6 months (Groups A and B). OPG and osteocalcin were measured, and bone age was assessed radiologically. Statistical analyses included ANOVA, Spearman’s correlations, and binomial logistic regression. Results: OPG levels were highest in newly diagnosed children (Group A), showing a non-significant decrease after gluten-free diet initiation. OPG correlated negatively with age and height in CD patients and controls, and positively with hemoglobin and iron in Group B. Logistic regression revealed no significant predictive value of OPG for delayed bone age, although a trend was observed in Group B (p = 0.091). Children in long-term remission exhibited bone maturation patterns similar to healthy peers. Conclusions: OPG levels reflect disease activity and growth delay in pediatric CD but lack predictive power for delayed bone age. While OPG may serve as a secondary marker of bone turnover and inflammatory status, it is not suitable as a standalone biomarker for skeletal maturation. These findings highlight the need for integrative biomarker panels to guide bone health monitoring in children with CD. Full article

The mode pairing quantum key distribution (MP-QKD) protocol has attracted considerable attention for its capability to ensure high secure key rates over long distances without requiring global phase locking. However, ensuring symmetric channels for the MP-QKD protocol is challenging in practical quantum communication networks. Previous studies on the asymmetric MP-QKD protocol have relied on ideal decoy state assumptions and infinite-key analysis, which are unattainable for real-world deployment. In this paper, we conduct a security analysis of the asymmetric MP-QKD protocol with the finite-key analysis, where we discard the previously impractical assumptions made in the decoy state method. Combined with statistical fluctuation analysis, we globally optimized the 10 independent parameters in the asymmetric MP-QKD protocol by employing our modified particle swarm optimization. Through further analysis, the simulation results demonstrate that our work achieves improved secure key rates and transmission distances compared to the strategy with additional attenuation. We further investigate the relationship between the intensities and probabilities of signal, decoy, and vacuum states with transmission distance, facilitating their more efficient deployment in future quantum networks. Full article

The rapid integration of the Internet of Medical Things (IoMT) into healthcare systems raises urgent demands for secure communication mechanisms capable of protecting sensitive patient data. Quantum key agreement (QKA), a collaborative approach to key generation based on quantum principles, provides an attractive alternative to traditional quantum key distribution (QKD), as it eliminates dependence on a trusted authority and ensures equal participation from all users. QKA demonstrates particular suitability for IoMT’s decentralized medical networks by eliminating trusted authority dependence while ensuring equitable participation among all participants. This addresses fundamental challenges where centralized trust models introduce vulnerabilities and asymmetric access patterns that compromise egalitarian principles essential for medical data sharing. However, practical QKA applications in IoMT remain limited, particularly for schemes that avoid complex entanglement operations and authenticated classical channels. Among the few QKA protocols employing Grover’s search algorithm (GSA), existing proposals potentially suffer from limitations in fairness and security. In this paper, the author proposes an improved GSA-based QKA protocol that ensures fairness, security, and correctness without requiring an authenticated classical communication channel. The proposed scheme guarantees that each participant’s input equally contributes to the final key, preventing manipulation by any user subgroup. The scheme combines Grover’s algorithm with the decoy photon technique to ensure secure quantum transmission. Security analysis confirms resistance to external attacks, including intercept-resend, entanglement probes, and device-level exploits, as well as insider threats such as parameter manipulation. Fairness is achieved through a symmetric protocol design rooted in quantum mechanical principles. Efficiency evaluation shows a theoretical efficiency of approximately 25%, while eliminating the need for quantum memory. These results position the proposed protocol as a practical and scalable solution for future secure quantum communication systems, particularly within distributed IoMT environments. Full article

Ransomware is a group of malware that aims to make computing resources unavailable, demanding a ransom amount to return control back to users. Ransomware can be classified into two types: crypto-ransomware and locker ransomware. Crypto-ransomware employs strong encryption and prevents users’ access to the system. Locker ransomware makes access unavailable to users either by locking the boot sector or the user’s desktop. The proposed solution is an anomaly-based ransomware detection and prevention system consisting of post- and pre-encryption detection stages. The developed IDS is capable of detecting ransomware attacks by monitoring the usage of resources, triggered by anomalous behavior during an active attack. By analyzing the recorded parameters after recovery and logging any adverse effects, we were able to train the system for better detection patterns. The proposed solution allows for detection and intervention against the crypto and locker types of ransomware attacks. In previous work, the authors introduced a novel anti-ransomware tool for Windows platforms, known as R-Locker, which demonstrates high effectiveness and efficiency in countering ransomware attacks. The R-Locker solution employs “honeyfiles”, which serve as decoy files to attract ransomware activities. Upon the detection of any malicious attempts to access or alter these honeyfiles, R-Locker automatically activates countermeasures to thwart the ransomware infection and mitigate its impact. Building on our prior R-Locker framework this work introduces a multi-stage detection architecture with resource–behavioral hybrid analysis, achieving cross-platform efficacy against evolving ransomware families not addressed previously. Full article

Background: The treatment and management of atrial fibrillation poses substantial complexity. A delicate balance in the trade-off between the minimising risk of stroke without increasing the risk of bleeding through anticoagulant optimisations. Natural compounds are often associated with low-toxicity effects, and their effects on atrial fibrillation have yet to be fully understood. Whilst deep learning (a subtype of machine learning that uses multiple layers of artificial neural networks) methods may be useful for drug compound interaction and discovery analysis, graphical processing units (GPUs) are expensive and often required for deep learning. Furthermore, in limited-resource settings, such as low- and middle-income countries, such technology may not be easily available. Objectives: This study aims to discover the presence of any new therapeutic candidates from a large set of natural compounds that may support the future treatment and management of atrial fibrillation anywhere using a low-cost technique. The objective is to develop a deep learning approach under a low-resource setting where suitable high-performance NVIDIA graphics processing units (GPUs) are not available and to apply to atrial fibrillation as a case study. Methods: The primary training dataset is the MINER-DTI dataset from the BIOSNAP collection. It includes 13,741 DTI pairs from DrugBank, 4510 drug compounds, and 2181 protein targets. Deep cross-modal attention modelling was developed and applied. The Database of Useful Decoys (DUD-E) was used to fine-tune the model using contrastive learning. This application and evaluation of the model were performed on the natural compound NPASS 2018 dataset as well as a dataset curated by a clinical pharmacist and a clinical scientist. Results: the new model showed good performance when compared to existing state-of-the-art approaches under low-resource settings in both the validation set (PR AUC: 0.8118 vs. 0.7154) and test set (PR AUC: 0.8134 vs. 0.7206). Tenascin-C (TNC; NPC306696) and deferoxamine (NPC262615) were identified as strong natural compound interactors of the arrhythmogenic targets ADRB1 and HCN1, respectively. A strong natural compound interactor of the bleeding-related target Factor X was also identified as sequoiaflavone (NPC194593). Conclusions: This study presented a new high-performing model under low-resource settings that identified new natural therapeutic candidates for pharmacological cardioversion and anticoagulation. Full article

Quantum Key Distribution (QKD) enables the sharing of cryptographic keys secured by quantum mechanics. The BB84 protocol assumes single-photon sources, but practical systems rely on weak coherent pulses vulnerable to Photon-Number-Splitting (PNS) attacks. The Gottesman–Lo–Lütkenhaus–Preskill (GLLP) framework addresses these imperfections, deriving secure key rate bounds under limited PNS scenarios. The decoy-state protocol further improves performance by refining single-photon yield estimates, but still considers multi-photon states as insecure, thereby limiting intensities and constraining key rate and distance. More recently, finite-key security bounds for decoy-state QKD have been extended to address general attacks, ensuring security against adversaries capable of exploiting arbitrary strategies. In this work, we focus on a specific class of attacks, the generalized PNS attack, and demonstrate that higher pulse intensities can be securely used by employing Bayesian inference to estimate key parameters directly from observed data. By raising the pulse intensity to 10 photons, we achieve a 50-fold increase in key rate and a 62.2% increase in operational range (about 200 km) compared to the decoy-state protocol. Furthermore, we accurately model after-pulsing using a Hidden Markov Model (HMM) and reveal inaccuracies in decoy-state calculations that may produce erroneous key-rate estimates. While this methodology does not address all possible attacks, it provides a new approach to security proofs in QKD by shifting from worst-case assumption analysis to observation-dependent inference, advancing the reach and efficiency of discrete-variable QKD protocols. Full article

Increasing recognition of the importance of the tumor microenvironment (TME) in cancer therapeutic strategies has led to more efforts to target molecules in the TME. Vasorin (VASN) is a transmembrane glycoprotein that can be cleaved and released into the extracellular matrix in a soluble form (sVASN), which is regarded as a decoy that inhibits the TGF-β signaling pathway. VASN is upregulated under hypoxic or tumorigenic conditions to regulate tumor progression. In this study, cell surface CD71 was identified as a specific binding protein of sVASN and mediated the internalization of sVASN in cancerous, endothelial and T cells. Endocytosed sVASN enhanced the nuclear translocation of p-STAT3(Tyr705), leading to the activation of a cascade of genes, ultimately contributing to tumor malignant progression. In cancer cells, sVASN promoted cell proliferation and migration by upregulating the YAP1/TAZ or mTOR-AKT pathways and it promotes stemness maintenance by regulating Notch1. In endothelial cells, sVASN facilitated angiogenesis through the VEGF signaling pathway. In T cells, sVASN inhibited the activation of T cells through AKT pathway. This study elucidated the mechanism by which sVASN acts as a tumor-promoting factor to accelerate tumor malignant progression through cell-surface CD71 and presented sVASN as a novel target for cancer therapy. Full article

Glaesserella parasuis (G. parasuis) causes vascular inflammation in piglets, resulting in vascular damage. However, the mechanism causing vascular inflammation remains unclear. Baicalin possesses an anti-inflammatory function. Tumor necrosis factor-like weak inducer of apoptosis (TWEAK) has been implicated in immunosuppression. CD163, a scavenger receptor expressed on macrophages that acts as a decoy receptor for TWEAK, plays a crucial role in the regulation of autophagy and inflammation. This research investigated the efficacy of baicalin in reducing immunosuppression elicited by G. parasuis through the regulation of CD163/TWEAK-mediated autophagy. The data demonstrated that G. parasuis altered routine blood indicators and biochemical parameters, increased cytokine production, and induced blood vessel tissue damage. G. parasuis reduced the CD3+ T cell proportion, CD3+CD4+ T cell proportion, and CD3+CD8+ T cell proportion in piglet blood. The proteomic analysis revealed that CD163 was differentially expressed in the blood vessels of challenged piglets. Baicalin was found to regulate CD163/TWEAK axis expression, inhibit Notch/Wnt signaling pathway activation, promote autophagy, and reduce NLRP3/Caspase 1 signaling pathway activation. Baicalin also decreased cytokine production and alleviated pathological tissue damage in the blood vessels of G. parasuis-challenged piglets. Taken together, this study indicates that baicalin alleviates G. parasuis-induced immunosuppression and might promote CD163/TWEAK-mediated autophagy. This finding suggests that baicalin could serve as a potential therapeutic agent to control G. parasuis infection and related vascular inflammation. Full article

Accurately predicting protein–ligand binding affinity is an important step in the drug discovery process. Deep learning (DL) methods have improved binding affinity prediction by using diverse categories of molecular data. However, many models rely heavily on interaction and sequence data, which impedes proper learning and limits performance in de novo applications. To address these limitations, we developed a novel graph neural network model, called StructureNet (structure-based graph neural network), to predict protein–ligand binding affinity. StructureNet improves existing DL methods by focusing entirely on structural descriptors to mitigate data memorization issues introduced by sequence and interaction data. StructureNet represents the protein and ligand structures as graphs, which are processed using a GNN-based ensemble deep learning model. StructureNet achieved a PCC of 0.68 and an AUC of 0.75 on the PDBBind v.2020 Refined Set, outperforming similar structure-based models. External validation on the DUDE-Z dataset showed that StructureNet can effectively distinguish between active and decoy ligands. Further testing on a small subset of well-known drugs indicates that StructureNet has high potential for rapid virtual screening applications. We also hybridized StructureNet with interaction- and sequence-based models to investigate their impact on testing accuracy and found minimal difference (0.01 PCC) between merged models and StructureNet as a standalone model. An ablation study found that geometric descriptors were the key drivers of model performance, with their removal leading to a PCC decrease of over 15.7%. Lastly, we tested StructureNet on ensembles of binding complex conformers generated using molecular dynamics (MD) simulations and found that incorporating multiple conformations of the same complex often improves model accuracy by capturing binding site flexibility. Overall, the results show that structural data alone are sufficient for binding affinity predictions and can address pattern recognition challenges introduced by sequence and interaction features. Additionally, structural representations of protein–ligand complexes can be considerably improved using geometric and topological descriptors. We made StructureNet GUI interface freely available online. Full article

The SCN9A gene, a critical regulator of pain perception, encodes the voltage-gated sodium channel Nav1.7, a key mediator of pain signal transmission. This study conducts a multimodal assessment of SCN9A, integrating genetic variation, structural architecture, and molecular dynamics to elucidate its role in pain regulation. Using advanced computational methods, I-TASSER simulations generated structural decoys of the SCN9A homology domain, producing an ensemble of conformational states. SPICKER clustering identified five representative models with a C-score of −3.19 and TM-score of 0.36 ± 0.12, reflecting moderate structural similarity to experimental templates while highlighting deviations that may underpin functional divergence. Validation via ProSA-web supported model reliability, yielding a Z-score of −1.63, consistent with native-like structures. Central to the analysis was the R1150W non-synonymous variant, a potential pathogenic variant. Structural modeling revealed localized stability in the mutant conformation but disrupted hydrogen bonding and altered charge distribution. Its pathogenicity was underscored by a high MetaRNN score (0.7978498) and proximity to evolutionarily conserved regions, suggesting functional importance. Notably, the variant lies within the Sodium-Ion-Transport-Associated Domain, where perturbations could impair ion conductance and channel gating—mechanisms critical for neuronal excitability. These findings illuminate how SCN9A variants disrupt pain signaling, linking genetic anomalies to molecular dysfunction. While computational insights advance mechanistic understanding, experimental validation is essential to confirm the variant’s impact on Nav1.7 dynamics and cellular physiology. By refining SCN9A’s molecular blueprint and highlighting its therapeutic potential as a target for precision analgesics, this work provides a roadmap for mitigating pain-related disorders through channel-specific modulation. Integrating structural bioinformatics with functional genomics, this study deciphers SCN9A’s role in pain biology, laying the groundwork for novel strategies to manage pathological pain. Full article

Background/Objectives: In the tumor microenvironment, hypoxia regulates genes that support tumor cell invasion and angiogenesis under the control of the hypoxia-inducible transcription factors (HIFs). Pleiotrophin (PTN) is a secreted protein that activates cell migration in endothelial and cancer cells that express α_νβ₃ integrin but has inhibitory effects in cells that do not express α_νβ₃ integrin. In both cases, the protein tyrosine phosphatase receptor zeta 1 (PTPRZ1) seems to mediate the effects of PTN. In the present work, we studied the effect of hypoxia on PTN and PTPRZ1 expression and the functional consequences of this effect. Methods: Western blot, quantitative real-time PCR, and luciferase assays were used to study the impact of hypoxia at the protein, mRNA, and transcriptional levels, respectively. Decoy oligonucleotides (ODNs), siRNA technology, and plasmid overexpression were used to study the involvement of the transcription factors studied. Functional assays were used to study the effect of hypoxia on cell proliferation and migration. Results: Hypoxia increases PTN expression through the transcriptional activation of the corresponding gene in α_νβ₃ integrin-expressing cells. The transcription factors HIF-1α, HIF-2α, and AP-1 mediate the up-regulation of PTN by hypoxia. Functional assays in endothelial cells from PTN knockout mice or endothelial and cancer cells following the downregulation of PTN expression showed that PTN negatively affects chemical hypoxia-induced cell proliferation and migration. In cancer cells that do not express α_νβ₃ integrin, hypoxia or chemical hypoxia inhibits PTN expression in a HIF-1α-, HIF-2α-, and AP-1-independent manner. The expression of PTPRZ1 is up-regulated by chemical hypoxia, is HIF-1α- and HIF-2α-dependent, and seems to limit the activation of HIF-1α, at least in endothelial cells. Conclusions: Hypoxia or chemical hypoxia regulates PTN and PTPRZ1 expressions to restrict the stimulatory effects of hypoxia on endothelial and cancer cell migration. Full article

Alzheimer’s disease (AD), one of the neurodegenerative disorders, afflicts negatively across the whole world. Due to its complex etiology, no available treatments are disease-altering. This study aimed to explore isolated saponins profiles from Platycodon grandiflorum in the binding pockets of six target proteins of AD using computational and quantum chemistry simulations. Initially, saponin compounds were docked to AD enzymes, such as GSK-3β and synapsin I, II, and III. The subsequent research from MD simulations of the best three docked compounds (polygalacin D2, polygalacin D, and platycodin D) suggested that their profiles match with the binding of standard active drugs like ifenprodil and donepezil to the six enzymes. Moreover, analyzing DFT quantum calculations of top-scoring compounds fully unravels their electronic and quantum properties and potential in anti-AD. The subtle differences between polygalacin D and D2, and platycodin D, were studied at the level of theory DFT/B3LYP, showing that the electron-donating effect of the hydroxy ethyl group in platycodin D rendering this compound of moderate electrophilicity and reactivity. Polygalacin D2 diglucoside substituent in position-2 contributed to its best binding and intermolecular interactions more than polygalacin D and prosapogenin D, which acted as the negative decoy drug. Full article

FGFRL1 (fibroblast growth factor receptor-like 1) is a newly identified member of the FGFR family. Its extracellular domain resembles the four conventional FGFRs, while its intracellular part lacks the tyrosine kinase domain necessary for FGF-mediated signal transduction. At first, it was only considered a “decoy receptor”. However, recent studies have demonstrated that FGFRL1 is a multifunctional molecule involved in prenatal and postnatal growth of cartilage and osteogenesis, the development of embryonic kidney and diaphragm, the modulation of cellular biological behaviors, and cell signal transduction. The functional abnormalities of FGFRL1 contribute to human diseases including congenital disease, hypertension, osteoporosis, degenerative diseases of the central nervous system, and different kinds of tumors. The present review summarizes the research progress of FGFRL1, especially its subcellular location, molecular function, and associated human disease. These data may offer valuable resources for further studying the molecular function of FGFRL1 and disclosing the mechanism of its related human diseases. Full article

Ransomware, a type of malware that first appeared in 1989, encrypts user files and demands money for decryption, causing increasing global damage. To reduce the impact of ransomware, various file-based detection technologies are being developed; however, these have limitations, such as difficulties in detecting ransomware that bypasses traditional methods like decoy files. A newer approach measures file entropy to detect infected files, but attackers counter this by using encoding algorithms like Base64 to bypass detection thresholds. Additionally, attackers can neutralize detection through format-preserving encryption (FPE), which allows files to be encrypted without changing their format, complicating detection. In this article, we present a machine learning-based method for detecting ransomware-infected files encrypted using FPE techniques. We employed various machine learning models, including K-Nearest Neighbors (KNN), Logistic Regression, and Decision Tree, and found that most trained models—except for Logistic Regression and Multi-Layer Perceptron (MLP)—effectively detected ransomware-infected files encrypted with FPE. In summary, to counter the ransomware neutralization attack using FPE and entropy manipulation, this paper proposes a machine learning-based method for detecting files infected with such manipulated ransomware entropy. The experimental results showed an average precision of 94.64% across various datasets, indicating that the proposed method effectively detects ransomware-infected files. Therefore, the findings of this study offer a solution to address new ransomware attacks that aim to bypass entropy-based detection techniques, contributing to the advancement of ransomware detection and the protection of users’ files and systems. Full article