Background: Latent tuberculosis infection (LTBI) is the principal reservoir for active tuberculosis, with >85% of cases attributable to reactivation. Bacillus Calmette-Guérin fails to block this transition, leaving a critical gap in prevention. Methods: An immunoinformatics/reverse-vaccinology pipeline was applied to seven dormancy-related antigens retrieved from Mycobrowser. T-cell epitopes were predicted with NetMHCI/IIpan-4.1 and B-cell epitopes with ABCpred; antigenicity, allergenicity, and toxicity were evaluated with VaxiJen, AllerTOP, and ToxinPred. Secondary/tertiary structures were modeled with PSIPRED and AlphaFold-3; docking to Toll-like receptors (TLR) 2/4 and 100 ns molecular dynamics simulations assessed complex stability. Immune responses were simulated with C-ImmSim, and the mRNA sequence was human-codon-optimized using ExpOptimizer. Results: The resulting construct, RP14914P, encodes 14 cytotoxic T lymphocyte, 9 helper T lymphocyte, and 14 B-cell epitopes within an 866-aa, 90.4 kDa polypeptide. Antigenicity score = 0.7797, immunogenicity score = 8.58629. and no toxicity or allergenicity was predicted. Physicochemical analysis: instability index = 28.65, and solubility = 0.513. Estimated population coverage is 82.35% and 99.67% for Human Leukocyte Antigen (HLA)-I and HLA-II globally. Docking energies: −1477.8 kcal/mol (TLR2) and −1480.1 kcal/mol (TLR4). Molecular dynamics trajectories confirm stable binding. Immune simulation predicts potent activation of Natural Killer cells, macrophages, and dendritic cells, Th1 polarization, high interferon-γ/interleukin-2 secretion, and durable memory. Conclusions: In silico analyses predict that RP14914P exhibits favorable immunogenicity, safety, and broad population coverage, suggesting its potential as a promising mRNA vaccine candidate to prevent LTBI reactivation. However, these computational predictions require thorough experimental validation to confirm the vaccine’s immunogenicity and protective efficacy.
This study reports successful synthesis and application of a multifunctional magnetic nanocomposite, comprising MWCNTs, MnO₂, and mealworm skin-extracted chitin, as a splendid sorbent for preconcentration and determination of narcotic drugs papaverine, noscapine, codeine, and morphine in complex biological matrices, using d-μ-SPE (dispersive micro-solid-phase extraction). Characterization via FT-IR, FE-SEM, XRD, EDS, TGA, VSM, and BET analyses confirmed the nanocomposite's structural integrity, high surface area (107.68 m²/g), mesoporosity, and superparamagnetic behaviour, which altogether enhance adsorption capacity and facilitate facile and rapid magnetic separation. Adsorption studies demonstrated adherence to Langmuir and pseudo-second-order models, indicative of monolayer adsorption on homogeneous active sites. Statistical optimization using Central Composite Design allowed precise modulation of parameters influencing adsorption and desorption phases, improving extraction recoveries and method reproducibility. Optimal conditions for maximum analyte recovery within short extraction and desorption times included a sample pH around neutrality (7.3), adsorbent dosage of 5.8 mg, and the usage of a 50:50 ethanol-acetonitrile solvent mixture for desorption. With LODs as low as 0.10 ng/mL, broad linear dynamic ranges, and relative standard deviations under 4.8%, the method exhibited remarkable analytical performance. Application to real samples, including plasma, breast milk, urine, and saliva, yielded recoveries between 93.36% and 102.85%, which vouchsafed robustness and minimal matrix interference. Overall, exploiting a novel magnetic nanocomposite, the new environmentally-compatible, rapid, and sensitive UA-D-μ-SPE method offers a powerful tool for trace-level detection of narcotic drugs, leading to enhanced capabilities in forensic and clinical toxicology.
Cardiovascular disease is a major cause of death worldwide and a global socio-economic problem. To date, there are numerous studies focused on finding new biomarkers of cardiovascular diseases. High-technological methods such as mass spectrometry (MS), high-performance liquid chromatography (HPLC), and nuclear magnetic resonance (NMR) spectroscopy enable us to record thousands of metabolites of organs and tissues. Studying organisms at a molecular level contributes to an in-depth understanding of preclinical conditions of various diseases. Metabolomics reflects the dynamics of metabolism distribution, including environmental influences, allowing us to create a metabolic profile of the patient. The aim of this review was to analyze current data on metabolomic and proteomic biomarkers in the diagnosis of cardiovascular diseases. The search databases were used to select studies on the potential clinical and diagnostic application of proteomic and metabolomic markers in cardiology. The selected sources were subjected to qualitative and thematic analysis. All biomarkers were grouped according to the pathophysiological process (inflammation, blood coagulation and lipid metabolism disorders, myocardial necrosis, etc.). The association of changes in metabolomic and proteomic profiles with the activation of pathogenic processes in the cardiovascular system was demonstrated. The use of these multivariate markers, individually or in combination, will increase the accuracy of early diagnosis and the effectiveness of treatment. This article also highlights the limitations of the method and possible ways to solve them.
Prismatic tensegrity structures represent a novel paradigm of robotic design, characterized by tensioned wires and compressed bars in a form of twisted prism with a fixed base and a flexible top. These structures present an alternative to classic stationary manipulators as they offer several advantages, such as stability of the system, lightweight construction, impact resistance, high payload capacity, and the ability to operate in unstructured environments. However, despite these benefits, prismatic tensegrity-based robot designs face challenges in areas such as the restricted horizontal workspace. To address this issue, this research letter proposes the first prismatic tensegrity manipulator with an active bar mechanism. The proposed robot aims to expand the workspace by adding reachability in the Z-axis. We simplify the control of the manipulator using a neural network. Furthermore, this study highlights the differences between classic active wire and novel active bar actuation mechanisms in tensegrity robot control. This article is organized as follows: robot design concept, research methodology, control strategy, experiments and results, and conclusions.
Hepatocellular carcinoma (HCC) often exhibits limited responsiveness to immune checkpoint inhibitors (ICIs), largely due to an immunosuppressive tumor microenvironment (TME). Regulated cell death (RCD) pathways, including ferroptosis, necroptosis, and pyroptosis, possess immunogenic properties that may influence tumor–immune interactions and therapeutic responses. However, the prognostic significance of RCD-related genes and their relationship with immune suppression and anti–PD-1 resistance remain insufficiently understood. Two bulk RNA-seq datasets (GSE181947 and GSE248516) representing immunologically distinct HCC subtypes were analyzed to identify differentially expressed genes (DEGs). These were intersected with curated ferroptosis-, necroptosis-, and pyroptosis-related gene sets, yielding 36 differentially expressed RCD-related genes (DE-RCDRGs). Functional enrichment, protein–protein interaction (STRING and CytoHubba), and survival analyses (Kaplan–Meier Plotter, TCGA-LIHC) were performed to prioritize hub genes. Clinical correlations and epigenetic regulation were assessed using UALCAN. Expression validation was conducted across 24 liver cancer cell lines using Human Protein Atlas (HPA) RNA-seq data. Additionally, deleterious non-synonymous SNPs (nsSNPs) in prioritized genes were structurally characterized using integrative in silico modeling. Ten hub genes were identified, with CEP55, DLGAP5, and EZH2 emerging as key prognostic markers. These genes were significantly overexpressed in tumors, associated with advanced stage and poor differentiation, and showed aberrant DNA methylation. Functional enrichment linked them to oxidative stress response, mitotic regulation, and epigenetic control. Cell-line analysis showed CEP55 and DLGAP5 enrichment in SNU-series models, while EZH2 was highly expressed in HuH-6, Hep3B, and Huh7. Structural analysis further identified deleterious nsSNPs affecting critical functional domains. CEP55, DLGAP5, and EZH2 are identified as RCD-associated biomarkers linked to immune suppression and immunotherapy resistance in HCC. Integrated machine learning–based multi-omic analysis identifies CEP55, DLGAP5, and EZH2 as regulated cell death–associated biomarkers in hepatocellular carcinoma. These genes are linked to ferroptosis and necroptosis pathways, epigenetic dysregulation, immunosuppressive tumor microenvironment features (CD8⁺ T-cell exclusion and M2 macrophage polarization), and resistance to anti-PD-1 immunotherapy, highlighting their potential value for prognostic stratification and precision immunotherapy.
