Search Results (303)

Background/Objectives: The neonatal Fc receptor (FcRn) is a heterodimeric protein composed of a heavy α-chain with an MHC class I-like fold and β₂-microglobulin. It plays a crucial role in maintaining the homeostasis and pharmacokinetics of immunoglobulin G (IgG) and albumin through pH-dependent recycling. The production of soluble recombinant FcRn is technically challenging due to its heterodimeric structure and the presence of a transmembrane domain. This study aimed to develop a polycistronic construct enabling the co-expression of FcRn subunits from a single transcript and to evaluate the functional activity of the resulting protein in CHO-K1 cells. Methods: Integration vectors (pComV-FcRn-B2M) were designed to encode FcRn and β₂-microglobulin linked via self-cleaving 2A peptides (P2A, E2A, F2A, T2A). Stable producer cell lines were generated using the Sleeping Beauty transposon system. The purified proteins were characterized by SDS-PAGE, Western blotting, and size-exclusion chromatography (SEC). Functional activity was assessed by ELISA and bio-layer interferometry (BLI). Results: Electrophoretic and chromatographic analyses confirmed the expected subunit composition and demonstrated that over 95% of the recombinant protein was monomeric. Functional assays revealed pH-dependent IgG binding, with strong interaction at pH 6.0 and negligible binding at pH 7.5. BLI measurements showed high affinity consistent with native FcRn function (KD = 3.15 nM at pH 6.0). Conclusions: The developed polycistronic construct containing a P2A peptide with a GSG linker enabled efficient production of functional FcRn in CHO-K1 cells (yield up to 2.23 mg/mL). The P2A variant demonstrated the highest efficiency and can serve as a reference system for screening Fc-engineered antibodies with optimized pharmacokinetic properties. Full article

Antimicrobial peptides (AMPs), evolutionarily conserved components of the immune system, have attracted considerable attention as promising therapeutic candidates. Derived from diverse organisms, AMPs represent a heterogeneous class of molecules, typically cationic, which facilitates their initial electrostatic interaction with anionic microbial membranes. Unlike conventional single-target antibiotics, AMPs utilize rapid, multi-target mechanisms, primarily physical membrane disruption, which results in a significantly lower incidence of resistance emergence. Their broad-spectrum antimicrobial activity, capacity to modulate host immunity, and unique mechanisms of action make them inherently less susceptible to resistance compared with traditional antibiotics. Despite these advantages, the clinical translation of natural AMPs remains limited by several challenges, including poor in vivo stability, and potential cytotoxicity. Bioengineering technology offers innovative solutions to these limitations of AMPs. Two techniques have demonstrated promise: (i) a chimeric recombinant of AMPs with stable scaffold, such as human serum albumin and antibody Fc domain and (ii) chemical modification approaches, such as lipidation. This review provides a comprehensive overview of AMPs, highlighting their origins, structures, and mechanisms of antimicrobial activity, followed by recent advances in bioengineering platforms designed to overcome their therapeutic limitations. By integrating natural AMPs with bioengineering and nanotechnologies, AMPs may be developed into next-generation antibiotics. Full article

With the continuous growth of user reviews, identifying underlying sentiment across multi-source texts efficiently and accurately has become a significant challenge in NLP. Traditional single models in cross-domain sentiment analysis often exhibit insufficient stability, limited generalization capabilities, and sensitivity to class imbalance. Existing ensemble methods predominantly rely on static weighting or voting strategies among homogeneous models, failing to fully leverage the complementary advantages between models. To address these issues, this study proposes a heterogeneous ensemble sentiment classification model integrating multi-view features and dynamic weighting. At the feature learning layer, the model constructs three complementary base learners, a RoBERTa-FC for extracting global semantic features, a BERT-BiGRU for capturing temporal dependencies, and a TextCNN-Attention for focusing on local semantic features, thereby achieving multi-level text representation. At the decision layer, a meta-learner is used to fuse multi-view features, and dynamic uncertainty weighting and attention weighting strategies are employed to adaptively adjust outputs from different base learners. Experimental results across multiple domains demonstrate that the proposed model consistently outperforms single learners and comparison methods in terms of Accuracy, Precision, Recall, F1 Score, and Macro-AUC. On average, the ensemble model achieves a Macro-AUC of 0.9582 ± 0.023 across five datasets, with an Accuracy of 0.9423, an F1 Score of 0.9590, and a Macro-AUC of 0.9797 on the AlY_ds dataset. Moreover, in cross-dataset ranking evaluation based on equally weighted metrics, the model consistently ranks within the top two, confirming its superior cross-domain adaptability and robustness. These findings highlight the effectiveness of the proposed framework in enhancing sentiment classification performance and provide valuable insights for future research on lightweight dynamic ensembles, multilingual, and multimodal applications. Full article

Calcium signaling is essential for coordinating plant responses to diverse stimuli and regulating growth and development. Among calcium sensors, calmodulin (CaM) and CaM-like proteins (CMLs) represent a class that, despite increasing research, remains incompletely characterized in wheat, with many interacting partners and biological functions remaining largely elusive. This study conducted bioinformatics analyses of subgroup II CaM/CMLs, characterizing their phylogenetic relationships, conserved motifs, sequence features, and cis-elements. Expression analysis revealed that TaCML49-B was significantly upregulated in roots under salt stress. Moreover, TaCML49-B was localized to nucleus, cytoplasm, and membrane. Function characterization demonstrated that overexpression of TaCML49-B in Arabidopsis enhanced salt tolerance, whereas the BSMV-VIGS silencing of TaCML49-B reduced salt resistance in wheat. Furthermore, STRING database prediction analysis and bimolecular fluorescence complementation (BiFC) assay confirmed that TaCML49-B can physically interact with TaIQD23, which encodes an IQ67 domain protein, suggesting its potential involvement in the salt stress signaling pathway. Collectively, our findings indicate that TaCML49-B functions as a positive role in wheat salt stress response, thereby providing novel insights into the functions of TaCML genes and calcium signaling in wheat. Full article

Aluminum alloys under long-term service or repetitive stress are prone to small fatigue cracks (FCs) with arbitrary orientations, necessitating eddy current probes with focused magnetic fields and directional selectivity for reliable detection. This study presents a flexible printed circuit board (FPCB) probe with a double-layer planar excitation coil and a double-layer differential receiving coil. The excitation coil employs a reverse-wound design to enhance magnetic field directionality and focusing, while the differential receiving coil improves sensitivity and suppresses common-mode noise. The probe is optimized by adjusting the excitation coil overlap and the excitation–receiving coil angles to maximize eddy current concentration and detection signals. Finite element simulations and experiments confirm the system’s effectiveness in detecting surface cracks of varying sizes and orientations. To further characterize these defects, two time-domain features are extracted: the peak-to-peak value ($\Delta$P), reflecting amplitude variations associated with defect size and orientation, and the signal width ($\Delta$W), primarily correlated with defect angle. However, substantial overlap in their value ranges for defects with different parameters means that these features alone cannot identify which specific parameter has changed, making prior defect classification using a Transformer-based approach necessary for accurate quantitative analysis. The proposed method demonstrates reliable performance and clear interpretability for defect evaluation in aluminum components. Full article

Staphylococcus aureus is a major pathogen responsible for staphylococcal food poisoning (SFP), with its pathogenicity primarily dependent on staphylococcal enterotoxins (SEs). Among these, staphylococcal enterotoxin A (SEA) is a critical risk factor due to its high toxicity, high detection rate (accounting for 80% of SFP cases), strong thermal stability, and resistance to hydrolysis. Traditional SEA immunoassays, such as enzyme-linked immunosorbent assay (ELISA), are prone to false-positive results caused by nonspecific binding interference from S. aureus surface protein A (SpA). In recent years, nanobodies (single-domain heavy-chain antibodies) have emerged as an ideal alternative to address SpA interference owing to their small molecular weight (15 kDa), high affinity, robust stability, and lack of Fc regions. In this study, based on a previously developed highly specific monoclonal antibody against SEA (mAb-4C6), four anti-SEA nanobodies paired with mAb-4C6 were obtained through two-part (four-round) of biopanning from a naive nanobody phage display library. Among these, SEA-4-20 and SEA-4-31 were selected as optimal candidates and paired with mAb-4C6 to construct double-antibody sandwich ELISAs. The detection limits for SEA were 0.135 ng/mL and 0.137 ng/mL, respectively, with effective elimination of SpA interference. This approach provides a reliable tool for rapid and accurate detection of SEA in food, clinical, and environmental samples. Full article

Functional bowel disorders (FBDs) are chronic gastrointestinal conditions characterized by recurrent symptoms associated with gut microbiota dysbiosis. Although accumulating evidence suggests that probiotics can improve symptoms in patients with FBD, the underlying mechanisms remain to be fully elucidated. In this randomized, double-blind, placebo-controlled clinical trial, 38 adults meeting the Rome IV diagnostic criteria of functional constipation (FC) and functional diarrhea (FD) received either a multi-strain probiotic complex or placebo for 8 weeks. Clinical outcomes were evaluated using the Irritable Bowel Syndrome Severity Scoring System (IBS-SSS), bowel habits questionnaire, and IBS Quality of Life (IBS-QoL) instrument. Fecal samples were collected at baseline and at week 8 for gut microbiota profiling via 16S rRNA gene sequencing and metabolomic analysis using gas chromatography–mass spectrometry. Probiotic supplementation significantly reduced the severity of abdominal bloating and its interference with quality of life, and improved the body image domain of the IBS-QoL. Beta diversity analysis showed significant temporal shifts in the probiotic group, while 16S rRNA sequencing revealed an increased relative abundance of Faecalibacterium prausnitzii and Blautia stercoris. Fecal metabolomic analysis further indicated elevated levels of metabolites implicated in the gut–brain axis. Multi-strain probiotic supplementation alleviated gastrointestinal symptoms and improved aspects of psychosocial well-being in adults with FBDs, potentially through modulation of the human gut microbiome. Full article

Student trust plays a pivotal role in shaping the future integration of artificial intelligence (AI) in higher education. This study investigates how AI Facilitating Conditions (FCs), Performance Expectancy (PE), and task type influence students’ System-like Trust (AST) and Human-like Trust (AHT) in AI and further examines the mediating role of human-like trust in fostering the willingness to continue AI-assisted learning. Drawing on valid data collected from 466 Chinese university students, we employed partial least squares structural equation modeling (PLS-SEM) in combination with artificial neural networks (ANN) to test the hypothesized relationships, mediating mechanisms and the relative importance of influencing factors. The findings indicate that AI facilitating conditions significantly enhance both system-like trust and usage intention; performance expectancy exerts a positive effect on both forms of trust, with particularly strong effects observed in subjective tasks. Moreover, system-like trust positively promotes human-like trust, and together, these dimensions jointly strengthen students’ intention to engage in AI-assisted learning. Results from the ANN analysis further highlight that performance expectancy, system-like trust, and facilitating conditions are the primary determinants of system-like trust, human-like trust, and usage intention, respectively. This study extends the application of interpersonal trust theory to the AI domain and offers theoretical insights for fostering more positive and effective patterns of AI adoption in higher education. Full article

Olfaction plays a crucial role in fish feeding behaviors and ecological adaptation. However, systematic studies on its transcriptional regulation and molecular evolutionary mechanisms in herbivorous and carnivorous fishes remain scarce. In this study, we analyzed four Xenocyprididae species: two herbivorous (Ctenopharyngodon idella and Megalobrama amblycephala) and two carnivorous (Elopichthys bambusa and Culter alburnus), using olfactory rosette transcriptome sequencing and cross-species comparisons. The number of unigenes per species ranged from 40,229 to 42,405, with BUSCO completeness exceeding 89.2%. Functional annotation was performed using six major databases. Olfactory-related candidate genes were identified based on Pfam domains (7tm_4) and KEGG pathways (ko04740), revealing 8–19 olfactory receptor genes per species. These candidate genes were predominantly enriched in the olfactory transduction and neuroactive ligand–receptor interaction pathways. A total of 3681 single-copy orthologous genes were identified, and their expression profiles exhibited clear interspecific divergence without forming strict clustering by dietary type. High-threshold differentially expressed trend genes (|log₂FC| ≥ 4) were enriched in pathways related to RNA processing, metabolite transport, and xenobiotic metabolism, suggesting that the olfactory system may participate in diverse adaptive responses. Ka/Ks analysis indicated that most homologous genes were under purifying selection, with only 0.87–2.07% showing positive selection. These positively selected genes were enriched in pathways related to immune response and neural regulation, implying potential roles in adaptive evolution associated with ecological behavior. Furthermore, the olfactory-related gene oard1 exhibited Ka/Ks > 1 in the E. bambusa vs. C. idella comparison. qRT-PCR validation confirmed the reliability of the RNA-Seq data. This work is the first to integrate two complementary indicators—expression trends and evolutionary rates—to systematically investigate the transcriptional regulation and molecular evolution of the olfactory system in Xenocyprididae species under the context of dietary differentiation, providing valuable reference data for understanding the perceptual basis of dietary adaptation in freshwater fish. Full article

Background: Obstructive sleep apnea (OSA) is highly prevalent in the early stages of Alzheimer’s disease (AD), and its hallmark, sleep fragmentation, may accelerate cognitive decline. Continuous positive airway pressure (CPAP) improves OSA-related hypoxia during slow-wave sleep, but its cognitive benefits in AD remain unclear. Methods: We performed a 12-month sub-analysis of a prospective, longitudinal pilot study that enrolled 21 adults (median age = 77 yr; 71% women) with Mild Cognitive Impairment (MCI) with AD confirmed biomarkers and polysomnography-diagnosed OSA. All participants underwent baseline overnight polysomnography (PSG) and neuropsychological testing (Clinical Dementia Rating (CDR), Mini-Mental State Examination (MMSE), Repeatable Battery for the Assessment of Neuropsychological Status (RBANS)) that were repeated after 12 months. Twelve participants were CPAP-compliant (moderate/severe OSA) and nine were non-users (mild OSA/intolerance). Cognitive change scores (Δ = 12 months -baseline) were compared with Generalized Linear Models (GLM) adjusted for baseline cognition and Apnea–Hypopnea Index (AHI); associations between baseline sleep parameters and cognitive trajectories were examined. And the association of sleep variables with the use of CPAP was also evaluated. Results: Compared with non-users, CPAP users showed significantly slower global decline (Δ MMSE: p = 0.016) and improvements in overall cognition (Δ RBANS Total: p = 0.028) and RBANS sub-domains (Δ RBANS FC: p = 0.010; Δ RBANS SF: p = 0.045). Longer baseline non-rapid eye movement (NREM) stage 3 and rapid eye movement (REM) sleep, greater total sleep time and sleep efficiency, and right-side sleeping were each linked to better cognitive outcomes, whereas extended NREM stage 2, wakefulness, and supine sleeping were associated with poorer trajectories. Conclusions: Twelve months of CPAP use was associated with attenuated cognitive decline and domain-specific gains in AD-related MCI with OSA. Sleep architecture and body position during sleep predicted cognitive outcomes, underscoring the therapeutic relevance of optimizing breathing and sleep quality. Larger, longer-term trials are warranted to confirm CPAP’s disease-modifying potential and to clarify the mechanistic role of sleep in AD progression. Full article

Antibodies, also called immunoglobulins, play a key role in the body’s immune response, binding to specific molecular targets. Of the five classes of antibodies, IgG has found the greatest clinical application. The article presents the mechanisms of antibody action, including interactions with FcR receptors on leukocytes, complement activation, and direct cytotoxic interactions, as well as the main methods of antibody production, which include hybridoma technology, phage display, and production using transgenic animals and their modifications, which allowed for the production of antibodies with reduced immunogenicity and increased their effectiveness and safety of use. It also characterizes various types of antibodies and presents the differences between them resulting from the structure and content of individual protein domains encoded by human genes and genes from other species. Antibodies are currently one of the most important groups of biological drugs used in the treatment of autoimmune, infectious, and neoplastic diseases. The properties of these large biomolecules and the achievements in the field of obtaining and modifying antibodies mean that they are currently the subject of many studies. New forms of antibodies, such as antibody–drug conjugates with highly potent cytotoxic agents, bispecific antibodies, and nanobodies, demonstrate an innovative approach to the treatment of cancer and autoimmune diseases. The dynamic development of the antibody market indicates its growing importance in modern pharmacy and medicine. Further research in this area may lead to the development of more effective and precise therapies, as well as to increase the safety of their use. Full article

Fractional-order calculus (FOC) has gained significant attention in electric vehicle (EV) energy storage and management systems, as it provides enhanced modeling and analysis capabilities compared to traditional integer-order approaches. This review presents a comprehensive survey of recent advancements in the application of FOC to EV energy storage systems, including lithium-ion batteries (LIBs), supercapacitors (SCs), and fuel cells (FCs), as well as their integration within energy management systems (EMS). The review focuses on developments in electrochemical, equivalent circuit, and data-driven models formulated in the fractional-order domain, which improve the representation of nonlinear, memory-dependent, and multi-scale dynamics of energy storage devices. It also discusses the benefits and limitations of current FOC-based models, identifies open challenges such as computational feasibility and parameter identification, and outlines future research directions. Overall, the findings indicate that FOC offers a robust framework with significant potential to advance next-generation EV energy storage and management systems. Full article

Donor-specific anti-HLA antibodies (DSAs) bind to donor vascular endothelial cells and mediate allograft rejection (AMR), but a clinical challenge for which targeted therapeutic options remain limited. We used a multiplexed single-antigen bead (SAB) assay to detect anti-human leukocyte antigen (HLA) antibodies. Based on the antigens which patient’s antibodies aganist to, we developed bivalent HLA-Fc fusion proteins composed of HLA-derived antigenic domains and human IgG1-Fc effector regions (rA24-Fc and rB13-Fc). Specific binding and functional activity of the HLA-Fc proteins were further validated by flow cytometry, ELISA, complement-dependent cytotoxicity (CDC) and antibody-dependent cellular cytotoxicity (ADCC) assays. Our findings demonstrate that the fusion proteins rA24-Fc and rB13-Fc significantly reduced HLA-specific antibody reactivity in vitro. Notably, rA24-Fc and rB13-Fc selectively bound to B-cell hybridomas (e.g., mouse W6/32 cells) expressing membrane immunoglobulins (BCR) which bound to the most HLA class I antigens. Importantly, rA24-Fc and rB13-Fc elicited antigen-specific, Fc-dependent elimination of the specific B-cell hybridomas. This study highlights HLA-Fc fusion proteins as a promising therapeutic strategy for the antigen-specific suppression of depletion of alloreactive B cells through dual cytotoxic mechanisms. This precision targeted to BCR of B cells approach is used to apply to the treatment of antibody-mediated rejection. Full article

Myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) is characterized by the predominance of optic neuritis, myelitis, acute disseminated encephalomyelitis (ADEM), and cortical encephalitis, and can be diagnosed by the presence of pathogenic immunoglobulin G (IgG) antibodies targeting the extracellular domain of MOG in the serum and cerebrospinal fluid (CSF). Initially considered a variant of multiple sclerosis (MS) or neuromyelitis optica spectrum disorder (NMOSD), it is now widely recognized as a separate entity, supported by converging evidence from serological, pathological, and clinical studies. Patients with MOGAD often exhibit better recovery from acute attacks; however, their clinical and pathological features vary based on the immunological role of MOG-IgG via antibody- or complement-mediated perivenous demyelinating pathology, in addition to MOG-specific cellular immunity, resulting in heterogeneous demyelinated lesions from vanishing benign forms to tissue necrosis, even though MOGAD is not a mild disease. The key is the immunological mechanism of devastating lesion coalescence and long-term degenerating mechanisms, which may still accrue, particularly in the relapsing, progressing, and aggressive clinical course of encephalomyelitis. The warning features of the severe clinical forms are: (1) fulminant acute multifocal lesions or multiphasic ADEM transitioning to diffuse (Schilder-type) or tumefactive lesions; (2) cortical or subcortical lesions related to brain atrophy and/or refractory epilepsy (Rasmussen-type); (3) longitudinally extended spinal cord lesions severely affected with residual symptoms. In addition, it is cautious for patients refractory to acute stage early 1st treatment including intravenous methylprednisolone treatment and apheresis with residual symptoms and relapse activity with immunoglobulin and other 2nd line treatments including B cell depletion therapy. Persistent MOG-IgG high titration, intrathecal production of MOG-IgG, and suggestive markers of higher disease activity, such as cerebrospinal fluid interleukin-6 and complement C5b-9, could be identified as promising markers of higher disease activity, worsening of disability, and poor prognosis, and used to identify signs of escalating treatment strategies. It is promising of currently ongoing investigational antibodies against anti-interleukin-6 receptor and the neonatal Fc receptor. Moreover, due to possible refractory issues such as the intrathecal production of autoantibody and the involvement of complement in the worsening of the lesion, further developments of other mechanisms of action such as chimeric antigen receptor T-cell (CAR-T) and anti-complement therapies are warranted in the future. Full article

Background/Objectives: Single-domain immunoglobulins are small protein modules with specific affinities. Among them, the variable domains of heavy chains of heavy-chain-only antibodies (VHH) as the antigen-binding fragment of heavy-chain-only antibodies (also termed nanobodies) have been widely investigated for their applicability, e.g., therapeutics and immunodiagnostics. However, despite their advantageous biochemical and biophysical characteristics, protein aggregation throughout recombinant synthesis is a serious drawback in the development of nanobodies with application perspectives. Therefore, we aimed to develop a computational method to predict the aggregation propensity of VHH antibodies for the selection of promising candidates in early discovery. Methods: We employed a deep learning-based structure prediction for VHHs and derived from it likely biophysical and biochemical properties of the framework region 2 with relevance for aggregation. A total of 106 nanobody variants were produced by recombinant expression and characterized for their aggregation behavior using size exclusion chromatography (SEC). Results: Quantitative characteristics of framework region 2 patches were combined into a function that defines an aggregation score (AS) predicting the aggregation propensities of VHH variants. AS was evaluated for its capability to forecast recombinant VHH aggregation by experimentally studying VHH Fc-fusion proteins for their aggregation. We observed a clear correlation between the calculated aggregation score and the actual aggregation propensities of biochemically characterized VHHs Fc-fusion proteins. Moreover, we implemented an easily accessible pipeline of software modules to design nanobodies with desired solubility properties. Conclusions: AI-based prediction of VHH structures, followed by analysis of framework region 2 properties, can be used to predict the aggregation propensities of VHHs, providing a convenient and efficient tool for selecting stable recombinant nanobodies. Full article