Search Results (1,130)

Pine wilt disease (PWD), caused by the pine wood nematode (PWN) Bursaphelenchus xylophilus, is a devastating pine disease that is characterized by rapid transmission, high lethality, and limited control options. In our previous study, the fucosyltransferase gene (fut) which encoded fucosyltransferase (FUT) was found to be a putative virulence determinant in PWN, which regulates pathogenicity of nematodes. To investigate the functional role of the fut gene in PWN, a comprehensive analysis was conducted to understand its molecular structure and biological activity. The full-length open reading frame (ORF) of fut was amplified using reverse transcription PCR (RT-PCR) and successfully ligated into the pET-28a expression vector. Heterologous expression of the recombinant FUT was achieved in Escherichia coli Rosetta (DE3) through induction with 1.0 mM isopropyl-β-D-thiogalactoside (IPTG), followed by purification via nickel-nitrilotriacetic acid (Ni-NTA) affinity chromatography. Biochemical characterization revealed that the recombinant FUT exhibited optimal enzymatic activity at ‌30 °C‌ and ‌pH 8.0‌, respectively. Furthermore, RNA interference (RNAi) validated by RT-qPCR was used to explore the biological functions of fut in PWN, and results indicated that downregulation of the fut gene could significantly reduce the vitality, reproduction, pathogenicity, development, and lifespan of PWN. Furthermore, gallic acid as an inhibitor of FUT displayed a strong inhibitory effect on recombinant FUT activity and nematicidal activity against PWNs in vitro and could alleviate the wilt symptom of pine seedlings inoculated with PWNs at a concentration of 100 μg/mL, indicating that it has the potential to be a novel nematicide. Collectively, these results establish fut as a critical virulence determinant in PWN and highlight its potential as a molecular target for controlling pine wilt disease. Full article

Background: Monitoring training load and recovery is essential for performance optimization and injury prevention in youth football. However, predicting subjective recovery in preadolescent athletes remains challenging due to biological variability and the multidimensional nature of training responses. This exploratory study examined whether supervised machine learning (ML) models could predict Total Quality of Recovery (TQR) using integrated external load, internal load, anthropometric and maturational variables collected over one competitive microcycle. Methods: Forty male sub-elite U11 and U13 football players (age 10.3 ± 0.7 years; height 1.43 ± 0.08 m; body mass 38.6 ± 6.2 kg; BMI 18.7 ± 2.1 kg/m²) completed a microcycle comprising four training sessions (MD-4 to MD-1) and one official match (MD). A total of 158 performance-related variables were extracted, including external load (GPS-derived metrics), internal load (RPE and sRPE), heart rate indicators (U13 only), anthropometric and maturational measures, and tactical–cognitive indices (FUT-SAT). After preprocessing and aggregation at the player level, five supervised ML algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB)—were trained using a 70/30 train–test split and 5-fold cross-validation to classify TQR into Low, Moderate, and High categories. Results: Tree-based models (DT, GB) demonstrated the highest predictive performance, whereas linear and distance-based approaches (SVM, KNN) showed lower discriminative ability. Anthropometric and maturational factors emerged as the most influential predictors of TQR, with external and internal load contributing modestly. Predictive accuracy was moderate, reflecting the developmental variability characteristics of this age group. Conclusions: Using combined physiological, mechanical, and maturational data, these ML-based monitoring systems can simulate subjective recovery in young football players, offering potential as decision-support tools in youth sub-elite football and encouraging a more holistic and individualized approach to training and recovery management. Full article

Unmanned underwater vehicles (UUVs) capable of agile, high-speed maneuvering in complex environments require propulsion systems that can dynamically modulate three-dimensional forces. The California sea lion (Zalophus californianus) provides an exceptional biological model, using its foreflippers to achieve rapid turns and powerful propulsion. However, the specific kinematic mechanisms that govern instantaneous force generation from its powerful foreflippers remain poorly quantified. This study experimentally characterizes the time-varying thrust and lift produced by a bio-robotic sea lion foreflipper to determine how flipper twist, sweep, and phase overlap modulate propulsive forces. A three-degree-of-freedom bio-robotic flipper with a simplified, low-aspect-ratio planform and single compliant hinge was tested in a circulating flow tank, executing parameterized power and paddle strokes in both isolated and combined-phase trials. The time-resolved force data reveal that the propulsive stroke functions as a tunable hybrid system. The power phase acts as a force-vectoring mechanism, where the flipper’s twist angle reorients the resultant vector: thrust is maximized in a broad, robust range peaking near 45°, while lift increases monotonically to 90°. The paddle phase operates as a flow-insensitive, geometrically driven thruster, where twist angle (0° optimal) regulates thrust by altering the presented surface area. In the full stroke, a temporal-phase overlap governs thrust augmentation, while the power-phase twist provides robust steering control. Within the tested inertial flow regime (Re ≈ 10⁴–10⁵), this control map is highly consistent with propulsion dominated by geometric momentum redirection and impulse timing, rather than circulation-based lift. These findings establish a practical, experimentally derived control map linking kinematic inputs to propulsive force vectors, providing a foundation for the design and control of agile, bio-inspired underwater vehicles. Full article

Cervical cancer (CC) can be prevented through continuous screening and the timely detection of cervical intraepithelial neoplasia (CIN) using immunohistochemistry techniques to identify biomarker expressions. In a previous study, we proposed nuclear REST loss as a biomarker in precancerous lesions and CC; however, no validated antibodies are available for detecting REST in cytology or cervical tissues. Although we have developed an IgM-type anti-REST monoclonal antibody capable of detecting REST in liquid-based cytology cells, it was not useful for the detection of REST in cervical tissues by immunohistochemistry. The main objective of this study is to generate single-chain variable fragments (scFvs) for the clinical evaluation of REST in cervical tissues from women with CIN and CC. Using RNA from an IgM-producing hybridoma anti-REST, we conducted RT-PCR and PCR to obtain the coding sequences for the variable regions of the heavy and light chains. These sequences were joined with a linker to create a single-chain antibody. The scFv was then cloned into the pSyn1 vector, expressed in E. coli TG1, and purified through chromatography. Subsequently, it was characterized using immunological methods to assess its biological activity and employed to evaluate REST expression in cytological samples and cervical tissues. The anti-REST scFv represents an innovative detection tool that retains the antigen recognition of the parental IgM while overcoming its size limitation, enabling tissue penetration and detection of REST in cervical samples. Its application facilitates the identification of REST in cervical samples, reinforcing REST’s potential as a diagnostic biomarker for CC and CIN. Full article

Background/Objectives: Cell-mediated and peptide-assisted delivery systems have emerged as powerful platforms at the intersection of chemistry, nanotechnology, and molecular medicine. By leveraging the intrinsic targeting, transport, and signaling capacities of living cells and bioinspired peptides, these systems facilitate the delivery of therapeutic agents across otherwise restrictive biological barriers such as the blood–brain barrier (BBB) and the tumor microenvironment. This review aims to summarize recent advances in engineered cell carriers, peptide vectors, and hybrid nanostructures designed for enhanced intracellular and tissue-specific delivery. Methods: We surveyed recent literature covering molecular design principles, mechanistic studies, and in vitro/in vivo evaluations of cell-mediated and peptide-enabled delivery platforms. Emphasis was placed on neuro-oncology, immunotherapy, and regenerative medicine, with particular focus on uptake pathways, endosomal escape mechanisms, and structure–function relationships. Results: Analysis of current strategies reveals significant progress in optimizing cell-based transport systems, peptide conjugates, and multifunctional nanostructures for the targeted delivery of drugs, nucleic acids, and immunomodulatory agents. Key innovations include improved BBB penetration, enhanced tumor homing, and more efficient cytosolic delivery enabled by advanced peptide designs and engineered cellular carriers. Several platforms have progressed toward clinical translation, underscoring their therapeutic potential. Conclusions: Cell-mediated and peptide-assisted delivery technologies represent a rapidly evolving frontier with broad relevance to next-generation therapeutics. Despite notable advances, challenges remain in scalability, manufacturing, safety, and regulatory approval. Continued integration of chemical design, molecular engineering, and translational research will be essential to fully realize the clinical impact of these delivery systems. Full article

Mangrove forests are biodiverse and highly productive coastal ecosystems, fundamental to fisheries and tourism. However, they are severely threatened by human activities and invasive species, particularly in port areas such as the Port of Santos, necessitating effective environmental management. This study aimed to analyze the risks of biological invasion in mangrove ecosystems stemming from port activities, with a focus on the Port of Santos (PS), Brazil. To achieve this, we conducted a bibliometric review using the Web of Science and Scopus databases, analyzed vessel traffic flows arriving at the PS over 14 years (from 2010 to 2024), and discussed alternatives to address the challenge of biological invasion. The review revealed a significant gap in the scientific literature, as few studies (9.9%, n = 71) address the intersection of maritime transport, invasive species, and mangroves in Latin American contexts. The intense and constant flow of international vessels into the Port of Santos, totaling 15,193 arrivals from more than 200 ports worldwide between 2010 and 2024, poses a persistent threat of biological invasion. This high-volume connectivity, with several foreign hubs exceeding 300 departures in the period, reinforces the role of ships as vectors transporting exotic species in ballast water and through hull fouling. This can destabilize local ecosystems and cause significant socioeconomic losses unless control measures, mediated by effective policies, regulations, and technologies, are implemented in the short term. A spatiotemporal analysis of vessel traffic flows over a 14-year period revealed persistent high-risk corridors for bioinvasion, directly linking maritime activity patterns to the threat level for adjacent mangrove ecosystems. The data indicate a substantial challenge for the PS, yet one with a high potential for resolution in the medium term, contingent upon investment in technology and regulation. Full article

In this paper, we propose the Adaptive Volcano Support Vector Machine (AVSVM)—a novel classification model inspired by the dynamic behavior of volcanic eruptions—for the purpose of enhancing malware detection. Unlike conventional SVMs that rely on static decision boundaries, AVSVM introduces biologically inspired mechanisms such as pressure estimation, eruption-triggered kernel perturbation, lava flow-based margin refinement, and an exponential cooling schedule. These components work synergistically to enable real-time adjustment of the decision surface, allowing the classifier to escape local optima, mitigate class overlap, and stabilize under high-dimensional, noisy, and imbalanced data conditions commonly found in malware detection tasks. Extensive experiments were conducted on the UNSW-NB15 and KDD Cup 1999 datasets, comparing AVSVM to baseline classifiers including traditional SVM, PSO-SVM, and CNN under identical computational settings. On the UNSW-NB15 dataset, AVSVM achieved an accuracy of 96.7%, recall of 95.4%, precision of 96.1%, F₁-score of 95.75%, and a false positive rate of only 3.1%, outperforming all benchmarks. Similar improvements were observed on the KDD dataset. In addition, AVSVM demonstrated smooth convergence behavior and statistically significant gains (p < 0.05) across all pairwise comparisons. These results validate the effectiveness of incorporating biologically motivated adaptivity into classical margin-based classifiers and position AVSVM as a promising tool for intelligent malware detection systems. Full article

Increased atmospheric nitrogen (N) deposition alters the formation and stability of soil organic carbon (SOC) in fragile ecosystems. While biochar (BC) amendment represents a promising strategy for augmenting soil carbon sequestration, its impact on the stability of the SOC pool under high N deposition remains unclear. In this study, we conducted a two-year field trial with three replicates to investigate the effects of combined N (0 and 9 g N·m⁻²·yr⁻¹) and BC (0, 20, and 40 t·ha⁻¹) addition on the stability of the SOC pool in restored grasslands on the Loess Plateau. We assessed SOC pool stability by examining the influence of soil microbial carbon utilization efficiency (CUE), metabolic constraints, and community composition on the content of particulate organic carbon (POC) and mineral-associated organic carbon (MAOC). The results indicate that in comparison to the control treatment (N0BC0), the addition of both high N (N9BC0) and BC (N0BC20 and N0BC40) significantly promoted the accumulation of POC by 15.78%, 9.87%, and 11.05%, respectively. Conversely, the content of MAOC was suppressed under the N9BC0 (−10.64%) and N0BC40 (−8.29%) treatments. However, the combination of high N and BC treatments resulted in increased levels of SOC, POC, and MAOC, while simultaneously reducing the MAOC/POC ratio, with all parameters reaching their peak under the N9BC40 treatment. Meanwhile, high N and BC additions led to differences in bacterial community structure, increased CUE, and enzyme vector angle. Notably, high N shifted the dominant factor of BC on MAOC/POC from physicochemical properties to biological factors. Microbes drive CUE to influence changes in MAOC by adapting to metabolic limitations and stoichiometric imbalances. In contrast, POC is primarily influenced by physicochemical properties. Overall, high additions of N and BC have been shown to reduce the stability of SOC by promoting the accumulation of POC. However, an addition rate of 40 t·ha⁻¹ of BC was found to be more effective in mitigating the negative impacts of high N addition on MAOC. This strategy can serve as an effective management approach for enhancing SOC sequestration in vulnerable regions of the Loess Plateau. Full article

Anaerobic digestion (AD) is a nonlinear and disturbance-sensitive process in which instability is often induced by feedstock variability and biological fluctuations. To address this challenge, this study develops an entropy-guided machine learning framework that integrates parameter prediction, uncertainty quantification, and entropy-based evaluation of AD operation. Using six months of industrial data (~10,000 samples), three models—support vector machine (SVM), random forest (RF), and artificial neural network (ANN)—were compared for predicting biogas yield, fermentation temperature, and volatile fatty acid (VFA) concentration. The ANN achieved the highest performance (accuracy = 96%, F1 = 0.95, root mean square error (RMSE) = 1.2 m³/t) and also exhibited the lowest prediction error entropy, indicating reduced uncertainty compared to RF and SVM. Feature entropy and permutation analysis consistently identified feed solids, organic matter, and feed rate as the most influential variables (>85% contribution), in agreement with the RF importance ranking. When applied as a real-time prediction and decision-support tool in the plant (“sensor → prediction → programmable logic controller (PLC)/operation → feedback”), the ANN model was associated with a reduction in gas-yield fluctuation from approximately ±18% to ±5%, a decrease in process entropy, and an improvement in operational stability of about 23%. Techno-economic and life-cycle assessments further indicated a 12–15 USD/t lower operating cost, 8–10% energy savings, and 5–7% CO₂ reduction compared with baseline operation. Overall, this study demonstrates that combining machine learning with entropy-based uncertainty analysis offers a reliable and interpretable pathway for more stable and low-carbon AD operation. Full article

Potato (Solanum tuberosum L.) is the third most important food crop worldwide and a cornerstone of food security across the Andean region. However, its production is increasingly threatened by the potato psyllid Bactericera cockerelli (Šulc), the vector of Candidatus Liberibacter solanacearum, the causal agent of the purple-top complex associated with zebra chip disease, which severely reduces both tuber yield and quality. This study was conducted from September 2024 to February 2025 in the province of Huancabamba, Peru, to evaluate the efficacy of biological and chemical control agents against B. cockerelli under field conditions. A randomized complete block design was implemented with five treatments and four replicates, totaling 20 experimental units, each consisting of 20 potato plants (S. tuberosum L.), of which 10 plants were evaluated. Treatments included an untreated control (T0), a chemical control (thiamethoxam + lambda-cyhalothrin, abamectin, and imidacloprid) (T1), and three biological control agents: Beauveria bassiana CCB LE-265 (>1.5 × 10¹⁰ conidia g⁻¹) (T2), Paecilomyces lilacinus strain 251 (1.0 × 10¹⁰ conidia g⁻¹) (T3), and Metarhizium anisopliae (1.0 × 10¹⁰ conidia g⁻¹) (T4). Foliar applications targeted eggs, nymphs, and adults of the psyllid. Results indicated that B. cockerelli mortality across developmental stages was lower under biological treatments compared with T1, which achieved the lowest probability of purple-top symptom expression (46%) and a zebra chip incidence of 60.60%. Among the biological agents, M. anisopliae (T4) reduced incidence to 56.60%, while P. lilacinus (T3) demonstrated consistent suppression of nymphal populations. In terms of yield, T1 achieved the highest tuber weight (198.86 g plant⁻¹) and number of tubers (7.74 plant⁻¹), followed by T3 (5.08) and T4 (4.24). Nevertheless, all treatments exhibited low yields and small tuber sizes, likely due to unfavorable environmental conditions and the presence of the invasive pest. Overall, chemical control was more effective than biological agents; however, the latter showed considerable potential for integration into sustainable pest management programs. Importantly, vector suppression alone does not guarantee the absence of purple-top complex symptoms or zebra chip disease in potato tubers. Full article

Adenoviral vector vaccines were pivotal for COVID-19 control, but postmarketing safety surveillance has identified venous-predominant thrombotic risks not fully explained by platelet-centric mechanisms. We tested an RBC-associated hypothesis using an Ad5 vector-rAd/HA(PR8) rat model within a predefined sub-hemolytic window (<10% hemolysis). Ex vivo, we quantified RBC surface phosphatidylserine (PS) exposure, morphology remodeling by scanning electron microscopy, and microvesicle generation, all aligning with increased procoagulant activity. RBCs also exhibited dose-dependent increases in thrombin generation 4 h after intravenous exposure (10⁸–10⁹ OPU/Rat). In vivo, an inferior vena cava thrombosis model showed a pronounced, dose-responsive rise in thrombus burden, consistent with increased thrombogenic potential. Together, these integrated data provide experimental evidence consistent with RBC involvement under adenoviral exposure, supporting a biologically plausible link to the venous-predominant epidemiology observed during the COVID-19 vaccination era. Reported clinical adenoviral vaccine doses are of the same order of magnitude as the exposures tested here, supporting translational relevance while not implying inter-species or product equivalence. Incorporating RBC-focused endpoints, including PS exposure, morphology indices, microvesicle counts, and thrombin generation, into preclinical and early clinical assessments may enhance safety evaluation and inform vector design to mitigate venous thrombotic risk. Full article

Background: Hyaluronan, or hyaluronic acid (HA), is a glycosaminoglycan with structural and signaling functions playing key roles in human skin homeostasis. It ensures hydration and biomechanical properties of this tissue as well as regulates cell adhesion, migration, proliferation, and inflammation. Its biocompatibility, viscoelastic properties, biological functions, and large-scale sustainable bioproduction made this polysaccharide a hero molecule of the cosmetic industry. Methods: A literature search was conducted to discuss the skin and hair benefits of the external use of HA and its derivatives. Four main questions were addressed: What are the different forms of HA in cosmetic formulations? What about their safety? Does HA penetrate human skin and hair? What are the benefits and mode of actions of HA, and its derivatives, in the fields of cosmetic and dermatology? Results: The analysis revealed HA below 100 kDa to penetrate skin, and lower molecular weight being able to reach the dermis. The safety of HA-containing formulations has been evaluated in several clinical trials and is supported by independent reports of commercial ingredients. We described HA molecules having beneficial effects on skin and hair, as well as their mode of action. Conclusions: This review provides comprehensive information on the nature and efficacy of topical HA, and its derivatives, in cosmetic applications, with an emphasis on hair care. New areas of research were highlighted as the vectorization of high-molecular-weight HA. Full article

Background/Objectives: Vector-borne diseases like malaria remain a major global health concern, worsened by insecticide resistance in mosquito populations. Quinoline-based compounds have been extensively studied for their pharmacological effects, including antimalarial and larvicidal properties. Modifying quinoline structures with hydrazone groups may enhance their biological activity and physicochemical properties. This study reports the synthesis, structural characterization, and larvicidal testing of a new series of aryl hydrazones (6a–i) derived from 8-trifluoromethyl quinoline. Methods: Compounds 6a–i were prepared via condensation reactions and characterized using ¹H NMR, ¹⁹F-NMR, ¹³C NMR, and HRMS techniques. Their larvicidal activity was tested against Anopheles arabiensis. Single-crystal X-ray diffraction (XRD) was performed on compound 6d to determine its three-dimensional structure. Hirshfeld surface analysis, fingerprint plots, and interaction energy calculations (HF/3-21G) were used to examine intermolecular interactions. Quantum chemical parameters were computed using density functional theory (DFT). Molecular docking studies were performed for the synthesized compounds 6a–i against the target acetylcholinesterase from the malaria vector (6ARY). In silico ADMET properties were also calculated to evaluate the drug-likeness of all the tested compounds. Results: Compound 6a showed the highest larvicidal activity, causing significant mortality in Anopheles arabiensis larvae. Single-crystal XRD analysis of 6d revealed a monoclinic crystal system with space group P2₁/c, stabilized by N–H···N intermolecular hydrogen bonds. Hirshfeld analysis identified H···H (22.0%) and C···H (12.1%) interactions as key contributors to molecular packing. Density functional theory results indicated a favorable HOMO–LUMO energy gap, supporting molecular stability and good electronic distribution. The most active compounds, 6a and 6d, also showed strong binding interactions with the target protein 6ARY and satisfactory ADMET properties. The BOILED-Egg model is a powerful tool for predicting both blood–brain barrier (BBB) and gastrointestinal permeation by calculating the lipophilicity and polarity of the reported compounds 6a–i. Conclusions: The synthesized arylhydrazone derivatives demonstrated promising larvicidal activity. Combined crystallographic and computational studies support their structural stability and suitability for further development as eco-friendly bioactive agents in malaria vector control. Full article

The clinical management of major depressive disorder remains hampered by a trial-and-error approach to treatment selection, a challenge that current diagnostic and static predictive models have failed to address. While artificial intelligence (AI) applications have focused on classifying a patient’s present state, they lack the ability to forecast the trajectory of their future response. This study addresses this critical gap by proposing a new theoretical framework that conceptualises depression treatment response as a complex dynamic system. Drawing a powerful analogy from the engineering field of structural health monitoring and damage prognosis, which forecasts the remaining useful life of a system, we shift the paradigm from diagnosis to prognosis. We introduce three core constructs: the Patient State Vector (PSV), a multimodal baseline of a patient’s clinical, biological, and digital phenotype; the Therapeutic Impulse Function (TIF), a formal representation of a treatment’s properties; and the Predicted Recovery Trajectory (PRT), the forecasted path of symptom severity over time. The central thesis of the framework is that a patient’s PRT emerges from the dynamic interaction between their initial PSV and a given TIF. We present a series of testable propositions, such as how early fluctuations in PRT can classify patients into distinct “dynamic phenotypes” predictive of long-term outcomes. By integrating mechanisms across neurobiology, behaviour, and pharmacology within an SHM-inspired framework, this prognostic theory aims to provide a new systems-based paradigm for personalised psychiatry, moving beyond static prediction to a mechanistic understanding of recovery. This cross-disciplinary adaptation illustrates how SHM-derived principles of state assessment, load modelling, and prognosis can inform new frontiers in predictive health modelling. Full article

Radiopharmaceutical development usually requires chelators to route radiometals to specific cancer targets. However, there is no universal chelator. To reduce off-target side effects, new chelators are needed as the radiometal toolset grows. DOTA is the most often used chelator for radiometals in radiopharmaceutical development, however DOTA’s extreme conditions make it unsuitable for labeling heat sensitive biological vectors. The ideal chelator should be thermodynamically and kinetically stable, allowing labeling under mild conditions (~37 °C). In recent years, new hybrid chelators with enhanced characteristics have emerged, warranting further investigation for medical applications. This paper aims to discuss some of these promising chelators. Full article