Search Results (20,531)

As an emerging energy-saving approach, bio-inspired drag reduction technology has become a key research direction for reducing energy consumption and greenhouse gas emissions. This study introduces the latest research progress on bio-inspired microstructured surfaces in the field of underwater drag reduction, focusing on analyzing the drag reduction mechanism, preparation process, and application effect of the three major technological paths; namely, bio-inspired non-smooth surfaces, bio-inspired superhydrophobic surfaces, and bio-inspired modified coatings. Bio-inspired non-smooth surfaces can significantly reduce the wall shear stress by regulating the flow characteristics of the turbulent boundary layer through microstructure design. Bio-inspired superhydrophobic surfaces form stable gas–liquid interfaces through the construction of micro-nanostructures and reduce frictional resistance by utilizing the slip boundary effect. Bio-inspired modified coatings, on the other hand, realize the synergistic function of drag reduction and antifouling through targeted chemical modification of materials and design of micro-nanostructures. Although these technologies have made significant progress in drag reduction performance, their engineering applications still face bottlenecks such as manufacturing process complexity, gas layer stability, and durability. Future research should focus on the analysis of drag reduction mechanisms and optimization of material properties under multi-physical field coupling conditions, the development of efficient and low-cost manufacturing processes, and the enhancement of surface stability and adaptability through dynamic self-healing coatings and smart response materials. It is hoped that the latest research status of bio-inspired drag reduction technology reviewed in this study provides a theoretical basis and technical reference for the sustainable development and energy-saving design of ships and underwater vehicles. Full article

Introduction: Urinary tract infections (UTIs) remain a significant public health issue worldwide, particularly affecting the geriatric population with increased morbidity and mortality. Aging-related immune changes, comorbidities, and urogenital abnormalities contribute to the higher incidence and complexity of UTIs in elderly patients. Antimicrobial resistance, especially extended-spectrum beta-lactamase (ESBL) production and carbapenem resistance, poses a major challenge in managing UTIs in this group. Methods: This retrospective, multicenter study included 776 patients aged 65 and older, hospitalized with a diagnosis of urinary tract infection between January 2019 and August 2024. Clinical, laboratory, and microbiological data were collected and analyzed. Urine samples were obtained under sterile conditions and pathogens identified using conventional and automated systems. Antibiotic susceptibility testing was performed according to CLSI standards. Logistic regression analyses were conducted to identify factors associated with ESBL production, carbapenem resistance, and mortality. Results: Among the patients, the median age was 78.9 years, with 45.5% female. ESBL production was detected in 56.8% of E. coli isolates and carbapenem resistance in 1.2%. Klebsiella species exhibited higher carbapenem resistance (37.8%). Independent predictors of ESBL production included the presence of urogenital cancer and antibiotic use within the past three months. Carbapenem resistance was associated with recent hospitalization, absence of kidney stones, and infection with non-E. coli pathogens. Mortality was independently associated with intensive care admission at presentation, altered mental status, Gram-positive infections, and comorbidities such as chronic obstructive pulmonary disease and urinary incontinence. Discussion: Our findings suggest that urinary pathogens and resistance patterns in elderly patients are similar to those in younger adults reported in the literature, highlighting the need for age-specific awareness in empiric therapy. The identification of risk factors for multidrug-resistant organisms emphasizes the importance of targeted antibiotic stewardship, especially in high-risk geriatric populations. Multicenter data contribute to regional understanding of resistance trends, aiding clinicians in optimizing management strategies for elderly patients with UTIs. Conclusions: This study highlights that E. coli and Klebsiella species are the primary causes of UTIs in the elderly, with resistance patterns similar to those seen in younger adults. The findings also contribute important data on risk factors for ESBL production and carbapenem resistance, supported by a robust patient sample. Full article

Blast-induced polytrauma (BIPT) is a common injury among military personnel exposed to explosive blasts. It is increasingly recognized as a complex, multisystem disorder that extends beyond neurological damage to include systemic metabolic and inflammatory dysfunction. Adipokines, particularly leptin and adiponectin, are hormones secreted by adipose tissue and are emerging as key mediators in the pathophysiology of traumatic brain injuries. Yet, their long-term dynamics following blast exposure remain unclear. This study investigated the temporal profiles of plasma leptin and adiponectin in a longitudinal rat model of BIPT. Adult male Sprague Dawley rats were subjected to either a single (B) or repeated (BB) blast exposure (20 psi) or served as sham controls. Plasma samples were collected at 24 h, 1 month, 6 months, and 12 months post-exposure, and adipokine levels were measured using Enzyme-linked Immunosorbent Assay. Adiponectin levels exhibited a biphasic response: both B and BB groups showed significant early decrease at 24 h and 1 month compared to sham animals, followed by robust elevation at 6 and 12 months, particularly in the repeated blast group. In contrast, leptin levels remained unchanged acutely but rose significantly at 6 and 12 months post-blast, with the BB group again showing the highest levels. These patterns indicate sustained, exposure-dependent dysregulation of adipokine signaling after blast trauma. The study provides the first longitudinal profile of systemic adipokine responses to BIPT, revealing their potential as accessible biomarkers and therapeutic targets. These findings support a model of chronic metabolic and inflammatory imbalance in BIPT and warrant further investigation in human cohorts and mechanistic studies. Full article

Centriole duplication is a vital process for cellular organisation and function, underpinning essential activities such as cell division, microtubule organisation and ciliogenesis. This review summarises the latest research on the mechanisms and regulatory pathways that control this process, focusing on important proteins such as polo-like kinase 4 (PLK4), SCL/TAL1 interrupting locus (STIL) and spindle assembly abnormal protein 6 (SAS-6). This study examines the complex steps involved in semi-conservative duplication, from initiation in the G1–S phase to the maturation of centrioles during the cell cycle. Additionally, we will explore the consequences of dysregulated centriole duplication. Dysregulation of this process can lead to centrosome amplification and subsequent chromosomal instability. These factors are implicated in several cancers and developmental disorders. By integrating recent study findings, this review emphasises the importance of centriole duplication in maintaining cellular homeostasis and its potential as a therapeutic target in disease contexts. The presented findings aim to provide a fundamental understanding that may inform future research directions and clinical interventions related to centriole biology. Full article

Alkalihalophilus marmarensis is an obligate alkaliphile with exceptional tolerance to high-pH environments, making it a promising candidate for industrial bioprocesses that require contamination-resistant and extremophilic production platforms. However, its practical deployment is hindered by limited biomass formation under extreme conditions, which constrains overall productivity. This study presents a model-driven investigation of how pH (8.8 and 10.5), culture duration (24 and 48 h), and nitrogen source composition (peptone and meat extract) affect cell dry mass, lactate, and protease synthesis. Using the response surface methodology and multi-objective optimization, we established predictive models (R² up to 0.92) and uncovered key trade-offs in biomass and metabolite yields. Our findings reveal that peptone concentration critically shapes the metabolic output, with low levels inhibiting growth and high levels suppressing protease activity. Maximum cell dry mass (4.5 g/L), lactate (19.3 g/L), and protease activity (43.5 U/mL) were achieved under distinct conditions, highlighting the potential for targeted process tuning. While the model validation confirmed predictions for lactate, deviations in cell dry mass and protease outputs underscore the complexity of growth–product interdependencies under nutrient-limited regimes. This work delivers a foundational framework for developing fermentations with A. marmarensis and advancing its application in sustainable, high-pH industrial bioprocesses. The insights gained here can be further leveraged through synthetic biology and bioprocess engineering to fully exploit the metabolic potential of obligate alkaliphiles like A. marmarensis. Full article

Introduction: Scattering of kernels due to angular dehiscence is a key bottleneck in mechanized harvesting of oilseed rape. Materials and Methods: In this study, a dual-track “genotype–phenotype” screening strategy was established by innovatively integrating high-throughput KASP molecular marker technology and a standardized random collision phenotyping system for the complex quantitative trait of angular resistance. Results: Through the systematic evaluation of 634 oilseed rape hybrid progenies, it was found that the KASP marker S12.68, targeting the cleavage resistance locus (BnSHP1) on chromosome C9, achieved a 73.34% introgression rate (465/634), which was significantly higher than the traditional breeding efficiency (<40%). Phenotypic characterization screened seven excellent resources with cracking resistance index (SRI) > 0.6, of which four reached the high resistance standard (SRI > 0.8), including the core materials NR21/KL01 (SRI = 1.0) and YuYou342/KL01 (SRI = 0.97). Six breeding intermediate materials (44.7–48.7% oil content, mycosphaerella resistance MR grade or above) were created, combining high resistance to chipping and excellent agronomic traits. For the first time, it was found that local germplasm YuYou342 (non-KL01-derived line) was purely susceptible at the S12.68 locus (SRI = 0.86), but its angiosperm vascular bundles density was significantly increased by 37% compared with that of the susceptible material 0911 (p < 0.01); and the material 187308 (SRI = 0.78), although purely susceptible at S12.68, had a 2.8-fold downregulation in expression of the angiosperm-related gene, BnIND1, and a 2.8-fold downregulation of expression of the angiosperm-related gene, BnIND1. expression was significantly downregulated 2.8-fold (q < 0.05), indicating the existence of a novel resistance mechanism independent of the primary effector locus. Conclusions: The results of this research provide an efficient technical platform and breakthrough germplasm resources for oilseed rape crack angle resistance breeding, which is of great practical significance for promoting the whole mechanized production. Full article

Cryptococcus neoformans is an opportunistic fungal pathogen and causative agent of cryptococcosis and cryptococcal meningitis (CM). Cryptococcal disease accounts for up to 19% of AIDS-related mortalities globally, warranting its label as a pathogen of critical priority by the World Health Organization. Standard treatments for CM rely heavily on high doses of antifungal agents for long periods of time, contributing to the growing issue of antifungal resistance. Moreover, mortality rates for CM are still incredibly high (13–78%). Attempts to create new and effective treatments have been slow due to the complex and diverse set of immune-evasive and survival-enhancing virulence factors that C. neoformans employs. To bolster the development of better clinical tools, deeper study into host–Cryptococcus proteomes is needed to identify clinically relevant proteins, pathways, antigens, and beneficial host response mechanisms. Mass spectrometry-based proteomics approaches serve as invaluable tools for investigating these complex questions. Here, we discuss some of the insights into cryptococcal disease and biology learned using proteomics, including target proteins and pathways regulating Cryptococcus virulence factors, metabolism, and host defense responses. By utilizing proteomics to probe deeper into these protein interaction networks, new clinical tools for detecting, diagnosing, and treating C. neoformans can be developed. Full article

Migraine is a neurovascular paroxysmal disorder characterized by neurogenic inflammation and has a remarkable impact on the quality of life. The Food and Drug Administration (FDA) approved onabotulinumtoxin A in 2010 for the prophylactic treatment of chronic migraine. Today, in its 4th decade, it is approved in 100 countries for 15 main indications. Its mechanism of action, based on the inhibition of neurotransmitter release from primary sensory neurons, is very complex: it affords antinociception, but it also has an analgesic effect on neuropathic pain conditions and reduces the need for rescue medications. Genetic variants have been investigated for their potential role in the pathogenesis and clinical expression of migraine and of the response to treatments. These studies primarily involved genes associated with vascular regulation and cardiovascular pathology, including those encoding angiotensin-converting enzyme (ACE) and methylenetetrahydrofolate reductase (MTHFR). However, epigenetics and, particularly, genetic and epigenetic modifications are still poorly studied in terms of understanding the mechanisms implicated in susceptibility to migraine, aura, chronification and response to symptomatic and preventive treatments. In particular, the aim of the present study is to gather evidence on the genetic variants and epigenetic modifications affecting the pathway of the calcitonin gene-related peptide (CGRP), the target of onabotulinumtoxin A and of all the novel monoclonal antibodies. Full article

With the global increase in maritime activities, the frequency of maritime accidents has risen, underscoring the urgent need for faster and more efficient search and rescue (SAR) solutions. This study presents an intelligent unmanned aerial vehicle (UAV)-based maritime rescue system that combines GPS-driven dynamic path planning with vision-based dual-target detection and tracking. Developed within the Gazebo simulation environment and based on modular ROS architecture, the system supports stable takeoff and smooth transitions between multi-rotor and fixed-wing flight modes. An external command module enables real-time waypoint updates. This study proposes three path-planning schemes based on the characteristics of drones. Comparative experiments have demonstrated that the triangular path is the optimal route. Compared with the other schemes, this path reduces the flight distance by 30–40%. Robust target recognition is achieved using a darknet-ROS implementation of the YOLOv4 model, enhanced with data augmentation to improve performance in complex maritime conditions. A monocular vision-based ranging algorithm ensures accurate distance estimation and continuous tracking of rescue vessels. Furthermore, a dual-target-tracking algorithm—integrating motion prediction with color-based landing zone recognition—achieves a 96% success rate in precision landings under dynamic conditions. Experimental results show a 4% increase in the overall mission success rate compared to traditional SAR methods, along with significant gains in responsiveness and reliability. This research delivers a technically innovative and cost-effective UAV solution, offering strong potential for real-world maritime emergency response applications. Full article

Infrared small-target detection remains a critical challenge in military reconnaissance, environmental monitoring, forest-fire prevention, and search-and-rescue operations, owing to the targets’ extremely small size, sparse texture, low signal-to-noise ratio, and complex background interference. Traditional convolutional neural networks (CNNs) struggle to detect such weak, low-contrast objects due to their limited receptive fields and insufficient feature extraction capabilities. To overcome these limitations, we propose a Multi-Scale Edge-Aware Convolution (MEAC) module that enhances feature representation for small infrared targets without increasing parameter count or computational cost. Specifically, MEAC fuses (1) original local features, (2) multi-scale context captured via dilated convolutions, and (3) high-contrast edge cues derived from differential Gaussian filters. After fusing these branches, channel and spatial attention mechanisms are applied to adaptively emphasize critical regions, further improving feature discrimination. The MEAC module is fully compatible with standard convolutional layers and can be seamlessly embedded into various network architectures. Extensive experiments on three public infrared small-target datasets (SIRSTD-UAVB, IRSTDv1, and IRSTD-1K) demonstrate that networks augmented with MEAC significantly outperform baseline models using standard convolutions. When compared to eleven mainstream convolution modules (ACmix, AKConv, DRConv, DSConv, LSKConv, MixConv, PConv, ODConv, GConv, and Involution), our method consistently achieves the highest detection accuracy and robustness. Experiments conducted across multiple versions, including YOLOv10, YOLOv11, and YOLOv12, as well as various network levels, demonstrate that the MEAC module achieves stable improvements in performance metrics while slightly increasing computational and parameter complexity. These results validate the MEAC module’s significant advantages in enhancing the detection of small and weak objects and suppressing interference from complex backgrounds. These results validate MEAC’s effectiveness in enhancing weak small-target detection and suppressing complex background noise, highlighting its strong generalization ability and practical application potential. Full article

Atopic dermatitis (AD) is a chronic, relapsing inflammatory skin condition characterized by intense pruritus and a significant impact on a patient’s quality of life. Despite advancements in understanding AD pathophysiology, there remains a critical need for innovative therapeutic options to better manage this debilitating disease. This review focuses on the evolving landscape of biological therapies for AD, offering insights into their role, mechanisms of action, and potential to revolutionize patient care. In this review, we explore the underlying immunological mechanisms of AD, particularly the role of cytokines and immune pathways implicated in the disease, and how targeted biological therapies modulate these pathways. Current FDA- and EMA-approved biologics, such as Dupilumab, are also discussed in terms of their mechanisms of action, efficacy, and safety. Additionally, we compare their effectiveness, highlighting the benefits and limitations observed in clinical practice. Emerging biological therapies currently under development offer new hope, with innovative targets like IL-13, IL-31, and thymic stromal lymphopoietin (TSLP) representing promising avenues for intervention. We also delve into personalized medicine, emphasizing the importance of biomarkers for predicting treatment response and stratifying AD patients to optimize therapeutic outcomes. Moreover, the synergistic potential of combining biologics with traditional therapies is reviewed, along with a discussion of the challenges involved, including safety, long-term efficacy, and patient adherence. We address the future direction of AD treatment, including microbiome-targeting biologics and the development of next-generation immune modulators. We highlight a new era of targeted treatment possibilities for this complex condition. Full article

Cancer remains one of the most formidable challenges to human health; hence, developing effective treatments is critical for saving lives. An important strategy involves reactivating tumor suppressor genes, particularly p53, by targeting their negative regulator MDM2, which is essential in promoting cell cycle arrest and apoptosis. Leveraging a drug repurposing approach, we screened over 24,000 clinically tested molecules to identify new MDM2 inhibitors. A key innovation of this work is the development and application of a selective cleaning algorithm that systematically filters assay data to mitigate noise and inconsistencies inherent in large-scale bioactivity datasets. This approach significantly improved the predictive accuracy of our machine learning model for pIC₅₀ values, reducing RMSE by 21.6% and achieving state-of-the-art performance (R² = 0.87)—a substantial improvement over standard data preprocessing pipelines. The optimized model was integrated with structure-based virtual screening via molecular docking to prioritize repurposing candidate compounds. We identified two clinical CB1 antagonists, MePPEP and otenabant, and the statin drug atorvastatin as promising repurposing candidates based on their high predicted potency and binding affinity toward MDM2. Interactions with the related proteins MDM4 and BCL2 suggest these compounds may enhance p53 restoration through multi-target mechanisms. Quantum mechanical (ONIOM) optimizations and molecular dynamics simulations confirmed the stability and favorable interaction profiles of the selected protein–ligand complexes, resembling that of navtemadlin, a known MDM2 inhibitor. This multiscale, accuracy-boosted workflow introduces a novel data-curation strategy that substantially enhances AI model performance and enables efficient drug repurposing against challenging cancer targets. Full article

Vessel detection technology based on marine remote sensing imagery is of great importance. However, it often faces challenges, such as small vessel targets, cloud occlusion, insufficient data volume, and severely imbalanced class distribution in datasets. These issues result in conventional models failing to meet the accuracy requirements for practical applications. In this paper, we first construct a novel remote sensing vessel image dataset that includes various complex scenarios and enhance the data volume and diversity through data augmentation techniques. Secondly, we address the class imbalance between foreground (small vessels) and background in remote sensing imagery from two perspectives: the sensitivity of IoU metrics to small object localization errors and the innovative design of a cost-sensitive loss function. Specifically, at the dataset level, we select vessel targets appearing in the original dataset as templates and randomly copy–paste several instances onto arbitrary positions. This enriches the diversity of target samples per image and mitigates the impact of data imbalance on the detection task. At the algorithm level, we introduce the Normalized Wasserstein Distance (NWD) to compute the similarity between bounding boxes. This enhances the importance of small target information during training and strengthens the model’s cost-sensitive learning capabilities. Ablation studies reveal that detection performance is optimal when the weight assigned to the NWD metric in the model’s loss function matches the overall proportion of small objects in the dataset. Comparative experiments show that the proposed NWD-YOLO achieves Precision, Recall, and ${\mathrm{AP}}_{50}$ scores of 0.967, 0.958, and 0.971, respectively, meeting the accuracy requirements of real-world applications. Full article

Background/Objectives: The treatment of multiple myeloma (MM) remains a challenge, as almost all patients will eventually relapse. Proteasome inhibitors are a cornerstone in the management of MM. Unfortunately, validated biomarkers predicting drug response are largely missing. Therefore, we aimed to identify genes associated with drug resistance or sensitization to proteasome inhibitors. Methods: We performed genome-wide CRISPR-Cas9 knockout (KO) screens in human KMS-28-BM myeloma cells to identify genetic determinants associated with resistance or sensitization to proteasome inhibitors. Results: We show that KO of KLF13 and PSMC4 induces drug resistance, while NUDCD2, OSER1 and HERC1 KO cause drug sensitization. Subsequently, we focused on top sensitization hit, NUDCD2, which acts as a co-chaperone of Hsp90 to regulate the LIS1/dynein complex. RNA sequencing showed downregulation of genes involved in the ERAD pathway and in ER-associated ubiquitin-dependent protein catabolic processes in both untreated and carfilzomib-treated NUDCD2 KO cells, suggesting that NUDCD2 depletion alters protein degradation. Furthermore, bortezomib-treated NUDCD2 KO cells showed a decreased expression of genes that have a function in oxidative phosphorylation and the mitochondrial membrane, such as Carnitine Palmitoyltransferase 1A (CPT1A). CPT1A catalyzes the uptake of long chain fatty acids into mitochondria. Mitochondrial lipid metabolism has recently been reported as a possible therapeutic target for MM drug sensitivity. Conclusions: These results contribute to the search for therapeutic targets that can sensitize MM patients to proteasome inhibitors. Full article

In this post-digital age, writing assessment has been markedly influenced by advancements in artificial intelligence (AI), emphasizing the role of automated formative feedback in supporting second language (L2) writing. This study investigates how Norwegian teachers use an AI-driven automated feedback tool, the Essay Assessment Technology (EAT), in process writing for the first time. Framed by the second and third-order barriers framework, we looked at teachers’ beliefs and the design level changes that they made in their teaching. Data were collected in Autumn 2022, during the testing of EAT’s first prototype. Teachers were first introduced to EAT in a workshop. A total of 3 English as a second language teachers from different schools were informants in this study. Teachers then used EAT in the classroom with their 9th-grade students (13 years old). Through individual teacher interviews, this descriptive qualitative study explores teachers’ perceptions, user experiences, and pedagogical decisions when incorporating EAT into their practices. The findings indicate that teachers’ beliefs about technology and its role in student learning, as well as their views on students’ responsibilities in task completion, significantly influence their instructional choices. Additionally, teachers not only adopt AI-driven tools but are also able to reflect and solve complex teaching and learning activities in the classroom, which demonstrates that these teachers have applied design thinking processes in integrating technology in their teaching. Based on the results in this study, we suggest the need for targeted professional development to support effective technology integration. Full article