Search Results (5,627)

Background/Objectives: Classical swine fever (CSF) continues to challenge global eradication efforts, particularly in endemic regions, where pregnant sows face heightened risks of vertical transmission following exposure to CSFV. Methods: This study evaluates the early protective efficacy of FlagT4G, a novel live attenuated DIVA-compatible vaccine. Pregnant sows were vaccinated at mid-gestation and challenged 14 days later with a highly virulent CSFV strain. Results: FlagT4G conferred complete clinical protection, preventing both maternal viremia and transplacental transmission. No CSFV RNA, specific antibodies, or IFN-α were detected in fetal samples from vaccinated animals. In contrast, unvaccinated sows exhibited clinical signs, high viral loads, and widespread fetal infection. Interestingly, early protection was observed even in the absence of strong humoral responses in some vaccinated sows, suggesting a potential role for innate or T-cell-mediated immunity in conferring rapid protection. Conclusions: The demonstrated efficacy of FlagT4G within two weeks of vaccination underscores its feasibility for integration into emergency vaccination programs. Its DIVA compatibility and ability to induce early fetal protection against highly virulent CSFV strains position it as a promising tool for CSF control and eradication strategies. Full article

Q Fever is a zoonosis caused by Coxiella burnetii that affects domestic and wild ruminants, leading to reproductive disorders. In humans, the disease can manifest with acute and chronic clinical manifestations. Veterinarians, as healthcare professionals in close contact with animals, serve both as the first line of defence in preventing infection at the animal–human interface and as an important sentinel group for the rapid detection of outbreaks. The aim of this study was to assess the knowledge, perception, and attitude of veterinarians in Southern Spain regarding Q Fever. To this end, an online survey was designed, validated, and conducted among veterinarians in the province of Malaga, with a final participation of 97 individuals, predominantly from the private sector (clinic, livestock, agri-food, etc.). The data obtained reflected a general lack of knowledge about the disease, particularly concerning its epidemiology and infection prevention. Regarding perception and attitude, a significant percentage of respondents stated they did not use protective equipment when handling susceptible animals and only sought information about the disease in response to outbreak declarations. The study emphasised the significance of promoting training in zoonotic diseases during and after graduation, the relevance of official channels in occupational risk prevention, and the utility of epidemiological surveys as a tool to identify and address potential gaps in knowledge related to this disease. Full article

Computational Fluid Dynamics (CFD) is regarded as an important tool for analyzing the flow field, thermal environment, and air quality around the built environment. However, for built environment applications, the high computational cost of CFD hinders large-scale scenario simulation and efficient design optimization. In the field of built environment research, surrogate modeling has become a key technology to connect the needs of high-fidelity CFD simulation and rapid prediction, whereas the low-dimensional nature of traditional surrogate models is unable to match the physical complexity and prediction needs of built flow fields. Therefore, combining machine learning (ML) with CFD to predict flow fields in built environments offers a promising way to increase simulation speed while maintaining reasonable accuracy. This review briefly reviews traditional surrogate models and focuses on ML-based surrogate models, especially the specific application of neural network architectures in rapidly predicting flow fields in the built environment. The review indicates that ML accelerates the three core aspects of CFD, namely mesh preprocessing, numerical solving, and post-processing visualization, in order to achieve efficient coupled CFD simulation. Although ML surrogate models still face challenges such as data availability, multi-physics field coupling, and generalization capability, the emergence of physical information-driven data enhancement techniques effectively alleviates the above problems. Meanwhile, the integration of traditional methods with ML can further enhance the comprehensive performance of surrogate models. Notably, the online ministry of trained ML models using transfer learning strategies deserves further research. These advances will provide an important basis for advancing efficient and accurate operational solutions in sustainable building design and operation. Full article

Extrapulmonary tuberculosis (EPTB) remains diagnostically challenging due to its paucibacillary nature and variable presentation. Xpert and culture are limited in EPTB diagnosis due to sampling challenges, low sensitivity, and long turnaround times. This study evaluated the performance of the MPT64 antigen detection test for diagnosing EPTB, particularly tuberculous lymphadenitis (TBLN) and tuberculous pleuritis (TBP), in a high-TB, low-HIV setting. Conducted at Gulab-Devi Hospital, Lahore, Pakistan, this study evaluated the MPT64 test’s performance against conventional diagnostic methods, including culture, histopathology, and the Xpert MTB/RIF assay. Lymph node biopsies were collected, and cell blocks were made from aspirated pleural fluid from patients clinically presumed to have EPTB. Of 338 patients, 318 (94%) were diagnosed with EPTB. For TBLN, MPT64 demonstrated higher sensitivity (84%) than Xpert (48%); for TBP, the sensitivity was 51% versus 7%, respectively. Among histopathology-confirmed TBLN cases, MPT64 outperformed both culture and Xpert (85% vs. 58% and 47%). Due to the low number of non-TB cases, specificity could not be reliably assessed. The MPT64 test shows promise as a rapid, sensitive diagnostic tool for EPTB, particularly TBLN, in routine settings. While sensitivity is notably superior to Xpert, further studies are needed to evaluate its specificity and broader diagnostic utility. Full article

Background/Objectives: The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic resistance. Somatic alterations in key pathway genes—including KRAS, NRAS, BRAF, and EGFR—are pivotal to clinical decision-making in precision oncology. However, the integration of these genomic events with clinical and demographic data remains hindered by fragmented resources and a lack of accessible analytical frameworks. To address this challenge, we developed AI-HOPE-RTK-RAS, a domain-specialized conversational artificial intelligence (AI) system designed to enable natural language-based, integrative analysis of RTK-RAS pathway alterations in CRC. Methods: AI-HOPE-RTK-RAS employs a modular architecture combining large language models (LLMs), a natural language-to-code translation engine, and a backend analytics pipeline operating on harmonized multi-dimensional datasets from cBioPortal. Unlike general-purpose AI platforms, this system is purpose-built for real-time exploration of RTK-RAS biology within CRC cohorts. The platform supports mutation frequency profiling, odds ratio testing, survival modeling, and stratified analyses across clinical, genomic, and demographic parameters. Validation included reproduction of known mutation trends and exploratory evaluation of co-alterations, therapy response, and ancestry-specific mutation patterns. Results: AI-HOPE-RTK-RAS enabled rapid, dialogue-driven interrogation of CRC datasets, confirming established patterns and revealing novel associations with translational relevance. Among early-onset CRC (EOCRC) patients, the prevalence of RTK-RAS alterations was significantly lower compared to late-onset disease (67.97% vs. 79.9%; OR = 0.534, p = 0.014), suggesting the involvement of alternative oncogenic drivers. In KRAS-mutant patients receiving Bevacizumab, early-stage disease (Stages I–III) was associated with superior overall survival relative to Stage IV (p = 0.0004). In contrast, BRAF-mutant tumors with microsatellite-stable (MSS) status displayed poorer prognosis despite higher chemotherapy exposure (OR = 7.226, p < 0.001; p = 0.0000). Among EOCRC patients treated with FOLFOX, RTK-RAS alterations were linked to worse outcomes (p = 0.0262). The system also identified ancestry-enriched noncanonical mutations—including CBL, MAPK3, and NF1—with NF1 mutations significantly associated with improved prognosis (p = 1 × 10⁻⁵). Conclusions: AI-HOPE-RTK-RAS exemplifies a new class of conversational AI platforms tailored to precision oncology, enabling integrative, real-time analysis of clinically and biologically complex questions. Its ability to uncover both canonical and ancestry-specific patterns in RTK-RAS dysregulation—especially in EOCRC and populations with disproportionate health burdens—underscores its utility in advancing equitable, personalized cancer care. This work demonstrates the translational potential of domain-optimized AI tools to accelerate biomarker discovery, support therapeutic stratification, and democratize access to multi-omic analysis. Full article

Antiphospholipid syndrome (APS) is an autoimmune disorder characterized by venous and arterial thrombosis and obstetric complications, driven by antiphospholipid antibodies (APLAs). This review synthesizes the latest advancements and current understanding, diagnosis, and treatment of APS. APLAs, including lupus anticoagulant (LAC), anticardiolipin (aCL), and anti-β2-glycoprotein I (aβ2-GPI), interfere with coagulation and endothelial function, as well as with placental health. APS can be primary or secondary; it is often associated with systemic autoimmune diseases like lupus. The pathogenesis of APS remains only partially understood. APLAs promote thrombosis through endothelial damage, platelet activation, and inflammatory signaling pathways. Laboratory diagnosis relies on persistent positivity for APLAs and LAC through tests like ELISA and clotting assays, following a three-step confirmation process. New integrated test systems have been introduced to improve standardization. Classification criteria have evolved, with the 2023 EULAR-ACR criteria providing a weighted, domain-based scoring system, enhancing diagnostic precision. Catastrophic APS (CAPS) is a severe, rare manifestation of APS, characterized by multi-organ failure due to rapid, widespread microthrombosis and systemic inflammation, which requires urgent anticoagulation. Seronegative APS is proposed for patients with clinical features of APS but negative standard antibody tests, possibly due to non-criteria antibodies or transient immunosuppression. Treatment primarily involves long-term anticoagulation with vitamin K antagonists; direct oral anticoagulants are generally not recommended. APS diagnosis and management remain complex due to clinical heterogeneity and laboratory challenges. Continued refinement of diagnostic tools and criteria is essential for improving outcomes in this life-threatening condition. Full article

Resistance of various bacterial pathogens to the activity of clinically approved drugs currently leads to serious infections, rapid spread of difficult-to-treat diseases, and even death. Taking the threats for human health in mind, researchers are focused on the isolation and characterization of novel natural products, including plant secondary metabolites. These molecules serve as inspiration and a suitable structural platform in the design and development of novel semi-synthetic and synthetic derivatives. All considered compounds have to be adequately evaluated in silico, in vitro, and in vivo using relevant approaches. The current review paper briefly focuses on the chemical and metabolic properties of resveratrol (1), as well as its oligomeric structures, viniferins, and viniferin-based molecules. The core scaffolds of these compounds contain so-called privileged structures, which are also present in many clinically approved drugs, indicating that those natural, properly substituted semi-synthetic, and synthetic molecules can provide a notably broad spectrum of beneficial pharmacological activities, including very impressive antimicrobial efficiency. Except for spectral verification of their structures, these compounds suffer from the determination or prediction of other structural and physicochemical characteristics. Therefore, the structure–activity relationships for specific dihydrodimeric and dimeric viniferins, their bioisosteres, and derivatives with notable efficacy in vitro, especially against chosen Gram-positive bacterial strains, are summarized. In addition, a set of descriptors related to their structural, physicochemical, pharmacokinetic, and toxicological properties is generated using various computational tools. The obtained values are compared to those of clinically approved drugs. The particular relationships between these in silico parameters are also explored. Full article

We propose a dynamic monitoring and precision fertilization decision system for agricultural soil nutrients, integrating UAV remote sensing and GIS technologies to address the limitations of traditional soil nutrient assessment methods. The proposed method combines multi-source data fusion, including hyperspectral and multispectral UAV imagery with ground sensor data, to achieve high-resolution spatial and spectral analysis of soil nutrients. Real-time data processing algorithms enable rapid updates of soil nutrient status, while a time-series dynamic model captures seasonal variations and crop growth stage influences, improving prediction accuracy (RMSE reductions of 43–70% for nitrogen, phosphorus, and potassium compared to conventional laboratory-based methods and satellite NDVI approaches). The experimental validation compared the proposed system against two conventional approaches: (1) laboratory soil testing with standardized fertilization recommendations and (2) satellite NDVI-based fertilization. Field trials across three distinct agroecological zones demonstrated that the proposed system reduced fertilizer inputs by 18–27% while increasing crop yields by 4–11%, outperforming both conventional methods. Furthermore, an intelligent fertilization decision model generates tailored fertilization plans by analyzing real-time soil conditions, crop demands, and climate factors, with continuous learning enhancing its precision over time. The system also incorporates GIS-based visualization tools, providing intuitive spatial representations of nutrient distributions and interactive functionalities for detailed insights. Our approach significantly advances precision agriculture by automating the entire workflow from data collection to decision-making, reducing resource waste and optimizing crop yields. The integration of UAV remote sensing, dynamic modeling, and machine learning distinguishes this work from conventional static systems, offering a scalable and adaptive framework for sustainable farming practices. Full article

Work-related musculoskeletal disorders (WMSDs) are among the most prevalent occupational health issues, particularly affecting public transport drivers due to prolonged sitting, constrained postures, and poorly adaptable cabins. This study addresses the ergonomic risks associated with tram driving, aiming to evaluate biomechanical load and postural stress in relation to drivers’ anthropometric characteristics. A combined methodological approach was applied, integrating two standardized observational tools—RULA and REBA—with anthropometric modeling based on three representatives European morphotypes (SmallW, MidM, and TallM). ErgoFellow 3.0 software was used for digital posture evaluation, and lumbar moments at the L4/L5 vertebral level were calculated to estimate lumbar loading. The analysis was simulation-based, using digital human models, and no real subjects were involved. The results revealed uniform REBA (Rapid Entire Body Assessment) and RULA (Rapid Upper Limb Assessment) scores of 6 across all morphotypes, indicating moderate to high risk and a need for ergonomic intervention. Lumbar moments ranged from 51.35 Nm (SmallW) to 101.67 Nm (TallM), with the tallest model slightly exceeding the recommended ergonomic thresholds. These findings highlight a systemic mismatch between cabin design and user variability. In conclusion, ergonomic improvements such as adjustable seating, better control layout, and driver education are essential to reduce the risk of WMSDs. The study proposes a replicable methodology combining anthropometric, observational, and biomechanical tools for evaluating and improving transport workstation design. Full article

Passion fruit (Passiflora edulis Sims), belonging to the Passifloraceae family, is an economically important plant in tropical and subtropical regions. The advances in functional genomics research of passion fruit have been significantly hindered by its recalcitrance to regeneration and stable transformation. This study establishes the first efficient Agrobacterium rhizogenes-mediated hairy root transformation system for passion fruit. Utilizing the eGFP marker gene, transformation efficiencies of 11.3% were initially achieved with strains K599, MSU440, and C58C1, with K599 proving most effective. Key transformation parameters were systematically optimized to achieve the following: OD₆₀₀ = 0.6, infection duration 30 min, acetosyringone concentration 100 μM, and a dark co-cultivation period of 2 days. The system’s utility was further enhanced by incorporating the red visual marker RUBY, enabling direct, instrument-free identification of transgenic roots via betaxanthin accumulation. Finally, this system was applied for functional analysis using PeMYB123, which may be involved in proanthocyanidin accumulation. Overexpression of PeMYB123 produced a higher content of proanthocyanidin in hairy roots. Additionally, the PeANR gene involved in the proanthocyanidin pathway was strongly activated in the transgenic hairy roots. This rapid and efficient visually simplified hairy root transformation system provides a powerful tool for functional gene studies in passion fruit. Full article

Background: Airway management in patients with obstructive sleep apnea (OSA) presents unique challenges for anesthesiologists and other airway practitioners. This comprehensive review examines current evidence and clinical practices for managing difficult airways in this high-risk population. OSA is characterized by specific anatomical and physiological alterations that increase both the likelihood of encountering difficult intubation and the risk of rapid desaturation during airway manipulation. Methods: Preoperative assessment of OSA patients requires integration of traditional difficult airway evaluation with OSA-specific considerations, including severity indices, oxygen desaturation patterns, and continuous positive airway pressure dependency. Conventional direct laryngoscopy often proves inadequate in these patients, prompting the development and refinement of alternative approaches. Videolaryngoscopy has emerged as a particularly valuable technique in OSA patients, offering improved glottic visualization while maintaining physiologic positioning. Flexible endoscopic techniques, particularly awake flexible bronchoscopic intubation, remain essential for high-risk scenarios, though they require considerable expertise. Results: Recent technological innovations have produced hybrid devices combining multiple modalities to address the specific challenges presented by OSA patients. Adjunctive tools and techniques, including specialized introducers, exchange catheters, and high-flow nasal oxygen, play critical roles in extending safe apnea time and facilitating successful intubation. Professional society guidelines now incorporate OSA-specific recommendations, emphasizing thorough preparation, appropriate device selection, and comprehensive monitoring. Conclusions: Effective management ultimately requires not only appropriate technology but also systematic preparation, strategic device selection, and meticulous execution. As OSA prevalence continues to rise globally, optimizing airway management approaches for this challenging population remains a critical priority for patient safety. Full article

Background: This meta-analysis aimed to evaluate the dentoalveolar changes of miniscrew-assisted rapid palatal expansion (MARPE) compared with conventional rapid palatal expansion (CRPE) in growing patients (≤16 years). Methods: A systematic and comprehensive literature search was carried out independently by two reviewers using both MeSH terms and free-text keywords across PubMed, the Cochrane Library, and Embase, with studies published through February 2025 included. The risk of bias was assessed using the Cochrane ROB 2.0 tool. The GRADE system was employed to determine evidence quality. Results: Of the 462 initially screened articles, 6 met the inclusion criteria and were selected for quantitative synthesis. Most studies had a low risk of bias with some concerns in reporting. The pooled standardized mean difference (SMD) for tooth inclination changes in CRPE compared with MARPE was 0.98 (95% confidence interval (CI), 0.54 to 1.42; p < 0.01). The test for overall effect was significant (p < 0.01), but no significant differences were found between the subgroups. The pooled SMD for buccal bone thickness changes in CRPE compared with MARPE was 0.69 (95% CI, 0.37 to 1.00; p < 0.01). The test for overall effect was significant (p < 0.01), and there were substantial differences between the subgroups. The supporting evidence ranged in certainty from moderate to low. Conclusions: MARPE was more effective than CRPE in minimizing the buccal tipping and buccal bone loss of the maxillary first premolars and first molars. However, to further confirm these outcomes and guide evidence-based clinical practice, well-designed randomized controlled trials with long-term follow-up are necessary. Full article

Rapid vaccine availability is essential for effective epidemic and pandemic response. Building on the Coalition for Epidemic Preparedness Innovations (CEPI) 100 Days Mission, which aims to have new vaccines ready for initial authorization and manufacturing at scale within 100 days of recognition of a pandemic pathogen, the CEPI has developed a Chemistry, Manufacturing and Controls (CMC) Rapid Response Framework to define technical and logistical CMC requirements to enable rapid vaccine availability. Central to this framework is the availability of adaptable vaccine platforms that can be readily tailored to emerging pathogens. To support strategic decision-making and identify gaps in platform capabilities, CEPI has created the Platform Readiness Dashboard. This tool provides a structured, multi-dimensional initial assessment of platform maturity across six key categories: Adaptability, Compatibility, Suitability, Regulatory, Manufacturing, and Facility Readiness. Each category includes specific technical and operational considerations scored using a color-coded system to reflect outbreak response readiness level. This Dashboard aims to enable vaccine developers, manufacturers, funders, and outbreak response teams to evaluate platform strengths and limitations at any given time, informing funding, preparedness and response activities. By offering a dynamic view of essential platform readiness indicators, the dashboard can communicate progress supporting faster responses to future health emergencies. Full article

Background/Objectives: Septoplasty is a commonly performed surgical procedure aimed at correcting nasal septal deviations, to improve nasal airflow and respiratory function. Traditional approaches to septal correction rely on either direct visualization or endoscopic guidance. Recently, a novel technology known as exoscopy has been introduced into surgical practice. Exoscopy is an “advanced magnification system” that provides an enlarged, three-dimensional view of the operating field. In this article, we present our experience with exoscope-assisted septoplasty, developed over the last two years, and compare it with our extensive experience using the endoscopic approach. Methods: Our case series includes 26 patients, predominantly males and young adults, who underwent exoscope-assisted septoplasty. We discuss the primary advantages of this technique and, most importantly, provide an analysis of its learning curve. The cohort of patients treated using the exoscopic approach was compared with a control group of 26 patients who underwent endoscope-guided septoplasty, randomly selected from our broader clinical database. Finally, we present a representative surgical case that details all phases of the exoscope-assisted procedure. Results: Our surgical experience has demonstrated that exoscopy is a safe and effective tool for performing septoplasty. Moreover, the learning curve associated with this technique exhibits a rapid and progressive improvement. Notably, exoscopy provides a substantial educational benefit for trainees and medical students, as it enables them to share the same visual perspective as the lead surgeon. Conclusions: Although further studies are required to validate this approach, we believe that exoscopy represents a promising advancement for a wide range of head and neck procedures, and certainly for septoplasty. Full article

Variable selection (VS) is a critical step in developing predictive binary classification (BC) models. Many traditional methods for assessing the added value of a candidate variable provide global performance summaries and lack an interpretable graphical summary of results. To address this limitation, we developed the U-smile method, a residual-based, post hoc evaluation approach for assessing prediction improvements and worsening separately for events and non-events. The U-smile method produces three families of interpretable BA-RB-I coefficients at three levels of generality and a standardized graphical summary through U-smile and prediction improvement–worsening (PIW) plots, enabling transparent, interpretable, and explainable VS. Validated in balanced and imbalanced BC scenarios, the method proved robust to class imbalance and collinearity, and more sensitive than traditional metrics in detecting subtle but meaningful effects. Moreover, the method’s intuitive visual output (U-smile plot) facilitates the rapid communication of results to non-technical stakeholders, bridging the gap between data science and applied decision-making. The U-smile method supports both local and global evaluations and complements existing explainable machine learning (XML) and artificial intelligence (XAI) tools without overlapping in their functions. The U-smile method offers a transparency-enhancing and human-oriented approach for ethical and fair VS, making it highly suited for high-stakes domains, e.g., healthcare and public health. Full article