Search Results (14,683)

There is a global demand for reducing the adoption of traditional chemical insecticides in agriculture. Among the most promising alternatives, botanical insecticides have been increasingly gaining attention due to their efficacy combined with a more environmentally safe impact. Among the different botanical insecticides commercially available, oxymatrine is an alkaloid found in the roots of Sophora flavescens which exhibits wide insecticide activity. However, their side-effects on non-target organisms have not been extensively evaluated. Therefore, this study aimed to investigate in laboratory conditions the insecticidal potential of a commercial botanical insecticide (Matrine^®) based on ethanolic extract of S. flavescens roots at 0.2; 0.6; 1.0; 1.4; 1.8; and 2.2 L of commercial product per hectare to control third-instar larvae of Rachiplusia nu and its selectivity in the egg parasitoid Trichogramma pretiosum. Overall, our results showed that the ethanolic extract of S. flavescens is an efficient tool to control R. nu from 0.6 to 2.2 L/ha, with similar R. nu mortality at 48 and 72 h after spraying (close to 100% mortality) associated with no impact to pupae and minimum impact to adults (slightly harmful) of the egg parasitoid. The botanical insecticide was classified as harmless to the pupae and slightly harmful to the adults of T. pretiosum according to the International Organization for Biological Control (IOBC) protocols. Thus, the use of the ethanolic extract of S. flavescens emerges as a relevant alternative to control R. nu, which needs to be confirmed in future field trials. Full article

Background/Objectives: Emerging evidence links diabetes to increased cancer risk. This study aimed to assess the association between diabetes mellitus (DM)(type 1, type 2, or gestational) and the development of head and neck cancer. Methods: An umbrella review was conducted using systematic searches in Cochrane, EBSCO, Wiley, ScienceDirect, and PubMed (January 2000–January 2024), registered in PROSPERO (CRD42024512151). Included were systematic reviews (SRs) and meta-analyses (MAs) of observational studies. Article selection followed the PRISMA guidelines; the quality and risk of bias of the selected studies were assessed with the Joanna Briggs Institute Checklist. The GROOVE tool was used to identify double counting. Two independent reviewers screened studies, with a third resolving disagreements. Results: Seven SRs were included. While DM has been widely examined in cancer research, few studies specifically targeted head and neck cancers. Of the 20 associations between various cancer sites and diabetes types, 9 (45%) showed a statistically significant positive correlation. The strongest evidence was for overall cancer risk (RR = 1.22, 95% CI: 1.16–1.29, p < 0.001). Oral cancer showed elevated risks (RRR = 1.13, p = 0.009; OR = 1.32, p < 0.001; HR = 1.73, p < 0.05; RR = 1.28, p < 0.05). Increased risks were also observed for oropharyngeal (RR = 1.18; HR = 1.53), head and neck (HR = 1.47), and nasopharyngeal cancer (OR = 1.40), all p < 0.05. Heterogeneity was low in two reviews, unreported in one, and high in four. Five SRs reported associated risk factors. Conclusions: While some associations between DM and cancer appear significant, evidence remains limited and inconsistent, particularly for oral cancer. Further standardized, high-quality research is needed to clarify the link across head and neck cancer subtypes. Full article

The root of Plumbago indica L. is commercially available in herbal markets in both crude and powdered forms. P. indica root is a key ingredient in numerous polyherbal formulations. However, P. indica has two closely related species, P. zeylanica L. and P. auriculata Lam. Since only P. indica is traditionally used in Thai polyherbal products, adulteration with other species could potentially compromise the therapeutic efficacy and overall effectiveness of these formulations. To address this issue, a MassARRAY iPLEX assay was developed to accurately identify and differentiate P. indica from its closely related species. Five single nucleotide polymorphism (SNP) sites—positions 18, 112, 577, 623, and 652—within the internal transcribed spacer (ITS) region were selected as genetic markers for species identification. The assay demonstrated high accuracy in identifying P. indica and was capable of detecting the species at DNA concentrations as low as 0.01 ng/µL. Additionally, the assay successfully identified P. zeylanica in commercial crude drug samples, highlighting potential instances of adulteration. Furthermore, it was able to distinguish P. indica in mixed samples containing P. indica, along with either P. zeylanica or P. auriculata. The developed MassARRAY iPLEX assay proves to be a reliable and effective molecular tool for authenticating P. indica raw materials. Its application holds significant potential for ensuring the integrity of herbal products by preventing misidentification and adulteration. Full article

Three-dimensional imaging technologies are increasingly used in breast reconstructive and plastic surgery due to their potential for efficient and accurate preoperative assessment and planning. This study systematically evaluates the accuracy and consistency of six commercially available 3D scanning applications (apps)—Structure Sensor, 3D Scanner App, Heges, Polycam, SureScan, and Kiri—in reconstructing the female torso. To avoid variability introduced by human subjects, a silicone breast mannequin model was scanned, with fiducial markers placed at known anatomical landmarks. Manual distance measurements were obtained using calipers by two independent evaluators and compared to digital measurements extracted from 3D reconstructions in Blender software. Each scan was repeated six times per application to ensure reliability. SureScan demonstrated the lowest mean error (2.9 mm), followed by Structure Sensor (3.0 mm), Heges (3.6 mm), 3D Scanner App (4.4 mm), Kiri (5.0 mm), and Polycam (21.4 mm), which showed the highest error and variability. Even the app using an external depth sensor (Structure Sensor) showed no statistically significant accuracy advantage over those using only the iPad’s built-in camera (except for Polycam), underscoring that software is the primary driver of performance, not hardware (alone). This work provides practical insights for selecting mobile 3D scanning tools in clinical workflows and highlights key limitations, such as scaling errors and alignment artifacts. Future work should include patient-based validation and explore deep learning to enhance reconstruction quality. Ultimately, this study lays the foundation for more accessible and cost-effective 3D imaging in surgical practice, showing that smartphone-based tools can produce clinically useful scans. Full article

Infectious diseases poses a growing public health challenge. The COVID-19 pandemic has further emphasized the urgent need for rapid, accessible diagnostics. This study presents the development of an integrated, flexible point-of-care (POC) diagnostic system for the rapid detection of Corynebacterium diphtheriae, the pathogen responsible for diphtheria. The system comprises a microfluidic polymerase chain reaction (micro-PCR) device and an electrochemical DNA biosensor, both fabricated on flexible substrates. The micro-PCR platform offers rapid DNA amplification overcoming the time limitations of conventional thermocyclers. The biosensor utilizes specific molecular recognition and an electrochemical transducer to detect the amplified DNA fragment, providing a clear and direct indication of the pathogen’s presence. The combined system demonstrates the effective amplification and detection of a gene fragment from a toxic strain of C. diphtheriae, chosen due to its increasing incidence. The design leverages lab-on-a-chip (LOC) and microfluidic technologies to minimize reagent use, reduce cost, and support portability. Key challenges in microsystem design—such as flow control, material selection, and reagent compatibility—were addressed through optimized fabrication techniques and system integration. This work highlights the feasibility of using flexible, integrated microfluidic and biosensor platforms for the rapid, on-site detection of infectious agents. The modular and scalable nature of the system suggests potential for adaptation to a wide range of pathogens, supporting broader applications in global health diagnostics. The approach provides a promising foundation for next-generation POC diagnostic tools. Full article

Photobiomodulation (PBM) utilizes different wavelengths of light to modulate cellular functions and has emerged as a promising approach in regenerative medicine. In this study, we examined the effects of blue (455 nm), red (660 nm), and near-infrared (810 nm) light, both individually and in combination, on human adipose-derived mesenchymal stem/stromal cells (adMSCs). A single, short-term exposure of adMSCs in suspension to these wavelengths using an integrating sphere revealed distinct wavelength- and dose-dependent cellular responses. Blue light exposure led to a dose-dependent increase in intracellular reactive oxygen species, accompanied by reduced cell proliferation, metabolic activity, interleukin-6/interleukin-8 secretion, and adipogenic differentiation. In contrast, red and near-infrared light preserved cell viability and metabolic function while enhancing cell migration, consistent with their documented ability to stimulate proliferation and mitochondrial activity in mesenchymal stem cells. These findings highlight the necessity of precise wavelength and dosage selection in PBM applications and support the potential of PBM as a customizable tool for optimizing patient-specific regenerative therapies. Full article

The European plum (Prunus domestica L.) is a hexaploid species that is grown worldwide for its tasty fruits. Many pomological forms and varieties exist, and thus it is important for genebank curators, breeders, growers, and/or control authorities to distinguish them with certainty. The purpose of this study was to select and verify a set of simple sequence repeat (SSR) markers for reliable genotyping, and to optimize their use in a one-reaction format for easy routine practice. After testing 78 SSR markers from different diploid Prunus species, 8 SSR markers were selected, multiplexed, and successfully verified as being able to distinguish all 242 unique genotypes tested. The selected markers were relatively easily scored and highly heterogenic, giving more than 35 alleles/genotype on average. The allele atlas was created to become a valuable tool for allele calling that should lead to standardized and reliable genotyping results between laboratories. The population analysis confirmed high diversity of the Czech germplasm collection used. The kit was also successfully tested for diploid “plums” of various origins and interspecies hybrids, as these are sometimes phenotypically indistinguishable from hexaploid European plums. The one-tube approach substantially simplified the plum genotyping laboratory workflow, minimizes errors, and saves labor, time, and money. Full article

The construction sector significantly contributes to global environmental degradation through intensive resource extraction, high energy consumption, and substantial waste generation. Addressing this unsustainable trajectory requires integrated approaches that simultaneously improve operational efficiency and material circularity. Lean Construction (LC) and Circular Economy (CE) offer complementary frameworks for enhancing process performance and reducing environmental impacts. However, their combined implementation remains underdeveloped and fragmented. This study conducts a systematic literature review (SLR) of 18 peer-reviewed articles published between 2010 and 2025, selected using PRISMA 2020 guidelines and sourced from Scopus and Web of Science databases. A mixed-method approach combines bibliometric mapping and qualitative content analysis to investigate how LC and CE are jointly operationalized in construction contexts. The findings reveal that LC improves cost, time, and workflow reliability, while CE enables reuse, modularity, and lifecycle extension. Integration is further supported by digital tools—such as Building Information Modelling (BIM), Design for Manufacture and Assembly (DfMA), and digital twins—which enhance traceability and flow optimization. Nonetheless, persistent barriers—including supply chain fragmentation, lack of standards, and regulatory gaps—continue to constrain widespread adoption. This review identifies six strategic enablers for LC-CE integration: crossdisciplinary competencies, collaborative governance, interoperable digital systems, standardized indicators, incentive-based regulation, and pilot demonstrator projects. By consolidating fragmented evidence, the study provides a structured research agenda and practical insights to guide the transition toward more circular, efficient, and sustainable construction practices. Full article

Respiratory diseases represent a persistent global health challenge, underscoring the need for intelligent, accurate, and personalized diagnostic and therapeutic systems. Existing methods frequently suffer from limitations in diagnostic precision, lack of individualized treatment, and constrained adaptability to complex clinical scenarios. To address these challenges, our study introduces a modular AI-powered framework that integrates an audio-based disease classification model with simulated molecular biomarker profiles to evaluate the feasibility of future multimodal diagnostic extensions, alongside a synthetic-data-driven prescription recommendation engine. The disease classification model analyzes respiratory sound recordings and accurately distinguishes among eight clinical classes: bronchiectasis, pneumonia, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), asthma, chronic obstructive pulmonary disease (COPD), bronchiolitis, and healthy respiratory state. The proposed model achieved a classification accuracy of 99.99% on a holdout test set, including 94.2% accuracy on pediatric samples. In parallel, the prescription module provides individualized treatment recommendations comprising drug, dosage, and frequency trained on a carefully constructed synthetic dataset designed to emulate real-world prescribing logic.The model achieved over 99% accuracy in medication prediction tasks, outperforming baseline models such as those discussed in research. Minimal misclassification in the confusion matrix and strong clinician agreement on 200 prescriptions (Cohen’s $\kappa$ = 0.91 [0.87–0.94] for drug selection, 0.78 [0.74–0.81] for dosage, 0.96 [0.93–0.98] for frequency) further affirm the system’s reliability. Adjusted clinician disagreement rates were 2.7% (drug), 6.4% (dosage), and 1.5% (frequency). SHAP analysis identified age and smoking as key predictors, enhancing model explainability. Dosage accuracy was 91.3%, and most disagreements occurred in renal-impaired and pediatric cases. However, our study is presented strictly as a proof-of-concept. The use of synthetic data and the absence of access to real patient records constitute key limitations. A trialed clinical deployment was conducted under a controlled environment with a positive rate of satisfaction from experts and users, but the proposed system must undergo extensive validation with de-identified electronic medical records (EMRs) and regulatory scrutiny before it can be considered for practical application. Nonetheless, the findings offer a promising foundation for the future development of clinically viable AI-assisted respiratory care tools. Full article

Efficient and sustainable extraction of bioactive benzopyrans from Hypericum polyanthemum Klotzsch ex Reichardt (Hypericaceae) remains underexplored, despite their potential applications. The current study aimed to optimize this process by integrating computational simulation and experimental extraction with suitable solvents. The COSMO-RS model was employed to screen deep eutectic solvents (DESs), indicating lactic acid/glycine/water 3:1:3 (DES 1) as a highly promising candidate based on activity coefficients at infinite dilution for target benzopyrans (HP1, HP2, HP3). Ultrasound-assisted extraction (UAE) was then conducted using the proposed DES as well as hexane, and the extracts were analyzed via high-performance liquid chromatography (HPLC) and spectrophotometry for total phenolic content (TPC). The results for DES 1 showed yields for benzopyrans HP1 (1.43 ± 0.09 mg/g plant) and HP2 (0.55 ± 0.04 mg/g plant) close to those obtained in the hexane extract (1.65 and 0.78 mg/g plant, respectively), corroborating the use of COSMO-RS for solvent screening. Kinetic analysis using an adapted Crank diffusion model successfully described the mass transfer process for DES 1 (R² > 0.98, mean average percent error < 9%), indicating diffusion control and allowing estimation of effective diffusion coefficients. This work confirms COSMO-RS as a valuable tool for solvent selection and demonstrates that UAE with the identified DES provides an efficient, greener approach for extracting valuable benzopyrans, offering a foundation for further process optimization. Full article

The work presents the design of an Oscillating Water Column (OWC) system with a nominal capacity of 16 kW, proposed as a contribution to reducing the energy gap in Ecuador, where electricity demand surpasses supply. The province of Santa Elena was selected as a promising site due to its favorable wave conditions and coastal location. The design process involved identifying areas with high wave energy potential, conducting a brief mathematical modeling analysis, and defining the parameters required for the system. Computational Fluid Dynamics (CFD) simulations were carried out in two stages: In the first stage, OpenFOAM was used to evaluate wave behavior, specifically flow velocity and pressure, before the water enters the generation chamber. In the second stage, a different CFD tool was used, incorporating the output data from OpenFOAM to simulate the energy conversion process inside the Wells turbine. This analysis focused on how the turbine captures and transforms the wave energy into usable power. The results show that, under ideal conditions, the system achieves an average power output of 11 kW. These findings suggest that implementing this type of system in coastal regions of Ecuador is both viable and beneficial for local energy development. Full article

Background: Cancer is one of the leading causes of morbidity and mortality worldwide and in Spain. Individuals with cancer are at a higher risk of suicide compared to the general population due to both general and disease-specific risk factors. Objective: To update knowledge on nursing care measures to address the risk of suicide in cancer patients. Methods: A narrative review was conducted by searching PubMed, WOS, Scopus, and CINAHL during February and March 2025. The inclusion criteria comprised original qualitative, quantitative, and/or mixed-methods studies related to the topic of the review. Results: Of the 289 identified studies, 23 were selected. Twelve studies of cancer patients, ten studies of healthcare professionals, and one study of caregivers and survivors were included. Regarding suicide risk factors, eight studies addressed demographic aspects, fifteen socio-economic factors, twenty-one psycho-emotional factors, and seventeen physical factors. Key risk factors included male sex, advanced age, social isolation, lack of social support, hopelessness, and physical deterioration. Seventeen studies highlighted the need for continuous and comprehensive nursing care using validated tools for systematic assessment of suicide risk. Eight emphasised the importance of ongoing training in suicide prevention, which is essential for developing communication skills and improving therapeutic relationships. Five studies underscored the relevance of a holistic approach that addresses the physical, emotional, social, and spiritual dimensions of patient care. Six extended this approach to include family members and caregivers. Conclusions: Suicide risk in cancer patients is associated with multiple risk factors. Emotional support and a comprehensive, continuous nursing approach—based on systematic assessments, specialised training, and a holistic focus—are key to effective suicide prevention. Full article

The subject of this research is the original project concept of the life-centered city, which focuses on the planning and design of sustainable solutions for urban landscape transformation. This concept prioritizes the well-being and needs of all life on Earth, including not only humans but also animals and their natural habitats. The aim of this article is to propose ways to implement the life-centered city concept into the strategic development policies of cities and identify sustainable urban landscape solutions that foster the creation of interspecies spaces. The research employs a comparative analysis of selected European cities, neighborhoods, and urban microspaces that are progressively adapting to climate change, addressing the needs of various users, and prioritizing the development of interspecies spaces. A detailed study focuses on the Polish city of Gliwice, which serves as a pilot example of applying the life-centered city model to local landscapes. Our findings suggest that the life-centered city concept, when effectively integrated into city development strategies and implemented within the urban fabric, can act as a proactive tool for transforming urban landscapes to better accommodate both people and nature. It supports the creation of a sustainable built environment that is inclusive, resilient, and adaptable to change. Full article

The ongoing decline in global biodiversity constitutes a critical challenge for environmental science, necessitating the prompt development of effective monitoring frameworks and conservation protocols to safeguard the structure and function of natural ecosystems. Recent progress in ecoacoustic monitoring, supported by advances in artificial intelligence, might finally offer scalable tools for systematic biodiversity assessment. In this study, we evaluate the performance of BirdNET, a state-of-the-art deep learning model for avian sound recognition, in the context of selected bird species characteristic of the Italian Alpine region. To this end, we assemble a comprehensive, manually annotated audio dataset targeting key regional species, and we investigate a variety of strategies for model adaptation, including fine-tuning with data augmentation techniques to enhance recognition under challenging recording conditions. As a baseline, we also develop and evaluate a simple Convolutional Neural Network (CNN) trained exclusively on our domain-specific dataset. Our findings indicate that BirdNET performance can be greatly improved by fine-tuning the pre-trained network with data collected within the specific regional soundscape, outperforming both the original BirdNET and the baseline CNN by a significant margin. These findings underscore the importance of environmental adaptation and data variability for the development of automated ecoacoustic monitoring devices while highlighting the potential of deep learning methods in supporting conservation efforts and informing soundscape management in protected areas. Full article

In the field of green chemistry, the development of more sustainable and cost-efficient methods for synthesizing primary amines is of paramount importance, with catalyst research being central to this effort. This work presents a facile, aqueous-phase synthesis of highly active cobalt catalysts (Co-Ph@SiO₂(x)) via pyrolysis of silica-supported cobalt–phenanthroline complexes. The optimized Co-Ph@SiO₂(900) catalyst achieved exceptional performance (>99% conversion, >98% selectivity) in the reductive amination of acetophenone to 1-phenylethanamine using NH₃/H₂. Systematic studies revealed that its exceptional performance originates from the in situ pyrolysis of the cobalt–phyllosilicate complex. This process promotes the uniform distribution of metal cobalt nanoparticles, simultaneously enhancing porosity and imparting bifunctional (acidic and basic) properties to the catalyst, resulting in outstanding catalytic activity and selectivity. The catalyst demonstrated broad applicability, efficiently converting diverse ketones (aryl-alkyl, dialkyl, bioactive) and aldehydes (halogenated, heterocyclic, biomass-derived) into primary amines with high yields (up to 99%) and chemoselectivity (>40 examples). This sustainable, non-noble metal-based catalyst system offers significant potential for industrial primary amine synthesis and provides a versatile tool for developing highly selective and active heterogeneous catalysts. Full article