Search Results (143)

Supercritical CO₂ (S-CO₂) extraction is one of the most employed techniques for the extraction of bioactive compounds for its safety, effectiveness, cost-efficiency, and good environmental compliance. Smyrnium olusatrum L. (Apiaceae) is an aromatic plant of great interest due to its potential applications in pharmaceutical, agrochemical, and oleochemical fields. Its bioactivity is caused by furanosesquiterpenes, mainly represented by isofuranodiene (IFD). The extraction of this compound is usually achieved through Soxhlet or hydrodistillation. However, the latter usually leads to the thermal Cope rearrangement of IFD into its isomer curzerene, resulting in low recovery. This study reported for the first time the optimization of S-CO₂ extraction of IFD from S. olusatrum schizocarps. Pressure (MPa), extraction time (min), and static mode (%) were varied while the temperature was maintained at 45 °C to avoid IFD thermal degradation. The optimized process (50 MPa, 60 min, 25% static mode) provided an extraction yield and an IFD recovery of 8.50 and 0.94% and avoided the thermal degradation of the compound. This study demonstrated that S-CO₂ extraction is a valuable alternative to conventional hydrodistillation (extraction yield and IFD recovery of 2.64 and 0.77%) and Soxhlet (extraction yield and IFD recovery of 9.49 and 0.85%) to recover IFD from S. olusatrum. Full article

Endocrine-disrupting chemicals (EDCs) perturb hormonal homeostasis, dysregulating multiple biological pathways and subsequently resulting in adverse health outcomes, including impaired reproductive function. Bisphenols represent an important subclass of EDCs with widespread use in polycarbonate plastics, thermal paper formulations, epoxy resins, and various everyday consumer products. Bisphenol A (BPA) was the first bisphenol to be synthesized, with extensive industrial applications. However, the concerns over its potential health risks, most notably reproductive dysfunction, prompted the development and introduction of several structurally related BPA analogues. That said, studies on the potential hormonal effects of these BPA analogues remain limited. Therefore, strengthening the evidence base on their reproductive safety evaluation remains an essential priority for ensuring their safe application, and this study contributes to that broader objective. The study aimed to explore the potential endocrine-disrupting activity of two commonly used BPA analogues, bisphenol F (BPF) and bisphenol S (BPS), on reproductive hormone signalling, contributing to ongoing safety assessment efforts. The molecular interactions of these analogues with the estrogen receptor-α (ERα) and estrogen receptor-β (ERβ) were analyzed through structural binding characterization employing the induced fit docking (IFD) approach using the Schrödinger 2019 suite. The overall results revealed that the two indicated BPA analogues were placed successfully in the ligand-binding pockets of ERα and ERβ. Their binding pattern and molecular interactions showed certain similarities; however, they did not fully replicate the amino acid residue environment of the native ligands of ERα and ERβ, estradiol. Notably, the binding energy estimations revealed that BPF and BPS showed substantially lower values than those calculated for native ligands of ERα and ERβ. In summary, this study suggests that BPF and BPS exhibit lower predicted binding affinity toward ERα and ERβ under the applied molecular docking conditions. However, these computational findings do not establish receptor activation, endocrine potency, or safety outcomes, and the potential involvement of other reproductive signalling pathways warrants further investigation. Full article

This paper examines the philosophy and practice of interfaith dialogue (IFD) developed by Daisaku Ikeda (1928–2023), a prominent religious leader and peace philosopher. It explores how his dialogical approach can contribute to the United Nations’ Sustainable Development Goals (SDGs) and pathways to global peace. Ikeda’s dialogue is not confined to doctrinal debate or temporary reconciliation among faith communities. Rather, it is framed as a transformative process in which participants from diverse religious and civilizational traditions rebuild relationships through mutual respect and understanding, thereby contributing to personal transformation and broader societal change. Focusing on Ikeda’s core concepts—humanism, the dignity of life, and human revolution—this study first clarifies the philosophical foundations of his interfaith dialogue rooted in Nichiren Buddhism and a life-affirming worldview. It then examines major dialogues with global thinkers and leaders (e.g., Arnold J. Toynbee, Linus Pauling, Mikhail Gorbachev, and Johan Galtung) and selected institutional practices associated with Soka Gakkai International (SGI), the Institute of Oriental Philosophy (IOP), and the Ikeda Center for Peace, Learning, and Dialogue. These cases illustrate how Ikeda’s IFD functions as praxis for civilizational understanding, social cohesion, conflict transformation, and solidarity for the public good. The paper further analyzes the linkages between Ikeda’s IFD and SDG 16 (Peace, Justice and Strong Institutions), SDG 17 (Partnerships for the Goals), SDG 4 (Quality Education—especially Target 4.7 on Global Citizenship Education), and SDG 10 (Reduced Inequalities). It argues that IFD can operate as both a normative and practical resource for mitigating religious conflict, strengthening inclusion, enhancing global citizenship education and education for sustainable development (ESD), and fostering multistakeholder partnerships. The paper also reflects on the challenges of translating an approach grounded in a particular religious tradition into broader SDG governance contexts. Full article

Invasive fungal diseases (IFDs) represent an escalating global health threat, compounded by the rapid emergence of antifungal resistance (AFR). This review synthesizes the contemporary landscape of AFR from clinical and microbiological perspectives, providing actionable insights for clinical practitioners. We examine the epidemiology of critical pathogens, including Candidozyma auris, clonal Candida parapsilosis, azole-resistant Aspergillus fumigatus, and dissect the underlying molecular mechanisms, from genetic mutations in ERG11 and cyp51A to novel emerging epigenetic and adaptive strategies. We critically appraise the diagnostic gap between phenotypic testing and clinical urgency, highlighting the role of rapid molecular assays and next-generation sequencing. Finally, we evaluate evidence-based therapeutic strategies, including the integration of novel agents such as rezafungin, ibrexafungerp, olorofim, and fosmanogepix), while emphasizing the imperative of antifungal stewardship, infection prevention and control in mitigating resistance, and “One-Health” interventions. Full article

Invasive fungal disease (IFD) is one of the leading causes of morbidity and mortality in immunocompromised pediatric patients. This is a multicenter prospective cohort study with a nested retrospective analysis aimed at identifying risk factors for IFD in immunocompromised children with cancer and episodes of persistent high-risk febrile neutropenia (HRFN). One hundred and seventy-four episodes of persistent HRFN were analyzed, of which 34 (19.5%) were confirmed as IFD, 52.9% were caused by filamentous fungi, and 47.1% by yeasts. Logistic regression and survival analyses identified the following significant risk factors for IFD: male sex (OR 4.04), adolescence (OR 4.65), C-reactive protein ≥ 90 mg/L at admission (OR 3.13), and transfer to a critical care unit (OR 10.73). The predictive model demonstrated strong discriminatory capacity (AUC 0.84), with 79.4% sensitivity and 82.1% specificity. These findings highlight that adolescents, particularly males with severe clinical conditions and elevated inflammatory markers, are at the highest risk for IFD during episodes of HRFN. The proposed risk factor-based model may support early risk stratification and guide targeted antifungal prophylaxis or therapy, potentially improving outcomes in this population. Validation an external cohort is required to confirm these results and optimize clinical applicability. Full article

(1) Background: Invasive fungal diseases (IFDs) represent significant challenges in clinical practice, particularly among immunocompromised individuals, leading to substantial morbidity and mortality. The present document aims to provide evidence-based consensus for the timely initiation of antifungal treatment, focusing on early empiric approaches among immunocompromised patients. (2) Methods: A multidisciplinary expert panel of nine healthcare professionals (HCPs) reviewed the literature, including guidelines and consensus reports (2013–2023; PubMed, Scopus). The panel defined appropriate empiric antifungal approaches for invasive candidiasis, aspergillosis, and mucormycosis among hematological and critically ill patients. Consensus was defined as ≥75% agreement. (3) Results: A total of 47 statements were included. The experts recommend that early targeted antifungal therapy is critical for high-risk patients with suspected IFDs. Empiric therapy may be initiated before definitive diagnosis, considering the local fungal prevalence and the patient’s risk category. Close monitoring is essential, and switching between antifungal classes may be necessary for patients who experience deterioration or side effects. The transition from intravenous to oral therapy depends on the specific infection, the availability of therapeutic drug monitoring, and the patient’s progress. (4) Conclusions: Implementing this targeted, early approach may improve the outcomes of vulnerable patients with IFDs. Full article

Maize is a globally major crop; however, the prevalence of mixed-aged seeds in the market complicates consumer selection and impedes the healthy development of the maize industry. This study introduces a novel method for identifying maize seeds of different storage ages. Seeds were categorized into three age groups: new seeds, one-year stored, and two-year stored, with 300 seeds per group. Hyperspectral images of all 900 samples were acquired using a visible and near-infrared (Vis-NIR) hyperspectral imaging system. To achieve optimal results with minimal spectral data, a feature wavelength selection algorithm based on Inter-Class Feature Differences (IFD) was proposed. When only using the selected three key wavelengths, combined with the linear discriminant analysis (LDA) algorithm, the discrimination accuracy among three different age groups reached 85.67%, while the discrimination accuracy between new and aged seeds achieved 95.33%. Compared to two commonly used variable selection algorithms—Successive Projections Algorithm (SPA) and Random Frog (RF), the proposed IFD method demonstrated superior performance when only a limited number of key wavelengths were used for modeling. These results indicate that the proposed algorithm offers an effective and efficient solution for maize seed age discrimination, showing great potential for practical application. Full article

Schiff bases are bioactive compounds that have been synthesized by many researchers in recent years. They may also exhibit strong antimicrobial activities against various pathogenic microorganisms in both medicine and veterinary applications. The synthesis of new Schiff base-derived compounds remains of interest due to the increasing problem of antibiotic-resistance in clinical practice. Seven new Schiff base derivatives were synthesized, and their chemical structures were characterized using FT-IR, ¹H/¹³C NMR, and LCMS-MS analyses. The antimicrobial activities of thesyntesized compounds against various pathogenic bacteria, yeasts, and fungi were evaluated using the disk-diffusion method, and their MIC values were also determined. In addition, one representative microorganisms from each class were selected for molecular docking studies. IFD analyses were performed for the 4f and 4g ligands using the dihydrofolate reductase enzyme. Spectroscopic analyses confirmed the structures of the synthesized compounds, revealing the presence of characteristic imine functionalities and validating the integrity of the molecular frameworks. Antimicrobial assays demonstrated that several derivatives exhibited measurable activity, with compounds 4f and 4g showing the most potent effects, displaying MIC values of 32 µg/mL against B. cereus and E. faecalis, respectively. Molecular docking studies further indicated that both 4f and 4g bind efficiently to the DHFR active site. These findings indicate that among the synthesized Schiff base derivatives, compounds 4f and 4g exhibit particularly promising antimicrobial activity, warranting further pharmacological evaluation and medicinal chemistry optimization. Full article

The detection of small targets in infrared imagery captured by unmanned aerial vehicles (UAVs) is critical for surveillance and monitoring applications. However, this task is challenged by the small target size, low signal-to-noise ratio, and the limited computational resources of UAV platforms. To address these issues, this paper proposes IFD-YOLO, a novel lightweight detector based on YOLOv11n, specifically designed for onboard infrared sensing systems. Our framework introduces three key improvements. First, a RepViT backbone enhances both global and local feature extraction. Second, a C3k2-DyGhost module performs dynamic and efficient feature fusion. Third, an Adaptive Fusion-IoU (AF-IoU) loss improves bounding-box regression accuracy for small targets. Extensive experiments on the HIT-UAV and IRSTD-1k datasets demonstrate that IFD-YOLO achieves a superior balance between accuracy and efficiency. Compared to YOLOv11n, our model improves mAP@50 and mAP@50:95 by 4.9% and 3.1%, respectively, while simultaneously reducing the number of parameters and GFLOPs by 23% and 21%. These results validate the strong potential of IFD-YOLO for real-time infrared sensing tasks on resource-constrained UAV platforms. Full article

Intelligent Fault Diagnosis (IFD) systems are integral to predictive maintenance and real-time monitoring but often encounter challenges such as data scarcity, non-linearity, and changing operational conditions. To address these challenges, we propose an enhanced transfer learning framework that integrates the Universal Adaptation Network (UAN) with Spectral-normalized Neural Gaussian Process (SNGP), WideResNet, and attention mechanisms, including self-attention and an outlier attention layer. UAN’s flexibility bridges diverse fault conditions, while SNGP’s robustness enables uncertainty quantification for more reliable diagnostics. WideResNet’s architectural depth captures complex fault patterns, and the attention mechanisms focus the diagnostic process. Additionally, we employ Optuna for hyperparameter optimization, using a structured study to fine-tune model parameters and ensure optimal performance. The proposed approach is evaluated on benchmark datasets, demonstrating superior fault identification accuracy, adaptability to varying operational conditions, and resilience against data anomalies compared to existing models. Our findings highlight the potential of advanced machine learning techniques in IFD, setting a new standard for applying these methods in complex diagnostic environments. Full article

Invasive fungal disease (IFD) remains a life-threatening complication in patients with hematological diseases. Isavuconazole was approved by the FDA for primary treatment of invasive aspergillosis and mucormycosis. While clinical trials have demonstrated its efficacy, data on its use in hematology patients remain limited. This study aims to evaluate the real-world effectiveness and safety of isavuconazole in this population. We conducted a single-center, retrospective study of hematology patients who received isavuconazole for IFD between 1 June 2022, and 31 July 2024, at West China Hospital, Sichuan University. A total of 66 patients with proven (n = 9), probable (n = 17), or possible (n = 40) IFD were included in the study. Acute leukemia (AL) was the most common underlying disease, affecting 27 patients (40.9%), followed by non-Hodgkin’s lymphoma (NHL) and myelodysplastic syndrome (MDS). Over 80.0% of patients received oral isavuconazole. At 6 weeks of follow-up, a favorable response was observed in 57.6% of patients, increasing to 71.2% at 12 weeks. Factors associated with achieving complete response in isavuconazole treatment included receiving isavuconazole as primary treatment (OR = 0.10, p = 0.01) and reaching complete/partial remission (CR/PR) of the primary hematological disease (OR = 0.07, p = 0.003). The all-cause mortality rates were under 30.0%. The use of isavuconazole as primary antifungal therapy (p < 0.05) and achieving CR/PR in the underlying hematological disease (p < 0.05) were two independent predictors of improved clinical outcomes. Adverse events were reported in 33.3% of patients, and no adverse events led to discontinuation of treatment. Our study demonstrated that isavuconazole is an effective and well-tolerated treatment for IFD in hematology patients. The oral formulation provided comparable efficacy and enhanced compliance, potentially leading to improved outcomes and optimizing the management strategy. The generalizability of our findings may be limited by the single-center, retrospective nature; further validation through prospective, multi-center studies is needed. Full article

To develop and validate a predictive model for assessing the risk of short-term mortality in patients with invasive fungal diseases (IFDs) following cardiac surgery. This retrospective study analyzed clinical data from patients diagnosed with postoperative IFDs in the cardiac surgical intensive care unit (ICU) of Qilu Hospital of Shandong University (QLH), between January 2020 and December 2023. A total of 98 patients were included and divided into a non-survival group (n = 42) and a survival group (n = 56) based on 28-day mortality. Demographic, clinical, and postoperative parameters were collected. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for variable selection, and selected variables were then entered into multivariate logistic regression to identify independent risk factors. A nomogram was developed, and its predictive performance was evaluated using the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve (CIC). Multivariate logistic regression, following variable selection by LASSO, identified a history of smoking, an elevated SOFA score, mean arterial pressure (MAP) below 70 mmHg, and tachyarrhythmia as independent risk factors for short-term mortality in this cohort (p < 0.05). The prediction model demonstrated excellent discrimination, with an area under the ROC curve (AUC) of 0.886 (95% CI: 0.816–0.957). The calibration curve showed good agreement between predicted and observed outcomes, with a mean absolute error of 0.023. Decision curve analysis indicated a net clinical benefit across a threshold probability range of 0.1 to 0.87. The clinical impact curve confirmed a high concordance between predicted mortality and actual outcomes. A history of smoking, an elevated SOFA score, MAP below 70 mmHg, and tachyarrhythmia independently predict short-term mortality in patients with IFDs after cardiac surgery. Therefore, the nomogram constructed from these factors provides an accurate and clinically applicable tool for risk stratification. Full article

With the rapid development of intelligent connected vehicle technology, false data injection (FDI) attacks have become a major challenge in the Internet of Vehicles (IoV). While deep learning methods can effectively identify such attacks, the dynamic, distributed architecture of the IoV and limited computing resources hinder both privacy protection and lightweight computation. To address this, we propose FedIFD, a federated learning (FL)-based detection method for false data injection attacks. The lightweight threat detection model utilizes basic safety messages (BSM) for local incremental training, and the Q-FedCG algorithm compresses gradients for global aggregation. Original features are reshaped using a time window. To ensure temporal and spatial consistency, a sliding average strategy aligns samples before spatial feature extraction. A dual-branch architecture enables parallel extraction of spatiotemporal features: a three-layer stacked Bidirectional Long Short-Term Memory (BiLSTM) captures temporal dependencies, and a lightweight Transformer models spatial relationships. A dynamic feature fusion weight matrix calculates attention scores for adaptive feature weighting. Finally, a differentiated pooling strategy is applied to emphasize critical features. Experiments on the VeReMi dataset show that the accuracy reaches 97.8%. Full article

Background: The presence of (1→3)-β-D-Glucan (BDG) in serum may be indicative of invasive fungal disease (IFD), but even without IFD, elevated BDG can be associated with adverse patient outcomes. Methods: COVID-19-infected patients (n = 125) who were screened for IFD with fungal biomarkers were evaluated to assess the prognostic value of BDG. BDG was correlated with patients’ mortality, considering the influences of IFD and anti-fungal therapy (AFT). Results: A BDG concentration > 31 pg/mL was associated with significant mortality in the absence of documented IFD and without subsequent antifungal therapy (≤31 pg/mL: 28% vs. >31 pg/mL: 91%; p = 0.0001). In patients without IFD but with BDG > 31 pg/mL, mortality dropped to 50% when AFT was administered. In patients with BDG > 31 pg/mL and neither IFD nor AFT, the average probability of death was 3.38-fold greater. Conclusions: Elevated serum BDG is associated with significant mortality in COVID-19-infected patients without IFD, irrespective of AFT. A BDG-associated proinflammatory response might be driving the high mortality. BDG serves as a prognostic marker in COVID-19-infected patients with or without IFD. When BDG is very low (≤31 pg/mL) the likelihood of death remains consistent with the background mortality rates for COVID-19 within the ICU. Full article

Improving the decoding accuracy of biological signals has been a research focus for decades to advance health, automation, and robotic industries. However, challenges like inter-subject variability, data scarcity, and multifunctional variability cause low decoding accuracy, thus hindering the practical deployment of biological signal paradigms. This paper proposes a multifunctional biological signals network (Multi-BioSig-Net) that addresses the aforementioned issues by devising a novel blind few-shot learning (FSL) technique to quickly adapt to multiple target domains without needing a pre-trained model. Specifically, our proposed multimodal similarity extractor (MMSE) and self-multiple domain adaptation (SMDA) modules address data scarcity and inter-subject variability issues by exploiting and enhancing the similarity between multimodal samples and quickly adapting the target domains by adaptively adjusting the parameters’ weights and position, respectively. For multifunctional learning, we proposed inter-function discriminator (IFD) that discriminates the classes by extracting inter-class common features and then subtracts them from both classes to avoid false prediction of the proposed model due to overfitting on the common features. Furthermore, we proposed a holistic-local fusion (HLF) module that exploits contextual-detailed features to adapt the scale-varying features across multiple functions. In addition, fractal dimension estimation (FDE) was employed for the classification of left-hand motor imagery (LMI) and right-hand motor imagery (RMI), confirming that proposed method can effectively extract the discriminative features for this task. The effectiveness of our proposed algorithm was assessed quantitatively and statistically against competent state-of-the-art (SOTA) algorithms utilizing three public datasets, demonstrating that our proposed algorithm outperformed SOTA algorithms. Full article