Search Results (4)

Grain serves as an essential cornerstone for sustaining life and social stability. However, during storage grain is often invaded by mold, which leads to mildew issues. This problem diminishes nutrient content and food quality and raises safety concerns, including toxin production, which can cause serious economic losses and catastrophic market stability and national food security conditions. Accordingly, implementing effective measures to prevent and control mold is crucial for ensuring grain storage safety. This paper analyzes the molds that affect grain during storage, discussing their varieties, environmental needs, and potential hazards. It also expounds on corresponding prevention and control measures, including physical methods, chemical approaches, innovative mold inhibitors derived from microbes and plants, and micro–nano prevention and control technology. These measures demonstrate significant mold suppression by destroying the cell structure of mold or inhibiting its physiological processes. In particular, micro–nano technology enables the effective embedding and controlled release of active ingredients. It can prolong the release duration and enhance antibacterial stability, thus achieving more effective control effects. Furthermore, it can be concluded that these strategies provide a theoretical foundation to enhance the safety and efficiency of grain storage. Additionally, they assist in more effectively addressing mold-related challenges while ensuring food security. Full article

This study proposes a deep learning-based method for monitoring the growth of Botrytis cinerea and evaluating the effectiveness of control measures. It aims to address the limitations of traditional statistical analysis methods in capturing non-linear relationships and multi-factor synergistic effects. The method integrates colony growth environment data and images as network inputs, achieving real-time prediction of colony area through an improved RepVGG network. The innovations include (1) combining channel attention mechanism, multi-head self-attention mechanism, and multi-scale feature extractor to improve prediction accuracy and (2) introducing the Shapley value algorithm to achieve a precise quantitative analysis of environmental variables’ contribution to colony growth. Experimental results show that the validation loss of this method reaches 0.007, with a mean absolute error of 0.0148, outperforming other comparative models. This study enriches the theory of gray mold control and provides information technology for optimizing and selecting its inhibitors. Full article

Severe equine asthma is the most common globally widespread non-infectious equine respiratory disease (together with its mild and moderate form), which is associated with exposure to hay dust and mold spores, has certain similarities to human asthma, and continues to represent a therapeutic problem. Immunomodulatory CpG-ODN, bound to gelatin nanoparticles as a drug delivery system, were successfully administered by inhalation to severe equine asthmatic patients in several studies. It was possible to demonstrate a significant, sustained, and allergen-independent one-to-eight-week improvement in key clinical parameters: the arterial partial pressure of oxygen, the quantity and viscosity of tracheal mucus, and neutrophilic inflammatory cells in the respiratory tracts of the severe equine asthmatic subjects. At the immunological level, an upregulation of the regulatory antiallergic and anti-inflammatory cytokine IL-10 as well as a downregulation of the proallergic IL-4 and proinflammatory IFN-γ in the respiratory tracts of the severe equine asthmatic patients were identified in the treatment groups. CD4⁺ T lymphocytes in the respiratory tracts of the asthmatic horses were demonstrated to downregulate the mRNA expression of Tbet and IL-8. Concentrations of matrix metalloproteinase-2 and -9 and tissue inhibitors of metalloproteinase-2 were significantly decreased directly after the treatment as well as six weeks post-treatment. This innovative therapeutic concept thus opens new perspectives in the treatment of severe equine asthma and possibly also that of human asthma. Full article

An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals—drugs, pesticides, and environmental pollutants—interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D ¹³C and 1D ¹⁵N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D ¹³C-NMR and ¹⁵N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold² descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models. Full article