Aflatoxin biosynthesis regulators AflR and AflS: DNA binding affinity, stoichiometry, and kinetics.

Aflatoxins (AFs), potent foodborne carcinogens produced by Aspergillus fungi, pose significant health risks worldwide and present challenges to food safety and productivity in the food chain. Novel strategies for disrupting AF production, cultivating resilient crops, and detecting contaminated food are urgently needed. Understanding the regulatory mechanisms of AF production is pivotal for targeted interventions to mitigate toxin accumulation in food and feed. The gene cluster responsible for AF biosynthesis encodes biosynthetic enzymes and pathway-specific regulators, notably AflR and AflS. While AflR, a DNA-binding protein, activates gene transcription within the cluster, AflS enhances AF production through mechanisms that are not fully understood. In this study, we developed protocols to purify recombinant AflR and AflS proteins and utilized multiple assays to characterize their interactions with DNA. Our biophysical analysis indicated that AflR and AflS form a complex. AflS exhibited no DNA-binding capability on its own but unexpectedly reduced the DNA-binding affinity of AflR. Additionally, we found that AflR achieves its binding specificity through a mechanism in which either two copies of AflR or its complex with AflS bind to target sites on DNA in a highly cooperative manner. The estimated values of the interaction parameters of AflR, AflS and DNA target sites constitute a fundamental framework against which the function and mechanisms of other AF biosynthesis regulators can be compared.

Development of oral formulation of Lepidium seeds significantly decreases the high blood glucose levels in diabetic rats: in vitro formulation and in vivo antidiabetic performance.

BACKGROUND: Lepidium sativum, Garden Cress (GC), seeds have a lot of natural molecules with a pronounced activity against different disorders. It was reported that GC seeds have the ability to lower the blood glucose level. AIM: The aim of this work was to formulate GC seeds into oral tablets containing a fixed dose of the grounded seeds. Furthermore, the anti-diabetic performance of the prepared tablets was studied in the streptozotocin rats' model in comparison with positive control metformin. METHODS: Micrometrics of GC grounded seeds with different excipients were investigated. Then, GC tablets were prepared via direct compression technique. GC tablets were characterized for their uniformity of dosage unit, friability, hardness, disintegration time, and in vitro release. The antidiabetic effect was studied in rats for a period of 28 days. Glycosylated hemoglobin, liver performance, and lipid levels include total cholesterol (TC), triglycerides (TGs), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) were also estimated. In addition, histopathological study of liver and pancreas was also performed. RESULTS: Prosolv®EasyTab produced tablets with higher hardness, lower disintegration time, and fast release. GC tablets significantly lower the elevated blood glucose level. In addition, they have antihyperlipidemic activity, hepatocellular protective role and restore the histology of the liver and pancreas. CONCLUSION: GC tablets could be a promising alternative formulation to control the high blood glucose level in diabetic rats rather than chemically derivatized drugs.

A novel spectrofluorimetric method for determination of lomefloxacin adopting on zinc(II) chelation strategy: Application in human plasma.

A new sensitive and instantaneous spectrofluorimetric method for efficient determination of lomefloxacin (LMX) in its pure, dosage form and human plasma was designed. The developed method depends on formation of a metal-chelation compound of LMX as a ligand with zinc(II) in a buffer of acetate (pH 5.5). The following parameters; type of metal, concentration of metal, pH, type of buffer and diluting solvent were optimized. After carefully investigation; 0.2 mM zinc, 2.0 ml acetate buffer (pH 5.5) and water as diluting solvent were set as optimum reaction conditions. Under these conditions, a large increase in the intensity of the fluorescence of LMX was attained at 450 after excitation at 284 nm. The limits of detection and quantification were 5.8 and 1.9 ng ml-1 , respectively, with linearity range of 10.0 to 500.0 ng ml-1 . The binding mode of LMX and zinc(II) ion (Zn2+ ) was found to be 2:1, respectively, and confirmed by Job's plot method. Furthermore, it extended to the analysis of LMX in the spiked plasma of humans with percentage recovery (98.70 ± 0.97 to 100.30 ± 1.69%, n = 3).

XDecompo: Explainable Decomposition Approach in Convolutional Neural Networks for Tumour Image Classification.

Of the various tumour types, colorectal cancer and brain tumours are still considered among the most serious and deadly diseases in the world. Therefore, many researchers are interested in improving the accuracy and reliability of diagnostic medical machine learning models. In computer-aided diagnosis, self-supervised learning has been proven to be an effective solution when dealing with datasets with insufficient data annotations. However, medical image datasets often suffer from data irregularities, making the recognition task even more challenging. The class decomposition approach has provided a robust solution to such a challenging problem by simplifying the learning of class boundaries of a dataset. In this paper, we propose a robust self-supervised model, called XDecompo, to improve the transferability of features from the pretext task to the downstream task. XDecompo has been designed based on an affinity propagation-based class decomposition to effectively encourage learning of the class boundaries in the downstream task. XDecompo has an explainable component to highlight important pixels that contribute to classification and explain the effect of class decomposition on improving the speciality of extracted features. We also explore the generalisability of XDecompo in handling different medical datasets, such as histopathology for colorectal cancer and brain tumour images. The quantitative results demonstrate the robustness of XDecompo with high accuracy of 96.16% and 94.30% for CRC and brain tumour images, respectively. XDecompo has demonstrated its generalization capability and achieved high classification accuracy (both quantitatively and qualitatively) in different medical image datasets, compared with other models. Moreover, a post hoc explainable method has been used to validate the feature transferability, demonstrating highly accurate feature representations.

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network.

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and a deep CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 93.1% (with a sensitivity of 100%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases.

Biocontrol of Fusarium graminearum, a Causal Agent of Fusarium Head Blight of Wheat, and Deoxynivalenol Accumulation: From In Vitro to In Planta.

Crop diseases caused by Fusarium graminearum threaten crop production in both commercial and smallholder farming. F. graminearum produces deoxynivalenol mycotoxin, which is stable during food and feed processing. Therefore, the best way to prevent the sporulation of pathogens is to develop new prevention strategies. Plant-based pesticides, i.e., natural fungicides, have recently gained interest in crop protection as alternatives to synthetic fungicides. Herein we show that treatment with the methanolic extract of medicinal plant Zanthoxylum bungeanum (M20 extract), decreased F. graminearum growth and abrogated DON production. The F. graminearum DNA levels were monitored by a quantitative TaqMan real-time PCR, while DON accumulation was assessed by HPLC quantification. This M20 extract was mainly composed of four flavonoids: quercetin, epicatechin, kaempferol-3-O-rhamnoside, and hyperoside. The in vitro bioassay, which measured the percent inhibition of fungal growth, showed that co-inoculation of four F. graminearum strains with the M20 extract inhibited the fungal growth up to 48.5%. After biocontrol treatments, F. graminearum DNA level was reduced up to 85.5% compared to that of wheat heads, which received F. graminearum mixture only. Moreover, DON production was decreased in wheat heads by 73% after biocontrol treatment; meanwhile in wheat heads inoculated with F. graminearum conidia, an average of 2.263 ± 0.8 mg/kg DON was detected. Overall, this study is a successful case from in vitro research to in planta, giving useful information for wheat protection against F. graminearum responsible for Fusarium Head Blight and DON accumulation in grains. Further studies are needed to study the mechanism by which M20 extract inhibited the DON production and what changes happened to the DON biosynthetic pathway genes.

Single-step noncompetitive immunocomplex immunoassay for rapid aflatoxin detection.

Owing to the high carcinogenicity of aflatoxins, these toxic secondary metabolites pose a severe risk to human and animal health and can have major economic implications. Herein, we report the development of a noncompetitive immunoassay for aflatoxins based on a monoclonal capture antibody and a unique anti-immunocomplex (anti-IC) antibody fragment (scFv) isolated from a synthetic antibody repertoire. The anti-IC scFv recognizes the immunocomplex and enables the development of noncompetitive sandwich-type assays despite the small size of the analyte. The single-step assay developed in this work, with a detection limit of 70 pg mL-1, could detect aflatoxins within 15 min. The assay was applied to the analysis of spiked food samples, and the results showed that the method could provide a rapid and simple tool for aflatoxin detection. Moreover, the work demonstrates the potential of anti-IC antibodies and non-competitive immunoassays for the analysis of small molecule contaminants.

A methanolic extract of Zanthoxylum bungeanum modulates secondary metabolism regulator genes in Aspergillus flavus and shuts down aflatoxin production.

Aflatoxin B1 (AFB1) is a food-borne toxin produced by Aspergillus flavus and a few similar fungi. Natural anti-aflatoxigenic compounds are used as alternatives to chemical fungicides to prevent AFB1 accumulation. We found that a methanolic extract of the food additive Zanthoxylum bungeanum shuts down AFB1 production in A. flavus. A methanol sub-fraction (M20) showed the highest total phenolic/flavonoid content and the most potent antioxidant activity. Mass spectrometry analyses identified four flavonoids in M20: quercetin, epicatechin, kaempferol-3-O-rhamnoside, and hyperoside. The anti-aflatoxigenic potency of M20 (IC50: 2-4 µg/mL) was significantly higher than its anti-proliferation potency (IC50: 1800-1900 µg/mL). RNA-seq data indicated that M20 triggers significant transcriptional changes in 18 of 56 secondary metabolite pathways in A. flavus, including repression of the AFB1 biosynthesis pathway. Expression of aflR, the specific activator of the AFB1 pathway, was not changed by M20 treatment, suggesting that repression of the pathway is mediated by global regulators. Consistent with this, the Velvet complex, a prominent regulator of secondary metabolism and fungal development, was downregulated. Decreased expression of the conidial development regulators brlA and Medusa, genes that orchestrate redox responses, and GPCR/oxylipin-based signal transduction further suggests a broad cellular response to M20. Z. bungeanum extracts may facilitate the development of safe AFB1 control strategies.

4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection.

Due to the high availability of large-scale annotated image datasets, knowledge transfer from pretrained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this article, we propose a novel deep convolutional neural network, which we called self-supervised super sample decomposition for transfer learning (4S-DT) model. The 4S-DT encourages a coarse-to-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabeled chest X-ray images. 4S-DT helps in improving the robustness of knowledge transformation via a downstream learning strategy with a class-decomposition (CD) layer to simplify the local structure of the data. The 4S-DT can deal with any irregularities in the image dataset by investigating its class boundaries using a downstream CD mechanism. We used 50000 unlabeled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar. The 4S-DT has achieved a high accuracy of 99.8% on the larger of the two datasets used in the experimental study and an accuracy of 97.54% on the smaller dataset, which was enriched by augmented images, out of which all real COVID-19 cases were detected.

A Polyphasic Approach to Compare the Genomic Profiles of Aflatoxigenic and Non-Aflatoxigenic Isolates of Aspergillus Section Flavi.

Aflatoxins (AF) are highly toxic compounds produced by Aspergillus section Flavi. They spoil food crops and present a serious global health hazard to humans and livestock. The aim of this study was to examine the phylogenetic relationships among aflatoxigenic and non-aflatoxigenic Aspergillus isolates. A polyphasic approach combining phylogenetic, sequence, and toxin analyses was applied to 40 Aspergillus section Flavi isolates collected from eight countries around the world (USA, Philippines, Egypt, India, Australia, Indonesia, China, and Uganda). This allows one to pinpoint the key genomic features that distinguish AF producing and non-producing isolates. Based on molecular identification, 32 (80%) were identified as A. flavus, three (7.5%) as A. parasiticus, three (7.5%) as A. nomius and one (2.5%) as A. tamarii. Toxin analysis showed that 22 (55%) Aspergillus isolates were aflatoxigenic. The majority of the toxic isolates (62.5%) originated from Egypt. The highest aflatoxin production potential was observed in an A. nomius isolate which is originally isolated from the Philippines. DNA-based molecular markers such as random amplified polymorphic DNA (RAPD) and inter-simple sequence repeats (ISSR) were used to evaluate the genetic diversity and phylogenetic relationships among these 40 Aspergillus isolates, which were originally selected from 80 isolates. The percentage of polymorphic bands in three RAPD and three ISSR primers was 81.9% and 79.37%, respectively. Analysis of molecular variance showed significant diversity within the populations, 92% for RAPD and 85% for ISSR primers. The average of Polymorphism Information Content (PIC), Marker Index (MI), Nei's gene diversity (H) and Shannon's diversity index (I) in ISSR markers are higher than those in RAPD markers. Based on banding patterns and gene diversities values, we observed that the ISSR-PCR provides clearer data and is more successful in genetic diversity analyses than RAPD-PCR. Dendrograms generated from UPGMA (Unweighted Pair Group Method with Arithmetic Mean) cluster analyses for RAPD and ISSR markers were related to the geographic origin.