Edible and medicinal mushrooms are extensively cultivated and commercially consumed around the world. However, green mold disease (causal agent, Trichoderma spp.) has resulted in severe crop losses on mushroom farms worldwide in recent years and has become an obstacle to the development of the Ganoderma industry in China. In this study, a new species and a new fungal pathogen on Ganoderma sichuanense fruitbodies were identified based on the morphological characteristics and phylogenetic analysis of two genes, the translation elongation factor 1-α (TEF1) and the second-largest subunit of RNA polymerase II (RPB2) genes. The new species, Trichoderma ganodermatigerum sp. nov., belongs to the Harzianum clade, and the new fungal pathogen was identified as Trichoderma koningiopsis. Furthermore, in order to better understand the interaction between Trichoderma and mushrooms, as well as the potential biocontrol value of pathogenic Trichoderma, we summarized the Trichoderma species and their mushroom hosts as best as possible, and the phylogenetic relationships within mushroom pathogenic Trichoderma species were discussed.
Prostate cancer is the 2nd most commonly occurring male cancer and the 4th most common cancer overall. Early detection and diagnosis are important for clinical treatment. Atomic force microscopy (AFM)-based techniques have been shown to have potential in detecting malignant cancers and artificial intelligence can improve the accuracy of diagnostic and prognostic prediction tests. In this study, the classification of AFM images of prostate cells was performed using machine learning. For early prediction, we used the support vector machine (SVM) to classification prostate cells and compare the classification performance with the remaining four conventional classifiers such as logistic regression (LR), stochastic gradient descent (SGD), K-nearest neighbours (KNN), random forest (RF). Most of the classifiers did well after using the feature selection method (BorutaShap). The results show that the accuracy (ACC) of the features selected using the BorutaShap algorithm combined with the SVM classifier can reach 82.5%. Our current study demonstrates that AFM imaging combined with machine learning can be used to identify prostate cancer cells with an effective classification performance and robustness.
Ovarian cancer is a disease with a high mortality rate in women. The important reasons for high mortality rate of ovarian cancer is the difficulty in early detection. The process of cell carcinogenesis is often accompanied by changes in surface nanostructure of cell membrane. In this study, atomic force microscopy (AFM) was used to obtain the nanostructure features of ovarian cancer cells. IOSE-80 (human ovarian normal cells) and Caov3 (human ovarian cancer cells) cell lines were selected and the morphology of the cell nuclear regions were measured using AFM Quantitative Imaging (QI) mode, which can offer information of hight, adhesion and slope channels. The surface parameters of the cell obtained from the three channels were analyzed. The results showed that there were significant statistical differences in parameters Root-mean-square height (Sq), Skewness (Ssk), Maximum height (Sz) and Arithemetic mean height (Sa) of adhesion channel, Sq, Ssk and Sa of hight channel. These findings indicate that the three channel in AFM imaging can offer different information of the surface nanostructure and the combination of these feature parameters may improve the identification accuracy of cancer. Our study will provide a new idea for the early diagnosis of ovarian cancer based on the nanostructure features of cell surface at the single-cell level.
Paxillus, a type of ectomycorrhizal fungi distributed widely in the world, is also an essential category for researching bioactive substances and pharmacological functions. We discovered fruitbodies of Paxillus involutus covered in a layer of white mycelium in 2020. Cladobotryum verticillatum, a pathogenic fungus related to cobweb disease, was isolated and identified based on morphological and phylogenetic features. Koch's postulates were used to confirm the pathogenicity. The host range test revealed that C. verticillatum could cause disease in all examined mushrooms except Ganoderma sichuanense. To our knowledge, C. verticillatum is a new record species in China and a new pathogen on Paxillus involutus.
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