Controlling sap-sucking insect pests with recombinant endophytes expressing plant lectin

We developed a novel pest management strategy, which uses endophytes to express anti-pest plant lectins. Fungal endophyte of Chaetomium globosum YY-11 with anti-fungi activities was isolated from rape seedlings, and bacterial endophytes of SJ-10 (Enterobacter sp.) and WB (Bacillus subtilis) were isolated from rice seedlings. Pinellia ternate agglutinin gene was cloned into SJ-10 and WB for expression by a shuttle vector, and YY-11 was mediated by Agrobacterium tumefaciens. Positive transformants were evaluated using PCR and Western blot assay. Recombinant endophytes colonized most of crops, and resistance of rice seedlings, which were inoculated with the recombinant endophytic bacteria, to white backed planthoppers was dramatically enhanced by decreasing the survival and fecundity of white backed planthoppers. Rape inoculated with recombinant endophytic fungi significantly inhibited the growth and reproduction of aphids. Recombinant endophytes expressing PTA may endow hosts with resistance against sap-sucking pests

Synthesis, properties, and optical applications of noble metal nanoparticle-biomolecule conjugates

Noble metal nanoparticles, such as gold or silver nanoparticles and nanorods, exhibit unique photonic, electronic and catalytic properties. Functionalization of noble metal nanoparticles with biomolecules (e. g., protein and DNA) produces systems that possess numerous applications in catalysis, delivery, therapy, imaging, sensing, constructing nanostructures and controlling the structure of biomolecules. In this paper, the recent development of noble metal nanoparticle-biomolecule conjugates is reviewed from the following three aspects: (1) synthesis of noble metal nanoparticle-biomolecule systems by electrostatic adsorption, direct chemisorption of thiol derivatives, covalent binding through bifunctional linkers and specific affinity interactions; (2) the photonic properties and bioactivation of noble metal nanoparticle-biomolecule conjugates; and (3) the optical applications of such systems in biosensors, and medical imaging, diagnosis, and therapy. The conjugation of Au and Ag nanoparticles with biomolecules and the most recent optical applications of the resulting systems have been focused on

Prediction value study of breast cancer tumor infiltrating lymphocyte levels based on ultrasound imaging radiomics

ObjectiveConstruct models based on grayscale ultrasound and radiomics and compare the efficacy of different models in preoperatively predicting the level of tumor-infiltrating lymphocytes in breast cancer.Materials and methodsThis study retrospectively collected clinical data and preoperative ultrasound images from 185 breast cancer patients confirmed by surgical pathology. Patients were randomly divided into a training set (n=111) and a testing set (n=74) using a 6:4 ratio. Based on a 10% threshold for tumor-infiltrating lymphocytes (TIL) levels, patients were classified into low-level and high-level groups. Radiomic features were extracted and selected using the training set. The evaluation included assessing the relationship between TIL levels and both radiomic features and grayscale ultrasound features. Subsequently, grayscale ultrasound models, radiomic models, and nomograms combining radiomics score (Rad-score) and grayscale ultrasound features were established. The predictive performance of different models was evaluated through receiver operating characteristic (ROC) analysis. Calibration curves assessed the fit of the nomograms, and decision curve analysis (DCA) evaluated the clinical effectiveness of the models.ResultsUnivariate analyses and multivariate logistic regression analyses revealed that indistinct margin (P<0.001, Odds Ratio [OR]=0.214, 95% Confidence Interval [CI]: 0.103-1.026), posterior acoustic enhancement (P=0.027, OR=2.585, 95% CI: 1.116-5.987), and ipsilateral axillary lymph node enlargement (P=0.001, OR=4.214, 95% CI: 1.798-9.875) were independent predictive factors for high levels of TIL in breast cancer. In comparison to grayscale ultrasound model (Training set: Area under curve [AUC] 0.795; Testing set: AUC 0.720) and radiomics model (Training set: AUC 0.803; Testing set: AUC 0.759), the nomogram demonstrated superior discriminative ability on both the training (AUC 0.884) and testing (AUC 0.820) datasets. Calibration curves indicated high consistency between the nomogram model’s predicted probability of breast cancer TIL levels and the actual occurrence probability. DCA revealed that the radiomics model and the nomogram model achieved higher clinical net benefits compared to the grayscale ultrasound model.ConclusionThe nomogram based on preoperative ultrasound radiomics features exhibits robust predictive capacity for the non-invasive evaluation of breast cancer TIL levels, potentially providing a significant basis for individualized treatment decisions in breast cancer

Synthesis, properties, and optical applications of noble metal nanoparticle-biomolecule conjugates

China-MOST [2008DFA51230]; National Basic Research Program of China [2007CB936603]; National Natural Science Foundation of China [11074207, 60776007]Noble metal nanoparticles, such as gold or silver nanoparticles and nanorods, exhibit unique photonic, electronic and catalytic properties. Functionalization of noble metal nanoparticles with biomolecules (e. g., protein and DNA) produces systems that possess numerous applications in catalysis, delivery, therapy, imaging, sensing, constructing nanostructures and controlling the structure of biomolecules. In this paper, the recent development of noble metal nanoparticle-biomolecule conjugates is reviewed from the following three aspects: (1) synthesis of noble metal nanoparticle-biomolecule systems by electrostatic adsorption, direct chemisorption of thiol derivatives, covalent binding through bifunctional linkers and specific affinity interactions; (2) the photonic properties and bioactivation of noble metal nanoparticle-biomolecule conjugates; and (3) the optical applications of such systems in biosensors, and medical imaging, diagnosis, and therapy. The conjugation of Au and Ag nanoparticles with biomolecules and the most recent optical applications of the resulting systems have been focused on

Claudin-1/4 as directly target gene of HIF-1α can feedback regulating HIF-1α by PI3K-AKT-mTOR and impact the proliferation of esophageal squamous cell though Rho GTPase and p-JNK pathway

Immunohistochemical microarray comprising 80 patients with esophageal squamous cell carcinoma (ESCC) and discovered that the expression of CLDN1 and CLDN4 were significantly higher in cancer tissues compared to para-cancerous tissues. Furthermore, CLDN4 significantly affected the overall survival of cancer patients. When two ESCC cell lines (TE1, KYSE410) were exposed to hypoxia (0.1% O2), CLDN1/4 was shown to influence the occurrence and development of esophageal cancer. Compared with the control culture group, the cancer cells cultured under hypoxic conditions exhibited obvious changes in CLDN1 and CLDN4 expression at both the mRNA and protein levels. Through genetic intervention and Chip, we found that HIF-1α could directly regulate the expression of CLDN1 and CLDN4 in cancer cells. Hypoxia can affect the proliferation and apoptosis of cancer cells by regulating the PI3K-Akt-mTOR pathway. Molecular analysis further revealed that CLDN1 and CLDN4 can participate in the regulation process and had a feedback regulatory effect on HIF-1α expression in cancer cells. In vitro cellular experiments and vivo experiments in nude mice further revealed that changes in CLDN4 expression in cancer cells could affect the proliferation of cancer cells via regulation of Rho GTP and p-JNK pathway. Whether CLDN4 can be target for the treatment of ESCC needs further research

Research on performance evaluation and optimization theory for thermal microscope imaging systems

Infrared imaging theory is an important theoretical basis for the design of infrared imaging systems, but there is no research on infrared imaging theory for designing thermal microscope imaging systems. Therefore, we studied the performance evaluation and optimization theory of thermal microscope imaging systems. In this paper, we analyzed the difference in spectral radiant flux between thermal microscope imaging and telephoto thermal imaging. The expression of signal-to-noise ratio of the output image of the thermal microscope imaging systems was derived, based on the analysis of the characteristics of thermal microscope imaging. We studied the performance evaluation model of thermal microscope imaging systems based on the minimum resolvable temperature difference and the minimum detectable temperature difference. Simulation and analysis of different detectors (ideal photon detector and ideal thermal detector) were also carried out. Finally, based on the conclusion of theoretical research, we carried out a system design and image acquisition experiment. The results show that the theoretical study of thermal microscope imaging systems in this paper can provide reference for the performance evaluation and optimization of thermal microscope imaging systems

Combined features in region of interest for brain tumor segmentation

Diagnosis of brain tumor gliomas is a challenging task in medical image analysis due to its complexity, the less regularity of tumor structures, and the diversity of tissue textures and shapes. Semantic segmentation approaches using deep learning have consistently outperformed the previous methods in this challenging task. However, deep learning is insufficient to provide the required local features related to tissue texture changes due to tumor growth. This paper designs a hybrid method arising from this need, which incorporates machine-learned and hand-crafted features. A semantic segmentation network (SegNet) is used to generate the machine-learned features, while the grey-level co-occurrence matrix (GLCM)-based texture features construct the hand-crafted features. In addition, the proposed approach only takes the region of interest (ROI), which represents the extension of the complete tumor structure, as input, and suppresses the intensity of other irrelevant area. A decision tree (DT) is used to classify the pixels of ROI MRI images into different parts of tumors, i.e. edema, necrosis and enhanced tumor. The method was evaluated on BRATS 2017 dataset. The results demonstrate that the proposed model provides promising segmentation in brain tumor structure. The F-measures for automatic brain tumor segmentation against ground truth are 0.98, 0.75 and 0.69 for whole tumor, core and enhanced tumor, respectively