[Preparation and Tumor Targeting Analysis of Cell Membrane Nanovesicles Derived from Breast Cancer Cells].

Objective: To prepare cell membrane nanovesicles (NVs) derived from breast cancer cells, to explore their basic characteristics, tumor cell endocytosis, and in vivo distribution in a tumor-bearing mouse model, and to investigate their tumor targeting properties. Methods: 4T1 breast cancer cells were cultured in vitro. The cell membrane of 4T1 cells was isolated through ultracentrifugation and NVs were formulated with a liposome extruder. The size distribution of NVs was determined by way of dynamic light scattering, and the morphology properties of the NVs were examined with transmission electron microscope. The stability of NVs was analyzed by measuring the diameter changes of NVs submerged in phosphate-buffered saline (PBS). The biocompatibility of NVs was investigated by measuring the viability of dendritic cells treated with NVs at different concentrations (5, 10, 20, 50, and 100 mg·L -1) by CCK-8 assay. Fluorescence microscopy was used to analyze the cellular uptake of NVs by breast cancer cells. A mice model of breast cancer model was established with mice bearing subcutaneous xenograft of 4T1 cells. The mice were treated with Cy5.5-labeled NVs injected via the tail vein and the in vivo distribution of NVs was analyzed with an imaging system for small live animals. Results: The results showed that NVs derived from 4T1 breast cancer cells were successfully prepared. The NVs had a mean diameter of 123.2 nm and exhibited a hollow spherical structure under transmission electron microscope. No obvious change in the size of the NVs was observed after 7 days of incubation in PBS solution. CCK-8 assay results showed that the viability of dendritic cells treated with NVs at different concentrations was always higher than 90%. Fluorescence microscopic imaging showed that NVs could be efficiently internalized into breast cancer cells. in vivo biodistribution analysis revealed that breast cancer cell-derived NVs showed higher distribution in tumor tissue than the NVs prepared with normal cells did. Conclusion: We successfully prepared cell membrane NVs derived from 4T1 breast cancer cells. These NVs had efficient cellular uptake by breast cancer cells and sound tumor targeting properties.

Multilevel Coupled Hybrids Made of Porous Cobalt Oxides and Graphene for High-Performance Lithium Storage.

Metal oxide coupling with carbon materials holds great promise for lithium storage. Herein, multilevel coupled cobalt oxide-graphene (CoO/CO3 O4 -G) hybrids were fabricated by in situ assembly of Co hydroxide precursors and a calcination process. The oxygen-containing functional groups on the graphene surface act as bridging sites and tend to bond with Co2+ ions, effectively modifying the morphology and structure of the Co species. The as-obtained CoO/CO3 O4 -G hybrids are composed of unique CoO/CO3 O4 porous nanoparticles uniformly anchored on graphene sheets, as confirmed by a series of characterization analyses. Benefiting from these structural characteristics, the CoO/CO3 O4 -G hybrids used as an anode can deliver a high capacity of about 1080 mA h g-1 reversibly at 0.1 Ag-1 in the voltage range between 3.0 and 0.01 V, which is remarkably superior to that of the CoO hexagonal sheets in the absence of graphene. The high reversible capacity of the CoO/CO3 O4 -G hybrids is retained at elevated current densities, for example, a capacity of approximately 455 mA h g-1 can be achieved at a current rate as high as 4 A g-1 , indicative of its potential for high-performance lithium-ion batteries.

Correction: Individual dairy cow identification based on lightweight convolutional neural network.

[This corrects the article DOI: 10.1371/journal.pone.0260510.].

Construction and validation of a prognostic signature based on necroptosis-related genes in hepatocellular carcinoma.

BACKGROUND: Necroptosis is a necrotic programmed cell death with potent immunogenicity. Due to the dual effects of necroptosis on tumor growth, metastasis and immunosuppression, we evaluated the prognostic value of necroptosis-related genes (NRGs) in hepatocellular carcinoma (HCC). METHODS: We first analyzed RNA sequencing and clinical HCC patient data obtained to develop an NRG prognostic signature based on the TCGA dataset. Differentially expressed NRGs were further evaluated by GO and KEGG pathway analyses. Next, we conducted univariate and multivariate Cox regression analyses to build a prognostic model. We also used the dataset obtained from the International Cancer Genome Consortium (ICGC) database to verify the signature. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to investigate the immunotherapy response. Furthermore, we investigated the relationship between the prediction signature and chemotherapy treatment response in HCC. RESULTS: We first identified 36 differentially expressed genes out of 159 NRGs in hepatocellular carcinoma. Enrichment analysis showed that they were mainly enriched in the necroptosis pathway. Four NRGs were screened by Cox regression analysis to establish a prognostic model. The survival analysis revealed that the overall survival of patients with high-risk scores was significantly shorter than that of patients with low-risk scores. The nomogram demonstrated satisfactory discrimination and calibration. The calibration curves validated a fine concordance between the nomogram prediction and actual observation. The efficacy of the necroptosis-related signature was also validated by an independent dataset and immunohistochemistry experiments. TIDE analysis revealed that patients in the high-risk group were possibly more susceptible to immunotherapy. Furthermore, high-risk patients were found to be more sensitive to conventional chemotherapeutic medicines such as bleomycin, bortezomib, and imatinib. CONCLUSION: We identified 4 necroptosis-related genes and established a prognostic risk model that could potentially predict prognosis and response to chemotherapy and immunotherapy in HCC patients in the future.

Identification of ferroptotic genes and phenotypes in idiopathic nonobstructive azoospermia.

Effective treatments for nonobstructive azoospermia (NOA), which affects 1% of all men globally, are limited by undefined pathogenic mechanisms, especially in idiopathic NOA (iNOA). Here, we tried to identify the functional ferroptosis-related genes and phenotypes involved in iNOA. Differentially expressed ferroptotic genes were identified from iNOA mRNA microarray datasets by bioinformatic analyses, and these ferroptotic genes were subsequently filtered by various algorithms. Then, receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic ability of the abovementioned genes for iNOA. Generally, 11 differentially expressed ferroptotic genes were downregulated, and five genes were upregulated in iNOA samples. Four genes, including DUSP1, GPX4, HSD17B11, and SLC2A8, were technically selected and determined to be potential biomarkers for iNOA. Subsequently, similar expression levels were validated at both the RNA and protein levels in the iNOA specimens. Finally, morphologic and biochemical assays were applied to define the ferroptotic phenotypes in testes. The ferroptotic features, like shrunken mitochondria with electron-dense membranes and a reduction in cristae were observed across various cell types within iNOA patients, accompanied by the overload of ferrous ions and increased lipid peroxidation production. Our findings demonstrated that these ferroptosis genes could be involved in the underlying pathogenesis mechanisms of iNOA by regulating ferroptosis and serve as potential diagnostic biomarkers. Also, the ferroptotic phenotypes were identified in iNOA patients.

Identification and validation of a m7G-related lncRNA signature for predicting the prognosis and therapy response in hepatocellular carcinoma.

BACKGROUND: N7-methylguanosine (m7G) is one of the most common RNA posttranscriptional modifications; however, its potential role in hepatocellular carcinoma (HCC) remains unknown. We developed a prediction signature based on m7G-related long noncoding RNAs (lncRNAs) to predict HCC prognosis and provide a reference for immunotherapy and chemotherapy. METHODS: RNA-seq data from The Cancer Genome Atlas (TCGA) database and relevant clinical data were used. Univariate and multivariate Cox regression analyses were conducted to identify m7G-related lncRNAs with prognostic value to build a predictive signature. We evaluated the prognostic value and clinical relevance of this signature and explored the correlation between the predictive signature and the chemotherapy treatment response of HCC. Moreover, an in vitro study to validate the function of CASC19 was performed. RESULTS: Six m7G-related lncRNAs were identified to create a signature. This signature was considered an independent risk factor for the prognosis of patients with HCC. TIDE analyses showed that the high-risk group might be more sensitive to immunotherapy. ssGSEA indicated that the predictive signature was strongly related to the immune activities of HCC. HCC in high-risk patients was more sensitive to the common chemotherapy drugs bleomycin, doxorubicin, gemcitabine, and lenalidomide. In vitro knockdown of CASC19 inhibited the proliferation, migration and invasion of HCC cells. CONCLUSION: We established a 6 m7G-related lncRNA signature that may assist in predicting the prognosis and response to chemotherapy and immunotherapy of HCC.

Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq Combined with Multiple Machine Learning Identified a Novel Immune Signature in Diabetic Nephropathy.

Background: Increasing evidence suggests that immune modulation contributes to the pathogenesis and progression of diabetic nephropathy (DN). However, the role of immune modulation in DN has not been elucidated. The purpose of this study was to search for potential immune-related therapeutic targets and molecular mechanisms of DN. Methods: Gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. A total of 1793 immune-related genes were acquired from the Immunology Database and Analysis Portal (ImmPort). Weighted gene co-expression network analysis (WGCNA) was performed for GSE142025, and the red and turquoise co-expression modules were found to be key for DN progression. We utilized four machine learning algorithms, namely, random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), and k-nearest neighbor (KNN), to evaluate the diagnostic value of hub genes. Immune infiltration patterns were analyzed using the CIBERSORT algorithm, and the correlation between immune cell type abundance and hub gene expression was also investigated. Results: A total of 77 immune-related genes of advanced DN were selected for subsequent analyzes. Functional enrichment analysis showed that the regulation of cytokine-cytokine receptor interactions and immune cell function play a corresponding role in the progression of DN. The final 10 hub genes were identified through multiple datasets. In addition, the expression levels of the identified hub genes were corroborated through a rat model. The RF model exhibited the highest AUC. CIBERSORT analysis and single-cell sequencing analysis revealed changes in immune infiltration patterns between control subjects and DN patients. Several potential drugs to reverse the altered hub genes were identified through the Drug-Gene Interaction database (DGIdb). Conclusion: This pioneering work provided a novel immunological perspective on the progression of DN, identifying key immune-related genes and potential drug targets, thus stimulating future mechanistic research and therapeutic target identification for DN.

Lightweight-Convolutional Neural Network for Apple Leaf Disease Identification.

As a widely consumed fruit worldwide, it is extremely important to prevent and control disease in apple trees. In this research, we designed convolutional neural networks (CNNs) for five diseases that affect apple tree leaves based on the AlexNet model. First, the coarse-grained features of the disease are extracted in the model using dilated convolution, which helps to maintain a large receptive field while reducing the number of parameters. The parallel convolution module is added to extract leaf disease features at multiple scales. Subsequently, the series 3 × 3 convolutions shortcut connection allows the model to deal with additional nonlinearities. Further, the attention mechanism is added to all aggregated output modules to better fit channel features and reduce the impact of a complex background on the model performance. Finally, the two fully connected layers are replaced by global pooling to reduce the number of model parameters, to ensure that the features are not lost. The final recognition accuracy of the model is 97.36%, and the size of the model is 5.87 MB. In comparison with five other models, our model design is reasonable and has good robustness; further, the results show that the proposed model is lightweight and can identify apple leaf diseases with high accuracy.

Lightweight individual cow identification based on Ghost combined with attention mechanism.

Individual cow identification is a prerequisite for intelligent dairy farming management, and is important for achieving accurate and informative dairy farming. Computer vision-based approaches are widely considered because of their non-contact and practical advantages. In this study, a method based on the combination of Ghost and attention mechanism is proposed to improve ReNet50 to achieve non-contact individual recognition of cows. In the model, coarse-grained features of cows are extracted using a large sensory field of cavity convolution, while reducing the number of model parameters to some extent. ResNet50 consists of two Bottlenecks with different structures, and a plug-and-play Ghost module is inserted between the two Bottlenecks to reduce the number of parameters and computation of the model using common linear operations without reducing the feature map. In addition, the convolutional block attention module (CBAM) is introduced after each stage of the model to help the model to give different weights to each part of the input and extract the more critical and important information. In our experiments, a total of 13 cows' side view images were collected to train the model, and the final recognition accuracy of the model was 98.58%, which was 4.8 percentage points better than the recognition accuracy of the original ResNet50, the number of model parameters was reduced by 24.85 times, and the model size was only 3.61 MB. In addition, to verify the validity of the model, it is compared with other networks and the results show that our model has good robustness. This research overcomes the shortcomings of traditional recognition methods that require human extraction of features, and provides theoretical references for further animal recognition.

DenseNet weed recognition model combining local variance preprocessing and attention mechanism.

Introduction: The purpose of this paper is to effectively and accurately identify weed species in crop fields in complex environments. There are many kinds of weeds in the detection area, which are densely distributed. Methods: The paper proposes the use of local variance pre-processing method for background segmentation and data enhancement, which effectively removes the complex background and redundant information from the data, and prevents the experiment from overfitting, which can improve the accuracy rate significantly. Then, based on the optimization improvement of DenseNet network, Efficient Channel Attention (ECA) mechanism is introduced after the convolutional layer to increase the weight of important features, strengthen the weed features and suppress the background features. Results: Using the processed images to train the model, the accuracy rate reaches 97.98%, which is a great improvement, and the comprehensive performance is higher than that of DenseNet, VGGNet-16, VGGNet-19, ResNet-50, DANet, DNANet, and U-Net models. Discussion: The experimental data show that the model and method we designed are well suited to solve the problem of accurate identification of crop and weed species in complex environments, laying a solid technical foundation for the development of intelligent weeding robots.

Individual dairy cow identification based on lightweight convolutional neural network.

In actual farms, individual livestock identification technology relies on large models with slow recognition speeds, which seriously restricts its practical application. In this study, we use deep learning to recognize the features of individual cows. Alexnet is used as a skeleton network for a lightweight convolutional neural network that can recognise individual cows in images with complex backgrounds. The model is improved for multiple multiscale convolutions of Alexnet using the short-circuit connected BasicBlock to fit the desired values and avoid gradient disappearance or explosion. An improved inception module and attention mechanism are added to extract features at multiple scales to enhance the detection of feature points. In experiments, side-view images of 13 cows were collected. The proposed method achieved 97.95% accuracy in cow identification with a single training time of only 6 s, which is one-sixth that of the original Alexnet. To verify the validity of the model, the dataset and experimental parameters were kept constant and compared with the results of Vgg16, Resnet50, Mobilnet V2 and GoogLenet. The proposed model ensured high accuracy while having the smallest parameter size of 6.51 MB, which is 1.3 times less than that of the Mobilnet V2 network, which is famous for its light weight. This method overcomes the defects of traditional methods, which require artificial extraction of features, are often not robust enough, have slow recognition speeds, and require large numbers of parameters in the recognition model. The proposed method works with images with complex backgrounds, making it suitable for actual farming environments. It also provides a reference for the identification of individual cows in images with complex backgrounds.

Combination of doxorubicin liposomes with left atrial appendage radiofrequency catheter ablation to reduce post-ablation recovery of electrical conduction.

AIMS: To determine whether use of radiofrequency catheter ablation (RFCA) combined with intravenously administered liposomal doxorubicin (L-DOX) facilitates a reduction in the recovery of post-ablation electrical conduction. METHODS: Circumferential ablation was performed on the epicardial surface of the left atrial appendage (LAA) in New Zealand White rabbits, and L-DOX was then administered intravenously. Fluorescence spectrophotometry was used to assess reagent bio-distribution, while Western blots and immunohistochemistry were used to assess the localization of the apoptotic markers Bcl-2, Bax, and cleaved CASP3 in the LAA. Liver, kidney, and cardiac functions were also measured to evaluate the safety of this approach. RESULTS: At 1 week and 1 month after RFCA, a pacing electrocardiogram could not be detected in most of the rabbits that had received the combined RFCA and L-DOX therapy. L-DOX began to target the LAA on the second day after RFCA. L-DOX treatment increased the apoptosis of cardiomyocytes in the regions peripheral to the necrotic area induced by RFCA. Doxorubicin had some effect on liver and kidney function, but these effects were reversible and did not affect survival. CONCLUSION: The present results provide evidence that L-DOX treatment can reduce the recovery of electrical conduction after RFCA therapy owing to L-DOX-induced apoptosis of cardiomyocytes in the ablated area and the proximal transition zone of the LAA.

A Trace C2H2 Sensor Based on an Absorption Spectrum Technique Using a Mid-Infrared Interband Cascade Laser.

In this study, tunable diode laser absorption spectroscopy (TDLAS) combined with wavelength modulation spectroscopy (WMS) was used to develop a trace C2H2 sensor based on the principle of gas absorption spectroscopy. The core of this sensor is an interband cascade laser that releases wavelength locks to the best absorption line of C2H2 at 3305 cm-1 (3026 nm) using a driving current and a working temperature control. As the detected result was influenced by 1/f noise caused by the laser or external environmental factors, the TDLAS-WMS technology was used to suppress the 1/f noise effectively, to obtain a better minimum detection limit (MDL) performance. The experimental results using C2H2 gas with five different concentrations show a good linear relationship between the peak value of the second harmonic signal and the gas concentration, with a linearity of 0.9987 and detection accuracy of 0.4%. In total, 1 ppmv of C2H2 gas sample was used for a 2 h observation experiment. The data show that the MDL is low as 1 ppbv at an integration time of 63 s. In addition, the sensor can be realized by changing the wavelength of the laser to detect a variety of gases, which shows the flexibility and practicability of the proposed sensor.

MsmK, an ATPase, Contributes to Utilization of Multiple Carbohydrates and Host Colonization of Streptococcus suis.

Acquisition and metabolism of carbohydrates are essential for host colonization and pathogenesis of bacterial pathogens. Different bacteria can uptake different lines of carbohydrates via ABC transporters, in which ATPase subunits energize the transport though ATP hydrolysis. Some ABC transporters possess their own ATPases, while some share a common ATPase. Here we identified MsmK, an ATPase from Streptococcus suis, an emerging zoonotic bacterium causing dead infections in pigs and humans. Genetic and biochemistry studies revealed that the MsmK was responsible for the utilization of raffinose, melibiose, maltotetraose, glycogen and maltotriose. In infected mice, the msmK-deletion mutant showed significant defects of survival and colonization when compared with its parental and complementary strains. Taken together, MsmK is an ATPase that contributes to multiple carbohydrates utilization and host colonization of S. suis. This study gives new insight into our understanding of the carbohydrates utilization and its relationship to the pathogenesis of this zoonotic pathogen.

Activation of glycine site and GluN2B subunit of NMDA receptors is necessary for ERK/CREB signaling cascade in rostral anterior cingulate cortex in rats: implications for affective pain.

OBJECTIVE: The rostral anterior cingulate cortex (rACC) is implicated in processing the emotional component of pain. N-methyl-D-aspartate receptors (NMDARs) are highly expressed in the rACC and mediate pain-related affect by activating a signaling pathway that involves cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA) and/or extracellular regulated kinase (ERK)/cAMP-response element-binding protein (CREB). The present study investigated the contributions of the NMDAR glycine site and GluN2B subunit to the activation of ERK and CREB both in vitro and in vivo in rat rACC. METHODS: Immunohistochemistry and Western blot analysis were used to separately assess the expression of phospho-ERK (pERK) and phospho-CREB (pCREB) in vitro and in vivo. Double immunostaining was also used to determine the colocalization of pERK and pCREB. RESULTS: Both bath application of NMDA in brain slices in vitro and intraplantar injection of formalin into the rat hindpaw in vivo induced significant up-regulation of pERK and pCREB in the rACC, which was inhibited by the NMDAR antagonist DL-2-amino-5-phospho-novaleric acid. Selective blockade of the NMDAR GluN2B subunit and the glycine-binding site, or degradation of endogenous D-serine, a co-agonist for the glycine site, significantly decreased the up-regulation of pERK and pCREB expression in the rACC. Further, the activated ERK predominantly colocalized with CREB. CONCLUSION: Either the glycine site or the GluN2B subunit of NMDARs participates in the phosphorylation of ERK and CREB induced by bath application of NMDA in brain slices or hindpaw injection of 5% formalin in rats, and these might be fundamental molecular mechanisms underlying pain affect.