The expression and significance of PD-L1 in condyloma acuminatum.

BACKGROUND: It has been reported that programmed death-ligand 1 (PD-L1) is highly expressed in cells during viral infection, which helps the virus escape host immunity. However, the relationship between human papillomavirus (HPV) and PD-L1 in condyloma acuminatum and whether they participate in immunosuppression have not been reported. In this paper, we aimed to explore the expression and significance of PD-L1 in condyloma acuminatum. METHODS: The expression of PD-L1 in the wart of condyloma acuminatum patients and the foreskin of healthy individuals was evaluated. Lentivirus transfection was used to introduce the HPV11-E7 gene into HaCaT cells to investigate whether HPV infection could affect the expression of PD-L1. The successfully constructed HPV11-E7 HaCaT cells were cocultured with Jurkat cells, and Jurkat cell apoptosis and proliferation as well as the Jurkat cell cycle were evaluated by flow cytometry and cell counting kit-8 (CCK-8) assays. RESULTS: PD-L1 was highly expressed in keratinocytes of genital warts. Through the construction of a cell model, we found that HPV11-E7 could upregulate the expression of PD-L1 in HaCaT cells. Furthermore, HPV11-E7 HaCaT cells can promote the apoptosis of Jurkat cells, inhibit the proliferation of Jurkat cells and mediate the cell cycle arrest of Jurkat cells through the PD-1/PD-L1 signalling pathway. CONCLUSIONS: HPV infection may upregulate PD-L1 expression in the keratinocytes of genital warts and participate in the inhibition of local T-cell function.

Downregulation of Sonic hedgehog signaling induces G2-arrest in genital warts.

BACKGROUND: Human papillomavirus (HPV) infected keratinocyte dysfunction results in the formation of genital warts, and the specific role of Sonic hedgehog (SHh) signaling in genital warts remains elusive. Thus, this study aimed to identify the correlation between wart formation and SHh signaling. MATERIALS AND METHODS: In this study, nine male patients with genital warts were recruited, and the expression of SHh and its downstream signal molecules Patched-1 and GLI family zinc finger 1 (Ptch1 and Gli1) was detected. Moreover, G2-phase cells in the collected genital warts samples were assessed with normal foreskin samples as a comparison. HPV6/11 were detected via in situ hybridization (ISH), and SHh expression of the corresponding paraffin sections was determined via immunohistochemical staining (IHC). In addition, an in vitro down-regulated SHh model was constructed by siRNA transfection of the HaCaT cell line, and the cell cycle was detected at 36 h by flow cytometry with propidium iodide staining. RESULTS: SHh, Ptch1, and Gli1 in warts were significantly downregulated in the condyloma acuminatum (CA) group compared to the normal foreskin group. G2-phase cells in the middle section of the spinous layer of CA wart tissues were significantly increased. Moreover, the expression of HPV-DNA was amplified and negatively correlated with SHh activity in CA wart tissues. Lastly, the downregulation of SHh-induced G2 arrest in vitro. CONCLUSIONS: The downregulation of the SHh signaling promotes HPV replication and the formation of warts by inducing G2/M arrest in the keratinocytes of CA.

Brain-derived neurotrophic factor-TrkB signaling in the medial prefrontal cortex plays a role in the anhedonia-like phenotype after spared nerve injury.

Although depressive symptoms including anhedonia (i.e., loss of pleasure) frequently accompany pain, little is known about the risk factors contributing to individual differences in pain-induced anhedonia. In this study, we examined if signaling of brain-derived neurotrophic factor (BDNF) and its receptor tropomyosin-receptor-kinase B (TrkB) contribute to individual differences in the development of neuropathic pain-induced anhedonia. Rats were randomly subjected to spared nerved ligation (SNI) or sham surgery. The SNI rats were divided into two groups based on the results of a sucrose preference test. Rats with anhedonia-like phenotype displayed lower tissue levels of BDNF in the medial prefrontal cortex (mPFC) compared with rats without anhedonia-like phenotype and sham-operated rats. In contrast, tissue levels of BDNF in the nucleus accumbens (NAc) of rats with an anhedonia-like phenotype were higher compared with those of rats without anhedonia-like phenotype and sham-operated rats. Furthermore, tissue levels of BDNF in the hippocampus, L2-5 spinal cord, muscle, and liver from both rats with or without anhedonia-like phenotype were lower compared with those of sham-operated rats. A single injection of 7,8-dihydroxyflavone (10 mg/kg; TrkB agonist), but not ANA-12 (0.5 mg/kg; TrkB antagonist), ameliorated reduced sucrose preference and reduced BDNF-TrkB signaling in the mPFC in the rats with anhedonia-like phenotype. These findings suggest that reduced BDNF-TrkB signaling in the mPFC might contribute to neuropathic pain-induced anhedonia, and that TrkB agonists could be potential therapeutic drugs for pain-induced anhedonia.

Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells.

This work investigated the high-throughput classification performance of microscopic images of mesenchymal stem cells (MSCs) using a hyperspectral imaging-based separable convolutional neural network (CNN) (H-SCNN) model. Human bone marrow mesenchymal stem cells (hBMSCs) were cultured, and microscopic images were acquired using a fully automated microscope. Flow cytometry (FCT) was employed for functional classification. Subsequently, the H-SCNN model was established. The hyperspectral microscopic (HSM) images were created, and the spatial-spectral combined distance (SSCD) was employed to derive the spatial-spectral neighbors (SSNs) for each pixel in the training set to determine the optimal parameters. Then, a separable CNN (SCNN) was adopted instead of the classic convolutional layer. Additionally, cultured cells were seeded into 96-well plates, and high-functioning hBMSCs were screened using both manual visual inspection (MV group) and the H-SCNN model (H-SCNN group), with each group consisting of 96 samples. FCT served as the benchmark to compare the area under the curve (AUC), F1 score, accuracy (Acc), sensitivity (Sen), specificity (Spe), positive predictive value (PPV), and negative predictive value (NPV) between the manual and model groups. The best classification Acc was 0.862 when using window size of 9 and 12 SSNs. The classification Acc of the SCNN model, ResNet model, and VGGNet model gradually increased with the increase in sample size, reaching 89.56 ± 3.09, 80.61 ± 2.83, and 80.06 ± 3.01%, respectively at the sample size of 100. The corresponding training time for the SCNN model was significantly shorter at 21.32 ± 1.09 min compared to ResNet (36.09 ± 3.11 min) and VGGNet models (34.73 ± 3.72 min) (P < 0.05). Furthermore, the classification AUC, F1 score, Acc, Sen, Spe, PPV, and NPV were all higher in the H-SCNN group, with significantly less time required (P < 0.05). Microscopic images based on the H-SCNN model proved to be effective for the classification assessment of hBMSCs, demonstrating excellent performance in classification Acc and efficiency, enabling its potential to be a powerful tool in future MSCs research.

Cell Recognition Using BP Neural Network Edge Computing.

This exploration is to solve the efficiency and accuracy of cell recognition in biological experiments. Neural network technology is applied to the research of cell image recognition. The cell image recognition problem is solved by constructing an image recognition algorithm. First, with an in-depth understanding of computer functions, as a basic intelligent algorithm, the artificial neural network (ANN) is widely used to solve the problem of image recognition. Recently, the backpropagation neural network (BPNN) algorithm has developed into a powerful pattern recognition tool and has been widely used in image edge detection. Then, the structural model of BPNN is introduced in detail. Given the complexity of cell image recognition, an algorithm based on the ANN and BPNN is used to solve this problem. The BPNN algorithm has multiple advantages, such as simple structure, easy hardware implementation, and good learning effect. Next, an image recognition algorithm based on the BPNN is designed and the image recognition process is optimized in combination with edge computing technology to improve the efficiency of algorithm recognition. The experimental results show that compared with the traditional image pattern recognition algorithm, the recognition accuracy of the designed algorithm for cell images is higher than 93.12%, so it has more advantages for processing the cell image algorithm. The results show that the BPNN edge computing can improve the scientific accuracy of cell recognition results, suggesting that edge computing based on the BPNN has a significant practical value for the research and application of cell recognition.

Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning.

The current work aims to strengthen the research of segmentation, detection, and tracking methods of stem cell image in the fields of regenerative medicine and tissue damage restoration. Firstly, based on the relevant theories of stem cell image segmentation, digital twins (DTs), and lightweight deep learning, a new phase contrast microscope is introduced through the research of optical microscope. Secondly, the results of DTs method and phase contrast imaging principle are compared in stem cell image segmentation and detection. Finally, a lightweight deep learning model is introduced in the segmentation and tracking of stem cell image to observe the gray value and mean value before and after stem cell image movement and stem cell division. The results show that phase contrast microscope can increase the phase contrast and amplitude difference of stem cell image and solve the problem of stem cell image segmentation to a certain extent. The detection results of DTs method are compared with phase contrast imaging principle. It indicates that not only can DTs method make the image contour more accurate and clearer, but also its accuracy, recall, and F1 score are 0.038, 0.024, and 0.043 higher than those of the phase contrast imaging method. The lightweight deep learning model is applied to the segmentation and tracking of stem cell image. It is found that the gray value and mean value of stem cell image before and after movement and stem cell division do not change significantly. Hence, the application of DTs and lightweight deep learning methods in the segmentation, detection, and tracking of stem cell image has great reference significance for the development of biology and medicine.

Performance for rotor system of hybrid electromagnetic bearing and elastic foil gas bearing with dynamic characteristics analysis under deep learning.

The bearing-rotor system is prone to faults during operation, so it is necessary to analyze the dynamic characteristics of the bearing-rotor system to discuss the optimal structure of the convolutional neural network (CNN) in system fault detection and classification. The turbo expander is undertaken as the research object. Firstly, the hybrid magnetic bearing-rotor system is modeled into the form of four stiffness coefficients and four damping coefficients, so as to analyze and explain the dynamic characteristics of the system. Secondly, the ambient pressure is introduced to analyze the dynamic characteristics of the elastic foil gas bearing-rotor system based on the changes in the dynamic stiffness and dynamic damping of the gas bearing. Finally, the CNN is introduced to be applied in the detection of faults of bearing-rotor system through determining the parameters of the constructed CNN. The results show that the displacement of the rotor increases and the stiffness decreases with the acceleration of the speed of the electromagnetic bearing. The maximum displacement of the rotor can reach 135µm, and the maximum stiffness can be reduced to 35×105N/m. Increase of ambient pressure causes enhancement of main stiffness of the gas bearing, and the main damping decreases accordingly. Analysis of the classification accuracy and loss function based on the CNN model shows that the convolution kernel size of 7*1 and the batch size of 128 can realize the best performance of CNN in fault classification. This provides a data support and reference for studying the dynamic characteristics of the bearing-rotor system and for the optimization of CNN structure in fault classification and detection.

Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning.

The hybrid electromagnetic and elastic foil gas bearing is explored based on the radial basis function (RBF) neural network in this study so as to improve its stabilization in work. The related principles and structure of hybrid electromagnetic and elastic foil gas bearings is introduced firstly. Then, the proportional, integral, and derivative (PID) bearing controller is introduced and improved into two controllers: IPD and CPID. The controllers and hybrid bearing system are controlled based on the RBF neural network based on deep learning. The characteristics of the hybrid bearing system are explored at the end of this study, and the control simulation research is developed based on the Simulink simulation platform. The effects of the PID, IPD, and CIPD controllers based on the RBF neural network are compared, and they are also compared based on the traditional particle swarm optimization (PSO). The results show that the thickness, spread angle, and rotation speed of the elastic foil have great impacts on the bearing system. The proposed CIPD bearing control method based on RBF neural network has the shortest response time and the best control effect. The controller parameter tuning optimization starts to converge after one generation, which is the fastest iteration. It proves that RBF neural network control based on deep learning has high feasibility in hybrid bearing system. Therefore, the results provide an important reference for the application of deep learning in rotating machinery.