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Abstract Despite advancements in chemotherapy and the availability of novel therapies, the outcome of adult patients with B-cell acute lymphoblastic leukemia (B-ALL) remains unsatisfactory. Therefore, it is necessary to understand the molecular mechanisms underlying the progression of B-ALL. Brahma-related gene 1 (BRG1) is a poor prognostic factor for multiple cancers. Here, the expression of BRG1 was found to be higher in patients with B-ALL, irrespective of the molecular subtype, than in healthy individuals, and its overexpression was associated with a poor prognosis. Upregulation of BRG1 accelerated cell cycle progression into the S phase, resulting in increased cell proliferation, whereas its downregulation facilitated the apoptosis of B-ALL cells. Mechanistically, BRG1 occupies the transcriptional activation site of PPP2R1A, thereby inhibiting its expression and activating the PI3K/AKT signaling pathway to regulate the proto-oncogenes c-Myc and BCL-2. Consistently, silencing of BRG1 and administration of PFI-3 (a specific inhibitor targeting BRG1) significantly inhibited the progression of leukemia and effectively prolonged survival in cell-derived xenograft mouse models of B-ALL. Altogether, this study demonstrates that BRG1-induced overactivation of the PPP2R1A/PI3K/AKT signaling pathway plays an important role in promoting the progression of B-ALL. Therefore, targeting BRG1 represents a promising strategy for the treatment of B-ALL in adults.

Abstract The ability to precisely engineer nanostructures underpins a wide range of applications in areas such as electronics, optics, and biomedical sciences. Here we present a novel approach for the growth of nanoparticle assemblies that leverages the unique properties of superfluid helium. Unlike viscous solvents at or near room temperature, superfluid helium provides an unperturbed and cold environment in which weak van der Waals interactions between molecular templates and metal atoms become significant and can define the spatial arrangement of nanoparticles. To demonstrate this concept, diol and porphyrin-based molecules are employed as templates to grow gold nanoparticle assemblies in superfluid helium droplets. After soft-landing on a solid surface to remove the helium, transmission electron microscopy (TEM) imaging shows the growth of gold nanoparticles at specific binding sites within the molecular templates where the interaction between gold atoms and the molecular template is at its strongest.

Abstract The response to programmed death-1 (PD-1) blockade varies in hepatocellular carcinoma (HCC). We utilize a panel of 16 serum factors to show that a circulating level of serum amyloid A (SAA) > 20.0 mg/L has the highest accuracy in predicting anti-PD-1 resistance in HCC. Further experiments show a correlation between peritumoral SAA expression and circulating SAA levels in patients with progressive disease after PD-1 inhibition. In vitro experiments demonstrate that SAA induces neutrophils to express PD-L1 through glycolytic activation via an LDHA/STAT3 pathway and to release oncostatin M, thereby attenuating cytotoxic T cell function. In vivo, genetic or pharmacological inhibition of STAT3 or SAA eliminates neutrophil-mediated immunosuppression and enhances antitumor efficacy of anti-PD-1 treatment. This study indicates that SAA may be a critical inflammatory cytokine implicated in anti-PD-1 resistance in HCC. Targeting SAA-induced PD-L1+ neutrophils through STAT3 or SAA inhibition may present a potential approach for overcoming anti-PD1 resistance.

Abstract Background Toosendanin (TSN) exhibits potent antitumor activity against various tumor cell lines. However, its efficacy against oral squamous cell carcinoma (OSCC) remains unknown. Here, we investigated the effects of TSN on OSCC cells in vitro and verified them in vivo using a patient-derived xenograft (PDX) model. Methods The effect of TSN on OSCC cells was investigated by cytotoxicity assays and flow cytometry. The expression of proteins was detected by western blotting. An OSCC PDX model was constructed to further investigate the role of TSN in regulating the function of OSCC. Results The cell viability of CAL27 and HN6 cells decreased as the concentration of TSN increased within the experimental range. Compared with controls, TSN at lower doses inhibited cell proliferation and induced apoptosis through S-phase cell cycle arrest. TSN inhibited OSCC cell proliferation by downregulating the STAT3 pathway through the inhibition of STAT3 phosphorylation. After successful construction of the OSCC PDX model with high pathological homology to the primary tumor and treatment with an intraperitoneal injection of TSN, we showed that TSN significantly reduced the tumor size of the PDX model mice without obvious toxicity. Conclusions Both in vitro and in vivo, TSN significantly inhibits the proliferation and promoted apoptosis of OSCC cells. Furthermore, TSN demonstrates potent inhibition of STAT3 phosphorylation, indicating its potential as a promising therapeutic agent for OSCC. Therefore, TSN holds great promise as a viable drug candidate for the treatment of OSCC.

Abstract Neutrophil extracellular traps (NETs) have been categorized as a form of inflammatory cell death mode of neutrophils (NETosis) involved in natural immunity and the regulation of adaptive immunity. More and more studies revealed the ability of NETs to reshape the tumor immune microenvironment (TIME) by limiting antitumor effector cells, which may impair the efficacy of immunotherapy. To explore whether NETs-related genes make vital impacts on Colon carcinoma (COAD), we have carried out a systematic analysis and showed several findings in the present work. First, we obtained the patient's data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset, aiming to detect two NETs-associated subtypes by consensus clustering. For the purpose of annotating the roles of NETs-related pathways, gene ontology enrichment analyses were adopted. Next, we constructed a 6 novel NETs-related genes score using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model. We found that the NETs risk score was notably upregulated in COAD patient samples, and its levels were notably correlated with tumor clinicopathological and immune traits. Then, according to NETs-associated molecular subtypes and the risk signature, this study compared immune cell infiltration calculated through the estimate, CIBERSORT, TIMER, ssGSEA algorithms, tumor immune dysfunction, as well as exclusion (TIDE). Furthermore, we confirm that MPO(myeloperoxidase) was significantly upregulated in COAD patient samples, and its levels were significantly linked to tumor malignancy and clinic outcome. Moreover, multiplex immunohistochemistry (mIHC) spatial analysis confirmed that MPO was closely related to Treg and PD-1 + Treg in spatial location which suggested MPO may paly an important role in TIME formation. Altogether, the obtained results indicated that a six NETs-related genes prognostic signature was conducive to estimating the prognosis and response of chemo-/immuno-therapy of COAD patients.

Objective: Acute myeloid leukemia (AML) is a malignant hematologic cancer with poor prognosis. Emerging evidence suggests a close association between AML progression and hypoxia. The purpose of this study was to establish a new risk prognostic model for AML based on hypoxia-related genes, and to explore the mechanisms by which hypoxia-related genes affect the prognosis of AML based on tumor immune microenvironment (TIME) and drug resistance.Methods: The AML patient samples obtained from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were classified into C1 and C2 based on hypoxia-related genes, followed by analysis utilizing Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA). Through univariate and LASSO Cox regression analysis, the hypoxia-related hub genes 26S proteasome non-ATPase regulatory subunit 11 (PSMD11) and 26S proteasome non-ATPase regulatory subunit 14 (PSMD14) were identified to construct the model. AML patient samples were obtained from the TARGET and The Cancer Genome Atlas (TCGA) databases, serving as the training and the validation sets, and were stratified into high-risk and low-risk group according to the median risk score. The correlations between the model and TIME and anti-tumor drugs were analysed using CIBERSORT and Genomics of Drug Sensitivity in Cancer (GDSC) databases. The expressions of PSMD11/PSMD14 in clinical samples and AML sensitive and drug-resistant cell lines were detected by Western blot and real-time PCR.Results: The C1 group with high expression of hypoxia-related genes had lower overall survival (OS). Immune-related signaling pathways were different between C1/C2, and hypoxia was positively correlated with the activation of mammalian target of rapamycin (mTOR) signaling pathway. The model had good accuracy in both the training and the validation sets. The high-risk group exhibited lower OS and TIME activity, and was more sensitive to several anti-tumor drugs. PSMD11/PSMD14 were highly expressed in relapsed patients and AML drug-resistant cell lines.Conclusion: The established novel risk prognostic model and experiment results offer valuable insights for predicting AML prognosis and guiding drug selection. It also provides a fundamental framework for the mechanisms through which hypoxia impacts AML prognosis by modulating TIME and drug resistance.

Summary: Background: The pathology of keloid and especially the roles of bacteria on it were not well understood. Methods: In this study, multi-omics analyses including microbiome, metaproteomics, metabolomic, single-cell transcriptome and cell-derived xenograft (CDX) mice model were used to explore the roles of bacteria on keloid disease. Findings: We found that the types of bacteria are significantly different between keloid and healthy skin. The 16S rRNA sequencing and metaproteomics showed that more catalase (CAT) negative bacteria, Clostridium and Roseburia existed in keloid compared with the adjacent healthy skin. In addition, protein mass spectrometry shows that CAT is one of the differentially expressed proteins (DEPs). Overexpression of CAT inhibited the proliferation, migration and invasion of keloid fibroblasts, and these characteristics were opposite when CAT was knocked down. Furthermore, the CDX model showed that Clostridium butyricum promote the growth of patient's keloid fibroblasts in BALB/c female nude mice, while CAT positive bacteria Bacillus subtilis inhibited it. Single-cell RNA sequencing verified that oxidative stress was up-regulated and CAT was down-regulated in mesenchymal-like fibroblasts of keloid. Interpretation: In conclusion, our findings suggest that bacteria and CAT contribute to keloid disease. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Abstract Pulmonary arterial hypertension‐targeted therapies in portopulmonary hypertension (PoPH) are scarce, let alone for patients with chronic liver failure (CLF) and hepatopulmonary syndrome (HPS). A 48‐year male was admitted to the hospital because of cirrhosis for 18 years, systemic oedema, and chest distress after exercise for 1 week. He was diagnosed with CLF, PoPH, and HPS. After 7 weeks of macitentan treatment, the patient's activity tolerance, pulmonary artery systolic pressure, arterial partial pressure of oxygen (PaO2), cTNI, and NT‐proBNP changes indicated gradual recovery, without hepatic safety concerns. This case indicated that administering macitentan in patients diagnosed as PoPH (with CLF and HPS) may be efficient and safe enough in a clinical setting.

Frequency agility refers to the rapid variation of the carrier frequency of adjacent pulses, which is an effective radar active antijamming method against frequency spot jamming. Variation patterns of traditional pseudo-random frequency hopping methods are susceptible to analysis and decryption, rendering them ineffective against increasingly sophisticated jamming strategies. Although existing reinforcement learning-based methods can adaptively optimize frequency hopping strategies, they are limited in adapting to the diversity and dynamics of jamming strategies, resulting in poor performance in the face of complex unknown jamming strategies. This paper proposes an AK-MADDPG (Adaptive K-th order history-based Multi-Agent Deep Deterministic Policy Gradient) method for designing frequency hopping strategies in frequency agile radar. Signal pulses within a coherent processing interval are treated as agents, learning to optimize their hopping strategies in the case of unknown jamming strategies. Agents dynamically adjust their carrier frequencies to evade jamming and collaborate with others to enhance antijamming efficacy. This approach exploits cooperative relationships among the pulses, providing additional information for optimized frequency hopping strategies. In addition, an adaptive K-th order history method has been introduced into the algorithm to capture long-term dependencies in sequential data. Simulation results demonstrate the superior performance of the proposed method.

Nitzschia dubiiformis Hust 1939 is a globally distributed species belonging to the family Bacillariaceae. This study reported the complete chloroplast genome sequences of N. dubiiformis. The genome of N. dubiiformis was 179,935 bp in length, consisting of 48,722 bp of large single copy, 103,427 bp of small single copy, and 13,893 bp of a pair of inverted repeat regions. It encoded 188 genes, including 151 protein-coding genes, 6 rRNA and 31 tRNA genes. The GC content of complete chloroplast genome was 30.4%. The phylogenomic analysis suggests that there is a close relationship between N. dubiiformis and N. traheaformis.

Synthetic aperture radar (SAR) images of ships have complex background interference, multi-scale targets, and irregular distribution characteristics. However, existing mainstream SAR ship detection algorithms rely on manually designed hyperparameters. This results in poor robustness, which makes it difficult to effectively balance the detection accuracy and speed. To solve these problems, a novel anchor-free SAR ship detection algorithm based on self-balancing position attention (SBPA) is proposed. First, a lightweight feature extraction backbone (GhostVS-Net) is designed by FOCUS, ghost, and separable convolution modules to extract feature information. This helps to extract the contour features of ships and suppress unrelated interference, making it more suitable for the scattering characteristics of SAR images. Second, an SBPA module is designed, which balances the local image features under multiple receptive fields, and aggregates the global position information and spatial context information. The proposed SBPA module considers the characteristics of background, scale, and distribution of SAR images to obtain greatly improved positioning accuracy. Finally, the feature pyramid network is applied to fuse the scale context information, and further improve the accuracy of detection. Experimental results on the SSDD and HRSID datasets show that the proposed network bears feature of accurate detection and strong robustness, with the mean average precision reaches 99.72% and 95.30%, which reveals that it outperforms all current state-of-the-art algorithms with super high performances.

In this report, we describe the first case of infective endocarditis caused by Mycobacterium kansasii in a 45-year-old male patient who presented with a 10-day fever and decompensated cirrhosis. Despite negative results in blood culture and pathology, we employed metagenomic next-generation sequencing (mNGS) to analyze the genome sequences of both the host and microbe. The copy number variation (CNV) indicated a high risk of liver disease in the patient, which correlated with biochemical examination findings. Notably, M. kansasii sequences were detected in peripheral blood samples and confirmed through Sanger sequencing. Unfortunately, the patient’s condition deteriorated, leading to his demise prior to heart surgery. Nevertheless, we propose that mNGS could be a novel approach for diagnosing M. kansasii infection, particularly in cases where blood culture and pathology results are unavailable. It is important to consider M. kansasii infection as a potential cause of endocarditis and initiate appropriate anti-infection treatment.

The mechanism of hydrolysis and extension diffusion of heavy metal pollution elements infiltrated into rock is analyzed by the theory of ion hydrolysis displacement. The hydrolysis properties of typical elements such as cadmium, zinc, lead, and copper are verified by convective dispersion model, and the diffusion law and fission characteristics of heavy metal with different hydrolysis constant are discussed. A three-dimensional constitutive relation model of rock extension diffusion surface is established by combining viscoelastic monomer model with a damage monomer model. Considering the influence of diffusion coefficient, hydrolysis constant, deformation factor, and other parameters, the rationality of the test results and model fitting results of heavy metal invading rock are verified. The results show that the replacement rate of colloidal mineral elements in rock varies with different hydrolysis constant, when the hydrolysis constant is large, the extension diffusion rate in rock is large; otherwise, the extension diffusion rate is small. Constitutive relation curves of polluted rock with different lithologies are in good agreement with the fitting results of the combination model under the influence of the same test conditions and the same parameters.

Abstract Background Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults. Given the high relapse rate, more effective treatments are needed to improve clinical outcomes. We previously demonstrated that heme oxygenase 1 (HO1) is overexpressed in AML, while the functional roles of HO1 remain unclear. Methods Bioinformatics analysis and flow cytometry were conducted to assess the association between HO1 levels and immune cells or immune checkpoint/ligand molecules in AML patients. Primary natural killer (NK) cells were purified and subsequently co-cultured in vitro with transduced AML cells to determine the effects of HO1 expression on NK cell functions. AML mice models were established to investigate the effects of HO1 expression on cytotoxic effects of NK cells in vivo. The molecular mechanism was studied by flow cytometry, quantitative real-time PCR (qRT-PCR), western blotting, and immunoprecipitation. Results Bioinformatics analysis indicated a correlation between HO1 expression and the AML immune microenvironment. The present study findings indicated that HO1 specifically downregulates the expression of CD48, a ligand of the NK cell-activating receptor 2B4, thus decreasing the cytotoxic effect of NK cells. HO1 overexpression promoted tumor growth and inhibited the cytotoxic effect of NK cells in the AML mice model. Mechanistic investigations found that HO1 directly interacted with Sirt1 and increased its expression and deacetylase activity. With the overexpression of HO1, increased Sirt1 in AML cells enabled histone H3K27 deacetylation to suppress CD48 transcription and expression. Administration of Sirt1 inhibitor restored the expression of CD48. Conclusions Collectively, HO1 promotes NK cell dysfunction in AML. Therefore, restoring NK cell function by inhibiting HO1 activity is a potential immunotherapeutic approach against AML.

In this study, biological ash samples were prepared by establishing a high-capacity microwave integrated device for carbonizing and ashing. Radioactive nuclides in biologically derived ash samples using high-purity germanium gamma spectrometry were directly measured for rapid determination of γ radionuclide activity concentration in environmental bio-samples from nuclear power plant surroundings. A comparison with the traditional ash preparation method was also conducted. The results showed that the microwave-ash preparation method can improve the preparation efficiency of biological samples by approximately 2 to 5 times, with a relative deviation of less than 14.8% for parallel samples and a spike recovery rate ranging from 87.0% to 116%. A paired T-test revealsed no significant difference between the two methods. The proposed method has the advantages of high accuracy, high analytical efficiency, simple operation, greater security, and environmental friendliness. It is an ideal method for quickly determining γ radionuclide activity concentration in biological samples.

Abstract Cellular therapies offer a promising therapeutic strategy for the highly malignant brain tumor, glioblastoma (GBM). However, their clinical translation is limited by the lack of effective target identification and stringent testing in pre-clinical models that replicate standard treatment in GBM patients. In this study, we show the detection of cell surface death receptor (DR) target on CD146-enriched circulating tumor cells (CTC) captured from the blood of mice bearing GBM and patients diagnosed with GBM. Next, we developed allogeneic “off-the-shelf” clinical-grade bifunctional mesenchymal stem cells (MSCBif) expressing DR-targeted ligand and a safety kill switch. We show that biodegradable hydrogel encapsulated MSCBif (EnMSCBif) has a profound therapeutic efficacy in mice bearing patient-derived invasive, primary and recurrent GBM tumors following surgical resection. Activation of the kill switch enhances the efficacy of MSCBif and results in their elimination post-tumor treatment which can be tracked by positron emission tomography (PET) imaging. This study establishes a foundation towards a clinical trial of EnMSCBif in primary and recurrent GBM patients.

Objectives: To investigate the clinical significance of Chloride Intracellular Channel 1 (CLIC1) expression in esophageal squamous cell carcinoma (ESCC) and its functional contribution and molecular mechanisms to the progression of ESCC. Methods: CLIC1 expression was analyzed by immunohistochemistry (IHC) in a cohort of 86 ESCC tissue specimens and paired normal adjacent esophageal tissues. Associations between clinicopathological features of ESCC and CLIC1 expression were determined. In vitro analyses examined CLIC1 expression in the ESCC cell lines KYSE150 and TE1 using RT-PCR and Western blotting. The downstream pathways of CLIC1 were detected by lentiviral shRNA knockdown and subsequent proteomic analyses. CLIC1 siRNA knockdown was performed in ESCC cell lines KYSE150 and TE1 and the functional effects of CLIC1 on the growth and proliferation of ESCC cells were evaluated combined with cell viability and colony formation assays; the mTOR signaling pathway-related proteins were detected by Western blotting based on the previous proteomic data. Results: CLIC1 expression was significantly increased in ex vivo ESCC tissues compared with corresponding normal tissues, and the up-regulation was associated with clinical tumor node metastasis (TNM) classifications. Knockdown of CLIC1 inhibited in vitro cell proliferation of ESCC cell lines KYSE150 and TE1. CLIC1 knockdown down-regulated the protein expression of p-mTOR and the downstream targets Rictor and p-4EBP1 in both KYSE150 and TE1 cell lines. And the CLIC1 knockdown induced inhibition of cell proliferation on ESCC cells could be rescued by mTOR overexpression. Conclusions: CLIC1 expression increases during esophageal carcinogenesis and it may functionally contribute to the progression of ESCC through growth promotion effects by promoting the mTOR and downstream signaling pathway. CLIC1 therefore constitutes a candidate molecular biomarker of ESCC.

Let $ k, l, m_1, m_2 $ be positive integers and let both $ p $ and $ q $ be odd primes such that $ p^k = 2^{m_1}-a^{m_2} $ and $ q^l = 2^{m_1}+a^{m_2} $ where $ a $ is odd prime with $ a\equiv 5\pmod 8 $ and $ a\not\equiv 1\pmod 5 $. In this paper, using only the elementary methods of factorization, congruence methods and the quadratic reciprocity law, we show that the exponential Diophantine equation $ \left(\frac{q^{2l}-p^{2k}}{2}n\right)^x+(p^kq^ln)^y = \left(\frac{q^{2l}+p^{2k}}{2}n\right)^z $ has only the positive integer solution $ (x, y, z) = (2, 2, 2) $.

Introduction. Osteonecrosis (ON) of the femoral head (FH) – (ONFH) is an intractable disease that causes progressive femoral head collapse, severe pain, and gait disturbance. We report a case of avascular necrosis of the femoral head following an occult femoral neck (FN) stress fracture. Case report. A 55-year-old woman presented to our department with a chief complaint of low back pain that radiated into the left anterolateral thigh over the period of two months. Her left anterolateral thigh became progressively more painful over the past two weeks. No abnormal findings indicative of ONFH or an occult fracture of the FN were detected by X-ray or computed tomography, but an occult insufficiency fracture of the left FN was identified on magnetic resonance imaging (MRI). The diagnosis of FN stress fracture was delayed, resulting in femoral head necrosis. The fracture was treated with total hip arthroplasty, and the resected FH was subjected to histopathology (HP). Based on the HP findings, the final diagnosis of this case was ONFH with an occult fracture of the left FN. Clinical symptoms were relieved postoperatively. Conclusion. An early MRI examination is recommended in patients presenting with a suspected stress fracture of the FN to avoid FH necrosis due to a delayed diagnosis.

Deep-learning-based synthetic aperture radar automatic target recognition (SAR-ATR) plays a significant role in the military and civilian fields. However, data limitation and large computational cost are still severe challenges in the actual application of SAR-ATR. To improve the performance of the convolutional neural network (CNN) model with limited data samples in SAR-ATR, this article proposes a novel multidomain feature subspace fusion representation learning method, i.e., a lightweight cascaded multidomain attention network, namely, LW-CMDANet. First, we design a four-layer CNN model to perform hierarchical feature representation learning via the hinge loss function, which can efficiently alleviate the overfitting problem of the CNN model by a nongreedy training style with a small dataset. Then, a cascaded multidomain attention module, based on discrete cosine transform and discrete wavelet transform, is embedded into the previous CNN to further complete the class-specific feature extraction from both the frequency and wavelet transform domains of the input feature maps. Thus, the multidomain attention can enhance the feature extraction ability of previous nongreedy learning manner, to effectively improve the recognition accuracy of the CNN model. Experimental results on small SAR datasets show that our proposed method can achieve better or competitive performance than that of many current existing state-of-the-art methods in terms of recognition accuracy and computational cost.

Recent studies have shown that autoencoder-based models can achieve superior performance on anomaly detection tasks due to their excellent ability to fit complex data in an unsupervised manner. In this work, we propose a novel autoencoder-based model, named StackVAE-G that can significantly bring the efficiency and interpretability to multivariate time series anomaly detection. Specifically, we utilize the similarities across the time series channels by the stacking block-wise reconstruction with a weight-sharing scheme to reduce the size of learned models and also relieve the overfitting to unknown noises in the training data. We also leverage a graph learning module to learn a sparse adjacency matrix to explicitly capture the stable interrelation structure among multiple time series channels for the interpretable pattern reconstruction of interrelated channels. Combining these two modules, we introduce the stacking block-wise VAE (variational autoencoder) with GNN (graph neural network) model for multivariate time series anomaly detection. We conduct extensive experiments on three commonly used public datasets, showing that our model achieves comparable (even better) performance with the state-of-the-art models and meanwhile requires much less computation and memory cost. Furthermore, we demonstrate that the adjacency matrix learned by our model accurately captures the interrelation among multiple channels, and can provide valuable information for failure diagnosis applications.

The confrontation between radar and jammer is increasingly competitive in electromagnetic spectrum warfare. The current radar anti-jamming methods are constrained to some extent in complex electromagnetic environment. It is necessary to study not only radar specific action benefit but also confrontation strategy. To improve the anti-jamming capacity of radar system, a game theory-based optimization method is proposed to enhance the decision-making of anti-jamming strategy in this paper. First, we analyzed the radar winning conditions and discussed the temporal constraints of the recognition and preparation process of the radar and jammer actions. Second, a temporal sequence interaction based dynamic game model between radar and jammer is constructed. Then, Q-learning is performed to optimize the radar anti-jamming strategies with the temporal sequence interaction gain as the objective function. The simulation experimental results show that the proposed strategy can significantly improve the radar winning probability in the confrontation.

The accurate prevention and control of pear tree diseases, especially the precise segmentation of leaf diseases, poses a serious challenge to fruit farmers globally. Given the possibility of disease areas being minute with ambiguous boundaries, accurate segmentation becomes difficult. In this study, we propose a pear leaf disease segmentation model named MFBP-UNet. It is based on the UNet network architecture and integrates a Multi-scale Feature Extraction (MFE) module and a Tokenized Multilayer Perceptron (BATok-MLP) module with dynamic sparse attention. The MFE enhances the extraction of detail and semantic features, while the BATok-MLP successfully fuses regional and global attention, striking an effective balance in the extraction capabilities of both global and local information. Additionally, we pioneered the use of a diffusion model for data augmentation. By integrating and analyzing different augmentation methods, we further improved the model’s training accuracy and robustness. Experimental results reveal that, compared to other segmentation networks, MFBP-UNet shows a significant improvement across all performance metrics. Specifically, MFBP-UNet achieves scores of 86.15%, 93.53%, 90.89%, and 0.922 on MIoU, MP, MPA, and Dice metrics, marking respective improvements of 5.75%, 5.79%, 1.08%, and 0.074 over the UNet model. These results demonstrate the MFBP-UNet model’s superior performance and generalization capabilities in pear leaf disease segmentation and its inherent potential to address analogous challenges in natural environment segmentation tasks.

This paper proposes an optimization method to improve the radar anti-jamming strategy by using the predictability of left game interval. Firstly, we propose the concept of key time nodes in radar/jammer confrontation and analyze its predictability. Secondly, we analyze the radar-winning scenarios by considering the temporal constraints and construct the actual utility matrix of the radar. Then, we describe the optimization algorithm using radar-winning probability statistics based on the prediction of left game interval. Finally, we carry out a simulation experiment by comparing it with other anti-jamming strategies to verify the rationality, and the result shows that the proposed method can significantly improve the radar’s winning probability in the confrontation. By using the proposed anti-jamming strategy optimization method just at the key time nodes, the imperceptibility from the jammer is improved, and its long-term superiority can be maintained in the confrontation.

Intelligent ship detection based on synthetic aperture radar (SAR) is vital in maritime situational awareness. Deep learning methods have great advantages in SAR ship detection. However, the methods do not strike a balance between lightweight and accuracy. In this article, we propose an end-to-end lightweight SAR target detection algorithm, multi-level Laplacian pyramid denoising network (LPDNet). Firstly, an intelligent denoising method based on the multi-level Laplacian transform is proposed. Through Convolutional Neural Network (CNN)-based threshold suppression, the denoising becomes adaptive to every SAR image via back-propagation and makes the denoising processing supervised. Secondly, channel modeling is proposed to combine the spatial domain and frequency domain information. Multi-dimensional information enhances the detection effect. Thirdly, the Convolutional Block Attention Module (CBAM) is introduced into the feature fusion module of the basic framework (Yolox-tiny) so that different weights are given to each pixel of the feature map to highlight the effective features. Experiments on SSDD and AIR SARShip-1.0 demonstrate that the proposed method achieves 97.14% AP with a speed of 24.68FPS and 92.19% AP with a speed of 23.42FPS, respectively, with only 5.1 M parameters, which verifies the accuracy, efficiency, and lightweight of the proposed method.

PurposeTo assess the association between sarcopenia and the risk of early biliary infection (EBI) after percutaneous transhepatic biliary stent (PTBS) placement in patients with inoperable biliary tract cancer (BTC).Patients and methodsIn this single center, retrospective observational study, patients diagnosed with inoperable BTC undergoing PTBS placement between January 2013 and July 2021 were enrolled. Preoperative sarcopenia was defined based on skeletal muscle mass measured by computed tomography images on the level of third lumbar vertebra within one month before PTBS placement. Patients were divided into two groups in accordance with the status of sarcopenia. Univariate and further multivariate logistic analyses were performed to determine predictors for EBI. Stratified and interactive analyses were conducted to investigate the stability of results. Further receiver operating characteristic curve was performed to determine the predictive value of sarcopenia on EBI after PTBS placement.ResultsTotally, 134 patients were included in this retrospective study, with 45 (33.6%) patients characterized as sarcopenia. The incidence rate of EBI was 26.9% (36/134). Multivariate analyses demonstrated that sarcopenia [Odds ratio (OR), 2.75; 95%CI: 1.11–6.77; P=0.028], obstruction length (OR, 1.04; 95%CI: 1.00–1.08; P=0.030) and diabetes (OR, 2.46; 95%CI: 1.01–5.96; P=0.047) were significant predictors of EBI. There were no significant interactions in different subgroups (P for interaction > 0.05). Moreover, the areas under the curves (AUC) revealed that the combined index containing sarcopenia, obstruction length, and diabetes showed the better predictive value (AUC= 0.723) than either one alone.ConclusionSarcopenia increased the risk of EBI in patients with inoperable BTC after PTBS placement. Preoperative assessment of sarcopenia may aid in risk stratification. Patients with sarcopenia should be given intensive monitoring.

Abstract. Background:. Hyperbaric oxygen treatment (HBOT) has been demonstrated to influence the keloid recurrence rate after surgery and to relieve keloid symptoms and other pathological processes in keloids. To explore the mechanism of the effect of HBOT on keloids, tumor immune gene expression and immune cell infiltration were studied in this work. Methods:. From February 2021 to April 2021, HBOT was carried out on keloid patients four times before surgery. Keloid tissue samples were collected and divided into an HBOT group (keloid with HBOT before surgery [HK] group, n = 6) and a non-HBOT group (K group, n = 6). Tumor gene expression was analyzed with an Oncomine Immune Response Research Assay kit. Data were mined with R package. The differentially expressed genes between the groups were compared. Hub genes between the groups were determined and verified with Quantitative Real-time PCR. Immune cell infiltration was analyzed based on CIBERSORT deconvolution algorithm analysis of gene expression and verified with immunohistochemistry (IHC). Results:. Inflammatory cell infiltration was reduced in the HK group. There were 178 upregulated genes and 217 downregulated genes. Ten hub genes were identified, including Integrin Subunit Alpha M (ITGAM), interleukin (IL)-4, IL-6, IL-2, Protein Tyrosine Phosphatase Receptor Type C (PTPRC), CD86, transforming growth factor (TGF), CD80, CTLA4, and IL-10. CD80, ITGAM, IL-4, and PTPRC with significantly downregulated expression were identified. IL-10 and IL-2 were upregulated in the HK group but without a significant difference. Infiltration differences of CD8 lymphocyte T cells, CD4 lymphocyte T-activated memory cells, and dendritic resting cells were identified with gene CIBERSORT deconvolution algorithm analysis. Infiltration levels of CD4 lymphocyte T cell in the HK group were significantly higher than those of the K group in IHC verification. Conclusion:. HBOT affected tumor gene expression and immune cell infiltration in keloids. CD4 lymphocyte T cell, especially activated memory CD4+T, might be the key regulatory immune cell, and its related gene expression needs further study.

Keloids, characterized by skin fibrosis and excessive accumulation of extracellular matrix, remain a therapeutic challenge. In this study, we systematically capture the cellular composition of keloids by the single-cell RNA sequencing technique. Our results indicated that there are significant differences in most cell types present between 12 pairs of keloid and adjacent normal tissue. We found that fibroblasts, endothelial cells, mast cells, mural cells, and Schwann cells increased significantly in keloid. The proportion of mesenchymal fibroblast subpopulations in keloids was markedly higher than those in the surrounding normal skin tissue. Furthermore, we found that the immune profiles between two groups varied significantly. The proportion of macrophages in the keloid was significantly elevated compared to the surrounding normal tissue, while cDC2 cells significantly decreased. Hotspot and pseudotime trajectory analysis indicated two modules of macrophage cells (Module2: highly expresses RNASE1, C1QA, CD163, CD14, C1QC, FCGRT, MS4A7; Module10: highly expresses APOC1, CTSB, CTSL, TYROBP), which exhibited the characteristics of tumor-associated macrophages, were upregulated in more-advanced keloid cells. Subsequently, the analysis of cellular communication networks suggested that a macrophage-centered communication regulatory network may exist in keloids and that fibroblasts in keloids may facilitate the transition and proliferation of M2 macrophages, which contributes to further comprehension of the immunological features of keloids. Overall, we delineate the immunology landscape of keloids and present new insights into the mechanisms involved in its formation in this study.

Attacking a naval vessel with multiple missiles is an important way to improve the hit rate of missiles. Missile-borne radars need to complete detection and antijamming tasks to guide missiles, but communication between these radars is often difficult. In this paper, an optimization method based on multi-agent reinforcement learning is proposed for the collaborative detection and antijamming tasks of multiple radars against one naval vessel. We consider the collaborative radars as one player to make their confrontation with the naval vessel a two-person zero-sum game. With temporal constraints of the radar’s and jammer’s recognition and preparation interval, the game focuses on taking a favorable position at the end of the confrontation. It is assumed the total jamming capability of a shipborne jammer is constant and limited, and the shipborne jammer allocates the jamming capability in the radar’s direction according to the radar threat assessment result and its probability of successful detection. The radars work collaboratively through prior centralized training and obtain a good performance by decentralized execution. The proposed method can make radars collaborate to detect the naval vessel, rather than only considering the detection result of each radar itself. Experimental results show that the proposed method in this paper is effective, improving the winning probability to 10% and 25% in the two-radar and four-radar scenarios, respectively.

The echoes collected by wideband radar systems provide abundant information on target scatterers, which is beneficial to target detection, classification, and recognition. However, as the radar range resolution increases, range cell migration (RCM) during the coherent integration (CI) period happens much easier, which may cause a degradation of target detection probability. In addition, due to the target’s orientation and structure relative to the radar, the distribution characteristics of the target scatterers in high-resolution range profiles (HRRPs) and the detection window length may vary from pulse to pulse, which may reduce the performance of traditional energy integration (EI) detectors. To solve those problems, moving range-spread target (RST) detection combining the modified keystone transform (MKT) and improved EI (IEI) is proposed in this paper. Firstly, based on waveform entropy minimization, MKT using hunter–prey optimization (HPO) is introduced to reduce the CI gain loss. The target Doppler ambiguity factor is estimated using such an effective optimization technique. Then, the IEI detector optimized by the adaptive threshold and detection window is utilized to achieve target detection, which minimizes the sensitivity of the traditional EI detector to the detection window length. The proposed method significantly improves the performance of moving RSTs in sea clutter without prior knowledge of the target Doppler ambiguity factor. Experiments are conducted by comparing the proposed method with other competing methods on both simulation data and real sea clutter data. The results demonstrate that the proposed method can obtain the CI more efficiently and has a higher detection probability.

Uncontrolled proliferation is an important cancer cell biomarker, which plays a critical role in carcinogenesis, progression and development of resistance to chemotherapy. An improved understanding of novel genes modulating cancer cell proliferation and mechanism will help develop new therapeutic strategies. The nuclear factor erythroid 2-related factor 2 (Nrf2), a transcription factor, decreases apoptosis when its expression is upregulated. However, the relationship between Nrf2 and Vincristine (VCR) chemotherapy resistance in B-cell acute lymphoblastic leukemia (B-ALL) is not yet established. Our results showed that Nrf2 levels could sufficiently modulate the sensitivity of B-ALL cells to VCRby regulating an apoptotic protein, i.e., the Bcl-2 agonist of cell death (BAD). Chemotherapeutic agents used for the treatment of B-ALL induced Nrf2 overactivation and PI3K-AKT pathway activation in the cells, independent of the resistance to chemotherapy; thus, a potential resistance loop during treatment for B-ALL with a drug combination is established. Therefore, B-ALL patients with a high expression of Nrf2 might mean induction chemotherapy with VCR effective little.

Abstract Aims This study aims to analyse whether inhaled nitric oxide (iNO) was beneficial in the treatment of coronavirus disease 2019 (COVID‐19) patients with pulmonary hypertension. Methods and results Five critically ill COVID‐19 patients with pulmonary hypertension designated Cases 1–5 were retrospectively included. Clinical data before and after iNO treatment were serially collected and compared between patients with or without iNO treatment. The five cases experienced pulmonary artery systolic pressure (PASP) elevation (≥50 mmHg) at 30, 24, 33, 23, and 24 days after illness onset (d.a.o), respectively. Cases 1–3 received iNO treatment on the 24th, 13th, and 1st day after the first elevation of PASP, with concentrations varied from 10 to 20 ppm based on the changes of PASP and blood pressure for 10, 9, and 5 days, respectively. Upon iNO treatment, PASP of Cases 1 and 2 returned to normal on the 10th day and 1st day, and maintained between 50 and 58 mmHg in Case 3. Pa02/Fi02 increased from 88 to 124, 51 to 118, and 146 to 244, respectively. SPO2 increased from 91% to 97% for Case 1 and maintained a high level above 97% for Case 2. Cardiac function remained normal in the three patients after treatment. Moreover, Cases 1 and 3 survived from severe acute respiratory syndrome coronavirus 2 infection, while Case 2 finally died on the 36th day after the first elevation of PASP due to severe complications. Both cases who did not receive iNO treatment experienced a sudden decrease of PASP and Pa02/Fi02 due to right heart failure and then died. Conclusions Inhaled nitric oxide treatment was beneficial in reducing and stabilizing the PASP and might also reduce the risk of right heart failure in COVID‐19 with pulmonary hypertension.

In order to address the issue of edge-blurring and improve the accuracy and robustness of scene flow estimation under motion occlusions, we in this article propose a piecewise 3D scene flow estimation approach with semantic segmentation, named SS-SF. First, we utilize the semantic optical flow to initialize the 3D plane and its rigid motion parameters, and then produce the initial mappings of pixel-to-segment and segment-to-plane of the input left and right image sequences. Second, we plan a novel energy function to optimize the initial mappings by using a semantic segmentation constraint term to regularize the classical scene flow model, which the optimized mappings are employed to update the assignment and motion parameters of each pixel. Third, we adopt the semantic label to extract the occlusion pixels and exploit an occlusion handling constraint to enhance the robustness of the scene flow estimation. Finally, we compare the proposed SS-SF model with several state-of-the-art approaches by using the KITTI and MPI-Sintel databases. The experimental results demonstrate that the proposed method has the advanced accuracy and robustness in scene flow estimation, especially owns the capacities of edge-preserving and occlusion handling.

Pore space of tight sandstone samples exhibits fractal characteristics. Nuclear magnetic resonance is an effective method for pore size characterization. This paper focuses on fractal characteristics of pore size from nuclear magnetic resonance (NMR) of tight sandstone samples. The relationship between the fractal dimension from NMR with pore structure and water saturation is parameterized by analyzing experimental data. Based on it, a pore structure characterization and classification method for water-saturated tight sandstone and a water saturation prediction method in a gas-bearing sandstone reservoir have been proposed. To verify the models, the fractal dimension from NMR of 19 tight sandstone samples selected from the gas-bearing tight sandstone reservoir of Shahejie Formation in Nanpu Sag and that of 16 of them under different water saturation states are analyzed. The application result of new methods in the gas-bearing tight sandstone reservoir of Shahejie Formation in Nanpu Sag shows consistency with experimental data. This paper has facilitated the development of the NMR application by providing a non-electrical logging idea in reservoir quality evaluation and water saturation prediction. It provides a valuable scientific resource for reservoir engineering and petrophysics of unconventional reservoir types, such as tight sandstone, low porosity, and low permeability sandstone, shale, and carbonate rock reservoirs.

Background: Keloid is a skin fibroproliferative disease with unknown pathogenesis. Metabolomics provides a new perspective for revealing biomarkers related to metabolites and their metabolic mechanisms.Method: Metabolomics and transcriptomics were used for data analysis. Quality control of the data was performed to standardize the data. Principal component analysis (PCA), PLS-DA, OPLS-DA, univariate analysis, CIBERSORT, neural network model, and machine learning correlation analysis were used to calculate differential metabolites. The molecular mechanisms of characteristic metabolites and differentially expressed genes were identified through enrichment analysis and topological analysis.Result: Compared with normal tissue, lipids have a tendency to decrease in keloids, while peptides have a tendency to increase in keloids. Significantly different metabolites between the two groups were identified by random forest analysis, including 1-methylnicotinamide, 4-hydroxyproline, 5-hydroxylysine, and l-prolinamide. The metabolic pathways which play important roles in the pathogenesis of keloids included arachidonic acid metabolism and d-arginine and d-ornithine metabolism. Metabolomic profiling reveals that 5-hydroxylysine and 1-methylnicotinamide are metabolic indicators of keloid severity. The high-risk early warning index for 5-hydroxylysine is 4 × 108-6.3×108 (p = 0.0008), and the high-risk predictive index for 1-methylnicotinamide is 0.95 × 107-1.6×107 (p = 0.0022).Conclusion: This study was the first to reveal the metabolome profile and transcriptome of keloids. Differential metabolites and metabolic pathways were calculated by machine learning. Metabolomic profiling reveals that 5-hydroxylysine and 1-methylnicotinamide may be metabolic indicators of keloid severity.

Cutaneous squamous cell carcinoma (CSCC) is a malignant proliferation of cutaneous epithelium that has been observed to have an alarming rise in incidence. Numerous studies have demonstrated microRNAs (miRNAs or miRs) as important biomarkers in the diagnosis, prognosis, and treatment of CSCC. This study aims to investigate the effects of miR-203 on the behaviors of CSCC cells and possible mechanisms associated with protein regulator of cytokinesis-1 (PRC1) and Wnt/β-catenin signaling pathway. PRC1 was suggested as a target of miR-203 in squamous cell carcinoma cell line 1 (SCL-1) cells by dual-luciferase reporter gene assay. Based on the immunohistochemical staining and qRT-PCR, PRC1 was abundantly expressed while miR-203 was poorly expressed in CSCC tissues. miR-203 mimic or inhibitor was transfected into SCL-1 cells to upregulate or downregulate its expression. Upregulation of miR-203 downregulated PRC1 expression to block the Wnt/β-catenin signaling pathway. By conducting 3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT), scratch test, and Transwell and flow cytometric analyses, miR-203 was witnessed to restrain SCL-1 cell proliferation, migration, and invasion while accelerating their apoptosis. The rescue experiments addressed that inhibition of the Wnt/β-catenin signaling pathway conferred the anti-tumor effect of miR-203. These results establish a tumor-suppressive role for miR-203 in CSCC cell line SCL-1. Hence, miR-203 has promising potential as a therapeutic target for CSCC.

Abstract Background Nakajo-Nishimura syndrome (NNS) is an autosomal recessive heredity disorder, one of a spectrum of autoinflammatory diseases named proteasome-associated autoinflammatory syndrome (PRAAS) caused by mutations of PSMB8 gene. NNS is characterized by pernio-like skin rashes, intermittent fever, and long clubbed fingers and toes with joint contractures, partially with progressive lipomuscular atrophy, emaciation, hepatosplenomegaly and basal ganglion calcification. Case presentation We presented a sporadic case of NNS with compound heterozygous mutations in the PSMB8 gene. The 4-year-old boy was affected by progressive erythematous plaques on his nose and gradually involved hands and feet later with characteristic appearance of long clubbed fingers. The repetitive periodic intermittent fever was recorded. By gene sequencing, novel compound heterozygous mutations c.373C > T (p.R125C) and c.355G > A (p.D119N) in the PSMB8 gene were found. The patient responded well to low dosage of oral methylprednisolone. Conclusions We reported novel compound heterozygous mutations in PSMB8 in a sporadic Chinese NNS patient.

Dysregulation of microRNAs (miRNAs) is acknowledged in human cutaneous squamous cell carcinoma (cSCC). We hereby evaluated the ability of miRNA-216b (miR-216b) to impact human cSCC. cSCC tissues with corresponding adjacent normal tissues were collected from 40 patients diagnosed with cSCC where the expression pattern of miR-216b and targeting protein for Xenopus kinesin-like protein 2 (TPX2) was determined by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) and western blot analysis. A431 cells were transfected with miR-216b mimic, miR-216b inhibitor, or short interfering RNA against TPX2 to evaluate cell proliferation, invasion, migration, and apoptosis using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, scratch test, Transwell assay, and flow cytometry. TPX2 was highly expressed in cSCC tissues while miR-216b was poorly expressed in association with tumor differentiation, lymph node metastasis, and tumor node metastasis staging in patients with cSCC. In response to overexpressed miR-216b or silenced TPX2, cSCC cell proliferation, invasion, and migration were suppressed and apoptosis was stimulated, along with activated p53 signaling. Thus, upregulated miR-216b was capable of promoting apoptosis and inhibiting proliferation, invasion, and migration of cSCC cells by downregulating TPX2 through activation of the p53 signaling, highlighting a novel biomarker for novel treatment modalities against cSCC.

The backbone network of oil and gas pipelines in China has begun to take shape, and the construction of intelligent pipeline network becomes possible on the base of increasingly perfect operation and maintenance system and the application of big data, artificial intelligence, and technologies of the Internet of Things. The construction of intelligent pipeline network undertaken by China National Petroleum Corporation was exemplified to introduce and summarize the construction progress of intelligent pipeline network at the present stage in terms of science and technology, information, and the Internet of Things based on the existing design scheme of intelligent pipeline network. Then the latest achievements were introduced from the pilot projects on China–Russia Eastern Gas Pipeline and China–Myanmar oil and gas pipelines, and the problems to be solved were set forth for the construction of intelligent pipeline network. The targets of the intelligent pipeline operation will be realized, including comprehensive and uniform data, interactive and visual perception, integrated and interconnected system, precise and matched supply, intelligent and efficient operation, and controllable prediction and prewarning, by defining the principles and objectives of pipeline network construction and developing an effective method for the construction of intelligent pipeline network by practical research.

Existing self-supervised monocular depth estimation methods usually explore increasingly large networks to achieve accurate estimation results. However, larger networks are more difficult to train and require more storage space. To balance the network size and the computational accuracy, we propose in this article a compact lightweight network for monocular depth estimation, named LW-Net. First, we construct a compact network by designing an iterative decoder with shared weights and a lightweight pyramid encoder. The proposed network includes significantly fewer parameters than most of the existing monocular depth estimation networks. Second, we exploit a self-supervised training strategy by combining the proposed LW-Net model with a pose network, and we then use a hybrid loss function to train the decoder and encoder separately. The proposed training strategy results in the LW-Net model achieving a better performance in terms of estimation accuracy than other methods. Finally, we respectively run the proposed LW-Net model on the KITTI and Make3D datasets to conduct a comprehensive comparison with several state-of-the-art methods. The experimental results demonstrate that our method performs the best in terms of computational accuracy while utilizing the fewest parameters. Specifically, the model parameters of our method are reduced by 46.6%, the time cost is decreased by 7.69%, and the frame rate is increased by 5.19% compared with the existing state-of-the-art method.

Recently, the interpolation of correspondences method has been widely used in optical flow estimation, because it produces an accurate flow field and costs little runtimes. However, most of the existing matching-based optical flow methods are usually susceptible to non-rigid motion and large displacements. We propose in this article a large displacement optical flow estimation method based on robust interpolation of sparse correspondences, named Riscflow. First, we utilize the deep matching model to achieve an initial matching result of two consecutive frames, and then we exploit a grid-based motion statistics optimization scheme to remove the outliers from the initial matching field. Second, we propose a random forest-based motion boundary extraction model and construct a sparse-to-dense interpolation method by using the boundary information to prevent the dense matching field from edge-blurring. Third, we design a global optical flow estimation method by using an energy function to optimize the dense matching field. Finally, we respectively run the proposed method on the MPI-Sintel and UCF101 databases to conduct a comprehensive comparison with some state-of-the-art optical flow approaches including the variational methods, the matching-based methods, and the deep learning-based methods. The comparison results demonstrate that the proposed method has high accuracy and good robustness of optical flow estimation, and especially gains the benefit of edge-preserving under non-rigid motion and large displacements.

The basic leucine zipper (bZIP) transcription factors, which harbor a conserved bZIP domain composed of two regions, a DNA-binding basic region and a Leu Zipper region, operate as important switches of transcription networks in eukaryotes. However, this gene family has not been systematically characterized in cotton (Gossypium hirsutum). Here, we identified 197 bZIP family members in cotton. The chromosome distribution pattern indicates that the GhbZIP genes have undergone 53 genome-wide segmental and 7 tandem duplication events which contribute to the expansion of the cotton bZIP family. Phylogenetic analysis showed that cotton GhbZIP proteins cluster into 13 subfamilies, and homologous protein pairs showed similar characteristics. Inspection of the DNA-binding basic region and leucine repeat heptads within the bZIP domains indicated different DNA-binding site specificities as well as dimerization properties among different groups. Comprehensive expression analysis indicated the most highly and differentially expressed genes in root and leaf that might play significant roles in cotton response to drought stress. GhABF3D was identified as a highly and differentially expressed bZIP family gene in cotton leaf and root under drought stress treatments that likely controls drought stress responses in cotton. These data provide useful information for further functional analysis of the GhbZIP gene family and its potential application in crop improvement.

Background: A keloid is a benign skin tumor that extends beyond the initial injury area, and its pathologic mechanism remains unclear. Method: High-throughput sequencing data were obtained from normal skin tissue of patients with keloids (Group N) and healthy controls (Group C). Important genes were mined by bioinformatics analysis and identified by RT–qPCR, Western blotting, immunohistochemistry and immunofluorescence assays. The CIBERSORT algorithm was used to convert gene expression information into immune cell information. Flow cytometry was used to verify the key immune cells. Fluorescence-activated cell sorting coculture and CCK8 experiments were used to explore the effect of CD8+ T cells on keloid-associated fibroblasts. Neural network models were used to construct associations among CD28, CD8+ T cells and the severity of keloids and to identify high-risk values. Result: The expression levels of costimulatory molecules (CD28, CD80, CD86 and CD40L) in the skin tissue of patients with keloids were higher than the levels in healthy people (p < 0.05). The number of CD8+ T cells was significantly higher in Group N than in Group C (p < 0.05). The fluorescence intensities of CD28 and CD8+ T cells in Group N were significantly higher than those in Group C (p = 0.0051). The number and viability of fibroblasts cocultured with CD8+ T cells were significantly reduced compared with those of the control (p < 0.05). The expression of CD28 and CD8+ T cells as the input layer may be predictors of the severity of keloids with mVSS as the output layer. The high-risk early warning indicator for CD28 is 10–34, and the high-risk predictive indicator for CD8+ T cells is 13–28. Conclusions: The abnormal expression of costimulatory molecules may lead to the abnormal activation of CD8+ T cells. CD8+ T cells may drive keloid-associated immunosuppression. The expression of CD28 and CD8+ T cells as an input layer may be a predictor of keloid severity. CD28 and CD8+ T cells play an important role in the development of keloids.

Porokeratosis (PK) is defined as hyperpigmented macules or patches with a distinctive, ridge-like hyperkeratotic border which is histologically characterized by a cornoid lamella. Here, we report two cases of linear porokeratosis which converted to multiple cutaneous squamous cell carcinoma after long history progression. In addition, patient 2 was accompanied by secondary dermal amyloid deposits, which was rare reported.

Objective To summarize the clinical features and treatment of iatrogenic vas deferens injury in the inguinal region. Methods Between January, 2014 and June, 2018, we collected the data of 17 patients treated in our hospital for obstructive azoospermia resulting from a previous inguinal hernia surgery for analysis of their clinical features, findings by surgical exploration, and characteristics of vas deferens injuries. All the patients underwent microsurgical vas deferens anastomosis and the mid-term and long-term outcomes of the patients were followed up. Results Fourteen of the 17 patients underwent vasovasostomy or vasoepididymostomy successfully. The patients were followed up for a mean of 19.6±9.5 months after the microsurgeries, and recanalization was achieved in 13 patients with a mean sperm count of (33.1±25.6)×106/L; natural conception of the spouses was achieved in 6 cases after the surgeries. Conclusions Inguinal hernia surgery is one of the main causes of vas deferens obstruction to result in obstructive azoospermia, and recanalization can be achieved in the majority of such cases by microvasectomy.