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ObjectiveTo investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated.MethodsA total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the “rms” package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model.ResultsThe mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model.ConclusionPD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

Background: The role of Ferroptosis in the course of sepsis-induced myopathy is yet unclear. The objective of our work is to identify key genes connected with Ferroptosis in sepsis-induced myopathy and investigate possible pharmaceutical targets related to this process. This research aims to provide new insights into the management of sepsis-induced myopathy. Methods: We got the GSE13205 dataset from the Gene Expression Omnibus (GEO) and extracted Ferroptosis-associated genes from the FerrDb database. After conducting a functional annotation analysis of these genes, we created a protein-protein interaction network using Cytoscape software to identify important genes. Subsequently, we employed CMap to investigate prospective pharmaceuticals that could target these crucial genes. Results: A total of 61 genes that are expressed differently (DEGs) have been found concerning Ferroptosis. These genes are involved in a wide range of biological functions, including reacting to signals from outside the cell and the availability of nutrients, programmed cell death, controlling apoptosis, and responding to peptides, chemical stressors, and hormones. The KEGG pathway study revealed that these pathways are involved in Ferroptosis, autophagy, P53 signaling, PI3K-Akt signaling, mTOR signaling, HIF-1 signaling, endocrine resistance, and different tumorigenic processes. In addition, we created a network that shows the simultaneous expression of important genes and determined the top 10 medications that have the potential to treat sepsis-induced myopathy. Conclusion: The bioinformatics research undertaken sheds insight into the probable role of Ferroptosis-associated genes in sepsis-induced myopathy. The identified critical genes show potential as therapeutic targets for treating sepsis-induced myopathy, offering opportunities for the development of tailored medicines.

Abstract Background Multimodal techniques-assisted resection of glioma under general anesthesia (GA) has been shown to achieve similar clinical outcomes as awake craniotomy (AC) in some studies. In this study, we aim to validate the use of multimodal techniques can achieve the maximal safe resection of high-grade glioma involving language areas (HGILAs) under GA. Methods HGILAs cases were reviewed and collected between January 2009 and December 2020 in our center. Patients were separated into multimodal group (using neuronavigation, intraoperative MRI combined with direct electrical stimulation [DES] and neuromonitoring [IONM]) and conventional group (neuronavigation alone) and clinical outcomes were compared between groups. Studies of HGILAs were reviewed systematically and the meta-analysis results of previous (GA or AC) studies were compared with our results. Results Finally, there were 263 patients in multimodal group and 137 patients in conventional group. Compared to the conventional group, the multimodal group achieved the higher median EOR (100% versus 94.32%, P

The receiver operating characteristic (ROC) curve is a valuable statistical tool in medical research. It assesses a biomarker’s ability to distinguish between diseased and healthy individuals. The area under the ROC curve (AUC) and the Youden index (J) are common summary indices used to evaluate a biomarker’s diagnostic accuracy. Simultaneously examining AUC and J offers a more comprehensive understanding of the ROC curve’s characteristics. In this paper, we utilize a semiparametric density ratio model to link the distributions of a biomarker for healthy and diseased individuals. Under this model, we establish the joint asymptotic normality of the maximum empirical likelihood estimator of (AUC,J) and construct an asymptotically valid confidence region for (AUC,J). Furthermore, we propose a new test to determine whether a biomarker simultaneously exceeds prespecified target values of AUC0 and J0 with the null hypothesis H0:AUC≤AUC0 or J≤J0 against the alternative hypothesis Ha:AUC>AUC0 and J>J0. Simulation studies and a real data example on Duchenne Muscular Dystrophy are used to demonstrate the effectiveness of our proposed method and highlight its advantages over existing methods.

An improved DCGAN tire defect image generation model was proposed to solve the problems of poor image quality, unstable model frame and slow model convergence in the data expansion method of deep convolutional generative adversarial network. The residual network and attention mechanism were embedded in DCGAN model to improve the feature extraction ability of the model. At the same time, the DCGAN loss function JS divergence was abandoned and Wasserstein distance with gradient penalty term was used to improve the stability of model training. The experimental results show that the quality of tire defect images generated by this model is better than that generated by DCGAN, WGAN, CGAN and SAGAN,with an average FID value of 11628 and a minimum FID value of 8494. The proposed model can stably generate tire defect images with better quality, which provides an effective way for expanding tire defect sample dataset and alleviates the problem of small sample in the development of deep learning in the field of defect detection.

The resource endowments, development levels and emission reduction potential of different provinces in China are different. Accurately judging regional differences of carbon neutrality potential of each province is helpful to analyze the low-carbon development strategy. In this research, a comprehensive evaluation index system for carbon neutrality potential is constructed, and the carbon neutrality potential score of China from 2003 to 2019 is calculated by sparse principal components(SPC), and dynamically evaluated by combining time ordered weighted average operator (TOWA operator). The results show that: (1) The evaluation score of carbon neutrality potential is greatly affected by the sustainable driving force, and the overall trend is on the rise. But there is a significant gap between provinces, showing the distribution characteristics of “high in the east and low in the central and western regions”. (2) The upward trend of sustainable driving force is obvious, there is a momentum of accelerated development in recent years. The gap between provinces is large. (3) The resilient driving force showed an increasing trend, but some provinces have experienced a decline since 2017. (4) The growth trend of coordination and transformational resilience is flat. The development of provinces is relatively balanced. This provides a reference for the analysis of each region to formulate low-carbon development strategies tailored to local conditions.

Introduction: Fuzhuan brick tea (FBT) is a worldwide popular beverage which has the appreciable potential in regulating glycometabolism. However, the reports on the hypoglycemic mechanism of FBT remain limited.Methods: In this study, the hypoglycemic effect of FBT was evaluated in a pharmacological experiment based on Kunming mice. Global metabolomics and network pharmacology were combined to discover the potential target metabolites and genes. In addition, the real-time quantitative polymerase chain reaction (RT-qPCR) analysis was performed for verification.Results: Seven potential target metabolites and six potential target genes were screened using the integrated approach. After RT-qPCR analysis, it was found that the mRNA expression of VEGFA, KDR, MAPK14, and PPARA showed significant differences between normal and diabetes mellitus mice, with a retracement after FBT treatment.Conclusion: These results indicated that the hypoglycemic effect of FBT was associated with its anti-inflammatory activities and regulation of lipid metabolism disorders. The exploration of the hypoglycemic mechanism of FBT would be meaningful for its further application and development.

Abstract The procollagen C-protease enhancer (PCOLCE) has been identified to influence tumor growth and metastasis in multiple cancers. However, the relationship between PCOLCE activity and the progression of gliomas remains largely unknown. Glioma RNA-seq data were derived from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas databases for analysis. Kaplan–Meier survival curve, clinical characterization correlation, univariate and multivariate Cox, and receiver operating characteristic curve analyses were performed to assess the prognostic role of PCOLCE. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to determine the functions or pathways associated with PCOLCE. The ESTIMATE and CIBERSORT algorithms, Spearman’s rank correlation analysis, and Tumor Immune Estimation Resource (TIMER) databases were used to explore the relationship between PCOLCE and immune infiltration. Correlation analysis between PCOLCE, related genes, and immune cell markers was conducted using the TIMER database. Immunophenoscore assays were performed to determine differential PCOLCE expression levels in glioma. The sensitivity of multi-drugs were determined to explore potential chemotherapeutic agents in between PCOLCE. Compared to normal brain tissue, PCOLCE expression was increased in glioma and correlated with shorter overall survival (OS). Furthermore, significant differences were observed in the immune scores and immune cell infiltration levels. PCOLCE is positively associated with immune checkpoints and many immune markers. Additionally, PCOLCE expression was higher in gliomas with higher IPS Z-scores in CGGA. High expression of PCOLCE increased sensitivity to multiple chemotherapy agents in CGGA (P

Abstract Medical images exhibit multi‐granularity and high obscurity along boundaries. As representative work, the U‐Net and its variants exhibit two shortcomings on medical image segmentation: (a) they expand the range of reception fields by applying addition or concatenate operators to features with different reception fields, which disrupts the distribution of the essential feature of objects; (b) they utilize the downsampling or atrous convolution to characterize multi‐granular features of objects, which can obtain a large range of reception fields but leads to blur boundaries of objects. A Shuffling Atrous Convolutional U‐Net (SACNet) for circumventing those issues is proposed. The significant component of SACNet is the Shuffling Atrous Convolution (SAC) module, which fuses different atrous convolutional layers together by using a shuffle concatenate operation, so that the features from the same channel (which correspond to the same attribute of objects) are merged together. Besides the SAC modules, SACNet utilizes an EP module during the fine and medium levels to enhance the boundaries of objects, and utilizes a Transformer module during the coarse level to capture an overall correlation of pixels. Experiments on three medical image segmentation tasks: abdominal organ, cardiac, and skin lesion segmentation demonstrate that, SACNet outperforms several state‐of‐the‐art methods and facilitates easy transplant to other semantic segmentation tasks.

Summary: Extracellular vesicles (EVs) can mediate intercellular communication, including signaling between the placenta and maternal tissues. Human placental explant culture is a versatile in vitro model system to investigate placental function. We performed systematic studies in different tissue culture media types and oxygen tensions to identify a defined serum-free culture condition that supports high trophoblast viability and metabolism, as well as the release of similar populations of EVs, compared to traditional undefined conditions that contain media additives potentially contaminated with exogenous EVs. We also determined the time frame in which trophoblast viability and functionality remain optimal. Multiplex vesicle flow cytometry with classical EV and placenta-specific markers revealed three separate populations of explant-derived EVs: small CD63+ EVs; large PLAP+ EVs; and CD63-/PLAP- EVs. These culture and analytical approaches will enable in vitro modeling of short-term effects of environmental perturbations associated with pregnancy complications on placental function and EV release.

Abstract BRD7 functions as a crucial tumor suppressor in numerous malignancies including nasopharyngeal carcinoma (NPC). However, its function and exact mechanisms involved in tumor progression are not well understood. Here, we found that the B7BS was a potential enhancer region of BIRC2, and BRD7 negatively regulated the transcriptional activity and expression of BIRC2 by targeting the activation of the BIRC2 enhancer. Moreover, BIRC2 promoted cell proliferation, migration, invasion as well as xenograft tumor growth and metastasis in vivo, thus functioning as an oncogene in NPC. Furthermore, the recovery of BIRC2 expression could rescue the inhibitory effect of BRD7 on cell proliferation, migration, invasion and xenograft tumor growth and metastasis. In addition, BIRC2 was highly-expressed in NPC tissues, and positively correlated with the TNM stage and negatively correlated with the expression of BRD7. Therefore, these results suggest that BRD7 suppresses tumor growth and metastasis thus functioning as a tumor suppressor at least partially by negatively regulating the enhancer activity and expression of BIRC2, and targeting the BRD7/BIRC2 regulation axis might be a potential strategy for the diagnosis and treatment of NPC.

Due to cost-effectiveness and high efficiency, two-phase case-control sampling has been widely used in epidemiology studies. We develop a semi-parametric empirical likelihood approach to two-phase case-control data under the logistic regression model. We show that the maximum empirical likelihood estimator has an asymptotically normal distribution, and the empirical likelihood ratio follows an asymptotically central chi-square distribution. We find that the maximum empirical likelihood estimator is equal to Breslow and Holubkov (1997)'s maximum likelihood estimator. Even so, the limiting distribution of the likelihood ratio, likelihood-ratio-based interval, and test are all new. Furthermore, we construct new Kolmogorov–Smirnov type goodness-of-fit tests to test the validation of the underlying logistic regression model. Our simulation results and a real application show that the likelihood-ratio-based interval and test have certain merits over the Wald-type counterparts and that the proposed goodness-of-fit test is valid.

BackgroundPersistent Inflammation, Immunosuppression, and Catabolism Syndrome (PIICS) is a significant contributor to adverse long-term outcomes in severe trauma patients.ObjectiveThe objective of this study was to establish and validate a PIICS predictive model in severe trauma patients, providing a practical tool for early clinical prediction.Patients and methodsAdult severe trauma patients with an Injury Severity Score (ISS) of ≥16, admitted between October 2020 and December 2022, were randomly divided into a training set and a validation set in a 7:3 ratio. Patients were classified into PIICS and non-PIICS groups based on diagnostic criteria. LASSO regression was used to select appropriate variables for constructing the prognostic model. A logistic regression model was developed and presented in the form of a nomogram. The performance of the model was evaluated using calibration and ROC curves.ResultsA total of 215 patients were included, consisting of 155 males (72.1%) and 60 females (27.9%), with a median age of 51 years (range: 38–59). NRS2002, ISS, APACHE II, and SOFA scores were selected using LASSO regression to construct the prognostic model. The AUC of the ROC analysis for the predictive model in the validation set was 0.84 (95% CI 0.72–0.95). The Hosmer-Lemeshow test in the validation set yielded a χ2 value of 14.74, with a value of p of 0.098.ConclusionAn accurate and easily implementable PIICS risk prediction model was established. It can enhance risk stratification during hospitalization for severe trauma patients, providing a novel approach for prognostic prediction.

Sepsis is described as an immune response disorder of the host to infection in which microorganisms play a non-negligible role. Most survivors of sepsis experience ICU-acquired weakness, also known as septic myopathy, characterized by skeletal muscle atrophy, weakness, and irreparable damage/regenerated or dysfunctional. The mechanism of sepsis-induced myopathy is currently unclear. It has been believed that this state is triggered by circulating pathogens and their related harmful factors, leading to impaired muscle metabolism. Sepsis and its resulting alterations in the intestinal microbiota are associated with sepsis-related organ dysfunction, including skeletal muscle wasting. There are also some studies on interventions targeting the flora, including fecal microbiota transplants, the addition of dietary fiber and probiotics in enteral feeding products, etc., aiming to improve sepsis-related myopathy. In this review, we critically assess the potential mechanisms and therapeutic prospects of intestinal flora in the development of septic myopathy.

In the era of big data, divide-and-conquer, parallel, and distributed inference methods have become increasingly popular. How to effectively use the calibration information from each machine in parallel computation has become a challenging task for statisticians and computer scientists. Many newly developed methods have roots in traditional statistical approaches that make use of calibration information. In this paper, we first review some classical statistical methods for using calibration information, including simple meta-analysis methods, parametric likelihood, empirical likelihood, and the generalized method of moments. We further investigate how these methods incorporate summarized or auxiliary information from previous studies, related studies, or populations. We find that the methods based on summarized data usually have little or nearly no efficiency loss compared with the corresponding methods based on all-individual data. Finally, we review some recently developed big data analysis methods including communication-efficient distributed approaches, renewal estimation, and incremental inference as examples of the latest developments in methods using calibration information.

Depending on the asymptotical independence of periodograms, exponential tilted (ET) likelihood, as an effective nonparametric statistical method, is developed to deal with time series in this paper. Similar to empirical likelihood (EL), it still suffers from two drawbacks: the non-definition problem of the likelihood function and the under-coverage probability of confidence region. To overcome these two problems, we further proposed the adjusted ET (AET) likelihood. With a specific adjustment level, our simulation studies indicate that the AET method achieves a higher-order coverage precision than the unadjusted ET method. In addition, due to the good performance of ET under moment model misspecification [Schennach, S. M. (2007). Point estimation with exponentially tilted empirical likelihood. The Annals of Statistics, 35(2), 634–672. https://doi.org/10.1214/009053606000001208], we show that the one-order property of point estimate is preserved for the misspecified spectral estimating equations of the autoregressive coefficient of AR(1). The simulation results illustrate that the point estimates of the ET outperform those of the EL and their hybrid in terms of standard deviation. A real data set is analyzed for illustration purpose.

BackgroundTear trough deformity is one of the most common complaints in clinical settings. The correction of this groove is challenging in facial rejuvenation. The lower eyelid blepharoplasty varies with different conditions. A novel approach of using orbital fat in the lower eyelid to increase the volume of the infraorbital rim with granule fat injection has been applied in our institution for more than 5 years.ObjectivesThis article aims to describe the detailed steps of our technique and verify its effectiveness by a cadaveric head dissection after surgical simulation.MethodsIn this study, a total of 172 patients with tear trough deformity underwent lower eyelid orbital rim augmentation with fat filling in the sub-periosteum pocket. According to Barton's grades, 152 patients underwent lower eyelid orbital rim augmentation with orbital fat filling, 12 patients had it combined with autologous granule fat from other body parts, and 8 patients received only transconjunctival fat removal to correct tear trough.ResultsThe modified Goldberg score system was used to compare preoperative and postoperative photographs. Patients were satisfied with the cosmetic results. Excessive protruding fat was released, and the tear trough groove was flattened by using autologous orbital fat transplantation. The lower eyelid sulcus deformities were well-corrected. To further illustrate the anatomical structure of the lower eyelid area and injection layers, six cadaveric heads were used for surgical simulation and demonstrated the effectiveness of our technique.ConclusionsThis study indicated that the infraorbital rim could be increased by transplanting orbital fat to the pocket, which was dissected under the periosteum, and the procedure has been verified as reliable and effective.Evidence-based medicine (EBM) levelLevel II.

Sporadic Alzheimer’s disease (sAD) is the most common neurodegenerative disease worldwide, which is characterized by the progressive cognitive dysfunction and behavioral impairment. Here, we generated a human induced pluripotent stem cell (iPSC) line from the peripheral blood mononuclear cells (PBMCs) isolated from a 78-year-old male patient clinically diagnosed with sAD. The iPSC line expressed pluripotency markers, showed normal karyotype, and had the ability to differentiate into three germ layers in vitro. This iPSC line may provide a powerful tool for modeling AD in vitro and studying the pathogenesis of sAD.

In this study, a static railway track smoothness detection system based on laser reference, which can measure various track smoothness parameters by using multiple sensors, is proposed. Furthermore, in order to improve the measurement accuracy and stability of the system, this paper also conducted three key analyses based on the static track measurement system. By using a liquid double-wedge automatic compensation device to compensate the horizontal angle of the beam, a mathematical model of liquid double-wedge automatic compensation was established. Then, by using an optical ring grating system to ring-grate and characterize the laser spot, the collimation efficiency of the system was improved when measuring at long distances. For the special ring grating spot image, an adaptive image processing algorithm was proposed, which can achieve sub-pixel-level positioning accuracy. This study also conducted a field measurement experiment, comparing the experimental data obtained via the static track measurement system with the results of existing track measurement products, and verifying that the static track measurement system has high measurement accuracy and stability.

Hyphantria cunea (Drury), a destructive polyphagous pest, has been spreading southward after invading northern China, which indicates that this insect species is facing a huge thermal challenge. Small heat shock proteins (sHSPs) function as ATP-independent molecular chaperones that protect insects from heat stress damage. In order to explore the role of sHSPs in the thermotolerance of H. cunea, five novel sHSP genes of H. cunea were cloned, including an orthologous gene (HcHSP21.4) and four species-specific sHSP genes (HcHSP18.9, HcHSP20.1, HcHSP21.5, and HcHSP29.8). Bioinformatics analysis showed that the proteins encoded by these five HcHSPs contained typical α-crystallin domains. Quantitative real-time PCR analysis revealed the ubiquitous expression of all HcHSPs across all developmental stages of H. cunea, with the highest expression levels in pupae and adults. Four species-specific HcHSPs were sensitive to high temperatures. The expression levels of HcHSPs were significantly up-regulated under heat stress and increased with increasing temperature. The expression levels of HcHSPs in eggs exhibited an initial up-regulation in response to a temperature of 40 °C. In other developmental stages, the transcription of HcHSPs was immediately up-regulated at 30 °C or 35 °C. HcHSPs transcripts were abundant in the cuticle before and after heat shock. The expression of HcHSP21.4 showed weak responses to heat stress and constitutive expression in the tissues tested. These results suggest that most of the HcHSPs are involved in high-temperature response and may also have functions in the normal development and reproduction of H. cunea.

The positional accuracy and orientation accuracy of industrial robots are crucial technical indicators for determining their applicability in industrial scenarios. However, the majority of current calibration methods for industrial robots only consider positional errors, neglecting the significance of orientation accuracy. This paper presents a more accurate error model and parameter calibration method for industrial robots based on six degrees-of-freedom position and orientation to identify the actual structural parameters. Firstly, based on the modified Denavit–Hartenberg parameters, the transformation errors of the tool coordinate system and measurement coordinate frame were introduced to establish a geometric parameter error model with positional and orientation accuracy as the optimization objectives. Secondly, to address the drawback of falling into local optima when identifying geometric parameters simultaneously, a geometric parameter cross-identification method based on the Levenberg–Marquardt algorithm is proposed. Lastly, the linear relationship between the parameters was analyzed, and a scheme for not calibrating some geometric parameters under specific conditions was given. Simulation results demonstrated that, under the premise of existing transformation errors, the proposed geometric parameter error model can accurately identify the actual structural parameters of industrial robots. After calibration, the positional error at the robot’s flange end decreased from 1.9536 mm to 0.0122 mm, and the orientation error decreased from 1.46 × 10−2 rad to 1.31 × 10−4 rad. Furthermore, compared to identifying the geometric parameters simultaneously, the proposed cross-identification method has a wider convergence range.

NASICON (Na superionic conductor)-type cathode materials for sodium-ion batteries (SIBs) have attracted extensive attention due to their mechanically robust three-dimensional (3D) framework, which has sufficient open channels for fast Na+ transportation. However, they usually suffer from inferior electronic conductivity and low capacity, which severely limit their practical applications. To solve these issues, we need to deeply understand the structural evolution, redox mechanisms, and electrode/electrolyte interface reactions during cycling. Recently, rapid developments in synchrotron X-ray techniques, neutron-based resources, magnetic resonance, as well as optical and electron microscopy have brought numerous opportunities to gain deep insights into the Na-storage behaviors of NASICON cathodes. In this review, we summarize the detection principles of advanced characterization techniques used with typical NASICON-structured cathode materials for SIBs. The special focus is on both operando and ex situ techniques, which help to investigate the relationships among phase, composition, and valence variations within electrochemical responses. Fresh electrochemical measurements and theoretical computations are also included to reveal the kinetics and energy-storage mechanisms of electrodes upon charge/discharge. Finally, we describe potential new developments in NASICON-cathodes with optimized SIB systems, foreseeing a bright future for them, achievable through the rational application of advanced diagnostic methods.

Flavonoids are one of the most important bioactive components in litchi (Litchi chinensis Sonn.) seeds and have broad-spectrum antiviral and antitumor activities. Litchi seeds have been shown to inhibit the proliferation of cancer cells and induce apoptosis, particularly effective against breast and liver cancers. Elucidating the distribution of flavonoids is important for understanding their physiological and biochemical functions and facilitating their efficient extraction and utilization. However, the spatial distribution patterns and expression states of flavonoids in litchi seeds remain unclear. Herein, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used for in situ detection and imaging of the distribution of flavonoids in litchi seed tissue sections for the first time. Fifteen flavonoid ion signals, including liquiritigenin, apigenin, naringenin, luteolin, dihydrokaempferol, daidzein, quercetin, taxifolin, kaempferol, isorhamnetin, myricetin, catechin, quercetin 3-β-d-glucoside, baicalin, and rutin, were successfully detected and imaged in situ through MALDI-MSI in the positive ion mode using 2-mercaptobenzothiazole as a matrix. The results clearly showed the heterogeneous distribution of flavonoids, indicating the potential of litchi seeds for flavonoid compound extraction. MALDI-MS-based multi-imaging enhanced the visualization of spatial distribution and expression states of flavonoids. Thus, apart from improving our understanding of the spatial distribution of flavonoids in litchi seeds, our findings also facilitate the development of MALDI-MSI-based metabolomics as a novel effective molecular imaging tool for evaluating the spatial distribution of endogenous compounds.

Septic myopathy, also known as ICU acquired weakness (ICU-AW), is a characteristic clinical symptom of patients with sepsis, mainly manifested as skeletal muscle weakness and muscular atrophy, which affects the respiratory and motor systems of patients, reduces the quality of life, and even threatens the survival of patients. Melatonin is one of the hormones secreted by the pineal gland. Previous studies have found that melatonin has anti-inflammatory, free radical scavenging, antioxidant stress, autophagic lysosome regulation, mitochondrial protection, and other multiple biological functions and plays a protective role in sepsis-related multiple organ dysfunction. Given the results of previous studies, we believe that melatonin may play an excellent regulatory role in the repair and regeneration of skeletal muscle atrophy in septic myopathy. Melatonin, as an over-the-counter drug, has the potential to be an early, complementary treatment for clinical trials. Based on previous research results, this article aims to critically discuss and review the effects of melatonin on sepsis and skeletal muscle depletion.

PurposeTo establish a predictive model to predict the occurrence of language deficit for patients after surgery of glioma involving language areas (GILAs) under general anesthesia (GA).MethodsPatients with GILAs were retrospectively collected in our center between January 2009 and December 2020. Clinical variables (age, sex, aphasia quotient [AQ], seizures and KPS), tumor-related variables (recurrent tumor or not, volume, language cortices invaded or not, shortest distance to language areas [SDLA], supplementary motor area or premotor area [SMA/PMA] involved or not and WHO grade) and intraoperative multimodal techniques (used or not) were analyzed by univariate and multivariate analysis to identify their association with temporary or permanent language deficits (TLD/PLD). The predictive model was established according to the identified significant variables. Receiver operating characteristic (ROC) curve was used to assess the accuracy of the predictive model.ResultsAmong 530 patients with GILAs, 498 patients and 441 patients were eligible to assess TLD and PLD respectively. The multimodal group had the higher EOR and rate of GTR than conventional group. The incidence of PLD was 13.4% in multimodal group, which was much lower than that (27.6%, P

Abstract Background Post-resistance progress in paclitaxel (PTX) treatment remains a major challenge in tumor treatment. A high dose of PTX was used for cell lines to analyze the changes in molecular expression. The miR-378d was sharply reduced in surviving cells, but the role of miR-378d in Esophageal squamous cell carcinoma (ESCC) remained unclear. Methods We analyzed the relationship between miR-378d expression and the clinicopathological features of ESCC. We constructed miR-378d silent expression cell lines to study phenotypes and molecular mechanisms. Results The miR-378d expression was associated with good prognosis in patients with ESCC. miR-378d inhibition promoted chemo-resistance, monoclonal formation, EMT, migration, invasion, stemness, and metastasis of ESCC cells. miR-378d can target downregulated AKT1. Conclusions Therefore, miR-378d expression is a good prognostic factor of patients with ESCC and regulates the malignant phenotype of tumor cells through AKT.

Skeletal muscle atrophy is a common complication in survivors of sepsis, which affects the respiratory and motor functions of patients, thus severely impacting their quality of life and long-term survival. Although several advances have been made in investigations on the pathogenetic mechanism of sepsis-induced skeletal muscle atrophy, the underlying mechanisms remain unclear. Findings from recent studies suggest that the nucleotide-binding and oligomerisation domain (NOD)-like receptor family pyrin domain containing 3 (NLRP3) inflammasome, a regulator of inflammation, may be crucial in the development of skeletal muscle atrophy. NLRP3 inhibitors contribute to the inhibition of catabolic processes, skeletal muscle atrophy and cachexia-induced inflammation. Here, we review the mechanisms by which NLRP3 mediates these responses and analyse how NLRP3 affects muscle wasting during inflammation.

Background: Glioma is the most common intracranial malignancy. Immune-infiltration and tumour stemness are associated with the prognosis of glioma. Although pleckstrin homology containing family A, number 4 (PLEKHA4) is widely expressed in various human cancers, its role in glioma remains unclear. Methods: We examined the features and clinical significance of PLEKHA4 in gliomas by analysing relevant data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. Gene set enrichment analysis (GSEA) was performed to determine the possible functions and pathways involving PLEKHA4 in glioma. The relationship between PLEKHA4 expression and the degree of oncogenic dedifferentiation was analysed using stemness scores (ss) calculated from epigenetic and transcriptomic features. We also explored the relationship between PLEKHA4 expression and immune cell infiltration in gliomas using the CIBERSORT databases. Furthermore, drug sensitivity analysis was performed using datasets from the GDSC and GTRP databases. In addition, we performed relevant in vitro experimental studies. Results: PLEKHA4 DNA hypomethylation status was associated with its high expression in glioma tissues as well as poor prognoses. Univariate and multivariate Cox analyses indicated that PLEKHA4 expression may be considered as an independent prognostic factor in patients with glioma. GSEA indicated that high PLEKHA4 expression was associated with Janus kinase (JAK)/signal transducer and activator of transcription (STAT), Wingless-Type MMTV Integration Site Family (Wnt), JUN N-terminal kinase (JNK) signalling pathways and involved in apoptotic, cytoskeletal, and cell adhesion biological processes (BPs). In addition, increased PLEKHA4 expression was associated with higher glioma stemness scores than lower PLEKHA4 expression levels. Furthermore, the expression of PLEKHA4 was shown to be associated with glioma infiltration by CD4+ T cells, B cells, neutrophils, macrophages, and dendritic cells. Drug sensitivity analysis also showed that PLEKHA4 expression was negatively correlated with the sensitivity of several small molecule kinase inhibitors. Furthermore, in vitro experiments confirmed that PLEKHA4 knockdown inhibited the proliferation of glioma cells. Conclusions: PLEKHA4 is highly expressed in glioma tissues and correlated with tumour stemness, immune cell infiltration and proliferation, suggesting its potential as a novel prognostic biomarker and therapeutic target in glioma.

Learning novel classes with a few samples per class is a very challenging task in deep learning. To mitigate this issue, previous studies have utilized an additional dataset with extensively labeled samples to realize transfer learning. Alternatively, many studies have used unlabeled samples that originated from the novel dataset to achieve few-shot learning, i.e., semi-supervised few-shot learning. In this paper, an easy but efficient semi-supervised few-shot learning model is proposed to address the embeddings mismatch problem that results from inconsistent data distributions between the novel and base datasets, where samples with the same label approach each other while samples with different labels separate from each other in the feature space. This model emphasizes pseudo-labeling guided contrastive learning. We also develop a novel local factor clustering module to improve the ability to obtain pseudo-labels from unlabeled samples, and this module fuses the local feature information of labeled and unlabeled samples. We report our experimental results on the mini-ImageNet and tiered-ImageNet datasets for both five-way one-shot and five-way five-shot settings and achieve better performance than previous models. In particular, the classification accuracy of our model is improved by approximately 11.53% and 14.87% compared to the most advanced semi-supervised few-shot learning model we know in the five-way one-shot scenario. Moreover, ablation experiments in this paper show that our proposed clustering strategy demonstrates accuracy improvements of about 4.00% in the five-way one-shot and five-way five-shot scenarios compared to two popular clustering methods.

Diet restriction (DR) ameliorates obesity by regulating mitochondrial function. Cardiolipin (CL), a mitochondrial phospholipid, is closely associated with mitochondrial function. This study aimed to evaluate the anti-obesity effects of graded levels of DR based on mitochondrial CL levels in the liver. Obese mice were treated with 0%, 20%, 40%, and 60% reductions in the normal diet compared to normal animals (0 DR, 20 DR, 40 DR, and 60 DR groups, respectively). Biochemical and histopathological analyses were performed to evaluate the ameliorative effects of DR on obese mice. The altered profile of mitochondrial CL in the liver was explored using a targeted metabolomics strategy by ultra-high-pressure liquid chromatography MS/MS coupled with quadrupole time-of-flight mass spectrometry. Finally, gene expression associated with CL biosynthesis and remodeling was quantified. Tissue histopathology and biochemical index evaluations revealed significant improvements in the liver after DR, except for the 60 DR group. The variation in mitochondrial CL distribution and DR levels showed an inverted U-shape, and the CL content in the 40 DR group was the most upregulated. This result is consistent with the results of the target metabolomic analysis, which showed that 40 DR presented more variation. Furthermore, DR led to increased gene expression associated with CL biosynthesis and remodeling. This study provides new insights into the mitochondrial mechanisms underlying DR intervention in obesity.

To investigate the influences of volcanic activities on shale reservoirs, a case study was carried out on the Permian Mount Emei basalt and coal-bearing shale of Longtan Formation in the Southern Guizhou Depression. Based on the analytical results of total organic carbon content, XRD, vitrinite reflectance and cryogenic nitrogen and carbon dioxide adsorptions, the effects of volcanic activities on the hydrocarbon generation, mineral composition and reservoir microstructure of coal-bearing shale were discussed. The results showed that volcanic activities had obvious effects on the hydrocarbon generation, mineral composition and pore structure of coal-bearing shale. They significantly promoted the evolution and maturation of organic matters in shale, which quickly entered the over-mature stage (Ro% increased from 2.0% to 2.88%), and accelerated the hydrocarbon generation and expulsion efficiency. With the decrease of the distance to the basalt, the contents of clastic minerals and carbonate minerals increased, the content of clay minerals decreased, and the contents of illite and chlorite changed regularly, which indicated that the evolution of shale diagenesis had also been affected to a certain extent. The heat brought by volcanic activities had an important influence on the pores of reservoirs. As it got closer to basalt, the proportion of micropores increased while the content of mesopores and macropores decreased. It was speculated that the thermal effects of volcanic activities promoted the formation of organic pores and also affected the development of inorganic pores.

BackgroundUnexplained recurrent spontaneous abortion is a serious reproductive problem of unknown etiology. Thyroid peroxidase antibodies (TPO-Ab) may be associated with pregnancy outcomes in unexplained recurrent spontaneous abortion with normal thyroid function.ObjectiveThis study aimed to investigate the relationship between TPO-Ab and the first trimester miscarriage rate/live birth rate in women of unexplained recurrent spontaneous abortion with normal thyroid function.MethodsWe retrospectively analyzed the clinical data of 297 women who met our strict inclusion criteria, comparing the first trimester miscarriage rate/live birth rate between the TPO-Ab positive and TPO-Ab negative groups. For the same purpose, we also performed subgroup analysis.ResultsOf the included women, 76 (25.6%) were TPO-Ab positive, and 221 (74.4%) were negative. First trimester miscarriage rate differed between the two groups (36.8% vs 24.0%, RR = 1.54, 95% CI: 1.05–2.24, P = 0.030). In the younger subgroup (

Aiming at the problem of poor precision performance of traditional Bayesian model saliency detection algorithm, a Bayesian model saliency detection algorithm based on improved convex hull was proposed. Firstly, the foreground of the image was extracted by the manifold ranking algorithm, which was used as the prior probability in Bayesian model. Secondly, Harris corner detection algorithm based on color enhancement was used to detect the feature points of the image in three color spaces of RGB, HSV and CIELab; the convex hulls in RGB, HSV and CIELab spaces were constructed respectively; and the intersection of convex hulls were obtained. Thirdly, the saliency region map was calculated by Bayesian model according to the prior probability and the observed likelihood probability obtained by combining convex hulls and color histograms. Finally, the proposed algorithm was tested in two public data sets MSRA and ECSSD. The experimental results show that the proposed algorithm can suppress the background noise effectively and detect the salient areas completely. The test results of F-measure value in MSRA and ECSSD databases are 0.87 and 0.71 respectively, and the accuracy-recall rate curve is higher than that of traditional classical algorithms in complex image databases. The proposed algorithm improves the detection effect of the traditional classical algorithm and the accuracy of saliency map detection.

PurposeThe majority of solitary brain metastases appear similar to glioblastomas (GBMs) on magnetic resonance imaging (MRI). This study aimed to develop and validate an MRI-based model to differentiate intracranial metastases from GBMs using automated machine learning.Materials and MethodsRadiomics features from 354 patients with brain metastases and 354 with GBMs were used to build prediction algorithms based on T2-weighted images, contrast-enhanced (CE) T1-weighted images, or both. The data of these subjects were subjected to a nested 10-fold split in the training and testing groups to build the best algorithms using the tree-based pipeline optimization tool (TPOT). The algorithms were independently validated using data from 124 institutional patients with solitary brain metastases and 103 patients with GBMs from the cancer genome atlas.ResultsThree groups of models were developed. The average areas under the receiver operating characteristic curve (AUCs) were 0.856 for CE T1-weighted images, 0.976 for T2-weighted images, and 0.988 for a combination in the testing groups, and the AUCs of the groups of models in the independent validation were 0.687, 0.831, and 0.867, respectively. A total of 149 radiomics features were considered as the most valuable features for the differential diagnosis of GBMs and metastases.ConclusionThe models established by TPOT can distinguish glioblastoma from solitary brain metastases well, and its non-invasiveness, convenience, and robustness make it potentially useful for clinical applications.

Multivariate mixtures are encountered in situations where the data are repeated or clustered measurements in the presence of heterogeneity among the observations with unknown proportions. In such situations, the main interest may be not only in estimating the component parameters, but also in obtaining reliable estimates of the mixing proportions. In this paper, we propose an empirical likelihood approach combined with a novel dimension reduction procedure for estimating parameters of a two-component multivariate mixture model. The performance of the new method is compared to fully parametric as well as almost nonparametric methods used in the literature.

ObjectiveIschemic infarction of pituitary apoplexy (PA) is a rare type of pituitary apoplexy. This study aims to characterize ischemic PA via clinical presentations, imaging data, histopathological manifestations, and focus on the management and prognosis of the disease.MethodsThis study retrospectively identified 46 patients with ischemic PA confirmed using histopathology at a single institution from January 2013 to December 2020. The clinical presentations, imaging data, laboratory examination, management, and outcomes were collected. We then summarized the clinical presentations, imaging features, intraoperative findings, and histopathological manifestations, and compared the outcomes based on the timing of surgical intervention.ResultsHeadache was the most common initial symptom (95.65%, 44/46), followed by visual disturbance (89.13%, 41/46), and nausea and vomiting (58.70%, 27/46). 91.3% of the patients had at least one pituitary dysfunction, with hypogonadism being the most common endocrine dysfunction (84.78%, 39/46). Cortisol dysfunction occurred in 24 (52.17%) patients and thyroid dysfunction occurred in 17 (36.96%). Typical rim enhancement and thickening of the sphenoid sinus on MRI were seen in 35 (85.37%) and 26 (56.52%) patients, respectively. Except for one patient with asymptomatic apoplexy, the remaining patients underwent early (≤ 1 week, 12 patients) and delayed (> 1 week, 33 patients) transsphenoidal surgery. Total tumor resection was achieved in 27 patients and subtotal tumor resection in 19 patients. At surgery, cottage cheese–like necrosis was observed in 50% (23/46) of the patients. At the last follow-up of 5.5 ± 2.7 years, 92.68% (38/41) of the patients had gained a significant improvement in visual disturbance regardless of surgical timing, and 65% of the patients were still receiving long-term hormone replacement therapy.ConclusionPatients with ischemic PA can be accurately diagnosed by typical imaging characteristics preoperatively. The timing of surgical intervention does not significantly affect the resolution of neurological and endocrinological dysfunctions. Preoperative endocrine dysfunctions are common and usually appear to be poor after surgical intervention.

Recently end-to-end (E2E) automatic speech recognition (ASR) models have achieved promising performance. However, existing models tend to adopt increasing model sizes and suffer from expensive resource consumption for real-world applications. To compress E2E ASR models and obtain smaller model sizes, we propose a comprehensive compression platform named LWMD (light-weight model designing), which consists of two essential parts: a light-weight architecture search (LWAS) framework and a differentiable structured pruning (DSP) algorithm. On the one hand, the LWAS framework adopts the neural architecture search (NAS) technique to automatically search light-weight architectures for E2E ASR models. By integrating different architecture topologies of existing models together, LWAS designs a topology-fused search space. Furthermore, combined with the E2E ASR training criterion, LWAS develops a resource-aware search algorithm to select light-weight architectures from the search space. On the other hand, given the searched architectures, the DSP algorithm performs structured pruning to reduce parameter numbers further. With a Gumbel re-parameter trick, DSP builds a stronger correlation between the pruning criterion and the model performance than conventional pruning methods. And an attention-similarity loss function is further developed for better performance. On two mandarin datasets, Aishell-1 and HKUST, the compression results are well evaluated and analyzed to demonstrate the effectiveness of the LWMD platform.

With the great breakthrough of supervised learning in the field of denoising, more and more works focus on end-to-end learning to train denoisers. In practice, however, it can be very challenging to obtain labels in support of this approach. The premise of this method is effective is that there is certain data support, but in practice, it is particularly difficult to obtain labels in the training data. Several unsupervised denoisers have emerged in recent years; however, to ensure their effectiveness, the noise model must be determined in advance, which limits the practical use of unsupervised denoising.n addition, obtaining inaccurate noise prior to noise estimation algorithms leads to low denoising accuracy. Therefore, we design a more practical denoiser that requires neither clean images as training labels nor noise model assumptions. Our method also needs the support of the noise model; the difference is that the model is generated by a residual image and a random mask during the network training process, and the input and target of the network are generated from a single noisy image and the noise model. At the same time, an unsupervised module and a pseudo supervised module are trained. The extensive experiments demonstrate the effectiveness of our framework and even surpass the accuracy of supervised denoising.

PurposeThe present study aimed to preoperatively predict the status of 1p/19q based on radiomics analysis in patients with World Health Organization (WHO) grade II gliomas.MethodsThis retrospective study enrolled 157 patients with WHO grade II gliomas (76 patients with astrocytomas with mutant IDH, 16 patients with astrocytomas with wild-type IDH, and 65 patients with oligodendrogliomas with mutant IDH and 1p/19q codeletion). Radiomic features were extracted from magnetic resonance images, including T1-weighted, T2-weighted, and contrast T1-weighted images. Elastic net and support vector machines with radial basis function kernel were applied in nested 10-fold cross-validation loops to predict the 1p/19q status. Receiver operating characteristic analysis and precision-recall analysis were used to evaluate the model performance. Student’s t-tests were then used to compare the posterior probabilities of 1p/19q co-deletion prediction in the group with different 1p/19q status.ResultsSix valuable radiomic features, along with age, were selected with the nested 10-fold cross-validation loops. Five features showed significant difference in patients with different 1p/19q status. The area under curve and accuracy of the predictive model were 0.8079 (95% confidence interval, 0.733–0.8755) and 0.758 (0.6879–0.8217), respectively, and the F1-score of the precision-recall curve achieved 0.6667 (0.5201–0.7705). The posterior probabilities in the 1p/19q co-deletion group were significantly different from the non-deletion group.ConclusionCombined radiomics analysis and machine learning showed potential clinical utility in the preoperative prediction of 1p/19q status, which can aid in making customized neurosurgery plans and glioma management strategies before postoperative pathology.

Abstract Background The relationships among energy metabolic levels, behavioral and other physiological traits help to determine the trade-off of energy allocation between different traits and the evolution of life-history driven by natural selection. However, these relationships may be distinctive in selected animal taxa because of their unique traits. In the present study, the relationships among energy metabolic levels, behavioral defense strategies, and antioxidant capacity were explored in three freshwater turtle species with different shell morphologies, by assessing responses to attack, righting time, shell morphology, whole-organism metabolic rates, tissue metabolic enzyme activities and antioxidant levels. Results The Chinese three-keeled pond turtles, Chinemys reevesii, showed a passive defense strategy, relatively larger shells, a higher resting metabolic rate (RMR) and higher antioxidant levels compared to the snapping turtle, Chelydra serpentina, or the Chinese soft-shelled turtle, Pelodiscus sinensis. These latter two species both showed an active defense strategy, a higher factorial aerobic scope and better muscle anaerobic metabolic capacity but relatively smaller shells, lower RMR and antioxidant capacity. Conclusion Our results indicate a negative relationship between RMR and activity levels in behavioral defense strategies along small-big shell continuum among the three turtle species. We also found a positive relationship between antioxidant capacity and energy metabolism but a negative one between antioxidant capacity and activity levels in defense strategies. The present study indicated a role of turtle shell in forming unique relationship between energy metabolic levels and behaviors in freshwater turtle taxa and a possible trade-off between the maintenance of physiological homeostasis and activity levels in energy allocation.

This paper addresses the angle tracking and vibration suppression for a flexible manipulator with uncertain parameters. Based on the partial differential equation (PDE) model, a unified framework of weighted multiple neural network boundary control (WMNNBC) is proposed to deal with the jumping parameters, in which neural networks are designed as the local boundary controllers to suppress vibrations. A novel proportion-derivative-like machine learning algorithm is developed to guarantee the learning convergence. Besides, the weighting algorithm is used to fuse multiple local neural network controllers to generate the appropriate global control signals with the variations of plant parameters. The stability of the overall closed-loop system is proved by the virtual equivalent system (VES) theory. The simulations are implemented to illustrate the feasibility and control performance of the proposed WMNNBC strategy.

This paper studies the tracking control problem for nonlinear system with largely jumping parameters. To deal with the large uncertainties of system parameters, an RBF networks-based weighted multi-model adaptive control (WMMAC) strategy is designed, in which the model set can be constructed to cover and approximate the variation range of the plant parameters. Correspondingly, the RBF network controller set generates the appropriate global control signals when parameters jumping, and better transient performance is obtained. Different from existing neural network learning methods, two novel learning rules based on the difference value between the current value of an objective function and its optimal value for the RBF networks are developed to achieve a fast convergence rate. The stability of the overall closed-loop system is validated by the virtual equivalent system (VES) theory, and favorable performance of the proposed control strategy is demonstrated by the numerical simulations.

In order to the make the magnetic field synchronous with the actual current in the current interruption experiment, an external excitation of axial magnetic field for intermediate-frequency vacuum arc experiment and research is designed and tested in this paper. The excitation system is consisted of intermediate-frequency current transformers (CTs) and Helmholtz coil. CTs are used to induct current in main circuit and the Helmholtz coil can be excited by the secondary current. Thus the axial magnetic field (AMF), which is synchronous with the primary current, is generated inside the Helmholtz coil. And different from traditional magnetic field excitation system, the magnetic field generated by commercial AMF contact can be simulated. The test is implemented to test the excitation system. The phase difference between the primary side and secondary side of the CT is less than 10'. The steady state error of B is within 1% and the dynamic error of B/I is within 1.5% as well. The results turn out to be consistent with the theoretical value, which proves the effectiveness of this external excitation system.

BRD7 functions as a crucial tumor suppressor in numerous malignancies. However, the effects of BRD7 on colorectal cancer (CRC) progression are still unknown. Here, based on the BRD7 knockout (BRD7–/–) and BRD7flox/flox (BRD7+/+) mouse models constructed in our previous work, we established an azoxymethane/dextran sodium sulfate (AOM/DSS)-induced mouse model. BRD7+/+ mice were found to be highly susceptible to AOM/DSS-induced colitis-associated CRC, and BRD7 significantly promoted cell proliferation and cell cycle G1/S transition but showed no significant effect on cell apoptosis. Furthermore, BRD7 interacted with c-Myc and stabilized c-Myc by inhibiting its ubiquitin–proteasome-dependent degradation. Moreover, restoring the expression of c-Myc in BRD7-silenced CRC cells restored cell proliferation, cell cycle progression, and tumor growth in vitro and in vivo. In addition, BRD7 and c-Myc were both significantly upregulated in CRC patients, and high expression of these proteins was associated with clinical stage and poor prognosis in CRC patients. Collectively, BRD7 functions as an oncogene and promotes CRC progression by regulating the ubiquitin–proteasome-dependent stabilization of c-Myc protein. Targeting the BRD7/c-Myc axis could be a potential therapeutic strategy for CRC.

Background: Epidemiological studies have revealed that sulfur dioxides (SO2) can increase the risk of pregnancy complications such as missed abortion in the first trimester, stillbirth, preterm birth, small for gestational age, gestational diabetes mellitus and preeclampsia, but the mechanisms underlying these findings remains unknown. What is known, however, is that trophoblasts, a type of fetal cell exerting vital immunologic functions to maintain a successful pregnancy, are usually involved in the pathogenic mechanism of pregnancy complications. Objective: To study the effect of SO2 derivatives (bisulfite and sulfite, 1:3 M/M) on the function of trophoblasts. Methods: Swan.71 trophoblast cells were treated with various concentrations of SO2 derivatives to determine the effect of SO2 derivatives on cellular viability by CKK8. Flow cytometry was performed to analyze the effect of SO2 derivatives on apoptosis, cell cycle and intracellular ROS. Wound healing assay and transwell assay were conducted to examine the migration and invasion of Swan.71 cells. Inflammation-related cytokines in the supernatant (IL-1β, IL-6, IL-8, IL-10 and TNF-α) were measured by IMMULITE®1000 Systems (SIEMENS). The expression level of NLRP3, Caspase1, MMP9, MMP2, STAT3, and p-STAT3 were evaluated by Western Blotting. Results: Exposure to SO2 derivatives significantly decreased cellular viability, arrested cell cycle at S/G2/M phase and induced cell apoptosis of Swan.71 trophoblasts. In addition, the migration and invasion of Swan.71 cell were significantly inhibited. SO2 derivatives also significantly increased IL-1β secretion while it is NLRP3/Caspase1 independent. IL-6 secretion was significant inhibited accompanied by decreased STAT3 phosphorylation and expression of MMP2 and MMP9. The intracellular ROS level was significantly suppressed by SO2 derivatives. Conclusion: SO2 derivatives exert toxic effects on trophoblasts which results in: suppressing cellular viability and intracellular ROS level, interfering with cell proliferation through arresting cell cycle, inducing cell apoptosis, disturbing inflammation-related cytokines secretion and inhibiting motility. Decreased ROS/IL-6/STAT3 levels play a role in inhibited cell viability, cell cycle arrest, apoptosis and defective motility.

The detection of mutations in telomerase reverse transcriptase promoter (pTERT) is important since preoperative diagnosis of pTERT status helps with evaluating prognosis and determining the surgical strategy. Here, we aimed to establish a radiomics-based machine-learning algorithm and evaluated its performance with regard to the prediction of mutations in pTERT in patients with World Health Organization (WHO) grade II gliomas. In total, 164 patients with WHO grade II gliomas were enrolled in this retrospective study. We extracted a total of 1,293 radiomics features from multi-parametric magnetic resonance imaging scans. Elastic net (used for feature selection) and support vector machine with linear kernel were applied in nested 10-fold cross-validation loops. The predictive model was evaluated by receiver operating characteristic and precision-recall analyses. We performed an unpaired t-test to compare the posterior predictive probabilities among patients with differing pTERT statuses. We selected 12 valuable radiomics features using nested 10-fold cross-validation loops. The area under the curve (AUC) was 0.8446 (95% confidence interval [CI], 0.7735–0.9065) with an optimal summed value of sensitivity of 0.9355 (95% CI, 0.8802–0.9788) and specificity of 0.6197 (95% CI, 0.5071–0.7371). The overall accuracy was 0.7988 (95% CI, 0.7378–0.8598). The F1-score was 0.8406 (95% CI, 0.7684–0.902) with an optimal precision of 0.7632 (95% CI, 0.6818–0.8364) and recall of 0.9355 (95% CI, 0.8802–0.9788). Posterior probabilities of pTERT mutations were significantly different between patients with wild-type and mutant TERT promoters. Our findings suggest that a radiomics analysis with a machine-learning algorithm can be useful for predicting pTERT status in patients with WHO grade II glioma and may aid in glioma management.

Patients who survive the acute phase of sepsis can progress to persistent inflammation, immunosuppression, and catabolism syndrome (PICS), which usually results in extended recovery periods and multiple complications. Alpinetin is a flavonoid isolated from Alpinia katsumadai Hayata that has been demonstrated to have anti-inflammatory, antibacterial, and antioxidant activities. The aim of this study was to investigate whether the administration of alpinetin could attenuate PICS in a septic mouse model. Mice were randomly divided into four groups: the (1) sham-operated group, (2) sham+alpinetin (1 mg/kg intravenously infused for once per day after sham operation), (3) cecal ligation and puncture (CLP), and (4) CLP+alpinetin (50 mg/kg intravenously infused for once per day after CLP). Eight days after sham operation or CLP surgery, mice were euthanized for subsequent examination. Alpinetin significantly improved the survival of septic mice. Also, it attenuated the CLP-induced persistent inflammation, immunosuppression, and catabolism syndrome. The level of plasma proinflammatory cytokines and apoptosis of T lymphocytes were obviously decreased by alpinetin as well. Moreover, oxidative stress in the organs was compelling lower in the alpinetin-treated CLP mice. In this clinically relevant model of sepsis, alpinetin ameliorates CLP-induced organ dysfunction and improves the likelihood of survival, possibly through suppressing the inflammatory response, oxidative stress, and apoptosis. These findings suggested that alpinetin could be a potential novel therapeutic approach to prevent sepsis-induced PICS.

Unmanned aerial vehicle is a typical field robot which can work in many unstructured environments like mines, forests, and even radiation areas. In our mine monitoring system built in a northeast province of China, special designed unmanned aerial vehicle is applied to take photos and perceive the environment. We select a series of image-based techniques to process aerial pictures to monitor the slope. The visual features are initially refined by histogram equalization. Then, the rocks and cracks can be detected by different digital image processing operators, like Canny, so as to assess displacements. Advanced semantic segmentation model, U-Net, is also selected to process the problem. Experimental results show that both Canny and U-Net can perceive the edges in pictures effectively, better than other operators. In addition, we model the inspection mission for mine slopes into a traveling salesman problem, then plan the path for unmanned aerial vehicle by swarm intelligence-based optimization.

Background/Aims: Antimicrobial peptides are effective promoters of wound healing but are susceptible to degradation. In this study, we replaced the GIGDP unit on the N-terminal of the endogenous human antimicrobial peptide hBD-2 with APKAM to produce A-hBD-2 and analyzed the effect on wound healing both in vitro and in vivo. Methods: The effects of A-hBD-2 and hBD-2 on cytotoxicity and proliferation in keratinocytes were assessed by Cell Counting Kit-8 assay. The structural stability and antimicrobial activity of hBD-2 and A-hBD-2 were evaluated against Staphylococcus aureus. RNA and proteins levels were evaluated by real-time PCR and western blotting, respectively. Cell migration was evaluated using a transwell assay. Cell cycle analysis was performed by flow cytometry. Wound healing was assessed in Sprague-Dawley rats. Epidermal thickness was evaluated by hematoxylin and eosin staining. Results: We found that hBD-2 exhibited cytotoxicity at high concentrations and decreased the structural stability in the presence of high sodium chloride concentrations. A-hBD-2 exhibited increased structural stability and antimicrobial activity, and had lower cytotoxicity in keratinocytes. A-hBD-2 increased the migration and proliferation of keratinocytes via phosphorylation of EGFR and STAT3 and suppressed terminal differentiation of keratinocytes. We also found that A-hBD-2 elicited mobilization of intracellular Ca2+ and stimulated keratinocytes to produce pro- and anti-inflammatory cytokines and chemokines via phospholipase C activation. Furthermore, A-hBD-2 promoted wound healing in vivo. Conclusion: Our data suggest that A-hBD-2 may be a promising candidate therapy for wound healing.