Regulation of NCOA4-mediated iron recycling ameliorates paraquat-induced lung injury by inhibiting ferroptosis.

Paraquat (PQ) is an irreplaceable insecticide in many countries for the advantage of fast-acting and broad-spectrum. However, PQ was classified as the most prevailing poisoning substance for suicide with no specific antidote. Therefore, it is imperative to develop more effective therapeutic agents for the treatment of PQ poisoning. In the present study, both the RNA-Seq and the application of various cell death inhibitors reflected that ferroptosis exerts a crucial regulatory role in PQ poisoning. Moreover, we found PQ strengthens lipid peroxidation as evidenced by different experimental approaches. Of note, pretreatment of iron chelation agent DFO could ameliorate the ferroptotic cell death and alleviate the ferroptosis-related events. Mechanistically, PQ treatment intensively impaired mitochondrial homeostasis, enhanced phosphorylation of AMPK, accelerated the autophagy flux and triggered the activation of Nuclear receptor coactivator 4-ferritin heavy chain (NCOA4-FTH) axis. Importantly, the activation of autophagy was observed prior to the degradation of ferritin, and inhibition of autophagy could inhibit the accumulation of iron caused by the ferritinophagy process. Genetic and pharmacological inhibition of ferritinophagy could alleviate the lethal oxidative events, and rescue the ferroptotic cell death. Excitingly, in the mouse models of PQ poisoning, both the administration of DFO and adeno-associated virus-mediated FTH overexpression significantly reduced PQ-induced ferroptosis and improved the pathological characteristics of pulmonary fibrosis. In summary, the current work provides an in-depth study on the mechanism of PQ intoxication, describes a framework for the further understanding of ferroptosis in PQ-associated biological processes, and demonstrates modulation of iron metabolism may act as a promising therapeutic agent for the management of PQ toxicity.

Patient-derived iPSCs link elevated mitochondrial respiratory complex I function to osteosarcoma in Rothmund-Thomson syndrome.

Rothmund-Thomson syndrome (RTS) is an autosomal recessive genetic disorder characterized by poikiloderma, small stature, skeletal anomalies, sparse brows/lashes, cataracts, and predisposition to cancer. Type 2 RTS patients with biallelic RECQL4 pathogenic variants have multiple skeletal anomalies and a significantly increased incidence of osteosarcoma. Here, we generated RTS patient-derived induced pluripotent stem cells (iPSCs) to dissect the pathological signaling leading to RTS patient-associated osteosarcoma. RTS iPSC-derived osteoblasts showed defective osteogenic differentiation and gain of in vitro tumorigenic ability. Transcriptome analysis of RTS osteoblasts validated decreased bone morphogenesis while revealing aberrantly upregulated mitochondrial respiratory complex I gene expression. RTS osteoblast metabolic assays demonstrated elevated mitochondrial respiratory complex I function, increased oxidative phosphorylation (OXPHOS), and increased ATP production. Inhibition of mitochondrial respiratory complex I activity by IACS-010759 selectively suppressed cellular respiration and cell proliferation of RTS osteoblasts. Furthermore, systems analysis of IACS-010759-induced changes in RTS osteoblasts revealed that chemical inhibition of mitochondrial respiratory complex I impaired cell proliferation, induced senescence, and decreased MAPK signaling and cell cycle associated genes, but increased H19 and ribosomal protein genes. In summary, our study suggests that mitochondrial respiratory complex I is a potential therapeutic target for RTS-associated osteosarcoma and provides future insights for clinical treatment strategies.

A Novel Fault Diagnosis Method of High-Speed Train Based on Few-Shot Learning.

Ensuring the safe and stable operation of high-speed trains necessitates real-time monitoring and diagnostics of their suspension systems. While machine learning technology is widely employed for industrial equipment fault diagnosis, its effective application relies on the availability of a large dataset with annotated fault data for model training. However, in practice, the availability of informational data samples is often insufficient, with most of them being unlabeled. The challenge arises when traditional machine learning methods encounter a scarcity of training data, leading to overfitting due to limited information. To address this issue, this paper proposes a novel few-shot learning method for high-speed train fault diagnosis, incorporating sensor-perturbation injection and meta-confidence learning to improve detection accuracy. Experimental results demonstrate the superior performance of the proposed method, which introduces perturbations, compared to existing methods. The impact of perturbation effects and class numbers on fault detection is analyzed, confirming the effectiveness of our learning strategy.

Identification of Risk Factors and Phenotypes of Surgical Site Infection in Patients After Abdominal Surgery.

OBJECTIVES: We aimed to determine the current incidence rate and risk factors for surgical site infection (SSI) after abdominal surgery in China and to further demonstrate the clinical features of patients with SSI. BACKGROUND: Contemporary epidemiology and clinical features of SSI after abdominal surgery remain poorly characterized. METHODS: A prospective multicenter cohort study was conducted from March 2021 to February 2022; the study included patients who underwent abdominal surgery at 42 hospitals in China. Multivariable logistic regression analysis was performed to identify risk factors for SSI. Latent class analysis (LCA) was used to explore the population characteristics of SSI. RESULTS: In total, 23,982 patients were included in the study, of whom 1.8% developed SSI. There was a higher SSI incidence in open surgery (5.0%) than in laparoscopic or robotic surgeries (0.9%). Multivariable logistic regression indicated that the independent risk factors for SSI after abdominal surgery were older age, chronic liver disease, mechanical bowel preparation, oral antibiotic bowel preparation, colon or pancreas surgery, contaminated or dirty wounds, open surgery, and colostomy/ileostomy. LCA revealed 4 subphenotypes in patients undergoing abdominal surgery. Types α and ß were mild subclasses with a lower SSI incidence; whereas types γ and δ were the critical subgroups with a higher SSI incidence, but their clinical features were different. CONCLUSIONS: LCA identified 4 subphenotypes in patients who underwent abdominal surgery. Types γ and δ were critical subgroups with a higher SSI incidence. This phenotype classification can be used to predict SSI after abdominal surgery.

CS-iCCA, A New Clinically Based Staging System for Intrahepatic Cholangiocarcinoma: Establishment and External Validation.

INTRODUCTION: Intrahepatic cholangiocarcinoma (iCCA) is a primary liver malignancy with poor prognosis. Current prognostic methods are most accurate for patients with surgically resectable disease. However, a significant proportion of patients with iCCA are not surgical candidates. We aimed to develop a generalizable staging system based on clinical variables to determine prognosis of all patients with iCCA. METHODS: The derivation cohort included 436 patients with iCCA seen between 2000 and 2011. For external validation, 249 patients with iCCA seen from 2000 to 2014 were enrolled. Survival analysis was performed to identify prognostic predictors. All-cause mortality was the primary end point. RESULTS: Eastern Cooperative Oncology Group status, tumor number, tumor size, metastasis, albumin, and carbohydrate antigen 19-9 were incorporated into a 4-stage algorithm. Kaplan-Meier estimates for 1-year survival were 87.1% (95% confidence interval [CI] 76.1-99.7), 72.7% (95% CI 63.4-83.4), 48.0% (95% CI 41.2-56.0), and 16% (95% CI 11-23.5), respectively, for stages I, II, III, and IV. Univariate analysis yielded significant differences in risk of death for stages II (hazard ratio [HR] 1.71; 95% CI 1.0-2.8), III (HR 3.32; 95% CI 2.07-5.31), and IV (HR 7.44; 95% CI 4.61-12.01) compared with stage I (reference). Concordance indices showed the new staging system was superior to the TNM staging for predicting mortality in the derivation cohort, P < 0.0001. In the validation cohort, however, the difference between the 2 staging systems was not significant. DISCUSSION: The proposed independently validated staging system uses nonhistopathologic data to successfully stratify patients into 4 stages. This staging system has better prognostic accuracy compared with the TNM staging and can assist physicians and patients in treatment of iCCA.

Biallelic variants in CRIPT cause a Rothmund-Thomson-like syndrome with increased cellular senescence.

PURPOSE: Rothmund-Thomson syndrome (RTS) is characterized by poikiloderma, sparse hair, small stature, skeletal defects, cancer, and cataracts, resembling features of premature aging. RECQL4 and ANAPC1 are the 2 known disease genes associated with RTS in >70% of cases. We describe RTS-like features in 5 individuals with biallelic variants in CRIPT (OMIM 615789). METHODS: Two newly identified and 4 published individuals with CRIPT variants were systematically compared with those with RTS using clinical data, computational analysis of photographs, histologic analysis of skin, and cellular studies on fibroblasts. RESULTS: All CRIPT individuals fulfilled the diagnostic criteria for RTS and additionally had neurodevelopmental delay and seizures. Using computational gestalt analysis, CRIPT individuals showed greatest facial similarity with individuals with RTS. Skin biopsies revealed a high expression of senescence markers (p53/p16/p21) and the senescence-associated ß-galactosidase activity was elevated in CRIPT-deficient fibroblasts. RECQL4- and CRIPT-deficient fibroblasts showed an unremarkable mitotic progression and unremarkable number of mitotic errors and no or only mild sensitivity to genotoxic stress by ionizing radiation, mitomycin C, hydroxyurea, etoposide, and potassium bromate. CONCLUSION: CRIPT causes an RTS-like syndrome associated with neurodevelopmental delay and epilepsy. At the cellular level, RECQL4- and CRIPT-deficient cells display increased senescence, suggesting shared molecular mechanisms leading to the clinical phenotypes.

Comprehensive analysis identifies CLEC1B as a potential prognostic biomarker in hepatocellular carcinoma.

BACKGROUND: C-type lectin domain family 1 member B (CLEC1B, encoding the CLEC-2 protein), a member of the C-type lectin superfamily, is a type II transmembrane receptor involved in platelet activation, angiogenesis, and immune and inflammatory responses. However, data regarding its function and clinical prognostic value in hepatocellular carcinoma (HCC) remain scarce. METHODS: The expression of CLEC1B was explored using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. RT-qPCR, western blot, and immunohistochemistry assays were employed to validate the downregulation of CLEC1B. Univariate Cox regression and survival analyses were used to evaluate the prognostic value of CLEC1B. Gene Set Enrichment Analysis (GSEA) was conducted to investigate the potential association between cancer hallmarks and CLEC1B expression. The TISIDB database was applied to search for the correlation between immune cell infiltration levels and CLEC1B expression. The association between CLEC1B and immunomodulators was conducted by Spearman correlation analysis based on the Sangerbox platform. Annexin V-FITC/PI apoptosis kit was used for the detection of cell apoptosis. RESULTS: The expression of CLEC1B was low in various tumors and exhibited a promising clinical prognostic value for HCC patients. The expression level of CLEC1B was tightly associated with the infiltration of various immune cells in the HCC tumor microenvironment (TME) and positively correlated with a bulk of immunomodulators. In addition, CLEC1B and its related genes or interacting proteins are implicated in multiple immune-related processes and signaling pathways. Moreover, overexpression of CLEC1B significantly influenced the treatment effects of sorafenib on HCC cells. CONCLUSIONS: Our results reveal that CLEC1B could serve as a potential prognostic biomarker and may be a novel immunoregulator for HCC. However, its function in immune regulation should be further explored.

DNA barcodes and morphology reveal new species within the Rheotanytarsus guineensis species group from China (Diptera: Chironomidae).

The Rheotanytarsus guineensis species group (Diptera: Chironomidae) is a species diverse and taxonomically difficult group. Using DNA barcodes, we found five new species within the R. guineensis species group and reviewed the species group based on adult males from China. Rheotanytarsus guoae Lin & Yao sp. n., Rheotanytarsus miaoae Lin & Yao sp. n., Rheotanytarsus qiangi Lin & Yao sp. n., Rheotanytarsus yueqingensis Lin & Yao sp. n., and Rheotanytarsus yui Lin & Yao sp. n. are all described and figured. A key to known adult males of the R. guineensis species group worldwide is provided for the first time.

Study on association of serum uric acid levels with bipolar disorder: systematic review and meta-analysis in Chinese patients.

BACKGROUND: The purine system represented by uric acid may be involved in the pathogenesis of bipolar disorder, This study intends to explore the association of serum uric acid levels with bipolar disorder in Chinese patients through meta-analysis. METHODS: Electronic databases, including PubMed, Embase, Web of Science, and China National Knowledge Infrastructure (CNKI), searching from inception to December 2022. Randomized Controlled Trials that reported serum uric acid levels and bipolar disorder were included. Two investigators independently extracted data and RevMan5.4 and Stata14.2 were used for statistical analyses. RESULTS: Twenty-eight studies with 4482 bipolar disorder, 1568 depression, 785 schizophrenia, and 2876 healthy control subjects were included in this meta-analysis. The results of the meta-analysis showed that serum uric acid levels in the bipolar disorder group were significantly higher than those in depression [SMD 0.53 (0.37, 0.70), p < 0.00001], schizophrenia [SMD 0.27 (0.05, 0.49), p = 0.02] and healthy control group [SMD 0.87 (0.67, 1.06), p < 0.00001]. Subgroup-analysis showed that in Chinese people with bipolar disorder, uric acid levels of the manic episode were higher than the depressed episode [SMD 0.31 (0.22, 0.41), p < 0.00001]. CONCLUSION: Our results indicated a strong association between serum uric acid levels and bipolar disorder in Chinese patients, but further studies about whether uric acid levels can be a biomarker for bipolar disorder still need to investigate.

Between-Class Adversarial Training for Improving Adversarial Robustness of Image Classification.

Deep neural networks (DNNs) have been known to be vulnerable to adversarial attacks. Adversarial training (AT) is, so far, the only method that can guarantee the robustness of DNNs to adversarial attacks. However, the robustness generalization accuracy gain of AT is still far lower than the standard generalization accuracy of an undefended model, and there is known to be a trade-off between the standard generalization accuracy and the robustness generalization accuracy of an adversarially trained model. In order to improve the robustness generalization and the standard generalization performance trade-off of AT, we propose a novel defense algorithm called Between-Class Adversarial Training (BCAT) that combines Between-Class learning (BC-learning) with standard AT. Specifically, BCAT mixes two adversarial examples from different classes and uses the mixed between-class adversarial examples to train a model instead of original adversarial examples during AT. We further propose BCAT+ which adopts a more powerful mixing method. BCAT and BCAT+ impose effective regularization on the feature distribution of adversarial examples to enlarge between-class distance, thus improving the robustness generalization and the standard generalization performance of AT. The proposed algorithms do not introduce any hyperparameters into standard AT; therefore, the process of hyperparameters searching can be avoided. We evaluate the proposed algorithms under both white-box attacks and black-box attacks using a spectrum of perturbation values on CIFAR-10, CIFAR-100, and SVHN datasets. The research findings indicate that our algorithms achieve better global robustness generalization performance than the state-of-the-art adversarial defense methods.

Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning.

The safe and comfortable operation of high-speed trains has attracted extensive attention. With the operation of the train, the performance of high-speed train bogie components inevitably degrades and eventually leads to failures. At present, it is a common method to achieve performance degradation estimation of bogie components by processing high-speed train vibration signals and analyzing the information contained in the signals. In the face of complex signals, the usage of information theory, such as information entropy, to achieve performance degradation estimations is not satisfactory, and recent studies have more often used deep learning methods instead of traditional methods, such as information theory or signal processing, to obtain higher estimation accuracy. However, current research is more focused on the estimation for a certain component of the bogie and does not consider the bogie as a whole system to accomplish the performance degradation estimation task for several key components at the same time. In this paper, based on soft parameter sharing multi-task deep learning, a multi-task and multi-scale convolutional neural network is proposed to realize performance degradation state estimations of key components of a high-speed train bogie. Firstly, the structure takes into account the multi-scale characteristics of high-speed train vibration signals and uses a multi-scale convolution structure to better extract the key features of the signal. Secondly, considering that the vibration signal of high-speed trains contains the information of all components, the soft parameter sharing method is adopted to realize feature sharing in the depth structure and improve the utilization of information. The effectiveness and superiority of the structure proposed by the experiment is a feasible scheme for improving the performance degradation estimation of a high-speed train bogie.

Remote Sensing Image of The Landsat 8-9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function.

Utilizing low-rank prior data in compressed sensing (CS) schemes for Landsat 8-9 remote sensing images (RSIs) has recently received widespread attention. Nevertheless, most CS algorithms focus on the sparsity of an RSI and ignore its low-rank (LR) nature. Therefore, this paper proposes a new CS reconstruction algorithm for Landsat 8-9 remote sensing images based on a non-local optimization framework (NLOF) that is combined with non-convex Laplace functions (NCLF) used for the low-rank approximation (LAA). Since the developed algorithm is based on an approximate low-rank model of the Laplace function, it can adaptively assign different weights to different singular values. Moreover, exploiting the structural sparsity (SS) and low-rank (LR) between the image patches enables the restored image to obtain better CS reconstruction results of Landsat 8-9 RSI than the existing models. For the proposed scheme, first, a CS reconstruction model is proposed using the non-local low-rank regularization (NLLRR) and variational framework. Then, the image patch grouping and Laplace function are used as regularization/penalty terms to constrain the CS reconstruction model. Finally, to effectively solve the rank minimization problem, the alternating direction multiplier method (ADMM) is used to solve the model. Extensive numerical experimental results demonstrate that the non-local variational framework (NLVF) combined with the low-rank approximate regularization (LRAR) method of non-convex Laplace function (NCLF) can obtain better reconstruction results than the more advanced image CS reconstruction algorithms. At the same time, the model preserves the details of Landsat 8-9 RSIs and the boundaries of the transition areas.

Mutations in ANAPC1, Encoding a Scaffold Subunit of the Anaphase-Promoting Complex, Cause Rothmund-Thomson Syndrome Type 1.

Rothmund-Thomson syndrome (RTS) is an autosomal-recessive disorder characterized by poikiloderma, sparse hair, short stature, and skeletal anomalies. Type 2 RTS, which is defined by the presence of bi-allelic mutations in RECQL4, is characterized by increased cancer susceptibility and skeletal anomalies, whereas the genetic basis of RTS type 1, which is associated with juvenile cataracts, is unknown. We studied ten individuals, from seven families, who had RTS type 1 and identified a deep intronic splicing mutation of the ANAPC1 gene, a component of the anaphase-promoting complex/cyclosome (APC/C), in all affected individuals, either in the homozygous state or in trans with another mutation. Fibroblast studies showed that the intronic mutation causes the activation of a 95 bp pseudoexon, leading to mRNAs with premature termination codons and nonsense-mediated decay, decreased ANAPC1 protein levels, and prolongation of interphase. Interestingly, mice that were heterozygous for a knockout mutation have an increased incidence of cataracts. Our results demonstrate that deficiency in the APC/C is a cause of RTS type 1 and suggest a possible link between the APC/C and RECQL4 helicase because both proteins are involved in DNA repair and replication.

A Systematic Review and Meta-Analysis of Metabolic Syndrome Prevalence in Chinese Inpatients with Bipolar Disorder.

The aim of the work was to study the prevalence of metabolic syndrome in Chinese patients with bipolar disorder. We searched Chinese literature related to the study in prevalence of metabolic syndrome in bipolar disorder in Chinese language, among which results such as comments, letters, reviews and case reports were excluded. The prevalence of metabolic syndrome in bipolar disorder was researched and discussed. A total of 1562 subjects were included in 11 studies. The prevalence of MetS in bipolar disorder was 33% (95% CI=0.29-0.37), which was higher significantly than normal control (10.82%), but similar to schizophrenia (31.59%). The 41.41% prevalence of MetS in male patients was higher significantly than that in females (26.83%).The prevalence of MetS in BD treated by AAP was 47.54%, by MS was 19.19%, by MS+AAP was 40%.The prevalence of MetS in BD treated by carbamazepine was 28.21%, by lithium was 30%, by valproate was 21.71%, by clozepine was 51.43%, by olanzapine was 39.84%, by quetiapine was 39.44%, and by risperidone was 35%. The prevalence of MetS in bipolar disorder was 33% (95% CI=0.29-0.37), which was higher significantly than normal control (10.82%), but similar to schizophrenia (31.59%). AAP and MS were the main one risks of MetS in BD.

A preoperative prediction model based on Lymphocyte-C-reactive protein ratio predicts postoperative anastomotic leakage in patients with colorectal carcinoma: a retrospective study.

BACKGROUND & AIMS: Lymphocyte-C-reactive Protein Ratio (LCR) has been demonstrated as a promising new marker for predicting surgical and oncological outcomes in colorectal carcinoma (CRC). However, anastomotic leakage (AL) is also likely related to this inflammatory marker. Herein, we aimed to identify preoperative predictors of AL and build and develop a novel model able to identify patients at risk of developing AL. METHODS: We collected 858 patients with CRC undergoing elective radical operation between 2007 and 2018 at a single center were retrospectively reviewed. We performed univariable and multivariable analyses and built a multivariable model that predicts AL based on preoperative factors. Propensity adjustment was used to correct the bias introduced by non-random matching of the LCR. The model's performance was evaluated by using the area under the receiver operator characteristic curves (AUROCs), decision curve analysis (DCA), Brier scores, D statistics, and R2 values. RESULTS: Age, nutrition risk screening 2002 (NRS2002) score, tumor location and LCR, together with hemoglobin < 90 g/l, were independent predictors of AL. The models built on these variables showed good performance (internal validation: c-statistic = 0.851 (95%CI 0.803-0.965), Brier score = 0.049; temporal validation: c-statistic = 0.777 (95%CI 0.823-0.979), Brier score = 0.096). A regression equation to predict the AL was also established by multiple linear regression analysis: [Age(≥ 60 year) × 1.281] + [NRS2002(≥ 3) × 1.341] + [Tumor location(pt.) × 1.348]-[LCR(≤ 6000) × 1.593]-[Hemoglobin(< 90 g/L) × 1.589]-6.12. CONCLUSION: Preoperative LCR is an independent predictive factor for AL. A novel model combining LCR values, age, tumor location, and NRS2002 provided an excellent preoperative prediction of AL in patients with CRC. The nomogram can help clinical decision-making and support future research.

Few-shot contrastive learning for image classification and its application to insulator identification.

This paper presents a novel discriminative Few-shot learning architecture based on batch compact loss. Currently, Convolutional Neural Network (CNN) has achieved reasonably good performance in image recognition. Most existing CNN methods facilitate classifiers to learn discriminating patterns to identify existing categories trained with large samples. However, learning to recognize novel categories from a few examples is a challenging task. To address this, we propose the Residual Compact Network to train a deep neural network to learn hierarchical nonlinear transformations to project image pairs into the same latent feature space, under which the distance of each positive pair is reduced. To better use the commonality of class-level features for category recognition, we develop a batch compact loss to form robust feature representations relevant to a category. The proposed methods are evaluated on several datasets. Experimental evaluations show that our proposed method achieves acceptable results in Few-shot learning.

Mid- and Long-Term Efficacy of Surgical Treatment of L1-2 Vertebral Tuberculosis with Subdiaphragmatic Extraperitoneal Approach.

BACKGROUND The L1-2 vertebral segment is the most common site of spinal tuberculosis. Traditional thoracoabdominal surgery in this segment risks trauma and complications. This study analyzed the surgical efficacy of the subdiaphragmatic extraperitoneal approach in the treatment of L1-2 spinal tuberculosis. MATERIAL AND METHODS Retrospective analysis of 67 patients with L1-2 vertebral tuberculosis who underwent posterior internal fixation was performed: 35 patients underwent the subdiaphragmatic extraperitoneal approach (group A) and 32 underwent the thoracoabdominal approach (group B). Operation time, intraoperative blood loss, postoperative hospital stay, postoperative nerve function recovery, deformity correction, bone graft fusion, lesion healing, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and complications were observed. RESULTS In group A and group B, intraoperative blood loss was 712.00±64.66 mL and 1104.38±131.34 mL; average operation time was 3.16±0.67 h and 5.16±1.07 h; and postoperative hospital stay was 9.60±2.64 days and 13.69±3.87 days, respectively. At 6 months and 5 years after surgery, neurological function, visual analog scale score, and Cobb angle of all patients were significantly improved compared with those before surgery; ESR and CRP decreased to normal levels; lesions completely cured; and all patients had good bone graft fusion. Pulmonary complications occurred in 2 patients in group A and in 14 patients in group B. CONCLUSIONS The efficacy of subdiaphragmatic extraperitoneal approach was similar to that of the thoracoabdominal approach for L1-2 spinal tuberculosis, but the former has the advantages of less surgical trauma, shorter operation time, less intraoperative bleeding, and fewer postoperative pulmonary complications.

Adversarial Gaussian Denoiser for Multiple-Level Image Denoising.

Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods.

[Silencing LncRNA SNHG7 alleviates hypoxia/reoxygenation-induced cardiomyocyte damage by regulating the expression of miR-181b-5p].

OBJECTIVE: To study the effect of silencing LncRNA SNHG7 on hypoxia/reoxygenation (H/R)-induced cardiomyocyte injury and its targeted regulation on miR-181b-5p. METHODS: Rat cardiomyocytes H9c2 were cultured in vitro and randomly divided into control group, H/R group, H/R + si-NC group, H/R + si-SNHG7 group, H/R + si-SNHG7 + anti-miR-NC group and H/R + si-SNHG7 + anti-miR-181b-5p group. The content of lactate dehydrogenase (LDH), malondialedhyde (MDA) and the activity of superoxide dismutase (SOD) were detected. Flow cytometry was carried out to detect the rate of apoptosis. qRT-PCR was used to detect the expression of SNHG7 and miR-181b-5p. Dual luciferase report experiment was used to verify the targeting relationship between SNHG7 and miR-181b-5p. Western blotting was used to detect the expression of Bax and Bcl-2. RESULTS: Compared with the control group, the H/R group showed significantly increased SNHG7 expression in cardiomyocytes, reduced miR-181b-5p expression, higher levels of LDH and MDA, reduced activity of SOD, increased cell apoptosis rate, higher level of Bax protein, and reduced level of Bcl-2 protein (all P< 0.05). Compared with the H/R and H/R + si-NC groups, the H/R + si-SNHG7 group had significantly reduced level of LDH and MDA, increased activity of SOD, reduced apoptosis rate, reduced level of Bax protein, increased level of Bcl-2 protein (all P< 0.05). The dual luciferase report experiment confirmed that SNHG7 could target miR-181b-5p. Interference with the expression of miR-181b-5p could reduce the effect of silencing SNHG7 on H/R-induced cardiomyocyte oxidative stress and apoptosis. CONCLUSION: Silencing SNHG7 may inhibit H/R-induced cardiomyocyte oxidative stress and apoptosis by up-regulating the expression of miR-181b-5p, thereby exerting a protective effect on cardiomyocytes.

RECQ DNA Helicases and Osteosarcoma.

The RECQ family of DNA helicases is a conserved group of enzymes that plays an important role in maintaining genomic stability. Humans possess five RECQ helicase genes, and mutations in three of them - BLM, WRN, and RECQL4 - are associated with the genetic disorders Bloom syndrome, Werner syndrome, and Rothmund-Thomson syndrome (RTS), respectively. These syndromes share overlapping clinical features, and importantly they are all associated with an increased risk of cancer. Patients with RTS have the highest specific risk of developing osteosarcoma compared to all other cancer predisposition syndromes; therefore, RTS serves as a relevant model to study the pathogenesis and molecular genetics of osteosarcoma. The "tumor suppressor" function of the RECQ helicases continues to be an area of active investigation. This chapter will focus primarily on the known cellular functions of RECQL4 and how these may relate to tumorigenesis, as well as ongoing efforts to understand RECQL4's functions in vivo using animal models. Understanding the RECQ pathways will provide insight into avenues for novel cancer therapies in the future.