FAM122A Is Required for Mesendodermal and Cardiac Differentiation of Embryonic Stem Cells.

Mesendodermal specification and cardiac differentiation are key issues for developmental biology and heart regeneration medicine. Previously, we demonstrated that FAM122A, a highly conserved housekeeping gene, is an endogenous inhibitor of protein phosphatase 2A (PP2A) and participates in multifaceted physiological and pathological processes. However, the in vivo function of FAM122A is largely unknown. In this study, we observed that Fam122 deletion resulted in embryonic lethality with severe defects of cardiovascular developments and significantly attenuated cardiac functions in conditional cardiac-specific knockout mice. More importantly, Fam122a deficiency impaired mesendodermal specification and cardiac differentiation from mouse embryonic stem cells but showed no influence on pluripotent identity. Mechanical investigation revealed that the impaired differentiation potential was caused by the dysregulation of histone modification and Wnt and Hippo signaling pathways through modulation of PP2A activity. These findings suggest that FAM122A is a novel and critical regulator in mesendodermal specification and cardiac differentiation. This research not only significantly extends our understanding of the regulatory network of mesendodermal/cardiac differentiation but also proposes the potential significance of FAM122A in cardiac regeneration.

REG3A promotes proliferation and DDP resistance of ovarian cancer cells by activating the PI3K/Akt signaling pathway.

This study explored the effect of Regenerating Islet-Derived 3-Alpha (REG3A) on ovarian cancer (OC) progression. REG3A expression was scrutinized in clinical tissues of 97 OC cases by quantitative real-time polymerase chain reaction (qRT-PCR). REG3A expression in OC cells and cisplatin (DDP) resistance OC cells was regulated by transfection. LY294002 (10 µM, inhibitor of the PI3K/Akt signaling pathway) was used to treat OC cells and DDP resistance OC cells. Cell counting kit-8 and methyl-thiazolyl-tetrazolium assays were applied for proliferation and DDP resistance detection. Flow cytometry was utilized for cell cycle and apoptosis analysis. The effect of REG3A on the OC cell in vivo growth was researched by establishing xenograft tumor model via using nude mice using nude mice. The expression of genes in clinical samples, cells and xenograft tumor tissues was investigated by qRT-PCR, Western blot and immunohistochemistry. As a result, REG3A was over-expressed in OC patients and cells, associating with dismal prognosis of patients. REG3A knockdown repressed proliferation, DDP resistance, induced cell cycle arrest and apoptosis of OC cells, and reduced the expression MDR-1, Cyclin D1, Cleaved caspase 3 proteins and the PI3K/Akt signaling pathway activity in OC cells. LY294002 treatment abrogated the promotion effect of REG3A on OC cell proliferation, apoptosis inhibition and DDP resistance. REG3A knockdown suppressed the in vivo growth of OC cells. Thus, REG3A promoted proliferation and DDP resistance of OC cells by activating the PI3K/Akt signaling pathway. REG3A might be a promising target for the clinical treatment of OC.

Imaging process matched neural network for complex wavefront retrieval with a higher space-bandwidth product.

Recently, deep learning (DL) has shown great potential in complex wavefront retrieval (CWR). However, the application of DL in CWR does not match well with the physical diffraction process. The state-of-the-art DL-based CWR methods crop full-size diffraction patterns down to a smaller size to save computational resources. However, cropping reduces the space-bandwidth product (SBP). In order to solve the trade-off between computational resources and SBP, we propose an imaging process matched neural network (IPMnet). IPMnet accepts full-size diffraction patterns with a larger SBP as inputs and retrieves a higher resolution and a larger field of view of the complex wavefront. We verify the effectiveness of the proposed IPMnet through simulations and experiments.

Extraction of the Microstructure of Wool Fabrics Based on Structure Tensor.

The trends of "fashionalization", "personalization" and "customization" of wool fabrics have prompted the textile industry to change the original processing design based on the experience of engineers and trial production. In order to adapt to the promotion of intelligent production, the microstructure of wool fabrics is introduced into the finishing process. This article presents an automated method to extract the microstructure from the micro-CT data of woven wool fabrics. Firstly, image processing was performed on the 3D micro-CT images of the fabric. The raw grayscale data were converted into eigenvectors of the structure tensor to segment the individual yarns. These data were then used to calculate the three parameters of diameter, spacing and the path of the center points of the yarn for the microstructure. The experimental results showed that the proposed method was quite accurate and robust on woven single-ply tweed fabrics.

Fiber selectivity of peripheral neuropathy in patients with Parkinson's disease.

OBJECTIVE: To determine the function of each type of peripheral nerve fiber and investigate the possible role of levodopa (LD) in peripheral neuropathy (PN) in Parkinson's disease (PD) patients. METHODS: We enrolled 60 patients with idiopathic PD. All PD patients were divided into three groups: levodopa exposure >3 years (LELD), levodopa exposure ≤3 years (SELD) and de novo patients with PD (NOLD). The current perception threshold (CPT), which was measured by Neurometer at 2000, 250 and 5 Hz, the level of homocysteine, Vitamin B12 and folic acid in plasma, were compared with those of sex- and age-matched healthy controls (HCs). RESULTS: Current perception threshold was higher at 250 Hz (p < .05) and 5 Hz (p < .05) in the LELD group than the NOLD, SELD, and control group. CPT was lower at 5 Hz in the NOLD than in the HCs group (p < .05). The CPT of the more affected side of PD patients was positively correlated with H-Y stage at 5 Hz current stimulation (r = .42, p = .01). Multivariate logistic regression analysis showed that elevated homocysteine levels were the risk factor of sensory nerve injury in PD patients (p < .01). Serum homocysteine levels were positively correlated with levodopa (LD) daily dose, LD equivalent daily dose, and LD cumulative lifetime dose (p < .05). CONCLUSIONS: Peripheral neuropathy in PD patients can occur in the early stage of PD exhibiting as hyperesthesia and is fiber selectivity, especially for Aδ and C nerve fibers. PN in PD patients is related to PD itself and long-term LD exposure. Elevated plasma homocysteine is a risk factor for PN in PD patients.

Sex and onset-age-related features of excessive daytime sleepiness and night-time sleep in patients with Parkinson's disease.

BACKGROUND: The clinical characteristics of Parkinson's disease (PD) differ between men and women, and late- and early-onset patients, including motor symptoms and some nonmotor symptoms, such as cognition, anxiety, and depression. OBJECTIVE: To explore the features of excessive daytime sleepiness (EDS) and night-time sleep quality in PD patients of different sexes and age at onset (AAO). METHODS: Demographic data and clinical characteristics of 586 PD patients were collected. Epworth Sleepiness Scale (ESS) and Pittsburgh Sleep Quality Index (PSQI) were used to investigate the daytime drowsiness and nocturnal sleep. Multivariate logistic regression analysis was used to explore the risk factors of EDS and poor night-time sleep quality. RESULTS: Sleep disorders were common in PD patients. EDS was more prominent in men than in women. There was no significant difference in ESS scores between late-onset PD (LOPD) and early-onset PD. LOPD patients had a higher probability of poor night-time sleep quality. Male sex, disease duration, and depression were risk factors for EDS. In all patients of both sexes and all AAO, depression was a risk factor for poor night-time sleep. CONCLUSION: More attention should be paid to sleep disorders of PD patients, especially male LOPD patients. Depression is a common risk factor for EDS and poor sleep quality in PD patients.

Multimodal Image Analysis of Sexual Dimorphism in Developing Childhood Brain.

It is well known that there exist great differences in human brain anatomy and functions between males and females. With the development of noninvasive neuroimaging techniques, the sex differences in adult human brain have been well studied in some researches. However, the sexual dimorphism of human brain anatomy and functions has not been sufficiently described during the developmental period of early childhood brain when the sex differences emerge in behavior. This study was to identify specific patterns of the sexual dimorphism in developing brain structure of early childhood using multimodal brain image analysis. We have performed a multivariate and data-driven analysis by combining multiple neuroimaging technologies including the 3D T1-weighted structural MR images (sMRI) and diffusion tensor imaging (DTI) in a prospective cohort of 188 children (128 males and 60 females) between the ages of 0 and 15. First, the brain images were segmented into 90 regions of interest (ROIs) based on the AAL template to extract the ROI volume and connectivity features. Then, the individual multimodal imaging biomarkers were identified associated with the sex differences. Finally, the selected features from multi-modality neuroimages were combined using multi-kernel support vector machine for classifications of male or female. The method achieved sex classification accuracy 72% for the children between the ages of 0 and 15. And the volumes of right precuneus and right postcentral were more related to sexual dimorphism of developing brain structure than those of other regions in sMRI (p < 0.002). In DTI, the connections between right middle occipital gyrus and right inferior occipital gyrus, between left superior marginal gyrus and left middle temporal gyrus, and between right triangle inferior frontal gyrus and right putamen are also more relevant to the sexual dimorphism than others (p < 0.005). The brain regions and connections related to the sexual differences were identified with sMRI and DTI in early developing brain structure by using the multimodal image analysis. And these sexually dimorphic patterns of brain may be related to the observed sex differences in developing childhood brain structure and connectivity.

FAM122A maintains DNA stability possibly through the regulation of topoisomerase IIα expression.

FAM122A is a housekeeping gene and highly conserved in mammals. More recently, we have demonstrated that FAM122A is essential for maintaining the growth of hepatocellular carcinoma cells, in which we unexpectedly found that FAM122A deletion increases γH2AX protein level, suggesting that FAM122A may participate in the regulation of DNA homeostasis or stability. In this study, we continued to investigate the potential role of FAM122A in DNA damage and/or repair. We found that CRISPR/Cas9-mediated FAM122A deletion enhances endogenous DNA damages in cancer cells but not in normal cells, demonstrating a significant increase in γH2AX protein and foci formation of γH2AX and 53BP1, as well as DNA breaks by comet assay. Further, we found that FAM122A deletion greatly increases TOP2α protein level, and significantly and specifically enhances TOP2 poisons (etoposide and doxorubicin)-induced DNA damage effects in cancer cells. Moreover, FAM122A is found to be interacted with TOP2α, instead of TOP2ß. However, FAM122A knockout doesn't affect the intracellular ROS levels and the process of DNA repair after removal of etoposide with short-term stimulation, suggesting that FAM122A deletion-enhanced DNA damage does not result from endogenous overproduction of ROS and/or impairment of DNA repair ability. Collectively, our study provides the first demonstration that FAM122A is critical for maintaining DNA stability probably by modulating TOP2α protein, and FAM122A deletion combined with TOP2-targeted drugs may represent a potential novel chemotherapeutic strategy for cancer patients.

FAM122A supports the growth of hepatocellular carcinoma cells and its deletion enhances Doxorubicin-induced cytotoxicity.

FAM122A is a highly conserved protein in mammals, however its function is still largely unknown so far. In this study, we investigated the potential role of FAM122A in hepatocellular carcinoma (HCC). By analyzing HCC patient cohorts from RNA sequencing datasets, we found the expression level of FAM122A mRNA is significantly upregulated in HCC patients. Moreover, this abnormally higher expression pattern of FAM122A protein was also found in partial HCC tumor tissues, compared with the normal parts. Further, we demonstrated that CRISPR/Cas9-mediated FAM122A knockout significantly inhibits the growth, clonogenic potential and xenografts of HCC cells, induces cell cycle arrest and reduces the expression of proliferation-related genes. Interestingly, FAM122A deletion significantly enhances the cytotoxicity effect of Doxorubicin (Dox), a drug used in standard chemotherapy in HCC patients. In contrary, overexpression of FAM122A not only promotes HCC cell growth, but also inhibits Dox-induced DNA damage and cell death. Considering that FAM122A is previously identified as an endogenous inhibitor of PP2A, we asked whether FAM122A regulating HCC cell growth is associated with PP2A. The results showed FAM122A can also modulate PP2A activity in HCC cells although the modulated effect is relatively slight, however, treatment with a PP2A inhibitor okadaic acid did not rescue the inhibitory effects of cell growth and proliferation in FAM122A deletion cells, indicating that FAM122A may support HCC cell growth independent of its ability to modulate PP2A. Collectively, these results suggest that FAM122A is required for maintaining HCC cell growth, and its elimination combined with chemotherapy may represent a potential novel therapeutic strategy for HCC patients.

A multi-task fully deep convolutional neural network for contactless fingerprint minutiae extraction.

With the outbreak and wide spread of novel coronavirus (COVID-19), contactless fingerprint recognition has attracted more attention for personal recognition because it can provide significantly higher user convenience and hygiene than the traditional contact-based fingerprint recognition. However, it is still challenging to achieve a highly accurate recognition due to the low ridge-valley contrast and pose variances of contactless fingerprints. Minutiae points are a kind of ridge flow discontinuities, and robust and accurate extraction is an important step for most automatic fingerprint recognition algorithms. Most of existing methods are based on two stages which locate the minutiae points first and then compute their directions. The two-stage method cannot make full use of location and direction information. In this paper, we propose a multi-task fully deep convolutional neural network for jointly learning the minutiae location detection and its corresponding direction computation which operates directly on the whole gray scale contactless fingerprints. The proposed method consists of offline training and online testing stages. In the training stage, a fully deep convolutional neural network is built for the tasks of minutiae detection and its direction regression, with an attention mechanism to make the direction regression branch concentrate on the minutiae points. A new loss function is proposed to jointly learn the tasks of minutiae detection and its direction regression from the whole fingerprints. In the testing stage, the trained network is applied on the whole contactless fingerprint to generate the minutiae location and direction maps. The proposed multi-task leaning method performs better than the individual single task and it operates directly on the raw gray-scale contactless fingerprints without preprocessing. The results on three contactless fingerprint datasets show the proposed algorithm performs better than other minutiae extraction algorithms and the commercial software.

A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. Mild cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can delay its progression, no effective cures are available for AD. Accurate early-stage diagnosis of AD is vital for the prevention and intervention of the disease progression. Hippocampus is one of the first affected brain regions in AD. To help AD diagnosis, the shape and volume of the hippocampus are often measured using structural magnetic resonance imaging (MRI). However, these features encode limited information and may suffer from segmentation errors. Additionally, the extraction of these features is independent of the classification model, which could result in sub-optimal performance. In this study, we propose a multi-model deep learning framework based on convolutional neural network (CNN) for joint automatic hippocampal segmentation and AD classification using structural MRI data. Firstly, a multi-task deep CNN model is constructed for jointly learning hippocampal segmentation and disease classification. Then, we construct a 3D Densely Connected Convolutional Networks (3D DenseNet) to learn features of the 3D patches extracted based on the hippocampal segmentation results for the classification task. Finally, the learned features from the multi-task CNN and DenseNet models are combined to classify disease status. Our method is evaluated on the baseline T1-weighted structural MRI data collected from 97 AD, 233 MCI, 119 Normal Control (NC) subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The proposed method achieves a dice similarity coefficient of 87.0% for hippocampal segmentation. In addition, the proposed method achieves an accuracy of 88.9% and an AUC (area under the ROC curve) of 92.5% for classifying AD vs. NC subjects, and an accuracy of 76.2% and an AUC of 77.5% for classifying MCI vs. NC subjects. Our empirical study also demonstrates that the proposed multi-model method outperforms the single-model methods and several other competing methods.

Assembled Step Emulsification Device for Multiplex Droplet Digital Polymerase Chain Reaction.

Digital PCR is a powerful method for absolute nucleic acid quantification with unprecedented accuracy and precision. To promote the wider use and application of digital PCR, several major challenges still exist, including reduction of cost, integration of the instrumental platform, and simplification of operations. This paper describes a reusable microfluidic device that generates nanoliter droplet arrays based on step emulsification for the on-chip multiplex digital PCR of eight samples simultaneously. The device contains two glass plates that can be quickly assembled with prefilled mineral oil. Droplets are simply generated through the arrays of step emulsification nozzles driven by a single pressure controller and are self-assembled into monolayer droplet arrays in U-shaped chambers. The use of mineral oil eliminates bubble generation; thus, no overpressure is required during thermocycling. Moreover, the device can be reused many times after disassembly and a brief cleaning procedure, which significantly reduces the cost of the device per dPCR assays. The device was able to detect template DNA at concentrations as low as 10 copies/µL with a dynamic range of approximately 4 logs. We applied this device in the quantitative assessment of HER2 copy number variation, which is important for targeted therapy and prognosis of breast cancer. The performance was validated by 16 clinical samples, obtaining similar results to commercial digital PCR. We envision that this low-cost, reusable, and user-friendly device can be broadly used in various applications.

Plasma Long Non-Coding RNA (lncRNA) GAS5 is a New Biomarker for Coronary Artery Disease.

Our study aimed to investigate the diagnostic value of long non-coding RNA (lncRNA) GAS5 for coronary artery disease (CAD) and to explore the mechanism of the role of GAS5 in CAD. A total of 30 patients with CAD were selected from January 2015 to January 2017 in The First Hospital of Tianmen. In addition, 30 healthy individuals were selected as a control group, and patients with various other types of cardiovascular diseases were also selected. Expression of GAS5 in plasma of all participants was detected by quantitative real-time PCR. Receiver operating characteristic (ROC) curve analysis was performed to investigate the diagnostic value of GAS5 for CAD. Levels of mammalian target of rapamycin (mTOR) and phospho-mTOR (p-mTOR) in human primary coronary artery endothelial cells (HCAECs) were detected by western blotting. Compared with normal healthy people, expression level of lncRNA Novlnc6 was significantly reduced in patients with CAD and diabetes mellitus, but not in patients with other types of cardiovascular diseases, such as hypertension, abnormal aortic aneurysm, viral myocarditis. In addition, the expression level of GAS5 was significantly lower in patients with CAD compared to patients with diabetes mellitus. ROC curve analysis showed that GAS5 may serve as a promising biomarker for CAD. GAS5 knockdown and overexpression showed no significant effect on the level of mTOR) in HCAECs. However, GAS5 knockdown significantly increased the level of phospho-mTOR (p-mTOR), and GAS5 overexpression significantly decreased the level of p-mTOR. Treatment with mTOR inhibitor and activator showed no significant effect on expression of GAS5 in HCAECs. GAS5 plays a role as upstream regulator of the mTOR pathway to participate in the development of CAD. GAS5 was specifically downregulated in patients with CAD, and it may serve as a promising biomarker for CAD.

A Modified Nerve-Sparing Panhysterectomy for Benign Uterine Diseases: Techniques and Evaluation of Postoperative Pelvic Dysfunctions.

OBJECTIVE: To describe a modified nerve-sparing panhysterectomy and to investigate the feasibility and impact of this nerve-sparing technique in improving postoperative pelvic visceral dysfunctions of benign uterine disease patients. METHODS: From January 2008 to January 2010, a total of 300 patients diagnosed with benign uterine diseases at the Second Affiliated Hospital of Nantong University were enrolled. Of those, 150 randomly selected patients underwent modified panhysterectomy (research group), while the other 150 patients underwent conventional panhysterectomy (control group). The surgery-related parameters, including operation time, intraoperative blood loss, length of hospital stay, postoperative indwelling catheter time, and first voiding and defecation time were compared between the two groups. The extent of nerve damage in both groups was examined using the nerve-specific marker S-100 via immunohistochemistry. Besides, postoperative assessments of bladder and bowel functions were conducted within 1 year after the operation. RESULTS: The surgery-related parameters in the two groups showed no significant difference (p > 0.05). Immunohistochemistry results showed significantly reduced damage of the nerves in the research group. We also found a better bladder and bowel function in the research group (p < 0.05) and in younger patients (p < 0.05) compared with that in the control group. Recovery trends of the bladder and bowel function were found in both groups (χ(2) = 7.512, p = 0.006 in the research group; χ(2) = 7.299, p = 0.007 in the control group). CONCLUSION: Modified panhysterectomy for benign uterine diseases seems feasible and safe, with the main advantage of improving postoperative urocystic and rectal dysfunctions through the preservation of the pelvic autonomic nerves.

Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis.

Pattern classification methods have been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer's disease (AD) and its early stage such as mild cognitive impairment (MCI). By considering the nature of pathological changes, a large number of features related to both local brain regions and interbrain regions can be extracted for classification. However, it is challenging to design a single global classifier to integrate all these features for effective classification, due to the issue of small sample size. To this end, we propose a hierarchical ensemble classification method to combine multilevel classifiers by gradually integrating a large number of features from both local brain regions and interbrain regions. Thus, the large-scale classification problem can be divided into a set of small-scale and easier-to-solve problems in a bottom-up and local-to-global fashion, for more accurate classification. To demonstrate its performance, we use the spatially normalized grey matter (GM) of each MR brain image as imaging features. Specifically, we first partition the whole brain image into a number of local brain regions and, for each brain region, we build two low-level classifiers to transform local imaging features and the inter-region correlations into high-level features. Then, we generate multiple high-level classifiers, with each evaluating the high-level features from the respective brain regions. Finally, we combine the outputs of all high-level classifiers for making a final classification. Our method has been evaluated using the baseline MR images of 652 subjects (including 198 AD patients, 225 MCI patients, and 229 normal controls (NC)) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our classification method can achieve the accuracies of 92.0% and 85.3% for classifications of AD versus NC and MCI versus NC, respectively, demonstrating very promising classification performance compared to the state-of-the-art classification methods.

Methylation of OPCML promoter in ovarian cancer tissues predicts poor patient survival.

BACKGROUND: Ovarian cancer is a lethal gynecological malignancy largely due to the lack of biomarkers for early detection and treatment options. Opioid binding protein/cell adhesion molecule-like gene (OPCML) has a tumor-suppressor function in ovarian cancer, and epigenetic inactivation of OPCML induces oncogenic transformation of human ovarian surface epithelial cells. METHODS: This study investigated OPCML promoter methylation levels in ovarian cancer tissues. A total of 30 normal ovarian, 85 benign ovarian tumor, and 102 ovarian cancer tissues were subjected to quantitative methylation-specific PCR analysis of OPCML methylation. Four ovarian cancer cell lines were cultured and treated with the DNA demethylation agent 5-aza-2'-deoxycytidine (5-AZA) for restoring OPCML expression. RESULTS: The data showed that 80 of 102 (78.4%) ovarian cancer tissues and 28 of 85 (32.9%) benign ovarian tumors had a methylated OPCML gene promoter. In contrast, there was no OPCML gene promoter methylation in any of the 30 normal ovarian samples. OPCML promoter methylation was significantly associated with an older age of the patients (p=0.022), an advanced pathological stage of ovarian cancer (p=0.023), and poor overall survival of ovarian cancer patients (p<0.001). Multivariate analysis data showed that pathological stage, age, and OPCML promoter methylation were all independent factors to predict overall survival of patients. Furthermore, 5-AZA was able to restore expression of OPCML mRNA and protein in ovarian cancer cell lines. CONCLUSIONS: These data indicate that detection of OPCML gene promoter methylation could be a useful biomarker for predicting the prognosis of ovarian cancer patients and disease progression.

Transformer based multi-modal MRI fusion for prediction of post-menstrual age and neonatal brain development analysis.

The brain development during the perinatal period is characterized by rapid changes in both structure and function, which have significant impact on the cognitive and behavioral abilities later in life. Accurate assessment of brain age is a crucial indicator for brain development maturity and can help predict the risk of neonatal pathology. However, evaluating neonatal brains using magnetic resonance imaging (MRI) is challenging due to its complexity, multi-dimension, and noise with subtle alterations. In this paper, we propose a multi-modal deep learning framework based on transformers for precise post-menstrual age (PMA) estimation and brain development analysis using T2-weighted structural MRI (T2-sMRI) and diffusion MRI (dMRI) data. First, we build a two-stream dense network to learn modality-specific features from T2-sMRI and dMRI of brain individually. Then, a transformer module based on self-attention mechanism integrates these features for PMA prediction and preterm/term classification. Finally, saliency maps on brain templates are used to enhance the interpretability of results. Our method is evaluated on the multi-modal MRI dataset of the developing Human Connectome Project (dHCP), which contains 592 neonates, including 478 term-born and 114 preterm-born subjects. The results demonstrate that our method achieves a 0.5-week mean absolute error (MAE) in PMA estimation for term-born subjects. Notably, preterm-born subjects exhibit delayed brain development, worsening with increasing prematurity. Our method also achieves 95% accuracy in classification of term-born and preterm-born subjects, revealing significant group differences.

[Retracted] MicroRNA­30e inhibits adhesion, migration, invasion and cell cycle progression of prostate cancer cells via inhibition of the activation of the MAPK signaling pathway by downregulating CHRM3.

Following the publication of the above article, a concerned reader drew to the Editor's attention that certain of the Transwell invasion assay data shown in Fig. 7B on p. 451 were strikingly similar to data that had appeared in Fig. 3D in a previously published paper written by different authors at a different research institute, which had been received at the journal Cancer Letters at around the same time, and which has subsequently been retracted [Gu J, Wang Y, Wang X, Zhou D, Shao C, Zhou M and He Z: Downregulation of lncRNA GAS5 confers tamoxifen resistance by activating miR­222 in breast cancer. Cancer Lett 434: 1­10, 2018]. In addition, there were potentially anomalous features associated with the western blot and cell cycle data in this paper.  In view of the fact that certain of the data in the above article were also submitted to a different journal within the space of a few days, the Editor of International Journal of Oncology has decided that this paper should be retracted from the publication. The authors were asked for an explanation to account for these concerns, but the Editorial Office did not receive a reply. The Editor apologizes to the readership for any inconvenience caused. [International Journal of Oncology 54: 443­454, 2019; DOI: 10.3892/ijo.2018.4647].

Tongqiao Huoxue Decoction inhibits ferroptosis by facilitating ACSL4 ubiquitination degradation for neuroprotection against cerebral ischemia-reperfusion injury.

BACKGROUND: Cerebral ischemia-reperfusion injury (CIRI) refers to brain tissue injury caused by the temporary interruption of cerebral blood flow ischemia followed by the restoration of reperfusion, which is the main cause of post-stroke brain injury. A traditional Chinese herbal preparation called Tongqiao Huoxue Decoction (TQHX) has shown promise in reducing CIRI in rats. However, the mechanism of this herbal preparation for CIRI remains unclear. PURPOSE: This study aimed to evaluate the therapeutic effect of TQHX extract on rats with CIRI and to further explore the underlying mechanisms. METHODS: The active ingredients of TQHX extract were quantified by the high-performance liquid chromatography (HPLC) condition. We conducted thorough investigations to assess the effects of TQHX on CIRI and ferroptosis using oxygen-glucose deprivation/reperfusion (OGD/R)-treated PC12 cells as an in vitro model and transient middle cerebral artery occlusion (tMCAO) animals as an in vivo model. The neurological score assessment was performed to evaluate the neuroprotective effects of TQHX extract on tMCAO rats. Using histologic methods to study the extent of cerebral infarction, blood-brain barrier, and rat brain tissue. We examined the impact of TQHX on ferroptosis-related markers of Fe2+, superoxide dismutase (SOD), reactive oxygen species (ROS), and malondialdehyde (MDA) in the brain tissue. In addition, the expression of key proteins and markers of ferroptosis, as well as key factors associated with Acyl-CoA synthetase long-chain family member 4 (ACSL4) were detected by Western blot and quantitative real-time PCR (RT-qPCR). RESULTS: TQHX extract could decrease the Longa score and extent of cerebral infarction of tMCAO rats, which exerted the function of neuroprotection. Additionally, TQHX treatment efficiently decreased levels of MDA and ROS while increasing the expression of SOD and ferroptosis-related proteins including ferritin heavy chain 1 (FTH1) and glutathione peroxidase 4 (GPX4) at the transcription and translation level. Meanwhile, TQHX provided strong protection against oxidative stress and ferritin accumulation by increasing the ubiquitination and degradation of ACSL4. The injection of OE-ACSL4 reversed the effects of TQHX on neuroprotection and ferroptosis inhibition in PC12 cells. The injection of shACSL4 reversely validate the crucial role of ACSL4 in CIRI rat treatment. CONCLUSION: This work shows that TQHX promotes the ubiquitination-mediated degradation of ACSL4, which improves oxidative stress and inhibits the beginning of ferroptosis in cells. TQHX provides a possible path for additional research in CIRI therapies, advancing translational investigations.