Learning-prolonged maintenance of stimulus information in CA1 and subiculum during trace fear conditioning.

Temporal associative learning binds discontiguous conditional stimuli (CSs) and unconditional stimuli (USs), possibly by maintaining CS information in the hippocampus after its offset. Yet, how learning regulates such maintenance of CS information in hippocampal circuits remains largely unclear. Using the auditory trace fear conditioning (TFC) paradigm, we identify a projection from the CA1 to the subiculum critical for TFC. Deep-brain calcium imaging shows that the peak of trace activity in the CA1 and subiculum is extended toward the US and that the CS representation during the trace period is enhanced during learning. Interestingly, such plasticity is consolidated only in the CA1, not the subiculum, after training. Moreover, CA1 neurons, but not subiculum neurons, increasingly become active during CS-and-trace and shock periods, respectively, and correlate with CS-evoked fear retrieval afterward. These results indicate that learning dynamically enhances stimulus information maintenance in the CA1-subiculum circuit during learning while storing CS and US memories primarily in the CA1 area.

Scene memories are biased toward high-probability views.

Visual scenes are often remembered as if they were observed from a different viewpoint. Some scenes are remembered as farther than they appeared, and others as closer. These memory distortions-also known as boundary extension and contraction-are strikingly consistent for a given scene, but their cause remains unknown. We tested whether these distortions can be explained by an inferential process that adjusts scene memories toward high-probability views, using viewing depth as a test case. We first carried out a large-scale analysis of depth maps of natural indoor scenes to quantify the statistical probability of views in depth. We then assessed human observers' memory for these scenes at various depths and found that viewpoint judgments were consistently biased toward the modal depth, even when just a few seconds elapsed between viewing and reporting. Thus, scenes closer than the modal depth showed a boundary-extension bias (remembered as farther-away), and scenes farther than the modal depth showed a boundary-contraction bias (remembered as closer). By contrast, scenes at the modal depth did not elicit a consistent bias in either direction. This same pattern of results was observed in a follow-up experiment using tightly controlled stimuli from virtual environments. Together, these findings show that scene memories are biased toward statistically probable views, which may serve to increase the accuracy of noisy or incomplete scene representations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Stanniocalcin 1 promotes metastasis, lipid metabolism and cisplatin chemoresistance via the FOXC2/ITGB6 signaling axis in ovarian cancer.

BACKGROUND: Stanniocalcin 1 (STC1) plays an integral role in ovarian cancer (OC). However, the functional role of STC1 in metastasis, lipid metabolism and cisplatin (DDP) chemoresistance in OC is not fully understood. METHODS: Single-cell sequencing and IHC analysis were performed to reveal STC1 expression profiles in patient tissues. Metastasis, lipid metabolism and DDP chemoresistance were subsequently assessed. Cell-based in vitro and in vivo assays were subsequently conducted to gain insight into the underlying mechanism of STC1 in OC. RESULTS: Single-cell sequencing assays and IHC analysis verified that STC1 expression was significantly enhanced in OC tissues compared with para-carcinoma tissues, and it was further up-regulated in peritoneal metastasis tissues compared with OC tissues. In vitro and in vivo experiments demonstrated that STC1 promoted metastasis, lipid metabolism and DDP chemoresistance in OC. Simultaneously, STC1 promoted lipid metabolism by up-regulating lipid-related genes such as UCP1, TOM20 and perilipin1. Mechanistically, STC1 directly bound to integrin ß6 (ITGB6) to activate the PI3K signaling pathway. Moreover, STC1 was directly regulated by Forkhead box C2 (FOXC2) in OC. Notably, targeting STC1 and the FOXC2/ITGB6 signaling axis was related to DDP chemoresistance in vitro. CONCLUSIONS: Overall, these findings revealed that STC1 promoted metastasis, lipid metabolism and DDP chemoresistance via the FOXC2/ITGB6 signaling axis in OC. Thus, STC1 may be used as a prognostic indicator in patients with metastatic OC. Meanwhile, STC1 could be a therapeutic target in OC patients, especially those who have developed chemoresistance to DDP.

Long non-coding RNA CTSLP8 mediates ovarian cancer progression and chemotherapy resistance by modulating cellular glycolysis and regulating c-Myc expression through PKM2.

PURPOSE: Long non-coding RNAs (lncRNAs) play vital roles in tumor progression and resistance. Ovarian cancer (OC), a common gynecological cancer, is associated with poor prognosis as it can progress to peritoneal metastasis and develop resistance to chemotherapy. This study aimed to examine the role of lncRNAs in the development of chemotherapy resistance in OC. METHODS: The clinical samples were divided into chemotherapy-sensitive and chemotherapy-resistant groups based on the chemotherapy response at follow-up. The glycolysis levels in the two groups were analyzed using positron emission tomography/computed tomography (PET/CT) scanning and immunohistochemistry. GEO dataset analysis revealed the expression of CTSLP8 in chemotherapy-resistant patients with OC. Two pairs of normal and diamminodichloroplatinum (DDP)-resistant cells were transfected with CTSLP8 overexpression and knockdown constructs to examine the functions of CTSLP8 in the OC cells and elucidate the underlying mechanisms. The in vivo effect of CTSLP8 overexpression and knockdown on the chemotherapy response of tumors was examined using a mouse subcutaneous tumor model. The tissue chips were subjected to fluorescence in situ hybridization and immunohistochemical (IHC) staining to examine the correlation among CTSLP8 expression, DDP resistance, and prognosis in OC. RESULTS: The dataset analysis demonstrated that CTSLP8 was upregulated in chemotherapy-resistant tumor tissues. CTSLP8 promoted the proliferation and development of DDP resistance in the OC cells. Moreover, CTSLP8 promoted c-Myc expression by facilitating the binding of PKM2 to the promoter region of c-Myc, thereby upregulating glycolysis. The analysis of tissue chips revealed that the upregulation of CTSLP8 was associated with the development of DDP resistance and poor prognosis in patients with OC. CONCLUSIONS: These findings indicate that CTSLP8 forms a complex with PKM2 to regulate c-Myc, and this action results in the upregulation of cellular glycolysis, consequently promoting OC progression and development of chemotherapy resistance. HEADLIGHTS: 1. CTSLP8 was upregulated in the chemotherapy-resistant tumor tissues. 2. CTSLP8 promoted the proliferation and cisplatin resistance in the OC cells. 3. CTSLP8 promoted glycolysis by facilitating the binding of PKM2 to the promoter region of c-Myc. 4. Inhibition of CTSLP8 or the combination of c-Myc inhibitors with cisplatin were potential therapeutic strategies for chemotherapy-resistant of OC.

Ovarian cancer: epigenetics, drug resistance, and progression.

Ovarian cancer (OC) is one of the most common malignant tumors in women. OC is associated with the activation of oncogenes, the inactivation of tumor suppressor genes, and the activation of abnormal cell signaling pathways. Moreover, epigenetic processes have been found to play an important role in OC tumorigenesis. Epigenetic processes do not change DNA sequences but regulate gene expression through DNA methylation, histone modification, and non-coding RNA. This review comprehensively considers the importance of epigenetics in OC, with a focus on microRNA and long non-coding RNA. These types of RNA are promising molecular markers and therapeutic targets that may support precision medicine in OC. DNA methylation inhibitors and histone deacetylase inhibitors may be useful for such targeting, with a possible novel approach combining these two therapies. Currently, the clinical application of such epigenetic approaches is limited by multiple obstacles, including the heterogeneity of OC, insufficient sample sizes in reported studies, and non-optimized methods for detecting potential tumor markers. Nonetheless, the application of epigenetic approaches to OC patient diagnosis, treatment, and prognosis is a promising area for future clinical investigation.

NOL4L, a novel nuclear protein, promotes cell proliferation and metastasis by enhancing the PI3K/AKT pathway in ovarian cancer.

Nucleolar protein 4-like (NOL4L) was first identified in acute myeloid leukaemia. Then, it was verified to be involved in cell progression in neuroblastoma. However, the functional role of NOL4L in tumor proliferation and metastasis and the underlying molecular mechanism(s) are not fully understood. Immunohistochemistry (IHC) assays were performed in patient tissues to reveal NOL4L expression profiles. Then, we knocked down NOL4L in two ovarian cancer cell lines (Skov3-ip1 and Hey), and cell-based in-vitro and in-vivo assays were subsequently conducted to gain insight into the underlying mechanism of NOL4L in ovarian cancer. We confirmed that the expression of NOL4L was higher in tumor tissues, especially in peritoneal metastatic tissues. Furthermore, we observed that NOL4L was related to prognosis in ovarian cancer patients. Next, we conducted CCK-8 assays, colony formation assays, migration and invasion experiments and wound healing assays and verified that NOL4L could promote proliferation and metastasis in ovarian cancer cells. In addition, NOL4L promoted tumor progression and metastasis in a nude mouse model. Mechanistically, we demonstrated that NOL4L influenced gene expression in the PI3K/AKT pathway. Overall, our study provides genetic and biochemical evidence that NOL4L is critical for tumor progression and metastasis in ovarian cancer cells. Thus, it could serve as a target for antimetastatic therapy in ovarian cancer.

The lnc-CTSLP8 upregulates CTSL1 as a competitive endogenous RNA and promotes ovarian cancer metastasis.

BACKGROUND: Ovarian cancer is highly lethal and has a poor prognosis due to metastasis. Long non-coding RNAs (lncRNAs) are key regulators of tumor development, but their role in ovarian cancer metastasis remains unclear. METHODS: The expression of lnc-CTSLP8 in ovarian cancer was analyzed in public databases (TCGA and GEO) and validated via qRT-PCR. Lnc-CTSLP8 overexpression and knockout cell lines were constructed using a lentiviral vector and the CRISP/Cas9 system. Cell proliferation, colony formation, migration, and invasion were analyzed. An ovarian orthotopic tumor mouse model was used for the in vivo study. Changes in autophagosomes, autolysosomes, and mitochondria in ovarian cancer cells were observed via transmission electron microscopy. EMT markers were detected by immunoblotting and immunofluorescence assays. RNA immunoprecipitation, RNA pull-down, and dual luciferase reporter assays were performed to confirm the interaction between lnc-CTSLP8 and miR-199a-5p. RESULTS: A novel pseudogene, lnc-CTSLP8, was identified in ovarian cancer, with significantly elevated expression in metastatic tumor tissues compared to primary ovarian tumors. When overexpressed, lnc-CTSLP8 promoted ovarian cancer in vitro and in vivo by acting as a sponge for miR-199a-5p. Autophagy and EMT in ovarian cancer were also enhanced by lnc-CTSLP8. Mechanistically, lnc-CTSLP8 upregulated CTSL1 as a competitive endogenous RNA and exhibited oncogenic effects. Moreover, CTSL1 inhibitor treatment and miR-199a-5p overexpression abrogated the effects of lnc-CTSLP8 overexpression. CONCLUSIONS: lnc-CTSLP8 acts as a ceRNA in ovarian cancer and represents a potential therapeutic target for metastatic ovarian cancer.

A Comprehensive Risk Assessment Model for Ovarian Cancer Patients with Phospho-STAT3 and IL-31 as Immune Infiltration Relevant Genes.

INTRODUCTION: Ovarian carcinoma is a malignant tumor with a high mortality rate and a lack of effective treatment options for patients at advanced stages. For improving outcomes and helping patients with poor prognosis, choose a suitable therapy and an excellent risk assessment model and new treatment options are needed. MATERIALS AND METHODS: Ovarian cancer gene expression profile of GSE32062 was downloaded from the NCBI GEO database for screening differentially expressed genes (DEGs) between well and poor prognosis groups using limma package in R (version 3.4.1). Prognosis-related genes and clinical prognostic factors were obtained from univariate and multivariate Cox regression analyses, and a comprehensive risk assessment model was constructed using a Pathway Dysregulation Score (PDS) matrix, Cox-Proportional Hazards (Cox-PH) regression, as well as L1-least absolute shrinkage and selection operator (L1-LASSO) penalization. Then, significant DEGs were converted to pathways and optimal prognosis-related pathways were screened. Finally, risk prediction models based on pathways, genes involved in pathways, and comprehensive clinical risk factors with pathways were built. Their prognostic functions were assessed in verification sets. Besides, genes involved in immune-pathways were checked for immune infiltration using immunohistochemistry. RESULTS: A superior risk assessment model involving 9 optimal combinations of pathways and one clinical factor was constructed. The pathway-based model was found to be superior to the gene-based model. Phospho-STAT3 (from JAK-STAT signaling pathway) and IL-31 (from DEGs) were found to be related to immune infiltration. CONCLUSION: We have generated a comprehensive risk assessment model consisting of a clinical risk factor and pathways that showed a possible bright foreground. The set of significant pathways might play as a better prognosis model which is more accurate and applicable than the DEG set. Besides, p-STAT3 and IL-31 showing correlation to immune infiltration of ovarian cancer tissues may be potential therapeutic targets for treating ovarian cancers.

Aberrant Expression of a Non-muscle RBFOX2 Isoform Triggers Cardiac Conduction Defects in Myotonic Dystrophy.

Myotonic dystrophy type 1 (DM1) is a multisystemic genetic disorder caused by the CTG repeat expansion in the 3'-untranslated region of DMPK gene. Heart dysfunctions occur in ∼80% of DM1 patients and are the second leading cause of DM1-related deaths. Herein, we report that upregulation of a non-muscle splice isoform of RNA-binding protein RBFOX2 in DM1 heart tissue-due to altered splicing factor and microRNA activities-induces cardiac conduction defects in DM1 individuals. Mice engineered to express the non-muscle RBFOX240 isoform in heart via tetracycline-inducible transgenesis, or CRISPR/Cas9-mediated genome editing, reproduced DM1-related cardiac conduction delay and spontaneous episodes of arrhythmia. Further, by integrating RNA binding with cardiac transcriptome datasets from DM1 patients and mice expressing the non-muscle RBFOX2 isoform, we identified RBFOX240-driven splicing defects in voltage-gated sodium and potassium channels, which alter their electrophysiological properties. Thus, our results uncover a trans-dominant role for an aberrantly expressed RBFOX240 isoform in DM1 cardiac pathogenesis.

Development and internal validation of a Nomogram for preoperative prediction of surgical treatment effect on cesarean section diverticulum.

BACKGROUND: The aim of this study was to develop and validate an individualized score based on preoperative parameters to predict patient outcomes after vaginal repair of cesarean section diverticulum. METHODS: This is a retrospective cohort study (Canadian Task Force classification II-2). Patients were enrolled between Jun 11, 2012, to May 27, 2016. Multivariable logistic regression analyses were used to construct the predictive model. Then, we generated a nomogram to assess the individualized risk of poor prognosis after operation. This prediction model included information from 167 eligible patients diagnosed with cesarean section diverticulum who underwent vaginal repair. Class-A healing group was defined as CSD patients who had menstruation duration of no more than 7 days and a thickness of the remaining muscular layer of no less than 5.8 mm after vaginal repair according to conferences. Others were included in the non-class-A healing group. A final nomogram was computed using a multivariable logistic regression model. RESULTS: The factors contained in the individualized prediction nomogram included the depth/ the thickness of the remaining muscular layer ratio, number of menstruation days before surgery, White blood cell and fibrinogen. This model demonstrated adequate discrimination and calibration (C-index = 0.718). There was a significant difference in the number of postmenstrual spotting days (12.98 ± 3.86 VS 14.46 ± 2.86, P = 0.022) and depth/ the thickness of the remaining muscular layer ratio (2.81 ± 1.54 VS 4.00 ± 3.09, P = 0.001) between two groups. Decision curve analysis showed that this nomogram was clinically useful. CONCLUSIONS: This cesarean section diverticulum score can predict the outcomes of cesarean section diverticulum and can be useful for counseling patients who are making treatment decisions.

Coupling logistic model tree and random subspace to predict the landslide susceptibility areas with considering the uncertainty of environmental features.

Landslide disasters cause huge casualties and economic losses every year, how to accurately forecast the landslides has always been an important issue in geo-environment research. In this paper, a hybrid machine learning approach RSLMT is firstly proposed by coupling Random Subspace (RS) and Logistic Model Tree (LMT) for producing a landslide susceptibility map (LSM). With this method, the uncertainty introduced by input features is considered, the problem of overfitting is solved by reducing dimensions to increase the prediction rate of landslide occurrence. Moreover, the uncertainty of prediction will be deeply discussed with the rank probability score (RPS) series, which is an important evaluation of uncertainty but rarely used in LSM. Qingchuan county, China was taken as a study area. 12 landslide causal factors were selected and their contribution on landslide occurrence was evaluated by ReliefF method. In addition, Logistic Model Tree (LMT), Naive Bayes (NB) and Logistic Regression (LR) were researched for comparison. The results showed that RSLMT (AUC = 0.815) outperformed LMT (AUC = 0.805), NB (AUC = 0.771), LR (AUC = 0.785). LSM of Qingchuan county was produced using the novel model, it indicated that landslides tend to occur along with the fault belts and the middle-low mountain area that is strongly influenced by the large numbers of human engineering activities.

Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors.

The fragile ecological environment near mines provide advantageous conditions for the development of landslides. Mine landslide susceptibility mapping is of great importance for mine geo-environment control and restoration planning. In this paper, a total of 493 landslides in Shangli County, China were collected through historical landslide inventory. 16 spectral, geomorphic and hydrological predictive factors, mainly derived from Landsat 8 imagery and Global Digital Elevation Model (ASTER GDEM), were prepared initially for landslide susceptibility assessment. Predictive capability of these factors was evaluated by using the value of variance inflation factor and information gain ratio. Three models, namely artificial neural network (ANN), support vector machine (SVM) and information value model (IVM), were applied to assess the mine landslide sensitivity. The receiver operating characteristic curve (ROC) and rank probability score were used to validate and compare the comprehensive predictive capabilities of three models involving uncertainty. Results showed that ANN model achieved higher prediction capability, proving its advantage of solve nonlinear and complex problems. Comparing the estimated landslide susceptibility map with the ground-truth one, the high-prone area tends to be located in the middle area with multiple fault distributions and the steeply sloped hill.

Deregulation of RNA Metabolism in Microsatellite Expansion Diseases.

RNA metabolism impacts different steps of mRNA life cycle including splicing, polyadenylation, nucleo-cytoplasmic export, translation, and decay. Growing evidence indicates that defects in any of these steps lead to devastating diseases in humans. This chapter reviews the various RNA metabolic mechanisms that are disrupted in Myotonic Dystrophy-a trinucleotide repeat expansion disease-due to dysregulation of RNA-Binding Proteins. We also compare Myotonic Dystrophy to other microsatellite expansion disorders and describe how some of these mechanisms commonly exert direct versus indirect effects toward disease pathologies.

Mifepristone combined with ethacridine lactate for the second-trimester pregnancy termination in women with placenta previa and/or prior cesarean deliveries.

PURPOSE: This study was aimed to evaluate the safety and efficacy of the second-trimester medical abortions using mifepristone and ethacridine lactate in women with placenta previa and/or prior cesarean deliveries. METHODS: The patients who underwent a second-trimester pregnancy termination from January 2009 to December 2015 were retrospectively analyzed. The eligible patients were assigned to four groups based on placentation and cesarean history. The abortion interval (AI), blood loss, hospital stays, incidence of curettage, and transfusion were reviewed. RESULTS: Two women underwent cesarean sections for placenta increta. Finally, 443 patients were enrolled in this study, including 92 with placenta previa, 153 with prior cesarean deliveries, 36 with the both factors, and 236 with normal placentation and no cesarean delivery history. All the included cases had a successful vaginal delivery. There was no significant difference in AI, hospital stay, rate of hemorrhage, and transfusion among the four groups. Patients with prior cesarean section had higher blood loss than the normal group (P = 0.0017), as well as patients with both placenta previa and prior cesarean (P = 0.0018). However, there was no obvious blood loss in patients with placenta previa when compared with normal placetal patients (P = 0.23). No uterine rupture occurred in all patients. CONCLUSIONS: Mifepristone combined with ethacridine lactate is safe and effective for patients with low placentation or/and prior cesarean in the second-trimester pregnancy termination.