Frizzled class receptor 5 contributes to ovarian cancer chemoresistance through aldehyde dehydrogenase 1A1.

BACKGROUND: Chemoresistance is associated with tumor relapse and unfavorable prognosis. Multiple mechanisms underlying chemoresistance have been elucidated, including stemness and DNA damage repair. Here, the involvement of the WNT receptor, FZD5, in ovarian cancer (OC) chemoresistance was investigated. METHODS: OC cells were analyzed using in vitro techniques including cell transfection, western blot, immunofluorescence and phalloidin staining, CCK8 assay, colony formation, flowcytometry, real-time PCR, and tumorisphere formation. Pearson correlation analysis of the expression levels of relevant genes was conducted using data from the CCLE database. Further, the behavior of OC cells in vivo was assessed by generation of a mouse xenograft model. RESULTS: Functional studies in OC cells showed that FZD5 contributes to epithelial phenotype maintenance, growth, stemness, HR repair, and chemoresistance. Mechanistically, FZD5 modulates the expression of ALDH1A1, a functional marker for cancer stem-like cells, in a ß-catenin-dependent manner. ALDH1A1 activates Akt signaling, further upregulating RAD51 and BRCA1, to promote HR repair. CONCLUSIONS: Taken together, these findings demonstrate that the FZD5-ALDH1A1-Akt pathway is responsible for OC cell survival, and targeting this pathway can sensitize OC cells to DNA damage-based therapy.

ERTNet: an interpretable transformer-based framework for EEG emotion recognition.

Background: Emotion recognition using EEG signals enables clinicians to assess patients' emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy. Methods: We developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between channels. Subsequently, the transformer module processes the feature maps to integrate high-level spatiotemporal features, enabling the identification of the prevailing emotional state. Results: Experiments' results demonstrated that our model excels in diverse emotion classification, achieving an accuracy of 74.23% ± 2.59% on the dimensional model (DEAP) and 67.17% ± 1.70% on the discrete model (SEED-V). These results surpass the performances of both CNN and LSTM-based counterparts. Through interpretive analysis, we ascertained that the beta and gamma bands in the EEG signals exert the most significant impact on emotion recognition performance. Notably, our model can independently tailor a Gaussian-like convolution kernel, effectively filtering high-frequency noise from the input EEG data. Discussion: Given its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface.

Tumor-liver interface in MRI of liver metastasis enables prediction of EGFR mutation in patients with lung cancer: A proof-of-concept study.

BACKGROUND: Preoperative prediction of the epidermal growth factor receptor (EGFR) status in non-small-cell lung cancer (NSCLC) patients with liver metastasis (LM) may have potential clinical values for assisting in treatment decision-making. PURPOSE: To explore the value of tumor-liver interface (TLI)-based magnetic resonance imaging (MRI) radiomics for detecting the EGFR mutation in NSCLC patients with LM. METHODS: This retrospective study included 123 and 44 patients from hospital 1 (between Feb. 2018 and Dec. 2021) and hospital 2 (between Nov. 2015 and Aug. 2022), respectively. The patients received contrast-enhanced T1-weighted (CET1) and T2-weighted (T2W) liver MRI scans before treatment. Radiomics features were extracted from MRI images of TLI and the whole tumor region, separately. The least absolute shrinkage and selection operator (LASSO) regression was used to screen the features and establish radiomics signatures (RSs) based on TLI (RS-TLI) and the whole tumor (RS-W). The RSs were evaluated by the receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 5 and 6 features were identified highly correlated with the EGFR mutation status from TLI and the whole tumor, respectively. The RS-TLI showed better prediction performance than RS-W in the training (AUCs, RS-TLI vs. RS-W, 0.842 vs. 0.797), internal validation (AUCs, RS-TLI vs. RS-W, 0.771 vs. 0.676) and external validation (AUCs, RS-TLI vs. RS-W, 0.733 vs. 0.679) cohort. CONCLUSION: Our study demonstrated that TLI-based radiomics can improve prediction performance of the EGFR mutation in lung cancer patients with LM. The established multi-parametric MRI radiomics models may be used as new markers that can potentially assist in personalized treatment planning.

Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC.

PURPOSE: To develop and externally validate subregional radiomics for predicting therapeutic response to anti-PD1 therapy in non-small-cell lung cancer (NSCLC). METHODS: Sixty-six patients from center 1 served as training and internal validation cohorts. Thirty patients from center 2 and thirty patients from center 3 served as external validation 1 and external validation 2 cohorts, respectively. The lesions identified on CT scans were subdivided into two phenotypically consistent subregions by automatic clustering on the patient-level and population-level (denoted as marginal S1 and inner S2). Handcrafted and deep learning-based features were extracted separately from the entire tumor region and subregions, then selected using the intraclass correlation coefficient and least absolute shrinkage and selection operator regression (LASSO). Radiomics signatures (RSs) were built integrating the selected features and correlation coefficients using a logistic regression method. Area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the RSs. RESULTS: RSs derived from S1 outperformed those from S2 and the whole tumor region for both handcrafted and deep learning features. The Fusion-RS incorporating the two feature types achieved the best prediction performance in training (AUC = 0.947, 95 % Confidence Interval [CI] 0.905-0.989, SPE = 0.895, SEN = 0.878), internal validation (AUC = 0.875, 95 % CI: 0.782-0.969, SPE = 0.724, SEN = 0.952), external validation 1 (AUC = 0.836, 95 % CI: 0.694-0.977, SPE = 1.000, SEN = 0.533) and external validation 2 (AUC = 0.783, 95 % CI: 0.613-0.953, SPE = 0.765, SEN = 0.692) cohorts. CONCLUSIONS: Subregional radiomics analysis can be useful for predicting therapeutic response to anti-PD1 therapy. The developed Fusion-RS may be considered as a potential non-invasive tool for individual treatment managements.

Adipose-derived stem cells with miR-150-5p inhibition laden in hydroxyapatite/tricalcium phosphate ceramic powders promote osteogenesis via regulating Notch3 and activating FAK/ERK and RhoA.

Adipose-derived mesenchymal stem cells (ADSCs) are multipotent stromal cells and play huge role in forming and repairing bone tissues. Emerging evidence shows that MicroRNAs (miRNAs) are involved in ADSCs differentiation. Here, we explored the role of miR-150-5p and its related mechanisms in ADSCs osteogenesis. Real-time PCR was used to determine miR-150-5p expression during ADSCs osteogenesis. miR-150-5p inhibitors, miR-150-5p ADV or short hairpin RNA (shRNA) of Notch3 were transfected to ADSCs for analyzing the effects on osteogenesis. The mixture of hydroxyapatite/tricalcium phosphate (HA/TCP) ceramic powders and transfected ADSCs was implanted into BALB/C nude mice. Micro-CT and histological methods were performed to evaluate the new bone formation. Compared with negative control (NC) and miR-150-5p overexpression, inhibition of miR-150-5p increased ADSCs osteogenesis by regulating Notch3. MiR-150-5p overexpression decreased the expression of pFAK, pERK1/2, and RhoA, while these were up-regulated when miR-150-5p was inhibited, or notch3 was silenced. Furthermore, miR-150-5p inhibition partially reversed the suppression effect of notch3 knockdown on osteogenesis in vitro and in vivo. This study demonstrated the critical function of miR-150-5p during osteogenesis. The combination of ADSCs with miR-150-5p inhibition and HA/TCP might be a promising strategy for bone damage repair. STATEMENT OF SIGNIFICANCE: Osteoporosis is a common chronic metabolic bone disease in humans. Bone tissue engineering based on mesenchymal stem cells, biomaterials, and growth factors, provides a promising way to treat osteoporosis and bone defects. ADSCs commonly differentiate into adipose cells, they can also differentiate into osteogenic cell lineages. Nucleic acids and protein have usually been considered as regulators of ADSCs osteogenic differentiation. In the current study, we demonstrated the combination of ADSCs with miR-150-5p inhibition and hydroxyapatite/tricalcium phosphate ceramic powders enhanced bone regeneration. Furthermore, miR-150-5p/Notch3 axis regulating osteogenesis via the FAK/ERK1/2 and RhoA pathway was assessed. The current study showed the application of ADSCs in bone regeneration might be a promising strategy for osteoporosis and bone damage repairing.

Abnormal ECG detection based on an adversarial autoencoder.

Automatic detection and alarm of abnormal electrocardiogram (ECG) events play an important role in an ECG monitor system; however, popular classification models based on supervised learning fail to detect abnormal ECG effectively. Thus, we propose an ECG anomaly detection framework (ECG-AAE) based on an adversarial autoencoder and temporal convolutional network (TCN) which consists of three modules (autoencoder, discriminator, and outlier detector). The ECG-AAE framework is trained only with normal ECG data. Normal ECG signals could be mapped into latent feature space and then reconstructed as the original ECG signal back in our model, while abnormal ECG signals could not. Here, the TCN is employed to extract features of normal ECG data. Then, our model is evaluated on an MIT-BIH arrhythmia dataset and CMUH dataset, with an accuracy, precision, recall, F1-score, and AUC of 0.9673, 0.9854, 0.9486, 0.9666, and 0.9672 and of 0.9358, 0.9816, 0.8882, 0.9325, and 0.9358, respectively. The result indicates that the ECG-AAE can detect abnormal ECG efficiently, with its performance better than other popular outlier detection methods.

Intratumoral analysis of digital breast tomosynthesis for predicting the Ki-67 level in breast cancer: A multi-center radiomics study.

PURPOSE: To non-invasively evaluate the Ki-67 level in digital breast tomosynthesis (DBT) images of breast cancer (BC) patients based on subregional radiomics. METHODS: A total of 266 patients who underwent DBT scans were consecutively enrolled at two centers, between September 2017 and September 2021. The whole tumor region was partitioned into various intratumoral subregions, based on individual- and population-level clustering. Handcrafted radiomics and deep learning-based features were extracted from the subregions and from the whole tumor region, and were selected by least absolute shrinkage and selection operator (LASSO) regression, yielding radiomics signatures (RSs). The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the developed RSs. RESULTS: Each breast tumor region was partitioned into an inner subregion (S1) and a marginal subregion (S2). The RSs derived from S1 always generated higher AUCs compared with those from S2 or from the whole tumor region (W), for the external validation cohort (AUCs, S1 vs. W, handcrafted RSs: 0.583 [95% CI, 0.429-0.727] vs. 0.559 [95% CI, 0.405-0.705], p-value: 0.920; deep RSs: 0.670 [95% CI, 0.516-0.802] vs. 0.551 [95% CI, 0.397-0.698], p-value: 0.776). The fusion RSs, combining handcrafted and deep learning-based features derived from S1, yielded the highest AUCs of 0.820 (95% CI, 0.714-0.900) and 0.792 (95% CI, 0.647-0.897) for the internal and external validation cohorts, respectively. CONCLUSIONS: The subregional radiomics approach can accurately predict the Ki-67 level based on DBT data; thus, it may be used as a potential non-invasive tool for preoperative treatment planning in BC.

[Classification Model of Corneal Opacity Based on Digital Image Features].

OBJECTIVE: According to the digital image features of corneal opacity, a multi classification model of support vector machine (SVM) was established to explore the objective quantification method of corneal opacity. METHODS: The cornea digital images of dead pigs were collected, part of the color features and texture features were extracted according to the previous experience, and the SVM multi classification model was established. The test results of the model were evaluated by precision, sensitivity and F1 scores. The optimal feature subset was found by SVM-RFE combined with cross validation to optimize the model. RESULTS: In the classification of corneal opacity, the highest F1 score was 0.974 4, and the number of features in the optimal feature subset was 126. CONCLUSIONS: The SVM multi classification model can classify the degree of corneal opacity.

Automatic consecutive context perceived transformer GAN for serial sectioning image blind inpainting.

BACKGROUND AND OBJECTIVE: Serial sectioning is the routine method in histology study. In order to restore the defective images in section stack, and overcome the limitations of manual annotation of broken areas, we developed a fully automatic approach for locating and restoring the defective image stack. METHODS: We proposed a novel end-to-end framework named automatic consecutive context perceived transformer GAN (ACCP-GAN) for fully automatic serial sectioning image blind inpainting. The first stage network (auto-detection module) was designed to detect the broken areas and repair them roughly, then guided the second stage network (refined inpainting module) to generate these expected patches precisely; therefore, the segmentation part was integrated into restoring part. The transformer module (SPTransformer), based on self-attention mechanism, was introduced to make the refined inpainting module focus on the features from neighboring images to help in correcting inpainting results. Moreover, gated convolution was largely used to extract features from normal parts in the defective image. The framework was trained and validated on the N7 dataset (803 images), and the generalization ability of the model was tested on the E17 (701 images) and N5 (413 images) datasets, all of these images were collected for previous kidney study. RESULTS: N7 dataset was divided into training, validation, and test sets with a ratio of 6:2:2. Our model performed well in broken areas segmentation with the accuracy = 0.9995. The final restoration got the best performance with FSIM = 0.9478, MS-SSIM = 0.9592, PSNR = 29.7903, VIF = 0.8543, and FID = 47.2252 compared to the popular inpainting methods. The model was further tested on E15 and N5 datasets, and the generalization ability was satisfying. CONCLUSIONS: Our method could detect and restore the defective serial sectioning image stack automatically, even the broken patches were large on an individual image. The newly designed SPTransformer performed well in feature extraction. This method reduced the workload of manual annotation and improved the analysis or application of large scale sectioning image stack in histology research.

Ultrastructural identification of developing proximal tubules based on three-dimensional reconstruction.

BACKGROUND: The cellular mechanisms involved in the development of proximal tubules are not only associated with morphogenesis in fetal life, but also with restoration of damaged tubules in adulthood. Knowledge about morphological features of cell differentiation and proliferation along the developing tubule is insufficient, which hinders identification of the cellular origin. OBJECTIVES: This study aimed to investigate ultrastructures of the proximal tubule at different stages of nephrogenesis. METHODS: Electron microscopy was used and guided by computer-assisted tubular tracing to identify the cellular structures. RESULTS: Renal vesicles and S-shaped bodies revealed more proliferative features, such as densely-packed fusiform-shaped cells with numerous protein-producing organelles than membrane specializations typical for mature tubules. At the capillary-loop stage the proximal tubules demonstrated all characteristics of the mature tubules, but not as developed, including shorter but densely packed microvilli, fewer lateral processes with cell-cell contacts, lower basal membrane infoldings, and lower mitochondrial volume density. However, they exhibited an elaborated endocytic system above the nucleus, indicating a membrane transport is being established. Abundant free- and endoplasmic reticulum-adhered ribosomes and Golgi complexes reflected active protein synthesis for cell growth and proliferation. Interestingly, electron dense cells were occasionally intermixed with electron lucent cells characterized by various organelles in less cytosol and a larger nucleus with abundant euchromatin, which is a feature of active proliferation. CONCLUSIONS: These ultrastructures indicate that the morphogenesis of the developing proximal tubule corresponds to the gradually established physiological activities. The two different cellular electron densities may suggest distinctive differentiation of the cells along the tubule.

The relationship between ambient temperature and acute respiratory and cardiovascular diseases in Shenyang, China.

The purpose of this study was to analyze the acute effect of ambient temperature on hospitalization due to acute exacerbation of chronic obstructive pulmonary disease (AECOPD), stroke, and myocardial infarction (MI) in Shenyang, China. We used the distributed delayed nonlinear model to evaluate the impact of ambient temperature on respiratory and cardiovascular diseases. The study population was divided into four groups: < 65 group and ≥ 65 age groups, female and male groups. The < 65 age group of AECOPD patients was more likely to be affected by high ambient temperature, while the ≥ 65 age group of AECOPD patients was more sensitive to low ambient temperature. The hospitalization risk of MI admission increased in the ≥ 65 age group at 1-8 days delay under low ambient temperature conditions.

MRI Based Radiomics Approach With Deep Learning for Prediction of Vessel Invasion in Early-Stage Cervical Cancer.

This article aims to build deep learning-based radiomic methods in differentiating vessel invasion from non-vessel invasion in cervical cancer with multi-parametric MRI data. A set of 1,070 dynamic T1 contrast-enhanced (DCE-T1) and 986 T2 weighted imaging (T2WI) MRI images from 167 early-stage cervical cancer patients (January 2014 - August 2018) were used to train and validate deep learning models. Predictive performances were evaluated using receiver operating characteristic (ROC) curve and confusion matrix analysis, with the DCE-T1 showing more discriminative results than T2WI MRI. By adopting an attention ensemble learning strategy that integrates both MRI sequences, the highest average area was obtained under the ROC curve (AUC) of 0.911 (Sensitivity = 0.881 and Specificity = 0.752). The superior performances in this article, when compared to existing radiomic methods, indicate that a wealth of deep learning-based radiomics could be developed to aid radiologists in preoperatively predicting vessel invasion in cervical cancer patients.

[Automatic Identification and Classification Diagnosis of Atrial Ventricular Hypertrophy Electrocardiogram Based on Convolutional Neural Network].

OBJECTIVE: Identifying Atrial Ventricular Hypertrophy Electrocardiogram (AVH ECG)and diagnosing the classification of theirs automatically. METHODS: The ECG data used in this experiment was collected from the First Affiliated Hospital of China Medical University. CNN are combined with conventional methods and a 10 layers of one dimensional CNN are created in this experiment to extract the features of ECG signals automatically and achieve the function of classifying. ROC, sensitivity and F1-score are used here to evaluate the effects of the model. RESULTS: In the experiment of identifying AVH ECG, the AUC of test dataset is 0.991, while in the experiment of classifying AVH ECG, the maximal F1-score can reach 0.992. CONCLUSIONS: The CNN model created in this experiment can achieve the auxiliary diagnosis of AVH ECG.

Research Progress on Surface Treatments of Biodegradable Mg Alloys: A Review.

Mg alloys have attracted extensive attention in the biomedical fields owing to their great biocompatibility, suitable mechanical properties, and biodegradability, etc. However, the fast degradation rate restricts the application of Mg alloys. Thus, the surface treatment of Mg alloys is considered as one of the most effective ways to enhance the corrosion resistance of Mg alloys. Nevertheless, simple processing to improve the corrosion resistance can no longer meet the growing biofunctional clinical requirements. Therefore, functionalized processing has become one of the key development directions for surface treatment in the future, such as functionalized Mg alloys with antibacterial property and hydrophobicity. There are few papers that review the functionalized processing of surface treatment. This review summarized and compared the major advances of the surface treatment (anticorrosion processing and functionalized processing) of Mg alloys. Then, some potential research suggestions are proposed, which may provide a reference for the development of Mg alloys.

Associations of psychological distress with positive psychological variables and activities of daily living among stroke patients: a cross-sectional study.

BACKGROUND: Depression and anxiety result in psychological distress, which can further affect mental status and quality of life in stroke patients. Exploring the associations between positive psychological variables and symptoms of psychological distress following stroke is of great significance for further psychological interventions. METHODS: A total of 710 stroke patients from the five largest cities in Liaoning Province in China were enrolled into the present study in July 2014. All patients independently completed the questionnaires with respect to psychological distress and positive psychological variables. Depressive and anxiety symptoms were evaluated using Center for Epidemiologic Studies Depression Scale (CES-D) and Self-Rating Anxiety Scale, respectively. Positive psychological variables were evaluated using Perceived Social Support Scale, Adult Hope Scale (AHS), General Perceived Self-Efficacy Scale and Resilience Scale-14 (RS-14). Activities of Daily Living (ADL) was measured using Barthel Index. Factors associated with psychological variables and depressive and anxiety symptoms were identified using t-test, ANOVA, correlation and hierarchical linear regression analysis. RESULTS: Depressive and anxiety symptoms were present in 600 of 710 (84.51%) and 537 of 710 (75.63%) stroke patients enrolled, respectively. Social support (ß = - 0.111, p < 0.001) and hope (ß = - 0.120, p < 0.001) were negatively associated with both depressive and anxiety symptoms. Resilience (ß = - 0.179, p < 0.001) was negatively associated with depressive symptoms. Self-efficacy (ß = - 0.135, p < 0.001) was negatively associated with anxiety symptoms. Hierarchical regression analyses indicated that ADL accounted for 10.0 and 6.0% of the variance of depressive and anxiety symptoms, respectively. Social support, resilience, self-efficacy and hope as a whole accounted for 7.5 and 5.3% of the variance of depressive and anxiety symptoms. CONCLUSIONS: The high frequency of depressive and anxiety symptoms among Chinese stroke survivors should receive attentions from all stakeholders. Findings suggested that intervention strategies on ADL, social support, hope, resilience and self-efficacy could be developed to improve psychosocial outcomes for stroke survivors.

A Low-Noise-Level Heart Sound System Based on Novel Thorax-Integration Head Design and Wavelet Denoising Algorithm.

Along with the great performance in diagnosing cardiovascular diseases, current stethoscopes perform unsatisfactorily in controlling undesired noise caused by the surrounding environment and detector operation. In this case, a low-noise-level heart sound system was designed to inhibit noise by a novel thorax-integration head with a flexible electric film. A hardware filter bank and wavelet-based algorithm were employed to enhance the recorded heart sounds from the system. In the experiments, we used the new system and the 3M™ Littmann® Model 3200 Electronic Stethoscope separately to record heart sounds in different noisy environments. The results illustrated that the average estimated noise ratio represented 21.26% and the lowest represented only 12.47% compared to the 3M stethoscope, demonstrating the better performance in denoising ability of this system than state-of-the-art equipment. Furthermore, based on the heart sounds recorded with this system, some diagnosis results were achieved from an expert and compared to echocardiography reports. The diagnoses were correct except for two uncertain items, which greatly confirmed the fact that this system could reserve complete pathological information in the end.

MicroRNA-150 Modulates Adipogenic Differentiation of Adipose-Derived Stem Cells by Targeting Notch3.

MicroRNAs (miRNAs) influence stem cell functions, including mobilization, proliferation, and differentiation. miR-150 is abundantly expressed in monocytes. Knockdown of miR-150 promotes bone marrow stem cell migration. The role of miR-150 in adipose-derived stem cells (ADSCs) is unclear. In this study, the effects of miR-150 on adipogenic differentiation and proliferation of ADSCs were investigated. ADSCs were isolated from the inguinal adipose tissue of wild-type (WT) and miR-150 knockout (KO) mice and were induced for adipogenic differentiation. The miR-150 level was detected by real-time PCR. ADSCs were transfected by miR-150 or small-interfering RNA (siRNA) of Notch3. MTT assay and colony formation assay were performed in miR-150 knockdown and control ADSCs. Real-time PCR showed that miR-150 was expressed in ADSCs. miR-150 knockdown significantly decreased the capacity of adipogenic differentiation of ADSCs, as compared with their counterparts from WT mice. It is intriguing that the overexpression of miR-150 significantly increased C/EBPα and PPAR-γ expression and lipid formation in ADSCs with adipogenic induction. Overexpression of miR-150 significantly decreased Notch3 expression in ADSCs compared with the control groups. Furthermore, Notch3 inhibition promoted the adipogenic differentiation in ADSCs. miR-150 also suppressed proliferation potential and the expression of Nanog in ADSCs. In summary, this study demonstrates, for the first time, that miR-150 promotes adipogenic differentiation and inhibits proliferation of ADSCs. miR-150 regulates adipogenic differentiation of ADSCs, likely mediated by the downregulation of Notch3.

[Automatic Identifcation of Heart Block Precise Location Based on Sparse Connection Residual Network].

OBJECTIVE: To classify Right Bundle Branch Block (RBBB),Left Bundle Branch Block (LBBB) and normal ECG signals automatically. METHODS: The MIT-BIH database was used as experimental data sources.The training set and test set were extracted for training and testing network models.Based on convolutional neural network,this paper proposed the core algorithm:sparse connection residual network.Compared the sparse connected residual network with classic network models,then evaluated the recognition effect of the model. RESULTS: The accuracy of the test set the MIT-BIH database was 95.2%,the result is better than classic network models. CONCLUSIONS: The algorithm proposed in this paper can assist doctors in the diagnosis of heart block related disease and place a high value on clinical application.

Morphology of the initial nephron-collecting duct connection in mice using computerized 3D tracing and electron microscopy.

Recently, the cellular origin of the connecting tubule (CNT) has been genetically characterized. The CNT is a segment between two embryonically different structures, the collecting duct originating from ureteric bud (UB), and the nephron derived from the cap mesenchyme. However, the cellular detail at the initial connection is limited. The present study demonstrated that the initial connection was composed of cells which were closely associated with the renal vesicle (RV), the initial nephron, and connected with the basal epithelium of the terminal UB tip at discrete points. The identification of the RV and UB tip was based on tracing of tubules on serial epoxy sections at mouse embryonic day 17.5. The cells at the initial connection were characterized by 1) irregularly-shaped nuclei and cells with cytoplasmic processes, 2) electron dense nuclei, 3) abundant intercellular spaces, 4) extensive cell-cell contacts with cell junctions, often zonulae adherences and occasionally focal fusion of opposing plasma membranes, and 5) numerous mitochondria, densely packed rosette-like polyribosomes, and widespread rER in the cytoplasm. Moreover, the tracing revealed that a terminal UB tip frequently connected to two nephrons at different developing stages. The UB tips, the initial connections, and the distal tubules of the S-shaped bodies did not express Na+-Cl- cotransporter, H+-ATPase, or aquaporin 2, while they were expressed in immature CNT of the capillary-loop stage nephrons throughout the kidney development. Consequently, the cells at the initial connection exhibit the morphological features suggestive of energy demanding, protein producing, and intercellular communicating. The cell morphology together with transporter development indicates that these cells serve several functions during the development of the initial connection, and that these functions are different from the cells' final functions as transportation.

ERp57­small interfering RNA silencing can enhance the sensitivity of drug­resistant human ovarian cancer cells to paclitaxel.

ERp57 has been identified to be associated with the chemoresistance of human ovarian cancer. However, its biological roles in the chemoresistance phenotype remain unclear. In the present study, the association of ERp57 with paclitaxel­resistant cellular behavior was investigated and the sensitivity enhancement of chemoresistant human ovarian cancer cells to paclitaxel was examined using ERp57­small interfering (si)RNA silencing. Cell viability, cell proliferation, cell apoptosis and cell migration were detected using an MTT assay, clonogenic assay, flow cytometry analysis and transwell assay. Furthermore, mRNA expression levels of ERp57 and protein expression levels of ERp57, STAT3, phosphorylated STAT3, PCNA, nucelolin, TUBB3, P-gp, vimentin, Bcl-2, Bax, Bcl-xl, p53, MMP1, MMP2 and MMP9 of paclitaxel-sensitive human SKOV3 ovarian cancer cells were compared with paclitaxel-resistant counterpart SKOV3/tax using the real-time PCR and western blot analysis. ERp57 was highly expressed in the paclitaxel­resistant SKOV3/tax cells, and experimental results concluded that the paclitaxel­resistance phenotype was due primarily to the activation of the STAT3 signaling pathway. ERp57 overexpression by lentiviral particle infection decreased the sensitivity of SKOV3 cells to paclitaxel. Furthermore, ERp57­siRNA silencing restored paclitaxel sensitivity of SKOV3/tax cells. Notably, the IC50 value of ERp57­siRNA silenced SKOV3/tax cells was reduced to the original level and colony survival was significantly decreased in comparison with that of SKOV3/tax cells. Additionally, co­treatment of ERp57­siRNA silencing and paclitaxel could inhibit the STAT3 signaling pathway and downregulate the expression levels of downstream proteins. Notably, ERp57­siRNA and 100 nM paclitaxel co­treatment downregulated Bcl­2, Bcl­xl, MMP2, MMP9, TUBB3 and P­gp expression levels and upregulated the expression of Bax protein. Furthermore, co­treatment promoted change of the isoform of p53 to p53/p47. Bioinformatics analyses supported the experimental observations that ERp57 was associated with drug resistance in ovarian cancer. The present study implies that ERp57 is a potential therapeutic target for the treatment of paclitaxel­resistant human ovarian cancer.