Prediction of histopathologic grades of bladder cancer with radiomics based on MRI: Comparison with traditional MRI.

PURPOSE: To compare biparametric magnetic resonance imaging (bp-MRI) radiomics signatures and traditional MRI model for the preoperative prediction of bladder cancer (BCa) grade. MATERIALS AND METHODS: This retrospective study included 255 consecutive patients with pathologically confirmed 113 low-grade and 142 high-grade BCa. The traditional MRI nomogram model was developed using univariate and multivariate logistic regression by the mean apparent diffusion coefficient (ADC), vesical imaging reporting and data system, tumor size, and the number of tumors. Volumes of interest were manually drawn on T2-weighted imaging (T2WI) and ADC maps by 2 radiologists. Using one-way analysis of variance, correlation, and least absolute shrinkage and selection operator methods to select features. Then, a logistic regression classifier was used to develop the radiomics signatures. Receiver operating characteristic (ROC) analysis was used to compare the diagnostic abilities of the radiomics and traditional MRI models by the DeLong test. Finally, decision curve analysis was performed by estimating the clinical usefulness of the 2 models. RESULTS: The area under the ROC curves (AUCs) of the traditional MRI model were 0.841 in the training cohort and 0.806 in the validation cohort. The AUCs of the 3 groups of radiomics model [ADC, T2WI, bp-MRI (ADC and T2WI)] were 0.888, 0.875, and 0.899 in the training cohort and 0.863, 0.805, and 0.867 in the validation cohort, respectively. The combined radiomics model achieved higher AUCs than the traditional MRI model. decision curve analysis indicated that the radiomics model had higher net benefits than the traditional MRI model. CONCLUSION: The bp-MRI radiomics model may help distinguish high-grade and low-grade BCa and outperforming the traditional MRI model. Multicenter validation is needed to acquire high-level evidence for its clinical application.

Meta-analysis of dynamic contrast enhancement and diffusion-weighted MRI for differentiation of benign from malignant non-mass enhancement breast lesions.

Purpose: The objective of this study was to conduct a meta-analysis comparing the diagnostic efficacy of models based on diffusion-weighted imaging (DWI)-MRI, dynamic contrast enhancement (DCE)-MRI, and combination models (DCE and DWI) in distinguishing benign from malignant non-mass enhancement (NME) breast lesions. Materials and methods: PubMed, Embase, and Cochrane Library were searched, from inception to January 30, 2023, for studies that used DCE or DWI-MRI for the prediction of NME breast cancer patients. A bivariate random-effects model was used to calculate the meta-analytic sensitivity, specificity, and area under the curve (AUC) of the DCE, DWI, and combination models. Subgroup analysis and meta-regression analysis were performed to find the source of heterogeneity. Results: Of the 838 articles screened, 18 were eligible for analysis (13 on DCE, five on DWI, and four studies reporting the diagnostic accuracy of both DCE and DWI). The funnel plot showed no publication bias (p > 0.5). The pooled sensitivity and specificity and the AUC of the DCE, DWI, and combination models were 0.58, 0.72, and 0.70, respectively; 0.84, 0.69, and 0.84, respectively; and 0.88, 0.79, 0.90, respectively. The meta-analysis found no evidence of a threshold effect and significant heterogeneity among trials in terms of DCE sensitivity and specificity, as well as DWI specificity alone (I2 > 75%). The meta-regression revealed that different diagnostic criteria contributed to the DCE study's heterogeneity (p < 0.05). Different reference criteria significantly influenced the heterogeneity of the DWI model (p < 0.05). Subgroup analysis revealed that clustered ring enhancement (CRE) had the highest pooled specificity (0.92) among other DCE features. The apparent diffusion coefficient (ADC) with a mean threshold <1.3 × 10-3 mm2/s had a slightly higher sensitivity of 0.86 compared to 0.82 with an ADC of ≥1.3 × 10-3 mm2/s. Conclusion: The combination model (DCE and DWI) outperformed DCE or DWI alone in identifying benign and malignant NME lesions. The DCE-CRE feature was the most specific test for ruling in NME cancers.

An MRI-based radiomics nomogram in predicting histologic grade of non-muscle-invasive bladder cancer.

Background: Non-muscle-invasive bladder cancer (NMIBC) is categorized into high and low grades with different clinical treatments and prognoses. Thus, accurate preoperative evaluation of the histologic NMIBC grade through imaging techniques is essential. Objectives: To develop and validate an MRI-based radiomics nomogram for individualized prediction of NMIBC grading. Methods: The study included 169 consecutive patients with NMIBC (training cohort: n = 118, validation cohort: n = 51). A total of 3148 radiomic features were extracted, and one-way analysis of variance and least absolute shrinkage and selection operator were used to select features for building the radiomics score(Rad-score). Three models to predict NMIBC grading were developed using logistic regression analysis: a clinical model, a radiomics model and a radiomics-clinical combined nomogram model. The discrimination and calibration power and clinical applicability of the models were evaluated. The diagnostic performance of each model was compared by determining the area under the curve (AUC) in receiver operating characteristic (ROC) curve analysis. Results: A total of 24 features were used to build the Rad-score. A clinical model, a radiomics model, and a radiomics-clinical nomogram model that incorporated the Rad-score, age, and number of tumors were constructed. The radiomics model and nomogram showed AUCs of 0.910 and 0.931 in the validation set, which outperformed the clinical model (0.745). The decision curve analysis also showed that the radiomics model and combined nomogram model yielded higher net benefits than the clinical model. Conclusion: A radiomics-clinical combined nomogram model has the potential to be used as a non-invasive tool for the differentiating low-from high-grade NMIBCs.

MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies.

OBJECTIVE: The aim of this study was to establish a predictive nomogram for predicting prostate cancer (PCa) in patients with gray-zone prostate-specific antigen (PSA) levels (4-10.0 ng/mL) based on radiomics and other traditional clinical parameters. METHODS: In all, 274 patients with gray-zone PSA levels were included in this retrospective study. They were randomly divided into training and validation sets (n = 191 and 83, respectively). Data on the clinical risk factors related to PCa with gray-zone PSA levels (such as Prostate Imaging Reporting and Data System, version 2.1 [PI-RADS V2.1] category, age, prostate volume, and serum PSA level) were collected for all patients. Lesion volumes of interest (VOI) from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) imaging were annotated by two radiologists. The radiomics model, clinical model, and combined prediction model, which was presented on a nomogram by incorporating the radiomics signature and clinical and radiological risk factors for PCa, were developed using logistic regression. The area under the receiver operator characteristic (AUC-ROC) and decision, calibration curve were used to compare the three models for the diagnosis of PCa with gray-zone PSA levels. RESULTS: The predictive nomogram (AUC: 0.953) incorporating the radiomics score and PI-RADS V2.1 category, age, and the radiomics model (AUC: 0.941) afforded much higher diagnostic efficacy than the clinical model (AUC: 0.866). The addition of the rad score could improve the discriminatory performance of the clinical model. The decision curve analysis indicated that the radiomics or combined model could be more beneficial compared to the clinical model for the prediction of PCa. The nomogram showed good agreement for detecting PCa with gray-zone PSA levels between prediction and histopathologic confirmation. CONCLUSION: The nomogram, which combined the radiomics score and PI-RADS V2.1 category and age, is an effective and non-invasive method for predicting PCa. Furthermore, as well as good calibration and is clinically useful, which could reduce unnecessary prostate biopsies in patients having PCa with gray-zone PSA levels.

A meta-analysis of MRI-based radiomic features for predicting lymph node metastasis in patients with cervical cancer.

PURPOSE: To evaluate the ability of preoperative MRI-based radiomic features in predicting lymph node metastasis (LNM) in patients with cervical cancer. METHODS: PubMed, Embase, Web of Science, Cochrane Library databases, and four Chinese databases were searched to identify relevant studies published up until October 22, 2021. Two reviewers screened all papers independently for eligibility. We included diagnostic accuracy studies that used radiomics-MRI for LNM in patients with cervical cancer, using histopathology as the reference standard.Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score. Overall diagnostic odds ratio (DOR), sensitivity, specificity and area under the curve (AUC) were calculated to assess the prediction efficacy of MRI-based radiomic features in patients with cervical cancer. Spearman's correlation coefficient was calculated and subgroup analysis performed to investigate causes of heterogeneity. RESULTS: Twelve studies comprising 793 female patients were included. The pooled DOR, sensitivity, specificity, and AUC of radiomics in detecting LNM were 12.08 [confidence interval (CI) 8.18, 17.85], 80% (72%, 87%), 76% (72%, 80%), and 0.83 (0.76, 0.89), respectively. The meta-analysis showed significant heterogeneity among the included studies. No threshold effect was detected. Subgroup analysis showed that multiple sequences, and radiomics combined with clinical factors, radiomics approach [DOR:15.49 (6.06, 39.62), 18.93 (8.46, 42.38), and 10.63 (6.23, 18.12), respectively] could slightly improve diagnostic performance compared with apparent diffusion coefficient-based radiomic features, T2 + dynamic contrast-enhanced MRI-based radiomic features, T2 images-based radiomic features, single radiomics, and human reading [DOR: 4.9 (1.91, 12.74), 7.63 (3.78, 15.38), 8.31 (3.05, 22.61), 16.10 (9.10, 28.47), and 6.46 (3.08, 13.56), respectively]. CONCLUSION: Our meta-analysis showed that preoperative MRI-based radiomic features performs well in predicting LNM in patients with cervical cancer. This noninvasive and convenient tool may be used to facilitate preoperative identification of LNM.

The Diagnostic Performance of Machine Learning-Based Radiomics of DCE-MRI in Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Meta-Analysis.

OBJECTIVE: The aim of this study was to perform a meta-analysis to evaluate the diagnostic performance of machine learning(ML)-based radiomics of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) DCE-MRI in predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis(SLNM) in breast cancer. METHODS: English and Chinese databases were searched for original studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) were used to assess the methodological quality of the included studies. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were used to summarize the diagnostic accuracy. Spearman's correlation coefficient and subgroup analysis were performed to investigate the cause of the heterogeneity. RESULTS: Thirteen studies (1618 participants) were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.82 (0.75, 0.87), 0.83 (0.74, 0.89), 21.56 (10.60, 43.85), and 0.89 (0.86, 0.91), respectively. The meta-analysis showed significant heterogeneity among the included studies. There was no threshold effect in the test. The result of subgroup analysis showed that ML, 3.0 T, area of interest comprising the ALN, being manually drawn, and including ALNs and combined sentinel lymph node (SLN)s and ALNs groups could slightly improve diagnostic performance compared to deep learning, 1.5 T, area of interest comprising the breast tumor, semiautomatic scanning, and the SLN, respectively. CONCLUSIONS: ML-based radiomics of DCE-MRI has the potential to predict ALNM and SLNM accurately. The heterogeneity of the ALNM and SLNM diagnoses included between the studies is a major limitation.

Evaluating intracranial artery dissection by using three-dimensional simultaneous non-contrast angiography and intra-plaque hemorrhage high-resolution magnetic resonance imaging: a retrospective study.

BACKGROUND: There was no previous report on the three-dimensional simultaneous non-contrast angiography and intra-plaque hemorrhage (3D-SNAP) magnetic resonance imaging (MRI) sequence to diagnose intracranial artery dissection (IAD). PURPOSE: To improve the diagnostic accuracy and guide the clinical treatment for IAD by elucidating its pathological features using 3D-SNAP MRI. MATERIAL AND METHODS: From January 2015 to September 2018, 113 patients with suspected IAD were analyzed. They were divided into IAD and non-IAD groups according to the spontaneous coronary artery dissection (SCAD) criteria. All patients underwent 3D-SNAP, 3D-TOF, T2W imaging, 3D-PD, 3D-T1W-VISTA, and 3D-T1WCE) using 3.0-T MRI; clinical data were collected. The IAD imaging findings (intramural hematoma, double lumen, intimal flap, aneurysmal dilatation, stenosis, or occlusion) in every sequence were analyzed. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic efficiency of each sequence. RESULTS: There was a significant difference in the probability of intramural hematoma, relative signal intensity of intramural hematoma, double lumen, stenosis, or occlusion signs on 3D-TOF, T2W, 3D-PD, 3D-T1W-VISTA, 3D-SNAP, and 3D-T1WCE sequences (P<0.05). The 3D-SNAP and 3D-T1WCE sequences were most sensitive for diagnosing intramural hematoma and displaying double-lumen signs, respectively. The diagnostic efficiency of the 3D-SNAP sequence combined with 3D-T1WCE was the highest (area under the curve [AUC] 0.966). The AUC value of the 3D-SNAP sequence (AUC 0.897) was slightly inferior to that of 3D-T1W enhancement (AUC 0.903). CONCLUSION: 3D-SNAP MRI is a non-invasive and effective method and had the greatest potential among those methods tested for improving the diagnostic accuracy for IAD.

Altered Gray Matter Volume and Functional Connectivity in Patients With Vestibular Migraine.

SUBJECTS: Vestibular migraine (VM) is the most common neurological cause of vertigo in adults. Previous neuroimaging studies have reported structural alterations in areas associated with pain and vestibular processing. However, it is unclear whether altered resting-state functional connectivity (FC) exists in brain regions with structural abnormalities in patients with VM. METHODS: Resting-state functional magnetic resonance imaging (MRI) and three-dimensional T1-weighed MRI were performed in 30 patients with VM and 30 healthy controls (HCs). Patients underwent an evaluation of migraine and dizziness severity. FC and voxel-based morphometry (VBM) were performed using DPABI 4.3 and CAT12, respectively. The association between changes in gray matter (GM) volume or FC and clinical parameters was also analyzed. RESULTS: Compared with HCs, patients with VM demonstrated a reduced GM volume in the bilateral parietoinsular vestibular cortex (PIVC), right middle frontal gyrus, and precuneus. The GM volume of the left PIVC was negatively associated with Dizziness Handicap Inventory score in patients with VM. Taking this region as a seed region, we further observed increased FC between the left primary somatosensory cortex (S1)/inferior parietal lobule (IPL) and the left PIVC in patients with VM. CONCLUSION: FC between regions with a decline in GM volume (the PIVC and S1/IPL) is altered in patients with VM, suggesting that abnormalities in vestibular cortical network could be useful for understanding the underlying mechanisms of VM.

Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature.

Purpose: This study aimed to investigate the value of biparametric magnetic resonance imaging (bp-MRI)-based radiomics signatures for the preoperative prediction of prostate cancer (PCa) grade compared with visual assessments by radiologists based on the Prostate Imaging Reporting and Data System Version 2.1 (PI-RADS V2.1) scores of multiparametric MRI (mp-MRI). Methods: This retrospective study included 142 consecutive patients with histologically confirmed PCa who were undergoing mp-MRI before surgery. MRI images were scored and evaluated by two independent radiologists using PI-RADS V2.1. The radiomics workflow was divided into five steps: (a) image selection and segmentation, (b) feature extraction, (c) feature selection, (d) model establishment, and (e) model evaluation. Three machine learning algorithms (random forest tree (RF), logistic regression, and support vector machine (SVM)) were constructed to differentiate high-grade from low-grade PCa. Receiver operating characteristic (ROC) analysis was used to compare the machine learning-based analysis of bp-MRI radiomics models with PI-RADS V2.1. Results: In all, 8 stable radiomics features out of 804 extracted features based on T2-weighted imaging (T2WI) and ADC sequences were selected. Radiomics signatures successfully categorized high-grade and low-grade PCa cases (P < 0.05) in both the training and test datasets. The radiomics model-based RF method (area under the curve, AUC: 0.982; 0.918), logistic regression (AUC: 0.886; 0.886), and SVM (AUC: 0.943; 0.913) in both the training and test cohorts had better diagnostic performance than PI-RADS V2.1 (AUC: 0.767; 0.813) when predicting PCa grade. Conclusions: The results of this clinical study indicate that machine learning-based analysis of bp-MRI radiomic models may be helpful for distinguishing high-grade and low-grade PCa that outperformed the PI-RADS V2.1 scores based on mp-MRI. The machine learning algorithm RF model was slightly better.

Long non-coding RNA ABHD11-AS1 promotes colorectal cancer development through regulation of miR-133a/SOX4 axis.

Recently, lncRNA has been verified to regulate the development and progression of tumor. LncRNA ABHD11-AS1 has been proven to serve as an oncogene in several cancers. However, the role of ABHD11-AS1 in colorectal cancer remains totally unknown. In the present study, qRT-PCR assay revealed that ABHD11-AS1 expression was markedly higher in colorectal cancer tissues and cell lines. In addition, patients who displayed overexpression of ABHD11-AS1 showed a significantly poorer progression free survival (PFS) and overall survival (OS) by Kaplan-Meier analysis. Loss-of-function experiments suggested that silencing of ABHD11-AS1 expression could significantly reduce the proliferation, colony formation, migration and invasion of colorectal cancer cells, and increase cell apoptosis. Moreover, bioinformatics analysis, biotin pull-down assay, luciferase reporter assay, and RIP assay disclosed that ABHD11-AS1 straightly interacted with miR-133a. We also found that SOX4 was a downstream target of miR-133a and ABHD11-AS1 subsequently exerted its biological effects via modulating the expression of SOX4 in colorectal cancer cells. Collectively, these findings manifested that the ABHD11-AS1/miR-133a/SOX4 axis may be a cogitable and promising therapeutic target for colorectal cancer.

Stretchable degradable and electroactive shape memory copolymers with tunable recovery temperature enhance myogenic differentiation.

Development of flexible degradable electroactive shape memory polymers (ESMPs) with tunable switching temperature (around body temperature) for tissue engineering is still a challenge. Here we designed and synthesized a series of shape memory copolymers with electroactivity, super stretchability and tunable recovery temperature based on poly(ε-caprolactone) (PCL) with different molecular weight and conductive amino capped aniline trimer, and demonstrated their potential to enhance myogenic differentiation from C2C12 myoblast cells. We characterized the copolymers by Fourier transform infrared spectroscopy (FT-IR), proton nuclear magnetic resonance (1H NMR), cyclic voltammetry (CV), ultraviolet-visible spectroscopy (UV-vis), differential scanning calorimetry (DSC), shape memory test, tensile test and in vitro enzymatic degradation study. The electroactive biodegradable shape memory copolymers showed great elasticity, tunable recovery temperature around 37°C, and good shape memory properties. Furthermore, proliferation and differentiation of C2C12 myoblasts were investigated on electroactive copolymers films, and they greatly enhanced the proliferation, myotube formation and related myogenic differentiation genes expression of C2C12 myoblasts compared to the pure PCL with molecular weight of 80,000. Our study suggests that these electroactive, highly stretchable, biodegradable shape memory polymers with tunable recovery temperature near the body temperature have great potential in skeletal muscle tissue engineering application. STATEMENT OF SIGNIFICANCE: Conducting polymers can regulate cell behavior such cell adhesion, proliferation, and differentiation with or without electrical stimulation. Therefore, they have great potential for electrical signal sensitive tissue regeneration. Although conducting biomaterials with degradability have been developed, highly stretchable and electroactive degradable copolymers for soft tissue engineering have been rarely reported. On the other hand, shape memory polymers (SMPs) have been widely used in biomedical fields. However, SMPs based on polyesters usually are biologically inert. This work reported the design of super stretchable electroactive degradable SMPs based on polycaprolactone and aniline trimer with tunable recovery temperature around body temperature. These flexible electroactive SMPs facilitated the proliferation and differentiation of C2C12 myoblast cells compared with polycaprolactone, indicating that they are excellent scaffolding biomaterials in tissue engineering to repair skeletal muscle and possibly other tissues.

Synthesis of SnO2 versus Sn crystals within N-doped porous carbon nanofibers via electrospinning towards high-performance lithium ion batteries.

The design of tin-based anode materials (SnO2 or Sn) has become a major concern for lithium ion batteries (LIBs) owing to their different inherent characteristics. Herein, particulate SnO2 or Sn crystals coupled with porous N-doped carbon nanofibers (denoted as SnO2/PCNFs and Sn/PCNFs, respectively) are fabricated via the electrospinning method. The electrochemical behaviors of both SnO2/PCNFs and Sn/PCNFs are systematically investigated as anodes for LIBs. When coupled with porous carbon nanofibers, both SnO2 nanoparticles and Sn micro/nanoparticles display superior cycling and rate performances. SnO2/PCNFs and Sn/PCNFs deliver discharge capacities of 998 and 710 mA h g(-1) after 140 cycles (at 100, 200, 500 and 1000 mA g(-1) each for 10 cycles and then 100 cycles at 100 mA g(-1)), respectively. However, the Sn/PCNF electrodes show better cycling stability at higher current densities, delivering higher discharge capacities of 700 and 550 mA h g(-1) than that of SnO2/PCNFs (685 and 424 mA h g(-1)) after 160 cycles at 200 and 500 mA g(-1), respectively. The different superior electrochemical performance is attributed to the introduction of porous N-doped carbon nanofibers and their self-constructed networks, which, on the one hand, greatly decrease the charge-transfer resistance due to the high conductivity of N-doped carbon fibers; on the other hand, the porous carbon nanofibers with numerous voids and flexible one-dimensional (1D) structures efficiently alleviate the volume changes of SnO2 and Sn during the Li-Sn alloying-dealloying processes. Moreover, the discussion of the electrochemical behaviors of SnO2vs. Sn would provide new insights into the design of tin-based anode materials for practical applications, and the current strategy demonstrates great potential in the rational design of metallic tin-based anode materials.

Electroactive degradable copolymers enhancing osteogenic differentiation from bone marrow derived mesenchymal stem cells.

Mesenchymal stem cells (MSCs) have attracted great interest in the field of regenerative medicine, particularly in bone regeneration. Osteogenic differentiation from MSCs is regulated by various environmental factors including hormones, growth factors, chemicals and physical cues from biomaterials. We present the use of electroactive degradable copolymers to guide the osteogenic differentiation from bone marrow derived MSCs (BMSCs). The biodegradable conductive copolymers based on polylactide and tunable contents of the aniline oligomer were synthesized by ring opening polymerization and free radical polymerization, and subsequent functionalization with the aniline tetramer. Electroactive nanofibrous scaffolds were created via a thermally induced phase separation technique. The cell culture of BMSCs and MC3T3-E1 cells on electroactive copolymers showed that these copolymers were cytocompatible and the proliferation for both cells was significantly enhanced. Osteogenic differentiation from BMSCs on the electroactive copolymers was promoted compared to polylactide in terms of gene expression and von Kossa staining. Furthermore, protein adsorption on the electroactive copolymer surface was greatly increased, and this may contribute to the enhanced proliferation and differentiation of BMSCs. This is the first report about degradable electroactive polymers directing osteogenic differentiation from BMSCs and the results indicated that electroactive degradable polymers have great potential for application in bone regeneration.

Electroactive nanofibrous biomimetic scaffolds by thermally induced phase separation.

Fabrication of functional nanofibrous biomimetic scaffolds for tissue regeneration remains a challenge. This work demonstrates that functional nanofibrous scaffolds were fabricated from blends of polylactide with other functional polymers by a thermally induced phase separation (TIPS) technique, here exemplified by the fabrication of electroactive nanofibrous scaffolds from the blends of polylactide and an electroactive degradable tetraaniline-polylactide-tetraaniline (TPT) block copolymer by TIPS. The TPT copolymer was synthesized by coupling reaction between the carboxyl-capped tetraaniline and polylactide. The chemical structure, electroactivity, thermal properties and mechanical properties of TPT and polylactide/TPT blend films were characterized. The copolymer blends were fabricated into electroactive nanofibrous scaffolds by TIPS. The effect of aniline tetramer content, polymer concentration and phase separation temperature on the diameter of nanofibers was investigated. The adhesion and proliferation of C2C12 myoblast cells and protein adsorption on the electroactive biodegradable substrates were evaluated, and the results show that the electroactive materials are nontoxic and could enhance the C2C12 cell proliferation without electrical stimulation, and adsorbed more proteins compared to polylactide. The electrical stimulation on the electroactive substrates significantly increased the cell proliferation of C2C12 myoblasts. This work opens the way to fabricate functional nanofibrous scaffolds from the blends of polylactide and other functional polymers by TIPS.