Search

Background: To compare biparametric (bp) MRI radiomics signatures and traditional MRI model for the preoperative prediction of bladder cancer (BCa) grade. Methods: This retrospective study included 255 consecutive patients with pathologically confirmed 113 low-grade and 142 high-grade BCa who underwent preoperative MRI, including T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC). The traditional MRI nomogram model was developed using univariate and multivariate logistic regression by the mean apparent diffusion coefficient (mADC), vesical imaging reporting and data system (VI-RADS) scoring, tumor size and number of tumors. Volumes of interest were manually drawn on T2WI and ADC maps by two radiologists. Using ANOVA, correlation and LASSO methods to select features. Then, a logistic regression (LR) classifier was used to develop the radiomics signatures in the training set and assessed in the validation set. 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 (DCA) was performed by estimating the clinical usefulness of the two models in both the training and validation sets. Results: The areas 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 three groups of radiomics model [ADC, T2WI, bp-MRI (ADC and T2WI)]-based logistic regression analysis algorithms 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 and was compared using the DeLong test (P = 0.026 and 0.023 in the training and validation cohorts, respectively). DCA indicated that the radiomics model had higher net benefits than the traditional MRI model. Conclusions: The bp-MRI radiomics model may be helpful for distinguishing high-grade and low-grade BCa and outperformed the traditional MRI model. Multicenter validation is needed to acquire high-level evidence for its clinical application.

This paper combines improved GrowCut and Zernik feature extraction and ensemble learning techniques such as KNN, SVM, and MLP algorithms to prostate cancer detection and lesion segmentation in MRI. We use improved GrowCut algorithm for segmentation of the suspected cancer area and the combination of machine learning algorithms such as KNN, SVM, and MLP in the ensemble learning system to detect prostate cancer. We found that the accuracy of this method, which is a combination of several methods, improved by about 20% compared to other methods. Other metrics such as precision, recall, and error of proposed method have been improved.

To evaluate the ability of preoperative MRI-based radiomic features in predicting lymph node metastasis (LNM) in patients with cervical cancer.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.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].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.

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.

Aiming to the problem of measurement set partition of multiple extended target, a measurement set partition algorithm based on kernel fuzzy C-means (KFCM) clustering is proposed. Firstly, according to the FCM algorithm, the initial cluster centers are generated. Then, the distance function of FCM algorithm are changed by kernel function. Finally, this paper constructs new interation cycle to obtain tracking results. Experimental results shows that the proposed algorithm improves the performance of multiple extended target tracking compared with FCM partition and K-means++ partition effectively.

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.

Human posture recognition is gaining increasing attention due to its widespread application. Besides optical and wearable methods for human posture recognition, radar is also an important sensor with the advantage of independence of the condition of work. In this paper, it is proposed to recognize human posture based on multi-channel Synthetic Aperture Radar (SAR) at 77GHz. We obtain 2D radar images of human postures through synthetic aperture technology, and then obtain 3D radar images through multi-channel interferometry technology. By analyzing radar images with different postures and different resolutions, we find that the height distribution characteristics of radar images are discrepant under different postures. We propose to use the statistical characteristics of their height distribution to discriminate common human postures, like standing, sitting/squatting and lying. The final experimental results verify the feasibility and effectiveness of proposed method.

Indoor synthetic aperture radar (SAR) obtains the image of a room with a track at one end of the room. Imaging algorithms used in this geometry should preserve the precise hyperbolic range history. Back-Projection algorithm (BPA) and Range-Migration algorithm (RMA) are two typical algorithms that could be used in indoor SAR imaging applications. The performance of the two algorithms is compared using the data collected by a 77 GHz radar, and the specific characteristics of indoor SAR images are analyzed. The enclosed environment causes strong multiple reflections and produces mirror images beyond the physical size of the room. When implementing RMA, the aperture data should be severely zero-padded or a window in range-azimuth-frequency domain should be used to avoid the aliasing in the image. The imaging geometry varies at different locations in the room, and the targets with smaller spectrum support will be attenuated by the wavenumber domain interpolation in RMA. These results are helpful in indoor SAR image comprehension and object identification.

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(e-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.

With the FMCW SAR working at high squint, the coupling between the range and azimuth greatly increases. A linear range walk correction(LRWC) is adopt to reduce the range-azimuth coupling and to remove the linear component of target range cell migration. After the LRWC, we can use the side-looking method for the squint SAR. Then the azimuth nonlinear chirp scaling(ANLCS) is applied to equalize the azimuth frequency modulation rate for azimuth compression in azimuth processing. In this paper, we compare and analyze range processing and the ANLCS with different accuracy in our scenes. And the simulation experiments of different processing provide a basis for choosing a better imaging algorithm for our requirement.

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.

Electrically conductive hydrogels have great potential for biomedical applications. However, they usually exhibited poor mechanical properties. We present a simple approach to prepare injectable conductive interpenetrating polymer network (IPN) hydrogels with enhanced mechanical properties. The in situ forming IPN conductive hydrogels were based on gelatin-graft-polyaniline and carboxymethyl-chitosan, which were crosslinked with oxidized dextran via Schiff base at physiological conditions. Storage modulus of the injectable conductive IPN hydrogels was greatly increased. Gelation time, swelling ratio, pore size and conductivity of these injectable hydrogels were adjusted by polyaniline content. Electroactivity of the hydrogels was studied by UV-vis and cyclic voltammetry. Cytocompatibility of the hydrogels was investigated by cell adhesion and proliferation of C2C12 myoblasts and adipose-derived mesenchymal stem cells. The in vivo biocompatibility of the hydrogels was confirmed by H&E staining. These injectable conductive hydrogels with good mechanical properties and biocompatibility as bioactive biomaterials have great potential for drug delivery and tissue engineering.

Cytocompatible electrically conducting hydrogels based on amphoteric carboxymethyl chitosan (CMCS) and aniline oligomers with bioactive molecule delivery properties were presented. A series of conductive CMCS hydrogels with different aniline pentamer (AP) content were synthesized by a one-pot reaction with the combination of grafting and crosslinking reaction via glutaraldehyde. The conductivities of the swollen hydrogels are between 1.14 × 10−4 and 4.23 × 10−4 S/cm by tuning the AP content. Swelling ratio was controlled by the AP content and crosslinking degree of the hydrogels. The hydrogels showed absorption capacity of diclofenac sodium (DCS) as a model molecule and released DCS in a controlled manner. Morphologies and mechanical properties of the hydrogels were characterized by SEM and rheometer, respectively. The biocompatibility of the hydrogels was confirmed by C2C12 myoblast cells using Live/Dead assay and Alamar blue assay. These conducting hydrogels with controlled release capacity as bioactive scaffolds have potential application for tissue regeneration.

Electroactive injectable degradable hydrogels have great potential as bioactive scaffolds for tissue regeneration. We present the development of a series of in situ forming biodegradable electroactive hydrogels which were synthesized by in situ crosslinking of gelatin-graft-polyaniline (GP) by genipin at body temperature. The chemical structure and electroactivity of the GP co-polymers was confirmed. The formation of in situ hydrogels was demonstrated by the test tube inversion method and rheology measurement. Gelation time, swelling ratio and degradation rate of the hydrogels were controlled by the polyaniline content and genipin content. The conductivity of the hydrogels in the swollen state increased with increasing the polyaniline content in the materials. Interestingly, the hydrogels exhibited a linear release profile of in situ encapsulated diclofenac sodium. The non-cytotoxicity of the hydrogels was confirmed via cell adhesion and proliferation by using bone marrow mesenchymal stem cells and rat C2C12 myoblast cells. These in situ formed degradable electroactive hydrogels represent a new class of biomaterials and as biomimetic scaffolds they have great potential for sophisticated tissue engineering, such as bone, muscle and neural regeneration.

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.