Computer-Aided Classification of Breast Lesions Based on US RF Time Series Using a Novel Machine Learning Approach.

OBJECTIVES: One of the most promising adjuncts for screening breast cancer is ultrasound (US) radio-frequency (RF) time series. It has the superiority of not requiring any supplementary equipment over other methods. This research aimed to propound a machine learning (ML) approach for automatically classifying benign, probably benign, suspicious, and malignant breast lesions based on the features extracted from the accumulated US RF time series. METHODS: In this article, 220 data of the aforementioned categories, recorded from 118 patients, were analyzed. The dataset, named RFTSBU, was registered by a SuperSonic Imagine Aixplorer medical/research system equipped with a linear transducer. The regions of interest (ROIs) of the B-mode images were manually selected by an expert radiologist before computing the suggested features. Regarding time, frequency, and time-frequency domains, 291 various features were extracted from each ROI. Finally, the features were classified by a pioneering technique named the reference classification method (RCM). Furthermore, the Lee filter was applied to evaluate the effectiveness of reducing speckle noise on the outcomes. RESULTS: The accuracy of two-class, three-class, and four-class classifications were respectively calculated 98.59 ± 0.71%, 98.13 ± 0.69%, and 96.10 ± 0.66% (considering 10 repetitions) while support vector machine (SVM) and K-nearest neighbor (KNN) classifiers with 5-fold cross-validation were utilized. CONCLUSIONS: This article represented the proposed approach, named CCRFML, to distinguish between breast lesions based on registered in vivo RF time series employing an ML framework. The proposed method's impressive level of classification accuracy attests to its capability of effectively assisting medical professionals in the noninvasive differentiation of breast lesions.

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach.

Objective: Radio Frequency Time Series (RF TS) is a cutting-edge ultrasound approach in tissue typing. The RF TS does not provide dynamic insights into the propagation medium; when the tissue and probe are fixed. We previously proposed the innovative RFTSDP method in which the RF data are recorded while stimulating the tissue. Applying stimulation can unveil the mechanical characteristics of the tissue in RF echo. Materials and methods: In this study, an apparatus was developed to induce vibrations at different frequencies to the medium. Data were collected from four PVA phantoms simulating the nonlinear behaviors of healthy, fibroadenoma, cyst, and cancerous breast tissues. Raw focused, raw, and beamformed ultrafast data were collected under conditions of no stimulation, constant force, and various vibrational stimulations using the Supersonic Imagine Aixplorer clinical/research ultrasound imaging system. Time domain (TD), spectral, and nonlinear features were extracted from each RF TS. Support Vector Machine (SVM), Random Forest, and Decision Tree algorithms were employed for classification. Results: The optimal outcome was achieved using the SVM classifier considering 19 features extracted from beamformed ultrafast data recorded while applying vibration at the frequency of 65 Hz. The classification accuracy, specificity, and precision were 98.44 ± 0.20 %, 99.49 ± 0.01 %, and 98.53 ± 0.04 %, respectively. Applying RFTSDP, a notable 24.45 % improvement in accuracy was observed compared to the case of fixed probe assessing the recorded raw focused data. Conclusions: External vibration at an appropriate frequency, as applied in RFTSDP, incorporates beneficial information about the medium and its dynamic characteristics into the RF TS, which can improve tissue characterization.

In Vitro Evaluation of Pore Size Graded Bone Scaffolds with Different Material Composition.

The demand for biomimetic and biocompatible scaffolds in equivalence of structure and material composition for the regeneration of bone tissue is relevantly high. This article is investigating a novel three-dimensional (3D) printed porous structure called bone bricks with a gradient pore size mimicking the structure of the bone tissue. Poly-ɛ-caprolactone (PCL) combined with ceramics such as hydroxyapatite (HA), ß-tricalcium phosphate (TCP), and bioglass 45S5 were successfully mixed using a melt blending method and fabricated with the use of screw-assisted extrusion-based additive manufacturing system. Bone bricks containing the same material concentration (20 wt%) were biologically characterized through proliferation and differentiation tests. Scanning electron microscopy (SEM) was used to investigate the morphology of cells on the surface of bone bricks, whereas energy dispersive X-ray (EDX) spectroscopy was used to investigate the element composition on the surface of the bone bricks. Confocal imaging was used to investigate the number of differentiated cells on the surface of bone bricks. Proliferation results showed that bone bricks containing PCL/HA content are presenting higher proliferation properties, whereas differentiation results showed that bone bricks containing PCL/Bioglass 45S5 are presenting higher differentiation properties. Confocal imaging results showed that bone bricks containing PCL/Bioglass 45S5 are presenting a higher number of differentiated cells on their surface compared with the other material contents.

Decoding temporal muscle synergy patterns based on brain activity for upper extremity in ADL movements.

Muscle synergies have been hypothesized as specific predefined motor primitives that the central nervous system can reduce the complexity of motor control by using them, but how these are expressed in brain activity is ambiguous yet. The main purpose of this paper is to develop synergy-based neural decoding of motor primitives, so for the first time, brain activity and muscle synergy map of the upper extremity was investigated in the activity of daily living movements. To find the relationship between brain activities and muscle synergies, electroencephalogram (EEG) and electromyogram (EMG) signals were acquired simultaneously during activities of daily living. To extract the maximum correlation of neural commands with muscle synergies, application of a combined partial least squares and canonical correlation analysis (PLS-CCA) method was proposed. The Elman neural network was used to decode the relationship between extracted motor commands and muscle synergies. The performance of proposed method was evaluated with tenfold cross-validation and muscle synergy estimation of brain activity with R, VAF, and MSE of 84 ± 2.6%, 70 ± 4.7%, and 0.00011 ± 0.00002 were quantified respectively. Furthermore, the similarity between actual and reconstructed muscle activations was achieved more than 92% for correlation coefficient. To compare with the existing methods, our results showed significantly more accuracy of the model performance. Our results confirm that use of the expression of muscle synergies in brain activity can estimate the neural decoding performance for motor control that can be used to develop neurorehabilitation tools such as neuroprosthesis.

Exosomes from SHED-MSC regulate polarization and stress oxidative indexes in THP-1 derived M1 macrophages.

OBJECTIVE: The inhibition of M1 macrophages may be interesting for targeted therapy with mesenchymal stem cell-derived Exosomes (MSC-EXOs). This study aimed to investigate the stem cells of human exfoliated deciduous teeth-derived EXOs (SHED-MSC-EXOs) effect on regulating the pro- and anti-oxidant indexes and inhibiting M1 macrophage polarization. Besides, an in-silico analysis of SHED-MSC-EXO miRNAs as the highest frequency of small RNAs in the exosomes was performed to discover the possible mechanism. METHODS: The flow cytometry analysis of CD80 and CD86 as M1-specific markers confirmed the polarization of macrophages derived from THP-1 cells. After exosome isolation, characterization, and internalization, THP-1-derived M1 macrophages were treated with SHED-MSC-EXOs. M1-specific markers and pro- and anti-oxidant indexes were evaluated. For in-silico analysis of SHED-MSC-EXOs miRNAs, initial miRNA array data of SHED-EXOs is collected from GEO, and the interaction of the miRNAs in M1 macrophage polarization (M1P), mitochondrial oxidative stress (MOS) and LPS-induced oxidative stress (LOS) were analyzed by miRWalk 3.0 server. Outcomes were filtered by 75th percentile signal intensity, score cut-off ≥0.95, minimum free energy (MEF)≤ -20 kcal/mol, and seed = 1. RESULTS: It shows a decrease in the expression of CD80 and CD81, a reduction in pro-oxidant indicators, and an increase in the anti-oxidant indexes (P < 0.05). Computational analysis showed that eight microRNAs of SHED-MSC-EXO miRNAs can bind to and interfere with the expression of candidate genes in the M1P, MOS, and LOS pathways simultaneously. CONCLUSION: SHED-MSCs-EXOs can be utilized to treat conditions related to M1 macrophage-induced diseases (M1IDs) due to their unique physical properties and ability to penetrate target cells easily.