Classification of multi-feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks.

BACKGROUND: Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI-RADS) category 4 has the highest false-positive value of about 30% among five categories. The classification task in BI-RADS category 4 is challenging and has not been fully studied. PURPOSE: This work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B-mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy. METHODS: First, original US images coming directly from the hospital were cropped and resized. In 1385 B-mode US BI-RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K-means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B-mode images, K-means clustering images and entropy images, they are fused in a three-channel form multi-feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1-score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro-Wilk test. DeLong test and independent t-test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti-log compression was conducted but no improvement has shown in CNNs classification results. RESULTS: With multi-feature fusion images, DenseNet121 has highest accuracy of 80.22 ± 1.45% compared to other CNNs, precision of 77.97 ± 2.89% and AUC of 0.82 ± 0.01. Multi-feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B-mode images (p < 0.05). CONCLUSION: The CNNs with multi-feature fusion show a good potential of reducing the false-positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false-positive rate in breast tumor within US BI-RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise.

Transcranial ultrasound estimation of viscoelasticity and fluidity in brain tumors aided by transcranial shear waves.

Cerebral diseases, such as brain tumors, are intricately linked to the mechanical properties of brain tissues. Estimating the mechanical properties of brain tumors using transcranial ultrasound is a promising approach. However, the complexity of cranial features introduces challenges, such as ultrasound attenuation and interference from multidirectional transcranial shear waves induced by impact vibrations. To address these issues, this study proposes a transcranial ultrasound estimation method assisted by transcranial shear vibrations. Transcranial vibrations apply shear forces on the parietal bone, inducing unidirectional transcranial shear waves within brain tissue, as validated through simulations. Shear waves at different frequencies were captured via transcranial ultrasound, which were used to assess the viscoelasticity and fluidity of brain tumors. Transcranial experimental validations were conducted in 3D-printed models with tumor phantoms and ex vivo animal tumors. Vibration safety assessments were also performed. The results demonstrate that transcranial ultrasound can detect micron displacements induced by transcranial shear waves. In phantom and ex vivo animal experiments, speed distribution maps were employed to determine the size and location of one or two tumors enclosed in the skull model. The results revealed that the proposed approach could detect tumors with a minimum diameter of 0.8 cm and an inter-tumor distance of 0.8 cm. Notably, significant differences in viscoelasticity and fluidity between normal brain tissue and brain tumors were found (p<0.001). The maximum assessment errors for the elasticity, viscosity, and fluidity using transcranial ultrasound were 11.90%, 4.82%, and 0.73%, respectively, indicating that fluidity was more robust than viscoelasticity. The maximum accelerations of the skull were only 3.21 ms-2.

Noninvasive assessment of pressure distribution and fractional flow in middle cerebral artery using microbubbles and plane wave in vitro.

Fractional flow has been proposed for quantifying the degree of functional stenosis in cerebral arteries. Herein, subharmonic aided pressure estimation (SHAPE) combined with plane wave (PW) transmission was employed to noninvasively estimate the pressure distribution and fractional flow in the middle cerebral artery (MCA) in vitro. Consequently, the effects of incident sound pressure (peak negative pressures of 86-653 kPa), pulse repetition frequency (PRF), number of pulses, and blood flow rate on the subharmonic pressure relationship were investigated. The radio frequency data were stored and beamformed offline, and the subharmonic amplitude over a 0.4 MHz bandwidth was extracted using a 12-cycle PW at 4 MHz. The optimal incident sound pressure was 217 kPa without skull (sensitivity = 0.09 dB/mmHg; r2 = 0.997) and 410 kPa with skull (median sensitivity = 0.06 dB/mmHg; median r2 = 0.981). The optimal PRF was 500 Hz, as this value affords the highest sensitivity (0.09 dB/mmHg; r2 = 0.976) and temporal resolution. In addition, the blood flow rate exhibited a lesser effect on the subharmonic pressure relationship in our experimental setup. Using the optimized parameters, the blood pressure distribution and fractional flow (FFs) were measured. As such, the FFs value was in high agreement with the value measured using the pressure sensor (FFm). The mean ± standard deviations of the FF difference (FFm - FFs) were 0.03 ± 0.06 without skull and 0.01 ± 0.05 with skull.

Clutter filtering of angular domain data for contrast-free ultrafast microvascular imaging.

Objective. Contrast-free microvascular imaging is clinically valuable for the assessment of physiological status and the early diagnosis of diseases. Effective clutter filtering is essential for microvascular visualization without contrast enhancement. Singular value decomposition (SVD)-based spatiotemporal filter has been widely used to suppress clutter. However, clinical real-time imaging relies on short ensembles (dozens of frames), which limits the implementation of SVD filtering due to the large error of eigen-correlated estimations and high dependence on optimal threshold when used in such ensembles.Approach. To address the above challenges of imaging in short ensembles, two optimized filters of angular domain data are proposed in this paper: grouped angle SVD (GA-SVD) and angular-coherence-based higher-order SVD (AC-HOSVD). GA-SVD applies SVD to the concatenation of all angular data to improve clutter rejection performance in short ensembles, while AC-HOSVD applies HOSVD to the angular data tensor and utilizes angular coherence in addition to spatial and temporal features for filtering. Feasible threshold selection strategies in each feature space are provided. The clutter rejection performance of the proposed filters and SVD was evaluated with Doppler phantom andin vivostudies at different cases. Moreover, the robustness of the filters was explored under wrong singular value threshold estimation, and their computational complexity was studied.Main results. Qualitative and quantitative results indicated that GA-SVD and AC-HOSVD can effectively improve clutter rejection performance in short ensembles, especially AC-HOSVD. Notably, the proposed methods using 20 frames had similar image quality to SVD using 100 frames.In vivostudies showed that compared to SVD, GA-SVD increased the signal-to-noise-ratio (SNR) by 6.03 dB on average, and AC-HOSVD increased the SNR by 8.93 dB on average. Furthermore, AC-HOSVD remained better power Doppler image quality under non-optimal thresholds, followed by GA-SVD.Significance. The proposed filters can greatly enhance contrast-free microvascular visualization in short ensembles and have potential for different clinical translations due to the performance differences.

Effect of tissue viscoelasticity and adjacent phase-changed microbubbles on vaporization process and direct growth threshold of nanodroplet in an ultrasonic field.

Understanding the behavior of nanodroplets converted into microbubbles with applied ultrasound is an important problem in tumor therapeutical and diagnostic applications. In this study, a comprehensive model is proposed to investigate the vaporization process and the direct growth threshold of the nanodroplet by following the vapor bubble growth, especially attention devoted to the effect of tissue viscoelasticity and adjacent phase-changed microbubbles (PCMBs). It is shown that the ultrasonic energy must be sufficiently strong to counterbalance the natural condensation of the vapor bubble and the tissue stiffness-inhibitory effect. The softer tissue with a lower shear modulus favors the vaporization process, and the nanodroplet has a lower direct growth threshold in the softer tissue. Moreover, the adjacent PCMBs show a suppression effect on the vaporization process due to the negative value of the secondary Bjerknes force, implying an attractive force, preventing the nanodroplet from escaping from the constraint of the adjacent PCMBs. However, according to the linear scattering theory, the attractive force signifies that the constraint is weak, causing the direct growth threshold to increase in the range of 0.09-0.24 MPa. The weak increase in threshold demonstrates that the direct growth threshold is relatively unaffected by the adjacent PCMBs. The prediction results of our model are in good agreement with the experiment results obtained by the echo enhancement method, in which the threshold is relatively independent of the intermediate concentration. The findings presented here provide physical insight that will be further helpful in understanding the complex behavior of the nanodroplet responses to ultrasound in practical medical applications.

Boiling Histotripsy Using Dual-Frequency Protocol on Murine Breast Tumor Model and Promotes Immune Activation.

Histotripsy is an ultrasound-guided, noninvasive, nonthermal ablation therapy that can mechanically lyse target tissues. There have been no reports of enhanced histotripsy for large-volume triple-negative breast cancer (TNBC). This study aims to verify the ability of a novel approach of dual-frequency mode combined with two-stage millisecond-length ultrasound pulses (DF-TS) to accelerate the treatment of murine subcutaneous 4T1 tumors and determine immune changes after treatment. A custom-designed 1.1-/2.2-MHz two-element confocal-annular array was used to treat approximately 6-mm tumors under ultrasound guidance and real-time monitoring. Two-stage millisecond-length ultrasound pulses were used to generate approximate cuboid ablation volumes (diagonal 5-6 mm) within each tumor, with a dose of 100 pulses/point. Immune effects were characterized by changes of pro-inflammatory cytokine levels and infiltration levels of immune cells. In all targeted treatment areas, bubble cloud activity was visualized by ultrasound monitoring. The novel protocol resulted in elliptical and controllable sized lesions, reducing the number of scanning points, and was generally well tolerated. After treatment, tumor growth experienced a seven-day stagnation period, the survival period of mice was prolonged, and the levels of pro-inflammatory cytokines and immune cell infiltration increased. This study demonstrates that DF-TS boiling histotripsy (BH) has a noninvasive, efficient, and precise ablation ability for TNBC and potentially enhances immune responses.

Focused acoustic vortex-mediated sonochemotherapy for the amplification of immunogenic cell death combined with checkpoint blockade to potentiate cancer immunotherapy.

Sonodynamic therapy (SDT) as an auxiliary modality of cancer immunotherapy enhances systemic anti-tumor immunity. However, the efficiency of SDT-mediated immunotherapy based on conventional focused ultrasound (FUS) is restricted by the tiny focal region of FUS. Focused acoustic vortex (FAV) possessing a larger focal region, can induce stronger cavitation and thermal effects than FUS with the same parameters, having the potential to overcome this issue. This research investigated the feasibility of FAV-mediated sonochemotherapy combined with the immune checkpoint blockade (ICB) to reshape immunosuppressive tumor microenvironment (TME), inhibit tumor growth and lung metastasis. Sonosensitizer chlorin e6 (Ce6) and chemotherapeutic agent doxorubicin (Dox) were co-loaded into microbubble-liposome complex to compose Ce6/Dox@Lip@MBs (CDLM) for "all-in-one" synergistic sonochemotherapy, whose main components were clinical approved. FAV-activated CDLM significantly enriched immunogenic cell death (ICD) inducers in tumors and amplified ICD of cancer cells compared with FUS-activated CDLM. Furthermore, the amplified-ICD combined with ICB increased the infiltration of cytotoxic T lymphocytes and natural killer cells, polarized M2 macrophages to M1 macrophages, and decreased regulatory T cells. This study provides a multifunctional strategy for enriching ICD inducers in tumors and amplifying ICD to ameliorate immunosuppressive TME and potentiate systemic anti-tumor immunity.

Incremental learning for an evolving stream of medical ultrasound images via counterfactual thinking.

Despite the fact that traditional deep learning (DL) approaches provide promising accuracy and efficiency in medical ultrasound image analysis, they cannot replace the physician in making a diagnosis since the DL model is only appropriate in static application scenarios. Currently, most DL-based models are incapable of learning new tasks in the dynamic clinical environments due to the catastrophic forgetting of old tasks. To address the above problem, we propose an incremental classifier that is sequentially trained on evolving tasks for medical ultrasound images by counterfactual thinking. Specifically, the proposed model consists of a feature extractor and a classifier that can add new classes at any time during training. Toward a more discriminative model in the continual learning setting, a contrastive strategy is designed to leverage fine-grained information by generating a series of counterfactual regions. For model optimization, we design a multi-task loss made up of a knowledge distillation loss, a cross-entropy loss, and a contrasting loss. This objective jointly enjoys the merits of less forgetting, better accuracy, and fine-grained information utilization. A newly collected dataset with 52 medical ultrasound classification tasks is used to demonstrate the effectiveness of our method. The proposed approach achieves 76.59%, 11.67%, and 7.93% in terms of the average incremental accuracy, forgetting rate, and feature retention, respectively.

Ultrasound image segmentation using Gamma combined with Bayesian model for focused-ultrasound-surgery lesion recognition.

This study aims to investigate the feasibility of combined segmentation for the separation of lesions from non-ablated regions, which allows surgeons to easily distinguish, measure, and evaluate the lesion area, thereby improving the quality of high-intensity focused-ultrasound (HIFU) surgery used for the non-invasive tumor treatment. Given that the flexible shape of the Gamma mixture model (GΓMM) fits the complex statistical distribution of samples, a method combining the GΓMM and Bayes framework is constructed for the classification of samples to obtain the segmentation result. An appropriate normalization range and parameters can be used to rapidly obtain a good performance of GΓMM segmentation. The performance values of the proposed method under four metrics (Dice score: 85%, Jaccard coefficient: 75%, recall: 86%, and accuracy: 96%) are better than those of conventional approaches including Otsu and Region growing. Furthermore, the statistical result of sample intensity indicates that the finding of the GΓMM is similar to that obtained by the manual method. These results indicate the stability and reliability of the GΓMM combined with the Bayes framework for the segmentation of HIFU lesions in ultrasound images. The experimental results show the possibility of combining the GΓMM with the Bayes framework to segment lesion areas and evaluate the effect of therapeutic ultrasound.

Three-dimensional ultrasound image reconstruction based on 3D-ResNet in the musculoskeletal system using a 1D probe:ex vivoandin vivofeasibility studies.

Objective. Three-dimensional (3D) ultrasound (US) is needed to provide sonographers with a more intuitive panoramic view of the complex anatomical structure, especially the musculoskeletal system. In actual scanning, sonographers may perform fast scanning using a one-dimensional (1D) array probe .at random angles to gain rapid feedback, which leads to a large US image interval and missing regions in the reconstructed volume.Approach.In this study, a 3D residual network (3D-ResNet) modified by a 3D global residual branch (3D-GRB) and two 3D local residual branches (3D-LRBs) was proposed to retain detail and reconstruct high-quality 3D US volumes with high efficiency using only sparse two-dimensional (2D) US images. The feasibility and performance of the proposed algorithm were evaluated onex vivoandin vivosets.Main results. High-quality 3D US volumes in the fingers, radial and ulnar bones, and metacarpophalangeal joints were obtained by the 3D-ResNet, respectively. Their axial, coronal, and sagittal slices exhibited rich texture and speckle details. Compared with kernel regression, voxel nearest-neighborhood, squared distance weighted methods, and a 3D convolution neural network in the ablation study, the mean peak-signal-to-noise ratio and mean structure similarity of the 3D-ResNet were up to 28.53 ± 1.29 dB and 0.98 ± 0.01, respectively, and the corresponding mean absolute error dropped to 0.023 ± 0.003 with a better resolution gain of 1.22 ± 0.19 and shorter reconstruction time.Significance.These results illustrate that the proposed algorithm can rapidly reconstruct high-quality 3D US volumes in the musculoskeletal system in cases of a large amount of data loss. This suggests that the proposed algorithm has the potential to provide rapid feedback and precise analysis of stereoscopic details in complex and meticulous musculoskeletal system scanning with a less limited scanning speed and pose variations for the 1D array probe.

Ratiometric Fluorescent Detection of Ultrasound-Regulated ATP Release: An Ultrasound-Resistant Cu,N-Doped Carbon Nanosphere.

Focused ultrasound, as a protocol of cancer therapy, might induce extracellular adenosine triphosphate (ATP) release, which could enhance cancer immunotherapy and be monitored as a therapeutic marker. To achieve an ATP-detecting probe resistant to ultrasound irradiation, we constructed a Cu/N-doped carbon nanosphere (CNS), which has two fluorescence (FL) emissions at 438 and 578 nm to detect ultrasound-regulated ATP release. The addition of ATP to Cu/N-doped CNS was conducted to recover the FL intensity at 438 nm, where ATP enhanced the FL intensity probably via intramolecular charge transfer (ICT) primarily and hydrogen-bond-induced emission (HBIE) secondarily. The ratiometric probe was sensitive to detect micro ATP (0.2-0.6 µM) with the limit of detection (LOD) of 0.068 µM. The detection of ultrasound-regulated ATP release by Cu,N-CNS/RhB showed that ATP release was enhanced by the long-pulsed ultrasound irradiation at 1.1 MHz (+37%, p < 0.01) and reduced by the short-pulsed ultrasound irradiation at 5 MHz (-78%, p < 0.001). Moreover, no significant difference in ATP release was detected between the control group and the dual-frequency ultrasound irradiation group (+4%). It is consistent with the results of ATP detection by the ATP-kit. Besides, all-ATP detection was developed to prove that the CNS had ultrasound-resistant properties, which means it could bear the irradiation of focused ultrasound in different patterns and detect all-ATP in real time. In the study, the ultrasound-resistant probe has the advantages of simple preparation, high specificity, low limit of detection, good biocompatibility, and cell imaging ability. It has great potential to act as a multifunctional ultrasound theranostic agent for simultaneous ultrasound therapy, ATP detection, and monitoring.

Velocity field estimation in transcranial small vessel using super-resolution ultrasound imaging velocimetry.

Based on the diameter and position information of small vessels obtained by transcranial super-resolution imaging using 3 MHz low-frequency chirp plane waves, a Gaussian-like non-linear compression was adopted to compress the blood flow signals in spatiotemporal filtering (STF) data to a precise region, and then estimate the blood flow velocity field inside the region over the adjacent time intervals using ultrasound imaging velocimetry (UIV). Imaging parameters, such as the mechanical index (MI), frame rate, and microbubble (MB) concentration, are critical during the estimation of velocity fields over a short time at high MB contrast agent concentrations. These were optimized through experiments and algorithms, in which dividing the connected domain was proposed to calculate MB cluster spot centroid spacing (SCS) and the spot-to-flow area ratio (SFAR) to determine the suitable MB concentration. The results of the in vitro experiments showed that the estimation of the small vessel flow velocity field was consistent with the theoretical results; the velocity field resolution for vessels with diameters of 0.5 mm and 0.3 mm was 36 µm and 21 µm, and the error between the mean velocity and the theoretical value was 0.7 % and 0.67 %, respectively.

Non-Invasive Ultrasound Modulation of Solitary Tract Nucleus Exerts a Sustainable Antihypertensive Effect in Spontaneously Hypertensive Rats.

OBJECTIVE: We applied the method of non-invasive ultrasound (US) neuromodulation to regulate blood pressure (BP) by stimulating the solitary tract nucleus (NTS) of spontaneously hypertensive rats (SHRs). METHODS: The rats were exposed to US stimulation for 20 mins every day for two months. Morphology and function of the hypertensive target organs (heart and kidney) were then examined by echocardiography and immunohistochemical staining. C-fos immunofluorescence assays were used to evaluate neuronal activity in the US stimulated areas and to explore related neural pathways. Moreover, the effects of US stimulation on biochemical indicators angiotensinII (ANGII), aldosterone (Aldo), endothelin-1 (ET-1), atrial natriuretic factor (ANF), cortisol (Cor) in SHRs were detected. In addition, HE, TUNEL, and Nissl staining were performed to evaluate the safety of long-term transcranial US stimulation. RESULTS: After two months of US stimulation, systolic blood pressure (SBP) decreased from 170 ± 1.1 mmHg to 158 ± 1.8 mmHg, p < 0.01. What's more, US stimulation effectively inhibited the pathological process of target organs from both morphological and functional levels. With US stimulation, neuronal activities were also significantly enhanced in the NTS, ventrolateral periaqueductal gray (vlPAG), and the caudal ventrolateral medulla (CVLM) region. And US stimulation did not cause brain tissue damage. Meanwhile, the plasma levels of ANF, ANGII, Aldo, and Cor content were inhibited. CONCLUSION: US stimulation of the NTS could significantly lower BP in SHRs. SIGNIFICANCE: Non-invasive transcranial US stimulation acting on the NTS might be a potential therapeutic intervention due to its efficacy and safety.

The sustainable antihypertensive and target organ damage protective effect of transcranial focused ultrasound stimulation in spontaneously hypertensive rats.

OBJECTIVE: In this study, we aimed to investigate the sustainable antihypertensive effects and protection against target organ damage caused by low-intensity focused ultrasound (LIFU) stimulation and the underlying mechanism in spontaneously hypertensive rats (SHRs) model. METHODS AND RESULTS: SHRs were treated with ultrasound stimulation of the ventrolateral periaqueductal gray (VlPAG) for 20 min every day for 2 months. Systolic blood pressure (SBP) was compared among normotensive Wistar-Kyoto rats, SHR control group, SHR Sham group, and SHR LIFU stimulation group. Cardiac ultrasound imaging and hematoxylin-eosin and Masson staining of the heart and kidney were performed to assess target organ damage. The c-fos immunofluorescence analysis and plasma levels of angiotensin II, aldosterone, hydrocortisone, and endothelin-1 were measured to investigate the neurohumoral and organ systems involved. We found that SBP was reduced from 172 ±â€Š4.2 mmHg to 141 ±â€Š2.1 mmHg after 1 month of LIFU stimulation, P  < 0.01. The next month of treatment can maintain the rat's blood pressure at 146 ±â€Š4.2 mmHg at the end of the experiment. LIFU stimulation reverses left ventricular hypertrophy and improves heart and kidney function. Furthermore, LIFU stimulation enhanced the neural activity from the VLPAG to the caudal ventrolateral medulla and reduced the plasma levels of ANGII and Aldo. CONCLUSION: We concluded that LIFU stimulation has a sustainable antihypertensive effect and protects against target organ damage by activating antihypertensive neural pathways from VLPAG to the caudal ventrolateral medulla and further inhibiting the renin-angiotensin system (RAS) activity, thereby supporting a novel and noninvasive alternative therapy to treat hypertension.

Monitoring of thermal lesions in ultrasound using fully convolutional neural networks: A preclinical study.

Accurate monitoring of thermal ablation regions is an important guarantee for successful ablation treatment, which mainly depends on the subjective judgment of radiologists in current clinical practice. This work innovatively applied fully convolutional neural networks (FCNs) for detection and monitoring of thermal ablation regions in ultrasound (US) and comprehensively compared the performance of VGG16-FCN, U-Net, UNet++, Attention U-Net, MultiResUNet, and ResUNet, which have shown outstanding performance in medical image segmentation. The input of the models was US echo envelope data backscattered from the ablated regions. Excised porcine liver ablation dataset and clinical liver tumors ablation dataset were respectively used to evaluate the prediction ability of the models. With 1000 excised porcine liver ablation samples for training and 200 samples for testing, the UNet++ achieves both the highest Dice score (DSC) of 0.7824 ± 0.1098 and the best Hausdorff distance (HD) of 2.70 ± 1.38 mm. Additionally, considering potential clinical usage, we also tested the model generalizability by training on the excised dataset and testing on the clinical data, in which we obtained the performance with the highest DSC obtained by the ResUNet and the best HD by the UNet++. Our comparative study suggests that both UNet++ and ResUNet have relatively outstanding segmentation performance among all compared models, which are potential candidates for automatic segmentation of thermal ablation regions in US during clinical ablation treatment.

High-Performance Delivery of a CRISPR Interference System via Lipid-Polymer Hybrid Nanoparticles Combined with Ultrasound-Mediated Microbubble Destruction for Tumor-Specific Gene Repression.

The dCas9-based CRISPR interference (CRISPRi) system efficiently silences genes without causing detectable off-target activity, thus showing great potential for the treatment of cancer at the transcriptional level. However, due to the large size of the commonly used CRISPRi system, effective delivery of the system has been a challenge that hinders its application in the clinic. Herein, a combination of pH-responsive lipid-polymer hybrid nanoparticles (PLPNs) and ultrasound-mediated microbubble destruction (UMMD) is used for the delivery of the CRISPRi system. The core-shell structure of PLPNs can effectively be loaded with the CRISPRi plasmid, and increases the time spent in the circulating in vivo, and "actively target" cancer cells. Moreover, the combination of PLPNs with UMMD achieves a higher cellular uptake of the CRISPRi plasmid in vitro and retention in vivo. Furthermore, when PLPNs loaded with a CRISPRi plasmid that targets microRNA-10b (miR-10b) are used in combination with UMMD, it results in the effective repression of miR-10b in breast cancer, simultaneous disturbance of multiple cell migration and invasion-related signaling pathways, and a significant inhibition of lung metastasis. Thus, the established system presents a versatile, highly efficient, and safe strategy for delivery of the CRISPRi system both in vitro and in vivo.

Passive acoustic mapping with absolute time-of-flight information and delay-multiply-sum beamforming.

BACKGROUND: Passive acoustic mapping (PAM) is showing increasing application potential in monitoring ultrasound therapy by spatially resolving cavitation activity. PAM with the relative time-of-flight information leads to poor axial resolution when implemented with ultrasound diagnostic transducers. Through utilizing the absolute time-of-flight information preserved by the transmit-receive synchronization and applying the common delay-sum (DS) beamforming algorithm, PAM axial resolution can be greatly improved in the short-pulse excitation scenario, as with active ultrasound imaging. However, PAM with the absolute time-of-flight information (referred as AtPAM) suffers from low imaging resolution and weak interference suppression when the DS algorithm is applied. PURPOSE: This study aims to propose an enhanced AtPAM algorithm based on delay-multiply-sum (DMS) beamforming, to address the shortcomings of the DS-based AtPAM algorithm. METHODS: In DMS beamforming, the element signals delayed by the absolute time delays are first processed with a signed square-root operation and then multiplied in pairs and finally summed, the resulting beamformed output is further band-pass filtered. The performances of DS- and DMS-based AtPAMs are compared by experiments, in which an ultrasound diagnostic transducer (a linear array) is employed to passively sense the wire signals generated by an unfocused ultrasound transducer and the cavitation signals generated by a focused therapeutic ultrasound transducer in a flow phantom. The AtPAM image quality is assessed by main-lobe width (MLW), intensity valley value (IVV), area of pixels (AOP), signal-to-interference ratio (SIR), and signal-to-noise ratio (SNR). RESULTS: The single-wire experimental results show that compared to the DS algorithm, the DMS algorithm leads to an enhanced AtPAM image with a decreased transverse MLW of 0.15 mm and an improved SIR and SNR of 31.50 and 18.77 dB. For the four-wire images, the transverse (axial) IVV is decreased by 18.37 dB (13.11 dB) and the SIR (the SNR) is increased by 26.13 dB (18.47 dB) when using the DMS algorithm. The cavitation activity is better highlighted by DMS-based AtPAM, which decreases the AOP by 0.81 mm2 (-10-dB level) and 4.43 mm2 (-20-dB level) and increases the SIR and SNR by 20.14 and 10.48 dB respectively. The pixel distributions of AtPAM images of both wires and cavitation activity also indicate a better suppression of the DMS algorithm in sidelobe and noise. CONCLUSIONS: The experimental results illustrate that the DMS algorithm can improve the image quality of AtPAM compared to the DS algorithm. DMS-based AtPAM is beneficial for detecting cavitation activity during short-pulse ultrasound exposure with high resolution, and further for monitoring short-pulse ultrasound therapy.

A progressively dual reconstruction network for plane wave beamforming with both paired and unpaired training data.

High-frame-rate plane wave (PW) imaging suffers from unsatisfactory image quality due to the absence of focus in transmission. Although coherent compounding from tens of PWs can improve PW image quality, it in turn results in a decreased frame rate, which is limited for tracking fast moving tissues. To overcome the trade-off between frame rate and image quality, we propose a progressively dual reconstruction network (PDRN) to achieve adaptive beamforming and enhance the image quality via both supervised and transfer learning in the condition of single or a few PWs transmission. Specifically, the proposed model contains a progressive network and a dual network to form a close loop and provide collaborative supervision for model optimization. The progressive network takes the channel delay of each spatial point as input and progressively learns coherent compounding beamformed data with increased numbers of steered PWs step by step. The dual network learns the downsampling process and reconstructs the beamformed data with decreased numbers of steered PWs, which reduces the space of the possible learning functions and improves the model's discriminative ability. In addition, the dual network is leveraged to perform transfer learning for the training data without sufficient steered PWs. The simulated, in vivo, vocal cords (VCs), and public available CUBDL dataset are collected for model evaluation.

Dual ligand-capped gold nanoclusters for the smart detection of specific reactive oxygen species.

Quantitative detection of different types of reactive oxygen species (ROS) is vital for understanding the crucial roles of them in biological processes. However, few researches achieved the detection of multiple types of ROS with one probe until now. Given this, we designed and prepared fluorescent gold nanoclusters capped by dual ligand bovine serum albumin and lysozyme (BSA-LYS-AuNCs), which could detect 3 specific types of ROS based on its different fluorescent responses to H2O2, •OH and ClO-, respectively. The limit of detection (LOD) of H2O2, •OH, and ClO- was as low as 0.82 µM, 0.45 µM, and 0.62 µM. Moreover, as an important ROS type, ClO- was detected with high sensitivity and low LOD by BSA-LYS-AuNCs. It was also proved that the crosslinking of protein mainly contributed to the unique fluorescent characteristics of the probe exposing to ClO-. Furthermore, the fluorescent probe achieved the smart detection of hROS (including •OH and ClO-) and wROS (the form of H2O2) in the real sample, which could also been applied specifically to the detection of antioxidants, e.g. ascorbic acid. The gold nanoclusters developed have high potential for the smart detection of multiple ROS in the body fluid of organisms.