Visualization of Microcirculation at Acupoints in vivo of Alzheimer's Disease Animal Model with Photoacoustic Microscope: A Pilot Study.

Background: Alzheimer's disease may be effectively treated with acupoint-based acupuncture, which is acknowledged globally. However, more research is needed to understand the alterations in acupoints that occur throughout the illness and acupuncture treatment. Objective: This research investigated the differences in acupoint microcirculation between normal mice and AD animals in vivo. This research also examined how acupuncture affected AD animal models and acupoint microcirculation. Methods: 6-month-old SAMP8 mice were divided into two groups: the AD group and the acupuncture group. Additionally, SAMR1 mice of the same month were included as the normal group. The study involved subjecting a group of mice to 28 consecutive days of acupuncture at the ST36 (Zusanli) and CV12 (Zhongwan) acupoints. Following this treatment, the Morris water maze test was conducted to assess the mice's learning and memory abilities; the acoustic-resolution photoacoustic microscope (AR-PAM) imaging system was utilized to observe the microcirculation in CV12 acupoint region and head-specific region of each group of mice. Results: In comparison to the control group, the mice in the AD group exhibited a considerable decline in their learning and memory capabilities (p < 0.01). In comparison to the control group, the vascular in the CV12 region and head-specific region in mice from the AD group exhibited a considerable reduction in length, distance, and diameter r (p < 0.01). The implementation of acupuncture treatment had the potential to enhance the aforementioned condition to a certain degree. Conclusions: These findings offered tangible visual evidence that supports the ongoing investigation into the underlying mechanisms of acupuncture's therapeutic effects.

Research on Coated Tool Life and Wear in Ta-2.5W Alloy Turning.

Due to its inherent high hardness, strength, and plasticity, tantalum-tungsten (Ta-W) alloy poses a considerable challenge in machining, resulting in pronounced tool wear, diminished tool lifespan, and suboptimal surface quality. This study undertook experiments utilizing uncoated carbide tools, TiAlN-coated carbide tools, and AlTiN-coated carbide tools for machining Ta-2.5W alloy. The investigation delved into the intricacies of surface temperature, tool longevity, and the distinctive wear characteristics under varying coating materials and cutting parameters. Concurrently, a comprehensive exploration of the wear mechanisms affecting the tools was conducted. Among the observed wear modes, flank wear emerged as the predominant issue for turning tools. Across all three tool types, adhesive wear and diffusion wear were identified as the principal wear mechanisms, with the TiAlN-coated tools displaying a reduced level of wear compared to their AlTiN-coated counterparts. The experimental findings conclusively revealed that TiAlN-coated carbide tools exhibited an extended tool lifespan in comparison to uncoated carbide tools and AlTiN-coated carbide tools, signifying superior cutting performance.

Investigation of correlation between shear wave elastography and lymphangiogenesis in invasive breast cancer and diagnosis of axillary lymph node metastasis.

BACKGROUND: Accurate evaluation of axillary lymph node metastasis (LNM) in breast cancer is very important. A large number of hyperplastic and dilated lymphangiogenesis cases can usually be found in the pericancerous tissue of breast cancer to promote the occurrence of tumor metastasis.Shear wave elastography (SWE) can be used as an important means for evaluating pericancerous stiffness. We determined the stiffness of the pericancerous by SWE to diagnose LNM and lymphangiogenesis in invasive breast cancer (IBC). METHODS: Patients with clinical T1-T2 stage IBC who received surgical treatment in our hospital from June 2020 to December 2020 were retrospectively enrolled. A total of 299 patients were eventually included in the preliminary study, which included an investigation of clinicopathological features, ultrasonic characteristics, and SWE parameters. Multivariable logistic regression analysis was used to establish diagnostic model and evaluated its diagnostic performance of LNM. The correlation among SWE values, collagen volume fraction (CVF), and microlymphatic density (MLD) in primary breast cancer lesions was analyzed in another 97 patients. RESULTS: The logistic regression model is Logit(P)=-1.878 + 0.992*LVI-2.010*posterior feature enhancement + 1.230*posterior feature shadowing + 0.102*posterior feature combined pattern + 0.009*Emax. The optimum cutoff value of the logistic regression model was 0.365, and the AUC (95% CI) was 0.697 (0.636-0.758); the sensitivity (70.7 vs. 54.3), positive predictive value (PPV) (54.0 vs. 50.8), negative predictive value (NPV) (76.9 vs. 69.7), and accuracy (65.2 vs. 61.9) were all higher than Emax. There was no correlation between the SWE parameters and MLD in primary breast cancer lesions. CONCLUSIONS: The logistic regression model can help us to determine LNM, thus providing more imaging basis for the selection of preoperative treatment. The SWE parameter of the primary breast cancer lesion cannot reflect the peritumoral lymphangiogenesis, and we still need to find a new ultrasonic imaging method.

Diagnostic value of applying preoperative breast ultrasound and clinicopathologic features to predict axillary lymph node burden in early invasive breast cancer: a study of 1247 patients.

BACKGROUND: Since the Z0011 trial, the assessment of axillary lymph node status has been redirected from the previous assessment of the occurrence of lymph node metastasis alone to the assessment of the degree of lymph node loading. Our aim was to apply preoperative breast ultrasound and clinicopathological features to predict the diagnostic value of axillary lymph node load in early invasive breast cancer. METHODS: The 1247 lesions were divided into a high lymph node burden group and a limited lymph node burden group according to axillary lymph node status. Univariate and multifactorial analyses were used to predict the differences in clinicopathological characteristics and breast ultrasound characteristics between the two groups with high and limited lymph node burden. Pathological findings were used as the gold standard. RESULTS: Univariate analysis showed significant differences in ki-67, maximum diameter (MD), lesion distance from the nipple, lesion distance from the skin, MS, and some characteristic ultrasound features (P < 0.05). In multifactorial analysis, the ultrasound features of breast tumors that were associated with a high lymph node burden at the axilla included MD (odds ratio [OR], 1.043; P < 0.001), shape (OR, 2.422; P = 0.0018), hyperechoic halo (OR, 2.546; P < 0.001), shadowing in posterior features (OR, 2.155; P = 0.007), and suspicious lymph nodes on axillary ultrasound (OR, 1.418; P = 0.031). The five risk factors were used to build the predictive model, and it achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.702. CONCLUSION: Breast ultrasound features and clinicopathological features are better predictors of high lymph node burden in early invasive breast cancer, and this prediction helps to develop more effective treatment plans.

Early Prediction of Response to Neoadjuvant Chemotherapy Using Quantitative Parameters on Automated Breast Ultrasound Combined with Contrast-Enhanced Ultrasound in Breast Cancer.

OBJECTIVE: This prospective study was aimed at evaluating the role of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in the early prediction of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. METHODS: Forty-three patients with pathologically confirmed invasive breast cancer treated with NAC were included. The standard for evaluation of response to NAC was based on surgery within 21 d of completing treatment. The patients were classified as having a pathological complete response (pCR) and a non-pCR. All patients underwent CEUS and ABUS 1 wk before receiving NAC and after two treatment cycles. The rising time (RT), time to peak (TTP), peak intensity (PI), wash-in slope (WIS) and wash-in area under the curve (Wi-AUC) were measured on the CEUS images before and after NAC. The maximum tumor diameters in the coronal and sagittal planes were measured on ABUS, and the tumor volume (V) was calculated. The difference (∆) in each parameter between the two treatment time points was compared. Binary logistic regression analysis was used to identify the predictive value of each parameter. RESULTS: ∆V, ∆TTP and ∆PI were independent predictors of pCR. The CEUS-ABUS model achieved the highest AUC (0.950), followed by those based on CEUS (0.918) and ABUS (0.891) alone. CONCLUSION: The CEUS-ABUS model could be used clinically to optimize the treatment of patients with breast cancer.

Automated Breast Ultrasound (ABUS)-based radiomics nomogram: an individualized tool for predicting axillary lymph node tumor burden in patients with early breast cancer.

OBJECTIVES: Preoperative evaluation of axillary lymph node (ALN) status is an essential part of deciding the appropriate treatment. According to ACOSOG Z0011 trials, the new goal of the ALN status evaluation is tumor burden (low burden, < 3 positive ALNs; high burden, ≥ 3 positive ALNs), instead of metastasis or non-metastasis. We aimed to develop a radiomics nomogram integrating clinicopathologic features, ABUS imaging features and radiomics features from ABUS for predicting ALN tumor burden in early breast cancer. METHODS: A total of 310 patients with breast cancer were enrolled. Radiomics score was generated from the ABUS images. Multivariate logistic regression analysis was used to develop the predicting model, we incorporated the radiomics score, ABUS imaging features and clinicopathologic features, and this was presented with a radiomics nomogram. Besides, we separately constructed an ABUS model to analyze the performance of ABUS imaging features in predicting ALN tumor burden. The performance of the models was assessed through discrimination, calibration curve, and decision curve. RESULTS: The radiomics score, which consisted of 13 selected features, showed moderate discriminative ability (AUC 0.794 and 0.789 in the training and test sets). The ABUS model, comprising diameter, hyperechoic halo, and retraction phenomenon, showed moderate predictive ability (AUC 0.772 and 0.736 in the training and test sets). The ABUS radiomics nomogram, integrating radiomics score with retraction phenomenon and US-reported ALN status, showed an accurate agreement between ALN tumor burden and pathological verification (AUC 0.876 and 0.851 in the training and test sets). The decision curves showed that ABUS radiomics nomogram was clinically useful and more excellent than US-reported ALN status by experienced radiologists. CONCLUSIONS: The ABUS radiomics nomogram, with non-invasive, individualized and precise assessment, may assist clinicians to determine the optimal treatment strategy and avoid overtreatment.

Clinical value of combined preoperative-postoperative neutrophil-to-lymphocyte ratio in predicting hepatocellular carcinoma prognosis after radiofrequency ablation.

OBJECTIVE: Previous studies focused on the prognostic significance of the pre- or post-operative neutrophil-lymphocyte ratio (NLR); the significance of combined pre- and post-operative NLR (PP-NLR) remains unknown. Therefore, we investigated the value of PP-NLR for predicting prognosis after radiofrequency ablation (RFA) in patients with hepatocellular carcinoma (HCC) to improve treatment and prolong survival. METHODS: We investigated pre- and post-operative NLR and PP-NLR in predicting prognosis after RFA in patients with HCC. Optimal thresholds for leukocytes, lymphocytes, neutrophils, and NLR before and after RFA were retrospectively assessed in patients with HCC who had undergone RFA between January 2018 and June 2019 in Harbin Medical University Cancer Hospital. Risk factors for early HCC recurrence and those affecting recurrence-free survival (RFS) were analyzed. RESULTS: The respective pre- and post-operative optimal thresholds were as follows: neutrophils, 3.431 and 4.975; leukocytes, 5.575 and 6.61; lymphocytes, 1.455 and 1.025; and NLR, 1.53 and 4.36. Univariate analysis revealed tumor number; alpha-fetoprotein level; post-operative leukocytes, lymphocytes, NLR, and neutrophils; pre-operative neutrophils and NLR; and PP-NLR as factors influencing early recurrence and RFS. Multivariate analysis indicated PP-NLR as an independent risk factor for poor RFS and early recurrence. CONCLUSION: PP-NLR was more effective for predicting prognosis than pre- or post-operative NLR alone for patients with HCC. ADVANCES IN KNOWLEDGE: The novelty of this study lies in the combination of pre- and post-operative NLR, namely PP-NLR, to study its prognostic value for HCC patients after RFA, which has not been found in previous studies. The contribution of our study is that PP-NLR can provide clinicians with a new reference index to judge the prognosis of patients and make timely treatment to help patients improve their prognosis.

Prediction model of axillary lymph node status using automated breast ultrasound (ABUS) and ki-67 status in early-stage breast cancer.

BACKGROUND: Automated breast ultrasound (ABUS) is a useful choice in breast disease diagnosis. The axillary lymph node (ALN) status is crucial for predicting the clinical classification and deciding on the treatment of early-stage breast cancer (EBC) and could be the primary indicator of locoregional recurrence. We aimed to establish a prediction model using ABUS features of primary breast cancer to predict ALN status. METHODS: A total of 469 lesions were divided into the axillary lymph node metastasis (ALNM) group and the no ALNM (NALNM) group. Univariate analysis and multivariate analysis were used to analyze the difference of clinical factors and ABUS features between the two groups, and a predictive model of ALNM was established. Pathological results were as the gold standard. RESULTS: Ki-67, maximum diameter (MD), posterior feature shadowing or enhancement and hyperechoic halo were significant risk factors for ALNM in multivariate logistic regression analysis (P < 0.05). The four risk factors were used to build the predictive model, and it achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.791 (95% CI: 0.751, 0.831). The accuracy, sensitivity and specificity of the prediction model were 72.5%, 69.1% and 75.26%. The positive predictive value (PPV) and negative predictive value (NPV) were 66.08% and 79.93%, respectively. Distance to skin, MD, margin, shape, internal echo pattern, orientation, posterior features, and hyperechoic halo showed significant differences between stage I and stage II (P < 0.001). CONCLUSION: ABUS features and Ki-67 can meaningfully predict ALNM in EBC and the prediction model may facilitate a more effective therapeutic schedule.

Performance of novel deep learning network with the incorporation of the automatic segmentation network for diagnosis of breast cancer in automated breast ultrasound.

OBJECTIVE: To develop novel deep learning network (DLN) with the incorporation of the automatic segmentation network (ASN) for morphological analysis and determined the performance for diagnosis breast cancer in automated breast ultrasound (ABUS). METHODS: A total of 769 breast tumors were enrolled in this study and were randomly divided into training set and test set at 600 vs. 169. The novel DLNs (Resent v2, ResNet50 v2, ResNet101 v2) added a new ASN to the traditional ResNet networks and extracted morphological information of breast tumors. The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic (ROC) curve (AUC), and average precision (AP) were calculated. The diagnostic performances of novel DLNs were compared with those of two radiologists with different experience. RESULTS: The ResNet34 v2 model had higher specificity (76.81%) and PPV (82.22%) than the other two, the ResNet50 v2 model had higher accuracy (78.11%) and NPV (72.86%), and the ResNet101 v2 model had higher sensitivity (85.00%). According to the AUCs and APs, the novel ResNet101 v2 model produced the best result (AUC 0.85 and AP 0.90) compared with the remaining five DLNs. Compared with the novice radiologist, the novel DLNs performed better. The F1 score was increased from 0.77 to 0.78, 0.81, and 0.82 by three novel DLNs. However, their diagnostic performance was worse than that of the experienced radiologist. CONCLUSIONS: The novel DLNs performed better than traditional DLNs and may be helpful for novice radiologists to improve their diagnostic performance of breast cancer in ABUS. KEY POINTS: • A novel automatic segmentation network to extract morphological information was successfully developed and implemented with ResNet deep learning networks. • The novel deep learning networks in our research performed better than the traditional deep learning networks in the diagnosis of breast cancer using ABUS images. • The novel deep learning networks in our research may be useful for novice radiologists to improve diagnostic performance.

Prediction of high nodal burden in invasive breast cancer by quantitative shear wave elastography.

BACKGROUND: Axillary imaging has been earmarked to forecast high nodal burden [≥3 metastatic axillary lymph nodes (ALN)] instead of lymph node metastasis since the Z0011 trial period. We aimed to ascertain the possibility of utilising quantitative shear wave elastography (SWE) to forecast high nodal burden in invasive breast cancer (IBC). METHODS: In our hospital, 324 patients with clinical T1-T2N0 IBC who underwent surgery from June 2020 to October 2020 were analyzed retrospectively. A total of 273 patients (84.3%) were categorized as having a limited nodal burden, while 51 patients (15.7%) had a high nodal burden. The two groups were compared in terms of clinicopathological traits, ultrasonic features, and SWE values. The diagnostic performance for prediction of high nodal burden with the optimal cutoff values was drawn by SWE value. RESULTS: The optimal cutoff values for forecasting high nodal burden were as demonstrated: 119.52 kPa for tumor Emax, 97.31 kPa for tumor Emean, 19.38 for tumor Esd, 26.22 kPa for ALN Emax, 19.79 kPa for ALN Emean, 2.32 for ALN Eratio, 3.34 for ALN Esd. Combined with the ratings of sensitivity and specificity, ALN Emax could be chosen as the optimal index if the best diagnostic achievement was contemplated (AUC: 0.856; 95% CI: 0.802-0.909). CONCLUSIONS: An Emax cutoff 26.22 kPa of ALN, 72% of women with a high nodal burden of axillary disease would be detected, but if used for clinical decision making, 13% of women with a limited nodal burden disease would be potentially over treated. This data can allow us to appropriately ascertain this subgroup and can be used as one of the therapeutic implementation resources for patient decision support.

Dependability of Automated Breast Ultrasound (ABUS) in Assessing Breast Imaging Reporting and Data System (BI-RADS) Category and Size of Malignant Breast Lesions Compared with Handheld Ultrasound (HHUS) and Mammography (MG).

PURPOSE: This study aimed to evaluate the dependability of automated breast ultrasound (ABUS) compared with handheld ultrasound (HHUS) and mammography (MG) on the Breast Imaging Reporting and Data System (BI-RADS) category and size assessment of malignant breast lesions. PATIENTS AND METHODS: A total of 344 confirmed malignant lesions were recruited. All participants underwent MG, HHUS, and ABUS examinations. Agreements on the BI-RADS category were evaluated. Lesion size assessed using the three methods was compared with the size of the pathological result as the control. Regarding the four major molecular subtypes, correlation coefficients between size on imaging and pathology were also evaluated. RESULTS: The agreement between ABUS and HHUS on the BI-RADS category was 86.63% (kappa = 0.77), whereas it was 32.22% (kappa = 0.10) between ABUS and MG. Imaging lesion size compared to pathologic lesion size was assessed correctly in 36.92%/52.91% (ABUS), 33.14%/48.84% (HHUS) and 33.44%/43.87% (MG), with the threshold of 3 mm/5 mm, respectively. The correlation coefficient of size of ABUS-Pathology (0.75, Spearman) was statistically higher than that of the MG-Pathology (0.58, Spearman) with P < 0.01, but not different from that of the HHUS-Pathology (0.74, Spearman) with P > 0.05. The correlation coefficient of ABUS-Pathology was statistically higher than that of MG-Pathology in the triple-negative subtype, luminal B subtype, and luminal A subtype (P<0.01). CONCLUSION: The agreement between ABUS and HHUS in the BI-RADS category was good, whereas that between ABUS and MG was poor. ABUS and HHUS allowed a more accurate assessment of malignant tumor size compared to MG.

RNA-Seq Technology Reveals the Mechanism of SDT Combined With Novel Nanobubbles Against HCC.

Sonodynamic therapy is widely used in the treatment and research of hepatocellular carcinoma. A novel targeted nanobubble complex mediated with Hematoporphyrin monomethyl ether and Lonidamine was structured as a sensitizer, characterized the properties, and studied the therapeutic effect on hepatocellular carcinoma. The complexes can promote the apoptosis of hepatocellular carcinoma cells and work better in combination with sonodynamic therapy. The differential expression of multiple types of RNA in hepatocellular carcinoma with sonodynamic therapy can be identified accurately with high-throughput RNA sequencing. The differential expressions of mRNA, lncRNA, and circRNA were analyzed by RNA-Seq. The enrichment analyses (Gene Ontology and KEGG) prompted the meaningful genes and pathways in the process of sonodynamic therapy in hepatocellular carcinoma cells. HMME-LND@C3F8-NBs conjugated with ultrasound is confirmed efficiently for inhibiting the development of hepatocellular carcinoma cells, and it is a combination of multiple genes and mechanisms.

Structure of LINC00511-siRNA-conjugated nanobubbles and improvement of cisplatin sensitivity on triple negative breast cancer.

The drug resistance of triple negative breast cancer (TNBC) is considered as a major obstacle for the curative effect of chemotherapy. Long intergenic noncoding RNA 00511 (LINC00511) has been considered as a target gene of drug resistance. A novel theranostic agent loaded with LINC00511-siRNA to deliver siRNA was structured, and the responses of drug sensitivity in TNBC were detected. Next-generation high-throughput RNA sequencing (RNA-Seq) was performed to accurately analyze the differential expression of mRNAs and lncRNA targets after LINC00511-siRNA transfection with low-frequency ultrasound (LFUS). The LINC00511-siRNA conjugated nanobubble complexes showed appropriate characterization, with a mean diameter of 516.1 ± 24.7 nm and a zeta potential of -38.05 ± 0.24 mV. The transfection efficiency of nanobubble complexes was approximately 50% with LFUS. By RNA-Seq, the differential expressions of lncRNA transcripts and mRNA transcripts were identified, and then analyzed. The GO and KEGG enrichment analyses revealed the TNBC drug resistance related target genes and pathways. The combination of LFUS irradiation and nanobubble complexes is regarded as an efficient and safe method for siRNA transfection. The TNBC drug resistance occurs as a result of synergistic reactions between a variety of genes and a variety of pathways.

WITHDRAWN: Application of Echocardiography Information Technology in Monitoring Cardiac Function and Peripheral Neurotoxicity of HPV Patients Infected with Breast Cancer after Chemotherapy.

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Evaluation of therapeutic effect of targeting nanobubbles conjugated with NET-1 siRNA by shear wave elastography: an in vivo study of hepatocellular carcinoma bearing mice model.

This study aimed at investigating the tumor stiffness of hepatocellular carcinoma (HCC) bearing mice model in vivo to evaluate the therapeutic efficacy of targeting nanobubbles (TNBS) conjugated with NET-1 siRNA (NET-1 siRNA-TNBS). Also tested whether shear wave elastography (SWE) could demonstrate the pathological tumor changes and used to monitor therapeutic efficacy as a noninvasive method. The HCC bearing mice model was established by injecting human HCC cell line (HepG2). The mice were then divided into three groups randomly, and were treated with TNBS conjugated with NET-1 siRNA, TNBS conjugated with negative control gene, and saline as control. US-SWE was performed for three times. SWE values of all the tumors in three groups were increased with tumor growth. Emax was correlated with tumor size (p < .05). NET-1 gene (treatment group) significantly delayed the growth of tumor size compared to other two groups (p < .0001), showing a significantly increased Emax (p < .05). Immunohistochemical results showed that the NET-1 protein expression was significantly lower than the negative control and blank groups. In conclusion, TNBS conjugated with NET-1 siRNA inhibited tumor growth and prolonged the life of experimental animals. SWE provided a noninvasive and real time imaging method to detect the changes in tumor development.

Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy.

BACKGROUND AND AIMS: According to guidelines, endoscopic resection should only be performed for patients whose early gastric cancer invasion depth is within the mucosa or submucosa of the stomach regardless of lymph node involvement. The accurate prediction of invasion depth based on endoscopic images is crucial for screening patients for endoscopic resection. We constructed a convolutional neural network computer-aided detection (CNN-CAD) system based on endoscopic images to determine invasion depth and screen patients for endoscopic resection. METHODS: Endoscopic images of gastric cancer tumors were obtained from the Endoscopy Center of Zhongshan Hospital. An artificial intelligence-based CNN-CAD system was developed through transfer learning leveraging a state-of-the-art pretrained CNN architecture, ResNet50. A total of 790 images served as a development dataset and another 203 images as a test dataset. We used the CNN-CAD system to determine the invasion depth of gastric cancer and evaluated the system's classification accuracy by calculating its sensitivity, specificity, and area under the receiver operating characteristic curve. RESULTS: The area under the receiver operating characteristic curve for the CNN-CAD system was .94 (95% confidence interval [CI], .90-.97). At a threshold value of .5, sensitivity was 76.47%, and specificity 95.56%. Overall accuracy was 89.16%. Positive and negative predictive values were 89.66% and 88.97%, respectively. The CNN-CAD system achieved significantly higher accuracy (by 17.25%; 95% CI, 11.63-22.59) and specificity (by 32.21%; 95% CI, 26.78-37.44) than human endoscopists. CONCLUSIONS: We constructed a CNN-CAD system to determine the invasion depth of gastric cancer with high accuracy and specificity. This system distinguished early gastric cancer from deeper submucosal invasion and minimized overestimation of invasion depth, which could reduce unnecessary gastrectomy.

Targeted delivery of reduced graphene oxide nanosheets using multifunctional ultrasound nanobubbles for visualization and enhanced photothermal therapy.

Ultrasound molecular imaging as a promising strategy, which involved the use of molecularly targeted contrast agents, combined the advantages of contrast-enhanced ultrasound with the photothermal effect of reduced graphene oxide (rGO). METHODS AND RESULTS: The heparin sulfate proteoglycan glypican-3 (GPC3) is a potential molecular target for hepatocellular carcinoma (HCC). In this study, we covalently linked biotinylated GPC3 antibody to PEGylated nano-rGO to obtain GPC3-modified rGO-PEG (rGO-GPC3), and then combined rGO-GPC3 with avidinylated nanobubbles (NBs) using biotin-avidin system to prepare NBs-GPC3-rGO with photothermal effect and dispersibility, solubility in physiological environment. The average size of NBs-GPC3-rGO complex was 700.4±52.9 nm due to the polymerization of biotin-avidin system. Scanning electron microscope (SEM) showed NBs-GPC3-rGO attached to human hepatocellular carcinoma HepG2 cell. The ultrasound-targeted nanobubble destruction (UTND) technology make use of the physical energy of ultrasound exposure for the improvement of rGO delivery. Compared with other control groups, the highest nanobubble destruction efficiency of NBs-GPC3-rGO was attributed to the dissection effect of rGO on UTND. This is a positive feedback effect that leads to an increase in the concentration of rGO around the HepG2 cell. So NBs-GPC3-rGO using UTND and near-infrared (NIR) irradiation resulted in cell viability within 24 h, 48 h, 72 h lower than other treatment groups. CONCLUSION: This work established NBs-GPC3-rGO as an ultrasonic photothermal agent due to its suitable size, imaging capability, photothermal efficiency for visual photothermal therapy in vitro.

Apatinib-loaded lipid nanobubbles combined with ultrasound-targeted nanobubble destruction for synergistic treatment of HepG2 cells in vitro.

PURPOSE: Apatinib, an oral small-molecule antiangiogenetic medicine, is used to treat patients with advanced hepatocellular carcinoma. However, its systemic toxic side effects cannot be ignored. The ultrasound (US)-targeted nanobubble destruction technology can minimize systemic drug exposure and maximize therapeutic efficacy. The aim of this study was to develop novel GPC3-targeted and drug-loaded nanobubbles (NBs) and further assess the associated therapeutic effects on hepatocellular carcinoma cells in vitro. MATERIALS AND METHODS: Apatinib-loaded NBs were prepared by a mechanical vibration method. GPC3, a liver tumor homing peptide, was coated onto the surface of apatinib-loaded NBs through biotin-avidin interactions to target liver cancer HepG2 cells. The effects of different treatment groups on cell proliferation, cell cycle, and apoptosis of HepG2 cells were tested. RESULTS: The NBs could achieve 68% of optimal drug encapsulation. In addition, ligand binding assays demonstrated that attachment of targeted NBs to human HepG2 liver cancer cells was highly efficient. Furthermore, cell proliferation assays indicated that the antiproliferative activities of GPC3-targeted and apatinib-loaded NBs in combination with US (1 MHz, 1 W/cm2, 30 s) were, respectively, 44.11%±2.84%, 57.09%±6.38%, and 67.51%±2.89% after 24, 48, and 72 h of treatment. Treatment with GPC3-targeted and apatinib-loaded NBs also resulted in a higher proportion of cells in the G1 phase compared with other treatment groups such as apatinib only and nontargeted apatinib-loaded NBs when US was utilized. CONCLUSION: US-targeted and drug-loaded nanobubble destruction successfully achieved selective growth inhibition and apoptosis in HepG2 cells in vitro. Therefore, GPC3-targeted and apatinib-loaded NBs can be considered a novel chemotherapeutic approach for treating liver cancer in combination with US.

Shear wave elastography combined with the thyroid imaging reporting and data system for malignancy risk stratification in thyroid nodules.

To retrospectively evaluate the diagnostic performance of shear wave elastography (SWE) and thyroid imaging reporting and data system (TI-RADS) in differentiating malignant and benign thyroid nodules. A total of 313 thyroid nodules in 227 patients were included. All thyroid nodules were underwent SWE and TI-RADS before fine needle aspiration biopsy and/or surgery. SWE elasticity indices of the maximum (Emax), mean (Emean), minimum (Emin) and elastic ratio (ER) in thyroid nodules were measured. Nodules with solid component, marked hypoechogenicity, poorly defined margins, micro-calcifications, and a taller-than-wide shape were classified as suspicious at gray-scale ultrasonography. The level of TI-RADS was determined according to the number of suspicious ultrasonography features. The combined methods of SWE and TI-RADS in thyroid nodules were calculated. In the 313 nodules, 194 were malignant, and 119 were benign. SWE and TI-RADS were significantly higher in malignant nodules than benign nodules (P < 0.001). The most accurate SWE cut-off value, 51.95 kPa for Emax, achieved a sensitivity of 81.44% and a specificity of 83.19% for discriminating malignant nodules from benign nodules. There are two methods in combination with SWE and TI-RADS. The one is "tandem" method, which has a higher specificity (95.80%), positive likelihood ratio (18.16) and positive predictive value (96.73%). The other one is "parallel" method, which shows sensitivity (94.85%), negative likelihood ratio (0.07) and negative predictive value (90.00%).We believe that the methods could be used as a simple tool to stratify the risk of thyroid nodules accurately.

Early Evaluation of Relative Changes in Tumor Stiffness by Shear Wave Elastography Predicts the Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer.

OBJECTIVES: Neoadjuvant chemotherapy plays an important role in comprehensive therapy for breast cancer, but response prediction is imperfect. Shear wave elastography (SWE) is a novel technique that can quantitatively evaluate tissue stiffness. In this study, we sought to investigate the application value of SWE for early prediction of the response to neoadjuvant chemotherapy in patients with breast cancer. METHODS: We prospectively evaluated tumor stiffness in 62 patients with breast cancer using SWE, which was performed at baseline and after the second cycle of neoadjuvant chemotherapy. After chemotherapy, all of the patients underwent surgery. We investigated the correlations between the relative changes in tumor stiffness (Δ stiffness) after 2 cycles of chemotherapy and the pathologic response to the therapy. RESULTS: Compared with baseline values, tumor stiffness after 2 cycles of neoadjuvant chemotherapy was significantly decreased in responders (P < .001) but not in nonresponders (P = .172). The Δstiffness was significantly higher in responders (-42.194%) than in nonresponders (-23.593%; P = .001). As determined at either the baseline or after the second cycle of chemotherapy, tumor stiffness was significantly lower in responders than in nonresponders (P = .033 and .009, respectively). The Δ stiffness threshold for distinguishing between responders and nonresponders was -36.1% (72.92% sensitivity and 85.71% specificity). Furthermore, correlating Δ stiffness with clinical and pathologic characteristics, we found that estrogen and progesterone receptor expression showed statistically significant correlations with Δ stiffness (estrogen receptor, P = .008; progesterone receptor, P = .023). CONCLUSIONS: Early evaluation of relative changes in tumor stiffness using SWE could effectively predict the response to neoadjuvant chemotherapy in patients with breast cancer and might indicate better therapeutic strategies on a timelier basis.