Evaluation of standard breast ultrasonography by adding two-dimensional and three-dimensional shear wave elastography: a prospective, multicenter trial.

OBJECTIVE: To reduce the number of biopsies performed on benign breast lesions categorized as BI-RADS 4-5, we investigated the diagnostic performance of combined two-dimensional and three-dimensional shear wave elastography (2D + 3D SWE) with standard breast ultrasonography (US) for the BI-RADS assessment of breast lesions. METHODS: A total of 897 breast lesions, categorized as BI-RADS 3-5, were subjected to standard breast US and supplemented by 2D SWE only and 2D + 3D SWE analysis. Based on the malignancy rate of less than 2% for BI-RADS 3, lesions assessed by standard breast US were reclassified with SWE assessment. RESULTS: After standard breast US evaluation, 268 (46.1%) participants underwent benign biopsies in BI-RADS 4-5 lesions. By using separated cutoffs for upstaging BI-RADS 3 at 120 kPa and downstaging BI-RADS 4a at 90 kPa in 2D + 3D SWE reclassification, 123 (21.2%) participants underwent benign biopsy, resulting in a 54.1% reduction (123 versus 268). CONCLUSION: Combining 2D + 3D SWE with standard breast US for reclassification of BI-RADS lesions may achieve a reduction in benign biopsies in BI-RADS 4-5 lesions without sacrificing sensitivity unacceptably. CLINICAL RELEVANCE STATEMENT: Combining 2D + 3D SWE with US effectively reduces benign biopsies in breast lesions with categories 4-5, potentially improving diagnostic accuracy of BI-RADS assessment for patients with breast lesions. TRIAL REGISTRATION: ChiCTR1900026556 KEY POINTS: • Reduce benign biopsy is necessary in breast lesions with BI-RADS 4-5 category. • A reduction of 54.1% on benign biopsies in BI-RADS 4-5 lesions was achieved using 2D + 3D SWE reclassification. • Adding 2D + 3D SWE to standard breast US improved the diagnostic performance of BI-RADS assessment on breast lesions: specificity increased from 54 to 79%, and PPV increased from 54 to 71%, with slight loss in sensitivity (97.2% versus 98.7%) and NPV (98.1% versus 98.7%).

[Fucoidan-Modified Phase-Transitional Contrast Agent for Ultrasound Imaging and Targeting of Hepatoma Cells].

Objective: To prepare a fucoidan-modified phase-transitional contrast agent (FPCA) and to evaluate its in vitro capabilities for ultrasound imaging and targeting of hepatoma cells. Methods: Nano-liposomes encapsulated with perfluoropentane were prepared using thin-film hydration and ultrasonic emulsification methods. Then, FPCA nanoparticles were prepared through chemical grafting of fucoidan and the characterization of their physical and chemical properties was performed. After applying external stimuli of heating with hot water bath and microwave irradiation, the phase-transition status of FPCA was observed with microscope. The imaging abilities of phase-transited FPCA on two-dimensional ultrasound and contrast-enhanced ultrasound were observed with ultrasonic diagnostic instrument. The ability of FPCA to target at hepatoma cells was evaluated and verified with fluorescence confocal observation and flow cytometry analysis. Results: The FPCA prepared in the study had an average diameter of (222.1±32.5) nm, displaying spherical appearance, good dispersion, good stability, and good biocompatibility. The phase-transition of FPCA was induced by both heating with hot water bath and microwave irradiation. For phase transition, the optimal temperature was found to be 50 ℃ and the preferred microwave power was 1.5 W/cm 2. Moreover, after phase transition, FPCA showed significant imaging enhancement on both two-dimensional ultrasonography and contrast-enhanced ultrasonography. Through fluorescein isothiocyanate (FITC) labeling, FPCA could specifically bind with hepatoma cells at a high binding rate of (96.19±1.62)%, while it rarely bound with normal liver cells, showing a binding rate of less than 10%. Conclusion: A new type of phase-transitional ultrasound contrast agent with good stability and biocompatibility was successfully prepared. It not only could enhance ultrasound imaging through phase transition, but also had specific active hepatoma cell-targeting properties.

Artificial intelligence diagnosis based on breast ultrasound imaging. / åŸºäºŽä¹³è ºè¶ å£°å›¾åƒçš„äººå·¥æ™ºèƒ½è¯Šæ–­.

Breast cancer has now become the leading cancer in women. The development of breast ultrasound artificial intelligence (AI) diagnostic technology is conducive to promoting the precise diagnosis and treatment of breast cancer and alleviating the heavy medical burden due to the unbalanced regional development in China. In recent years, on the basis of improving diagnostic efficiency, AI technology has been continuously combined with various clinical application scenarios, thereby providing more comprehensive and reliable evidence-based suggestions for clinical decision-making. Although AI diagnostic technologies based on conventional breast ultrasound gray-scale images and cutting-edge technologies such as three-dimensional (3D) imaging and elastography have been developed to some extent, there are still technical pain points, diffusion difficulties and ethical dilemmas in the development of AI diagnostic technologies for breast ultrasound.

Automated location of thyroid nodules in ultrasound images with improved YOLOV3 network.

BACKGROUND: Thyroid ultrasonography is widely used to diagnose thyroid nodules in clinics. Automatic localization of nodules can promote the development of intelligent thyroid diagnosis and reduce workload of radiologists. However, besides the ultrasound image has low contrast and high noise, the thyroid nodules are diverse in shape and vary greatly in size. Thus, thyroid nodule detection in ultrasound images is still a challenging task. OBJECTIVE: This study proposes an automatic detection algorithm to locate nodules in B ultrasound images and Doppler ultrasound images. This method can be used to screen thyroid nodules and provide a basis for subsequent automatic segmentation and intelligent diagnosis. METHODS: We develop and optimize an improved YOLOV3 model for detecting thyroid nodules in ultrasound images with B-mode and Doppler mode. Improvements include (1) using the high-resolution network (HRNet) as the basic network for gradually extracting high-level semantic features to reduce the missed detection and misdetection, (2) optimizing the loss function for single target detection like nodules, and (3) obtaining the anchor boxes by clustering the candidate frames of real nodules in the dataset. RESULTS: The experimental results of applying to 8000 clinical ultrasound images show that the new method developed and tested in this study can effectively detect thyroid nodules. The method achieves 94.53% mean precision and 95.00% mean recall. CONCLUTIONS: The study demonstrates a new automated method that enables to achieve high detection accuracy and effectively locate thyroid nodules in various ultrasound images without any user interaction, which indicates its potential clinical application value for the thyroid nodule screening.

Diagnostic reliability of elastography in thyroid nodules reported as indeterminate at prior fine-needle aspiration cytology (FNAC): a systematic review and Bayesian meta-analysis.

OBJECTIVES: To assess the diagnostic yields of elastography in thyroid nodules reported as indeterminate in FNAC according to guidelines. METHODS: Databases of Medline, Embase, and Cochrane Central were searched till 31 October 2019. Two different reviewers check the studies and extracted the data. The diagnostic accuracy and yield were quantitatively synthesized using Bayesian bivariate model in R. RESULTS: Twenty studies with 1734 indeterminate thyroid nodules undergoing elastography were included. The summary estimates of sensitivity and specificity were 0.766 (95% credible interval (CrI), 0.686-0.835) and 0.867 (95% CrI, 0.780-0.931), respectively. The summary estimate for diagnostic odds ratio (DOR) was 25.9 (95% CrI, 12.8-46.2). Summary receiver operating characteristic plots for elastography showed a right-diagonal curvilinear relationship, suggesting a trade-off between sensitivity and specificity, and the estimate of area under curve (AUC) was 0.743. The summary estimates for positive and negative likelihood ratios were 6.6 (95% CrI, 4.2-11.3) and 0.27 (95% CrI, 0.21-0.36), respectively. CONCLUSIONS: Elastography had fair diagnostic yields in indeterminate thyroid nodules. Shear wave elastography and strain ratio elastography could be more efficient in diagnosis and should evolve in the next years while combing elastography with ultrasound would contribute more to sensitivity and specificity currently. KEY POINTS: • Elastography has fair diagnostic yields in indeterminate thyroid nodules. • Shear wave elastography and strain ratio elastography are more efficient than real-time elastography. • Combining elastography and other ultrasound techniques improves evaluation of indeterminate thyroid nodules.

Ultrasound Diagnosis of Breast Lymphoma and the Identification of Breast Infiltrating Ductal Carcinoma.

OBJECTIVES: By analyzing the B-mode ultrasound and color Doppler flow imaging characteristics of breast lymphoma (BL) and breast infiltrating ductal carcinoma (BIDC), we expected to discriminate these diseases. METHODS: Thirty-two patients with BL and 30 with BIDC confirmed pathologically were selected. The BL group was divided into nodular and diffuse groups. We analyzed and compared the general and imaging characteristics of the BL subgroups and the BIDC group. RESULTS: The mean maximum diameter of BL was 54.93 ± 43.74 cm, and that of BIDC was 23.90 ± 6.79 cm (P < .05). The differences between the nodular BL and BIDC groups in a circumscribed margin (60.00% versus 20.00%), calcification (20.00% versus 53.33%), aggregation characteristics (0.00% versus 53.33%), and density (73.33% versus 10.00%) were statistically significant (P < .05). The differences between the diffuse BL and BIDC groups in calcification (6.67% versus 53.33%), aggregation characteristics (6.67% versus 53.33%) and density (40.00% versus 10.00%) were statistically significant (P < .05). The difference in a circumscribed margin (60% versus 13.33%) between the BL subgroups was statistically significant (P < .05). The blood flow signal in BL lesions was richer than that in BIDC lesions (P < .05). CONCLUSIONS: Extrasuperior-quadrant single lesions in the BL group were larger than those in the BIDC group. The edges of the lesions in the nodular BL group were circumscribed and dense. Lesions in the diffuse BL group did not have a circumscribed margin, calcification, aggregation characteristics, or density. The blood flow signal in BL lesions was richer than that in BIDC lesions.

Diagnostic Efficiency of the Breast Ultrasound Computer-Aided Prediction Model Based on Convolutional Neural Network in Breast Cancer.

This study aimed to construct a breast ultrasound computer-aided prediction model based on the convolutional neural network (CNN) and investigate its diagnostic efficiency in breast cancer. A retrospective analysis was carried out, including 5000 breast ultrasound images (benign: 2500; malignant: 2500) as the training group. Different prediction models were constructed using CNN (based on InceptionV3, VGG16, ResNet50, and VGG19). Additionally, the constructed prediction models were tested using 1007 images of the test group (benign: 788; malignant: 219). The receiver operating characteristic curves were drawn, and the corresponding areas under the curve (AUCs) were obtained. The model with the highest AUC was selected, and its diagnostic accuracy was compared with that obtained by sonographers who performed and interpreted ultrasonographic examinations using 683 images of the comparison group (benign: 493; malignant: 190). In the model test with the test group images, the AUCs of the constructed InceptionV3, VGG16, ResNet50, and VGG19 models were 0.905, 0.866, 0.851, and 0.847, respectively. The InceptionV3 model showed the largest AUC, with statistically significant differences compared with the other models (P < 0.05). In the classification of the comparison group images, the AUC (0.913) of the InceptionV3 model was larger than that (0.846) obtained by sonographers, showing a statistically significant difference (P < 0.05). The breast ultrasound computer-aided prediction model based on CNN showed high accuracy in the prediction of breast cancer.

[Multiple Calcification of the Nipple Areola Complex: a Case Report of Infiltrating Syringomatous Adenoma of the Nipple].

A 41 years old female patient experienced enlarged right nipple more than 6 years ago. Physical examination showed enlarged and hardened right nipple (about 2 cm) but no tenderness. Mammographic results showed the mixed calcification pattern, which was presenting multiple sizes of circular and granular calcification of the nipple areola complex. Ultrasound revealed large calcification in the nipple areola area with posterior acoustic shadow. This patient underwent extensive excision of the nipple areola complex. The surgical margin was confirmed to be negative. Postoperative pathology confirmed an infiltrating syringomatous adenoma with ossification in some areas. Tumor cell infiltration was not found at the surgical margin. After 4 years of follow-up, no recurrence was found. The mammography and ultrasound findings of this benign tumor were significantly different from those reported in previous literatures. We should improve the understanding of the variousness of clinical imaging manifestations of this disease.

[The Characteristics of Ultrasonography and Thermal Tomography in Various Molecular Subtypes of Breast Cancer].

OBJECTIVE: To explore the relationship between the imaging features of ultrasonography and thermal tomography and molecular subtypes of breast cancer. METHODS: 404 female breast cancer patients with complete imaging data and pathological findings from January 2014 to June 2017 were reviewed in the West China Hospital of Sichuan University. Breast cancer pathological molecules were classified into Luminal A like type, Luminal B like type, human epidermal growth receptor-2 (HER-2) overexpression type and Basal like type according to the expression of various immune markers. The correlation of ultrasonographic BI-RADS signs, thermal tomography characteristics and immunohistochemical results of breast cancer was analyzed. RESULTS: Breast cancer lesions with regular morphology, sharp margins, and enhanced posterior echo were more common in Basal like type; Microcalcification was more likely tend to appear in HER-2 expression breast cancer than other subtypes; The q-r curve of Luminal A like breast cancer was nearly 30°, and that was more common between 30°and 45° of HER-2 expression and Basal like breast cancer；The ratio of vertical and horizontal ≥1 of tumors and limited lymph node metastasis could not be used for distinguishing between different subtypes. CONCLUSION: Different molecular subtypes of breast cancer may behave routine ultrasound and thermal tomography imaging features.

New one-step model of breast tumor locating based on deep learning.

BACKGROUND: Breast cancer has the highest cancer prevalence rate among the women worldwide. Early detection of breast cancer is crucial for successful treatment and reducing cancer mortality rate. However, tumor detection of breast ultrasound (US) image is still a challenging work in computer-aided diagnosis (CAD). OBJECTIVE: This study aims to develop a novel automated algorithm for breast tumor detection based on deep learning. METHODS: We proposed a new deep learning network named One-step model which have one input and two outputs, the first one was the segmentation result and the other one was used for false-positive reduction. The proposed One-step model includes three key components: Base-net, Seg-net, and Cls-net based on Anchor Box. The model chose DenseNet to construct Base-net, the decoder part of RefineNet as Seg-net, and connected several middle layers of Base-net and Seg-net to Cls-net. From the first output acquired by Base-net and Seg-net, the model detected a series of suspicious lesion regions. Then the second output from the Cls-net was used to recognize and reduce the false-positive regions. RESULTS: Experimental results showed that the new model achieved competitive detection result with 90.78% F1 score, which was 8.55% higher than Single Shot MultiBox Detector (SSD) method. In addition, running new model is also computational efficient and has comparative cost effect as SSD. CONCLUSIONS: We established a novel One-step model which improves location accuracy by generating more precise bounding box via Seg-net and removing false targets by another object detection network (Cls-net). On the other hand, a real-time detection of tumor is achieved by sharing the common Base-net. The experimental results showed that the new model performed well on various irregular and blurred ultrasound images. As a result, this study demonstrated feasibility of applying deep learning scheme to detect breast lesions depicting on US image.

Salient object detection method for breast tumor in ultrasound images based on absorbing Markov chain.

BACKGROUND: Automatic detection of tumor in breast ultrasound (BUS) images is important for the subsequent image processing and has been researched for decades. However, there still lacks a robust method due to poor quality of BUS images. OBJECTIVE: To propose and test a salient object detection method for BUS images. METHODS: BUS image is preprocessed by an adaptively selective replacement and speckle reducing anisotropic diffusion (SRAD) algorithm. Then, the preprocessed image is segmented into super pixels by a simple linear iterative clustering (SLIC) algorithm to form a graph model, and the saliency of the nodes in the graph is calculated by using the absorbed time of absorbing Markov chain (AMC). Finally, the initial saliency map is optimized by the recurrent time of ergodic Markov chain (EMC) and a distance weighting formula. RESULTS: Results of the proposed method were compared both qualitatively and quantitatively with two saliency detection models. It was observed that the proposed method outperformed the comparison models and yielded the highest Accuracy value (97.49% vs. 86.63% and 90.33%) using a dataset of 1000 BUS images. CONCLUSIONS: After the adaptively selective replacement, AMC can effectively distinguish tumors from background by random walks.

The sonographic findings of micropapillary pattern in pure mucinous carcinoma of the breast.

BACKGROUND: The aim of this study was to describe the sonographic features of pure mucinous carcinoma with micropapillary pattern (MUMPC) and compare them with conventional pure mucinous breast carcinoma without micropapillary architecture (cPMBC) and mixed mucinous breast carcinoma (MMBC). METHODS: Eighty-eight patients (17 MUMPCs, 43 cPMBCs, and 28 MMBCs) were included in the study. Sonographic features according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon for ultrasound (US) were recorded and analyzed for each patient. The age, sonographic lesion size, menstrual status, mass location, palpation, tenderness, and axillary lymph node metastasis (LNM) were also analyzed. RESULTS: Most of the MUMPCs showed an irregular shape (82.4%, 14/17), a parallel orientation (94.1%, 16/17), a non-circumscribed margin (88.2%, 15/17), and distal acoustic enhancement (88.2%, 15/17). Furthermore, MUMPC had mixed cystic and solid components (35.3%, 6/17) and hypoechoic (29.4%, 5/17) and isoechoic (35.3%, 6/17) structures, with calcification (29.4%, 5/17) and blood flow (41.2%, 7/17) within the tumor. The differences in sonographic features were not found between the MUMPC and cPMBC and between the MUMPC and MMBC. Moreover, there was no significant difference between the three groups based on age, menstrual status, mass location, palpation, and tenderness (p > 0.05). Similar axillary LNMs were observed between MUMPC and cPMBC (p > 0.05), but both MUMPC and cPMBC were statistically different from MMBC (p < 0.05), so as the lesion size. CONCLUSIONS: At this particular stage, it is challenging to distinguish MUMPC from cPMBC and MMBC on ultrasound according to the BI-RADS-US lexicon.

[Clinical and Ultrasonographic Features of Hepatic Infantile Hemangioendothelioma].

OBJECTIVE: To investigate the clinical manifestations and ultrasound features of hepatic infantile hemangioendothelioma (IHE). METHODS: This study retrospectively analyzed the clinical features and ultrasound characteristics of 8 patients of IHE,whose diagnosis was confirmed by surgery or liver biopsy from January 2010 to October 2016. RESULTS: Among the eight IHE patients,the male-to-female ratio was 1:1,with the mean age ranged from 15 d to 3 yr.,of which six individuals were younger than 3 months old. Seven patients had single lesion and one case was multiple. The average size of the lesions was (8.2±1.0) cm diameter. Hypoechoic (4 cases) and echogenic lesions (3 cases) were shown by echography,the lesions border and morphology were clearly defined in 7 cases.Furthermore,internal echoes were mostly inhomogeneous (4 cases),and calcification or peripheral halo were even visible. Color Dopplar flow imaging (CDFI) showed internal visible line or more abundant blood flow signal in 6 cases,mainly to venous blood flow. CONCLUSION: The age of onset for hepatic IHE seems less than three months. The echography with single lesion with clear border,regular solid mass or calcification in the tumor,should come to the possibility of IHE.

[Preparation of VEGFR2 Targeted Ultrasound Microbubbles by Freeze-drying and Targeting Study in vitro].

OBJECTIVE: To prepare vascular endothelial growth factor receptor2 (VEGFR2) targeted ultrasound contrast agent (microbubbles, MBs) by freeze-dried method and to evaluate its contrast enhanced effect and targeting capability through in vitro experiments. METHODS: Targeted MBs were prepared using the biotin-avidin linkage to conjugate rat anti-mouse VEGFR2 monoclonal antibody to the surface of biotinylated MBs. Morphology, size and distribution of MBs were assessed. The binding of streptavidin (FITC marker) and VEGFR2 monoclonal antibody (PE labeled rabbit IgG) to MBs was verified by immunofluorescence staining. In vitro targeting experiments were performed with human umbilical vein endothelial cells (HUVECs). The binding capacity of MBs to HUVECs were detected by three groups including untargeted MBs group (adding 1×107 untargeted MBs), antibody presaturation added VEGFR2 targeted MBs group (after being incubated with excess VEGFR2 antibody, 1×107 VEGFR2 targeted ultrasound MBs were added) and VEGFR2 targeted MBs group (adding 1×107 VEGFR2 targeted ultrasound MBs). Contrast enhanced effects of VEGFR2 targeted MBs were preliminarily examined using an ultrasound imaging system and a home-made extracorporeal circulating device. RESULTS: The monoclonal antibody of streptomycin and rat anti-mouse VEGFR2 can be combined with the biotinized MBs to construct the targeted ultrasound MBs of VEGFR2 by immunofluorescence staining. Under the microscope, VEGFR2 targeted MBs were round, uniform in size and uniform in distribution, with a mean diameter of (1.31±0.93) µm. Microscopy showed a small number of MBs around HUVECs in non-targeted MBs group, almost no MBs around HUVECs of antibody presaturation+VEGFR2 targeted ultrasound MBs group, and many MBs around HUVECs of VEGFR2 targeted ultrasound MBs group. The binding capacity was significantly higher than that of untargeted MBs. The self-made MBs developed well and no significant attenuation was observed as time extension in the mode of enhanced ultrasonography. CONCLUSIONS: The freeze-drying method can be used to prepare VEGFR2 targeted ultrasound contrast agent, which has good in vitro targeting ability and contrast enhanced effects for ultrasound molecular imaging.

Discovery of pyrazolo[1,5-a]pyrimidine-3-carbonitrile derivatives as a new class of histone lysine demethylase 4D (KDM4D) inhibitors.

Herein we report the discovery of a series of new small molecule inhibitors of histone lysine demethylase 4D (KDM4D). Molecular docking was first performed to screen for new KDM4D inhibitors from various chemical databases. Two hit compounds were retrieved. Further structural optimization and structure-activity relationship (SAR) analysis were carried out to the more selective one, compound 2, which led to the discovery of several new KDM4D inhibitors. Among them, compound 10r is the most potent one with an IC50 value of 0.41±0.03µM against KDM4D. Overall, compound 10r could be taken as a good lead compound for further studies.

[The Diagnostic Value of Double Contrast Enhanced Ultrasound in Colorectal Neoplasms].

OBJECTIVES: To determine the value of double contrast enhanced ultrasound (DCEUS) in detecting colorectal neoplasms. METHODS: The conventional abdominal ultrasound and DCEUS (hydrocolonal ultrasound+intravenous contrast-enhanced ultrasonography) were performed in 77 patients with 79 colorectal tumors. The clinical, ultrasonic and pathological data of the patients were reviewed. RESULTS: The conventional abdominal ultrasound detected 32 colorectal tumors (32/79, with a sensitivity of 40.1%), compared with 68 tumors found in DCEUS (68/79, with a sensitivity of 86.1%). The tumors were located accurately in 21.9%(7/32) and 88.2%(60/68) cases for the conventional abdominal ultrasound and DCEUS, respectively ( P<0.05). DCEUS accurately located 94.4%(51/54)cased with colon neoplasm. Two types of enhancing modes were found with colorectal neoplasm: entirely homogeneous enhancing and heterogeneous enhancing perpendicularly from the serosal part to the mucosal part. The enhancing modes were associated with the morphology of neoplasm. Thirty-nine segmented-thickened tumors were found with perpendicularly enhancing from the serosal surface to the mucosal surface, while 29 protruding tumors were found with enhancing from their pedicles or fund. CONCLUSIONS: DCEUS has higher detection sensitivity and location accuracy than conventional ultrasound.

[Breast Thermal Tomography and High Frequency Ultrasound Imaging in Predicting Breast Cancer: a Preliminary Study].

OBJECTIVE: To determine the clinical value of breast thermal tomography and high frequency ultrasound imaging in diagnosing breast cancer. METHODS: Breast thermal tomography and high frequency ultrasound imaging were performed in 298 patients with breast lumps. The results were compared with pathological diagnosis. RESULTS: The ultrasound imaging had a sensitivity, specificity and the area under the curve (AUC) of 99.02%, 62.78% and 0.814, respectively, compared with 83.33%, 83.16% and 0.830 of thermal tomography, for diagnosing breast cancer. The two imaging results showed statistical significance in the test of non-inferiority (P < 0.001). A combination of the two imaging results produced a sensitivity, specificity and AUC of 83.33%, 89.79% and 0.866, respectively. CONCLUSION: Thermal tomography is not inferior to ultrasonography in detecting breast cancer. The two combined can improve specificity and accuracy of the diagnosis of breast cancer.

Enhanced phase stability in passive analog photonic links with coherent Rayleigh noise reduction.

Minimizing phase fluctuation along passive analog fiber link is proposed and experimentally demonstrated. By utilizing three different optical wavelengths, we could significantly reduce the effect of coherent Rayleigh noise (CRN). In addition, a phase-locked loop is employed for dynamic phase fluctuation compensation. The RMS phase jitter within two-hour period is reduced to ~1.7131 ps over 40-km fiber link.

Prolyl hydroxylase domain protein 2 silencing enhances the survival and paracrine function of transplanted adipose-derived stem cells in infarcted myocardium.

RATIONALE: Transplantation of stem cells into damaged hearts has had modest success as a treatment for ischemic heart disease. One of the limitations is the poor stem cell survival in the diseased microenvironment. Prolyl hydroxylase domain protein 2 (PHD2) is a cellular oxygen sensor that regulates 2 key transcription factors involved in cell survival and inflammation: hypoxia-inducible factor and nuclear factor-κB. OBJECTIVE: We studied whether and how PHD2 silencing in human adipose-derived stem cells (ADSCs) enhances their cardioprotective effects after transplantation into infarcted hearts. METHODS AND RESULTS: ADSCs were transduced with lentiviral short hairpin RNA against prolyl hydroxylase domain protein 2 (shPHD2) to silence PHD2. ADSCs, with or without shPHD2, were transplanted after myocardial infarction in mice. ADSCs reduced cardiomyocyte apoptosis, fibrosis, and infarct size and improved cardiac function. shPHD2-ADSCs exerted significantly more protection. PHD2 silencing induced greater ADSC survival, which was abolished by short hairpin RNA against hypoxia-inducible factor-1α. Conditioned medium from shPHD2-ADSCs decreased cardiomyocyte apoptosis. Insulin-like growth factor-1 (IGF-1) levels were significantly higher in the conditioned medium of shPHD2-ADSCs versus ADSCs, and depletion of IGF-1 attenuated the cardioprotective effects of shPHD2-ADSC-conditioned medium. Nuclear factor-κB activation was induced by shPHD2 to induce IGF-1 secretion via binding to IGF-1 gene promoter. CONCLUSIONS: PHD2 silencing promotes ADSCs survival in infarcted hearts and enhances their paracrine function to protect cardiomyocytes. The prosurvival effect of shPHD2 on ADSCs is hypoxia-inducible factor-1α dependent, and the enhanced paracrine function of shPHD2-ADSCs is associated with nuclear factor-κB-mediated IGF-1 upregulation. PHD2 silencing in stem cells may be a novel strategy for enhancing the effectiveness of stem cell therapy after myocardial infarction.