Ultrasound-visible engineered bacteria for tumor chemo-immunotherapy.

Our previous work developed acoustic response bacteria, which enable the precise tuning of transgene expression through ultrasound. However, it is still difficult to visualize these bacteria in order to guide the sound wave to precisely irradiate them. Here, we develop ultrasound-visible engineered bacteria and chemically modify them with doxorubicin (DOX) on their surfaces. These engineered bacteria (Ec@DIG-GVs) can produce gas vesicles (GVs), providing a real-time imaging guide for remote hyperthermia high-intensity focused ultrasound (hHIFU) to induce the expression of the interferon (IFN)-γ gene. The production of IFN-γ can kill tumor cells, induce macrophage polarization from the M2 to the M1 phenotype, and promote the maturation of dendritic cells. DOX can be released in the acidic tumor microenvironment, resulting in immunogenic cell death of tumor cells. The concurrent effects of IFN-γ and DOX activate a tumor-specific T cell response, producing the synergistic anti-tumor efficacy. Our study provides a promising strategy for bacteria-mediated tumor chemo-immunotherapy.

Genetic Modulation of Biosynthetic Gas Vesicles for Ultrasound Imaging.

Gas vesicles (GVs) from microorganisms are genetically air-filled protein nanostructures, and serve as a new class of nanoscale contrast agents for ultrasound imaging. Recently, the genetically encoded GV gene clusters have been heterologously expressed in Escherichia coli, allowing these genetically engineered bacteria to be visualized in vivo in a real-time manner by ultrasound. However, most of the GV genes remained functionally uncharacterized, which makes it difficult to regulate and modify GVs for broad medical applications. Here, the impact of GV proteins on GV formation is systematically investigated. The results first uncovered that the deletions of GvpR or GvpU resulted in the formation of a larger proportion of small, biconical GVs compared to the full-length construct, and the deletion of GvpT resulted in a larger portion of large GVs. Meanwhile, the combination of gene deletions has resulted in several genotypes of ultrasmall GVs that span from 50 to 20 nm. Furthermore, the results showed that E. coli carrying the ΔGvpCRTU mutant can produce strong ultrasound contrast signals in mouse liver. In conclusion, the study provides new insights into the roles of GV proteins in GV formation and produce ultrasmall GVs with a wide range of in vivo research.

Application of Genetically Encoded Molecular Imaging Probes in Tumor Imaging.

In recent years, imaging technology has made rapid progress to improve the sensitivity of tumor diagnostic. With the development of genetic engineering and synthetic biology, various genetically encoded molecular imaging probes have also been extensively developed. As a biomedical imaging method with excellent detectable sensitivity and spatial resolution, genetically encoded molecular imaging has great application potential in the visualization of cellular and molecular functions during tumor development. Compared to chemosynthetic dyes and nanoparticles with an imaging function, genetically encoded molecular imaging probes can more easily label specific cells or proteins of interest in tumor tissues and have higher stability and tissue contrast in vivo. Therefore, genetically encoded molecular imaging probes have attracted increasing attention from researchers in engineering and biomedicine. In this review, we aimed to introduce the genetically encoded molecular imaging probes and further explained their applications in tumor imaging.

Evaluation of oocyte maturity using artificial intelligence quantification of follicle volume biomarker by three-dimensional ultrasound.

RESEARCH QUESTION: Can a novel deep learning-based follicle volume biomarker using three-dimensional ultrasound (3D-US) be established to aid in the assessment of oocyte maturity, timing of HCG administration and the individual prediction of ovarian hyper-response? DESIGN: A total of 515 IVF cases were enrolled, and 3D-US scanning was carried out on HCG administration day. A follicle volume biomarker established by means of a deep learning-based segmentation algorithm was used to calculate optimal leading follicle volume for predicting number of mature oocytes retrieved and optimizing HCG trigger timing. Performance of the novel biomarker cut-off value was compared with conventional two-dimensional ultrasound (2D-US) follicular diameter measurements in assessing oocyte retrieval outcome. Moreover, demographics, infertility work-up and ultrasound biomarkers were used to build models for predicting ovarian hyper-response. RESULTS: On the basis of the deep learning method, the optimal cut-off value of the follicle volume biomarker was determined to be 0.5 cm3 for predicting number of mature oocytes retrieved; its performance was significantly better than the conventional method (two-dimensional diameter measurement ≥10 mm). The cut-off value for leading follicle volume to optimize HCG trigger timing was determined to be 3.0 cm3 and was significantly associated with a higher number of mature oocytes retrieved (P = 0.01). Accuracy of the multi-layer perceptron model was better than two-dimensional diameter measurement (0.890 versus 0.785) and other multivariate classifiers in predicting ovarian hyper-response (P < 0.001). CONCLUSIONS: Deep learning segmentation methods and multivariate classifiers based on 3D-US were found to be potentially effective approaches for assessing mature oocyte retrieval outcome and individual prediction of ovarian hyper-response.

Nanosized Contrast Agents in Ultrasound Molecular Imaging.

Applying nanosized ultrasound contrast agents (nUCAs) in molecular imaging has received considerable attention. nUCAs have been instrumental in ultrasound molecular imaging to enhance sensitivity, identification, and quantification. nUCAs can achieve high performance in molecular imaging, which was influenced by synthetic formulations and size. This review presents an overview of nUCAs from different synthetic formulations with a discussion on imaging and detection technology. Then we also review the progress of nUCAs in preclinical application and highlight the recent challenges of nUCAs.

Contrastive rendering with semi-supervised learning for ovary and follicle segmentation from 3D ultrasound.

Segmentation of ovary and follicles from 3D ultrasound (US) is the crucial technique of measurement tools for female infertility diagnosis. Since manual segmentation is time-consuming and operator-dependent, an accurate and fast segmentation method is highly demanded. However, it is challenging for current deep-learning based methods to segment ovary and follicles precisely due to ambiguous boundaries and insufficient annotations. In this paper, we propose a contrastive rendering (C-Rend) framework to segment ovary and follicles with detail-refined boundaries. Furthermore, we incorporate the proposed C-Rend with a semi-supervised learning (SSL) framework, leveraging unlabeled data for better performance. Highlights of this paper include: (1) A rendering task is performed to estimate boundary accurately via enriched feature representation learning. (2) Point-wise contrastive learning is proposed to enhance the similarity of intra-class points and contrastively decrease the similarity of inter-class points. (3) The C-Rend plays a complementary role for the SSL framework in uncertainty-aware learning, which could provide reliable supervision information and achieve superior segmentation performance. Through extensive validation on large in-house datasets with partial annotations, our method outperforms state-of-the-art methods in various evaluation metrics for both the ovary and follicles.

CR-Unet-Based Ultrasonic Follicle Monitoring to Reduce Diameter Variability and Generate Area Automatically as a Novel Biomarker for Follicular Maturity.

Follicle size is closely related to ovarian function and is an important biomarker in transvaginal ultrasound examinations for assessing follicular maturity during an assisted reproduction cycle. However, manual measurement is time consuming and subject to high inter- and intra- observer variability. Based on the deep learning model CR-Unet described in our previous study, the aim of our present study was to investigate further the feasibility of using this model in clinical practice by validating its performance in reducing the inter- and intra-observer variability of follicle diameter measurement. This study also investigated whether follicular area is a better biomarker than diameter in assessing follicular maturity. Data on 106 ovaries and 230 follicles collected from 80 cases of single follicular cycles and 26 cases of multiple follicular cycles constituted the validation set. Intra-observer variability was 0.973 and 0.982 for the senior sonographer and junior sonographer in single follicular cycles and 0.979 (0.971, 0.985) and 0.920 (0.892, 0.943) in multiple follicular cycles, respectively, while CR-Unet had no intra-group variation. Bland-Altman plot analysis indicated that the 95% limits of agreement between senior sonographer and CR-Unet (-2.1 to 1.1 mm, -2.02 to 0.75 mm) were smaller than those between senior sonographer and junior sonographer (-1.51 to 1.15 mm, -2.1 to 1.56 mm) in single and multiple follicular cycles. The average operating times of diameter measurement taken by the junior sonographer, senior sonographer and CR-Unet were 7.54 ± 1.8, 4.87 ± 0.84 and 1.66 ± 0.76 s, respectively (p < 0.001). Correlation analysis indicated that both manual and automated follicular area correlated better with follicular volume than diameter. The deep learning algorithm and the new biomarker of follicular area hold potential for clinical application of ultrasonic follicular monitoring.