Label-free shotgun proteomics and metabolite analysis reveal a significant metabolic shift during citrus fruit development.

Label-free LC-MS/MS-based shot-gun proteomics was used to quantify the differential protein synthesis and metabolite profiling in order to assess metabolic changes during the development of citrus fruits. Our results suggested the occurrence of a metabolic change during citrus fruit maturation, where the organic acid and amino acid accumulation seen during the early stages of development shifted into sugar synthesis during the later stage of citrus fruit development. The expression of invertases remained unchanged, while an invertase inhibitor was up-regulated towards maturation. The increased expression of sucrose-phosphate synthase and sucrose-6-phosphate phosphatase and the rapid sugar accumulation suggest that sucrose is also being synthesized in citrus juice sac cells during the later stage of fruit development

Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging studies, including the SR task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures, but rather they were conventional SR techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues.Comment: 10 pages, 9 figure

Fully-automatic deep learning-based analysis for determination of the invasiveness of breast cancer cells in an acoustic trap

A single-beam acoustic trapping technique has been shown to be very useful for determining the invasiveness of suspended breast cancer cells in an acoustic trap with a manual calcium analysis method. However, for the rapid translation of the technology into the clinic, the development of an efficient/accurate analytical method is needed. We, therefore, develop a fully-automatic deep learning-based calcium image analysis algorithm for determining the invasiveness of suspended breast cancer cells using a single-beam acoustic trapping system. The algorithm allows to segment cells, find trapped cells, and quantify their calcium changes over time. For better segmentation of calcium fluorescent cells even with vague boundaries, a novel deep learning architecture with multi-scale/multi-channel convolution operations (MM-Net) is devised and constructed by a target inversion training method. The MM-Net outperforms other deep learning models in the cell segmentation. Also, a detection/quantification algorithm is developed and implemented to automatically determine the invasiveness of a trapped cell. For the evaluation of the algorithm, it is applied to quantify the invasiveness of breast cancer cells. The results show that the algorithm offers similar performance to the manual calcium analysis method for determining the invasiveness of cancer cells, suggesting that it may serve as a novel tool to automatically determine the invasiveness of cancer cells with high-efficiency. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.1

Translation of Korean Medicine Use to ICD-Codes Using National Health Insurance Service-National Sample Cohort

Background. Korean medicine was incorporated into the Korean Classification of Diseases (KCD) 6 through the development of U codes (U20–U99). Studies of the burden of disease have used summary measures such as disability-adjusted life years. Although Korean medicine is included in the official health care system, studies of the burden of disease that include Korean medicine are lacking. Methods. A data-based approach was used with National Health Insurance Service-National Sample Cohort data for the year 2012. U code diagnoses for patients covered by National Health Insurance were collected. Using the main disease and subdisease codes, the proportion of U codes was redistributed into the related KCD 6 codes and visualized. U code and KCD code relevance was appraised prior to the analysis by consultation with medical professionals and from the beta draft version of the International Classification of Diseases-11 traditional medicine chapter. Results. This approach enabled redistribution of U codes into KCD 6 codes. Musculoskeletal diseases had the greatest increase in the burden of disease through this approach. Conclusion. This study provides a possible method of incorporating Korean medicine into burden of disease analyses through a data-based approach. Further studies should analyze potential yearly differences