Imaging peritoneal blood vessels through optical coherence tomography angiography for laparoscopic surgery.

Laparoscopic surgery presents challenges in identifying blood vessels due to lack of tactile feedback. The image-guided laparoscopic surgical tool (IGLaST) integrated with optical coherence tomography (OCT) has potential for in vivo blood vessel imaging; however, distinguishing vessels from surrounding tissue remains a challenge. In this study, we propose utilizing an inter-A-line intensity differentiation-based OCT angiography (OCTA) to improve visualization of blood vessels. By evaluating a tissue phantom with varying flow speeds, we optimized the system's blood flow imaging capabilities in terms of minimum detectable flow and contrast-to-noise ratio. In vivo experiments on rat and porcine models, successfully visualized previously unidentified blood vessels and concealed blood flows beneath the 1 mm depth peritoneum. Qualitative comparison of various OCTA algorithms indicated that the intensity differentiation-based algorithm performed best for our application. We believe that implementing IGLaST with OCTA can enhance surgical outcomes and reduce procedure time in laparoscopic surgeries.

Lateral image reconstruction of optical coherence tomography using one-dimensional deep deconvolution network.

BACKGROUND AND OBJECTIVES: Optical coherence tomography (OCT) is a cross-sectional imaging method utilizing a low coherence interferometry. The lateral resolution of the OCT is limited by the numerical aperture (NA) of the imaging lens. Using a high NA lens improves the lateral resolution but reduces the depth of focus (DOF). In this study, we propose a method to improve the lateral resolution of OCT images by end-to-end training of a deep 1-D deconvolution network without use of high-resolution images. MATERIALS AND METHODS: To improve the lateral resolution of the OCT, we trained the 1-D deconvolution network using lateral profiles of OCT images and the beam spot size. We used our image-guided laparoscopic surgical tool (IGLaST) to acquire OCT images of nonbiological and biological samples ex vivo. The OCT images were then blurred by applying Gaussian functions with various full width half maximums ranging from 40 to 160 µm. The network was trained using the blurred OCT images as input and the non-blurred original OCT images as output. We quantitatively evaluated the developed network in terms of similarity and signal-to-ratio (SNR), using in-vivo images of mesenteric tissue from a porcine model that was not used for training. In addition, we performed knife-edge tests and qualitative evaluation of the network to show the lateral resolution improvement of ex-vivo and in-vivo OCT images. RESULTS: The proposed method showed an improvement of image quality on both blurred images and non-blurred images. When the proposed deconvolution network was applied, the similarity to the non-blurred image was improved by 1.29 times, and the SNR was improved by 1.76 dB compared to the artificially blurred images, which was superior to the conventional deconvolution method. The knife-edge tests at distances at 200 to 1000 µm from the imaging probe showed an approximately 1.2 times improvement in lateral resolution. In addition, through qualitative evaluation, it was found that the image quality of both ex-vivo and in-vivo tissue images was improved with clear structure and less noise. CONCLUSIONS: This study showed the ability of the 1-D deconvolution network to improve the image quality of OCT images with variable lateral resolution. We were able to train the network with a small amount of data by constraining the network in 1-D. The quantitative evaluation showed better results than conventional deconvolution methods for various amount of blurring. Qualitative evaluation showed analogous results with quantitative results. This simple yet powerful image restoration method provides improved lateral resolution and suppresses background noise, making it applicable to a variety of OCT imaging applications.

Enhancement of anti-inflammatory and immunomodulatory effects of adipose-derived human mesenchymal stem cells by making uniform spheroid on the new nano-patterned plates.

Human mesenchymal stem cells (MSCs) are known to have anti-inflammatory and immunomodulatory functions; thus, several MSC products have been applied as cell therapy in clinical trials worldwide. Recent studies have demonstrated that MSC spheroids have superior anti-inflammatory and immunomodulatory functions to a single cell suspension. Current methods to prepare MSC spheroids include hanging drop, concave microwell aggregation, spinner flask, and gravity circulation. However, all these methods have limitations such as low scalability, easy cell clumping, low viability, and irregular size distribution. Here, we present a nano-patterned culture plasticware named PAMcell™ 3D plate to overcome these limitations. Nano-sized silica particles (700 nm) coated with RGD peptide were arrayed into fusiform onto the PLGA film. This uniform array enabled the seeded MSCs to grow only on the silica particles, forming uniform-sized semi-spheroids within 48 h. These MSC spheroids have been shown to have enhanced stemness, anti-inflammatory, and immunomodulatory functions, as revealed by the increased expression of stem cell markers (Oct4, Sox2, and Nanog), anti-inflammatory (IL-10, TSG6, and IDO), and immunomodulatory molecules (HGF, VEGF, CXCR4) both at mRNA and protein expression levels. Furthermore, these MSC spheroids demonstrated an increased palliative effect on glycemic control in a multiple low-dose streptozotocin-induced diabetes model compared with the same number of MSC single cell suspensions. Taken together, this study presents a new method to produce uniform-sized MSC spheroids with enhanced anti-inflammatory and immunomodulatory functions in vitro and in vivo.

A Digital Shade-Matching Device for Dental Color Determination Using the Support Vector Machine Algorithm.

In this study, we developed a digital shade-matching device for dental color determination using the support vector machine (SVM) algorithm. Shade-matching was performed using shade tabs. For the hardware, the typically used intraoral camera was modified to apply the cross-polarization scheme and block the light from outside, which can lead to shade-matching errors. For reliable experiments, a precise robot arm with ±0.1 mm position repeatability and a specially designed jig to fix the position of the VITA 3D-master (3D) shade tabs were used. For consistent color performance, color calibration was performed with five standard colors having color values as the mean color values of the five shade tabs of the 3D. By using the SVM algorithm, hyperplanes and support vectors for 3D shade tabs were obtained with a database organized using five developed devices. Subsequently, shade matching was performed by measuring 3D shade tabs, as opposed to real teeth, with three additional devices. On average, more than 90% matching accuracy and a less than 1% failure rate were achieved with all devices for 10 measurements. In addition, we compared the classification algorithm with other classification algorithms, such as logistic regression, random forest, and k-nearest neighbors, using the leave-pair-out cross-validation method to verify the classification performance of the SVM algorithm. Our proposed scheme can be an optimum solution for the quantitative measurement of tooth color with high accuracy.

Delayed revascularization of islets after transplantation by IL-6 blockade in pig to non-human primate islet xenotransplantation model.

BACKGROUND: Pancreatic islet transplantation is currently proven as a promising treatment for type 1 diabetes patients with labile glycemic control and severe hypoglycemia unawareness. Upon islet transplantation, revascularization is essential for proper functioning of the transplanted islets. As IL-6 is important for endothelial cell survival and systemic inflammation related to xenograft, the effect of IL-6 receptor antagonist, tocilizumab, on revascularization of the transplanted islets was examined in pig to non-human primate islet xenotransplantation model. Also, the endothelial cell origin in a new vessel of the transplanted pig islets was determined. METHODS: Pig islets were isolated from designated pathogen-free (DPF) SNU miniature pigs and transplanted via portal vein into five streptozotocin-induced diabetic monkeys. One group (n = 2, basal group) was treated with anti-thymoglobulin (ATG), anti-CD40 antibody (2C10R4), sirolimus, and tacrolimus, and the other group was additionally given tocilizumab on top of basal immunosuppression (n = 3, Tocilizumab group). To confirm IL-6 blocking effect, C-reactive protein (CRP) levels and serum IL-6 concentration were measured. Scheduled biopsy of the margin of the posterior segment right lobe inferior of the liver was performed at 3 weeks after transplantation to assess the degree of revascularization of the transplanted islets. Immunohistochemical staining using anti-insulin, anti-CD31 antibodies, and lectin IB4 was conducted to find the origin of endothelial cells in the islet graft. RESULTS: CRP significantly increased at 1~2 days after transplantation in Basal group, but not in Tocilizumab group, and higher serum IL-6 concentration was measured in latter group, showing the biological potency of tocilizumab. In Basal group, well-developed endothelial cells were observed on the peri- and intraislet area, whereas the number of CD31+ cells in the intraislet space was significantly reduced in Tocilizumab group. Finally, new endothelial cells in the pig islet graft were positive for CD31, but not for lectin IB4, suggesting that they are originated from the recipient monkey. CONCLUSIONS: Our results demonstrated that tocilizumab can delay revascularization of the transplanted islet, although this effect had no significant correlation to the overall islet graft survival. In the pig to NHP islet xenotransplantation model, the endothelial cells from recipient monkey form new blood vessels in and around pig islets.

Electrothermal MEMS parallel plate rotation for single-imager stereoscopic endoscopes.

This work reports electrothermal MEMS parallel plate-rotation (PPR) for a single-imager based stereoscopic endoscope. A thin optical plate was directly connected to an electrothermal MEMS microactuator with bimorph structures of thin silicon and aluminum layers. The fabricated MEMS PPR device precisely rotates an transparent optical plate up to 37° prior to an endoscopic camera and creates the binocular disparities, comparable to those from binocular cameras with a baseline distance over 100 µm. The anaglyph 3D images and disparity maps were successfully achieved by extracting the local binocular disparities from two optical images captured at the relative positions. The physical volume of MEMS PPR is well fit in 3.4 mm x 3.3 mm x 1 mm. This method provides a new direction for compact stereoscopic 3D endoscopic imaging systems.

Development, implementation, and a cognitive evaluation of a definitional question answering system for physicians.

The published medical literature and online medical resources are important sources to help physicians make patient treatment decisions. Traditional sources used for information retrieval (e.g., PubMed) often return a list of documents in response to a user's query. Frequently the number of returned documents from large knowledge repositories is large and makes information seeking practical only "after hours" and not in the clinical setting. This study developed novel algorithms, and designed, implemented, and evaluated a medical definitional question answering system (MedQA). MedQA automatically analyzed a large number of electronic documents to generate short and coherent answers in response to definitional questions (i.e., questions with the format of "What is X?"). Our preliminary cognitive evaluation shows that MedQA out-performed three other online information systems (Google, OneLook, and PubMed) in two important efficiency criteria; namely, time spent and number of actions taken for a physician to identify a definition. It is our contention that question answering systems that aggregate pertinent information scattered across different documents have the potential to address clinical information needs within a timeframe necessary to meet the demands of clinicians.

Accessing bioscience images from abstract sentences.

Images (e.g., figures) are important experimental results that are typically reported in bioscience full-text articles. Biologists need to access images to validate research facts and to formulate or to test novel research hypotheses. On the other hand, biologists live in an age of information explosion. As thousands of biomedical articles are published every day, systems that help biologists efficiently access images in literature would greatly facilitate biomedical research. We hypothesize that much of image content reported in a full-text article can be summarized by the sentences in the abstract of the article. In our study, more than one hundred biologists had tested this hypothesis and more than 40 biologists had evaluated a novel user-interface BioEx that allows biologists to access images directly from abstract sentences. Our results show that 87.8% biologists were in favor of BioEx over two other baseline user-interfaces. We further developed systems that explored hierarchical clustering algorithms to automatically identify abstract sentences that summarize the images. One of the systems achieves a precision of 100% that corresponds to a recall of 4.6%.

A newly synthesized photostable retinol derivative (retinyl N-formyl aspartamate) for photodamaged skin: profilometric evaluation of 24-week study.

BACKGROUND: Retinol (vitamin A) is used in the cosmetics industry as an antiwrinkle agent. However, its photoinstability and skin irritation potential make it challenging to use in general cosmetic formulations. OBJECTIVE: The aim of this study was to assess the efficacy of a newly synthesized photostable retinol derivative (retinyl N-formyl aspartamate) in patients with photodamaged skin. Retinyl N-formyl aspartamate is a newly synthesized retinol derivative with higher photostability, and a similar effect on collagenase expression level as retinol. METHODS: In all, 29 Korean women (age range: 31-54 years), who were not pregnant, nursing, or undergoing any concurrent therapy, were enrolled in this study. A total of 24 patients completed a 24-week trial of retinyl N-formyl aspartamate twice daily on the left half of the face and a placebo on the right half of the face. A clinical evaluation, photographs, and silicone replicas of both crow's-feet areas were taken at baseline and at weeks 12, 20, and 24. Skin replicas were then analyzed using an optical profilometry technique. The standard wrinkle and roughness features were then calculated and statistically analyzed. The tolerance profile of the product was also clinically evaluated during the study. RESULTS: The 24 women who completed this study showed more improvement on the left side of the crow's-feet area in terms of the signs of photodamage than on the right side according to both their own (P < .001) and the investigator's (P < .05) evaluations. These results were confirmed by skin replica analyses. The average roughness showed significant improvement (P < .001). The smoothness depth was improved, but this was not statistically significant. One patient noted burning and prickling sensations, and she withdrew during the study. LIMITATIONS: Pigmentation changes were not assessed, investigators were not blinded, and the study size was relatively small. CONCLUSION: In this small study retinyl N-formyl aspartamate applied on a photodamaged face twice daily was significantly more effective than a placebo without severe side effects.

Exploring supervised and unsupervised methods to detect topics in biomedical text.

BACKGROUND: Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature. RESULTS: We have explored the methods of Topic Spotting, a task of text categorization that applies the supervised machine-learning technique naïve Bayes to assign automatically a document into one or more predefined topics; and Topic Clustering, which apply unsupervised hierarchical clustering algorithms to aggregate documents into clusters such that each cluster represents a topic. We have applied our methods to detect topics of more than fifteen thousand of articles that represent over sixteen thousand entries in the Online Mendelian Inheritance in Man (OMIM) database. We have explored bag of words as the features. Additionally, we have explored semantic features; namely, the Medical Subject Headings (MeSH) that are assigned to the MEDLINE records, and the Unified Medical Language System (UMLS) semantic types that correspond to the MeSH terms, in addition to bag of words, to facilitate the tasks of topic detection. Our results indicate that incorporating the MeSH terms and the UMLS semantic types as additional features enhances the performance of topic detection and the naïve Bayes has the highest accuracy, 66.4%, for predicting the topic of an OMIM article as one of the total twenty-five topics. CONCLUSION: Our results indicate that the supervised topic spotting methods outperformed the unsupervised topic clustering; on the other hand, the unsupervised topic clustering methods have the advantages of being robust and applicable in real world settings.

Beyond information retrieval--medical question answering.

Physicians have many questions when caring for patients, and frequently need to seek answers for their questions. Information retrieval systems (e.g., PubMed) typically return a list of documents in response to a user's query. Frequently the number of returned documents is large and makes physicians' information seeking "practical only 'after hours' and not in the clinical settings". Question answering techniques are based on automatically analyzing thousands of electronic documents to generate short-text answers in response to clinical questions that are posed by physicians. The authors address physicians' information needs and described the design, implementation, and evaluation of the medical question answering system (MedQA). Although our long term goal is to enable MedQA to answer all types of medical questions, currently, we implemented MedQA to integrate information retrieval, extraction, and summarization techniques to automatically generate paragraph-level text for definitional questions (i.e., "What is X?"). MedQA can be accessed at http://www.dbmi.columbia.edu/~yuh9001/research/MedQA.html.

Question analysis for biomedical question answering.

We are developing a biomedical question answering system. This paper describes our system's architecture and our question analysis component. Specifically, we have explored the use of various supervised machine learning approaches to filter out unanswerable questions based on physicians' annotations.