A method for human teratogen detection by geometrically confined cell differentiation and migration

Unintended exposure to teratogenic compounds can lead to various birth defects; however current animal-based testing is limited by time, cost and high inter-species variability. Here, we developed a human-relevant in vitro model, which recapitulated two cellular events characteristic of embryogenesis, to identify potentially teratogenic compounds. We spatially directed mesoendoderm differentiation, epithelial-mesenchymal transition and the ensuing cell migration in micropatterned human pluripotent stem cell (hPSC) colonies to collectively form an annular mesoendoderm pattern. Teratogens could disrupt the two cellular processes to alter the morphology of the mesoendoderm pattern. Image processing and statistical algorithms were developed to quantify and classify the compounds’ teratogenic potential. We not only could measure dose-dependent effects but also correctly classify species-specific drug (Thalidomide) and false negative drug (D-penicillamine) in the conventional mouse embryonic stem cell test. This model offers a scalable screening platform to mitigate the risks of teratogen exposures in human.Singapore. Agency for Science, Technology and ResearchJanssen Pharmaceutical Ltd. (Grant R-185-000-182-592)Janssen Pharmaceutical Ltd. (Grant R-185-000-228-592)Singapore-MIT Alliance Computational and Systems Biology Flagship Project (C-382-641-001-091)Mechanobiology Institute, Singapore (R-714-001-003-271

Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification

The accurate staging of liver fibrosis is of paramount importance to determine the state of disease progression, therapy responses, and to optimize disease treatment strategies. Non-linear optical microscopy techniques such as two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG) can image the endogenous signals of tissue structures and can be used for fibrosis assessment on non-stained tissue samples. While image analysis of collagen in SHG images was consistently addressed until now, cellular and tissue information included in TPEF images, such as inflammatory and hepatic cell damage, equally important as collagen deposition imaged by SHG, remain poorly exploited to date. We address this situation by experimenting liver fibrosis quantification and scoring using a combined approach based on TPEF liver surface imaging on a Thioacetamide-induced rat model and a gradient based Bag-of-Features (BoF) image classification strategy. We report the assessed performance results and discuss the influence of specific BoF parameters to the performance of the fibrosis scoring framework.Romania. Executive Agency for Higher Education, Research, Development and Innovation Funding (research grant PN-II-PT-PCCA-2011-3.2-1162)Rectors' Conference of the Swiss Universities (SCIEX NMS-CH research fellowship nr. 12.135)Singapore. Agency for Science, Technology and Research (R-185-000-182-592)Singapore. Biomedical Research CouncilInstitute of Bioengineering and Nanotechnology (Singapore)Singapore-MIT Alliance (Computational and Systems Biology Flagship Project funding (C-382-641-001-091))Singapore-MIT Alliance for Research and Technology (SMART BioSyM and Mechanobiology Institute of Singapore (R-714-001-003-271)

A nomogram based on collagen signature for predicting the immunoscore in colorectal cancer

ObjectivesThe Immunoscore can categorize patients into high- and low-risk groups for prognostication in colorectal cancer (CRC). Collagen plays an important role in immunomodulatory functions in the tumor microenvironment (TME). However, the correlation between collagen and the Immunoscore in the TME is unclear. This study aimed to construct a collagen signature to illuminate the relationship between collagen structure and Immunoscore.MethodsA total of 327 consecutive patients with stage I-III stage CRC were included in a training cohort. The fully quantitative collagen features were extracted at the tumor center and invasive margin of the specimens using multiphoton imaging. LASSO regression was applied to construct the collagen signature. The association of the collagen signature with Immunoscore was assessed. A collagen nomogram was developed by incorporating the collagen signature and clinicopathological predictors after multivariable logistic regression. The performance of the collagen nomogram was evaluated via calibration, discrimination, and clinical usefulness and then tested in an independent validation cohort. The prognostic values of the collagen nomogram were assessed using Cox regression and the Kaplan−Meier method.ResultsThe collagen signature was constructed based on 16 collagen features, which included 6 collagen features from the tumor center and 10 collagen features from the invasive margin. Patients with a high collagen signature were more likely to show a low Immunoscore (Lo IS) in both cohorts (P<0.001). A collagen nomogram integrating the collagen signature and clinicopathological predictors was developed. The collagen nomogram yielded satisfactory discrimination and calibration, with an AUC of 0.925 (95% CI: 0.895-0.956) in the training cohort and 0.911 (95% CI: 0.872-0.949) in the validation cohort. Decision curve analysis confirmed that the collagen nomogram was clinically useful. Furthermore, the collagen nomogram-predicted subgroup was significantly associated with prognosis. Moreover, patients with a low-probability Lo IS, rather than a high-probability Lo IS, could benefit from chemotherapy in high-risk stage II and stage III CRC patients.ConclusionsThe collagen signature is significantly associated with the Immunoscore in the TME, and the collagen nomogram has the potential to individualize the prediction of the Immunoscore and identify CRC patients who could benefit from adjuvant chemotherapy

Computer-aided-diagnosis of liver fibrosis using non-linear optics microscopy

Excessive accumulation of extracellular matrix results in fibrosis, which is the hallmark of chronic liver diseases. The role of liver biopsy as the gold standard for liver fibrosis assessment has recently been challenged due to inter- and intra-observer variation and sampling error. We have developed qFibrosis - a fully-automated classification of liver fibrosis through quantitative extraction of pathology-relevant features using non-linear optics microscopy, trained and tested in both animal and human studies. qFibrosis faithfully recapitulates the liver fibrosis staging performed by pathologists, and is robust with reference to sampling size. It can significantly predict staging underestimation in short biopsy cores, thus aiding in the correction of sampling error-mediated intra-observer variation. qFibrosis can predict the staging underestimation of the non-expert pathologist, thus further aiding in the correction of inter-observer variation. qFibrosis can also significantly differentiate intra-stage cirrhosis changes that can be monitored for making informed clinical decisions, and for predicting possible prognostic outcomes. qFibrosis has the potential to expedite the re-establishment of liver biopsy as the gold standard for assessment of fibrosis in chronic liver diseases. Furthermore, we have hypothesized that the less invasive liver surface imaging could serve as a favourable alternative to biopsy. We established a Capsule Index based on significant parameters extracted from the non-linear optics microscopy images of liver capsule from two fibrosis rat models. The Capsule Index is capable of differentiating different fibrosis stages in both animal models, making it possible to quantitatively stage liver fibrosis via liver surface imaging without biopsy.DOCTOR OF PHILOSOPHY (SAS

Assessment of liver steatosis and fibrosis in rats using integrated coherent anti-Stokes Raman scattering and multiphoton imaging technique

We report the implementation of a unique integrated coherent anti-Stokes Raman scattering (CARS), second-harmonic generation (SHG), and two-photon excitation fluorescence (TPEF) microscopy imaging technique developed for label-free monitoring of the progression of liver steatosis and fibrosis generated in a bile duct ligation (BDL) rat model. Among the 21 adult rats used in this study, 18 rats were performed with BDL surgery and sacrificed each week from weeks 1 to 6 (n = 3 per week), respectively; whereas 3 rats as control were sacrificed at week 0. Colocalized imaging of the aggregated hepatic fats, collagen fibrils, and hepatocyte morphologies in liver tissue is realized by using the integrated CARS, SHG, and TPEF technique. The results show that there are significant accumulations of hepatic lipid droplets and collagen fibrils associated with severe hepatocyte necrosis in BDL rat liver as compared to a normal liver tissue. The volume of normal hepatocytes keeps decreasing and the fiber collagen content in BDL rat liver follows a growing trend until week 6; whereas the hepatic fat content reaches a maximum in week 4 and then appears to stop growing in week 6, indicating that liver steatosis and fibrosis induced in a BDL rat liver model may develop at different rates. This work demonstrates that the integrated CARS and multiphoton microscopy imaging technique has the potential to provide an effective means for early diagnosis and detection of liver steatosis and fibrosis without labeling.Singapore. Biomedical Research CouncilSingapore. National Medical Research CouncilInstitute of Bioengineering and Nanotechnology (Singapore)Singapore. Biomedical Research CouncilSingapore. Agency for Science, Technology and ResearchMechanobiology Institute, SingaporeSingapore-MIT Alliance Computational and Systems Biology Flagship ProjectSingapore-MIT Alliance for Research and TechnologyJasssen Cila

In vivo, label-free, three-dimensional quantitative imaging of liver surface using multi-photon microscopy

Various structural features on the liver surface reflect functional changes in the liver. The visualization of these surface features with molecular specificity is of particular relevance to understanding the physiology and diseases of the liver. Using multi-photon microscopy (MPM), we have developed a label-free, three-dimensional quantitative and sensitive method to visualize various structural features of liver surface in living rat. MPM could quantitatively image the microstructural features of liver surface with respect to the sinuosity of collagen fiber, the elastic fiber structure, the ratio between elastin and collagen, collagen content, and the metabolic state of the hepatocytes that are correlative with the pathophysiologically induced changes in the regions of interest. This study highlights the potential of this technique as a useful tool for pathophysiological studies and possible diagnosis of the liver diseases with further development. © 2014 AIP Publishing LLC

Rapid and label-free detection of gastrointestinal stromal tumor via a combination of two-photon microscopy and imaging analysis

Abstract Background Gastrointestinal stromal tumor (GIST) is currently regarded as a potentially malignant tumor, and early diagnosis is the best way to improve its prognosis. Therefore, it will be meaningful to develop a new method for auxiliary diagnosis of this disease. Methods Here we try out a new means to detect GIST by combining two-photon imaging with automatic image processing strategy. Results Experimental results show that two-photon microscopy has the ability to label-freely identify the structural characteristics of GIST such as tumor cells, desmoplastic reaction, which are entirely different from those from gastric adenocarcinoma. Moreover, an image processing approach is used to extract eight collagen morphological features from tumor microenvironment and normal muscularis, and statistical analysis demonstrates that there are significant differences in three features—fiber area, density and cross-link density. The three morphological characteristics may be considered as optical imaging biomarkers to differentiate between normal and abnormal tissues. Conclusion With continued improvement and refinement of this technology, we believe that two-photon microscopy will be an efficient surveillance tool for GIST and lead to better management of this disease

Do Silvi-Medicinal Plantations Affect Tree Litter Decomposition and Nutrient Mineralization?

In a silvi-medicinal system, the plant secondary metabolites (PSMs) released from medicinal herbs could affect tree litter decomposition and nutrient release. However, the specific effects of PSMs on arboreous litter decomposition are still not well understood. In this study, the extracts of nine types of medicinal herbs were used to treat Pinus armandii Franch. and Larix gmelinii (Rupr.) Kuzen. litter during a simulated half-year decomposition. The effects of the extracts on the decomposition and the N and P release of the conifer litter were investigated. The results indicated that most of the medicinal herb extracts significantly inhibited the late decomposition of P. armandii litter, whereas only two of them accelerated the entire decomposition process. Only a few significantly affected the decomposition of the L. gmelinii litter. Four of the nine types of extract significantly inhibited the N and P release of the P. armandii litter, while 3/9 and 6/9 inhibited the N and P release of the L. gmelinii litter, respectively. The accelerating effects of the extracts on the cellulase activity and the inhibitory effects on the polyphenol oxidase activity might be responsible for the early acceleration and late inhibition of litter decomposition, while the effects of the extracts on the activities of protease and phosphatase might not be the main reason for the inhibitory or accelerating effects on the N and P release. In general, the inhibitory effects of medicinal herbs on the nutrient cycling of ecosystems should be taken into consideration when building silvi-medicinal systems, especially in P. armandii forests

M-region segmentation of pharyngeal swab image based on improved U-Net Model

The main method to diagnose COVID-19 is a nucleic acid test from a throat swab. Routine manual collection methods expose medical personnel to high-risk environment, which has a high risk of cross-infection. A throat swab sampling robot was developed to take the place of medical staff. The automatic segmentation of M-region in the pharyngeal swab image, which plays a core guiding role when the robot takes a throat swab sample. Aiming at the problem of discontinuous or fuzzy boundary in M -region of oral cavity, the segmentation accuracy is affected. An improved U -Net model is proposed and a new multi-scale feature fusion module with channel attention mechanism is presented. The ability of adaptive learning is enhanced and the segmentation precision of M -region with discontinuous or fuzzy edges is increased. Oral images of 45 volunteers were collected for training and testing. Experimental results showed that the model could accurately segment M-region in pharyngeal swab images, and compared with other segmentation networks, it has better indexes of segmentation precision

M-region segmentation of pharyngeal swab image based on improved U-Net Model

The main method to diagnose COVID-19 is a nucleic acid test from a throat swab. Routine manual collection methods expose medical personnel to high-risk environment, which has a high risk of cross-infection. A throat swab sampling robot was developed to take the place of medical staff. The automatic segmentation of M-region in the pharyngeal swab image, which plays a core guiding role when the robot takes a throat swab sample. Aiming at the problem of discontinuous or fuzzy boundary in M -region of oral cavity, the segmentation accuracy is affected. An improved U -Net model is proposed and a new multi-scale feature fusion module with channel attention mechanism is presented. The ability of adaptive learning is enhanced and the segmentation precision of M -region with discontinuous or fuzzy edges is increased. Oral images of 45 volunteers were collected for training and testing. Experimental results showed that the model could accurately segment M-region in pharyngeal swab images, and compared with other segmentation networks, it has better indexes of segmentation precision