Inorganic arsenic (iAs) is one of the most toxic metalloids which could accumulate in marine species, especially in clams, causing serious ecological risk. Marine clams accumulate high level of iAs in different tissues. Currently, Fluorescence Lifetime Microscopy (FLIM) technique has provided quantitative information in biochemical diagnosis. In this study, we applied FLIM method into analyzing Hematoxylin and eosin (H&E) stained sections of arsenic exposed Ruditapes philippinarum. The clams were exposed under different concentrations of As(Ⅲ) and As(V) for thirty days. Fluorescent images of the H&E stained hepatopancreas tissue were obtained with FLIM system, followed with data analysis for fluorescence lifetime values. The average fluorescence lifetime of sections in the control group was around 250 ps. The average lifetime value in the As(V) group was slightly increased to around 280-300 ps. The average lifetime value in the As(III) group achieved a significant increase to around 340 ps. These results suggested a higher extent of structural change in As(Ⅲ) exposed group than As(V) group. As a result, this work has provided quantitative evaluation standard for the toxicity of marine mollusk based on fluorescence lifetime imaging method.
Esophageal carcinoma is a common clinical cancer and with sixth occurrence frequency in the world, has a low five-year survival rate. Among different types of esophageal carcinoma, esophageal squamous cell carcinoma (ESCC) has the highest incidence rate and has a poor prognosis after surgery. For clinical diagnosis, hematoxylin and eosin (H&E) stained sections of diseased tissues are considered as the “golden standard”. However, determination of the tumor regions is usually relied on professional experience, which is time consuming and has a high misdiagnosis rate. Currently, novel optical imaging tools such as multi-photon excitation imaging and fluorescence lifetime imaging microscopy (FLIM) have been applied in clinical diagnosis. FLIM contains advantages of accurate measurement and high sensitivity to microenvironment. In this work, we constructed mice orthotopic esophageal cancer model to investigate the characteristics of esophageal tumor cells. Then FLIM technique were used to investigate H&E stained sections from both healthy control mice and ESCC mice, providing difference between the fluorescence lifetime values of normal tissues and those of the pathological tissues. Results also revealed an alteration of the fluorescence lifetime values of esophageal stratum corneum, which might be generated through tumor extrusion. Furthermore, the fluorescence lifetime values of tumor cells are distinctly smaller than those of the surrounding stroma, indicating an accurate identification the lesion area. In conclusion, the fluorescence lifetime images obtained by FLIM could provide a quantitative method in future pathological identification.
As one of the most fatal cancers, pancreatic cancer generated nearly a half million of new cases world-widely in 2021. The cure rate of pancreatic cancer is extremely low, whose five-year survival rate after surgery is less than 5%, leading to a great demand of early diagnosis. Currently, the aspiration biopsy with hematoxylin-eosin (H&E) staining is considered as one golden standard for clinical cancer diagnosis. However, the accuracy of the identification of the biopsy is unsatisfied, which is highly affected by the experience of doctors. Here, we have used fluorescence lifetime imaging microscopy (FLIM) method to analyze H&E stained sections, providing quantitative data for further identification. Mice were randomly selected, divided into the experimental group injected with pancreatic cancer cells and the control group. Then the pancreatic tissues of both groups were obtained and stained with H&E dye solution. Next, the sections were imaged with FLIM system, providing fluorescence lifetime data for analysis. The results showed that the average fluorescence lifetime value of normal tissues was around 30.5% less than that of cancerous tissues. Moreover, phasor plot software was applied to distinguish certain regions (such as desmoplasia), which were not identified in either the bright-field-image mode nor the fluorescence lifetime mode. In conclusion, FLIM technique on H&E stained sections has generated quantitative information to identify cancerous tissues as well as desmoplasia, leading to an improved diagnostic accuracy. This FLIM-based method in analyzing H&E stained sections has a high potential in further quantitative diagnosis of different types of cancers.
Stroke is a group of diseases with severe brain tissue damage, which are caused by either the sudden rupture of brain blood vessels (cerebral hemorrhage) or brain blood vessel obstruction, leading to rapid changes and high mortality. The diagnosis of stroke mainly relies on medical imaging techniques, including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), which require experienced radiologists to guarantee suitable accuracy. However, the amount of brain CT image data is extremely large, usually exceeding the technical capabilities of radiologists. Currently, artificial intelligence has been applied into CT image analysis in order to achieve high sensitivity and specific diagnosis results for clinical examinations. In this work, we obtained CT images from a database (CQ500), including epidural hemorrhage, cerebral parenchymal hemorrhage and intraventricular hemorrhage. Then, we introduced a deep-learning algorithm based on U-Net model, which was trained to generate image segmentation, providing a calculated accuracy of prediction yield. The results showed that the average intersection ratio of the final model on the test set could reach the value of 0.96. Briefly, artificial intelligence in this work can efficiently improve the analysis of brain CT images, suggesting an important development direction for future medical imaging auxiliary diagnosis.
In recent years, lung cancer has become one of the most lethal factors to human beings. Clinical data show that the probability of lung nodules developed into lung cancer is about 30%. Due to the lack of obvious symptoms, around 70% of lung cancer patients in China are in advanced stage of lung cancer when firstly diagnosed. Therefore, early identification of lung nodules is of great significance for early diagnosis and therapy. Currently, artificial intelligence has been widely used to generate predictive model of lung nodules by learning algorithms adapted to image characteristics, leading to improved accuracy and higher sensitivity of diagnosis of early lung cancer. In this work, Luna16 (lung nodule analysis 2016, containing a total of 888 low-dose chest Computed Tomography (CT) thin-slice plain scan lesions) were selected as the data set, providing a total of 1018 CT slices with the most representative shape of lung nodules in this analysis. Next, this project was performed on Baidu AI Studio platform, applying both U-Net and PSP Net to train a model of rapid detection of lung nodules. The training process generated a model providing a rapid and accurate identification of lung nodules larger than 3 mm in diameter. Results showed that the accuracy of U-Net was higher than that of PSP Net, indicating a high potential in further clinical diagnosis in lung cancer.
As a kind of microalgae, Spirulina plays an important role in fish culture, food processing industry, medical treatment and bioenergetic development due to its reasonable nutritional composition and high hydrogenase activity. However, the purity of Spirulina , which could be significantly affected by virus infection and miscellaneous algal issues, has great impact on the quality of the product. Thus, periodic Spirulina detection is necessary for quality control of Spirulina culture. Currently, there are two main methods of Spirulina detection: the optical microscopic method and the fluorescence detection method. The former has higher accuracy and a lower speed while the latter has a higher speed in a sample destructing mode. Deep learning-based method has the ability to accelerate data processing. Meanwhile, it can achieve high accuracy by model training and validation. In this work, we have applied deep learning to Spirulina detection to achieve a higher accuracy rate. The process was divided into four main steps: Spirulina culture, image acquisition, image preprocessing and YOLO-v3 model training. The hyperparametric modulation was carried out to determine the appropriate training parameters, providing a trained model with mAP of 0.839 at a detection speed of 20.53 fps. It has great application potential in quantity detection and size detection of cultured Spirulina .
Here, a doxorubicin (Dox)-Cu complex is prepared and used as thiols-cleavable Dox prodrug in FLIM imaging studies. The Dox-Cu complex has no fluorescence and its absorption spectrum shows a significant red shift as compared to the raw Dox. When the complex is mixed with biothiol compound, the fluorescence of Dox is restored. In this paper, both Dox and Dox-Cu complexes are used in the fluorescence lifetime analysis, cell incubation and FLIM imaging studies. The way of intracellular transport and the changes in fluorescence lifetime of two compounds are basically the same. This indicates that the Dox-Cu complex has been decomposed into free Dox molecules and Cu-biothiol complex inside the cell. This research provides a valuable example for the study of Dox prodrug by FLIM technique, promoting the application of FLIM technique in drug research.
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