Jing Wang

Bio: Jing Wang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Fundus (eye) & Fluorescence-lifetime imaging microscopy. The author has an hindex of 3, co-authored 8 publications receiving 19 citations. Previous affiliations of Jing Wang include University of Science and Technology of China.

Carbon Dots for Intracellular pH Sensing with Fluorescence Lifetime Imaging Microscopy.

Abstract: The monitoring of intracellular pH is of great importance for understanding intracellular trafficking and functions. It has various limitations for biosensing based on the fluorescence intensity or spectra study. In this research, pH-sensitive carbon dots (CDs) were employed for intracellular pH sensing with fluorescence lifetime imaging microscopy (FLIM) for the first time. FLIM is a highly sensitive method that is used to detect a microenvironment and it can overcome the limitations of biosensing methods based on fluorescence intensity. The different groups on the CDs surfaces changing with pH environments led to different fluorescence lifetime values. The CDs aqueous solution had a gradual change from 1.6 ns to 3.7 ns in the fluorescence lifetime with a pH range of 2.6-8.6. Similar fluorescence lifetime changes were found in pH buffer-treated living cells. The detection of lysosomes, cytoplasm, and nuclei in living cells was achieved by measuring the fluorescence lifetime of CDs. In particular, a phasor FLIM analysis was used to improve the pH imaging. Moreover, the effects of the coenzymes, amino acids, and proteins on the fluorescence lifetime of CDs were examined in order to mimic the complex microenvironment inside the cells.

Weakly supervised anomaly segmentation in retinal OCT images using an adversarial learning approach

Abstract: Lesion detection is a critical component of disease diagnosis, but the manual segmentation of lesions in medical images is time-consuming and experience-demanding. These issues have recently been addressed through deep learning models. However, most of the existing algorithms were developed using supervised training, which requires time-intensive manual labeling and prevents the model from detecting unaware lesions. As such, this study proposes a weakly supervised learning network based on CycleGAN for lesions segmentation in full-width optical coherence tomography (OCT) images. The model was trained to reconstruct underlying normal anatomic structures from abnormal input images, then the lesions can be detected by calculating the difference between the input and output images. A customized network architecture and a multi-scale similarity perceptual reconstruction loss were used to extend the CycleGAN model to transfer between objects exhibiting shape deformations. The proposed technique was validated using an open-source retinal OCT image dataset. Image-level anomaly detection and pixel-level lesion detection results were assessed using area-under-curve (AUC) and the Dice similarity coefficient, producing results of 96.94% and 0.8239, respectively, higher than all comparative methods. The average test time required to generate a single full-width image was 0.039 s, which is shorter than that reported in recent studies. These results indicate that our model can accurately detect and segment retinopathy lesions in real-time, without the need for supervised labeling. And we hope this method will be helpful to accelerate the clinical diagnosis process and reduce the misdiagnosis rate.

Domain adaptation model for retinopathy detection from cross-domain OCT images

Abstract: A deep neural network (DNN) can assist in retinopathy screening by automatically classifying patients into normal and abnormal categories according to optical coherence tomography (OCT) images. Typically, OCT images captured from different devices show heterogeneous appearances because of different scan settings; thus, the DNN model trained from one domain may fail if applied directly to a new domain. As data labels are difficult to acquire, we proposed a generative adversarial network-based domain adaptation model to address the cross-domain OCT images classification task, which can extract invariant and discriminative characteristics shared by different domains without incurring additional labeling cost. A feature generator, a Wasserstein distance estimator, a domain discriminator, and a classifier were included in the model to enforce the extraction of domain invariant representations. We applied the model to OCT images as well as public digit images. Results show that the model can significantly improve the classification accuracy of cross-domain images.

Generating Fundus Fluorescence Angiography Images from Structure Fundus Images Using Generative Adversarial Networks

Abstract: Fluorescein angiography can provide a map of retinal vascular structure and function, which is commonly used in ophthalmology diagnosis, however, this imaging modality may pose risks of harm to the patients. To help physicians reduce the potential risks of diagnosis, an image translation method is adopted. In this work, we proposed a conditional generative adversarial network(GAN) - based method to directly learn the mapping relationship between structure fundus images and fundus fluorescence angiography images. Moreover, local saliency maps, which define each pixel's importance, are used to define a novel saliency loss in the GAN cost function. This facilitates more accurate learning of small-vessel and fluorescein leakage features.

Detecting benign uterine tumors by autofluorescence lifetime imaging microscopy through adjacent healthy cervical tissues

Abstract: The endogenous fluorophores such as reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD) in cells and tissues can be imaged by fluorescence lifetime...

Universal adversarial attacks on deep neural networks for medical image classification

Abstract: Deep neural networks (DNNs) are widely investigated in medical image classification to achieve automated support for clinical diagnosis. It is necessary to evaluate the robustness of medical DNN tasks against adversarial attacks, as high-stake decision-making will be made based on the diagnosis. Several previous studies have considered simple adversarial attacks. However, the vulnerability of DNNs to more realistic and higher risk attacks, such as universal adversarial perturbation (UAP), which is a single perturbation that can induce DNN failure in most classification tasks has not been evaluated yet. We focus on three representative DNN-based medical image classification tasks (i.e., skin cancer, referable diabetic retinopathy, and pneumonia classifications) and investigate their vulnerability to the seven model architectures of UAPs. We demonstrate that DNNs are vulnerable to both nontargeted UAPs, which cause a task failure resulting in an input being assigned an incorrect class, and to targeted UAPs, which cause the DNN to classify an input into a specific class. The almost imperceptible UAPs achieved > 80% success rates for nontargeted and targeted attacks. The vulnerability to UAPs depended very little on the model architecture. Moreover, we discovered that adversarial retraining, which is known to be an effective method for adversarial defenses, increased DNNs’ robustness against UAPs in only very few cases. Unlike previous assumptions, the results indicate that DNN-based clinical diagnosis is easier to deceive because of adversarial attacks. Adversaries can cause failed diagnoses at lower costs (e.g., without consideration of data distribution); moreover, they can affect the diagnosis. The effects of adversarial defenses may not be limited. Our findings emphasize that more careful consideration is required in developing DNNs for medical imaging and their practical applications.

Carbon Nanomaterials: Synthesis, Functionalization and Sensing Applications

Abstract: Recent advances in nanomaterial design and synthesis has resulted in robust sensing systems that display superior analytical performance. The use of nanomaterials within sensors has accelerated new routes and opportunities for the detection of analytes or target molecules. Among others, carbon-based sensors have reported biocompatibility, better sensitivity, better selectivity and lower limits of detection to reveal a wide range of organic and inorganic molecules. Carbon nanomaterials are among the most extensively studied materials because of their unique properties spanning from the high specific surface area, high carrier mobility, high electrical conductivity, flexibility, and optical transparency fostering their use in sensing applications. In this paper, a comprehensive review has been made to cover recent developments in the field of carbon-based nanomaterials for sensing applications. The review describes nanomaterials like fullerenes, carbon onions, carbon quantum dots, nanodiamonds, carbon nanotubes, and graphene. Synthesis of these nanostructures has been discussed along with their functionalization methods. The recent application of all these nanomaterials in sensing applications has been highlighted for the principal applicative field and the future prospects and possibilities have been outlined.

Highly fluorescent carbon dots as novel theranostic agents for biomedical applications.

Abstract: As an emerging fluorescent nanomaterial, carbon dots (CDs) exhibit many attractive physicochemical features, including excellent photoluminescence properties, good biocompatibility, low toxicity and the ability to maintain the unique properties of the raw material. Therefore, CDs have been intensively pursued for a wide range of applications, such as bioimaging, drug delivery, biosensors and antibacterial agents. In this review, we systematically summarize the synthesis methods of these CDs, their photoluminescence mechanisms, and the approaches for enhancing their fluorescence properties. Particularly, we summarize the recent research on the synthesis of CDs from drug molecules as raw materials and introduce the representative application aspects of these fascinating CDs. Finally, we look into the future direction of CDs in the biomedical field and discuss the challenges encountered in the current development.

Visual pH Sensors: From a Chemical Perspective to New Bioengineered Materials

Abstract: Many human activities and cellular functions depend upon precise pH values, and pH monitoring is considered a fundamental task. Colorimetric and fluorescence sensors for pH measurements are chemical and biochemical tools able to sense protons and produce a visible signal. These pH sensors are gaining widespread attention as non-destructive tools, visible to the human eye, that are capable of a real-time and in-situ response. Optical “visual” sensors are expanding researchers’ interests in many chemical contexts and are routinely used for biological, environmental, and medical applications. In this review we provide an overview of trending colorimetric, fluorescent, or dual-mode responsive visual pH sensors. These sensors include molecular synthetic organic sensors, metal organic frameworks (MOF), engineered sensing nanomaterials, and bioengineered sensors. We review different typological chemical entities of visual pH sensors, three-dimensional structures, and signaling mechanisms for pH sensing and applications; developed in the past five years. The progression of this review from simple organic molecules to biological macromolecules seeks to benefit beginners and scientists embarking on a project of pH sensing development, who needs background information and a quick update on advances in the field. Lessons learned from these tools will aid pH determination projects and provide new ways of thinking for cell bioimaging or other cutting-edge in vivo applications.