Choroidal Layer Analysis in OCT images via Ambiguous Boundary-aware Attention.

Optical Coherence Tomography (OCT) is a commonly used retina imaging technique, and it is capable of revealing the morphology of the choroid. However, the segmentation and quantitative analysis of the sublayers and vessels in choroid are rarely explored, primarily due to the indistinct boundaries of choroidal sublayers, and imbalanced distribution of vessels observed in OCT imagery. In this paper, we propose a novel two-stage architecture called Choroidal Layer Analysis network (CLA), that may be considered the first attempt in this research community for joint segmentation of choroidal sublayers and choroidal vessels in OCT images. CLA employs the encoder-decoder network with the residual U-shape module as the backbone. In order to empower the ability of the segmentation model to identify the inconspicuous boundaries of choroidal sublayers, we introduce an Ambiguous Boundary Attention block (ABA) into the bottleneck of the encoder-decoder network in the first stage. For more accurate segmentation of large choroidal vessels with ambiguous contours and imbalanced spatial distribution, the second stage introduces an active contour-based loss to refine the contours of choroidal vessels simultaneously with precise identification of each vessel via contextual modeling. To train, test and validate the proposed model, we conducted a choroidal segmentation dataset containing 800 OCT images, with their sublayers and large choroidal vessels manually annotated. Experimental results demonstrate the superiority of the proposed approach compared with other state-of-the-art segmentation networks in large margins. It is worth noting that we also reconstructed the large choroidal vessels in three-dimensional (3D) based on the segmentation results, and multiple 3D morphological parameters were calculated. The statistical analysis of these parameters demonstrates significant differences between the healthy control and high myopia group, and this further confirms the proposed work may facilitate subsequent disease understanding and clinical decision-making.

A powerful method for In Situ and rapid detection of trace nanoplastics in water-Mie scattering.

The pervasive presence of nanoplastics (NPs) in environmental media has raised significant concerns regarding their implications for environmental safety and human health. However, owing to their tiny size and low level in the environment, there is still a lack of effective methods for measuring the amount of NPs. Leveraging the principles of Mie scattering, a novel approach for rapid in situ quantitative detection of small NPs in low concentrations in water has been developed. A limit of detection of 4.2 µg/L for in situ quantitative detection of polystyrene microspheres as small as 25 nm was achieved, and satisfactory recoveries and relative standard deviations were obtained. The results of three self-ground NPs showed that the method can quantitatively detect the concentration of NPs in a mixture of different particle sizes. The satisfactory recoveries (82.4% to 110.3%) of the self-ground NPs verified the good anti-interference ability of the method. The total concentrations of the NPs in the five brands of commercial bottled water were 0.07 to 0.39 µg/L, which were directly detected by the method. The proposed method presents a potential approach for conducting in situ and real-time environmental risk assessments of NPs on human and ecosystem health in actual water environments.

Correction: Integrated biosensors for monitoring microphysiological systems.

Correction for 'Integrated biosensors for monitoring microphysiological systems' by Lei Mou et al., Lab Chip, 2022, 22, 3801-3816, https://doi.org/10.1039/D2LC00262K.

A Contrast-Enhanced Tri-Modal MRI Technique for High-Performance Hypoxia Imaging of Breast Cancer.

Personalized radiotherapy strategies enabled by the construction of hypoxia-guided biological target volumes (BTVs) can overcome hypoxia-induced radioresistance by delivering high-dose radiotherapy to targeted hypoxic areas of the tumor. However, the construction of hypoxia-guided BTVs is difficult owing to lack of precise visualization of hypoxic areas. This study synthesizes a hypoxia-responsive T1 , T2 , T2 mapping tri-modal MRI molecular nanoprobe (SPION@ND) and provides precise imaging of hypoxic tumor areas by utilizing the advantageous features of tri-modal magnetic resonance imaging (MRI). SPION@ND exhibits hypoxia-triggered dispersion-aggregation structural transformation. Dispersed SPION@ND can be used for routine clinical BTV construction using T1 -contrast MRI. Conversely, aggregated SPION@ND can be used for tumor hypoxia imaging assessment using T2 -contrast MRI. Moreover, by introducing T2 mapping, this work designs a novel method (adjustable threshold-based hypoxia assessment) for the precise assessment of tumor hypoxia confidence area and hypoxia level. Eventually this work successfully obtains hypoxia tumor target and accurates hypoxia tumor target, and achieves a one-stop hypoxia-guided BTV construction. Compared to the positron emission tomography-based hypoxia assessment, SPION@ND provides a new method that allows safe and convenient imaging of hypoxic tumor areas in clinical settings.

Inositol hexaphosphate enhances chemotherapy by reversing senescence induced by persistently activated PERK and diphthamide modification of eEF2.

Oxaliplatin is an important initial chemotherapy benefiting advanced-stage colorectal cancer patients. Frustratingly, acquired oxaliplatin resistance always occurs after sequential chemotherapy with diverse antineoplastic drugs. Therefore, an exploration of the mechanism of oxaliplatin resistance formation in-depth is urgently needed. We generated oxaliplatin-resistant colorectal cancer models by four representative compounds, and RNA-seq revealed that oxaliplatin resistance was mainly the result of cells' response to stimulus. Moreover, we proved persistent stimulus-induced endoplasmic reticulum stress (ERs) and associated cellular senescence were the core causes of oxaliplatin resistance. In addition, we screened diverse phytochemicals for ER inhibitors in silico, identifying inositol hexaphosphate (IP6), whose strong binding was confirmed by surface plasmon resonance. Finally, we confirmed the ability of IP6 to reverse colorectal cancer chemoresistance and investigated the mechanism of IP6 in the inhibition of diphthamide modification of eukaryotic elongation factor 2 (eEF2) and PERK activation. Our study demonstrated that oxaliplatin resistance contributed to cell senescence induced by persistently activated PERK and diphthamide modification of eEF2 levels, which were specifically reversed by combination therapy with IP6.

Rapid integration of screen-printed electrodes into thermoplastic organ-on-a-chip devices for real-time monitoring of trans-endothelial electrical resistance.

Trans-endothelial electrical resistance (TEER) is one of the most widely used indicators to quantify the barrier integrity of endothelial layers. Over the last decade, the integration of TEER sensors into organ-on-a-chip (OOC) platforms has gained increasing interest for its efficient and effective measurement of TEER in OOCs. To date, microfabricated electrodes or direct insertion of wires has been used to integrate TEER sensors into OOCs, with each method having advantages and disadvantages. In this study, we developed a TEER-SPE chip consisting of carbon-based screen-printed electrodes (SPEs) embedded in a poly(methyl methacrylate) (PMMA)-based multi-layered microfluidic device with a porous poly(ethylene terephthalate) membrane in-between. As proof of concept, we demonstrated the successful cultures of hCMEC/D3 cells and the formation of confluent monolayers in the TEER-SPE chip and obtained TEER measurements for 4 days. Additionally, the TEER-SPE chip could detect changes in the barrier integrity due to shear stress or an inflammatory cytokine (i.e., tumor necrosis factor-α). The novel approach enables a low-cost and facile fabrication of carbon-based SPEs on PMMA substrates and the subsequent assembly of PMMA layers for rapid prototyping. Being cost-effective and cleanroom-free, our method lowers the existing logistical and technical barriers presenting itself as another step forward to the broader adoption of OOCs with TEER measurement capability.

Isoliquiritigenin induces neurodevelopmental-toxicity and anxiety-like behavior in zebrafish larvae.

Isoliquiritigenin, a flavonoid compound, exhibits a variety of pharmacological properties, including anti-inflammatory, anti-oxidative, anti-microbial, anti-viral, and anti-tumor effects. In the past few years, the consumption of isoliquiritigenin-containing dietary supplements has increased due to their health benefits. Although the neuroprotective effects of isoliquiritigenin have been well-investigated, these studies were performed in cells and adult animals. The potential effects of isoliquiritigenin on the development, especially the neurodevelopment, of certain populations, such as zebrafish larvae, have not been investigated. In this study, zebrafish larvae were employed as a model to investigate the effects of isoliquiritigenin on development and neurodevelopment. Zebrafish embryos treated with high concentrations of isoliquiritigenin (10 and 15 µM) exhibited high rates of mortality, hatching, and malformation, indicating that isoliquiritigenin can affect zebrafish development. In addition, isoliquiritigenin impeded the development of central nervous system regions and the length of dopaminergic neurons located in midbrains and thalami of transgenic zebrafish larvae. The locomotor ability of zebrafish larvae exposed to high concentrations of isoliquiritigenin was negatively affected. The total distance and the average velocity significantly decreased, and anxiety-related behaviors were observed under light-dark challenge. Furthermore, the levels of gap43, tuba1b, mbp, hcrt, vmat2, and pomc, which mediate neurodevelopment, neurotoxicity, and anxiety were significantly decreased in zebrafish larvae exposed to isoliquiritigenin. These results indicate that isoliquiritigenin can disrupt the development of dopaminergic neurons and the function of the central nervous system in zebrafish, causing anxiety-like symptoms.

Recent Advances in Bioinspired Hydrogels: Materials, Devices, and Biosignal Computing.

The remarkable ability of biological systems to sense and adapt to complex environmental conditions has inspired new materials and novel designs for next-generation wearable devices. Hydrogels are being intensively investigated for their versatile functions in wearable devices due to their superior softness, biocompatibility, and rapid stimulus response. This review focuses on recent strategies for developing bioinspired hydrogel wearable devices that can accommodate mechanical strain and integrate seamlessly with biological systems. We will provide an overview of different types of bioinspired hydrogels tailored for wearable devices. Next, we will discuss the recent progress of bioinspired hydrogel wearable devices such as electronic skin and smart contact lenses. Also, we will comprehensively summarize biosignal readout methods for hydrogel wearable devices as well as advances in powering and wireless data transmission technologies. Finally, current challenges facing these wearable devices are discussed, and future directions are proposed.

Global-local multi-stage temporal convolutional network for cataract surgery phase recognition.

BACKGROUND: Surgical video phase recognition is an essential technique in computer-assisted surgical systems for monitoring surgical procedures, which can assist surgeons in standardizing procedures and enhancing postsurgical assessment and indexing. However, the high similarity between the phases and temporal variations of cataract videos still poses the greatest challenge for video phase recognition. METHODS: In this paper, we introduce a global-local multi-stage temporal convolutional network (GL-MSTCN) to explore the subtle differences between high similarity surgical phases and mitigate the temporal variations of surgical videos. The presented work consists of a triple-stream network (i.e., pupil stream, instrument stream, and video frame stream) and a multi-stage temporal convolutional network. The triple-stream network first detects the pupil and surgical instruments regions in the frame separately and then obtains the fine-grained semantic features of the video frames. The proposed multi-stage temporal convolutional network improves the surgical phase recognition performance by capturing longer time series features through dilated convolutional layers with varying receptive fields. RESULTS: Our method is thoroughly validated on the CSVideo dataset with 32 cataract surgery videos and the public Cataract101 dataset with 101 cataract surgery videos, outperforming state-of-the-art approaches with 95.8% and 96.5% accuracy, respectively. CONCLUSIONS: The experimental results show that the use of global and local feature information can effectively enhance the model to explore fine-grained features and mitigate temporal and spatial variations, thus improving the surgical phase recognition performance of the proposed GL-MSTCN.

Integrated biosensors for monitoring microphysiological systems.

Microphysiological systems (MPSs), also known as organ-on-a-chip models, aim to recapitulate the functional components of human tissues or organs in vitro. Over the last decade, with the advances in biomaterials, 3D bioprinting, and microfluidics, numerous MPSs have emerged with applications to study diseased and healthy tissue models. Various organs have been modeled using MPS technology, such as the heart, liver, lung, and blood-brain barrier. An important aspect of in vitro modeling is the accurate phenotypical and functional characterization of the modeled organ. However, most conventional characterization methods are invasive and destructive and do not allow continuous monitoring of the cells in culture. On the other hand, microfluidic biosensors enable in-line, real-time sensing of target molecules with an excellent limit of detection and in a non-invasive manner, thereby effectively overcoming the limitation of the traditional techniques. Consequently, microfluidic biosensors have been increasingly integrated into MPSs and used for in-line target detection. This review discusses the state-of-the-art microfluidic biosensors by providing specific examples, detailing their main advantages in monitoring MPSs, and highlighting current developments in this field. Finally, we describe the remaining challenges and potential future developments to advance the current state-of-the-art in integrated microfluidic biosensors.

3D vessel-like structure segmentation in medical images by an edge-reinforced network.

The vessel-like structure in biomedical images, such as within cerebrovascular and nervous pathologies, is an essential biomarker in understanding diseases' mechanisms and in diagnosing and treating diseases. However, existing vessel-like structure segmentation methods often produce unsatisfactory results due to challenging segmentations for crisp edges. The edge and nonedge voxels of the vessel-like structure in three-dimensional (3D) medical images usually have a highly imbalanced distribution as most voxels are non-edge, making it challenging to find crisp edges. In this work, we propose a generic neural network for the segmentation of the vessel-like structures in different 3D medical imaging modalities. The new edge-reinforced neural network (ER-Net) is based on an encoder-decoder architecture. Moreover, a reverse edge attention module and an edge-reinforced optimization loss are proposed to increase the weight of the voxels on the edge of the given 3D volume to discover and better preserve the spatial edge information. A feature selection module is further introduced to select discriminative features adaptively from an encoder and decoder simultaneously, which aims to increase the weight of edge voxels, thus significantly improving the segmentation performance. The proposed method is thoroughly validated using four publicly accessible datasets, and the experimental results demonstrate that the proposed method generally outperforms other state-of-the-art algorithms for various metrics.

Organ-On-A-Chip Models of the Blood-Brain Barrier: Recent Advances and Future Prospects.

The human brain and central nervous system (CNS) present unique challenges in drug development for neurological diseases. One major obstacle is the blood-brain barrier (BBB), which hampers the effective delivery of therapeutic molecules into the brain while protecting it from blood-born neurotoxic substances and maintaining CNS homeostasis. For BBB research, traditional in vitro models rely upon Petri dishes or Transwell systems. However, these static models lack essential microenvironmental factors such as shear stress and proper cell-cell interactions. To this end, organ-on-a-chip (OoC) technology has emerged as a new in vitro modeling approach to better recapitulate the highly dynamic in vivo human brain microenvironment so-called the neural vascular unit (NVU). Such BBB-on-a-chip models have made substantial progress over the last decade, and concurrently there has been increasing interest in modeling various neurological diseases such as Alzheimer's disease and Parkinson's disease using OoC technology. In addition, with recent advances in other scientific technologies, several new opportunities to improve the BBB-on-a-chip platform via multidisciplinary approaches are available. In this review, an overview of the NVU and OoC technology is provided, recent progress and applications of BBB-on-a-chip for personalized medicine and drug discovery are discussed, and current challenges and future directions are delineated.

Multi-Scale Interactive Network With Artery/Vein Discriminator for Retinal Vessel Classification.

Automatic classification of retinal arteries and veins plays an important role in assisting clinicians to diagnosis cardiovascular and eye-related diseases. However, due to the high degree of anatomical variation across the population, and the presence of inconsistent labels by the subjective judgment of annotators in available training data, most of existing methods generally suffer from blood vessel discontinuity and arteriovenous confusion, the artery/vein (A/V) classification task still faces great challenges. In this work, we propose a multi-scale interactive network with A/V discriminator for retinal artery and vein recognition, which can reduce the arteriovenous confusion and alleviate the disturbance of noisy label. A multi-scale interaction (MI) module is designed in encoder for realizing the cross-space multi-scale features interaction of fundus images, effectively integrate high-level and low-level context information. In particular, we also design an ingenious A/V discriminator (AVD) that utilizes the independent and shared information between arteries and veins, and combine with topology loss, to further strengthen the learning ability of model to resolve the arteriovenous confusion. In addition, we adopt a sample re-weighting (SW) strategy to effectively alleviate the disturbance from data labeling errors. The proposed model is verified on three publicly available fundus image datasets (AV-DRIVE, HRF, LES-AV) and a private dataset. We achieve the accuracy of 97.47%, 96.91%, 97.79%, and 98.18% respectively on these four datasets. Extensive experimental results demonstrate that our method achieves competitive performance compared with state-of-the-art methods for A/V classification. To address the problem of training data scarcity, we publicly release 100 fundus images with A/V annotations to promote relevant research in the community.

DeepGrading: Deep Learning Grading of Corneal Nerve Tortuosity.

Accurate estimation and quantification of the corneal nerve fiber tortuosity in corneal confocal microscopy (CCM) is of great importance for disease understanding and clinical decision-making. However, the grading of corneal nerve tortuosity remains a great challenge due to the lack of agreements on the definition and quantification of tortuosity. In this paper, we propose a fully automated deep learning method that performs image-level tortuosity grading of corneal nerves, which is based on CCM images and segmented corneal nerves to further improve the grading accuracy with interpretability principles. The proposed method consists of two stages: 1) A pre-trained feature extraction backbone over ImageNet is fine-tuned with a proposed novel bilinear attention (BA) module for the prediction of the regions of interest (ROIs) and coarse grading of the image. The BA module enhances the ability of the network to model long-range dependencies and global contexts of nerve fibers by capturing second-order statistics of high-level features. 2) An auxiliary tortuosity grading network (AuxNet) is proposed to obtain an auxiliary grading over the identified ROIs, enabling the coarse and additional gradings to be finally fused together for more accurate final results. The experimental results show that our method surpasses existing methods in tortuosity grading, and achieves an overall accuracy of 85.64% in four-level classification. We also validate it over a clinical dataset, and the statistical analysis demonstrates a significant difference of tortuosity levels between healthy control and diabetes group. We have released a dataset with 1500 CCM images and their manual annotations of four tortuosity levels for public access. The code is available at: https://github.com/iMED-Lab/TortuosityGrading.

Multiplexed Lab-on-a-Chip Bioassays for Testing Antibodies against SARS-CoV-2 and Its Variants in Multiple Individuals.

Neutralization assays that can measure neutralizing antibodies in serum are vital for large-scale serodiagnosis and vaccine evaluation. Here, we establish multiplexed lab-on-a-chip bioassays for testing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants. Compared with enzyme-linked immunosorbent assay (ELISA), our method exhibits a low consumption of sample and reagents (10 µL), a low limit of detection (LOD: 0.08 ng/mL), a quick sample-to-answer time (about 70 min), and multiplexed ability (5 targets in each of 7 samples in one assay). We can also increase the throughput as needed. The concentrations of antibodies against RBD, D614G, N501Y, E484K, and L452R/E484Q-mutants after two doses of vaccines are 6.6 ± 3.6, 8.7 ± 4.6, 3.4 ± 2.8, 3.8 ± 2.8, and 2.8 ± 2.3 ng/mL, respectively. This suggests that neutralizing activities against N501Y, E484K, and L452R/E484Q-mutants were less effective than RBD and D614G-mutant. We performed a plaque reduction neutralization test (PRNT) for all volunteers. Compared with PRNT, our assay is fast, accurate, inexpensive, and multiplexed with multiple-sample processing ability, which is good for large-scale serodiagnosis and vaccine evaluation.

Liquid metal-polymer conductor-based wireless, battery-free epidermal patch.

Wearable epidermal patch can seamlessly monitor biological signals in real-time. Here, we report a liquid metal-polymer conductor-based wireless epidermal patch. The epidermal patch is made of a new conductive material called liquid metal-polymer conductors (LMPC). LMPC is made by casting and peeling off polymers from patterned liquid metal particles. Our printable conductors present good stretchability, repeatability, and biocompatibility. We fabricate LMPC-based antenna and wire, which achieves wireless signal communication and power supply. To demonstrate the capability of our LMPC-based antenna and wire, we fabricate an epidermal patch to analyze metabolites, electrolytes, and urea in sweat. When a portable device is close to the epidermal sensor, the device can power and read the sensor through LMPC-based antenna. The epidermal patch exhibited good analytical performance for sweat analysis with a low limit of detection, fast response time, and multiplex detection capabilities. This epidermal patch opens the possibility for a broad range of non-invasive diagnostic tools that can be used for health monitoring in the general population.

Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning.

Trigeminal neuralgia caused by paroxysmal and severe pain in the distribution of the trigeminal nerve is a rare chronic pain disorder. It is generally accepted that compression of the trigeminal root entry zone by vascular structures is the major cause of primary trigeminal neuralgia, and vascular decompression is the prior choice in neurosurgical treatment. Therefore, accurate preoperative modeling/segmentation/visualization of trigeminal nerve and its surrounding cerebrovascular is important to surgical planning. In this paper, we propose an automated method to segment trigeminal nerve and its surrounding cerebrovascular in the root entry zone, and to further reconstruct and visual these anatomical structures in three-dimensional (3D) Magnetic Resonance Angiography (MRA). The proposed method contains a two-stage neural network. Firstly, a preliminary confidence map of different anatomical structures is produced by a coarse segmentation stage. Secondly, a refinement segmentation stage is proposed to refine and optimize the coarse segmentation map. To model the spatial and morphological relationship between trigeminal nerve and cerebrovascular structures, the proposed network detects the trigeminal nerve, cerebrovasculature, and brainstem simultaneously. The method has been evaluated on a dataset including 50 MRA volumes, and the experimental results show the state-of-the-art performance of the proposed method with an average Dice similarity coefficient, Hausdorff distance, and average surface distance error of 0.8645, 0.2414, and 0.4296 on multi-tissue segmentation, respectively.

Anticonvulsant activity of melatonin and its success in ameliorating epileptic comorbidity-like symptoms in zebrafish.

Epilepsy is one of common neurological disorders, greatly distresses the well-being of the sufferers. Melatonin has been used in clinical anti-epileptic studies, but its effect on epileptic comorbidities is unknown, and the underlying mechanism needs further investigation. Herein, by generating PTZ-induced zebrafish seizure model, we carried out interdisciplinary research using neurobehavioral assays, bioelectrical detection, molecular biology, and network pharmacology to investigate the activity of melatonin as well as its pharmacological mechanisms. We found melatonin suppressed seizure-like behavior by using zebrafish regular locomotor assays. Zebrafish freezing and bursting activity assays revealed the ameliorative effect of melatonin on comorbidity-like symptoms. The preliminary screening results of neurobehavioral assays were further verified by the expression of key genes involved in neuronal activity, neurodevelopment, depression and anxiety, as well as electrical signal recording from the midbrain of zebrafish. Subsequently, network pharmacology was introduced to identify potential targets of melatonin and its pathways. Real-time qPCR and protein-protein interaction (PPI) were conducted to confirm the underlying mechanisms associated with glutathione metabolism. We also found that melatonin receptors were involved in this process, which were regulated in response to melatonin exposure before PTZ treatment. The antagonists of melatonin receptors affected anticonvulsant activity of melatonin. Overall, current study revealed the considerable ameliorative effects of melatonin on seizure and epileptic comorbidity-like symptoms and unveiled the underlying mechanism. This study provides an animal model for the clinical application of melatonin in the treatment of epilepsy and its comorbidities.