Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review.

Background: The convergence of smartphone technology and artificial intelligence (AI) has revolutionized the landscape of ophthalmic care, offering unprecedented opportunities for diagnosis, monitoring, and management of ocular conditions. Nevertheless, there is a lack of systematic studies on discussing the integration of smartphone and AI in this field. Main text: This review includes 52 studies, and explores the integration of smartphones and AI in ophthalmology, delineating its collective impact on screening methodologies, disease detection, telemedicine initiatives, and patient management. The collective findings from the curated studies indicate promising performance of the smartphone-based AI screening for various ocular diseases which encompass major retinal diseases, glaucoma, cataract, visual impairment in children and ocular surface diseases. Moreover, the utilization of smartphone-based imaging modalities, coupled with AI algorithms, is able to provide timely, efficient and cost-effective screening for ocular pathologies. This modality can also facilitate patient self-monitoring, remote patient monitoring and enhancing accessibility to eye care services, particularly in underserved regions. Challenges involving data privacy, algorithm validation, regulatory frameworks and issues of trust are still need to be addressed. Furthermore, evaluation on real-world implementation is imperative as well, and real-world prospective studies are currently lacking. Conclusions: Smartphone ocular imaging merged with AI enables earlier, precise diagnoses, personalized treatments, and enhanced service accessibility in eye care. Collaboration is crucial to navigate ethical and data security challenges while responsibly leveraging these innovations, promising a potential revolution in care access and global eye health equity.

Cross-modality transfer learning with knowledge infusion for diabetic retinopathy grading.

Background: Ultra-wide-field (UWF) fundus photography represents an emerging retinal imaging technique offering a broader field of view, thus enhancing its utility in screening and diagnosing various eye diseases, notably diabetic retinopathy (DR). However, the application of computer-aided diagnosis for DR using UWF images confronts two major challenges. The first challenge arises from the limited availability of labeled UWF data, making it daunting to train diagnostic models due to the high cost associated with manual annotation of medical images. Secondly, existing models' performance requires enhancement due to the absence of prior knowledge to guide the learning process. Purpose: By leveraging extensively annotated datasets within the field, which encompass large-scale, high-quality color fundus image datasets annotated at either image-level or pixel-level, our objective is to transfer knowledge from these datasets to our target domain through unsupervised domain adaptation. Methods: Our approach presents a robust model for assessing the severity of diabetic retinopathy (DR) by leveraging unsupervised lesion-aware domain adaptation in ultra-wide-field (UWF) images. Furthermore, to harness the wealth of detailed annotations in publicly available color fundus image datasets, we integrate an adversarial lesion map generator. This generator supplements the grading model by incorporating auxiliary lesion information, drawing inspiration from the clinical methodology of evaluating DR severity by identifying and quantifying associated lesions. Results: We conducted both quantitative and qualitative evaluations of our proposed method. In particular, among the six representative DR grading methods, our approach achieved an accuracy (ACC) of 68.18% and a precision (pre) of 67.43%. Additionally, we conducted extensive experiments in ablation studies to validate the effectiveness of each component of our proposed method. Conclusion: In conclusion, our method not only improves the accuracy of DR grading, but also enhances the interpretability of the results, providing clinicians with a reliable DR grading scheme.

Multi-granularity learning of explicit geometric constraint and contrast for label-efficient medical image segmentation and differentiable clinical function assessment.

Automated segmentation is a challenging task in medical image analysis that usually requires a large amount of manually labeled data. However, most current supervised learning based algorithms suffer from insufficient manual annotations, posing a significant difficulty for accurate and robust segmentation. In addition, most current semi-supervised methods lack explicit representations of geometric structure and semantic information, restricting segmentation accuracy. In this work, we propose a hybrid framework to learn polygon vertices, region masks, and their boundaries in a weakly/semi-supervised manner that significantly advances geometric and semantic representations. Firstly, we propose multi-granularity learning of explicit geometric structure constraints via polygon vertices (PolyV) and pixel-wise region (PixelR) segmentation masks in a semi-supervised manner. Secondly, we propose eliminating boundary ambiguity by using an explicit contrastive objective to learn a discriminative feature space of boundary contours at the pixel level with limited annotations. Thirdly, we exploit the task-specific clinical domain knowledge to differentiate the clinical function assessment end-to-end. The ground truth of clinical function assessment, on the other hand, can serve as auxiliary weak supervision for PolyV and PixelR learning. We evaluate the proposed framework on two tasks, including optic disc (OD) and cup (OC) segmentation along with vertical cup-to-disc ratio (vCDR) estimation in fundus images; left ventricle (LV) segmentation at end-diastolic and end-systolic frames along with ejection fraction (LVEF) estimation in two-dimensional echocardiography images. Experiments on nine large-scale datasets of the two tasks under different label settings demonstrate our model's superior performance on segmentation and clinical function assessment.

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.

Deep segmentation of OCTA for evaluation and association of changes of retinal microvasculature with Alzheimer's disease and mild cognitive impairment.

BACKGROUND: Optical coherence tomography angiography (OCTA) enables fast and non-invasive high-resolution imaging of retinal microvasculature and is suggested as a potential tool in the early detection of retinal microvascular changes in Alzheimer's Disease (AD). We developed a standardised OCTA analysis framework and compared their extracted parameters among controls and AD/mild cognitive impairment (MCI) in a cross-section study. METHODS: We defined and extracted geometrical parameters of retinal microvasculature at different retinal layers and in the foveal avascular zone (FAZ) from segmented OCTA images obtained using well-validated state-of-the-art deep learning models. We studied these parameters in 158 subjects (62 healthy control, 55 AD and 41 MCI) using logistic regression to determine their potential in predicting the status of our subjects. RESULTS: In the AD group, there was a significant decrease in vessel area and length densities in the inner vascular complexes (IVC) compared with controls. The number of vascular bifurcations in AD is also significantly lower than that of healthy people. The MCI group demonstrated a decrease in vascular area, length densities, vascular fractal dimension and the number of bifurcations in both the superficial vascular complexes (SVC) and the IVC compared with controls. A larger vascular tortuosity in the IVC, and a larger roundness of FAZ in the SVC, can also be observed in MCI compared with controls. CONCLUSION: Our study demonstrates the applicability of OCTA for the diagnosis of AD and MCI, and provides a standard tool for future clinical service and research. Biomarkers from retinal OCTA images can provide useful information for clinical decision-making and diagnosis of AD and MCI.

Design and synthesis of cantharidin derivative DCZ5418 as a TRIP13 inhibitor with anti-multiple myeloma activity in vitro and in vivo.

Natural product cantharidin can inhibit multiple myeloma cell growth in vitro, while serious adverse effects limited its clinical application. Therefore, the structural modification of cantharidin is needed. Herein, inspired by the structural similarity of the aliphatic endocyclic moiety in cantharidin and TRIP13 inhibitor DCZ0415, we designed and synthesized DCZ5418 and its nineteen derivatives. The molecular docking study indicated that DCZ5418 had a similar binding mode to TRIP13 protein as DCZ0415 while with a stronger docking score. Moreover, the bioassay studies of the MM-cells viability inhibition, TRIP13 protein binding affinity and enzyme inhibiting activity showed that DCZ5418 had good anti-MM activity in vitro and definite interaction with TRIP13 protein. The acute toxicity test of DCZ5418 showed less toxicity in vivo than cantharidin. Furthermore, DCZ5418 showed good anti-MM effects in vivo with a lower dose administration than DCZ0415 (15 mg/kg vs 25 mg/kg) on the tumor xenograft models. Thus, we obtained a new TRIP13 inhibitor DCZ5418 with improved safety and good activity in vivo, which provides a new example of lead optimization by using the structural fragments of natural products.

Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction.

Machine learning has been increasingly utilized in the field of protein engineering, and research directed at predicting the effects of protein mutations has attracted increasing attention. Among them, so far, the best results have been achieved by related methods based on protein language models, which are trained on a large number of unlabeled protein sequences to capture the generally hidden evolutionary rules in protein sequences, and are therefore able to predict their fitness from protein sequences. Although numerous similar models and methods have been successfully employed in practical protein engineering processes, the majority of the studies have been limited to how to construct more complex language models to capture richer protein sequence feature information and utilize this feature information for unsupervised protein fitness prediction. There remains considerable untapped potential in these developed models, such as whether the prediction performance can be further improved by integrating different models to further improve the accuracy of prediction. Furthermore, how to utilize large-scale models for prediction methods of mutational effects on quantifiable properties of proteins due to the nonlinear relationship between protein fitness and the quantification of specific functionalities has yet to be explored thoroughly. In this study, we propose an ensemble learning approach for predicting mutational effects of proteins integrating protein sequence features extracted from multiple large protein language models, as well as evolutionarily coupled features extracted in homologous sequences, while comparing the differences between linear regression and deep learning models in mapping these features to quantifiable functional changes. We tested our approach on a dataset of 17 protein deep mutation scans and indicated that the integrated approach together with linear regression enables the models to have higher prediction accuracy and generalization. Moreover, we further illustrated the reliability of the integrated approach by exploring the differences in the predictive performance of the models across species and protein sequence lengths, as well as by visualizing clustering of ensemble and non-ensemble features.

Automated evaluation of retinal hyperreflective foci changes in diabetic macular edema patients before and after intravitreal injection.

Purpose: Fast and automated reconstruction of retinal hyperreflective foci (HRF) is of great importance for many eye-related disease understanding. In this paper, we introduced a new automated framework, driven by recent advances in deep learning to automatically extract 12 three-dimensional parameters from the segmented hyperreflective foci in optical coherence tomography (OCT). Methods: Unlike traditional convolutional neural networks, which struggle with long-range feature correlations, we introduce a spatial and channel attention module within the bottleneck layer, integrated into the nnU-Net architecture. Spatial Attention Block aggregates features across spatial locations to capture related features, while Channel Attention Block heightens channel feature contrasts. The proposed model was trained and tested on 162 retinal OCT volumes of patients with diabetic macular edema (DME), yielding robust segmentation outcomes. We further investigate HRF's potential as a biomarker of DME. Results: Results unveil notable discrepancies in the amount and volume of HRF subtypes. In the whole retinal layer (WR), the mean distance from HRF to the retinal pigmented epithelium was significantly reduced after treatment. In WR, the improvement in central macular thickness resulting from intravitreal injection treatment was positively correlated with the mean distance from HRF subtypes to the fovea. Conclusion: Our study demonstrates the applicability of OCT for automated quantification of retinal HRF in DME patients, offering an objective, quantitative approach for clinical and research applications.

Development and external validation of a mixed-effects deep learning model to diagnose COVID-19 from CT imaging.

Background: The automatic analysis of medical images has the potential improve diagnostic accuracy while reducing the strain on clinicians. Current methods analyzing 3D-like imaging data, such as computerized tomography imaging, often treat each image slice as individual slices. This may not be able to appropriately model the relationship between slices. Methods: Our proposed method utilizes a mixed-effects model within the deep learning framework to model the relationship between slices. We externally validated this method on a data set taken from a different country and compared our results against other proposed methods. We evaluated the discrimination, calibration, and clinical usefulness of our model using a range of measures. Finally, we carried out a sensitivity analysis to demonstrate our methods robustness to noise and missing data. Results: In the external geographic validation set our model showed excellent performance with an AUROC of 0.930 (95%CI: 0.914, 0.947), with a sensitivity and specificity, PPV, and NPV of 0.778 (0.720, 0.828), 0.882 (0.853, 0.908), 0.744 (0.686, 0.797), and 0.900 (0.872, 0.924) at the 0.5 probability cut-off point. Our model also maintained good calibration in the external validation dataset, while other methods showed poor calibration. Conclusion: Deep learning can reduce stress on healthcare systems by automatically screening CT imaging for COVID-19. Our method showed improved generalizability in external validation compared to previous published methods. However, deep learning models must be robustly assessed using various performance measures and externally validated in each setting. In addition, best practice guidelines for developing and reporting predictive models are vital for the safe adoption of such models.

Design, synthesis and biological evaluation of chalcone derivatives as potent and orally active hCYP3A4 inhibitors.

Human cytochrome P450 3A4 (hCYP3A4), one of the most important drug-metabolizing enzymes, catalyze the metabolic clearance of ∼50% therapeutic drugs. CYP3A4 inhibitors have been used for improving the in vivo efficacy of hCYP3A4-substrate drugs. However, most of existing hCYP3A4 inhibitors may trigger serious adverse effects or undesirable effects on endogenous metabolism. This study aimed to discover potent and orally active hCYP3A4 inhibitors from chalcone derivatives and to test their anti-hCYP3A4 effects both in vitro and in vivo. Following three rounds of screening and structural optimization, the isoquinoline chalcones were found with excellently anti-hCYP3A4 effects. SAR studies showed that introducing an isoquinoline ring on the A-ring significantly enhanced anti-CYP3A4 effect, generating A10 (IC50 = 102.10 nM) as a promising lead compound. The 2nd round of SAR studies showed that introducing a substituent group at the para position of the carbonyl group on B-ring strongly improved the anti-CYP3A4 effect. As a result, C6 was identified as the most potent hCYP3A4 inhibitor (IC50 = 43.93 nM) in human liver microsomes (HLMs). C6 also displayed potent anti-hCYP3A4 effect in living cells (IC50 = 153.00 nM), which was superior to the positive inhibitor ketoconazole (IC50 = 251.00 nM). Mechanistic studies revealed that C6 could potently inhibit CYP3A4-catalyzed N-ethyl-1,8-naphthalimide (NEN) hydroxylation in a competitive manner (Ki = 30.00 nM). Moreover, C6 exhibited suitable metabolic stability in HLMs and showed good safety profiles in mice. In vivo tests demonstrated that C6 (100 mg/kg, orally administration) significantly increased the AUC(0-inf) of midazolam by 3.63-fold, and strongly prolonged its half-life by 1.66-fold compared with the vehicle group in mice. Collectively, our findings revealed the SARs of chalcone derivatives as hCYP3A4 inhibitors and offered several potent chalcone-type hCYP3A4 inhibitors, while C6 could serve as a good lead compound for developing novel, orally active CYP3A4 inhibitors with improved druglikeness properties.

Deep learning for detecting visually impaired cataracts using fundus images.

Purpose: To develop a visual function-based deep learning system (DLS) using fundus images to screen for visually impaired cataracts. Materials and methods: A total of 8,395 fundus images (5,245 subjects) with corresponding visual function parameters collected from three clinical centers were used to develop and evaluate a DLS for classifying non-cataracts, mild cataracts, and visually impaired cataracts. Three deep learning algorithms (DenseNet121, Inception V3, and ResNet50) were leveraged to train models to obtain the best one for the system. The performance of the system was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results: The AUC of the best algorithm (DenseNet121) on the internal test dataset and the two external test datasets were 0.998 (95% CI, 0.996-0.999) to 0.999 (95% CI, 0.998-1.000),0.938 (95% CI, 0.924-0.951) to 0.966 (95% CI, 0.946-0.983) and 0.937 (95% CI, 0.918-0.953) to 0.977 (95% CI, 0.962-0.989), respectively. In the comparison between the system and cataract specialists, better performance was observed in the system for detecting visually impaired cataracts (p < 0.05). Conclusion: Our study shows the potential of a function-focused screening tool to identify visually impaired cataracts from fundus images, enabling timely patient referral to tertiary eye hospitals.

Design, synthesis and biological evaluation of salicylanilides as novel allosteric inhibitors of human pancreatic lipase.

Obesity is a growing global health problem and is associated with increased prevalence of many metabolic disorders, including diabetes, hypertension and cardiovascular disease. Pancreatic lipase (PL) has been validated as a key target for developing anti-obesity agents, owing to its crucial role in lipid digestion and absorption. In the past few decades, porcine PL (pPL) is always used as the enzyme source for screening PL inhibitors, which generate numerous pPL inhibitors but the potent inhibitors against human PL (hPL) are rarely reported. Herein, a series of salicylanilide derivatives were designed and synthesized, while their anti-hPL effects were assayed by a fluorescence-based biochemical approach. To investigate the structure-activity relationships of salicylanilide derivatives as hPL inhibitors in detail, structural modifications on three rings (A, B and C) of the salicylanilide skeleton were performed. Among all tested compounds, 2t and 2u were found possessing the most potent anti-PL activity, showing IC50 values of 1.86 µM and 1.63 µM, respectively. Inhibition kinetic analyses suggested that both 2t and 2u could effectively inhibit hPL in a non-competitive manner, with the ki value of 1.67 µM and 1.70 µM, respectively. Fluorescence quenching assays suggested that two inhibitors could quench the fluorescence of hPL via a static quenching procedure. Molecular docking simulations suggested that 2t and 2u could tightly bind on an allosteric site of hPL. Collectively, the structure-activity relationships of salicylanilide derivatives as hPL inhibitors were carefully investigated, while two newly identified reversible hPL inhibitors (2t and 2u) could be used as promising lead compounds to develop novel anti-obesity drugs.

3D Visual Saliency: An Independent Perceptual Measure or a Derivative of 2D Image Saliency?

While 3D visual saliency aims to predict regional importance of 3D surfaces in agreement with human visual perception and has been well researched in computer vision and graphics, latest work with eye-tracking experiments shows that state-of-the-art 3D visual saliency methods remain poor at predicting human fixations. Cues emerging prominently from these experiments suggest that 3D visual saliency might associate with 2D image saliency. This paper proposes a framework that combines a Generative Adversarial Network and a Conditional Random Field for learning visual saliency of both a single 3D object and a scene composed of multiple 3D objects with image saliency ground truth to 1) investigate whether 3D visual saliency is an independent perceptual measure or just a derivative of image saliency and 2) provide a weakly supervised method for more accurately predicting 3D visual saliency. Through extensive experiments, we not only demonstrate that our method significantly outperforms the state-of-the-art approaches, but also manage to answer the interesting and worthy question proposed within the title of this paper.

StruNet: Perceptual and low-rank regularized transformer for medical image denoising.

BACKGROUND: Various types of noise artifacts inevitably exist in some medical imaging modalities due to limitations of imaging techniques, which impair either clinical diagnosis or subsequent analysis. Recently, deep learning approaches have been rapidly developed and applied on medical images for noise removal or image quality enhancement. Nevertheless, due to complexity and diversity of noise distribution representations in different medical imaging modalities, most of the existing deep learning frameworks are incapable to flexibly remove noise artifacts while retaining detailed information. As a result, it remains challenging to design an effective and unified medical image denoising method that will work across a variety of noise artifacts for different imaging modalities without requiring specialized knowledge in performing the task. PURPOSE: In this paper, we propose a novel encoder-decoder architecture called Swin transformer-based residual u-shape Network (StruNet), for medical image denoising. METHODS: Our StruNet adopts a well-designed block as the backbone of the encoder-decoder architecture, which integrates Swin Transformer modules with residual block in parallel connection. Swin Transformer modules could effectively learn hierarchical representations of noise artifacts via self-attention mechanism in non-overlapping shifted windows and cross-window connection, while residual block is advantageous to compensate loss of detailed information via shortcut connection. Furthermore, perceptual loss and low-rank regularization are incorporated into loss function respectively in order to constrain the denoising results on feature-level consistency and low-rank characteristics. RESULTS: To evaluate the performance of the proposed method, we have conducted experiments on three medical imaging modalities including computed tomography (CT), optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA). CONCLUSIONS: The results demonstrate that the proposed architecture yields a promising performance of suppressing multiform noise artifacts existing in different imaging modalities.

Association of retinal microvascular abnormalities and neuromyelitis optica spectrum disorders with optical coherence tomography angiography.

Introduction: Neuromyelitis optica spectrum disorders (NMOSD) are autoimmune central nervous system diseases characterized by the immune system's abnormal attack on glial cells and neurons. Optic neuritis (ON) is one of the indicators of NMOSD, often starting unilaterally and potentially affecting both eyes later in the disease progression, leading to visual impairment. Optical coherence tomography angiography (OCTA) has the potential to aid in the early diagnosis of NMOSD by examining ophthalmic imaging and may offer a window for disease prevention. Methods: In this study, we collected OCTA images from 22 NMOSD patients (44 images) and 25 healthy individuals (50 images) to investigate retinal microvascular changes in NMOSD. We employed effective retinal microvascular segmentation and foveal avascular zone (FAZ) segmentation techniques to extract key OCTA structures for biomarker analysis. A total of 12 microvascular features were extracted using specifically designed methods based on the segmentation results. The OCTA images of NMOSD patients were classified into two groups: optic neuritis (ON) and non-optic neuritis (non-ON). Each group was compared separately with a healthy control (HC) group. Results: Statistical analysis revealed that the non-ON group displayed shape changes in the deep layer of the retina, specifically in the FAZ. However, there were no significant microvascular differences between the non-ON group and the HC group. In contrast, the ON group exhibited microvascular degeneration in both superficial and deep retinal layers. Sub-regional analysis revealed that pathological variations predominantly occurred on the side affected by ON, particularly within the internal ring near the FAZ. Discussion: The findings of this study highlight the potential of OCTA in evaluating retinal microvascular changes associated with NMOSD. The shape alterations observed in the FAZ of the non-ON group suggest localized vascular abnormalities. In the ON group, microvascular degeneration in both superficial and deep retinal layers indicates more extensive vascular damage. Sub-regional analysis further emphasizes the impact of optic neuritis on pathological variations, particularly near the FAZ's internal ring. Conclusion: This study provides insights into the retinal microvascular changes associated with NMOSD using OCTA imaging. The identified biomarkers and observed alterations may contribute to the early diagnosis and monitoring of NMOSD, potentially offering a time window for intervention and prevention of disease progression.

Joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images.

Background: Ultra-Wide-Field (UWF) fundus imaging is an essential diagnostic tool for identifying ophthalmologic diseases, as it captures detailed retinal structures within a wider field of view (FOV). However, the presence of eyelashes along the edge of the eyelids can cast shadows and obscure the view of fundus imaging, which hinders reliable interpretation and subsequent screening of fundus diseases. Despite its limitations, there are currently no effective methods or datasets available for removing eyelash artifacts from UWF fundus images. This research aims to develop an effective approach for eyelash artifact removal and thus improve the visual quality of UWF fundus images for accurate analysis and diagnosis. Methods: To address this issue, we first constructed two UWF fundus datasets: the paired synthetic eyelashes (PSE) dataset and the unpaired real eyelashes (uPRE) dataset. Then we proposed a deep learning architecture called Joint Conditional Generative Adversarial Networks (JcGAN) to remove eyelash artifacts from UWF fundus images. JcGAN employs a shared generator with two discriminators for joint learning of both real and synthetic eyelash artifacts. Furthermore, we designed a background refinement module that refines background information and is trained with the generator in an end-to-end manner. Results: Experimental results on both PSE and uPRE datasets demonstrate the superiority of the proposed JcGAN over several state-of-the-art deep learning approaches. Compared with the best existing method, JcGAN improves PSNR and SSIM by 4.82% and 0.23%, respectively. In addition, we also verified that eyelash artifact removal via JcGAN could significantly improve vessel segmentation performance in UWF fundus images. Assessment via vessel segmentation illustrates that the sensitivity, Dice coefficient and area under curve (AUC) of ResU-Net have respectively increased by 3.64%, 1.54%, and 1.43% after eyelash artifact removal using JcGAN. Conclusion: The proposed JcGAN effectively removes eyelash artifacts in UWF images, resulting in improved visibility of retinal vessels. Our method can facilitate better processing and analysis of retinal vessels and has the potential to improve diagnostic outcomes.

OCTAve: 2D En Face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning With Locality Augmentation.

OBJECTIVE: While the microvasculature annotation within Optical Coherence Tomography Angiography (OCTA) can be leveraged using deep-learning techniques, expensive annotation processes are required to create sufficient training data. One way to avoid the expensive annotation is to use a type of weak annotation in which only the center of the vessel is annotated. However, retaining the final segmentation quality with roughly annotated data remains a challenge. METHODS: Our proposed methods, called OCTAve, provide a new way of using weak-annotation for microvasculature segmentation. Since the centerline labels are similar to scribble annotations, we attempted to solve this problem by using the scribble-based weakly-supervised learning method. Even though the initial results look promising, we found that the method could be significantly improved by adding our novel self-supervised deep supervision method based on Kullback-Liebler divergence. RESULTS: The study on large public datasets with different annotation styles (i.e., ROSE, OCTA-500) demonstrates that our proposed method gives better quantitative and qualitative results than the baseline methods and a naive approach, with a p-value less than 0.001 on dice's coefficient and a lot fewer artifacts. CONCLUSION: The segmentation results are both qualitatively and quantitatively superior to baseline weakly-supervised methods when using scribble-based weakly-supervised learning augmented with self-supervised deep supervision, with an average drop in segmentation performance of less than 10%. SIGNIFICANCE: This work gives a new perspective on how weakly-supervised learning can be used to reduce the cost of annotating microvasculature, which can make the annotating process easier and reduce the amount of work for domain experts.

Discovery of seven-membered ring berberine analogues as highly potent and specific hCES2A inhibitors.

Human carboxylesterase 2A (hCES2A) is a key serine hydrolase responsible for the metabolic clearance of large number of compounds bearing the ester- or amide-bond(s). Inhibition of hCES2A can relieve the chemotherapy-induced toxicity and alter the pharmacokinetic bahaviors of some orally administrate esters-containing agents. However, most of the hCES2A inhibitors show poor cell-membrane permeability and poor specificity. Herein, guided by the structure activity relationships (SAR) of fifteen natural alkaloids against hCES2A, fifteen new seven-membered ring berberine analogues were designed and synthesized, and their anti-hCES2A activities were evaluated. Among all tested compounds, compound 28 showed potent anti-hCES2A effect (IC50 = 1.66 µM) and excellent selectivity over hCES1A (IC50 > 100 µM). The SAR analysis revealed that the seven-membered ring of these berberine analogues was a crucial moiety for hCES2A inhibition, while the secondary amine group of the ring-C is important for improving their specificity over other serine hydrolases. Inhibition kinetic analyses and molecular dynamic simulation demonstrated that 28 strongly inhibited hCES2A in a mixed-inhibition manner, with an estimated Ki value of 1.035 µM. Moreover, 28 could inhibit intracellular hCES2A in living HepG2 cells and exhibited suitable metabolic stability. Collectively, the SAR of seven-membered ring berberine analogues as hCES2A inhibitors were studied, while compound 28 acted as a promising candidate for developing highly selective hCES2A inhibitors.

Adversarial-based latent space alignment network for left atrial appendage segmentation in transesophageal echocardiography images.

Left atrial appendage (LAA) is a leading cause of atrial fibrillation and thrombosis in cardiovascular disease. Clinicians can rely on LAA occlusion (LAAO) to effectively prevent and treat ischaemic strokes attributed to the LAA. The correct selection of the LAAO is one of the most critical stages in the successful surgical process, which relies on the quantification of the anatomical structure of the LAA for successful intervention in LAAO. In this paper, we propose an adversarial-based latent space alignment framework for LAA segmentation in transesophageal echocardiography (TEE) images by introducing prior knowledge from the label. The proposed method consists of an LAA segmentation network, a label reconstruction network, and a latent space alignment loss. To be specific, we first employ ConvNeXt as the backbone of the segmentation and reconstruction network to enhance the feature extraction capability of the encoder. The label reconstruction network then encodes the prior shape features from the LAA labels to the latent space. The latent space alignment loss consists of the adversarial-based alignment and the contrast learning losses. It can motivate the segmentation network to learn the prior shape features of the labels, thus improving the accuracy of LAA edge segmentation. The proposed method was evaluated on a TEE dataset including 1,783 images and the experimental results showed that the proposed method outperformed other state-of-the-art LAA segmentation methods with Dice coefficient, AUC, ACC, G-mean, and Kappa of 0.831, 0.917, 0.989, 0.911, and 0.825, respectively.

Development of Yin-Yang ligand for cannabinoid receptors.

Cannabinoid receptors (CBs), including CB1 and CB2, are the key components of a lipid signaling endocannabinoid system (ECS). Development of synthetic cannabinoids has been attractive to modulate ECS functions. CB1 and CB2 are structurally closely related subtypes but with distinct functions. While most efforts focus on the development of selective ligands for single subtype to circumvent the undesired off-target effect, Yin-Yang ligands with opposite pharmacological activities simultaneously on two subtypes, offer unique therapeutic potential. Herein we report the development of a new Yin-Yang ligand which functions as an antagonist for CB1 and concurrently an agonist for CB2. We found that in the pyrazole-cored scaffold, the arm of N1-phenyl group could be a switch, modification of which yielded various ligands with distinct activities. As such, the ortho-morpholine substitution exerted the desired Yin-Yang bifunctionality which, based on the docking study and molecular dynamic simulation, was proposed to be resulted from the hydrogen bonding with S173 and S285 in CB1 and CB2, respectively. Our results demonstrated the feasibility of structure guided ligand evolution for challenging Yin-Yang ligand.