The comparison of white-to-white via triple person-times caliper measuring and machine-measuring in V4c implantable collamer lens implantation.

This study aimed to compare the differences and characteristics of white-to-white (WTW) values obtained before V4c implantation using triple person-times caliper, IOL-Master 700, Pentacam HR, and UBM, and to assess their correlation with vaulting. A total of 930 myopia patients (1842 eyes) who were interested in undergoing ICL surgery were assessed before the procedure using various instruments. The WTW measurements were obtained using a triple person-times caliper, Pentacam HR, and IOL-Master 700, whereas the angle-to-angle (ATA) measurements were obtained using UBM. The size of the ICL was subsequently calculated using triple person-times caliper measurements. The vault of the ICL was assessed using Pentacam HR three months after the surgery. The WTW was determined to be 11.30 ± 0.29 mm, 11.43 ± 0.29 mm, and11.86 ± 0.38 mm, respectively, using the triple person-times caliper, Pentacam HR, and IOL-Master 700. The measurement of ATA was 11.57 ± 0.51 mm, as done by UBM. The ICL vault was measured to be 400.97 ± 198.46 µm when examined with Pentacam HR three monthsafter the procedure. The linear regression analyses of ICL size and WTW of triple person-times caliper, ICL vault and WTW were (R = 0.703, p < 0.001; R = 0.0969, p < 0.001) respectively. The highest correlation was found between IOL-Master and Pentacam HR (r = 0.766, p = 0.000). The lowest correlation was found between UBM and Pentacam HR (r = 0.358, p = 0.002). Bland-Altman analysis showed that the 95% limits of agreement (LoA) were the triple person-times caliper and Pentacam HR (- 0.573, 0.298) and the triple person-times caliper and UBM (- 1.15, - 0.605). This indicated a strong agreement between the triple person-times caliper and Pentacam HR and a lack of agreement between the triple person-times caliper and UBM. Triple person-times caliper measurements offer excellent maneuverability, practicality, and reliable outcomes for determining ICL vaults. Measurements obtained using the triple-person caliper were less differece than those obtained using the Pentacam HR.

TauFlowNet: Revealing latent propagation mechanism of tau aggregates using deep neural transport equations.

Mounting evidence shows that Alzheimer's disease (AD) is characterized by the propagation of tau aggregates throughout the brain in a prion-like manner. Since current pathology imaging technologies only provide a spatial mapping of tau accumulation, computational modeling becomes indispensable in analyzing the spatiotemporal propagation patterns of widespread tau aggregates from the longitudinal data. However, current state-of-the-art works focus on the longitudinal change of focal patterns, lacking a system-level understanding of the tau propagation mechanism that can explain and forecast the cascade of tau accumulation. To address this limitation, we conceptualize that the intercellular spreading of tau pathology forms a dynamic system where each node (brain region) is ubiquitously wired with other nodes while interacting with the build-up of pathological burdens. In this context, we formulate the biological process of tau spreading in a principled potential energy transport model (constrained by brain network topology), which allows us to develop an explainable neural network for uncovering the spatiotemporal dynamics of tau propagation from the longitudinal tau-PET scans. Specifically, we first translate the transport equation into a GNN (graph neural network) backbone, where the spreading flows are essentially driven by the potential energy of tau accumulation at each node. Conventional GNNs employ a l2-norm graph smoothness prior, resulting in nearly equal potential energies across nodes, leading to vanishing flows. Following this clue, we introduce the total variation (TV) into the graph transport model, where the nature of system's Euler-Lagrange equations is to maximize the spreading flow while minimizing the overall potential energy. On top of this min-max optimization scenario, we design a generative adversarial network (GAN-like) to characterize the TV-based spreading flow of tau aggregates, coined TauFlowNet. We evaluate our TauFlowNet on ADNI and OASIS datasets in terms of the prediction accuracy of future tau accumulation and explore the propagation mechanism of tau aggregates as the disease progresses. Compared to the current counterpart methods, our physics-informed deep model yields more accurate and interpretable results, demonstrating great potential in discovering novel neurobiological mechanisms through the lens of machine learning.

The comparison of corneal higher-order aberration and surgically induced astigmatism between the clear corneal incision and the limbus tunnel incision of posterior chamber implantable collamer lens implantation.

BACKGROUND: This study aimed to compare the corneal high-order aberrations and surgically induced astigmatism between the clear corneal incision and limbus tunnel incision for posterior chamber implantable collamer lens (ICL/TICL) implantation. METHODS: A total of 127 eyes from 73 myopic patients underwent ICL V4c implantation, with 70 eyes receiving clear corneal incisions and 57 eyes receiving limbus tunnel incisions. The anterior and back corneal surfaces were measured and the Root Mean Square of all activated aberrations (TRMS) was calculated, including higher-order aberration (HOA RMS), spherical aberration Z40, coma coefficients (Coma RMS) Z3-1 Z31, and surgically induced astigmatism (SIA). The measurements were taken preoperatively and postoperatively at 1 day, 1 week, and 1, 3, and 6 months. In this study, the corneal higher-order aberration was estimated as the Zernike coefficient calculated up to 5th order. The measurements were taken at a maximum diameter of 6.5 mm using Pentacam. RESULTS: One week after the operation, the corneal back Z31 of the clear corneal incision group was 0.06 ± 0.06, while the limbus tunnel incision group showed a measurement of 0.05 ± 0.06 (p = 0.031). The corneal back Z40 of the clear corneal incision group was -0.02 ± 0.25, compared to -0.04 ± 0.21 in the limbus tunnel incision group (p = 0.01). One month after the operation, the corneal back SIA of the clear corneal incision group was 0.11 ± 0.11, compared to 0.08 ± 0.11of the limbus tunnel incision group (p = 0.013), the corneal total SIA of the clear corneal incision group was 0.33 ± 0.30, compared to 0.15 ± 0.16 in the limbus tunnel incision group (p = 0.004); the clear corneal incision group exhibited higher levels of back astigmatism and total SIA than the limbus tunnel incision in the post-operation one month period. During the 6- month post-operative follow-up period, no significant difference in Z31, Z40, and other HOA RMS data was observed between the two groups. The total SIA of the corneal incision group and the limbus tunnel incision group were 0.24 ± 0.14 and 0.33 ± 0.32, respectively (p = 0.393), showing no significant difference between the two groups 6 months after the operation. CONCLUSION: Our data showed no significant difference in the high-order aberration and SIA between clear corneal incision and limbus tunnel incision up to 6 months after ICL-V4c implantation.

Developing Explainable Deep Model for Discovering Novel Control Mechanism of Neuro-Dynamics.

Human brain is a complex system composed of many components that interact with each other. A well-designed computational model, usually in the format of partial differential equations (PDEs), is vital to understand the working mechanisms that can explain dynamic and self-organized behaviors. However, the model formulation and parameters are often tuned empirically based on the predefined domain-specific knowledge, which lags behind the emerging paradigm of discovering novel mechanisms from the unprecedented amount of spatiotemporal data. To address this limitation, we sought to link the power of deep neural networks and physics principles of complex systems, which allows us to design explainable deep models for uncovering the mechanistic role of how human brain (the most sophisticated complex system) maintains controllable functions while interacting with external stimulations. In the spirit of optimal control, we present a unified framework to design an explainable deep model that describes the dynamic behaviors of underlying neurobiological processes, allowing us to understand the latent control mechanism at a system level. We have uncovered the pathophysiological mechanism of Alzheimer's disease to the extent of controllability of disease progression, where the dissected system-level understanding enables higher prediction accuracy for disease progression and better explainability for disease etiology than conventional (black box) deep models.

Uncovering Diverse Mechanistic Spreading Pathways in Disease Progression of Alzheimer's Disease.

Background: The AT[N] research framework focuses on three major biomarkers in Alzheimer's disease (AD): amyloid-ß deposition (A), pathologic tau (T), and neurodegeneration [N]. Objective: We hypothesize that the diverse mechanisms such as A⟶T and A⟶[N] pathways from one brain region to others, may underlie the wide variation in clinical symptoms. We aim to uncover the causal-like effect of regional AT[N] biomarkers on cognitive decline as well as the interaction with non-modifiable risk factors such as age and APOE4. Methods: We apply multi-variate statistical inference to uncover all possible mechanistic spreading pathways through which the aggregation of an upstream biomarker (e.g., increased amyloid level) in a particular brain region indirectly impacts cognitive decline, via the cascade build-up of a downstream biomarker (e.g., reduced metabolism level) in another brain region. Furthermore, we investigate the survival time for each identified region-to-region pathological pathway toward the AD onset. Results: We have identified a collection of critical brain regions on which the amyloid burdens exert an indirect effect on the decline in memory and executive function (EF) domain, being mediated by the reduction of metabolism level at other brain regions. APOE4 status has been found not only involved in many A⟶N mechanistic pathways but also significantly contributes to the risk of developing AD. Conclusion: Our major findings include 1) the region-to-region A⟶N⟶MEM and A⟶N⟶MEM pathways exhibit distinct spatial patterns; 2) APOE4 is significantly associated with both direct and indirect effects on the cognitive decline while sex difference has not been identified in the mediation analysis.

AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images.

Malaria is a significant public health concern, with ∼95% of cases occurring in Africa, but accurate and timely diagnosis is problematic in remote and low-income areas. Here, we developed an artificial intelligence-based object detection system for malaria diagnosis (AIDMAN). In this system, the YOLOv5 model is used to detect cells in a thin blood smear. An attentional aligner model (AAM) is then applied for cellular classification that consists of multi-scale features, a local context aligner, and multi-scale attention. Finally, a convolutional neural network classifier is applied for diagnosis using blood-smear images, reducing interference caused by false positive cells. The results demonstrate that AIDMAN handles interference well, with a diagnostic accuracy of 98.62% for cells and 97% for blood-smear images. The prospective clinical validation accuracy of 98.44% is comparable to that of microscopists. AIDMAN shows clinically acceptable detection of malaria parasites and could aid malaria diagnosis, especially in areas lacking experienced parasitologists and equipment.

An Online Mammography Database with Biopsy Confirmed Types.

Breast carcinoma is the second largest cancer in the world among women. Early detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients' lifespan. Mammography, a noninvasive imaging tool with low cost, is widely used to diagnose breast disease at an early stage due to its high sensitivity. Although some public mammography datasets are useful, there is still a lack of open access datasets that expand beyond the white population as well as missing biopsy confirmation or with unknown molecular subtypes. To fill this gap, we build a database containing two online breast mammographies. The dataset named by Chinese Mammography Database (CMMD) contains 3712 mammographies involved 1775 patients, which is divided into two branches. The first dataset CMMD1 contains 1026 cases (2214 mammographies) with biopsy confirmed type of benign or malignant tumors. The second dataset CMMD2 includes 1498 mammographies for 749 patients with known molecular subtypes. Our database is constructed to enrich the diversity of mammography data and promote the development of relevant fields.

DeepGA for automatically estimating fetal gestational age through ultrasound imaging.

Accurate estimation of gestational age (GA) is vital for identifying fetal abnormalities. Conventionally, GA is estimated by measuring the morphology of the cranium, abdomen, and femur manually and inputting them into the classic Hadlock formula to assess fetal growth. However, this procedure incurs considerable overhead and suffers from bias caused by the operators, yielding suboptimal estimations. To address this challenge, we develop an automatic DeepGA model to achieve fully automatic GA prediction in an end-to-end manner. Our model uses a deep segmentation model (DeepSeg) to accurately identify and segment three critical tissues, including the cranium, abdomen, and femur, in which their morphology is automatically extracted. After that, we are able to directly estimate the GA via a deep regression model (DeepReg). We evaluate DeepGA on a large dataset, including 10,413 ultrasound images from 7113 subjects. It achieves superior performance over the traditional measurement approach, with a mean absolute estimation error (MAE) of 5 days. Our DeepGA model is a novel automatic solution on the basis of artificial intelligence learning that can help radiologists improve the performance of GA estimation in various clinical scenarios, thereby enhancing the efficiency of prenatal examinations.

Two-Dimensional Unsupervised Feature Selection via Sparse Feature Filter.

Unsupervised feature selection is a vital yet challenging topic for effective data learning. Recently, 2-D feature selection methods show good performance on image analysis by utilizing the structure information of image. Current 2-D methods usually adopt a sparse regularization to spotlight the key features. However, such scheme introduces additional hyperparameter needed for pruning, limiting the applicability of unsupervised algorithms. To overcome these challenges, we design a feature filter to estimate the weight of image features for unsupervised feature selection. Theoretical analysis shows that a sparse regularization can be derived from the feature filter by transformation, indicating that the filter plays the same role as the popular sparse regularization does. We deploy two distinct strategies in terms of feature selection, called multiple feature filters and single common feature filter. The former divides the optimization problem into multiple independent subproblems and selects features that meet the respective interests of each subproblem. The latter selects features that are in the interest of the overall optimization problem. Extensive experiments on seven benchmark datasets show that our unsupervised 2-D weight-based feature selection methods achieve superior performance over the state-of-the-art methods.

Selective editing of a peptide skeleton via C-N bond formation at N-terminal aliphatic side chains.

The applications of peptides and peptidomimetics have been demonstrated in the fields of therapeutics, diagnostics, and chemical biology. Strategies for the direct late-stage modification of peptides and peptidomimetics are highly desirable in modern drug discovery. Transition-metal-catalyzed C-H functionalization is emerging as a powerful strategy for late-stage peptide modification that is able to construct functional groups or increase skeletal diversity. However, the installation of directing groups is necessary to control the site selectivity. In this work, we describe a transition metal-free strategy for late-stage peptide modification. In this strategy, a linear aliphatic side chain at the peptide N-terminus is cyclized to deliver a proline skeleton via site-selective δ-C(sp3)-H functionalization under visible light. Natural and unnatural amino acids are demonstrated as suitable substrates with the transformations proceeding with excellent regio- and stereo-selectivity.

Delayed diffuse lamellar keratitis after small-incision lenticule extraction related to immunoglobulin A nephropathy: A case report.

BACKGROUND: Diffuse lamellar keratitis (DLK) is a complication of laser-assisted in situ keratomileusis (LASIK). This condition can also develop after small-incision lenticule extraction (SMILE) with a distinctive appearance. We report the case involving a female patient with delayed onset DLK accompanied by immunoglobulin A (IgA) nephropathy. CASE SUMMARY: A 22-year-old woman was referred to our department for DLK and a decline in vision 1 mo after undergoing SMILE. The initial examination showed grade 2 DLK in the flap involving the central visual axis of the right eye. She was immediately administered with a large dose of a topical steroid for 30 d. However, the treatment was ineffective. Her vision deteriorated from 10/20 to 6/20, and DLK gradually worsened from grade 2 to 4. Eventually, interface washout was performed, after which her vision improved. DLK completely disappeared 2 mo after washout. Six months after SMILE, the patient was diagnosed with IgA nephropathy due to a 4-year history of interstitial hematuria. CONCLUSION: DLK is a typical complication of LASIK but can also develop after SMILE. Topical steroid therapy was ineffective in our patient, and interface washout was required. IgA nephropathy could be one of the factors contributing to the development of delayed DLK after SMILE.

Uncovering shape signatures of resting-state functional connectivity by geometric deep learning on Riemannian manifold.

Functional neural activities manifest geometric patterns, as evidenced by the evolving network topology of functional connectivities (FC) even in the resting state. In this work, we propose a novel manifold-based geometric neural network for functional brain networks (called "Geo-Net4Net" for short) to learn the intrinsic low-dimensional feature representations of resting-state brain networks on the Riemannian manifold. This tool allows us to answer the scientific question of how the spontaneous fluctuation of FC supports behavior and cognition. We deploy a set of positive maps and rectified linear unit (ReLU) layers to uncover the intrinsic low-dimensional feature representations of functional brain networks on the Riemannian manifold taking advantage of the symmetric positive-definite (SPD) form of the correlation matrices. Due to the lack of well-defined ground truth in the resting state, existing learning-based methods are limited to unsupervised methodologies. To go beyond this boundary, we propose to self-supervise the feature representation learning of resting-state functional networks by leveraging the task-based counterparts occurring before and after the underlying resting state. With this extra heuristic, our Geo-Net4Net allows us to establish a more reasonable understanding of resting-state FCs by capturing the geometric patterns (aka. spectral/shape signature) associated with resting states on the Riemannian manifold. We have conducted extensive experiments on both simulated data and task-based functional resonance magnetic imaging (fMRI) data from the Human Connectome Project (HCP) database, where our Geo-Net4Net not only achieves more accurate change detection results than other state-of-the-art counterpart methods but also yields ubiquitous geometric patterns that manifest putative insights into brain function.

Learning Brain Dynamics of Evolving Manifold Functional MRI Data Using Geometric-Attention Neural Network.

Functional connectivities (FC) of brain network manifest remarkable geometric patterns, which is the gateway to understanding brain dynamics. In this work, we present a novel geometric-attention neural network to characterize the time-evolving brain state change from the functional neuroimages by tracking the trajectory of functional dynamics on high-dimension Riemannian manifold of symmetric positive definite (SPD) matrices. Specifically, we put the spotlight on learning the common state-specific manifold signatures that represent the underlying cognition. In this context, the driving force of our neural network is tied up with the learning of the evolution functionals on the Riemannian manifold of SPD matrix that underlies the known evolving brain states. To do so, we train a convolution neural network (CNN) on the Riemannian manifold of SPD matrices to seek for the putative low-dimension feature representations, followed by an end-to-end recurrent neural network (RNN) to yield the time-varying mapping function of SPD matrices which fits the evolutionary trajectories of the underlying states. Furthermore, we devise a geometric attention mechanism in CNN, allowing us to discover the latent geometric patterns in SPD matrices that are associated with the underlying states. Notably, our work has the potential to understand how brain function emerges behavior by investigating the geometrical patterns from functional brain networks, which is essentially a correlation matrix of neuronal activity signals. Our proposed manifold-based neural network achieves promising results in predicting brain state changes on both simulated data and task functional neuroimaging data from Human Connectome Project, which implies great applicability in neuroscience studies.

SeqSeg: A sequential method to achieve nasopharyngeal carcinoma segmentation free from background dominance.

Reliable nasopharyngeal carcinoma (NPC) segmentation plays an important role in radiotherapy planning. However, recent deep learning methods fail to achieve satisfactory NPC segmentation in magnetic resonance (MR) images, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable border, making it indiscernible from tightly connected surrounding tissues from immense and complex backgrounds. To address such background dominance problems, this paper proposes a sequential method (SeqSeg) to achieve accurate NPC segmentation. Specifically, the proposed SeqSeg is devoted to solving the problem at two scales: the instance level and feature level. At the instance level, SeqSeg is forced to focus attention on the tumor and its surrounding tissue through the deep Q-learning (DQL)-based NPC detection model by prelocating the tumor and reducing the scale of the segmentation background. Next, at the feature level, SeqSeg uses high-level semantic features in deeper layers to guide feature learning in shallower layers, thus directing the channel-wise and region-wise attention to mine tumor-related features to perform accurate segmentation. The performance of our proposed method is evaluated by extensive experiments on the large NPC dataset containing 1101 patients. The experimental results demonstrated that the proposed SeqSeg not only outperforms several state-of-the-art methods but also achieves better performance in multi-device and multi-center datasets.

NPCNet: Jointly Segment Primary Nasopharyngeal Carcinoma Tumors and Metastatic Lymph Nodes in MR Images.

Nasopharyngeal carcinoma (NPC) is a malignant tumor whose survivability is greatly improved if early diagnosis and timely treatment are provided. Accurate segmentation of both the primary NPC tumors and metastatic lymph nodes (MLNs) is crucial for patient staging and radiotherapy scheduling. However, existing studies mainly focus on the segmentation of primary tumors, eliding the recognition of MLNs, and thus fail to comprehensively provide a landscape for tumor identification. There are three main challenges in segmenting primary NPC tumors and MLNs: variable location, variable size, and irregular boundary. To address these challenges, we propose an automatic segmentation network, named by NPCNet, to achieve segmentation of primary NPC tumors and MLNs simultaneously. Specifically, we design three modules, including position enhancement module (PEM), scale enhancement module (SEM), and boundary enhancement module (BEM), to address the above challenges. First, the PEM enhances the feature representations of the most suspicious regions. Subsequently, the SEM captures multiscale context information and target context information. Finally, the BEM rectifies the unreliable predictions in the segmentation mask. To that end, extensive experiments are conducted on our dataset of 9124 samples collected from 754 patients. Empirical results demonstrate that each module realizes its designed functionalities and is complementary to the others. By incorporating the three proposed modules together, our model achieves state-of-the-art performance compared with nine popular models.

Deep Multiview Clustering via Iteratively Self-Supervised Universal and Specific Space Learning.

Multiview clustering seeks to partition objects via leveraging cross-view relations to provide a comprehensive description of the same objects. Most existing methods assume that different views are linear transformable or merely sampling from a common latent space. Such rigid assumptions betray reality, thus leading to unsatisfactory performance. To tackle the issue, we propose to learn both common and specific sampling spaces for each view to fully exploit their collaborative representations. The common space corresponds to the universal self-representation basis for all views, while the specific spaces are the view-specific basis accordingly. An iterative self-supervision scheme is conducted to strengthen the learned affinity matrix. The clustering is modeled by a convex optimization. We first solve its linear formulation by the popular scheme. Then, we employ the deep autoencoder structure to exploit its deep nonlinear formulation. The extensive experimental results on six real-world datasets demonstrate that the proposed model achieves uniform superiority over the benchmark methods.

Asymmetric Synthesis of 3-Methyleneindolines via Rhodium(I)-Catalyzed Alkynylative Cyclization of N-(o-Alkynylaryl)imines.

The first asymmetric synthesis of 3-methyleneindolines from alkynyl imines has been developed via a rhodium-catalyzed tandem process: regioselective alkynylation of the internal alkynes and subsequent intramolecular addition to the imines. The reaction proceeded with unconventional chemoselectivity and provided 3-methyleneindolines with good yields (up to 82% yield) and high enantioselectivities (up to 97% ee). Moreover, this transformation also features mild reaction conditions, perfect atom economy, and a broad substrate scope.

Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort.

Corona Virus Disease (COVID-19) has spread globally quickly, and has resulted in a large number of causalities and medical resources insufficiency in many countries. Reverse-transcriptase polymerase chain reaction (RT-PCR) testing is adopted as biopsy tool for confirmation of virus infection. However, its accuracy is as low as 60-70%, which is inefficient to uncover the infected. In comparison, the chest CT has been considered as the prior choice in diagnosis and monitoring progress of COVID-19 infection. Although the COVID-19 diagnostic systems based on artificial intelligence have been developed for assisting doctors in diagnosis, the small sample size and the excessive time consumption limit their applications. To this end, this paper proposed a diagnosis prototype system for COVID-19 infection testing. The proposed deep learning model is trained and is tested on 2267 CT sequences from 1357 patients clinically confirmed with COVID-19 and 1235 CT sequences from non-infected people. The main highlights of the prototype system are: (1) no data augmentation is needed to accurately discriminate the COVID-19 from normal controls with the specificity of 0.92 and sensitivity of 0.93; (2) the raw DICOM image is not necessary in testing. Highly compressed image like Jpeg can be used to allow a quick diagnosis; and (3) it discriminates the virus infection within 6 seconds and thus allows an online test with light cost. We also applied our model on 48 asymptomatic patients diagnosed with COVID-19. We found that: (1) the positive rate of RT-PCR assay is 63.5% (687/1082). (2) 45.8% (22/48) of the RT-PCR assay is negative for asymptomatic patients, yet the accuracy of CT scans is 95.8%. The online detection system is available: http://212.64.70.65/covid .

Single Fundus Image Super-Resolution Via Cascaded Channel-Wise Attention Network.

Fundus image is commonly used in aiding the diagnosis of ophthalmic diseases. A high-resolution (HR) image is valuable to provide the anatomic information on the eye conditions. Recently, image super-resolution (SR) though learning model has been shown to be an economic yet effective way to satisfy the high demands in the clinical practice. However, the reported methods ignore the mutual dependencies of low-and high-resolution images and did not fully exploit the dependencies between channels. To tackle with the drawbacks, we propose a novel network for fundus image SR, named by Fundus Cascaded Channel-wise Attention Network (FC-CAN). The proposed FCCAN cascades channel attention module and dense module jointly to exploit the semantic interdependencies both frequency and domain information across channels. The channel attention module rescales channel maps in spatial domain, while the dense module preserves the HR components by up- and down-sampling operation. Experimental results demonstrate the superiority of our net-work in comparison with the six methods.