Single domain antibody multimers confer protection against rabies infection

Post-exposure prophylactic (PEP) neutralizing antibodies against Rabies are the most effective way to prevent infection-related fatality. The outer envelope glycoprotein of the Rabies virus (RABV) is the most significant surface antigen for generating virus-neutralizing antibodies. The small size and uncompromised functional specificity of single domain antibodies (sdAbs) can be exploited in the fields of experimental therapeutic applications for infectious diseases through formatting flexibilities to increase their avidity towards target antigens. In this study, we used phage display technique to select and identify sdAbs that were specific for the RABV glycoprotein from a naïve llama-derived antibody library. To increase their neutralizing potencies, the sdAbs were fused with a coiled-coil peptide derived from the human cartilage oligomeric matrix protein (COMP48) to form homogenous pentavalent multimers, known as combodies. Compared to monovalent sdAbs, the combodies, namely 26424 and 26434, exhibited high avidity and were able to neutralize 85-fold higher input of RABV (CVS-11 strain) pseudotypes in vitro, as a result of multimerization, while retaining their specificities for target antigen. 26424 and 26434 were capable of neutralizing CVS-11 pseudotypes in vitro by 90–95% as compared to human rabies immunoglobulin (HRIG), currently used for PEP in Rabies. The multimeric sdAbs were also demonstrated to be partially protective for mice that were infected with lethal doses of rabies virus in vivo. The results demonstrate that the combodies could be valuable tools in understanding viral mechanisms, diagnosis and possible anti-viral candidate for RABV infection

Remote Sensing and Texture Image Classification Network Based on Deep Learning Integrated with Binary Coding and Sinkhorn Distance

In the past two decades, traditional hand-crafted feature based methods and deep feature based methods have successively played the most important role in image classification. In some cases, hand-crafted features still provide better performance than deep features. This paper proposes an innovative network based on deep learning integrated with binary coding and Sinkhorn distance (DBSNet) for remote sensing and texture image classification. The statistical texture features of the image extracted by uniform local binary pattern (ULBP) are introduced as a supplement for deep features extracted by ResNet-50 to enhance the discriminability of features. After the feature fusion, both diversity and redundancy of the features have increased, thus we propose the Sinkhorn loss where an entropy regularization term plays a key role in removing redundant information and training the model quickly and efficiently. Image classification experiments are performed on two texture datasets and five remote sensing datasets. The results show that the statistical texture features of the image extracted by ULBP complement the deep features, and the new Sinkhorn loss performs better than the commonly used softmax loss. The performance of the proposed algorithm DBSNet ranks in the top three on the remote sensing datasets compared with other state-of-the-art algorithms

Lifting Scheme-Based Deep Neural Network for Remote Sensing Scene Classification

Recently, convolutional neural networks (CNNs) achieve impressive results on remote sensing scene classification, which is a fundamental problem for scene semantic understanding. However, convolution, the most essential operation in CNNs, restricts the development of CNN-based methods for scene classification. Convolution is not efficient enough for high-resolution remote sensing images and limited in extracting discriminative features due to its linearity. Thus, there has been growing interest in improving the convolutional layer. The hardware implementation of the JPEG2000 standard relies on the lifting scheme to perform wavelet transform (WT). Compared with the convolution-based two-channel filter bank method of WT, the lifting scheme is faster, taking up less storage and having the ability of nonlinear transformation. Therefore, the lifting scheme can be regarded as a better alternative implementation for convolution in vanilla CNNs. This paper introduces the lifting scheme into deep learning and addresses the problems that only fixed and finite wavelet bases can be replaced by the lifting scheme, and the parameters cannot be updated through backpropagation. This paper proves that any convolutional layer in vanilla CNNs can be substituted by an equivalent lifting scheme. A lifting scheme-based deep neural network (LSNet) is presented to promote network applications on computational-limited platforms and utilize the nonlinearity of the lifting scheme to enhance performance. LSNet is validated on the CIFAR-100 dataset and the overall accuracies increase by 2.48% and 1.38% in the 1D and 2D experiments respectively. Experimental results on the AID which is one of the newest remote sensing scene dataset demonstrate that 1D LSNet and 2D LSNet achieve 2.05% and 0.45% accuracy improvement compared with the vanilla CNNs respectively

Intracranial-Pressure-Monitoring-Assisted Management Associated with Favorable Outcomes in Moderate Traumatic Brain Injury Patients with a GCS of 9–11

Objective: With a mortality rate of 10–30%, a moderate traumatic brain injury (mTBI) is one of the most variable traumas. The indications for intracranial pressure (ICP) monitoring in patients with mTBI and the effects of ICP on patients’ outcomes are uncertain. The purpose of this study was to examine the indications of ICP monitoring (ICPm) and its effects on the long-term functional outcomes of mTBI patients. Methods: Patients with Glasgow Coma Scale (GCS) scores of 9–11 at Tangdu hospital, between January 2015 and December 2021, were enrolled and treated in this retrospective cohort study. We assessed practice variations in ICP interventions using the therapy intensity level (TIL). Six-month mortality and a Glasgow Outcome Scale Extended (GOS-E) score were the main outcomes. The secondary outcome was neurological deterioration (ND) events. The indication and the estimated impact of ICPm on the functional outcome were investigated by using binary regression analyses. Results: Of the 350 patients, 145 underwent ICP monitoring-assisted management, and the other 205 patients received a standard control based on imaging or clinical examinations. A GCS ≤ 10 (OR 1.751 (95% CI 1.216–3.023), p = 0.003), midline shift (mm) ≥ 2.5 (OR 3.916 (95% CI 2.076–7.386) p < 0.001), and SDH (OR 1.772 (95% CI 1.065–2.949) p = 0.028) were predictors of ICP. Patients who had ICPm (14/145 (9.7%)) had a decreased 6-month mortality rate compared to those who were not monitored (40/205 (19.5%), p = 0.011). ICPm was linked to both improved neurological outcomes at 6 months (OR 0.815 (95% CI 0.712–0.933), p = 0.003) and a lower ND rate (2 = 11.375, p = 0.010). A higher mean ICP (17.32 ± 3.52, t = −6.047, p < 0.001) and a more significant number of ICP > 15 mmHg (27 (9–45.5), Z = −5.406, p < 0.001) or ICP > 20 mmHg (5 (0–23), Z = −4.635, p < 0.001) 72 h after injury were associated with unfavorable outcomes. The best unfavorable GOS-E cutoff value of different ICP characteristics showed that the mean ICP was >15.8 mmHg (AUC 0.698; 95% CI, 0.606–0.789, p < 0.001), the number of ICP > 15 mmHg was >25.5 (AUC 0.681; 95% CI, 0.587–0.774, p < 0.001), and the number of ICP > 20 mmHg was >6 (AUC 0.660; 95% CI, 0.561–0.759, p < 0.001). The total TIL score during the first 72 h post-injury in the non-ICP group (9 (8, 11)) was lower than that of the ICP group (13 (9, 17), Z = −8.388, p < 0.001), and was associated with unfavorable outcomes. Conclusion: ICPm-assisted management was associated with better clinical outcomes six months after discharge and lower incidences of ND for seven days post-injury. A mean ICP > 15.8 mmHg, the number of ICP > 15 mmHg > 25.5, or the number of ICP > 20 mmHg > 6 implicate an unfavorable long-term prognosis after 72 h of an mTBI

Fully Convolutional Networks and a Manifold Graph Embedding-Based Algorithm for PolSAR Image Classification

With the rapid development of artificial intelligence, how to take advantage of deep learning and big data to classify polarimetric synthetic aperture radar (PolSAR) imagery is a hot topic in the field of remote sensing. As a key step for PolSAR image classification, feature extraction technology based on target decomposition is relatively mature, and how to extract discriminative spatial features and integrate these features with polarized information to maximize the classification accuracy is the core issue. In this context, this paper proposes a PolSAR image classification algorithm based on fully convolutional networks (FCNs) and a manifold graph embedding model. First, to describe different types of land objects more comprehensively, various polarized features of PolSAR images are extracted through seven kinds of traditional decomposition methods. Afterwards, drawing on transfer learning, the decomposed features are fed into multiple parallel and pre-trained FCN-8s models to learn deep multi-scale spatial features. Feature maps from the last layer of each FCN model are concatenated to obtain spatial polarization features with high dimensions. Then, a manifold graph embedding model is adopted to seek an effective and compact representation for spatially polarized features in a manifold subspace, simultaneously removing redundant information. Finally, a support vector machine (SVM) is selected as the classifier for pixel-level classification in a manifold subspace. Extensive experiments on three PolSAR datasets demonstrate that the proposed algorithm achieves a superior classification performance

Detection of monosodium urate depositions and atherosclerotic plaques in the cardiovascular system by dual-energy computed tomography

Aim: The study aimed to explore the relationship between urate deposition and surrounding atherosclerotic plaques, and to confirm the contribution of urate deposition to the development of coronary atherosclerosis. Methods and results: The present study employed Dual-energy CT (DECT) material separation technology through calcium score scan to access the presence of MSU crystal deposition in coronary atherosclerotic plaques in patients with clinically suspected coronary heart diseases undergoing DECT. DECT showed that among 872 patients, 441 had plaques in coronary arteries; the incidence of plaque was 50.6 %. The patients were divided in the atherosclerotic plaque vs. non-plaque groups. There were significant differences in age, sex, blood pressure, blood glucose, serum creatinine, and history of gout and hyperuricemia between the plaque and non-plaque groups (all P < 0.05). Among the patients with coronary plaques, there were 348 patients (78.9 %) with simple atherosclerotic plaque (AP), 8 (1.8 %) with simple urate depositions (UD), and 85 (19.3 %) with urate depositions and atherosclerotic plaques (UDAP). The multivariable analysis showed that urate deposition was independently associated with plaques after adjustment for age, sex, blood pressure, blood glucose, serum creatinine, history of gout, and history of hyperuricemia (OR = 13.69, 95%CI: 7.53–22.95, P = 0.035). UPAP patients had significantly higher coronary calcium scores than AP patients [210.1 (625.2) AU vs 58.2 (182.5) AU, P < 0.001] Urate deposition (16.7 mm3) positively correlated with plaque calcification (73.8 mm³) in UPAP patients (r = 0.325, P < 0.001). Conclusion: Patients with gout or a history of hyperuricemia were more likely to exhibit UDAP. Urate deposition was independently associated with plaques

Insights into the N‑Heterocyclic Carbene (NHC)-Catalyzed Oxidative γ‑C(sp³)–H Deprotonation of Alkylenals and Cascade [4 + 2] Cycloaddition with Alkenylisoxazoles

The N-heterocyclic carbene (NHC)-catalyzed oxidative C–H deprotonations have attracted increasing attention; however, the general mechanism regarding this kind of oxidative organocatalysis remains unclear. In this paper, the competing mechanisms and origin of the stereoselectivity of the NHC-catalyzed oxidative γ-C­(sp³)–H deprotonation of alkylenals and cascade [4 + 2] cycloaddition with alkenylisoxazoles were systematically investigated for the first time using density functional theory (DFT). The computed results indicate that the oxidation of the Breslow intermediate by 3,3′,5,5′-tetra-<i>tert</i>-butyl diphenoquinone (DQ) via a hydride transfer to oxygen (HTO) pathway is the most favorable among the four competing pathways. In addition, the analyses demonstrate that oxidant DQ plays a double role, i.e., strengthening the acidity of the hydrogen of the γ-carbon of alkylenal and forming π···π interactions with conjugated CC bonds to promote the γ-C­(sp³)–H deprotonation. The NHC catalyst acts as a Lewis base, and the hydrogen-bond network between the NHC and the substrate formed in the key Michael addition step is responsible for the origin of the stereoselectivity. Further DFT calculations reveal that the nonpolar solvent can stabilize the nonpolar <i>R</i> isomer but destabilize the polar <i>S</i> isomer for the stereoselectivity-determining transition states, thus improving the stereoselectivity

Nucleoside-Based Ultrasensitive Fluorescent Probe for the Dual-Mode Imaging of Microviscosity in Living Cells

Microviscosity changes of living cells have a far-reaching influence on diffusion and movement capacity of RNA and, more seriously, could modify RNA functions in living cells. Fluorescent rotor, whose fluorescence responds to different environmental viscosities, holds great potential for the imaging of viscosity in biosystem. Although many fluorescent rotors have been reported for viscosity, the fluorogenic rotor with ultrasensitivity for the determination of microviscosity (<10 cP) was rarely reported. Herein, we report a nucleoside-based two-photon fluorescent rotor (<b>dABp-3</b>) that can selectively and ultrasensitively image microviscosity in RNA region of living cells for the first time. 2′-Deoxyadenosine is selected as an electron donor to permit energy transfer via the acetylenic bond to acceptor, a typical boron dipyrromethene moiety. Another highlight, <b>dABp-3</b> is based on 2′-deoxyadenosine, which result in its recognition capacity for RNA. <b>dABp-3</b> with ultrasensitivity provides a varied linear response to the microrange viscosity (1.8–6.0 cP) in RNA region of living cells on dual-modetwo-photon ratio mode and fluorescence lifetime mode. After screening and optimization, advantageously, <b>dABp-3</b> can be used to screen reticulocytes from mature blood cells of thrombosis models in vitro and in vivo because of targeting RNA, while simultaneously image microviscosity changes in these cells. So, <b>dABp-3</b> as an analytical tool holds considerable promise for bioimaging and monitoring of microviscosity changes in complex biological systems