Long non-coding RNA-NONMMMUT004552.2 regulates the unloading-induced bone loss through the miRNA-15b-5p/Syne1 in mice.

Exercise-induced mechanical loading can increase bone strength whilst mechanical unloading enhances bone-loss. Here, we investigated the role of lncRNA NONMMUT004552.2 in unloading-induced bone-loss. Knockout of lncRNA NONMMUT004552.2 in hindlimb-unloaded mice caused an increase in the bone formation and osteoblast activity. The silencing of lncRNA NONMMUT004552.2 also decreased the osteoblast apoptosis and expression of Bax and cleaved caspase-3, increased Bcl-2 protein expression in MC3T3-E1 cells. Mechanistic investigations demonstrated that NONMMUT004552.2 functions as a competing endogenous RNA (ceRNA) to facilitate the protein expression of spectrin repeat containing, nuclear envelope 1 (Syne1) by competitively binding miR-15b-5p and subsequently inhibits the osteoblast differentiation and bone formation in the microgravity unloading environment. These data highlight the importance of the lncRNA NONMMUT004552.2/miR-15b-5p/Syne1 axis for the treatment of osteoporosis.

Comparison of prediction models for cardiovascular and mortality risk in people with type 2 diabetes: An external validation in 23 685 adults included in the UK Biobank.

AIMS: To validate cardiovascular risk prediction models for individuals with diabetes using the UK Biobank in order to assess their applicability. METHODS: We externally validated 19 cardiovascular risk scores from seven risk prediction models (Chang et al., Framingham, University of Hong Kong-Singapore [HKU-SG], Li et al, RECODe [risk equations for complications of type 2 diabetes], SCORE [Systematic Coronary Risk Evaluation] and the UK Prospective Diabetes Study Outcomes Model 2 [UKPDS OM2]), identified from systematic reviews, using UK Biobank data from 2006 to 2021 (n = 23 685; participant age 40-71 years, 63.5% male). We evaluated performance by assessing the discrimination and calibration of the models for the endpoints of mortality, cardiovascular mortality, congestive heart failure, myocardial infarction, stroke, and ischaemic heart disease. RESULTS: Over a total of 269 430 person-years of follow-up (median 11.89 years), the models showed low-to-moderate discrimination performance on external validation (concordance indices [c-indices] 0.50-0.71). Most models had low calibration with overprediction of the observed risk. RECODe outperformed other models across four comparable endpoints for discrimination: all-cause mortality (c-index 0.67, 95% confidence interval [CI] 0.65-0.69), congestive heart failure (c-index 0.71, 95% CI 0.69-0.72), myocardial infarction (c-index 0.67, 95% CI 0.65-0.68); and stroke (c-index 0.65, 95% CI 0.62-0.68), and for calibration (except for all-cause mortality). The UKPDS OM2 had comparable performance to RECODe for all-cause mortality (c-index 0.67, 95% CI 0.66-0.69) and cardiovascular mortality (c-index 0.71, 95% CI 0.70-0.73), but worse performance for other outcomes. The models performed better for younger participants and somewhat better for non-White ethnicities. Models developed from non-Western datasets showed worse performance in our UK-based validation set. CONCLUSIONS: The RECODe model led to better risk estimations in this predominantly White European population. Further validation is needed in non-Western populations to assess generalizability to other populations.

A rare case of infected urinothorax.

Urinothorax is a rare cause of pleural effusion. Infected urinothorax is even rarer. Here we present a case of infected urinothorax from renal mass causing obstructive uropathy. Patient improved with pleural drainage and a multidisciplinary approach of treatment between team involving urologist and pulmonologist. This case highlights the complexity in the diagnosis and management of infected urinothorax.

A New Biocontrol Agent Bacillus velezensis SF334 against Rubber Tree Fungal Leaf Anthracnose and Its Genome Analysis of Versatile Plant Probiotic Traits.

Natural rubber is an important national strategic and industrial raw material. The leaf anthracnose of rubber trees caused by the Colletotrichum species is one of the important factors restricting the yields of natural rubber. In this study, we isolated and identified strain Bacillus velezensis SF334, which exhibited significant antagonistic activity against both C. australisinense and C. siamense, the dominant species of Colletotrichum causing rubber tree leaf anthracnose in the Hainan province of China, from a pool of 223 bacterial strains. The cell suspensions of SF334 had a significant prevention effect for the leaf anthracnose of rubber trees, with an efficacy of 79.67% against C. siamense and 71.8% against C. australisinense. We demonstrated that SF334 can lead to the lysis of C. australisinense and C. siamense mycelia by causing mycelial expansion, resulting in mycelial rupture and subsequent death. B. velezensis SF334 also harbors some plant probiotic traits, such as secreting siderophore, protease, cellulase, pectinase, and the auxin of indole-3-acetic acid (IAA), and it has broad-spectrum antifungal activity against some important plant pathogenic fungi. The genome combined with comparative genomic analyses indicated that SF334 possesses most genes of the central metabolic and gene clusters of secondary metabolites in B. velezensis strains. To our knowledge, this is the first time a Bacillus velezensis strain has been reported as a promising biocontrol agent against the leaf anthracnose of rubber trees caused by C. siamense and C. australisinense. The results suggest that B. velezensis could be a potential candidate agent for the leaf anthracnose of rubber trees.

Deep convolutional dictionary learning network for sparse view CT reconstruction with a group sparse prior.

Purpose Numerous techniques based on deep learning have been utilized in sparse view computed tomography (CT) imaging. Nevertheless, the majority of techniques are instinctively constructed utilizing state-of-the-art opaque convolutional neural networks (CNNs) and lack interpretability. Moreover, CNNs tend to focus on local receptive fields and neglect nonlocal self-similarity prior information. Obtaining diagnostically valuable images from sparsely sampled projections is a challenging and ill-posed task. Method To address this issue, we propose a unique and understandable model named DCDL-GS for sparse view CT imaging. This model relies on a network comprised of convolutional dictionary learning and a nonlocal group sparse prior. To enhance the quality of image reconstruction, we utilize a neural network in conjunction with a statistical iterative reconstruction framework and perform a set number of iterations. Inspired by group sparsity priors, we adopt a novel group thresholding operation to improve the feature representation and constraint ability and obtain a theoretical interpretation. Furthermore, our DCDL-GS model incorporates filtered backprojection (FBP) reconstruction, fast sliding window nonlocal self-similarity operations, and a lightweight and interpretable convolutional dictionary learning network to enhance the applicability of the model. Results The efficiency of our proposed DCDL-GS model in preserving edges and recovering features is demonstrated by the visual results obtained on the LDCT-P and UIH datasets. Compared to the results of the most advanced techniques, the quantitative results are enhanced, with increases of 0.6-0.8 dB for the peak signal-to-noise ratio (PSNR), 0.005-0.01 for the structural similarity index measure (SSIM), and 1-1.3 for the regulated Fréchet inception distance (rFID) on the test dataset. The quantitative results also show the effectiveness of our proposed deep convolution iterative reconstruction module and nonlocal group sparse prior. Conclusion In this paper, we create a consolidated and enhanced mathematical model by integrating projection data and prior knowledge of images into a deep iterative model. The model is more practical and interpretable than existing approaches. The results from the experiment show that the proposed model performs well in comparison to the others.

Multiple sequence alignment-based RNA language model and its application to structural inference.

Compared with proteins, DNA and RNA are more difficult languages to interpret because four-letter coded DNA/RNA sequences have less information content than 20-letter coded protein sequences. While BERT (Bidirectional Encoder Representations from Transformers)-like language models have been developed for RNA, they are ineffective at capturing the evolutionary information from homologous sequences because unlike proteins, RNA sequences are less conserved. Here, we have developed an unsupervised multiple sequence alignment-based RNA language model (RNA-MSM) by utilizing homologous sequences from an automatic pipeline, RNAcmap, as it can provide significantly more homologous sequences than manually annotated Rfam. We demonstrate that the resulting unsupervised, two-dimensional attention maps and one-dimensional embeddings from RNA-MSM contain structural information. In fact, they can be directly mapped with high accuracy to 2D base pairing probabilities and 1D solvent accessibilities, respectively. Further fine-tuning led to significantly improved performance on these two downstream tasks compared with existing state-of-the-art techniques including SPOT-RNA2 and RNAsnap2. By comparison, RNA-FM, a BERT-based RNA language model, performs worse than one-hot encoding with its embedding in base pair and solvent-accessible surface area prediction. We anticipate that the pre-trained RNA-MSM model can be fine-tuned on many other tasks related to RNA structure and function.

Computationally efficient composite triple beat cancellation for DML-based DA-RoF fronthaul.

The digital-analog radio-over-fiber (DA-RoF) scheme offers a high-fidelity and spectrally efficient solution for future mobile fronthaul. However, to be implemented in the low-cost directly modulated laser with direct detection (DML-DD) link, both the digital and analog parts in DA-RoF modulation would suffer from the composite second-order (CSO) and composite triple beat (CTB) caused by the chirp-dispersion interaction. In this Letter, we propose and experimentally demonstrate a computationally efficient composite triple beat cancellation (CTB-C) algorithm for DA-RoF fronthaul in the dispersion-uncompensated C-band DML-DD link. The CSO and CTB are suppressed at the receiver-side DSP based on the theoretical model of these nonlinear distortions. In the proof-of-concept experiment, a 1.2-dB improvement in the recovered signal-to-noise ratio (SNR) is obtained with 5.5-GHz 1024-QAM orthogonal frequency division multiplexing (OFDM) signal after 10-km standard single-mode fiber (SSMF) transmission. The proposed CTB-C technique does not require the training process and performs close to the Volterra-based feed-forward equalizer (VFE) under the complexity constraint.

Comparative Analyses of Reproductive Caste Types Reveal Vitellogenin Genes Involved in Queen Fertility in Solenopsis invicta.

The red imported fire ant (Solenopsis invicta Buren) is a social pest species with a robust reproductive ability that causes extensive damage. Identification of the genes involved in queen fertility is critical in order to better understand the reproductive biology and screening for the potential molecular targets in S. invicta. Here, we used the mRNA deep sequencing (RNA-seq) approach to identify differentially expressed genes (DEGs) in the transcriptomes of three reproductive caste types of S. invicta, including queen (QA) and winged female (FA) and male (MA) ants. The genes that were specific to and highly expressed in the queens were then screened, and the Vg2 and Vg3 genes were chosen as targets to explore their functions in oogenesis and fertility. A minimum of 6.08 giga bases (Gb) of clean reads was obtained from all samples, with a mapping rate > 89.78%. There were 7524, 7133, and 977 DEGs identified in the MA vs. QA, MA vs. FA, and FA vs. QA comparisons, respectively. qRT-PCR was used to validate 10 randomly selected DEGs, including vitellogenin 2 (Vg2) and 3 (Vg3), and their expression patterns were mostly consistent with the RNA-seq data. The S. invicta Vgs included conserved domains and motifs that are commonly found in most insect Vgs. SiVg2 and SiVg3 were highly expressed in queens and winged females and were most highly expressed in the thorax, followed by the fat body, head, and epidermis. Evaluation based on a loss-of-function-based knockdown analysis showed that the downregulation of either or both of these genes resulted in smaller ovaries, less oogenesis, and less egg production. The results of transcriptional sequencing provide a foundation for clarifying the regulators of queen fertility in S. invicta. The functions of SiVg2 and SiVg3 as regulators of oogenesis highlight their importance in queen fecundity and their potential as targets of reproductive disruption in S. invicta control.

Targeting and cytotoxicity of chimeric antigen receptor T cells grafted with PD1 extramembrane domain.

BACKGROUND: Immunosuppression induced by programmed cell death protein 1 (PD1) presents a significant constraint on the effectiveness of chimeric antigen receptor (CAR)-T therapy. The potential of combining PD1/PDL1 (Programmed cell death 1 ligand 1) axis blockade with CAR-T cell therapy is promising. However, developing a highly efficient and minimally toxic approach requires further exploration. Our attempt to devise a novel CAR structure capable of recognizing both tumor antigens and PDL1 encountered challenges since direct targeting of PDL1 resulted in systemic adverse effects. METHODS: In this research, we innovatively engineered novel CARs by grafting the PD1 domain into a conventional second-generation (2G) CAR specifically targeting CD19. These CARs exist in two distinct forms: one with PD1 extramembrane domain (EMD) directly linked to a transmembrane domain (TMD), referred to as PE CAR, and the other with PD1 EMD connected to a TMD via a CD8 hinge domain (HD), known as PE8HT CAR. To evaluate their efficacy, we conducted comprehensive assessments of their cytotoxicity, cytokine release, and potential off-target effects both in vitro and in vivo using tumor models that overexpress CD19/PDL1. RESULTS: The findings of our study indicate that PE CAR demonstrates enhanced cytotoxicity and reduced cytokine release specifically towards CD19 + PDL1 + tumor cells, without off-target effects to CD19-PDL1 + tumor cells, in contrast to 2G CAR-T cells. Additionally, PE CAR showed ameliorative differentiation, exhaustion, and apoptosis phenotypes as assessed by flow cytometry, RNA-sequencing, and metabolic parameter analysis, after encountering CD19 + PDL1 + tumor cells. CONCLUSION: Our results revealed that CAR grafted with PD1 exhibits enhanced antitumor activity with lower cytokine release and no PD1-related off-target toxicity in tumor models that overexpress CD19 and PDL1. These findings suggest that our CAR design holds the potential for effectively addressing the PD1 signal.

Dynamic controllable residual generative adversarial network for low-dose computed tomography imaging.

Background: Computed tomography (CT) imaging technology has become an indispensable auxiliary method in medical diagnosis and treatment. In mitigating the radiation damage caused by X-rays, low-dose computed tomography (LDCT) scanning is becoming more widely applied. However, LDCT scanning reduces the signal-to-noise ratio of the projection, and the resulting images suffer from serious streak artifacts and spot noise. In particular, the intensity of noise and artifacts varies significantly across different body parts under a single low-dose protocol. Methods: To improve the quality of different degraded LDCT images in a unified framework, we developed a generative adversarial learning framework with a dynamic controllable residual. First, the generator network consists of the basic subnetwork and the conditional subnetwork. Inspired by the dynamic control strategy, we designed the basic subnetwork to adopt a residual architecture, with the conditional subnetwork providing weights to control the residual intensity. Second, we chose the Visual Geometry Group Network-128 (VGG-128) as the discriminator to improve the noise artifact suppression and feature retention ability of the generator. Additionally, a hybrid loss function was specifically designed, including the mean square error (MSE) loss, structural similarity index metric (SSIM) loss, adversarial loss, and gradient penalty (GP) loss. Results: The results obtained on two datasets show the competitive performance of the proposed framework, with a 3.22 dB peak signal-to-noise ratio (PSNR) margin, 0.03 SSIM margin, and 0.2 contrast-to-noise ratio margin on the Challenge data and a 1.0 dB PSNR margin and 0.01 SSIM margin on the real data. Conclusions: Experimental results demonstrated the competitive performance of the proposed method in terms of noise decrease, structural retention, and visual impression improvement.

Antagonistic transcriptome profile reveals potential mechanisms of action on Xanthomonas oryzae pv. oryzicola by the cell-free supernatants of Bacillus velezensis 504, a versatile plant probiotic bacterium.

Bacterial leaf streak (BLS) of rice is a severe disease caused by the bacterial pathogen Xanthomonas oryzae pv. oryzicola (Xoc) that has gradually become the fourth major disease on rice in some rice-growing regions in southern China. Previously, we isolated a Bacillus velezensis strain 504 that exhibited apparent antagonistic activity against the Xoc wild-type strain RS105, and found that B. velezensis 504 was a potential biocontrol agent for BLS. However, the underlying mechanisms of antagonism and biocontrol are not completely understood. Here we mine the genomic data of B. velezensis 504, and the comparative transcriptomic data of Xoc RS105 treated by the cell-free supernatants (CFSs) of B. velezensis 504 to define differentially expressed genes (DEGs). We show that B. velezensis 504 shares over 89% conserved genes with FZB42 and SQR9, two representative model strains of B. velezensis, but 504 is more closely related to FZB42 than SQR9, as well as B. velezensis 504 possesses the secondary metabolite gene clusters encoding the essential anti-Xoc agents difficidin and bacilysin. We conclude that approximately 77% of Xoc RS105 coding sequences are differentially expressed by the CFSs of B. velezensis 504, which significantly downregulates genes involved in signal transduction, oxidative phosphorylation, transmembrane transport, cell motility, cell division, DNA translation, and five physiological metabolisms, as well as depresses an additional set of virulence-associated genes encoding the type III secretion, type II secretion system, type VI secretion system, type IV pilus, lipopolysaccharides and exopolysaccharides. We also show that B. velezensis 504 is a potential biocontrol agent for bacterial blight of rice exhibiting relative control efficiencies over 70% on two susceptible cultivars, and can efficiently antagonize against some important plant pathogenic fungi including Colletotrichum siamense and C. australisinense that are thought to be the two dominant pathogenic species causing leaf anthracnose of rubber tree in Hainan province of China. B. velezensis 504 also harbors some characteristics of plant growth-promoting rhizobacterium such as secreting protease and siderophore, and stimulating plant growth. This study reveals the potential biocontrol mechanisms of B. velezensis against BLS, and also suggests that B. velezensis 504 is a versatile plant probiotic bacterium.

Scatter correction for cone-beam CT via scatter kernel superposition-inspired convolutional neural network.

Objective.X-ray scatter leads to signal bias and degrades the image quality in Computed Tomography imaging. Conventional real-time scatter estimation and correction methods include the scatter kernel superposition (SKS) methods, which approximate x-ray scatter field as a convolution of the scatter sources and scatter propagation kernels to reflect the spatial spreading of scatter x-ray photons. SKS methods are fast to implement but generally suffer from low accuracy due to the difficulties in determining the scatter kernels.Approach.To address such a problem, this work describes a new scatter estimation and correction method by combining the concept of SKS methods and convolutional neural network. Unlike conventional SKS methods which estimate the scatter amplitude and the scatter kernel based on the value of an individual pixel, the proposed method generates the scatter amplitude maps and the scatter width maps from projection images through a neural network, from which the final estimated scatter field is calculated based on a convolution process.Main Results.By incorporating physics in the network design, the proposed method requires fewer trainable parameters compared with another deep learning-based method (Deep Scatter Estimation). Both numerical simulations and physical experiments demonstrate that the proposed SKS-inspired convolutional neural network outperforms the conventional SKS method and other deep learning-based methods in both qualitative and quantitative aspects.Significance.The proposed method can effectively correct the scatter-related artifacts with a SKS-inspired convolutional neural network design.

Linear convergence of the subspace constrained mean shift algorithm: from Euclidean to directional data.

This paper studies the linear convergence of the subspace constrained mean shift (SCMS) algorithm, a well-known algorithm for identifying a density ridge defined by a kernel density estimator. By arguing that the SCMS algorithm is a special variant of a subspace constrained gradient ascent (SCGA) algorithm with an adaptive step size, we derive the linear convergence of such SCGA algorithm. While the existing research focuses mainly on density ridges in the Euclidean space, we generalize density ridges and the SCMS algorithm to directional data. In particular, we establish the stability theorem of density ridges with directional data and prove the linear convergence of our proposed directional SCMS algorithm.

DREAM-Net: Deep Residual Error Iterative Minimization Network for Sparse-View CT Reconstruction.

Sparse-view Computed Tomography (CT) has the ability to reduce radiation dose and shorten the scan time, while the severe streak artifacts will compromise anatomical information. How to reconstruct high-quality images from sparsely sampled projections is a challenging ill-posed problem. In this context, we propose the unrolled Deep Residual Error iterAtive Minimization Network (DREAM-Net) based on a novel iterative reconstruction framework to synergize the merits of deep learning and iterative reconstruction. DREAM-Net performs constraints using deep neural networks in the projection domain, residual space, and image domain simultaneously, which is different from the routine practice in deep iterative reconstruction frameworks. First, a projection inpainting module completes the missing views to fully explore the latent relationship between projection data and reconstructed images. Then, the residual awareness module attempts to estimate the accurate residual image after transforming the projection error into the image space. Finally, the image refinement module learns a non-standard regularizer to further fine-tune the intermediate image. There is no need to empirically adjust the weights of different terms in DREAM-Net because the hyper-parameters are embedded implicitly in network modules. Qualitative and quantitative results have demonstrated the promising performance of DREAM-Net in artifact removal and structural fidelity.

TIME-Net: Transformer-Integrated Multi-Encoder Network for limited-angle artifact removal in dual-energy CBCT.

Dual-energy cone-beam computed tomography (DE-CBCT) is a promising imaging technique with foreseeable clinical applications. DE-CBCT images acquired with two different spectra can provide material-specific information. Meanwhile, the anatomical consistency and energy-domain correlation result in significant information redundancy, which could be exploited to improve image quality. In this context, this paper develops the Transformer-Integrated Multi-Encoder Network (TIME-Net) for DE-CBCT to remove the limited-angle artifacts. TIME-Net comprises three encoders (image encoder, prior encoder, and transformer encoder), two decoders (low- and high-energy decoders), and one feature fusion module. Three encoders extract various features for image restoration. The feature fusion module compresses these features into more compact shared features and feeds them to the decoders. Two decoders perform differential learning for DE-CBCT images. By design, TIME-Net could obtain high-quality DE-CBCT images using two complementary quarter-scans, holding great potential to reduce radiation dose and shorten the acquisition time. Qualitative and quantitative analyses based on simulated data and real rat data have demonstrated the promising performance of TIME-Net in artifact removal, subtle structure restoration, and reconstruction accuracy preservation. Two clinical applications, virtual non-contrast (VNC) imaging and iodine quantification, have proved the potential utility of the DE-CBCT images provided by TIME-Net.

Comprehensive control of PM 2.5 capture and ozone emission in two-stage electrostatic precipitators.

Indoor air purification is extremely urgent to eliminate the health threat of PM 2.5, VOCs and microbial aerosol for exposing people, for which ESPs enjoy exceptional advantage for its special high-voltage characteristic. However, the secondary air pollutant of ozone is produced to possibly cause potential risk. In this work, six kinds of two-stage ESPs containing various charger and collector units, whose structure and size design are determined according to the indoor application, are developed to investigate the comprehensive control of PM 2.5 capture and ozone emission. Responsive surface methodology is employed to explore the relationship among ozone concentration, wire number, charger current and airflow velocity, and obtain regression model for predicting ozone emission. The comprehensive evaluation standard considering efficiency-ozone double factors is proposed to optimize structure design and working conditions of two-stage ESPs. Experimental results show that two-stage ESPs with a unit ratio of >3/4 can keep relatively good stable state, whose current reduction is in around 10 µA, for preventing particle charging function of charger from basically affecting. For the two-stage ESP with Ra = 2/5, it finds the optimization of working conditions of collector can bring rapid improvement of collection efficiency for 0.25 µm particles, which reaches up to be >60 %, while the optimization of that of the charger can only result in an enhancement of <30 %. RSM analysis exhibits a strong connection between the interactive effect of charger current and airflow velocity for presenting a steep response surface. Based on comprehensive control of PM 2.5 and ozone pollutants, it suggests the two-stage ESP with Ra = 2/5 is selected at the first priority and then that with Ra = 1/6, while two-stage ESP with Ra = 4/3 is not recommended for unsatisfied consequence of both of PM 2.5 capture and ozone emission.

sconce: a cosmic web finder for spherical and conic geometries.

The latticework structure known as the cosmic web provides a valuable insight into the assembly history of large-scale structures. Despite the variety of methods to identify the cosmic web structures, they mostly rely on the assumption that galaxies are embedded in a Euclidean geometric space. Here, we present a novel cosmic web identifier called sconce (Spherical and CONic Cosmic wEb finder) that inherently considers the 2D (RA, DEC) spherical or the 3D (RA, DEC, z) conic geometry. The proposed algorithms in sconce generalize the well-known subspace constrained mean shift (scms) method and primarily address the predominant filament detection problem. They are intrinsic to the spherical/conic geometry and invariant to data rotations. We further test the efficacy of our method with an artificial cross-shaped filament example and apply it to the SDSS galaxy catalogue, revealing that the 2D spherical version of our algorithms is robust even in regions of high declination. Finally, using N-body simulations from Illustris, we show that the 3D conic version of our algorithms is more robust in detecting filaments than the standard scms method under the redshift distortions caused by the peculiar velocities of haloes. Our cosmic web finder is packaged in python as sconce-scms and has been made publicly available.

IGFBP2 function as a novel biomarker for active lupus nephritis.

In search for new targets for the diagnosis and treatment of lupus nephritis (LN), we employed TMT-liquid chromatography-triple quadrupole mass spectrometry (TMT-LC-MS/MS) combined with RNA-seq and identified a panel of proteins that was dysregulated both at protein level and mRNA level in active LN patients compared with healthy controls. We chose to study the role of IGFBP2 since it is a relatively understudied protein in the context of LN. We further validated that IGFBP2 significantly increased and correlated with SLE activity index in active LN patients. The receiver operator characteristic (ROC) curve suggested that plasma IGFBP2 had a high diagnostic efficiency for distinguishing between inactive and active LN patients (AUC = 0.992; 95% CI = 0.974-1.000; P < 0.001). We demonstrated neutralizing IGFBP2-downregulated CD4+ T cell activation, upregulated the ratio of Treg, downregulated AKT/mTOR/4E-BP1 pathway, and significantly improved nephritis in MRL/lpr mice. In all, our work demonstrated IGFBP2 as a biomarker specific for active LN and blocking IGFBP2 could be a new target for treating LN. KEY MESSAGES : Plasma IGFBP2 is a promising diagnostic marker for distinguishing stable LN from active LN, and it is also a predictor for the poor prognosis of LN. Blockade of IGFBP2 can significantly improve the pathological damage of LN. IGFBP2 may regulate activation of CD4+ T and Treg ratio. Neutralizing IGFBP2 downregulates AKT/mTOR/4E-BP1 pathway.

PRIOR: Prior-Regularized Iterative Optimization Reconstruction For 4D CBCT.

4D cone-beam computed tomography (CBCT) is an important imaging modality in image-guided radiation therapy to address the motion-induced artifacts caused by organ movements during the respiratory process. However, due to the extremely sparse projection data for each temporal phase, 4D CBCT reconstructions will suffer from severe streaking artifacts. Therefore, to tackle the streak artifacts and provide high-quality images, we proposed a framework termed Prior-Regularized Iterative Optimization Reconstruction (PRIOR) for 4D CBCT. The PRIOR framework combines the physics-based model and data-driven method simultaneously, with powerful feature extracting capacity, significantly promoting the image quality compared to single model-based or deep learning-based methods. Besides, we designed a specialized deep learning model named PRIOR-Net, which can effectively excavate the static information in the prior image reconstructed from the fully-sampled projections at the encoding stage to improve the reconstruction performance for individual phase-resolved images. Both the simulated and clinical 4D CBCT datasets were performed to evaluate the performance of the PRIOR-Net and the PRIOR framework. Compared with the advanced 4D CBCT reconstruction methods, the proposed methods achieve promising results quantitatively and qualitatively in streak artifact suppression, soft tissue restoration, and tiny detail preservation.

MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging.

BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) has become increasingly important for alleviating X-ray radiation damage. However, reducing the administered radiation dose may lead to degraded CT images with amplified mottle noise and nonstationary streak artifacts. Previous studies have confirmed that deep learning (DL) is promising for improving LDCT imaging. However, most DL-based frameworks are built intuitively, lack interpretability, and suffer from image detail information loss, which has become a general challenging issue. METHODS: A multiscale reweighted convolutional coding neural network (MRCON-Net) is developed to address the above problems. MRCON-Net is compact and more explainable than other networks. First, inspired by the learning-based reweighted iterative soft thresholding algorithm (ISTA), we extend traditional convolutional sparse coding (CSC) to its reweighted convolutional learning form. Second, we use dilated convolution to extract multiscale image features, allowing our single model to capture the correlations between features of different scales. Finally, to automatically adjust the elements in the feature code to correct the obtained solution, a channel attention (CA) mechanism is utilized to learn appropriate weights. RESULTS: The visual results obtained based on the American Association of Physicians in Medicine (AAPM) Challenge and United Image Healthcare (UIH) clinical datasets confirm that the proposed model significantly reduces serious artifact noise while retaining the desired structures. Quantitative results show that the average structural similarity index measurement (SSIM) and peak signal-to-noise ratio (PSNR) achieved on the AAPM Challenge dataset are 0.9491 and 40.66, respectively, and the SSIM and PSNR achieved on the UIH clinical dataset are 0.915 and 42.44, respectively; these are promising quantitative results. CONCLUSION: Compared with recent state-of-the-art methods, the proposed model achieves subtle structure-enhanced LDCT imaging. In addition, through ablation studies, the components of the proposed model are validated to achieve performance improvements.