Distillation of multi-class cervical lesion cell detection via synthesis-aided pre-training and patch-level feature alignment.

Automated detection of cervical abnormal cells from Thin-prep cytologic test (TCT) images is crucial for efficient cervical abnormal screening using computer-aided diagnosis systems. However, the construction of the detection model is hindered by the preparation of the training images, which usually suffers from issues of class imbalance and incomplete annotations. Additionally, existing methods often overlook the visual feature correlations among cells, which are crucial in cervical lesion cell detection as pathologists commonly rely on surrounding cells for identification. In this paper, we propose a distillation framework that utilizes a patch-level pre-training network to guide the training of an image-level detection network, which can be applied to various detectors without changing their architectures during inference. The main contribution is three-fold: (1) We propose the Balanced Pre-training Model (BPM) as the patch-level cervical cell classification model, which employs an image synthesis model to construct a class-balanced patch dataset for pre-training. (2) We design the Score Correction Loss (SCL) to enable the detection network to distill knowledge from the BPM model, thereby mitigating the impact of incomplete annotations. (3) We design the Patch Correlation Consistency (PCC) strategy to exploit the correlation information of extracted cells, consistent with the behavior of cytopathologists. Experiments on public and private datasets demonstrate the superior performance of the proposed distillation method, as well as its adaptability to various detection architectures.

Spatial attention-based implicit neural representation for arbitrary reduction of MRI slice spacing.

Magnetic resonance (MR) images collected in 2D clinical protocols typically have large inter-slice spacing, resulting in high in-plane resolution and reduced through-plane resolution. Super-resolution technique can enhance the through-plane resolution of MR images to facilitate downstream visualization and computer-aided diagnosis. However, most existing works train the super-resolution network at a fixed scaling factor, which is not friendly to clinical scenes of varying inter-slice spacing in MR scanning. Inspired by the recent progress in implicit neural representation, we propose a Spatial Attention-based Implicit Neural Representation (SA-INR) network for arbitrary reduction of MR inter-slice spacing. The SA-INR aims to represent an MR image as a continuous implicit function of 3D coordinates. In this way, the SA-INR can reconstruct the MR image with arbitrary inter-slice spacing by continuously sampling the coordinates in 3D space. In particular, a local-aware spatial attention operation is introduced to model nearby voxels and their affinity more accurately in a larger receptive field. Meanwhile, to improve the computational efficiency, a gradient-guided gating mask is proposed for applying the local-aware spatial attention to selected areas only. We evaluate our method on the public HCP-1200 dataset and the clinical knee MR dataset to demonstrate its superiority over other existing methods.

Randomizing Human Brain Function Representation for Brain Disease Diagnosis.

Resting-state fMRI (rs-fMRI) is an effective tool for quantifying functional connectivity (FC), which plays a crucial role in exploring various brain diseases. Due to the high dimensionality of fMRI data, FC is typically computed based on the region of interest (ROI), whose parcellation relies on a pre-defined atlas. However, utilizing the brain atlas poses several challenges including (1) subjective selection bias in choosing from various brain atlases, (2) parcellation of each subject's brain with the same atlas yet disregarding individual specificity; (3) lack of interaction between brain region parcellation and downstream ROI-based FC analysis. To address these limitations, we propose a novel randomizing strategy for generating brain function representation to facilitate neural disease diagnosis. Specifically, we randomly sample brain patches, thus avoiding ROI parcellations of the brain atlas. Then, we introduce a new brain function representation framework for the sampled patches. Each patch has its function description by referring to anchor patches, as well as the position description. Furthermore, we design an adaptive-selection-assisted Transformer network to optimize and integrate the function representations of all sampled patches within each brain for neural disease diagnosis. To validate our framework, we conduct extensive evaluations on three datasets, and the experimental results establish the effectiveness and generality of our proposed method, offering a promising avenue for advancing neural disease diagnosis beyond the confines of traditional atlas-based methods. Our code is available at https://github.com/mjliu2020/RandomFR.

sTBI-GAN: An adversarial learning approach for data synthesis on traumatic brain segmentation.

Automatic brain segmentation of magnetic resonance images (MRIs) from severe traumatic brain injury (sTBI) patients is critical for brain abnormality assessments and brain network analysis. Construction of sTBI brain segmentation model requires manually annotated MR scans of sTBI patients, which becomes a challenging problem as it is quite impractical to implement sufficient annotations for sTBI images with large deformations and lesion erosion. Data augmentation techniques can be applied to alleviate the issue of limited training samples. However, conventional data augmentation strategies such as spatial and intensity transformation are unable to synthesize the deformation and lesions in traumatic brains, which limits the performance of the subsequent segmentation task. To address these issues, we propose a novel medical image inpainting model named sTBI-GAN to synthesize labeled sTBI MR scans by adversarial inpainting. The main strength of our sTBI-GAN method is that it can generate sTBI images and corresponding labels simultaneously, which has not been achieved in previous inpainting methods for medical images. We first generate the inpainted image under the guidance of edge information following a coarse-to-fine manner, and then the synthesized MR image is used as the prior for label inpainting. Furthermore, we introduce a registration-based template augmentation pipeline to increase the diversity of the synthesized image pairs and enhance the capacity of data augmentation. Experimental results show that the proposed sTBI-GAN method can synthesize high-quality labeled sTBI images, which greatly improves the 2D and 3D traumatic brain segmentation performance compared with the alternatives. Code is available at .

CSSNet: Cascaded spatial shift network for multi-organ segmentation.

Multi-organ segmentation is vital for clinical diagnosis and treatment. Although CNN and its extensions are popular in organ segmentation, they suffer from the local receptive field. In contrast, MultiLayer-Perceptron-based models (e.g., MLP-Mixer) have a global receptive field. However, these MLP-based models employ fully connected layers with many parameters and tend to overfit on sample-deficient medical image datasets. Therefore, we propose a Cascaded Spatial Shift Network, CSSNet, for multi-organ segmentation. Specifically, we design a novel cascaded spatial shift block to reduce the number of model parameters and aggregate feature segments in a cascaded way for efficient and effective feature extraction. Then, we propose a feature refinement network to aggregate multi-scale features with location information, and enhance the multi-scale features along the channel and spatial axis to obtain a high-quality feature map. Finally, we employ a self-attention-based fusion strategy to focus on the discriminative feature information for better multi-organ segmentation performance. Experimental results on the Synapse (multiply organs) and LiTS (liver & tumor) datasets demonstrate that our CSSNet achieves promising segmentation performance compared with CNN, MLP, and Transformer models. The source code will be available at https://github.com/zkyseu/CSSNet.

A Radiographic Analysis of Coronal Morphological Parameters of Lower Limbs in Chinese Non-knee Osteoarthritis Populations.

OBJECTIVES: Analyzing the lower limb coronal morphological parameters in populations without knee osteoarthritis (KOA) holds significant value in predicting, diagnosing, and formulating surgical strategies for KOA. This study aimed to comprehensively analyze the variability in these parameters among Chinese non-KOA populations, employing a substantial sample size. METHODS: A cross-sectional retrospective analysis was performed on the Chinese non-KOA populations (n = 407; 49.9% females). The study employed an in-house developed artificial intelligence software to meticulously assess the coronal morphological parameters of all 814 lower limbs. The parameters evaluated included the hip-knee-ankle angle (HKAA), weight-bearing line ratio (WBLR), joint line convergence angle (JLCA), mechanical lateral-proximal-femoral angle (mLPFA), mechanical lateral-distal-femoral angle (mLDFA), mechanical medial-proximal-tibial angle (mMPTA), and mechanical lateral-distal-tibial angle (mLDTA). Differences in these parameters were compared between left and right limbs, different genders, and different age groups (with 50 years as the cut-off point). RESULTS: HKAA and JLCA exhibited left-right differences (left vs. right: 178.2° ± 3.0° vs. 178.6° ± 2.9° for HKAA, p = 0.001; and 1.8° ± 1.5° vs. 1.4° ± 1.6° for JLCA, p < 0.001); except for the mLPFA, all other parameters show gender-related differences (male vs. female: 177.9° ± 2.8° vs. 179.0° ± 3.0° for HKAA, p < 0.001; 1.5° ± 1.5° vs. 1.8° ± 1.7° for JLCA, p = 0.003; 87.1° ± 2.1° vs. 88.1° ± 2.1° for mMPTA, p < 0.001; 90.2° ± 4.0° vs. 91.1° ± 3.2° for mLDTA, p < 0.001; 38.7% ± 12.9% vs. 43.6% ± 14.1% for WBLR, p < 0.001; and 87.7° ± 2.3° vs. 87.4° ± 2.7° for mLDTA, p = 0.045); mLPFA increase with age (younger vs. older: 90.1° ± 7.2° vs. 93.4° ± 4.9° for mLPFA, p < 0.001), while no statistical difference exists for other parameters. CONCLUSIONS: There were differences in lower limb coronal morphological parameters among Chinese non-KOA populations between left and right sides, different genders, and age.

RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation.

Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which requires a significant amount of expert annotated samples that are high-cost and laborious. Semi-supervised image segmentation can alleviate the problem by utilizing a large number of unlabeled images along with limited labeled images. However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches. To address the issues above, we propose a novel semi-supervised segmentation method named as Rectified Contrastive Pseudo Supervision (RCPS), which combines a rectified pseudo supervision and voxel-level contrastive learning to improve the effectiveness of semi-supervised segmentation. Particularly, we design a novel rectification strategy for the pseudo supervision method based on uncertainty estimation and consistency regularization to reduce the noise influence in pseudo labels. Furthermore, we introduce a bidirectional voxel contrastive loss in the network to ensure intra-class consistency and inter-class contrast in feature space, which increases class separability in the segmentation. The proposed RCPS segmentation method has been validated on two public datasets and an in-house clinical dataset. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art methods in semi-supervised medical image segmentation. The source code is available at https://github.com/hsiangyuzhao/RCPS.

Comparison of Immunogenicity of Alum and MF59-Like Adjuvant Inactivated SARS-CoV-2 Vaccines Against SARS-CoV-2 Variants in Elderly Mice.

The constant emergence of variants of concern (VOCs) challenges the effectiveness of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines over time. This is most concerning in clinically vulnerable groups, such as older adults. This study aimed to determine whether the novel adjuvant MF59-like adjuvant can improve cross-immunity against VOCs in aged animals. We compared the humoral and cellular immune responses of Alum and MF59-like adjuvant-formulated inactivated coronavirus disease 2019 (COVID-19) vaccines against prototype and SARS-CoV-2 variants in 18-month-old mice. Our results showed that two doses of the MF59-like adjuvant inactivated vaccines induced more robust binding and pseudo-neutralizing antibodies (Nabs) against the SARS-CoV-2 prototype and VOCs compared to the Alum-adjuvant and reduced Omicron variant escapes from Nabs in aged mice. The humoral immune responses of inactivated vaccines were much lower against VOCs than the prototype with or without adjuvants; however, T cell responses against VOCs were not affected. In addition, Alum and MF59-like adjuvanted vaccines induced Th1-biased immune responses with increased interferon-gamma and interleukin (IL)-2 secreting cells, and hardly detectable IL-4 and IL-5. Furthermore, the MF59-like adjuvant vaccine produced 1.9-2.0 times higher cross-reactive T cell responses against the SARS-CoV-2 prototype and VOCs than the Alum adjuvant. Therefore, our data have important implications for vaccine adjuvant strategies against SARS-CoV-2 VOCs in older adults.

Automatic risk prediction of intracranial aneurysm on CTA image with convolutional neural networks and radiomics analysis.

Background: Intracranial aneurysm (IA) is a nodular protrusion of the arterial wall caused by the localized abnormal enlargement of the lumen of a brain artery, which is the primary cause of subarachnoid hemorrhage. Accurate rupture risk prediction can effectively aid treatment planning, but conventional rupture risk estimation based on clinical information is subjective and time-consuming. Methods: We propose a novel classification method based on the CTA images for differentiating aneurysms that are prone to rupture. The main contribution of this study is that the learning-based method proposed in this study leverages deep learning and radiomics features and integrates clinical information for a more accurate prediction of the risk of rupture. Specifically, we first extracted the provided aneurysm regions from the CTA images as 3D patches with the lesions located at their centers. Then, we employed an encoder using a 3D convolutional neural network (CNN) to extract complex latent features automatically. These features were then combined with radiomics features and clinical information. We further applied the LASSO regression method to find optimal features that are highly relevant to the rupture risk information, which is fed into a support vector machine (SVM) for final rupture risk prediction. Results: The experimental results demonstrate that our classification method can achieve accuracy and AUC scores of 89.78% and 89.09%, respectively, outperforming all the alternative methods. Discussion: Our study indicates that the incorporation of CNN and radiomics analysis can improve the prediction performance, and the selected optimal feature set can provide essential biomarkers for the determination of rupture risk, which is also of great clinical importance for individualized treatment planning and patient care of IA.

Evolutionary game study of crowdsourcing open innovation synergy mechanism.

Open innovation crowdsourcing can help enterprises meet the challenges of a rapidly changing environment and improve their innovation performance. This study introduces network externalities as influencing factors of the crowdsourcing open innovation synergy mechanism. This study constructed the game payment matrix of the crowdsourcing open innovation synergy mechanism, and the evolutionary game method obtained the equilibrium solution of the crowdsourcing open innovation synergy mechanism. The impact of changes in the main influencing factors on the issuers' and receivers' willingness to collaborate and innovate was explored through numerical and case studies. The study shows that the higher the synergy benefit and its allocation coefficient need to be within a reasonable range for the willingness to collaborate and innovate to increase; the lower the original cost of both parties, and the higher the cost reduction coefficient under the policy support of the crowdsourcing platform, the higher the willingness to collaborate and innovate; the higher the network externality and the lower the penalty for breach of contract, the higher the desire to collaborate and innovate. The study recommends strengthening non-school education to guide innovation for all, and refining relevant policies to tailor innovation to local conditions. This study provides a new perspective and theoretical guidance for enterprises to build a crowdsourcing open innovation synergy mechanism and is a valuable reference for open innovation management.

Cartilage morphometry and magnetic susceptibility measurement for knee osteoarthritis with automatic cartilage segmentation.

Background: Automatic segmentation of knee cartilage and quantification of cartilage parameters are crucial for the early detection and treatment of knee osteoarthritis (OA). The aim of this study was to develop an automatic cartilage segmentation method for three-dimensional water-selective (3D_WATS) cartilage magnetic resonance imaging (MRI) and conduct cartilage morphometry and magnetic susceptibility measurements such as cartilage thickness, volume, and susceptibility values for knee OA assessment. Methods: Sixty-five consecutively sampled subjects, who had undergone health checks at our hospital, were enrolled in this cross-sectional study and were divided into three groups: 20 normal, 20 mild OA, 25 severe OA. Sagittal 3D_WATS sequence was used to image cartilage at 3T. The raw magnitude images were used for cartilage segmentation and the phase images were used for quantitative susceptibility mapping (QSM)-based assessment. Manual cartilage segmentation was performed by two experienced radiologists, and the automatic segmentation model was constructed using nnU-Net. Quantitative cartilage parameters were extracted from the magnitude and phase images based on the cartilage segmentation. Pearson correlation coefficient and intra-class correlation coefficient (ICC) were then used to assess the consistency of obtained cartilage parameters between automatic and manual segmentation. Cartilage thickness, volume, and susceptibility values among different groups were compared using one-way analysis of variance (ANOVA). Support vector machine (SVM) was used to further verify the classification validity of automatically extracted cartilage parameters. Results: The constructed cartilage segmentation model based on nnU-Net achieved an average Dice score of 0.93. The consistency of cartilage thickness, volume, and susceptibility values calculated using automatic and manual segmentations ranged from 0.98 to 0.99 (95% CI: 0.89-1.00) for the Pearson correlation coefficient, and from 0.91-0.99 (95% CI: 0.86-0.99) for ICC, respectively. Significant differences were found in OA patients; including decreases in cartilage thickness, volume, and mean susceptibility values (P<0.05), and increases in standard deviation (SD) of susceptibility values (P<0.01). Moreover, the automatically extracted cartilage parameters can achieve an AUC value of 0.94 (95% CI: 0.89-0.96) for OA classification using the SVM classifier. Conclusions: The 3D_WATS cartilage MR imaging allows simultaneously automated assessment of cartilage morphometry and magnetic susceptibility for evaluating the severity of OA using the proposed cartilage segmentation method.

Changes of Functional Brain Network in Neonates with Different Degrees of Hypoxic-Ischemic Encephalopathy.

Background: Neonatal hypoxic-ischemic encephalopathy (HIE) is the main cause of neonatal death and disability worldwide. At present, there are few researches on the application of resting-state functional magnetic resonance imaging (rs-fMRI) to explore the brain development of HIE children. This study aimed to explore the changes of brain function in neonates with different degrees of HIE using rs-fMRI. Methods: From February 2018 to May 2020, 44 patients with HIE were recruited, including 21 mild patients and 23 moderate and severe patients. The recruited patients were scanned by conventional and functional magnetic resonance image, and the method of amplitude of low-frequency fluctuation and connecting edge analysis of brain network was used. Results: Compared with the mild group, the connections between the right supplementary motor area and the right precentral gyrus, the right lingual gyrus and the right hippocampus, the left calcarine cortex and the right amygdala, and the right pallidus and the right posterior cingulate cortex in the moderate and severe groups were reduced (t values were 4.04, 4.04, 4.04, 4.07, all p < 0.001, uncorrected). Conclusion: By analyzing the functional connection changes of brain network in infants with different degrees of HIE, the findings of the current study suggested that neonates with moderate to severe HIE lag behind those with mild HIE in emotional processing, sensory movement, cognitive function, and learning and memory. Chinese Clinical Trial Registry registration number: ChiCTR1800016409.

Study on the Evolution of the Game of Willingness to Cooperate between Residents and Separation Enterprises in Waste Separation Considering the Convenience of Separation Facilities.

The distributivity and complexity of separation facilities in waste separation cooperation are incorporated into the factors influencing the payoff of waste separation cooperation. The game payment matrix of waste separation cooperation is constructed based on the distributivity and complexity of separation facilities. The equilibrium solution of waste separation cooperation is obtained through the evolutionary game. The influence of different changes in distributivity and complexity of separation facilities on the willingness to cooperate in waste separation is explored through numerical analysis of cases. The study shows that when the distributivity of separation facilities is certain, the lower the complexity of separation facilities, the higher the willingness of residents and enterprises to cooperate; when the complexity of separation facilities is certain, the willingness of residents and enterprises to cooperate rises and then falls with the increase of distributivity of separation facilities; finally, when the distributivity and complexity of separation facilities change at the same time, the willingness of residents and enterprises to cooperate shows different changes with the different changes of two separation facilities convenience factors.

Enhanced brain parcellation via abnormality inpainting for neuroimage-based consciousness evaluation of hydrocephalus patients by lumbar drainage.

Brain network analysis based on structural and functional magnetic resonance imaging (MRI) is considered as an effective method for consciousness evaluation of hydrocephalus patients, which can also be applied to facilitate the ameliorative effect of lumbar cerebrospinal fluid drainage (LCFD). Automatic brain parcellation is a prerequisite for brain network construction. However, hydrocephalus images usually have large deformations and lesion erosions, which becomes challenging for ensuring effective brain parcellation works. In this paper, we develop a novel and robust method for segmenting brain regions of hydrocephalus images. Our main contribution is to design an innovative inpainting method that can amend the large deformations and lesion erosions in hydrocephalus images, and synthesize the normal brain version without injury. The synthesized images can effectively support brain parcellation tasks and lay the foundation for the subsequent brain network construction work. Specifically, the novelty of the inpainting method is that it can utilize the symmetric properties of the brain structure to ensure the quality of the synthesized results. Experiments show that the proposed brain abnormality inpainting method can effectively aid the brain network construction, and improve the CRS-R score estimation which represents the patient's consciousness states. Furthermore, the brain network analysis based on our enhanced brain parcellation method has demonstrated potential imaging biomarkers for better interpreting and understanding the recovery of consciousness in patients with secondary hydrocephalus.

Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution.

Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability. Cartilage defects are regarded as major manifestations of knee OA, which are visible by magnetic resonance imaging (MRI). Thus early detection and assessment for knee cartilage defects are important for protecting patients from knee OA. In this way, many attempts have been made on knee cartilage defect assessment by applying convolutional neural networks (CNNs) to knee MRI. However, the physiologic characteristics of the cartilage may hinder such efforts: the cartilage is a thin curved layer, implying that only a small portion of voxels in knee MRI can contribute to the cartilage defect assessment; heterogeneous scanning protocols further challenge the feasibility of the CNNs in clinical practice; the CNN-based knee cartilage evaluation results lack interpretability. To address these challenges, we model the cartilages structure and appearance from knee MRI into a graph representation, which is capable of handling highly diverse clinical data. Then, guided by the cartilage graph representation, we design a non-Euclidean deep learning network with the self-attention mechanism, to extract cartilage features in the local and global, and to derive the final assessment with a visualized result. Our comprehensive experiments show that the proposed method yields superior performance in knee cartilage defect assessment, plus its convenient 3D visualization for interpretability.

Differentiation of benign and malignant spinal schwannoma using guided attention inference networks on multi-source MRI: comparison with radiomics method and radiologist-based clinical assessment.

BACKGROUND: Differentiating diagnosis between the benign schwannoma and the malignant counterparts merely by neuroimaging is not always clear and remains still confounding in many cases because of atypical imaging presentation encountered in clinic and the lack of specific diagnostic markers. PURPOSE: To construct and validate a novel deep learning model based on multi-source magnetic resonance imaging (MRI) in automatically differentiating malignant spinal schwannoma from benign. MATERIAL AND METHODS: We retrospectively reviewed MRI imaging data from 119 patients with the initial diagnosis of benign or malignant spinal schwannoma confirmed by postoperative pathology. A novel convolutional neural network (CNN)-based deep learning model named GAIN-CP (Guided Attention Inference Network with Clinical Priors) was constructed. An ablation study for the fivefold cross-validation and cross-source experiments were conducted to validate the novel model. The diagnosis performance among our GAIN-CP model, the conventional radiomics model, and the radiologist-based clinical assessment were compared using the area under the receiver operating characteristic curve (AUC) and balanced accuracy (BAC). RESULTS: The AUC score of the proposed GAIN method is 0.83, which outperforms the radiomics method (0.65) and the evaluations from the radiologists (0.67). By incorporating both the image data and the clinical prior features, our GAIN-CP achieves an AUC score of 0.95. The GAIN-CP also achieves the best performance on fivefold cross-validation and cross-source experiments. CONCLUSION: The novel GAIN-CP method can successfully classify malignant spinal schwannoma from benign cases using the provided multi-source MR images exhibiting good prospect in clinical diagnosis.

Evolutionary Game Analysis of Government and Residents' Participation in Waste Separation Based on Cumulative Prospect Theory.

Government and residents' participation in waste separation is a complex non-cooperative game process, and the evolutionary game can explain the behavior of participating subjects well. Considering that the traditional evolutionary game cannot satisfactorily explain the irrational psychology and risk preference factors of the participating issues, this study combines the prospect theory and evolutionary game, uses the prospect value function to supplement and improve the parameters of the evolutionary game payment matrix, and analyzes the evolutionary stabilization strategy. To verify the theoretical results, simulation experiments and impact analysis were conducted, and meaningful results were obtained: There are two stable evolutionary strategies in the system, namely higher participation benefits for residents and lower participation costs and opportunity costs, and reasonable direct benefit distribution coefficients all help to increase the participation rate of waste separation. This study can provide some scientific suggestions for the government to design and build a waste-separation system.

Immune Persistence against SARS-CoV-2 after Primary and Booster Immunization in Humans: A Large-Scale Prospective Cohort Study.

Amid the ongoing global COVID-19 pandemic, limited literature exists on immune persistence after primary immunization and the immunogenic features of booster vaccines administered at different time intervals. Therefore, this study aimed to determine the immune attenuation of neutralizing antibodies against the SARS-CoV-2 wild-type strain, and Delta and Omicron variants 12 months after the primary administration of the COVID-19 inactivated vaccine and evaluate the immune response after a booster administration at different time intervals. A total of 514 individuals were followed up after primary immunization and were vaccinated with a booster. Neutralizing antibodies against the wild-type strain and Delta and Omicron variant spike proteins were measured using pseudovirus neutralization assays. The geometric mean titers (GMTs) after the primary and booster immunizations were 12.09 and 61.48 for the wild-type strain, 11.67 and 40.33 for the Delta variant, and 8.51 and 29.31 for the Omicron variant, respectively. The GMTs against the wild-type strain declined gradually during the 12 months after the primary immunization, and were lower against the two variants. After implementing a booster immunization with a 6 month interval, the GMTs against the wild-type strain were higher than those obtained beyond the 7 month interval; however, the GMTs against the two variants were not statistically different across 3-12 month intervals. Overall, SARS-CoV-2 variants showed remarkable declines in immune persistence, especially against the Omicron variant. The booster administration interval could be shortened to 3 months in endemic areas of the Omicron variant, whereas an appropriate prolonging of the booster administration interval did not affect the booster immunization effect.

Differentiation between spinal multiple myeloma and metastases originated from lung using multi-view attention-guided network.

Purpose: Multiple myeloma (MM) and metastasis originated are the two common malignancy diseases in the spine. They usually show similar imaging patterns and are highly demanded to differentiate for precision diagnosis and treatment planning. The objective of this study is therefore to construct a novel deep-learning-based method for effective differentiation of two diseases, with the comparative study of traditional radiomics analysis. Methods: We retrospectively enrolled a total of 217 patients with 269 lesions, who were diagnosed with spinal MM (79 cases, 81 lesions) or spinal metastases originated from lung cancer (138 cases, 188 lesions) confirmed by postoperative pathology. Magnetic resonance imaging (MRI) sequences of all patients were collected and reviewed. A novel deep learning model of the Multi-view Attention-Guided Network (MAGN) was constructed based on contrast-enhanced T1WI (CET1) sequences. The constructed model extracts features from three views (sagittal, coronal and axial) and fused them for a more comprehensive differentiation analysis, and the attention guidance strategy is adopted for improving the classification performance, and increasing the interpretability of the method. The diagnostic efficiency among MAGN, radiomics model and the radiologist assessment were compared by the area under the receiver operating characteristic curve (AUC). Results: Ablation studies were conducted to demonstrate the validity of multi-view fusion and attention guidance strategies: It has shown that the diagnostic model using multi-view fusion achieved higher diagnostic performance [ACC (0.79), AUC (0.77) and F1-score (0.67)] than those using single-view (sagittal, axial and coronal) images. Besides, MAGN incorporating attention guidance strategy further boosted performance as the ACC, AUC and F1-scores reached 0.81, 0.78 and 0.71, respectively. In addition, the MAGN outperforms the radiomics methods and radiologist assessment. The highest ACC, AUC and F1-score for the latter two methods were 0.71, 0.76 & 0.54, and 0.69, 0.71, & 0.65, respectively. Conclusions: The proposed MAGN can achieve satisfactory performance in differentiating spinal MM between metastases originating from lung cancer, which also outperforms the radiomics method and radiologist assessment.

Abnormal resting-state functional connectivity of hippocampal subregions in children with primary nocturnal enuresis.

Objective: Previous neuroimaging studies have shown abnormal brain-bladder control network in children with primary nocturnal enuresis (PNE). The hippocampus, which has long been considered to be an important nerve center for memory and emotion, has also been confirmed to be activating during micturition in several human imaging studies. However, few studies have explored hippocampus-related functional networks of PNE in children. In this study, the whole resting-state functional connectivity (RSFC) of hippocampus was investigated in children with PNE. Methods: Functional magnetic resonance imaging data of 30 children with PNE and 29 matched healthy controls (HCs) were analyzed in our study. We used the seed-based RSFC method to evaluate the functional connectivity of hippocampal subregions defined according to the Human Brainnetome Atlas. Correlation analyses were also processed to investigate their relationship with disease duration time, bed-wetting frequency, and bladder volume. Results: Compared with HCs, children with PNE showed abnormal RSFC of the left rostral hippocampus (rHipp) with right fusiform gyrus, right Rolandic operculum, left inferior parietal lobule, and right precentral gyrus, respectively. Moreover, decreased RSFC of the left caudal hippocampus (cHipp) with right fusiform gyrus and right supplementary motor area was discovered in the PNE group. There were no significant results in the right rHipp and cHipp seeds after multiple comparison corrections. In addition, disease duration time was negatively correlated with RSFC of the left rHipp with right Rolandic operculum (r = -0.386, p = 0.035, uncorrected) and the left cHipp with right fusiform gyrus (r = -0.483, p = 0.007, uncorrected) in the PNE group, respectively. In the Receiver Operating Characteristic (ROC) analysis, all the above results of RSFC achieved significant performance. Conclusions: To our knowledge, this is the first attempt to examine the RSFC patterns of hippocampal subregions in children with PNE. These findings indicated that children with PNE have potential dysfunctions in the limbic network, sensorimotor network, default mode network, and frontoparietal network. These networks may become less efficient with disease duration time, inducing impairments in brain-bladder control, cognition, memory, and emotion. Further prospective research with dynamic observation of brain imaging, bladder function, cognition, memory, and emotion is warranted.