COVID-19 acts like a stress test, uncovering the vulnerable part of the human body: a retrospective study of 1640 cases in China.

BACKGROUND: Since the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection exhibits multi-organ damage with diverse complications, the correlation between age, gender, medical history and clinical manifestations of novel coronavirus disease 2019 (COVID-19) patients was investigated. METHODS: 1640 patients who were infected with SARS-CoV-2 and hospitalized at the First Affiliated Hospital of Ningbo University from 22 December 2022 to 1 March 2023 were categorized and analysed. Normal distribution test and variance homogeneity test were performed. Based on the test results, one-way analysis of variance, Pearson's chi-squared test and logistic regression analysis were conducted in the study. RESULTS: According to the ANOVA, there was a significant difference in the age distribution (P = .001) between different clinical presentations, while gender did not (P = .06). And regression analysis showed that age, hypertension, atherosclerosis and cancer were significant hazard factors for the development of predominant clinical manifestations in patients hospitalized with novel COVID-19. Additionally, infection with SARS-CoV-2 has the potential to exacerbate the burden on specific diseased or related organs. CONCLUSION: The elderly who are infected with SARS-CoV-2 ought to be treated with emphasis not only on antiviral therapy but also on individualized treatment that takes their medical history and comorbidities into account.

Deubiquitinating PABPC1 by USP10 upregulates CLK2 translation to promote tumor progression in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is extremely malignant with limited treatment options. Deubiquitinases (DUBs), which cleave ubiquitin on substrates, can regulate tumor progression and are appealing therapeutic targets, but there are few related studies in PDAC. In our study, we screened the expression levels and prognostic value of USP family members based on published databases and selected USP10 as the potential interventional target in PDAC. IHC staining of the PDAC microarray revealed that USP10 expression was an adverse clinical feature of PDAC. USP10 promoted tumor growth both in vivo and in vitro in PDAC. Co-IP experiments revealed that USP10 directly interacts with PABPC1. Deubiquitination assays revealed that USP10 decreased the K27/29-linked ubiquitination level of the RRM2 domain of PABPC1. Deubiquitinated PABPC1 was able to couple more CLK2 mRNA and eIF4G1, which increased the translation efficiency. Replacing PABPC1 with a mutant that could not be ubiquitinated impaired USP10 knock-down-mediated tumor suppression in PDAC. Targeting USP10 significantly delayed the growth of cell-derived xenograft and patient-derived xenograft tumors. Collectively, our study first identified USP10 as the DUB of PABPC1 and provided a rationale for potential therapeutic options for PDAC with high USP10 expression.

nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer.

Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community. Given the ability to exploit long-term dependencies, transformers are promising to help atypical convolutional neural networks to learn more contextualized visual representations. However, most of recently proposed transformer-based segmentation approaches simply treated transformers as assisted modules to help encode global context into convolutional representations. To address this issue, we introduce nnFormer (i.e., not-another transFormer), a 3D transformer for volumetric medical image segmentation. nnFormer not only exploits the combination of interleaved convolution and self-attention operations, but also introduces local and global volume-based self-attention mechanism to learn volume representations. Moreover, nnFormer proposes to use skip attention to replace the traditional concatenation/summation operations in skip connections in U-Net like architecture. Experiments show that nnFormer significantly outperforms previous transformer-based counterparts by large margins on three public datasets. Compared to nnUNet, the most widely recognized convnet-based 3D medical segmentation model, nnFormer produces significantly lower HD95 and is much more computationally efficient. Furthermore, we show that nnFormer and nnUNet are highly complementary to each other in model ensembling. Codes and models of nnFormer are available at https://git.io/JSf3i.

Serum antinuclear antibodies associate with severe disease activity in neuromyelitis optica spectrum disorder.

BACKGROUND: Studies suggest that antinuclear antibodies (ANAs) may correlate with the long-term prognosis of Neuromyelitis optica spectrum disorder (NMOSD). In this study, we investigated ANAs in Chinese patients with NMOSD and their relationship with disease outcomes. METHODS: We retrospectively collected data from 525 patients diagnosed with NMOSD at West China Hospital between September 1, 2009, and October 1, 2021. Patients were classified into two groups: NMOSD with ANA (+) or without ANA (-). We compared the clinical characteristics, relapse rate, severe attacks, laboratory tests, Expanded Disability Status Scale (EDSS), and prognosis between the two groups. RESULTS: Among the 525 NMOSD patients, those with ANA showed a higher frequency of AQP4-IgG (94.1% vs 79.3%, p < 0.001, false discovery rate (FDR) corrected p < 0.001), and anti-SSA (p < 0.001, FDR corrected p < 0.001), anti-SSB (p < 0.001, FDR corrected p < 0.001), anti-Ro52 antibodies (p < 0.001, FDR corrected p < 0.001), than those without ANA. ANA was detected in 403 patients during the acute phase. Patients with ANA (+) had higher EDSS scores in the acute stage (4.0 vs. 3.75, p = 0.013, FDR corrected p = 0.029) and at final follow-up (p = 0.032, FDR corrected p = 0.064). NMOSD patients with ANA (+) had a higher frequency of severe acute myelitis attack, severe acute myelitis and optic neuritis attack, motor and visual disability, compared to those with ANA (-) (42.1% vs. 27.8%, p = 0.001, FDR corrected p = 0.004, 19.3% vs. 10.3%, p = 0.004, FDR corrected p = 0.018, and 11.1% vs. 4.8%, p = 0.008, FDR corrected p = 0.022 respectively). The two groups had no significant difference in the annual recurrence rate (ARR). CONCLUSION: ANA may be associated with more severe disease activity and disability in NMOSD.

A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics.

During the diagnostic process, clinicians leverage multimodal information, such as the chief complaint, medical images and laboratory test results. Deep-learning models for aiding diagnosis have yet to meet this requirement of leveraging multimodal information. Here we report a transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. Rather than learning modality-specific features, the model leverages embedding layers to convert images and unstructured and structured text into visual tokens and text tokens, and uses bidirectional blocks with intramodal and intermodal attention to learn holistic representations of radiographs, the unstructured chief complaint and clinical history, and structured clinical information such as laboratory test results and patient demographic information. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary disease (by 12% and 9%, respectively) and in the prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and 7%, respectively). Unified multimodal transformer-based models may help streamline the triaging of patients and facilitate the clinical decision-making process.

A Unified Visual Information Preservation Framework for Self-supervised Pre-Training in Medical Image Analysis.

Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations. Codes and models are available at https://github.com/RL4M/PCRLv2.

Relation Matters: Foreground-Aware Graph-Based Relational Reasoning for Domain Adaptive Object Detection.

Domain Adaptive Object Detection (DAOD) focuses on improving the generalization ability of object detectors via knowledge transfer. Recent advances in DAOD strive to change the emphasis of the adaptation process from global to local in virtue of fine-grained feature alignment methods. However, both the global and local alignment approaches fail to capture the topological relations among different foreground objects as the explicit dependencies and interactions between and within domains are neglected. In this case, only seeking one-vs-one alignment does not necessarily ensure the precise knowledge transfer. Moreover, conventional alignment-based approaches may be vulnerable to catastrophic overfitting regarding those less transferable regions (e.g., backgrounds) due to the accumulation of inaccurate localization results in the target domain. To remedy these issues, we first formulate DAOD as an open-set domain adaptation problem, in which the foregrounds and backgrounds are seen as the "known classes" and "unknown class" respectively. Accordingly, we propose a new and general framework for DAOD, named Foreground-aware Graph-based Relational Reasoning (FGRR), which incorporates graph structures into the detection pipeline to explicitly model the intra- and inter-domain foreground object relations on both pixel and semantic spaces, thereby endowing the DAOD model with the capability of relational reasoning beyond the popular alignment-based paradigm. FGRR first identifies the foreground pixels and regions by searching reliable correspondence and cross-domain similarity regularization respectively. The inter-domain visual and semantic correlations are hierarchically modeled via bipartite graph structures, and the intra-domain relations are encoded via graph attention mechanisms. Through message-passing, each node aggregates semantic and contextual information from the same and opposite domain to substantially enhance its expressive power. Empirical results demonstrate that the proposed FGRR exceeds the state-of-the-art performance on four DAOD benchmarks.

GraVIS: Grouping Augmented Views From Independent Sources for Dermatology Analysis.

Self-supervised representation learning has been extremely successful in medical image analysis, as it requires no human annotations to provide transferable representations for downstream tasks. Recent self-supervised learning methods are dominated by noise-contrastive estimation (NCE, also known as contrastive learning), which aims to learn invariant visual representations by contrasting one homogeneous image pair with a large number of heterogeneous image pairs in each training step. Nonetheless, NCE-based approaches still suffer from one major problem that is one homogeneous pair is not enough to extract robust and invariant semantic information. Inspired by the archetypical triplet loss, we propose GraVIS, which is specifically optimized for learning self-supervised features from dermatology images, to group homogeneous dermatology images while separating heterogeneous ones. In addition, a hardness-aware attention is introduced and incorporated to address the importance of homogeneous image views with similar appearance instead of those dissimilar homogeneous ones. GraVIS significantly outperforms its transfer learning and self-supervised learning counterparts in both lesion segmentation and disease classification tasks, sometimes by 5 percents under extremely limited supervision. More importantly, when equipped with the pre-trained weights provided by GraVIS, a single model could achieve better results than winners that heavily rely on ensemble strategies in the well-known ISIC 2017 challenge. Code is available at https://bit.ly/3xiFyjx.

Development and validation of an abnormality-derived deep-learning diagnostic system for major respiratory diseases.

Respiratory diseases impose a tremendous global health burden on large patient populations. In this study, we aimed to develop DeepMRDTR, a deep learning-based medical image interpretation system for the diagnosis of major respiratory diseases based on the automated identification of a wide range of radiological abnormalities through computed tomography (CT) and chest X-ray (CXR) from real-world, large-scale datasets. DeepMRDTR comprises four networks (two CT-Nets and two CXR-Nets) that exploit contrastive learning to generate pre-training parameters that are fine-tuned on the retrospective dataset collected from a single institution. The performance of DeepMRDTR was evaluated for abnormality identification and disease diagnosis on data from two different institutions: one was an internal testing dataset from the same institution as the training data and the second was collected from an external institution to evaluate the model generalizability and robustness to an unrelated population dataset. In such a difficult multi-class diagnosis task, our system achieved the average area under the receiver operating characteristic curve (AUC) of 0.856 (95% confidence interval (CI):0.843-0.868) and 0.841 (95%CI:0.832-0.887) for abnormality identification, and 0.900 (95%CI:0.872-0.958) and 0.866 (95%CI:0.832-0.887) for major respiratory diseases' diagnosis on CT and CXR datasets, respectively. Furthermore, to achieve a clinically actionable diagnosis, we deployed a preliminary version of DeepMRDTR into the clinical workflow, which was performed on par with senior experts in disease diagnosis, with an AUC of 0.890 and a Cohen's k of 0.746-0.877 at a reasonable timescale; these findings demonstrate the potential to accelerate the medical workflow to facilitate early diagnosis as a triage tool for respiratory diseases which supports improved clinical diagnoses and decision-making.

Virtual Reality in Treatment for Psychological Problems in First-Line Health Care Professionals Fighting COVID-19 Pandemic: A Case Series.

ABSTRACT: Virtual reality therapy (VRT) is a new psychotherapeutic approach integrating virtual reality technology and psychotherapy. This case series aimed to study effectiveness of VRT in treating psychological problems. We described four cases of first-line health care professionals with emerging clinically significant early psychological problems during the COVID-19 outbreak, and specifically received the VRT treatment. We compared the Patient Health Questionnaire 9 items (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), PHQ-15, and Athens Insomnia Scale to evaluate psychological symptoms and sleep quality before and after sessions. All four cases showed a reduction in scale comparison. General scores of the PHQ-9 reduced 65%, GAD-7 reduced 52.17%, PHQ-15 decreased 38.17%, and scores of the Athens Insomnia Scale reduced 67.44%. Meanwhile, a reduction in depression, anxiety, psychosomatic, and sleeping symptoms was also found, which decreased 76.92% in general. These results are highly significant statistically. This case series demonstrated the effectiveness of VRT on psychological problems as a promising approach to apply on various psychological distress and disorders.

MASS: Modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired CT and MRI images.

Training deep segmentation models for medical images often requires a large amount of labeled data. To tackle this issue, semi-supervised segmentation has been employed to produce satisfactory delineation results with affordable labeling cost. However, traditional semi-supervised segmentation methods fail to exploit unpaired multi-modal data, which are widely adopted in today's clinical routine. In this paper, we address this point by proposing Modality-collAborative Semi-Supervised segmentation (i.e., MASS), which utilizes the modality-independent knowledge learned from unpaired CT and MRI scans. To exploit such knowledge, MASS uses cross-modal consistency to regularize deep segmentation models in aspects of both semantic and anatomical spaces, from which MASS learns intra- and inter-modal correspondences to warp atlases' labels for making predictions. For better capturing inter-modal correspondence, from a perspective of feature alignment, we propose a contrastive similarity loss to regularize the latent space of both modalities in order to learn generalized and robust modality-independent representations. Compared to semi-supervised and multi-modal segmentation counterparts, the proposed MASS brings nearly 6% improvements under extremely limited supervision.

Brain structural and functional connectivity alterations are associated with fatigue in neuromyelitis optica spectrum disorder.

BACKGROUND: Many patients with neurological disorders experience chronic fatigue, but the neural mechanisms involved are unclear. OBJECTIVE: Here we investigated whether the brain structural and functional connectivity alterations were involved in fatigue related to neuromyelitis optica spectrum disorder (NMOSD). METHODS: This prospective pilot study used structural and resting-state functional brain magnetic resonance imaging to compare total cortical thickness, cortical surface area, deep gray matter volume and functional connectivity (FC) between 33 patients with NMOSD and 20 healthy controls (HCs). Patients were subgrouped as low fatigue (LF) and high fatigue (HF). RESULTS: HF patients scored higher on the Hamilton Anxiety Rating Scale and Hamilton Rating Scale for Depression than LF patients and HCs. The two patient subgroups and HC group did not differ significantly in cortical thickness, cortical surface area and volumes of the bilateral caudate nucleus, bilateral putamen, bilateral amygdala, bilateral hippocampus, bilateral thalamus proper or right nucleus accumbens (p > 0.05). However, after correcting for age, sex, years of education, anxiety and depression, HF patients showed larger left pallidum than HCs (0.1573 ± 0.0214 vs 0.1372 ± 0.0145, p = 0.009). Meanwhile, both LF patients (0.0377 ± 0.0052 vs 0.0417 ± 0.0052, p = 0.009) and HF patients (0.0361 ± 0.0071 vs 0.0417 ± 0.0052, p = 0.013) showed smaller left nucleus accumbens than HCs.. Compared with LF patients, HF patients showed significantly decreased FC between the left pallidum and bilateral cerebellar posterior lobes. CONCLUSIONS: This was the first evidence linking structural and functional alterations in the brain to fatigue in NMOSD, and in the future, long term follow-up was necessary.

Effects of Molecular Structure on Organic Contaminants' Degradation Efficiency and Dominant ROS in the Advanced Oxidation Process with Multiple ROS.

In this study, the previously overlooked effects of contaminants' molecular structure on their degradation efficiencies and dominant reactive oxygen species (ROS) in advanced oxidation processes (AOPs) are investigated with a peroxymonosulfate (PMS) activation system selected as the typical AOP system. Averagely, degradation efficiencies of 19 contaminants are discrepant in the CoCaAl-LDO/PMS system with production of SO4•-, •OH, and 1O2. Density functional theory calculations indicated that compounds with high EHOMO, low-energy gap (ΔE = ELUMO - EHOMO), and low vertical ionization potential are more vulnerable to be attacked. Further analysis disclosed that the dominant ROS was the same one when treating similar types of contaminants, namely SO4•-, 1O2, 1O2, and •OH for the degradation of CBZ-like compounds, SAs, bisphenol, and triazine compounds, respectively. This phenomenon may be caused by the contaminants' structures especially the commonly shared or basic parent structures which can affect their effective reaction time and second-order rate constants with ROS, thus influencing the contribution of each ROS during its degradation. Overall, the new insights gained in this study provide a basis for designing more effective AOPs to improve their practical application in wastewater treatment.

An enriched environment reduces hippocampal inflammatory response and improves cognitive function in a mouse model of stroke.

An enriched environment is used as a behavioral intervention therapy that applies sensory, motor, and social stimulation, and has been used in basic and clinical research of various neurological diseases. In this study, we established mouse models of photothrombotic stroke and, 24 hours later, raised them in a standard, enriched, or isolated environment for 4 weeks. Compared with the mice raised in a standard environment, the cognitive function of mice raised in an enriched environment was better and the pathological damage in the hippocampal CA1 region was remarkably alleviated. Furthermore, protein expression levels of tumor necrosis factor receptor-associated factor 6, nuclear factor κB p65, interleukin-6, and tumor necrosis factor α, and the mRNA expression level of tumor necrosis factor receptor-associated factor 6 were greatly lower, while the expression level of miR-146a-5p was higher. Compared with the mice raised in a standard environment, changes in these indices in mice raised in an isolated environment were opposite to mice raised in an enriched environment. These findings suggest that different living environments affect the hippocampal inflammatory response and cognitive function in a mouse model of stroke. An enriched environment can improve cognitive function following stroke through up-regulation of miR-146a-5p expression and a reduction in the inflammatory response.

Risk Factors for Generalization in Patients with Ocular Myasthenia Gravis: A Multicenter Retrospective Cohort Study.

INTRODUCTION: Many patients with ocular myasthenia gravis (OMG) progress to generalized disease within the first 2 years of the onset of ocular symptoms. Several retrospective studies have identified risk factors associated with generalization, however these studies included patients on immunosuppression therapy or those undergoing thymectomy, which may reduce the generalization risk. In this study we explored the risk factors for generalization in non-immunosuppressed and non-thymectomized patients with OMG. METHODS: Data from patients with OMG treated at seven tertiary hospitals in China were retrospectively reviewed. Clinical characteristics, including sex, age at onset, symptoms at onset, comorbid autoimmune diseases, neostigmine test response, repetitive nerve stimulation (RNS) findings, presence of serum anti-acetylcholine receptor antibody (AChR-Ab), and thymic status based on radiological and pathological studies, were collected. The main outcome measure was disease generalization. The follow-up period was defined as the date of ocular symptom onset to the date of confirmation of generalization or immunotherapy initiation, or last follow-up (defined as 60 months). The Cox proportional hazards model was used to assess the risk factors for generalization. RESULTS: Overall, 572 patients (269 women) were eligible for inclusion in the analysis, of whom 144 developed generalization. The mean (standard deviation) onset age was 45.5 (19.8) years, and the median (interquartile range) follow-up period was 14.5 (7.0-47.3) months. Multivariable Cox regression analysis demonstrated that both early-onset (adjusted hazard ratio [aHR] 5.34; 95% confidence interval [CI] 1.64-17.36; p = 0.005) and late-onset (aHR 7.18; 95% CI 2.22-23.27; p = 0.001) in adulthood, abnormal RNS findings (aHR 3.01; 95% CI 1.97-4.61; p < 0.001), seropositivity for AChR-Ab (aHR 2.58; 95% CI 1.26-5.26; p = 0.01), and thymoma (aHR 1.62; 95% CI 1.05-2.49; p = 0.03) were independently associated with increased risk of generalization. CONCLUSION: The risk of generalization increased significantly in patients with adult-onset OMG, abnormal RNS findings, seropositivity for AChR-Ab, and thymoma, suggesting that these risk factors may predict OMG generalization.

Anesthetic technique for awake artery malformation clipping with motor evoked potential and somatosensory evoked potential: A case report.

BACKGROUND: Awake craniotomy has been widely used for tumor resection, epilepsy surgery, deep brain stimulation, and carotid endarterectomy. The report on awake artery malformation clipping is rare, especially for anesthesia management. CASE SUMMARY: A 62-year-old female diagnosed with malformation of anterior cerebral artery at the right side. We clipped the artery malformation with intraoperative neuromonitoring (IONM) in awake craniotomy. Spontaneous respiration was maintained throughout the procedure by nasopharyngeal airway during the surgery successfully. CONCLUSION: The technique of monitoring anesthesia care can be performed successfully for the patient with IONM.

[Effect and Mechanism of Treating Experimental Autoimmune Encephalomyelitis in Mice with Butylphthalide Combined with Bone Marrow Mesenchymal Stem Cells].

OBJECTIVE: To explore the efficacy and mechanism of using 3-n-butylphthalide (NBP) in combination with bone marrow mesenchymal stem cells (BMSCs) in the treatment of experimental autoimmune encephalomyelitis (EAE) in mice. METHODS: Myelin oligodendrocyte glycoprotein (MOG35-55) was used for the induction and establishment of the EAE model in C57BL/6 mice. The mice were randomly assigned to the EAE group, which received intraperitoneal injection of phosphate-buffered saline (PBS), the NBP-treated EAE group, or the NBP group, which received intraperitoneal injection of NBP, the BMSCs transplantion EAE group, or the BMSCs group, which received BMSCs injected into the lateral ventricle and intraperitoneal injection of PBS, and the BMSCs and NBP combination treatment EAE group, or the BMSCs+NBP group, which received BMSCs injected into the lateral ventricle and intraperitoneal injection of NBP. Each group had 10 mice, while ten normal mice were used as the blank control group receiving intraperitoneal injection of PBS. The neurological function scores were documented daily. The mice were sacrificed 22 days after EAE induction, and the demyelination state of of the spinal cords was observed through Luxol fast blue (LFB) staining. In addition, the levels of serum interleukin-6 (IL-6), IL-10, IL-17, IL-22 and transforming growth factor-ß (TGF-ß) were examined with ELISA. The levels of glial fibrillary acidic protein (GFAP), microtubule associated protein-2 (MAP-2) and myelin basic protein (MBP) in the brain were examined with immunofluorescence staining. Western blot was used to check the expressions of nuclear factor (NF)-κB pathway, phosphoinositide-3 kinase (PI3K)/protein kinase B (PKB or Akt) pathway, IL-17 and forkhead box P3 (Foxp3) in the spinal cords. RESULTS: The neurological function scores and average scores of each treatment group were significantly lower than those of the EAE group ( P<0.05). The scores of the BMSCs+NBP group decreased more significantly than those of the single treatment groups (the NBP group and the BMSCs group) ( P<0.05). LFB staining results of the spinal cords were consistent with the neurological function scores and the average scores. Compared with the EAE group, the levels of pro-inflammatory cytokines, including IL-6, IL-17 and IL-22, significantly decreased ( P<0.05), and the levels of anti-inflammatory cytokines IL-10 and TGF-ß significantly increased ( P<0.05). The change in cytokine expression was more significant in the BMSCs+NBP group ( P<0.05). The expressions of GFAP, MAP-2 and MBP in the BMSCs+NBP group were significantly higher than those of the BMSCs group ( P<0.05). Compared with the EAE group, the p-NF-κB/NF-κB ratio and the IL-17/Foxp3 ratio in NBP group, BMSCs group and BMSCs+NBP group decreased, while P-IκBα/IκBα, p-pI3k/PI3K and P-Akt/Akt ratios increased, especially in the BMSCs+NBP group( P<0.05). CONCLUSION: The combined treatment of NBP and BMSCs can help alleviate the symptoms of EAE model mice, showing better efficacy than treatment with NBP or BMSCs alone. The mechanism is related to the inhibition of the NF-κB pathway to regulate Th17/Foxp3 ratio and the activation of the PI3K/Akt pathway to promote the neurogenic differentiation of BMSCs.

Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition.

Multi-label image recognition is a practical and challenging task compared to single-label image classification. However, previous works may be suboptimal because of a great number of object proposals or complex attentional region generation modules. In this paper, we propose a simple but efficient two-stream framework to recognize multi-category objects from global image to local regions, similar to how human beings perceive objects. To bridge the gap between global and local streams, we propose a multi-class attentional region module which aims to make the number of attentional regions as small as possible and keep the diversity of these regions as high as possible. Our method can efficiently and effectively recognize multi-class objects with an affordable computation cost and a parameter-free region localization module. Over three benchmarks on multi-label image classification, our method achieves new state-of-the-art results with a single model only using image semantics without label dependency. In addition, the effectiveness of the proposed method is extensively demonstrated under different factors such as global pooling strategy, input size and network architecture. Code has been made available at https://github.com/gaobb/MCAR.

Evaluation and comparison of accurate automated spinal curvature estimation algorithms with spinal anterior-posterior X-Ray images: The AASCE2019 challenge.

Scoliosis is a common medical condition, which occurs most often during the growth spurt just before puberty. Untreated Scoliosis may cause long-term sequelae. Therefore, accurate automated quantitative estimation of spinal curvature is an important task for the clinical evaluation and treatment planning of Scoliosis. A couple of attempts have been made for automated Cobb angle estimation on single-view x-rays. It is very challenging to achieve a highly accurate automated estimation of Cobb angles because it is difficult to utilize x-rays efficiently. With the idea of developing methods for accurate automated spinal curvature estimation, AASCE2019 challenge provides spinal anterior-posterior x-ray images with manual labels for training and testing the participating methods. We review eight top-ranked methods from 12 teams. Experimental results show that overall the best performing method achieved a symmetric mean absolute percentage (SMAPE) of 21.71%. Limitations and possible future directions are also described in the paper. We hope the dataset in AASCE2019 and this paper could provide insights into quantitative measurement of the spine.

SSMD: Semi-Supervised medical image detection with adaptive consistency and heterogeneous perturbation.

Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised methods with additional unlabeled data. However, as a fundamental task, semi-supervised object detection has not gained enough attention in the field of medical image analysis. In this paper, we propose a novel Semi-Supervised Medical image Detector (SSMD). The motivation behind SSMD is to provide free yet effective supervision for unlabeled data, by regularizing the predictions at each position to be consistent. To achieve the above idea, we develop a novel adaptive consistency cost function to regularize different components in the predictions. Moreover, we introduce heterogeneous perturbation strategies that work in both feature space and image space, so that the proposed detector is promising to produce powerful image representations and robust predictions. Extensive experimental results show that the proposed SSMD achieves the state-of-the-art performance at a wide range of settings. We also demonstrate the strength of each proposed module with comprehensive ablation studies.