Efficacy of azathioprine, mycophenolate mofetil, and rituximab in the treatment of neuromyelitis optica spectrum disorder and analysis of prognostic factors.

OBJECTIVE: The study aims to compare the efficacies of the immunosuppressants most commonly prescribed for patients with neuromyelitis optica spectrum disorder (NMOSD). The predictors, which might be associated with relapse and disability in NMOSD, were also analyzed. METHODS: This retrospective study included NMOSD patients treated with azathioprine (AZA), mycophenolate mofetil (MMF), and rituximab (RTX). The annual relapse rate (ARR) and the incidence rates of adverse events were compared. Cox proportional-hazards model calculated the potential predictors of NMOSD relapse and disability. RESULTS: A total of 83 patients were included. The median treatment time of AZA group (n = 34), MMF group (n = 20), and RTX group (n = 29) were 19.5, 15.5, and 12 months, respectively. ARR of the three groups reduced significantly after treatment. In the three groups, 55.9%, 50%, and 79.3% of patients, respectively, were free from relapse. However, the difference among the three groups was of no statistical significance, possibly due to the small sample size. During the treatment, 32.4%, 15%, and 24.1% of patients experienced adverse events in the AZA group, MMF group, and RTX group, respectively. Additionally, the multivariate Cox analyses indicated that history of a severe attack and disease duration were associated with the risk of relapse after immunotherapy. Late-onset (≥ 50 years old) NMOSD patients were probably more susceptible to motor disability, and those with optic neuritis at onset were more likely to develop visual disability. CONCLUSIONS: AZA, MMF, and low-dose RTX were all effective in reducing the relapse rate in NMOSD. The age at onset, disease duration, history of severe attacks, and primary syndromes might be significant prognostic predictors in NMOSD.

Cross-Domain Mutual-Assistance Learning Framework for Fully Automated Diagnosis of Primary Tumor in Nasopharyngeal Carcinoma.

Accurate T-staging of nasopharyngeal carcinoma (NPC) holds paramount importance in guiding treatment decisions and prognosticating outcomes for distinct risk groups. Regrettably, the landscape of deep learning-based techniques for T-staging in NPC remains sparse, and existing methodologies often exhibit suboptimal performance due to their neglect of crucial domain-specific knowledge pertinent to primary tumor diagnosis. To address these issues, we propose a new cross-domain mutual-assistance learning framework for fully automated diagnosis of primary tumor using H&N MR images. Specifically, we tackle primary tumor diagnosis task with the convolutional neural network consisting of a 3D cross-domain knowledge perception network (CKP net) for excavated cross-domain-invariant features emphasizing tumor intensity variations and internal tumor heterogeneity, and a multi-domain mutual-information sharing fusion network (M2SF net), comprising a dual-pathway domain-specific representation module and a mutual information fusion module, for intelligently gauging and amalgamating multi-domain, multi-scale T-stage diagnosis-oriented features. The proposed 3D cross-domain mutual-assistance learning framework not only embraces task-specific multi-domain diagnostic knowledge but also automates the entire process of primary tumor diagnosis. We evaluate our model on an internal and an external MR images dataset in a three-fold cross-validation paradigm. Exhaustive experimental results demonstrate that our method outperforms the other algorithms, and obtains promising performance for tumor segmentation and T-staging. These findings underscore its potential for clinical application, offering valuable assistance to clinicians in treatment decision-making and prognostication for various risk groups.

A transformer-based multi-task deep learning model for simultaneous T-stage identification and segmentation of nasopharyngeal carcinoma.

Background: Accurate tumor target contouring and T staging are vital for precision radiation therapy in nasopharyngeal carcinoma (NPC). Identifying T-stage and contouring the Gross tumor volume (GTV) manually is a laborious and highly time-consuming process. Previous deep learning-based studies have mainly been focused on tumor segmentation, and few studies have specifically addressed the tumor staging of NPC. Objectives: To bridge this gap, we aim to devise a model that can simultaneously identify T-stage and perform accurate segmentation of GTV in NPC. Materials and methods: We have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: delineating the tumor contour and identifying T-stage. Our retrospective study involved contrast-enhanced T1-weighted images (CE-T1WI) of 320 NPC patients (T-stage: T1-T4) collected between 2017 and 2020 at our institution, which were randomly allocated into three cohorts for three-fold cross-validations, and conducted the external validation using an independent test set. We evaluated the predictive performance using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy (ACC), with a 95% confidence interval (CI), and the contouring performance using the Dice similarity coefficient (DSC) and average surface distance (ASD). Results: Our multi-task model exhibited sound performance in GTV contouring (median DSC: 0.74; ASD: 0.97 mm) and T staging (AUC: 0.85, 95% CI: 0.82-0.87) across 320 patients. In early T category tumors, the model achieved a median DSC of 0.74 and ASD of 0.98 mm, while in advanced T category tumors, it reached a median DSC of 0.74 and ASD of 0.96 mm. The accuracy of automated T staging was 76% (126 of 166) for early stages (T1-T2) and 64% (99 of 154) for advanced stages (T3-T4). Moreover, experimental results show that our multi-task model outperformed the other single-task models. Conclusions: This study emphasized the potential of multi-task model for simultaneously delineating the tumor contour and identifying T-stage. The multi-task model harnesses the synergy between these interrelated learning tasks, leading to improvements in the performance of both tasks. The performance demonstrates the potential of our work for delineating the tumor contour and identifying T-stage and suggests that it can be a practical tool for supporting clinical precision radiation therapy.

Highly crystalline cellulose microparticles from dealginated seaweed waste ameliorate high fat-sugar diet-induced hyperlipidemia in mice by modulating gut microbiota.

The effects of seaweed cellulose (SC) on high fat-sugar diet (HFSD)-induced glucolipid metabolism disorders in mice and potential mechanisms were investigated. SC was isolated from dealginated residues of giant kelp (Macrocystis pyrifera), with a crystallinity index of 85.51 % and an average particle size of 678.2 nm. Administering SC to C57BL/6 mice at 250 or 500 mg/kg BW/day via intragastric gavage for six weeks apparently inhibited the development of HFSD-induced obesity, dyslipidemia, insulin resistance, oxidative stress and liver damage. Notably, SC intervention partially restored the structure and composition of the gut microbiota altered by the HFSD, substantially lowering the Firmicutes to Bacteroidetes ratio, and greatly increasing the relative abundance of Lactobacillus, Bifidobacterium, Oscillospira, Bacteroides and Akkermansia, which contributed to improved short-chain fatty acid (SCFA) production. Supplementing with a higher dose of SC led to more significant increases in total SCFA (67.57 %), acetate (64.56 %), propionate (73.52 %) and butyrate (66.23 %) concentrations in the rectal contents of HFSD-fed mice. The results indicated that highly crystalline SC microparticles could modulate gut microbiota dysbiosis and ameliorate HFSD-induced obesity and related metabolic syndrome in mice. Furthermore, particle size might have crucial impact on the prebiotic effects of cellulose as insoluble dietary fiber.

MsGoF: Breast lesion classification on ultrasound images by multi-scale gradational-order fusion framework.

BACKGROUND AND OBJECTIVE: Predicting the malignant potential of breast lesions based on breast ultrasound (BUS) images is a crucial component of computer-aided diagnosis system for breast cancers. However, since breast lesions in BUS images generally have various shapes with relatively low contrast and present complex textures, it still remains challenging to accurately identify the malignant potential of breast lesions. METHODS: In this paper, we propose a multi-scale gradational-order fusion framework to make full advantages of multi-scale representations incorporating with gradational-order characteristics of BUS images for breast lesions classification. Specifically, we first construct a spatial context aggregation module to generate multi-scale context representations from the original BUS images. Subsequently, multi-scale representations are efficiently fused in feature fusion block that is armed with special fusion strategies to comprehensively capture morphological characteristics of breast lesions. To better characterize complex textures and enhance non-linear modeling capability, we further propose isotropous gradational-order feature module in the feature fusion block to learn and combine multi-order representations. Finally, these multi-scale gradational-order representations are utilized to perform prediction for the malignant potential of breast lesions. RESULTS: The proposed model was evaluated on three open datasets by using 5-fold cross-validation. The experimental results (Accuracy: 85.32%, Sensitivity: 85.24%, Specificity: 88.57%, AUC: 90.63% on dataset A; Accuracy: 76.48%, Sensitivity: 72.45%, Specificity: 80.42%, AUC: 78.98% on dataset B) demonstrate that the proposed method achieves the promising performance when compared with other deep learning-based methods in BUS classification task. CONCLUSIONS: The proposed method has demonstrated a promising potential to predict malignant potential of breast lesion using ultrasound image in an end-to-end manner.

A multi-perspective information aggregation network for automatedT-staging detection of nasopharyngeal carcinoma.

AccurateT-staging is important when planning personalized radiotherapy. However,T-staging via manual slice-by-slice inspection is time-consuming while tumor sizes and shapes are heterogeneous, and junior physicians find such inspection challenging. With inspiration from oncological diagnostics, we developed a multi-perspective aggregation network that incorporated various diagnosis-oriented knowledge which allowed automated nasopharyngeal carcinomaT-staging detection (TSD Net). Specifically, our TSD Net was designed in multi-branch architecture, which can capture tumor size and shape information (basic knowledge), strongly correlated contextual features, and associations between the tumor and surrounding tissues. We defined the association between the tumor and surrounding tissues by a signed distance map which can embed points and tumor contours in higher-dimensional spaces, yielding valuable information regarding the locations of tissue associations. TSD Net finally outputs aT1-T4 stage prediction by aggregating data from the three branches. We evaluated TSD Net by using the T1-weighted contrast-enhanced magnetic resonance imaging database of 320 patients in a three-fold cross-validation manner. The results show that the proposed method achieves a mean area under the curve (AUC) as high as 87.95%. We also compared our method to traditional classifiers and a deep learning-based method. Our TSD Net is efficient and accurate and outperforms other methods.

Physical, Emotional, Medical, and Socioeconomic Status of Patients With NMOSD: A Cross-Sectional Survey of 123 Cases From a Single Center in North China.

Objective: This study aimed to assess the physical, emotional, medical, and socioeconomic conditions of patients with neuromyelitis optica spectrum disorder (NMOSD) in North China. Methods: A cross-sectional survey of patients with NMOSD was performed, based on an established questionnaire from the Multiple Sclerosis Patient Survival Report 2018. Logistic regression analysis was conducted to define the significant determinants of certain physical or emotional characteristics of patients. A total of 123 patients were included. Results: A total of 63.4% of participants were initially diagnosed with conditions other than NMOSD, with a median delay of 6 months for accurate diagnosis. An aggregate of 72.2% of patients had one or more relapses, corresponding to an annual relapse rate of 0.8. Paresthesia was the most frequent physical symptom among patients both at disease onset (53.7%) and throughout the duration of the disease (86.2%). Onset in elderly (>50 years) patients was associated with an annual Expanded Disability Status Scale increase ≥1, compared with onset in younger (<30 years) patients (P = 0.001, OR = 7.83). A total of 76.4% of patients had received attack-prevention treatments in the remission phase, and 31.7 and 10.6% of patients had ever been administered rituximab and traditional Chinese medicine, respectively. Additionally, 63.4 and 43.1% of patients reported participating in few or no social activities and being out of work because of the disease. To be noted, 76.4% of patients reported suffering from negative emotions, with the most frequent being worry (60.2%), with 20.3% of patients experiencing suicidal thoughts. The inability to work and participating in few or no social activities due to NMOSD were two determinants of experiencing negative emotions (P work = 0.03, ORwork = 3.34; P socialactivities = 0.02, ORsocialactivities = 3.19). Conclusion: This study reported patient perspectives on NMOSD in North China, whereby demonstrating that the inability to work and participating in few or no social activities due to NMOSD rather than the physical impairment caused by the disease, was directly associated with patients experiencing negative emotions. This insight offers potential ways to manage patients' negative emotions by enhancing family and social support and facilitating active employment.

Effect of jianpiyiwei capsule on gastric precancerous lesions in rats.

AIM: To evaluate the therapeutic effect of compound Chinese drugs, Jianpiyiwei capsule (JPYW) on gastric precancerous lesions in rats and to explore its mechanism of action. METHODS: Model of gastric precancerous lesions was constructed in male Wistar rats: a metal spring was inserted and fixed through pyloric sphincter. One week after recovery, each rat was given 50-60 degrees hot paste containing 150 g/L NaCl 2 mL orally, twice a week for 15 weeks. Then 10 normal and 11 model rats were anaesthetized, after the measurement of gastric mucosa blood flow (GMBF), the rats were killed and the mucosal hexosamines and malonic dialdehyde (MDA) were measured. The morphological changes of gastric mucosa were observed macroscopically and microscopically, and by an automatic imaging analysis system. Other rats were treated with JPYW 1.5 g/kg.d(-1) or 4.5 g/kg.d(-1), or distilled water as negative control respectively (n=10 in each group). After 12 weeks, all the rats were examined as above. RESULTS: The gastric mucosa of model rats showed chronic atrophic gastritis with dysplasia and intestinal metaplasia (IM), GMBF and hexosamine content were reduced significantly and MDA was increased as compared to the normal group (P<0.01). After 12 weeks treatment, the pathological changes of the negative control group became worsened, while in JPYW treated groups the changes were modified with significant increase of GMBF and reduction of MDA, although the hexosamine concentration increased only mildly. CONCLUSION: JPYW increases GMBF and reduces MDA content in gastric mucosa and has therapeutic effects on gastric precancerous lesions.