A new intestinal supplement 'Synbiotics' therapeutically regulates gut microbiota and activates PPARs pathway to inhibit Alzheimer's disease progression in mouse models.

We aimed to investigate the role of Synbiotic preparations on the interaction of gut microbiota with AD development. APP/PS1 mice were randomized into APP/PS1 and Synbiotics groups, and C57BL/6J mice were used as wild type (WT) control group. The mice in the Synbiotics group and the APP/PS1 group were given Synbiotics and xylo-oligosaccharides for 3 months, respectively. The mice in the WT group were given the same amount of normal saline. Cognitive function was measured. Positron emission computed tomography/magnetic resonance imaging (PET/MRI) was used to detect fasting blood glucose level. Immunohistochemical assay, ELISA, western blot and qRT-PCR were carried out to detect inflammatory factors. DNA extraction of fecal sample was performed to carry out sequencing. Bioinformatics analysis, metabolites sample preparation and Liquid Chromatograph Mass Spectrometer (LC/MS) analysis were also performed. Synbiotics treatment can significantly ameliorate learning and memory competence by inhibiting Aß protein deposition. Different bacteria in the intestine were significantly improved and changes in gut microbiota can affect the intestinal metabolism to affect multiple potential pathways after Synbiotics treatment. Synbiotics treatment can activate peroxisome proliferator activated receptor (PPARs) signaling pathway and significantly reduce neuroinflammation in APP/PS1 mice brains. Synbiotics treatment can effectively reduce neuro-inflammatory response through the regulation of intestinal microflora to delay AD development.

Predictive Value of Serum microRNA-29b-3p in Recurrence of Atrial Fibrillation After Radiofrequency Catheter Ablation.

Objective: Atrial fibrillation (AF) is a common arrhythmia. This study explored serum miR-29b-3p expression in AF patients and its value in predicting AF recurrence after radiofrequency catheter ablation (RFCA). Methods: Totally 100 AF patients who underwent RFCA were enrolled, with 100 individuals without AF as controls. Serum miR-29b-3p expression in participants was determined using RT-qPCR. The correlation between miR-29b-3p and atrial fibrosis markers (FGF-21/FGF-23) was assessed by Pearson analysis. The diagnostic efficacy of serum miR-29b-3p and FGF-21/FGF-23 in predicting AF recurrence after RFCA was analyzed by the receiver operating characteristic (ROC) curves. The Kaplan-Meier method was adopted to evaluate the effect of miR-29b-3p expression on the incidence of AF recurrence after RFCA. The independent risk factors for AF recurrence after RFCA were analyzed by logistic regression analysis. Results: Serum miR-29b-3p was poorly expressed in AF patients. After RFCA, AF patients showed elevated serum miR-29b-3p expression. Serum miR-29b-3p expression in AF patients negatively correlated with serum FGF-21 and FGF-23 concentrations. The cut-off values of serum miR-29b-3p, FGF-21, and FGF-23 in identifying AF recurrence were 0.860 (sensitivity: 100.00%, specificity: 39.71%), 222.2 pg/mL (sensitivity: 96.88%, specificity: 32.35%) and 216.3 ng/mL (sensitivity: 53.13%, specificity: 70.59%), respectively. Patients with low miR-29b-3p expression had a significantly higher incidence of AF recurrence than patients with high miR-29b-3p expression. Serum miR-29b-3p expression was one of the independent risk factors for AF recurrence after RFCA. Conclusion: Low miR-29b-3p expression in AF patients has certain predictive values and is one of the independent risk factors for AF recurrence after RFCA.

Benign and malignant classification of breast tumor ultrasound images using conventional radiomics and transfer learning features: A multicenter retrospective study.

This study aims to establish an effective benign and malignant classification model for breast tumor ultrasound images by using conventional radiomics and transfer learning features. We collaborated with a local hospital and collected a base dataset (Dataset A) consisting of 1050 cases of single lesion 2D ultrasound images from patients, with a total of 593 benign and 357 malignant tumor cases. The experimental approach comprises three main parts: conventional radiomics, transfer learning, and feature fusion. Furthermore, we assessed the model's generalizability by utilizing multicenter data obtained from Datasets B and C. The results from conventional radiomics indicated that the SVM classifier achieved the highest balanced accuracy of 0.791, while XGBoost obtained the highest AUC of 0.854. For transfer learning, we extracted deep features from ResNet50, Inception-v3, DenseNet121, MNASNet, and MobileNet. Among these models, MNASNet, with 640-dimensional deep features, yielded the optimal performance, with a balanced accuracy of 0.866, AUC of 0.937, sensitivity of 0.819, and specificity of 0.913. In the feature fusion phase, we trained SVM, ExtraTrees, XGBoost, and LightGBM with early fusion features and evaluated them with weighted voting. This approach achieved the highest balanced accuracy of 0.964 and AUC of 0.981. Combining conventional radiomics and transfer learning features demonstrated clear advantages over using individual features for breast tumor ultrasound image classification. This automated diagnostic model can ease patient burden and provide additional diagnostic support to radiologists. The performance of this model encourages future prospective research in this domain.

Constructing the Optimal Classification Model for Benign and Malignant Breast Tumors Based on Multifeature Analysis from Multimodal Images.

The purpose of this study was to fuse conventional radiomic and deep features from digital breast tomosynthesis craniocaudal projection (DBT-CC) and ultrasound (US) images to establish a multimodal benign-malignant classification model and evaluate its clinical value. Data were obtained from a total of 487 patients at three centers, each of whom underwent DBT-CC and US examinations. A total of 322 patients from dataset 1 were used to construct the model, while 165 patients from datasets 2 and 3 formed the prospective testing cohort. Two radiologists with 10-20 years of work experience and three sonographers with 12-20 years of work experience semiautomatically segmented the lesions using ITK-SNAP software while considering the surrounding tissue. For the experiments, we extracted conventional radiomic and deep features from tumors from DBT-CCs and US images using PyRadiomics and Inception-v3. Additionally, we extracted conventional radiomic features from four peritumoral layers around the tumors via DBT-CC and US images. Features were fused separately from the intratumoral and peritumoral regions. For the models, we tested the SVM, KNN, decision tree, RF, XGBoost, and LightGBM classifiers. Early fusion and late fusion (ensemble and stacking) strategies were employed for feature fusion. Using the SVM classifier, stacking fusion of deep features and three peritumoral radiomic features from tumors in DBT-CC and US images achieved the optimal performance, with an accuracy and AUC of 0.953 and 0.959 [CI: 0.886-0.996], a sensitivity and specificity of 0.952 [CI: 0.888-0.992] and 0.955 [0.868-0.985], and a precision of 0.976. The experimental results indicate that the fusion model of deep features and peritumoral radiomic features from tumors in DBT-CC and US images shows promise in differentiating benign and malignant breast tumors.

Tubeimuside I improves the efficacy of a therapeutic Fusobacterium nucleatum dendritic cell-based vaccine against colorectal cancer.

Introduction: Fusobacterium nucleatum (F. nucleatum) infection has been confirmed to be associated with the development, chemoresistance, and immune evasion of colorectal cancer (CRC). The complex relationship between the microorganism, host cells, and the immune system throughout all stages of CRC progression, which makes the development of new therapeutic methods difficult. Methods: We developed a new dendritic cell (DC) vaccine to investigate the antitumor efficacy of CRC immunotherapy strategies. By mediating a specific mode of interaction between the bacteria, tumor, and host, we found a new plant-derived adjuvant, tubeimuside I (TBI), which simultaneously improved the DC vaccine efficacy and inhibited the F. nucleatum infection. Encapsulating TBI in a nanoemulsion greatly improved the drug efficacy and reduced the drug dosage and administration times. Results: The nanoemulsion encapsulated TBI DC vaccine exhibited an excellent antibacterial and antitumor effect and improved the survival rate of CRC mice by inhibiting tumor development and progression. Discussion: In this study, we provide a effective strategy for developing a DC-based vaccine against CRC and underlies the importance of further understanding the mechanism of CRC processes caused by F. nucleatum.

PSR-Nets: Deep neural networks with prior shift regularization for PET/CT based automatic, accurate, and calibrated whole-body lymphoma segmentation.

Lymphoma is a type of lymphatic tissue originated cancer. Automatic and accurate lymphoma segmentation is critical for its diagnosis and prognosis yet challenging due to the severely class-imbalanced problem. Generally, deep neural networks trained with class-observation-frequency based re-weighting loss functions are used to address this problem. However, the majority class can be under-weighted by them, due to the existence of data overlap. Besides, they are more mis-calibrated. To resolve these, we propose a neural network with prior-shift regularization (PSR-Net), which comprises a UNet-like backbone with re-weighting loss functions, and a prior-shift regularization (PSR) module including a prior-shift layer (PSL), a regularizer generation layer (RGL), and an expected prediction confidence updating layer (EPCUL). We first propose a trainable expected prediction confidence (EPC) for each class. Periodically, PSL shifts a prior training dataset to a more informative dataset based on EPCs; RGL presents a generalized informative-voxel-aware (GIVA) loss with EPCs and calculates it on the informative dataset for model finetuning in back-propagation; and EPCUL updates EPCs to refresh PSL and RRL in next forward-propagation. PSR-Net is trained in a two- stage manner. The backbone is first trained with re-weighting loss functions, then we reload the best saved model for the backbone and continue to train it with the weighted sum of the re-weighting loss functions, the GIVA regularizer and the L2 loss function of EPCs for regularization fine-tuning. Extensive experiments are performed based on PET/CT volumes with advanced stage lymphomas. Our PSR-Net achieves 95.12% sensitivity and 87.18% Dice coefficient, demonstrating the effectiveness of PSR-Net, when compared to the baselines and the state-of-the-arts.

Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images.

BACKGROUND: The rapid development of artificial intelligence technology has improved the capability of automatic breast cancer diagnosis, compared to traditional machine learning methods. Convolutional Neural Network (CNN) can automatically select high efficiency features, which helps to improve the level of computer-aided diagnosis (CAD). It can improve the performance of distinguishing benign and malignant breast ultrasound (BUS) tumor images, making rapid breast tumor screening possible. RESULTS: The classification model was evaluated with a different dataset of 100 BUS tumor images (50 benign cases and 50 malignant cases), which was not used in network training. Evaluation indicators include accuracy, sensitivity, specificity, and area under curve (AUC) value. The results in the Fus2Net model had an accuracy of 92%, the sensitivity reached 95.65%, the specificity reached 88.89%, and the AUC value reached 0.97 for classifying BUS tumor images. CONCLUSIONS: The experiment compared the existing CNN-categorized architecture, and the Fus2Net architecture we customed has more advantages in a comprehensive performance. The obtained results demonstrated that the Fus2Net classification method we proposed can better assist radiologists in the diagnosis of benign and malignant BUS tumor images. METHODS: The existing public datasets are small and the amount of data suffer from the balance issue. In this paper, we provide a relatively larger dataset with a total of 1052 ultrasound images, including 696 benign images and 356 malignant images, which were collected from a local hospital. We proposed a novel CNN named Fus2Net for the benign and malignant classification of BUS tumor images and it contains two self-designed feature extraction modules. To evaluate how the classifier generalizes on the experimental dataset, we employed the training set (646 benign cases and 306 malignant cases) for tenfold cross-validation. Meanwhile, to solve the balance of the dataset, the training data were augmented before being fed into the Fus2Net. In the experiment, we used hyperparameter fine-tuning and regularization technology to make the Fus2Net convergence.

Neurodevelopmental outcomes of extremely preterm infants in southern China: A multicenter study.

OBJECTIVE: To assess treatment outcomes and associated factors of extremely preterm infants (EPIs) in GuangXi, China. METHODS: This was a retrospective study consisting of 131 eligible cases with gestational age (GA) between 22 and 28 weeks, and infants were followed until 18-24 months. Data including clinical characteristics, perinatal factors and after-birth conditions were collected from the neonatal intensive care unit in 10 hospitals in Guangxi from January 1st 2010 until May 31st 2016. RESULTS: During that period, 307 EPIs were born in the hospitals. 137 infants died in hospital after their parents decided to withdraw clinical treatment, and 11 infants died despite full resuscitation was provided. Of the 159 surviving infants, 28 infants were lost to follow-up. In total, 131 infants who survived and were presented to follow-up at 18-24 months of age were enrolled into this study. Of the 131 infants evaluated at 18-24 months follow-up, 47 (35.9%) were diagnosed with neurodevelopmental disability (ND), and 84 (64%) demonstrated on tract motor and language skills. The incidence of chorioamnionitis, early onset sepsis (EOS), bronchopulmonary dysplasia (BPD) were all higher in the group of infants who were diagnosed with ND compared to those with normal motor language development (NML), the duration of mechanical ventilation (MV) was longer in ND group, and the higher incidence of ND was seen in the smaller GA babies (p < 0.05). Adjusted the BPD severity, GA was a protective factor of neurodevelopmental outcome (combined OR = 0.338, 95% CI: 0.145-0.791). In EPIs with moderate BPD and severe BPD, chorioamnionitis was a risk factor of ND (OR = 10.313 and 5.778，respectively, 95% CI: 1.389-6.486 and 1.444-23.119, respectively). The Logistic regression analysis showed that GA (OR = 0.207, 95%CI = 0.047-0.917) was a protective factor for ND, and chorioamnionitis (OR = 6.010, 95%CI: 1.331-27.138), moderate-to-severe BPD (OR = 4.285, 95%CI: 1.495-12.287), the duration of MV (OR = 3.508, 95%CI: 2.077-5.926) were independent risk factors for ND in EPIs. CONCLUSIONS: Chorioamnionitis, moderate-to-severe BPD, and the duration of MV were associated with neurodevelopmental disability in EPIs. The smaller the GA, the higher incidence of neurodevelopmental disability.

A study of 131iodine-labeling of histamine-indomethacin: its in vivo therapeutic effect and anti-tumor mechanisms in Lewis-bearing lung cancer.

BACKGROUND: In our research,we study the effect of 131iodine-labeled histamine-indomethacin (131I-His-IN). We focus on its in vivo therapeutic effect and anti-tumor mechanisms in Lewis-bearing lung cancer. METHODS: 131I-His-IN was administered by garage to the mice. At different timepoints, we made autoradiography (ARG) slices to observe the distribution of 131I-His-IN in the cellular, and the sliced samples underwent hematoxylin and eosin (HE) staining for observation of tumor necrosis. Before treatment, the groups of mice underwent 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography-computed tomography (PET-CT) scans ,and they were then given physiologic saline, iodine 131 (131I), indomethacin (IN), Histamine-indomethacin (His-IN), and 131I-His-IN, respectively, three times daily for seven days. Seven days later, all the mice underwent 18F-FDG PET-CT scans again. We calculated the maximum standard uptake value (SUVmax) of the region of interest (ROI) and tumor inhibition rate at the same time. RESULTS: In ARG groups, black silver particle was concentrated in the nucleus and cytoplasm. 131I-His-IN mainly concentrated in tumor tissues. At 8 hours after 131I-His-IN, the radioactivity uptake in tumor tissue was higher than in other organs (F=3.46, P<0.05). For the 18F-FDG PET-CT imaging, the tumor tissuses SUVmax of the ROI was lower compared to other groups after the treatment with 131I-His-IN. The tumor inhibitory rate (54.8%) in 131I-His-IN group was higher than in other groups, too. In the 131I-His-IN group the vascular endothelial growth factor (VEGF) decreased gradually compared to other groups. The tumor tissue necrotized obviously in 131I-His-IN group. CONCLUSIONS: Through these animal experiments, we found 131I-His-IN could inhibit the Lewis lung cancer cells. 131I-His-IN focused at the cell nucleus and cytoplasm. It could reduce VEGF and increase tumor inhibitory rate. At the same time, 18F-FDG PET-CT scan could be used for a curative effect and monitoring of disease prognosis.