Relationship between maternal-infant gut microbiota and infant food allergy.

The gut microbiota plays a crucial role in food allergies. We sought to identify characteristics of the maternal gut microbiota in the third trimester and the infant gut microbiota in early life and the association of these microbiotas with infant food allergy. A total of 68 healthy pregnant women and their full-term newborns were selected from a cohort of 202 mother-infant pairs; among them, 24 infants had been diagnosed with food allergy within 1 year of age, whereas 44 infants were healthy without allergic symptoms.

Antibody-Targeted TNFRSF Activation for Cancer Immunotherapy: The Role of FcγRIIB Cross-Linking.

Co-stimulation signaling in various types of immune cells modulates immune responses in physiology and disease. Tumor necrosis factor receptor superfamily (TNFRSF) members such as CD40, OX40 and CD137/4-1BB are expressed on myeloid cells and/or lymphocytes, and they regulate antigen presentation and adaptive immune activities. TNFRSF agonistic antibodies have been evaluated extensively in preclinical models, and the robust antitumor immune responses and efficacy have encouraged continued clinical investigations for the last two decades.

Association Between Breastmilk Microbiota and Food Allergy in Infants.

Regulating the composition of human breastmilk has the potential to prevent allergic diseases early in life. The composition of breastmilk is complex, comprising varying levels of oligosaccharides, immunoactive molecules, vitamins, metabolites, and microbes. Although several studies have examined the relationship between different components of breastmilk and infant food allergies, few have investigated the relationship between microorganisms in breastmilk and infant food allergy.

S-CUDA: Self-cleansing unsupervised domain adaptation for medical image segmentation.

Medical image segmentation tasks hitherto have achieved excellent progresses with large-scale datasets, which empowers us to train potent deep convolutional neural networks (DCNNs). However, labeling such large-scale datasets is laborious and error-prone, which leads the noisy (or incorrect) labels to be an ubiquitous problem in the real-world scenarios. In addition, data collected from different sites usually exhibit significant data distribution shift (or domain shift).

Multiple Kernel Clustering With Compressed Subspace Alignment.

Multiple kernel clustering (MKC) has recently achieved remarkable progress in fusing multisource information to boost the clustering performance. However, the O(n) memory consumption and O(n) computational complexity prohibit these methods from being applied into median- or large-scale applications, where n denotes the number of samples. To address these issues, we carefully redesign the formulation of subspace segmentation-based MKC, which reduces the memory and computational complexity to O(n) and O(n) , respectively.

Dynamic Joint Domain Adaptation Network for Motor Imagery Classification.

Electroencephalogram (EEG) has been widely used in brain computer interface (BCI) due to its convenience and reliability. The EEG-based BCI applications are majorly limited by the time-consuming calibration procedure for discriminative feature representation and classification. Existing EEG classification methods either heavily depend on the handcrafted features or require adequate annotated samples at each session for calibration.

Three Copies of Four Interferon Receptor Genes Underlie a Mild Type I Interferonopathy in Down Syndrome.

Down syndrome (DS) is characterized by the occurrence of three copies of human chromosome 21 (HSA21). HSA21 contains a cluster of four interferon receptor (IFN-R) genes: IFNAR1, IFNAR2, IFNGR2, and IL10RB. DS patients often develop mucocutaneous infections and autoimmune diseases, mimicking patients with heterozygous gain-of-function (GOF) STAT1 mutations, which enhance cellular responses to three types of interferon (IFN).

Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages.

High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. Conventional survival prediction based on clinical information is subjective and could be inaccurate.

Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks.

High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI).

Multi-Label Nonlinear Matrix Completion With Transductive Multi-Task Feature Selection for Joint MGMT and IDH1 Status Prediction of Patient With High-Grade Gliomas.

The O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation and isocitrate dehydrogenase 1 (IDH1) mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which has limited their wider clinical implementation. Accurate presurgical prediction of their statuses based on preoperative multimodal neuroimaging is of great clinical value for a better treatment plan.

Multi-label Inductive Matrix Completion for Joint MGMT and IDH1 Status Prediction for Glioma Patients.

MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which is laborious, invasive and time-consuming. Accurate presurgical prediction of their statuses based on preoperative imaging data is of great clinical value towards better treatment plan.

Exploring diagnosis and imaging biomarkers of Parkinson's disease via iterative canonical correlation analysis based feature selection.

Parkinson's disease (PD) is a neurodegenerative disorder that progressively hampers the brain functions and leads to various movement and non-motor symptoms. However, it is difficult to attain early-stage PD diagnosis based on the subjective judgment of physicians in clinical routines. Therefore, automatic and accurate diagnosis of PD is highly demanded, so that the corresponding treatment can be implemented more appropriately.

Outcome Prediction for Patient with High-Grade Gliomas from Brain Functional and Structural Networks.

High-grade glioma (HGG) is a lethal cancer, which is characterized by very poor prognosis. To help optimize treatment strategy, accurate preoperative prediction of HGG patient's outcome (i.e.

Feature Selection Based on Iterative Canonical Correlation Analysis for Automatic Diagnosis of Parkinson's Disease.

Parkinson's disease (PD) is a major progressive neurodegenerative disorder. Accurate diagnosis of PD is crucial to control the symptoms appropriately. However, its clinical diagnosis mostly relies on the subjective judgment of physicians and the clinical symptoms that often appear late.

DNMT1 cooperates with MBD4 to inhibit the expression of Glucocorticoid-induced TNFR-related protein in human T cells.

Glucocorticoid-induced TNFR-related protein (GITR) is constitutively expressed in T regulatory (Treg) cells and regulates their suppressive function. We identified two methylated CpG islands in the Gitr locus. Using a ChIP assay, we demonstrate that both DNMT1 and methyl-CpG-binding domain Protein 4 (MBD4) bind to the Gitr promoter.

3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients.

High-grade glioma is the most aggressive and severe brain tumor that leads to death of almost 50% patients in 1-2 years. Thus, accurate prognosis for glioma patients would provide essential guidelines for their treatment planning. Conventional survival prediction generally utilizes clinical information and limited handcrafted features from magnetic resonance images (MRI), which is often time consuming, laborious and subjective.

Utilizing a Newly Designed Scale for Evaluating Family Support and Its Association with Exclusive Breastfeeding.

Background: Although a woman's perception of her family members' support has long been established to be an influential factor on exclusive breastfeeding (EBF), it still has not been specified and quantified as a facilitator and guidance for practice.

Objective: To investigate in new mothers the association between EBF and maternal perception of family support with a standardized scale that classified support into nine items of behavioral or psychological support.

Method: A cross-sectional survey was carried out among 655 new mothers to collect information on their breastfeeding behavior and their corresponding family support at a baby-friendly hospital in Beijing, China.

IL-17A Exacerbates Fibrosis by Promoting the Proinflammatory and Profibrotic Function of Orbital Fibroblasts in TAO.

Context: The development of thyroid-associated ophthalmopathy (TAO) is associated with self-immune dysfunction. Recent findings in TAO and Graves' disease indicate that IL-17A may also be involved in the autoimmunity of TAO.

Objective: We sought to investigate the pathogenic function of IL-17A-producing T cells in TAO.

Fabrication of superhydrophilic-underwater superoleophobic inorganic anti-corrosive membranes for high-efficiency oil/water separation.

The issue of oil/water separation has recently become a global concern due to the frequency of oil spills and the increase in industrial waste water. Thus, membrane-based materials with unique wettability are desired to separate both of these from a mixture. Nevertheless, the fabrication of energy efficient and stable membranes appropriate for the separation process remains challenging.

Inherited CARD9 deficiency in otherwise healthy children and adults with Candida species-induced meningoencephalitis, colitis, or both.

Background: Invasive infections of the central nervous system (CNS) or digestive tract caused by commensal fungi of the genus Candida are rare and life-threatening. The known risk factors include acquired and inherited immunodeficiencies, with patients often displaying a history of multiple infections. Cases of meningoencephalitis, colitis, or both caused by Candida species remain unexplained.

Inherited CARD9 deficiency in 2 unrelated patients with invasive Exophiala infection.

Background: Exophiala species are mostly responsible for skin infections. Invasive Exophiala dermatitidis disease is a rare and frequently fatal infection, with 42 cases reported. About half of these cases had no known risk factors.

Using DNA chips for identification of tephritid pest species.

Background: The ability correctly to identify species in a rapid and reliable manner is critical in many situations. For insects in particular, the primary tools for such identification rely on adult-stage morphological characters. For a number of reasons, however, there is a clear need for alternatives.

Deep dermatophytosis and inherited CARD9 deficiency.

Background: Deep dermatophytosis is a severe and sometimes life-threatening fungal infection caused by dermatophytes. It is characterized by extensive dermal and subcutaneous tissue invasion and by frequent dissemination to the lymph nodes and, occasionally, the central nervous system. The condition is different from common superficial dermatophyte infection and has been reported in patients with no known immunodeficiency.

Combined deletion of two Condensin II system genes (NCAPG2 and MCPH1) in a case of severe microcephaly and mental deficiency.

7qter deletion syndrome includes prenatal and/or postnatal growth retardation, microcephaly, psychomotor delay or mental retardation and a characteristic dysmorphism. If clinical features are well described, the molecular mechanisms underlying the 7qter deletion syndrome remain unknown. Those deletions usually arise de novo.