Sirolimus alleviated intractable diarrhea of IPEX syndrome: a case report and literature review.

Background: Immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome is a rare single-gene X-linked immunodeficiency disease caused by mutations in the forkhead box protein 3 (FOXP3) gene. The typical clinical manifestations of IPEX mainly include severe atopic dermatitis, insulin-dependent type 1 diabetes mellitus, and intractable diarrhea.

Case Presentation: Here, we report a boy with intractable diarrhea diagnosed with early-onset IPEX syndrome due to the c.

Fecal Microbiota Transplantation in Children with Autism.

Purpose: This research aimed to explore the clinical efficacy of fecal microbiota transplantation (FMT) in treating children with autism spectrum disorder (ASD).

Methods: In this single-arm prospective study, every participant received FMT therapy, followed by an 8-week follow-up. Children unable to swallow lyophilized capsules (Caps) received fecal solution through transendoscopic enteral tube (TET) or nasal jejunal tube (NJT) approaches.

Gut Microbial and Metabolic Features Associated With Infection Recurrence in Children.

Background: Recurrent infection (CDI) is a critical clinical issue due to the increase in incidence and difficulty in treatment. We aimed to identify gut microbial and metabolic features associated with disease recurrence in a group of pediatric CDI patients.

Methods: A total of 84 children with primary CDI were prospectively enrolled in the study.

Successful treatment of juvenile polyposis of infancy with sirolimus: a case report.

Background: Infantile Juvenile polyposis of infantile (JPI) is a rare and aggressive form of juvenile polyposis syndrome (JPS) typically diagnosed in the first year of life. It often carries a poor prognosis due to chronic gastrointestinal bleeding, protein-losing enteropathy, malnutrition and immune deficiency.

Case Presentation: We report a case of a girl initially presented with pallor at 7 months of age, which progressed to gastrointestinal bleeding and protein-losing enteropathy.

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data.

Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery.

Prior image-based generative adversarial learning for multi-material decomposition in photon counting computed tomography.

Background: Photon counting detector computed tomography (PCD-CT) is a novel promising technique providing higher spatial resolution, lower radiation dose and greater energy spectrum differentiation, which create more possibilities to improve image quality. Multi-material decomposition is an attractive application for PCD-CT to identify complicated materials and provide accurate quantitative analysis. However, limited by the finite photon counting rate in each energy window of photon counting detector, the noise problem hinders the decomposition of high-quality basis material images.

The interaction between oral microbiota and gut microbiota in atherosclerosis.

Atherosclerosis (AS) is a complex disease caused by multiple pathological factors threatening human health-the pathogenesis is yet to be fully elucidated. In recent years, studies have exhibited that the onset of AS is closely involved with oral and gut microbiota, which may initiate or worsen atherosclerotic processes through several mechanisms. As for how the two microbiomes affect AS, existing mechanisms include invading plaque, producing active metabolites, releasing lipopolysaccharide (LPS), and inducing elevated levels of inflammatory mediators.

MISD-IR: material-image subspace decomposition-based iterative reconstruction with spectrum estimation for dual-energy computed tomography.

Background: Dual-energy computed tomography (DECT) is a promising technique, which can provide unique capability for material quantification. The iterative reconstruction of material maps requires spectral information and its accuracy is affected by spectral mismatch. Simultaneously estimating the spectra and reconstructing material maps avoids extra workload on spectrum estimation and the negative impact of spectral mismatch.

Effects and microbiota changes following oral lyophilized fecal microbiota transplantation in children with autism spectrum disorder.

Background And Purpose: Autism spectrum disorder (ASD) is a group of heterogeneous neurodevelopmental disorders that is characterized by core features in social communication impairment and restricted, repetitive sensory-motor behaviors. This study aimed to further investigate the utilization of fecal microbiota transplantation (FMT) in children with ASD, both with and without gastrointestinal (GI) symptoms, evaluate the effect of FMT and analyze the alterations in bacterial and fungal composition within the gut microbiota.

Methods: A total of 38 children diagnosed with ASD participated in the study and underwent oral lyophilized FMT treatment.

Probiotic properties and the ameliorative effect on DSS-induced colitis of human milk-derived Lactobacillus gasseri SHMB 0001.

Human milk contains a variety of microorganisms that exert benefit for human health. In the current study, we isolated a novel Lactobacillus gasseri strain named Lactobacillus gasseri (L. gasseri) SHMB 0001 from human milk and aimed to evaluate the probiotic characteristics and protective effects on murine colitis of the strain.

A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data.

In this study, we introduce TESA (weighted two-stage alignment), an innovative motif prediction tool that refines the identification of DNA-binding protein motifs, essential for deciphering transcriptional regulatory mechanisms. Unlike traditional algorithms that rely solely on sequence data, TESA integrates the high-resolution chromatin immunoprecipitation (ChIP) signal, specifically from ChIP-exonuclease (ChIP-exo), by assigning weights to sequence positions, thereby enhancing motif discovery. TESA employs a nuanced approach combining a binomial distribution model with a graph model, further supported by a "bookend" model, to improve the accuracy of predicting motifs of varying lengths.

A dual-energy CT reconstruction method based on anchor network from dual quarter scans.

Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is an attractive research to promote the applications of DECT in wide range areas and reduce the radiation dose as low as reasonably achievable. In this work, we design a novel DECT imaging scheme with dual quarter scans and propose an efficient method to reconstruct the desired DECT images from the dual limited-angle projection data.

CEMIG: prediction of the cis-regulatory motif using the de Bruijn graph from ATAC-seq.

Sequence motif discovery algorithms enhance the identification of novel deoxyribonucleic acid sequences with pivotal biological significance, especially transcription factor (TF)-binding motifs. The advent of assay for transposase-accessible chromatin using sequencing (ATAC-seq) has broadened the toolkit for motif characterization. Nonetheless, prevailing computational approaches have focused on delineating TF-binding footprints, with motif discovery receiving less attention.

Deep Learning-Assisted Measurements of Photoreceptor Ellipsoid Zone Area and Outer Segment Volume as Biomarkers for Retinitis Pigmentosa.

The manual segmentation of retinal layers from OCT scan images is time-consuming and costly. The deep learning approach has potential for the automatic delineation of retinal layers to significantly reduce the burden of human graders. In this study, we compared deep learning model (DLM) segmentation with manual correction (DLM-MC) to conventional manual grading (MG) for the measurements of the photoreceptor ellipsoid zone (EZ) area and outer segment (OS) volume in retinitis pigmentosa (RP) to assess whether DLM-MC can be a new gold standard for retinal layer segmentation and for the measurement of retinal layer metrics.

Remote Monitoring of Visual Function in Patients with Maculopathy: The Aphelion Study.

Introduction: Remote monitoring of vision, using tools such as the shape discrimination hyperacuity (SDH) test, can detect disease activity in patients with maculopathy. We determined the in-clinic accuracy and repeatability of three myVisionTrack expanded version (mVTx) tests for self-testing of visual acuity (VA) and contrast sensitivity.

Methods: Aphelion, a single-arm, prospective study conducted at two sites in the USA, included adults with any maculopathy and a baseline VA of 0.

Deep Learning-Facilitated Study of the Rate of Change in Photoreceptor Outer Segment Metrics in RPGR-Related X-Linked Retinitis Pigmentosa.

Purpose: The aim of this retrospective cohort study was to obtain three-dimensional (3D) photoreceptor outer segment (OS) metrics measurements with the assistance of a deep learning model (DLM) and to evaluate the longitudinal change in OS metrics and associated factors in retinitis pigmentosa GTPase regulator (RPGR) X-linked retinitis pigmentosa (XLRP).

Methods: The study included 34 male patients with RPGR-associated XLRP who had preserved ellipsoid zone (EZ) within their spectral-domain optical coherence tomography volume scans and an approximate 2-year or longer follow-up. Volume scans were segmented using a DLM with manual correction for EZ and apical retinal pigment epithelium (RPE).

F. prausnitzii-derived extracellular vesicles attenuate experimental colitis by regulating intestinal homeostasis in mice.

Background: Emerging evidence has shown that extracellular vesicles (EVs) derived from gut bacteria play a crucial role in microbiota-host interactions. Here, we aimed to evaluate the attenuating effect of EVs derived from a reduced commensal bacterium, F. prausnitzii (Fp-EVs), in inflammatory bowel disease (IBD) on dextran sulfate sodium (DSS)-induced colitis in mice.

Association of multimorbidity patterns with motoric cognitive risk syndrome among older adults: Evidence from a China longitudinal study.

Objectives: Motoric cognitive risk syndrome (MCR), a pre-dementia syndrome, is characterized by slow gait and subjective cognitive complaints among older adults. This study assessed the relationship between multimorbidity, its patterns, and MCR.

Methods: Data for this study were obtained from three waves (2011, 2013, and 2015) of the China Health and Retirement Longitudinal Study.

The interplay between oral microbiota, gut microbiota and systematic diseases.

Over the past two decades, the importance of microbiota in health and disease has become evident. The human gut microbiota and oral microbiota are the largest and second-largest microbiome in the human body, respectively, and they are physically connected as the oral cavity is the beginning of the digestive system. Emerging and exciting evidence has shown complex and important connections between gut microbiota and oral microbiota.

Altered gut microbiota and microbial metabolism in children with hepatic glycogen storage disease: a case-control study.

Background: Accumulating evidence has demonstrated that gut microbiota dysbiosis correlated with altered metabolism are implicated in liver metabolic diseases. However, data on pediatric hepatic glycogen storage disease (GSD) are limited. Here, we aimed to investigate the features of the gut microbiota and metabolites in hepatic GSD children from China.

Home vision monitoring in patients with maculopathy: current and future options for digital technologies.

Patients with macular pathology, including that caused by age-related macular degeneration and diabetic macular oedema, must attend frequent in-clinic monitoring appointments to detect onset of disease activity requiring treatment and to monitor progression of existing disease. In-person clinical monitoring places a significant burden on patients, caregivers and healthcare systems and is limited in that it only provides clinicians with a snapshot of the patient's disease status. The advent of remote monitoring technologies offers the potential for patients to test their own retinal health at home in collaboration with clinicians, reducing the need for in-clinic appointments.

Generation and application of a convolutional neural networks algorithm in evaluating stool consistency in diapers.

Aim: The aim of the study was to develop a deep convolutional neural networks (CNNs) algorithm for automated assessment of stool consistency from diaper photographs and test its performance under real-world conditions.

Methods: Diaper photographs were enrolled via a mobile phone application. The stool consistency was assessed independently according to the Brussels Infant and Toddler Stool Scale (BITSS) by paediatricians.