Patch-free streaming contrast-rebalanced dichoptic cartoons versus patching for treatment of amblyopia in children aged 3 to 5 years: a pilot, randomized clinical trial.

BACKGROUND: We developed and tested a dichoptic treatment designed for younger children that can be viewed freely and involves a dichoptic manipulation of a popular animation series that enables contrast-rebalancing without disrupting fusion. Our aim was to assess whether this novel amblyopia treatment is superior to patching in children aged 3-5 years. METHODS: A total of 34 children with amblyopia were randomly assigned to contrast-rebalanced dichoptic cartoons (4 hours/week) or patching (14 hours/week) for 2 weeks. Children in the cartoon group continued watching cartoons for an additional 2 weeks. Designed to target the youngest and most treatable children, the dichoptic cartoons presented the entire scene to the amblyopic eye at 100% contrast, while the fellow eye view was presented at reduced contrast with the main character omitted. Best-corrected visual acuity (BCVA), stereoacuity, suppression, and manual dexterity were measured at each visit. RESULTS: After 2 weeks, improvement in amblyopic eye BCVA was greater for dichoptic treatment than for patching, with a mean improvement of 0.11 ± 0.08 versus 0.06 ± 0.09 logMAR, respectively (P = 0.04). Stereoacuity, suppression, and manual dexterity did not improve significantly more in the dichoptic group than the patching group at 2 weeks. After 4 weeks of dichoptic cartoon treatment, mean visual acuity improvement in the dichoptic group was 0.16 logMAR (95% CI, 0.10-0.21). CONCLUSIONS: In our study cohort, a contrast-rebalanced dichoptic cartoon was more effective than patching in treating childhood amblyopia after 2 weeks. Dichoptic cartoons that rebalance contrast to overcome suppression provide an additional treatment option for amblyopia in young children.

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

Background: Recurrent Clostridioides difficile 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. Fecal samples collected at the initial diagnosis were subjected to 16S rRNA gene sequencing and targeted metabolomics analysis to profile the bacterial composition and metabolome. Results: Twenty-six of 84 (31.0%) pediatric CDI patients experienced recurrence. The alpha diversity of the fecal microbiota was significantly lower in the recurrent group than in the nonrecurrent group, and the beta diversity was different from that of the nonrecurrent group. Taxonomic profiles revealed that the relative abundances of multiple bacterial taxa significantly differed between the recurrent and nonrecurrent groups. Linear discriminant analysis effect size analysis identified several bacterial genera that discriminated between recurrent and nonrecurrent groups, including Parabacteroides, Coprococcus, Dialister, and Clostridium. Recurrent bacteria presented lower abundances of several short-chain fatty acid (SCFA)-producing bacteria (Faecalibacterium, Butyricicoccus, Clostridium, Roseburia, and Ruminococcus), which were correlated with reduced fecal SCFA levels. In addition, several bile acids, including lithocholic acid (LCA), 12-ketoLCA, trihydroxycholestanoic acid, and deoxycholic acid, were decreased in recurrent patients. Conclusions: Our study suggests that the differing gut microbiota profiles in pediatric CDI patients may contribute to disease recurrence by modulating SCFA concentrations and bile acid profiles. The gut microbiota and metabolite signatures may be used to predict disease recurrence in children with CDI.

Deep learning aided measurement of outer retinal layer metrics as biomarkers for inherited retinal degenerations: opportunities and challenges.

PURPOSE OF REVIEW: The purpose of this review was to provide a summary of currently available retinal imaging and visual function testing methods for assessing inherited retinal degenerations (IRDs), with the emphasis on the application of deep learning (DL) approaches to assist the determination of structural biomarkers for IRDs. RECENT FINDINGS: (clinical trials for IRDs; discover effective biomarkers as endpoints; DL applications in processing retinal images to detect disease-related structural changes). SUMMARY: Assessing photoreceptor loss is a direct way to evaluate IRDs. Outer retinal layer structures, including outer nuclear layer, ellipsoid zone, photoreceptor outer segment, RPE, are potential structural biomarkers for IRDs. More work may be needed on structure and function relationship.

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. Endoscopic examination, which included both upper gastrointestinal endoscopy and enteroscopy, showed diffuse polyposis. Histopathology results indicated the presence of juvenile polyps with no dysplasia in all removed polyps. Genetic testing identified a 2.1 Mb deletion on chromosome 10q23.2q23.31 involving the phosphatase and tensin homolog (PTEN) and bone morphogenetic protein receptor type IA (BMPR1A) genes. Treatment with sirolimus initiated at 10 months of age led to a reduction in the need for blood and albumin infusions, improved patient growth, and quality of life. While the frequency of endoscopic evaluations decreased with sirolimus, regular endoscopic polypectomy every 5 months remained necessary. However, discontinuation of sirolimus resulted in polyp recurrence after 2 months due to pneumonia. CONCLUSION: This case highlights sirolimus treatment can alleviate many complications of JPI, it does not eliminate the need for aggressive polypectomy.

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. METHODS: To address this issue, an end-to-end multi-material decomposition network based on prior images is proposed in this paper. First, the reconstructed images corresponding to the full spectrum with less noise are introduced as prior information to improve the overall signal-to-noise ratio of the data. Then, a generative adversarial network is designed to mine the relationship between reconstructed images and basis material images based on the information interaction of material decomposition. Furthermore, a weighted edge loss is introduced to adapt to the structural differences of different basis material images. RESULTS: To verify the performance of the proposed method, simulation and real studies are carried out. In simulation study of structured fibro-glandular tissue model, the results show that the proposed method decreased the root mean square error by 67 % and 26 % on adipose, 66 % and 28 % on fibroglandular, 52 % and 8 % on calcification, compared to butterfly network and dual interactive Wasserstein generative adversarial network. CONCLUSION: Experimentally, the proposed method shows certain advantages over other methods on noise suppression effect, detail retention ability and decomposition accuracy.

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. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.

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. However, existing methods are not satisfactory in image detail preservation, edge retention, and convergence rate. The purpose of this paper was to mine the similarity between the reconstructed images and the material images to improve the imaging quality, and to design an effective iteration strategy to improve the convergence efficiency. Methods: The material-image subspace decomposition-based iterative reconstruction (MISD-IR) with spectrum estimation was proposed for DECT. MISD-IR is an optimized model combining spectral estimation and material reconstruction with fast convergence speed and promising noise suppression capability. We proposed to reconstruct the material maps based on extended simultaneous algebraic reconstruction techniques and estimation of the spectrum with model spectral. To stabilize the iteration and alleviate the influence of errors, we introduced a weighted proximal operator based on the block coordinate descending algorithm (WP-BCD). Furthermore, the reconstructed computed tomography (CT) images were introduced to suppress the noise based on subspace decomposition, which relies on non-local regularization to prevent noise accumulation. Results: In numerical experiments, the results of MISD-IR were closer to the ground truth compared with other methods. In real scanning data experiments, the results of MISD-IR showed sharper edges and details. Compared with other one-step iterative methods in the experiment, the running time of MISD-IR was reduced by 75%. Conclusions: The proposed MISD-IR can achieve accurate material decomposition (MD) without known energy spectrum in advance, and has good retention of image edges and details. Compared with other one-step iterative methods, it has high convergence efficiency.

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. Considering the possible profound connection between oral and gut microbiota, the effect of the interaction between the two microbiomes on the initiation and progression of AS has been investigated. Findings are oral microbiota can lead to gut dysbiosis, and exacerbate intestinal inflammation. Nevertheless, relevant research is not commendably refined and a concrete review is needed. Hence, in this review, we summarize the most recent mechanisms of the oral microbiota and gut microbiota on AS, illustrate an overview of the current clinical and epidemiological evidence to support the bidirectional connection between the two microbiomes and AS.

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. The dosage of the FMT treatment was determined based on a ratio of 1 g of donor stool per 1 kg of recipient body weight, with a frequency of once every 4 weeks for a total of 12 weeks. In addition, 30 healthy controls (HC) were included in the analysis. The clinical efficacy of FMT was evaluated, while the composition of fecal bacteria and fungi was determined using 16S rRNA and ITS gene sequencing methods. Results: Median age of the 38 children with ASD was 7 years. Among these children, 84.2% (32 of 38) were boys and 81.6% (31 of 38) exhibited GI symptoms, with indigestion, constipation and diarrhea being the most common symptoms. Sample collections and assessments were conducted at baseline (week 0), post-treatment (week 12) and follow-up (week 20). At the end of the follow-up phase after FMT treatment, the autism behavior checklist (ABC) scores decreased by 23% from baseline, and there was a 10% reduction in scores on the childhood autism rating scale (CARS), a 6% reduction in scores on the social responsiveness scale (SRS) and a 10% reduction in scores on the sleep disturbance scale for children (SDSC). In addition, short-term adverse events observed included vomiting and fever in 2 participants, which were self-limiting and resolved within 24 h, and no long-term adverse events were observed. Although there was no significant difference in alpha and beta diversity in children with ASD before and after FMT therapy, the FMT treatment resulted in alterations in the relative abundances of various bacterial and fungal genera in the samples of ASD patients. Comparisons between children with ASD and healthy controls (HC) revealed statistically significant differences in microbial abundance before and after FMT. Blautia, Sellimonas, Saccharomycopsis and Cystobasidium were more abundant in children with ASD than in HC, while Dorea were less abundant. After FMT treatment, levels of Blautia, Sellimonas, Saccharomycopsis and Cystobasidium decreased, while levels of Dorea increased. Moreover, the increased abundances of Fusicatenibacter, Erysipelotrichaceae_UCG-003, Saccharomyces, Rhodotorula, Cutaneotrichosporon and Zygosaccharomyces were negatively correlated with the scores of ASD core symptoms. Conclusions: Oral lyophilized FMT could improve GI and ASD related symptoms, as well as sleep disturbances, and alter the gut bacterial and fungal microbiota composition in children with ASD. Clinical Trial Registration: Chinese Clinical Trial Registry, ChiCTR2200055943. Registered 28 January 2022, www.chictr.org.cn.

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. The results showed that L. gasseri SHMB 0001 possessed promising potential probiotic characteristics, including good tolerance against artificial gastric and intestinal fluids, adhesion to Caco-2 cells, susceptibility to antibiotic, no hemolytic activity, and without signs of toxicity or infection in mice. Administration of L. gasseri SHMB 0001 (1 × 108 CFU per gram of mouse weight per day) reduced weight loss, the disease activity index, and colon shortening in mice during murine colitis conditions. Histopathological analysis revealed that L. gasseri SHMB 0001 treatment attenuated epithelial damage and inflammatory infiltration in the colon. L. gasseri SHMB 0001 treatment increased the expression of colonic occludin and claudin-1 while decreasing the expression of pro-inflammatory cytokine genes. L. gasseri SHMB 0001 modified the composition and structure of the gut microbiota community and partially recovered the Clusters of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways altered by dextran sulfate sodium (DSS). Overall, our results indicated that the human breast milk-derived L. gasseri SHMB 0001 exhibited promising probiotic properties and ameliorative effect on DSS-induced colitis in mice. L. gasseri SHMB 0001 may be applied as a promising probiotic against intestinal inflammation in the future. PRACTICAL APPLICATION: L. gasseri SHMB 0001 isolated from human breast milk showed good tolerance to gastrointestinal environment, safety, and protective effect against DSS-induced mice colitis via enforcing gut barrier, downregulating pro-inflammatory cytokines, and modulating gut microbiota. L. gasseri SHMB 0001 may be a promising probiotic candidate for the treatment of intestinal inflammation.

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. Our evaluation, utilizing an extensive compilation of 90 prokaryotic ChIP-exo datasets from proChIPdb and 167 H. sapiens datasets, compared TESA's performance against seven established tools. The results indicate TESA's improved precision in motif identification, suggesting its valuable contribution to the field of genomic research.

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. We first study the characteristics of limited-angle artifacts under dual quarter scans scheme, and find that the negative and positive artifacts of DECT images are complementarily distributed in image domain because the corresponding X-rays of high- and low-energy scans are symmetric. Inspired by this finding, a fusion CT image is generated by integrating the limited-angle DECT images of dual quarter scans. This strategy enhances the true image information and suppresses the limited-angle artifacts, thereby restoring the image edges and inner structures. Utilizing the capability of neural network in the modeling of nonlinear problem, a novel Anchor network with single-entry double-out architecture is designed in this work to yield the desired DECT images from the generated fusion CT image. Experimental results on the simulated and real data verify the effectiveness of the proposed method. This work enables DECT on imaging configurations with half-scan and largely reduces scanning angles and radiation doses.

Antimicrobial Resistance of Clostridioides difficile in Children from a Tertiary Pediatric Hospital in Shanghai, China.

Background: Our previous study reported a high rate of recurrence in children with Clostridioides difficile (C. difficile) infection (CDI) after conventional antibiotic therapy. Here, we aimed to explore whether metronidazole and vancomycin resistant C. difficile isolates are circulating in pediatric CDI. Methods: Antimicrobial susceptibility testing (AST) using the agar dilution method according to the Clinical and Laboratory Standard Institute (CLSI) were performed on C. difficile isolates collected from children with CDI between 2019 and 2022 at the Shanghai Children's Hospital. Whole-genome sequencing (WGS) was performed on all C. difficile isolates, and the presence of antibiotic resistance genes (ARGs) were identified using Resfinder and the Comprehensive Antibiotic Resistance Database (CARD). The presence of plasmid pCD-METRO was detected using SRST2 (v0.2.0) against 8 pCD-METRO coding sequences. Results: A total of 50 C. difficile isolates were collected from stools of CDI children. The overall resistance rate on all isolates was 30.00% for metronidazole, 6.00% for vancomycin, 0% for rifaximin, 2.00% for rifampin, 24.00% for meropenem, 100.00% for ceftriaxone and clindamycin, 86.00% for erythromycin, 30.0% for levofloxacin, and 50.0% for tetracycline. Multidrug-resistant (MDR) was presented in 44 isolates (88.00%). Sixteen reported potential ARGs relating with resistance to antibiotic classes of aminoglycoside (AAC(6')-Ie-APH(2")-Ia, aad(6), ANT(6)-Ib, APH(2")-If, APH(3')-IIIa), lincosamide-clindamycin-erythromycin (ErmB, ErmQ), fluoroquinolones (CdeA), glycopeptides (vanRG), nucleoside (SAT-4), tetracycline (tetM, tetA(P), tetB(P), tetO), and trimethoprim (dfrF) were identified. However, the pCD-METRO plasmid and vanA/B were not detected in any isolates. Conclusion: C. difficile isolates from children with reduced susceptibility to metronidazole and vancomycin are emerging in pediatric CDI in China. The lack of pCD-METRO plasmid and vanA/B associated with reduced antibiotic susceptibility suggests there are additional mechanisms of resistance.

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 0.7 log of minimum angle of resolution (logMAR) (Snellen 20/100) or better. Participants completed the mVTx tests (tumbling E, Landolt C, contrast sensitivity, and SDH) and standard clinical tests (near and distance Early Treatment Diabetic Retinopathy Study [ETDRS] charts and the Pelli-Robson contrast sensitivity chart). Test-retest repeatability and agreement between the mVTx tests and the corresponding clinical test were assessed by Bland-Altman analyses. Participants also completed a usability survey. RESULTS: The mean age of the 122 participants was 67 years. The most common diagnosis was age-related macular degeneration (42% of patients). The tumbling E test had a test-retest 95% limit of agreement (LoA) of ± 0.18 logMAR; the Landolt C test, ± 0.23 logMAR; the SDH test, ± 0.24 logMAR; and the contrast sensitivity test, ± 0.32 log contrast threshold (logCT). Compared with the distance ETDRS chart, the LoA was ± 0.35 logMAR for the tumbling E test (mean difference, - 0.07 logMAR) and ± 0.39 logMAR for the Landolt C test (mean difference, 0.03 logMAR). For the contrast sensitivity test, the LoA compared with the Pelli-Robson chart was ± 0.30 logCT (mean difference, - 0.25 logCT). Most participants (85%) reported that they learned the tests quickly. The tumbling E test scored the highest on ease of use. CONCLUSION: The mVTx tests of VA are accurate and repeatable, supporting their potential use alongside the SDH test to detect disease progression remotely between clinic visits.

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. Ninety-six high-speed 9 mm 31-line volume scans obtained from 48 patients with RPGR-associated XLRP were selected based on the following criteria: the presence of an EZ band within the scan limit and a detectable EZ in at least three B-scans in a volume scan. All the B-scan images in each volume scan were manually segmented for the EZ and proximal retinal pigment epithelium (pRPE) by two experienced human graders to serve as the ground truth for comparison. The test volume scans were also segmented by a DLM and then manually corrected for EZ and pRPE by the same two graders to obtain DLM-MC segmentation. The EZ area and OS volume were determined by interpolating the discrete two-dimensional B-scan EZ-pRPE layer over the scan area. Dice similarity, Bland-Altman analysis, correlation, and linear regression analyses were conducted to assess the agreement between DLM-MC and MG for the EZ area and OS volume measurements. For the EZ area, the overall mean dice score (SD) between DLM-MC and MG was 0.8524 (0.0821), which was comparable to 0.8417 (0.1111) between two MGs. For the EZ area > 1 mm2, the average dice score increased to 0.8799 (0.0614). When comparing DLM-MC to MG, the Bland-Altman plots revealed a mean difference (SE) of 0.0132 (0.0953) mm2 and a coefficient of repeatability (CoR) of 1.8303 mm2 for the EZ area and a mean difference (SE) of 0.0080 (0.0020) mm3 and a CoR of 0.0381 mm3 for the OS volume. The correlation coefficients (95% CI) were 0.9928 (0.9892-0.9952) and 0.9938 (0.9906-0.9958) for the EZ area and OS volume, respectively. The linear regression slopes (95% CI) were 0.9598 (0.9399-0.9797) and 1.0104 (0.9909-1.0298), respectively. The results from this study suggest that the manual correction of deep learning model segmentation can generate EZ area and OS volume measurements in excellent agreement with those of conventional manual grading in RP. Because DLM-MC is more efficient for retinal layer segmentation from OCT scan images, it has the potential to reduce the burden of human graders in obtaining quantitative measurements of biomarkers for assessing disease progression and treatment outcomes in RP.

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. Participants who were aged 60 years and older and had complete data at baseline as well as complete data about MCR at follow-up were selected. Patients without MCR at baseline were selected for further analyses. Longitudinal associations between multimorbidity, its patterns, and MCR were examined using a Cox proportional hazards model. Multimorbidity patterns were classified using latent class analysis. RESULTS: A total of 4923 respondents were included at baseline, 43.47% of whom had multimorbidity. Additionally, the prevalence of MCR at baseline was 12.61%. After adjusting for covariates, multimorbidity was positively associated with MCR (hazard ratio [HR] = 1.33, 95% confidence interval [CI] = 1.06-1.68). A higher number of multimorbidity was also significantly associated with an increased risk of developing MCR (HR = 1.10, 95% CI = 1.02-1.19). Three multimorbidity patterns were selected: relatively healthy pattern, respiratory pattern, and cardiovascular pattern. Older adults with the cardiovascular pattern were 1.57 times more likely to develop MCR than those with the relatively healthy pattern (HR = 1.57, 95% CI = 1.16-2.13). There was no significant difference between the relatively healthy pattern and the respiratory pattern (HR = 1.31, 95% CI = 0.91-1.92). CONCLUSIONS: MCR is highly prevalent among older Chinese adults. MCR may be exacerbated by multimorbidity. For older adults with multimorbidity (especially cardiovascular multimorbidity), attention should be paid to MCR to achieve early detection, diagnosis, and treatment.

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. RESULTS: Fp-EVs isolated by ultracentrifugation and typically exhibited a double concave disc shape with an average diameter of 172 nm. Fp-EVs treatment reduced DSS-induced weight loss, disease activity index (DAI) score, colon length shortening, histological damage, neutrophil infiltration and increased intestinal epithelial apoptotic cells in DSS-induced colitis mice. Fp-EVs upregulated the protein expression of zona occludens (ZO)-1 and Occludin and increased the ratio of Tregs in the colon tissue of colitis mice. Furthermore, Fp-EVs downregulated the expression of the proinflammatory cytokines interleukin-1ß (IL-1ß), IL-2, IL-6, IL-12a, IL-17a, Interferon-γ (IFN-γ), tumor necrosis factor - α (TNF-α), granulocyte-macrophage colony stimulating factor (GM-CSF) and upregulated the anti-inflammatory cytokines IL-4, IL-10, and transforming growth factor ß (TGF-ß) in DSS-treated mice. Moreover, Fp-EV treatment markedly reduced the phosphorylation of these proteins Nuclear factor-κB (NF-κB) and Mitogen activated protein kinase (MAPK), and regulated the expression of nuclear factor erythroid 2-related factor (Nrf2) and heme oxygenase-1 (HO-1). CONCLUSION: Our findings revealed that Fp-EVs attenuated DSS-induced colitis by modulating the intestinal mucosal barrier function and immunological profile. Our findings reveal that Fp-EVs attenuate DSS-induced colitis by modulating intestinal mucosal barrier function and the immunological profile.

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). OS metrics were measured from 3D EZ-RPE layers of volume scans. Linear mixed-effects models were used to calculate the rate of change in OS metrics and the associated factors, including baseline age, baseline OS metrics, and follow-up duration. Results: The mean (standard deviation) of progression rates were -0.28 (0.43) µm/y, -0.73 (0.61) mm2/y, and -0.014 (0.012) mm3/y for OS thickness, EZ area, and OS volume, respectively. In multivariable analysis, the progression rates of EZ area and OS volume were strongly associated with their baseline values, with faster decline in eyes with larger baseline values (P ≤ 0.003), and nonlinearly associated with the baseline age (P ≤ 0.003). OS thickness decline was not associated with its baseline value (P = 0.32). Conclusions: These results provide evidence to support using OS metrics as biomarkers to assess the progression of XLRP and as the outcome measures of clinical trials. Given that their progression rates are dependent on their baseline values, the baseline EZ area and OS volume should be considered in the design and statistical analysis of future clinical trials. Deep learning may provide a useful tool to reduce the burden of human graders to analyze OCT scan images and to facilitate the assessment of disease progression and treatment trials for retinitis pigmentosa.

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. Methods: Totals of 22 hepatic GSD patients and 16 age- and gender-matched healthy children were enrolled from the Shanghai Children's Hospital, China. Pediatric GSD patients were confirmed as having hepatic GSD via genetic diagnosis and/or liver biopsy pathology. The control group comprised children without any history of chronic diseases or clinically relevant GSD or symptoms of any other metabolic diseases. The baseline characteristics of the two groups were gender- and age-matched matched using chi-squared test and the Mann-Whitney U test, respectively. The gut microbiota, bile acids (BAs), and short chain fatty acids (SCFAs) were determined from the feces using 16S ribosomal RNA (rRNA) gene sequencing, ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), and gas chromatography-mass spectrometry (GC-MS), respectively. Results: The alpha diversity of fecal microbiome was significantly lower in hepatic GSD patients [observed species richness (Sobs): P=0.011; abundance-based coverage estimator (ACE): P=0.011; Chao: P=0.011; Shannon: P<0.001], and their microbial community was more distanced from that of the control [principal coordinate analysis (PCoA) on genus level, unweighted UniFrac: P=0.011]. Relative abundances of phyla Firmicutes (P=0.030) and Bacteroidetes (P=0.029), families Lachnospiraceae (P=0.012), Ruminococcaceae (P=0.008), and Peptostreptococcaceare (P=0.031), genera Blautia (P=0.017), Eubacterium_hallii_group (P=0.032), and Faecalibacterium (P=0.017) were decreased, whereas phyla Actinobacteria (P=0.033), Proteobacteria (P=0.049), families Bifidobacteriaceae (P=0.030), Lactobacillaceae (P=0.034), and Veillonellaceae (P=0.033), genera Lactobacillus (P=0.011), Enterobater (P=0.034), and Veillonella (P=0.014) were increased in hepatic GSD. Altered microbial metabolisms were characterized by increased abundances of primary BAs (P=0.009) and decreased concentrations of SCFAs in hepatic GSD children. Furthermore, the altered bacterial genera were correlated with the changes of both fecal BAs and SCFAs. Conclusions: The hepatic GSD patients in this study presented with gut microbiota dysbiosis which correlated with altered BAs metabolism and fecal SCFAs changes. Further studies are needed to investigate the driver of these changes mediated by either the genetic defect, disease status, or diet therapy.

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. The interplay of the two microbiomes may contribute to the pathological processes of many diseases, including diabetes, rheumatoid arthritis, nonalcoholic fatty liver disease, inflammatory bowel disease, pancreatic cancer, colorectal cancer, and so on. In this review, we discuss possible routes and factors of oral microbiota to affect gut microbiota, and the contribution of this interplay between oral and gut microbiota to systemic diseases. Although most studies are association studies, recently, there have been increasing mechanistic investigations. This review aims to enhance the interest in the connection between oral and gut microbiota, and shows the tangible impact of this connection on human health.