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.

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.

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.

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.

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.

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. In this review we discuss visual function tests, both existing and novel, that have the potential for remote use and consider their suitability for discriminating the presence of disease and progression of disease. We then review the clinical evidence supporting the use of mobile applications for monitoring of visual function from clinical development through to validation studies and real-world implementation. This review identified seven app-based visual function tests: four that have already received some form of regulatory clearance and three under development. The evidence included in this review shows that remote monitoring offers great potential for patients with macular pathology to monitor their condition from home, reducing the need for burdensome clinic visits and expanding clinicians' understanding of patients' retinal health beyond traditional clinical monitoring. In order to instil confidence in the use of remote monitoring in both patients and clinicians further longitudinal real-world studies are now warranted.

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. These images were randomised into a training data set and a test data set. After training and testing, the new algorithm was used under real-world conditions by parents. RESULTS: There was an overall agreement of 92.9% between paediatricians and the CNN-generated algorithm. Post hoc classification into the validated 4 categories of the BITSS yielded an agreement of 95.4%. Spearman correlation analysis across the ranking of 7 BITSS photographs and validated 4 categories showed a significant correlation of rho = 0.93 (95% CI, 0.92, 0.94; p < 0.001) and rho = 0.92 (95% CI, 0.90, 0.93; p < 0.001), respectively. The real-world application yielded further insights into changes in stool consistency between age categories and mode of feeding. CONCLUSION: The new CNN-based algorithm is able to reliably identify stool consistency from diaper photographs and may support the communication between parents and paediatricians.

Altered gut microbiota composition in children and their caregivers infected with the SARS-CoV-2 Omicron variant.

BACKGROUND: Gut microbiota alterations have been implicated in the pathogenesis of coronavirus disease 2019 (COVID-19). This study aimed to explore gut microbiota changes in a prospective cohort of COVID-19 children and their asymptomatic caregivers infected with the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) Omicron variant. METHODS: A total of 186 participants, including 59 COVID-19 children, 50 asymptomatic adult caregivers, 52 healthy children (HC), and 25 healthy adults (HA), were recruited between 15 April and 31 May 2022. The gut microbiota composition was determined by 16S rRNA gene sequencing in fecal samples collected from the participants. Gut microbiota functional profiling was performed by using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) software. RESULTS: The gut microbiota analysis of beta diversity revealed that the fecal microbial community of COVID-19 children remained far distantly related to HC. The relative abundances of the phyla Actinobacteria and Firmicutes were decreased, whereas Bacteroidetes, Proteobacteria, and Verrucomicrobiota were increased in COVID-19 children. Feces from COVID-19 children exhibited notably lower abundances of the genera Blautia, Bifidobacterium, Fusicatenibacter, Streptococcus, and Romboutsia and higher abundances of the genera Prevotella, Lachnoclostridium, Escherichia-Shigella, and Bacteroides than those from HC. The enterotype distributions of COVID-19 children were characterized by a high prevalence of enterotype Bacteroides. Similar changes in gut microbiota compositions were observed in asymptomatic caregivers. Furthermore, the microbial metabolic activities of KEGG (Kyoto Encyclopedia of Genes and Genomes) and COG (cluster of orthologous groups of proteins) pathways were perturbed in feces from subjects infected with the SARS-CoV-2 Omicron variant. CONCLUSION: Our data reveal altered gut microbiota compositions in both COVID-19 children and their asymptomatic caregivers infected with the SARS-CoV-2 Omicron variant, which further implicates the critical role of gut microbiota in COVID-19 pathogenesis.

Projection domain processing for low-dose CT reconstruction based on subspace identification.

PURPOSE: Low-dose computed tomography (LDCT) has promising potential for dose reduction in medical applications, while suffering from low image quality caused by noise. Therefore, it is in urgent need for developing new algorithms to obtain high-quality images for LDCT. METHODS: This study tries to exploit the sparse and low-rank properties of images and proposes a new algorithm based on subspace identification. The collection of transmission data is sparsely represented by singular value decomposition and the eigen-images are then denoised by block-matching frames. Then, the projection is regularized by the correlation information under the frame of prior image compressed sensing (PICCS). With the application of a typical analytical algorithm on the processed projection, the target images are obtained. Both numerical simulations and real data verifications are carried out to test the proposed algorithm. The numerical simulations data is obtained based on real clinical scanning three-dimensional data and the real data is obtained by scanning experimental head phantom. RESULTS: In simulation experiment, using new algorithm boots the means of PSNR and SSIM by 1 dB and 0.05, respectively, compared with BM3D under the Gaussian noise with variance 0.04. Meanwhile, on the real data, the proposed algorithm exhibits superiority over compared algorithms in terms of noise suppression, detail preservation and computational overhead. The means of PSNR and SSIM are improved by 1.84 dB and 0.1, respectively, compared with BM3D under the Gaussian noise with variance 0.04. CONCLUSION: This study demonstrates the feasibility and advantages of a new algorithm based on subspace identification for LDCT. It exploits the similarity among three-dimensional data to improve the image quality in a concise way and shows a promising potential on future clinical diagnosis.

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. Herein, we present Cis rEgulatory Motif Influence using de Bruijn Graph (CEMIG), an algorithm leveraging de Bruijn and Hamming distance graph paradigms to predict and map motif sites. Assessment on 129 ATAC-seq datasets from the Cistrome Data Browser demonstrates CEMIG's exceptional performance, surpassing three established methodologies on four evaluative metrics. CEMIG accurately identifies both cell-type-specific and common TF motifs within GM12878 and K562 cell lines, demonstrating its comparative genomic capabilities in the identification of evolutionary conservation and cell-type specificity. In-depth transcriptional and functional genomic studies have validated the functional relevance of CEMIG-identified motifs across various cell types. CEMIG is available at https://github.com/OSU-BMBL/CEMIG, developed in C++ to ensure cross-platform compatibility with Linux, macOS and Windows operating systems.

Case Report: Eosinophilic gastritis with pyloric stenosis in immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome.

Immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome is a rare X-linked recessive immunodeficiency caused by mutations in the forkhead box protein 3 (FOXP3) gene. IPEX is characterized by the onset of intractable diarrhea, type 1 diabetes mellitus (T1DM), and eczema in the early stages of life. The typical clinic triad for IPEX is not always seen. Here, we report a 15-year-old male patient with atypical IPEX syndrome complicated with severe eosinophilic gastritis (EG) and pyloric stenosis. The patient had noticeable eczema during the first year of life and had a history of food allergies. At the age of 3 years, the patient was diagnosed with EG, Helicobacter pylori (HP) infection, pyloric stenosis with recurrent vomiting, and failure to thrive. The patient did not respond to long-term symptomatic treatments in the following years, including methylprednisolone, proton pump inhibitors (PPI), L-glutamine and sodium gualenate granules, anti-HP therapy, and balloon dilation. At the age of 12 years, the patient received surgical interventions, including a laparoscopic jejunostomy feeding tube placement, gastrojejunal anastomosis bypass, and jejunal-jejunal end-to-side anastomosis. Intractable diarrhea and T1DM were not present in the patient. At the age of 14 years, the patient was diagnosed with IPEX syndrome due to a c.748-750del (p.Lys250del) mutation in the leucine zipper domain of the FOXP3 protein. The patient underwent matched sibling peripheral blood hematopoietic stem cell transplantation (HSCT) and showed good evolution after 3 months of HSCT. In summary, this case report provides information of unusual gastrointestinal findings in IPEX syndrome and highlights the need for increased awareness and early diagnosis of IPEX syndrome, which is vital for improving the patient's outcome.

Fecal Microbiota Transplantation in Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders that begin in infancy. In recent years, the incidence of ASD in the world is increasing year by year. At present, the etiology and pathogenesis of ASD are not clear, and effective treatments are still lacking. In addition to neurobehavioral symptoms, children with ASD often have obvious gastrointestinal symptoms. Gut microbiota is a large microbial community in the human gut, which is closely related to the nervous system and can affect brain development and behavior through the neuroendocrine, neuroimmune and autonomic nervous systems, forming a microbiota-gut-brain axis connection. Recent studies have shown that children with ASD have significant gut microbiota and metabolic disorders, and fecal microbiota transplantation (FMT) is expected to improve ASD-related symptoms by regulating gut microbiota and metabolism. This review paper will therefore focus on FMT in the treatment of ASD, and FMT is effective in improving gastrointestinal and neurobehavioral symptoms in children with ASD.