Fecal Microbiota Transplantation for Sleep Disturbance in Post-acute COVID-19 Syndrome.

BACKGROUND & AIMS: Post-acute COVID-19 syndrome (PACS) is associated with sleep disturbance, but treatment options are limited. The etiology of PACS may be secondary to alterations in the gut microbiome. Here, we report the efficacy of fecal microbiota transplantation (FMT) in alleviating post-COVID insomnia symptoms in a nonrandomized, open-label prospective interventional study. METHODS: Between September 22, 2022, and May 22, 2023, we recruited 60 PACS patients with insomnia defined as Insomnia Severity Index (ISI) ≥8 and assigned them to the FMT group (FMT at weeks 0, 2, 4, and 8; n = 30) or the control group (n = 30). The primary outcome was clinical remission defined by an ISI of <8 at 12 weeks. Secondary outcomes included changes in the Pittsburgh Sleep Quality Index, Generalized Anxiety Disorder-7 scale, Epworth Sleepiness Scale, Multidimensional Fatigue Inventory, blood cortisol and melatonin, and gut microbiome analysis on metagenomic sequencing. RESULTS: At week 12, more patients in the FMT than the control group had insomnia remission (37.9% vs 10.0%; P = .018). The FMT group showed a decrease in ISI score (P < .0001), Pittsburgh Sleep Quality Index (P < .0001), Generalized Anxiety Disorder-7 scale (P = .0019), Epworth Sleepiness Scale (P = .0057), and blood cortisol concentration (P = .035) from baseline to week 12, but there was no significant change in the control group. There was enrichment of bacteria such as Gemmiger formicilis and depletion of microbial pathways producing menaquinol derivatives after FMT. The gut microbiome profile resembled that of the donor in FMT responders but not in nonresponders at week 12. There was no serious adverse event. CONCLUSIONS: This pilot study showed that FMT could be effective and safe in alleviating post-COVID insomnia, and further clinical trials are warranted. CLINICALTRIALS: gov, Number: NCT05556733.

Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems.

The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased idle times. To solve the problems of missing sensor data in an ETC gantry system with large volumes and insufficient traffic detection among ETC gantries, this study constructs a high-order tensor model based on the analysis of the high-dimensional, sparse, large-volume, and heterogeneous characteristics of ETC gantry data. In addition, a missing data completion method for the ETC gantry data is proposed based on an improved dynamic tensor flow model. This study approximates the decomposition of neighboring tensor blocks in the high-order tensor model of the ETC gantry data based on tensor Tucker decomposition and the Laplacian matrix. This method captures the correlations among space, time, and user information in the ETC gantry data. Case studies demonstrate that our method enhances ETC gantry data quality across various rates of missing data while also reducing computational complexity. For instance, at a less than 5% missing data rate, our approach reduced the RMSE for time vehicle distance by 0.0051, for traffic volume by 0.0056, and for interval speed by 0.0049 compared to the MATRIX method. These improvements not only indicate a potential for more precise traffic data analysis but also add value to the application of ETC systems and contribute to theoretical and practical advancements in the field.

New implications for prion diseases therapy and prophylaxis.

Prion diseases are rare, fatal, progressive neurodegenerative disorders that affect both animal and human. Human prion diseases mainly present as Creutzfeldt-Jakob disease (CJD). However, there are no curable therapies, and animal prion diseases may negatively affect the ecosystem and human society. Over the past five decades, scientists are devoting to finding available therapeutic or prophylactic agents for prion diseases. Numerous chemical compounds have been shown to be effective in experimental research on prion diseases, but with the limitations of toxicity, poor efficacy, and low pharmacokinetics. The earliest clinical treatments of CJD were almost carried out with anti-infectious agents that had little amelioration of the course. With the discovery of pathogenic misfolding prion protein (PrPSc) and increasing insights into prion biology, amounts of novel technologies have attempted to eliminate PrPSc. This review presents new perspectives on clinical and experimental prion diseases, including immunotherapy, gene therapy, small-molecule drug, and stem cell therapy. It further explores the prospects and challenge associated with these emerging therapeutic approaches for prion diseases.

Water-stable boroxine structure with dynamic covalent bonds.

Boroxines are significant structures in the production of covalent organic frameworks, anion receptors, self-healing materials, and others. However, their utilization in aqueous media is a formidable task due to hydrolytic instability. Here we report a water-stable boroxine structure discovered from 2-hydroxyphenylboronic acid. We find that, under ambient environments, 2-hydroxyphenylboronic acid undergoes spontaneous dehydration to form a dimer with dynamic covalent bonds and aggregation-induced enhanced emission activity. Intriguingly, upon exposure to water, the dimer rapidly transforms into a boroxine structure with excellent pH stability and water-compatible dynamic covalent bonds. Building upon these discoveries, we report the strong binding capacity of boroxines toward fluoride ions in aqueous media, and develop a boroxine-based hydrogel with high acid-base stability and reversible gel-sol transition. This discovery of the water-stable boroxine structure breaks the constraint of boroxines not being applicable in aqueous environments, opening a new era of researches in boroxine chemistry.

Evaluation of different screening tools as the first step of the GLIM framework: A cross-sectional study of Chinese cancer patients in an outpatient setting.

BACKGROUND: Ambulatory cancer patients are at high risk of malnutrition. Multiple nutrition screening and assessment tools are used in the outpatient setting. This study aimed to evaluate the efficacy of different nutrition screening tools as the first step of the Global Leadership Initiative on Malnutrition (GLIM) framework in Chinese ambulatory cancer patients. METHODS: A cross-sectional study was conducted in a tertiary hospital in China. Malnutrition diagnoses made by the GLIM framework using Malnutrition Screening Tool, Malnutrition Universal Screening Tool, Nutritional Risk Screening 2002, or short-form of Patient-Gernerated Subjective Global Assessment (PG-SGA) as the first step were compared with PG-SGA separately. RESULTS: Of the 562 included patients, 31.0% were diagnosed with malnutrition (PG-SGA: B + C), and 12.6% were diagnosed with severe malnutrition (PG-SGA: C). As the screening tool in the first step of the GLIM framework, the short form of PG-SGA (PG-SGA SF) with a cutoff value of ≥2 performed best in diagnosing malnutrition, with good sensitivity (SE) (80.5% [73.6-85.9]) and specificity (SP) (98.4% [96.5-99.4]) and substantial accordance (κ = 0.826), whereas PG-SGA SF with a cutoff value of ≥4 performed best in diagnosing severe malnutrition, with fair SE (62.0% [49.6-73.0]), good SP (96.7% [94.6-98.1]) and moderate accordance (κ = 0.629). CONCLUSION: Using PG-SGA as the standard, the GLIM framework using PG-SGA SF as the screening tool has good accordance with the PG-SGA regardless of severity grading. PG-SGA SF can be used as a valid screening tool in the GLIM framework.

Efficacy, acceptability and tolerability of second-generation antipsychotics for behavioural and psychological symptoms of dementia: a systematic review and network meta-analysis.

BACKGROUND: Behavioural and psychological symptoms of dementia (BPSD) are highly prevalent in people living with dementia. Second-generation antipsychotics (SGAs) are commonly used to treat BPSD, but their comparative efficacy and acceptability are unknown. METHODS: The standard mean difference (SMD) was used to pool the fixed effects of continuous outcomes. We calculated ORs with corresponding 95% credible intervals (CI) for the categorical variable. Efficacy was defined as the scores improved on the standardised scales. Acceptability was defined as the all-cause dropout rate. Tolerability was defined as the discontinuation rate due to adverse effects (AEs). The relative treatment rankings were reported with the surface under the cumulative curve. The AE outcomes included mortality, cerebrovascular adverse events (CVAEs), falls, sedation, extrapyramidal symptoms and urinary symptoms. RESULTS: Twenty randomised controlled trials with a total of 6374 individuals containing 5 types of SGAs (quetiapine, olanzapine, risperidone, brexpiprazole and aripiprazole) with intervention lengths ranging from 6 weeks to 36 weeks were included in this network meta-analysis. For the efficacy outcome, compared with the placebo, brexpiprazole (SMD=-1.77, 95% CI -2.80 to -0.74) was more efficacious, and brexpiprazole was better than quetiapine, olanzapine and aripiprazole. Regarding acceptability, only aripiprazole (OR=0.72, 95% CI 0.54 to 0.96) was better than the placebo, and aripiprazole was also better than brexpiprazole (OR=0.61, 95% CI 0.37 to 0.99). In terms of tolerability, olanzapine was worse than placebo (OR=6.02, 95% CI 2.87 to 12.66), risperidone (OR=3.67, 95% CI 1.66 to 8.11) and quetiapine (OR=3.71, 95% CI 1.46 to 9.42), while aripiprazole was better than olanzapine (OR=0.25, 95% CI 0.08 to 0.78). Quetiapine presented good safety in CVAE. Brexpiprazole has better safety in terms of falls and showed related safety in sedation among included SGAs. CONCLUSION: Brexpiprazole showing great efficacy in the treatment of BPSD, with aripiprazole showing the highest acceptability and olanzapine showing the worst tolerability. The results of this study may be used to guide decision-making.

Development of automated neural network prediction for echocardiographic left ventricular ejection fraction.

Introduction: The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF). Methods: This paper aimed to quantify LVEF automatically and accurately with the proposed pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey's method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline's accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF. Results: This method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson's correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p < 0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200 echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval 0.88 to 0.91) for HFrEF assessment. Conclusion: The automated neural network-based calculation of LVEF is comparable to expert clinicians performing time-consuming, frame-by-frame manual evaluations of cardiac systolic function.

Precise Structural Analysis of Neutral Glycans Using Aerolysin Mutant T240R Nanopore.

Glycans play vital roles in nearly all life processes of multicellular organisms, and understanding these activities is inseparable from elucidating the biological significance of glycans. However, glycan research has lagged behind that of DNA and protein due to the challenges posed by structural heterogeneity and isomerism (i.e., structures with equal molecular weights) the lack of high-efficiency structural analysis techniques. Nanopore technology has emerged as a sensitive single-molecule biosensor, shining a light on glycan analysis. However, a significant number of glycans are small and uncharged, making it challenging to elicit identifiable nanopore signals. Here we introduce a R-binaphthyl tag into glycans, which enhances the cation-π interaction between the derivatized glycan molecules and the nanopore interface, enabling the detection of neutral glycans with an aerolysin nanopore. This approach allows for the distinction of di-, tri-, and tetrasaccharides with monosaccharide resolution and has the potential for group discrimination, the monitoring of enzymatic transglycosylation reactions. Notably, the aerolysin mutant T240R achieves unambiguous identification of six disaccharide isomers, trisaccharide and tetrasaccharide linkage isomers. Molecular docking simulations reveal that multiple noncovalent interactions occur between residues R282, K238, and R240 and the glycans and R-binaphthyl tag, significantly slowing down their translocation across the nanopore. Importantly, we provide a demonstration of the kinetic translocation process of neutral glycan isomers, establishing a solid theoretical foundation for glycan nanopore analysis. The development of our technology could promote the analysis of glycan structural isomers and has the potential for nanopore-based glycan structural determination and sequencing.

Multikingdom and functional gut microbiota markers for autism spectrum disorder.

Associations between the gut microbiome and autism spectrum disorder (ASD) have been investigated although most studies have focused on the bacterial component of the microbiome. Whether gut archaea, fungi and viruses, or function of the gut microbiome, is altered in ASD is unclear. Here we performed metagenomic sequencing on faecal samples from 1,627 children (aged 1-13 years, 24.4% female) with or without ASD, with extensive phenotype data. Integrated analyses revealed that 14 archaea, 51 bacteria, 7 fungi, 18 viruses, 27 microbial genes and 12 metabolic pathways were altered in children with ASD. Machine learning using single-kingdom panels showed area under the curve (AUC) of 0.68 to 0.87 in differentiating children with ASD from those that are neurotypical. A panel of 31 multikingdom and functional markers showed a superior diagnostic accuracy with an AUC of 0.91, with comparable performance for males and females. Accuracy of the model was predominantly driven by the biosynthesis pathways of ubiquinol-7 or thiamine diphosphate, which were less abundant in children with ASD. Collectively, our findings highlight the potential application of multikingdom and functional gut microbiota markers as non-invasive diagnostic tools in ASD.

The gut microbiome associates with phenotypic manifestations of post-acute COVID-19 syndrome.

The mechanisms underlying the many phenotypic manifestations of post-acute COVID-19 syndrome (PACS) are poorly understood. Herein, we characterized the gut microbiome in heterogeneous cohorts of subjects with PACS and developed a multi-label machine learning model for using the microbiome to predict specific symptoms. Our processed data covered 585 bacterial species and 500 microbial pathways, explaining 12.7% of the inter-individual variability in PACS. Three gut-microbiome-based enterotypes were identified in subjects with PACS and associated with different phenotypic manifestations. The trained model showed an accuracy of 0.89 in predicting individual symptoms of PACS in the test set and maintained a sensitivity of 86% and a specificity of 82% in predicting upcoming symptoms in an independent longitudinal cohort of subjects before they developed PACS. This study demonstrates that the gut microbiome is associated with phenotypic manifestations of PACS, which has potential clinical utility for the prediction and diagnosis of PACS.