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.

Faecal 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 aetiology of PACS may be secondary to alterations in the gut microbiome. Here, we report the efficacy of faecal microbiota transplantation (FMT) in alleviating post-COVID insomnia symptoms in a non-randomised, 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 less than eight at 12 weeks. Secondary outcomes included changes in the Pittsburgh Sleep Quality Index (PSQI), Generalised Anxiety Disorder-7 scale (GAD-7), Epworth Sleepiness Scale (ESS), Multidimensional Fatigue Inventory (MFI), 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=0.018). The FMT group showed a decrease in ISI score (p<0.0001), PSQI (p<0.0001), GAD-7 (p=0.0019), ESS (p=0.0057) and blood cortisol concentration (p=0.035) 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. Gut microbiome profile resembled that of the donor in FMT responders but not in non-responders at week 12. There was no serious adverse event. CONCLUSION: This pilot study showed that FMT could be effective and safe in alleviating post-COVID insomnia and further clinical trials are warranted. CLINICALTRIALS: gov identifier: NCT05556733.

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.

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.

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.

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.

AI-based intra-tumor heterogeneity score of Ki67 expression as a prognostic marker for early-stage ER+/HER2- breast cancer.

Early-stage estrogen receptor positive and human epidermal growth factor receptor negative (ER+/HER2-) luminal breast cancer (BC) is quite heterogeneous and accounts for about 70% of all BCs. Ki67 is a proliferation marker that has a significant prognostic value in luminal BC despite the challenges in its assessment. There is increasing evidence that spatial colocalization, which measures the evenness of different types of cells, is clinically important in several types of cancer. However, reproducible quantification of intra-tumor spatial heterogeneity remains largely unexplored. We propose an automated pipeline for prognostication of luminal BC based on the analysis of spatial distribution of Ki67 expression in tumor cells using a large well-characterized cohort (n = 2,081). The proposed Ki67 colocalization (Ki67CL) score can stratify ER+/HER2- BC patients with high significance in terms of BC-specific survival (p < 0.00001) and distant metastasis-free survival (p = 0.0048). Ki67CL score is shown to be highly significant compared with the standard Ki67 index. In addition, we show that the proposed Ki67CL score can help identify luminal BC patients who can potentially benefit from adjuvant chemotherapy.

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.

Differentiating Alzheimer's disease from mild cognitive impairment: a quick screening tool based on machine learning.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder characterised by cognitive decline, behavioural and psychological symptoms of dementia (BPSD) and impairment of activities of daily living (ADL). Early differentiation of AD from mild cognitive impairment (MCI) is necessary. METHODS: A total of 458 patients newly diagnosed with AD and MCI were included. Eleven batteries were used to evaluate ADL, BPSD and cognitive function (ABC). Machine learning approaches including XGboost, classification and regression tree, Bayes, support vector machines and logical regression were used to build and verify the new tool. RESULTS: The Alzheimer's Disease Assessment Scale (ADAS-cog) word recognition task showed the best importance in judging AD and MCI, followed by correct numbers of auditory verbal learning test delay recall and ADAS-cog orientation. We also provided a selected ABC-Scale that covered ADL, BPSD and cognitive function with an estimated completion time of 18 min. The sensitivity was improved in the four models. CONCLUSION: The quick screen ABC-Scale covers three dimensions of ADL, BPSD and cognitive function with good efficiency in differentiating AD from MCI.

AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer.

Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in BC grading. Here we propose an objective Haematoxylin & Eosin (H&E) image-based prognostic marker for early-stage luminal/Her2-negative BReAst CancEr that we term as the BRACE marker. The proposed BRACE marker is derived from AI based assessment of heterogeneity in BC at a detailed level using the power of deep learning. The prognostic ability of the marker is validated in two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 and Cohort-B/Coventry: n = 311) on early-stage luminal/HER2-negative BC patients treated with endocrine therapy and with long-term follow-up. The BRACE marker is able to stratify patients for both distant metastasis free survival (p = 0.001, C-index: 0.73) and BC specific survival (p < 0.0001, C-index: 0.84) showing comparable prediction accuracy to Nottingham Prognostic Index and Magee scores, which are both derived from manual histopathological assessment, to identify luminal BC patients that may be likely to benefit from adjuvant chemotherapy.

Development and validation of artificial intelligence-based prescreening of large-bowel biopsies taken in the UK and Portugal: a retrospective cohort study.

BACKGROUND: Histopathological examination is a crucial step in the diagnosis and treatment of many major diseases. Aiming to facilitate diagnostic decision making and improve the workload of pathologists, we developed an artificial intelligence (AI)-based prescreening tool that analyses whole-slide images (WSIs) of large-bowel biopsies to identify typical, non-neoplastic, and neoplastic biopsies. METHODS: This retrospective cohort study was conducted with an internal development cohort of slides acquired from a hospital in the UK and three external validation cohorts of WSIs acquired from two hospitals in the UK and one clinical laboratory in Portugal. To learn the differential histological patterns from digitised WSIs of large-bowel biopsy slides, our proposed weakly supervised deep-learning model (Colorectal AI Model for Abnormality Detection [CAIMAN]) used slide-level diagnostic labels and no detailed cell or region-level annotations. The method was developed with an internal development cohort of 5054 biopsy slides from 2080 patients that were labelled with corresponding diagnostic categories assigned by pathologists. The three external validation cohorts, with a total of 1536 slides, were used for independent validation of CAIMAN. Each WSI was classified into one of three classes (ie, typical, atypical non-neoplastic, and atypical neoplastic). Prediction scores of image tiles were aggregated into three prediction scores for the whole slide, one for its likelihood of being typical, one for its likelihood of being non-neoplastic, and one for its likelihood of being neoplastic. The assessment of the external validation cohorts was conducted by the trained and frozen CAIMAN model. To evaluate model performance, we calculated area under the convex hull of the receiver operating characteristic curve (AUROC), area under the precision-recall curve, and specificity compared with our previously published iterative draw and rank sampling (IDaRS) algorithm. We also generated heat maps and saliency maps to analyse and visualise the relationship between the WSI diagnostic labels and spatial features of the tissue microenvironment. The main outcome of this study was the ability of CAIMAN to accurately identify typical and atypical WSIs of colon biopsies, which could potentially facilitate automatic removing of typical biopsies from the diagnostic workload in clinics. FINDINGS: A randomly selected subset of all large bowel biopsies was obtained between Jan 1, 2012, and Dec 31, 2017. The AI training, validation, and assessments were done between Jan 1, 2021, and Sept 30, 2022. WSIs with diagnostic labels were collected between Jan 1 and Sept 30, 2022. Our analysis showed no statistically significant differences across prediction scores from CAIMAN for typical and atypical classes based on anatomical sites of the biopsy. At 0·99 sensitivity, CAIMAN (specificity 0·5592) was more accurate than an IDaRS-based weakly supervised WSI-classification pipeline (0·4629) in identifying typical and atypical biopsies on cross-validation in the internal development cohort (p<0·0001). At 0·99 sensitivity, CAIMAN was also more accurate than IDaRS for two external validation cohorts (p<0·0001), but not for a third external validation cohort (p=0·10). CAIMAN provided higher specificity than IDaRS at some high-sensitivity thresholds (0·7763 vs 0·6222 for 0·95 sensitivity, 0·7126 vs 0·5407 for 0·97 sensitivity, and 0·5615 vs 0·3970 for 0·99 sensitivity on one of the external validation cohorts) and showed high classification performance in distinguishing between neoplastic biopsies (AUROC 0·9928, 95% CI 0·9927-0·9929), inflammatory biopsies (0·9658, 0·9655-0·9661), and atypical biopsies (0·9789, 0·9786-0·9792). On the three external validation cohorts, CAIMAN had AUROC values of 0·9431 (95% CI 0·9165-0·9697), 0·9576 (0·9568-0·9584), and 0·9636 (0·9615-0·9657) for the detection of atypical biopsies. Saliency maps supported the representation of disease heterogeneity in model predictions and its association with relevant histological features. INTERPRETATION: CAIMAN, with its high sensitivity in detecting atypical large-bowel biopsies, might be a promising improvement in clinical workflow efficiency and diagnostic decision making in prescreening of typical colorectal biopsies. FUNDING: The Pathology Image Data Lake for Analytics, Knowledge and Education Centre of Excellence; the UK Government's Industrial Strategy Challenge Fund; and Innovate UK on behalf of UK Research and Innovation.

Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence.

BACKGROUND: Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this study, we used artificial intelligence (AI) to assess the prognostic significance of TILs in a large well-characterised cohort of luminal BC. METHODS: Supervised deep learning model analysis of Haematoxylin and Eosin (H&E)-stained whole slide images (WSI) was applied to a cohort of 2231 luminal early-stage BC patients with long-term follow-up. Stromal TILs (sTILs) and intratumoural TILs (tTILs) were quantified and their spatial distribution within tumour tissue, as well as the proportion of stroma involved by sTILs were assessed. The association of TILs with clinicopathological parameters and patient outcome was determined. RESULTS: A strong positive linear correlation was observed between sTILs and tTILs. High sTILs and tTILs counts, as well as their proximity to stromal and tumour cells (co-occurrence) were associated with poor clinical outcomes and unfavourable clinicopathological parameters including high tumour grade, lymph node metastasis, large tumour size, and young age. AI-based assessment of the proportion of stroma composed of sTILs (as assessed visually in routine practice) was not predictive of patient outcome. tTILs was an independent predictor of worse patient outcome in multivariate Cox Regression analysis. CONCLUSION: AI-based detection of TILs counts, and their spatial distribution provides prognostic value in luminal early-stage BC patients. The utilisation of AI algorithms could provide a comprehensive assessment of TILs as a morphological variable in WSIs beyond eyeballing assessment.

Effects of White Fish Meal Replaced by Low-Quality Brown Fish Meal with Compound Additives on Growth Performance and Intestinal Health of Juvenile American Eel (Anguilla rostrata).

With a reduced supply and increased price of white fish meal (WFM), the exploration of a practical strategy to replace WFM is urgent for sustainable eel culture. A 70-day feeding trial was conducted to evaluate the effects of replacing WFM with low-quality brown fish meal (LQBFM) with compound additives (CAs) on the growth performance and intestinal health of juvenile American eels (Anguilla rostrata). The 300 fish (11.02 ± 0.02 g/fish) were randomly distributed in triplicate to four groups (control group, LQBFM20+CAs group, LQBFM30+CAs group and LQBFM40+CAs group). They were fed the diets with LQBFM replacing WFM at 0, 20%, 30% and 40%, respectively. The CAs were a mixture of Macleaya cordata extract, grape seed proanthocyanidins and compound acidifiers; its level in the diets of the trial groups was 0.50%. No significant differences were found in the growth performance between the control and LQBFM20+CAs groups (p > 0.05), whereas those values were significantly decreased in LQBFM30+CAs and LQBFM40+CAs groups (p < 0.05). Compared to the control group, the activity of glutamic-pyruvic transaminase was significantly increased in LQBFM30+CAs and LQBFM40+CAs groups, while lysozyme activity and complement 3 level were significantly decreased in those two groups (p < 0.05). There were decreased antioxidant potential and intestinal morphological indexes in the LQBFM30+CAs and LQBFM40+CAs groups, and no significant differences in those parameters were observed between the control group and LQBFM20+CAs group (p > 0.05). The intestinal microbiota at the phylum level or genus level was beneficially regulated in the LQBFM20+CAs group; similar results were not shown in the LQBFM40+CAs group. In conclusion, with 0.50% CA supplementation in the diet, LQBFM could replace 20% of WFM without detrimental effects on the growth and intestinal health of juvenile American eels and replacing 30% and 40%WFM with LQBFM might exert negative effects on this fish species.

A highly sensitive nanochannel device for the detection of SUMO1 peptides.

SUMOylation is an important and highly dynamic post-translational modification (PTM) process of protein, and its disequilibrium may cause various diseases, such as cancers and neurodegenerative disorders. SUMO proteins must be accurately detected to understand disease states and develop effective drugs. Reliable antibodies against SUMO2/3 are commercially available; however, efficient detectors are yet to be developed for SUMO1, which has only 50% homology with SUMO2 and SUMO3. Here, using phage display technology, we identified two cyclic peptide (CP) sequences that could specifically bind to the terminal dodecapeptide sequence of SUMO1. Then we combined the CPs and polyethylene terephthalate conical nanochannel films to fabricate a nanochannel device highly sensitive towards the SUMO1 terminal peptide and protein; sensitivity was achieved by ensuring marked variations in both transmembrane ionic current and Faraday current. The satisfactory SUMO1-sensing ability of this device makes it a promising tool for the time-point monitoring of the SENP1 enzyme-catalyzed de-SUMOylation reaction and cellular imaging. This study not only solves the challenge of SUMO1 precise recognition that could promote SUMO1 proteomics analysis, but also demonstrates the good potential of the nanochannel device in monitoring of enzymes and discovery of effective drugs.

Comprehensive analysis to long non-coding RNA-mediated high expression of GNG5 correlates with better prognosis and tumor immune infiltration of colon carcinoma.

BACKGROUND: Colorectal cancer is the third most common cancer and the fourth leading cause of cancer deaths. Prognosis is poor. The majority of patients are diagnosed with locally advanced or metastatic disease. Increasing evidence suggests G protein subunit gamma 5 (GNG5) play key roles in several types of human cancer. The key gating mechanisms in colorectal cancer remains unkown. METHODS: In this study, pan-cancer analyses have been performed for GNG5's expression. Prognosis using The Cancer Genome Atlas and The Genotype-Tissue Expression data found that GNG5 are activated oncogenes in colorectal cancer. Noncoding RNAs play increasingly appreciated gene-regulatory roles and long noncoding RNAs contributing to GNG5 overexpression. They were identified by a combination in silico computational analyses. We identified candidate regulators controlling colon carcinoma survival analysis and correlation analysis. RESULTS: The SNHG4/DRAIC-let-7c-5p axis was identified as the most progressive upstream lncRNA-related pathway of GNG5 in colorectal cancer. The GNG5 level was significantly negatively correlated with tumor immune cell infiltration, immune cell biomarkers, and immune checkpoint expression. CONCLUSIONS: Our findings elucidated that lncRNAs-mediated downregulation of GNG5 correlated with better prognosis and tumor immune infiltration in colorectal cancer.