Rethinking the classification of non‐digestible carbohydrates: Perspectives from the gut microbiome DOI
Songtao Fan, Zhihong Zhang, Qixing Nie

et al.

Comprehensive Reviews in Food Science and Food Safety, Journal Year: 2024, Volume and Issue: 23(6)

Published: Oct. 22, 2024

Abstract Clarification is required when the term “carbohydrate” used interchangeably with “saccharide” and “glycan.” Carbohydrate classification based on human digestive enzyme activities brings clarity to energy supply function of digestible sugars starch. However, categorizing structurally diverse non‐digestible carbohydrates (NDCs) make dietary intake recommendations for health promotion remains elusive. In this review, we present a summary strengths weaknesses traditional dichotomic classifications carbohydrates, which were introduced by food chemists, nutritionists, microbiologists. parallel, discuss current consensus commonly terms NDCs such as “dietary fiber,” “prebiotics,” “fermentable glycans” highlight their inherent differences from perspectives gut microbiome. Moreover, provide historical perspective development novel concepts microbiota‐accessible microbiota‐directed fiber, targeted prebiotics, glycobiome. Crucially, these proposed multidisciplinary scholars help distinguish interactions between summary, created inability enzymes fails denote Considering that microbiome possesses sophisticated systems harvest NDCs, subclassification should be realigned metabolism various microbes, particularly health‐promoting microbes. Such rigorous categorizations facilitate microbiome‐targeted therapeutic strategies incorporating specific types NDCs.

Language: Английский

Colonic drug delivery: Formulating the next generation of colon-targeted therapeutics DOI Creative Commons
Laura E. McCoubrey,

Alessia Favaron,

Atheer Awad

et al.

Journal of Controlled Release, Journal Year: 2022, Volume and Issue: 353, P. 1107 - 1126

Published: Dec. 25, 2022

Colonic drug delivery can facilitate access to unique therapeutic targets and has the potential enhance bioavailability whilst reducing off-target effects. Delivering drugs colon requires considered formulation development, as both oral rectal dosage forms encounter challenges if colon's distinct physiological environment is not appreciated. As opportunities surrounding colonic multiply, success of novel pharmaceuticals lies in their design. This review provides a modern insight into key parameters determining effective design development colon-targeted medicines. Influential features governing release, dissolution, stability, absorption are first discussed, followed by an overview most reliable strategies. Finally, appropriate vitro, vivo, silico preclinical investigations presented, with goal inspiring strategic new therapeutics.

Language: Английский

Citations

112
Faecal microbiome-based machine learning for multi-class disease diagnosis DOI Creative Commons
Qi Su, Qin Liu, Raphaela Iris Lau

et al.

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Nov. 10, 2022

Abstract Systemic characterisation of the human faecal microbiome provides opportunity to develop non-invasive approaches in diagnosis a major disease. However, shared microbial signatures across different diseases make accurate challenging single-disease models. Herein, we present machine-learning multi-class model using metagenomic dataset 2,320 individuals with nine well-characterised phenotypes, including colorectal cancer, adenomas, Crohn’s disease, ulcerative colitis, irritable bowel syndrome, obesity, cardiovascular post-acute COVID-19 syndrome and healthy individuals. Our processed data covers 325 species derived from 14.3 terabytes sequence. The trained achieves an area under receiver operating characteristic curve (AUROC) 0.90 0.99 (Interquartile range, IQR, 0.91–0.94) predicting independent test set, sensitivity 0.81 0.95 (IQR, 0.87–0.93) at specificity 0.76 0.98 (IQR 0.83–0.95). Metagenomic analysis public datasets 1,597 samples populations observes comparable predictions AUROC 0.69 0.91 0.79–0.87). Correlation top 50 disease phenotypes identifies 363 significant associations (FDR < 0.05). This microbiome-based multi-disease has potential clinical application diagnostics treatment response monitoring warrants further exploration.

Language: Английский

Citations

73
The long-term health outcomes, pathophysiological mechanisms and multidisciplinary management of long COVID DOI Creative Commons
Jingwei Li, Yun Zhou, Jiechao Ma

et al.

Signal Transduction and Targeted Therapy, Journal Year: 2023, Volume and Issue: 8(1)

Published: Nov. 1, 2023

Abstract There have been hundreds of millions cases coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome 2 (SARS-CoV-2). With the growing population recovered patients, it crucial to understand long-term consequences and management strategies. Although COVID-19 was initially considered an illness, recent evidence suggests that manifestations including but not limited those cardiovascular, respiratory, neuropsychiatric, gastrointestinal, reproductive, musculoskeletal systems may persist long after phase. These persistent manifestations, also referred as COVID, could impact all patients with across full spectrum illness severity. Herein, we comprehensively review current literature on highlighting its epidemiological understanding, vaccinations, organ-specific sequelae, pathophysiological mechanisms, multidisciplinary In addition, psychological psychosomatic factors underscored. Despite these findings diagnostic therapeutic strategies based previous experience pilot studies remain inadequate, well-designed clinical trials should be prioritized validate existing hypotheses. Thus, propose primary challenges concerning biological knowledge gaps efficient remedies well discuss corresponding recommendations.

Language: Английский

Citations

65
Gut microbiome for predicting immune checkpoint blockade-associated adverse events DOI Creative Commons

Muni Hu,

Xiaolin Lin, Tiantian Sun

et al.

Genome Medicine, Journal Year: 2024, Volume and Issue: 16(1)

Published: Jan. 19, 2024

Abstract Background The impact of the gut microbiome on initiation and intensity immune-related adverse events (irAEs) prompted by immune checkpoint inhibitors (ICIs) is widely acknowledged. Nevertheless, there inconsistency in microbial associations with irAEs reported across various studies. Methods We performed a comprehensive analysis leveraging dataset that included published data ( n = 317) in-house generated from 16S rRNA shotgun metagenome samples 115). utilized machine learning-based approach, specifically Random Forest (RF) algorithm, to construct microbiome-based classifier capable distinguishing between non-irAEs irAEs. Additionally, we conducted analysis, integrating transcriptome profiling, explore potential underlying mechanisms. Results identified specific species patients experiencing non-irAEs. RF classifier, developed using 14 features, demonstrated robust discriminatory power (AUC 0.88). Moreover, predictive score our exhibited significant discriminative capability for identifying two independent cohorts. Our functional revealed altered was characterized an increased menaquinone biosynthesis, accompanied elevated expression rate-limiting enzymes menH menC . Targeted metabolomics further highlighted notably higher abundance serum who did not develop compared group. Conclusions study underscores biomarkers predicting onset highlights menaquinone, metabolite derived community, as possible selective therapeutic agent modulating occurrence

Language: Английский

Citations

25
Pan‐Cancer Single‐Cell and Spatial‐Resolved Profiling Reveals the Immunosuppressive Role of APOE+ Macrophages in Immune Checkpoint Inhibitor Therapy DOI Creative Commons
Chuan Liu, Jindong Xie, Bo Lin

et al.

Advanced Science, Journal Year: 2024, Volume and Issue: 11(23)

Published: April 3, 2024

Abstract The heterogeneity of macrophages influences the response to immune checkpoint inhibitor (ICI) therapy. However, few studies explore impact APOE + on ICI therapy using single‐cell RNA sequencing (scRNA‐seq) and machine learning methods. scRNA‐seq bulk RNA‐seq data are Integrated construct an M.Sig model for predicting based distinct molecular signatures macrophage algorithms. Comprehensive analysis as well in vivo vitro experiments applied potential mechanisms affecting response. shows clear advantages efficacy prognosis pan‐cancer patients. proportion is higher non‐responders triple‐negative breast cancer compared with responders, interaction longer distance between CD8 exhausted T (Tex) cells confirmed by multiplex immunohistochemistry. In a 4T1 tumor‐bearing mice model, combined treatment best efficacy. real‐world immunotherapy accurately predicts pan‐cancer, which may be associated Tex cells.

Language: Английский

Citations

23
The gut microbiome associates with phenotypic manifestations of post-acute COVID-19 syndrome DOI
Qi Su, Raphaela Iris Lau, Qin Liu

et al.

Cell Host & Microbe, Journal Year: 2024, Volume and Issue: 32(5), P. 651 - 660.e4

Published: April 23, 2024

Language: Английский

Citations

19
A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions DOI Creative Commons
Bablu Kumar,

Erika Lorusso,

Bruno Fosso

et al.

Frontiers in Microbiology, Journal Year: 2024, Volume and Issue: 15

Published: Feb. 13, 2024

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition functional potential. However, a critical challenge in this field is the lack standard comprehensive metadata associated with raw data, hindering ability to perform robust data stratifications consider confounding factors. In review, we categorize publicly available microbiome five types: shotgun sequencing, amplicon metatranscriptomic, metabolomic, metaproteomic data. We explore importance for reuse address challenges collecting standardized metadata. also, assess limitations collection existing public repositories metagenomic This review emphasizes vital role interpreting comparing datasets highlights need protocols fully leverage data's Furthermore, future directions implementation Machine Learning (ML) retrieval, offering promising avenues deeper understanding ecological roles. Leveraging these tools will enhance capabilities dynamics diverse ecosystems. Finally, emphasize crucial ML models development.

Language: Английский

Citations

18
Noninvasive, microbiome-based diagnosis of inflammatory bowel disease DOI Creative Commons
Jiaying Zheng, Qianru Sun,

Mengjing Zhang

et al.

Nature Medicine, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 4, 2024

Language: Английский

Citations

15
Electro‐Optical Multiclassification Platform for Minimizing Occasional Inaccuracy in Point‐of‐Care Biomarker Detection DOI
Changhao Dai, Huiwen Xiong, Rui He

et al.

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(15)

Published: Jan. 30, 2024

On-site diagnostic tests that accurately identify disease biomarkers lay the foundation for self-healthcare applications. However, these routinely rely on single-mode signals and suffer from insufficient accuracy, especially multiplexed point-of-care (POCTs) within a few minutes. Here, this work develops dual-mode multiclassification platform integrates an electrochemiluminescence sensor field-effect transistor in microfluidic chip. The channel guides testing samples to flow across electro-optical units, which produce readouts by detecting infectious of tuberculosis (TB), human rhinovirus (HRV), group B streptococcus (GBS). Then, machine-learning classifiers generate three-dimensional (3D) hyperplanes diagnose different diseases. Dual-mode derived distinct mechanisms enhance anti-interference ability physically, machine-learning-aided diagnosis high-dimensional space reduces occasional inaccuracy mathematically. Clinical validation studies with 501 unprocessed indicate has accuracy approaching 99%, higher than 77%-93% rapid technologies at 100% statistical power (>150 clinical tests). Moreover, time is 5 min without trade-off accuracy. This solves issue on-site diagnosis, endowing POCT systems same as laboratory holding unique prospects complicated scenes personalized healthcare.

Language: Английский

Citations

12
Multikingdom and functional gut microbiota markers for autism spectrum disorder DOI
Qi Su, Oscar W.H. Wong,

Wenqi Lu

et al.

Nature Microbiology, Journal Year: 2024, Volume and Issue: 9(9), P. 2344 - 2355

Published: July 8, 2024

Language: Английский

Citations

11