Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease

Gut microbiome research: Revealing the pathological mechanisms and treatment strategies of type 2 diabetes

The high prevalence and disability rate of type 2 diabetes (T2D) caused a huge social burden to the world. Currently, new mechanisms and therapeutic approaches that may affect this disease are being sought. With in-depth research on the pathogenesis of T2D and growing advances in microbiome sequencing technology, the association between T2D and gut microbiota has been confirmed. The gut microbiota participates in the regulation of inflammation, intestinal permeability, short-chain fatty acid metabolism, branched-chain amino acid metabolism and bile acid metabolism, thereby affecting host glucose and lipid metabolism. Interventions focusing on the gut microbiota are gaining traction as a promising approach to T2D management. For example, dietary intervention, prebiotics and probiotics, faecal microbiota transplant and phage therapy. Meticulous experimental design and choice of analytical methods are crucial for obtaining accurate and meaningful results from microbiome studies. How to design gut microbiome research in T2D and choose different machine learning methods for data analysis are extremely critical to achieve personalized precision medicine.

Operationalizing Team Science at the Academic Cancer Center Network to Unveil the Structure and Function of the Gut Microbiome

Oncologists increasingly recognize the microbiome as an important facilitator of health as well as a contributor to disease, including, specifically, cancer. Our knowledge of the etiologies, mechanisms, and modulation of microbiome states that ameliorate or promote cancer continues to evolve. The progressive refinement and adoption of "omic" technologies (genomics, transcriptomics, proteomics, and metabolomics) and utilization of advanced computational methods accelerate this evolution. The academic cancer center network, with its immediate access to extensive, multidisciplinary expertise and scientific resources, has the potential to catalyze microbiome research. Here, we review our current understanding of the role of the gut microbiome in cancer prevention, predisposition, and response to therapy. We underscore the promise of operationalizing the academic cancer center network to uncover the structure and function of the gut microbiome; we highlight the unique microbiome-related expert resources available at the City of Hope of Comprehensive Cancer Center as an example of the potential of team science to achieve novel scientific and clinical discovery.

The brain-gut microbiota network (BGMN) is correlated with symptom severity and neurocognition in patients with schizophrenia

The association between the human brain and gut microbiota, known as the "brain-gut-microbiota axis", is involved in the neuropathological mechanisms of schizophrenia (SZ); however, its association patterns and correlations with symptom severity and neurocognition are still largely unknown. In this study, 43 SZ patients and 55 normal controls (NCs) were included, and resting-state functional magnetic resonance imaging (rs-fMRI) and gut microbiota data were acquired for each participant. First, the brain features of brain images and functional brain networks were computed from rs-fMRI data; the gut features of gut microbiota abundance and the gut microbiota network were computed from gut microbiota data. Second, we propose a novel methodology to construct an individual brain-gut microbiota network (BGMN) for each participant by combining the brain and gut features via multiple strategies. Third, discriminative models between SZ patients and NCs were built using the connectivity matrices of the BGMN as input features. Moreover, the correlations between the most discriminative features and the scores of symptom severity and neurocognition were analyzed in SZ patients. The results showed that the best discriminative model between SZ patients and NCs was achieved using the connectivity matrices of the BGMN when all the brain and gut features were integrated, with an accuracy of 0.90 and an area under the curve value of 0.97. The most discriminative features were related primarily to the genera Faecalibacterium and Collinsella, in which the genus Faecalibacterium was linked to the visual system and subcortical cortices and the genus Collinsella was linked to the default network and subcortical cortices. Furthermore, parts of the most discriminative features were significantly correlated with the scores of neurocognition in the SZ patients. The methodology for constructing individual BGMNs proposed in this study can help us reveal the associations between the brain and gut microbiota and understand the neuropathology of SZ.

Mental Health Disorders Due to Gut Microbiome Alteration and NLRP3 Inflammasome Activation After Spinal Cord Injury: Molecular Mechanisms, Promising Treatments, and Aids from Artificial Intelligence

Aside from its immediate traumatic effects, spinal cord injury (SCI) presents multiple secondary complications that can be harmful to those who have been affected by SCI. Among these secondary effects, gut dysbiosis (GD) and the activation of the NOD (nucleotide-binding oligomerization domain) like receptor-family pyrin-domain-containing three (NLRP3) inflammasome are of special interest for their roles in impacting mental health. Studies have found that the state of the gut microbiome is thrown into disarray after SCI, providing a chance for GD to occur. Metabolites such as short-chain fatty acids (SCFAs) and a variety of neurotransmitters produced by the gut microbiome are hampered by GD. This disrupts healthy cognitive processes and opens the door for SCI patients to be impacted by mental health disorders. Additionally, some studies have found an increased presence and activation of the NLRP3 inflammasome and its respective parts in SCI patients. Preclinical and clinical studies have shown that NLRP3 inflammasome plays a key role in the maturation of pro-inflammatory cytokines that can initiate and eventually aggravate mental health disorders after SCI. In addition to the mechanisms of GD and the NLRP3 inflammasome in intensifying mental health disorders after SCI, this review article further focuses on three promising treatments: fecal microbiome transplants, phytochemicals, and melatonin. Studies have found these treatments to be effective in combating the pathogenic mechanisms of GD and NLRP3 inflammasome, as well as alleviating the symptoms these complications may have on mental health. Another area of focus of this review article is exploring how artificial intelligence (AI) can be used to support treatments. AI models have already been developed to track changes in the gut microbiome, simulate drug-gut interactions, and design novel anti-NLRP3 inflammasome peptides. While these are promising, further research into the applications of AI for the treatment of mental health disorders in SCI is needed.

A bibliometric study on the impact of gut microbiota on the efficacy of immune checkpoint inhibitors in cancer patients: analysis of the top 100 cited articles

Background

Immune checkpoint inhibitors (ICIs) have transformed oncological treatment by modulating immune responses against tumors. However, their efficacy is subject to inter-patient variability and is associated with immune-related adverse events (irAEs). The human gut microbiota, a complex microbial ecosystem, is increasingly implicated in modulating responses to ICIs. This bibliometric analysis examines the 100 most-cited articles to elucidate trends and advancements in research concerning the gut microbiota's impact on ICI efficacy.

Methods

A systematic literature retrieval was conducted within the Web of Science Core Collection (WoSCC), focusing on the 100 most-cited articles. VOSviewer and CiteSpace were utilized for bibliometric analysis, examining collaborative patterns and keyword co-occurrences. The relationship between citing and cited entities was analyzed, and burst ranking identified research hotspots based on citation frequency.

Results

The 100 most-cited publications encompassed a range of disciplines, with a predominance of oncological research. The United States and China were leading in publication volume, with France and Canada also contributing significantly. French institutions, particularly INSERM and Université Paris Cite, were prolific. Routy, Bertrand and Zitvogel, Laurence were prominent among high-impact authors. Dominant keywords included "gut microbiota," "immunotherapy," "efficacy," and "cancer." The article by Routy et al. (2018) was the most frequently cited.

Conclusions

This study highlights the significant role of the gut microbiota in ICI development and efficacy, emphasizing the necessity for international and interdisciplinary collaboration. The research is progressively focusing on managing immunotherapy side effects and optimizing treatment strategies. Challenges, including individual variability in gut microbiota composition, persist. Further research is imperative to exploit the potential of the gut microbiota in cancer therapy, advocating for personalized approaches and a more profound comprehension of the underlying mechanisms.

The role of the microbiota-gut-brain axis and artificial intelligence in cognitive health of pediatric obstructive sleep apnea: A narrative review

Pediatric obstructive sleep apnea (OSA) is a prevalent sleep-related breathing disorder associated with significant neurocognitive and behavioral impairments. Recent studies have highlighted the role of gut microbiota and the microbiota-gut-brain axis (MGBA) in influencing cognitive health in children with OSA. This narrative review aims to summarize current knowledge on the relationship between gut microbiota, MGBA, and cognitive function in pediatric OSA. It also explores the potential of artificial intelligence and machine learning in advancing this field and identifying novel therapeutic strategies. Pediatric OSA is associated with gut dysbiosis, reduced microbial diversity, and metabolic disruptions. MGBA mechanisms, such as endocrine, immune, and neural pathways, link gut microbiota to cognitive outcomes. Artificial intelligence and machine learning methodologies offer promising tools to uncover microbial markers and mechanisms associated with cognitive deficits in OSA. Future research should focus on validating these findings through clinical trials and developing personalized therapeutic approaches targeting the gut microbiota.

Delineating the nexus between gut-intratumoral microbiome and osteo-immune system in bone metastases

Emerging insights in osteoimmunology have enabled researchers to explore in depth the role of immune modulation in regulating bone health. Bone is one of the common sites of metastasis notably in case of breast cancer, prostate cancer and several other cancer types. High calcium ion concentration and presence of several factors within the mineralized bone matrix including TGF-β, BMP etc., aid in tumor growth and proliferation. Accumulating evidence has substantiated the role of the gut-microbiota (GM) in tumorigenesis, further providing a strong impetus for the growing "immune-cancer-gut microbiota" relationship. Recent advancements in research further highlight the importance of the intra-tumor microbiota in conjunction with GM in cancer metastasis. Intratumoral microbiota owing to their ability to cause genetic instability, mutations, and epigenetic modifications within the tumor microenvironment, has been recognized to affect cancer cell physiology. The host microbiota and immune system crosstalk shapes the innate and adaptive arms of the immune system, which is the key player in cancer progression. In this review, we aim to decipher the role of microorganisms mediating bone metastasis by shedding light on the immuno-onco-microbiome (IOM) axis. We discussed the feasible cancer therapeutic interventions based on the modulation of the microbiome-immune cell axis which includes prebiotics, probiotics, and postbiotics. Here, we leverage the conceptual framework based on the published articles on microbiota-based therapies to target bone metastases. Understanding this complicated nexus will provide insights into fundamental factors governing bone metastases which will subsequently help in managing this malignancy with better efficacy.

AI in microbiome-related healthcare

Artificial intelligence (AI) has the potential to transform clinical practice and healthcare. Following impressive advancements in fields such as computer vision and medical imaging, AI is poised to drive changes in microbiome-based healthcare while facing challenges specific to the field. This review describes the state-of-the-art use of AI in microbiome-related healthcare. It points out limitations across topics such as data handling, AI modelling and safeguarding patient privacy. Furthermore, we indicate how these current shortcomings could be overcome in the future and discuss the influence and opportunities of increasingly complex data on microbiome-based healthcare.

Gut Microbiota Are a Novel Source of Biomarkers for Immunotherapy in Non-Small-Cell Lung Cancer (NSCLC)

Despite the recent availability of immune checkpoint inhibitors, not all patients affected by Non-Small-Cell Lung Cancer (NSCLC) benefit from immunotherapy. The reason for this variability relies on a variety of factors which may allow for the identification of novel biomarkers. Presently, a variety of biomarkers are under investigation, including the PD1/PDL1 axis, the tumor mutational burden, and the microbiota. The latter is made by all the bacteria and other microorganisms hosted in our body. The gut microbiota is the most represented and has been involved in different physiological and pathological events, including cancer. In this light, it appears that all conditions modifying the gut microbiota can influence cancer, its treatment, and its treatment-related toxicities. The aim of this review is to analyze all the conditions influencing the gut microbiota and, therefore, affecting the response to immunotherapy, iRAEs, and their management in NSCLC patients. The investigation of the landscape of these biological events can allow for novel insights into the optimal management of NSCLC immunotherapy.

Interwoven processes in fish development: microbial community succession and immune maturation

Fishes are hosts for many microorganisms that provide them with beneficial effects on growth, immune system development, nutrition and protection against pathogens. In order to avoid spreading of infectious diseases in aquaculture, prevention includes vaccinations and routine disinfection of eggs and equipment, while curative treatments consist in the administration of antibiotics. Vaccination processes can stress the fish and require substantial farmer's investment. Additionally, disinfection and antibiotics are not specific, and while they may be effective in the short term, they have major drawbacks in the long term. Indeed, they eliminate beneficial bacteria which are useful for the host and promote the raising of antibiotic resistance in beneficial, commensal but also in pathogenic bacterial strains. Numerous publications highlight the importance that plays the diversified microbial community colonizing fish (i.e., microbiota) in the development, health and ultimately survival of their host. This review targets the current knowledge on the bidirectional communication between the microbiota and the fish immune system during fish development. It explores the extent of this mutualistic relationship: on one hand, the effect that microbes exert on the immune system ontogeny of fishes, and on the other hand, the impact of critical steps in immune system development on the microbial recruitment and succession throughout their life. We will first describe the immune system and its ontogeny and gene expression steps in the immune system development of fishes. Secondly, the plurality of the microbiotas (depending on host organism, organ, and development stage) will be reviewed. Then, a description of the constant interactions between microbiota and immune system throughout the fish's life stages will be discussed. Healthy microbiotas allow immune system maturation and modulation of inflammation, both of which contribute to immune homeostasis. Thus, immune equilibrium is closely linked to microbiota stability and to the stages of microbial community succession during the host development. We will provide examples from several fish species and describe more extensively the mechanisms occurring in zebrafish model because immune system ontogeny is much more finely described for this species, thanks to the many existing zebrafish mutants which allow more precise investigations. We will conclude on how the conceptual framework associated to the research on the immune system will benefit from considering the relations between microbiota and immune system maturation. More precisely, the development of active tolerance of the microbiota from the earliest stages of life enables the sustainable establishment of a complex healthy microbial community in the adult host. Establishing a balanced host-microbiota interaction avoids triggering deleterious inflammation, and maintains immunological and microbiological homeostasis.

The future of artificial intelligence in clinical nutrition

PURPOSE OF REVIEW

Artificial intelligence has reached the clinical nutrition field. To perform personalized medicine, numerous tools can be used. In this review, we describe how the physician can utilize the growing healthcare databases to develop deep learning and machine learning algorithms, thus helping to improve screening, assessment, prediction of clinical events and outcomes related to clinical nutrition.

RECENT FINDINGS

Artificial intelligence can be applied to all the fields of clinical nutrition. Improving screening tools, identifying malnourished cancer patients or obesity using large databases has been achieved. In intensive care, machine learning has been able to predict enteral feeding intolerance, diarrhea, or refeeding hypophosphatemia. The outcome of patients with cancer can also be improved. Microbiota and metabolomics profiles are better integrated with the clinical condition using machine learning. However, ethical considerations and limitations of the use of artificial intelligence should be considered.

SUMMARY

Artificial intelligence is here to support the decision-making process of health professionals. Knowing not only its limitations but also its power will allow precision medicine in clinical nutrition as well as in the rest of the medical practice.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

Unveiling the Connection between Microbiota and Depressive Disorder through Machine Learning

In the last few years, investigation of the gut-brain axis and the connection between the gut microbiota and the human nervous system and mental health has become one of the most popular topics. Correlations between the taxonomic and functional changes in gut microbiota and major depressive disorder have been shown in several studies. Machine learning provides a promising approach to analyze large-scale metagenomic data and identify biomarkers associated with depression. In this work, machine learning algorithms, such as random forest, elastic net, and You Only Look Once (YOLO), were utilized to detect significant features in microbiome samples and classify individuals based on their disorder status. The analysis was conducted on metagenomic data obtained during the study of gut microbiota of healthy people and patients with major depressive disorder. The YOLO method showed the greatest effectiveness in the analysis of the metagenomic samples and confirmed the experimental results on the critical importance of a reduction in the amount of Faecalibacterium prausnitzii for the manifestation of depression. These findings could contribute to a better understanding of the role of the gut microbiota in major depressive disorder and potentially lead the way for novel diagnostic and therapeutic strategies.

The Gut-Liver-Brain Axis: From the Head to the Feet

The gut-liver-brain axis, a multifaceted network of communication, intricately connects the enteric, hepatic, and central nervous systems [...].