16S rRNA gene sequencing and machine learning reveal correlation between drug abuse and human host gut microbiota

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.

Gut microbiota signatures of vulnerability to food addiction in mice and humans

OBJECTIVE

Food addiction is a multifactorial disorder characterised by a loss of control over food intake that may promote obesity and alter gut microbiota composition. We have investigated the potential involvement of the gut microbiota in the mechanisms underlying food addiction.

DESIGN

We used the Yale Food Addiction Scale (YFAS) 2.0 criteria to classify extreme food addiction in mouse and human subpopulations to identify gut microbiota signatures associated with vulnerability to this disorder.

RESULTS

Both animal and human cohorts showed important similarities in the gut microbiota signatures linked to food addiction. The signatures suggested possible non-beneficial effects of bacteria belonging to the Proteobacteria phylum and potential protective effects of Actinobacteria against the development of food addiction in both cohorts of humans and mice. A decreased relative abundance of the species Blautia wexlerae was observed in addicted humans and of Blautia genus in addicted mice. Administration of the non-digestible carbohydrates, lactulose and rhamnose, known to favour Blautia growth, led to increased relative abundance of Blautia in mice faeces in parallel with dramatic improvements in food addiction. A similar improvement was revealed after oral administration of Blautia wexlerae as a beneficial microbe.

CONCLUSION

By understanding the crosstalk between this behavioural alteration and gut microbiota, these findings constitute a step forward to future treatments for food addiction and related eating disorders.

Evaluating the Clinical Validity and Reliability of Artificial Intelligence-Enabled Diagnostic Tools in Neuropsychiatric Disorders

Neuropsychiatric disorders (NPDs) pose a substantial burden on the healthcare system. The major challenge in diagnosing NPDs is the subjective assessment by the physician which can lead to inaccurate and delayed diagnosis. Recent studies have depicted that the integration of artificial intelligence (AI) in neuropsychiatry could potentially revolutionize the field by precisely diagnosing complex neurological and mental health disorders in a timely fashion and providing individualized management strategies. In this narrative review, the authors have examined the current status of AI tools in assessing neuropsychiatric disorders and evaluated their validity and reliability in the existing literature. The analysis of various datasets including MRI scans, EEG, facial expressions, social media posts, texts, and laboratory samples in the accurate diagnosis of neuropsychiatric conditions using machine learning has been profoundly explored in this article. The recent trials and tribulations in various neuropsychiatric disorders encouraging future scope in the utility and application of AI have been discussed. Overall machine learning has proved to be feasible and applicable in the field of neuropsychiatry and it is about time that research translates to clinical settings for favorable patient outcomes. Future trials should focus on presenting higher quality evidence for superior adaptability and establish guidelines for healthcare providers to maintain standards.

Plasma endocannabinoidome and fecal microbiota interplay in people with HIV and subclinical coronary artery disease: Results from the Canadian HIV and Aging Cohort Study

Chronic HIV infection is associated with accelerated coronary artery disease (CAD) due to chronic inflammation. The expanded endocannabinoid system (eCBome) and gut microbiota modulate each other and are key regulators of cardiovascular functions and inflammation. We herein investigated the interplay between plasma eCBome mediators and gut microbiota in people with HIV (PWH) and/or subclinical CAD versus HIV-uninfected individuals. CAD was determined by coronary computed tomography (CT) angiography performed on all participants. Plasma eCBome mediator and fecal microbiota composition were assessed by tandem mass spectrometry and 16S rDNA sequencing, respectively. HIV infection was associated with perturbed plasma eCBome mediators characterized by an inverse relationship between anandamide and N-acyl-ethanolamines (NAEs) versus 2-AG and 2-monoacylglycerols (MAGs). Plasma triglyceride levels were positively associated with MAGs. Several fecal bacterial taxa were altered in HIV-CAD+ versus controls and correlated with plasma eCBome mediators. CAD-associated taxonomic alterations in fecal bacterial taxa were not found in PWH.

Research progress on the application of 16S rRNA gene sequencing and machine learning in forensic microbiome individual identification

Forensic microbiome research is a field with a wide range of applications and a number of protocols have been developed for its use in this area of research. As individuals host radically different microbiota, the human microbiome is expected to become a new biomarker for forensic identification. To achieve an effective use of this procedure an understanding of factors which can alter the human microbiome and determinations of stable and changing elements will be critical in selecting appropriate targets for investigation. The 16S rRNA gene, which is notable for its conservation and specificity, represents a potentially ideal marker for forensic microbiome identification. Gene sequencing involving 16S rRNA is currently the method of choice for use in investigating microbiomes. While the sequencing involved with microbiome determinations can generate large multi-dimensional datasets that can be difficult to analyze and interpret, machine learning methods can be useful in surmounting this analytical challenge. In this review, we describe the research methods and related sequencing technologies currently available for application of 16S rRNA gene sequencing and machine learning in the field of forensic identification. In addition, we assess the potential value of 16S rRNA and machine learning in forensic microbiome science.