AIMC Topic: Saliva

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Identification of cariogenic bacteria by click chemistry mediated polyethylene glycolized graphyne nanozymes.

Mikrochimica acta
Dental caries, one of the most common oral diseases, is mainly induced by multiple cariogenic bacteria in the oral microenvironment, so it is important to construct a method that can identify oral multiply cariogenic bacteria. Herein, a machine learn...

Microbiome-based prediction of allogeneic hematopoietic stem cell transplantation outcome.

Genome medicine
BACKGROUND: Allogeneic hematopoietic stem cell transplantation (HSCT) is potentially curative for hematologic malignancies but is frequently complicated by relapse and immune-mediated complications, such as graft-versus-host disease (GVHD). Emerging ...

Ensemble learning for microbiome-based caries diagnosis: multi-group modeling and biological interpretation from salivary and plaque metagenomic data.

BMC oral health
BACKGROUND: Oral microbiota is a major etiological factor in the development of dental caries. Next-generation sequencing techniques have been widely used, generating vast amounts of data which is underexplored. The advancement of artificial intellig...

AgNWs-COF SERS biosensor for oral cancer diagnosis based on exhaled breath and saliva.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Over recent years, surface-enhanced Raman spectroscopy (SERS) has shown its unparalleled sensitivity and molecular specificity in biomedical applications. However, noninvasive and sensitive detection of biomarkers with conventional SERS for oral canc...

Prediction of postoperative infection through early-stage salivary microbiota following kidney transplantation using machine learning techniques.

Renal failure
Kidney transplantation (KT) is an effective treatment for end-stage renal disease; however, the lifelong immunosuppressive regimen increases the risk of infection, presenting significant clinical, and economic challenges. Identifying predictive bioma...

Potential use of saliva infrared spectra and machine learning for a minimally invasive screening test for congenital syphilis in infants.

Scientific reports
Congenital syphilis is a global public health issue, and its diagnostic complexity poses a challenge to early treatment. Fourier Transform Infrared Spectroscopy (FTIR) is a promising technological tool that facilitates the detection and diagnosis of ...

Oral and Gut Dysbiosis in Migraine: Oral Microbial Signatures as Biomarkers of Migraine.

Neurology(R) neuroimmunology & neuroinflammation
BACKGROUND AND OBJECTIVES: Emerging evidence suggests that oral health conditions may exacerbate migraine, and saliva is a potential source of biomarkers for migraine. The 3-way interaction of the oral-gut-brain axis has been implicated in several ne...

Body movements as biomarkers: Machine Learning-based prediction of HPA axis reactivity to stress.

Psychoneuroendocrinology
Body movements and posture provide valuable insights into stress responses, yet their relationship with endocrine biomarkers of the stress response remains underexplored. This study investigates whether movement patterns during the Trier Social Stres...

Visualizing fatigue mechanisms in non-communicable diseases: an integrative approach with multi-omics and machine learning.

BMC medical informatics and decision making
BACKGROUND: Fatigue is a prevalent and debilitating symptom of non-communicable diseases (NCDs); however, its biological basis are not well-defined. This exploratory study aimed to identify key biological drivers of fatigue by integrating metabolomic...

Comparison of salivary statherin and beta-defensin-2 levels, oral health behaviors, and demographic factors in children with and without early childhood caries.

BMC oral health
BACKGROUND: Early childhood caries (ECC) is a widespread pediatric dental condition that is influenced by a combination of biological, behavioral, and demographic factors. Salivary biomarkers, including beta-defensin-2 (BD-2) and statherin (STATH), o...