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Record W4388014598 · doi:10.1111/odi.14796

Artificial intelligence, cell therapies, acupuncture, and tight junctions: Advances in salivary research

2023· editorial· en· W4388014598 on OpenAlexaff
Simon D. Tran

Bibliographic record

VenueOral Diseases · 2023
Typeeditorial
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsMcGill University
Fundersnot available
KeywordsOral medicineOral healthCitationLibrary scienceBiomedical sciencesMedicinePsychologyFamily medicineComputer scienceDentistryPathology

Abstract

fetched live from OpenAlex

Many scientific disciplines, including salivary research, are benefiting from major advances in science such as the use of artificial intelligence to accelerate discovery, cell-based therapies for intractable diseases, and improved analytical instrumentations to study cellular structures. Also, oriental therapies, such as acupuncture that is widely accepted in the East, are becoming increasingly popular in the West. In this issue of Oral Diseases, the section on Advances in Salivary Research includes four invited papers on topics relevant to researchers, clinicians, and students. The first paper is a review of salivary biomarker discovery and validation using artificial intelligence by Adeoye and Su from the University of Hong Kong (Adeoye & Su, 2023). Second is a review of cell-based therapies to treat salivary dysfunctions due to radiotherapy and Sjögren's syndrome by I and colleagues from Nagasaki University, Japan (I et al., 2023). Third is a review of tight junctions in the regulation of salivary gland secretion by Cong and colleagues from Peking University (Cong et al., 2023). Fourth is an original paper on the use of acupuncture to treat Sjögren's syndrome patients and nonobese diabetic mice with Sjögren's-like disease by Liu and colleagues from the China Academy of Chinese Medical Sciences (Liu et al., 2023). Artificial intelligence (AI) can accelerate research and discovery because of currently available large data sets, fast computing, and new algorithms. AI has revolutionized research workflows by providing accurate predictions (Wang et al., 2023). Adeoye and Su reviewed AI current techniques for salivary biomarker discovery and validation in oral diseases (Adeoye & Su, 2023). AI uses contemporary analytical techniques, multi-omics data sets, and patient information to optimize the selection, validation, and operationalization of potential salivary biomarkers for diagnosing and managing oral diseases. Adeoye and Su listed in great detail the current applications of AI liquid (saliva) biopsy platforms for biomarker discovery and validation in oral cancers, dental caries, periodontal diseases, temporomandibular joint dysfunctions, halitosis, Sjogren syndrome, oral lichen planus, and oral mucositis. Cell therapies involve the administration of cells as living agents to fight diseases. Recently, there has been a growth in the clinical deployment of cell therapies in the pharmaceutical sector (Bashor et al., 2022). Certain cell therapies have received regulatory approval and are being marketed in Europe, Japan, and North America. Examples are chimeric antigen receptor (CAR)-T cells for the treatment of lymphoid cancers, limbal stem cells to repair damaged corneal epithelial, and adipose-derived mesenchymal stem cells to treat fistulas in Crohn's disease (Bashor et al., 2022). I and colleagues reviewed the current progress of cell therapies for salivary gland dysfunction, such as salivary hypofunction due to radiotherapy or Sjögren's syndrome (I et al., 2023). The authors reported that from over 100 experimental studies supporting the therapeutic potential of cell therapies to treat salivary hypofunction, seven studies have been tested in clinical studies for their safety and efficacy. These promising cell therapies to treat salivary hypofunction used either mesenchymal stem cells (MSCs) from adipose tissue, MSCs from bone marrow, MSCs from umbilical cord, or enhanced-mononuclear cells (E-MNC) from peripheral blood. Of particular note is the E-MNC approach which harvests cells from a blood sample and enhances the anti-inflammatory and vasculogenic characteristics of isolated mononuclear cells in a serum-free defined culture media supplemented with five factors for 5–7 days before reinjecting these E-MNC back to the patient. Tight junctions function as selective gates controlling paracellular diffusion of ions and solutes across epithelial and endothelial cells, define apical and basolateral membrane domains (cell polarization), and affect cell signaling, gene expression, and cell proliferation (Zihni et al., 2016). In a comprehensive review, Cong and colleagues enrich our understanding of the cellular and molecular functions of epithelial and endothelial cells tight junctions in the regulation of salivary secretion during physiological and pathophysiological conditions such as those encountered in Sjögren's syndrome, diabetes mellitus, and radiotherapy (Cong et al., 2023). Acupuncture, a traditional Chinese medicine nonpharmacologic approach, has been used for over 2000 years to treat numerous disorders, including xerostomia. Acupuncture is gaining popularity outside of China but as with any medical treatments, there are associated adverse events (Chan et al., 2017). Clinical studies testing acupuncture as a therapy for Sjögren's syndrome are still being standardized for their reporting on primary and long-term outcomes (Liu et al., 2021). Liu and colleagues, an experienced group of acupuncturists from the China Academy of Chinese Medical Sciences in Beijing, report promising results from a clinical study assessing the role of acupuncture on patients with Sjögren's syndrome as well as on nonobese diabetic mice in regulating cytokines and the expression of aquaporins (Liu et al., 2023). Overall, the series on ‘Advances in Salivary Research’ allows us to bring internationally established researchers and clinicians together to share their knowledge and expertise for the advancement of salivary research. Oral Diseases is proud to support this initiative to our journal readership worldwide.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.056
GPT teacher head0.382
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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