Gingival Crevicular Fluid Peptidome Profiling in Healthy and in Periodontal Diseases

Diagnostic Potential of Salivary Interleukin-1β and IL-10 for Distinguishing Periodontal Health From Periodontitis and Stable From Unstable Periodontitis: A Case-Control Study

Objective: This case-control study aimed to investigate the diagnostic accuracy of salivary interleukin (IL)-1β, IL-10, and IL-1β/IL-10 ratio to discriminate periodontitis from periodontal health and stable from unstable periodontitis. Methods: Saliva samples were collected from 135 (healthy on an intact periodontium = 45 [as healthy control], stable periodontitis = 39, and unstable periodontitis = 51) participants, and then clinical periodontal parameters were recorded. An enzyme-linked immunosorbent assay was used to determine salivary levels of IL-1β and IL-10. Area under the curves (AUCs), sensitivity, and specificity of IL-1β, IL-10, and IL-1β/IL-10 were estimated to discriminate between groups. Result: The level of salivary IL-1β was significantly higher in unstable periodontitis than in stable periodontitis and healthy control groups (426 ± 59, 247 ± 55, and 204 ± 36 pg/ml [picograms per milliliter], respectively). While the level of salivary IL-10 was significantly higher in the control group (360.7 ± 80.5 pg/ml) than unstable periodontitis group (146.92 ± 1.8 pg/ml), no significant difference was found between the control and stable periodontitis (317.04 ± 59.8 pg/ml) groups. IL-1β, IL-10, and IL-1β/IL-10 had significant diagnostic accuracy for differentiating healthy control from unstable periodontitis (AUCs = 0.99, 0.96, and 1; sensitivity = 0.98,1, and 1; specificity = 0.95, 0.95, and 1, respectively). Similarly, they showed significant diagnostic accuracy in distinguishing unstable from stable periodontitis (AUCs = 0.98, 0.99, and 1; sensitivity = 0.94, 1, and 1; specificity = 0.94, 0.97, and 1, respectively). Conclusion: Salivary IL-1β, IL-10, and IL-1β/IL-10 have a high potential to discriminate healthy control from periodontitis and stable from unstable periodontitis. Trial Registration: ClinicalTrials.gov identifier: NCT05722613.

Mediating and moderating effects of plasma proteomic biomarkers on the association between poor oral health problems and incident dementia: The UK Biobank study

The plasma proteome can mediate poor oral health problems (POHP)'s link to incident dementia. We screened 37,269 UK Biobank participants 50-74 years old (2006-2010) for prevalent POHP, further tested against 1463 plasma proteins and incident dementia over up to 15 years of follow-up. Total effect (TE) of POHP-dementia through plasma proteomic markers was decomposed into pure indirect effect (PIE), interaction referent (INTREF), controlled direct effect (CDE), or mediated interaction (INTMED). POHP increased the risk of all-cause dementia by 17% (P < 0.05). Growth differentiation factor 15 (GDF15) exhibited the strongest mediating effects (PIE > 0, P < 0.001), explaining 28% the total effect of POHP on dementia, as a pure indirect effect. A first principal component encompassing top 4 mediators (GDF15, IL19, MMP12, and ACVRL1), explained 11% of the POHP-dementia effect as a pure indirect effect. Pathway analysis including all mediators (k = 173 plasma proteins) revealed the involvement of the immune system, signal transduction, metabolism, disease, and gene expression, while STRING analysis indicated that top mediators within the first principal component were also represented in the two largest proteomic clusters. The dominant biological GO pathway for the GDF15 cluster was GO:0007169 labeled as "transmembrane receptor protein tyrosine kinase signaling pathway." Dementia is linked to POHP mediated by GDF15 among several proteomic markers.

Using SWATH-MS to identify new molecular biomarkers in gingival crevicular fluid for detecting periodontitis and its response to treatment

AIM

To identify new biomarkers to detect untreated and treated periodontitis in gingival crevicular fluid (GCF) using sequential window acquisition of all theoretical mass spectra (SWATH-MS).

MATERIALS AND METHODS

GCF samples were collected from 44 periodontally healthy subjects and 40 with periodontitis (Stages III-IV). In the latter, 25 improved clinically 2 months after treatment. Samples were analysed using SWATH-MS, and proteins were identified by the UniProt human-specific database. The diagnostic capability of the proteins was determined with generalized additive models to distinguish the three clinical conditions.

RESULTS

In the untreated periodontitis vs. periodontal health modelling, five proteins showed excellent or good bias-corrected (bc)-sensitivity/bc-specificity values of >80%. These were GAPDH, ZG16B, carbonic anhydrase 1, plasma protease inhibitor C1 and haemoglobin subunit beta. GAPDH with MMP-9, MMP-8, zinc-α-2-glycoprotein and neutrophil gelatinase-associated lipocalin and ZG16B with cornulin provided increased bc-sensitivity/bc-specificity of >95%. For distinguishing treated periodontitis vs. periodontal health, most of these proteins and their combinations revealed a predictive ability similar to previous modelling. No model obtained relevant results to differentiate between periodontitis conditions.

CONCLUSIONS

New single and dual GCF protein biomarkers showed outstanding results in discriminating untreated and treated periodontitis from periodontal health. Periodontitis conditions were indistinguishable. Future research must validate these findings.

Oral and Gingival Crevicular Fluid Biomarkers for Jawbone Turnover Diseases: A Scoping Review

Gingival crevicular fluid (GCF) and oral fluid have emerged as promising diagnostic tools for detecting biomarkers. This review aimed to evaluate the existing literature on using oral fluids as a source of biomarkers for bone turnover diseases affecting the jawbone. A comprehensive search strategy was executed between August 2014 and August 2024 across five major databases (Web of Science, EBSCOhost Dentistry & Oral Sciences Source, Cochrane Library, Scopus, and PubMed) and grey literature sources. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was applied. The screening was facilitated using Rayyan at rayyan.ai and Endnote X20 software tools, culminating in the evaluation of 14,965 citations from databases and 34 from grey literature. Following rigorous scrutiny, 37 articles were selected for inclusion in this review, encompassing diseases such as periodontitis, medication-related osteonecrosis of the jaw (MRONJ), and osteoporosis. The quality of the included observational studies was assessed using the Revised Risk of Bias Assessment Tool for Non-Randomized Studies (RoBANS 2). Interleukin-1 beta (IL-1β), sclerostin, osteoprotegerin (OPG), and interleukin-34 (IL-34) emerged as significant biomarkers in GCF, and they were mainly from periodontitis and osteoporosis. Osteocalcin (OC), IL-1β, tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), OPG, and matrix metalloproteinase-9 (MMP-9) were significant in oral fluid or saliva, and they were from periodontitis, MRONJ, and osteoporosis. These findings underscore the potential use of oral fluids, which are regarded as non-invasive tools for biomarker identification in bone turnover. Many biomarkers overlap, and it is important to identify other specific biomarkers to enable accurate diagnosis of these conditions.

Role of innate host defense proteins in oral cancerogenesis

It is nowadays well accepted that chronic inflammation plays a pivotal role in tumor initiation and progression. Under this aspect, the oral cavity is predestined to examine this connection because periodontitis is a highly prevalent chronic inflammatory disease and oral squamous cell carcinomas are the most common oral malignant lesions. In this review, we describe how particular molecules of the human innate host defense system may participate as molecular links between these two important chronic noncommunicable diseases (NCDs). Specific focus is directed toward antimicrobial polypeptides, such as the cathelicidin LL-37 and human defensins, as well as S100 proteins and alarmins. We report in which way these peptides and proteins are able to initiate and support oral tumorigenesis, showing direct mechanisms by binding to growth-stimulating cell surface receptors and/or indirect effects, for example, inducing tumor-promoting genes. Finally, bacterial challenges with impact on oral cancerogenesis are briefly addressed.

Untargeted Metabolomics Analysis of Gingival Tissue in Patients with Severe Periodontitis

The purpose of this study was to determine potential metabolic biomarkers and therapeutic drugs in the gingival tissue of individuals with periodontitis. Liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) were used to analyze the gingival tissue samples from 20 patients with severe periodontitis and 20 healthy controls. Differential metabolites were identified using variable important in projection (VIP) values from the orthogonal partial least squares discrimination analysis (OPLS-DA) model and then verified for significance between groups using a two-tailed Student's t test. In total, 65 metabolites were enriched in 33 metabolic pathways, with 40 showing a significant increase and 25 expressing a significant decrease. In addition, it was found that patients with severe periodontitis have abnormalities in metabolic pathways, such as glucose metabolism, purine metabolism, amino acid metabolism, and so on. Furthermore, based on a multidimensional analysis, 12 different metabolites may be the potential biomarkers of severe periodontitis. The experiment's raw data have been uploaded to the MetaboLights database, and the project number is MTBLS8357. Moreover, osteogenesis differentiation characteristics were detected in the selected metabolites. The findings may provide a basis for the study of diagnostic biomarkers and therapeutic metabolites in severe periodontitis.

Accuracy of periodontitis diagnosis obtained using multiple molecular biomarkers in oral fluids: A systematic review and meta-analysis

AIM

To determine the accuracy of biomarker combinations in gingival crevicular fluid (GCF) and saliva through meta-analysis to diagnose periodontitis in systemically healthy subjects.

METHODS

Studies on combining two or more biomarkers providing a binary classification table, sensitivity/specificity values or group sizes in subjects diagnosed with periodontitis were included. The search was performed in August 2022 through PUBMED, EMBASE, Cochrane, LILACS, SCOPUS and Web of Science. The methodological quality of the articles selected was evaluated using the QUADAS-2 checklist. Hierarchical summary receiver operating characteristic modelling was employed to perform the meta-analyses (CRD42020175021).

RESULTS

Twenty-one combinations in GCF and 47 in saliva were evaluated. Meta-analyses were possible for six salivary combinations (median sensitivity/specificity values): IL-6 with MMP-8 (86.2%/80.5%); IL-1β with IL-6 (83.0%/83.7%); IL-1β with MMP-8 (82.7%/80.8%); MIP-1α with MMP-8 (71.0%/75.6%); IL-1β, IL-6 and MMP-8 (81.8%/84.3%); and IL-1β, IL-6, MIP-1α and MMP-8 (76.6%/79.7%).

CONCLUSIONS

Two-biomarker combinations in oral fluids show high diagnostic accuracy for periodontitis, which is not substantially improved by incorporating more biomarkers. In saliva, the dual combinations of IL-1β, IL-6 and MMP-8 have an excellent ability to detect periodontitis and a good capacity to detect non-periodontitis. Because of the limited number of biomarker combinations evaluated, further research is required to corroborate these observations.

Cytokine expression in gingival crevicular fluid around teeth opposing dental implants and 3-unit fixed partial dentures in a cross-sectional study

OBJECTIVE

This study aimed to study the cytokines in gingival crevicular fluid (GCF) of the teeth opposing to dental implants and 3-unit fixed partial dentures (FPDs).

MATERIALS AND METHODS

A total of 74 participants were recruited for this cross-sectional study. Based on the status of lower first molars, the participants were divided into dental implants group and 3-unit FPDs group. Social index and oral hygiene were recorded. Occlusal loading was evaluated with a T-scan. GCF was sampled from the upper first molar and assessed with a commercial cytokine assay kit.

RESULTS

Forty three dental implants patients and 31 3-unit FPDs patients received all of the clinical and laboratory evaluation. The dental implants group had a higher occlusion force distribution on first molars region. IL-10, IL-17, RANK had a higher mean in dental implants group and was associated with occlusion force of first molar. There was a weakly association between IL-10 and dental implants in the binary logistic regression analyses.

CONCLUSIONS

In this study, the teeth opposing implants have a higher level of cytokines in the GCF than teeth opposing to 3-unit FPDs in periodontal healthy participants because of the poor osseoperception of dental implants. IL-10 might reflect a higher occlusion force in dental implants region.

CLINICAL RELEVANCE

This study provided that different tooth restoration methods could influence the periodontal status of the contact teeth.

Mass Spectrometry-Based Untargeted Approaches to Reveal Diagnostic Signatures of Male Infertility in Seminal Plasma: A New Laboratory Perspective for the Clinical Management of Infertility?

Male infertility has been recognized as a global health problem. Semen analysis, although considered the golden standard, may not provide a confident male infertility diagnosis alone. Hence, there is the urgent request for an innovative and reliable platform to detect biomarkers of infertility. The rapid expansion of mass spectrometry (MS) technology in the field of the 'omics' disciplines, has incredibly proved the great potential of MS-based diagnostic tests to revolutionize the future of pathology, microbiology and laboratory medicine. Despite the increasing success in the microbiology area, MS-biomarkers of male infertility currently remain a proteomic challenge. In order to address this issue, this review encompasses proteomics investigations by untargeted approaches with a special focus on experimental designs and strategies (bottom-up and top-down) for seminal fluid proteome profiling. The studies reported here witness the efforts of the scientific community to address these investigations aimed at the discovery of MS-biomarkers of male infertility. Proteomics untargeted approaches, depending on the study design, might provide a great plethora of biomarkers not only for a male infertility diagnosis, but also to address a new MS-biomarkers classification of infertility subtypes. From the early detection to the evaluation of infertility grade, new MS-derived biomarkers might also predict long-term outcomes and clinical management of infertility.

Revealing the Hidden Diagnostic Clues of Male Infertility from Human Seminal Plasma by Dispersive Solid Phase Extraction and MALDI-TOF MS

Seminal plasma (SP) mirrors the local pathophysiology of the male reproductive system and represents a non-invasive fluid for the study of infertility. Matrix-Assisted Laser Desorption/Ionization-Time-of-Flight Mass Spectrometry (MALDI-TOF-MS) provides a high-throughput platform to rapidly extrapolate the diagnostic profiles of information-rich patterns. In this study, dispersive solid phase extraction (d-SPE) combined with MALDI-TOF-MS was applied for the first time to the human SP, with the aim of revealing a diagnostic signature for male infertility. Commercially available octadecyl (C₁₈)-, octyl (C₈)-bonded silica sorbents and hexagonal mesoporous silica (HMS) were tested and the robustness of MALDI-TOF peptide profiling was evaluated. Best performances were obtained for C₁₈-bonded silica with the highest detection of peaks and the lowest variation of spectral features. To assess the diagnostic potential of the method, C₁₈-bonded silica d-SPE and MALDI-TOF-MS were used to generate enriched endogenous peptide profiles of SP from 15 fertile and 15 non-fertile donors. Principal component analysis (PCA) successfully separated fertile from non-fertile men into two different clusters. An array of seven semenogelin-derived peptides was found to distinguish the two groups, with high statistical significance. These findings, while providing a rapid and convenient route to selectively enrich native components of SP peptidome, strongly reinforce the prominent role of semenogelins in male infertility.

Clinical Significance of Inflammatory Factors, Osteocalcin, and Matrix Metalloproteinase-8 in Gingival Crevicular Fluid in Drug Treatment of Severe Periodontitis

Whether gingival crevicular fluid (GCF) indexes in patients with severe periodontitis affect the efficacy of drug treatment was a new direction of recent research. At present, there were few studies on the effects of inflammatory indicators, BGP, and MMP-8 levels in GCF on the efficacy of drug treatment in such patients. So the purpose of this study was to observe the changes in osteocalcin (BGP), matrix metalloproteinase-8 (MMP-8), and inflammatory indexes levels in GCF of patients with severe periodontitis. The correlation between the above indexes and the effect of drug treatment in the patients was analyzed, in order to provide guidance for improving the clinical curative effect of severe periodontitis. A retrospective analysis was conducted to collect the baseline data of patients with severe periodontitis who were treated with Minocycline Hydrochloride Ointment in our hospital. The inflammatory indicators, BGP, and MMP-8 levels in GCF were analyzed before drug treatment, and the treatment effect on the patients was counted. Logistic regression was used to analyze the correlation between BGP, MMP-8, and inflammatory indicators levels in GCF and the drug treatment effect on the patients. After statistical analysis, we found that the response rate was 69% and the inefficiency was 31%. There were no significant differences in C-reactive protein (CRP) and tumor necrosis factor-α (TNF-α) levels between the inefficacy group and efficacy group (P > 0.05). Compared with the efficacy group, the levels of interleukin-6 (IL-6), interleukin-1β (IL-1β), interleukin-8 (IL-8), BGP, and MMP-8 were increased in the inefficacy group. High levels of IL-6, IL-1β, IL-8, BGP, and MMP-8 were associated with ineffective drug treatment in patients with severe periodontitis (all OR >1 and P < 0.05). Levels of IL-6, IL-1β, IL-8, BGP, and MMP-8 predicted that the AUCs of drug treatment failure in patients with severe periodontitis were all greater than 0.7, which were 1.398, 1.458, 1.244, 1.012, and 1.012, respectively. From this, we can conclude that increased levels of BGP, MMP-8, and inflammatory indicators such as IL-6, IL-1β, and IL-8 in GCF would increase the risk of ineffective drug treatment in patients with severe periodontitis. The clinical treatment plan could be adjusted according to the levels of the above indicators in GCF to improve the effectiveness of drug treatment in patients.

Quantitative proteomic analysis of gingival crevicular fluids to identify novel biomarkers of gingival recession in orthodontic patients

OBJECTIVE

To identify gingival recession-related biomarkers in orthodontic patients, we compared the proteome of gingival crevicular fluids (GCF) from healthy gingiva without orthodontic treatment (GH), healthy gingiva undergoing orthodontic treatment (OGH), and recessed gingiva undergoing orthodontic treatment (OGR).

METHODS

GCF samples were obtained from the anterior teeth of 15 volunteers (n = 5/group). Quantitative proteomic analysis was performed using DIA-based liquid chromatography-tandem mass spectrometry (LC-MS/MS). Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to annotate differentially expressed proteins (DEPs). Receiver-operating characteristic (ROC) analysis was performed to detect and filter biomarker candidates, while Protein-Protein Interaction (PPI) Networks were utilized to determine the interactions between these DEPs.

RESULTS

A total of 253, 238, and 101 DEPs were found in OGR vs. OGH, OGR vs. GH, and OGH vs. GH groups, respectively. Based on the Venn diagram of three groups, 128 DEPs in OGR vs. OGH group were identified as specific proteins associated with progressive gingival recession (GR) during orthodontic treatment. Molecular function analysis showed that 128 DEPs were enriched in "molecular binding", including antigen binding, RNA binding, double-stranded RNA binding, cadherin binding involved in cell-cell adhesion, vinculin binding, S100 protein binding, and Ral GTPase binding. The majority of these DEPs were also involved in cytoskeletal regulation. In addition, biological process analysis showed an enrichment in translation, while cellular component analysis indicated that 128 DEPs were related to extracellular exosome. Furthermore, Ribosome and Phagosome were the top two terms in KEGG analysis. The results of ROC analysis demonstrated that 26 proteins could be potential biomarker candidates for GR. PPI networks analysis predicted that IQGAP1, ACTN1, TLN1, VASP, FN1, FERMT3, MYO1C, RALA, RPL35, SEC61G, KPNB1, and NPM1 could be involved in the development of GR via cytoskeletal regulation.

CONCLUSIONS

In summary, we identified several GCF proteins associated with GR after orthodontic treatment. These findings could contribute to the prevention of GR in susceptible patients before the initiation of orthodontic treatment.

SIGNIFICANCE

Orthodontic patients with GR often report esthetic defects or root hypersensitivity during orthodontic treatment, especially at the anterior teeth site. GCF, rich in protein, is an easily accessible source of potential biomarkers for the diagnosis of periodontal diseases; however, little is known about the changes in GCF proteome associated with GR in orthodontic patients. In this study we firstly used DIA-based LC-MS/MS to evaluate the proteome and to identify the biomarker candidates for GR in orthodontic patients. These findings will improve our understanding of GR during orthodontic treatment, and could contribute to an earlier diagnosis, or even prevention, of GR in susceptible populations before orthodontic treatment.

Metabolomics Research in Periodontal Disease by Mass Spectrometry

Periodontology is a newer field relative to other areas of dentistry. Remarkable progress has been made in recent years in periodontology in terms of both research and clinical applications, with researchers worldwide now focusing on periodontology. With recent advances in mass spectrometry technology, metabolomics research is now widely conducted in various research fields. Metabolomics, which is also termed metabolomic analysis, is a technology that enables the comprehensive analysis of small-molecule metabolites in living organisms. With the development of metabolite analysis, methods using gas chromatography–mass spectrometry, liquid chromatography–mass spectrometry, capillary electrophoresis–mass spectrometry, etc. have progressed, making it possible to analyze a wider range of metabolites and to detect metabolites at lower concentrations. Metabolomics is widely used for research in the food, plant, microbial, and medical fields. This paper provides an introduction to metabolomic analysis and a review of the increasing applications of metabolomic analysis in periodontal disease research using mass spectrometry technology.

Proteomics Disclose the Potential of Gingival Crevicular Fluid (GCF) as a Source of Biomarkers for Severe Periodontitis

Periodontal disease is a widespread disorder comprising gingivitis, a mild early gum inflammation, and periodontitis, a more severe multifactorial inflammatory disease that, if left untreated, can lead to the gradual destruction of the tooth-supporting apparatus. To date, effective etiopathogenetic models fully explaining the clinical features of periodontal disease are not available. Obviously, a better understanding of periodontal disease could facilitate its diagnosis and improve its treatment. The purpose of this study was to employ a proteomic approach to analyze the gingival crevicular fluid (GCF) of patients with severe periodontitis, in search of potential biomarkers. GCF samples, collected from both periodontally healthy sites (H-GCF) and the periodontal pocket (D-GCF), were subjected to a comparison analysis using sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). A total of 26 significantly different proteins, 14 up-regulated and 12 down-regulated in D-GCF vs. H-GCF, were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The main expressed proteins were inflammatory molecules, immune responders, and host enzymes. Most of these proteins were functionally connected using the STRING analysis database. Once validated in a large scale-study, these proteins could represent a cluster of promising biomarkers capable of making a valuable contribution for a better assessment of periodontitis.

A comparative proteomic analysis to define the influencing factors on gingival crevicular fluid using LC-MS/MS

Gingival crevicular fluid (GCF) is a promising biofluid for disease identification and biomarker searching in periodontology. This study aimed to investigate the possible influencing factors, including tooth site, sex and age, on the normal GCF proteome. Forty periodontal healthy adults were randomly divided into a training group and a testing group. In the training group, GCF samples from 12 adults were analyzed using the iTRAQ 2D LC-MS/MS method. The influencing factors, tooth site (including periodontitis-susceptible and -insusceptible tooth sites), sex and age, and related differential proteins were defined and functionally annotated. The important differential proteins from 28 adults in the testing group were then validated by PRM analysis. An average of approximately 5 differential proteins were found between tooth sites of periodontitis-susceptible and -insusceptible sites. Eighty-five differentially expressed proteins were obtained between sexes in the young group, while only 7 sex-associated proteins were found in the old group. A total of 203 and 235 age-associated proteins were found in the male and female groups, respectively. The differential protein functional annotation showed that sex-related proteins were mainly related to immune function and metabolism, and age-related proteins were primarily associated with inflammation, lipid metabolism and immune function. In the testing group, a total of 4 sex-related proteins and 12 age-related proteins were validated by PRM analysis. SIGNIFICANCE: The influences of tooth site, sex and age in GCF proteomics in periodontal health were firstly analyzed using LC-MS/MS. Tooth site showed a small influence on the GCF proteome. The sex effect was significant in young adults, but its influence in old adults is small. Age is an important impact factor for the GCF proteome. These findings enrich the knowledge about the normal GCF proteome and might benefit future disease analyses.

Searching for essential genes and drug discovery in breast cancer and periodontitis via text mining and bioinformatics analysis

The primary purpose of the study was (1) to search for the essential genes associated with breast cancer and periodontitis, and (2) to identify candidate drugs targeted to these genes for expanding the potential drug indications. The genes related to both breast cancer and periodontitis were determined by text mining. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis were performed on these genes, and protein-protein interaction analysis was carried out to export significant module genes. Drug-gene interaction database was employed for potential drug discovery. We identified 221 genes common to both breast cancer and periodontitis. The top six significant enrichment terms and 15 enriched signal pathways were selected. Among 24 significant genes demonstrated as a gene cluster, we found SERPINA1 and TF were significantly related to poor overall survival between the relatively high and low groups in patients. Using the final two genes, 12 drugs were identified that had potential therapeutic effects. SERPINA1 and TF were screened out as essential genes related to both breast cancer and periodontitis, targeting 12 candidate drugs that may expand drug indications. Drug discovery using text mining and analysis of different databases can promote the identification of existing drugs that have the potential of administration to improve treatment in breast cancer.

Diagnostic potential and future directions of matrix metalloproteinases as biomarkers in gingival crevicular fluid of oral and systemic diseases

Gingival crevicular fluid (GCF) is a physiological fluid and an inflammatory serum exudate derived from the gingival plexus of blood vessels and mixed with host tissues and subgingival plaque flows. In addition to proteins, GCF contains a diverse population of cells, including desquamated epithelial cells, cytokines, electrolytes, and bacteria from adjacent plaques. Recently, matrix metalloproteinases(MMPs), which are endopeptidases that are active against extracellular macromolecules, in GCF have been revealed as potential utility biomarkers for the diagnosis and follow-up of oral and systemic diseases, thereby facilitating the early evaluation of malignancy risk and the monitoring of disease progression and treatment response. Tissue inhibitors of metalloproteinases (TIMPs) are specific inhibitors of matrixins that participate in the regulation of local activities of MMPs in tissues. This review provides an overview of the latest findings on the diagnostic and prognostic values of MMPs and TIMPs in GCF of oral and systemic diseases, including periodontal disease, pulpitis, peri-implantitis and cardiovascular disease as well as the extraction, detection and analytical methods for GCF.

Optimal Examination Sites for Periodontal Disease Evaluation: Applying the Item Response Theory Graded Response Model

Periodontal examination data have a complex structure. For epidemiological studies, mass screenings, and public health use, a simple index that represents the periodontal condition is necessary. Periodontal indices for partial examination of selected teeth have been developed. However, the selected teeth vary between indices, and a justification for the selection of examination teeth has not been presented. We applied a graded response model based on the item response theory to select optimal examination teeth and sites that represent periodontal conditions. Data were obtained from 254 patients who participated in a multicenter follow-up study. Baseline data were obtained from initial follow-up. Optimal examination sites were selected using item information calculated by graded response modeling. Twelve sites—maxillary 2nd premolar (palatal-medial), 1st premolar (palatal-distal), canine (palatal-medial), lateral incisor (palatal-central), central incisor (palatal-distal) and mandibular 1st premolar (lingual, medial)—were selected. Mean values for clinical attachment level, probing pocket depth, and bleeding on probing by full mouth examinations were used for objective variables. Measuring the clinical parameters of these sites can predict the results of full mouth examination. For calculating the periodontal index by partial oral examination, a justification for the selection of examination sites is essential. This study presents an evidence-based partial examination methodology and its modeling.