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Self-reported periodontitis:A multiethnic community-based validation study
No full textObjective: Self-reported periodontal measures offer a practical alternative to clinical examinations in large-scale studies, but few validation efforts exist in Asia, where periodontitis is highly prevalent. Moreover, the diagnostic performance of individual self-reported measures and the relevant sociodemographic factors differs across populations. This study aims to clinically validate self-reported periodontal measures in a multiethnic community-based population and to develop several predictive models that combine sociodemographic characteristics and self-reported periodontal measures. Methods: We analysed cross-sectional data from 426 diabetes-free participants in Singapore who completed the Centers for Disease Control and Prevention/American Academy of Periodontology (CDC-AAP) self-reported periodontal questionnaire and underwent full-mouth periodontal examinations. Periodontitis status was defined using the 2012 CDC-AAP case definitions. Multivariable logistic regressions and area under the curve (AUC) analyses evaluated predictive models for periodontitis. Additionally, we performed an exploratory analysis, training and testing five machine learning models to predict periodontitis. Results: Participants had a mean age of 48.9±9.9 years, 55.6% were female, and 16.4%, 42.5%, and 18.1% had mild, moderate, and severe periodontitis, respectively. A multivariable model incorporating one self-reported question (loose teeth), age, and ethnicity demonstrated good discrimination for severe periodontitis (AUC=0.76; sensitivity/specificity=0.60/0.80). While the machine learning models achieved similar AUC (0.67-0.76), they tended to be highly specific (0.99-0.75) but had much lower sensitivity (0.12-0.63). Conclusion: Selected self-reported periodontitis questions were useful for detecting severe periodontitis in this population. While machine learning models integrated sociodemographic factors with all 8 self-reported questions, larger and more diverse datasets are needed to enhance model robustness and generalisability across international populations. Clinical significance: Self-reported periodontal measures validated in a multiethnic population demonstrated good predictive value for severe periodontitis when combining self-report of loose teeth with demographic variables age, gender, and ethnicity. The application of machine learning models further enhanced diagnostic performance, highlighting potential for scalable, data-driven periodontal screening and surveillance across diverse populations.</p
Single cell proteomic analysis defines discrete neutrophil functional states in human glioblastoma
Get PDFNeutrophils are vital innate immune cells shown to infiltrate glioblastomas, however we currently lack the molecular understanding of their functional states within the tumour niche. Given that neutrophils are known to display a prominent discordance between mRNA and protein abundance, we developed ultra-sensitive mini-bulk and single cell proteomic (SCP) workflows to study the heterogeneity of peripheral blood and tumour associated neutrophils (TAN) from patients with glioblastoma. Mini-bulk analysis enabled a deeper protein coverage of circulating immature, mature and TAN populations, defining signatures of maturity and demonstrating that TANs resemble mature circulating neutrophils. Analysis of the SCP data results in the detection of >1100 proteins from a single TAN providing a detailed characterization of neutrophil subsets in glioblastoma. Our approach shows evidence of pathogenic and anti-tumorigenic clusters and discovers cell states invisible to scRNAseq, opening new opportunities to selectively target pro-tumoural neutrophil states.</p
Correction:Loss of the tumour suppressor LKB1/STK11 uncovers a leptin-mediated sensitivity mechanism to mitochondrial uncouplers for targeted cancer therapy (Molecular Cancer, (2024), 23, 1, (147), 10.1186/s12943-024-02061-4)
No full textCorrection: Mol Cancer 23, 147 (2024) Following publication of the original correspondence [1], the authors wish to extend their acknowledgements to provide a more clear and precise credit to Dr P Haramis, Dr L Mans and Dr G Gargiulo’s work that was included in their manuscript. First, they would like to thank Dr P Haramis and Dr L Mans for the work done in zebrafish and cite the data published in Dr Mans’ doctoral thesis [2] used in Figs. 1 C-F, S1B-E and S2 of the manuscript. They also thank Dr Haramis for her original concept of exploiting the hypermetabolic phenotype of lkb1 mutant zebrafish in a synthetic lethality screen. Finally, they would like to extend their acknowledgements to Dr G Gargiulo for the analysis of the RNA-seq experiments on zebrafish performed at the Genomics Core Facility of the NKI.</p
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