Aim: The aim of this review was to answer the following question: Can periodontal measures be used to identify dental patients with undiagnosed hyperglycaemia?
Materials And Methods: Systematic searches of electronic databases and the grey literature were carried out to identify studies developing and/or validating prediction models, based on any periodontal measure, to screen adults for undiagnosed hyperglycaemia (pre-diabetes and diabetes). Risk of bias was evaluated using the PRediction mOdel risk-of-Bias ASsessment Tool (PROBAST).
Results: Ten studies were identified, of which eight were model development studies. The remaining two studies reported the external validation of one existing prediction model. The periodontal prediction model with some evidence of external validation showed moderate diagnostic performance in the development sample but lower performance in the external validation samples. According to PROBAST, all studies had high risk of bias mainly due to methodological limitations in data analysis, but also in the recruitment of participants, choice and measurement of periodontal predictors and diabetes.
Conclusions: There is a need for more robust external validation studies of existing prediction models adhering to current recommendations. Dental professionals who see patients at risk of diabetes and routinely collect periodontal measures have an important role to play in their identification and referral.
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http://dx.doi.org/10.1111/jcpe.13596 | DOI Listing |
Sci Rep
December 2024
School of Pharmacy, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Cuproptosis, a newly identified form of cell death, has drawn increasing attention for its association with various cancers, though its specific role in colorectal cancer (CRC) remains unclear. In this study, transcriptomic and clinical data from CRC patients available in the TCGA database were analyzed to investigate the impact of cuproptosis. Differentially expressed genes linked to cuproptosis were identified using Weighted Gene Co-Expression Network Analysis (WGCNA).
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Medicine, Institute of Biomedicine, University of Eastern Finland, Yliopistonranta 1, PO Box 1627, 70211 Kuopio, Finland.
The selection of biomarker panels in omics data, challenged by numerous molecular features and limited samples, often requires the use of machine learning methods paired with wrapper feature selection techniques, like genetic algorithms. They test various feature sets-potential biomarker solutions-to fine-tune a machine learning model's performance for supervised tasks, such as classifying cancer subtypes. This optimization process is undertaken using validation sets to evaluate and identify the most effective feature combinations.
View Article and Find Full Text PDFCureus
December 2024
General Surgery, Aneurin Bevan University Health Board, Newport, GBR.
Aim: To assess recent colonoscopies and CT scans in conjunction with the feacal immunochemical test (FIT) for possibly downgrading urgent suspected cancer (USC) referrals.
Methods: A retrospective single-centre study was conducted, including all USC referrals for colonoscopy in 2022, excluding anal cancers. The CT and colonoscopy findings for a two-year period prior to the referral, along with the FIT result (if done), were noted.
J Inflamm Res
December 2024
Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, 40014, People's Republic of China.
Purpose: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive lung disease. PANoptosis, a unique inflammatory programmed cell death, it manifests as the simultaneous activation of signaling markers for pyroptosis, apoptosis, and necroptosis. However, research on the role of PANoptosis in the development of IPF is currently limited.
View Article and Find Full Text PDFBMC Med
December 2024
Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing, 100191, China.
Background: Risk prediction models can identify individuals at high risk of chronic liver disease (CLD), but there is limited evidence on the performance of various models in diverse populations. We aimed to systematically review CLD prediction models, meta-analyze their performance, and externally validate them in 0.5 million Chinese adults in the China Kadoorie Biobank (CKB).
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