The cortisol awakening response (CAR) can be assessed from saliva samples collected at home, which confers ecological validity but lacks researcher oversight. Participant non-adherence to requested saliva sampling regimes leads to inaccurate CAR estimates. Moderate sampling delays of just 8 (5-15) min between awakening and commencement of saliva sampling are reported to result in over-estimated CAR magnitude and earlier peaking. This has been attributed to an observed 'latent' period in which cortisol secretion does not increase for up to 10-min after awakening. Replication of this finding is essential as the findings have considerable implications for CAR research. Healthy participants (n=26) collected saliva samples at 5-min intervals for 60min on 2 consecutive typical weekdays. Full electronic monitoring of awakening and sampling enabled exclusion of non-adherent data (i.e., delays of greater than 5min between awakening and collection of the first sample). In the 0-15min post awakening segment of the CAR a quadratic effect was observed, with no difference between the awakening and 5 and 10min samples. Moderate sampling delays will shift assessment of the CAR just sufficiently along the time axis to not impact upon measurement of the first sample but to remove the immediate post-awakening latent period from CAR estimates-whilst retaining later estimates of elevated cortisol secretion. The implication from these results is that accurate CAR measures can only be determined from data with strict adherence to commencement of saliva sampling following awakening.
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http://dx.doi.org/10.1016/j.psyneuen.2015.08.011 | DOI Listing |
Microrna
January 2025
Department of Periodontics. Panineeya Institute of Dental Sciences and Research Center. Road no. 5, Kamala Nagar, Dilsukh Nagar, Hyderabad, 500060, India.
Background: Periodontitis destroys the tooth's supporting structures and attachment apparatus. Local or systemic factors can cause it. Traditionally, diagnosis is based on clinical parameters that may not consistently reflect an accurate confirmation.
View Article and Find Full Text PDFBMC Oral Health
January 2025
Innovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No.37, Guoxue Lane, Wuhou District, Chengdu, China.
Background: Diabetes with its highly prevalence has become a major contributor to the burden of health care costs worldwide. Recent unequivocal evidence has revealed a bidirectional link between oral health and diabetes. In this study, the effects of the Oral Health Promotion Program (OHPP) on oral hygiene, oral health-related quality of life and glycated haemoglobin (HbA1c) levels in diabetic elderly were examined.
View Article and Find Full Text PDFAAPS J
January 2025
National Center On Addiction and Doping, National Institute of Health, Viale Regina Elena 299, 00161, Rome, Italy.
Nowadays, synthetic cathinones (SCs) is the second more representative subclass of New Psychoactive Substances, accounting for 104 analogues in the illegal market. Since its first report in 2011, α-pyrrolidinovalerophenone (α-PVP) gained popularity among drug users, provoking an increased number of intoxications. Nonetheless, pharmacokinetics data is still limited in the literature.
View Article and Find Full Text PDFNPJ Biofilms Microbiomes
January 2025
Department of Health and Genomics, FISABIO Foundation, Valencia, Spain.
We have previously demonstrated that subgingival levels of nitrate-reducing bacteria, as well as the in vitro salivary nitrate reduction capacity (NRC), were diminished in periodontitis patients, increasing after periodontal treatment. However, it remains unclear if an impaired NRC in periodontitis can affect systemic health. To determine this, the effect of nitrate-rich beetroot juice (BRJ) on blood pressure was determined in 15 periodontitis patients before and 70 days after periodontal treatment (i.
View Article and Find Full Text PDFAnal Chim Acta
January 2025
Christian Doppler Laboratory for Innovative Gut Health Concepts of Livestock, Austria; BOKU University, Vienna, Dept. IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, Tulln, Austria.
Background: Untargeted metabolomics requires robust and reliable strategies for data processing to extract relevant information form the underlying raw data. Multiple platforms for data processing are available, but the choice of software tool can have an impact on the analysis. This study provides a comprehensive evaluation of four workflows based on commonly used metabolomics software tools: XCMS, Compound Discoverer, MS-DIAL, and MZmine.
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