Clinical and Investigative Medicine最新文献

Purpose: Through comprehensive bioinformatics analysis based on the immune microenvironment, this study aimed to identify immune-related RNA biomarkers that indicate aneurysmal subarachnoid hemorrhage (aSAH).

Methods: The GSE73378 dataset was downloaded from the National Center for Biotechnology Information GEO database, providing blood from 107 normal controls and 103 patients with aSAH. The immune infiltration types in the aSAH blood samples were assessed and RNAs that were differentially expressed (DE) between 1) the aSAH and control groups and 2) the immune infiltration groups (high and low) were identified. The intersecting genes were subjected to weighted gene co-expression network analysis followed by co-expression network construction. The aSAH-related genes and pathways were identified from the Comparative Toxicogenomics Database: update 2019.

Results: A total of three DE long non-coding RNAs (lncRNAs) and 301 DE mRNAs were identified. Of the 301 mRNAs, 91 were significantly enriched in three modules. Based on the 91 mRNAs and three lncRNAs, a co-expression network related to the disease pathway was constructed. This pathway consisted of 16 factors, including the 13 mRNAs (e.g., TNFSF13B, TNFSF10, MYD88, GNA12 and NSMAF) and three lncRNAs (FAM66A, LINC00954 and CELF2-AS2), as well as six pathways, including the NF-κB, toll-like receptor, and sphingolipid signalling pathways.

Conclusion: TNFSF13B, MYD88, GNA12, NSMAF, FAM66A, LINC00954 and CELF2-AS2 may serve as biomarkers for aSAH. The NF-κB, toll-like receptor and sphingolipid signalling pathways may play critical roles in the progression of aSAH.

Purpose: Disease prevalence estimates from population-based administrative databases are often biased due to measurement (misclassification) errors. The purpose of this article is to review the methodology for estimating disease prevalence in administrative data, with a focus on bias correction.

Source: Several approaches to bias correction in administrative data were reviewed and application of these methods was demonstrated using an example from the literature: physician claims and hospitalization data were employed to estimate diabetes prevalence in Ontario, Canada.

Findings: Misclassification bias in prevalence estimates from administrative data can be reduced by developing and selecting an optimal algorithm for case identification, applying a bias correction formula, or using statistical modelling. An algorithm for which sensitivity equals positive predictive value provides an unbiased estimate of prevalence. Bias reduction methods generally require information about the measurement properties of the algorithm, such as sensitivity, specificity, or predictive value. These properties depend on disease type, prevalence, algorithm definition (including the observation window), and may vary by population and time. Prevalence estimates can be improved by applying multivariable disease prediction models.

Conclusion: Frequency of a positive case identification algorithm in administrative data is generally not equivalent to disease prevalence. Although prevalence estimates can be corrected for bias using known measurement properties of the algorithm, these properties may be difficult to estimate accurately; therefore, disease prevalence estimates based on administrative data must be treated with caution.

For over 40 years the journal of Clinical and Investigative Medicine (CIM) has published articles of interest to clinician investigators in Canada and elsewhere. We strive for the highest standards and to remain relevant to our readers, but we cannot achieve this goal without the help of our reviewers, all of whom play a vital role in maintaining the integrity of the scientific process. Without their efforts, academic excellence would falter.

Purpose: The association between subclinical hypothyroidism (SCH) and cardiovascular risk, particularly with a TSH <10 µIU/ml, remains controversial. The objective of our study was to assess the association between SCH and cardiovascular risk through carotid intima-media thickness, and alternatively, to evaluate its change after treatment with levothyroxine.

Methods: A total of 54 individuals were included in the study: 18 with SCH; 18 with overt hypothyroidism (OH); and 18 healthy controls (HC). The carotid intima-media thickness was measured in each group. In SCH, follow-up was performed at three and six months after the start of levothyroxine treatment.

Results: The mean age of the total population at baseline was 35.8 years. The median TSH in SCH was 6.15 µIU/ml. The carotid intima-media thickness (mean and standard deviation) was greater in SCH in comparison to the HC group: right common carotid artery (RCCA), 0.486 ± 0.106 mm and 0.413 ± 0.075 mm in SCH and HC, respectively, p=0.01 and left common carotid artery (LCCA), 0.511 ± 0.144 mm and 0.427 mm ± 0.090 in SCH and HC, respectively, p=0.03). In patients with SCH, there was a decrease in the carotid intima-media thickness after treatment with levothyroxine (RCCA and LCCA, p <0.05 at three and six months).

Conclusions: There was an association between increased carotid intima-media thickness in patients with SCH in comparison with HC, even with a TSH <10 µIU/ml. The increase was reversed with levothyroxine therapy. The association of this increased thickness with important cardiovascular outcomes remains uncertain and should be evaluated in future studies.

Purpose: The coronavirus disease 2019 (COVID-19) pandemic has deeply altered social and working environments among health care workers. These health care workers are therefore at risk of additional psychosocial strain and ensuing metal health symptoms, which indirectly affects patient care. In this study, we aimed to assess the psychosocial and psychopathological impact of COVID-19 among acute care mental health and addictions staff.

Methods: This study is a cross-sectional survey and contains a sample size of 60 mental health and addiction acute care workers recruited from within Nova Scotia Health Authority. The survey was constructed using the online survey system, Opinio, and consisted of three sections: demographic variables (gender, age group and profession); the DASS-21 Questionnaire (which provides dimensional measures of stress, anxiety and depression); and the MBI-HSS (MP) Questionnaire (which measures three dimensions of burnout-emotional exhaustion, depersonalization and personal achievement).

Results: The majority of participants had at least one pathologic score on the DASS-21 and MBI-HSS (MP) sections (75.5% and 93.5%, respectively). The median severity on the DASS-21 and MBI-HSS (MP) were both moderate, with the younger age group (20-35 years) having more significant burnout scores (p = 0.0494). Simple logistic regression showed a significant relationship between burnout severity and pathologic distress, and simple linear regression showed significant correlation between DASS-21 and MBI-HSS (MP) scores, with a R2 value of 0.4633.

Conclusion: More planning, programs, resources and further research are needed to support wellness and recovery of all health care professionals who work at the mental health and addictions acute care unit.