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NSG 7220 Week 2 Discussion SU

The discussion assignment provides a forum for discussing relevant topics for this week based on the course competencies covered.

For this assignment, make sure you post your initial response to the Discussion Area by the due date assigned.

To support your work, use your course and text readings and also use the South University Online Library. As in all assignments, cite your sources in your work and provide references for the citations in APA format.

Start reviewing and responding to the postings of your classmates as early in the week as possible. Respond to at least two of your classmates’ initial postings. Participate in the discussion by asking a question, providing a statement of clarification, providing a point of view with a rationale, challenging an aspect of the discussion, or indicating a relationship between two or more lines of reasoning in the discussion. Cite sources in your responses to other classmates. Complete your participation for this assignment by the end of the week.

Biophysical Evidence and Application

In this assignment, you will discuss biophysical evidence pertaining to your topic. Everything we do in nursing should be supported by evidence. Whether we are looking to write a policy or nursing standard or make a change in practice to promote patient safety, we look to research (evidence) to guide us in our decision making. Visit the South University Online Library and retrieve two peer-reviewed articles pertaining to biophysical research and your topic of interest. Critique each article, highlighting the strengths and weaknesses of each article. Interpret the statistical analysis and significance of results. Finally, analyze the feasibility of applying this evidence to your topic of interest in your current practicum setting. Both articles should support nursing standards and patient safety within your chosen topic.

The articles I retrieved from the University library search both revolve around the use of diagnostic imaging to determine if detectible brain structure changes correlate with depressive manifestations.  The query in both articles aimed to see if a change in brain structure is noted among individuals with depression and if screening by imagin can this be used as a tool for earlier diagnosing or assessing of risk factors for mental health issues.  While my topic does revolve around screening, the type of screening explored in these articles is not similar as I am developing an idea with screening tools, such as the GAD-7.  The specificity of the diagnostic imaging explored in these articles is not feasible for my clinic setting, as the diagnostics involved are very technical and require special equipment and staff trained to use and interpret findings.  Neither study showed statistical significance in brain imaging that correlated to depressive symptoms.

Article 1 – A magnetic resonance imaging-based morphometric and structural covariance network study of Brazilian adolescents stratified by depression risk.

Rohrsetzer et al. (2023) offer that new research into neuroimaging studies that look at structural changes in the brain have shown mixed results related to depressive symptoms. This study aimed to determine if magnetic resonance imaging (MRI) will provide visible morphometric changes and biomarkers that can be correlated to depressive symptoms.  Over 18 months 150 participants divided into three groups, low risk, high risk, and major depressive disorder were given an MRI.

Strengths of this study include background data that suggests there are altered patterns of regulatory pathways in those with depression (Rohrsetzer et al., 2023).  The sample size of 150 divided into three groups was a good sample size. Lastly, the study used a composite risk score to correlate imaging changes to a standard screening tool among the test subjects.   The low-risk group was found to have a 1.3% risk of developing depression, while the high-risk group over 8% from the composite risk screening tool (Rohrsetzer et al., 2023).  However, of all comparisons performed among studied groups, no significant differences were found in the imaging studies.

Weaknesses for this study is that they did not identify if participants were taking medications and or therapies that may affect brain function. Additionally, this study did not identify exclusions for participation, Lastly, specific statistical information was not offered, only that the results indicated no significance. 

Article 2 – Exploring the course of adolescent anxiety and depression: Associations with white matter tract microstructure.

Roelofs et al. (2022) utilized diffusion tensor imaging (DTI) to identify alterations in white matter that may correlate to clinical symptoms of depression in adolescents. The theory for this study is that baseline changes to the neurobiology of the brain will be seen in adolescents who suffer from depression.

Strengths of this study include foundational research that shows alterations in emotion-processing networks.  Roelofs et al. (2022) used this as a rationale to support their theory that prior study results infer functional or structural changes would be seen in imaging.  Independent psychological assessments were performed on each participant to obtain baseline data on a standard assessment inventory tool which were used to assess symptoms and assign t-scores, which identify upper and lower bounds of data (Stat Analytica, 2023) .  Another strength is that this study also accounted for developmental stage by using a PDS scale. 

Over 18 months two groups INT, the study group, an HC, the healthy control, were examined 3 times at 6-month intervals.  Each group included approximately 30 participants.  One weakness of this study is that the study group included individuals with both clinical depression and at least one diagnosis of comorbid anxiety, which could include unforeseen variables. Additionally, this test group was identified as also having a recent change in antidepressant treatment, but discussion of medication use/disuse was not discussed in the study.

The results of this study also showed no statistical difference in any of the groups or imaging completed.

References

Roelofs, E. F., Bas-Hoogendam, J. M., van der Werff, S. J. A., Valstar, S. D., van der Wee, N. J. A., & Vermeiren, R. (2022). Exploring the course of adolescent anxiety and depression: associations with white matter tract microstructure. European Archives of Psychiatry and Clinical Neuroscience, 272(5), 849-858. https://doi.org/10.1007/s00406-021-01347-8

Rohrsetzer, F., Balardin, J. B., Picon, F., Sato, J. R., Battel, L., Viduani, A., Manfro, P. H., Yoon, L., Kohrt, B. A., Fisher, H. L., Mondelli, V., Swartz, J. R., & Kieling, C. (2023). An MRI-based morphometric and structural covariance network study of Brazilian adolescents stratified by depression risk. Brazilian Journal of Psychiatry, 45(4), 318-326. https://doi.org/10.47626/1516-4446-2023-3037

Stat Analytica. (2023). Understanding the T Score formula: A comprehensive guide. statanalytica.com/blog/t-score-formula/

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