Vitamin D, Depressive Symptoms, and Covid-19 Pandemic
Bibliographic record
Abstract
Since the COVID-19 outbreak, studies across diverse countries have strongly pointed toward the emergence of a mental health crisis, with a dramatic increase in the prevalence of depressive psychopathology and suicidal tendencies. Vitamin D deficiency has been associated with an increased risk of mental health problems as well as individual responses to stress. Studies have discussed the relationship between low serum vitamin D concentrations and depressive symptoms, suggesting that maintaining adequate concentrations of serum vitamin D seems to have a protective effect against it. Vitamin D was found to contribute to improved serotonergic neurotransmission in the experimental model of depression by regulating serotonin metabolism. The signaling of 1,25-dihydroxyvitamin D3, the active form of vitamin D, through vitamin D receptor (VDR) induces the expression of the gene of tryptophan hydroxylase 2 (TPH2), influences the expression of serotonin reuptake transporter (SERT) as well as the levels of monoamine oxidase-A (MAO-A), the enzyme responsible for serotonin catabolism. Vitamin D also presents a relevant link with chronobiological interplay, which could influence the development of depressive symptoms when unbalance between light-dark cycles occurs. In this Perspective, we discussed the significant role of vitamin D in the elevation of stress-related depressive symptoms during the COVID-19 pandemic. It is suggested that vitamin D monitoring and, when deficiency is detected, supplementation could be considered as an important healthcare measure while lockdown and social isolation procedures last during the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".