Association of hematological coefficients with markers of inflammation and oxidative stress in schizophrenia: results of a pilot analysis
Bibliographic record
Abstract
Relevance. To date, there is convincing evidence in favour of the immuno-inflammatory hypothesis of the etiopathogenesis of schizophrenia. As markers of immuno-inflammatory disorders in schizophrenia, a small number of studies have examined haematological indices (HI) and systemic inflammation coefficients (SIC): neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR) and platelet-lymphocyte ratio (PLR). Considering that HI may hypothetically reflect immuno-inflammatory processes in schizophrenia, it is relevant to assess their relationship with other known biochemical markers of inflammation and oxidative stress in this disease. The aim of this study was to evaluate the association of HI with biochemical markers of inflammation and oxidative stress in schizophrenia, as well as with the severity of clinical symptoms. Materials and methods: 50 patients with schizophrenia were examined: 42 women, 8 men, age 36 [7] years (median and interquartile range, hereafter Me [Q1; Q3]). Clinical assessment was performed using the Schizophrenia Positive and Negative Syndrome Scale and the Calgary Depression Scale for Schizophrenia. HI, homocysteine (Hc), tetrahydrobioterine (BH4), reduced glutathione (GSH), interleukin-6 (IL-6), C-reactive protein (CRP) and tumour necrosis factor-alpha (TNF-a) were studied. Results: Serum CRP levels were significantly correlated with leukocyte count, total monocyte, basophil and eosinophil counts, and granulocyte counts, but not with SIC (NLP, MLR and PLR). TNF-a has stronger direct correlations with a number of HI (granulocyte and erythrocyte counts, leukocyte counts at the level of trend towards significance) and coefficients (NLP, MLR at the level of trend). Conclusions: HI in the studied sample of patients with schizophrenia correlate with the data obtained by foreign authors, which indicates the prospect of studying this topic in the Russian population. Further evaluation of the relationship between HI (SIC) and serum TNF-a seems relevant. The study of associations of biochemical immuno-inflammatory markers with SIC further requires a thorough assessment of the stage of disease development, duration of its course, peculiarities of response to therapy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".