Editorial: Further findings in the role of inflammation in the etiology and treatment of schizophrenia
Bibliographic record
Abstract
Further findings in the role of inflammation in the etiology and treatment of schizophrenia Current biomedical knowledge makes it possible to ascertain how brain function represented by thought is subject to a series of changes in a pathological sense as the condition of anatomical microstructures in the brain changes and neurophysiology changes in response to environmental stimuli (1).Increasing evidence has pointed to the implication of inflammation in schizophrenia, and patients with diagnoses afferent to its spectrum may have increased pro-inflammatory markers (2) and a higher prevalence of inflammatory diseases.In addition, genetic and epigenetic studies have highlighted the role of immune and endocrinological systems in schizophrenia (3), and some clinical trials have detected antipsychotic effects of anti-inflammatory drugs (4).In light of these considerations, the Research Topic aimed to evaluate various molecular, biological, genetic, and neuroimaging aspects due to the consequences of inflammation to identify risk factors, predictors, and possible protective factors or specific treatment methods.Regarding routine immunological laboratory parameters, Skalniak et al. highlight how their values measured at admission may act to improve positive symptoms in schizophrenia after treatment.From the data analysis, the authors repeatedly highlight the altered Creactive protein (CRP) levels in patients with schizophrenia compared with healthy controls.According to further psychometric evaluation using the PANSS scale, correlation with inflammation parameters is present in positive PANSS scales on admission; these parameters also decline significantly after drug treatment.The authors found that for PANSS subscales representing arousal and disorganization of thought processes, the immunological parameters C4 and CRP, respectively, parametrically modify the outcome of drug treatment.Further assessing negative symptoms, fT3, glucose and creatinine levels appear to be substantial modifiers, while creatinine affects the arousal subscales and HDL affects the subscales describing negative emotions.In their study on schizophrenia and cellular senescence candidate gene screening, machine learning, diagnostic models, and drug prediction, Feng et al. demonstrate through KEGG analysis the connection between Epstein-Barr virus (EBV) infection and schizophrenia-related Frontiers in Psychiatry frontiersin.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.020 | 0.022 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".