Carga microbiana, dor, inflamação e atraso na cicatrização
Bibliographic record
Abstract
Background: It is estimated that over 400 million people worldwide suffer from hard to heal wounds, with a high social and economic cost constituting a burden for the health care system and society.The big current challenge for the management of those injuries is the prevention, early diagnosis and treatment of bacterial burden and its organization in biofilm, which has been identified as one of the contributing factors of chronic inflammation and delayed wound healing.Biofilm is defined as an aggregated of microorganisms organized as a community, embedded within an extracellular polymeric matrix which confers them immunity against antimicrobials.Besides delayed wound healing, a potential indicator of this problem is pain, symptom present in more than 60% of people suffering from wounds, originating stress, social isolation and interrupting daily life activities with high impact in the quality of life in consequence.Wound-related pain has not been fully understood or properly assessed and managed in clinical settings, due to the scarcity of research that explores the intricate relationships between the factors involved.Objective: The present study aims to identify and analyze the association between microbial load, pain, inflammation, and healing, in hard-to-heal wounds, as well as the impact on health-related quality of life.Methods: This is an observational and longitudinal prospective cohort study with four weeks of follow-up that included an Enterostomal Therapy service in São Paulo (Brazil).The data were collected in a pilot study with 10? patients corresponding to each type of chronic and acute wound (Vasculogenic Ulcers, Diabetic Foot Ulcers, Pressure Injuries, Surgical Complex Wound, Skin tears and secondary trauma wounds), totalizing an initial sample of 60 patients; after this analysis, it will be calculated the definitive sample size.After getting patients informed consent, data collection will be done by clinical records review, interview and physical wound assessment using the web-based research electronic data capture system REDCap ® .As data collection forms, will be used a sociodemographic and clinical data tool and Bates-Jansen wound assessment tool for wound description.Adapted and validated questionnaires to English and Brazilian Portuguese, on wound-related pain will be applied, such as Brief Inventory of Pain-reduced version, McGill Pain Questionnaire-Short Form, Neuropathic Symptom Rating Scale, and Numerical Pain Scale.Also, the Perceived Stress Scale and the Ferrans & Powers' Quality of Life Index -Wound version will be used.Tissue samples and wound fluid will be collected by swabs to identify inflammatory mediators (IL-1β, IL-6, TNF-α), metalloproteases (MMP 3, 6,9) and exudate samples will be analyzed through laboratory techniques of molecular biology (ELISA, PNA FISH) and microscopy.As a complimentary assessment, the bacterial burden will also be verified using Moleculight ® UV light camera, and the inflammation index calculated by thermography (wound temperature study) will be used as an indirect indicator.Data will be statistically analyzed with SPSS 24.0 program, there will be performed descriptive and probabilistic statistics including the verification of correlations (Pearson or Spearman tests) and associations between variables (univariate parametric or non-parametric tests) and finally the possible predictors of delayed wound healing, inflammation and pain (multivariate regression models).Results: The obtained results will allow a better understanding of the association between microbial load and inflammation; favouring the development of new approaches for the treatment of pain in patients with chronic hard-to-heal wounds.The comparison of the variables between acute and chronic wounds will contribute to the creation of differentiated interventions for the effective prevention of acute wound chronicity.The group plans to develop future randomized clinical trials to define how the treatment of oxidative stress and microbial burden can reduce inflammation and improve chronic pain.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".