A review of nutrition in neuropathic pain of leprosy
Bibliographic record
Abstract
Leprosy is a neglected tropical disease (NTD) that continues to burden low- and middle-income countries (LMICs), despite being eliminated as a public health concern by the World Health Organization (WHO) in 2000. The causative agents, Mycobacterium leprae and Mycobacterium lepromatosis, affect nearly 200,000 individuals globally each year, with over 19,000 new cases detected in the Americas in 2020 alone. Canada has experienced an increasing incidence of leprosy, due to rising levels of travel and migration from endemic areas, reaching over 37,000 individuals with leprosy by the end of 2020. Patients experience a spectrum of signs and symptoms including hypopigmented cutaneous macules alongside peripheral neuropathy including peripheral neuropathic pain (PNP) and disabling sensory neuropathies. Despite the development of effective and curative therapeutics via multidrug therapy (MDT), many barriers to treatment adherence and effective immunological control of the pathogen challenge the care of patients with leprosy. Socioeconomic barriers, such as disability-related social stigma and often undiagnosed nutritional deficiencies, have resulted in heightened disease severity. PNP therapeutics are associated with significant side effects and remain ineffective as the majority of individuals will not experience a greater than 30% reduction of symptoms. Nutrient supplementation is known to be instrumental in reducing host oxidative stress, strengthening the immune system and mitigating comorbidities. Likewise, dietary lifestyle interventions known to be physiologically beneficial have recently emerged as powerful tools conferring neuroprotective effects, potentially mitigating PNP severity. However, a significant knowledge gap concerning the effect of adequate nutrition on host immunological control of leprosy and PNP severity exists. Further evaluation of this relationship will provide key insight into the pathogenesis of leprosy, strengthening the current body of literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".