Reply: Retinopathy, histidine-rich protein-2 and perfusion pressure in cerebral malaria
Bibliographic record
Abstract
Sir, We thank Kariuki and Newton for their letter which raises several interesting points. We value their contributions to this discussion. Failure of cerebral autoregulation may be an important step in the cerebral malaria disease process. Ideally our review would have not only included comparisons of autoregulatory function in retinal and cerebral vessels, but also other important subjects such as the nature of the blood-tissue barriers, distribution of endothelial receptors, and vessel ultrastructure in retina and brain. Comparing and contrasting the retina with other areas of the CNS in terms of these and other features could well provide valuable insights, not just into cerebral malaria, but for a whole range of neurovascular diseases. We hope our paper will help to stimulate individual reviews on these topics. The utility of Plasmodium falciparum histidine-rich protein 2 (pfHRP2) as a biomarker in severe malaria has recently been reviewed (Manning and Davis, 2013). In African children with severe malaria, high pfHRP2 has been associated with anaemia, coma and death (Hendriksen et al., 2012), and distinguishes presence/absence of cerebral sequestration or malarial retinopathy in separate derivation and validation cohorts (Seydel et al., 2012). pfHRP2 shows clear promise as an important indicator of P. falciparum pathogenesis, and further evaluation as a biomarker of disease severity seems warranted. However, as with any prospective biomarker, evaluation must consider the biological context (Buyse et al., 2010). For example, some strains of P. falciparum do not produce pfHRP2 (Gamboa et al., 2010). Estimates of total body parasite load may provide useful information at a population level, but subject level variance in important biological parameters leads to improbable values for individual patients (Hendriksen et al., 2012). Biological context is equally important when considering retinal features as potential markers of cerebral damage. This concern motivated our review. The available evidence suggests that malarial retinopathy, in the context of clinically defined paediatric cerebral malaria (Newton et al., 1998), does indeed reflect similar disease processes in the brain. Although empirical associations between retinopathy and cerebral histopathology necessarily come from limited populations, similarities between retina and brain in terms of anatomy and physiology imply that these associations are also likely to exist more broadly. This biological context suggests inference of several distinct pathological processes from the retina to the brain is plausible, including sequestration, haemorrhage, blood-tissue barrier breakdown, and ischaemia (MacCormick et al., 2014). Consequently, retinal imaging in severe malaria has the potential to provide information on a range of interdependent pathological processes, taking place within the CNS. In the same way that parasite detection by microscopy depends on an experienced technician, accurate detection of malarial retinopathy depends on the observer and the method. There is a need for a standardized approach to malarial retinopathy. This will facilitate accurate comparisons between studies, and help to define the characteristics and clinical associations of both malarial retinopathy and cerebral malaria more generally. Progressive technological advances in retinal imaging are likely to increase the utility of retinal observations. Far from precluding the use of other disease markers, retinal imaging represents an opportunity to assess novel biomarkers, both for their clinical use and to investigate relationships between multiple aspects of cerebral pathogenesis. More work is needed to compare retina–brain biological characteristics, describe empirical associations between retina, brain, and prospective biomarkers (including pfHRP2), and to standardize imaging methodology in malaria research.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.028 | 0.030 |
| Insufficient payload (model declined to judge) | 0.002 | 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".