The Bacterial and Viral Complexity of Postinfectious Hydrocephalus in Uganda
Bibliographic record
Abstract
Abstract Postinfectious hydrocephalus (PIH), often following neonatal sepsis, is the most common cause of pediatric hydrocephalus world-wide, yet the microbial pathogens remain uncharacterized. Characterization of the microbial agents causing PIH would lead to an emphasis shift from surgical palliation of cerebrospinal fluid (CSF) accumulation to prevention. We examined blood and CSF from 100 consecutive cases of PIH and control cases of non-postinfectious hydrocephalus (NPIH) in infants in Uganda. Genomic testing was undertaken for bacterial, fungal, and parasitic DNA, DNA and RNA sequencing for viral identification, and extensive bacterial culture recovery. We uncovered a major contribution to PIH from Paenibacillus , upon a background of frequent cytomegalovirus (CMV) infection. CMV was only found in CSF in PIH cases. A facultatively anaerobic isolate was recovered. Assembly of the genome revealed a strain of P. thiaminolyticus . In mice, this isolate designated strain Mbale , was lethal in contrast with the benign reference strain. These findings point to the value of an unbiased pan-microbial approach to characterize PIH in settings where the organisms remain unknown, and enables a pathway towards more optimal treatment and prevention of the proximate neonatal infections. One Sentence Summary We have discovered a novel strain of bacteria upon a frequent viral background underlying postinfectious hydrocephalus in Uganda.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".