Polymerase Chain Reaction Detection of Culture-Negative Klebsiella pneumoniae Endophthalmitis: A Case Report
Bibliographic record
Abstract
Introduction: For cases of culture-negative endophthalmitis, 16S ribosomal deoxyribonucleic acid (16S RNA) real-time polymerase chain reaction (RT-PCR) may offer greater diagnostic yield than traditional microbial cultures. Our case presents an unusual clinical course, which supports the use of 16S RNA RT-PCR, even after negative microbial cultures, to secure a pathogenic diagnosis. Case Presentation: A 49-year-old male with human immunodeficiency virus (HIV) infection presented with fever and cough, accompanied by acute bilateral vision reduction, photophobia, and eye pain. Clinically, his examination showed severe panuveitis in both eyes. Investigations showed elevated white blood cells, C-reactive protein, cluster of differentiation 4 count of 180/μL, HIV viral load of <40 copies/mL, and unexpectedly, aqueous and blood cultures were negative. An autoimmune workup was also negative. Given this, intravitreal antibiotics were administered alongside systemic antibiotics. Subsequent chest computed tomography showed pulmonary cavitations and liver lesions, and despite negative culture results, a 16S rRNA RT-PCR of the aqueous humor detected Klebsiella pneumoniae genetic material. The patient completed 6 weeks of ceftriaxone and multiple bilateral vitrectomies for recurrent retinal detachments, likely due to retinal necrosis. Conclusion: Clinicians may consider alternative etiologies after a negative microbial culture. This teaching case supports the use of 16S RT-PCR to more rigorously rule out infectious causes of panuveitis, especially in immunocompromised patients, to avoid premature consideration of other differential diagnoses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".