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Clinical, pathological, and molecular characterization of feline leprosy syndrome in the western USA

2004· article· en· W2043055368 on OpenAlexaboutno aff
J. E. Foley, Thelma Lee Gross, Niki Drazenovich, F. Ramiro-Ibáñez, E. Anacleto

Bibliographic record

VenueVeterinary Dermatology · 2004
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsnot available
Fundersnot available
KeywordsCATSLeprosyHistopathologyPolymerase chain reactionPathology16S ribosomal RNAMycobacterium lepraePathologicalMedicineNecrosisBiologyVeterinary medicineDermatologyBacteriaInternal medicineGeneGenetics

Abstract

fetched live from OpenAlex

Feline leprosy syndrome is caused by multiple species of mycobacteria; at least two histomorphologic subtypes have been reported from Australia and Canada, and PCR/DNA sequencing studies suggest that several species of mycobacteria may be implicated. To the authors’ knowledge, similar studies from the United States have not been performed. Ten cats with skin lesions characteristic of feline leprosy syndrome and acid‐fast confirmation of organisms were included in the study. The cats were evaluated by clinical follow‐up, histopathology, and molecular characterization (PCR with DNA sequencing). Eight of 10 cats were from coastal cities of Hawaii, Washington or California; two cats were from the coastal mountain range in California, approximately 30 miles inland. Additional environmental risk factors included access to the outdoors in nine cats, four of which were observed hunting. Skin lesions ranged from mildly alopecic and swollen to nodular and ulcerated. Lesions were evaluated histopathologically, using both H&E and Fites acid‐fast stains for necrosis, number and distribution of acid‐fast bacteria, and visibility of organisms on H&E examination. PCR and DNA sequencing of a fragment of the 16S rRNA gene was performed in all cases. Lesions from four cats yielded Mycobacterium visibilis /IWGMT 90242 species, as previously identified in Canadian but not Australian cases; in each of these, large numbers of acid‐fast bacteria were seen diffusely within non‐necrotic lesions. These organisms also were visible with H&E examination. Lesions from four cats were associated with M. lepraemurium: three had necrosis with few (three cases) to moderate (one case) numbers of acid‐fast bacteria that were not visible upon H&E examination. Organisms tended to cluster in necrotic foci. The remaining lesions from two cats included one each of Rhodococcus erythropolis and M. kansasii ;both of these had M. lepraemurium ‐type histomorphology. Clinically, cats with M. visibilis/ IWGMTinfection tended to be older (mean age 10.3 years, P = 0.008) and have larger numbers of lesions (range four to too numerous to count) with recurrence (three cats), unexplained mortality(one cat), or concurrent disease (one cat coinfected with T. gondii ). Cats with M. lepraemurium , or histomorphologically similar Rhodococcus erythropolis and M. kansasii infections,were younger (mean age 2.2 years) and tended to have few lesions (range 1–5; mean 2.2). These cases responded completely to excision and treatment with miscellaneous broad‐spectrum antibiotics. The presence of two clinically and histomorphologically distinct syndromes supports previous reports from Canada and Australia. Clinical differences from Australian cases were identified, specifically the benign course of M. lepraemurium ‐typeinfections. The significance of the Rhodococcus erythropolis and M. kansasii is not clear; however, multiple species of mycobacteria have been recently identified by PCR in Canadian cases of feline leprosy syndrome. Geographic differences in specific organisms associated with feline leprosy syndrome may reflect local differences in risk factors, possibly due to differing ecologies of M. lepraemurium and M. visibilis in Australia, Hawaii and the continental United States. Funding: Self‐funded.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.363
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2004
Admission routes1
Has abstractyes

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