Majeed Syndrome: Five Cases With Novel Mutations From Unrelated Families in India With a Review of Literature
Bibliographic record
Abstract
Objective Majeed syndrome (MJS) is an autosomal recessive, systemic autoinflammatory disease (SAID) caused by biallelic loss-of-function variants in the LPIN2 gene. It is characterized by early-onset chronic recurrent multifocal osteomyelitis (CRMO), dyserythropoietic anemia, and neutrophilic dermatosis. We analyzed a cohort of uncharacterized Indian patients for pathogenic variants in LPIN2 and other genes associated with SAIDs. Methods We performed whole-exome sequencing (WES) for 1 patient and next-generation sequencing (NGS) targeted gene panel for SAIDs in 3 patients. One patient was a referral from neurology after clinical exome sequencing identified a novel variant in LPIN2 . We reviewed the literature for all published studies of mutation-positive MJS patients and have summarized their clinical features and disease-causing variants. Results We describe the largest series of patients with MJS outside of the Middle East. All 5 patients are homozygous for novel, possibly pathogenic variants in the LPIN2 gene. Two of these variants are missense substitutions, and 3 are predicted to alter transcript splicing and create a truncated protein. In addition to the classical features of CRMO and anemia, patients exhibited previously unreported features, including abdominal pain, recurrent diarrhea/ear discharge, and erythema nodosum. Conclusion Patients with MJS may present initially to different specialists, and thus it is important to create awareness in the medical community. In India, consanguinity is a common sociocultural factor in many ethnic communities and an abbreviated NGS gene panel for autoinflammatory diseases should include MJS. The unavailability of interleukin 1 inhibitors in some countries poses a treatment challenge.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".