Bacterial Profile and Antimicrobial Susceptibility Patterns in Diabetic Foot Ulcers: A Cross‑Sectional Study in Bangladesh
Bibliographic record
Abstract
Introduction Diabetic foot ulcers (DFUs) are a significant cause of amputation and mortality in low‑ and middle‑income countries. However, Bangladeshi data on the bacterial spectrum and antimicrobial susceptibility patterns remain scarce. Methods In a cross‑sectional study at Chattogram Diabetic General Hospital, Bangladesh, 106 adults with DFUs provided deep tissue specimens as per grading for aerobic culture. Bacterial isolates were identified by standard methods and tested against 16 antibiotics according to Clinical and Laboratory Standards Institute (CLSI) disc diffusion guidelines. Clinical and sociodemographic data were summarised descriptively; resistance patterns were visualised using heat‑map clustering. Results Culture was positive in 81.1% (86/106) of participants. Across all participants, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) were co‑dominant (each 26.4%, 28/106), followed by Klebsiella pneumoniae (K. pneumoniae) and Enterococcus species (spp.) (each 11.3%, 12/106), and Pseudomonas aeruginosa (P. aeruginosa) (9.4%, 10/106); polymicrobial infection was 3.8% (4/106). Resistance was widespread. In the Gram‑positive panel, erythromycin showed the highest resistance (S. aureus 92.9%; Enterococcus spp. 83.3%), with ampicillin/vancomycin/linezolid also high. In the Gram-negative panel, ampicillin, ciprofloxacin and cefuroxime carried heavy resistance burdens (reaching 100% in P. aeruginosa). By contrast, resistance to piperacillin-tazobactam and imipenem was low in E. coli (10.7% and 14.3%, respectively); in K. pneumoniae it was higher for piperacillin-tazobactam than imipenem (41.7% vs 16.7%); and in P. aeruginosa the pattern was reversed, with 10.0% resistant to piperacillin-tazobactam and 40.0% resistant to imipenem. Tigecycline retained 0% resistance across all taxa. Row‑wise clustering separated high‑ from lower‑resistance drug groups, making organism‑specific patterns immediately interpretable. Conclusion Gram‑negative organisms were more frequent overall, and resistance to several commonly used β‑lactams and fluoroquinolones was high. Tigecycline showed the best preserved activity. Empirical treatment should be guided by local data, prioritise agents with retained activity against both Gram‑positive and Gram‑negative pathogens, and be promptly narrowed once susceptibilities are available. Continued local surveillance and strong antimicrobial stewardship are essential to limit further resistance.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".