Comparative Study of Bacterial Isolates Organism-wise Comparison (%) Organism Present Study (%) Lipsky BA (2012) Citron DM (2007) Shanmugam P (2013) Gadepalli R (2006) Sekhar S (2014) Escherichia coli 8.14% 10% 12% 14% 9% 11% Klebsiella pneumoniae 15.11% 8% 10% 18% 16% 14% Pseudomonas aeruginosa 31.40% 15% 18% 28% 32% 30% Acinetobacter spp. 9.30% 5% 6% 10% 12% 8% Proteus mirabilis 6.98% 6% 7% 9% 8% 7% Staphylococcus aureus (MRSA) 6.98% 20% 22% 18% 15% 17% Staphylococcus aureus 4.65% 25% 20% 16% 18% 19% Enterococcus spp. 1.17% 4% 5% 3% 2% 3% Proteus vulgaris 16.28% 7% 8% 12% 14% 13% interpretation and bar chart
ls -la /home/daytona/workspace/ 2>/dev/null || echo "workspace empty or missing"
mkdir -p /home/daytona/workspace/bacterial-study && python3 << 'EOF' import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import matplotlib.patches as mpatches import numpy as np organisms = [ "E. coli", "K. pneumoniae", "P. aeruginosa", "Acinetobacter spp.", "P. mirabilis", "S. aureus\n(MRSA)", "S. aureus", "Enterococcus spp.", "P. vulgaris" ] studies = { "Present Study": [8.14, 15.11, 31.40, 9.30, 6.98, 6.98, 4.65, 1.17, 16.28], "Lipsky BA (2012)": [10, 8, 15, 5, 6, 20, 25, 4, 7], "Citron DM (2007)": [12, 10, 18, 6, 7, 22, 20, 5, 8], "Shanmugam P (2013)": [14, 18, 28, 10, 9, 18, 16, 3, 12], "Gadepalli R (2006)": [9, 16, 32, 12, 8, 15, 18, 2, 14], "Sekhar S (2014)": [11, 14, 30, 8, 7, 17, 19, 3, 13], } colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b'] n_groups = len(organisms) n_studies = len(studies) bar_width = 0.13 index = np.arange(n_groups) fig, ax = plt.subplots(figsize=(18, 8)) for i, (study, values) in enumerate(studies.items()): offset = (i - n_studies / 2 + 0.5) * bar_width bars = ax.bar(index + offset, values, bar_width, label=study, color=colors[i], alpha=0.88, edgecolor='white', linewidth=0.5) ax.set_xlabel('Bacterial Organism', fontsize=13, fontweight='bold', labelpad=10) ax.set_ylabel('Percentage of Isolates (%)', fontsize=13, fontweight='bold', labelpad=10) ax.set_title('Comparative Study of Bacterial Isolates\nOrganism-wise Distribution Across Studies (%)', fontsize=15, fontweight='bold', pad=15) ax.set_xticks(index) ax.set_xticklabels(organisms, fontsize=9.5) ax.set_ylim(0, 38) ax.yaxis.grid(True, linestyle='--', alpha=0.6) ax.set_axisbelow(True) legend = ax.legend(title='Studies', title_fontsize=10, fontsize=9, loc='upper right', framealpha=0.9, edgecolor='grey', ncol=2) # Highlight present study bars with a star annotation present_values = studies["Present Study"] for j, v in enumerate(present_values): offset = (0 - n_studies / 2 + 0.5) * bar_width ax.annotate('★', xy=(index[j] + offset, v + 0.3), ha='center', va='bottom', fontsize=7, color='#1f77b4') ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) plt.tight_layout() plt.savefig('/home/daytona/workspace/bacterial-study/bacterial_isolates_comparison.png', dpi=180, bbox_inches='tight', facecolor='white') print("Chart saved successfully.") EOF
bacterial isolates comparison
Image · PNG
Interpretation
Overall Pattern
Organism-by-Organism Analysis
| Organism | Present Study | Trend vs. Comparators | Significance |
|---|---|---|---|
| P. aeruginosa (31.4%) | Highest isolate | Higher than Lipsky/Citron; matches Gadepalli | Dominant pathogen in this cohort; indicates chronic or hospital-associated wound infections |
| P. vulgaris (16.28%) | 2nd highest | Markedly higher than all comparators (7–13%) | A notable finding — suggests this study population may have polymicrobial, chronic, or immunocompromised wounds |
| K. pneumoniae (15.11%) | 3rd highest | Comparable to Shanmugam (18%) and Gadepalli (16%) | Consistent across South Asian studies; aligns with community-acquired Gram-negative bacteraemia |
| Acinetobacter spp. (9.3%) | Moderate | Higher than Lipsky/Citron; similar to Gadepalli | Reflects nosocomial or ICU-acquired isolates |
| E. coli (8.14%) | Low-moderate | Lower than most comparators (9–14%) | Relatively underrepresented; may reflect patient selection or geographic variation |
| P. mirabilis (6.98%) | Low | Consistent across all studies (6–9%) | A reliably minor but persistent contributor |
| S. aureus (MRSA) (6.98%) | Low | Markedly lower than all comparators (15–22%) | Significant departure — could indicate a Gram-negative predominant setting, prior antibiotic selection pressure, or regional epidemiology |
| S. aureus (4.65%) | Very low | Substantially lower than comparators (16–25%) | Along with MRSA, total S. aureus (~11.6%) is much lower than Western studies; this is the most striking divergence |
| Enterococcus spp. (1.17%) | Negligible | Consistently lowest across all studies | Minor pathogen in wound infections |
Key Findings
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Gram-negative shift: The present study shows a pronounced Gram-negative predominance. Combined S. aureus (MRSA + non-MRSA) is only ~11.6% vs. 40–47% in Lipsky (2012) and Citron (2007). This divergence likely reflects geographic epidemiology (South Asian vs. Western cohorts) and prior broad-spectrum antibiotic exposure.
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P. aeruginosa dominance (31.4%): Consistent with South Asian studies (Gadepalli 32%, Shanmugam 28%, Sekhar 30%), but far above Western reports. Clinically important due to intrinsic resistance and biofilm formation.
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P. vulgaris overrepresentation (16.28%): This is the most distinctive finding compared to all reference studies (7–13%). Proteus vulgaris is associated with urease production, alkaline urine, and complex polymicrobial infections — its elevation warrants attention for empirical antibiotic protocol design.
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MRSA underreporting or true reduction: The low MRSA rate (6.98%) compared to Western studies (15–22%) may reflect either true epidemiological differences or underdetection due to limited phenotypic screening (confirm via genotypic MRSA testing).
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Clinical implication: Empirical therapy in this population should prioritize coverage of P. aeruginosa, K. pneumoniae, and Proteus spp. — anti-pseudomonal beta-lactams (piperacillin-tazobactam, carbapenems) or fluoroquinolones (where sensitivity is confirmed) would be most relevant, rather than MRSA-targeted agents (vancomycin/linezolid) which may be less critical here.