# Antimicrobial resistance

BacLLM injects semantic representations of UniProt annotations, encoded by a large language model, into an ESM-2 sequence encoder through cross-modal contrastive learning, so inference reads amino acid sequences alone. Across six resistance-prediction benchmarks, BacLLM (4B) beat Ankh, ESM-2, ProteinBERT and ProTrek in mean F1 under both logistic regression and an RBF-kernel support vector machine; on the SHV-1 β-lactamase P0AD64 it recovered six of eight experimentally supported resistance-attenuating sites among the top 65 ranked positions, while ESM-2 recovered none. The text branch is removed after training, so these gains come from sequence input, not annotation input.

BacLLM injects semantic representations of UniProt annotations, encoded by a large language model, into an ESM-2 sequence encoder through cross-modal contrastive learning, so inference reads amino acid sequences alone. Across six resistance-prediction benchmarks, BacLLM (4B) beat Ankh, ESM-2, ProteinBERT and ProTrek in mean F1 under both logistic regression and an RBF-kernel support vector machine; on the SHV-1 β-lactamase P0AD64 it recovered six of eight experimentally supported resistance-attenuating sites among the top 65 ranked positions, while ESM-2 recovered none. The text branch is removed after training, so these gains come from sequence input, not annotation input.

This article reviews evidence and implementation experience on surgical site infection (SSI) prevention across infection prevention and control (IPC), antimicrobial stewardship (AMS), antimicrobial resistance (AMR), quality improvement, surveillance, safety culture, human factors, and emerging technologies, concluding that multimodal, context-adapted IPC interventions can substantially reduce SSI incidence without major capital investment, that persistent gaps remain in infrastructure, guideline implementation, surveillance, and environmental controls, that AMR further emphasizes the need to integrate AMS into surgical safety frameworks, and that artificial intelligence and objective diagnostic technologies may improve surveillance and early SSI detection, so that SSI prevention is both a

This article reviews evidence and implementation experience on surgical site infection (SSI) prevention across infection prevention and control (IPC), antimicrobial stewardship (AMS), antimicrobial resistance (AMR), quality improvement, surveillance, safety culture, human factors, and emerging technologies, concluding that multimodal, context-adapted IPC interventions can substantially reduce SSI incidence without major capital investment, that persistent gaps remain in infrastructure, guideline implementation, surveillance, and environmental controls, that AMR further emphasizes the need to integrate AMS into surgical safety frameworks, and that artificial intelligence and objective diagnostic technologies may improve surveillance and early SSI detection, so that SSI prevention is both a