The challenges of combatting antimicrobial resistance in the Philippines

Protein embeddings improve phage-host interaction prediction

With the growing interest in using phages to combat antimicrobial resistance, computational methods for predicting phage-host interactions have been explored to help shortlist candidate phages. Most existing models consider entire proteomes and rely on manual feature engineering, which poses difficulty in selecting the most informative sequence properties to serve as input to the model. In this paper, we framed phage-host interaction prediction as a multiclass classification problem that takes as input the embeddings of a phage's receptor-binding proteins, which are known to be the key machinery for host recognition, and predicts the host genus. We explored different protein language models to automatically encode these protein sequences into dense embeddings without the need for additional alignment or structural information. We show that the use of embeddings of receptor-binding proteins presents improvements over handcrafted genomic and protein sequence features. The highest performance was obtained using the transformer-based protein language model ProtT5, resulting in a 3% to 4% increase in weighted F1 and recall scores across different prediction confidence thresholds, compared to using selected handcrafted sequence features.

A national online survey of Filipinos' knowledge, attitude, and awareness of antibiotic use and resistance: A cross-sectional study

BACKGROUND

Antibiotic resistance is one of the 21st century's most challenging clinical and public health issues. However, this health issue remains underreported in the Philippines.

AIM

This study examined Filipinos' knowledge, attitude, and awareness regarding antibiotic use and resistance and the associated predictive variables of antibiotic resistance.

METHODS

A cross-sectional design and Strengthening the Reporting of Observational Studies in Epidemiology guidelines were used in this study. Convenience sampling of 3767 participants completed the Eurobarometer survey on antibiotic resistance from October 2021 to February 2022. χ² and regression analysis were used to analyze the data.

RESULTS

Most participants were familiar with the popular types of available antibiotics. Males and healthcare workers had a higher percentage of correct responses on antibiotic resistance knowledge. Participants had moderate to high knowledge levels of antibiotic resistance. Age, educational attainment, profession, antibiotic use in the previous year, and household members taking antibiotics were significant predictors of the level of knowledge of antibiotic resistance. There was a significant difference in participants' sex, age, and educational attainment in their attitudes toward acquisition, hygienic practices, and the role of health professionals in antibiotic resistance.

CONCLUSION

Government agencies and policymakers should consider the identified predictors when establishing policies on antibiotic resistance. This will ensure that antibiotic use is safe and effective.