Correlation between the use of antibiotics and development of a resistant bacterial infection in patients in the ICU

Artificial intelligence-accelerated high-throughput screening of antibiotic combinations on a microfluidic combinatorial droplet system

Microfluidic platforms have been employed as an effective tool for drug screening and exhibit the advantages of lower reagent consumption, higher throughput and a higher degree of automation. Despite the great advancement, it remains challenging to screen complex antibiotic combinations in a simple, high-throughput and systematic manner. Meanwhile, the large amounts of datasets generated during the screening process generally outpace the abilities of the conventional manual or semi-automatic data analysis. To address these issues, we propose an artificial intelligence-accelerated high-throughput combinatorial drug evaluation system (AI-HTCDES), which not only allows high-throughput production of antibiotic combinations with varying concentrations, but can also automatically analyze the dynamic growth of bacteria under the action of different antibiotic combinations. Based on this system, several antibiotic combinations displaying an additive effect are discovered, and the dosage regimens of each component in the combinations are determined. This strategy not only provides useful guidance in the clinical use of antibiotic combination therapy and personalized medicine, but also offers a promising tool for the combinatorial screenings of other medicines.

Development of a rapid and sensitive analytical system for Pseudomonas aeruginosa based on reverse transcription quantitative PCR targeting of rRNA molecules

For Pseudomonas aeruginosa (PA), infection control and appropriate antimicrobial treatment have become important issues. Diagnosis is critical in managing PA infection, but conventional methods are not highly accurate or rapid. We developed a new PA quantification system based on 23S rRNA-targeted reverse transcription quantitative PCR (RT-qPCR). We confirmed that RT-qPCR can quantify PA directly from clinical samples quickly (within 6 h) and with high sensitivity (blood, 1 cell/mL; stool, 100 cells/g) and without cross-reaction. Also, under antibiotic treatment, PA viable counts detected by this system correlated well with the inflammatory response of infected Caco-2 cells compared to other methods such as culturing and qPCR. Next, we utilized this system on fecal samples collected from 65 septic ICU patients and 44 healthy volunteers to identify ICU infection status. We confirmed that the PA detection ratio in ICU patients was significantly higher than that in healthy volunteers (49.2% vs. 13.6%, P < 0.05). Additionally, we monitored drug-resistant PA in 4 ICU patients by this system. The trends in PA counts accurately reflected various treatment backgrounds such as antibiotic use and mechanical ventilator use. Our results suggest that this RT-qPCR system is beneficial for the early diagnosis and evaluation of appropriate antibacterial treatment and may be a useful tool in combating PA infection.