Antimicrobial stewardship and targeted therapies in the changing landscape of maternal sepsis.

Pregnant and postnatal women are a high-risk population particularly prone to rapid progression to sepsis with significant morbidity and mortality worldwide. Moreover, severe maternal infections can have a serious detrimental impact on neonates with almost 1 million neonatal deaths annually attributed to maternal infection or sepsis. In this review we discuss the susceptibility of pregnant women and their specific physiological and immunological adaptations that contribute to their vulnerability to sepsis, the implications for the neonate, as well as the issues with antimicrobial stewardship and the challenges this poses when attempting to reach a balance between clinical care and urgent treatment. Finally, we review advancements in the development of pregnancy-specific diagnostic and therapeutic approaches and how these can be used to optimize the care of pregnant women and neonates.

Wireless implantable bioelectronics with a direct electron transfer lactate enzyme for detection of surgical site infection in orthopaedics.

Periprosthetic infection is one of the most devastating complications following orthopaedic surgery. Rapid detection of an infection can change the treatment pathway and improve outcomes for the patient. In here, we propose a miniaturized lactate biosensor developed on a flexible substrate and integrated on a small-form bone implant to detect infection. The methods for lactate biosensor fabrication and integration on a bone implant are fully described within this study. The system performance was comprehensively electrochemically characterised, including with L-lactate solutions prepared in phosphate-buffered saline and culture medium, and interferents such as acetaminophen and ascorbic acid. A proof-of-concept demonstration was then conducted with ex vivo ovine femoral heads incubated with and without exposure to Staphylococcus epidermidis. The sensitivity, current density and limit-of-detection levels achieved by the biosensor were 1.25 µA mM-1, 1.51 µA.M-1.mm-2 and 66 µM, respectively. The system was insensitive to acetaminophen, while sensitivity to ascorbic acid was half that of the sensitivity to L-lactate. In the ex vivo bone model, S. epidermidis infection was detected within 5 h of implantation, while the control sample led to no change in the sensor readings. This pioneering work demonstrates a pathway to improving orthopaedic outcomes by enabling early infection diagnosis.

Towards pharmacokinetic boosting of phenoxymethylpenicillin (penicillin-V) using probenecid for the treatment of bacterial infections.

In the face of increasing antimicrobial tolerance and resistance there is a global obligation to optimise oral antimicrobial dosing strategies including narrow spectrum penicillins, such as penicillin-V. We conducted a randomised, crossover study in healthy volunteers to characterise the influence of probenecid on penicillin-V pharmacokinetics and estimate the pharmacodynamics against Streptococcus pneumoniae. Twenty participants took six doses of penicillin-V (250 mg, 500 mg or 750 mg four times daily) with and without probenecid. Total and free concentrations of penicillin-V and probenecid were measured at two timepoints. A pharmacokinetic model was developed, and the probability of target attainment (PTA) calculated. The mean difference (95% CI) between penicillin-V alone and in combination with probenecid for serum total and free penicillin-V concentrations was significantly different at both timepoints (total: 45 min 4.32 (3.20-5.32) mg/L p < 0.001, 180 min 2.2 (1.58-3.25) mg/L p < 0.001; free: 45 min 1.15 (0.88-1.42) mg/L p < 0.001, 180 min 0.5 (0.35-0.76) mg/L p < 0.001). There was no difference between the timepoints in probenecid concentrations. PTA analysis shows probenecid allows a fourfold increase in MIC cover. Addition of probenecid was safe and well tolerated. The data support further research into improved dosing structures for complex outpatient therapy and might also be used to address penicillin supply shortages.

Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study.

BACKGROUND: Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commercially available wearable (SmartCare) utilising photoplethysmography (PPG) to stratify clinical risk for a cohort of hospitalised patients with dengue in Vietnam. METHODS: We performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified between 3 severity classes: i) uncomplicated (ward-based), ii) moderate-severe (emergency department-based), and iii) severe (ICU-based). Data from paediatric patients were split into 2 classes: i) severe (during ICU stay) and ii) follow-up (14-21 days after the illness onset). Model performances were evaluated using standard classification metrics and 5-fold stratified cross-validation. FINDINGS: We included PPG and clinical data from 132 adults and 15 paediatric patients with a median age of 28 (IQR, 21-35) and 12 (IQR, 9-13) years respectively. 1781 h of PPG data were available for analysis. The best performing convolutional neural network models (CNN) achieved a precision of 0.785 and recall of 0.771 in classifying adult patients according to severity class and a precision of 0.891 and recall of 0.891 in classifying between disease and post-disease state in paediatric patients. INTERPRETATION: We demonstrate that the use of a low-cost wearable provided clinically actionable data to differentiate between patients with dengue of varying severity. Continuous monitoring and connectivity to early warning systems could significantly benefit clinical care in dengue, particularly within an endemic setting. Work is currently underway to implement these models for dynamic risk predictions and assist in individualised patient care. FUNDING: EPSRC Centre for Doctoral Training in High-Performance Embedded and Distributed Systems (HiPEDS) (Grant: EP/L016796/1) and the Wellcome Trust (Grants: 215010/Z/18/Z and 215688/Z/19/Z).