METHODS: C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0-24. An external dataset was used for predictive performance evaluation.

RESULTS: Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C0) in patients have been obtained, AUC0-24 can be further predicted using the Catboost-based AUC-ML model combined with C0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%.

MATERIALS AND METHODS: In this trial, a total of 56 eligible subjects were randomly assigned to the fasting group and the postprandial group. The two groups were given 250 mg of the test and reference preparation, respectively. Liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was applied to determine the plasma concentration of cefalexin. PhoenixWinNonlin software (V7.0) was used to calculate the pharmacokinetic parameters of cefalexin using the non-compartmental model (NCA), and the bioequivalence and safety results were calculated by SAS (V9.4) software.

RESULTS: The main pharmacokinetic parameters of the test and reference preparations were as follows, the fasting group: Cmax 12.59 ± 2.65 μg/mL, 12.72 ± 2.28 μg/mL; AUC0-8h 20.43 ± 3.47 h×μg/mL, 20.66 ± 3.38 h×μg/mL; AUC0-∞ 20.77 ± 3.53 h×μg/mL, 21.02 ± 3.45 h×μg/mL; the postprandial group: Cmax 5.25 ± 0.94 μg/mL, 5.23 ± 0.80 μg/mL; AUC0-10h 16.92 ± 2.03 h×μg/mL, 17.09 ± 2.31 h×μg/mL; AUC0-∞ 17.33 ± 2.09 h×μg/mL, 17.67 ± 2.45 h×μg/mL.

METHODS: Using the recently completed genome sequences from P. malariae, P. ovale and P. knowlesi, a set of 33 candidate cell surface and secreted blood-stage antigens was selected and expressed in a recombinant form using a mammalian expression system. These proteins were added to an existing panel of antigens from P. falciparum and P. vivax and the immunoreactivity of IgG, IgM and IgA immunoglobulins from individuals diagnosed with infections to each of the five different Plasmodium species was evaluated by ELISA. Logistic regression modelling was used to quantify the ability of the responses to determine prior exposure to the different Plasmodium species.

RESULTS: Using sera from European travellers with diagnosed Plasmodium infections, antigens showing species-specific immunoreactivity were identified to select a panel of 22 proteins from five Plasmodium species for serological profiling. The immunoreactivity to the antigens in the panel of sera taken from travellers and individuals living in malaria-endemic regions with diagnosed infections showed moderate power to predict infections by each species, including P. ovale, P. malariae and P. knowlesi. Using a larger set of patient samples and logistic regression modelling it was shown that exposure to P. knowlesi could be accurately detected (AUC = 91%) using an antigen panel consisting of the P. knowlesi orthologues of MSP10, P12 and P38.

METHODS: Fifty-one adult patients with suspected bacterial sepsis on admission to the Emergency Department (ED) of a teaching hospital were included into the study. All relevant cultures and serology tests were performed. Serum levels for Group II Secretory Phospholipase A2 (sPLA2-IIA) and CD64 were subsequently analyzed.

RESULTS AND DISCUSSION: Sepsis was confirmed in 42 patients from a total of 51 recruited subjects. Twenty-one patients had culture-confirmed bacterial infections. Both biomarkers were shown to be good in distinguishing sepsis from non-sepsis groups. CD64 and sPLA2-IIA also demonstrated a strong correlation with early sepsis diagnosis in adults. The area under the curve (AUC) of both Receiver Operating Characteristic curves showed that sPLA2-IIA was better than CD64 (AUC = 0.93, 95% confidence interval (CI) = 0.83-0.97 and AUC = 0.88, 95% CI = 0.82-0.99, respectively). The optimum cutoff value was 2.13μg/l for sPLA2-IIA (sensitivity = 91%, specificity = 78%) and 45 antigen bound cell (abc) for CD64 (sensitivity = 81%, specificity = 89%). In diagnosing bacterial infections, sPLA2-IIA showed superiority over CD64 (AUC = 0.97, 95% CI = 0.85-0.96, and AUC = 0.95, 95% CI = 0.93-1.00, respectively). The optimum cutoff value for bacterial infection was 5.63μg/l for sPLA2-IIA (sensitivity = 94%, specificity = 94%) and 46abc for CD64 (sensitivity = 94%, specificity = 83%).

Materials and Methods: Sixty-three diabetic foot patients admitted from June 15, 2019 to February 15, 2020. Methods included one-on-one interview for clinico-demographic data, physical examination to determine the classification. Patients were followed-up and outcomes were determined. Pearson Chi-square or Fisher's Exact determined association between clinico-demographic data, the classifications, and outcomes. The receiver operating characteristic (ROC) curve determined predictive abilities of classification systems and paired analysis compared the curves. Area Under the Receiver Operating Characteristic Curve (AUC) values used to compare the prediction accuracy. Analysis was set at 95% CI.

Results: Results showed hypertension, duration of diabetes, and ambulation status were significantly associated with major amputation. WIFi showed the highest AUC of 0.899 (p = 0.000). However, paired analysis showed AUC differences between WIFi, Wagner, and University of Texas classifications by grade were not significantly different from each other.