Pharmacokinetics and pharmacodynamics of rocuronium in young adult and elderly patients undergoing elective surgery.

OBJECTIVE: To evaluate the impact of advanced age on rocuronium kinetic disposition in ASA I-III patients undergoing elective surgeries. METHODS: Young adult (20-50 years, n = 15) and elderly patients (65-85 years, n = 14) submitted to surgery under general anaesthesia were investigated. All patients were induced with individual intravenous doses of midazolam, rocuronium, fentanyl and propofol. Rocuronium-induced neuromuscular block was monitored by train of four stimulations of the adductor muscle of the thumb on the ulnar nerve. The pharmacokinetic parameters were calculated by non-compartmental analysis. The relationship between rocuronium plasma concentration and the neuromuscular blockade was described by a sigmoidal Emax model. KEY-FINDINGS: Elderly patients presented decreased Cl (2.1 ml/kg per min vs 2.8 ml/kg per min; P = 0.0123); increased AUC/dose (507.8 µg min/ml (mg/kg) vs 392.2 µg min/ml/(mg/kg); P = 0.0168) and reduced volume of distribution (285.4 ml/kg vs 435.6 ml/kg, P = 0.0434) compared to young adults. The concentrations required to achieve 50% of maximum neuromuscular block (EC50) were similar for young adult (338.8 ng/ml) and elderly (462.7 ng/ml) patients (P > 0.05). CONCLUSIONS: Elderly patients showed increased AUC/D and reduced total Cl compared to young adult patients due to the age-related reduced renal function. Differences in the PK-PD properties of rocuronium in elderly population are due to changes in drug disposition rather than to alterations in the sensitivity to the drug.

A simple and practical control of the authenticity of organic sugarcane samples based on the use of machine-learning algorithms and trace elements determination by inductively coupled plasma mass spectrometry.

A practical and easy control of the authenticity of organic sugarcane samples based on the use of machine-learning algorithms and trace elements determination by inductively coupled plasma mass spectrometry is proposed. Reference ranges for 32 chemical elements in 22 samples of sugarcane (13 organic and 9 non organic) were established and then two algorithms, Naive Bayes (NB) and Random Forest (RF), were evaluated to classify the samples. Accurate results (>90%) were obtained when using all variables (i.e., 32 elements). However, accuracy was improved (95.4% for NB) when only eight minerals (Rb, U, Al, Sr, Dy, Nb, Ta, Mo), chosen by a feature selection algorithm, were employed. Thus, the use of a fingerprint based on trace element levels associated with classification machine learning algorithms may be used as a simple alternative for authenticity evaluation of organic sugarcane samples.