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Background and Aims:Transversus abdominis plane (TAP) block is a popular technique for post-operative analgesia in abdominal surgeries. The aim of the study was to evaluate the relative efficacy of bupivacaine versus ropivacaine for post-operative analgesia using ultrasound-guided TAP block in laparoscopic cholecystectomies.Methods:Sixty adults undergoing elective laparoscopic cholecystectomy were randomised to receive ultrasound-guided TAP block at the end of the surgical procedure with either 0.25% bupivacaine (Group I, n = 30) or 0.375% ropivacaine (Group II, n = 30). All patients were assessed for post-operative pain and rescue analgesic consumption at 10 min, 30 min, 1 h, 4 h, 8 h, 12 h and 24 h time points. Means for normally distributed data were compared using Student's t-test, and proportions were compared using Chi-square or Fisher's exact test whichever was applicable.Results:Patients receiving ultrasound-guided TAP block with ropivacaine (Group II) had significantly lower pain scores when compared to patients who received the block with bupivacaine (Group I) at 10 min, 30 min and 1 h. However, both the drugs were equivalent for post-operative analgesia and 24 h cumulative rescue analgesic requirement (median [interquartile range]) (75.00 [75.00–75.00] in Group I vs. 75.00 [75.00–93.75] in Group II, P = 0.366).Conclusion:Ultrasound-guided TAP block with ropivacaine provides effective analgesia in the immediate post-operative period up to 1 h as compared to bupivacaine. However, both the drugs are similar in terms of 24 h cumulative rescue analgesic requirement.

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Osteoarthritic conditions in ACLT model in rats were attenuated by SOE administration.

Summary The paper presents modeling and simulation of ion‐sensitive field‐effect transistor (ISFET)‐based pH sensor with temperature‐dependent behavioral macromodel and proposes to compensate the temperature drift in the sensor using intelligent machine learning (ML) models. The macromodel is built using SPICE by introducing electrochemical parameters in a metal‐oxide‐semiconductor field‐effect transistor (MOSFET) model to simulate ISFET characteristics. We account for the temperature dependence of electrochemical and semiconductor parameters in our macromodel to increase its robustness. The macromodel is then exported as a subcircuit element, which is used to design the readout interface circuit. A simple constant‐voltage, constant‐current (CVCC) topology is utilized to generate the data for temperature drift in ISFET pH sensor, which is used to train and test state‐of‐the‐art ML‐based regression models in order to compensate the drift behavior. The experimental results demonstrate that the random forest (RF) technique achieves the best performance with very high correlation and low error rate. Corresponding curves for output signal using the trained models show highly temperature‐independent characteristics when tested for pH 2, 4, 7, 10, and 12, and we obtained a root mean squared error (RMS) variation of ΔpH ≤ 0.024 over a temperature range of 15°C to 55°C in comparison with ΔpH ≤ 1.346 for uncompensated output signal. This work establishes the framework for integration of ML techniques for drift compensation of ISFET chemical sensor to improve its performance.