Lactate is potentially a major energy source in brain, particularly following hypoxia/ischemia; however, the regulation of brain lactate metabolism is not well understood. Lactate dehydrogenase (LDH) isozymes in cytosol from primary cultures of neurons and astrocytes, and freshly isolated synaptic terminals (synaptosomes) from adult rat brain were separated by electrophoresis, visualized with an activity-based stain, and quantified. The activity and kinetics of LDH were determined in the same preparations. In synaptosomes, the forward reaction (pyruvate + NADH + H(+ )--> lactate + NAD(+)), which had a V (max) of 1,163 micromol/min/mg protein was 62% of the rate in astrocyte cytoplasm. In contrast, the reverse reaction (lactate + NAD(+ )--> pyruvate + NADH + H(+)), which had a V (max) of 268 micromol/min/mg protein was 237% of the rate in astrocytes. Although the relative distribution was different, all five isozymes of LDH were present in synaptosomes and primary cultures of cortical neurons and astrocytes from rat brain. LDH1 was 14.1% of the isozyme in synaptic terminals, but only 2.6% and 2.4% in neurons and astrocytes, respectively. LDH5 was considerably lower in synaptic terminals than in neurons and astrocytes, representing 20.4%, 37.3% and 34.8% of the isozyme in these preparations, respectively. The distribution of LDH isozymes in primary cultures of cortical neurons does not directly reflect the kinetics of LDH and the capacity for lactate oxidation. However, the kinetics of LDH in brain are consistent with the possible release of lactate by astrocytes and oxidative use of lactate for energy in synaptic terminals.

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The quality improvement project, which was implemented in 2 phases, successfully increased the FCOTS rate in our neurosurgical ORs from 33% to 68%.

Background: The placement of subthalamic nucleus (STN) deep brain stimulation (DBS) electrodes can be facilitated by intraoperative microelectrode recording (MER) of the STN. Objectives: Optimal anesthetic management during surgery remains unclear because of a lack of quantitative data of the effect of anesthetics on MER. Therefore, we measured the effects of dexmedetomidine (DEX) on MER measures of the STN commonly taken intraoperatively. Methods: MER from 45 patients was retrospectively compared between patients treated with remifentanil (REMI) alone or both REMI and DEX, which are the 2 main standards of care at our center. The measures examined were population activity, such as root mean square, STN length, and number of passes yielding STN, and the single-neuron measures of firing rate and variability. Results: The addition of DEX does not affect population measures (number of passes: DEX+REMI, n = 68, REMI only, n = 154), or neuronal firing rates (number of neurons: DEX+REMI, n = 64, REMI only, n = 72), but firing rate variability was reduced. Conclusions: In this cohort, population-based measures routinely used for electrode placement in the STN were unaffected by DEX when added to REMI. Neuronal firing rates were also unaffected, but their variability was reduced, even beyond 20 min after cessation.

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The operating room (OR) is a complex environment that accounts for a significant proportion of total hospital revenue and expenses. Efficient management of patient throughput is necessary to optimize this valuable resource. Accurate scheduling of surgical cases can improve OR efficiency, and yet, it is fraught with inherent challenges. Various strategies aimed at first case ontime starts, OR turnover times, and delays in postanesthesia care unit (PACU) admissions are used to manage patient throughput and will be discussed in detail. The impact of perioperative teams, trainees, and clinical expertise are some of the additional considerations for OR efficiency. Machine learning methods, in combination with large electronic health databases, are a more recent approach to further optimize patient throughput from the preoperative arena to the PACU.