Pilot evaluation of a novel unilateral onychectomy model and efficacy of an extended release buprenorphine product

Abstract Background Non-steroidal anti-inflammatory drugs (NSAIDs), transdermal fentanyl patches, and transmucosal buprenorphine are probably the most commonly used options for providing post-operative analgesia in the early at-home period. However, these require daily administration or are associated with abuse concerns. One of the significant unmet needs in veterinary surgery and pain management is for longer acting opioids for cats to effectively bridge the gap between the in-hospital and at-home recovery periods. A proof of concept study of an extended release formulation of buprenorphine HCL (ER-Bup) was conducted using objective kinetic measures and a unilateral onychectomy model. Using a blinded, randomized, two period crossover design, four cats were allocated to control (saline) or ER-Bup (0.6 mg/kg, subcutaneously [SC]) treatment groups. All animals underwent a unilateral forelimb onychectomy per period with a washout/recovery period in between. Observational pain scores and kinetic data (using a pressure sensitive walkway [PSW]) were collected prior to (baseline) and at intervals for 72 h following surgery. Symmetry indices were derived for kinetic variables (peak vertical force [PVF]; vertical impulse [VI]) of each forelimb for landing following a jump and for walking. A rescue analgesic protocol was in place. Effect of surgery and treatment were evaluated using a mixed model statistical approach. Results No cats required rescue analgesics based on subjective pain score. ER-Bup had a positive influence on subjective pain scores during the 72 h postsurgery (p = 0.0473). PVF and VI of the operated limb were significantly decreased for both landing (p < 0.0001 and p < 0.0001) and walking (p < 0.0001 and p < 0.0001 respectively) compared to control. ER-Bup resulted in significantly decreased asymmetry in limb use during landing (PVF, p < 0.0001; VI, p < 0.0001) and walking (PVF, p = 0.0002, VI, p < 0.0001). The novel use of data collected following a jump from an elevated platform appeared to provide all desired information and was easier to collect than walking data. Conclusion This study demonstrates that SC administration of ER-Bup may be an effective analgesic for a 72 h period postoperatively. Furthermore, landing onto a PSW from an elevated perch may be a useful and efficient way to assess analgesics in cats using a unilateral model of limb pain

Modelos de predicción de mortalidad en cirugía cardiaca: diseño de un modelo, evaluación de modelos generales y análisis de mortalidad ajustada a riesgo

Las intervenciones de cirugía cardiaca tienen costes elevados, emplean recursos complejos, y se asocian a riesgos quirúrgicos medios comparativamente más elevados que la mayor parte de las demás disciplinas quirúrgicas. Es un área de especial interés para los estudios de evaluación de riesgo y de análisis de calidad. El parámetro más empleado como variable de medición es la mortalidad hospitalaria, por su relevancia clínica, facilidad de medir, y estandarización. La necesidad de establecer rangos de mortalidad aceptable ha originado iniciativas nacionales para medirlo. Ningún organismo emite datos de mortalidad por tipo de procedimiento ajustado a riesgo de los servicios cardioquirúrgicos adaptados a criterios acordados con las sociedades científicas. Las fuentes existentes son las memorias de resultados de las Consejerías, los registros de gastos y el Registro de Actividad de la SECTCV. Ninguna de estas fuentes contiene información paciente a paciente, ni información pronóstica de los pacientes. La fuente más útil a nivel clínico es el Registro de Intervenciones de la SECTCV. El análisis de la mortalidad debe tener en cuenta el riesgo de los pacientes. Los modelos de predicción de mortalidad estiman este riesgo..

Reading the ground: Understanding the response of bioelectric microbes to anthropogenic compounds in soil based terrestrial microbial fuel cells

Electrogenic bacteria produce power in soil based terrestrial microbial fuel cells (tMFCs) by growing on electrodes and transferring electrons released from the breakdown of substrates. The direction and magnitude of voltage production is hypothesized to be dependent on the available substrates. A sensor technology was developed for compounds indicative of anthropological activity by exposing tMFCs to gasoline, petroleum, 2,4-dinitrotoluene, fertilizer, and urea. A machine learning classifier was trained to identify compounds based on the voltage patterns. After 5 to 10 days, the mean voltage stabilized (+/- 0.5 mV). After the entire incubation, voltage ranged from -59.1 mV to 631.8 mV, with the tMFCs containing urea and gasoline producing the highest (624 mV) and lowest (-9 mV) average voltage, respectively. The machine learning algorithm effectively discerned between gasoline, urea, and fertilizer with greater than 94% accuracy, demonstrating that this technology could be successfully operated as an environmental sensor for change detection.</jats:p

The additional external stimuli may solicit a repeatable response in electrogenic bacteria embedded on electrodes in soil.

The additional external stimuli may solicit a repeatable response in electrogenic bacteria embedded on electrodes in soil.</p

Boxplots of end state voltage data across the six treatments (control, gasoline, DNT, urea, fertilizer, or petroleum).

Each treatment has sample size n = 6.</p

Diagram of microbial fuel cell construction.

Wetted soil (with and without contaminant) was distributed below and above the anode before the cathode was placed on top. To reduce the loss of moisture the ports around the wires were sealed with a sticky tack.</p

NM500 interpretation of anthropogenic compounds, with only the top 3 most accurate compounds from Fig 5 used for training.

Method used is 6-point grouping with Radial Basis function and “Unanimous” interpreter on standardized data. All answers are marked as “Identified”.</p

Evolution of voltage from tMFCs for the duration of the incubation.

Lines indicate mean voltage. Bars indicate standard error of n = 6 replicates.</p

Comparison of the baseline linear mixed effects (LME) model (without using treatments) and a LME model using the treatments as a predictor.

Comparison of the baseline linear mixed effects (LME) model (without using treatments) and a LME model using the treatments as a predictor.</p