Intraperitoneal aerosolization of bupivacaine is a safe and effective method in controlling postoperative pain in laparoscopic Roux-en-Y gastric bypass.

INTRODUCTION: Obesity is a worldwide problem and has grown in severity in the last few decades making bariatric surgery and, in particular, laparoscopic banding and Roux-en-Y gastric bypass efficacious and cost-effective procedures. The laparoscopic approach has been shown to offer significant healthcare benefits, of particular interests are reports of decreased postoperative pain resulting in a shorter hospital stay and an earlier return to normal activity. However, many patients still experience significant pain, including shoulder tip pain, that require strong analgesia including opiates during their early recovery period. The aims of this study were to establish the safe use of the aerosolization technique in bariatric surgery and to investigate the possible benefits in reducing postoperative pain. METHODS: In this study, fifty patients undergoing laparoscopic gastric bypass were recruited and divided into two groups; control (n = 25) and therapeutic (n = 25). The control group received intraperitoneal aerosolization of 10 mL of 0.9% normal saline while the therapeutic group received 10 mL of 0.5% bupivacaine. All the patients had standard preoperative, intraoperative, and postoperative care. Pain scores were carried out by the nursing staff in recovery and 6 h, 12 h and 24 h postoperatively using a standard 0-10 pain scoring scale. In addition, opiate consumption via patient-controlled analgesia (PCA) was recorded. RESULTS: Aerosolized bupivacaine reduced postoperative pain in comparison to normal saline (p < 0.05). However, PCA usage showed no statistically significant change from the control group. CONCLUSION: The aims of this study were achieved and we were able to establish the safe use of the aerosolization technique in bariatric surgery and its benefits in reducing postoperative pain

Towards a pragmatic epilepsy classification: Future considerations

The Integrated Epilepsy Classification was recently proposed to merge the 2017 International League Against Epilepsy classification and the four-dimensional epilepsy classification updated in 2019. The efforts in developing the concept of an Integrated Epilepsy Classification scheme are encouraging. However, consideration of brain age, validation in contexts that differ in socioeconomic status and with poor healthcare infrastructure, and incorporation of a team-based approach are necessary. These advancements allow for better clinical management of people with epilepsy and empower people with epilepsy globally

Learning from the comorbidities of epilepsy

PURPOSE OF REVIEW: Comorbidities are a common feature in epilepsy, but neither the entire spectrum nor the significance of such comorbidities has been fully explored. We review comorbidities associated with epilepsy and their associated burden, provide an overview of relationships, and discuss a new conceptualization of the comorbidities. RECENT FINDINGS: The epidemiology of the comorbidities of epilepsy and effects on health outcomes, healthcare use, and healthcare expenditures have been partly delineated. Distinct mechanisms of the associations have been suggested but not entirely ascertained. Movement from conceptualizing epilepsy as a condition to a symptom-complex has occurred. SUMMARY: Comorbidities are common among people with epilepsy and are associated with poorer clinical outcomes and quality of life, greater use of health resources, and increased expenditure. Becoming aware of the associated mechanisms and their uncertainty is central to understanding the relationships between epilepsy and comorbid health conditions, which have implications for diagnosis and screening, medical management, and surgical management. Conceptualizing comorbidities of epilepsy as precipitating factors and epilepsy as the symptom will improve the understanding of epilepsy and catalyze research and improvements in clinical practice

Drivers for the comorbidity of type 2 diabetes mellitus and epilepsy: A scoping review

Epilepsy, a common neurologic condition, is associated with a greater prevalence of type 2 diabetes mellitus (T2DM). We examined potential drivers for the comorbidity of epilepsy and T2DM in an attempt to elucidate possible biological mechanisms underlying the development of processes in individuals. We searched PubMed and Medline up to December 2019. Our search yielded 3361 articles, of which 82 were included in the scoping review. We reviewed articles focusing on the association of epilepsy and T2DM, drivers, and biological mechanisms. We found that epilepsy is associated with obesity and obesity is associated with T2DM. Treatment with valproate (either sodium or acid) is associated with weight increase and hyperinsulinemia, while topiramate causes weight loss. People with epilepsy are less likely to exercise, which is protective against obesity. Mitochondrial dysfunction and adiponectin deficiency are common to epilepsy and T2DM. One possible mechanism for the comorbidity is mitochondrial dysfunction and adiponectin deficiency, which promotes epilepsy, obesity, and T2DM. Another possible mechanism is that people with epilepsy are more likely to be obese because of the lack of exercise and the effects of some antiseizure medications (ASMs), which makes them susceptible to T2DM because of the development of mitochondrial dysfunction and adiponectin deficiency. A third mechanism is that people with epilepsy have greater mitochondrial dysfunction and lower adiponectin levels than people without epilepsy at baseline, which may exacerbate after treatment with ASMs. Future research involving a combined genetic and molecular pathway approach will likely yield valuable insight regarding the comorbidity of epilepsy and T2DM

Reducing Sudden Unexpected Death in Epilepsy: Considering Risk Factors, Pathophysiology and Strategies

Purpose of Review Sudden Unexpected Death in Epilepsy (SUDEP) is the commonest cause of epilepsy-related premature mortality in people with chronic epilepsy. It is the most devastating epilepsy outcome. We describe and discuss risk factors and possible pathophysiological mechanisms to elucidate possible preventative strategies to avert SUDEP. Recent Findings Sudden death accounts for a significant proportion of premature mortality in people with epilepsy compared to the general population. Unmodifiable risk factors include a history of neurologic insult, younger age of seizure-onset, longer epilepsy duration, a history of convulsions, symptomatic epilepsy, intellectual disability, and non-ambulatory status. Modifiable risk factors include the presence of convulsive seizures, increased seizure frequency, timely and appropriate use of antiseizure medications, polytherapy, alcoholism, and supervision while sleeping. Pathophysiology is unclear, but several possible mechanisms such as direct alteration of cardiorespiratory function, pulmonary impairment, electrocerebral shutdown, adenosine dysfunction, and genetic susceptibility suggested. Summary Methods to prevent SUDEP include increasing awareness of SUDEP, augmenting knowledge of unmodifiable risk factors, obtaining full seizure remission, addressing lifestyle factors such as supervision and prone positioning, and enacting protocols to increase the detection of and intervention for SUDEP. Further studies are required to characterize precisely and comprehensively SUDEP risk factors and pathophysiological drivers and develop evidence-based algorithms to minimize SUDEP in people with epilepsy

Periodotopy in the gerbil inferior colliculus: local clustering rather than a gradient map

Periodicities in sound waveforms are widespread, and shape important perceptual attributes of sound including rhythm and pitch. Previous studies have indicated that, in the inferior colliculus (IC), a key processing stage in the auditory midbrain, neurons tuned to different periodicities might be arranged along a periodotopic axis which runs approximately orthogonal to the tonotopic axis. Here we map out the topography of frequency and periodicity tuning in the IC of gerbils in unprecedented detail, using pure tones and different periodic sounds, including click trains, sinusoidally amplitude modulated (SAM) noise and iterated rippled noise. We found that while the tonotopic map exhibited a clear and highly reproducible gradient across all animals, periodotopic maps varied greatly across different types of periodic sound and from animal to animal. Furthermore, periodotopic gradients typically explained only about 10% of the variance in modulation tuning between recording sites. However, there was a strong local clustering of periodicity tuning at a spatial scale of ca. 0.5 mm, which also differed from animal to animal

Classifying epilepsy pragmatically: Past, present, and future

The classification of epilepsy is essential for people with epilepsy and their families, healthcare providers, physicians and researchers. The International League Against Epilepsy proposed updated seizure and epilepsy classifications in 2017, while another four-dimensional epilepsy classification was updated in 2019. An Integrated Epilepsy Classification system was proposed in 2020. Existing classifications, however, lack consideration of important pragmatic factors relevant to the day-to-day life of people with epilepsy and stakeholders. Despite promising developments, consideration of comorbidities in brain development, genetic causes, and environmental triggers of epilepsy remains largely user-dependent in existing classifications. Demographics of epilepsy have changed over time, while existing classification schemes exhibit caveats. A pragmatic classification scheme should incorporate these factors to provide a nuanced classification. Validation across disparate contexts will ensure widespread applicability and ease of use. A team-based approach may simplify communication between healthcare personnel, while an individual-centred perspective may empower people with epilepsy. Together, incorporating these elements into a modern but pragmatic classification scheme may ensure optimal care for people with epilepsy by emphasising cohesiveness among its myriad users. Technological advancements such as 7T MRI, next-generation sequencing, and artificial intelligence may affect future classification efforts

Efficient inference for time-varying behavior during learning

The process of learning new behaviors over time is a problem of great interest in both neuroscience and artificial intelligence. However, most standard analyses of animal training data either treat behavior as fixed or track only coarse performance statistics (e.g., accuracy, bias), providing limited insight into the evolution of the policies governing behavior. To overcome these limitations, we propose a dynamic psychophysical model that efficiently tracks trial-to-trial changes in behavior over the course of training. Our model consists of a dynamic logistic regression model, parametrized by a set of time-varying weights that express dependence on sensory stimuli as well as task-irrelevant covariates, such as stimulus, choice, and answer history. Our implementation scales to large behavioral datasets, allowing us to infer 500K parameters (e.g. 10 weights over 50K trials) in minutes on a desktop computer. We optimize hyperparameters governing how rapidly each weight evolves over time using the decoupled Laplace approximation, an efficient method for maximizing marginal likelihood in non-conjugate models. To illustrate performance, we apply our method to psychophysical data from both rats and human subjects learning a delayed sensory discrimination task. The model successfully tracks the psychophysical weights of rats over the course of training, capturing day-to-day and trial-to-trial fluctuations that underlie changes in performance, choice bias, and dependencies on task history. Finally, we investigate why rats frequently make mistakes on easy trials, and suggest that apparent lapses can be explained by sub-optimal weighting of known task covariates

Three-dimensional cephalometric evaluation of maxillary growth following in utero repair of cleft lip and alveolar-like defects in the mid-gestational sheep model

Objective: To evaluate maxillary growth following in utero repair of surgically created cleft lip and alveolar (CLA)-like defects by means of three-dimensional (3D) computer tomographic (CT) cephalometric analysis in the mid-gestational sheep model. Methods: In 12 sheep fetuses a unilateral CLA-like defect was created in utero (untreated control group: 4 fetuses). Four different bone grafts were used for the alveolar defect closure. After euthanasia, CT scans of the skulls of the fetuses, 3D re-constructions, and a 3D-CT cephalometric analysis were performed. Results: The comparisons between the operated and nonoperated skull sides as well as of the maxillary asymmetry among the experimental groups revealed no statistically significant differences of the 12 variables used. Conclusions: None of the surgical approaches used for the in utero correction of CLA-like defects seem to affect significantly postsurgical maxillary growth; however, when bone graft healing takes place, a tendency for almost normal maxillary growth can be observed. Copyright (c) 2006 S. Karger AG, Basel