The neurophysiology of intraoperative error: An EEG study of trainee surgeons during robotic-assisted surgery simulations.

Surgeons operate in mentally and physically demanding workspaces where the impact of error is highly consequential. Accurately characterizing the neurophysiology of surgeons during intraoperative error will help guide more accurate performance assessment and precision training for surgeons and other teleoperators. To better understand the neurophysiology of intraoperative error, we build and deploy a system for intraoperative error detection and electroencephalography (EEG) signal synchronization during robot-assisted surgery (RAS). We then examine the association between EEG data and detected errors. Our results suggest that there are significant EEG changes during intraoperative error that are detectable irrespective of surgical experience level

Short-latency afferent inhibition during selective finger movement

During individual finger movement, two opposite phenomena occur at the level of the central nervous system that could affect other intrinsic hand muscle representations, unintentional co-activation, and surround inhibition (SI). At rest, excitability in the motor cortex (M1) is inhibited at about 20 ms after electric stimulation of a peripheral nerve [short-latency afferent inhibition (SAI)]. We sought to determine whether SAI changes during selective index finger movement. Effects were measured by the response to transcranial magnetic stimulation in two functionally distinct target muscles of the hand [abductor digiti minimi muscle (ADM), first dorsal interosseus muscle (FDI)]. An increase in SAI in the ADM during index finger movement compared to at rest could help explain the genesis of SI. Electrical stimulation was applied to either the little finger (homotopic for ADM, heterotopic for FDI) or the index finger (heterotopic for ADM, homotopic for FDI). During index finger movement, homotopic SAI was present only in the ADM, and the effect of peripheral stimulation was greater when there was less co-activation. Heterotopic SAI found at rest disappeared with movement. We conclude that during movement, homotopic SAI on the muscle in the surround of the intended movement may contribute to SI

Effect of Alemtuzumab (CAMPATH 1-H) in patients with inclusion-body myositis

Sporadic inclusion-body myositis (sIBM) is the most common disabling, adult-onset, inflammatory myopathy histologically characterized by intense inflammation and vacuolar degeneration. In spite of T cell-mediated cytotoxicity and persistent, clonally expanded and antigen-driven endomysial T cells, the disease is resistant to immunotherapies. Alemtuzumab is a humanized monoclonal antibody that causes an immediate depletion or severe reduction of peripheral blood lymphocytes, lasting at least 6 months. We designed a proof-of-principle study to examine if one series of Alemtuzumab infusions in sIBM patients depletes not only peripheral blood lymphocytes but also endomysial T cells and alters the natural course of the disease. Thirteen sIBM patients with established 12-month natural history data received 0.3 mg/kg/day Alemtuzumab for 4 days. The study was powered to capture ≥10% increase strength 6 months after treatment. The primary end-point was disease stabilization compared to natural history, assessed by bi-monthly Quantitative Muscle Strength Testing and Medical Research Council strength measurements. Lymphocytes and T cell subsets were monitored concurrently in the blood and the repeated muscle biopsies. Alterations in the mRNA expression of inflammatory, stressor and degeneration-associated molecules were examined in the repeated biopsies. During a 12-month observation period, the patients’ total strength had declined by a mean of 14.9% based on Quantitative Muscle Strength Testing. Six months after therapy, the overall decline was only 1.9% (P < 0.002), corresponding to a 13% differential gain. Among those patients, four improved by a mean of 10% and six reported improved performance of daily activities. The benefit was more evident by the Medical Research Council scales, which demonstrated a decline in the total scores by 13.8% during the observation period but an improvement by 11.4% (P < 0.001) after 6 months, reaching the level of strength recorded 12 months earlier. Depletion of peripheral blood lymphocytes, including the naive and memory CD8+ cells, was noted 2 weeks after treatment and persisted up to 6 months. The effector CD45RA+CD62L­ cells, however, started to increase 2 months after therapy and peaked by the 4th month. Repeated muscle biopsies showed reduction of CD3 lymphocytes by a mean of 50% (P < 0.008), most prominent in the improved patients, and reduced mRNA expression of stressor molecules Fas, Mip-1a and αB-crystallin; the mRNA of desmin, a regeneration-associated molecule, increased. This proof-of-principle study provides insights into the pathogenesis of inclusion-body myositis and concludes that in sIBM one series of Alemtuzumab infusions can slow down disease progression up to 6 months, improve the strength of some patients, and reduce endomysial inflammation and stressor molecules. These encouraging results, the first in sIBM, warrant a future study with repeated infusions (Clinical Trials. Gov NCT00079768)

Rising statin use and effect on ischemic stroke outcome

BACKGROUND: Statins (3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors) have neuroprotective effects in experimental stroke models and are commonly prescribed in clinical practice. The aim of this study was to determine if patients taking statins before hospital admission for stroke had an improved clinical outcome. METHODS: This was an observational study of 436 patients admitted to the National Institutes of Health Suburban Hospital Stroke Program between July 2000 and December 2002. Self-reported risk factors for stroke were obtained on admission. Stroke severity was determined by the admission National Institutes of Health Stroke Scale score. Good outcome was defined as a Rankin score < 2 at discharge. Statistical analyses used univariate and multivariate logistic regression models. RESULTS: There were 436 patients with a final diagnosis of ischemic stroke; statin data were available for 433 of them. A total of 95/433 (22%) of patients were taking a statin when they were admitted, rising from 16% in 2000 to 26% in 2002. Fifty-one percent of patients taking statins had a good outcome compared to 38% of patients not taking statins (p = 0.03). After adjustment for confounding factors, statin pretreatment was associated with a 2.9 odds (95% CI: 1.2–6.7) of a good outcome at the time of hospital discharge. CONCLUSIONS: The proportion of patients taking statins when they are admitted with stroke is rising rapidly. Statin pretreatment was significantly associated with an improved functional outcome at discharge. This finding could support the early initiation of statin therapy after stroke

Alien Registration- Dambrosia, Fred (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/25770/thumbnail.jp

Alien Registration- Dambrosia, Fred (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/25770/thumbnail.jp

Machine Intelligence for Performant Surgical Robotics

Intelligent algorithms for measuring and augmenting performance during robotic-assisted surgery (RAS) in both human robot collaborative and autonomous system settings have the potential to benefit both surgeons and patients. Successful RAS depends on both human operator and robotic system performance. Measuring performance requires integrating, synchronizing and analyzing contemporaneous data from humans, robots, and task environments. Safety-critical tasks in dynamic, unstructured environments, such as RAS, require both high performing operators and robotic systems. Surgeons operate in mentally and physically demanding workspaces where the impact of error is highly consequential, and uncertainties in operating room (OR) robotic systems, particularly in kinematics and perception for autonomous applications, have meaningful implications for clinical outcomes. The purpose of this dissertation is to develop novel machine intelligence algorithms to quantitatively model and augment performance during robot-assisted surgery for both human operators and autonomous systems. For human operators, we detect intraoperative errors and analyze operator biometric data. For autonomous systems, we use perception algorithms to measure tool localization accuracy and visual scene uncertainty in surgical environments. Our results show that we can quantitatively analyze human intraoperative error and that our perception algorithms can more accurately localize surgical tools and measure visual scene uncertainty in surgical environments