Validation of a novel robotic-assisted partial nephrectomy surgical training model

What's known on the subject? and What does the study add? One area of particular growth for robotic surgery has been partial nephrectomy. Despite a perceived notion that robotic-assisted partial nephrectomy is more easily adaptable compared to laparoscopic partial nephrectomy, there is nonetheless an associated learning curve. Validated training models with a corresponding assessment method for robotic-assisted partial nephrectomy were previously unavailable. We have designed and validated a RAPN surgical model appropriate for resident and fellow training.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Spatio-temporal credit assignment in population learning

We present a model for plasticity induction in reinforcement learning which is based on a cascade of synaptic memory traces. In the cascade of these so called eligibility traces presynaptic input is first corre lated with postsynaptic events, next with the behavioral decisions and finally with the external reinforcement. A population of leaky integrate and fire neurons endowed with this plasticity scheme is studied by simulation on different tasks. For operant co nditioning with delayed reinforcement, learning succeeds even when the delay is so large that the delivered reward reflects the appropriateness, not of the immediately preceeding response, but of a decision made earlier on in the stimulus - decision sequence . So the proposed model does not rely on the temporal contiguity between decision and pertinent reward and thus provides a viable means of addressing the temporal credit assignment problem. In the same task, learning speeds up with increasing population si ze, showing that the plasticity cascade simultaneously addresses the spatial problem of assigning credit to the different population neurons. Simulations on other task such as sequential decision making serve to highlight the robustness of the proposed sch eme and, further, contrast its performance to that of temporal difference based approaches to reinforcement learning.

Coupling biomechanics to a cellular level model: An approach to patient-specific image driven multi-scale and multi-physics tumor simulation

Modeling of tumor growth has been performed according to various approaches addressing different biocomplexity levels and spatiotemporal scales. Mathematical treatments range from partial differential equation based diffusion models to rule-based cellular level simulators, aiming at both improving our quantitative understanding of the underlying biological processes and, in the mid- and long term, constructing reliable multi-scale predictive platforms to support patient-individualized treatment planning and optimization. The aim of this paper is to establish a multi-scale and multi-physics approach to tumor modeling taking into account both the cellular and the macroscopic mechanical level. Therefore, an already developed biomodel of clinical tumor growth and response to treatment is self-consistently coupled with a biomechanical model. Results are presented for the free growth case of the imageable component of an initially point-like glioblastoma multiforme tumor. The composite model leads to significant tumor shape corrections that are achieved through the utilization of environmental pressure information and the application of biomechanical principles. Using the ratio of smallest to largest moment of inertia of the tumor material to quantify the effect of our coupled approach, we have found a tumor shape correction of 20\% by coupling biomechanics to the cellular simulator as compared to a cellular simulation without preferred growth directions. We conclude that the integration of the two models provides additional morphological insight into realistic tumor growth behavior. Therefore, it might be used for the development of an advanced oncosimulator focusing on tumor types for which morphology plays an important role in surgical and/or radio-therapeutic treatment planning.

Spatio-Temporal Credit Assignment in Neuronal Population Learning

n learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.

Echinococcus multilocularis and its intermediate host: a model of parasite-host interplay

Host-parasite interactions in the E. multilocularis-intermediate host model depend on a subtle balance between cellular immunity, which is responsible for host's resistance towards the metacestode, the larval stage of the parasite, and tolerance induction and maintenance. The pathological features of alveolar echinococcosis. the disease caused by E. multilocularis, are related both to parasitic growth and to host's immune response, leading to fibrosis and necrosis, The disease spectrum is clearly dependent on the genetic background of the host as well as on acquired disturbances of Th1-related immunity. The laminated layer of the metacestode, and especially its carbohydrate components, plays a major role in tolerance induction. Th2-type and anti-inflammatory cytokines, IL-10 and TGF-beta, as well as nitric oxide, are involved in the maintenance of tolerance and partial inhibition of cytotoxic mechanisms. Results of studies in the experimental mouse model and in patients suggest that immune modulation with cytokines, such as interferon-alpha, or with specific antigens could be used in the future to treat patients with alveolar echinococcosis and/or to prevent this very severe parasitic disease.

Vaccination with the recombinant chimeric antigen recNcMIC3-1-R induces a non-protective Th2-type immune response in the pregnant mouse model for N. caninum infection

The major route of transmission of Neospora caninum in cattle is transplacentally from an infected cow to its progeny. Therefore, a vaccine should be able to prevent both the horizontal transmission from contaminated food or water and the vertical transmission. We have previously shown that a chimeric vaccine composed of predicted immunogenic epitopes of NcMIC3, NcMIC1 and NcROP2 (recNcMIC3-1-R) significantly reduced the cerebral infection in BALB/c mice. In this study, mice were first vaccinated, then mated and pregnant mice were challenged with 2×10(6)N. caninum tachyzoites at day 7-9 of pregnancy. Partial protection was only observed in the mice vaccinated with a tachyzoite crude protein extract but no protection against vertical transmission or cerebral infection in the dams was observed in the group vaccinated with recNcMIC3-1-R. Serological and cytokine analysis showed an overall lower cytokine level in sera associated with a dominant IL-4 expression and high IgG1 titers. Thus, the Th2-type immune response observed in the pregnant mice was not protective against experimental neosporosis, in contrary to the mixed Th1-/Th2-type immune response observed in the non-pregnant mouse model. These results demonstrate that the immunomodulation that occurs during pregnancy was not favorable for the protection against N. caninum infection conferred by vaccination with recNcMIC3-1-R.

Granulocyte colony-stimulating factor supports liver regeneration in a small-for-size liver remnant mouse model

Experimental partial hepatectomy of more than 80% of the liver weight bears an increased mortality in rodents, due to impaired hepatic regeneration in small-for-size liver remnants. Granulocyte colony-stimulating factor (G-CSF) promotes progenitor cell expansion and mobilization and also has immunomodulatory properties. The aim of this study was to determine the effect of systemically administered G-CSF on liver regeneration and animal survival in a small-for-size liver remnant mouse model. Mice were preconditioned daily for 5 days with subcutaneous injections of 5 microg G-CSF or aqua ad injectabile. Subsequently, 83% partial hepatectomy was performed by resecting the median, the left, the caudate, and the right inferior hepatic lobes in all animals. Daily sham or G-CSF injection was continued. Survival was significantly better in G-CSF-treated animals (P < 0.0001). At 36 and 48 h after microsurgical hepatic resection, markers of hepatic proliferation (Ki67, BrdU) were elevated in G-CSF-treated mice compared to sham injected control animals (P < 0.0001) and dry liver weight was increased (P < 0.05). G-CSF conditioning might prove to be useful in patients with small-for-size liver remnants after extended hepatic resections due to primary or secondary liver tumors or in the setting of split liver transplantation.

Animal model for percutaneous creation of traumatic thoracic aortic transection

PURPOSE: This study was conducted to create an animal model for thoracic aortic transection that is suitable for thoracic endograft research. MATERIALS AND METHODS: Percutaneous aortic transection creation was attempted in 12 sheep. A custom collapsible circumferential cutting device was inserted into the proximal descending thoracic aorta via a femoral approach with an 11-F delivery catheter. The device was deployed 2 cm distal to the left subclavian artery origin and rotated 20 times to create aortic transection. Aortic diameters, mean aortic pressures, and heart rates were tested for degrees of difference between measurements before and after the creation of transection. On necropsy, the extent of aortic damage was classified as none, nontransmural, or transmural, and aortic transection was classified as none, partial, or circumferential. RESULTS: On angiography, creation of transmural thoracic aortic transection was successful in 91.7% (11/12) of animals. Aortic transection was circumferential in 54.4% (6/11) of animals and partial in 45.6% (5/11) of animals. Mean aortic diameter was 19.6 +/- 3.4 mm (range 12-24 mm) pre-transection and 25.8 +/- 4.5 mm (range 17.8-33 mm) post-transection (P = .0003). Pre-transection, mean aortic pressure was 79 +/- 13.8 mmHg, and 64.6 +/- 15.8 mmHg 15 min post-transection (P = .041). Pre-transection, mean heart rate was 94.5 +/- 17.2 beats per minute (bpm), and 105.8 +/- 17.2 bpm 15 min post-transection (P = .0057). CONCLUSIONS: Thoracic aortic transection was successfully created percutaneously in most animals. The animals remained in hemodynamically stable condition for as long as 240 minutes after the creation of aortic injury. This percutaneous animal model is straightforward and may be of potential value for future thoracic endograft research.

Granulocyte colony stimulating factor supports liver regeneration in a surgical small for size liver remnant mouse model

ntense liver regeneration and almost 100% survival follows partial hepatectomy of up to 70% of liver mass in rodents. More extensive resections of 70 to 80% have an increased mortality and partial hepatectomies of >80% constantly lead to acute hepatic failure and death in mice. The aim of the study was to determine the effect of systemically administered granulocyte colony stimulating factor (G-CSF) on animal survival and liver regeneration in a small for size liver remnant mouse model after 83% partial hepatectomy (liver weight <0.8% of mouse body weight). Methods: Male Balb C mice (n=80, 20-24g) were preconditioned daily for five days with 5μg G-CSF subcutaneously or sham injected (aqua ad inj). Subsequently 83% hepatic resection was performed and daily sham or G-CSF injection continued. Survival was determined in both groups (G-CSF n=35; Sham: n=33). In a second series BrdU was injected (50mg/kg Body weight) two hours prior to tissue harvest and animals euthanized 36 and 48 hours after 83% liver resection (n=3 each group). To measure hepatic regeneration the BrdU labeling index and Ki67 expression were determined by immunohistochemistry by two independent observers. Harvested liver tissue was dried to constant weight at 65 deg C for 48 hours. Results: Survival was 0% in the sham group on day 3 postoperatively and significantly better (26.2% on day 7 and thereafter) in the G-CSF group (Log rank test: p<0.0001). Dry liver weight was increased in the G-CSF group (T-test: p<0.05) 36 hours after 83% partial hepatectomy. Ki67 expression was elevated in the G-CSF group at 36 hours (2.8±2.6% (Standard deviation) vs 0.03±0.2%; Rank sum test: p<0.0001) and at 48 hours (45.1±34.6% vs 0.7±1.0%; Rank sum test: p<0.0001) after 83% liver resection. BrdU labeling at 48 hours was 0.1±0.3% in the sham and 35.2±34.2% in the G-CSF group (Rank sum test: p<0.0001) Conclusions: The surgical 83% resection mouse model is suitable to test hepatic supportive regimens in the setting of small for size liver remnants. Administration of G-CSF supports hepatic regeneration after microsurgical 83% partial hepatectomy and leads to improved long-term survival in the mouse. G-CSF might prove to be a clinically valuable supportive substance in small for size liver remnants in humans after major hepatic resections due to primary or secondary liver tumors or in the setting of living related liver donation.

Structural model of the CopA copper ATPase of Enterococcus hirae based on chemical cross-linking

The CopA copper ATPase of Enterococcus hirae belongs to the family of heavy metal pumping CPx-type ATPases and shares 43% sequence similarity with the human Menkes and Wilson copper ATPases. Due to a lack of suitable protein crystals, only partial three-dimensional structures have so far been obtained for this family of ion pumps. We present a structural model of CopA derived by combining topological information obtained by intramolecular cross-linking with molecular modeling. Purified CopA was cross-linked with different bivalent reagents, followed by tryptic digestion and identification of cross-linked peptides by mass spectrometry. The structural proximity of tryptic fragments provided information about the structural arrangement of the hydrophilic protein domains, which was integrated into a three-dimensional model of CopA. Comparative modeling of CopA was guided by the sequence similarity to the calcium ATPase of the sarcoplasmic reticulum, Serca1, for which detailed structures are available. In addition, known partial structures of CPx-ATPase homologous to CopA were used as modeling templates. A docking approach was used to predict the orientation of the heavy metal binding domain of CopA relative to the core structure, which was verified by distance constraints derived from cross-links. The overall structural model of CopA resembles the Serca1 structure, but reveals distinctive features of CPx-type ATPases. A prominent feature is the positioning of the heavy metal binding domain. It features an orientation of the Cu binding ligands which is appropriate for the interaction with Cu-loaded metallochaperones in solution. Moreover, a novel model of the architecture of the intramembranous Cu binding sites could be derived.