Clinical experience with a multifunctional, flexible surgery system for endolumenal, single-port, and NOTES procedures

Single-port and incisionless surgical approaches hold the promise of fewer complications, reduced pain, faster recovery, and improved cosmesis compared with traditional open or laparoscopic approaches. The ability to select an access approach (i.e., endolumenal, single-port, transvaginal, or transgastric) with one platform may be important to optimization of individual patient results. The authors report their results using these four separate surgical approaches tailored to three different therapeutic procedures, all with the use of a single flexible platform, the Incisionless Operating Platform (IOP). After institutional review board approval, the IOP was used to perform nine cholecystectomies via transvaginal (TV) (n = 4), transgastric (TG) (n = 4), and single-port transumbilical (TU) (n = 1) access. Two appendectomies were performed via TG access. Endolumenal access was used for 18 gastric pouch and stoma reductions after Roux-en-Y gastric bypass. The TG and TV procedures involved the use of one to three trocars. The recorded data included safety, procedural success, operative time, patient pain assessment (on a 0–10 scale) at discharge, and length of hospital stay. Procedural success was achieved for 16 of 18 endolumenal procedures, 1 of 1 single-port procedure, and 10 of 10 NOTES procedures. For 5 of 10 NOTES procedures, only one small trocar was required. The mean operative times were 79 min for pouch with stoma reduction, 171 min for cholecystectomy, and 274 min for appendectomy. Of 29 patients, 27 were discharged in 24 h or less. The average pain scores were 0.44 for pouch with stoma reduction, 1.3 for cholecystectomy, and 2.5 for appendectomy. No significant complications occurred. The ergonomics of IOP allowed the surgeon to interface with the system using an endoscopic or laparoscopic orientation. Availability of a multifunctional, flexible surgery platform provides a choice of a single-port or incisionless surgical approach with the potential to reduce complications, pain, and recovery time while improving cosmesis

Advances in Targeting Signal Transduction Pathways

Over the past few years, significant advances have occurred in both our understanding of the complexity of signal transduction pathways as well as the isolation of specific inhibitors which target key components in those pathways. Furthermore critical information is being accrued regarding how genetic mutations can affect the sensitivity of various types of patients to targeted therapy. Finally, genetic mechanisms responsible for the development of resistance after targeted therapy are being discovered which may allow the creation of alternative therapies to overcome resistance. This review will discuss some of the highlights over the past few years on the roles of key signaling pathways in various diseases, the targeting of signal transduction pathways and the genetic mechanisms governing sensitivity and resistance to targeted therapies

Natural orifice surgery: initial clinical experience

Natural orifice translumenal endoscopic surgery (NOTES) has moved quickly from preclinical investigation to clinical implementation. However, several major technical problems limit clinical NOTES including safe access, retraction and dissection of the gallbladder, and clipping of key structures. This study aimed to identify challenges and develop solutions for NOTES during the initial clinical experience. Under an Institutional Review Board (IRB)-approved protocol, patients consented to a natural orifice operation for removal of either the gallbladder or the appendix via either the vagina or the stomach using a single umbilical trocar for safety and assistance. Nine transvaginal cholecystectomies, one transgastric appendectomy, and one transvaginal appendectomy have been completed to date. All but one patient were discharged on postoperative day 1 as per protocol. No complications occurred. The limited initial evidence from this study demonstrates that NOTES is feasible and safe. The addition of an umbilical trocar is a bridge allowing safe performance of NOTES procedures until better instruments become available. The addition of a flexible long grasper through the vagina and a flexible operating platform through the stomach has enabled the performance of NOTES in a safe and easily reproducible manner. The use of a uterine manipulator has facilitated visualization of the cul de sac in women with a uterus to allow for safe transvaginal access

Communication in Decision Making: Competition favors Inequality

We consider a multi-agent system in which the individual goal is to collect resources, but where the amount of collected resources depends also on others decision. Agents can communicate and can take advantage of being communicated other agents\u2019 plan: therefore they may develop more profitable strategies. We wonder if some kind of collective behaviour, with respect to communication, emerges in this system without being explicitly promoted. To investigate this aspect, we design three different scenarios, respectively a cooperative, a competitive, and a mixed one, in which agents\u2019 behaviors are individually learned by means of reinforcement learning. We consider different strategies concerning communication and learning, including no-communication, always-communication, and optional-communication. Experimental results show that always-communication leads to a collective behaviour with the best results in terms of both overall earned resources and equality between agents. On the other hand optional-communication strategy leads to similar collective strategies in some of these scenarios, but in other scenarios some agents develop individual behaviours that oppose to the collective welfare and thus result in high inequality

On the Impact of the Rules on Autonomous Drive Learning

Autonomous vehicles raise many ethical and moral issues that are not easy to deal with and that, if not addressed correctly, might be an obstacle to the advent of such a technological revolution. These issues are critical because autonomous vehicles will interact with human road users in new ways and current traffic rules might not be suitable for the resulting environment. We consider the problem of learning optimal behavior for autonomous vehicles using Reinforcement Learning in a simple road graph environment. In particular, we investigate the impact of traffic rules on the learned behaviors and consider a scenario where drivers are punished when they are not compliant with the rules, i.e., a scenario in which violation of traffic rules cannot be fully prevented. We perform an extensive experimental campaign, in a simulated environment, in which drivers are trained with and without rules, and assess the learned behaviors in terms of efficiency and safety. The results show that drivers trained with rules enforcement are willing to reduce their efficiency in exchange for being compliant to the rules, thus leading to more overall safety

Mechanisms of Social Learning in Evolved Artificial Life

Adaptation of agents in artificial life scenarios is especially effective when agents may evolve, i.e., inherit traits from their parents, and learn by interacting with the environment. The learning process may be boosted with forms of social learning, i.e., by allowing an agent to learn by combining its experiences with knowledge transferred among agents. In this work, we tackle two specific questions regarding social learning and evolution: (a) from whom learners should learn? (b) how should knowledge be transferred? We address these questions by experimentally investigating two scenarios: a simple one in which the mechanism for evolution and learning is easily interpretable; a more complex and realistic artificial life scenario in which agents compete for survival. Experimental results show that social learning is more profitable when (a) the learners learn from a small set of good teachers and (b) the knowledge to be transferred is determined by teachers experience, rather than learner experience

Selfish vs. global behavior promotion in car controller evolution

We consider collective tasks to be solved by simple agents synthesized automatically by means of neuroevolution. We investigate whether driving neuroevolution by promoting a form of selfish behavior, i.e., by optimizing a fitness index that synthesizes the behavior of each agent independent of any other agent, may also result in optimizing global, system-wide properties. We focus on a specific and challenging task, i.e., evolutionary synthesis of agent as car controller for a road traffic scenario. Based on an extensive simulation-based analysis, our results indicate that even by optimizing the behavior of each single agent, the resulting system-wide performance is comparable to the performance resulting from optimizing the behavior of the system as a whole. Furthermore, agents evolved with a fitness promoting selfish behavior appear to lead to a system that is globally more robust with respect to the presence of unskilled agents

Evolutionary Synthesis of Sensing Controllers for Voxel-based Soft Robots

Soft robots allow for interesting morphological and behavioral designs because they exhibit more degrees of freedom than robots composed of rigid parts. In particular, voxel-based soft robots (VSRs)\u2014aggregations of elastic cubic building blocks\u2014have attracted the interest of Robotics and Artificial Life researchers. VSRs can be controlled by changing the volume of individual blocks: simple, yet effective controllers that do not exploit the feedback of the environment, have been automatically designed by means of Evolutionary Algorithms (EAs). In this work we explore the possibility of evolving sensing controllers in the form of artificial neural networks: we hence allow the robot to sense the environment in which it moves. Although the search space for a sensing controller is larger than its non-sensing counterpart, we show that effective sensing controllers can be evolved which realize interesting locomotion behaviors. We also experimentally investigate the impact of the VSR morphology on the effectiveness of the search and verify that the sensing controllers are indeed able to exploit their sensing ability for better solving the locomotion task

On the Impact of the Rules on Autonomous Drive Learning

Autonomous vehicles raise many ethical and moral issues that are not easy to deal with and that, if not addressed correctly, might be an obstacle to the advent of such a technological revolution. These issues are critical because autonomous vehicles will interact with human road users in new ways and current traffic rules might not be suitable for the resulting environment. We consider the problem of learning optimal behavior for autonomous vehicles using Reinforcement Learning in a simple road graph environment. In particular, we investigate the impact of traffic rules on the learned behaviors and consider a scenario where drivers are punished when they are not compliant with the rules, i.e., a scenario in which violation of traffic rules cannot be fully prevented. We performed an extensive experimental campaign, in a simulated environment, in which drivers were trained with and without rules, and assessed the learned behaviors in terms of efficiency and safety. The results show that drivers trained with rules enforcement are willing to reduce their efficiency in exchange for being compliant to the rules, thus leading to higher overall safety