BackgroundRobotic assistance (RA) in the harvesting of internal thoracic artery during minimally invasive direct coronary artery bypass grafting (MIDCAB) provides several potential benefits for surgeon and patient in comparison with conventional MIDCAB. The two technical options have not been thoroughly compared in the literature yet. We aimed to perform this in our cohort with the use of propensity-score matching (PSM).MethodsThis was a retrospective comparison of all consecutive patients undergoing conventional MIDCAB (2005–2021) and RA-MIDCAB (2018–2021) at our institution with the use of PSM with 27 preoperative covariates.ResultsThroughout the study period 603 patients underwent conventional and 132 patients underwent RA-MIDCAB. One hundred and thirty matched pairs were selected for further comparison. PSM successfully eliminated all preoperative differences. Patients after RA-MIDCAB had lower 24 h blood loss post-operatively (300 vs. 450 ml, p = 0.002). They had shorter artificial ventilation time (6 vs. 7 h, p = 0.018) and hospital stay (6 vs. 8 days, p < 0.001). There was no difference in the risk of perioperative complications, short-term and mid-term mortality between the groups.ConclusionsRA-MIDCAB is an attractive alternative to conventional MIDCAB. It is associated with lower post-operative blood loss and potentially faster rehabilitation after surgery. The mortality and the risk of perioperative complications are comparable among the groups.

Background Tobacco smoking has been associated with an increased risk of complications after conventional coronary surgery. However, the impact of smoking on the risk of postoperative complications in minimally invasive coronary surgery is yet to be studied. We aimed to analyze the impact of the preoperative smoking status on the short- and long-term outcomes of minimally invasive direct coronary artery bypass grafting (MIDCAB) in the context of isolated surgical revascularization or in association with percutaneous coronary intervention. Methods This was a retrospective observational study of all patients undergoing MIDCAB at our institution between 2006 and 2020. Patients were divided into three groups: active smokers, ex-smokers who have quit smoking for at least 1 month before surgery, and non-smokers. The groups were compared using conventional statistical methods. Multivariate analysis was then performed where significant differences were found to eliminate bias. Results Throughout the study period, 541 patients underwent MIDCAB, of which 135 (25%) were active smokers, 183 (34%) were ex-smokers, and 223 (41%) were non-smokers. Smokers presented for surgery at a younger age (p < 0.0001), more frequently with a history of myocardial infarction (p < 0.001), peripheral artery disease (p < 0.001) and chronic obstructive pulmonary disease (p < 0.0001). Using multivariate analysis, active smoking was determined to be a significant risk factor for the need of urgent revascularization (odds ratio 2.36 [1.00–5.56], p = 0.049) and the composite of pulmonary complications (including pneumothorax, respiratory infection, respiratory dysfunction, subcutaneous emphysema and exacerbation of chronic obstructive pulmonary disease; odds ratio 2.84 [1.64–4.94], p < 0.001). Preoperative smoking status did not influence the long-term survival (p = 0.83). Conclusions In our study, active smokers presented for MIDCAB at a younger age and more often with signs of atherosclerotic disease (history of myocardial infarction and peripheral artery disease). Active smoking was found to be the most significant risk factor for postoperative pulmonary complications, and is also associated with a more frequent need for urgent surgery at diagnosis. Long-term postoperative survival is not affected by the preoperative smoking status.

Conversational AI systems are gaining a lot of attention recently in both industrial and scientific domains, providing a natural way of interaction between customers and adaptive intelligent systems. A key requirement in these systems is the ability to efficiently parse user queries, understand the intent behind each query, and provide adequate responses to users. Therefore, many applications such as conversation bots and smart IoT devices has a natural language understanding (LU) service integrated within. One of the greatest challenges of language understanding services is efficient utterance (sentence) representation in vector space, which is an essential step for most ML tasks. In this paper, we propose a novel approach for generating vector space representations of conversational utterances using pair-wise similarity metrics. The proposed approach uses only a few corpora to tune the weights of the similarity metric without relying on external general purpose ontologies. Our experiments confirm that the generated vectors can improve the performance of LU services in unsupervised, semi-supervised and supervised learning tasks over state-ofthe-art prior works.

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