The Effects of Ultrasound-Guided Transversus Abdominis Plane Block on Acute and Chronic Postsurgical Pain After Robotic Partial Nephrectomy: A Prospective Randomized Clinical Trial.

BACKGROUND: Use of a locoregional analgesia technique, such as the ultrasound-guided transversus abdominis plane block (TAPb), can improve postoperative pain management. We investigated the role of TAPb in robotic partial nephrectomy, a surgery burdened by severe postoperative pain. METHODS: In this prospective trial, patients with American Society of Anesthesiologists class I-III physical status undergoing robotic partial nephrectomy were randomly assigned to standard general anesthetic plus ultrasound-guided TAPb (TAP group) or sole standard general anesthetic (NO-TAP group). The primary end point was morphine consumption 24 hours after surgery. Secondary outcomes were postoperative nausea and vomiting in the first 24 hours, sensitivity, and acute and chronic pain, as measured by multiple indicators. RESULTS: A total of 96 patients were evaluated: 48 patients in the TAP group and 48 in the NO-TAP group. Median morphine consumption after 24 hours was higher in the NO-TAP group compared with the TAP group (14.1 ± 4.5 mg vs 10.6 ± 4.6, P < 0.008). The intensity of acute somatic pain and the presence of chronic pain at three and six months were higher in the NO-TAP group. CONCLUSIONS: Our results show that TAPb can significantly reduce morphine consumption and somatic pain, but not visceral pain. TAPb reduced the incidence of chronic pain.

A Prospective, Randomized, Clinical Trial on the Effects of a Valveless Trocar on Respiratory Mechanics During Robotic Radical Cystectomy: A Pilot Study.

BACKGROUND: Prolonged pneumoperitoneum and Trendelenburg positioning for robot-assisted radical cystectomy (RARC) are essential for optimizing visualization of the operative field, although they worsen hemodynamic and respiratory function. Our hypothesis is that the use of a valveless trocar (VT) may improve respiratory mechanics. METHODS: In this prospective, 2-arm parallel trial, patients ASA II to III undergoing RARC were randomly assigned into 2 groups: in the VT group, the capnoperitoneum was maintained with a VT; in the control group, the capnoperitoneum was maintained with a standard trocar (ST group). Inspiratory plateau pressure (Pplat), static compliance (Cstat), minute volume (MV), tidal volume (Vt), and carbon dioxide (CO2) elimination rate were recorded at these times: 15 minutes after anesthesia induction (T0), 10 minutes (T1) and 60 minutes (T2) after first robot docking, 10 minutes before first undocking (T3), 10 minutes (T4) and 60 minutes (T5) after second docking, 10 minutes before second undocking (T6), and 10 minutes before extubation (T7). The primary end point of the study was the assessment of Pplat mean value from T1 to T6. RESULTS: A total of 56 patients were evaluated: 28 patients in the VT group and 28 in the ST group. VT group had lower Pplat (means and standard error, VT group 30 [0.66] versus ST group 34 [0.66] cm H2O, with estimated mean difference and 95% confidence interval, -4.1 [-5.9 to -2.2], P < .01), lower MV (means and standard error, VT group 8.2 [0.22] versus ST group 9.8 [0.21] L min, P < .01), lower CO2 elimination rate (means and standard error, VT group 4.2 [0.25] versus ST group 5.4 [0.24] mL kg min, P < .01), lower end-tidal CO2 (ETCO2) (means and standard error, VT group 28.8 [0.48] versus ST group 31.3 [0.46] mm Hg, P < .01), and higher Cstat (means and standard error, VT group 26 [0.9] versus ST group 22.1 [0.9] mL cm H2O, P < .01). Both groups had similar Vt (P = .24). CONCLUSIONS: During RARC, use of a VT was associated with a significantly lower Pplat and improvement in other respiratory parameters.

Link prediction and feature relevance in knowledge networks: A machine learning approach.

We propose a supervised machine learning approach to predict partnership formation between universities. We focus on successful joint R&D projects funded by the Horizon 2020 programme in three research domains: Social Sciences and Humanities, Physical and Engineering Sciences, and Life Sciences. We perform two related analyses: link formation prediction, and feature importance detection. In predicting link formation, we consider two settings: one including all features, both exogenous (pertaining to the node) and endogenous (pertaining to the network); and one including only exogenous features (thus removing the network attributes of the nodes). Using out-of-sample cross-validated accuracy, we obtain 91% prediction accuracy when both types of attributes are used, and around 67% when using only the exogenous ones. This proves that partnership predictive power is on average 24% larger for universities already incumbent in the programme than for newcomers (for which network attributes are clearly unknown). As for feature importance, by computing super-learner average partial effects and elasticities, we find that the endogenous attributes are the most relevant in affecting the probability to generate a link, and observe a largely negative elasticity of the link probability to feature changes, fairly uniform across attributes and domains.

Machine learning prediction of academic collaboration networks.

We investigate the different roles played by nodes' network and non-network attributes in explaining the formation of European university collaborations from 2011 to 2016, in three European Research Council (ERC) domains: Social Sciences and Humanities (SSH), Physical and Engineering Sciences (PE), Life Sciences (LS), as well as multidisciplinary collaborations. On link formation in collaboration networks, existing research has not yet compared and simultaneously examined both network and non-network attributes. Using four machine learning predictive algorithms (LASSO, Neural Network, Gradient Boosting, and Random Forest) our results show that, over various model specifications: (i) best model link formation accuracy is larger than 80%, (ii) among the non-network attributes, public funding plays an important role in PE and LS, (iii) network attributes count more than non-network attributes for the formation, sensibly increasing accuracy, (iv) feature-importance scores show a different ordering in the four domains, thus signalling different modes of knowledge production and transmission taking place within these different scientific communities.

Outlining the Orientation Toward Socially Relevant Issues in Competitive R&D Funding Instruments.

While project-based funding in public R&D investments has grown in importance in all European countries over the last two decades, there is widespread concern among decision-makers about the actual orientation of project funding instruments to promote societal well-being. The capability of public R&D investment to improve the quality of citizens' lives implies the pursuit of "relevant" social objectives related to existing or emerging problems affecting individuals' lives and society. Particularly, when referring to project-funded research, the question of "relevance" in research objectives recalls the never-ending debate over how to translate policymakers' request for producing value from public investments in research activities into "usable results". The manuscript explores, using recent data collected at European level on public R&D funding, the portfolio of research project funding policy instruments of various public research funding organizations (RFOs) in order to shed light on how and to what extent it is oriented to address socially relevant issues. The authors examine the characterization of the single project funding instruments, which are intended to incorporate the motivations and targeted goals of public action, and the RFOs that manage them. They specifically assume that the actual orientation of funding instruments, beyond the declared objectives, is influenced by some features related to their implementation operated by the RFOs, such as the importance given to specific evaluation criteria and the composition of the evaluation panels in the selection process of the funding beneficiaries.

Recovery from Anesthesia after Robotic-Assisted Radical Cystectomy: Two Different Reversals of Neuromuscular Blockade.

During robot-assisted radical cystectomy (RARC), specific surgical conditions (a steep Trendelenburg position, prolonged pneumoperitoneum, effective myoresolution until the final stages of surgery) can seriously impair the outcomes. The aim of the study was to evaluate the incidence of postoperative nausea and vomiting (PONV) and ileus and the quality of cognitive function at the awakening in two groups of patients undergoing different reversals. In this randomized trial, patients that were American Society of Anesthesiologists physical status (ASA) ≤III candidates for RARC for bladder cancer were randomized into two groups: In the sugammadex (S) group, patients received 2 mg/kg of sugammadex as reversal of neuromuscolar blockade; in the neostigmine (N) group, antagonization was obtained with neostigmine 0.04 mg/kg + atropine 0.02 mg/kg. PONV was evaluated at 30 min, 6 and 24 h after anesthesia. Postoperative cognitive functions and time to resumption of intestinal transit were also investigated. A total of 109 patients were analyzed (54 in the S group and 55 in the N group). The incidence of early PONV was lower in the S group but not statistically significant (S group 25.9% vs. N group 29%; p = 0.711). The Mini-Mental State test mean value was higher in the S group vs. the N group (1 h after surgery: 29.3 (29; 30) vs. 27.6 (27; 30), p = 0.007; 4 h after surgery: 29.5 (30; 30) vs. 28.4 (28; 30), p = 0.05). We did not observe a significant decrease of the PONV after sugammadex administration versus neostigmine use. The Mini-Mental State test mean value was greater in the S group.