Wound closure is performed at the end of the procedure, when the attention of the surgical team may decrease due to tiredness. The aim of this study was to assess the influence of changing the surgical team for wound closure on the rate of surgical site infection (SSI).

A two-armed observational monocentric matched case-control study was performed in a time series design. During the baseline period, closure of the abdominal wall was performed by the main surgical team. The intervention consisted of closure of the abdominal wall and skin by an independent surgical team. Matching was based on gender, BMI, length of surgery, type of surgery, elective versus emergency surgery and ASA score. The primary outcome was SSI rate 30 days after surgery.

A total of 72 patients in the intervention group were matched with 72 patients in the baseline group. The SSI rate after 30 days in the intervention group was 10% (n = 7) and in the baseline group 21% (n = 15) (p = 0.064). Redo-Surgery as result of infection (e.g. opening the wound, drainage or reoperation) was significantly higher in the baseline group (19.4% vs 2.7%; p = 0.014). Mortality, length of stay, rehospitalisation and complication rates 30 days after surgery did not differ significantly.

Changing the surgical team for wound closure did not reduce the overall rate of SSI, but the rate of redo-surgery as a result of SSI. Despite being potentially beneficial, organizational factors are a main limiting factor of changing the surgical team for the wound closure.

Clinicaltrial.gov NCT04503642.

Clinicaltrial.gov NCT04503642.

Venous reconstruction has been recently demonstrated to be safe for tumours with invasion into portal vein and/or superior mesenteric vein. This study aims to compare the patency between various venous reconstructions.

This is retrospective study of 76 patients who underwent pancreaticoduodenectomy or total pancreatectomy with venous reconstruction from 2006 to 2018. Patient demographics, tumour histopathology, morbidity, mortality and patency were studied. Kaplan-Meier estimates were performed for primary venous patency.

Sixty-two patients underwent pancreaticoduodenectomy and 14 underwent total pancreatectomy. Forty-seven, 19 and 10 patients underwent primary repair, end-to-end anastomosis and interposition graft respectively. Major morbidity (Clavien-Dindo >grade 2) and 30-day mortality were 14/76(18.4%) and 1/76(1.3%) respectively. There were 12(15.8%) venous occlusion including 4(5.3%) acute occlusions. Overall 6-month, 1-year and 2-year primary patency was 89.1%, 92.5% and 92.3% respectively. 1-year primary patency of primary repair was superior to end-to-end anastomosis and interposition graft (primary repair 100%, end-to-end anastomosis 81.8%, interposition graft 66.7%, p = 0.045). Pairwise comparison also demonstrated superior 1-year patency of primary repair (adjusted p = 0.037). There was no significant difference between the cumulative venous patency for each venous reconstruction method primary repair 84±6%, end-to-end anastomosis 75±11% and interposition graft 76±15% (p = 0.561).

1-year primary venous patency of primary repair is superior to end-to-end anastomosis and interposition graft.

1-year primary venous patency of primary repair is superior to end-to-end anastomosis and interposition graft.It is well recognized that isolated cardiac muscle cells beat in a periodic manner. BGB-290 Recently, evidence indicates that other, non-muscle cells, also perform periodic motions that are either imperceptible under conventional lab microscope lens or practically not easily amenable for analysis of oscillation amplitude, frequency, phase of movement and its direction. Here, we create a real-time video analysis tool to visually magnify and explore sub-micron rhythmic movements performed by biological cells and the induced movements in their surroundings. Using this tool, we suggest that fibroblast cells perform small fluctuating movements with a dominant frequency that is dependent on their surrounding substrate and its stiffness.Speakers’ memory of sentence structure can persist and modulate the syntactic choices of subsequent utterances (i.e., structural priming). Much research on structural priming posited a multifactorial account by which an implicit learning process and a process related to explicit memory jointly contribute to the priming effect. Here, we tested two predictions from that account (1) that lexical repetition facilitates the retrieval of sentence structures from memory; (2) that priming is partly driven by a short-term explicit memory mechanism with limited resources. In two pairs of structural priming and sentence structure memory experiments, we examined the effects of structural priming and its modulation by lexical repetition as a function of cognitive load in native Dutch speakers. Cognitive load was manipulated by interspersing the prime and target trials with easy or difficult mathematical problems. Lexical repetition boosted both structural priming (Experiments 1a-2a) and memory for sentence structure (Experiments 1b-2b) and did so with a comparable magnitude. In Experiment 1, there were no load effects, but in Experiment 2, with a stronger manipulation of load, both the priming and memory effects were reduced with a larger cognitive load. The findings support an explicit memory mechanism in structural priming that is cue-dependent and attention-demanding, consistent with a multifactorial account of structural priming.Cloud computing has evolved the big data technologies to a consolidated paradigm with SPaaS (Streaming processing-as-a-service). With a number of enterprises offering cloud-based solutions to end-users and other small enterprises, there has been a boom in the volume of data, creating interest of both industry and academia in big data analytics, streaming applications, and social networking applications. With the companies shifting to cloud-based solutions as a service paradigm, the competition grows in the market. Good quality of service (QoS) is a must for the enterprises, as they strive to survive in a competitive environment. However, achieving reasonable QoS goals to meet SLA agreement cost-effectively is challenging due to variation in workload over time. This problem can be solved if the system has the ability to predict the workload for the near future. In this paper, we present a novel topology-refining scheme based on a workload prediction mechanism. Predictions are made through a model based on a combination of SVR, autoregressive, and moving average model with a feedback mechanism.