Purpose The purpose of this paper is to use the NACA 0015 symmetric hydrofoil as the research subject and control cloud cavitation on hydrofoils. Design/methodology/approach Based on observed distribution of caudal fin spines on fish, a bionic structure of fin-like spines is arranged on the hydrofoil suction surface, which maintains the cavitation in a quasi-steady state stage by eliminating the cyclic shedding process of cloud cavitation. Based on the modified shear stress transport k-ω turbulence model and the Zwart–Gerber–Belamri cavitation model, this paper compares and analyzes the NACA 0015 hydrofoil and the bionic NACA 0015 hydrofoil under condition of an angle of attack of 8° and a cavitation number of 0.8. Findings The results show that the average drag of the hydrofoil is reduced but the lift is decreased, and the lift-drag ratio is increased after arranging the bionic structure. The bionic structure can effectively reduce the turbulent kinetic energy and make the flow more stable; it also can effectively control the hydrofoil surface side-entrant jet and the vortex shedding process of the near wall region. Originality/value Based on the above conclusions, the bionic structure of fin-like spines can achieve a significant passive control in the hydrofoil cloud cavitation process.

Background: Increasing numbers of patients have recovered from severe coronavirus disease 2019 in Wuhan, China. This study aimed to evaluate the association of psychological distress with resting palpitations in recovered patients. Methods: In this prospective cohort study, consecutive patients who recovered from severe COVID-19 and complained of resting palpitations were included. Dynamic electrocardiogram (ECG) was continuously monitored for 2 hours while patients were at rest. A survey using a palpitation frequency scale and the Hospital Anxiety and Depression Scale (HADS) was administered to all participants. Results: Of the 289 consecutive patients who recovered from severe COVID-19, 24 patients (8.3%) suffered resting palpitation symptoms, and 22 patients were finally included. Twohour Holter monitoring showed that 18 (81.8%) patients had tachyarrhythmias, of which the most common was sinus tachycardia (17/22, 77.3%). However, patients with sinus tachycardia showed a similar frequency of palpitation episodes compared to those without sinus tachycardia. Anxiety (68.2%) and depression (59.1%) were prevalent among these recovered patients. Patients with anxiety or depression symptoms had a higher frequency of palpitation episodes than those without anxiety or depression symptoms. In addition, both the HADSanxiety score (r =0.609, P<0.01) and HADS-depression score (r =0.516, P=0.01) were positively related to the frequency of palpitation episodes. Conclusion: Symptoms of resting palpitations, manifested mainly by sinus tachycardia, are not uncommon in patients who recovered from severe COVID-19. Psychological distress (anxiety and depression) may be responsible, at least in part, for resting palpitation symptoms.

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Objective To explore the clinical value of the Gleason score upgrading (GSU)prediction model after radical prostatectomy (RP) based on a Bayesian network. Methods The data of 356 patients who underwent prostate biopsy and RP in our hospital from January 2018 to May 2021 were retrospectively analysed. Fourteen risk factors,including age, body mass index (BMI), total prostate-specific antigen (tPSA), prostate volume, total prostate-specific antigen density (PSAD), the number and proportion of positive biopsy cores, PI-RADS score, clinical stage and postoperative pathological characteristics, were included in the analysis. Data were used to establish a prediction model for Gleason score elevation based on the tree augmented naive (TAN) Bayesian algorithm. Moreover, the Bayesia Lab validation function was used to calculate the importance of polymorphic Birnbaum according to the results of the posterior analysis and to obtain the importance of each risk factor. ResultsIn the overall cohort, 110 patients (30.89%) had GSU. Based on all of the risk factors that were included in this study, the AUC of the model was 81.06%, and the accuracy was 76.64%. The importance ranking results showed that lymphatic metastasis, the number of positive biopsy cores, ISUP stage and PI-RADS score were the top four influencing factors for GSU after RP. ConclusionsThe prediction model of GSU after RP based on a Bayesian network has high accuracy andcan more accurately evaluate the Gleason score of prostate biopsy specimens and guide treatment decisions.