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BackgroundSteroid-resistant (SR) lower gastrointestinal (LGI) tract graft-versus-host disease (GVHD) is the predominant cause of morbidity and mortality from GVHD after allogeneic hematopoietic stem cell transplantation (allo-HSCT). The role of vedolizumab in the treatment of SR-LGI acute GVHD (aGVHD) remains uncertain. We aimed to assess the efficacy and safety of vedolizumab combined with basiliximab as second-line therapy for SR-LGI-aGVHD.MethodsThis study aimed to explore the efficacy of vedolizumab combined with basiliximab for SR-LGI-aGVHD. The primary endpoint was the overall response (OR) on day 28. Secondary and safety endpoints included durable OR at day 56, overall survival (OS), chronic GVHD (cGVHD), non-relapse mortality (NRM), failure-free survival (FFS), and adverse events.ResultsTwenty-eight patients with SR-LGI-aGVHD were included. The median time to start of combination therapy after SR-LGI-aGVHD diagnosis was 7 (range, 4–16) days. The overall response rate (ORR) at 28 days was 75.0% (95% CI: 54.8%–88.6%), and 18 achieved a complete response (CR) (64.3%, 95% CI: 44.1%–80.7%). The durable OR at day 56 was 64.3% (95% CI: 44.1%–80.7%). The 100-day, 6-month, and 12-month OS rates for the entire cohort of patients were 60.7% (95% CI: 45.1%–81.8%), 60.7% (95% CI: 45.1%–81.8%), and 47.6% (95% CI: 31.4%–72.1%), respectively. The median failure-free survival was 276 days; (95% CI: 50–not evaluable) 12-month NRM was 42.9% (95% CI: 24.1%–60.3%). The 1-year cumulative incidence of cGVHD was 35.7%. Within 180 days after study treatments, the most common grade 3 and 4 adverse events were infections. Nine (32.1%) patients developed cytomegalovirus (CMV) reactivation complicated with bacterial infections (25.0%, CMV infection; 7.1%, CMV viremia). Epstein–Barr virus (EBV) reactivation occurred in five patients (17.9%, 95% CI: 6.8%–37.6%). Only three patients (10.7%, 95% CI: 2.8%–29.4%) in our study developed pseudomembranous colitis.ConclusionsVedolizumab plus basiliximab demonstrated efficacy in severe SR-LGI-aGVHD and was well-tolerated. Vedolizumab plus basiliximab may be considered a potential treatment option for patients with LGI-aGVHD.

The Camellia oleifera fruit harvester, a specialized agricultural device, is engineered for efficient operation within the densely planted C. oleifera groves of China’s undulating terrains. Its design features a notably small footprint to navigate the constrained spaces between trees. With the enhancement of the functionality and power of the harvester, the engine compartment becomes even more congested. This, while beneficial for performance, complicates heat dissipation and reduces harvesting efficiency. In this study, experiments were initially conducted to collect temperature data from the main heat-generating components and parts susceptible to high temperatures within the harvester’s engine compartment. Subsequently, a 3D model was developed for numerical simulations, leading to the proposal of optimization schemes for the engine compartment’s structure and the validation of these schemes’ feasibility. A comparison of the experimental data, both before and after optimization, revealed a significant reduction in the surface temperatures of components within the engine compartment following optimization. As a result, the heat dissipation of the engine compartment has been greatly optimized. The harvester has demonstrated prolonged normal operation, enhancing the reliability and economy of the harvester.

ObjectiveThe objective of this study was to explore the distributed network effects of intracranial electrical stimulation in patients with medically refractory epilepsy using dynamic functional connectivity (dFC) and graph indicators.MethodsThe time-varying connectivity patterns of dFC (state-based metrics) as well as topological properties of static functional connectivity (sFC) and dFC (graph indicators) were assessed before and after the intracranial electrical stimulation. The sliding window method and k-means clustering were used for the analysis of dFC states, which were characterized by connectivity strength, occupancy rate, dwell time, and transition. Graph indicators for sFC and dFC were obtained using group statistical tests.ResultsDFCs were clustered into two connectivity configurations: a strongly connected state (state 1) and a sparsely connected state (state 2). After electrical stimulation, the dwell time and occupancy rate of state 1 decreased, while that of state 2 increased. Connectivity strengths of both state 1 and state 2 decreased. For graph indicators, the clustering coefficient, k-core, global efficiency, and local efficiency of patients showed a significant decrease, but the brain networks of patients exhibited higher modularity after electrical stimulation. Especially, for state 1, there was a significant decrease in functional connectivity strength after stimulation within and between the frontal lobe and temporary lobe, both of which are associated with the seizure onset.ConclusionOur findings demonstrated that intracranial electrical stimulation significantly changed the time-varying connectivity patterns and graph indicators of the brain in patients with medically refractory epilepsy. Specifically, the electrical stimulation decreased functional connectivity strength in both local-level and global-level networks. This might provide a mechanism of understanding for the distributed network effects of intracranial electrical stimulation and extend the knowledge of the pathophysiological network of medically refractory epilepsy.

Due to the growth characteristics of the flowers and fruits of camellia in the same period, the vibrating camellia fruit picking machine needs to ensure the constant rotational speed of the vibrating hydraulic motor when the picking mechanism is operating, to achieve a constant vibration frequency, to ensure that the camellia fruit can smoothly fall off the branches through vibration. In contrast, the camellia fruit does not fall off. In this regard, this paper deduced the state space equation of the camellia fruit picking machine’s valve-controlled vibrating hydraulic motor system and designed a fuzzy wavelet neural network PID controller (FWNN PID controller) based on the traditional incremental PID control principle. Then the designed vibration picking manipulator control system was simulated under no-load, 5 s load conditions, and load start conditions with MATLAB/Simulink, a general PID controller and a fuzzy RBF neural network PID controller (FRBFNN PID controller) were used to contrast with it. The results show that the general PID controller has a slow response speed and poor robustness, while fuzzy neural network PID controllers (including FWNN PID controller and FRBFNN PID controller) have a fast response speed and strong robustness, which can well meet the requirements of a specific vibration frequency. Finally, a field test was carried out. The results show that the FWNN PID control is better than the FRBFNN PID control. Furthermore, the FWNN PID controller obviously reduced the drop rate of camellia flowers within 6% while ensuring the picking efficiency above 90%, which can well meet the needs of the camellia fruit picking operation.

Objective:To determine the factors associated with quality of life of discharged patients with coronavirus disease 2019(COVID-19), so as to provide a basis for optimizing early intervention programs, preventing community from life restriction, and formulating appropriate community rehabilitation measures.Methods:A total of 57 discharged patients with COVID-19 who were cured from Wuhan China resources& Wisco general hospital from March to April 2020. In the"Questionnaire Star"platform from April to May 2020, the 12-item short form health survey version 2(SF-12V2) was used to evaluate life quality of life; the self-rating anxiety scale (SAS) was used to evaluate dyspnea anxiety status; the self-rating depression scale (SDS) was used to evaluate depression status; the modified medical research council dyspnea scores (mMRC) was used to evaluate difficulty of breathing. The quality of life of discharged patients with COVID-19 with different characteristics was compared. The correlation between the quality of life of patients and anxiety, depression and dyspnea and the related factors were analyzed.Results:A total of 57 questionnaires were distributed, and three duplicate and invalid questionnaires were eliminated, 54 valid questionnaires were obtained, with an effective questionnaire of 94.74%.①Quality of life of discharged patients with COVID-19: the physical component summary (PCS) scores and mental component summary (MCS) scores were (37.02±12.32) and (38.46±14.42) respectively; there were 3 cases (5.56%), 45 cases (83.33%), 5 cases (9.26%), and 1 case (1.85%) with dyspnea grade 0 to 3 respectively; 19 cases (35.19%) had an anxiety state (SAS≥50), and 19 cases (35.19%) had an anxiety state (SDS≥53). ②Comparison of quality of life of COVID-19 patients with different characteristics: patients with different disease types showed statistically significant differences in the SF-12V2 PCS(P< 0.05). ③Correlation analysis between quality of life, anxiety, depression and dyspnea: Pearson's correlation indicated that, anxiety (r=-0.34, P=0.011) and dyspnea (r=-0.39, P=0.003) were negatively correlated with the PCS scores, anxiety (r=-0.46, P=0.001) and depression (r=-0.40, P=0.002) were negatively correlated with the MCS scores.④Analysis of influencing factors on the quality of life of COVID-19 patients: multiple linear regression indicated that, sex (β=8.27), depression (β=-0.34) and severity of illness (β=-11.68) were significant predictors of the SF-12V2 PCS scores (P< 0.05). Anxiety (β=-0.62) was a significant predictor of the SF-12V2 MCS scores (P< 0.05).Conclusion:COVID-19 patients discharged from the hospital have problems with dyspnea, anxiety, depression, and the decline of quality of life. Sex, depression, anxiety and severity of illness are important factors for the decline of quality of life in patients with COVID-19. Patients with COVID-19(especially female patients and severe patients) should be screened for depression and anxiety as soon as possible after discharge and intervention should be carried out to reduce patients'negative emotions. Patients should be encouraged to participate in rehabilitation training appropriately to improve respiratory function and improve the quality of life.

As consumer-grade depth sensors provide an efficient and low-cost way to obtain point cloud data, an increasing number of applications regarding the acquisition and processing of livestock point clouds have been proposed. Curve skeletons are abstract representations of 3D data, and they have great potential for the analysis and understanding of livestock point clouds. Articulated skeleton extraction has been extensively studied on 2D and 3D data. Nevertheless, robust and accurate skeleton extraction from point set sequences captured by consumer-grade depth cameras remains challenging since such data are often corrupted by substantial noise and outliers. Additionally, few approaches have been proposed to overcome this problem. In this paper, we present a novel curve skeleton extraction method for point clouds of four-legged animals. First, the 2D top view of the livestock was constructed using the concave hull algorithm. The livestock data were divided into the left and right sides along the bilateral symmetry plane of the livestock. Then, the corresponding 2D side views were constructed. Second, discrete skeleton evolution (DSE) was utilized to extract the skeletons from those 2D views. Finally, we divided the extracted skeletons into torso branches and leg branches. We translated each leg skeleton point to the border of the nearest banded point cluster and then moved it to the approximate centre of the leg. The torso skeleton points were calculated according to their positions on the side view and top view. Extensive experiments show that quality curve skeletons can be extracted from many livestock species. Additionally, we compared our method with representative skeleton extraction approaches, and the results show that our method performs better in avoiding topological errors caused by the shape characteristics of livestock. Furthermore, we demonstrated the effectiveness of our extracted skeleton in detecting frames containing pigs with correct postures from the point cloud stream.