Analysis of single extracellular vesicles (EVs) has the potential to yield valuable label-free information on their morphological structure, biomarkers and therapeutic targets, though such analysis is hindered by the lack of reliable and quantitative measurements of the mechanical properties of these compliant nanoscale particles. The technical challenge in mechanical property measurements arises from the existing tools and methods that offer limited throughput, and the reported elastic moduli range over several orders of magnitude. Here, we report on a flow-based method complemented by transmission electron microscopy (TEM) imaging to provide a high throughput, whole EV deformation analysis for estimating the mechanical properties of liposarcoma-derived EVs as a function of their size. Our study includes extracting morphological data of EVs from a large dataset of 432 TEM images, with images containing single to multiple EVs, and implementing the thin-shell deformation theory. We estimated the elastic modulus, E = 0.16 ± 0.02 MPa (mean±SE) for small EVs (sEVs; 30-150 nm) and E = 0.17 ± 0.03 MPa (mean±SE) for large EVs (lEVs; >150 nm). To our knowledge, this is the first report on the mechanical property estimation of LPS-derived EVs and has the potential to establish a relationship between EV size and EV mechanical properties.

This study examined 430 Chinese college students\' engagement in arts activities and the psychological benefits derived from such activities. The research differentiated between various types of arts participation and ways of involvement and examined four potential positive psychological outcomes. The findings revealed correlations between (1) creative participation in the performing arts, \'flow\', and aesthetic emotions; (2) consumptive participation in the visual arts and aesthetic emotions; and (3) creative participation in the literary arts and ego identity. Holistic arts participation demonstrated a significantly positive relationship with flourishing. A path analysis showed that flow experience and aesthetic emotions served as mediators in the mechanism through which holistic arts participation affected flourishing, with a chained mediation effect from flow experience to ego identity. This study confirms that arts participation is an effective pathway for individual flourishing and that more diverse and profound engagement in the arts can lead to sustained and widespread happiness.

OBJECTIVE: The study aimed to examine the effect of mental imagery on sports performance. Specifically, it was tested whether imagining flying (i.e., air travel) increases jumping performance in a group of female volleyball players.
METHODS: The study included 46 female young athletes (mean age = 15.23 years; standard deviation = 2.4) divided into two groups: the experimental group viewed a three-dimensional video that simulated a flying experience, while the control group watched neutral footage. The jump performance of both groups was measured before and after viewing the videos, using the Vertec Like® instrument to assess jump height. Participants were assessed for their flow disposition, mental imagery skills, and image vividness.
RESULTS: Comparing pre-post jump performance scores, the experimental group showed a significant improvement over the control group, with a medium-large effect size (d = 0.634). There was no significant association between flow disposition, mental imagery skills, image vividness, and pre-post jumping performance differences (respectively: β = -0.107, p = .484; β = -0.008, p = .957; β = 0.024, p = .913).
CONCLUSIONS: These findings suggest that the experience of imagining flying, enhanced with an immersive video, has a positive effect on jumping performance in a one-session study with young female volleyball players. This effect does not appear to be associated with pre-existing characteristics or the vividness of the image itself.

Recently, the interest in the Helmet interface during non-invasive respiratory support (NIRS) has increased due to the COVID-19 pandemic. During NIRS, positive end-expiratory pressure (PEEP) can be given as continuous positive airway pressure (CPAP), which maintains a positive airway pressure throughout the whole respiratory cycle with Helmet as an interface (H-CPAP). The main disadvantage of the H-CPAP is the inability to measure tidal volume (VT). Opto-electronic plethysmography (OEP) is a non-invasive technique that is not sensitive to gas compression/expansion inside the helmet. OEP acquisitions were performed on 28 healthy volunteers (14 females and 14 males) at baseline and during Helmet CPAP. The effect of posture (semi-recumbent vs. prone), flow (50 vs. 60 L/min), and PEEP (0 vs. 5 vs. 10 cmH2O) on the ventilatory and thoracic-abdominal pattern and the operational volumes were investigated. Prone position limited vital capacity, abdominal expansion and chest wall recruitment. A constant flow of 60 L/min reduced the need for the subject to ventilate while having a slight recruitment effect (100 mL) in the semi-recumbent position. A progressive increasing recruitment was found with higher PEEP but limited by the prone position. It is possible to accurately measure tidal volume during H-CPAP to deliver non-invasive ventilatory support using opto-electronic plethysmography during different clinical settings.

The enhancement of flexibility, energy efficiency, and environmental friendliness constitutes a widely acknowledged trend in the development of urban infrastructure. The proliferation of various types of transportation vehicles exacerbates the complexity of traffic regulation. Intelligent transportation systems, leveraging real-time traffic status prediction technologies, such as velocity estimation, emerge as viable solutions for the efficacious management and control of urban road networks. The objective of this project is to address the complex task of increasing accuracy in predicting traffic conditions on a big scale using deep learning techniques. To accomplish the objective of the study, the historical traffic data of Paris and Istanbul within a certain timeframe were used, considering the impact of variables such as speed, traffic volume, and direction. Specifically, traffic movie clips based on 2 years of real-world data for the two cities were utilized. The movies were generated with HERE data derived from over 100 billion GPS (Global Positioning System) probe points collected from a substantial fleet of automobiles. The model presented by us, unlike the majority of previous ones, takes into account the cumulative impact of speed, flow, and direction. The developed model showed better results compared to the well-known models, in particular, in comparison with the SR-ResNet model. The pixel-wise MAE (mean absolute error) values for Paris and Istanbul were 4.299 and 3.884 respectively, compared to 4.551 and 3.993 for SR-ResNET. Thus, the created model demonstrated the possibilities for further enhancing the accuracy and efficacy of intelligent transportation systems, particularly in large urban centres, thereby facilitating heightened safety, energy efficiency, and convenience for road users. The obtained results will be useful for local policymakers responsible for infrastructure development planning, as well as for specialists and researchers in the field. Future research should investigate how to incorporate more sources of information, in particular previous information from physical traffic flow models, information about weather conditions, etc. into the deep learning framework, as well as further increasing of the throughput capacity and reducing processing time.

Introduction: Flow state, the optimal experience resulting from the equilibrium between perceived challenge and skill level, has been extensively studied in various domains. However, its occurrence in industrial settings has remained relatively unexplored. Notably, the literature predominantly focuses on Flow within mentally demanding tasks, which differ significantly from industrial tasks. Consequently, our understanding of emotional and physiological responses to varying challenge levels, specifically in the context of industry-like tasks, remains limited. Methods: To bridge this gap, we investigate how facial emotion estimation (valence, arousal) and Heart Rate Variability (HRV) features vary with the perceived challenge levels during industrial assembly tasks. Our study involves an assembly scenario that simulates an industrial human-robot collaboration task with three distinct challenge levels. As part of our study, we collected video, electrocardiogram (ECG), and NASA-TLX questionnaire data from 37 participants. Results: Our results demonstrate a significant difference in mean arousal and heart rate between the low-challenge (Boredom) condition and the other conditions. We also found a noticeable trend-level difference in mean heart rate between the adaptive (Flow) and high-challenge (Anxiety) conditions. Similar differences were also observed in a few other temporal HRV features like Mean NN and Triangular index. Considering the characteristics of typical industrial assembly tasks, we aim to facilitate Flow by detecting and balancing the perceived challenge levels. Leveraging our analysis results, we developed an HRV-based machine learning model for discerning perceived challenge levels, distinguishing between low and higher-challenge conditions. Discussion: This work deepens our understanding of emotional and physiological responses to perceived challenge levels in industrial contexts and provides valuable insights for the design of adaptive work environments.

4D-flow MRI is a promising technique for assessing vessel hemodynamics. However, its utilization is currently limited by the lack of reference values, particularly for pulmonary vessels. In this work, we have analysed flow and velocity in the pulmonary trunk (PT), left and right pulmonary arteries (LPA and RPA, respectively) in Landrace pigs at both rest and stress through the software MEVISFlow. Nine healthy Landrace pigs were acutely instrumented closed-chest and transported to the CMR facility for evaluation. After rest measurements, dobutamine was administered to achieve a 25% increase in heart rate compared to rest. 4D-flow MRI images have been analysed through MEVISFlow by two independent observers. Inter- and intra-observer reproducibility was quantified using intraclass correlation coefficient. A significant difference between rest and stress regarding flow and velocity in all the pulmonary vessels was observed. Mean flow increased 55% in PT, 75% in LPA and 40% in RPA. Mean peak velocity increased 55% in PT, 75% in LPA and 66% in RPA. A good-to-excellent reproducibility was observed in rest and stress for flow measurements in all three arteries. An excellent reproducibility for velocity was found in PT at rest and stress, a good one for LPA and RPA at rest, while poor reproducibility was found at stress. The current study showed that pulmonary flow and velocity assessed through 4D-flow MRI follow the physiological alterations during cardiac cycle and after stress induced by dobutamine. A clinical translation to assess pulmonary diseases with 4D-flow MRI under stress conditions needs investigation.

This study aimed to assess the adaptability and penetration depth capacity of recent bioceramic systems, including regular EndoSequence (BC) versus HiFlow (BCH) sealers in the presence of BC points. A total of 54 single-rooted teeth were instrumented and obturated with either the cold or warm compaction technique (n = 9), using either BC, BCH, or AH Plus (AHP) combined with BC points. The adaptation, film thickness, and gaps/voids were evaluated by scanning electron microscopy. The sealer/dentin interface was evaluated by Raman spectroscopy, and depth penetration was evaluated by a confocal laser scanning microscope. According to the normality test, the data were statistically analyzed by ANOVA or Kruskal-Wallis and Mann-Whitney U tests at p < 0.05. BCH sealer showed the significantly thinnest film with the greatest flow (p > 0.001), with further improvement when subjected to the warm compaction technique. Moreover, it exhibited close adaptation with deep penetration into radicular dentin, forming a tag-like structure. The Raman spectra also indicated close contact with the dentin surface. The use of BC sealer with BC points exhibited homogenous, single-unit obturation, either with a cold or warm technique. Furthermore, the use of the warm compaction technique with BCH sealer achieved a gap-free interface associated with tag-like structures, which exhibit the monoblock phenomenon.

OBJECTIVE: It is uncertain whether Thunderbeat has a place in harvesting the left internal mammary artery (LIMA) and whether skeletonization is superior to pedicle-harvested LIMA. Some investigations have shown improved flowrates in the skeletonized graft. The aim of this study was to compare 3 groups of harvesting techniques: Pedicled, surgical skeletonized and skeletonized with Thunderbeat in terms of flow rates in the LIMA and postoperative in-hospital outcomes.
METHODS: Patients undergoing coronary artery bypass grafting with the LIMA to the anterior descending artery were randomized to pedicled (n = 56), surgical skeletonized (n = 55) and skeletonized with Thunderbeat (n = 54). Main outcomes were blood flow and pulsatility index in the graft.
RESULTS: No statistical difference between groups regarding flow in LIMA or pulsatility index. Similarly, no difference in postoperative bleeding or days of hospitalization. The duration of harvesting was faster for the pedicled technique compared with surgical skeletonized and skeletonized with Thunderbeat [mean total min: pedicled 20.2 min standard deviation (SD) ± 5.4; surgical skeletonized 28.6 min SD ± 8.7; skeletonized with Thunderbeat 28.3 min SD ± 9.11, P < 0.001]. No grafts discarded due to faulty harvesting and there was no graft failure within hospital stay.
CONCLUSIONS: We found no difference between the harvesting methods except for a significantly faster harvesting time with the pedicled technique. However, non-touch skeletonized LIMA harvesting with Thunderbeat seems to be an effective alternative to traditional surgical skeletonized LIMA. The future will reveal whether patency is harvesting dependent.
BACKGROUND: ClinicalTrials.gov Identifier: NCT05562908.

BACKGROUND: The National Health Service (NHS) Talking Therapies program treats people with common mental health problems in England according to \"stepped care,\" in which lower-intensity interventions are offered in the first instance, where clinically appropriate. Limited resources and pressure to achieve service standards mean that program providers are exploring all opportunities to evaluate and improve the flow of patients through their service. Existing research has found variation in clinical performance and stepped care implementation across sites and has identified associations between service delivery and patient outcomes. Process mining offers a data-driven approach to analyzing and evaluating health care processes and systems, enabling comparison of presumed models of service delivery and their actual implementation in practice. The value and utility of applying process mining to NHS Talking Therapies data for the analysis of care pathways have not been studied.
OBJECTIVE: A better understanding of systems of service delivery will support improvements and planned program expansion. Therefore, this study aims to demonstrate the value and utility of applying process mining to NHS Talking Therapies care pathways using electronic health records.
METHODS: Routine collection of a wide variety of data regarding activity and patient outcomes underpins the Talking Therapies program. In our study, anonymized individual patient referral records from two sites over a 2-year period were analyzed using process mining to visualize the care pathway process by mapping the care pathway and identifying common pathway routes.
RESULTS: Process mining enabled the identification and visualization of patient flows directly from routinely collected data. These visualizations illustrated waiting periods and identified potential bottlenecks, such as the wait for higher-intensity cognitive behavioral therapy (CBT) at site 1. Furthermore, we observed that patients discharged from treatment waiting lists appeared to experience longer wait durations than those who started treatment. Process mining allowed analysis of treatment pathways, showing that patients commonly experienced treatment routes that involved either low- or high-intensity interventions alone. Of the most common routes, >5 times as many patients experienced direct access to high-intensity treatment rather than stepped care. Overall, 3.32% (site 1: 1507/45,401) and 4.19% (site 2: 527/12,590) of all patients experienced stepped care.
CONCLUSIONS: Our findings demonstrate how process mining can be applied to Talking Therapies care pathways to evaluate pathway performance, explore relationships among performance issues, and highlight systemic issues, such as stepped care being relatively uncommon within a stepped care system. Integration of process mining capability into routine monitoring will enable NHS Talking Therapies service stakeholders to explore such issues from a process perspective. These insights will provide value to services by identifying areas for service improvement, providing evidence for capacity planning decisions, and facilitating better quality analysis into how health systems can affect patient outcomes.