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.

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.

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.

In today\'s fast paced, attention-demanding society, executive functions and attentional resources are often taxed. Individuals need ways to sustain and restore these resources. We first review the concepts of attention and restoration, as instantiated in Attention Restoration Theory (ART). ART emphasizes the role of nature in restoring attention. We then discuss the essentials of experiments on the causal influences of nature. Next, we expand the concept of ART to include modern, designed environments. We outline a wider perspective termed attentional ecology, in which attention behavior is viewed within a larger system involving the human and their interactions with environmental demands over time. When the ecology is optimal, mental functioning can be a positive \"flow\" that is productive, sustainable for the individual, and sometimes creative.

Herein, we present the first examples of amino acid decarboxylation via photochemically activated carbonyl sulfide (COS) elimination of the corresponding thioacids. This method offers a mild approach for the decarboxylation of amino acids, furnishing N-alkyl amino derivatives. The methodology was compatible with amino acids displaying both polar and hydrophobic sidechains and was tolerant towards widely used amino acid-protecting groups. The compatibility of the reaction with continuous-flow conditions demonstrates the scalability of the process.

Background: Few data exist on the comparative long-term outcomes of severe aortic stenosis (AS) patients with different flow-gradient patterns undergoing transcatheter aortic valve implantation (TAVI). This study sought to evaluate the impact of the pre-TAVI flow-gradient pattern on long-term clinical outcomes after TAVI and assess changes in the left ventricular ejection fraction (LVEF) of different subtypes of AS patients following TAVI. Methods: Consecutive patients with severe AS undergoing TAVI in our institution were screened and prospectively enrolled. Patients were divided into four subgroups according to pre-TAVI flow/gradient pattern: (i) low flow-low gradient (LF-LG): stroke volume indexed (SVi) ≤ 35 mL/m2 and mean gradient (MG) < 40 mmHg); (ii) normal flow-low gradient (NF-LG): SVi > 35 mL/m2 and MG < 40 mmHg; (iii) low flow-high gradient (LF-HG): Svi 35 mL/m2 and MG ≥ 40 mmHg and (iv) normal flow-high gradient (NF-HG): SVi > 35 mL/m2 and MG ≥ 40 mmHg. Transthoracic echocardiography was repeated at 1-year follow-up. Clinical follow-up was obtained at 12 months, and yearly thereafter until 5-year follow-up was complete for all patients. Results: A total of 272 patients with complete echocardiographic and clinical follow-up were included in our analysis. Their mean age was 80 ± 7 years and the majority of patients (N = 138, 50.8%) were women. 62 patients (22.8% of the study population) were distributed in the LF-LG group, 98 patients (36%) were LF-HG patients, 95 patients (34.9%) were NF-HG, and 17 patients (6.3%) were NF-LG. There was a greater prevalence of comorbidities among LF-LG AS patients. One-year all-cause mortality differed significantly between the four subgroups of AS patients (log-rank p: 0.022) and was more prevalent among LF-LG patients (25.8%) compared to LF-HG (11.3%), NF-HG (6.3%) and NF-LG patients (18.8%). At 5-year follow-up, global mortality remained persistently higher among LF-LG patients (64.5%) compared to LF-HG (47.9%), NF-HG (42.9%), and NF-LG patients (58.8%) (log-rank p: 0.029). At multivariable Cox hazard regression analysis, baseline SVi (HR: 0.951, 95% C.I.; 0.918-0.984), the presence of at least moderate tricuspid regurgitation at baseline (HR: 3.091, 95% C.I: 1.645-5.809) and at least moderate paravalvular leak (PVL) post-TAVI (HR: 1.456, 95% C.I.: 1.106-1.792) were significant independent predictors of late global mortality. LF-LG patients and LF-HG patients exhibited a significant increase in LVEF at 1-year follow-up. A lower LVEF (p < 0.001) and a lower Svi (p < 0.001) at baseline were associated with LVEF improvement at 1-year. Conclusions: Patients with LF-LG AS have acceptable 1-year outcomes with significant improvement in LVEF at 1-year follow-up, but exhibit exceedingly high 5-year mortality following TAVI. The presence of low transvalvular flow and at least moderate tricuspid regurgitation at baseline and significant paravalvular leak post-TAVI were associated with poorer long-term outcomes in the entire cohort of AS patients. The presence of a low LVEF or a low SVi predicts LVEF improvement at 1-year.