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PloS One 2024This paper investigates the association between several mental health indicators (depression, anxiety, stress, and loneliness) and the overall tendency to follow...
This paper investigates the association between several mental health indicators (depression, anxiety, stress, and loneliness) and the overall tendency to follow official recommendations regarding self-protection against COVID-19 (i.e., overall compliance). We employ panel data from the COME-HERE survey, collected over four waves, on 7,766 individuals (22,878 observations) from France, Germany, Italy, Spain, and Sweden. Employing a flexible specification that allows the association to be non-monotonic, we find a U-shaped relationship, in which transitions to low and high levels of mental health are associated with higher overall compliance, while transitions to medium levels of mental health are associated with less overall compliance. Moreover, anxiety, stress, and loneliness levels at baseline (i.e., at wave 1) also have a U-shaped effect on overall compliance later (i.e., recommendations are followed best by those with lowest and highest levels of anxiety, stress, and loneliness at baseline, while following the recommendations is lowest for those with moderate levels of these variables). These U shapes, which are robust to several specifications, may explain some of the ambiguous results reported in the previous literature. Additionally, we observe a U-shaped association between the mental health indicators and a number of specific health behaviours (including washing hands and mask wearing). Importantly, most of these specific behaviours play a role in overall compliance. Finally, we uncover the role of gender composition effects in some of the results. While variations in depression and stress are negatively associated with variations in overall compliance for men, the association is positive for women. The U-shaped relation in the full sample (composed of males and females) will reflect first the negative slope for males and then the positive slope for females.
Topics: Humans; COVID-19; Male; Female; Mental Health; Anxiety; Middle Aged; Adult; Depression; Loneliness; Stress, Psychological; SARS-CoV-2; Aged; Surveys and Questionnaires; Health Behavior; Young Adult
PubMed: 38917072
DOI: 10.1371/journal.pone.0305833 -
PloS One 2024When combating a respiratory disease outbreak, the effectiveness of protective measures hinges on spontaneous shifts in human behavior driven by risk perception and...
When combating a respiratory disease outbreak, the effectiveness of protective measures hinges on spontaneous shifts in human behavior driven by risk perception and careful cost-benefit analysis. In this study, a novel concept has been introduced, integrating social distancing and mask-wearing strategies into a unified framework that combines evolutionary game theory with an extended classical epidemic model. To yield deeper insights into human decision-making during COVID-19, we integrate both the prevalent dilemma faced at the epidemic's onset regarding mask-wearing and social distancing practices, along with a comprehensive cost-benefit analysis. We explore the often-overlooked aspect of effective mask adoption among undetected infectious individuals to evaluate the significance of source control. Both undetected and detected infectious individuals can significantly reduce the risk of infection for non-masked individuals by wearing effective facemasks. When the economical burden of mask usage becomes unsustainable in the community, promoting affordable and safe social distancing becomes vital in slowing the epidemic's progress, allowing crucial time for public health preparedness. In contrast, as the indirect expenses associated with safe social distancing escalate, affordable and effective facemask usage could be a feasible option. In our analysis, it was observed that during periods of heightened infection risk, there is a noticeable surge in public interest and dedication to complying with social distancing measures. However, its impact diminishes beyond a certain disease transmission threshold, as this strategy cannot completely eliminate the disease burden in the community. Maximum public compliance with social distancing and mask-wearing strategies can be achieved when they are affordable for the community. While implementing both strategies together could ultimately reduce the epidemic's effective reproduction number ([Formula: see text]) to below one, countries still have the flexibility to prioritize either of them, easing strictness on the other based on their socio-economic conditions.
Topics: Humans; Masks; COVID-19; Game Theory; Physical Distancing; SARS-CoV-2; Cost-Benefit Analysis
PubMed: 38917069
DOI: 10.1371/journal.pone.0301915 -
JMIR Human Factors Jun 2024In pandemic situations, digital contact tracing (DCT) can be an effective way to assess one's risk of infection and inform others in case of infection. DCT apps can...
BACKGROUND
In pandemic situations, digital contact tracing (DCT) can be an effective way to assess one's risk of infection and inform others in case of infection. DCT apps can support the information gathering and analysis processes of users aiming to trace contacts. However, users' use intention and use of DCT information may depend on the perceived benefits of contact tracing. While existing research has examined acceptance in DCT, automation-related user experience factors have been overlooked.
OBJECTIVE
We pursued three goals: (1) to analyze how automation-related user experience (ie, perceived trustworthiness, traceability, and usefulness) relates to user behavior toward a DCT app, (2) to contextualize these effects with health behavior factors (ie, threat appraisal and moral obligation), and (3) to collect qualitative data on user demands for improved DCT communication.
METHODS
Survey data were collected from 317 users of a nationwide-distributed DCT app during the COVID-19 pandemic after it had been in app stores for >1 year using a web-based convenience sample. We assessed automation-related user experience. In addition, we assessed threat appraisal and moral obligation regarding DCT use to estimate a partial least squares structural equation model predicting use intention. To provide practical steps to improve the user experience, we surveyed users' needs for improved communication of information via the app and analyzed their responses using thematic analysis.
RESULTS
Data validity and perceived usefulness showed a significant correlation of r=0.38 (P<.001), goal congruity and perceived usefulness correlated at r=0.47 (P<.001), and result diagnosticity and perceived usefulness had a strong correlation of r=0.56 (P<.001). In addition, a correlation of r=0.35 (P<.001) was observed between Subjective Information Processing Awareness and perceived usefulness, suggesting that automation-related changes might influence the perceived utility of DCT. Finally, a moderate positive correlation of r=0.47 (P<.001) was found between perceived usefulness and use intention, highlighting the connection between user experience variables and use intention. Partial least squares structural equation modeling explained 55.6% of the variance in use intention, with the strongest direct predictor being perceived trustworthiness (β=.54; P<.001) followed by moral obligation (β=.22; P<.001). Based on the qualitative data, users mainly demanded more detailed information about contacts (eg, place and time of contact). They also wanted to share information (eg, whether they wore a mask) to improve the accuracy and diagnosticity of risk calculation.
CONCLUSIONS
The perceived result diagnosticity of DCT apps is crucial for perceived trustworthiness and use intention. By designing for high diagnosticity for the user, DCT apps could improve their support in the action regulation of users, resulting in higher perceived trustworthiness and use in pandemic situations. In general, automation-related user experience has greater importance for use intention than general health behavior or experience.
Topics: Humans; Contact Tracing; Mobile Applications; Cross-Sectional Studies; COVID-19; Female; Male; Adult; Surveys and Questionnaires; Middle Aged
PubMed: 38916941
DOI: 10.2196/53940 -
Ibrain 2024This review comprehensively assesses the epidemiology, interaction, and impact on patient outcomes of perioperative sleep disorders (SD) and perioperative neurocognitive... (Review)
Review
This review comprehensively assesses the epidemiology, interaction, and impact on patient outcomes of perioperative sleep disorders (SD) and perioperative neurocognitive disorders (PND) in the elderly. The incidence of SD and PND during the perioperative period in older adults is alarmingly high, with SD significantly contributing to the occurrence of postoperative delirium. However, the clinical evidence linking SD to PND remains insufficient, despite substantial preclinical data. Therefore, this study focuses on the underlying mechanisms between SD and PND, underscoring that potential mechanisms driving SD-induced PND include uncontrolled central nervous inflammation, blood-brain barrier disruption, circadian rhythm disturbances, glial cell dysfunction, neuronal and synaptic abnormalities, impaired central metabolic waste clearance, gut microbiome dysbiosis, hippocampal oxidative stress, and altered brain network connectivity. Additionally, the review also evaluates the effectiveness of various sleep interventions, both pharmacological and nonpharmacological, in mitigating PND. Strategies such as earplugs, eye masks, restoring circadian rhythms, physical exercise, noninvasive brain stimulation, dexmedetomidine, and melatonin receptor agonists have shown efficacy in reducing PND incidence. The impact of other sleep-improvement drugs (e.g., orexin receptor antagonists) and methods (e.g., cognitive-behavioral therapy for insomnia) on PND is still unclear. However, certain drugs used for treating SD (e.g., antidepressants and first-generation antihistamines) may potentially aggravate PND. By providing valuable insights and references, this review aimed to enhance the understanding and management of PND in older adults based on SD.
PubMed: 38915944
DOI: 10.1002/ibra.12167 -
AIP Advances 2024Lithium niobate (LN) is used in diverse applications such as spectroscopy, remote sensing, and quantum communications. The emergence of lithium-niobate-on-insulator...
Lithium niobate (LN) is used in diverse applications such as spectroscopy, remote sensing, and quantum communications. The emergence of lithium-niobate-on-insulator (LNOI) technology and its commercial accessibility represent significant milestones. This technology aids in harnessing the full potential of LN's properties, such as achieving tight mode confinement and strong overlap with applied electric fields, which has enabled LNOI-based electro-optic modulators to have ultra-broad bandwidths with low-voltage operation and low power consumption. Consequently, LNOI devices are emerging as competitive contenders in the integrated photonics landscape. However, the nanofabrication, particularly LN etching, presents a notable challenge. LN is hard, dense, and chemically inert. It has anisotropic etch behavior and a propensity to produce material redeposition during the reactive-ion plasma etch process. These factors make fabricating low-loss LNOI waveguides (WGs) challenging. Recognizing the pivotal role of addressing these fabrication challenges for obtaining low-loss WGs, our research focuses on a systematic study of various process steps in fabricating LNOI WGs and other photonic structures. In particular, our study involves (i) careful selection of hard mask materials, (ii) optimization of inductively coupled plasma etch parameters, and finally, (iii) determining the optimal post-etch cleaning approach to remove redeposited material on the sidewalls of the etched photonic structures. Using the recipe established, we realized optical WGs with total (propagation and coupling) loss value of -10.5 dB, comparable to established values found in the literature. Our findings broaden our understanding of optimizing fabrication processes for low-loss lithium-niobate waveguides and can serve as an accessible resource in advancing LNOI technology.
PubMed: 38915883
DOI: 10.1063/6.0003522 -
Microsystems & Nanoengineering 2024Reservoir computing (RC) is a bio-inspired neural network structure which can be implemented in hardware with ease. It has been applied across various fields such as...
Reservoir computing (RC) is a bio-inspired neural network structure which can be implemented in hardware with ease. It has been applied across various fields such as memristors, and electrochemical reactions, among which the micro-electro-mechanical systems (MEMS) is supposed to be the closest to sensing and computing integration. While previous MEMS RCs have demonstrated their potential as reservoirs, the amplitude modulation mode was found to be inadequate for computing directly upon sensing. To achieve this objective, this paper introduces a novel MEMS reservoir computing system based on stiffness modulation, where natural signals directly influence the system stiffness as input. Under this innovative concept, information can be processed locally without the need for advanced data collection and pre-processing. We present an integrated RC system characterized by small volume and low power consumption, eliminating complicated setups in traditional MEMS RC for data discretization and transduction. Both simulation and experiment were conducted on our accelerometer. We performed nonlinearity tuning for the resonator and optimized the post-processing algorithm by introducing a digital mask operator. Consequently, our MEMS RC is capable of both classification and forecasting, surpassing the capabilities of our previous non-delay-based architecture. Our method successfully processed word classification, with a 99.8% accuracy, and chaos forecasting, with a 0.0305 normalized mean square error (NMSE), demonstrating its adaptability for multi-scene data processing. This work is essential as it presents a novel MEMS RC with stiffness modulation, offering a simplified, efficient approach to integrate sensing and computing. Our approach has initiated edge computing, enabling emergent applications in MEMS for local computations.
PubMed: 38915829
DOI: 10.1038/s41378-024-00701-9 -
Risk Management and Healthcare Policy 2024After the declaration by the World Health Organization signaling the conclusion of the COVID-19 pandemic, most countries lifted mandatory mask-wearing regulations. This...
PURPOSE
After the declaration by the World Health Organization signaling the conclusion of the COVID-19 pandemic, most countries lifted mandatory mask-wearing regulations. This study aimed to investigate factors such as risk perception and political ideology associated with continued adherence to mask-wearing among specific populations, particularly when it is no longer deemed necessary.
METHODS
We conducted a cross-sectional study including a sample of 1001 respondents stratified by sex, age (≥ 18 years), and region from January 31 to February 2, 2023, after the mandatory mask regulation was lifted in South Korea. Multivariate logistic regression models were applied to estimate the relationships between risk perceptions, political ideology, and mask-wearing maintenance, adjusting for factors such as sex, age, occupation, and trust in the government.
RESULTS
Our results indicated significant associations between age, self-reported household economic status, political ideology, affective risk perception, and perceived effectiveness of the government's COVID-related measures with indoor mask-wearing. Specifically, liberals were more likely to keep mask-wearing indoors than conservatives (adjusted odds ratio [aOR]: 2.19; 95% confidence interval [CI]: 1.33-3.59); and those who perceived a greater affective risk of COVID-19 (aOR: 2.47; 95% CI: 1.96-3.10), along with those who perceived the government's countermeasures as inadequate, were more inclined to maintain the habit of wearing masks indoors (aOR: 1.90; 95% CI: 1.19-3.03).
CONCLUSION
Our study highlighted the multifaceted factors influencing mask-wearing behavior in the post-COVID-19 era. Even after adjusting for various confounding factors, such as age, sex, and trust in the government, an association remained between affective risk perception, political ideology, and mask-wearing behavior. However, further research for psychological mechanisms is needed to foster a culture of preventive behaviors proportional to the risk of infection.
PubMed: 38915789
DOI: 10.2147/RMHP.S463739 -
BioRxiv : the Preprint Server For... Jun 2024Autofluorescence microscopy uses intrinsic sources of molecular contrast to provide cellular-level information without extrinsic labels. However, traditional cell...
Autofluorescence microscopy uses intrinsic sources of molecular contrast to provide cellular-level information without extrinsic labels. However, traditional cell segmentation tools are often optimized for high signal-to-noise ratio (SNR) images, such as fluorescently labeled cells, and unsurprisingly perform poorly on low SNR autofluorescence images. Therefore, new cell segmentation tools are needed for autofluorescence microscopy. Cellpose is a deep learning network that is generalizable across diverse cell microscopy images and automatically segments single cells to improve throughput and reduce inter-human biases. This study aims to validate Cellpose for autofluorescence imaging, specifically from multiphoton intensity images of NAD(P)H. Manually segmented nuclear masks of NAD(P)H images were used to train new Cellpose models. These models were applied to PANC-1 cells treated with metabolic inhibitors and patient-derived cancer organoids (across 9 patients) treated with chemotherapies. These datasets include co-registered fluorescence lifetime imaging microscopy (FLIM) of NAD(P)H and FAD, so fluorescence decay parameters and the optical redox ratio (ORR) were compared between masks generated by the new Cellpose model and manual segmentation. The Dice score between repeated manually segmented masks was significantly lower than that of repeated Cellpose masks (p<0.0001) indicating greater reproducibility between Cellpose masks. There was also a high correlation (R >0.9) between Cellpose and manually segmented masks for the ORR, mean NAD(P)H lifetime, and mean FAD lifetime across 2D and 3D cell culture treatment conditions. Masks generated from Cellpose and manual segmentation also maintain similar means, variances, and effect sizes between treatments for the ORR and FLIM parameters. Overall, Cellpose provides a fast, reliable, reproducible, and accurate method to segment single cells in autofluorescence microscopy images such that functional changes in cells are accurately captured in both 2D and 3D culture.
PubMed: 38915614
DOI: 10.1101/2024.06.07.597994 -
Journal of Cardiothoracic Surgery Jun 2024Endotracheal intubation is often associated with postoperative complications such as sore throat discomfort and hoarseness, reducing patient satisfaction and prolonging... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Endotracheal intubation is often associated with postoperative complications such as sore throat discomfort and hoarseness, reducing patient satisfaction and prolonging hospital stays. Laryngeal mask airway (LMA) plays a critical role in reducing airway complications related to endotracheal intubation. This meta-analysis was performed to determine the efficacy and safety of LMA in video-assisted thoracic surgery (VATS).
METHODS
The PubMed, Embase, Cochrane Library, Medline and Web of Science databases were searched for eligible studies from inception until October 5, 2023. Cochrane's tool (RoB 2) was used to evaluate the possibility biases of RCTs. We performed sensitivity analysis and subgroup analysis to assess the robustness of the results.
RESULTS
Seven articles were included in this meta-analysis. Compared with endotracheal intubation, there was no significant difference in the postoperative hospital stay (SMD = -0.47, 95% CI = -0.98-0.03, P = 0.06), intraoperative minimum SpO2 (SMD = 0.00, 95% CI = -0.49-0.49, P = 1.00), hypoxemia (RR = 1.00, 95% CI = 0.26-3.89, P = 1.00), intraoperative highest PetCO2 (SMD = 0.51, 95% CI = -0.12-1.15, P = 0.11), surgical field satisfaction (RR = 1.01, 95% CI = 0.98-1.03, P = 0.61), anesthesia time (SMD = -0.10, 95% CI = -0.30-0.10, P = 0.31), operation time (SMD = 0.06, 95% CI = -0.13-0.24, P = 0.55) and blood loss (SMD =- 0.13, 95% CI = -0.33-0.07, P = 0.21) in LMA group. However, LMA was associated with a lower incidence of throat discomfort (RR = 0.28, 95% CI = 0.17-0.48, P < 0.00001) and postoperative hoarseness (RR = 0.36, 95% CI = 0.16-0.81, P = 0.01), endotracheal intubation was found in connection with a longer postoperative awake time (SMD = -2.19, 95% CI = -3.49 - -0.89, P = 0.001).
CONCLUSION
Compared with endotracheal intubation, LMA can effectively reduce the incidence of throat discomfort and hoarseness post-VATS, and can accelerate the recovery from anesthesia. LMA appears to be an alternative to endotracheal intubation for some specific thoracic surgical procedures, and the efficacy and safety of LMA in VATS need to be further explored in the future.
Topics: Humans; Laryngeal Masks; Thoracic Surgery, Video-Assisted; Randomized Controlled Trials as Topic; Intubation, Intratracheal; Postoperative Complications; Length of Stay
PubMed: 38915035
DOI: 10.1186/s13019-024-02840-6 -
BMC Medical Imaging Jun 2024The assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the wound healing process....
BACKGROUND
The assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the wound healing process. Existing methods suffer significant limitations, such as user dependency, time-consuming nature, and lack of sensitivity, thus paving the way for automated analysis approaches.
METHODS
Hereby, three structurally different variations of U-net architectures based on convolutional neural networks (CNN) were implemented for the segmentation of in vitro wound healing microscopy images. The developed models were fed using two independent datasets after applying a novel augmentation method aimed at the more sensitive analysis of edges after the preprocessing. Then, predicted masks were utilized for the accurate calculation of wound areas. Eventually, the therapy efficacy-indicator wound areas were thoroughly compared with current well-known tools such as ImageJ and TScratch.
RESULTS
The average dice similarity coefficient (DSC) scores were obtained as 0.958 0.968 for U-net-based deep learning models. The averaged absolute percentage errors (PE) of predicted wound areas to ground truth were 6.41%, 3.70%, and 3.73%, respectively for U-net, U-net++, and Attention U-net, while ImageJ and TScratch had considerable averaged error rates of 22.59% and 33.88%, respectively.
CONCLUSIONS
Comparative analyses revealed that the developed models outperformed the conventional approaches in terms of analysis time and segmentation sensitivity. The developed models also hold great promise for the prediction of the in vitro wound area, regardless of the therapy-of-interest, cell line, magnification of the microscope, or other application-dependent parameters.
Topics: Deep Learning; Wound Healing; Microscopy; Humans; Image Processing, Computer-Assisted; Neural Networks, Computer
PubMed: 38914942
DOI: 10.1186/s12880-024-01332-2