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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 -
International Journal of Applied &... 2024Supraglottic devices have revolutionized the current practice of airway management. We compared the clinical performance of a recently introduced BlockBuster™...
BACKGROUND
Supraglottic devices have revolutionized the current practice of airway management. We compared the clinical performance of a recently introduced BlockBuster™ Laryngeal mask airway with i-gel in adult patients under general anesthesia.
METHODS
Following Institutional ethical clearance, the present study was conducted on 62 patients belonging to American Society of Anesthesiologists physical status 1 and 2 of either sex in the age group of 20-60 years under general anesthesia. Patients were randomly assigned to i-gel (I) and BlockBuster™ (B) groups (31 per group). Time for successful insertion, insertion success rate, ease of insertion, oropharyngeal leak pressures (OLPs), and complications were assessed.
RESULTS
Mean insertion time of device was less in Group I (13.52 ± 2.58 s) than that of Group B (14.10 ± 2.04 s), which was neither clinically nor statistically significant ( = 0.330). OLP in Group B (24.52 ± 2.77 cm of H2O) was found to be significantly higher compared to Group I (20.81 ± 2.56 cm of H2O) with < 0.001. Overall insertion and first attempt success was similar (i-gel 31/31 [100%] and 29/31 [93.5%] and BlockBuster™ 31/31 [100%] and 29/31 [93.5%], respectively). Ease of insertion ( = 0.684) and complications ( = 0.782) of both the devices were comparable.
CONCLUSIONS
Both the devices are useful and effective for airway management in adult under general anesthesia. Having a high OLP and comparable insertion time, risk of aspiration may be further reduced with the use of BlockBuster™ in comparison to i-gel.
PubMed: 38912364
DOI: 10.4103/ijabmr.ijabmr_520_23 -
GeoHealth Jun 2024Many infectious disease forecasting models in the United States (US) are built with data partitioned into geopolitical regions centered on human activity as opposed to...
Many infectious disease forecasting models in the United States (US) are built with data partitioned into geopolitical regions centered on human activity as opposed to regions defined by natural ecosystems; although useful for data collection and intervention, this has the potential to mask biological relationships between the environment and disease. We explored this concept by analyzing the correlations between climate and West Nile virus (WNV) case data aggregated to geopolitical and ecological regions. We compared correlations between minimum, maximum, and mean annual temperature; precipitation; and annual WNV neuroinvasive disease (WNND) case data from 2005 to 2019 when partitioned into (a) climate regions defined by the National Oceanic and Atmospheric Administration (NOAA) and (b) Level I ecoregions defined by the Environmental Protection Agency (EPA). We found that correlations between climate and WNND in NOAA climate regions and EPA ecoregions were often contradictory in both direction and magnitude, with EPA ecoregions more often supporting previously established biological hypotheses and environmental dynamics underlying vector-borne disease transmission. Using ecological regions to examine the relationships between climate and disease cases can enhance the predictive power of forecasts at various scales, motivating a conceptual shift in large-scale analyses from geopolitical frameworks to more ecologically meaningful regions.
PubMed: 38912225
DOI: 10.1029/2024GH001024 -
Medical Research Archives May 2024Respiratory fluid dynamics is integral to comprehending the transmission of infectious diseases and the effectiveness of interventions such as face masks and social...
On the efficacy of facial masks to suppress the spreading of pathogen-carrying saliva particles during human respiratory events: Insights gained via high-fidelity numerical modeling.
Respiratory fluid dynamics is integral to comprehending the transmission of infectious diseases and the effectiveness of interventions such as face masks and social distancing. In this research, we present our recent studies that investigate respiratory particle transport via high-fidelity large eddy simulation coupled with the Lagrangian particle tracking method. Based on our numerical simulation results for human respiratory events with and without face masks, we demonstrate that facial masks could significantly suppress particle spreading. The studied respiratory events include coughing and normal breathing through mouth and nose. Using the Lagrangian particle tracking simulation results, we elucidated the transport pathways of saliva particles during inhalation and exhalation of breathing cycles, contributing to our understanding of respiratory physiology and potential disease transmission routes. Our findings underscore the importance of respiratory fluid dynamics research in informing public health strategies to reduce the spread of respiratory infections. Combining advanced mathematical modeling techniques with experimental data will help future research on airborne disease transmission dynamics and the effectiveness of preventive measures such as face masks.
PubMed: 38911991
DOI: 10.18103/mra.v12i5.5441 -
MicroPublication Biology 2024Standardizing image datasets is essential for facilitating overall visual comparisons and enhancing compatibility with image-processing workflows. One way to achieve...
Standardizing image datasets is essential for facilitating overall visual comparisons and enhancing compatibility with image-processing workflows. One way to achieve homogeneity for images containing a single object is to align the object to a common orientation. Here, we propose the Virtual Orientation Tools (VOTj): a set of Fiji plugins to center and align an object of interest in images to a vertical or horizontal orientation. To process an image, the plugin requires either a mask outlining the object or a rough annotation of the object directly drawn by the user in the image. The current object orientation is retrieved using Principal Component Analysis (PCA), from which the optimal alignment is derived. The plugins support multi-dimensional images to allow, e.g., aligning individual time points of a time-lapse. The tools can be used for a variety of samples and imaging modalities. Besides, the plugins enable the interactive alignment of a list of images from a directory for batch execution and can be included in custom image-processing workflows using macro-recording.
PubMed: 38911438
DOI: 10.17912/micropub.biology.001221 -
Cureus May 2024Objective and background This study aimed to develop a deep convolutional neural network (DCNN) model capable of generating synthetic 4D magnetic resonance angiography...
Objective and background This study aimed to develop a deep convolutional neural network (DCNN) model capable of generating synthetic 4D magnetic resonance angiography (MRA) from 3D time-of-flight (TOF) images, allowing estimation of temporal changes in arterial flow. TOF MRA provides static information about arterial structures through maximum intensity projection (MIP) processing, but it does not capture the dynamic information of contrast agent circulation, which is lost during MIP processing. Considering the principles of TOF, it is hypothesized that dynamic information about arterial blood flow is latent within TOF signals. Although arterial spin labeling (ASL) can extract dynamic arterial information, ASL MRA has drawbacks, such as longer imaging times and lower spatial resolution than TOF MRA. This study's primary aim is to extend the utility of TOF MRA by training a machine-learning model on paired TOF and ASL data to extract latent dynamic information from TOF signals. Methods A DCNN combining a modified U-Net and a long-short-term memory (LSTM) network was trained on a dataset of 13 subjects (11 men and two women, aged 42-77 years) using paired 3D TOF MRA and 4D ASL MRA images. Subjects had no history of cerebral vessel occlusion or significant stenosis. The dataset was acquired using a 3T MRI system with a 32-channel head coil. Preprocessing involved resampling and intensity normalization of TOF and ASL images, followed by data augmentation and arterial mask generation. The model learned to extract flow information from TOF images and generate 8-phase 4D MRA images. The precision of flow estimation was evaluated using the coefficient of determination (R²) and Bland-Altman analysis. A board-certified neuroradiologist validated the quality of the images and the absence of significant stenosis in the major cerebral arteries. Results The generated 4D MRA images closely resembled the ground-truth ASL MRA data, with R² values of 0.92, 0.85, and 0.84 for the internal carotid artery (ICA), proximal middle cerebral artery (MCA), and distal MCA, respectively. Bland-Altman analysis revealed a systematic error of -0.06, with 95% agreement limits ranging from -0.18 to 0.12. Additionally, the model successfully identified flow abnormalities in a subject with left MCA stenosis, displaying a delayed peak and subsequent flattening distal to the stenosis, indicative of reduced blood flow. Visualization of the predicted arterial flow overlaid on the original TOF MRA images highlighted the spatial progression and dynamics of the flow. Conclusions The DCNN model effectively generated synthetic 4D MRA images from TOF images, demonstrating its potential to estimate temporal changes in arterial flow accurately. This non-invasive technique offers a promising alternative to conventional methods for visualizing and evaluating healthy and pathological flow dynamics. It has significant potential to improve the diagnosis and treatment of cerebrovascular diseases by providing detailed temporal flow information without the need for contrast agents or invasive procedures. The practical implementation of this model could enable the extraction of dynamic cerebral blood flow information from routine brain MRI examinations, contributing to the early diagnosis and management of cerebrovascular disorders.
PubMed: 38910733
DOI: 10.7759/cureus.60803 -
BioData Mining Jun 2024Deep learning shows great promise for medical image analysis but often lacks explainability, hindering its adoption in healthcare. Attribution techniques that explain...
Deep learning shows great promise for medical image analysis but often lacks explainability, hindering its adoption in healthcare. Attribution techniques that explain model reasoning can potentially increase trust in deep learning among clinical stakeholders. In the literature, much of the research on attribution in medical imaging focuses on visual inspection rather than statistical quantitative analysis.In this paper, we proposed an image-based saliency framework to enhance the explainability of deep learning models in medical image analysis. We use adaptive path-based gradient integration, gradient-free techniques, and class activation mapping along with its derivatives to attribute predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models.The proposed framework integrates qualitative and statistical quantitative assessments, employing Accuracy Information Curves (AICs) and Softmax Information Curves (SICs) to measure the effectiveness of saliency methods in retaining critical image information and their correlation with model predictions. Visual inspections indicate that methods such as ScoreCAM, XRAI, GradCAM, and GradCAM++ consistently produce focused and clinically interpretable attribution maps. These methods highlighted possible biomarkers, exposed model biases, and offered insights into the links between input features and predictions, demonstrating their ability to elucidate model reasoning on these datasets. Empirical evaluations reveal that ScoreCAM and XRAI are particularly effective in retaining relevant image regions, as reflected in their higher AUC values. However, SICs highlight variability, with instances of random saliency masks outperforming established methods, emphasizing the need for combining visual and empirical metrics for a comprehensive evaluation.The results underscore the importance of selecting appropriate saliency methods for specific medical imaging tasks and suggest that combining qualitative and quantitative approaches can enhance the transparency, trustworthiness, and clinical adoption of deep learning models in healthcare. This study advances model explainability to increase trust in deep learning among healthcare stakeholders by revealing the rationale behind predictions. Future research should refine empirical metrics for stability and reliability, include more diverse imaging modalities, and focus on improving model explainability to support clinical decision-making.
PubMed: 38909228
DOI: 10.1186/s13040-024-00370-4