Search Results (2,545)

The use of equipment such as dental handpieces and ultrasonic tips in the dental environment has potentially heightened the generation and spread of aerosols, which are dispersant particles contaminated by etiological factors. Although numerous types of personal protective equipment have been used to lower contact with contaminants, they generally do not exhibit excellent removal rates and user-friendliness in tandem. To solve this problem, we developed a prototype of an air-barrier device that forms an air curtain as well as performs suction and evaluated the effect of this newly developed device through a simulation study and experiments. The air-barrier device derived the improved design for reducing bioaerosols through the simulation results. The experiments also demonstrated that air-barrier devices are effective in reducing bioaerosols generated at a distance in a dental environment. In conclusion, this study demonstrates that air-barrier devices in dental environments can play an effective role in reducing contaminating particles. Full article

Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patients. We propose this model as a tool to generate predictive alerts for possible ARDS cases, enabling early diagnosis. Our team created a unique dataset of 7800 single-view chest-X-ray images labeled for the presence of bilateral or unilateral pulmonary opacities, or ‘equivocal’ images, by three blinded clinicians. We used a novel training technique that enables the CNN to explicitly predict the ‘equivocal’ class using an uncertainty-aware label smoothing loss. We achieved an Area under the Receiver Operating Characteristic Curve (AUROC) of 0.82 (95% CI: 0.80, 0.85), a precision of 0.75 (95% CI: 0.73, 0.78), and a sensitivity of 0.76 (95% CI: 0.73, 0.78) on the internal test set while achieving an (AUROC) of 0.84 (95% CI: 0.81, 0.86), a precision of 0.73 (95% CI: 0.63, 0.69), and a sensitivity of 0.73 (95% CI: 0.70, 0.75) on an external validation set. Further, our results show that this approach improves the model calibration and diagnostic odds ratio of the hypothesized alert tool, making it ideal for clinical decision support systems. Full article

Extant clinical research has underscored that patients suffering from atrial fibrillation (AF) bear an elevated risk for stroke, predominantly driven by the formation of thrombus in the left atrial appendage (LAA). As such, accurately identifying those at an increased risk of thrombosis becomes paramount to facilitate timely and effective treatment. This study was designed to shed light on the mechanisms underlying thrombus formation in the LAA by employing three-dimensional (3D) left atrium (LA) models of AF patients, which were constructed based on Computed Tomography (CT) imaging. The distinct benefits of Computational Fluid Dynamics (CFD) were leveraged to simulate the blood flow field within the LA, using three distinct blood flow models, both under AF and sinus rhythm (SR) conditions. The potential risk of thrombus formation was evaluated by analyzing the Relative Residence Time (RRT) and Endothelial Cell Activation Potential (ECAP) values. The results gleaned from this study affirm that all three blood flow models align with extant clinical guidelines, thereby enabling an effective prediction of thrombosis risk. However, noteworthy differences emerged when comparing the intricacies of the flow field and thrombosis risk across the three models. The single-phase non-Newtonian blood flow model resulted in comparatively lower residence times for blood within the LA and lower values for the Oscillatory Shear Index (OSI), RRT, and ECAP within the LAA. These findings suggest a reduced thrombosis risk. Conversely, the two-phase non-Newtonian blood flow model exhibited a higher residence time for blood and elevated RRT value within the LAA, suggesting an increased risk for thrombosis. Full article

Medical image segmentation can effectively identify lesions in medicine, but some small and rare lesions cannot be well identified. Existing studies do not take into account the uncertainty of the occurrence of diseased tissue, and the problem of long-tailed distribution of medical data. Meanwhile, the grayscale image obtained from Magnetic Resonance Imaging (MRI) detection has problems, such as the features being difficult to extract and invalid features being difficult to distinguish. In order to solve these problems, we propose a new weighted attention ResUNet (WA-ResUNet) and a class weight formula based on the number of images contained in the class, which improves the performance of the model in the low-frequency class and the overall effect of the model by improving the degree of attention paid to the valid features and invalid ones and rebalancing the learning efficiency among the classes. We evaluated our method on an uterine MRI dataset and compared it with the ResUNet. WA-ResUNet increased Intersection over Union (IoU) in the low-frequency class (Nabothian cysts) by 21.87%, and the overall mIoU increased by more than 6.5%. Full article

Background: The stability index estimation algorithm was derived and applied to develop and implement a balance ability diagnosis system that can be used in daily life. Methods: The system integrated an approach based on sensory function interaction, called the clinical test of sensory interaction with balance. A capacitance and resistance sensing type force mat was fabricated, and a stability index prediction algorithm was developed and applied using the center of pressure variables. The stability index prediction algorithm derived a center of pressure variable for 103 elderly people by Nintendo Wii Balance Board to predict the stability index of the balance system (Biodex SD), and the accuracy of this approach was confirmed. Results: As a result of testing with the test set, the linear regression model confirmed that the r-value ranged between 0.943 and 0.983. To confirm the similarity between the WBB and the flexible force mat, each measured center of pressure value was inputted and calculated in the developed regression model, and the result of the correlation coefficient validation confirmed an r-value of 0.96. Conclusion: The system developed in this study will be applicable to daily life in the home in the form of a floor mat. Full article

In response to the subjectivity, low accuracy, and high concealment of existing attack behavior prediction methods, a video-based impulsive aggression prediction method that integrates physiological parameters and facial expression information is proposed. This method uses imaging equipment to capture video and facial expression information containing the subject’s face and uses imaging photoplethysmography (IPPG) technology to obtain the subject’s heart rate variability parameters. Meanwhile, the ResNet-34 expression recognition model was constructed to obtain the subject’s facial expression information. Based on the random forest classification model, the physiological parameters and facial expression information obtained are used to predict individual impulsive aggression. Finally, an impulsive aggression induction experiment was designed to verify the method. The experimental results show that the accuracy of this method for predicting the presence or absence of impulsive aggression was 89.39%. This method proves the feasibility of applying physiological parameters and facial expression information to predict impulsive aggression. This article has important theoretical and practical value for exploring new impulsive aggression prediction methods. It also has significance in safety monitoring in special and public places such as prisons and rehabilitation centers. Full article

Traumatic brain injury (TBI) affects millions of people annually, and most cases are classified as mild TBI (mTBI). Ketamine is a potent trauma analgesic and anesthetic with anti-inflammatory properties. However, ketamine’s effects on post-mTBI outcomes are not well characterized. For the current study, we used the Closed-Head Impact Model of Engineered Rotational Acceleration (CHIMERA), which replicates the biomechanics of a closed-head impact with resulting free head movement. Adult male Sprague–Dawley rats sustained a single-session, repeated-impacts CHIMERA injury. An hour after the injury, rats received an intravenous ketamine infusion (0, 10, or 20 mg/kg, 2 h period), during which locomotor activity was monitored. Catheter blood samples were collected at 1, 3, 5, and 24 h after the CHIMERA injury for plasma cytokine assays. Behavioral assays were conducted on post-injury days (PID) 1 to 4 and included rotarod, locomotor activity, acoustic startle reflex (ASR), and pre-pulse inhibition (PPI). Brain tissue samples were collected at PID 4 and processed for GFAP (astrocytes), Iba-1 (microglia), and silver staining (axonal injury). Ketamine dose-dependently altered locomotor activity during the infusion and reduced KC/GRO, TNF-α, and IL-1β levels after the infusion. CHIMERA produced a delayed deficit in rotarod performance (PID 3) and significant axonal damage in the optic tract (PID 4), without significant changes in other behavioral or histological measures. Notably, subanesthetic doses of intravenous ketamine infusion after mTBI did not produce adverse effects on behavioral outcomes in PID 1–4 or neuroinflammation on PID 4. A further study is warranted to thoroughly investigate beneficial effects of IV ketamine on mTBI given multi-modal properties of ketamine in traumatic injury and stress. Full article

The quality of breast ultrasound images has a significant impact on the accuracy of disease diagnosis. Existing image quality assessment (IQA) methods usually use pixel-level feature statistical methods or end-to-end deep learning methods, which focus on the global image quality but ignore the image quality of the lesion region. However, in clinical practice, doctors’ evaluation of ultrasound image quality relies more on the local area of the lesion, which determines the diagnostic value of ultrasound images. In this study, a global–local integrated IQA framework for breast ultrasound images was proposed to learn doctors’ clinical evaluation standards. In this study, 1285 breast ultrasound images were collected and scored by experienced doctors. After being classified as either images with lesions or images without lesions, they were evaluated using soft-reference IQA or bilinear CNN IQA, respectively. Experiments showed that for ultrasound images with lesions, our proposed soft-reference IQA achieved PLCC 0.8418 with doctors’ annotation, while the existing end-to-end deep learning method that did not consider the local lesion features only achieved PLCC 0.6606. Due to the accuracy improvement for the images with lesions, our proposed global–local integrated IQA framework had better performance in the IQA task than the existing end-to-end deep learning method, with PLCC improving from 0.8306 to 0.8851. Full article

Three-dimensional planning of hip arthroplasty is associated with better visualisation of anatomical landmarks and enhanced mapping for preoperative implant sizing, which can lead to a decrease in surgical time and complications. Despite the advantages of hip resurfacing arthroplasty (HRA), it is considered a technically challenging procedure and associated with inaccurate implant placement. This study aimed to examine the validity, reliability, and usability of preoperative 3D Hip Planner software for HRA. Fifty random cases of various hip osteoarthritis severity were planned twice by two junior trainees using the 3D Hip Planner within a one-month interval. Outcome measures included femoral/cup implant size, stem-shaft angle, and cup inclination angle, and were assessed by comparing outcomes from 2D and 3D planning. An adapted unified theory of acceptance and use of technology (UTAUT) survey was used for software usability. Bland–Altman plots between 3D and 2D planning for stem-shaft and inclination angles showed mean differences of 0.7 and −0.6, respectively (r = 0.93, p < 0.001). Stem-shaft and inclination angles showed inter-rater reliability biases of around −2° and 3°, respectively. Chi-square and Pearson’s correlation for femoral implant size showed a significant association between the two assessors (r = 0.91, p < 0.001). The 3D test–retest coefficient of repeatability for stem-shaft and inclination angles were around ±2° and ±3°, respectively, with a strong significant association for femoral implant size (r = 0.98, p < 0.001). Survey analyses showed that 70–90% agreed that 3D planning improved expectancy in four domains. 3D hip planner appears to be valid and reliable in preoperative HRA and shows significant potential in optimising the quality and accuracy of surgical planning. Full article

This work demonstrated the feasibility of an industrial-scale aerated static pile composting system for treating one of the common biowastes—soybean curd residue. The mixing ratios of the feedstock were optimized to achieve a carbon–nitrogen ratio and a moisture level in the ranges of 25–35 and 60–70%, respectively. This open-air composting system required 6–7 months to obtain a mature compost. Solvita and seed germination tests further confirmed the maturity of the compost, with 25% compost extract concentration yielding the best germination index in the absence of phytotoxicity. The bacterial and fungal compositions of the compost piles were further examined with metagenomic analysis. Thermoactinomyces spp., Oceanobacillus spp., and Kroppenstedtia spp. were among the unique bacteria found, and Diutina rugosa, Thermomyces dupontii, and Candida taylorii were among the unique fungi found in the compost piles, suggesting the presence of good microorganisms for degrading the organic biowastes. Full article

Pentagalloyl glucose (PGG) is currently being investigated as a non-surgical treatment for abdominal aortic aneurysms (AAAs); however, the molecular mechanisms of action of PGG on the AAA matrix components and the intra-luminal thrombus (ILT) still need to be better understood. To assess these interactions, we utilized peptide fingerprinting and molecular docking simulations to predict the binding of PGG to vascular proteins in normal and aneurysmal aorta, including matrix metalloproteinases (MMPs), cytokines, and fibrin. We performed PGG diffusion studies in pure fibrin gels and human ILT samples. PGG was predicted to bind with high affinity to most vascular proteins, the active sites of MMPs, and several cytokines known to be present in AAAs. Finally, despite potential binding to fibrin, PGG was shown to diffuse readily through thrombus at physiologic pressures. In conclusion, PGG can bind to all the normal and aneurysmal aorta protein components with high affinity, potentially protecting the tissue from degradation and exerting anti-inflammatory activities. Diffusion studies showed that thrombus presence in AAAs is not a barrier to endovascular treatment. Together, these results provide a deeper understanding of the clinical potential of PGG as a non-surgical treatment of AAAs. Full article

The valorization of fruit and vegetable residues (such as carrot discard) and their microbial conversion into 2,3-butanediol (BDO) can be considered as a very interesting way to reduce food waste and sustainably originate high value-added products. This work analyzes the valorization of carrot discard as feedstock for 2,3-butanediol (BDO) production by Paenibacillus polymyxa DSM 365. The influences of stirring and the presence of tryptone (nitrogen source) are studied. Furthermore, in order to evaluate the influence of the pre-culture medium (nitrogen source, nutrients, and pH) and the substrate, fermentation assays in simple and mixture semi-defined media (glucose, fructose, and/or galactose) were also carried out. As a result, 18.8 g/L BDO, with a BDO yield of 0.43 g/g (86% of its theoretical value), could be obtained from carrot discard enzymatic hydrolysate at 100 rpm, no tryptone, and pre-culture Häßler medium. No hydrothermal pre-treatment was necessary for BDO production from carrot discard, which increases the profitability of the process. Therefore, 18.8 g BDO, as well as 2.5 g ethanol and 2.1 g acetoin by-products, could be obtained from 100 g of carrot discard (dry matter). Full article

Low bile acid excretion (BAE) is associated with a higher risk of coronary artery disease (CAD) and cerebrovascular disease (stroke). This study investigated BAE in patients with peripheral vascular disease (PVD) and carotid artery disease (CA) and those without these diseases, compared to patients with CAD, stroke, or no evidence of atherosclerosis. Patients with complaints of chest pain-suspected CAD, syncope, stroke/TIA, severe headache, intermittent claudication, or falls were enrolled. All received a 4-day standard diet with 490 mg of cholesterol and internal standard copper thiocyanate. Fecal BAE was measured using gas–liquid chromatography. One hundred and three patients, sixty-eight (66%) men and thirty-five women (34%), mean age range 60.9 ± 8.9 years, were enrolled in this prospective, 22-year follow-up study. Regression analysis showed that advanced age, total BAE, and excretion of the main fractions were the only significant independent factors that predicted prolonged survival (p < 0.001). Twenty-two years’ follow-up revealed only 15% of those with BAE <262.4 mg/24 h survived, compared to >60% of participants without atherosclerosis and a mean BAE of 676 mg/24 h. BAE was lower in patients with polyvascular atherosclerosis than in those with involvement of 1–3 vascular beds. Pearson correlations were found between total BAE and various fractions of BA, as well as HDL cholesterol. BAE and short-term survival were decreased among patients with PVD compared to those with CAD or stroke. Low BAE should be considered a valuable and independent risk factor for PVD. Full article

This technical paper introduces a novel organ preservation system based on isochoric (constant volume) supercooling. The system is designed to enhance the stability of the metastable supercooling state, offering potential long-term preservation of large biological organs at subfreezing temperatures without the need for cryoprotectant additives. Detailed technical designs and usage protocols are provided for researchers interested in exploring this field. The paper also presents a control system based on the thermodynamics of isochoric freezing, utilizing pressure monitoring for process control. Sham experiments were performed using whole pig liver sourced from a local food supplier to evaluate the system’s ability to sustain supercooling without ice nucleation for extended periods. The results demonstrated sustained supercooling without ice nucleation in pig liver tissue for 24 and 48 h. These findings suggest the potential of this technology for large-volume, cryoprotectant-free organ preservation with real-time control over the preservation process. The simplicity of the isochoric supercooling device and the design details provided in the paper are expected to serve as encouragement for other researchers in the field to pursue further research on isochoric supercooling. However, final evidence that these preserved organs can be successfully transplanted is still lacking. Full article

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. The dataset used for the research is the Lung Harvard 2 Dataset (LH2) which consists of 150 Adenocarcinoma subjects and 31 Mesothelioma subjects. The paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Nonlinear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%. Full article