Affiliation involving adipokines together with frailty in cardiovascular malfunction

This suggests that although some of the ticks removed prior to 24 h of attachment succeed in injecting a small amount of A. phagocytophilum, this amount is insufficient for stimulating humoral immunity and perhaps for establishing disseminated infection in BALB/c mice. Although A. phagocytophilum may be present in salivary glands of unfed I. scapularis nymphs, the amount of A. phagocytophilum initially contained in saliva appears insufficient to cause sustainable infection in a host. Replication and, maybe, reactivation of the agent for 12-24 h in a feeding tick is required before a mouse can be consistently infected.The co-pyrolysis of sewage sludge and biomass is considered a promising technique for reducing the volume of sewage sludge, adding value, and decreasing the risk associated with this waste. In this study, sewage sludge and cotton stalks were pyrolyzed together with different amounts of K2CO3 to evaluate the potential of chemical activation using K2CO3 for improving the porosity of the biochar formed and immobilizing the heavy metals present in it. It was found that K2CO3 activation effectively improved the pore structure and increased the aromaticity of the biochar. Moreover, K2CO3 activation transformed the heavy metals (Cu, Zn, Pb, Ni, Cr, and Cd) into more stable forms (oxidizable and residual fractions). The activation effect became more pronounced with increasing amount of added K2CO3, eventually resulting in a significant reduction in the mobility and bioavailability of the heavy metals in the biochar. Further analysis revealed that, during the co-pyrolysis process, K2CO3 activation resulted in a reductive atmosphere, increased the alkalinity of the biochar, and led to the formation CaO, CaCO3, and aluminosilicates, which aided the immobilization of the heavy metals. K2CO3 activation also effectively reduced the leachability, and thus, the environmental risks of the heavy metals. Thus, K2CO3 activation can improve the porosity of the biochar derived from sewage sludge/cotton stalks and aid the immobilization of the heavy metals in it.
User-independent recognition of exercise-induced fatigue from wearable motion data is challenging, due to inter-participant variability. This study aims to develop algorithms that can accurately estimate fatigue during exercise.
A novel approach for wearable sensor data augmentation was used to generate (via OpenSim) a large corpus of simulated wearable human motion data, based on a small corpus of human motion data measured using optical sensors. Simulated data is generated using detailed kinematic modelling with variations based on human anthropometry datasets. Using both the recorded and generated data, we trained three different neural networks (Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), DeepConvLSTM) to perform person-independent fatigue estimation from wearable motion data.
The estimation performance increased with the amount of simulated training data. Accuracy and correlation values were higher with the proposed data augmentation method as compared to other general time series augmentation methods (e.g, rotation, jettering, magnitude wrapping) with the same amount of training data. An accuracy of 87% and a Pearson correlation coefficient of 90% were achieved on unseen data when the DeepConvLSTM model was trained with the proposed augmented dataset.
The enlarged dataset significantly improves the prediction of inter-individual fatigue.
Appropriate augmentation techniques for biomechanical data can improve model accuracy and reduce the need for expensive data collection.
Appropriate augmentation techniques for biomechanical data can improve model accuracy and reduce the need for expensive data collection.In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. MV1035 In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.An in silico trial simulates a disease and its corresponding therapies on a cohort of virtual patients to support the development and evaluation of medical devices, drugs, and treatment. In silico trials have the potential to refine, reduce cost, and partially replace current in vivo studies, namely clinical trials and animal testing. We present the design and implementation of an in silico trial for treatment of acute ischemic stroke. We propose an event-based modelling approach for the simulation of a disease and injury, where changes to the state of the system (the events) are assumed to be instantaneous. Using this approach we are able to combine a diverse set of models, spanning multiple time scales, to model acute ischemic stroke, treatment, and resulting brain tissue injury. The in silico trial is designed to be modular to aid development and reproducibility. It provides a comprehensive framework for application to any potential in silico trial. A statistical population model is used to generate cohorts of virtual patients. Patient functional outcomes are also predicted with a statistical model, using treatment and injury results and the patient's clinical parameters. We demonstrate the functionality of the event-based modelling approach and trial framework by running proof of concept in silico trials. The proof of concept trials simulate the same cohort of patients twice once with successful treatment (successful recanalisation) and once with unsuccessful treatment (unsuccessful treatment). Ways to overcome some of the challenges and difficulties in setting up such an in silico trial are discussed, such as validation and computational limitations.
To fully enhance the feature extraction capabilities of deep learning models, so as to accurately diagnose coronavirus disease 2019 (COVID-19) based on chest CT images, a densely connected attention network (DenseANet) was constructed by utilizing the self-attention mechanism in deep learning.
During the construction of the DenseANet, we not only densely connected attention features within and between the feature extraction blocks with the same scale, but also densely connected attention features with different scales at the end of the deep model, thereby further enhancing the high-order features. In this way, as the depth of the deep model increases, the spatial attention features generated by different layers can be densely connected and gradually transferred to deeper layers. The DenseANet takes CT images of the lung fields segmented by an improved U-Net as inputs and outputs the probability of the patients suffering from COVID-19.
Compared with exiting attention networks, DenseANet can maximize the utilization of self-attention features at different depths in the model. A five-fold cross-validation experiment was performed on a dataset containing 2993 CT scans of 2121 patients, and experiments showed that the DenseANet can effectively locate the lung lesions of patients infected with SARS-CoV-2, and distinguish COVID-19, common pneumonia and normal controls with an average of 96.06% Acc and 0.989 AUC.
The DenseANet we proposed can generate strong attention features and achieve the best diagnosis results. In addition, the proposed method of densely connecting attention features can be easily extended to other advanced deep learning methods to improve their performance in related tasks.
The DenseANet we proposed can generate strong attention features and achieve the best diagnosis results. In addition, the proposed method of densely connecting attention features can be easily extended to other advanced deep learning methods to improve their performance in related tasks.Non-invasive multi-disease detection is an active technology that detects human diseases automatically. By observing images of the human body, computers can make inferences on disease detection based on artificial intelligence and computer vision techniques. The sublingual vein, lying on the lower part of the human tongue, is a critical identifier in non-invasive multi-disease detection, reflecting health status. However, few studies have fully investigated non-invasive multi-disease detection via the sublingual vein using a quantitative method. In this paper, a two-phase sublingual-based disease detection framework for non-invasive multi-disease detection was proposed. In this framework, sublingual vein region segmentation was performed on each image in the first phase to achieve the region with the highest probability of covering the sublingual vein. In the second phase, features in this region were extracted, and multi-class classification was applied to these features to output a detection result. To better represent the characterisation of the obtained sublingual vein region, multi-feature representations were generated of the sublingual vein region (based on color, texture, shape, and latent representation). The effectiveness of sublingual-based multi-disease detection was quantitatively evaluated, and the proposed framework was based on 1103 sublingual vein images from patients in different health status categories. The best multi-feature representation was generated based on color, texture, and latent representation features with the highest accuracy of 98.05%.Cytokines/chemokines regulate hematopoiesis, most having multiple cell actions. Numerous but not all chemokine family members act as negative regulators of hematopoietic progenitor cell (HPC) proliferation, but very little is known about such effects of the chemokine, CXCL15/Lungkine. We found that CXCL15/Lungkine-/- mice have greatly increased cycling of multi cytokine-stimulated bone marrow and spleen hematopoietic progenitor cells (HPCs CFU-GM, BFU-E, and CFU-GEMM) and CXCL15 is expressed in many bone marrow progenitor and other cell types. This suggests that CXCL15/Lungkine acts as a negative regulator of the cell cycling of these HPCs in vivo. Recombinant murine CXCL15/Lungkine, decreased numbers of functional HPCs during cytokine-enhanced ex-vivo culture of lineage negative mouse bone marrow cells. Moreover, CXCL15/Lungkine, through S-Phase specific actions, was able to suppress in vitro colony formation of normal wildtype mouse bone marrow CFU-GM, CFU-G, CFU-M, BFU-E, and CFU-GEMM. This clearly identifies the negative regulatory activity of CXCL15/Lungkine on proliferation of multiple types of mouse HPCs.
There are significant practice variations in antibiotic treatment for appendicitis, ranging from short-course narrow spectrum to long-course broad-spectrum. We sought to describe the modern microbial epidemiology of acute and perforated appendicitis in adults to help inform appropriate empiric coverage and support antibiotic stewardship initiatives.
This is a post-hoc secondary analysis of the Multicenter Study of the Treatment of Appendicitis in America Acute, Perforated, and Gangrenous (MUSTANG) which prospectively enrolled adult patients (age ≥ 18 years) diagnosed with appendicitis between January 2017 and June 2018 across 28 centers in the United States. We included all subjects with positive microbiologic cultures during primary or secondary (rescue after medical failure) appendectomy or percutaneous drainage. Culture yield was compared between low- and high-grade appendicitis as per the AAST classification.
A total of 3,471 patients were included 230 (7%) had cultures performed, and 179/230 (78%) had positive results.