Lovemarkussen9136

COVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagging dynamic deep learning network (B-DDLN) for diagnosing COVID-19 by intelligently recognizing its symptoms in X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, a bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects the feature extractor and bagging classifier in series. Experimental results verify that the proposed B-DDLN achieves 98.8889% testing accuracy, which shows the best diagnosis performance among the existing state-of-the-art methods on the open image set. It also provides evidence for further detection and treatment.Circulating tumor DNA (ctDNA) may reveal dynamic tumor status during therapy. We conducted serial ctDNA analysis to investigate potential association with clinical outcome in metastatic colorectal cancer (mCRC) patients receiving chemotherapy. Tissue KRAS/NRAS wild-type mCRC patients were enrolled and treated with first-line cetuximab-containing chemotherapy. ctDNA isolated from plasma were analyzed by next generation sequencing (NGS) with 16 targeted gene panel. Among 93 patients, 84 (90.3%) had at least 1 somatic mutation in baseline ctDNA samples (average 2.74). Five patients with KRAS or NRAS hotspot mutation in the ctDNA showed significantly worse progression-free survival (PFS) (p = 0.029). Changes in average variant allele frequency (VAF) in ctDNA showed significant correlation with tumor size change at the time of first response evaluation (p = 0.020) and progressive disease (PD) (p = 0.042). Patients whose average VAF decreased below cutoff ( less then  1%) at the first evaluation showed significantly better PFS (p  less then  0.001), and the average VAF change further discriminated the PFS in the patients in partial response (p = 0.018). At the time of PD, 54 new mutations including KRAS and MAP2K1 emerged in ctDNA. ctDNA sequencing can provide mutation profile that could better reflect tumor mutation status and predict treatment outcome.The increasing number and quality of randomized controlled trials (RCTs) employing transcranial direct current stimulation (tDCS) denote the rising awareness of neuroscientific community about its electroceutical potential and opening to include these treatments in the framework of medical therapies under the indications of the international authorities. The purpose of this quantitative review is to estimate the recommendation strength applying the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) criteria and PICO (population, intervention, comparison, outcome) model values for effective tDCS treatments on no-structural diseases, and to provide an estimate of Sham effect for future RCTs. Applying GRADE evaluation pathway, we searched in literature the tDCS-based RCTs in psychophysical diseases displaying a major involvement of brain electrical activity imbalances. Three independent authors agreed on Class 1 RCTs (18 studies) and meta-analyses were carried out using a random-effects model for pathologies sub-selected based on PICO and systemic involvement criteria. The meta-analysis integrated with extensive evidence of negligible side effects and low-cost, easy-to-use procedures, indicated that tDCS treatments for depression and fatigue in Multiple Sclerosis ranked between moderately and highly recommendable. find more For these interventions we reported the PICO variables, with left vs. right dorsolateral prefrontal target for 30 min/10 days against depression and bilateral somatosensory vs occipital target for 15 min/5 days against MS fatigue. An across-diseases meta-analysis devoted to the Sham effect provided references for power analysis in future tDCS RCTs on these clinical conditions. High-quality indications support tDCS as a promising tool to build electroceutical treatments against diseases involving neurodynamics alterations.Bioleaching of metal sulfide ores involves acidophilic microbes that catalyze the chemical dissolution of the metal sulfide bond that is enhanced by attached and planktonic cell mediated oxidation of iron(II)-ions and inorganic sulfur compounds. Leptospirillum spp. often predominate in sulfide mineral-containing environments, including bioheaps for copper recovery from chalcopyrite, as they are effective primary mineral colonizers and oxidize iron(II)-ions efficiently. In this study, we demonstrated a functional diffusible signal factor interspecies quorum sensing signaling mechanism in Leptospirillum ferriphilum and Leptospirillum ferrooxidans that produces (Z)-11-methyl-2-dodecenoic acid when grown with pyrite as energy source. In addition, pure diffusible signal factor and extracts from supernatants of pyrite grown Leptospirillum spp. inhibited biological iron oxidation in various species, and that pyrite grown Leptospirillum cells were less affected than iron grown cells to self inhibition. Finally, transcriptional analyses for the inhibition of iron-grown L. ferriphilum cells due to diffusible signal factor was compared with the response to exposure of cells to N- acyl-homoserine-lactone type quorum sensing signal compounds. The data suggested that Leptospirillum spp. diffusible signal factor production is a strategy for niche protection and defense against other microbes and it is proposed that this may be exploited to inhibit unwanted acidophile species.Placenta growth factor (PlGF) is a pro-inflammatory angiogenic mediator that promotes many pathologies including diabetic complications and atherosclerosis. Widespread endothelial dysfunction precedes the onset of these conditions. As very little is known of the mechanism(s) controlling PlGF expression in pathology we investigated the role of hyperglycaemia in the regulation of PlGF production in endothelial cells. Hyperglycaemia stimulated PlGF secretion in cultured primary endothelial cells, which was suppressed by IGF-1-mediated PI3K/Akt activation. Inhibition of PI3K activity resulted in significant PlGF mRNA up-regulation and protein secretion. Similarly, loss or inhibition of Akt activity significantly increased basal PlGF expression and prevented any further PlGF secretion in hyperglycaemia. Conversely, constitutive Akt activation blocked PlGF secretion irrespective of upstream PI3K activity demonstrating that Akt is a central regulator of PlGF expression. Knock-down of the Forkhead box O-1 (FOXO1) transcription factor, which is negatively regulated by Akt, suppressed both basal and hyperglycaemia-induced PlGF secretion, whilst FOXO1 gain-of-function up-regulated PlGF in vitro and in vivo. FOXO1 association to a FOXO binding sequence identified in the PlGF promoter also increased in hyperglycaemia. This study identifies the PI3K/Akt/FOXO1 signalling axis as a key regulator of PlGF expression and unifying pathway by which PlGF may contribute to common disorders characterised by endothelial dysfunction, providing a target for therapy.Longitudinal analyses of magnetoencephalography (MEG) data are essential for a full understanding of the pathophysiology of brain diseases and the development of brain activity over time. However, time-dependent factors, such as the recording environment and the type of MEG recording system may affect such longitudinal analyses. We hypothesized that, using source-space analysis, hardware and software differences between two recordings systems may be overcome, with the aim of finding consistent neurophysiological results. We studied eight healthy subjects who underwent three consecutive MEG recordings over 7 years, using two different MEG recordings systems; a 151-channel VSM-CTF system for the first two time points and a 306-channel Elekta Vectorview system for the third time point. We assessed the within (longitudinal) and between-subject (cross-sectional) consistency of power spectra and functional connectivity matrices. Consistency of within-subject spectral power and functional connectivity matrices was good and was not significantly different when using different MEG recording systems as compared to using the same system. Importantly, we confirmed that within-subject consistency values were higher than between-subject values. We demonstrated consistent neurophysiological findings in healthy subjects over a time span of seven years, despite using data recorded on different MEG systems and different implementations of the analysis pipeline.A new diagnosis method for the discriminative detection of laser-accelerated multi-MeV carbon ions from background oxygen ions utilizing solid-state nuclear track detectors (SSNTDs) is proposed. The idea is to combine two kinds of SSNTDs having different track registration sensitivities Bisphenol A polycarbonate detects carbon and the heavier ions, and polyethylene terephthalate detects oxygen and the heavier ions. The method is calibrated with mono-energetic carbon and oxygen ion beams from the heavy ion accelerator. Based on the calibration data, the method is applied to identify carbon ions accelerated from multilayered graphene targets irradiated by a high-power laser, where the generation of high-energy high-purity carbon ions is expected. It is found that 93 ± 1% of the accelerated heavy ions with energies larger than 14 MeV are carbons. The results thus obtained support that carbon-rich heavy ion acceleration is achieved.Radiomics is a method to mine large numbers of quantitative imaging features and develop predictive models. It has shown exciting promise for improved cancer decision support from early detection to personalized precision treatment, and therefore offers a desirable new direction for pancreatic cancer where the mortality remains high despite the current care and intense research. For radiomics, interobserver segmentation variability and its effect on radiomic feature stability is a crucial consideration. While investigations have been reported for high-contrast cancer sites such as lung cancer, no studies to date have investigated it on CT-based radiomics for pancreatic cancer. With three radiation oncology observers and three radiology observers independently contouring on the contrast CT of 21 pancreatic cancer patients, we conducted the first interobserver segmentation variability study on CT-based radiomics for pancreatic cancer. Moreover, our novel investigation assessed whether there exists an interdisciion for interobserver segmentation variability and its effect on CT-based radiomics for pancreatic cancer. An interesting interdisciplinary variability found in this study also introduces new considerations for the deployment of radiomics models.Gene co-expression networks (GCNs) have been developed as relevant analytical tools for the study of the gene expression patterns behind complex phenotypes. Determining the association between structure and function in GCNs is a current challenge in biomedical research. Several structural differences between GCNs of breast cancer and healthy phenotypes have been reported. In a previous study, using co-expression multilayer networks, we have shown that there are abrupt differences in the connectivity patterns of the GCN of basal-like breast cancer between top co-expressed gene-pairs and the remaining gene-pairs. Here, we compared the top-100,000 interactions networks for the four breast cancer phenotypes (Luminal-A, Luminal-B, Her2+ and Basal), in terms of structural properties. For this purpose, we used the graph-theoretical k-core of a network (maximal sub-network with nodes of degree at least k). We developed a comprehensive analysis of the network k-core ([Formula see text]) structures in cancer, and its relationship with biological functions.