Intestinal bacteria—a powerful weapon for fungal infections treatment

The morbidity and mortality of invasive fungal infections are rising gradually. In recent years, fungi have quietly evolved stronger defense capabilities and increased resistance to antibiotics, posing huge challenges to maintaining physical health. Therefore, developing new drugs and strategies to combat these invasive fungi is crucial. There are a large number of microorganisms in the intestinal tract of mammals, collectively referred to as intestinal microbiota. At the same time, these native microorganisms co-evolve with their hosts in symbiotic relationship. Recent researches have shown that some probiotics and intestinal symbiotic bacteria can inhibit the invasion and colonization of fungi. In this paper, we review the mechanism of some intestinal bacteria affecting the growth and invasion of fungi by targeting the virulence factors, quorum sensing system, secreting active metabolites or regulating the host anti-fungal immune response, so as to provide new strategies for resisting invasive fungal infection

DECODE:Deep Confidence Network for Robust Image Classification

The recent years have witnessed the success of deep convolutional neural networks for image classification and many related tasks. It should be pointed out that the existing training strategies assume there is a clean dataset for model learning. In elaborately constructed benchmark datasets, deep network has yielded promising performance under the assumption. However, in real-world applications, it is burdensome and expensive to collect sufficient clean training samples. On the other hand, collecting noisy labeled samples is much economical and practical, especially with the rapidly increasing amount of visual data in theWeb. Unfortunately, the accuracy of current deep models may drop dramatically even with 5% to 10% label noise. Therefore, enabling label noise resistant classification has become a crucial issue in the data driven deep learning approaches. In this paper, we propose a DEep COnfiDEnce network, DECODE, to address this issue. In particular, based on the distribution of mislabeled data, we adopt a confidence evaluation module which is able to determine the confidence that a sample is mislabeled. With the confidence, we further use a weighting strategy to assign different weights to different samples so that the model pays less attention to low confidence data which is more likely to be noise. In this way, the deep model is more robust to label noise. DECODE is designed to be general such that it can be easily combine with existing architectures. We conduct extensive experiments on several datasets and the results validate that DECODE can improve the accuracy of deep models trained with noisy data

Optimized Projection for Hashing

Hashing, which seeks for binary codes to represent data, has drawn increasing research interest in recent years. Most existing Hashing methods follow a projection-quantization framework which first projects high-dimensional data into compact low-dimensional space and then quantifies the compact data into binary codes. The projection step plays a key role in Hashing and academia has paid considerable attention to it. Previous works have proven that a good projection should simultaneously 1) preserve important information in original data, and 2) lead to compact representation with low quantization error. However, they adopted a greedy two-step strategy to consider the above two properties separately. In this paper, we empirically show that such a two-step strategy will result in a sub-optimal solution because the optimal solution to 1) limits the feasible set for the solution to 2). We put forward a novel projection learning method for Hashing, dubbed Optimized Projection (OPH). Specifically, we propose to learn the projection in a unified formulation which can find a good trade-off such that the overall performance can be optimized. A general framework is given such that OPH can be incorporated with different Hashing methods for different situations. We also introduce an effective gradient-based optimization algorithm for OPH. We carried out extensive experiments for Hashing-based Approximate Nearest Neighbor search and Content-based Data Retrieval on six benchmark datasets. The results show that OPH significantly outperforms several state-of-the-art related Hashing methods

Regulating Cytoplasmic Calcium Homeostasis Can Reduce Aluminum Toxicity in Yeast

Our previous study suggested that increased cytoplasmic calcium (Ca) signals may mediate aluminum (Al) toxicity in yeast (Saccharomyces cerevisiae). In this report, we found that a yeast mutant, pmc1, lacking the vacuolar calcium ion (Ca2+) pump Ca2+-ATPase (Pmc1p), was more sensitive to Al treatment than the wild-type strain. Overexpression of either PMC1 or an anti-apoptotic factor, such as Bcl-2, Ced-9 or PpBI-1, decreased cytoplasmic Ca2+ levels and rescued yeast from Al sensitivity in both the wild-type and pmc1 mutant. Moreover, pretreatment with the Ca2+ chelator BAPTA-AM sustained cytoplasmic Ca2+ at low levels in the presence of Al, effectively making the cells more tolerant to Al exposure. Quantitative RT-PCR revealed that the expression of calmodulin (CaM) and phospholipase C (PLC), which are in the Ca2+ signaling pathway, was down-regulated under Al stress. This effect was largely counteracted when cells overexpressed anti-apoptotic Ced-9 or were pretreated with BAPTA-AM. Taken together, our results suggest that the negative regulation of Al-induced cytoplasmic Ca signaling is a novel mechanism underlying internal resistance to Al toxicity

Effects of Neutrophil Extracellular Traps on Bovine Mammary Epithelial Cells in vitro

Bovine mastitis is a common infectious disease which causes huge economic losses in dairy cattle. Bovine mammary epithelial cell (BMEC) damage usually directly causes the decrease of milk production, which is one of the most important causes of economic loss. NETs, novel effector mechanisms, are reported to exacerbate the pathogenesis of several inflammatory diseases. NETs formation has also been observed in the milk and mammary glands of sheep. However, the effects and detailed mechanisms of NETs on BMEC damage remain unclear. Thus, we aim to examine the effects of NETs on BMECs in vitro, and further to investigate the detail mechanism. In this study, the cytotoxicity of NETs on BMECs was determined using lactic dehydrogenase (LDH) levels in culture supernatants. Histone-induced BMEC damage was examined by flow cytometry and immunofluorescence analysis. The activities of caspase 1, caspase 3, caspase 11, and NLRP3 was detected using western blotting and immunohistochemical analysis. The results showed that NETs and their component histone significantly increased cytotoxicity to BMECs, suggesting the critical role of NETs, and their component histone in BMEC damage. In addition, histone could also induce necrosis, pyroptosis, and apoptosis of BMECs, and the mechanisms by which histone leads to BMEC damage occurred via activating caspase 1, caspase 3, and NLRP3. Altogether, NETs formation regulates inflammation and BMEC damage in mastitis. Inhibiting excess NETs formation may be useful to ameliorate mammary gland damage associated with mastitis

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake