Comparison of hair from rectum cancer patients and from healthy persons by Raman microspectroscopy and imaging

AbstractIn this work, Raman microspectroscopy and imaging was employed to analyze cancer patients’ hair tissue. The comparison between the hair from rectum cancer patients and the hair from healthy people reveals some remarkable differences, such as for the rectum cancer patients, there are more lipids but less content of α-helix proteins in the hair medulla section. Though more statistic data are required to establish universary rules for practical and accurate diagnosis, this work based on case study demonstrates the possibility of applying Raman microspectroscopy to reveal abnormality in non-cancer tissues such as hair in order to predict and diagnose cancers

A Comprehensive Survey on Deep Graph Representation Learning

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future

Surgical treatment of patellar dislocation: A network meta-analysis of randomized control trials and cohort studies

BackgroundCurrently, there are many surgical options for patellar dislocation. The purpose of this study is to perform a network meta-analysis of the randomized controlled trials (RCTs) and cohort studies to determine the better treatment.MethodWe searched the Pubmed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, clinicaltrials.gov and who.int/trialsearch. Clinical outcomes included Kujala score, Lysholm score, International Knee Documentation Committee (IKDC) score, redislocation or recurrent instability. We conducted pairwise meta-analysis and network meta-analysis respectively using the frequentist model to compare the clinical outcomes.ResultsThere were 10 RCTs and 2 cohort studies with a total of 774 patients included in our study. In network meta-analysis, double-bundle medial patellofemoral ligament reconstruction (DB-MPFLR) achieved good results on functional scores. According to the surface under the cumulative ranking (SUCRA), DB-MPFLR had the highest probabilities of their protective effects on outcomes of Kujala score (SUCRA 96.5 %), IKDC score (SUCRA 100.0%) and redislocation (SUCRA 67.8%). However, DB-MPFLR (SUCRA 84.6%) comes second to SB-MPFLR (SUCRA 90.4%) in Lyshlom score. It is (SUCRA 70%) also inferior to vastus medialis plasty (VM-plasty) (SUCRA 81.9%) in preventing Recurrent instability. The results of subgroup analysis were similar.ConclusionOur study demonstrated that MPFLR showed better functional scores than other surgical options

Remarkable nucleation and growth of ultrafine particles from vehicular exhaust

High levels of ultrafine particles (UFPs; diameter of less than 50 nm) are frequently produced from new particle formation under urban conditions, with profound implications on human health, weather, and climate. However, the fundamental mechanisms of new particle formation remain elusive, and few experimental studies have realistically replicated the relevant atmospheric conditions. Previous experimental studies simulated oxidation of one compound or a mixture of a few compounds, and extrapolation of the laboratory results to chemically complex air was uncertain. Here, we show striking formation of UFPs in urban air from combining ambient and chamber measurements. By capturing the ambient conditions (i.e., temperature, relative humidity, sunlight, and the types and abundances of chemical species), we elucidate the roles of existing particles, photochemistry, and synergy of multipollutants in new particle formation. Aerosol nucleation in urban air is limited by existing particles but negligibly by nitrogen oxides. Photooxidation of vehicular exhaust yields abundant precursors, and organics, rather than sulfuric acid or base species, dominate formation of UFPs under urban conditions. Recognition of this source of UFPs is essential to assessing their impacts and developing mitigation policies. Our results imply that reduction of primary particles or removal of existing particles without simultaneously limiting organics from automobile emissions is ineffective and can even exacerbate this problem

Remarkable nucleation and growth of ultrafine particles from vehicular exhaust

High levels of ultrafine particles (UFPs; diameter of less than 50 nm) are frequently produced from new particle formation under urban conditions, with profound implications on human health, weather, and climate. However, the fundamental mechanisms of new particle formation remain elusive, and few experimental studies have realistically replicated the relevant atmospheric conditions. Previous experimental studies simulated oxidation of one compound or a mixture of a few compounds, and extrapolation of the laboratory results to chemically complex air was uncertain. Here, we show striking formation of UFPs in urban air from combining ambient and chamber measurements. By capturing the ambient conditions (i.e., temperature, relative humidity, sunlight, and the types and abundances of chemical species), we elucidate the roles of existing particles, photochemistry, and synergy of multipollutants in new particle formation. Aerosol nucleation in urban air is limited by existing particles but negligibly by nitrogen oxides. Photooxidation of vehicular exhaust yields abundant precursors, and organics, rather than sulfuric acid or base species, dominate formation of UFPs under urban conditions. Recognition of this source of UFPs is essential to assessing their impacts and developing mitigation policies. Our results imply that reduction of primary particles or removal of existing particles without simultaneously limiting organics from automobile emissions is ineffective and can even exacerbate this problem

Antibiotic-Induced Primary Biles Inhibit SARS-CoV-2 Endoribonuclease Nsp15 Activity in Mouse Gut

The gut microbiome profile of COVID-19 patients was found to correlate with a viral load of SARS-CoV-2, COVID-19 severity, and dysfunctional immune responses, suggesting that gut microbiota may be involved in anti-infection. In order to investigate the role of gut microbiota in anti-infection against SARS-CoV-2, we established a high-throughput in vitro screening system for COVID-19 therapeutics by targeting the endoribonuclease (Nsp15). We also evaluated the activity inhibition of the target by substances of intestinal origin, using a mouse model in an attempt to explore the interactions between gut microbiota and SARS-CoV-2. The results unexpectedly revealed that antibiotic treatment induced the appearance of substances with Nsp15 activity inhibition in the intestine of mice. Comprehensive analysis based on functional profiling of the fecal metagenomes and endoribonuclease assay of antibiotic-enriched bacteria and metabolites demonstrated that the Nsp15 inhibitors were the primary bile acids that accumulated in the gut as a result of antibiotic-induced deficiency of bile acid metabolizing microbes. This study provides a new perspective on the development of COVID-19 therapeutics using primary bile acids

Etiologic Diagnosis of Lower Respiratory Tract Bacterial Infections Using Sputum Samples and Quantitative Loop-Mediated Isothermal Amplification

Etiologic diagnoses of lower respiratory tract infections (LRTI) have been relying primarily on bacterial cultures that often fail to return useful results in time. Although DNA-based assays are more sensitive than bacterial cultures in detecting pathogens, the molecular results are often inconsistent and challenged by doubts on false positives, such as those due to system- and environment-derived contaminations. Here we report a nationwide cohort study on 2986 suspected LRTI patients across P. R. China. We compared the performance of a DNA-based assay qLAMP (quantitative Loop-mediated isothermal AMPlification) with that of standard bacterial cultures in detecting a panel of eight common respiratory bacterial pathogens from sputum samples. Our qLAMP assay detects the panel of pathogens in 1047(69.28%) patients from 1533 qualified patients at the end. We found that the bacterial titer quantified based on qLAMP is a predictor of probability that the bacterium in the sample can be detected in culture assay. The relatedness of the two assays fits a logistic regression curve. We used a piecewise linear function to define breakpoints where latent pathogen abruptly change its competitive relationship with others in the panel. These breakpoints, where pathogens start to propagate abnormally, are used as cutoffs to eliminate the influence of contaminations from normal flora. With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients. In conclusion, qLAMP is a reliable method in quantifying bacterial titer. Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship