Immune dysregulation in sepsis: experiences, lessons and perspectives.

Sepsis is a life-threatening organ dysfunction syndrome caused by dysregulated host responses to infection. Not only does sepsis pose a serious hazard to human health, but it also imposes a substantial economic burden on the healthcare system. The cornerstones of current treatment for sepsis remain source control, fluid resuscitation, and rapid administration of antibiotics, etc. To date, no drugs have been approved for treating sepsis, and most clinical trials of potential therapies have failed to reduce mortality. The immune response caused by the pathogen is complex, resulting in a dysregulated innate and adaptive immune response that, if not promptly controlled, can lead to excessive inflammation, immunosuppression, and failure to re-establish immune homeostasis. The impaired immune response in patients with sepsis and the potential immunotherapy to modulate the immune response causing excessive inflammation or enhancing immunity suggest the importance of demonstrating individualized therapy. Here, we review the immune dysfunction caused by sepsis, where immune cell production, effector cell function, and survival are directly affected during sepsis. In addition, we discuss potential immunotherapy in septic patients and highlight the need for precise treatment according to clinical and immune stratification

Second-Order Nonlinearity Assisted by Dual Surface Plasmon Resonance Modes in Perforated Gold Film

We have studied analytically the reflection assisted with surface plasmon through the square lattice perforated gold film. Under the excitation of the external electromagnetic field with one or two different frequencies, the second-order nonlinearity exists in this noncentrosymmetric metal-based metamaterial. We employed the two surface plasmon resonances modes with different lattice periods. With the excitation of two different plasmon resonances modes, the strong local field induces an expected increase of the second-order nonlinearity including second harmonic generation as well as the sum (difference) frequency generation. The field distributions results also indicate that the enhancement of sum frequency signals and difference frequency signals strongly depends on surface plasmon resonance effect

Development of genomic phenotype and immunophenotype of acute respiratory distress syndrome using autophagy and metabolism-related genes.

BackgroundDistinguishing ARDS phenotypes is of great importance for its precise treatment. In the study, we attempted to ascertain its phenotypes based on metabolic and autophagy-related genes and infiltrated immune cells.MethodsTranscription datasets of ARDS patients were obtained from Gene expression omnibus (GEO), autophagy and metabolic-related genes were from the Human Autophagy Database and the GeneCards Database, respectively. Autophagy and metabolism-related differentially expressed genes (AMRDEGs) were further identified by machine learning and processed for constructing the nomogram and the risk prediction model. Functional enrichment analyses of differentially expressed genes were performed between high- and low-risk groups. According to the protein-protein interaction network, these hub genes closely linked to increased risk of ARDS were identified with CytoHubba. ssGSEA and CIBERSORT was applied to analyze the infiltration pattern of immune cells in ARDS. Afterwards, immunologically characterized and molecular phenotypes were constructed according to infiltrated immune cells and hub genes.ResultsA total of 26 AMRDEGs were obtained, and CTSB and EEF2 were identified as crucial AMRDEGs. The predictive capability of the risk score, calculated based on the expression levels of CTSB and EEF2, was robust for ARDS in both the discovery cohort (AUC = 1) and the validation cohort (AUC = 0.826). The mean risk score was determined to be 2.231332, and based on this score, patients were classified into high-risk and low-risk groups. 371 differential genes in high- and low-risk groups were analyzed. ITGAM, TYROBP, ITGB2, SPI1, PLEK, FGR, MPO, S100A12, HCK, and MYC were identified as hub genes. A total of 12 infiltrated immune cells were differentially expressed and have correlations with hub genes. According to hub genes and implanted immune cells, ARDS patients were divided into two different molecular phenotypes (Group 1: n = 38; Group 2: n = 19) and two immune phenotypes (Cluster1: n = 22; Cluster2: n = 35), respectively.ConclusionThis study picked up hub genes of ARDS related to autophagy and metabolism and clustered ARDS patients into different molecular phenotypes and immunophenotypes, providing insights into the precision medicine of treating patients with ARDS

Development and external validation of a prognostic multivariable model on admission for hospitalized patients with COVID-19

Summary Background COVID-19 pandemic has developed rapidly and the ability to stratify the most vulnerable patients is vital. However, routinely used severity scoring systems are often low on diagnosis, even in non-survivors. Therefore, clinical prediction models for mortality are urgently required. Methods We developed and internally validated a multivariable logistic regression model to predict inpatient mortality in COVID-19 positive patients using data collected retrospectively from Tongji Hospital, Wuhan (299 patients). External validation was conducted using a retrospective cohort from Jinyintan Hospital, Wuhan (145 patients). Nine variables commonly measured in these acute settings were considered for model development, including age, biomarkers and comorbidities. Backwards stepwise selection and bootstrap resampling were used for model development and internal validation. We assessed discrimination via the C statistic, and calibration using calibration-in-the-large, calibration slopes and plots. Findings The final model included age, lymphocyte count, lactate dehydrogenase and SpO 2 as independent predictors of mortality. Discrimination of the model was excellent in both internal (c=0·89) and external (c=0·98) validation. Internal calibration was excellent (calibration slope=1). External validation showed some over-prediction of risk in low-risk individuals and under-prediction of risk in high-risk individuals prior to recalibration. Recalibration of the intercept and slope led to excellent performance of the model in independent data. Interpretation COVID-19 is a new disease and behaves differently from common critical illnesses. This study provides a new prediction model to identify patients with lethal COVID-19. Its practical reliance on commonly available parameters should improve usage of limited healthcare resources and patient survival rate. Funding This study was supported by following funding: Key Research and Development Plan of Jiangsu Province (BE2018743 and BE2019749), National Institute for Health Research (NIHR) (PDF-2018-11-ST2-006), British Heart Foundation (BHF) (PG/16/65/32313) and Liverpool University Hospitals NHS Foundation Trust in UK. Research in context Evidence before this study Since the outbreak of COVID-19, there has been a pressing need for development of a prognostic tool that is easy for clinicians to use. Recently, a Lancet publication showed that in a cohort of 191 patients with COVID-19, age, SOFA score and D-dimer measurements were associated with mortality. No other publication involving prognostic factors or models has been identified to date. Added value of this study In our cohorts of 444 patients from two hospitals, SOFA scores were low in the majority of patients on admission. The relevance of D-dimer could not be verified, as it is not included in routine laboratory tests. In this study, we have established a multivariable clinical prediction model using a development cohort of 299 patients from one hospital. After backwards selection, four variables, including age, lymphocyte count, lactate dehydrogenase and SpO 2 remained in the model to predict mortality. This has been validated internally and externally with a cohort of 145 patients from a different hospital. Discrimination of the model was excellent in both internal (c=0·89) and external (c=0·98) validation. Calibration plots showed excellent agreement between predicted and observed probabilities of mortality after recalibration of the model to account for underlying differences in the risk profile of the datasets. This demonstrated that the model is able to make reliable predictions in patients from different hospitals. In addition, these variables agree with pathological mechanisms and the model is easy to use in all types of clinical settings. Implication of all the available evidence After further external validation in different countries the model will enable better risk stratification and more targeted management of patients with COVID-19. With the nomogram, this model that is based on readily available parameters can help clinicians to stratify COVID-19 patients on diagnosis to use limited healthcare resources effectively and improve patient outcome

Quantitative Demonstration of Wear Rate and Dissipation Energy during Tension–Torsion Cyclic Loading of Steel Wires with Fretting Contact in Different Environmental Media

The wear rate and dissipation energy during tension–torsion cyclic loading of steel wires with fretting contact in different environmental media were explored in this study. Hysteresis loops of tangential force versus displacement amplitude (Ft-D) and torque versus torsion angle (T-θ), and their dissipation energies were obtained employing the self-made test rig. Morphologies of wear scars of steel wires were observed employing the white light interference surface morphology. The quantitative demonstration of the coefficient of cyclic wear of steel wire was carried out combining polynomial fitting, reconstruction of three-dimensional geometric model of wear scar and Archard’s equation. The results show that Ft-D curves reveal both decreases of the relative slip and dissipation energy in the order: corrosive media, deionized water and air. Increases of contact load and crossing angle caused overall decreases in the relative slip and dissipation energy, while the relative slip and dissipation energy both increased with increasing torsion angle. T-θ curves indicated the largest and smallest dissipation energies in cases of acid solution and deionized water, respectively. Increases of contact load, crossing angle and torsion angle caused increases in relative slip and dissipation energy due to cyclic torsional loading with fretting contact. The wear coefficient in cases of distinct environmental media decreased in this order: air, corrosive media and deionized water. Increases of the contact load, torsion angle and crossing angle all induced increases in the wear coefficient

FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging Framework

Recent work for extracting relations from texts has achieved excellent performance. However, most existing methods pay less attention to the efficiency, making it still challenging to quickly extract relations from massive or streaming text data in realistic scenarios. The main efficiency bottleneck is that these methods use a Transformer-based pre-trained language model for encoding, which heavily affects the training speed and inference speed. To address this issue, we propose a fast relation extraction model (FastRE) based on convolutional encoder and improved cascade binary tagging framework. Compared to previous work, FastRE employs several innovations to improve efficiency while also keeping promising performance. Concretely, FastRE adopts a novel convolutional encoder architecture combined with dilated convolution, gated unit and residual connection, which significantly reduces the computation cost of training and inference, while maintaining the satisfactory performance. Moreover, to improve the cascade binary tagging framework, FastRE first introduces a type-relation mapping mechanism to accelerate tagging efficiency and alleviate relation redundancy, and then utilizes a position-dependent adaptive thresholding strategy to obtain higher tagging accuracy and better model generalization. Experimental results demonstrate that FastRE is well balanced between efficiency and performance, and achieves 3-10x training speed, 7-15x inference speed faster, and 1/100 parameters compared to the state-of-the-art models, while the performance is still competitive.Comment: Accepted to IJCAI-ECAI 202

Tribo-Brake Characteristics between Brake Disc and Brake Shoe during Emergency Braking of Deep Coal Mine Hoist with the High Speed and Heavy Load

The friction wear and thermal fatigue cracking of the brake shoe and friction-induced self-excited vibration (frictional flutter) of the disc brake can easily occur during emergency braking of a deep coal mine hoist with at high speed and with a heavy load. Therefore, tribo-brake characteristics between the brake disc and brake shoe during emergency braking of a deep coal mine hoist are investigated in the present study. Scaled parameters of the disc brake of a deep coal mine hoist are determined by employing the similarity principle. Friction tests between friction disc and brake shoe are carried out to obtain the coefficient of friction in the case of high speed and large specific pressure between the friction disc and brake shoe. Coupled thermo-mechanical finite element analyses of the brake disc and brake shoe are established to investigate temperature and stress fields of the brake disc and brake shoe during emergency braking, which is validated by the engineering failure case. Effects of braking parameters on flutter characteristics between the brake disc and brake shoe are explored by employing a double-degrees-of-freedom vibration mechanism model. The results show that the maximum temperature, equivalent Von Mises stress and contact pressure are all located at the average friction radii of contact surfaces of the brake disc and brake shoe during emergency braking. The cage crashing accident in the case of high speed and heavy load in a typical coal mine shows crack marks and discontinuous burn marks at central locations of brake shoe and brake disc surfaces, respectively, which indicates frictional flutter characteristics between brake disc and brake shoe. During emergency braking, flutter time duration decreases with increasing initial braking speed and damping parameter; the flutter amplitude and frequency of the disc brake increases with increasing normal braking load and stiffness, respectively