CD274 (PD-L1) negatively regulates M1 macrophage polarization in ALI/ARDS

BackgroundAcute lung injury (ALI)/severe acute respiratory distress syndrome (ARDS) is a serious clinical syndrome characterized by a high mortality rate. The pathophysiological mechanisms underlying ALI/ARDS remain incompletely understood. Considering the crucial role of immune infiltration and macrophage polarization in the pathogenesis of ALI/ARDS, this study aims to identify key genes associated with both ALI/ARDS and M1 macrophage polarization, employing a combination of bioinformatics and experimental approaches. The findings could potentially reveal novel biomarkers for the diagnosis and management of ALI/ARDS.MethodsGene expression profiles relevant to ALI were retrieved from the GEO database to identify co-upregulated differentially expressed genes (DEGs). GO and KEGG analyses facilitated functional annotation and pathway elucidation. PPI networks were constructed to identify hub genes, and differences in immune cell infiltration were subsequently examined. The expression of hub genes in M1 versus M2 macrophages was evaluated using macrophage polarization datasets. The diagnostic utility of CD274 (PD-L1) for ARDS was assessed by receiver operating characteristic (ROC) analysis in a validation dataset. Experimental confirmation was conducted using two LPS-induced M1 macrophage models and an ALI mouse model. The role of CD274 (PD-L1) in M1 macrophage polarization and associated proinflammatory cytokine production was further investigated by siRNA-mediated silencing.ResultsA total of 99 co-upregulated DEGs were identified in two ALI-linked datasets. Enrichment analysis revealed that these DEGs were mainly involved in immune-inflammatory pathways. The following top 10 hub genes were identified from the PPI network: IL-6, IL-1β, CXCL10, CD274, CCL2, TLR2, CXCL1, CCL3, IFIT1, and IFIT3. Immune infiltration analysis revealed a significantly increased abundance of M1 and M2 macrophages in lung tissue from the ALI group compared to the control group. Subsequent analysis confirmed that CD274 (PD-L1), a key immunological checkpoint molecule, was highly expressed within M1 macrophages. ROC analysis validated CD274 (PD-L1) as a promising biomarker for the diagnosis of ARDS. Both in vitro and in vivo experiments supported the bioinformatics analysis and confirmed that the JAK-STAT3 pathway promotes CD274 (PD-L1) expression on M1 macrophages. Importantly, knockdown of CD274 (PD-L1) expression potentiated M1 macrophage polarization and enhanced proinflammatory cytokines production.ConclusionThis study demonstrates a significant correlation between CD274 (PD-L1) and M1 macrophages in ALI/ARDS. CD274 (PD-L1) functions as a negative regulator of M1 polarization and the secretion of proinflammatory cytokines in macrophages. These findings suggest potential new targets for the diagnosis and treatment of ALI/ARDS

Interaction between electrical storm and left ventricular ejection fraction as predictors of mortality in patients with implantable cardioverter defibrillator: A Chinese cohort study

AimsTo determine the interaction of electrical storm (ES) and impaired left ventircular ejection fraction (LVEF) on the mortality risk of patients with implantable cardioverter defibrillator (ICD).Methods and resultsA total of 554 Chinese ICD recipients from 2010 to 2014 were retrospectively included and the mean follow-up was 58 months. The proportions of dilated cardiomyopathy and the hypertrophic cardiomyopathy were 26.0% (144/554) and 5.6% (31/554), respectively. There were 8 cases with long QT syndrome, 6 with arrhythmogenic right ventricular cardiomyopathy and 2 with Brugada syndrome. Patients with prior MI accounted for 15.5% (86/554) and pre-implantation syncope accounted for 23.3% (129/554). A total of 199 (35.9%) patients had primary prevention indications for ICD therapy. Both ES and impaired LVEF (<40%) were independent predictors for all-cause mortality [hazard ratio (HR) 2.40, 95% CI 1.57–3.68, P < 0.001; HR 1.94, 95% CI 1.30–2.90, P = 0.001, respectively] and cardiovascular mortality (HR 4.63, 95% CI 2.68–7.98, P < 0.001; HR 2.56, 95% CI 1.47–4.44, p = 0.001, respectively). Compared with patients with preserved LVEF (≥40%) and without ES, patients with impaired LVEF and ES had highest all-cause and cardiovascular mortality risks (HR 4.17, 95% CI 2.16–8.06, P < 0.001; HR 11.91, 95% CI 5.55–25.56, P < 0.001, respectively). In patients with impaired LVEF, ES increased the all-cause and cardiovascular mortality risks (HR 1.84, 95% CI 1.00–3.37, P = 0.034; HR 4.86, 95% CI 2.39–9.86, P < 0.001, respectively). In patients with ES, the deleterious effects of impaired LVEF seemed confined to cardiovascular mortality (HR 2.54, 95% CI 1.25–5.14, p = 0.038), and the HR for all-cause mortality was not significant statistically (HR 1.14, 95% CI 0.54–2.38, P = 0.735).ConclusionBoth ES and impaired LVEF are independent predictors of mortality risk in this Chinese cohort of ICD recipients. The interaction of ES and impaired LVEF in patients significantly amplifies the deleterious effects of each other as distinct disease

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Real Time Electricity Demand Response for Sustainable Manufacturing Systems: Challenges and a Case Study

Due to the mounting electricity demand from industrial sector of United States and the expected huge investment for new generation capacity, the significance of load management at the customer side has been gradually recognized by both academia and industry. Compared with many research efforts in long term production planning to shave the peak demand, seldom work on real time electricity demand response while maintaining system throughput for the purpose of grid stability for manufacturing company can be found. In this paper, a brief literature review on system load management is provided and the existing challenges of real time electricity demand response for manufacturing systems are analyzed. Numerical case study about demand response implementation is performed to illustrate the possibility of the significant energy consumption reduction without negative impact on system throughput through appropriate real-time production control

Microstructure Characteristics of Porous NiTi Shape Memory Alloy Synthesized by Powder Metallurgy during Compressive Deformation at Room Temperature

Porous NiTi shape memory alloys (SMAs) possess compatible mechanical properties with human bones and can effectively reduce the risk of stress shielding and stress concentration; therefore, they have been termed promising candidates for orthopedic implants. However, microstructure characteristics of porous NiTi SMAs during plastic deformation have rarely been investigated. The present study aims to specifically investigate microstructure characteristics and the corresponding underlying mechanisms of fabricated porous NiTi SMAs via a conventional sintering (CS) process with NaCl space holder during compressive deformation at room temperature. To realize the aforementioned target, X-ray diffraction (XRD), scanning electron microscope (SEM), electron backscattered diffraction (EBSD), transmission electron microscopy (TEM), and high-resolution transmission electron microscopy (HRTEM) are applied in the present study. The results show that the fabricated porous NiTi SMA is 51.8% for porosity, 181.65 μm for the average pore size, and 0.78 μm for the average grain size. Many Ni4Ti3 and NiTi2 phases are formed in the mixed matrix with dominant B2 (NiTi) and some B19′ (NiTi). Severe inhomogeneous deformation happens within compressed specimens, leading to the occurrence of tangled dislocation and shear bands. Microcracks occur within fabricated porous NiTi SMAs at a deformation degree of 9.2%; then, they extend quickly to form macrocracks, which finally results in the failure of regions between pores. The observed nanocrystallization and amorphization around microcrack tips within the 12.5%-deformed sample can be attributed to the relatively small grain size and the grain segmentation effect via statistically stored dislocation (SSD) and geometrically necessary dislocation (GND)

Study of the Fire Behavior of Multilayer Cables in a Mine Tunnel

Fires caused by cables occur frequently in mines, which endanger the safety of workers. To explore the characteristics of a multilayer cable fire in a mine tunnel, multilayer cable fire simulations were carried out using the Fire Dynamics Simulator (FDS). The influence of cable tray spacing, ignition position, and tunnel ventilation speed on the characteristics of the fire were studied. The results showed that these factors change the amount of contact between the cable and air, the heat accumulation, and the heat transfer by the flame interaction between the cables. It was also noted that increasing the spacing or wind speed both made the peak of heat release rate (PHRR) initially increase and then decrease. The influence of wind speed on the cable burnout rate in the upstream and downstream sides of the fire source was not consistent, and the wind speed had a sensitive effect on the cable burn out rate in the upstream side of the fire source. The higher the ignition position was, the longer the arrival time of PHRR was and the slower the fire developed. There was a higher burn velocity close to the ceiling. The cable hooks obstructed the cable fire. This study provides a theoretical basis for cable fire prevention and control in mine tunnels

Nanoparticles suppress fluid instabilities in the thermal drawing of ultralong nanowires.

Ultra-long metal nanowires and their facile fabrication have been long sought after as they promise to offer substantial improvements of performance in numerous applications. However, ultra-long metal ultrafine/nanowires are beyond the capability of current manufacturing techniques, which impose limitations on their size and aspect ratio. Here we show that the limitations imposed by fluid instabilities with thermally drawn nanowires can be alleviated by adding tungsten carbide nanoparticles to the metal core to arrive at wire lengths more than 30 cm with diameters as low as 170 nm. The nanoparticles support thermal drawing in two ways, by increasing the viscosity of the metal and lowering the interfacial energy between the boron silicate and zinc phase. This mechanism of suppressing fluid instability by nanoparticles not only enables a scalable production of ultralong metal nanowires, but also serves for widespread applications in other fluid-related fields