Elevated IFNA1 and suppressed IL12p40 associated with persistent hyperinflammation in COVID-19 pneumonia

IntroductionDespite of massive endeavors to characterize inflammation in COVID-19 patients, the core network of inflammatory mediators responsible for severe pneumonia stillremain remains elusive. MethodsHere, we performed quantitative and kinetic analysis of 191 inflammatory factors in 955 plasma samples from 80 normal controls (sample n = 80) and 347 confirmed COVID-19 pneumonia patients (sample n = 875), including 8 deceased patients. ResultsDifferential expression analysis showed that 76% of plasmaproteins (145 factors) were upregulated in severe COVID-19 patients comparedwith moderate patients, confirming overt inflammatory responses in severe COVID-19 pneumonia patients. Global correlation analysis of the plasma factorsrevealed two core inflammatory modules, core I and II, comprising mainly myeloid cell and lymphoid cell compartments, respectively, with enhanced impact in a severity-dependent manner. We observed elevated IFNA1 and suppressed IL12p40, presenting a robust inverse correlation in severe patients, which was strongly associated with persistent hyperinflammation in 8.3% of moderate pneumonia patients and 59.4% of severe patients. DiscussionAberrant persistence of pulmonary and systemic inflammation might be associated with long COVID-19 sequelae. Our comprehensive analysis of inflammatory mediators in plasmarevealed the complexity of pneumonic inflammation in COVID-19 patients anddefined critical modules responsible for severe pneumonic progression

Ultra-compact terahertz 50:50 power splitter designed by a perceptron based algorithm

We designed and simulated an ultra-compact 1 × 2 power splitter operating in the terahertz region. A machine learning approach was implemented to design the photonic device. The designed power splitter has a footprint of 500 µm × 500 µm. We calculated the insertion loss using a three-dimensional finite difference time domain method. The calculated insertion loss was less than 4 dB over the operating wavelength range of 275–325 µm. The machine learning algorithm implemented in this work can be applied to the inverse design of various photonic devices.11