The TZM-bl assay measures antibody-mediated neutralization of HIV-1 as a function of reductions in HIV-1 Tat-regulated firefly luciferase (Luc) reporter gene expression after a single round of infection with Env-pseudotyped viruses. This assay has become the main endpoint neutralization assay used for the assessment of preclinical and clinical trial samples by a growing number of laboratories worldwide. Here we present the results of the formal optimization and validation of the TZM-bl assay, performed in compliance with Good Clinical Laboratory Practice (GCLP) guidelines. The assay was evaluated for specificity, accuracy, precision, limits of detection and quantitation, linearity, range and robustness. The validated manual TZM-bl assay was also adapted, optimized and qualified to an automated 384-well format.

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Predicting physical response of an artificially structured material is of particular interest for scientific and engineering applications. Here we use deep learning to predict optical response of artificially engineered nanophotonic devices. In addition to predicting forward approximation of transmission response for any given topology, this approach allows us to inversely approximate designs for a targeted optical response. Our Deep Neural Network (DNN) could design compact (2.6 × 2.6 μm2) silicon-on-insulator (SOI)-based 1 × 2 power splitters with various target splitting ratios in a fraction of a second. This model is trained to minimize the reflection (to smaller than ~ −20 dB) while achieving maximum transmission efficiency above 90% and target splitting specifications. This approach paves the way for rapid design of integrated photonic components relying on complex nanostructures.

Inhaled particulate matter 2.5 (PM2.5) can cause lung injury by inducing serious inflammation in lung tissue. Renin-angiotensin system (RAS) is involved in the pathogenesis of inflammatory lung diseases and regulates inflammatory response. Angiotensin-converting enzyme II (ACE2), which is produced through the angiotensin-converting enzyme (ACE)/angiotensin II (Ang II) axis, protects against lung disease. However, few studies have focused on the relationships between PM2.5 and ACE2. Therefore, we aimed to explore the role of ACE2 in PM2.5-induced acute lung injury (ALI). An animal model of PM2.5-induced ALI was established with wild type (C57BL/6, WT) and ACE2 gene knockout (ACE2 KO) mice. The mice were exposed to PM2.5 through intratracheal instillation once a day for 3 days (6.25 mg/kg/day) and then sacrificed at 2 days and 5 days after PM2.5 instillation. The results show that resting respiratory rate (RRR), levels of inflammatory cytokines, ACE and MMPs in the lungs of WT and ACE2 KO mice were significantly increased at 2 days postinstillation. At 5 days postinstillation, the PM2.5-induced ALI significantly recovered in the WT mice, but only partially recovered in the ACE2 KO mice. The results hint that PM2.5 could induce severe ALI through pulmonary inflammation, and the repair after acute PM2.5 postinstillation could be attenuated in the absence of ACE2. Additionally, our results show that PM2.5-induced ALI is associated with signaling p-ERK1/2 and p-STAT3 pathways and ACE2 knockdown could increase pulmonary p-STAT3 and p-ERK1/2 levels in the PM2.5-induced ALI.