Decidual glycodelin-A polarizes human monocytes into a decidual macrophage-like phenotype through Siglec-7

Decidual macrophages constitute 20-30% of the total leukocytes in the uterus of pregnant women, regulating the maternal immune tolerance and placenta development. Abnormal number or activities of decidual macrophages (dMs) are associated with fetal loss and pregnancy complications, such as preeclampsia. Monocytes differentiate into dMs in a decidua-specific microenvironment. Despite their important roles in pregnancy, the exact factors that regulate the differentiation into dMs remain unclear. Glycodelin-A (PAEP, hereafter referred to as GdA) is a glycoprotein that is abundantly present in the decidua, and plays an important role in fetomaternal defense and placental development. It modulates the differentiation and activity of several immune cell types residing in the decidua. In this study, we demonstrated that GdA induces the differentiation of human monocytes into dM-like phenotypes in terms of transcriptome, cell surface marker expression, secretome, and regulation of trophoblast and endothelial cell functions. We found that Sialic acid-binding Ig-like lectin 7 (Siglec-7) mediates the binding and biological actions of GdA in a sialic acid-dependent manner. We, therefore, suggest that GdA, induces the polarization of monocytes into dMs to regulate fetomatemal tolerance and placental development.Peer reviewe

Bullet Optical Fiber Humidity Sensor Based on Ag Nanoparticles Dispersed in Leaf Extract of Alstonia Scholaris

An optical fiber with a clad of Ag nanoparticles dispersed in leaf extract of Alstonia Scholaris is used as an optical humidity sensor. The fabricated sensor showed response to humidity in the range of 40-95%. The specialty of this sensor is that it can be used when stored at room temperature (25 oC) up to a maximum of 25 days with 90% retention of original sensitivity. These humidity sensing bio-films showed good operational efficiency for 5 cycles. The plastic optical fiber is versatile and can be used easily for humidity measurement with high sensitivity. The sensor exhibited a short response time of 4-5 sec. and recovery time of 45 sec with repeatability, reproducibility and low hysteresis effect. This Ag dispersed in leaf extract of Alstonia Scholaris showed higher humidity response compared to response shown by the leaf extract alone

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Screening COVID-19 by Swaasa AI platform using cough sounds: a cross-sectional study

Abstract The Advent of Artificial Intelligence (AI) has led to the use of auditory data for detecting various diseases, including COVID-19. SARS-CoV-2 infection has claimed more than six million lives to date and therefore, needs a robust screening technique to control the disease spread. In the present study we created and validated the Swaasa AI platform, which uses the signature cough sound and symptoms presented by patients to screen and prioritize COVID-19 patients. We collected cough data from 234 COVID-19 suspects to validate our Convolutional Neural Network (CNN) architecture and Feedforward Artificial Neural Network (FFANN) (tabular features) based algorithm. The final output from both models was combined to predict the likelihood of having the disease. During the clinical validation phase, our model showed a 75.54% accuracy rate in detecting the likely presence of COVID-19, with 95.45% sensitivity and 73.46% specificity. We conducted pilot testing on 183 presumptive COVID subjects, of which 58 were truly COVID-19 positive, resulting in a Positive Predictive Value of 70.73%. Due to the high cost and technical expertise required for currently available rapid screening methods, there is a need for a cost-effective and remote monitoring tool that can serve as a preliminary screening method for potential COVID-19 subjects. Therefore, Swaasa would be highly beneficial in detecting the disease and could have a significant impact in reducing its spread