Aspirin protects against preeclampsia via p38MAPK signaling pathway

Purpose: To investigate the protective effect of aspirin against preeclampsia and the involvement of p38MAPK signaling pathway in the process.Methods: Sixty pregnant women who underwent antenatal care and delivery at Chancheng Central Hospital from September 2020 to September 2022 were selected and equally assigned to control group (CG) and experimental group (EG). From the 12th week of gestation, EG was administered 100 mg of aspirin and 1000 mg of calcium carbonate daily, while CG was given only 1000 mg of calcium carbonate daily. Both groups were treated up to the 35th week of gestation. Thereafter, blood samples were taken for measurement of serum levels of p38MAPK. In addition, the blood pressure of the women was measured. The incidence of preeclampsia and maternal-infant outcomes were assessed.Results: EG had a lower p38MAPK level at week 35 of pregnancy, and lower blood pressure levels at the 27th and 35th weeks of gestation, than CG (p < 0.05). There were 5 cases of preeclampsia (16.7 %) in EG, and 13 cases (43.3 %) of preeclampsia in CG, with a lower incidence of preeclampsia in EG than in CG (ꭓ2 = 5.079, p < 0.05). The numbers of newborns through premature delivery and cesarean section, as well as Apgar score ≤ 7 were lower in EG than in CG (p < 0.05).Conclusion: Aspirin exerts a protective effect against preeclampsia through via p38MAPK signaling pathway. Therefore, aspirin treatment may be useful in reducing the incidence of preeclampsia and improving maternal-infant outcomes. However, further clinical trials are recommended prior to application in clinical practice

Fabrication and Optimization of High Aspect Ratio Through-Silicon-Vias Electroplating for 3D Inductor

In this study, the filling process of high aspect ratio through-silicon-vias (TSVs) under dense conditions using the electroplating method was efficiently achieved and optimized. Pulsed power was used as the experimental power source and the electroplating solution was prepared with various additive concentrations. Designed control variable experiments were conducted to determine the optimized method. In the control variable experiments, the relationship of multiple experimental variables, including current density (0.25–2 A/dm2), additive concentration (0.5–2 mL/L), and different shapes of TSVs (circle, oral, and square), were systematically analyzed. Considering the electroplating speed and quality, the influence of different factors on experimental results and the optimized parameters were determined. The results showed that increasing current density improved the electroplating speed but decreased the quality. Additives worked well, whereas their concentrations were controlled within a suitable range. The TSV shape also influenced the electroplating result. When the current density was 1.5 A/dm2 and the additive concentration was 1 mL/L, the TSV filling was relatively better. With the optimized parameters, 500-μm-deep TSVs with a high aspect ratio of 10:1 were fully filled in 20 h, and the via density reached 70/mm2. Finally, optimized parameters were adopted, and the electroplating of 1000-μm-deep TSVs with a diameter of 100 μm was completed in 45 h, which is the deepest and smallest through which a three-dimensional inductor has ever been successfully fabricated

pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing

This paper proposes a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing, enabling a CNN model developer to convince a user of the truthful CNN performance over non-public data from multiple testers, while respecting model privacy. To balance the security and efficiency issues, three new efforts are done by appropriately integrating homomorphic encryption (HE) and zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) primitives with the CNN testing. First, a CNN model to be tested is strategically partitioned into a private part kept locally by the model developer, and a public part outsourced to an outside server. Then, the private part runs over HE-protected test data sent by a tester and transmits its outputs to the public part for accomplishing subsequent computations of the CNN testing. Second, the correctness of the above CNN testing is enforced by generating zk-SNARK based proofs, with an emphasis on optimizing proving overhead for two-dimensional (2-D) convolution operations, since the operations dominate the performance bottleneck during generating proofs. We specifically present a new quadratic matrix programs (QMPs)-based arithmetic circuit with a single multiplication gate for expressing 2-D convolution operations between multiple filters and inputs in a batch manner. Third, we aggregate multiple proofs with respect to a same CNN model but different testers' test data (i.e., different statements) into one proof, and ensure that the validity of the aggregated proof implies the validity of the original multiple proofs. Lastly, our experimental results demonstrate that our QMPs-based zk-SNARK performs nearly 13.9$\times$faster than the existing QAPs-based zk-SNARK in proving time, and 17.6$\times$faster in Setup time, for high-dimension matrix multiplication