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- Marcotte, Harold, et al.
(författare)
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Conversion of monoclonal IgG to dimeric and secretory IgA restores neutralizing ability and prevents Infection of Omicron lineages
- 2024
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : NATL ACAD SCIENCES. - 0027-8424 .- 1091-6490. ; 121:3
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Tidskriftsartikel (refereegranskat)abstract
- The emergence of Omicron lineages and descendent subvariants continues to present a severe threat to the effectiveness of vaccines and therapeutic antibodies. We have previ- ously suggested that an insufficient mucosal immunoglobulin A (IgA) response induced by the mRNA vaccines is associated with a surge in breakthrough infections. Here, we further show that the intramuscular mRNA and/or inactivated vaccines cannot suffi- ciently boost the mucosal secretory IgA response in uninfected individuals, particu- larly against the Omicron variant. We thus engineered and characterized recombinant monomeric, dimeric, and secretory IgAl antibodies derived from four neutralizing IgG monoclonal antibodies (mAbs 01A05, rmAb23, DXP-604, and XG014) targeting the receptor-binding domain of the spike protein. Compared to their parental IgG antibod- ies, dimeric and secretory IgAl antibodies showed a higher neutralizing activity against different variants of concern (VOCs), in part due to an increased avidity. Importantly, the dimeric or secretory IgAl form of the DXP-604 antibody significantly outperformed its parental IgG antibody, and neutralized the Omicron lineages BA.1, BA.2, and BA.4/5 with a 25- to 75-fold increase in potency. In human angiotensin converting enzyme 2 (ACE2) transgenic mice, a single intranasal dose of the dimeric IgA DXP-604 conferred prophylactic and therapeutic protection against Omicron BA.5. Thus, dimeric or secre- tory IgA delivered by nasal administration may potentially be exploited for the treatment Iand prevention of Omicron infection, thereby providing an alternative tool for combating immune evasion by the current circulating subvariants and, potentially, future VOCs.
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| 2. |
- Tang, Jinrui, et al.
(författare)
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Data-Driven State of Health Estimation Method of Lithium-ion Batteries for Partial Charging Curves
- 2024
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Ingår i: IEEE Transactions on Energy Conversion. - 1558-0059 .- 0885-8969. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- State of health (SOH) is one of the most important performance indicators of lithium-ion batteries (LIBs). Accurate estimation of SOH is a prerequisite for the safe and reliable operation of LIBs. Traditional SOH estimation methods predominantly rely on complete charging cycle data acquired through laboratory testing. However, in practical application, the charging behaviors of electric vehicle users are random and unpredictable, making the partial charging curves difficult to utilize the traditional methods. This work introduces a novel data-driven approach to estimating a battery's SOH for partial charging cases. Firstly, a curve fitting method is proposed to extract health indicators (HIs) from partial charging voltage data, where novel HIs based on the energy-voltage curve are extracted. A composite Gaussian process regression-based data-driven method is proposed to achieve highly accurate SOH estimation. The method's adaptability to real-world partial charging habits is evaluated through three representative scenarios derived from extensive charging behavior reports of EV users. The impact of partial charging on HI extraction is analyzed based on the three identified scenarios. The proposed method is verified using a combination of our laboratory testing data and the Oxford open dataset. The results show that the proposed framework demonstrates the ability to estimate SOH accurately and strong robustness to various partial charging behaviors.
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