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This study introduced a novel dual fixation method for the pulmonary vasculature and lung tissue in pulmonary hypertension (PH) rats, addressing the limitations of traditional fixation methods that failed to accurately preserve the in vivo status of pulmonary vascular morphology. The modified method involved a dual fixation process, combining individualized ventilation support and vascular perfusion to simulate the respiratory motion, pulmonary artery pressure and right ventricular output of the rat under in vivo conditions. Utilizing a monocrotaline-induced PH rat model, this study compared the dual fixation with the traditional immersion fixation, focusing on the quantitative assessment of alveolar expansion degree, capillary patency, endothelial cell quantity and wall thickness of pulmonary vein and artery. The results demonstrated that the dual fixation is superior in maintaining the authenticity and integrity of lung tissue and more sensitive in the evaluation of pulmonary artery hypertrophy, providing a more reliable representation of pulmonary vascular remodeling associated with PH. [ABSTRACT FROM AUTHOR]

Over the past century, the field of antibody discovery has undergone significant evolution, excluding the current exploration stage of artificial intelligence‐based antibody generation and the often overlooked non‐animal sourced antibody discovery, which typically requires mature in vitro affinity and the selection of high‐quality antigen formulations. This journey has traversed various stages, from methods involving serum‐based antibody acquisition, the isolation of B cells capable of perpetual antibody production through hybridoma technology, to the in‐depth exploration of genetic material using the phage display system, and the current stage involving diverse single B cell screening techniques. Additionally, the emergence of machine learning has brought impressive scientific and technological breakthroughs across research domains, proving to be a powerful application in the field of antibody discovery. However, each technique comes with its limitations, such as variability and control challenges in serum‐based acquisition, lengthy and difficult hybridoma‐derived antibody development, potential limitations in sequence and epitope diversity due to immunization biases in phage display techniques, and costly single B cell screening. Protein mass spectrometry sequencing, with shorter acquisition time and lower costs, is seen as a shortcut by diagnostic companies, impacting traditional antibody development. In diagnostic antibody development, methodological differences in downstream assays and the impact of constant regions outside the Fv core are often neglected. This paper deeply analyzes challenges, proposing innovative strategies for the next generation of diagnostic antibody development. Aimed at moving closer to the gold standard of antibody discovery, these strategies enhance the competitiveness of diagnostic reagent products. [ABSTRACT FROM AUTHOR]

We report acute antibody responses to SARS-CoV-2 in 285 patients with COVID-19. Within 19 days after symptom onset, 100% of patients tested positive for antiviral immunoglobulin-G (IgG). Seroconversion for IgG and IgM occurred simultaneously or sequentially. Both IgG and IgM titers plateaued within 6 days after seroconversion. Serological testing may be helpful for the diagnosis of suspected patients with negative RT-PCR results and for the identification of asymptomatic infections. A cross-sectional study of hospitalized patients with COVID-19 and a longitudinal follow-up study of patients with COVID-19 suggest that SARS-CoV2-specific IgG or IgM seroconversion occurs within 20 days post symptom onset., Author(s): Quan-Xin Long [sup.1] , Bai-Zhong Liu [sup.2] , Hai-Jun Deng [sup.1] , Gui-Cheng Wu [sup.3] [sup.4] , Kun Deng [sup.5] , Yao-Kai Chen [sup.6] , Pu Liao [sup.7] , [...]

Social distancing restrictions have become a primary measure in curbing the spread of the COVID-19 pandemic. Given the fact that the survival probability of infected individuals and labour supply will decline during the pandemic, this study extends the overlapping generations (OLG) model to include the effects of COVID-19 on individuals' health and investigates the optimal social distancing policy from an economic perspective. Our results show that the pandemic can cause economic contraction, and a strict social distancing policy generally benefits economic growth. Nevertheless, absolute zero socializing may not be an optimal strategy. [ABSTRACT FROM AUTHOR]