Objective: To find the time window of normalization of tumor vasculature by endostar in tumor-bearing mice with ovarian cancer.
Methods: The nude murine model of ovarian cancer was established. The vasculature markers α-SMA (alpha-smooth muscle actin) and collagen IV covered tumor vessels and hypoxic zone following the treatment of endostar were monitored. According to the changes of microvascular morphology and tumor hypoxia zone, the time window was identified. Furthermore the treatment protocols of scheduling dosing of cisplatin plus endostar at different time points were designed. After treatment tumor volume, the parameters of microvascular density (MVD) and PCNA (proliferating cell nuclear antigen) were monitored to evaluate the effects of different protocols.
Results: At Days 4 and 6 post-treatment, more α-SMA and collagen IV covered vessels could be observed. The amount of microvasculature expressed α-SMA on days 4 compared with control was (15.3 ± 5.2) vs (4.3 ± 2.1)/mm(2) (P < 0.01); at Day 6, the result was (16.4 ± 4.6) vs (6.6 ± 2.4)/mm(2)(P < 0.01). The expression of collagen IV had a similar change. And the numbers of microvasculature expressing collagen IV was (14.7 ± 4.3) vs (6.7 ± 5.1)/mm(2) at Days 4 and 6 (P < 0.01); (18.4 ± 5.5) vs (7.1 ± 1.7)/mm(2) (P < 0.01). The expression of HIF-1α decreased in hypoxic area. In the rhES + DDP (d4-6) group, the value of microvessel density (MVD) decreased and the expression of PCNA significantly decreased versus other groups.
Conclusion: Endostar may normalize the tumor vasculature. And the time window is found at Days 4-6 post-treatment. During the time of vascular normalization, a combination therapy of endostar plus cisplatin has optimal efficacies.
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Arch Dermatol Res
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Department of Dermatology, Drexel University College of Medicine, 860 1St Avenue, Suite 8B, Philadelphia, PA, 19406, USA.
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Deep and accurate proteome analysis is crucial for understanding cellular processes and disease mechanisms; however, it is challenging to implement in routine settings. In this protocol, we combine a robust chromatographic platform with a high-performance mass spectrometric setup to enable routine yet in-depth proteome coverage for a broad community. This entails tip-based sample preparation and pre-formed gradients (Evosep One) combined with a trapped ion mobility time-of-flight mass spectrometer (timsTOF, Bruker).
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The Faculty of Data and Decisions Sciences, Technion - Israel Institute of Technology, Haifa, Israel.
Large Language Models (LLMs) have shown success in predicting neural signals associated with narrative processing, but their approach to integrating context over large timescales differs fundamentally from that of the human brain. In this study, we show how the brain, unlike LLMs that process large text windows in parallel, integrates short-term and long-term contextual information through an incremental mechanism. Using fMRI data from 219 participants listening to spoken narratives, we first demonstrate that LLMs predict brain activity effectively only when using short contextual windows of up to a few dozen words.
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