《基質生物學》(創(chuàng)建于1980年，前身為《膠原蛋白及其相關研究》)是一本致力于發(fā)表基質生物學研究最新成果的前沿期刊。我們歡迎與理解細胞外基質的細胞和分子病理生理學相關的文章。矩陣生物學專注于解決難以捉摸的問題，開辟新的思路和發(fā)現途徑，挑戰(zhàn)長期存在的生物學范式。考慮評審并最終發(fā)表的論文，必須包含與重要生理或病理過程相關的力學和功能見解支持的實質性進展。這些方面是至關重要的，是出版所必需的。矩陣生物學歡迎原創(chuàng)的研究文章，簡短的報告，評論，(迷你)評論和公告。更多信息請查看我們的作者指南。向矩陣生物學提交下一篇文章的理由:矩陣生物學的第一雜志;發(fā)表的文章對全世界的研究人員具有普遍的吸引力、價值和實用價值。高影響度量:在左側列中找到當前度量。該領域的關鍵研究人員組成的國際委員會。嚴格、公平、高效的同行評審流程。你的論文你的方式:沒有不必要的格式要求。靈活發(fā)布:您可以選擇發(fā)布開放訪問?？焖俪霭?您的文章可在接受后一周內在線。在線版本不收頁面費和顏色費。選擇聯(lián)合提交數據文章到數據簡要。

Matrix Biology (established in 1980 as Collagen and Related Research) is a cutting-edge journal that is devoted to publishing the latest results in matrix biology research. We welcome articles that reside at the nexus of understanding the cellular and molecular pathophysiology of the extracellular matrix. Matrix Biology focusses on solving elusive questions, opening new avenues of thought and discovery, and challenging longstanding biological paradigms.Papers considered for review, and ultimately for publication, must contain substantial advances supported by mechanistic and functional insights germane to important physiological or pathological processes. These aspects are crucial and required for publication.Matrix Biology welcomes original research articles, brief reports, commentaries, (mini) reviews, and announcements. Please view our Guide for Authors for more information.Reasons to submit your next article to Matrix Biology:The premier journal in matrix biology; published articles have a universal appeal, value, and utility to the researcher community worldwide.High impact metrics: find our current metrics in the left-hand column.An international board of key researchers in the field.Rigorous, fair and efficient peer review process.Your paper your way: no unnecessary formatting requests.Flexible publication: you can choose to publish open access.Rapid publication: your article available online within a week after acceptance.No page charges and no color charges for online version.Option of co-submitting a data article to Data in Brief.