最新醫(yī)學科研信息
(2014年6月6日)
目 錄
1、A brief history of altmetrics
2、乳腺癌相關基因變異增肺癌風險
3、抗肺癌新藥CM118將進入臨床
4、我國新增肺癌全球居首 吸煙喝酒成致癌主因
5、早產(chǎn)或與胎盤中微生物有關
6、從動物糞便中萃取牲畜飲用水
7、糞便移植逐漸為主流醫(yī)學界接受
8、腸促胰島素類藥物不增加急性胰腺炎風險
1、A brief history of altmetrics
“No one can read everything. We rely on filters to make sense of the scholarly literature, but the narrow, traditional filters are being swamped. However, the growth of new, online scholarly tools allows us to make new filters; these altmetrics reflect the broad, rapid impact of scholarship in this burgeoning ecosystem. We call for more tools and research based on altmetrics. (1)”
The above manifesto signaled the birth of altmetrics. It grew from the recognition that the social web provided opportunities to create new metrics for the impact or use of scholarly publications. These metrics could help scholars find important articles and perhaps also evaluate the impact of their articles. At the time there was already a field with similar goals, webometrics, which had created a number of indicators from the web for scholars (e.g., 2) and scholarly publications (e.g., 3), including genre-specific indicators, such as syllabus mentions (4). Moreover, article download indicators (e.g., 5) had also been previously investigated. Nevertheless, altmetrics have been radically more successful because of the wide range of social web services that could be harnessed, from Twitter to Mendeley, and because of the ease with which large scale data could be automatically harnessed from the social web through Applications Programming Interfaces (APIs). Academic research with multiple different approaches is needed to evaluate their value, however (6).
1 Scholarly use of the social web
Some research has investigated how scholars use social web services, giving insights into the kinds of activities that altmetrics might reflect. In some cases the answers seem straightforward; for example Mendeley is presumably used to store the academic references that users are interested in – perhaps articles that they have previously read or articles that they plan to read. Counts of article “Readers” in Mendeley might therefore be similar to citation counts in the sense that they could reflect the impact of an article. Mendeley has the advantage that its metrics could be available sooner than traditional citations, since there is no publication delay, and its user base is presumably wider than just publishing scientists. Nevertheless, there are biases, such as towards more junior researchers (7).
In comparison to Mendeley, Twitter has a wider user base and a wider range of potential uses. Nevertheless, it seems that only a minority of articles get tweeted – for example, perhaps as few as 10% of PubMed articles in the Web of Science 2010-2012 have been tweeted (8). Scholars seem to use Twitter to cite articles, but sometimes indirectly (9), which may cause problems for automatically harvesting these citations. Moreover, most tweet (link) citations seem to be relatively trivial in the sense of echoing an article title or a brief summary rather than critically engaging with it (10). There are also disciplinary differences in the extent to which Twitter is used and what it is used for (11) and so, as with citations, Twitter altmetrics should not be used to compare between fields. Another problem is that users may also indicate awareness of others’ work by tweeting to them or tweeting about their ideas without citing specific publications (12).
2 Evidence for the value of altmetrics
If article level altmetrics are to be useful to help direct potential readers to the more important articles in their field then evidence would be needed to show that articles with higher altmetric scores tended to be, in general, more useful to read. It would be difficult to get direct empirical verification, however, since data from readers about many articles would be needed to cross-reference with altmetric scores. Perhaps the most practical way to demonstrate the value of an altmetric is to show that it can be used to predict the number of future citations to articles, however, since citations are an established indicator of article impact, at least at the statistical level (more cited articles within a field tend to be more highly regarded by scholars, e.g., 13), even though there are many individual examples of articles for which citations are not a good guide to their value. This has been done for tweets to one online medical journal (14) and for citations in research blogs (15). This approach has double value because it shows that altmetric scores are not random but associate with an established (albeit controversial) impact measure and also shows that altmetrics can give earlier evidence of impact than can citation counts.
A second way of getting evidence of the value of altmetrics is to show that their values correlate with citation counts, without demonstrating that the former preceded the latter (of course, correlation does not imply causation and a lack of correlation does not imply worthlessness, but a correlation does imply a relationship with citation impact or at least some of the factors that cause citation impact). This gives some evidence of the validity of altmetrics as an impact indicator but not of their value as an early impact indicator. For example, a study showed that the number of Mendeley readers of articles in the Science and Nature magazines correlated with their citations, but did not prove that Mendeley reader data was available before citation counts (16).
Although the above studies provide good evidence that some altmetrics could have value as impact indicators for a small number of journals, larger scale studies are needed to check additional indicators and a wider range of journals in order to get more general evidence. In response, a large-scale study investigated 11 different altmetrics and up to 208,739 PubMed articles for evidence of a relationship between citations and altmetric scores gathered for 18 months from July 2011. The study found most altmetrics to have a statistically significant positive (Spearman) correlation with citations but one that was too small to be of practical significance (below 0.1). The exceptions were blogs (0.201), research highlights (0.373) and Twitter (-0.190). The reason for the negative correlation for Twitter, and perhaps also for the low correlations in many other cases, could be the rapid increase in citing academic articles in social media, leading to more recent articles being more mentioned even though they were less cited. This suggests that, in most cases, altmetrics have little value for comparing articles published at different points in time, even within the same year. To assess the ability of altmetrics to differentiate between articles published at the same time and in the same journal, the study ran a probabilistic test for up to 1,891 journals per metric to see whether more cited articles tended to have higher altmetric scores, benchmarking against approximately contemporary articles from the same journal. The results gave statistical evidence of an association between higher altmetric scores and citations for most of them for which sufficient data was available (Twitter, Facebook, research highlights, blogs, mainstream media, forums) (17). In summary, it seems that although many altmetrics may have value as indicators of impact, differences over time are critical and so altmetrics need to be normalized in some way in order to allow valid comparisons over time, or they should only be used to compare articles published at the same time (exception: blogs and research highlights).
3 Other uses for altmetrics
Altmetrics also have the potential to be used for impact indicators for individual researchers based upon their web presences, although this information should not be used as a primary source of impact information since the extent to which academics possess or exploit social web profiles is variable (e.g., 18; 19; 20). More widely, however, altmetrics should not be used to help evaluate academics for anything important, unless perhaps as complementary measures, because of the ease with which they can be manipulated. In particular, since social websites tend to have no quality control and no formal process to link users to offline identities it would be easy to systematically generate high altmetric scores for any given researcher or set of articles.
A promising future direction for research is to harness altmetrics in new ways in order to gain insights into aspects of research that were previously difficult to get data about, such as the extent to which articles from a field attract readerships from other fields (21) or the value of social media publicity for articles (22). Future research also needs to investigate disciplinary differences in the validity and value of different types of altmetrics. Currently it seems that most articles don’t get mentioned in the social web in a way that can be easily identified for use in altmetrics (e.g., 23), but this may change in the future.
4 References
(1) Priem, J., Taraborelli, D., Groth, P. & Neylon, C. (2010) “Altmetrics: A manifesto”, http://altmetrics.org/manifesto/
(2) Cronin, B., Snyder, H.W., Rosenbaum, H., Martinson, A. & Callahan, E. (1998) “Invoked on the Web”, Journal of the American Society for Information Science, Vol. 49, No. 14, pp. 1319-1328.
(3) Vaughan, L. & Shaw, D. (2003) “Bibliographic and web citations: what is the difference?”, Journal of the American Society for Information Science and Technology, Vol.54, No. 14, pp. 1313-1322.
(4) Kousha, K. & Thelwall, M. (2008) “Assessing the impact of disciplinary research on teaching: An automatic analysis of online syllabuses”, Journal of the American Society for Information Science and Technology, Vol. 59, No. 13, pp. 2060-2069.
(5) Shuai, X., Pepe, A., & Bollen, J. (2012) “How the scientific community reacts to newly submitted preprints: Article downloads, Twitter mentions, and citations”, PLOS ONE, Vol. 7 No. 11, e47523.
(6) Sud, P. & Thelwall, M. (2014) “Evaluating altmetrics”, Scientometrics, Vol. 98, No. 2, pp. 1131-1143.
(7) Mohammadi, E., Thelwall, M., Haustein, S. & Larivière, V. (in press) “Who reads research articles? An altmetrics analysis of Mendeley user categories”, Journal of the Association for Information Science and Technology.
(8) Haustein, S., Peters, I., Sugimoto, C.R., Thelwall, M. & Larivière, V. (in press) “Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature”, Journal of the Association for Information Science and Technology.
(9) Priem, J., & Costello, K.L. (2010) “How and why scholars cite on Twitter”, Proceedings of the American Society for Information Science and Technology, Vol. 47, pp. 1-4.
(10) Thelwall, M., Tsou, A., Weingart, S., Holmberg, K. & Haustein, S. (2013) “Tweeting links to academic articles”, Cybermetrics: International Journal of Scientometrics, Informetrics and Bibliometrics, Vol. 17, No. 1, paper 1.
(11) Holmberg, K. & Thelwall, M. (in press) “Disciplinary differences in Twitter scholarly communication”, Scientometrics.
(12) Weller, K., Dr?ge, E. & Puschmann, C. (2011) “Citation analysis in Twitter: Approaches for defining and measuring information flows within tweets during scientific conferences”, In Proceedings of Making Sense of Microposts Workshop (# MSM2011).
(13) Franceschet, M. & Costantini, A. (2011) “The first Italian research assessment exercise: A bibliometric perspective”, Journal of Informetrics, Vol. 5, No. 2, pp. 275-291.
(14) Eysenbach, G. (2011) “Can tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact”, Journal of Medical Internet Research, Vol.13, No. 4, e123.
(15) Shema, H., Bar-Ilan, J. & Thelwall, M. (2014) “Do blog citations correlate with a higher number of future citations? Research blogs as a potential source for alternative metrics”, Journal of the Association for Information Science and Technology, Vol. 65, No. 5, pp. 1018–1027.
(16) Li, X., Thelwall, M. & Giustini, D. (2012) “Validating online reference managers for scholarly impact measurement”, Scientometrics, Vol. 91, No. 2, pp. 461-471.
(17) Thelwall, M., Haustein, S., Larivière, V. & Sugimoto, C. (2013) “Do altmetrics work? Twitter and ten other candidates”, PLOS ONE, Vol. 8, No. 5, e64841. doi:10.1371/journal.pone.0064841
(18) Bar-Ilan, J., Haustein, S., Peters, I., Priem, J., Shema, H. & Terliesner, J. (2012) “Beyond citations: Scholars' visibility on the social Web”, Proceedings of 17th International Conference on Science and Technology Indicators (pp. 98-109), Montréal: Science-Metrix and OST.
(19) Haustein, S., Peters, I., Bar-Ilan, J., Priem, J., Shema, H. & Terliesner, J. (in press) “Coverage and adoption of altmetrics sources in the bibliometric community”, Scientometrics.
(20) Mas Bleda, A., Thelwall, M., Kousha, K. & Aguillo, I. (2013) “European highly cited scientists’ presence in the social web”, In 14th International Society of Scientometrics and Informetrics Conference (ISSI 2013) (pp. 98-109).
(21) Mohammadi, E. & Thelwall, M. (in press) “Mendeley readership altmetrics for the social sciences and humanities: Research evaluation and knowledge flows”, Journal of the Association for Information Science and Technology.
(22) Allen, H.G., Stanton, T.R., Di Pietro, F. & Moseley, G.L. (2013) “Social media release increases dissemination of original articles in the clinical pain sciences”, PloS ONE, Vol. 8, No. 7, e68914.
(23) Zahedi, Z., Costas, R. & Wouters, P. (in press) “How well developed are Altmetrics? Cross-disciplinary analysis of the presence of “alternative metrics” in scientific publications”, Scientometrics.
2、乳腺癌相關基因變異增肺癌風險
一項大規(guī)模國際研究發(fā)現(xiàn),一種與乳腺癌有關的基因變異會顯著增加肺癌風險,尤其是吸煙者如果出現(xiàn)這一基因變異,其患肺癌的風險要比不吸煙者高出近80倍。
研究小組在新一期英國《自然—遺傳學》雜志上介紹說,他們對約1.1萬名患有肺癌的歐洲人與約1.5萬名未患肺癌的人進行了基因狀況對比。結果發(fā)現(xiàn),一種名為“BRCA-2”的基因變異與肺癌患病風險明顯相關。而且醫(yī)學界早就發(fā)現(xiàn),“BRCA-2”與“BRCA-1”這兩種基因變異與乳腺癌、卵巢癌發(fā)病有關。研究人員據(jù)此認為目前針對乳腺癌的療法或許可改善肺癌治療。
“BRCA-2”基因的變異尤其應該引起吸煙者高度警惕。此次研究發(fā)現(xiàn),“BRCA-2”基因變異的吸煙者中,約四分之一的人會患肺癌,而不攜帶此基因變異的吸煙者患肺癌的風險約為15%。
參與研究的英國癌癥研究所專家說,與不吸煙者相比,吸煙者患肺癌的風險要高出40倍。而新研究發(fā)現(xiàn),出現(xiàn)“BRCA-2”基因變異的吸煙者患肺癌的風險高出近80倍,因此這一人群需要高度警惕這種風險,對他們來說最重要的是早日戒煙。
3、抗肺癌新藥CM118將進入臨床
日前,上海再新醫(yī)藥科技有限公司宣布,公司的抗肺癌新藥CM118項目已經(jīng)完成臨床前研究,并向上海市食品藥品監(jiān)督管理局提交臨床申請。
CM118是一種選擇性MET激酶和ALK激酶抑制劑,它是直接針對美國輝瑞公司已上市的新型ALK激酶抑制劑抗肺癌新藥克唑替尼而設計,在保留其優(yōu)點的基礎上,進一步克服克唑替尼的藥物相互作用的缺點,擴展這一類新型抗癌藥與其他抗癌藥聯(lián)合應用的可能性,大幅度提高治療效果。
臨床前試驗結果顯示,CM118很可能達到了預期的設計目標。同時,MET激酶與腫瘤生長、轉移擴散直接有關,它也是目前臨床常用的高端一類抗癌藥如厄洛替尼、吉非替尼、阿瓦斯汀等的重要耐藥性機制。MET激酶抑制劑的臨床試驗證明其對非小細胞肺癌等有很好的效果。
4、我國新增肺癌全球居首 吸煙喝酒成致癌主因
據(jù)世界衛(wèi)生組織下屬的國際癌癥研究機構發(fā)表的《2014年世界癌癥報告》指出,全球癌癥患者數(shù)量正以驚人的速度增加,其中中國新增肺癌全球居首,這也引發(fā)網(wǎng)友對于霧霾與肺癌的關系、戒煙是否真的能控制肺癌等的熱議。
吸煙喝酒成都市人致癌主因
據(jù)世界衛(wèi)生組織下屬的國際癌癥研究機構發(fā)表的《2014年世界癌癥報告》指出,2012年導致死亡最多的前三類癌癥依次是肺癌、肝癌和胃癌,它們都與生活方式密切相關。北京大學腫瘤醫(yī)院黨委副書記楊躍在接受中新網(wǎng)健康頻道專訪時表示,吸煙已成為肺癌發(fā)病的首因,根據(jù)統(tǒng)計,得肺癌的病人有87%跟吸煙相關,21%的冠心病人曾吸煙,全身的各個器官的腫瘤有31%跟吸煙相關,而我們肺部的慢性疾病加重更是有82%和跟吸煙相關。
“如果說霧霾是個大環(huán)境、外環(huán)境,對于肺癌而言還有一個凈環(huán)境,吸二手煙,生活場所,咱們生活的居室,這種小環(huán)境,也是非常重要,呼吸道直接誘導呼吸道得病的因素叫內環(huán)境,男性吸煙導致的二手煙對家族里的女性,如母親、姐姐等最有影響。”楊躍說道。
他表示,目前國際上現(xiàn)在又新的一個理念“三手煙”,也值得警惕和預防。所謂的三手煙,是男性沒有在家抽煙,他們在外頭應酬抽完煙以后,回家后沒有在屋里頭抽煙,也沒有給太太兒子帶來二手煙的機會,但是他們脫掉的衣服放在洗衣機旁邊,“太太要洗衣服,孩子們會看看兜里面有什么東西,這個抖的過程當中吸的是三手煙,已經(jīng)越來越多的被醫(yī)學家們關注了。”
除了吸煙以外,喝白酒、生活不規(guī)律、精神緊張是都市白領和民眾致癌的原因。楊躍透露,自身免疫力在老百姓來說是些很簡單的道理,比如這一宿值夜班了,過年人好不容易回來了,大家打個牌,看個春晚聊聊天,喝點小酒,吃個餃子,大半宿過去入睡已經(jīng)三四點鐘了,大家想想,這樣接連幾天下來,是不是突然發(fā)現(xiàn)體制明顯下降,感冒了,這些都說明生活不規(guī)律。
“一個突發(fā)的不規(guī)律的生活勢必要得病,得小病能過去,得大病就等于把定時炸彈潛伏在身體里了。人的作息時間就像一個生物鐘,生物鐘有一個波動,所以不管男性還是女性都一樣,該休息的時候休息,學會養(yǎng)精蓄銳。”楊躍建議道。
歐美國家推強行戒煙 中國主要靠媒體宣傳
隨著癌癥發(fā)病率的居高不下,近20年時間里,英法德美等國強行在國內實行戒煙,新加坡推行更強行的戒煙。不過我國在戒煙方面的教育主要是通過媒體進行宣傳。
楊躍在接受中新網(wǎng)健康頻道采訪時表示,近20年時間里,英法德等國強行在國內實行戒煙,盡量減少香煙。他們做的國民流行病肺癌調查發(fā)現(xiàn),吸煙者少了,肺癌發(fā)病率下降了。美國人也發(fā)現(xiàn)這個事實了,他們也重視這個,新加坡更加強行的戒煙。美國人也發(fā)現(xiàn),吸煙人數(shù)下降,肺癌發(fā)病率也下降了,美國人把一些高危吸煙人群強行送去體檢,做一個很簡單的胸部的CT。早診的人多了,他們發(fā)現(xiàn)治愈率也提高了。
“我國的報紙,網(wǎng)絡,電視,廣播都在宣傳吸煙有害,但這樣潛移默化的宣傳需要無時無刻去做。他說,國外戒煙分幾部曲,一是醫(yī)務人員、心理醫(yī)生的宣教,跟戒煙者接觸、交談,進一步到規(guī)勸:怎么從生活上注意,在哪些環(huán)節(jié)讓吸煙的想法淡化。國外有各種各樣的戒煙措施甚至藥品,醫(yī)院工作者會提供給抽煙人,甚至給他安排戒煙的時間段計劃表。”楊躍說,國外已經(jīng)把吸煙作為一種危險的生活習慣看待,必須戒掉。
同時楊躍還表示,目前我國戒煙多靠媒體和醫(yī)院宣傳健康知識,力度遠遠不夠,還需要建立分步驟的實際操作方法,從操作技術上多一些改變。“外部在監(jiān)督力度上還是應該加大,監(jiān)督方式上需要改變,多一些技巧。盡管國內現(xiàn)在到處都有禁止吸煙的標識場所,但再怎么標識,如醫(yī)院里也標識了整個外科大樓、門診大樓不許抽煙,但還是有些家屬就在這些大樓樓道里抽煙,“我們樓道是給病人去做鍛煉,走路,鍛煉,練習肺活量的地方,我們現(xiàn)在跟病人說不許到那兒去了,到那兒煙味太大了。”但在新加坡,將供吸煙使用的垃圾筒放在離住院大樓5米以外,有的人嫌遠就不抽了。
楊躍接著表示,光靠醫(yī)生和媒體工作者去宣傳吸煙有害成效不大,重點是讓吸煙的人去實施戒煙的過程。國外有借鑒的做法。國外戒煙是分幾部曲,有步驟、分階段進行。國外先是由戒煙或者醫(yī)務人員、心理醫(yī)生進行宣教,然后會跟吸煙者接觸、逐漸的交談、進一步規(guī)勸,讓吸煙者從生活上注意哪些環(huán)節(jié)去淡化吸煙的欲望,“乃至到在國外也有一些戒煙的措施甚至藥品,醫(yī)院會給吸煙者安排戒煙的時間段、計劃表,我國光靠媒體和醫(yī)生宣傳的作用還是不大。”
讓“小皇帝”規(guī)勸監(jiān)督家人戒煙是好方式
戒煙,對于每一個擁有長煙齡的人來說,都是一件痛苦的事,楊躍在接受中新網(wǎng)健康頻道采訪時表示,在戒煙的環(huán)節(jié)上,首先要給兒童或者青少年傳授健康知識,現(xiàn)在的家庭大多是獨生子女,父輩會比較重視孩子們的反應。“將吸煙有害的知識傳授給孩子,他第一時間的腦子里接觸了這個知識,他知道這個太可怕了,會介紹給爸爸媽媽”。
他認為,不僅是傳授知識,并且可以讓孩子去監(jiān)督父輩們戒煙,這是一種比較有效的勸誡方式。“其實通過我們的媒體、這些老師們的宣傳,監(jiān)督的力度還是不夠。但他們回到家里有一個小皇上式的孩子們去限制他們,效果可能好很多了。”
5、早產(chǎn)或與胎盤中微生物有關
美國一個研究小組說,孕婦胎盤并不像人們此前認為的那樣是一個無菌環(huán)境,而是生存著一個小型但多元的微生物群落。這些微生物直接影響胎兒的健康,甚至可能與胎兒是否早產(chǎn)有關。
微生物群落是細菌、病毒與真菌等的總稱。休斯敦貝勒醫(yī)學院副教授謝斯蒂?奧高及同事報告說,新研究表明,孕婦胎盤中的微生物可能來自口腔,說明孕婦口腔健康對胎兒健康至關重要。此外,早產(chǎn)胎兒與足月產(chǎn)胎兒的胎盤微生物群落組成存在明顯不同,說明胎盤微生物群落與早產(chǎn)之間可能存在關聯(lián)。
長期以來,醫(yī)學界認為胎盤是一個無菌的環(huán)境。而奧高等人此前發(fā)現(xiàn),嬰兒出生時腸道內就有微生物群落存在,但與孕婦陰道中的微生物群落并不匹配,因此猜測新生兒腸道內的微生物群落存在其他來源,最有可能就是胎盤。
在新研究中,奧高等人利用宏基因組鳥槍測序法,分析320個來自捐贈的胎盤的微生物組成,發(fā)現(xiàn)胎盤中有大約300種微生物存在,不過水平較低,其中大多數(shù)微生物都發(fā)揮著重要作用,比如為發(fā)育中的胎兒代謝維生素。
宏基因組又稱元基因組或生態(tài)基因組,是指與人類共生的全部微生物的基因總和。鳥槍測序首先將整個基因組打亂,切成隨機片段,然后測定每個小片段的序列,最終利用計算機對這些切片進行排序和組裝,并確定它們在基因組中的正確位置。
研究還表明,胎盤中數(shù)量最多的是腸道中常見的、不致病的大腸桿菌,兩種口腔菌坦納氏普雷沃氏菌與奈瑟菌數(shù)量也相對較多。總體而言,胎盤的微生物群落組成與在口腔中發(fā)現(xiàn)的微生物群落最為相似。
奧高猜測,口腔微生物可能首先“溜”入孕婦血液之中,然后遷徙到胎盤“定居”。她說,這說明女性懷孕期間保持口腔健康的重要性,“強化了一個長期以來的觀點——牙周疾病與早產(chǎn)風險存在關系”。
研究人員還發(fā)現(xiàn),早產(chǎn)孕婦胎盤的微生物群落組成不同于足月產(chǎn)孕婦的胎盤。但奧高表示,目前還不清楚是否是這種差異造成早產(chǎn),接下來計劃觀察500多名有早產(chǎn)風險的孕婦,以進一步探究其中的聯(lián)系,這也將有助于開發(fā)預測女性早產(chǎn)的診斷工具以及幫助她們預防早產(chǎn)的新策略。
6、從動物糞便中萃取牲畜飲用水
美國密西根州立大學的研究有突破,欲從糞便中萃取飲水,水的潔凈程度,給牲畜飲用,是綽綽有余。該項研究有望在今年內投入商業(yè)運用,這項技術對于水資源匱乏地區(qū)的畜牧業(yè)者,特別有價值。
報道稱,密西根州立大學的研究團隊,最先是運用微生物分離技術,從糞便中萃取能源和化學物質,后來,它們進一步透過超過濾、空氣脫吸以及逆滲透技術,萃取糞便中的水份,水的潔凈程度,給牲畜飲用,是綽綽有余。動物糞便中,有大約九成是水份,以密西根大學目前的技術,大概每100加侖(約合378.5升)的糞便,可以萃取到5加侖的水。
7、糞便移植逐漸為主流醫(yī)學界接受
糞便移植為治療很多疾病提供了新希望。但該領域的先驅者表示,還需要對它們進行更加科學地研究。
在擔任荷蘭阿姆斯特丹學術醫(yī)學中心(AMC)內科醫(yī)生之后不久,Max Nieuwdorp遇到了一個棘手的病例:一名81歲的女性因尿路感染引起的并發(fā)癥而入院治療。她有嚴重的褥瘡,且高燒不退、無法進食。在抗生素已經(jīng)消滅了病人的結腸微生物種群后,一種名為艱難梭菌的機會性致病菌入侵了她的身體,引起了嚴重腹瀉和炎癥性腸病。
成功案例
單單在美國,艱難梭菌這個“臭名昭著”的病原體在一年中已經(jīng)至少使1.4萬人喪命。治療中,這名女性患者使用了幾個療程的萬古霉素(這類病例中的常用抗生素)。但是,正如經(jīng)常發(fā)生的一樣,細菌產(chǎn)生了抗藥性。
Nieuwdorp不甘眼睜睜地看著病人生命的流逝。“我很年輕和幼稚。”他說,并開始檢索醫(yī)學期刊數(shù)據(jù)庫以尋找任何可以挽救病人生命的方法。當他找到1958年Ben Eiseman(當時是美國科羅拉多大學丹佛分校的內科醫(yī)生)的論文時,他知道自己該如何做了。我打算采取糞便移植的治療措施,Nieuwdorp告訴他的主管——Joep Bartelsman。
很快Bartelsman意識到Nieuwdorp并不是在開玩笑,他同意了Nieuwdorp的方案。治療方案很簡單:他們將對該病人進行結腸沖洗(希望借此也能清除艱難梭菌),并用來自捐贈者(她的兒子)的健康菌群替代。他們將她兒子的排泄物和鹽水混合,通過插在鼻子上的一個薄塑料管,將混合物直接注射入病人的十二指腸。
治療三天后,該病人出院了。Nieuwdorp和Bartelsman決定在接下來的幾個月治療另外6名艱難梭菌患者。由于這種不尋常的治療會令人尷尬,他們都會等到同事們午飯休息時才開展工作。其中4名病人立刻痊愈,另外兩人接受了來自第二名捐贈者的糞便移植。
但是,當Nieuwdorp將結果呈現(xiàn)在醫(yī)院會議上時,一名內科醫(yī)生提出了質疑:“如果你想通過糞便治療艱難梭菌感染者,你為什么不把該方法也應用到心血管病人身上呢?”
類似的懷疑已成過去時。現(xiàn)在很多醫(yī)生都同意艱難梭菌腸道感染能夠通過糞便移植的方法治愈。研究人員還認為,這種大規(guī)模替代腸道微生物菌群的方法也有助于治療其他疾病,例如炎癥性腸病、糖尿病和難以捉摸的慢性疲勞綜合征。越來越多的醫(yī)生采用了糞便移植這種治療措施。
Nieuwdorp說,現(xiàn)在仍缺失的是一個真正科學的方法來開展糞便移植。Nieuwdorp已經(jīng)成為推廣更多研究的主要倡導者。今年1月,AMC團隊在《新英格蘭醫(yī)學雜志》(NEJM)上發(fā)表的文章描述了一個糞便移植的隨機對照臨床試驗——這類研究首次被公開報道。Nieuwdorp還和其他實驗室科學家開展合作,以更好地理解其作用機制。他希望,這些研究最終能幫助醫(yī)生由糞便移植轉為更精細的治療手段:給病人注入選定的菌株。
成為主流
Eiseman開創(chuàng)性的論文發(fā)表在《外科學》雜志上,描述了用肛門灌注液狀糞便的方法治愈了4名患假膜性小腸結腸炎的病人。(癥狀和艱難梭菌嚴重感染的病人相似,但可能由一種不同的細菌引起。)這不是首次在醫(yī)療中使用糞便,用糞便懸浮液治療食物中毒和嚴重腹瀉首次由中國醫(yī)生于4世紀進行,到了17世紀,它們被用來治療有腸道疾病的乳牛。
2010年,《紐約時報》刊登了一篇文章——美國明尼阿波里斯市明尼蘇達大學醫(yī)學中心的胃腸病學家Alexander Khoruts用糞便移植的方法成功治愈了一名艱難梭菌嚴重感染的患者,之后美國學界對糞便移植的研究興趣愈發(fā)濃厚。Nieuwdorp說:“我意識到,為了讓這個療法能夠被醫(yī)生所接受,我們必須開展隨機臨床試驗。”
隨后的研究比較了糞便移植和萬古霉素或萬古霉素和腸道沖洗相結合的方式的療效。研究人員選定了120名患者,但研究數(shù)據(jù)和安全監(jiān)測在對43名病人進行試驗后即終止,因為繼續(xù)下去將不符合道德要求:94%接受糞便移植的患者得到治愈,相比而言,試驗對照組的數(shù)據(jù)分別只有31%和23%。這一結果被發(fā)表在NEJM上,“這使糞便移植又向主流醫(yī)學邁進了一步。”Khoruts說。
作用機制
了解糞便移植的作用原理是使治療更加安全的關鍵。捐獻者的糞便在術后會不會遺留在患者體內?哪一種細菌具有左右健康與疾病的能力?移植的微生物是如何與患者體內的微生物相互作用的?Nieuwdorp與荷蘭瓦赫寧根大學微生物生態(tài)學家Willem de Vos(厭氧菌類的專家)展開合作,他們的團隊是人類腸道領域研究的翹楚。de Vos說:“我們已經(jīng)證明,一些重要的菌種在艱難梭菌患者體內喪失了,而另外一些有害的菌種大行其道。”他的研究還證明,艱難梭菌患者體內的微生物多樣性程度僅僅與一名1歲大的兒童相當。但經(jīng)過抑制治療之后,來自捐贈者的厭氧性細菌會停留在患者的腸道內,幫助患者恢復微生物多樣性。
Nieuwdorp的同事還包括瑞典哥德堡大學的Fredrik Backhed,Backhed管理著一座家鼠實驗設施,那里的試驗對象會在完全無菌的條件下生長,使科學家得以研究特定菌種的效果。Nieuwdorp說:“我們正在對不同的捐贈者進行試驗,以便找出可以左右健康與疾病的超級細菌。”
實驗的希望在于:醫(yī)生最終能夠控制這些細菌的排泄與灌輸。但澳大利亞消化疾病中心的胃腸病學家Thomas Borody說,這種經(jīng)過培養(yǎng)的鋇灌腸可能會產(chǎn)生副作用,相比擁有完整生態(tài)系統(tǒng)的糞便,其治愈效果要低。并且隨著在實驗室中不斷繁殖,細菌可能會發(fā)生變異,喪失治愈能力。
許多人仍然相信鋇灌腸是行之有效的方法。最近,一個由日本東京大學Kenya Honda領導的小組報告:在治療患有結腸炎和過敏性腹瀉的老鼠的過程中,研究人員以17種無害的梭菌(曾被證明可以刺激免疫系統(tǒng)分泌調節(jié)T細胞)為治療手段,有效地抑制了免疫反應過度。
在一項名為RePOOPulate的實驗項目中,一個由加拿大金斯頓皇后大學的Elaine Petrof和圭爾夫大學的Emma Allen-Vercoe領導的研究小組,成功開發(fā)出一個由33個菌種組成的糞便裝置,用于治療艱難梭菌和炎癥性腸病。他們希望這些菌種在為完整的糞便移植提供幫助的同時風險更小。Allen-Vercoe最初培養(yǎng)了70個菌種,Petrof以每一種菌種的致病性和抗生素抗性為依據(jù),最終從中選出了33種。她說,在作最終選擇的時候她依靠的是自己的判斷力:“我會把這坨臭烘烘的東西塞進我媽媽的身體里嗎?不會!那么我將把這個菌種剔除出去。”
一家名為Rebiotix的美國公司也是同道中人。最近,美國食品藥品監(jiān)督管理局放行了旨在治愈艱難梭菌的一項臨床二期實驗。Rebiotix公司的創(chuàng)立者兼CEO Lee Jones在一份郵件中寫道:“我們并不認為本公司的產(chǎn)品是糞便移植,相反,我們正在開發(fā)的是一種基于生物醫(yī)藥形式的微生物修復治療方法。”
Nieuwdorp認為這種治療方法還存在多種可能性,但他認為要實現(xiàn)這些可能性需要時間。他說:“現(xiàn)在我36歲,如果到我60歲的時候微生物群分析可以成為醫(yī)院實驗室的標準程序,我將感到非常開心。”目前,糞便移植的禁忌已經(jīng)不復存在,Nieuwdorp對此感到非常開心。
8、腸促胰島素類藥物不增加急性胰腺炎風險
針對糖尿病現(xiàn)有的治療情況,比如胰島β細胞功能不斷衰竭、患者血糖水平持續(xù)升高、降糖藥物可能增加低血糖風險和患者體重增加以及繼發(fā)性藥物失效等問題,基于腸促胰島素的治療方案應運而生。
腸促胰島素是人體在進食后,腸道細胞分泌的一些多肽類激素,其作用是增加胰島素的分泌,以維持血糖正常,好比血糖的內源性“調節(jié)器”,只有在血糖升高的時候,才會刺激胰島素分泌。
胰升糖素樣肽-1(GLP-1)類似物和二肽基肽酶4(DPP-4)是新一代抗糖尿病藥物。而腸促胰島素類藥物是否會引起急性胰腺炎仍沒有一致的研究結果。
鑒于腸促胰島素類藥物使用人數(shù)不斷上升,并出于對這類藥物安全性方面的擔憂,來自加拿大猶太總醫(yī)院臨床流行病學中心的Faillie等進行了一項研究,探討腸促胰島素類藥物是否會增加急性胰腺炎風險。
該研究是一項以人口為基礎的隊列研究,研究數(shù)據(jù)來自英國臨床研究數(shù)據(jù)鏈中680家全科診所。從2007年1月1日到2012年3月31日期間,F(xiàn)aillie等對20748例腸促胰島素類藥物使用者與51712例磺酰脲類降糖藥使用者進行比較,并隨訪至2013年3月31日。采用Cox比例風險模型評估腸促胰島素類藥物與磺酰脲類藥物使用者的急性胰腺炎風險比。
結果顯示,腸促胰島素類藥物使用者的急性胰腺炎發(fā)生率為1.45/1000人年,而磺酰脲類藥物使用者為1.47/1000人年。相對磺酰脲類而言,腸促胰島素類藥物并不增加急性胰腺炎風險。
該研究提示,與磺酰脲類相比,腸促胰島素類藥物不增加急性胰腺炎風險。Faillie指出,雖然這一研究結果讓人們對腸促胰島素類藥物放心,但仍不能排除風險輕微升高,因此仍需要更多的研究來證實。
目前,已在我國上市的GLP-1類似物有:艾塞那肽注射液(百泌達)、利拉魯肽注射液(諾和力)等;DPP-4抑制劑已上市的有:西格列汀(捷諾維)、沙格列汀(安麗澤)、維格列汀(佳維樂)等