| 2 | Arabidopsis tic62 trol Mutant Lacking Thylakoid- Bound Ferredoxin-NADP+ Oxidoreductase Shows Distinct Metabolic Phenotype显示文摘FerredoxinNADP+ oxidoreductase (FNR ) ,在光合的电子转移链的最后步工作,当在叶绿体基质并且紧的可溶的蛋白质属于叶绿体膜,两个都存在。表面电浆子回声试金证明二 FNR isoforms, LFNR1 和 LFNR2,以一种 pH 依赖的方式经由 Tic62 和 TROL 蛋白质的 C 终端领域被绑在 thylakoid 膜。tic62 trol 两倍异种包含了 FNR 的减少的水平,只在可溶的基质发现。尽管变异的植物没显示出视觉显型或在在任何条件下面的 photosystems 的功能的缺点学习了, NADPH/NADP+ 的低比率被检测。自从 CO2 固定,能力没在 tic62 trol 植物之间不同并且野类型,看起来植物对把力量归结为很关键的反应保证植物的幸存和健康的漏斗有能力。然而, malate 脱氢酶的活动在变异的植物是下面调整的。显然,质体新陈代谢能在从轻反应指导电子到 stromal 新陈代谢并且这样应付实质的变化仅仅很少差别在 tic62 trol 植物的不变的代谢物水池尺寸是可见的。 | Minna Lintala Natalie Schuck Ina Thormaihlen Andreas Jungfer Katrin L. Weber Andreas P.M. Weber Peter Geigenberger Juirgen Soil Bettina Bolter Paula Mulo | 2014 | Molecular Plant2014，7，1： | 0 |
| 3 | Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis:A systematic review and meta-analysis显示文摘BACKGROUND Subepithelial lesions(SELs)are gastrointestinal tumors with heterogeneous malignant potential.Endoscopic ultrasonography(EUS)is the leading method for evaluation,but without histopathological analysis,precise differentiation of SEL risk is limited.Artificial intelligence(AI)is a promising aid for the diagnosis of gastrointestinal lesions in the absence of histopathology.AIM To determine the diagnostic accuracy of AI-assisted EUS in diagnosing SELs,especially lesions originating from the muscularis propria layer.METHODS Electronic databases including PubMed,EMBASE,and Cochrane Library were searched.Patients of any sex and>18 years,with SELs assessed by EUS AIassisted,with previous histopathological diagnosis,and presented sufficient data values which were extracted to construct a 2×2 table.The reference standard was histopathology.The primary outcome was the accuracy of AI for gastrointestinal stromal tumor(GIST).Secondary outcomes were AI-assisted EUS diagnosis for GIST vs gastrointestinal leiomyoma(GIL),the diagnostic performance of experienced endoscopists for GIST,and GIST vs GIL.Pooled sensitivity,specificity,positive,and negative predictive values were calculated.The corresponding summary receiver operating characteristic curve and post-test probability were also analyzed.RESULTS Eight retrospective studies with a total of 2355 patients and 44154 images were included in this meta-analysis.The AI-assisted EUS for GIST diagnosis showed a sensitivity of 92%[95%confidence interval(CI):0.89-0.95;P<0.01),specificity of 80%(95%CI:0.75-0.85;P<0.01),and area under the curve(AUC)of 0.949.For diagnosis of GIST vs GIL by AI-assisted EUS,specificity was 90%(95%CI:0.88-0.95;P=0.02)and AUC of 0.966.The experienced endoscopists’values were sensitivity of 72%(95%CI:0.67-0.76;P<0.01),specificity of 70%(95%CI:0.64-0.76;P<0.01),and AUC of 0.777 for GIST.Evaluating GIST vs GIL,the experts achieved a sensitivity of 73%(95%CI:0.65-0.80;P<0.01)and an AUC of 0.819.CONCLUSION AI-assisted EUS has high diagnostic accuracy for fourth-layer SELs,especially for GIST,demonstrating superiority compared to experienced endoscopists’and improving their diagnostic performance in the absence of invasive procedures. | Rômulo Sérgio Araújo Gomes Guilherme Henrique Peixoto de Oliveira Diogo Turiani Hourneaux de Moura Ana Paula Samy Tanaka Kotinda Carolina Ogawa Matsubayashi Bruno Salomão Hirsch Matheus de Oliveira Veras João Guilherme Ribeiro Jordão Sasso Roberto Paolo Trasolini Wanderley Marques Bernardo Eduardo Guimarães Hourneaux de Moura | 2023 | World Journal of Gastrointestinal Endoscopy2023，15，8： | 0 |
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