BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Saigusa D, Matsukawa N, Hishinuma E, Koshiba S. Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics. Drug Metab Pharmacokinet 2021;37:100373. [PMID: 33631535 DOI: 10.1016/j.dmpk.2020.11.008] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
1 Tian P, Chen Y, Hang J, Yu R, Zhao C, Zhao M, Wang M. Effect of Gypenosides on the composition of gut microbiota and metabolic activity in the treatment of CCl4-induced liver injury in rats. Journal of Functional Foods 2022;97:105233. [DOI: 10.1016/j.jff.2022.105233] [Reference Citation Analysis]
2 Podgórska B, Wielogórska-partyka M, Godzień J, Siemińska J, Ciborowski M, Szelachowska M, Krętowski A, Siewko K. Applications of Metabolomics in Calcium Metabolism Disorders in Humans. IJMS 2022;23:10407. [DOI: 10.3390/ijms231810407] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Grasso D, Pillozzi S, Tazza I, Bertelli M, Campanacci DA, Palchetti I, Bernini A. An improved NMR approach for metabolomics of intact serum samples. Anal Biochem 2022;:114826. [PMID: 35870512 DOI: 10.1016/j.ab.2022.114826] [Reference Citation Analysis]
4 Le MT, Shon HK, Nguyen HP, Lee CH, Kim KS, Na HK, Lee TG. Simultaneous Multiplexed Imaging of Biomolecules in Transgenic Mouse Brain Tissues Using Mass Spectrometry Imaging: A Multi-omic Approach. Anal Chem 2022. [PMID: 35696262 DOI: 10.1021/acs.analchem.2c00676] [Reference Citation Analysis]
5 Dias DB, Fritsche-guenther R, Gutmann F, Duda GN, Kirwan J, Poh PSP. A Comparison of Solvent-Based Extraction Methods to Assess the Central Carbon Metabolites in Mouse Bone and Muscle. Metabolites 2022;12:453. [DOI: 10.3390/metabo12050453] [Reference Citation Analysis]
6 Eid T. Harnessing Metabolomics to Advance Epilepsy Research. Epilepsy Curr. [DOI: 10.1177/15357597221074518] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Hissong R, Evans KR, Evans CR. Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics. Handbook of Experimental Pharmacology 2022. [DOI: 10.1007/164_2022_617] [Reference Citation Analysis]
8 Qin X, Hakenjos JM, Li F. LC-MS-Based Metabolomics in the Identification of Biomarkers Pertaining to Drug Toxicity: A New Narrative. Biomarkers in Toxicology 2022. [DOI: 10.1007/978-3-030-87225-0_34-1] [Reference Citation Analysis]
9 Uruno A, Saigusa D, Suzuki T, Yumoto A, Nakamura T, Matsukawa N, Yamazaki T, Saito R, Taguchi K, Suzuki M, Suzuki N, Otsuki A, Katsuoka F, Hishinuma E, Okada R, Koshiba S, Tomioka Y, Shimizu R, Shirakawa M, Kensler TW, Shiba D, Yamamoto M. Nrf2 plays a critical role in the metabolic response during and after spaceflight. Commun Biol 2021;4:1381. [PMID: 34887485 DOI: 10.1038/s42003-021-02904-6] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
10 Miyaguti NADS, Chiocchetti GME, Salgado CM, Lopes-Aguiar L, Viana LR, Blanchard L, Santos RWD, Gomes-Marcondes MCC. Walker-256 Tumour-Induced Cachexia Altered Liver Metabolomic Profile and Function in Weanling and Adult Rats. Metabolites 2021;11:831. [PMID: 34940589 DOI: 10.3390/metabo11120831] [Reference Citation Analysis]
11 Suzuki N, Iwamura Y, Nakai T, Kato K, Otsuki A, Uruno A, Saigusa D, Taguchi K, Suzuki M, Shimizu R, Yumoto A, Okada R, Shirakawa M, Shiba D, Takahashi S, Suzuki T, Yamamoto M. Gene expression changes related to bone mineralization, blood pressure and lipid metabolism in mouse kidneys after space travel. Kidney Int 2021:S0085-2538(21)01030-9. [PMID: 34767829 DOI: 10.1016/j.kint.2021.09.031] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
12 Oyenihi OR, Oyenihi AB, Erhabor JO, Matsabisa MG, Oguntibeju OO. Unravelling the Anticancer Mechanisms of Traditional Herbal Medicines with Metabolomics. Molecules 2021;26:6541. [PMID: 34770949 DOI: 10.3390/molecules26216541] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
13 Nielsen JE, Maltesen RG, Havelund JF, Færgeman NJ, Gotfredsen CH, Vestergård K, Kristensen SR, Pedersen S. Characterising Alzheimer's disease through integrative NMR- and LC-MS-based metabolomics. Metabol Open 2021;12:100125. [PMID: 34622190 DOI: 10.1016/j.metop.2021.100125] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
14 Saigusa D, Hishinuma E, Matsukawa N, Takahashi M, Inoue J, Tadaka S, Motoike IN, Hozawa A, Izumi Y, Bamba T, Kinoshita K, Ekroos K, Koshiba S, Yamamoto M. Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values. Metabolites 2021;11:652. [PMID: 34677367 DOI: 10.3390/metabo11100652] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Yamauchi T, Ochi D, Matsukawa N, Saigusa D, Ishikuro M, Obara T, Tsunemoto Y, Kumatani S, Yamashita R, Tanabe O, Minegishi N, Koshiba S, Metoki H, Kuriyama S, Yaegashi N, Yamamoto M, Nagasaki M, Hiyama S, Sugawara J. Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis. Sci Rep 2021;11:17777. [PMID: 34493809 DOI: 10.1038/s41598-021-97342-z] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
16 Charalampidi A, Kordou Z, Tsermpini EE, Bosganas P, Chantratita W, Fukunaga K, Mushiroda T, Patrinos GP, Koromina M. Pharmacogenomics variants are associated with BMI differences between individuals with bipolar and other psychiatric disorders. Pharmacogenomics 2021;22:749-60. [PMID: 34410167 DOI: 10.2217/pgs-2021-0012] [Reference Citation Analysis]
17 Hishinuma E, Shimada M, Matsukawa N, Saigusa D, Li B, Kudo K, Tsuji K, Shigeta S, Tokunaga H, Kumada K, Komine K, Shirota H, Aoki Y, Motoike IN, Yasuda J, Kinoshita K, Yamamoto M, Koshiba S, Yaegashi N. Wide-Targeted Metabolome Analysis Identifies Potential Biomarkers for Prognosis Prediction of Epithelial Ovarian Cancer. Toxins (Basel) 2021;13:461. [PMID: 34209281 DOI: 10.3390/toxins13070461] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]