666 research outputs found
sj-pdf-1-imr-10.1177_03000605211053281 - Supplemental material for Creation of a three-dimensional printed spine model for training in pain procedures
No full textSupplemental material, sj-pdf-1-imr-10.1177_03000605211053281 for Creation of a three-dimensional printed spine model for training in pain procedures by Jae Chul Koh, Yoo Kyung Jang, Hyunyoung Seong, Kae Hong Lee, Seungwoo Jun and Jong Bum Choi in Journal of International Medical Research</p
sj-pdf-2-imr-10.1177_03000605211053281 - Supplemental material for Creation of a three-dimensional printed spine model for training in pain procedures
No full textSupplemental material, sj-pdf-2-imr-10.1177_03000605211053281 for Creation of a three-dimensional printed spine model for training in pain procedures by Jae Chul Koh, Yoo Kyung Jang, Hyunyoung Seong, Kae Hong Lee, Seungwoo Jun and Jong Bum Choi in Journal of International Medical Research</p
Data of methylome and transcriptome derived from human dilated cardiomyopathy
No full textAbstractAlterations in DNA methylation and gene expression have been implicated in the development of human dilated cardiomyopathy (DCM). Differentially methylated probes (DMPs) and differentially expressed genes (DEGs) were identified between the left ventricle (LV, a pathological locus for DCM) and the right ventricle (RV, a proxy for normal hearts). The data in this DiB are for supporting our report entitled “Methylome analysis reveals alterations in DNA methylation in the regulatory regions of left ventricle development genes in human dilated cardiomyopathy” (Bong-Seok Jo, In-Uk Koh, Jae-Bum Bae, Ho-Yeong Yu, Eun-Seok Jeon, Hae-Young Lee, Jae-Joong Kim, Murim Choi, Sun Shim Choi, 2016) [1]
Thiopurine S-methyltransferase polymorphisms and the relationship between the mutant alleles and the adverse effects in systemic lupus erythematosus patients taking azathioprine
No full textObjective. The present study sought to elucidate the genetic basis of thiopurine methyltransferase (TPMT) polymorphism and subsequently to investigate the relationship between mutant TPMT and an adverse response observed in Korean patients with systemic lupus erythematosus (SLE) taking azathioprine (AZA). Methods. The TPMT genotype of 342 patients with SLE was determined by MALDI-TOF mass spectroinetry and correlated with the effects of clinical exposure to AZA. Results. TPMT polymorphism was detected in 17 of the 342 study subjects (5.0%), 12 heterozygous for the TPM-T*3C allele and 5 heterozygous for the TPMT*6 allele. Numerous patients taking AZA demonstrated adverse drug responses. Severe nausea occurred in 4 patients with the TPMT*3C allele, while I patient with the TPMT*6 allele suffered severe bone marrow toxicty Leucopenia (n = 17), nausea (n = 4), and abnormal liver function (n = 1) were suspected in 23 of the 94 lupus patients taking AZA. AZA was relatively well tolerated by the remainder of the patients. The heterozygous genotype for the TPMT*3C and *6 alleles was frequently detected it? Korean SLE patients. Conclusion. Contrary to previous hypotheses, this study identified no statistical correlation between TPMT geno-type and AZA toxicity We thus conclude that TMPT genotyping cannot replace regular blood monitoring in SLE patients receiving AZA treatment
sj-tif-1-tpx-10.1177_01926233211057128 – Supplemental material for Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats
No full textSupplemental material, sj-tif-1-tpx-10.1177_01926233211057128 for Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats by Ji-Hee Hwang, Hyun-Ji Kim, Heejin Park, Byoung-Seok Lee, Hwa-Young Son, Yong-Bum Kim, Sang-Yeop Jun, Jong-Hyun Park, Jaeku Lee and Jae-Woo Cho in Toxicologic Pathology</p
sj-tif-3-tpx-10.1177_01926233211057128 – Supplemental material for Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats
No full textSupplemental material, sj-tif-3-tpx-10.1177_01926233211057128 for Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats by Ji-Hee Hwang, Hyun-Ji Kim, Heejin Park, Byoung-Seok Lee, Hwa-Young Son, Yong-Bum Kim, Sang-Yeop Jun, Jong-Hyun Park, Jaeku Lee and Jae-Woo Cho in Toxicologic Pathology</p
sj-doc-1-tpx-10.1177_01926233211057128 – Supplemental material for Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats
No full textSupplemental material, sj-doc-1-tpx-10.1177_01926233211057128 for Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats by Ji-Hee Hwang, Hyun-Ji Kim, Heejin Park, Byoung-Seok Lee, Hwa-Young Son, Yong-Bum Kim, Sang-Yeop Jun, Jong-Hyun Park, Jaeku Lee and Jae-Woo Cho in Toxicologic Pathology</p
sj-tif-2-tpx-10.1177_01926233211057128 – Supplemental material for Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats
No full textSupplemental material, sj-tif-2-tpx-10.1177_01926233211057128 for Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats by Ji-Hee Hwang, Hyun-Ji Kim, Heejin Park, Byoung-Seok Lee, Hwa-Young Son, Yong-Bum Kim, Sang-Yeop Jun, Jong-Hyun Park, Jaeku Lee and Jae-Woo Cho in Toxicologic Pathology</p
sj-tif-4-tpx-10.1177_01926233211057128 – Supplemental material for Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats
No full textSupplemental material, sj-tif-4-tpx-10.1177_01926233211057128 for Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats by Ji-Hee Hwang, Hyun-Ji Kim, Heejin Park, Byoung-Seok Lee, Hwa-Young Son, Yong-Bum Kim, Sang-Yeop Jun, Jong-Hyun Park, Jaeku Lee and Jae-Woo Cho in Toxicologic Pathology</p
sj-tif-5-tpx-10.1177_01926233211057128 – Supplemental material for Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats
No full textSupplemental material, sj-tif-5-tpx-10.1177_01926233211057128 for Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats by Ji-Hee Hwang, Hyun-Ji Kim, Heejin Park, Byoung-Seok Lee, Hwa-Young Son, Yong-Bum Kim, Sang-Yeop Jun, Jong-Hyun Park, Jaeku Lee and Jae-Woo Cho in Toxicologic Pathology</p
- …
