Jie Yuan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Jie Yuan has a total of 41 co-authors. Here are the top co-authors by number of shared publications:
Bo Lan
1 shared publication- Chongping Li
Department of Human Cell Biology and Genetics, Joint Laboratory of Guangdong & Hong Kong Universities for Vascular Homeostasis and Diseases, School of Medicine, Shenzhen 518055, China
1 shared publication Cong Xu
Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
1 shared publicationDa Yin
Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
1 shared publication- Feng Lin
Department of Cardiology, Shenzhen Cardiovascular Minimally Invasive Medical Engineering Technology Research and Development Center, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
1 shared publication - Hao Chen
Department of Human Cell Biology and Genetics, Joint Laboratory of Guangdong & Hong Kong Universities for Vascular Homeostasis and Diseases, School of Medicine, Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Southern University of Science and Technology, Shenzhen 518055, China
1 shared publication
Latest Publications
- Open Access
- 2 November 2026
- Published by OAE Publishing Inc. in The Journal of Cardiovascular Aging
- Vol. 6 (4)
Machine-learning risk stratification for 12-month mortality and SGLT2 inhibitor-related absolute risk differences in elderly patients with heart failure and type 2 diabetes
Aim: Older adults with heart failure (HF) and type 2 diabetes mellitus (T2DM) face high 12-month mortality. We developed a routine-variable model for mortality prediction and explored SGLT2 inhibitor (SGLT2i)-related absolute risk differences across model-defined risk strata. Methods: This retrospective study included 500 hospitalized patients aged ≥ 75 years with HF and T2DM. Three feature sets (guideline-informed core, primary analysis, and complete imputed candidate) and ten algorithms were compared using repeated five-fold outer/four-fold inner cross-validation with training-fold preprocessing. Cox and multiple-imputation sensitivity analyses were performed. SGLT2i associations were assessed using propensity-score overlap weighting. Results: The binary modelling cohort included 434 patients with 167 deaths (38.5%). The nested selection workflow achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.729, area under the precision-recall curve of 0.612, and Brier score of 0.206. Cox sensitivity analysis yielded a censoring-adjusted 365-day AUROC of 0.755; multiple imputation gave an AUROC of 0.758 for the fixed gradient-boosting reference. Among 381 patients, weighted 12-month mortality was 16.1% in SGLT2i users vs. 31.1% in non-users, a difference of 15.0 percentage points (95%CI: 5.9-23.9). Stratum-specific differences were 6.5, 18.1, and 19.7 percentage points, with wide confidence intervals. A six-variable interactive interface supported risk communication. Conclusion: Routine clinical variables supported moderately discriminative mortality risk stratification under nested validation. SGLT2i use was associated with lower adjusted 12-month mortality overall; larger studies are needed to establish whether associations differ across risk strata. External and prospective validation remains necessary.
- 2 November 2026
- Published by OAE Publishing Inc. in The Journal of Cardiovascular Aging
- Vol. 6 (4)
Open AccessMachine-learning risk stratification for 12-month mortality and SGLT2 inhibitor-related absolute risk differences in elderly patients with heart failure and type 2 diabetes
Aim: Older adults with heart failure (HF) and type 2 diabetes mellitus (T2DM) face high 12-month mortality. We developed a routine-variable model for mortality prediction and explored SGLT2 inhibitor (SGLT2i)-related absolute risk differences across model-defined risk strata. Methods: This retrospective study included 500 hospitalized patients aged ≥ 75 years with HF and T2DM. Three feature sets (guideline-informed core, primary analysis, and complete imputed candidate) and ten algorithms were compared using repeated five-fold outer/four-fold inner cross-validation with training-fold preprocessing. Cox and multiple-imputation sensitivity analyses were performed. SGLT2i associations were assessed using propensity-score overlap weighting. Results: The binary modelling cohort included 434 patients with 167 deaths (38.5%). The nested selection workflow achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.729, area under the precision-recall curve of 0.612, and Brier score of 0.206. Cox sensitivity analysis yielded a censoring-adjusted 365-day AUROC of 0.755; multiple imputation gave an AUROC of 0.758 for the fixed gradient-boosting reference. Among 381 patients, weighted 12-month mortality was 16.1% in SGLT2i users vs. 31.1% in non-users, a difference of 15.0 percentage points (95%CI: 5.9-23.9). Stratum-specific differences were 6.5, 18.1, and 19.7 percentage points, with wide confidence intervals. A six-variable interactive interface supported risk communication. Conclusion: Routine clinical variables supported moderately discriminative mortality risk stratification under nested validation. SGLT2i use was associated with lower adjusted 12-month mortality overall; larger studies are needed to establish whether associations differ across risk strata. External and prospective validation remains necessary.
- Open Access
- 6 May 2026
- Published by Elsevier in Journal of Advanced Research
Multi-omics landscape and machine learning predictors of acute and chronic coronary syndrome diagnosis in young patients
- 6 May 2026
- Published by Elsevier in Journal of Advanced Research
Open AccessMulti-omics landscape and machine learning predictors of acute and chronic coronary syndrome diagnosis in young patients
- Open Access
- 2 September 2024
- Published by Springer Nature in EMBO Reports
- Vol. 25 (11) , 4655-4673
Abundant mRNA m1A modification in dinoflagellates: a new layer of gene regulation
Dinoflagellates, a class of unicellular eukaryotic phytoplankton, exhibit minimal transcriptional regulation, representing a unique model for exploring gene expression. The biosynthesis, distribution, regulation, and function of mRNA N1-methyladenosine (m 1 A) remain controversial due to its limited presence in typical eukaryotic mRNA. This study provides a comprehensive map of m 1 A in dinoflagellate mRNA and shows that m 1 A, rather than N6-methyladenosine (m 6 A), is the most prevalent internal mRNA modification in various dinoflagellate species, with an asymmetric distribution along mature transcripts. In Amphidinium carterae , we identify 6549 m 1 A sites characterized by a non-tRNA T-loop-like sequence motif within the transcripts of 3196 genes, many of which are involved in regulating carbon and nitrogen metabolism. Enriched within 3′UTRs, dinoflagellate mRNA m 1 A levels negatively correlate with translation efficiency. Nitrogen depletion further decreases mRNA m 1 A levels. Our data suggest that distinctive patterns of m 1 A modification might influence the expression of metabolism-related genes through translational control.
- 2 September 2024
- Published by Springer Nature in EMBO Reports
- Vol. 25 (11) , 4655-4673
Open AccessAbundant mRNA m1A modification in dinoflagellates: a new layer of gene regulation
Dinoflagellates, a class of unicellular eukaryotic phytoplankton, exhibit minimal transcriptional regulation, representing a unique model for exploring gene expression. The biosynthesis, distribution, regulation, and function of mRNA N1-methyladenosine (m 1 A) remain controversial due to its limited presence in typical eukaryotic mRNA. This study provides a comprehensive map of m 1 A in dinoflagellate mRNA and shows that m 1 A, rather than N6-methyladenosine (m 6 A), is the most prevalent internal mRNA modification in various dinoflagellate species, with an asymmetric distribution along mature transcripts. In Amphidinium carterae , we identify 6549 m 1 A sites characterized by a non-tRNA T-loop-like sequence motif within the transcripts of 3196 genes, many of which are involved in regulating carbon and nitrogen metabolism. Enriched within 3′UTRs, dinoflagellate mRNA m 1 A levels negatively correlate with translation efficiency. Nitrogen depletion further decreases mRNA m 1 A levels. Our data suggest that distinctive patterns of m 1 A modification might influence the expression of metabolism-related genes through translational control.