Yijun Sun
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View article: Stability and iron affinity of Donkey-hide gelatin hydrolysates: Implications for improving iron bioavailability and gut microbiota modulation
Stability and iron affinity of Donkey-hide gelatin hydrolysates: Implications for improving iron bioavailability and gut microbiota modulation Open
Iron deficiency anemia (IDA) is a prevalent nutritional disorder primarily treated with inorganic iron supplements. However, these interventions are often limited by poor bioavailability and gastrointestinal side effects. Food-derived prot…
View article: Beyond Over-Refusal: Scenario-Based Diagnostics and Post-Hoc Mitigation for Exaggerated Refusals in LLMs
Beyond Over-Refusal: Scenario-Based Diagnostics and Post-Hoc Mitigation for Exaggerated Refusals in LLMs Open
Large language models (LLMs) frequently produce false refusals, declining benign requests that contain terms resembling unsafe queries. We address this challenge by introducing two comprehensive benchmarks: the Exaggerated Safety Benchmark…
View article: Targeting USP21 to inhibit abdominal aortic aneurysm progression by suppressing the phenotypic transition of vascular smooth muscle cells
Targeting USP21 to inhibit abdominal aortic aneurysm progression by suppressing the phenotypic transition of vascular smooth muscle cells Open
Abdominal aortic aneurysm (AAA) is a life-threatening condition lacking effective treatment. We investigate the role of the deubiquitinating enzyme USP21 in AAA development. Proteomic analysis reveals significant upregulation of USP21 in m…
View article: Identifying Diagnostic Biomarkers for Electroacupuncture Treatment of Rheumatoid Arthritis Using Bioinformatic Analysis and Machine Learning Algorithms
Identifying Diagnostic Biomarkers for Electroacupuncture Treatment of Rheumatoid Arthritis Using Bioinformatic Analysis and Machine Learning Algorithms Open
This study employed three machine learning algorithms to identify potential diagnostic biomarkers for the alleviation of RA by EA. The biomarkers demonstrated robust diagnostic performance across multiple validation datasets. Furthermore, …
View article: Figure S2 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Figure S2 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Supplementary Figure S2
View article: Supplementary Tables S5-12 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Supplementary Tables S5-12 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Significantly varied genes, BEAM results, enriched pathways.
View article: Figure S4 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Figure S4 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Supplementary Figure S4
View article: Supplementary Data from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Supplementary Data from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Construction of combined model.
View article: Figure S3 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Figure S3 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Supplementary Figure S3
View article: Supplementary Tables S2-4 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Supplementary Tables S2-4 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
TCGA disease-related genes, states and paths.
View article: Supplementary Table S13 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Supplementary Table S13 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Putative driver genetic events in transitions.
View article: Supplementary Tables S14-15 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Supplementary Tables S14-15 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
GSE46691 disease-related genes, paths.
View article: Supplementary Table S1 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Supplementary Table S1 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Data, methods and softwares used.
View article: Figure S1 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Figure S1 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Supplementary Figure S1
View article: Metasurface spectrometers beyond resolution-sensitivity constraints
Metasurface spectrometers beyond resolution-sensitivity constraints Open
Conventional spectrometer designs necessitate a compromise between their resolution and sensitivity, especially as device and detector dimensions are scaled down. Here, we report on a miniaturizable spectrometer platform where light throug…
View article: A comprehensive benchmark study of methods for identifying significantly perturbed subnetworks in cancer
A comprehensive benchmark study of methods for identifying significantly perturbed subnetworks in cancer Open
Network-based methods utilize protein–protein interaction information to identify significantly perturbed subnetworks in cancer and to propose key molecular pathways. Numerous methods have been developed, but to date, a rigorous benchmark …
View article: A scoping review of the measurement and analysis of frailty in randomised controlled trials
A scoping review of the measurement and analysis of frailty in randomised controlled trials Open
Background Frailty is of increasing interest in trials, either as a target of intervention, as an outcome or as a potential treatment modifier. However, frailty measurement is often highly variable. This scoping review assessed how frailty…
View article: Figure 2 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Figure 2 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Progression-free survival functions of seven identified tumor states.
View article: Figure 4 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Figure 4 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Spearman’s rank correlation analysis of PSA level, TMB, and GII mapped onto four identified progression paths. A, PSA level. B, TMB. C, GII. The broken line in each plot indicates the branching events on the corresponding progression path,…
View article: Supplementary Table S13 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Supplementary Table S13 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Putative driver genetic events in transitions.
View article: Figure 6 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Figure 6 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
BEAM identified branch-dependent genes and molecular pathways associated with the divergence of ERG fusion–negative (path A) and ERG fusion–positive (path C) tumors. A, Heatmap of identified branch-dependent genes. The expression data of e…
View article: Supplementary Data from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Supplementary Data from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Construction of combined model.
View article: Supplementary Tables S14-15 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Supplementary Tables S14-15 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
GSE46691 disease-related genes, paths.
View article: Figure 3 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Figure 3 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Enrichment analysis of T-stage and summary GS mapped onto four identified progression paths. A, T-stage. Tumor samples with T-stages 3 and 4 were plotted on the top (red), and those with T-stages 1 and 2 plotted at the bottom (black). B, S…
View article: Figure S1 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Figure S1 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Supplementary Figure S1
View article: Supplementary Tables S5-12 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Supplementary Tables S5-12 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Significantly varied genes, BEAM results, enriched pathways.
View article: Figure 1 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Figure 1 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Progression model of prostate cancer constructed by using TCGA data. A, Visualization analysis showed a general trend of data distribution of normal and tumor samples. Each tumor sample was color-coded by its TMPRSS2–ERG fusion status. The…
View article: Supplementary Table S1 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Supplementary Table S1 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Data, methods and softwares used.
View article: Figure 5 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Figure 5 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Transcriptional signatures identified in four progression paths. A, Numbers of upregulated and downregulated genes identified in each path. B and C, KEGG pathways enriched in the upregulated and downregulated genes shared by the four paths…
View article: Figure S3 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States
Figure S3 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States Open
Supplementary Figure S3