Objective Recent advancements in single-cell RNA sequencing(scRNA-seq)have revolutionized the study of cellular heterogeneity,particularly within the hematological system.However,accurately annotating cell types remai...Objective Recent advancements in single-cell RNA sequencing(scRNA-seq)have revolutionized the study of cellular heterogeneity,particularly within the hematological system.However,accurately annotating cell types remains challenging due to the complexity of immune cells.To address this challenge,we develop a PAN-blood single-cell Data Annotator(scPANDA),which leverages a comprehensive 10-million-cell atlas to provide precise cell type annotation.Methods The atlas,constructed from data collected in 16 studies,incorporated rigorous quality control,preprocessing,and integration steps to ensure a high-quality reference for annotation.scPANDA utilizes a three layer inference approach,progressively refining cell types from broad compartments to specific clusters.Iterative clustering and harmonization processes were employed to maintain cell type purity throughout the analysis.Furthermore,the performance of scPANDA was evaluated in three external datasets.Results The atlas was structured hierarchically,consisting of 16 compartments,54 classes,4,460 low-level clusters(pd_cc_cl_tfs),and 611 high-level clusters(pmid_cts).Robust performance of the tool was demonstrated in annotating diverse immune scRNA-seq datasets,analyzing immune-tumor coexisting clusters in renal cell carcinoma,and identifying conserved cell clusters across species.Conclusion scPANDA exemplifies effective reference mapping with a large-scale atlas,enhancing the accuracy and reliability of blood cell type identification.展开更多
Objective Identification of the risk factors for extraordinary hidden blood loss(HBL) could clarify the underlying causes and provide more appropriate management. This study aims to identify the predictors of HBL in s...Objective Identification of the risk factors for extraordinary hidden blood loss(HBL) could clarify the underlying causes and provide more appropriate management. This study aims to identify the predictors of HBL in spinal surgery.Methods Medical records were retrospectively retrieved to collect the data of patients who undergoing posterior thoracic and lumbar fusion surgery or scoliosis surgery. Demographic information, perioperative visible blood loss volume, as well as laboratory results were recorded. The patients receiving fusion surgery or scoliosis surgery were further divided into the HBL positive subgroup and the HBL negative subgroup. Differences in the variables between the groups were then analyzed. Binary logistic regression analysis was performed to determine independent risk factors associated with HBL.Results For patients undergoing posterior spinal surgery, the independent risk factors associated with HBL were autologous transfusion(for fusion surgery P = 0.011, OR: 2.627, 95%CI: 1.574-2.782; for scoliosis surgery P < 0.001, OR: 2.268, 95%CI: 2.143-2.504) and allogeneic transfusion(for fusion surgery P < 0.001, OR: 6.487, 95%CI: 2.349-17.915; for scoliosis surgery P < 0.001, OR: 3.636, 95%CI: 2.389-5.231).Conclusion Intraoperative blood transfusion might be an early-warning indicator for perioperative HBL.展开更多
文摘Objective Recent advancements in single-cell RNA sequencing(scRNA-seq)have revolutionized the study of cellular heterogeneity,particularly within the hematological system.However,accurately annotating cell types remains challenging due to the complexity of immune cells.To address this challenge,we develop a PAN-blood single-cell Data Annotator(scPANDA),which leverages a comprehensive 10-million-cell atlas to provide precise cell type annotation.Methods The atlas,constructed from data collected in 16 studies,incorporated rigorous quality control,preprocessing,and integration steps to ensure a high-quality reference for annotation.scPANDA utilizes a three layer inference approach,progressively refining cell types from broad compartments to specific clusters.Iterative clustering and harmonization processes were employed to maintain cell type purity throughout the analysis.Furthermore,the performance of scPANDA was evaluated in three external datasets.Results The atlas was structured hierarchically,consisting of 16 compartments,54 classes,4,460 low-level clusters(pd_cc_cl_tfs),and 611 high-level clusters(pmid_cts).Robust performance of the tool was demonstrated in annotating diverse immune scRNA-seq datasets,analyzing immune-tumor coexisting clusters in renal cell carcinoma,and identifying conserved cell clusters across species.Conclusion scPANDA exemplifies effective reference mapping with a large-scale atlas,enhancing the accuracy and reliability of blood cell type identification.
文摘Objective Identification of the risk factors for extraordinary hidden blood loss(HBL) could clarify the underlying causes and provide more appropriate management. This study aims to identify the predictors of HBL in spinal surgery.Methods Medical records were retrospectively retrieved to collect the data of patients who undergoing posterior thoracic and lumbar fusion surgery or scoliosis surgery. Demographic information, perioperative visible blood loss volume, as well as laboratory results were recorded. The patients receiving fusion surgery or scoliosis surgery were further divided into the HBL positive subgroup and the HBL negative subgroup. Differences in the variables between the groups were then analyzed. Binary logistic regression analysis was performed to determine independent risk factors associated with HBL.Results For patients undergoing posterior spinal surgery, the independent risk factors associated with HBL were autologous transfusion(for fusion surgery P = 0.011, OR: 2.627, 95%CI: 1.574-2.782; for scoliosis surgery P < 0.001, OR: 2.268, 95%CI: 2.143-2.504) and allogeneic transfusion(for fusion surgery P < 0.001, OR: 6.487, 95%CI: 2.349-17.915; for scoliosis surgery P < 0.001, OR: 3.636, 95%CI: 2.389-5.231).Conclusion Intraoperative blood transfusion might be an early-warning indicator for perioperative HBL.
