Hematopoietic stem and progenitor cells can be enriched by implanting biomaterial into spatium intermusculare

Repair of cerebrospinal fluid leak during posterior thoracolumbar surgery using paraspinal muscle flap combined with fat graft

Objective

This study aimed to propose a novel surgical method via combination of fat graft and paraspinal muscle flap, in order to treat cerebrospinal fluid (CSF) leak during posterior thoracolumbar surgery. The clinical outcomes were also evaluated.

Methods

Data of a total of 71 patients who were diagnosed with intraoperative incidental durotomy and CSF leak after posterior thoracolumbar surgery in our hospital form January 2019 to January 2021 were retrospectively collected and analyzed. Among them, 34 and 37 patients were assigned into conventional suturing (CS) group and fat graft and paraspinal muscle flap (FPM) group, respectively. Patients’ demographic and clinical data were compared between the two groups.

Results

The average drainage tube time in the FPM group was 3.89 ± 1.17 days, which was shorter than that in the CS group (5.12 ± 1.56, P < 0.001). The drainage volume in the FPM group (281.08 ± 284.76 ml) was also smaller than that in the CS group (859.70 ± 553.11 ml, P < 0.001). Besides, 15 (44.11%) patients in the CS group complained of postural headache, which was more than that in the FPM group (7 patients, 18.91%). There was a statistically significant difference in postoperative visual analogue scale (VAS) score between the two groups (P = 0.013). Two patients underwent revision surgery resulting from incision nonunion and delayed meningeal cyst.

Conclusion

Fat graft combined with paraspinal muscle flap showed to be an effective method to repair CSF leak during posterior thoracolumbar surgery. The proposed method significantly reduced postoperative drainage tube time and postoperative drainage volume. It also decreased the incidence and the degree of postural headache. The proposed method showed satisfactory clinical outcomes, and it is worthy of promotion.

Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine

Hematopoiesis is a complicated process involving a series of biological sub-processes that lead to the formation of various blood components. A widely accepted model of early hematopoiesis proceeds from long-term hematopoietic stem cells (LT-HSCs) to multipotent progenitors (MPPs) and then to lineage-committed progenitors. However, the molecular mechanisms of early hematopoiesis have not been fully characterized. In this study, we applied a computational strategy to identify the gene expression signatures distinguishing three types of closely related hematopoietic cells collected in recent studies: (1) hematopoietic stem cell/multipotent progenitor cells; (2) LT-HSCs; and (3) hematopoietic progenitor cells. Each cell in these cell types was represented by its gene expression profile among a total number of 20,475 genes. The expression features were analyzed by a Monte-Carlo Feature Selection (MCFS) method, resulting in a feature list. Then, the incremental feature selection (IFS) and a support vector machine (SVM) optimized with a sequential minimum optimization (SMO) algorithm were employed to access the optimal classifier with the highest Matthews correlation coefficient (MCC) value of 0.889, in which 6698 features were used to represent cells. In addition, through an updated program of MCFS method, seventeen decision rules can be obtained, which can classify the three cell types with an overall accuracy of 0.812. Using a literature review, both the rules and the top features used for building the optimal classifier were confirmed to be commonly used or potential biological markers for distinguishing the three cell types of HSPCs. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.