Predictive value of cyst/tumor volume ratio of pituitary adenoma for tumor cell proliferation.

BACKGROUND: MRI has been widely used to predict the preoperative proliferative potential of pituitary adenoma (PA). However, the relationship between the cyst/tumor volume ratio (C/T ratio) and the proliferative potential of PA has not been reported. Herein, we determined the predictive value of the C/T ratio of PA for tumor cell proliferation. METHODS: The clinical data of 72 patients with PA and cystic change on MRI were retrospectively analyzed. PA volume, cyst volume, and C/T ratio were calculated. The corresponding intraoperative specimens were collected. Immunohistochemistry and hematoxylin-eosin staining were performed to evaluate the Ki67 index and nuclear atypia. Patients were categorized according to the Ki67 index (< 3% and ≥ 3%) and nuclear atypia (absence and presence). Univariate and multivariate analyses were used to identify the significant predictors of the Ki67 index and nuclear atypia. The receiver operating characteristic curve assessed the prediction ability of the significant predictors. RESULTS: Larger tumor volumes, smaller cyst volumes, and lower C/T ratios were found in patients with higher Ki67 indexes and those with nuclear atypia (P < 0.05). C/T ratio was an independent predictor of the Ki67 index (odds ratio = 0.010, 95% confidence interval = 0.000-0.462) and nuclear atypia (odds ratio = 0.010, 95% confidence interval = 0.000-0.250). The predictive value of the C/T ratio did not differ significantly from that of tumor volume (P > 0.05) but was better than that of cyst volume (P < 0.05). The area under the curve of the C/T ratio for predicting the Ki67 index and nuclear atypia was larger than that for predicting cyst volume and tumor volume. CONCLUSIONS: C/T ratios can be used to predict PA tumor proliferation preoperatively. Our findings may facilitate the selection of surgery timing and the efficacy evaluation of surgery.

Adenomyosis Accompanied by Multiple Hemorrhagic Cerebral Infarction: A Case Report.

This study aims to present a case of uterine adenomyosis accompanied by multiple hemorrhagic cerebral infarctions (CIs), summarize therapeutic experiences based on the literature review, and improve the clinical diagnosis and treatment of multiple hemorrhagic CIs. This paper describes a 46-year-old female with a four-year history of uterine adenomyosis complicated by multiple hemorrhagic CIs. During treatment, elevated levels of D-dimer, CA-125, and severe anemia were observed. Following internal medicine treatment targeting uterine adenomyosis and hemorrhagic CIs, the cerebral hemorrhage gradually resolved. Women presenting with multiple CIs, particularly hemorrhagic ones, should be evaluated for the presence of gynecological diseases. Treating gynecological conditions may aid in the management of multiple CIs.

Biomarkers for predicting the severity of spinal cord injury by proteomic analysis.

Purpose: Currently, there is a shortage of the protein biomarkers for classifying spinal cord injury (SCI) severity. We attempted to explore the candidate biomarkers for predicting SCI severity. Methods: SCI rat models with mild, moderate, and severe injury were constructed with an electro-mechanic impactor. The behavior assessment and pathological examinations were conducted before and after SCI. Then, quantitative liquid chromatography-mass spectrometry (LC-MS/MS) was performed in spinal cord tissues with different extents of injury. The differentially expressed proteins (DEPs) in SCI relative to controls were identified, followed by Mfuzz clustering, function enrichment analysis, and protein-protein interaction (PPI) network construction. The differential changes of candidate proteins were validated by using a parallel reaction monitoring (PRM) assay. Results: After SCI modeling, the motor function and mechanical pain sensitivity of SCI rats were impaired, dependent on the severity of the injury. A total of 154 DEPs overlapped in the mild, moderate, and severe SCI groups, among which 82 proteins were classified in clusters 1, 2, 3, 5, and 6 with similar expression patterns at different extents of injury. DEPs were closely related to inflammatory response and significantly enriched in the IL-17 signaling pathway. PPI network showed that Fgg (Fibrinogen gamma chain), Fga (Fibrinogen alpha chain), Serpinc1 (Antithrombin-III), and Fgb (Fibrinogen beta chain) in cluster 1 were significant nodes with the largest degrees. The upregulation of the significant nodes in SCI samples was validated by PRM. Conclusion: Fgg, Fga, and Fgb may be the putative biomarkers for assessing the extent of SCI.