Purpose: The effect of scinderin (SCIN) on cancer progression has been studied, but its role in glioma remains unknown. This study describes the value of SCIN for the diagnosis, prognosis, and treatment of glioma. Methods: The expression of SCIN was analyzed using the GEPIA, Oncomine, cBioPortal, and CGGA databases. GO/KEGG enrichment analysis of similar genes to SCIN were performed using the R software package, and the protein–protein interaction (PPI) network was analyzed by the STRING and GeneMANIA databases. The correlations of mRNA expression between SCIN and MMP2/9 were analyzed by TCGA glioma. Simultaneously, the TISIDB and TIMER databases were used to analyze the correlation between SCIN and immune infiltration. Finally, SCIN and MMP2/9 protein expression among different grades of glioma was performed and the results were obtained via immunohistochemistry and Western blot assays. We used the Kaplan–Meier method and Cox proportional hazards model to assess the impact of SCIN and MMP2/9 on glioma patients’ survival. The correlations between SCIN and MMP2/9 were analyzed by immunohistochemistry and Western blot assays. Results: SCIN was upregulated in glioma patients with a poor prognosis. The GO and KEGG enrichment analysis showed the functional relationship between SCIN and the immune cell activation and regulation. In addition, the expression of SCIN was related to MMP2/9 in glioma. The correlation analysis showed that SCIN expression was associated with tumor purity and immune infiltration. SCIN and MMP2/9 are negative prognostic factors resulting in worsening glioma patients’ survival. Conclusion: Our studies demonstrated that SCIN expression was associated with MMP2/9, immune infiltration, and a poor prognosis in glioma. SCIN may serve as a potential prognostic marker and an immune therapy target for glioma.

In recent years, the theory of brain glioma stem cells (BGSCs) has facilitated the study of gliomas. BGSCs have been accepted as the origin of gliomas and determine their biological features. Numerous efforts have been made to probe into the biological characteristics and behaviors of BGSCs. However, the culturing of target cells remains the essential first step for research on BGSCs. In this study, we established a simplified procedure to culture and isolate BGSCs from samples of clinical glioma patients. Samples of 17 glioma patients were included in the study, and the processed glioma cells were grown in serum-free stem cell media. After the tumor spheres appeared, a proliferation assay, a single-cell-derived colonies formation assay and an induced differentiation assay were carried out, followed by an immunocytochemistry assay. Serial passage was used to purify the target cells, whereas neither animal experiments nor sorting techniques were included. As a result, CD133+ BGSCs from 8 out of 17 patients were grown and maintained in a serum-free condition combined with EGF, FGF and B-27 supplements. The tumor sphere cells were serially passaged and showed pluripotency in an induced differentiation assay. Immunocytochemistry identified the committed markers (CD133, GFAP and TU-20) and confirmed the cells were BGSCs and their progeny. The results proved that CD133+ BGSCs from resected glioma tissue may be cultured in serum-free stem cell media, and may also be purified by conditioned culture combining serial passage, which is time-saving and cost-effective, and allows the cells to be used for subsequent research. The cell sorting techniques and animal experiments of tumorigenecity are optional. Thus, this modified procedure is more practical and feasible than other available procedures.

Solitary fibrous tumor (SFT) of the central nervous system is rare. It is predominantly benign and rarely malignant. There is no established standardized treatment regimen for malignant intracranial SFTs. We present a rare case of SFT in a 9-year-old girl with a space-occupying effect in the frontal-parietal lobes. She underwent craniotomy, and the mass was resected. Immunohistochemistry examination of the specimen showed that Ki-67 proliferation index staining was highly positive in 80% of tumor cells. Whole exome sequencing of the surgical tissue showed 38 somatic gene mutations and 1 gene amplification such as fibroblast growth factor receptor 4 or TP53. At 1.5 mo after surgery, head magnetic resonance imaging revealed that the tumor had recurred. The patient received 60 Gy and 30 fractions of intensity modulated radiotherapy. The patient then received anlotinib 8 mg po qd for 1-14 d of a 21 d cycle. Foll owing this regimen, the patient achieved stable disease for > 17 mo. Magnetic resonance imaging at 1.5 year after surgery showed that the tumor had not progressed. This is the first reported case of SFT of the central nervous system treated with surgery, radiotherapy and anlotinib. This regimen may be an effective treatment option for malignant intracranial SFT patients.

Bilateral basal ganglia hemorrhages are extremely rare and have very poor prognosis. We describe the case of a 52-year-old woman with a history of hypertension who experienced bilateral basal ganglia hemorrhages. We performed bilateral hematoma aspiration by minimally invasive surgery via frontal and temporal puncture points. We discuss the surgical procedure and review relevant literature pertaining to the underlying causes and complications of similar cases.

Automatic classification and survival prediction of pancreatic cancer based on computed tomography (CT) images have become a key research direction in the field of intelligent healthcare. Various deep learning-based diagnostic models have been developed for classifying pancreatic cancer from CT images. However, CT images often contain noise and a significant amount of irrelevant information unrelated to pancreatic cancer diagnosis. This irrelevant information weakens the discriminative power of the extracted features, leading to the problem of information redundancy. In addition, existing models generally suffer from poor feature generalization. To address these challenges, we propose a deep reconstruction bottleneck representation network (DRBRN) for pancreatic cancer diagnosis, based on Internet of Medical Things (IoMT) and CT imaging. In essence, the DRBRN leverages the information bottleneck principle and a reconstruction task to learn more compact and generalizable features from CT images. It effectively filters out redundant information and noise, preserving only the critical features relevant to the diagnosis of pancreatic cancer. The experimental results on real-world pancreatic cancer CT dataset demonstrate that our proposed DRBRN model achieved the best performance across all three metrics, with a Precision of 0.996, Accuracy of 0.995, and F1-Score of 0.995.