A ratiometric SERS strategy for the prediction of cancer cell proportion and guidance of glioma surgical resection.

Rapid and accurate identification of tumor boundaries is critical for the cure of glioma, but it is difficult due to the invasive nature of glioma cells. This paper aimed to explore a rapid diagnostic strategy based on a label-free surface-enhanced Raman scattering (SERS) technique for the quantitative detection of glioma cell proportion intraoperatively. With silver nanoparticles as substrate, an in-depth SERS analysis was performed on simulated clinical samples containing normal brain tissue and different concentrations of patient-derived glioma cells. The results revealed two universal characteristic peaks of 655 and 717 cm-1, which strongly correlated with glioma cell proportion regardless of individual differences. Based on the intensity ratio of the two peaks, a ratiometric SERS strategy for the quantification of glioma cells was established by employing an artificial neuron network model and a polynomial regression model. Such a strategy accurately estimated the proportion of glioma cells in simulated clinical samples (R2 = 0.98) and frozen samples (R2 = 0.85). More importantly, it accurately facilitated the delineation of tumor margins in freshly obtained samples. Taken together, this SERS-based method ensured a rapid and more detailed identification of tumor margins during surgical resection, which could be beneficial for intraoperative decision-making and pathological evaluation.

The biological behavior and clinical outcome of pituitary adenoma are affected by the microenvironment.

BACKGROUND: Pituitary adenoma is one of the most common brain tumors. Most pituitary adenomas are benign and can be cured by surgery and/or medication. However, some pituitary adenomas show aggressive growth with a fast growth rate and are resistant to conventional treatments such as surgery, drug therapy, and radiation therapy. These tumors, referred to as refractory pituitary adenomas, often relapse or regrow in the early postoperative period. The tumor microenvironment (TME) has recently been identified as an important factor affecting the biological manifestations of tumors and acts as the main battlefield between the tumor and the host immune system. MAIN BODY: In this review, we focus on describing TME in pituitary adenomas and refractory pituitary adenomas. Research on the immune microenvironment of pituitary adenomas is currently focused on immune cells such as macrophages and lymphocytes, and extensive research and experimental verifications are still required regarding other components of the TME. In particular, studies are needed to determine the role of the TME in the specific biological behaviors of refractory pituitary adenomas, such as high invasion, fast recurrence rate, and high tolerance to traditional treatments and to identify the mechanisms involved. CONCLUSION: Overall, we summarize the similarities and differences between the TME of pituitary adenomas and refractory pituitary adenomas as well as the changes in the biological behavior of pituitary adenomas that may be caused by the microenvironment. These changes greatly affect the outcome of patients.

Mdpg: a novel multi-disease diagnosis prediction method based on patient knowledge graphs.

Diagnosis prediction, a key factor in enhancing healthcare efficiency, remains a focal point in clinical decision support research. However, the time-series, sparse and multi-noise characteristics of electronic health record (EHR) data make it a great challenge. Existing methods commonly address these issues using RNNs and incorporating medical prior knowledge from medical knowledge bases, but they neglect the local spatial characteristics and spatial-temporal correlation of the data. Consequently, we propose MDPG, a diagnosis prediction model based on patient knowledge graphs. Initially, we represent the electronic visit records of patients as a patient-centered temporal knowledge graph, capturing the local spatial structure and temporal characteristics of the visit information. Subsequently, we design the spatial graph convolution block, temporal self-attention block, and spatial-temporal synchronous graph convolution block to capture the spatial, temporal, and spatial-temporal correlations embedded in them, respectively. Ultimately, we accomplish the prediction of patients' future states through multi-label classification. We conduct comprehensive experiments on two real-world datasets independently and evaluate the results using visit-level precision@k and code-level accuracy@k metrics. The experimental results demonstrate that MDPG outperforms all baseline models, yielding the best performance.

A Deep Learning Model for Automatic Segmentation of Intraparenchymal and Intraventricular Hemorrhage for Catheter Puncture Path Planning.

Intracerebral hemorrhage is the subtype of stroke with the highest mortality rate, especially when it also causes secondary intraventricular hemorrhage. The optimal surgical option for intracerebral hemorrhage remains one of the most controversial areas of neurosurgery. We aim to develop a deep learning model for the automatic segmentation of intraparenchymal and intraventricular hemorrhage for clinical catheter puncture path planning. First, we develop a 3D U-Net embedded with a multi-scale boundary aware module and a consistency loss for segmenting two types of hematoma in computed tomography images. The multi-scale boundary aware module can improve the model's ability to understand the two types of hematoma boundaries. The consistency loss can reduce the probability of classifying a pixel into two categories at the same time. Since different hematoma volumes and locations have different treatments. We also measure hematoma volume, estimate centroid deviation, and compare with clinical methods. Finally, we plan the puncture path and conduct clinical validation. We collected a total of 351 cases, and the test set contained 103 cases. For intraparenchymal hematomas, the accuracy can reach 96 % when the proposed method is applied for path planning. For intraventricular hematomas, the proposed model's segmentation efficiency and centroid prediction are superior to other comparable models. Experimental results and clinical practice show that the proposed model has potential for clinical application. In addition, our proposed method has no complicated modules and improves efficiency, with generalization ability.