Intracerebral hemorrhage (ICH) has one of the worst prognoses among patients with stroke. Surgical measures have been adopted to relieve the mass effect of the hematoma, and developing targeted therapy against secondary brain injury (SBI) after ICH is equally essential. Numerous preclinical and clinical studies have demonstrated that perihematomal edema (PHE) is a quantifiable marker of SBI after ICH and is associated with a poor prognosis. Thus, PHE has been considered a promising therapeutic target for ICH. However, the findings derived from existing studies on PHE are disparate and unclear. Therefore, it is necessary to classify, compare, and summarize the existing studies on PHE. In this review, we describe the growth characteristics and relevant underlying mechanism of PHE, analyze the contributions of different risk factors to PHE, present the potential impact of PHE on patient outcomes, and discuss the currently available therapeutic strategies.

Object: The standard transsphenoidal approach has been successfully used to resect most pituitary adenomas. However, as a result of the limited exposure provided by this procedure, complete surgical removal of pituitary adenomas with parasellar or retrosellar extension remains problematic. By additional bone removal of the cranial base, the extended transsphenoidal approach provides better exposure to the parasellar and clival region compared with the standard approach. The authors describe their surgical experience with the extended transsphenoidal approach to remove pituitary adenomas invading the anterior cranial base, cavernous sinus (CS), and clivus. Methods: Retrospective analysis was performed in 126 patients with pituitary adenomas that were surgically treated via the extended transsphenoidal approach between September 1999 and March 2008. There were 55 male and 71 female patients with a mean age of 43.4 years (range 12–75 years). There were 82 cases of macroadenoma and 44 cases of giant adenoma. Results: Gross-total resection was achieved in 78 patients (61.9%), subtotal resection in 43 (34.1%), and partial resection in 5 (4%). Postoperative complications included transient cerebrospinal rhinorrhea (7 cases), incomplete cranial nerve palsy (5), panhypopituitarism (5), internal carotid artery injury (2), monocular blindness (2), permanent diabetes insipidus (1), and perforation of the nasal septum (2). No intraoperative or postoperative death was observed. Conclusions: The extended transsphenoidal approach provides excellent exposure to pituitary adenomas invading the anterior cranial base, CS, and clivus. This approach enhances the degree of tumor resection and keeps postoperative complications relatively low. However, radical resection of tumors that are firm, highly invasive to the CS, or invading multidirectionally remains a big challenge. This procedure not only allows better visualization of the tumor and the neurovascular structures but also provides significant working space under the microscope, which facilitates intraoperative manipulation. Preoperative imaging studies and new techniques such as the neuronavigation system and the endoscope improve the efficacy and safety of tumor resection.

In addition to extracellular β-amyloid plaques and intracellular neurofibrillary tangles, neuroinflammation has been identified as a key pathological characteristic of Alzheimer’s disease (AD). Once activated, neuroinflammatory cells called microglia acquire different activation phenotypes. At the early stage of AD, activated microglia are mainly dominated by the neuroprotective and anti-inflammatory M2 phenotype. Conversely, in the later stage of AD, the excessive activation of microglia is considered detrimental and pro-inflammatory, turning into the M1 phenotype. Therapeutic strategies targeting the modulation of microglia may regulate their specific phenotype. Fortunately, with the rapid development of in vivo imaging methodologies, visualization of microglial activation has been well-explored. In this review, we summarize the critical role of activated microglia during the pathogenesis of AD and current studies concerning imaging of microglial activation in AD patients. We explore the possibilities for identifying activated microglial phenotypes with imaging techniques and highlight promising therapies that regulate the microglial phenotype in AD mice.

Context: The resection plan of pituitary adenoma (PA) needs preoperative observation of the sellar region. Radiomics prediction requires high-quality segmentations. Manual delineation is time-consuming and subject to rater variability. Objective: This work aims to create an automated segmentation method for the sellar region, several tools to extract invasiveness-related features, and evaluate their clinical usefulness by predicting the tumor consistency. Methods: Patients included were diagnosed with pituitary adenoma at Peking Union Medical College Hospital. A deep convolutional neural network, called gated-shaped U-net (GSU-Net), was created to automatically segment the sellar region into 8 classes. Five magnetic resonance imaging (MRI) features were extracted from the segmentation results, including tumor diameters, volume, optic chiasma height, Knosp grading system, and degree of internal carotid artery contact. The clinical usefulness of the proposed methods was evaluated by the diagnostic accuracy of the tumor consistency. Results: A total of 163 patients with confirmed pituitary adenoma were included as the first group and were randomly divided into a training data set and test data set (131 and 32 patients, respectively). Fifty patients with confirmed acromegaly were included as the second group. The Dice coefficient of pituitary adenoma in important image slices was 0.940. The proposed methods achieved accuracies of more than 80% for the prediction of 5 invasive-related MRI features. Methods derived from the automatic segmentation showed better performance than original methods and achieved areas under the curve of 0.840 and 0.920 for clinical models and radiomics models, respectively. Conclusion: The proposed methods could automatically segment the sellar region and extract features with high accuracy. The outstanding performance of the prediction of the tumor consistency indicates the methods’ clinical usefulness for supporting neurosurgeons in judging patients’ conditions, predicting prognosis, and other downstream tasks during the preoperative period.