Aneurysmal subarachnoid hemorrhage (aSAH) is one of the most severe neurological disorders, with a high mortality rate and severe disabling functional sequelae. Systemic inflammation following hemorrhagic stroke may play an important role in mediating intracranial and extracranial tissue damage. Previous studies showed that various systemic inflammatory biomarkers might be useful in predicting clinical outcomes. Anti-inflammatory treatment might be a promising therapeutic approach for improving the prognosis of patients with aSAH. This review summarizes the complicated interactions between the nervous system and the immune system.

Stroke, a neurological emergency, is a leading cause of death and disability in adults worldwide. In acute or rehabilitative stages, stroke survivors sustain variable neurological recovery with long-term disabilities. The influence of post-stroke nutritional status on long-term survival has not been confirmed. Using the United States National Health and Nutrition Examination Survey data (2001–2010), we conducted a matched-cohort analysis (929 and 1858 participants in stroke and non-stroke groups, respectively) to investigate the influence of nutritional elements on post-stroke survival. With significantly lower nutrient consumption, the mortality risk was 2.2 times higher in stroke patients compared to non-stroke patients (Kaplan–Meier method with Cox proportional hazards model: adjusted hazard ratio, 2.208; 95% confidence interval: 1.887–2.583; p < 0.001). For several nutritional elements, the lower consumption group had significantly shorter survival than the higher consumption stroke subgroup; moreover, stroke patients with the highest 25% nutritional intake for each nutritional element, except moisture and total fat, had significantly shorter survival than non-stroke patients with the lowest 25% nutrition. Malnutrition is highly prevalent in stroke patients and is associated with high mortality rates. The dynamic change in energy requirements throughout the disease course necessitates dietary adjustment to ensure adequate nutritional intake.

Oligometastatic non-small-cell lung cancer (NSCLC) is a distinct entity that is different from localized and disseminated diseases. The definition of oligometastatic NSCLC varies across studies in past decades owing to the use of different imaging modalities; however, a uniform definition of oligometastatic NSCLC has been proposed, and this may facilitate trial design and evaluation of certain interventions. Patients with oligometastatic NSCLC are candidates for curative-intent management, in which local ablative treatment, such as surgery or stereotactic radiosurgery, should be instituted to improve clinical outcomes. Although current guidelines recommend that local therapy for thoracic and metastatic lesions should be considered for patients with oligometastatic NSCLC with stable disease after systemic therapy, optimal management strategies for different oligometastatic sites have not been established. Additionally, the development of personalized therapies for individual patients with oligometastatic NSCLC to improve their quality of life and overall survival should also be addressed. Here, we review relevant articles on the management of patients with oligometastatic NSCLC and categorize the disease according to the site of metastases. Ongoing trials are also summarized to determine future directions and expectations for new treatment modalities to improve patient management.

Background/Objectives: Posterior cage migration is a clinically relevant complication after lumbar interbody fusion. Most reported risk factors are derived from open techniques, whereas evidence specific to minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) is limited. We evaluated factors associated with cage migration and symptomatic retropulsion in a large MIS-TLIF cohort. Methods: We retrospectively reviewed 650 consecutive patients undergoing MIS-TLIF, comprising 1126 fused motion segments. Cage migration was defined as posterior displacement > 3 mm compared with early postoperative radiographs. Demographic, clinical, surgical, and radiographic variables were compared between segments with and without migration. Cases with migration were further stratified by revision requirement. Results: Cage migration occurred in 27 of 1126 levels (2.4%). Seven cases required revision surgery for symptomatic cage retropulsion, corresponding to a level-based incidence of 0.6%. More posterior initial cage placement was significantly associated with subsequent migration. Age, body habitus, smoking, diabetes, endplate violation, and multilevel fusion were not associated with migration. Among migration cases, male sex was associated with higher odds of revision, and no radiographic or mechanical parameter predicted progression from radiographic migration to symptomatic retropulsion. In revision cases, the migrated cage was removed via the original approach, followed by contralateral placement of a new interbody cage using a minimally invasive technique. Conclusions: In this MIS-TLIF cohort, posterior initial cage placement was the primary factor associated with cage migration, consistent with prior open-series findings. Progression from migration to symptomatic retropulsion was not explained by mechanical parameters alone, suggesting a multifactorial process. These findings underscore the importance of meticulous cage positioning during MIS-TLIF and provide practical insights for postoperative surveillance and revision decision-making.

Background: Osteoporosis (OP) is characterized by reduced bone mineral density and increased fracture risk. Many spinal surgery patients have undiagnosed OP due to the lack of preoperative screening, leading to postoperative complications. Magnetic resonance imaging (MRI), a routine, non-invasive tool for spinal assessment, offers potential for opportunistic OP detection. This study aimed to develop deep learning models to identify OP using lumbar MRI. Methods: We retrospectively enrolled 218 patients (≥50 years) who underwent both lumbar MRI and dual-energy X-ray absorptiometry (DXA). After segmentation of vertebral bodies from T1- and T2-weighted MRI images, 738 images per sequence were extracted. Separate convolutional neural network (CNN) models were trained for each sequence. Model performance was evaluated using receiver operating characteristic curves and area under the curve (AUC). Results: Among tested classifiers, EfficientNet b4 showed the best performance. For the T1-weighted model, it achieved an AUC of 82%, with a sensitivity of 85% and specificity of 79%. For the T2-weighted model, the AUC was 83%, with a sensitivity of 86% and specificity of 80%. These results were superior to those of InceptionResNet v2 and ResNet-50 for both sequences. Conclusions: The AI models provided reliable OP classification without additional imaging or radiation. AI-based analysis of standard lumbar MRI sequences can accurately identify OP. These models may assist in early detection of undiagnosed OP in surgical candidates, enabling timely treatment and perioperative strategies to improve outcomes and reduce healthcare burden.