Alzheimer’s disease (AD) presents a public health challenge due to its progressive neurodegeneration, cognitive decline, and memory loss. The amyloid cascade hypothesis, which postulates that the accumulation of amyloid-beta (Aβ) peptides initiates a cascade leading to AD, has dominated research and therapeutic strategies. The failure of recent Aβ-targeted therapies to yield conclusive benefits necessitates further exploration of AD pathology. This review proposes the Mitochondrial–Neurovascular–Metabolic (MNM) hypothesis, which integrates mitochondrial dysfunction, impaired neurovascular regulation, and systemic metabolic disturbances as interrelated contributors to AD pathogenesis. Mitochondrial dysfunction, a hallmark of AD, leads to oxidative stress and bioenergetic failure. Concurrently, the breakdown of the blood–brain barrier (BBB) and impaired cerebral blood flow, which characterize neurovascular dysregulation, accelerate neurodegeneration. Metabolic disturbances such as glucose hypometabolism and insulin resistance further impair neuronal function and survival. This hypothesis highlights the interconnectedness of these pathways and suggests that therapeutic strategies targeting mitochondrial health, neurovascular integrity, and metabolic regulation may offer more effective interventions. The MNM hypothesis addresses these multifaceted aspects of AD, providing a comprehensive framework for understanding disease progression and developing novel therapeutic approaches. This approach paves the way for developing innovative therapeutic strategies that could significantly improve outcomes for millions affected worldwide.

Advances in neuro-oncology have transformed the diagnosis and management of brain tumors, which are among the most challenging malignancies due to their high mortality rates and complex neurological effects. Despite advancements in surgery and chemoradiotherapy, the prognosis for glioblastoma multiforme (GBM) and brain metastases remains poor, underscoring the need for innovative diagnostic strategies. This review highlights recent advancements in imaging techniques, liquid biopsies, and artificial intelligence (AI) applications addressing current diagnostic challenges. Advanced imaging techniques, including diffusion tensor imaging (DTI) and magnetic resonance spectroscopy (MRS), improve the differentiation of tumor progression from treatment-related changes. Additionally, novel positron emission tomography (PET) radiotracers, such as ¹⁸F-fluoropivalate, ¹⁸F-fluoroethyltyrosine, and ¹⁸F-fluluciclovine, facilitate metabolic profiling of high-grade gliomas. Liquid biopsy, a minimally invasive technique, enables real-time monitoring of biomarkers such as circulating tumor DNA (ctDNA), extracellular vesicles (EVs), circulating tumor cells (CTCs), and tumor-educated platelets (TEPs), enhancing diagnostic precision. AI-driven algorithms, such as convolutional neural networks, integrate diagnostic tools to improve accuracy, reduce interobserver variability, and accelerate clinical decision-making. These innovations advance personalized neuro-oncological care, offering new opportunities to improve outcomes for patients with central nervous system tumors. We advocate for future research integrating these tools into clinical workflows, addressing accessibility challenges, and standardizing methodologies to ensure broad applicability in neuro-oncology.

Background and Objectives: Postoperative pain and opioid requirements can vary widely, even among patients undergoing similar procedures. This narrative review examines how biological sex, age, frailty, chronic pain, preoperative opioid exposure, and psychological factors may affect postoperative pain, opioid response, and recovery. Materials and Methods: PubMed and Google Scholar were searched from database inception through 18 June 2026. Relevant systematic reviews, meta-analyses, clinical guidelines, randomized trials, and observational studies involving adult surgical patients were included, with a focus on postoperative pain, opioid use, adverse effects, persistent pain, and functional recovery. Human perioperative evidence was prioritized, while experimental and preclinical evidence was used selectively to explain relevant biological mechanisms. Results: The available evidence was heterogeneous across patient populations, surgical procedures, analgesic techniques, and outcome definitions. No single patient characteristic consistently predicts postoperative pain or opioid requirements. Female sex is associated with a greater risk of postoperative nausea and vomiting, but reported differences in pain and opioid efficacy are inconsistent. Older and frail patients may be more susceptible to sedation, respiratory depression, delirium, and other opioid-related adverse effects. Patients with chronic pain or prior opioid exposure often have more difficult postoperative pain control because of tolerance, dependence, or possible opioid-induced hyperalgesia. Anxiety, depression, and pain catastrophizing may also contribute to greater pain and poorer recovery, although their relationship with opioid consumption is less consistent. Conclusions: Perioperative analgesia should be based on the patient’s overall clinical picture rather than any single risk factor. The proposed framework is conceptual and has not been clinically validated. Considering these characteristics together may help clinicians select appropriate multimodal treatments, anticipate analgesic needs, monitor for adverse effects, and support recovery.