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.

Early cancer detection is crucial for improving survival rates and treatment outcomes. Electrochemical biosensors have emerged as powerful tools for early cancer detection due to their high sensitivity, specificity, and rapid detection capabilities. This review explores recent advancements (2015–2025) in electrochemical biosensors for cancer biomarker detection, their working principles, novel nanomaterial-based enhancements, challenges, and prospects for clinical applications. Specifically, we highlight the electrochemical detection of protein biomarkers (e.g., CEA, PSA, CRP), nucleic acid markers (ctDNA, miRNA, methylation patterns), and metabolic indicators, emphasizing their clinical relevance in early diagnosis and monitoring. Unlike previous reviews which focus on either biomarker classes or sensor platforms, this review uniquely integrates both factors. This review provides a novel perspective on how next-generation electrochemical biosensors can bridge the gap between laboratory development and real-world cancer diagnostics.

The human gut microbiome represents a complex ecosystem comprising numerous microorganisms critical to basic physiological processes. The gut microbiome’s composition and functionality influence surgical outcomes following orthopedic procedures. The purpose of this study was to evaluate the gut microbiota on critical aspects of orthopedic surgical outcomes. A comprehensive literature search was conducted via PubMed, the Cumulative Index for Nursing and Allied Health Literature (CINAHL), Google Scholar, Cochrane Library, Medline, and Web of Science. A total of 18 research articles of the 599 retrieved results were included in this study. Significant correlations were identified between microbial composition and surgical outcomes, including infection rates, inflammatory responses, and postoperative complications. Bacterial genera like Alistipes and Helicobacter increased postoperative cognitive dysfunction (POCD) risk, while short-chain fatty acid (SCFA)-producing bacteria showed negative correlations with inflammatory markers. Probiotic interventions reduced POCD incidence from 16.4% to 5.1% and modulated inflammatory responses. Additionally, bacterial composition was associated with critical surgical parameters such as bone healing, infection rate, and recovery trajectory. Inflammation, healing processes, and recovery trajectories are influenced by microbial composition in surgical settings. Targeted interventions, such as probiotics, show promise in reducing surgical risks and improving patient recovery.

Augmented reality (AR) has emerged as a promising intraoperative navigation tool in spine surgery, particularly for pedicle screw placement, which demands high spatial precision and carries risks of neurovascular injury. AR platforms offer real-time overlays of patient anatomy, aiming to enhance accuracy, reduce radiation exposure, and streamline operative workflow. This narrative review synthesizes the early clinical experiences, efficacy data, and barriers to adoption for AR-assisted navigation in spine surgery. It also explores the role of AR in surgical training, workflow optimization, and future integration into routine practice. A comprehensive search of PubMed and ScienceDirect was conducted for English-language studies from January 2004 to May 2025. Inclusion criteria focused on peer-reviewed trials evaluating AR in spinal procedures, with outcomes including screw placement accuracy, radiation exposure, operative time, and user experience. Across multiple platforms, including Microsoft HoloLens and Augmedics xVision, AR-assisted navigation demonstrated pedicle screw placement accuracy rates ranging from 93% to 100%, often with reduced fluoroscopy time and improved surgeon ergonomics. Preliminary data suggest utility in complex deformity surgeries and surgical training. However, widespread adoption remains limited by cost, integration challenges, and a lack of large-scale, multicenter trials. AR represents a viable adjunct to current navigation technologies in spine surgery, showing early promise in enhancing precision and efficiency. Broader adoption will require improved validation, standardization of training protocols, and cost-effectiveness data.

Background: The incorporation of biosensors into orthopedic implants, particularly tibial components in total knee arthroplasty (TKA), marks a new era in personalized joint replacement. These smart systems aim to provide real-time physiological and mechanical data, enabling dynamic postoperative monitoring and enhanced surgical precision. Objective: This narrative review synthesizes the current landscape of electrochemical biosensor-embedded tibial implants in TKA, exploring technical mechanisms, clinical applications, challenges, and future directions for translation into clinical practice. Methods: A comprehensive literature review was conducted across PubMed and Google Scholar. Articles were thematically categorized into technology design, integration strategies, preclinical and clinical evidence, regulatory frameworks, ethical considerations, and strategic recommendations. Findings were synthesized narratively and organized to support forward-looking system design. Results: Smart tibial implants have demonstrated feasibility in both bench and early clinical settings. Key advances include pressure-sensing intraoperative tools, inertial measurement units for remote gait tracking, and chemical biosensors for infection surveillance. However, the field remains limited by biological encapsulation, signal degradation, regulatory uncertainty, and data privacy challenges. Interdisciplinary design, standardized testing, translational funding, and ethical oversight are essential to scaling these innovations. Conclusions: Biosensor-enabled tibial components represent a promising convergence of orthopedics, electronics, and data science. By addressing the technological, biological, regulatory, and ethical gaps outlined herein, this field can transition from prototype to widespread clinical reality—offering new precision in arthroplasty care.