Magnetoelastic sensors, typically made of magnetostrictive and magnetically-soft materials, can be fabricated from commercially available materials into a variety of shapes and sizes for their intended applications. Since these sensors are wirelessly interrogated via magnetic fields, they are good candidates for use in both research and industry, where detection of environmental parameters in closed and controlled systems is necessary. Common applications for these sensors include the investigation of physical, chemical, and biological parameters based on changes in mass loading at the sensor surface which affect the sensor’s behavior at resonance. To improve the performance of these sensors, optimization of sensor geometry, size, and detection conditions are critical to increasing their mass sensitivity and detectible range. This work focuses on investigating how the geometry of the sensor influences its resonance spectrum, including the sensor’s shape, size, and aspect ratio. In addition to these factors, heterogeneity in resonance magnitude was mapped for the sensor surface and the effect of the magnetic bias field strength on the resonance spectrum was investigated. Analysis of the results indicates that the shape of the sensor has a strong influence on the emergent resonant modes. Reducing the size of the sensor decreased the sensor’s magnitude of resonance. The aspect ratio of the sensor, along with the bias field strength, was also observed to affect the magnitude of the signal; over or under biasing and aspect ratio extremes were observed to decrease the magnitude of resonance, indicating that these parameters can be optimized for a given shape and size of magnetoelastic sensor.

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.

Magnetoelastic sensors, which undergo mechanical resonance when interrogated with magnetic fields, can be functionalized to measure various physical quantities and chemical/biological analytes by tracking their resonance behaviors. The unique wireless and functionalizable nature of these sensors makes them good candidates for biological sensing applications, from the detection of specific bacteria to tracking force loading inside the human body. In this study, we evaluate the viability of magnetoelastic sensors based on a commercially available magnetoelastic material (Metglas 2826 MB) for wirelessly monitoring the attachment and growth of human mesenchymal stromal cells (hMSCs) in 2D in vitro cell culture. The results indicate that the changes in sensor resonance are linearly correlated with cell quantity. Experiments using a custom-built monitoring system also demonstrated the ability of this technology to collect temporal profiles of cell growth, which could elucidate key stages of cell proliferation based on acute features in the profile. Additionally, there was no observed change in the morphology of cells after they were subjected to magnetic and mechanical stimuli from the monitoring system, indicating that this method for tracking cell growth may have minimal impact on cell quality and potency.

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.