Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine

A molecular modeling study of pristine and Li-doped B16N16 nanocages for sensing G-series nerve agents using DFT-D3

The detection and removal of toxic warfare agents, such as G-series nerve agents, is a critical area of research for environmental safety and public health. This research uses density functional theory (DFT) to address the gap in understanding the molecular-level interactions of G-series nerve agents with boron nitride nanocages (BNNC) and lithium-doped boron nitride nanocages (Li-BNNC). The investigated nanostructures exhibited high negative adsorption energies, allowing the G-series nerve agents to adsorb strongly onto the BNNC and Li-BNNC surfaces. The Li-BNNC complexes undergo the chemisorption process with the adsorption energy, ranging from -31.819 kcal/mol to -33.635 kcal/mol. The findings of frontier molecular orbitals (FMOs) and density of states (DOS) indicated that the electronic characteristics of GS@BNNC and GS@Li-BNNC had been significantly changed, resulting in a smaller energy gap and higher conductivity. The Li-doping results in much lower energy gaps in Li-BNNC systems, such as 2.707 eV for Tabun@Li-BNNC, that cause higher electrical conductivity. Tabun@Li-BNNC has the highest electrical conductivity of 4.60 × 1012 among Li-doped systems, and Tabun@BNNC has a high conductivity of 2.84 × 1012 among undoped BNNC systems. Li-BNNC systems have higher electrical conductivity, which makes them good sensors for detecting G-series nerve agents. These findings provide a molecular-level understanding of the effect of Li-doping on BNNC-based nanomaterials and their potential for advancing nanotechnology-driven gas sensors.

Codes across (life)sciences

The concept of "code" connotes different meanings, intentions, and formalizations. From mathematics and computer sciences to psychology and culture, the term becomes less formal, more diverse, and sometimes appears ambiguous. In biology a growing number of codes ignite a debate about their role in evolution, biocomplexity, and agency, to name just a few. Here, a transdisciplinary group of code scientists attempts to capture the big picture of code research across their fields of interest. In this cross-sectional overview commonalities emerge that may pave the way towards a unified theory of life-based-on-codes. Codes underly cellular processes, perception, cognition, and communication. From ecosystems to human language, codes influence how individuals behave in groups, memorize, learn, and take part in cultural practices. Emotions like aggression, fear, anger, frustration, are important motivators of behaviour modulating mutual communication and sculpting individual experience. The inheritance of experience in form of innate release mechanisms, stereotyped behaviour, or archetypes may have phylogenetic and ontogenetic roots that rely on codes and impact our conscious decision making. Unconsciously, even our dreams draw on codes. In the future, conflation of different coding systems, e.g., from synthetic biology and generative artificial intelligence, will merge biological codes with machine logic and computer language to promote next-level transhumanism. Codes emerge as a currency converter between systems of life and between different scientific disciplines.

Core-Sheath Structured Yarn for Biomechanical Sensing in Health Monitoring

The rapidly evolving field of functional yarns has garnered substantial research attention due to their exceptional potential in enabling next-generation electronic textiles for wearable health monitoring, human-machine interfaces, and soft robotics. Despite notable advancements, the development of yarn-based strain sensors that simultaneously achieve high flexibility, stretchability, superior comfort, extended operational stability, and exceptional electrical performance remains a critical challenge, hindered by material limitations and structural design constraints. Here, we present a bioinspired, hierarchically structured core-sheath yarn sensor (CSSYS) engineered through an efficient dip-coating process, which synergistically integrates the two-dimensional conductive MXene nanosheets and one-dimensional silver nanowires (AgNWs). Furthermore, the sensor is encapsulated using a yarn-based protective layer, which not only preserves its inherent flexibility and wearability but also effectively mitigates oxidative degradation of the sensitive materials, thereby significantly enhancing long-term durability. Drawing inspiration from the natural architecture of plant stems-where the inner core provides structural integrity while a flexible outer sheath ensures adaptive protection-the CSSYS exhibits outstanding mechanical and electrical performance, including an ultralow strain detection limit (0.05%), an ultrahigh gauge factor (up to 744.45), rapid response kinetics (80 ms), a broad sensing range (0-230% strain), and exceptional cyclic stability (>20,000 cycles). These remarkable characteristics enable the CSSYS to precisely capture a broad spectrum of physiological signals, ranging from subtle arterial pulsations and respiratory rhythms to large-scale joint movements, demonstrating its immense potential for next-generation wearable health monitoring systems.

The Role of Artificial Intelligence in Identifying NF1 Gene Variants and Improving Diagnosis

Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder caused by mutations in the NF1 gene, typically diagnosed during early childhood and characterized by significant phenotypic heterogeneity. Despite advancements in next-generation sequencing (NGS), the diagnostic process remains challenging due to the gene's complexity, high mutational burden, and frequent identification of variants of uncertain significance (VUS). This review explores the emerging role of artificial intelligence (AI) in enhancing NF1 variant detection, classification, and interpretation. A systematic literature search was conducted across PubMed, IEEE Xplore, Google Scholar, and ResearchGate to identify recent studies applying AI technologies to NF1 genetic analysis, focusing on variant interpretation, structural modeling, tumor classification, and therapeutic prediction. The review highlights the application of AI-based tools such as VEST3, REVEL, ClinPred, and NF1-specific models like DITTO and RENOVO-NF1, which have demonstrated improved accuracy in classifying missense variants and reclassifying VUS. Structural modeling platforms like AlphaFold contribute further insights into the impact of NF1 mutations on neurofibromin structure and function. In addition, deep learning models, such as LTC neural networks, support tumor classification and therapeutic outcome prediction, particularly in NF1-associated complications like congenital pseudarthrosis of the tibia (CPT). The integration of AI methodologies offers substantial potential to improve diagnostic precision, enable early intervention, and support personalized medicine approaches. However, key challenges remain, including algorithmic bias, limited data diversity, and the need for functional validation. Ongoing refinement and clinical validation of these tools are essential to ensure their effective implementation and equitable use in NF1 diagnostics.

Robotic Ultrasound Scanning End-Effector with Adjustable Constant Contact Force

In modern medical treatment, ultrasound scanning provides a radiation-free medical imaging method for the diagnosis of soft tissues via skin contact. However, the exerted contact force heavily relies on the skill and experience of the operator, which poses great inspection instability. This article reports on a robotic ultrasound scanning system with a constant-force end-effector. Its uniqueness is the introduction of a hybrid active-passive force control approach to maintaining a constant contact force between the ultrasound probe and the continually changing surface. In particular, the passive constant-force mechanism provides strong buffering to the force variation. The active force control system improves flexibility and provides long-stroke positioning. Experimental tests on both silicone models and human volunteers demonstrate the capability of the proposed robotic ultrasound scanning system for obtaining qualified ultrasound images with high repeatability. Moreover, the ease of operation of the robotic US scanning system is verified. This work provides a promising method to assist doctors in conducting better and cushier ultrasound scanning imaging.

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry

The deployment of artificial intelligence (AI) is revolutionizing neuropharmacology and drug development, allowing the modulation of neurotransmitter systems at the personal level. This review focuses on the neuropharmacology and regulation of neurotransmitters using predictive modeling, closed-loop neuromodulation, and precision drug design. The fusion of AI with applications such as machine learning, deep-learning, and even computational modeling allows for the real-time tracking and enhancement of biological processes within the body. An exemplary application of AI is the use of DeepMind's AlphaFold to design new GABA reuptake inhibitors for epilepsy and anxiety. Likewise, Benevolent AI and IBM Watson have fast-tracked drug repositioning for neurodegenerative conditions. Furthermore, we identified new serotonin reuptake inhibitors for depression through AI screening. In addition, the application of Deep Brain Stimulation (DBS) settings using AI for patients with Parkinson's disease and for patients with major depressive disorder (MDD) using reinforcement learning-based transcranial magnetic stimulation (TMS) leads to better treatment. This review highlights other challenges including algorithm bias, ethical concerns, and limited clinical validation. Their proposal to incorporate AI with optogenetics, CRISPR, neuroprosthesis, and other advanced technologies fosters further exploration and refinement of precision neurotherapeutic approaches. By bridging computational neuroscience with clinical applications, AI has the potential to revolutionize neuropharmacology and improve patient-specific treatment strategies. We addressed critical challenges, such as algorithmic bias and ethical concerns, by proposing bias auditing, diverse datasets, explainable AI, and regulatory frameworks as practical solutions to ensure equitable and transparent AI applications in neurotransmitter modulation.

A roadmap for artificial intelligence in pain medicine: current status, opportunities, and requirements

PURPOSE OF REVIEW

Artificial intelligence (AI) represents a transformative opportunity for pain medicine, offering potential solutions to longstanding challenges in pain assessment and management. This review synthesizes the current state of AI applications with a strategic framework for implementation, highlighting established adaptation pathways from adjacent medical fields.

RECENT FINDINGS

In acute pain, AI systems have achieved regulatory approval for ultrasound guidance in regional anesthesia and shown promise in automated pain scoring through facial expression analysis. For chronic pain management, machine learning algorithms have improved diagnostic accuracy for musculoskeletal conditions and enhanced treatment selection through predictive modeling. Successful integration requires interdisciplinary collaboration and physician coleadership throughout the development process, with specific adaptations needed for pain-specific challenges.

SUMMARY

This roadmap outlines a comprehensive methodological framework for AI in pain medicine, emphasizing four key phases: problem definition, algorithm development, validation, and implementation. Critical areas for future development include perioperative pain trajectory prediction, real-time procedural guidance, and personalized treatment optimization. Success ultimately depends on maintaining strong partnerships between clinicians, developers, and researchers while addressing ethical, regulatory, and educational considerations.

The story of pain in people with dementia: a rationale for digital measures

BACKGROUND

The increasingly older world population presents new aging-related challenges, especially for persons with dementia unable to express their suffering. Pain intensity and the effect of pain treatment are difficult to assess via proxy rating and both under- and overtreatment lead to neuropsychiatric symptoms, inactivity, care-dependency and reduced quality of life. In this debate piece, we provide a rationale on why valid digitalization, sensing technology, and artificial intelligence should be explored to improve the assessment of pain in people with dementia.

MAIN TEXT

In dementia care, traditional pain assessment relies on observing the manifestations of typical pain behavior. At the same time, pain treatment is complicated by polypharmacy, potential side effects, and a lack of around-the-clock, timely measures. But proper pain treatment requires objective and accurate measures that capture both the levels of pain and the treatment effects. Sensing systems research for personalized pain assessment is underway, with some promising results regarding associations between physiological signals and pain. Digital phenotyping, making use of everyday sensor data for monitoring health behaviors such as patterns of sleep or movement, has shown potential in clinical trials and for future continuous observation. This emerging approach requires transdisciplinary collaboration between medical and engineering sciences, with user involvement and adherence to ethical practices.

CONCLUSION

Digital phenotyping based on physiological parameters and sensing technology may increase pain assessment objectivity in older adults with dementia. This technology must be designed with user involvement and validated; however, it opens possibilities to improve pain relief and care.

Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions

Artificial intelligence is rapidly transforming quality assurance in healthcare, driving advancements in diagnostics, surgery, and patient care. This review presents a comprehensive analysis of artificial intelligence integration-particularly convolutional and recurrent neural networks-across key clinical domains, significantly enhancing diagnostic accuracy, surgical performance, and pathology evaluation. Artificial intelligence-based approaches have demonstrated clear superiority over conventional methods: convolutional neural networks achieved 91.56% accuracy in scanner fault detection, surpassing manual inspections; endoscopic lesion detection sensitivity rose from 2.3% to 6.1% with artificial intelligence assistance; and gastric cancer invasion depth classification reached 89.16% accuracy, outperforming human endoscopists by 17.25%. In pathology, artificial intelligence achieved 93.2% accuracy in identifying out-of-focus regions and an F1 score of 0.94 in lymphocyte quantification, promoting faster and more reliable diagnostics. Similarly, artificial intelligence improved surgical workflow recognition with over 81% accuracy and exceeded 95% accuracy in skill assessment classification. Beyond traditional diagnostics and surgical support, AI-powered wearable sensors, drug delivery systems, and biointegrated devices are advancing personalized treatment by optimizing physiological monitoring, automating care protocols, and enhancing therapeutic precision. Despite these achievements, challenges remain in areas such as data standardization, ethical governance, and model generalizability. Overall, the findings underscore artificial intelligence's potential to outperform traditional techniques across multiple parameters, emphasizing the need for continued development, rigorous clinical validation, and interdisciplinary collaboration to fully realize its role in precision medicine and patient safety.

The vaso-occlusive pain crisis in sickle cell patients: A focus on pathogenesis

Vaso-occlusive pain crisis (VOC) is recognized as a prominent complication of sickle cell disease, accompanied by debilitating pain and serious consequences for patients, making it the primary cause of visits to hospital emergency departments. In the etiology of VOC, the intricate interaction of endothelial cells, hypoxia, inflammation, and the coagulation system is pivotal. Hemoglobin S polymerization under hypoxic conditions leads to the formation of rigid and adhesive red blood cells that interact with vascular endothelial cells and other blood cells, causing occlusion and subsequent inflammation. Hemolysis of red blood cells results in anemia and heightened inflammation, whereas oxidative stress and involvement of the coagulation system further complicate matters. In this review, we strive to examine the pathophysiology of VOC from these mentioned aspects by consolidating findings from various studies, as a comprehensive understanding of the causes of VOC is essential for the development of targeted therapeutic interventions and the prevention and management of pain, ultimately improving the quality of life for patients.

Trends and Hotspots in Nanomedicine Applications for Pain: A Bibliometric Analysis from 1999 to 2024

Background: Pain, especially chronic pain, is a leading cause of individuals seeking medical attention and presents a significant public health challenge due to its widespread prevalence and associated healthcare costs. Nanomedicine has shown considerable potential in pain management research. However, there is a lack of comprehensive bibliometric and trend analyses that explore the current status, research hotspots, and future directions of nanomedicine applications in pain. Methods: To fill this gap, we analyzed English language publications related to nanomedicine and pain from the Web of Science Core Collection, spanning the period from January 1, 1999, to May 24, 2024. The analysis focused on publication trends, contributions by countries/regions, institutions, journals, research categories, prominent authors, key references, and keywords. Results: A total of 2370 papers were included. China leads in the number of published papers (785, 33.12%) and hosts numerous high-output institutions and funding agencies, followed by the USA. The International Journal of Pharmaceutics emerged as the leading journal in terms of publication volume. A clear interdisciplinary platform has been established between nanomedicine and the field of pain. "Nanoparticles" and "drug delivery" were identified as high-frequency keywords. The drug delivery systems for pain treatment were considered the main research hotspots, particularly for chronic pain. The keyword citation bursts indicate that the pain of biomarker monitoring is a future trend. Conclusions: The application of nanomedicine in pain has advanced rapidly. Increased funding and international collaboration are necessary with future potential to expand from pain treatment to monitoring and diagnosis.

Smart Contact Lenses: Disease Monitoring and Treatment

Smart contact lenses (SCLs), an innovative evolution of conventional contact lenses, have recently attracted increasing attention for their substantial potential for use in the healthcare field. With advancements in materials science and medical technology, SCLs have integrated electronic information technology with biomedical engineering to enable the incorporation of various medical functionalities. Recent developments have focused on applying SCLs to provide intelligent, efficient, and personalized healthcare solutions in the surveillance, diagnosis, and treatment of chronic ocular surface inflammation, glaucoma, and diabetes complications.

Microneedles as a Promising Technology for Disease Monitoring and Drug Delivery: A Review

The delivery of molecules, such as DNA, RNA, peptides, and certain hydrophilic drugs, across the epidermal barrier poses a significant obstacle. Microneedle technology has emerged as a prominent area of focus in biomedical research because of its ability to deliver a wide range of biomolecules, vaccines, medicines, and other substances through the skin. Microneedles (MNs) form microchannels by disrupting the skin's structure, which compromises its barrier function, and facilitating the easy penetration of drugs into the skin. These devices enhance the administration of many therapeutic substances to the skin, enhancing their stability. Transcutaneous delivery of medications using a microneedle patch offers advantages over conventional drug administration methods. Microneedles containing active substances can be stimulated by different internal and external factors to result in the regulated release of the substances. To achieve efficient drug administration to the desired location, it is necessary to consider the design of needles with appropriate optimized characteristics. The choice of materials for developing and manufacturing these devices is vital in determining the pharmacodynamics and pharmacokinetics of drug delivery. This article provides the most recent update and overview of the numerous microneedle systems that utilize different activators to stimulate the release of active components from the microneedles. Further, it discusses the materials utilized for producing microneedles and the design strategies important in managing the release of drugs. An explanation of the commonly employed fabrication techniques in biomedical applications and electronics, particularly for integrated microneedle drug delivery systems, is discussed. To successfully implement microneedle technology in clinical settings, it is essential to comprehensively assess several factors, such as biocompatibility, drug stability, safety, and production cost. Finally, an in-depth review of these criteria and the difficulties and potential future direction of microneedles in delivering drugs and monitoring diseases is explored.

Advances in Biointegrated Wearable and Implantable Optoelectronic Devices for Cardiac Healthcare

With the prevalence of cardiovascular disease, it is imperative that medical monitoring and treatment become more instantaneous and comfortable for patients. Recently, wearable and implantable optoelectronic devices can be seamlessly integrated into human body to enable physiological monitoring and treatment in an imperceptible and spatiotemporally unconstrained manner, opening countless possibilities for the intelligent healthcare paradigm. To achieve biointegrated cardiac healthcare, researchers have focused on novel strategies for the construction of flexible/stretchable optoelectronic devices and systems. Here, we overview the progress of biointegrated flexible and stretchable optoelectronics for wearable and implantable cardiac healthcare devices. Firstly, the device design is addressed, including the mechanical design, interface adhesion, and encapsulation strategies. Next, the practical applications of optoelectronic devices for cardiac physiological monitoring, cardiac optogenetics, and nongenetic stimulation are presented. Finally, an outlook on biointegrated flexible and stretchable optoelectronic devices and systems for intelligent cardiac healthcare is discussed.