Frontiers in medical technology最新文献

The advent of messenger RNA (mRNA) therapeutics has revolutionized medicine, with its potential underscored by rapid advancements during the COVID-19 pandemic. Despite its promise, nucleic acid delivery remains a formidable challenge due to enzymatic degradation, cellular uptake barriers, and endosomal trapping. Therapeutic lipid nanoparticles (LNPs), pioneered in the 1970s, have emerged as the gold standard for delivering mRNA and other nucleic acids, offering unparalleled advantages in stability, biocompatibility, and cellular targeting. This review explores the evolution and design of LNPs, focusing on their role in hematologic therapies and platelet transfection, where unique challenges arise due to platelets' anucleate nature. The paper systematically evaluates the composition of LNPs, highlighting the role of ionizable, cationic, and neutral lipids in optimizing delivery efficiency, stability, and immune response modulation. Strategies to overcome platelet transfection barriers, including tailored lipid compositions and particle engineering, are discussed alongside advances in artificial intelligence (AI) for predictive nanoparticle design. Furthermore, it examines various nucleic acid cargoes, including mRNA, siRNA, and miRNA, and their therapeutic potential in addressing platelet-related disorders and advancing personalized medicine. Finally, the review delves into emerging technologies and the integration of AI to overcome existing barriers in nucleic acid delivery. By fostering interdisciplinary collaboration, this work aims to catalyze discoveries in LNP-based therapeutics and transformative advancements in hematologic treatments.

Background: Segmentation of ischaemic stroke lesions from magnetic resonance images (MRI) remains a challenging task mainly due to the confounding appearance of these lesions with other pathologies, and variations in their presentation depending on the lesion stage (i.e., hyper-acute, acute, subacute and chronic). Works on the theme have been reviewed, but none of the reviews have addressed the seminal question on what would be the optimal architecture to address this challenge. We systematically reviewed the literature (2015-2023) for deep learning algorithms that segment acute and/or subacute stroke lesions on brain MRI seeking to address this question, meta-analysed the data extracted, and evaluated the results.

Methods and materials: Our review, registered in PROSPERO (ID: CRD42023481551), involved a systematic search from January 2015 to December 2023 in the following databases: IEE Explore, MEDLINE, ScienceDirect, Web of Science, PubMed, Springer, and OpenReview.net. We extracted sample characteristics, stroke stage, imaging protocols, and algorithms, and meta-analysed the data extracted. We assessed the risk of bias using NIH's study quality assessment tool, and finally, evaluated our results using data from the ISLES-2015-SISS dataset.

Results: From 1485 papers, 41 were ultimately retained. 13/41 studies incorporated attention mechanisms in their architecture, and 39/41 studies used the Dice Similarity Coefficient to assess algorithm performance. The generalisability of the algorithms reviewed was generally below par. In our pilot analysis, the UResNet50 configuration, which was developed based on the most comprehensive architectural components identified from the reviewed studies, demonstrated a better segmentation performance than the attention-based AG-UResNet50.

Conclusion: We found no evidence that favours using attention mechanisms in deep learning architectures for acute stroke lesion segmentation on MRI data, and the use of a U-Net configuration with residual connections seems to be the most appropriate configuration for this task.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42023481551, PROSPERO CRD42023481551.

Background: Ischaemia is a critical complication, and can result in poor surgical outcomes. While intra-operative overt ischaemia can be perceived with the naked eye, timely recognition of borderline perfusion can prevent post-operative ischaemic complications, which is particularly relevant for colorectal anastomoses. Consequently, there is a clinical need for new technologies to intra-operatively assess tissue oxygenation (indicative of end organ perfusion), with minimal disruption to the surgical workflow. Here we present a hyperspectral imaging (HSI) system for laparoscopic surgery. This system provides live, easy to interpret, tissue oxygenation (StO₂) maps with associated quantitative values.

Methods: White light view and tissue oxygenation maps were reconstructed from a protoype laparoscopic Hyperspectral Surgical System (HSS). First, in a live porcine model (55 kg female), the mesentery of a small bowel loop was temporarily occluded with a laparoscopic grasper, then released whilst being imaged with HSI. The quantitative StO₂ values obtained from the HSS were compared with those of a non-invasive tissue oximetry probe (Moor VMS-Oxy, Moor Instruments Ltd, United Kingdom). Secondly, mimicking a laparoscopic colon resection and anastomosis, the colorectal junction was mobilised laparoscopically, exteriorised, transected, anastomosed and repositioned in the abdominal cavity. In order to compare healthy and ischaemic colon, the distal part was intentionally devascularised. Tissue oxygenation maps were compared with indocyanine green fluorescence angiography (ICG-FA) of the anastomotic region.

Results: The HSS was used as the primary scope to complete a laparoscopic colorectal anastomosis, providing a simultaneous white light view and hyperspectral information. Quantitative results from small bowel imaging were shown to correlate with measurements from the superficial tissue oximetry probe. Real-time tissue oxygenation maps were shown to visually correlate with ICG-FA.

Conclusion: The HSS can guide laparoscopic surgical procedures whilst providing visual and quantitative tissue oxygenation information in a live animal model. This paves the way for further studies to assess clinical applications.

The landscape of medical device regulation is rapidly evolving, driven by innovations and the need to bring these technologies to patients more efficiently. This review provides a comprehensive analysis of the accelerated access pathways for medical devices in the United States (US) and the European Union (EU), focusing on the Breakthrough Devices Program (BDP) in the US and the evolving regulatory framework within the EU. Analysis of Food and Drug Administration (FDA) data reveals that from 2015 to 2024, only 12.3% of the 1,041 BDP-designated devices received marketing authorization, with mean decision times of 152, 262, and 230 days for 510(k), de novo, and PMA pathways respectively-significantly faster than standard approvals for de novo (338 days) and PMA (399 days). In the EU, where no specific accelerated pathway exists, the recently implemented Medical Device Regulation and Health Technology Assessment Regulation aim to harmonize approval processes, with joint clinical assessments beginning in 2026. The analysis explores the interplay between regulatory approval, funding mechanisms, and coverage policies that collectively determine the accessibility of medical devices. The unique challenges associated with emerging technologies and the implementation of accelerated pathways are also discussed. We recommend global regulatory convergence through harmonized standards, mutual recognition agreements, and unified post-market surveillance systems to balance innovation with patient safety.

Background: Quantitative computed tomography (QCT) has received growing attention for its utility in bone mineral density (BMD) assessment and osteoporosis diagnosis.

Objective: To assess the accuracy and precision of lumbar spine BMD measurements obtained using low-dose iCare QCT, based on the European Spine Phantom (ESP).

Methods: Paired t-test was employed to compare BMD values measured under normal-dose and low-dose scan protocols using Mindways and iCare QCT systems. Accuracy was evaluated using relative measurement error (RME), and precision was assessed via relative standard deviation (RSD). Pearson correlation coefficients and Bland-Altman analysis were used to examine measurement correlation and agreement.

Results: For Mindways QCT, RMEs of L1-L3 were 11.89%, 6.94%, and 6.72% under normal-dose, and 6.65%, 10.5%, and 6.31% under low-dose, respectively. For iCare QCT, RMEs were 1.21%, 4.28%, and 8.88% under normal-dose, and 2.14%, 4.96%, and 8.59% under low-dose, respectively. RSDs of L1-L3 for Mindways QCT were 5.16%, 2.85%, and 0.47% under normal-dose, and 9.08%, 4.69%, and 0.49% under low-dose, respectively. For iCare QCT, RSDs were 1.11%, 0.81%, and 0.45% under normal-dose, and 2.34%, 0.85%, and 0.33% under low-dose, respectively. The radiation dose in the low-dose protocol was significantly reduced compared with the normal-dose protocol.

Conclusion: Low-dose iCare QCT exhibited high accuracy and precision in measuring lumbar spine BMD, achieving an approximately 85% reduction in radiation dose. These findings highlight its potential as a safer and reliable tool for clinical application.

Optogenetics has potentials for a treatment of retinitis pigmentosa and other rare degenerative retinal diseases. The technology allows controlling cell activity through combining genetic engineering and optical stimulation with light. First clinical studies are already being conducted, whereby the vision of participating patients who were blinded by retinitis pigmentosa was partially recovered. In view of the ongoing translational process, this paper examines regulatory aspects of preclinical and clinical research as well as a therapeutic application of optogenetics in ophthalmology. There is no prohibition or specific regulation of optogenetic methods in the European Union. Regarding preclinical research, legal issues related to animal research and stem cell research have importance. In clinical research and therapeutic applications, aspects of subjects' and patients' autonomy are relevant. Because at EU level, so far, no specific regulation exists for clinical studies in which a medicinal product and a medical device are evaluated simultaneously (combined studies) the requirements for clinical trials with medicinal products as well as those for clinical investigations on medical devices apply. This raises unresolved legal issues and is the case for optogenetic clinical studies, when for the gene transfer a viral vector classified as gene therapy medicinal product (GTMP) and for the light stimulation a device qualified as medical device are tested simultaneously. Medicinal products for optogenetic therapies of retinitis pigmentosa fulfill requirements for designation as orphan medicinal product, which goes along with regulatory and financial incentives. However, equivalent regulation does not exist for medical devices for rare diseases.

Drug-coated balloons (DCB) represent an emerging therapeutic alternative to drug-eluting stents (DES) for the treatment of coronary artery disease (CAD). Among the key advantages of DCB over DES are the absence of a permanent structure in the vessel and the potential for fast and homogeneous drug delivery. While DCB were first introduced for treatment of in-stent restenosis (ISR), their potential wider use in percutaneous coronary intervention (PCI) has recently been explored in several randomized clinical trials, including for treatment of de novo lesions. Moreover, new hybrid techniques that combine DES and DCB are being investigated to more effectively tackle complex cases. Despite the growing interest in DCB within the clinical community, the mechanisms of drug exchange and the interactions between the balloon, the polymeric coating and the vessel wall are yet to be fully understood. It is, therefore, perhaps surprising that the number of computational (in silico) models developed to study interventions involving these devices is small, especially given the mechanistic understanding that has been gained from computational studies of DES procedures over the last two decades. In this paper, we discuss the current and emerging clinical approaches for DCB use in PCI and review the computational models that have been developed thus far, underlining the potential challenges and opportunities in integrating in silico models of DCB into clinical practice.

Central nervous system infections (CNSI) are serious life-threatening conditions caused by bacteria, viruses, fungi, and parasites and lead to high morbidity and mortality worldwide. Therefore, rapid identification of causative organisms and appropriate treatment are important. The traditional identification methods are time-consuming and lack sensitivity and specificity. Although culture method is gold standard for CNSI, it is time-consuming and microbiology reporting requires several days. Multiplex PCR assays can detect multiple pathogens simultaneously in clinical samples and overcome the limitations of conventional identification techniques. Despite the availability of several commercial molecular-based platforms for the detection of pathogens causing CNSI, there are still limitations in terms of cost, false positive results, and false negative results, which are limited to targeted pathogens in the panel. Moreover, validation of many commercially available and in-house laboratory-developed molecular assays is still lacking. In addition, molecular diagnostic tests need to be used in correlation with the clinical context to ensure better diagnosis and management of infections.

The microbiome of the gut is a complex ecosystem that contains a wide variety of microbial species and functional capabilities. The microbiome has a significant impact on health and disease by affecting endocrinology, physiology, and neurology. It can change the progression of certain diseases and enhance treatment responses and tolerance. The gut microbiota plays a pivotal role in human health, influencing a wide range of physiological processes. Recent advances in computational tools and artificial intelligence (AI) have revolutionized the study of gut microbiota, enabling the identification of biomarkers that are critical for diagnosing and treating various diseases. This review hunts through the cutting-edge computational methodologies that integrate multi-omics data-such as metagenomics, metaproteomics, and metabolomics-providing a comprehensive understanding of the gut microbiome's composition and function. Additionally, machine learning (ML) approaches, including deep learning and network-based methods, are explored for their ability to uncover complex patterns within microbiome data, offering unprecedented insights into microbial interactions and their link to host health. By highlighting the synergy between traditional bioinformatics tools and advanced AI techniques, this review underscores the potential of these approaches in enhancing biomarker discovery and developing personalized therapeutic strategies. The convergence of computational advancements and microbiome research marks a significant step forward in precision medicine, paving the way for novel diagnostics and treatments tailored to individual microbiome profiles. Investigators have the ability to discover connections between the composition of microorganisms, the expression of genes, and the profiles of metabolites. Individual reactions to medicines that target gut microbes can be predicted by models driven by artificial intelligence. It is possible to obtain personalized and precision medicine by first gaining an understanding of the impact that the gut microbiota has on the development of disease. The application of machine learning allows for the customization of treatments to the specific microbial environment of an individual.

Introduction: Orthopedic procedures often require drilling of tunnels through bone, for instance for the introduction of implants. The currently used rigid bone drills make it challenging to reach all target areas without damaging surrounding anatomy. Steerable bone drills are a promising solution as they enable access to larger volumes and the creation of curved tunnels thereby reducing the risk of harm to surrounding anatomical structures.

Method: This review provides a comprehensive overview of steerable bone drill designs identified in patent literature via the Espacenet database and in scientific literature accessed via the Scopus data base. A Boolean search combined with pre-set inclusion criteria returned 78 literature references describing a variety of drill designs.

Results: These drill designs could be categorized based on how the drilling trajectory was defined. Three methods to influence the drilling trajectory were identified: (1) the device (57% of the sources), (2) the environment (15% of the sources): the path is defined based on the tissue interaction forces with the surrounding bone or (3) the user defines the drilling trajectory (28% of the sources).

Discussion: The comprehensive overview of steerable drilling methods provides insights in the possibilities in drill design and may be used as a source of inspiration for the design of novel steerable drill designs.