Progress in biomedical engineering (Bristol, England)最新文献

Schizophrenia is a complex and chronic psychiatric disorder that significantly impacts patients' quality of life. Ranking 12th among 310 diseases and injuries that result in disability, the number of patients suffering from schizophrenia continues to rise, emphasizing the urgent need for developing effective treatments. Despite the availability of effective antipsychotic drugs, over 80% of patients taking oral antipsychotics experience relapses, primarily caused by non-adherence as the high dosing frequency is required. In this review, we discuss about schizophrenia, its incidence, pathological causes, influencing factors, and the challenges of the current medications. Specifically, we explore nanocrystal technology and its application to paliperidone, making it one of the most successful long-acting antipsychotic drugs introduced to the market. We highlight the clinical advantages of paliperidone nanocrystals, including improved adherence, efficacy, long-term outcomes, patient satisfaction, safety, and cost-effectiveness. Additionally, we address the physicochemical factors influencing the drug's half-life, which crucially contribute to long-acting medications. Further studies on nanocrystal-based long-acting medications are crucial for enhancing their effectiveness and reliability. The successful development of paliperidone nanocrystals holds great promise as a significant approach for drug development, with potential applications for other chronic disease management.

Retinal implantation of an electrode array is an emerging treatment for vision loss caused by outer-retinal degeneration. This article collects and analyses harms associated with the treatment reported in the peer-reviewed literature, thus enabling informed decision-making by patients, clinicians, researchers, engineers, and policymakers. We searched MEDLINE, Embase, and clinical trials registries for peer-reviewed journal articles reporting harms outcomes. We extracted data from articles including study design, definitions of 'serious adverse event', and timing of adverse events. We applied the McMaster tool to these articles to assess the risk of bias in harms assessment and reporting. Our searches returned 585 abstracts. We reviewed the full text of 59 articles describing 11 different devices. McMaster scores ranged from 3 to 12 (maximum 15; higher scores indicate less risk). We compiled a comprehensive list of all serious and non-serious adverse events associated with retinal implantation. Several harms were common across devices. Our meta-analysis showed that serious adverse events are log-uniformly distributed throughout follow-up. Improved reporting and further clinical studies are needed to develop a reliable safety profile of retinal implantation. Our findings will help guide the design, conduct, and reporting of future clinical trials of retinal implantation and other emerging treatments for vision loss. (PROSPERO registration: CRD42022308123.).

Technical setups in today's operating rooms (ORs) are becoming increasingly complex, especially with the integration of applications which rely on the fusion of multiple information sources. While manufacturers have already started to make use of such approaches, the quest for fully integrated ORs becoming standard is still ongoing. We describe a variety of state-of-the-art projects that envision an OR of the future in order to identify missing building blocks. While these initial implementations of sensor fused ORs have shown to be promising, all current proposals lack a scalable networking backbone that serves the needs of future applications. We therefore discuss how the coming 6G standard's envisioned advancements can provide a flexible and intelligent platform to enable the fully integrated OR of the future.

This article summarizes a systematic literature review of deep neural network-based cognitive workload (CWL) estimation from electroencephalographic (EEG) signals. The focus of this article can be delineated into two main elements: first is the identification of experimental paradigms prevalently employed for CWL induction, and second, is an inquiry about the data structure and input formulations commonly utilized in deep neural networks (DNN)-based CWL detection. The survey revealed several experimental paradigms that can reliably induce either graded levels of CWL or a desired cognitive state due to sustained induction of CWL. This article has characterized them with respect to the number of distinct CWL levels, cognitive states, experimental environment, and agents in focus. Further, this literature analysis found that DNNs can successfully detect distinct levels of CWL despite the inter-subject and inter-session variability typically observed in EEG signals. Several methodologies were found using EEG signals in its native representation of a two-dimensional matrix as input to the classification algorithm, bypassing traditional feature selection steps. More often than not, researchers used DNNs as black-box type models, and only a few studies employed interpretable or explainable DNNs for CWL detection. However, these algorithms were mostly post hoc data analysis and classification schemes, and only a few studies adopted real-time CWL estimation methodologies. Further, it has been suggested that using interpretable deep learning methodologies may shed light on EEG correlates of CWL, but this remains mostly an unexplored area. This systematic review suggests using networks sensitive to temporal dependencies and appropriate input formulations for each type of DNN architecture to achieve robust classification performance. An additional suggestion is to utilize transfer learning methods to achieve high generalizability across tasks (task-independent classifiers), while simple cross-subject data pooling may achieve the same for subject-independent classifiers.

Mammography imaging remains the gold standard for breast cancer detection and diagnosis, but challenges in image quality can lead to misdiagnosis, increased radiation exposure, and higher healthcare costs. This comprehensive review evaluates traditional and machine learning-based techniques for improving mammography image quality, aiming to benefit clinicians and enhance diagnostic accuracy. Our literature search, spanning 2015 - 2024, identified 115 articles focusing on contrast enhancement and noise reduction methods, including histogram equalization, filtering, unsharp masking, fuzzy logic, transform-based techniques, and advanced machine learning approaches. Machine learning, particularly architectures integrating denoising autoencoders with convolutional neural networks, emerged as highly effective in enhancing image quality without compromising detail. The discussion highlights the success of these techniques in improving mammography images' visual quality. However, challenges such as high noise ratios, inconsistent evaluation metrics, and limited open-source datasets persist. Addressing these issues offers opportunities for future research to further advance mammography image enhancement methodologies.

Computational models of the heart are now being used to assess the effectiveness and feasibility of interventions through in-silico clinical trials (ISCTs). As the adoption and acceptance of ISCTs increases, best practices for reporting the methodology and analysing the results will emerge. Focusing in the area of cardiology, we aim to evaluate the types of ISCTs, their analysis methods and their reporting standards. To this end, we conducted a systematic review of cardiac ISCTs over the period of 1 January 2012-1 January 2022, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA). We considered cardiac ISCTs of human patient cohorts, and excluded studies of single individuals and those in which models were used to guide a procedure without comparing against a control group. We identified 36 publications that described cardiac ISCTs, with most of the studies coming from the US and the UK. In $75\%$ of the studies, a validation step was performed, although the specific type of validation varied between the studies. ANSYS FLUENT was the most commonly used software in $19\%$ of ISCTs. The specific software used was not reported in $14\%$ of the studies. Unlike clinical trials, we found a lack of consistent reporting of patient demographics, with $28\%$ of the studies not reporting them. Uncertainty quantification was limited, with sensitivity analysis performed in only $19\%$ of the studies. In $97\%$ of the ISCTs, no link was provided to provide easy access to the data or models used in the study. There was no consistent naming of study types with a wide range of studies that could potentially be considered ISCTs. There is a clear need for community agreement on minimal reporting standards on patient demographics, accepted standards for ISCT cohort quality control, uncertainty quantification, and increased model and data sharing.