Playing with propranolol.
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Anxiety Disorders↗
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Results for “Biomedical Enhancement”
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
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Safeguarding biomedical AI: a critical scoping review of privacy-enhancing technologies, hybrid approaches, and deployment models.
BACKGROUND: Biomedical artificial intelligence (AI) requires the integration of privacy-enhancing technologies (PETs) to safeguard sensitive clinical, imaging, and genomic data while preserving analytical utility. OBJECTIVES: This review critically and systematically maps applications of PETs across the biomedical AI lifecycle in accordance with PRISMA-ScR guidelines and evaluates their technical trade-offs, deployment feasibility, and residual risks. METHODS: We systematically searched PubMed, IEEE Xplore, ACM Digital Library, and Scopus for studies published between 2015 and 2025. Eligible studies addressed differential privacy, federated learning, secure multiparty computation, homomorphic encryption, or hybrid approaches in biomedical AI. Data were charted on PET type, modality, lifecycle stage, utility metrics, privacy parameters, and deployment considerations. A critical appraisal rubric assessed threat-model adequacy, methodological clarity, reproducibility, privacy-utility transparency, and deployment realism. Additionally, we hand-searched major venues (USENIX Security, NeurIPS, AAAI) and screened Google Scholar for grey literature, applying de-duplication across sources. RESULTS: We identified 87 studies spanning clinical decision support, genomics, and medical imaging. From 25,761 initial records, 3,754 underwent title/abstract screening and 1,968 underwent full-text assessment. PETs demonstrated distinct strengths and limitations: differential privacy provided provable guarantees but reduced performance on imbalanced data; federated learning improved data access but remained vulnerable to gradient leakage; and cryptographic methods ensured confidentiality at high computational cost. Synthetic data generation supported privacy-conscious data sharing and benchmarking but remained sensitive to disclosure risk, fidelity loss, and subgroup representation. Hybrid and emerging approaches, including trusted execution environments, zero-knowledge proofs, and privacy-preserving transformer architectures, mitigated composability gaps yet lacked full end-to-end assurance. Case studies at hospital and biobank scale illustrated practical feasibility and infrastructure demands. CONCLUSIONS: Situating PETs within technical and operational contexts clarifies their capabilities, limitations, and deployment challenges. Residual risks persist, including fairness concerns, inference-time leakage, and overreliance on PETs as compliance proxies. Sustained technical innovation and institutional governance remain essential for the trustworthy integration of PETs in biomedical AI.
biomedical AI↗
The chicken chorioallantoic membrane model for human surgeries and implants.
The fertilized chick egg, particularly its chorioallantoic membrane (CAM), has emerged as a valuable model in biomedical research due to its extensive applications in vascular studies, cancer investigations, surgical advancements including neurological, gynecological, urological, and retinal procedures, drug evaluation, and implant assessments. This review provides an in-depth examination of the chicken genome, structural composition of CAM, developmental progression, vascularization patterns, and cellular regulatory mechanisms. Furthermore, it underscores the CAM's significance in assessing therapeutic kinetics, biocompatibility, biodistribution, and drug effectiveness. A particular focus is placed on its role in analyzing vascular-disrupting agents (VDAs) for cancer treatment, alongside the incorporation of advanced imaging technologies such as photodynamic therapy, radiotherapy, positron emission tomography (PET)/computed tomography (CT) imaging, ultrasound techniques, and AI-driven detection methods for real-time vascular monitoring. By evaluating its advantages, limitations, and applications, this study establishes that CAM is a crucial alternative model for biomedical research, facilitating enhanced experimental design and methodological refinement.
angiogenesis↗
Titanium-protein interaction: changes with oxide layer thickness.
Since titanium is being used for various biomedical applications requiring enhanced blood compatibility, which may be partly due to its extremely stable oxide layer, an attempt is made here to understand the effect of oxide layer thickness on protein adsorption. Different thickness of oxide layers have been coated on titanium foil using anodizing method and thickness of oxide layers deposited have been measured by an ellipsometer. Studies of competitive adsorption of proteins, using I125 labelled protein from a mixture of 25 mg% albumin, 15 mg% gamma-globulin and 7.5 mg% fibrinogen indicate an increased adsorption of proteins onto the oxide layer coated surfaces compared to the bare surface.
Adsorption↗
[An analysis of the seasonal rise in hepatitis A morbidity at a children's center and ways to improve the sanitary epidemiologic surveillance for this infection among organized preschoolers].
The spread of hepatitis A in children's institution occurred through everyday contacts, the boundaries of the focus of infection embraced the whole institution, and the active detection of the prevailing subclinical forms of the disease proved to be possible by means of virological and biomedical tests. The enhancement of the effectiveness of clinical and epidemiological surveillance by its orientation to the control of the spread of this infection through water and everyday contacts, the rational organization of sanitary, microbiological and serological studies, the development of criteria of epidemiological safety of children's institutions is proposed.
Antigens, Viral↗
Digital image enhancement of cephalograms.
Quantification of cephalometric X-rays requires the use of hand tracings of the structures to delineate points, lines, and angles. The structures, especially soft tissue, are often obscure and are therefore approximated. Recent biomedical application of image enhancement by digital computer promises a method whereby landmarks in the cranium are more clearly defined. Development of this technique will enable a more extensive and accurate utilization of cephalometric radiographs for diagnostic and research purposes.
Cephalometry↗
PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.
MOTIVATION: Integrating multi-omics data provides valuable insights into biological processes by capturing information across multiple molecular layers, enabling a comprehensive understanding of complex diseases and driving advancements in precision medicine. However, existing computational methods for multi-omics integration face significant challenges, such as low reliability and poor generalizability, due to the high dimensionality and low sample size nature of omics data. RESULTS: To address these challenges, we present PEARL (Pearson-Enhanced spectrAl gRaph convoLutional networks), a novel deep graph learning method for biomedical classification and functional important omics features identification. PEARL leverages a simple yet effective learning architecture to achieve superior and robust performance in high-dimensional, low-sample-size multi-omics settings. Our results demonstrate that PEARL significantly outperforms existing state-of-the-art methods on both synthetic and real biomedical datasets. Furthermore, applied to Alzheimer's disease (AD) brain multi-omics data, features prioritized by PEARL lead to functionally important genes that demonstrate significant enrichment in AD-related pathways. These findings highlight PEARL's practical utility in biomedical research and its potential to enhance biological interpretability in multi-omics studies. AVAILABILITY AND IMPLEMENTATION: The source code of our computational framework is available at https://github.com/zqq121017/PEARL.
Multiomics↗
Delineation of clinical conditions: conceptual models of 'physical' and 'mental disorders.
The dysfunctional consequences of the Cartesian dichotomy have been enhanced by the power of biomedical technology. Technical virtuosity reifies the mechanical model and widens the gap between what patients seek and doctors provide. Patients suffer 'illnesses'; doctors diagnose and treat 'diseases'. Illnesses are experiences of discontinuities in states of being and perceived role performances. Diseases, in the scientific paradigm of modern medicine, are abnormalities in the function and/or structure of body organs and systems. Traditional healers also redefine illness as disease: because they share symbols and metaphors consonant with lay beliefs, their healing rituals are more responsive to the psychosocial context of illness. Psychiatric disorders offer an illuminating perspective on the basic medical dilemma. The paradigms for psychiatric practice include multiple and ostensibly contradictory models: organic, psychodynamic, behavioural and social. This mélange of concepts stems from the fact that the fundamental manifestations of psychosis are disordered behaviours. The psychotic patient remains a person; his self-concept and relationships with others are cental to the therapeutic encounter, whatever pharmacological adjuncts are employed. The same truths hold for all patients. The social matrix determines when and how the patient seeks what kind of help, his 'compliance' with the recommended regimen and, to a significant extent, the functional outcome. When physicians dismiss illness because ascertainable 'disease' is absent, they fail to meet their socially assigned responsibility. It is essential to reintegrate 'scientific' and 'social' concepts of disease and illness as a basis for a functional system of medical research and care.
Culture↗
Disease and illness. Distinctions between professional and popular ideas of sickness.
The dysfunctional consequences of the Cartesian dichotomy have been enhanced by the power of biomedical technology. Technical virtuosity reifies the mechanical model and widens the gap between what patients seek and doctors provide. Patients suffer "illnesses"; doctors diagnose and treat "diseases". Illnesses are experiences of discontinuities in states of being and perceived role performances. Diseases, in the scientific paradigm of modern medicine, are abnormalities in the function and/or structure of body organs and systems. Traditional healers also redefine illness as disease: because they share symbols and metaphors consonant with lay beliefs, their healing rituals are more responsive to the psychosocial context of illness. Psychiatric disorders offer an illuminating perspective on the basic medical dilemma. The paradigms for psychiatric practice include multiple and ostensibly contradictory models: organic, psychodynamic, behavioural and social. This mélange of concepts stems from the fact that the fundamental manifestations of psychosis are disordered behaviours. The psychotic patient remains a person; his self-concept and relationships with others are central to the therapeutic encounter, whatever pharmacological adjuncts are employed. The same truths hold for all patients. The social matrix determines when and how the patient seeks what kind of help, his "compliance" with the recommended regimen and, to a significant extent, the functional outcome. When physicians dismiss illness because ascertainable "disease" is absent, they fail to meet their socially assigned responsibility. It is essential to reintegrate "scientific" and "social" concepts of disease and illness as a basis for a functional system of medical research and care.
Attitude of Health Personnel↗
Catalyzing computational biology research at an academic institute through an interest network.
Biology has been transformed by the rapid development of computing and the concurrent rise of data-rich approaches such as, omics or high-resolution imaging. However, there is a persistent computational skills gap in the biomedical research workforce. Inherent limitations of classroom teaching and institutional core support highlight the need for accessible ways for researchers to explore developments in computational biology. An analysis of the Scripps Research Genomics Core revealed increases in the total number and diversity of experiments: the share of experiments other than bulk RNA- or DNA-sequencing increased from 34% to 60% within 10 years, requiring more tailored computational analyses. These challenges were tackled by forming a volunteer-led affinity group of approximately 300 academic biomedical researchers interested in computational biology, referred to as the Computational Biology and Bioinformatics (CBB) affinity group. This adaptive group has provided continuing education and networking opportunities through seminars, workshops, and coding sessions while evolving along with the needs of its members. A survey of CBB's impact confirmed the group's events increased the members' exposure to computational biology educational and research events (79% respondents) and networking opportunities (61% respondents). Thus, volunteer-led affinity groups may be a viable complement to traditional institutional resources for enhancing the application of computing in biomedical research.
Computational Biology↗
Statistics and biomedical research.
Medical observations of leukemia or Hodgkin's disease clusters in time and space have led statisticians to invent techniques to evaluate the probability that these occurrences were due to chance. A difficult computation in human genetics was simplified by a matrix devised by a non-genetical statistician, and readily adapted to newly emerging computer-processing. The experiences exemplify the special opportunity in Japan to enhance research by merging mathematical talent with biomedical observations.
Computers↗
A new model for enhanced information services in an academic medical center.
The information base used in the biomedical enterprise, already large, continues to expand at a striking rate. Networking and desktop computing technology is playing a more important role in the operations of academic medical centers. Integration efforts aimed at enhancing information access by using distributed computing are very substantial technical challenges. However, if these integration efforts focus only on the technical aspects, they are doomed to failure. New organizational approaches are also needed. This paper describes an new model for enhanced information services. This model calls for the central information supplier to provide a set of core services. Users, who may be individuals or units and generally have more insight into the nature of their problems, will be encouraged to add value to these core services in the form of specialization or customization to meet their unique and critical needs. This model provides a way to adapt and transform current organizational elements to effectively use the large information technology investments and to meet the increasing challenges of biomedical information use.
Academic Medical Centers↗
An introduction to interactive hypermedia.
Current computers can create and display documents that incorporate a variety of audiovisual media, and can be organized to allow the user, guided by curiosity and not by a fixed path through the material, to move through the information in non-linear pathways. These hypermedia documents and the concept of hypertext offer significant new possibilities for the creation of educational materials for the biomedical sciences. If the full capabilities of the computer are to be used to enhance the educational experience for learners, computer professionals need to collaborate with publishing and teaching professionals. Biomedical communications professionals can and should play a role in establishing and evaluating hypermedia documents for medical education.
Audiovisual Aids↗
A review on biomedical image processing and future trends.
The last two decades have witnessed a revolutionary development in the field of biomedical and diagnostic imaging. Imaging procedures and modalities which were only in the experimental research phase in the early part of the last two decades, have now become universally accepted clinical procedures. They include computerized tomography (CT), magnetic resonance imaging, ultrasound imaging, nuclear medicine imaging, computerized hematological cell analysis, etc. In the past, the conventional and relatively simple image processing techniques such as image enhancement, gray-level mapping, spectral analysis, region extraction, etc. have been modified for biomedical images and successfully applied for processing and analysis. The role of image enhancement, gray-level mapping, and image reconstruction from projections algorithms in CT and other radiological imaging modalities is well evident. Recently, many advances in biomedical image processing, analysis, and understanding algorithms have shown a great potential for enhancing and interpreting useful diagnostic information from these images more accurately. This paper presents a review on the current state-of-the-art techniques in biomedical image processing and comments on future trends.
Algorithms↗
Automated integration of external databases: a knowledge-based approach to enhancing rule-based expert systems.
Expert system applications in the biomedical domain have long been hampered by the difficulty inherent in maintaining and extending large knowledge bases. We have developed a knowledge-based method for automatically augmenting such knowledge bases. The method consists of automatically integrating data contained in commercially available, external, on-line databases with data contained in an expert system's knowledge base. We have built a prototype system, named DBX, using this technique to augment an expert system's knowledge base as a decision support aid and as a bibliographic retrieval tool. In this paper, we describe this prototype system in detail, illustrate its use and discuss the lessons we have learned in its implementation.
Asthma↗
[Characteristics of the design of power supply devices for radiodiagnostic apparatus].
Basic features specific for the X-ray power supply facilities feeding modern roentgenological units, which became necessary after the introduction of automatic exposure, decreasing-load conditions and X-ray image intensification relays. Classification of the feeding devices, depending upon the purpose of the apparatus is presented and recommendations for the adoption of suitable patterns for the main circuitry of the apparatus and methods of its calculation are given.
Biomedical Engineering↗
Implants, transplants, and other parts of the medicine of the future.
Today, therapies that rely on adding something "more than natural" to the patient are a commonplace of medical practice. Although we may view such therapies as recent developments of biomedical science and engineering, they are actually the culmination of efforts to replicate, replace, and enhance parts of the human anatomy that date from the early days of recorded history. A review of some of those early efforts is an instructive way to begin. It helps us to appreciate that current developments at the forefront of biomedical science and engineering will soon transform the implants and transplants of today into the relatively primitive implants and transplants of the past. A discussion of the present state of implants and transplants follows. It is necessary to allow us to appreciate the great potential offered by current biomedical science and engineering for future developments in the therapies of the "more than natural." And then, finally, we can offer a present view of the implants, transplants, and other parts of the medicine of the future.
Artificial Organs↗
The human genome project as social policy: implications for clinical medicine.
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Biomedical Research↗