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PubMed1978

Influence factors affecting career choice of preclinical medical technology students.

Over a seven-year period, data were gathered on 249 declared medical technology majors enrolled in an Introduction to Medical Technology course at the University of Iowa. The Kendall Tau C test for significance (p = less than .05) was utilized in determining the influence of several variables or factors in the students' choice of medical technology as a career. Such factors as the type of work, demand for medical technologists, and desire to help people were found to be highly motivating factors in choice. It appeared the motivation was primarily internalized with assistance sought from various sources. The decision of medical technology as a career was predominantly made in the junior/senior year in high school or freshman/sophomore year in college.

Career Choice↗

PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗

PubMed1976

[Effects of technological additives and heating range on some chemical and physical changes in canned meat. 2. Changes in redox potentials and selected quality characteristics].

The influence of technological additives and the range of heating on the redox potential, as well as on some quality features of canned meat was examined. The experiments showed, that the time of storage and the degree of heating of model preserves of meat influence on the redox potential. The technological additions as polyphosphates, ascorbic acid, gelatine and mixtures of these substances influence less on the redox potential. The analysis of each experimental factor showed, that on the secretion of meat juice occurring during can pasteurization or sterilization influence all experimental factors, as the kind of heating, the time of storage as well as the kind and the quantity of technological additives. The highest secretion of meat juice was found in cans with addition of ascorbic acid. Cans with addition of gelatine had the smallest content of jelly and consequently the lowest secretion of meat juice. It was also found a certain relation between the level of redox potential and the tested quality features of the model meat preserves.

Ascorbic Acid↗

PubMed2026

Application of Omics Technologies for Cowpea Improvement.

Cowpea (Vigna unguiculata) is a vital crop for food security, nutrition, and climate resilience in sub-Saharan African and other semi-arid regions. However, its improvement is constrained by the complexity of polygenic traits such as drought tolerance, pest resistance, and seed quality. Conventional breeding, while foundational, remains insufficient to address these challenges at the required pace. Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, provide new opportunities to dissect complex traits, identify candidate genes, and accelerate the development of resilient, high-yielding cultivars. This review presents a critical synthesis of current applications of omics technologies in cowpea improvement, highlighting their contributions to stress adaptation, nutritional enhancement, and precision breeding. The review also examines key technical and institutional constraints limiting the adoption of omics-assisted breeding in cowpea, including inadequate research infrastructure, challenges in multi-omics data integration, and limited technical capacity across breeding programs in sub-Saharan Africa. It discusses strategies to address these barriers through regional collaboration, investment in bioinformatics capacity, and the integration of computational approaches into breeding pipelines. Overall, the review concludes that combining multi-omics technologies with artificial intelligence and machine learning has strong potential to improve genotype-phenotype prediction, accelerate breeding decisions, and support the development of climate-resilient and nutritionally enhanced cowpea cultivars.

cowpea↗

PubMed2024

Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium.

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry-based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust "business cases" to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

Proteomics↗

PubMed2026

Innovative advances and clinical applications of cell-free DNA methylation detection technologies.

Advances in DNA methylation detection technologies have promoted disease-related cell-free DNA (cfDNA) analysis. CfDNA methylation profiling has the potential to serve as a promising clinical tool for early disease diagnosis. However, current detection technologies suffer from high costs, complex operational procedures, and insufficient sensitivity for low-input samples. Moreover, the definitive validation of its clinical value still awaits robust evidence from high-quality confirmatory studies. Therefore, this review begins by mapping the historical evolution of cfDNA methylation, followed by a comparison of the traditional approaches and recent breakthroughs in cfDNA methylation analysis. Specifically, this review systematically examines the two major strategies: the ones based on bisulfite-dependent DNA modification and the bisulfite-free methods, including the techniques for whole-genome methylation profiling and methods targeting specific genomic regions. Additionally, to evaluate the clinical application potential of these methods, this review comprehensively describes the details of these technologies, such as sample input requirements and sensing accuracy in detecting clinical samples. The future development of cfDNA methylation detection will focus on clinical translation, integrating technical innovations with the demands for efficient clinical diagnosis. We believe this review will help researchers select methods tailored to sample availability and clinical applicability.

Humans↗

PubMed2025

Gene regulation technologies for gene and cell therapy.

Gene therapy stands at the forefront of medical innovation, offering unique potential to treat the underlying causes of genetic disorders and broadly enable regenerative medicine. However, unregulated production of therapeutic genes can lead to decreased clinical utility due to various complications. Thus, many technologies for controlled gene expression are under development, including regulated transgenes, modulation of endogenous genes to leverage native biological regulation, mapping and repurposing of transcriptional regulatory networks, and engineered systems that dynamically react to cell state changes. Transformative therapies enabled by advances in tissue-specific promoters, inducible systems, and targeted delivery have already entered clinical testing and demonstrated significantly improved specificity and efficacy. This review highlights next-generation technologies under development to expand the reach of gene therapies by enabling precise modulation of gene expression. These technologies, including epigenome editing, antisense oligonucleotides, RNA editing, transcription factor-mediated reprogramming, and synthetic genetic circuits, have the potential to provide powerful control over cellular functions. Despite these remarkable achievements, challenges remain in optimizing delivery, minimizing off-target effects, and addressing regulatory hurdles. However, the ongoing integration of biological insights with engineering innovations promises to expand the potential for gene therapy, offering hope for treating not only rare genetic disorders but also complex multifactorial diseases.

Humans↗

PubMed2025

Accurate somatic small variant discovery for multiple sequencing technologies with DeepSomatic.

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies offer potential advantages in repeat mapping and variant phasing. We present DeepSomatic, a deep-learning method for detecting somatic small nucleotide variations and insertions and deletions from both short-read and long-read data. The method has modes for whole-genome and whole-exome sequencing and can run on tumor-normal, tumor-only and formalin-fixed paraffin-embedded samples. To train DeepSomatic and help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available the Cancer Standards Long-read Evaluation (CASTLE) dataset of six matched tumor-normal cell line pairs whole-genome sequenced with Illumina, PacBio HiFi and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples, both cell line and patient-derived, and across short-read and long-read sequencing technologies, DeepSomatic consistently outperforms existing callers.

Humans↗

PubMed2025

Giotto Suite: a multiscale and technology-agnostic spatial multiomics analysis ecosystem.

Emerging spatial multiomics technologies provide an increasingly large amount of information content at multiple scales. However, it remains challenging to efficiently represent and harmonize diverse spatial datasets. Here we present Giotto Suite, a suite of modular packages that provides scalable and extensible end-to-end solutions for multiscale and multiomic data analysis, integration and visualization. At its core, Giotto Suite is centered around an innovative data framework, allowing the representation and integration of spatial omics data in a technology-agnostic manner. Giotto Suite integrates molecular, morphology, spatial and annotated feature information to create a responsive and flexible workflow, as demonstrated by applications to several state-of-the-art spatial technologies. Furthermore, Giotto Suite builds upon interoperable interfaces and data structures that bridge the established fields of genomics and spatial data science in R, thereby enabling independent developers to create custom-engineered pipelines. As such, Giotto Suite creates an immersive and multiscale ecosystem for spatial multiomic data analysis.

Genomics↗

PubMed2026

De-extinction technology and its application to conservation.

De-extinction, once the realm of science fiction, has evolved into a tangible scientific endeavor thanks to breakthroughs in genome sequencing, engineering, advanced assisted reproductive technologies, and stem cell biology. Alongside this work are innovations in reintroduction science and artificial intelligence, which are refining strategies for species translocations, rewilding, and long-term ecosystem monitoring of de-extinct species and populations. While the primary motivation for de-extinction is restoring lost ecological functions to eroded ecosystems, each of these technologies can also be applied to conservation biology for de-endangerment, offering new solutions for biodiversity preservation. This review synthesizes the technological advancements emerging from de-extinction science and explores their broad applications in conservation, demonstrating how de-extinction is both about resurrecting lost species and about expanding the conservation toolkit to sustain and rebuild biodiversity in the face of accelerating environmental change.

Conservation of Natural Resources↗

PubMed2024

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies.

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies now offer potential advantages in terms of repeat mapping and variant phasing. We present DeepSomatic, a deep learning method for detecting somatic SNVs and insertions and deletions (indels) from both short-read and long-read data, with modes for whole-genome and exome sequencing, and able to run on tumor-normal, tumor-only, and with FFPE-prepared samples. To help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available a dataset of five matched tumor-normal cell line pairs sequenced with Illumina, PacBio HiFi, and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples and technologies (short-read and long-read), DeepSomatic consistently outperforms existing callers, particularly for indels.

Journal Article↗

PubMed1979

Policies toward medical technology: the case of electronic fetal monitoring.

Electronic fetal monitoring (EFM) is an example of a medical technology that has been widely accepted since its introduction in the mid-1960s. However, review of the literature does not provide convincing evidence of EFM efficacy, and four recent, controlled, clinical trails show little if any benefit in terms of preventing death or long-term disability of the baby. Public and private policies have largely acted to encourage use of EFM, and none have acted to slow or prevent its spread. This need for mechanisms to assure the timely evaluation of new medical technologies before they are accepted as a medical practice has led to a new medical devices program in the Food and Drug Administration, consensus development groups at the National Institutes of Health, and congressional legislation to establish a new National Center for Health Care Technology.

Equipment and Supplies↗

PubMed2026

Advances in organoids for personalized medicine: from technological development to clinical application.

Organoids, three-dimensional cell culture models derived from patient tissues or stem cells, have emerged as a cutting-edge technology in personalized medicine, owing to their remarkable ability to closely recapitulate in vivo tissue architecture and function. This review provides a comprehensive overview of the technological evolution and construction methodologies of organoids, highlighting their significant applications in oncology, genetic disorders, infectious diseases, and drug screening. This review examines how organoids enable precision medicine by preserving genomic fidelity, predicting drug sensitivity, and creating disease models via gene editing. Despite these advances, organoid technology faces several technical challenges that impede its full clinical translation. Addressing these obstacles is critical for realizing the potential of organoids in individualized therapeutic strategies. This article aims to delineate current progress and future directions in organoid research, furnishing a theoretical foundation and guiding future investigations towards enhancing personalized treatment paradigms.

disease modeling↗

PubMed2026

Beyond Canonical Neoantigens: Emerging Technologies for Identification of Noncanonical Antigens and Implications for Personalized Cancer Vaccines.

Over the past decade, advances in sequencing technologies and computational pipelines enabled the development of personalized cancer vaccines (PCVs). Current PCV strategies primarily target cancer neoantigens generated by non-synonymous DNA mutations, which can result in altered amino acid sequences capable of eliciting tumor-specific immune responses. More recently, a distinct class of tumor-specific antigens (TSA), termed noncanonical or cryptic antigens, has emerged as an additional source of immunogenic targets. Unlike canonical neoantigens, noncanonical antigens typically cannot be identified by tumor/normal whole-exome sequencing, as they do not arise from classical DNA mutations. Instead, they are often associated with less well recognized and/or aberrant processes in the pathways from DNA to human leukocyte antigen (HLA)-presented peptides. Examples include transposable elements, circular RNA, translation of alternative open reading frames and/or long non-coding RNA, among others. Emerging evidence suggests that noncanonical antigens represent a substantial portion of the tumor-specific immunopeptidome and, similar to canonical neoantigens, are absent during thymic selection and can evade central tolerance and elicit T cell responses. Technological advances have increasingly facilitated the identification of noncanonical antigens. Long-read RNA sequencing reveals noncanonical transcripts by improving transcriptome assembly, while ribosome profiling provides genome-wide maps of actively translated regions, facilitating the discovery of peptides from aberrant translation events. Specialized molecular approaches enable enrichment and sequencing of circular RNAs, and immunopeptidomics using mass spectrometry allows for direct characterization of HLA-presented peptides. Together, these technological advances have led to an increasing interest in prioritizing and targeting noncanonical antigens in the next generation of PCVs. This review provides an overview of the diverse origins of TSAs beyond classical neoantigens and discusses emerging approaches that may enable the integration of these antigens in future clinical trials.

circular RNA↗

PubMed2025

Multi-omics technologies: Novel tools and methods for assessing nerve injury and regeneration.

Recently, with the rapid advancement of multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, new tools and approaches have been introduced for studying nerve injury and regeneration. This review highlights the application and progress of multi-omics in uncovering the mechanisms of nerve injury, guiding the development of regenerative strategies, and promoting clinical translation. By integrating multi-omics datasets, researchers can comprehensively track dynamic molecular changes following nerve injury, including abnormal gene expression, disrupted protein signaling, altered metabolic programs, and shifts in the immune microenvironment. Single-cell multi-omics technologies resolve cellular heterogeneity, revealing the distinct functions of neurons, glial cells, and immune cell subpopulations during the injury response. Spatially resolved transcriptomics maintain the spatial context of lesion and regeneration sites, enabling precise localization for targeted interventions. Multi-omics technologies not only identify key molecular players involved in nerve regeneration but also create opportunities for personalized medicine. Nonetheless, integrating multi-omics data poses technical challenges, including high dimensionality, batch effects, and algorithmic constraints, while ethical concerns related to stem cell therapy and gene editing require stringent oversight. To transition from structural reconstruction to functional remodeling, future research should emphasize artificial intelligence-driven data integration, organ-on-a-chip modeling, and cross-disciplinary collaboration to overcome existing technical barriers and accelerate the clinical application of neuroregenerative therapies.

artificial intelligence↗

PubMed1978

Effects of hospital cost containment on the development and use of medical technology.

The early hope for more effective, safer, and less expensive care through the "medical technological revolution" is critically examined. Inflated costs--and imperfect expectations--have led to broad proposals for hospital cost containment which may constrain development and use of new technology. But concerns for care and cost will be better served by selective shifts in the type of technology employed and incentives for its use.

Capital Expenditures↗

PubMed1977

The diffusion of hospital technologies: some econometric evidence.

This paper examines the diffusion of five hospital technologies that have spread widely since 1950: the postoperative recovery room, the intensive care unit, the respiratory therapy department, diagnostic radioisotope facilities, and electroencephalograph. The regressions show that the logistic function describes the diffusion of hospital innovations as well as it does the diffusion of innovations in other industries, that the rates of diffusion for these technologies fall within the range established by work on other industries, and that the rates for technologies that were not yet widespread in the middle 1960s increased with the advent of Medicare and Medicaid.

Costs and Cost Analysis↗

PubMedDate not supplied

The diffusion of medical technology.

The diffusion of medical technology is a complex process dependent upon the character of the industry, cost of investment and adoption, sophistication of users and their environments, general-use standards, user prestige, patient distribution, government regulations, and scale of operation. The one positive influence with the greatest impact on the rate of adoption of a new technology is the third-party-payer system. The more we understand those parameters affecting the diffusion of technology in medicine, the more we will be able to provide society with good care in a reasonable cost.

Costs and Cost Analysis↗