Technology: future challenge is information gathering and coordination.
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Cost-Benefit Analysis↗
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Results for “Future technology”
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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Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.
Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.
Multiomics↗
New and future techniques in prenatal diagnosis.
Fetal evaluation may be accomplished in the future using techniques experimental at this time. These include direct visualization of the fetus by endoscopy, biopsy of fetal tissues, and indirect techniques accomplished in the laboratory. It is hoped that in the future technology may provide us with means to diagnose the abnormal fetus more accurately and rapidly than currently. Possible methods are discussed.
Biopsy↗
Large-scale simulation of coverage and error rate tradeoffs for cancer detection in cell-free DNA whole-genome sequencing.
MOTIVATION: Cell-free DNA (cfDNA) whole-genome sequencing (WGS) is a promising approach for detecting cancer recurrence. It enables cancer detection by identifying all tumor-derived cfDNA (ctDNA) molecules carrying somatic single nucleotide variants (sSNVs). While ideally, a sequencing platform should be highly accurate for reliable ctDNA detection, in reality, all sequencing platforms introduce sequencing errors that generate false positives indistinguishable from true SNVs. Understanding how sequencing parameters influence ctDNA detection sensitivity at low tumor fractions (TFs) in cfDNA samples is essential for guiding sequencing strategies in clinical contexts. To model cfDNA sequencing for tumor detection, which contains asymmetric noise and multiple interacting parameters, analytical modeling is intractable, motivating large-scale parallelized simulation. RESULTS: We developed a simulation framework to generate in silico cfDNA data across 10 cancer types. In total, 480 million cfDNA samples were simulated from tumor WGS profiles. Overall, the lowest detectable TF differs substantially between cancer types under identical sequencing conditions due to variations in mutational load. For cancers with high mutational load, 3× coverage with low-error techniques reliably detects TFs below 0.1%. In contrast, cancers with low mutational load require at least six-fold higher coverage to achieve comparable detection thresholds. Increasing sequencing quality scores from Q30 to Q55 at 30× coverage further enhances sensitivity, enabling detection of TFs as low as 1 × 10-5. This study provides a comprehensive framework for optimizing sequencing parameters, offering valuable guidance for tailoring future technology development for specific cancer types and clinical applications. AVAILABILITY AND IMPLEMENTATION: The code is publicly available at https://github.com/UMCUGenetics/cfdetect/tree/main.
Whole Genome Sequencing↗
The effects of biological motion on CT resolution.
A variety of periodic and aperiodic psyiologic motions affect the quality of CT images. This study was initiated to estimate what an optimal scan period should be in the design of CT units. Ultrashort scan periods were experimentally similated in the heart and liver to approximate the parameters of future technology.
Animals↗
A technology residency in the future?
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Forecasting↗
The future of female sterilization technology.
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Colposcopy↗
Recent advances in environmental antibiotic resistance genes detection and research focus: From genes to ecosystems.
Antibiotic resistance genes (ARGs) persistence and potential harm have become more widely recognized in the environment due to its fast-paced research. However, the bibliometric review on the detection, research hotspot, and development trend of environmental ARGs has not been widely conducted. It is essential to provide a comprehensive overview of the last 30 years of research on environmental ARGs to clarify the changes in the research landscape and ascertain future prospects. This study presents a visualized analysis of data from the Web of Science to enhance our understanding of ARGs. The findings indicate that solid-phase extraction provides a reliable method for extracting ARG. Technological advancements in commercial kits and microfluidics have facilitated the efficacy of ARGs extraction with significantly reducing processing times. PCR and its derivatives, DNA sequencing, and multi-omics technology are the prevalent methodologies for ARGs detection, enabling the expansion of ARG research from individual strains to more intricate microbial communities in the environment. Furthermore, due to the development of combination, hybridization and mass spectrometer technologies, considerable advancements have been achieved in terms of sensitivity and accuracy as well as lowering the cost of ARGs detection. Currently, high-frequency terms such as "Antibiotic Resistance, Antibiotics, and Metagenomics" are the center of attention for study in this area. Prominent topics include the investigation of anthropogenic impacts on environmental resistance, as well as the dynamics of migration, dissemination, and adaptation of environmental ARGs, etc. The research on environmental ARGs has made significant advancements in the fields of "Microbiology" and "Biotechnology Applied Microbiology". Over the past decade, there has been a notable increase in the fields of "Environmental Sciences Ecology" and "Engineering" with a similar growth trend observed in "Water Resources". These three domains are expected to continue driving extensive study within the realm of environmental ARGs.
Drug Resistance, Microbial↗
Chrom-Sig: de-noising 1D genomic profiles by signal processing methods.
MOTIVATION: Modern genomic research is driven by next-generation sequencing experiments such as ChIP-seq, CUT&Tag, and CUT&RUN that generate coverage files for transcription factor binding, as well as ATAC-seq that yield coverage files for chromatin accessibility. Due to the inherent technical noise present in the experimental protocols, researchers need statistically rigorous and computationally efficient methods to extract true biological signal from a mixture of signal and noise. However, existing approaches are often computationally demanding or require input or spike-in controls. RESULTS: We developed Chrom-Sig, a Python package to quickly de-noise 1D genomic coverage tracks by computing the empirical null distribution without prior assumptions or experimental controls. When tested on 19 ChIP-seq, CUT&RUN, ATAC-seq, and snATAC-seq datasets, Chrom-Sig can effectively decompose the data into signal and noise components. Notably, Chrom-Sig performs de-noising and peak calling in 1-2 h using around 20 GB of memory. The de-noised signal corroborates with biologically meaningful results: CTCF CUT&RUN data retained a high percentage of peaks overlapping CTCF binding motifs, while ATAC-seq and RNA Polymerase II data were enriched in enhancers and promoters. We envision Chrom-Sig to be a versatile and general tool for current and future genomic technologies. AVAILABILITY AND IMPLEMENTATION: Chrom-Sig is publicly available on GitHub (https://github.com/minjikimlab/chromsig) and Zenodo (doi: 10.5281/zenodo.17488772) under the MIT licence.
Genomics↗
Through the lens of bioenergy crops: advances, bottlenecks, and promises of plant engineering.
Advances in engineering of bioenergy crops were driven over the past years by adapting technological breakthroughs and accelerating conventional applications but also exposed intriguing challenges. New tools revealed rich interconnectivity in the exponentially growing and dynamic 'big' omics data' of metabolomes, transcriptomes, and genomes at previously inaccessible magnitude (global, cross-species, meta-) and resolution (single cell). Insights enabled fresh hypotheses and stimulated disciplines such as functional genomics with discovery of broad regulatory networks and their determinants, that is, DNA parts, including promoters, regulatory elements, and transcription factors. Their rational design, assembly into increasingly complex blueprints, and installation into diverse chassis is an existing frontier that may benefit from emerging technologies to address bottlenecks. Interweaving nature-inspired to fully synthetic parts has already allowed building of fine-tuned regulatory circuits, or new-to-nature metabolic routes insulated from the biological context of the chassis species. Similarly, developments and the evolving need for unifying principles in plant transformation and species-agnostic technologies highlight future opportunities for engineering the next generation of bioenergy plants.
Crops, Agricultural↗
Medical technology--an assessment for the future.
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Cost-Benefit Analysis↗
Digital twins in precision pharmacotherapy: emerging applications, challenges, and future directions.
Digital twin technology, defined as dynamic digital models that represent individual patients, is emerging as a promising paradigm in precision pharmacotherapy. The integration of pharmacokinetic and pharmacodynamic (PK/PD) modeling, clinical data, genomic information, and real-time patient monitoring enables digital twins to shift drug therapy away from population-based averages toward individualized, adaptive decisions. This narrative review explores conceptual frameworks, emerging applications, methodological approaches, clinical value, limitations, and future directions of digital twins in pharmacotherapy, with particular emphasis on the role of clinical pharmacists. Unlike broader digital twin reviews that primarily emphasize technical architectures, disease-specific applications, or pharmaceutical research and development, this review focuses on the clinical-pharmacy translation layer: how digital twin outputs can be interpreted, validated, communicated, and converted into actionable medication decisions at the bedside and across ambulatory care settings. Key applications include precision dosing, polypharmacy management, antimicrobial stewardship, and the optimization of complex therapies, alongside important ethical, regulatory, and implementation challenges.
clinical pharmacy↗
A status update on the unkept promise of high-frequency spinal cord stimulation for treatment-refractory chronic migraine.
INTRODUCTION: Chronic migraine (CM) refractory to conventional pharmacotherapy (r-CM) remains a debilitating neurological condition with limited therapeutic options. High-frequency spinal cord stimulation at 10 kilohertz (HF-SCS) has recently emerged as a distinct neuromodulatory paradigm for this patient population. Unlike traditional spinal cord stimulation, HF-SCS operates above the frequency range that generates paresthesia, thereby eliminating stimulation-induced sensation while potentially engaging unique analgesic mechanisms. AREAS COVERED: This focused narrative review synthesizes the available clinical evidence, technical considerations, and mechanistic hypotheses specifically pertaining to cervical HF-SCS for refractory chronic migraine (r-CM). The authors further provide their expert perspectives on the future of this technology as a treatment option for refractory chronic migraine. EXPERT OPINION: Current data from prospective open-label studies and retrospective case series suggest that cervical HF-SCS may reduce monthly migraine days, facilitate conversion from chronic to episodic migraine patterns in some implanted patients, and may improve headache-related disability and quality of life over at least 52 weeks of follow-up. The paresthesia-free nature of HF-SCS confers a distinct advantage for both patient tolerability and future trial design, as it permits sham-controlled methodologies that have historically been impossible with conventional neurostimulation. However, these findings remain preliminary and should be considered hypothesis-generating pending confirmation in adequately powered randomized sham-controlled trials.
Humans↗
Ligand-based directed differentiation to produce granulosa-like cells expressing steroidogenic enzyme genes.
The ovarian granulosa cells are responsible for producing hormones and supporting oocytes through maturation and meiotic resumption. There is a need to generate granulosa-like cells (GLCs) from human induced pluripotent stem cells (hiPSCs) to better model human gonadal development and to test the effects of exogenous or pharmaceutical compounds on the ovary. Here we report a rapid ligand-based protocol for differentiating hiPSCs into cells that express markers of the transient developmental lineages and steroidogenic pathway genes. Single-cell RNA-sequencing (scRNA-seq) analysis identified canonical granulosa cell genes were expressed in a subset of cells and identified new genes of interest that were significantly associated with computationally modeled pseudotime. HSD17B1 was expressed in resulting GLCs but at low levels, suggesting an immature granulosa cell phenotype. The GLCs were produced using a simple culture method that could be augmented for granulosa cell functions such as sustaining oocyte growth. Producing GLCs through protocols such as this one is a first step toward designing large-scale ovarian endocrinology assays and developing personalized cell-based fertility and hormone restoration technologies in the future. This rapid protocol produced cells that express steroidogenic enzyme genes etoc blurb. Kubo and colleagues present a 5-day rapid protocol to generate immature granulosa-like cells from hiPSCs. Cells differentiated with inhibition of DKK1, a WNT signaling target gene, expressed gonadal ridge markers and FOXL2 transcripts and protein. Additionally, steroidogenic enzyme genes were expressed. A small population of differentiated cells were identified as expressing early-stage granulosa cell genes by single-cell RNA-seq.
Female↗
CRISPR tools for T cells: targeting the genome, epigenome, and transcriptome.
T cell therapy has curative potential for many cancers. Despite impressive clinical efficacy in hematological malignancies, current T cell therapy still faces challenges related to sustaining responses, antigen escape, cytotoxicity, limited accessibility, and difficulties in treating solid tumors. The advent of CRISPR (clustered regularly interspaced short palindromic repeats) technologies provides a promising solution to these challenges. CRISPR technologies have grown from merely tools for gene knockout to sophisticated tools that can engineer cells at various levels of the genome, epigenome, and transcriptome. In this review we discuss recent technological advancements and how their application to T cells has the potential to steer the next generation of cellular therapy. We highlight emerging applications and current technological limitations that future tool development aims to overcome.
Humans↗
The emerging impact of CRISPR and gene editing on global crop improvement.
The advent of CRISPR-based genome editing has revolutionized crop improvement, offering unprecedented precision and efficiency in modifying key agronomic traits. This review comprehensively examines the mechanisms, applications, and future potential of CRISPR technology in enhancing global crop production. CRISPR-Cas systems, originally identified as adaptive immune mechanisms in bacteria and archaea, have been repurposed for targeted genome editing in plants. The CRISPR-Cas9 system, in particular, has emerged as a powerful tool for introducing site-specific double-strand breaks, enabling precise genetic modifications. The three-stage process of adaptation, expression, and interference underlies the CRISPR mechanism, with guide RNAs directing Cas endonucleases to specific genomic loci. Advances in CRISPR technology have expanded its applications beyond gene knockouts, encompassing base editing, prime editing, and epigenome editing. These innovations have facilitated the development of crops with enhanced yield, stress tolerance, disease resistance, nutritional content, and post-harvest quality. However, challenges related to off-target effects, regulatory hurdles, ethical concerns, and public acceptance must be addressed to fully harness the potential of CRISPR in agriculture. Integration of CRISPR with other cutting-edge technologies, such as synthetic biology, artificial intelligence, and high-throughput phenotyping, holds immense promise for accelerating crop improvement efforts. As research continues to refine CRISPR tools and expand their applicability across diverse plant species, this transformative technology is poised to play a pivotal role in shaping a sustainable, resilient, and productive global food system for future generations.
Gene Editing↗
A systematic review of macaque brain stimulation: Trends and future directions.
Neurostimulation techniques can powerfully modulate neural circuit activity and provide causal insights into the relationship between brain function and behavior. Macaque monkeys have long been a key animal model for brain stimulation studies. While stimulating the macaque brain with one or a few electrodes has already taught us much about brain function and dysfunction, recent technological advances promise a future with more precise stimulation using many more electrodes. However, such possibilities also increase the number of choices an experimenter has when designing their study. We can learn from a rich past, but a comprehensive overview of which brain regions have been studied and with what stimulation parameters is lacking. Here, we present a PRISMA-compliant systematic review of 734 macaque brain stimulation studies using electrical and/or optogenetic stimulation. We find a striking bias in which brain areas have traditionally been stimulated: a mere 10 brain regions account for half of all studies, with the remainder of studies investigating approximately 150 other areas. Across studies, stimulation frequency robustly predicted direct behavioral effects independent of brain region, while amplitude did not. Future studies could more systematically explore less studied regions through lower stimulation frequencies (e.g., 20-50 Hz) alongside established ranges (∼200 Hz). Tools such as fMRI or optical imaging can capture neural circuit engagement evoked by these frequencies, even when behavioral effects are absent or remain subtle. Our synthesis offers a guide towards the next steps in high-channel-count, high-precision stimulation approaches.
Animals↗
CRISPR-Cas technologies for precision genome editing in plants: advances, applications, and future perspectives.
Developing climate-smart crops with enhanced crop productivity, nutritional quality, resistance to biological and environmental stressors is vital for global food security. While hybrid breeding forms the cornerstone of modern crop improvement, conventional breeding approaches are limited by genetic barriers and prolonged breeding cycles. CRISPR-Cas based genome editing has revolutionized plant biology by allowing precise, efficient, and multiplex genetic modifications. This review provides a comprehensive synthesis of a recent advances in CRISPR-Cas technologies and their strategic applications in crop genetics and hybrid breeding. We summarize major genome-editing strategies, including gene knock-out, base editing (BE), knock-in, gene replacement, epigenome editing, and transcriptional regulation. Furthermore, we contrast stable, transient, and DNA-free delivery systems, highlighting ribonucleoprotein (RNP)-mediated delivery for minimizing off-target effects and avoiding transgene integration. We showcase how these technologies accelerate hybrid breeding by engineering male sterility systems, fixing heterosis, and generating high-throughput mutant libraries for trait discovery. Finally, we synthesize major bottlenecks in tissue culture-independent transformation and delivery systems, while outlining how emerging paradigms like de novo domestication and synthetic biology will shape the future of climate-resilient agriculture.
CRISPR/Cas↗