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At least 19 recordsLinked to original sources

PubMed2026

OmicsQ: a user-friendly platform for interactive quantitative omics data analysis.

MOTIVATION: High-throughput omics technologies generate complex datasets with thousands of features that are quantified across multiple experimental conditions, but often suffer from incomplete measurements, missing values, and individually fluctuating variances. This requires analytical tools for accurate, deep and insightful biological interpretation, capable of dealing with a large variety of data properties and different amounts of completeness. Software capable of handling such data complexity and integrating with external applications for downstream analysis remains rare and mostly relies on programming-based environments, limiting accessibility for researchers without computational expertise. RESULTS: We present OmicsQ, an interactive, web-based platform designed to streamline quantitative omics data analysis. OmicsQ provides an intuitive, browser-based visualization interface that integrates established statistical processing tools. Those include robust batch correction, automated experimental design annotation, and handling of missing data without imputation, which maintains data integrity and avoids artifacts from a priori assumptions. OmicsQ seamlessly interacts with external applications (e.g. PolySTest, VSClust, ComplexBrowser) for statistical testing, clustering, analysis of protein complex behavior, and pathway enrichment, offering a comprehensive and flexible workflow from data import to biological interpretation that is broadly applicable across domains. AVAILABILITY AND IMPLEMENTATION: OmicsQ is implemented in R and Shiny and is available at https://computproteomics.bmb.sdu.dk/app_direct/OmicsQ. Source code and installation instructions: https://github.com/computproteomics/OmicsQ, DOI: 10.5281/zenodo.17778420.

Software↗

PubMed1979

Aging in the rhesus monkey: effects on visual discrimination learning and reversal learning.

The behavior of aged rhesus monkeys (18 years and older) was compared to that of young monkeys (3 to 6 years old) to evaluate their relative abilities to learn a series of visual discrimination and discrimination reversal problems. Using a subject-paced, automated experimental procedure designed to optimize stimulus control and facilitate execution of choice responses, no consistent age-related differences were observed in the ability to learn new color and pattern discrimination problems of varying difficulty. However, a severe and consistent deficity on reversal learning did occur. A detailed analysis of this deficit revealed that not only did the aged monkeys take longer to extinguish the old habit and return to chance performance, but they continued to display a deficit in establishing accurate performance at above-chance levels as well. Since no reliable age differences were observed on the original discrimination learning problems, these data suggest that aging impairs mechanisms involved with response rigidity and/or susceptibility to intertrial proactive interference, more severely than those involved with the simple formation of new associations.

Aging↗

PubMed2026

AI-driven CRISPR screening: optimizing gene editing through automation and intelligent decision support.

BACKGROUND: CRISPR-based genetic screening has become a central methodology in functional genomics, enabling systematic interrogation of gene function, genetic interactions and context-dependent vulnerabilities at scale. However, the rapid expansion of screening modalities-including multi-condition designs, combinatorial perturbations, in vivo applications and single-cell readouts-has exposed fundamental limitations of heuristic-driven experimental design and post hoc statistical analysis. MAIN BODY: This Review synthesizes how artificial intelligence is reshaping CRISPR screening by introducing predictive, adaptive and system-level intelligence across the experimental lifecycle. We organize recent advances into two tightly coupled modules. First, machine learning and deep learning (ML/DL) methods optimize experimental design by learning context-dependent perturbation behavior, anticipating confounding effects and enabling iterative, information-efficient screening strategies. Second, large language model-agent (LLM-agent) systems complement these advances by externalizing scientific reasoning, integrating biological knowledge at scale and coordinating analysis and decision-making in human-in-the-loop workflows. CONCLUSIONS: Together, ML/DL and LLM-agent approaches reframe CRISPR screening from a static analytical pipeline into an intelligent experimental system, with important implications for robustness, scalability and biological discovery.

Artificial Intelligence↗

PubMed2026

Biological Parts in Yeast Synthetic Biology: From Regulatory Elements to Predictive Design Platforms.

Yeasts, particularly Saccharomyces cerevisiae, are important eukaryotic chassis for synthetic biology because of their tractable genetics, versatile toolkits, and broad utility in metabolic engineering and functional genomics. Progress in this field has been driven by biological parts that enable programmable control of gene expression and cellular behavior. Early efforts focused mainly on promoters, terminators, and other regulatory elements for tuning individual genes. However, as engineering expanded to multigene pathways, genetic circuits, and dynamic regulatory systems, the limits of part-centric design became clear. Part performance is often shaped by genomic context, chromatin state, host physiology, and interactions with other components, which restricts modularity and predictability. In response, yeast synthetic biology is shifting toward integrated design frameworks combining multilayer regulation, standardized assembly, automated experimentation, and computational modeling. This review provides an integrated perspective on the evolution of biological parts across DNA-, RNA-, and protein-level regulation, connecting these advances with assembly frameworks, biofoundries, and machine learning to trace the trajectory from part-centric engineering toward predictive, system-level design in yeast synthetic biology.

Biofoundry↗

PubMed1979

NCTR computer systems designed for toxicologic experimentation. V. Post-experiment information system.

The Post-Experiment Information System (PEIS) is an automated data collection and reporting system composed of three specialized subsystems: Pathology, Chemistry and Microbiology. These subsystems function either independently or collectively to construct and maintain a comprehensive data base of all experimental values derived from, or associated with, an animal carcass. All data are retrievable by the unique Carcass Identification (CID) number assigned at death, which is the correlative of the Unique Identification Number (UIN) assigned to the animal at birth and used throughout its lifespan. Elements processed under the PEIS include gross and microscopic pathological observations, organ weights, hematologic data, chemical data, and microbiological analyses. The ability of the system to integrate the post-experiment data with the information collected on an animal from birth (BIS) and during the experiment (EIS) provides a complete animal history to the Principal Investigator or other requestor.

Animals↗

PubMedDate not supplied

NCTR computer systems designed for toxicologic experimentation. III. Breeding information system.

The Breeding Information System (BIS) facilitates management control of the breeding colony operation at the National Center for Toxicological Research (NCTR). Although this automated data handling system was initially intended to support Animal Husbandry the system's basic design, flexibility or reporting, data manipulation capabilities, and integration with other NCTR data collection systems provides BIS with capabilities that have application to other groups including the Plans and Programs and most scientific areas. This description of the System is in terms of its potential value to these diverse user groups.

Animal Husbandry↗

PubMed1979

Ingestive behavior and composition of weight change during cyproheptadine administration.

The effect of cyproheptadine on spontaneous energy intake was studied by means of an automated (liquid diet) food-dispensing apparatus in two nonobese adults confined to a metabolic ward. The experimental design included both single and double-blind periods. Throughout, the composition of daily weight change was determined by the energy-nitrogen balance method. While on cyproheptadine, both subjects exhibited increases in energy intake with the following average composition of weight gain: protein 16%, fat 14% and water 70% (first subject), and protein 5%, fat 49% and water 46% (second subject). The cyproheptadine-induced increase in energy intake was statistically significant in one of the subjects, who was at his desirable weight level at the outset. The other subject was underweight initially and tended to gain throughout the experiment, although rate of weight gain appeared to be more rapid during the periods of cyproheptadine administration. Energy output in both subjects remained fairly constant throughout. We conclude that cyproheptadine induces weight gain of 'normal' composition by stimulating increased energy intake.

Adult↗

PubMedDate not supplied

Components and results of a new preparation technique for automated analysis of cervical samples.

Components and evaluations of a new preparative procedure for automated high-resolution analysis of cervical samples are presented. This procedure is based on sedimentation velocity separation of samples with subsequent fractionation of the separation column and centrifugal deposition of suspended cells on coated glass slides. A system for specimen collection and mailing of suspended samples is described. A new type of glass slide designed for automated analysis is presented, and centrifugal buckets for cell deposition on a 6-sq-cm area are described. Experimental results with different kinds of coating substances for glass slides as well as different isopyknic media are discussed, and data for differential cell counts are graphically demonstrated. Looking at the diagnostic accuracy and economic feasibility of this system, the authors realize that preparations have to be evaluated quantitatively and that constraints of sample size and processing time have to be taken into consideration for further developments.

Autoanalysis↗

PubMed2025

Incubator-Free Organoid Culture in a Sealed Recirculatory System.

Organoids are powerful tools for studying development and disease, offering realistic organ-like human and animal tissues and facilitating experimental observation compared to live animal models. However, traditional organoid culture methods require a humidified incubator. This requirement complicates culture due to evaporative losses and restricted access to instrumentation, hindering the potential of organoids as physiologically accurate models easily subjected to detailed experimental observation. We introduce a compact, automated, sealed, incubator-free recirculatory organoid culture platform that replaces the air-liquid interface with a nonporous polymer gas exchanger and a liquid-phase gas buffer. This design prevents evaporation and stabilizes oxygen, pH, and osmolarity without feedback control. It enables single-actuator media exchange, simplifying automation. Dispensing with the incubator, we improve access for instruments such as live cell microscopes. We demonstrate compatibility with continuous multi-week live imaging of vascular organoids and show that brain organoids in this system maintain metabolic viability, structural fidelity, and electrophysiological activity comparable to traditional shaker-based cultures in an incubator.

Journal Article↗

PubMed2026

Toward Class Imbalance and Uncertainty in Powder XRD Analysis: A Dual-Channel Fusion Network for Space Group Classification.

Accurate identification of space groups from powder X-ray diffraction (pXRD) is essential for understanding crystal structures and accelerating materials discovery. However, this task remains highly challenging due to inherent peak overlap, experimental noise, and the complexity of the 230-class classification problem. To address the critical issues of class imbalance and data scarcity, we first design a general physics-informed data augmentation pipeline. We then propose a dual-channel fusion uncertainty-aware network (DFUN) for automated space group classification. The DFUN architecture integrates two complementary feature representations: convolutional features extracted directly from raw diffraction profiles and domain-specific peak descriptors. These distinct representations are adaptively fused through a gating mechanism. Furthermore, to mitigate the inherent long-tailed distribution of crystallographic data, we employ a hybrid loss function that combines Focal Loss with Label Smoothing. Finally, we incorporate Monte Carlo Dropout to provide predictive uncertainty estimation, thereby enabling not only accurate classification but also a crucial assessment of the model's reliability. Evaluated on large-scale simulated data and two public data sets (opXRD and RRUFF), DFUN outperforms the evaluated baseline methods across the reported metrics. The framework also provides uncertainty-aware predictions, establishing DFUN as a robust and interpretable solution for high-throughput automated crystallographic analysis from powder diffraction.

Uncertainty↗

PubMed1979

An image analysis system for cervical cytology automation using nuclear DNA content.

An experimental computer/image analysis system has been used to investigate cytology automation techniques based on nuclear DNA measurement and morphological artefact rejector tests. The system automatically measures and normalizes the integrated optical density of cell nuclei in specially prepared cervical cytology specimens, and selects any objects with abnormally high values for further analysis. These are then analyzed by morphological and densitometric tests designed to eliminate false positive signals caused by non-nuclear artefacts. The coordinates of the remaining abnormal nuclei are recorded so that they can subsequently be relocated and examined by a cytotechnician. Preliminary results are given showing the measurement accuracy of the system and the performance of the artefact rejection tests.

Cell Nucleus↗

PubMed1979

Determination of total CO2 in plasma by automated flow-injection analysis.

We describe a procedure for measuring total CO2 in plasma, based on the principles of the flow-injection analysis technique, which makes use of unsegmented fast-flowing reagent streams, as developed by Růziĉka and co-workers. The further methodological design resembles the silicone-rubber membrane technique of Kenny and Cheng. CO2 in the sample is released by reaction with H2SO4. Appropriate amounts of CO2 permeate through the membrane that separates the acid reagent streams and a buffered cresol-red indicator system. The experimental set-up and functioning of this system are described.

Autoanalysis↗

PubMed2025

Sensitive and Visualized Detection of Hantavirus Using CRISPR/Cas12a Based on AutoCORDSv2 Design.

In recent years, detection technologies based on the CRISPR/Cas12a method have been extensively utilized in the fields of nucleic acid, enzyme, and macromolecule detection, thereby reinforcing their significant role in the detection landscape. Enhancing the simplicity of design, efficiency, and automation of the CRISPR/Cas12a detection system is essential for advancing its application in diagnostics. Recently, we developed an automated CRISPR/Cas12a design system named AutoCORDSv2. This system can process published genomic sequences of pathogenic bacteria in a high-throughput manner and automatically generate conserved and highly specific crRNA sequences, along with primer sequences for target amplification. This capability facilitates the specific and precise design of the CRISPR/Cas12a detection system. In this study, crRNAs targeting the Hantaan virus (HTNV) and Seoul virus (SEOV), as well as RT-PCR primers and RT-RPA primers, were designed using AutoCORDSv2. The experimental results demonstrated that the CRISPR/Cas12a system, automatically designed by AutoCORDSv2, was specific for the detection of both the HTNV and SEOV, with no cross-reactivity observed with other pathogens. The detection sensitivity reached 6 copies/μL (equivalent to 111 copies per amplification reaction), whether measured by a microplate reader or directly observed with the naked eye. The detection results for 50 samples were consistent with those obtained from commercial RT-qPCR kits, indicating high precision. Furthermore, the CRISPR/Cas12a system designed by AutoCORDSv2 can also be utilized for the development of a single-tube detection system with a sensitivity of 42 copies per reaction. This system combined with a 5-min extraction step and RT-RPA, further underscoring its potential for application.

CRISPR-Cas Systems↗

PubMed1976

Factors contributing to intra-individual variation of serum constituents: Physiological day-to-day variation in concentrations of 10 specific proteins in sera of healthy subjects.

Using an automated immunoprecipitin method, we assayed human sera for 10 proteins: haptoglobin, orosomucoid, transferrin, alpha1 antitrypsin, alpha2-macroglobulin, IgG, IGa, IgM, complement C3, and complement C4. Blood from 14 healthy subjects (25-40y) was sampled on six separate days. From each venipuncture serum was divided into four eliquots; two were assayed on the day of venipuncture and two were frozen and kept until the end of the study, when all of the frozen samples were analyzed in one batch. With this experimental design, batch-to-batch analytical variation could be estimated, and we avoided confounding it with the biological variation. Data analysis was based on the analysis of variance technique. The average physiological intra-individual coefficient of variation ranged from 2.5% for transferrin to 11.1% for orosomucoid. THe interindividual variation ranged from 9.5% for transferrin to 70.5% for haptoglobin and the ratio between intra-individual variation and interindividual variation ranged from 0.66 for IgM to 0.26 for orosomucoid and transferrin.

Adult↗

PubMed2026

Mendelian randomisation for rheumatology: beyond hype-what it's good for, what it can't do, and how to read it critically.

Mendelian randomisation (MR) has become abundant in the literature, with variation in quality and frequent overinterpretation of causality. This creates a problem for clinical readers, reviewers, and editors: some MR studies can sharpen causal thinking, prioritise drug targets, and challenge misleading observational claims, whereas others are little more than automated exposure-outcome scans with causal claims disproportionate to the evidence. MR can strengthen causal inference when randomised trials are impractical and conventional observational studies are vulnerable to confounding, reverse causation, or selection bias. In rheumatology, credible MR can contribute to questions about disease aetiology, modifiable risk factors, therapeutic target validation, adverse-effect anticipation, and phenotype validation. However, its interpretation depends on whether the exposure is plausibly instrumentable, whether the genetic instruments are biologically defensible, whether assumptions are interrogated in ways appropriate to the design, and whether findings are triangulated with clinical, observational, experimental, and mechanistic evidence. Instead of recapitulating all methodological issues of MR, this review aims to help rheumatologists distinguish robust MR from weak or overinterpreted analyses quickly. We provide an accessible framework for reading and triaging MR studies in rheumatology. Papers that use poorly justified instruments, treat medication use as drug-target evidence, interpret genetic liability as diagnosis, rely on mechanical sensitivity analyses, ignore prior evidence or ask no clinically meaningful question can often be passed over by readers. The goal is not to discourage MR in rheumatology, but to raise the standard; useful MR should clarify causal reasoning rather than simply generate another statistically significant association.

Journal Article↗

PubMed2026

Artificial Intelligence for Natural Products Discovery and Development.

Natural products (NPs) remain a cornerstone of modern drug discovery, offering stereochemical complexity and diverse bioactivities that precisely modulate therapeutic targets, refined through billions of years of evolution. However, their research has long been hindered by inefficient, empirical workflows, high resource consumption, structural complexity, and the "multicomponent, multi-target" nature of their mechanisms. The exponential growth of genomic, metabolomic, and spectral data has overwhelmed conventional analytical methods, exposing critical bottlenecks in handling high-dimensional, heterogeneous datasets that exceed human interpretive capacity. Artificial intelligence (AI) is emerging as a transformative paradigm to address these challenges, integrating multi-omics and chemical data to shift NP research from fragmented empiricism toward mechanism-driven, precision-oriented development. By leveraging deep learning architectures- including graph neural networks, Transformers, and diffusion-based generative models-AI enables systematic decoding of NP biosynthesis, automated structure elucidation, rational target identification, knowledge extraction from vast unstructured scientific literature, and de novo molecular design. This review comprehensively surveys recent advances in AI applications across the full NP discovery and development pipeline, encompassing genome mining, structure-based and ligand-based virtual screening, multimodal structural characterization, lead optimization, and biosynthetic pathway engineering. We further examine the emerging roles of protein-centric, molecule- centric, and multimodal foundation models, as well as large language models, in bridging genotype-to-chemotype gaps and unlocking unstructured scientific knowledge. Finally, we discuss critical challenges including data scarcity, representational limitations for complex stereochemistry, physical plausibility in generative models, and the urgent need for experimental validation, while outlining future directions toward autonomous experimentation, closed-loop optimization, and human-AI collaborative discovery.

Artificial intelligence↗

PubMed2025

Measuring Cell Dimensions in Fission Yeast Using Machine Learning.

In fission yeast (Schizosaccharomyces pombe), cell length is a crucial indicator of cell cycle progression. Microscopy screens that examine the effect of agents or genotypes suspected of altering genomic or metabolic stability and thus cell size are crucial for studying disruptions to cell cycle dynamics. This method is based on using an automated cell segmentation algorithm to measure S. pombe cells imaged by brightfield (BF) microscopy methods. PhotoPhenosizer (PP) is a machine learning-based tool designed for automated cell measuring and dimensional analysis of morphology frequency distributions. Integration of this method into large-scale pipelines for tracking cell dimension change streamlines morphological measurements, which facilitates the examination of cellular responses to genomic and metabolic stresses. In this protocol, we use PP to observe the effect of genomic instability on cell size dynamics over a 12-day chronological lifespan assay. Our results show that relative to wild-type cells, a replication stress mutant shows larger cells during chronological aging in excess glucose media. Our results are consistent with activation of checkpoints that regulate cell morphology in response to DNA damage. This method's application highlights the relevance of its incorporation in experimental routines that require large-scale image processing and its adoption by users with routine needs in S. pombe molecular research projects.

Schizosaccharomyces↗

PubMed2026

The future of pediatric vesicoureteral reflux management.

BACKGROUND AND OBJECTIVE: Vesicoureteral reflux (VUR) is a common condition in pediatric urology, yet important uncertainties persist regarding risk stratification, imaging strategies, and prevention of long-term renal damage. Emerging technologies may help address these challenges. This review provides a forward-looking overview of recent advances in artificial intelligence (AI) and immunomodulation that may influence future management of pediatric VUR. METHODS: A forward-looking literature review was performed using the PubMed database (January 2000-March 2025), focusing on studies addressing AI, immunomodulation, or vaccination in the context of VUR and urinary tract infections. Criteria of inclusion were the relevance to pediatric VUR, the novelty of the proposed concept, the potential clinical implications and, for the AI literature, the existence of a clinical evaluation of the algorithm on a dataset from patients. KEY FINDINGS AND LIMITATIONS: AI-based models show promising performance in supporting clinical decision-making, including prediction of the need for voiding cystourethrography, automated grading of VUR, estimation of recurrent urinary tract infection risk and prediction of chemoprophylaxis. These tools may facilitate more individualized diagnostic and therapeutic strategies, although current evidence is largely retrospective and requires prospective validation. Immunization and immunomodulatory approaches aim to reduce infection burden and modulate inflammatory pathways associated with renal scarring. While early experimental and adult clinical data are encouraging, pediatric-specific evidence remains limited, and clinical applicability in children with VUR is not yet established. CONCLUSION: Artificial intelligence and immunologically targeted strategies represent complementary, emerging approaches that may contribute to more personalized management of pediatric VUR. At present, both should be regarded as exploratory tools whose clinical impact will depend on further validation and appropriately designed pediatric studies.

Humans↗