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At least 973 records · Page 54Linked to original sources

PubMed2003

Microwave synthesis solutions from Personal Chemistry.

By fulfilling the specific design criteria required for safe and reproducible microwave synthesis, Personal Chemistry introduced the first microwave system designed for organic chemistry in 2000. The principle of microwave synthesis offers 3 major benefits to chemistry development: speed, reproducibility, and a scalable approach to compound synthesis. Additionally, significant advantages of our technical solution are ease of use, safety, verified synthesis methods to enable new users, and an efficient workflow that fits the drug discovery process. To gain the greatest advantages offered by microwave synthesis, Personal Chemistry has implemented a system called Coherent Synthesis. Incorporating planning systems, automation, distributed system access, searchable databases and reaction optimisation kits, the technology has already proved itself to dramatically improve the quality of output from medicinal chemistry for lead optimisation in all the major pharmaceutical and biotech companies.

Automation↗

PubMed2003

Fundamentals of image acquisition and processing in the digital era.

OBJECTIVES: To review the historic context for digital imaging in dentistry and to outline the fundamental issues related to digital imaging modalities. CONTEXT: Digital dental X-ray images can be achieved by scanning analog film radiographs (secondary capture), with photostimulable phosphors, or using solid-state detectors (e.g. charge-coupled device and complementary metal oxide semiconductor). There are four characteristics that are basic to all digital image detectors; namely, size of active area, signal-to-noise ratio, contrast resolution and the spatial resolution. To perceive structure in a radiographic image, there needs to be sufficient difference between contrasting densities. This primarily depends on the differences in the attenuation of the X-ray beam by adjacent tissues. It is also depends on the signal received; therefore, contrast tends to increase with increased exposure. Given adequate signal and sufficient differences in radiodensity, contrast will be sufficient to differentiate between adjacent structures, irrespective of the recording modality and processing used. Where contrast is not sufficient, digital images can sometimes be post-processed to disclose details that would otherwise go undetected. For example, cephalogram isodensity mapping can improve soft tissue detail. CONCLUSIONS: It is concluded that it could be a further decade or two before three-dimensional digital imaging systems entirely replace two-dimensional analog films. Such systems need not only to produce prettier images, but also to provide a demonstrable evidence-based higher standard of care at a cost that is not economically prohibitive for the practitioner or society, and which allows efficient and effective workflow within the business of dental practice.

History, 20th Century↗

PubMed2003

Longitudinal effects of lean production on employee outcomes and the mediating role of work characteristics.

The author discusses results from a 3 year quasi-experimental field study (N = 368), which suggest negative effects on employee outcomes after the implementation of 3 lean production practices: lean teams, assembly lines, and workflow formalization. Employees in all lean production groups were negatively affected, but those in assembly lines fared the worst, with reduced organizational commitment and role breadth self-efficacy and increased job depression. A nonequivalent control group had no negative changes in outcomes. Mediational analyses showed that the negative effects of lean production were at least partly attributable to declines in perceived work characteristics (job autonomy, skill utilization, and participation in decision making). The study also shows the longitudinal effects of these work characteristics on psychological outcomes. Implications for lean production, work design, and employee well-being are discussed.

Adult↗

PubMed2003

Cohesion and performance in groups: a meta-analytic clarification of construct relations.

Previous meta-analytic examinations of group cohesion and performance have focused primarily on contextual factors. This study examined issues relevant to applied researchers by providing a more detailed analysis of the criterion domain. In addition, the authors reinvestigated the role of components of cohesion using more modern meta-analytic methods and in light of different types of performance criteria. The results of the authors' meta-analyses revealed stronger correlations between cohesion and performance when performance was defined as behavior (as opposed to outcome), when it was assessed with efficiency measures (as opposed to effectiveness measures), and as patterns of team workflow became more intensive. In addition, and in contrast to B. Mullen and C. Copper's (1994) meta-analysis, the 3 main components of cohesion were independently related to the various performance domains. Implications for organizations and future research on cohesion and performance are discussed.

Attitude↗

PubMed2006

Computational prediction of proteotypic peptides for quantitative proteomics.

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).

Algorithms↗

PubMed2006

High-throughput RNAi screening by time-lapse imaging of live human cells.

RNA interference (RNAi) is a powerful tool to study gene function in cultured cells. Transfected cell microarrays in principle allow high-throughput phenotypic analysis after gene knockdown by microscopy. But bottlenecks in imaging and data analysis have limited such high-content screens to endpoint assays in fixed cells and determination of global parameters such as viability. Here we have overcome these limitations and developed an automated platform for high-content RNAi screening by time-lapse fluorescence microscopy of live HeLa cells expressing histone-GFP to report on chromosome segregation and structure. We automated all steps, including printing transfection-ready small interfering RNA (siRNA) microarrays, fluorescence imaging and computational phenotyping of digital images, in a high-throughput workflow. We validated this method in a pilot screen assaying cell division and delivered a sensitive, time-resolved phenoprint for each of the 49 endogenous genes we suppressed. This modular platform is scalable and makes the power of time-lapse microscopy available for genome-wide RNAi screens.

Automation↗

PubMed2026

Radiomics as a spatial context for treatment decision-making in head and neck cancer.

Radiomics has been widely explored as a non-invasive biomarker in head and neck squamous cell carcinoma (HNSCC), yet its clinical role remains unclear. Tissue-based biomarkers differ in their susceptibility to spatial sampling. Biomarkers such as PD-L1 expression, immune-cell infiltration, necrosis, and immune exclusion may exhibit substantial spatial heterogeneity, whereas HPV/p16 status and some genomic alterations are generally more stable across the tumor. Nevertheless, localized sampling may incompletely capture heterogeneity in selected clinical contexts. This mismatch becomes clinically relevant when treatment decisions, particularly for chemoradiotherapy, immunotherapy, or de-escalation, are based on potentially non-representative biopsy findings. In this narrative review, we argue that the role of radiomics is not to outperform established biomarkers, but to contextualize them by capturing spatial heterogeneity related to hypoxia, necrosis, stromal architecture, and immune exclusion. We synthesize current evidence linking radiomic features to these biological processes and map them to specific clinical decision points, including larynx preservation, immunotherapy stratification, and recurrence assessment. Rather than serving as a standalone predictor, radiomics may provide complementary spatial information that helps identify situations in which biopsy-derived biomarkers should be interpreted with caution. Although current evidence is largely retrospective, radiomics offers a pragmatic framework for integrating spatial information into biomarker-guided clinical workflows.

Journal Article↗

PubMed2025

The development and usability of 'The Genetics Navigator': a digital solution for adult and paediatric clinical genetics services.

Clinical genetic services address diverse genetic testing needs, but there is no comprehensive digital solution to meet this variety. We aimed to develop and test the usability of the Genetics Navigator (GN), a platform designed to enhance genetic services for paediatric and adult patients. The GN prototype was created with input from a patient and clinician advisory board, informed by prior research. Usability testing involved genetics patients (N = 14), parents of paediatric patients (N = 4), and the general public (N = 10). Participants provided feedback using the 'think aloud' method when using the platform. We used the System Usability Scale (SUS) for quantitative evaluation. Qualitative data were coded by platform section, item, and identified key areas for improvement. Building on the Genetics Adviser platform, we added video and written content for various genetic conditions and patient groups, including pre-test education, counselling, decision support, history collection, post-test result disclosure, and management. Key feedback during rounds of usability testing emphasized the need for a supportive design, seamless workflow, and engaging experience of the tool. The tool was modified to reflect the feedback, and the GN achieved an average SUS score of 87.7 ± 10.9 (N = 28), indicating above-average usability. Future research will evaluate its clinical and cost-effectiveness in a randomized trial.

Humans↗

PubMed2026

Quantitative RNA modification mapping by mass spectrometry with isobaric tags and nucleobase fragment analysis.

RNA modifications regulate RNA stability, translation, stress responses, and disease processes, yet their function remains poorly understood due to technical limitations in sequence analysis. Here, we present an RNA-specific isobaric tandem mass tagging (RMT) platform for omic-scale quantitative mapping of RNA modifications. The platform combines RNA-specific tags adapted from proteomics with an end-to-end workflow spanning sample preparation through data processing. Validation using synthetic oligonucleotides and total tRNA from Pseudomonas aeruginosa yielded reproducible quantification, with coefficients of variation below 5%. Together with nucleobase fragment analysis, we identified and quantified 24 RNA modifications in PA14 tRNAs, including previously undescribed m2A38 and Gm/Cm39, and assigned their corresponding writer enzymes. Further analyses of tRNAs from writer knockout strains and stressed cells revealed dynamic modification patterns, modification interdependencies, and their potential roles in stress adaptation. This method provides a robust, cost-effective platform for quantitative RNA modification mapping, enabling deeper biological insights.

RNA, Transfer↗

PubMed2026

Integrating multi-omics technologies to decipher microbiome functions.

Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.

Multiomics↗

PubMed2021

Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology.

Circulating tumor DNA (ctDNA) sequencing is being rapidly adopted in precision oncology, but the accuracy, sensitivity and reproducibility of ctDNA assays is poorly understood. Here we report the findings of a multi-site, cross-platform evaluation of the analytical performance of five industry-leading ctDNA assays. We evaluated each stage of the ctDNA sequencing workflow with simulations, synthetic DNA spike-in experiments and proficiency testing on standardized, cell-line-derived reference samples. Above 0.5% variant allele frequency, ctDNA mutations were detected with high sensitivity, precision and reproducibility by all five assays, whereas, below this limit, detection became unreliable and varied widely between assays, especially when input material was limited. Missed mutations (false negatives) were more common than erroneous candidates (false positives), indicating that the reliable sampling of rare ctDNA fragments is the key challenge for ctDNA assays. This comprehensive evaluation of the analytical performance of ctDNA assays serves to inform best practice guidelines and provides a resource for precision oncology.

Circulating Tumor DNA↗

PubMed2023

Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins.

Assessment of single-cell gene expression (single-cell RNA sequencing) and adaptive immune receptor (AIR) sequencing (scVDJ-seq) has been invaluable in studying lymphocyte biology. Here we introduce Dandelion, a computational pipeline for scVDJ-seq analysis. It enables the application of standard V(D)J analysis workflows to single-cell datasets, delivering improved V(D)J contig annotation and the identification of nonproductive and partially spliced contigs. We devised a strategy to create an AIR feature space that can be used for both differential V(D)J usage analysis and pseudotime trajectory inference. The application of Dandelion improved the alignment of human thymic development trajectories of double-positive T cells to mature single-positive CD4/CD8 T cells, generating predictions of factors regulating lineage commitment. Dandelion analysis of other cell compartments provided insights into the origins of human B1 cells and ILC/NK cell development, illustrating the power of our approach. Dandelion is available at https://www.github.com/zktuong/dandelion .

Humans↗

PubMed2025

Proteoform profiling of endogenous single cells from rat hippocampus at scale.

We perform intact proteoform profiling of 10,809 endogenous single cells from the rat hippocampus using single-cell proteoform imaging mass spectrometry (scPiMS). scPiMS directly extracts whole proteins and demonstrates high throughput for MS-based single-cell proteomics compared with existing approaches. We develop an informatics workflow dedicated to this datatype and use it to assign neurons, astrocytes or microglia cell types according to their proteoform signatures.

Animals↗

PubMed2025

Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.

Artificial intelligence models using digital histopathology slides stained with hematoxylin and eosin offer promising, tissue-preserving diagnostic tools for patients with cancer. Despite their advantages, their clinical utility in real-world settings remains unproven. Assessing EGFR mutations in lung adenocarcinoma demands rapid, accurate and cost-effective tests that preserve tissue for genomic sequencing. PCR-based assays provide rapid results but with reduced accuracy compared with next-generation sequencing and require additional tissue. Computational biomarkers leveraging modern foundation models can address these limitations. Here we assembled a large international clinical dataset of digital lung adenocarcinoma slides (N = 8,461) to develop a computational EGFR biomarker. Our model fine-tunes an open-source foundation model, improving task-specific performance with out-of-center generalization and clinical-grade accuracy on primary and metastatic specimens (mean area under the curve: internal 0.847, external 0.870). To evaluate real-world clinical translation, we conducted a prospective silent trial of the biomarker on primary samples, achieving an area under the curve of 0.890. The artificial-intelligence-assisted workflow reduced the number of rapid molecular tests needed by up to 43% while maintaining the current clinical standard performance. Our retrospective and prospective analyses demonstrate the real-world clinical utility of a computational pathology biomarker.

Humans↗

PubMed2025

Microbial signals in primary and metastatic brain tumors.

Gliomas and brain metastases are associated with poor prognosis, necessitating a deeper understanding of brain tumor biology and the development of effective therapeutic strategies. Although our group and others have demonstrated microbial presence in various tumors, recent controversies regarding cancer-type-specific intratumoral microbiota emphasize the importance of rigorous, orthogonal validation. This prospective, multi-institutional study included a total of 243 samples from 221 patients, comprising 168 glioma and brain metastases samples and 75 non-cancerous or tumor-adjacent tissues. Using stringent fluorescence in situ hybridization, immunohistochemistry and high-resolution spatial imaging, we detected intracellular bacterial 16S rRNA and lipopolysaccharides in both glioma and brain metastases samples, localized to tumor, immune and stromal cells. Custom 16S and metagenomic sequencing workflows identified taxa associated with intratumoral bacterial signals in the tumor microenvironment; however, standard culture methods did not yield readily cultivable microbiota. Spatial analyses revealed significant correlations between bacterial 16S signals and antimicrobial and immunometabolic signatures at regional, neighborhood and cellular levels. Furthermore, intratumoral 16S bacterial signals showed sequence overlap with matched oral and gut microbiota, suggesting a possible connection with distant communities. Together, these findings introduce microbial elements as a component of the brain tumor microenvironment and lay the foundation for future mechanistic and translational studies.

Humans↗

PubMed2026

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects function. However, the reliability of current benchmarks of spatially aware clustering (SAC) methods is undermined by their narrow focus on Visium and brain tissue datasets and the incorrect interpretation of manual annotation as ground truth. Here we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration and metric evaluation, enabling rapid inclusion of new methods and datasets. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods and shows that anatomical labels commonly used as ground truths are often biased, error prone and unsuitable for benchmarking. Rather than ranking methods, we propose a consensus-guided workflow where descriptive spatial metrics highlight high-entropy regions of method disagreement, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual SAC methods and manual annotations, highlighting the need for iterative, expert-in-the-loop evaluation.

Benchmarking↗

PubMed2025

Mass spectrometry-based top-down proteomics for proteoform profiling of protein coronas.

The protein corona is a layer of biomolecules-primarily proteins-that adsorbs to nanoparticle (NP) surfaces in biological fluids. If the purpose of the NP is therapeutic, this can have a profound effect on its biological activity and function in vivo. Protein corona formation can also be exploited for diagnostic purposes and to differentially enrich proteins for biomarker discovery. For all of these applications, it is useful to determine which proteins, and which specific proteoforms, bind to different types of NP. The traditional mass spectrometry (MS)-based bottom-up proteomics does not accurately identify specific proteoforms within the protein corona. This limitation impedes the nanomedicine field's ability to precisely predict the biological fate and pharmacokinetics of nanomedicines and their effectiveness in early-stage biomarker discovery and disease detection because many different proteoforms of the same gene could exist in the corona, and they have divergent biological functions. Here, we describe how to use capillary zone electrophoresis (CZE)-MS-based top-down proteomics to characterize the proteoform landscape of the protein corona. Our procedures detail the recovery of intact proteoforms from NP surfaces by using detergent-assisted proteoform elution and the measurement of these proteoforms by using CZE-tandem MS (MS/MS) and CZE-high-field asymmetric waveform ion mobility spectrometry (FAIMS)-MS/MS. The entire workflow is completed within 3-4 d. Using this protocol, hundreds of proteoforms from the protein corona of polystyrene NPs can be identified. Distinct protein corona proteoform profiles were observed from NPs with different physicochemical properties. The addition of FAIMS is beneficial for more in-depth proteoform characterization.

Proteomics↗

PubMed2026

Tracking-seq: a universal off-target detection approach for CRISPR-Cas genome editing.

Tracking-seq is a highly sensitive method for genome-wide detection of off-target effects in cells edited with diverse genome editing modalities, including Cas9, cytosine base editors, adenine base editors and prime editors. Since most genome editors induce DNA repair pathways and generate single-stranded DNA (ssDNA) intermediates, Tracking-seq leverages this process by tracking replication protein A-a key protein that binds and protects ssDNA-to identify on-target and off-target events. Here we provide a detailed protocol for Tracking-seq, covering genome editing of cells, extraction of replication protein A-bound ssDNA, sequencing library construction and data analysis using our custom computational tool Offtracker. Tracking-seq is applicable to various genome editing scenarios with low cell input, delivering high-performance results. The entire workflow, from genome editing to data analysis, can be completed within 1-2 weeks, making it a rapid solution for assessing genome-wide off-target activity.

CRISPR-Cas Systems↗