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At least 811 records · Page 45Linked to original sources

PubMed2026

Detection of antibiotic heteroresistance in clinical microbiology: current and emerging methodologies.

BACKGROUND: Antibiotic heteroresistance (HR) is characterised by the coexistence of susceptible and resistant subpopulations within an apparently isogenic bacterial isolate. Because routine antimicrobial susceptibility testing (AST) primarily assesses the dominant population, HR may escape detection, potentially leading to discrepancies between laboratory susceptibility categorisation and the underlying bacterial population structure. OBJECTIVES: To provide a critical and practice-oriented evaluation of current and emerging methodologies for HR detection and to discuss their strengths, limitations, and potential for clinical implementation. SOURCES: Narrative review based on PubMed searches, complemented by screening of key reference lists and relevant EUCAST and CLSI documents. Peer-reviewed literature was prioritised. CONTENT: Phenotypic approaches, particularly population analysis profiling, remain the reference method for HR definition, but their labour-intensive workflows, long turnaround times, and limited standardisation restrict routine implementation. Alternative strategies, including modified AST assays, metabolic assays, and single-cell platforms, offer gains in speed or throughput but require broader validation. Molecular approaches such as quantitative PCR, droplet digital PCR, targeted deep sequencing, and whole-genome sequencing improve detection of minority resistance determinants. Emerging computational frameworks, including machine learning models integrating phenotypic and genomic data, represent a promising frontier for scalable HR prediction. IMPLICATIONS: Available evidence supports the clinical relevance of HR, although its association with adverse outcomes varies across bacterial species and antibiotic classes. Harmonised methodologies and clinically validated interpretive criteria are needed to support integration of HR assessment into routine diagnostics. Prospective multicentre studies and further standardisation, including engagement with EUCAST and CLSI, will be important to advance clinical implementation.

Antimicrobial resistance↗

PubMed2004

JIDE: a new software for computer-aided design of hip prosthesis.

This work is aimed at developing an innovative simulation environment supporting and improving the design of standard joint implants (JPD integrated design environment (JIDE)). The conceptual workflow starts from the design of a new implant, by using conventional CAD programmes and completes with the generation of a report that summarises the goodness for a new implant against a database of human bone anatomies. For each dataset in the database, the JPD application calculates a set of quantitative indicators that will support the designer in the evaluation of its design on a statistical basis. The resulting system is thus directed to prostheses manufacturers and addresses a market segment that appears to have a steady growth in the future.

Computer Simulation↗

PubMed2005

PsN-Toolkit--a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM.

PsN-Toolkit is a collection of statistical tools for pharmacometric data analysis using the non-linear mixed effect modeling software NONMEM. The toolkit is object oriented and written in the programming language Perl using the programming library Perl-speaks-NONMEM (PsN). Five methods: the Bootstrap, the Jackknife, Log-likelihood Profiling, Case-deletion Diagnostics and Stepwise Covariate Model building are included as separate classes and may be used in user-written Perl scripts or through stand-alone command line applications. The tools are designed with the ability to cooperate and with an emphasis on common structures for workflow and result handling. Parallel execution of independent tool sections is supported on shared memory multiprocessor (SMP) computers, Mosix/openMosix clusters and distributed computing environments following the NorduGrid standard. In conclusion, PsN-Toolkit makes it easier to use the Bootstrap, the Jackknife, Log-likelihood Profiling, Case-deletion Diagnostics and Stepwise Covariate Model building in pharmacometric data analysis.

Cluster Analysis↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

A reproducible computational transcriptomic framework for cell-type-resolved fibroinflammatory-AKT remodeling in human heart failure.

BACKGROUND: Human heart failure involves multicellular transcriptional remodeling, but public transcriptomic studies often remain disconnected from cell-type localization and perturbational interpretation. METHODS: We developed a reproducible computational workflow integrating human left-ventricular bulk transcriptomes, donor-level cell-type pseudobulk results from a human heart-failure single-cell/single-nucleus atlas, external snRNA-seq support, curated module scoring, focused ligand-receptor prioritization and LINCS/L1000 perturbational matching. RESULTS: Cross-cohort analysis identified 14,358 same-direction HF-associated genes, including 1633 replicated HF-up and 785 replicated HF-down genes. Donor-level pseudobulk analysis localized disease remodeling to cardiomyocyte, fibroblast and myeloid compartments. Activated fibroblast and inflammatory myeloid programs defined a fibroinflammatory remodeling axis connected to context-dependent AKT-associated transcriptional shifts. External snRNA-seq support was strongest for fibroblast activation and AKT-associated remodeling, with etiology-dependent heterogeneity across validation resources. L1000FWD screening prioritized safety-aware perturbational hypotheses, including glimepiride and simvastatin as interpretable candidates requiring experimental validation. CONCLUSIONS: This study provides a computational transcriptomic framework linking reproducible human HF signatures, cell-type-resolved fibroinflammatory remodeling and perturbational genomic prioritization without claiming drug efficacy or AKT causality.

Humans↗

PubMed2026

Integrative proteomics and bioinformatics pipelines for PTM profiling.

Post-translational modifications (PTMs) regulate protein function across all life forms and allow plants to respond rapidly to biotic and abiotic stress. Over 450 PTM types have been described across organisms, of which 23-33 have been experimentally confirmed in plants, including phosphorylation, acetylation, methylation, glycosylation, ubiquitination, and sumoylation. These modifications are highly dynamic and often reversible, and frequently act in combination, or "crosstalk," to fine-tune cellular processes. Advances in high-resolution mass spectrometry and large-scale genome sequencing continue to expand the catalogue of known PTM sites, while machine learning and deep learning approaches increasingly support prediction of PTM site localization and function. Unlike broader surveys of plant PTMs, this review focuses specifically on O-phosphorylation and Lys-N(ε)-acetylation, the two best-characterized and most extensively crosstalking PTMs in plants, and integrates four perspectives: the historical development of proteomic and bioinformatics approaches to these modifications; current mass spectrometry-based workflows and enrichment strategies; the bioinformatics tools and databases available for their analysis; and the technical and species-related challenges, particularly in non-model plants, that currently limit their study. We close by outlining priority directions for future research, including multi-omics integration, AI-based prediction, and the translation of PTM knowledge into crop stress resilience and breeding applications.

Protein Processing, Post-Translational↗

PubMed2026

Targeting EGFR in cancer using Terminalia arjuna: An integrated In Silico, molecular dynamics, experimental validation, and network pharmacology study.

The Epidermal Growth Factor Receptor (EGFR) plays a pivotal role in 20-60% of cancer cases, including glioblastoma, lung adenocarcinoma, and head and neck squamous cell carcinoma, as reported in The Cancer Genome Atlas (TCGA) dataset. The present study employed an integrated in silico and experimental workflow to evaluate EGFR-targeted compounds from Terminalia arjuna. Drug-likeness and ADMET screening were performed, followed by molecular docking and 1000 ns molecular dynamics simulations. In vitro validation was conducted using cancer cell-based assays and network pharmacology to explore the molecular mechanisms associated with the identified compound. Screening shortlisted eight compounds from T. arjuna. Molecular docking identified Arjunaside C (-8.2 kcal/mol), Arjunapthanoloside (-7.7 kcal/mol), and Beta-sitosterol (-7.4 kcal/mol) as potential EGFR inhibitors compared to Erlotinib (-6.6 kcal/mol). Arjunapthanoloside formed more H-bonds and exhibited most stable interactions with EGFR. MD simulations at 1000 ns revealed lower RMSD, RMSF, SASA, and Rg values for the Arjunapthanoloside-EGFR complex, indicating enhanced stability. Direct binding validation was limited by the unavailability of purified Arjunapthanoloside; therefore, Arjuna extract was evaluated, which demonstrated potent cytotoxicity with an IC₅₀ of 9 µg/mL in H357 oral cancer cells. Flow cytometry confirmed apoptosis-mediated cell death by increased early- and late-apoptotic cell populations. Network pharmacology analysis further identified additional targets (MMP3, MMP7, MMP9, and HRAS) that are directly involved in various cancers. Overall, the findings provide new insights into the therapeutic potential of Arjunapthanoloside as a stable compound that interacts with EGFR from T. arjuna, highlighting its significance in EGFR-targeted anticancer research.

ErbB Receptors↗

PubMed2006

SIBAS: a blood bank information system and its 5-year implementation at Macau.

Automation systems and information technology can greatly help medical facilities to improve their working efficiency and optimize the whole workflow. This article surveys electronic information management in blood donation and transfusion service, and explores the rationale and archetype of blood bank information systems, then exemplifies a successful in-running system-Sistema Integrado de Bancos de Sangue (SIBAS), which is developed by the Institute of Systems and Computer Engineering of Macau (INESC-Macau) in cooperation with the Macau Blood Transfusion Center (CTS-Macau). Its implementation and the related lessons are briefly introduced too. In essence, this article is oriented to serve as a reference of contemporary blood bank information systems.

Blood Banks↗

PubMed2007

Open-source software for radiologists: a primer.

There is a wide variety of free (open-source) software available via the Internet which may be of interest to radiologists. This article will explore the use of open-source software in radiology to help streamline academic workflow and improve general efficiency and effectiveness by highlighting a number of the most useful applications currently available. These include really simple syndication applications, e-mail management, spreadsheet, word processing, database and presentation packages, as well as image and video editing software. How to incorporate this software into radiological practice will also be discussed.

Computer Graphics↗

PubMed2026

Artificial intelligence agents and agentic artificial intelligence applied to precision medicine.

Precision medicine seeks to individualise care by integrating multimodal biomedical data, yet most deployed clinical artificial intelligence (AI) remains assistive, providing predictions without managing workflows or adapting autonomously. Agentic AI, built on large language models (LLMs), has emerged as a paradigm characterised by autonomy, goal-directed reasoning, memory, planning and tool use. This review synthesises evidence on agentic AI and LLMs applied to precision medicine, encompassing drug discovery, genomics, oncology, rare disease diagnostics and clinical pharmacology. This review also examines architectural components, recent validation milestones and emerging challenges, including hallucination, sociodemographic bias and evolving regulatory frameworks across the FDA, the EU AI Act and the WHO.

agentic AI↗

PubMed2006

TuBaFrost 2: Standardising tissue collection and quality control procedures for a European virtual frozen tissue bank network.

Tumour Bank Networking presents a great challenge for oncological research as in order to carry out large-scale, multi-centre studies with minimal intrinsic bias, each tumour bank in the network must have some fundamental similarities and be using the same standardised and validated procedures. The European Human Frozen Tumour Tissue Bank (TuBaFrost) has responded to this need by the promotion of an integrated platform of tumour banks in Europe. The operational framework for TuBaFrost has drawn upon the best practice of standard workflows and operating procedures employed by members of the TuBaFrost project and key initiatives worldwide.

Biological Specimen Banks↗

PubMed2026

Liquid biopsy: a new window on the BRCA genes.

The Breast Cancer Susceptibility Gene (BRCA)-associated tumors represent a constantly evolving and intriguing scenario in oncology, in which the availability of novel systemic treatment, mainly including the poly (ADP-ribose) polymerase (PARP) inhibitors, has enabled an improved survival benefit in clinical subgroups. The expanding regulatory approvals of PARP inhibitors have inevitably reshaped the clinical indications for BRCA testing, moving the BRCA1/2 profiling from the traditional and preventive workflows to therapeutic paths. Despite advances in technology and treatment, substantial limitations remain in current genetic and genomic tools for the detection of deleterious BRCA1/2 variants. Germline and tumor tissue testing provide only a snapshot of a patient's disease, failing to capture the dynamic and longitudinal aspects of tumor clonal evolution. In this scenario, liquid biopsy (LB) profiling of BRCA1/2 genes, primarily as circulating tumor DNA, represents a highly active area of research potentially affecting many aspects of cancer screening, diagnosis, and monitoring in individuals who are carriers of BRCA1/2 deleterious variants. Beyond the attractive potential to surrogate the tumor tissue testing, to overcome the cancer spatial and temporal heterogeneity, and to monitor the tumor mutational profile over time, accurately detecting all clinically relevant BRCA genetic variants and epigenetic modifications using LB remains technically challenging.

BRCA1/2↗

PubMed2026

A point-of-use SERS assay for rapid detecting difenoconazole and flusilazole residues in fruit juices using Au/COF substrate.

We developed a ready-to-use surface-enhanced Raman scattering (SERS) sensor for rapid, pretreatment-free detection of difenoconazole (DIF) and flusilazole (FLU) in peach and lychee juices. The substrate combines Au nanoparticles (AuNPs) with covalent organic frameworks (COF) and is implemented on a portable 25-well plate, enabling in situ testing. Juices can be directly applied to the SERS-active Au/COF composite, allowing simultaneous adsorption and signal generation. The correlation between SERS intensity and logarithmic concentration yielded R-values between 0.925 and 0.986, meeting the monitoring needs of non-laboratory scenarios. The entire workflow completes within 12 min, offering a faster alternative to conventional methods while maintaining high sensitivity and reproducibility. Detection limits reach 0.96-1.22 ppb for DIF and FLU, both of which are below the regulatory maximum residue limits. Distinct SERS fingerprints enable reliable discrimination of mixed residues across juice matrices, supporting rapid on-site monitoring and cost-effective pesticide surveillance.

Triazoles↗

PubMed2026

LC-IMS-MS profiling of avocado acetogenins reveals tissue-dependent distribution and cultivar-specific metabolic signatures.

This study presents a comprehensive characterisation of acetogenin-related metabolites in avocado using an LC-IMS-MS workflow. A total of 26 metabolites were semi-quantified across peel, pulp and seed tissues from three cultivars (Hass, Bacon and Fuerte). The integration of ion mobility spectrometry enabled the generation of the first experimental database of collision cross section (CCS) values for avocado acetogenins, improving confidence in metabolite annotation. Results revealed a pronounced tissue-dependent distribution, with seeds and pulp as the primary reservoir of several acetogenins, whereas the peel consistently exhibited lower concentrations. In contrast, acetogenin levels remained largely stable throughout ripening. Clear cultivar-dependent differences were observed, with Hass displaying a distinct metabolic profile compared to Bacon and Fuerte. Multivariate analysis confirmed these findings, showing tissue-dependent cultivar differentiation. This study provides new insights into avocado chemical diversity and highlights the potential of avocado by-products as consistent and promising sources of bioactive acetogenins.

Persea↗

PubMed2025

The impact of genetic counselor involvement in genetic and genomic test order review: A scoping review.

PURPOSE: The increasing complexity of genetic technologies paired with more genetic tests being ordered by nongenetic health care providers, has resulted in an increase in the number of inappropriately ordered tests. Genetic counselors (GCs) are ideally suited to assess the appropriateness of a genetic test. METHODS: We performed a scoping review of GC involvement in utilization management initiatives in order to describe the impact of having GCs involved in this process. Five databases (MEDLINE, EMBASE, CINHAL, EBM reviews, and Web of Science Core Collection) and gray literature were searched. We considered literature published in English since 2010. RESULTS: A total of 51 studies were included. The most commonly evaluated outcomes included cancellation rate, economic efficiencies, impact on medical management, diagnostic rate, and time or triage efficiencies. Several studies also described GC impact on nongenetic health care providers. CONCLUSION: Employment of GCs in the laboratory has been implemented widely as a solution to test misordering. These studies describe ways in which GCs can be integrated into testing workflows to reduce the number of inappropriate tests and have wider impacts on nongenetic health care providers' ordering practices and the patient experience.

Humans↗

PubMed2025

Impact of laboratory-driven proactive reanalysis: Reclassification to positive in 5% of initially negative or uncertain exome sequencing cases.

PURPOSE: Reanalysis of exome sequencing (ES) data increases diagnostic utility; however, there is no consensus on when and under what circumstances reanalysis should occur. Requesting and performing ES reanalysis burdens both clinical and laboratory workflows. Maximizing the potential for reclassification is essential. Here, we describe the impact of a laboratory-driven proactive reanalysis process that triggers reanalysis when new evidence is identified. METHODS: We reviewed reanalysis outcomes of an ES cohort. Reanalysis events were categorized based on initiating factors (laboratory-driven proactive, family studies, and clinician-initiated). Laboratory-driven proactive reclassifications are prompted by systematic review of new scientific data. Outcomes were evaluated by initiating factors, reclassification types, evidence used, and time since original report. RESULTS: Overall, 23% of cases underwent at least 1 reanalysis, with 35% of reanalyses resulting in reclassification. There was a 4% increase in diagnostic yield, including 5% of initially unsolved ES receiving diagnostic reports. Diagnostic reclassifications rates were significantly higher for laboratory-driven proactive reanalyses (54%; P < .0001) than family studies (18%) and clinician-initiated reanalyses (4%). New gene-disease relationships were the most efficacious evidence source. Laboratory-driven proactive reclassifications occurred steadily over time. CONCLUSION: Laboratory-driven proactive reanalysis effectively provides more diagnostic reclassifications compared with clinician-initiated reanalysis. Laboratories should curate and integrate emerging evidence into ES reanalysis.

Humans↗

PubMed2025

Genetics first approach: Expanding the utility of genetic testing by nongeneticist physicians.

PURPOSE: The increasing demand for genetic testing and a global shortage of geneticists has significantly strained health care systems worldwide. This highlighted the need for new strategies aiming to increase testing accessibility, reduce wait times, and enhance patient care quality. METHODS: We implemented a 4-step program, "Genetics First," to empower nongeneticist physicians (NGPs) to play an active role in the process of genetic consultation and testing. The steps included (1) establishing criteria to identify suitable clinical domains, (2) selecting clinical indications within the domain through expert panel review, (3) designing tailored education and workflows for NGPs across indications, and (4) monitoring test outcomes and providing further support for complex cases. Test outcomes were compared between NGPs and clinical geneticists. RESULTS: Endocrinology was selected as the first domain, with 114 endocrinologists who completed the program. During the study, 260 gene panels were performed for monogenic diabetes, with NGPs initiating 68% of tests, leading to a 107% increase in referrals. The diagnostic yield was 30%, with no significant difference between NGP- and clinical geneticist-initiated tests. CONCLUSION: This study demonstrates the feasibility and impact of involving NGPs in genetic testing, offering a paradigm shift that could expand access to genetic testing and improve patients' care and clinical outcomes.

Humans↗

PubMed2025

Implementing customized genomic sequencing reports to empower providers in safety-net neonatal intensive care units.

PURPOSE: Through our implementation study providing rapid genomic sequencing (rGS) in safety-net neonatal intensive care units (NICUs), we investigated the feasibility and perceived usefulness of customized "clinical interpretive reports" (CIRs) to help neonatal providers with interpreting, disclosing, and managing care based on rGS results. METHODS: Enrolled infants received rGS through a clinically accredited vendor. We developed 5 CIR types to provide customized interpretation of rGS results and link results to clinical management considerations, research opportunities, and resources. We developed workflows to triage, create, and deliver CIRs within 3 business days. Providers received the vendor reports and CIRs, disclosed results, and completed post-disclosure surveys. We analyzed summary statistics for the first 100 cases. RESULTS: We delivered 97 of 100 CIRs (97%) within our goal time frame (average 1.3 days) and provided clinical management recommendations in 40 of 100 (40%). Neonatal providers completed the post-disclosure surveys for 86 of 100 disclosures (86%). Most reported using the CIR before disclosure (80/86, 93%) and found it helpful at providing useful information beyond the vendor report (79/80, 99%). CONCLUSION: It is feasible and useful to develop customized rGS reports to assist non-genetics providers in safety-net NICU settings. Similar approaches may hold promise for equitably advancing genomic care in non-NICU settings.

Humans↗