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PubMed2026

Circulating Tumor DNA in Breast Cancer: A Liquid Biopsy Revolution for Non-Invasive Genomic Profiling and Clinical Decision-Making.

Breast cancer remains the most frequently diagnosed cancer and a leading cause of cancer-related mortality among women worldwide, underscoring the need for accurate, minimally invasive biomarkers to support precision oncology. Conventional tissue biopsy remains the standard for molecular characterization but is limited by its invasiveness, inability to capture spatial and temporal tumor heterogeneity, and challenges in serial monitoring. Circulating tumor DNA (ctDNA), a tumor-derived fraction of cell-free DNA, has emerged as a promising liquid biopsy biomarker capable of providing real-time genomic information throughout disease progression. This narrative review examines recent advances in ctDNA biology, analytical technologies, clinical applications, current limitations, and future directions in breast cancer management. A structured literature search of PubMed/MEDLINE, Scopus, Embase, Web of Science, and Google Scholar identified relevant English-language publications from 2015 to 2026. Current evidence indicates that highly sensitive platforms, including digital PCR, BEAMing, and next-generation sequencing, can detect clinically actionable alterations in genes such as PIK3CA, ESR1, TP53, ERBB2, AKT1, and BRCA1/2. ctDNA has demonstrated particular utility in identifying minimal residual disease, monitoring therapeutic response, detecting emerging resistance mechanisms, and guiding targeted treatment selection in advanced breast cancer. However, applications in early cancer detection, population screening, and artificial intelligence-assisted clinical decision-making remain investigational. Widespread clinical implementation is constrained by low ctDNA abundance in early-stage disease, analytical variability, limited assay standardization, and cost considerations. Continued technological innovation, prospective multicenter validation, standardized testing protocols, and evidence-based clinical guidelines are essential to fully integrate ctDNA into routine precision breast cancer care.

breast cancer↗

PubMed2026

Application of emerging technologies in the antiviral field.

Viral diseases pose a serious threat to global public health, agriculture, and biosecurity. Conventional antiviral strategies are often limited by an incomplete understanding of disease mechanisms, poor targeting precision, and slow response times. Emerging technologies are now reshaping the landscape of antiviral research. This review examines the roles of four key frontiers, including organoid models, gene editing, AI-driven molecular design, and synthetic biology. Organoids provide physiologically relevant platforms that model virus-host interactions and disease progression. Viral infections remain a major challenge to human and animal health, agriculture, and biosecurity. Progress in antiviral research is constrained by the complexity of viral pathogenesis, the diversity and rapid evolution of viruses, and the limited translational relevance of some traditional model systems. Recent advances in organoid technology, gene editing, artificial intelligence, and synthetic biology are expanding the toolkit available for antiviral research and development. In this review, we discuss how these four technological frontiers contribute to disease modeling, target discovery, molecular design, and translational innovation. Organoids, in particular, provide physiologically relevant systems for investigating viral infection, tissue tropism, host responses, and pathogenesis. Gene editing tools, such as CRISPR, enable precise manipulation of host and viral genomes, facilitating the development of resistant organisms and next-generation vaccine platforms. AI technologies, including AlphaFold for structure prediction and platforms for de novo protein design, address long-standing bottlenecks in structural biology and offer powerful means to engineer antiviral proteins, antibodies, and vaccine antigens. Synthetic biology, guided by the Design-Build-Test-Learn cycle, integrates computational design, genetic assembly, and functional validation into a cohesive pipeline. Together, these technologies form a synergistic workflow that spans disease modeling, target discovery, molecular design, construction, testing, and iterative optimization. This integrated approach is shifting antiviral development from traditional empirical methods toward more precise, intelligent strategies. The review also highlights ongoing challenges in integration and scalability, stressing that high-quality biological datasets and stronger interdisciplinary collaboration are essential for realizing translational potential. By presenting a cohesive view of these converging methodologies, this review offers a framework to guide the intelligent evolution of antiviral strategies in both human and animal health.

Antiviral↗

PubMed2026

Immune dysregulatory disorders: perspective from solving a diagnostic odyssey.

PURPOSE OF REVIEW: Inborn errors of immunity (IEIs), once considered rare disorders characterized primarily by recurrent infections, are now recognized as a rapidly expanding group of diseases encompassing autoimmunity, autoinflammation, allergy, malignancy, and immune dysregulation. Advances in next-generation sequencing, functional immunology, and systems biology have revealed overlap between traditionally distinct disease categories and highlighted the complexity of genotype-phenotype relationships. RECENT FINDINGS: While this evolution has led to the discovery of hundreds of previously unrecognized disorders, it has also challenged conventional diagnostic paradigms and demonstrated how patients may have care spread across multiple specialties, without a clear medical home. These discoveries have also highlighted ongoing challenges translating scientific findings to the clinic including difficulties in accessing genomic testing, interpretation of variants of uncertain significance, impacts of incomplete penetrance and somatic mosaicism, and limited availability of specialized functional assays. Emerging computational approaches, including artificial intelligence, offer opportunities to accelerate diagnosis but cannot replace comprehensive clinical evaluation or longitudinal physician-patient relationships. SUMMARY: This perspective examines how the diagnostic odyssey for immune dysregulatory disorders has evolved, side-by-side with the changing framework for diagnosing rare immune diseases. We propose an integrated approach combining clinical phenotyping, genomics, functional validation, and multidisciplinary expertise to unite ongoing discovery between clinicians and scientists, diagnostics, and patient outcomes.

diagnostic odyssey↗

PubMed2006

One-class-at-a-time removal sequence planning method for multiclass classification problems.

Using dynamic programming, this work develops a one-class-at-a-time removal sequence planning method to decompose a multiclass classification problem into a series of two-class problems. Compared with previous decomposition methods, the approach has the following distinct features. First, under the one-class-at-a-time framework, the approach guarantees the optimality of the decomposition. Second, for a K-class problem, the number of binary classifiers required by the method is only K-1. Third, to achieve higher classification accuracy, the approach can easily be adapted to form a committee machine. A drawback of the approach is that its computational burden increases rapidly with the number of classes. To resolve this difficulty, a partial decomposition technique is introduced that reduces the computational cost by generating a suboptimal solution. Experimental results demonstrate that the proposed approach consistently outperforms two conventional decomposition methods.

Algorithms↗

PubMed2004

At the crossroads of evolutionary computation and music: self-programming synthesizers, swarm orchestras and the origins of melody.

This paper introduces three approaches to using Evolutionary Computation (EC) in Music (namely, engineering, creative and musicological approaches) and discusses examples of representative systems that have been developed within the last decade, with emphasis on more recent and innovative works. We begin by reviewing engineering applications of EC in Music Technology such as Genetic Algorithms and Cellular Automata sound synthesis, followed by an introduction to applications where EC has been used to generate musical compositions. Next, we introduce ongoing research into EC models to study the origins of music and detail our own research work on modelling the evolution of melody.

Algorithms↗

PubMed1985

Electrolaryngeal speech produced by laryngectomized subjects: perceptual characteristics.

The purpose of this study was to investigate the perceptual confusions individuals experience while listening to the speech of laryngectomized persons who used electrolarynges. Six talkers generated speech samples using two different models of electrolarynges. Intelligibility was evaluated from phonetic transcriptions of the speech samples. Confusion matrices were generated from the transcriptions. Talkers varied considerably in their intelligibility, but there was little difference in intelligibility for a given talker using either model of electrolarynx. The perceptual confusions revealed that most errors occurred on the voicing feature.

Humans↗

PubMed2026

Molecular biology and integrated strategies for activating cryptic biosynthetic gene clusters toward next-generation antibiotic discovery.

Antimicrobial resistance (AMR) has been identified as one of the 21st century's severest global public health crises. AMR led to an estimated 4.95 million deaths in 2019 and will claim 10 million lives a year by 2050 in the absence of targeted interventions. During the same period, the number of novel antibiotics discovered has decreased drastically as many researchers are rediscovering known antibiotics, non-model microorganisms are poorly understood or difficult to culture and antibiotic research and development investment has declined drastically. However, high-throughput whole genome sequencing and the subsequent application of bioinformatics in bacterial and fungal genomes have shown that a numerous of cryptic or silent biosynthetic gene clusters (BGCs) remain latent at ambient laboratory conditions since their genes are transcriptionally inactive. Cryptic BGCs represent a vast source of unique secondary metabolites, many of which may yield novel antibacterial, antifungal, anti-cancer and other potentially valuable natural products. This review discusses the biological relevance of cryptic BGCs, the major limiting factors that restricts their activation and novel strategies that have been employed to activate them and exploit their potential to produce novel natural products. The review focuses on biological approaches including CRISPR-Cas mediation for the activation of cryptic BGCs, promoter engineering, pathway refactoring, and heterologous expression; biochemical strategies such as Osman, OsMAC, Precursor Feeding, Chemical Elicitation, Epigenetic Regulation and Co-cultivation and technology-based strategies such as Genome mining, Microfluidic Cultivation systems, High-Throughput Screening, Metabolomics, Molecular Networking and Artificial Intelligence and Machine Learning based prediction of BGCs and their metabolites. The use of multi-omics technologies combined with synthetic biology to achieve better discovery, characterization and large-scale production of novel natural products is also discussed herein. Finally, we will talk about the ecological significance and evolutionary advantage of cryptic BGCs' role in interactions between microorganisms, such as competition, communication, symbiosis and environmental adaptability, so as to provide a useful background for accelerating next-generation antibiotics.

CRISPR-Cas activation↗

PubMed2005

Building an ontology of adverse drug reactions for automated signal generation in pharmacovigilance.

Automated signal generation in pharmacovigilance implements unsupervised statistical machine learning techniques in order to discover unknown adverse drug reactions (ADR) in spontaneous reporting systems. The impact of the terminology used for coding ADRs has not been addressed previously. The Medical Dictionary for Regulatory Activities (MedDRA) used worldwide in pharmacovigilance cases does not provide formal definitions of terms. We have built an ontology of ADRs to describe semantics of MedDRA terms. Ontological subsumption and approximate matching inferences allow a better grouping of medically related conditions. Signal generation performances are significantly improved but time consumption related to modelization remains very important.

Adverse Drug Reaction Reporting Systems↗

PubMed2005

Using a claims data-based sentinel system to improve compliance with clinical guidelines: results of a randomized prospective study.

OBJECTIVE: To demonstrate the potential effect of deploying a sentinel system that scans administrative claims information and clinical data to detect and mitigate errors in care and deviations from best medical practices. METHODS: Members (n = 39 462; age range, 12-64 years) of a midwestern managed care plan were randomly assigned to an intervention or a control group. The sentinel system was programmed with more than 1000 decision rules that were capable of generating clinical recommendations. Clinical recommendations triggered for subjects in the intervention group were relayed to treating physicians, and those for the control group were deferred to study end. RESULTS: Nine hundred eight clinical recommendations were issued to the intervention group. Among those in both groups who triggered recommendations, there were 19% fewer hospital admissions in the intervention group compared with the control group (P < .001). Charges among those whose recommendations were communicated were dollar 77.91 per member per month (pmpm) lower and paid claims were dollar 68.08 pmpm lower than among controls compared with the baseline values (P = .003 for both). Paid claims for the entire intervention group (with or without recommendations) were dollar 8.07 pmpm lower than those for the entire control group. In contrast, the intervention cost dollar 1.00 pmpm, suggesting an 8-fold return on investment. CONCLUSION: Ongoing use of a sentinel system to prompt clinically actionable, patient-specific alerts generated from administratively derived clinical data was associated with a reduction in hospitalization, medical costs, and morbidity.

Adult↗

PubMed1991

Modelling work practices: input to the design of a physician's workstation.

To ensure tight coupling between users' work practices and the system's model of these practices, designers need methods to generate accurate descriptions of what users actually do. This paper illustrates how we model physicians' information needs and translate knowledge about these needs into design specifications.

Artificial Intelligence↗

PubMed1997

Large scale protein modelling and model repository.

Knowledge-based molecular modelling of proteins has proven useful in many instances including the rational design of mutagenesis experiments, but it has been generally limited by the availability of expensive computer hardware and software. To overcome these limitations, we have developed the SWISS-MODEL server for automated knowledge-based protein modelling. The SWISS-MODEL server uses the Brookhaven Protein Data Bank as a source of structural information and automatically generates protein models for sequences which share significant similarities with at least one protein of known 3D-structure. We now use the software framework of the server to generate large collections of protein models. To store these models, we have established the SWISS-MODEL Repository, a new database for protein models generated by theoretical approaches. This repository is directly integrated with SWISS-PROT and other databases through the ExPASy World-Wide Web server (URL is http:(/)/www.expasy.ch).

Amino Acid Sequence↗

PubMed2002

[Intelligent system to perform a diagnostic protocol for lymphatic invasion in laryngeal cancer].

Laryngeal carcinoma is the most frequent malignant tumour in head and neck. Node invasion is known to be one of the most important prognostic factors. The aim of this study has been to design an intelligent system to perform a diagnostic algorithm of metastasic neck nodes. 122 clinical reports of patients diagnosed of laryngeal carcinoma in our department have been reviewed. The compiled data have been: tumor site, T stage, N stage (clinical, after CT scan and post-surgery). The method used to design the intelligent system has been the ID3, which is able to generate a minimal decision tree. Palpation has been the variable that has given more information about node invasion. CT has proved to be more efficient in supraglottic tumours. ID3 method has shown to be useful in performing diagnostic algorithms, specially when the number of cases and diagnostic tests are high.

Adult↗

PubMed2003

Multiobjective water resources systems analysis using genetic algorithms--application to Chou-Shui River Basin, Taiwan.

Multipurpose operation is adopted by most reservoirs in Taiwan in order to maximize the benefits of power generation, water supply, irrigation and recreational purposes. A multiobjective approach can be used to obtain trade-off curves among these multipurpose targets. The weighting method, in which different weighting factors are used for different purposes, was used in this research work. In Taiwan, most major reservoirs are operated by rule curves. Genetic algorithms with characteristics of artificial intelligence were applied to obtain the optimal rule curves of the multireservoir system under multipurpose operation in Chou-Shui River Basin in central Taiwan. The model results reveal that different shapes of rule curves under different weighting factors on targets can be efficiently obtained by genetic algorithms. Pareto optimal solutions for a trade-off between water supply and hydropower were obtained and analyzed.

Algorithms↗

PubMed2025

Improving Community-Based Care for Adolescents with ADHD: a Randomized Controlled Trial of Artificial Intelligence-Assisted Fidelity Supports.

Cognitive-behavioral treatments (CBTs) for adolescents with ADHD demonstrate promise of long-term effects on outcome. However, their implementation in routine care community clinics faces barriers that impact quantity, efficiency, and quality of delivery, as well as client outcomes. This study is a randomized controlled trial designed to evaluate the impact of an AI-assisted service delivery model on therapist implementation of Supporting Teens' Autonomy Daily (STAND), a CBT blended with Motivational Interviewing (MI) for adolescents with ADHD. Adolescents with ADHD (N = 51), who were clients at three community mental health agencies, received treatment from 23 therapists. There was randomization of adolescents and therapists to AI-assisted or standard implementation supports. In addition to standard supports (i.e., training, standard facilitation resources, technical assistance, case supervision), AI-assisted support package included digitized facilitation resources housed in a clinical dashboard (Care4), feedback on content fidelity, and AI-generated feedback on MI implementation quality. The AI-assisted group was associated with more efficient treatment delivery and lower number of appointments attended by the adolescent. There was also a significant decrement in MI quality over time in the AI-assisted group compared to the standard support group. Feedback in focus groups indicated that therapists perceived a task-oriented mindset to be associated with receipt of the AI-assisted support package, leading therapists to prioritize efficiency over relational aspects of therapy. Following the results of this trial, a future, larger RCT should examine the impact of the AI-assisted implementation model on mental health outcomes and cost savings to organizations, third party payers, and clients. Trial registration number: NCT05135065; https://www.clinicaltrials.gov ; Registered September 2021.

Humans↗

PubMedDate not supplied

Webifying a patient interview support application.

This paper reports on the software engineering challenges, and resultant benefits experienced, in porting an interactive, knowledge-based system from Microsoft Windows to the World Wide Web for evaluation purposes. The Patients Interview Support Application (PISA) is a program intended for operation by a non-expert clerk to interview an ambulatory primary care patient. The PISA code had to be re-written substantially to address the 'connectionless' nature of Web dialog and to work in terms of dynamically generated HTML forms; however, it was possible to avoid any revision of the central knowledge-base or inference engine. The resultant Web environment attracted thought-provoking and detailed feedback from users, indicating that significant attention can be obtained from the global community by mounting an interactive system on the Web. Specific enhancements to the PISA's artificial intelligence are suggested by user reaction. A future global health informatics 'marketplace' with a multidue of Web-based system components available for composition of health information systems is envisioned.

Artificial Intelligence↗

PubMed1991

Quantitative analysis of electroencephalograms: is there chaos in the future?

The history of quantitative, computerized electroencephalogram (EEG) analysis is reviewed. It is shown that, until very recently, the basic approach to EEG analysis involved the assumption that the EEG is stochastic. Consequently, statistical pattern recognition techniques, segmentation procedures, syntactic methods, knowledge-based approaches, and even artificial neural network methods have been developed with different levels of success. A fundamentally different approach to computerized EEG analysis, however, is making its way into the laboratories. The basic idea, inspired by recent advances in the area of non-linear dynamics, and especially the theory of chaos, is to view an EEG as the output of a deterministic system of relatively simple complexity, but containing non-linearities. This suggests that studying the geometrical dynamics of EEGs, and the development of neurophysiologically realistic models of EEG generation may produce more successful automated EEG analysis techniques than the classical, stochastic methods. Evidence supporting the non-linear dynamics paradigm is reviewed, and possible research paths are indicated.

Algorithms↗

PubMed1994

The design and construction of a medical simulation model.

This paper describes the design, construction and validation of a probabilistic simulation model of patients who present with abdominal pain. The model incorporates text-book medical knowledge, clinical judgment, and statistics collected from real cases. The knowledge representation combines techniques of Bayesian network modelling with ideas of logistic discrimination. The model is shown to generate convincing, realistic cases; large numbers of artificial cases with no missing observations can be generated quickly. This should make the model a useful tool for investigating factors which limit achievable computer accuracy in the diagnosis of abdominal pain.

Abdominal Pain↗

PubMed2005

CLAGen: a tool for clustering and annotating gene sequences using a suffix tree algorithm.

Most multiple gene sequence alignment methods rely on conventions regarding the score of a multiple alignment in pairwise fashion. Therefore, as the number of sequences increases, the runtime of sequencing expands exponentially. In order to solve the problem, this paper presents a multiple sequence alignment method using a linear-time suffix tree algorithm to cluster similar sequences at one time without pairwise alignment. After searching for common subsequences, cross-matching common subsequences were generated, and sometimes inexact matching was found. So, a procedure aimed at masking the inexact cross-matching pairs was suggested here. In addition, BLAST was combined with a clustering tool in order to annotate the clusters generated by suffix tree clustering. The proposed method for clustering and annotating genes consists of the following steps: (1) construction of a suffix tree; (2) searching and overlapping common subsequences; (3) grouping subsequence pairs; (4) masking cross-matching pairs; (5) clustering gene sequences; (6) annotating gene clusters by the BLAST search. The performance of the proposed system, CLAGen, was successfully evaluated with 42 gene sequences in a TCA cycle (a citrate cycle) of bacteria. The system generated 11 clusters and found the longest subsequences of each cluster, which are biologically significant.

Algorithms↗