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PubMed2025

Foliar disease resistance phenomics of fungal pathogens: image-based approaches for mapping quantitative resistance in cereal germplasm.

Host plant resistance is the most effective and environmentally sustainable means of reducing yield losses caused by fungal foliar pathogens of cereal species. Cereal genebank collections hold diverse pools of potentially underutilized disease resistance alleles, and cereal genomic resources are well advanced due to large-scale sequencing and genotyping efforts. Genome-Wide Association Studies (GWAS) have emerged as the predominant association genetics technique to initially discover novel disease resistance loci or alleles in these diverse collections. Traditional disease resistance phenotyping methods are reliant on visual estimation of disease symptom severity and have successfully supported genetic mapping studies either via GWAS or QTL mapping in biparental populations facilitating both marker development and gene cloning efforts. Due to foliar pathogens having a high capacity to evolve, there is a need to pyramid disease resistance genes with diverse mechanisms for durable control. Resistance expressed as a quantitative trait, known as quantitative resistance (QR), is hypothesized to be more durable, unlike major R-gene resistance that is race-specific and can be vulnerable to breaking down without gene stewardship. However, assessing QR visually is challenging, particularly when complicated by complex genotype × environment (G × E) effects in the field. High-throughput image-based phenotyping provides accurate and unbiased data that can support foliar disease resistance screening efforts of genebank collections using GWAS. In this review, we discuss image-based disease phenotyping based on macroscopic (visible symptoms) and microscopic features during the host-pathogen interaction. Quantitative image analysis approaches using conventional and artificial intelligence (AI) algorithms are also discussed.

Disease Resistance↗
PubMed2025

Haplotype stacking to improve stability of stripe rust resistance in wheat.

Genotype-by-environment interaction analysis and haplotype-level characterisation provide novel insights into the stability of stripe rust resistance. Breeding selection strategies are proposed to achieve rapid and stable genetic gains across environments. This study investigated stripe/yellow rust (YR) responses in the Vavilov wheat diversity panel evaluated across 11 field experiments conducted in Australia and Ethiopia during 2014-2021. Genotype-by-environment interaction (GEI) was analysed using a factor analytic (FA) model. Genotype-level selection was performed with overall performance (OP) and root-mean-square deviation (RMSD), which reflected average performance and stability of YR resistance across environments, respectively. Genomic estimated breeding values (GEBV) for these traits were calculated and compared with those from a multi-trait GBLUP model with average performance represented by the mean GEBV across environments and stability by the standard deviation of GEBV across environments. The FA-based and multi-trait GBLUP GEBV had high correlations. Haplotypes with large effects on OP and RMSD were identified using the local GEBV method. Favourable haplotypes were then used for stacking in breeding simulations, using the Vavilov collection as a base. Compared to truncation selection, optimal haplotype selection (OHS) using an artificial intelligence (AI)-based algorithm achieved longer-term genetic gains for both OP and RMSD (after many generations) by initially selecting founder parents that maximised favourable haplotypes. Simulations using YR responses from diverse environments that mimicked fluctuating environmental conditions across seasons were conducted to evaluate strategies for selection of YR resistance that is stable across years. Strategies which gave most weight to OP, but some weight to RMSD were optimal in these conditions, and substantially reduced variation of performance across years. This study provides useful information for breeding cultivars with both high YR resistance and high stability of resistance across environments.

Triticum↗
PubMed2026

A narrative review of what cohorts have taught us and how they have laid the foundation for much of our understanding of type 2 diabetes.

This narrative review provides a historical perspective on how observational research on type 2 diabetes has been developed and consolidated over the last 50 years and how well-designed cohort studies will provide us with knowledge for research and practice in the future and aid guideline development. We have included data from a large number of cohorts from every continent that have been used to study the development and/or progression of type 2 diabetes, including cohorts that are general population-based, disease-based, intervention-based and registry-based. We have structured the results from the past 50 years based on the following themes: diagnosis and screening, complications, risk factors and pathophysiology. We also discuss the strengths and weaknesses of observational research when compared with other research designs. Finally, we discuss the emerging and future directions for type 2 diabetes research using cohorts, which include novel developments, such as artificial intelligence, precision health and the exposome. We conclude that cohort research has significantly advanced our understanding of type 2 diabetes and aided guideline development, and complements experimental work, such as human randomised controlled trials and animal studies. Both approaches are essential and complementary in our pursuit to provide a more comprehensive understanding of the development and progression of type 2 diabetes, and to change dogma, practice and policies for better outcomes.

Humans↗
PubMed2026

Radiology considerations for the PREMIUM study: a multicenter randomized controlled trial of abbreviated MRI versus ultrasound for liver cancer screening in cirrhosis.

This paper describes the rationale and radiology considerations in the implementation of the Preventing Liver Cancer Mortality through Imaging with Ultrasound versus MRI (PREMIUM) study. PREMIUM is a multicenter, randomized controlled trial sponsored by the Department of Veterans Affairs comparing dynamic contrast-enhanced (DCE) abbreviated MRI (aMRI) plus serum AFP versus ultrasound (US) plus serum AFP for hepatocellular carcinoma (HCC) screening in patients with cirrhosis. PREMIUM aims to randomize 4,700 participants across over 47 Veterans Affairs Medical Centers to semiannual surveillance for up to eight years, with HCC-related mortality as the primary endpoint. To date, 35 sites have been activated with 1,085 patients randomized. To ensure uniform implementation and reporting of per-protocol screening, the PREMIUM Radiology Workgroup developed standardized imaging protocols, structured LI-RADS-based reporting templates, and a centralized training program for radiologists and technologists. They also perform ongoing quality control on both scans and reports. The aMRI protocols utilize multiphasic post-contrast imaging to allow LI-RADS scoring. A non-contrast-enhanced aMRI protocol is available for participants who develop renal impairment or contrast allergy during the study. US protocols conform to US LI-RADS standards. Structured reporting promotes consistency in documentation of findings, visualization scores, and follow-up recommendations. A centralized Image Repository was established, incorporating advanced de-identification methods to remove metadata and pixel-embedded protected health information from imaging files. More than 20,000 curated liver MRI and US exams are anticipated, supporting both trial outcomes and future radiomics and artificial intelligence research. PREMIUM aims to determine whether screening for HCC with a DCE aMRI protocol reduces HCC-related mortality and also facilitates ancillary studies utilizing the Image Repository.

Abbreviated MRI↗
PubMed2026

Metabolic convergence of diabetes and prostate cancer: from dysglycemia to tumor microenvironment reprogramming.

The relationship between diabetes mellitus and prostate cancer (PC) represents one of the most intriguing paradoxes in cancer epidemiology, with diabetic individuals exhibiting a reduced incidence of PC yet poorer prognosis following diagnosis. This apparent contradiction underscores the need for an integrated understanding of how systemic metabolic dysfunction influences prostate carcinogenesis and disease progression. The present review critically synthesizes contemporary epidemiological, mechanistic, and translational evidence to establish metabolic convergence as a unifying framework linking diabetes-associated metabolic abnormalities with PC biology. Current evidence indicates that chronic dysglycemia, hyperinsulinemia, insulin resistance, and endocrine perturbations orchestrate interconnected intracellular signaling networks involving PI3K-AKT-mTOR, AMPK, AGE-RAGE signaling, oxidative stress, mitochondrial dysfunction, and epigenetic reprogramming, collectively driving metabolic adaptation and tumor evolution. Beyond tumor-intrinsic mechanisms, diabetes profoundly remodels the prostate tumor microenvironment through alterations in stromal metabolism, cancer-associated fibroblast activation, adipocyte-tumor crosstalk, extracellular matrix (ECM) remodeling, hypoxic adaptation, and vascular dysfunction, while simultaneously promoting immunometabolic reprogramming characterized by macrophage polarization, T-cell dysfunction, immune checkpoint activation, and immune evasion. The review further examines the bidirectional interactions between antidiabetic therapies and PC treatment, critically evaluating the translational potential of metformin and emerging glucose-lowering agents within the context of precision metabolic therapeutics. Finally, future directions encompassing biomarker-guided patient stratification, longitudinal metabolic profiling, multi-omics integration, artificial intelligence, and clinically relevant mechanistic validation are discussed as essential components of next-generation precision oncology. Collectively, this review reframes diabetes as an active metabolic determinant of PC rather than a coincidental comorbidity and highlights metabolism-centered precision strategies as promising avenues for improving risk stratification, therapeutic decision-making, and clinical outcomes in diabetes-associated PC.

Humans↗
PubMed2003

A generalized locomotion CPG architecture based on oscillatory building blocks.

Neural oscillation is one of the most extensively investigated topics of artificial neural networks. Scientific approaches to the functionalities of both natural and artificial intelligences are strongly related to mechanisms underlying oscillatory activities. This paper concerns itself with the assumption of the existence of central pattern generators (CPGs), which are the plausible neural architectures with oscillatory capabilities, and presents a discrete and generalized approach to the functionality of locomotor CPGs of legged animals. Based on scheduling by multiple edge reversal (SMER), a primitive and deterministic distributed algorithm, it is shown how oscillatory building block (OBB) modules can be created and, hence, how OBB-based networks can be formulated as asymmetric Hopfield-like neural networks for the generation of complex coordinated rhythmic patterns observed among pairs of biological motor neurons working during different gait patterns. It is also shown that the resulting Hopfield-like network possesses the property of reproducing the whole spectrum of different gaits intrinsic to the target locomotor CPGs. Although the new approach is not restricted to the understanding of the neurolocomotor system of any particular animal, hexapodal and quadrupedal gait patterns are chosen as illustrations given the wide interest expressed by the ongoing research in the area.

Algorithms↗
PubMed2025

Decoding the distribution, structure-function-redox potential relationship and recent advances in fungal laccases: a systematic approach.

Laccases, categorized as multicopper oxidases, are recognized for their multifaceted roles in ecosystems and their utility in diverse industrial applications. Laccases from higher fungi, specifically Ascomycota and Basidiomycota, have garnered significant research interest due to their elevated redox potentials and their capacity to degrade lignin in decaying wood, alongside other industrial uses. Here, we have conducted a comprehensive and systematic analysis on fungal laccases using Web of Science, Scopus, PubMed, and ScienceDirect. The genomic distribution, phylogenetic affiliation, and structural organization of laccase-encoding genes in higher fungal species were investigated, as were the catalytic mechanisms of the corresponding enzymes. Additionally, the study explores the correlation between structural domains and redox potential, as well as the impact of post-translational modifications like glycosylation on enzyme activity. Furthermore, the recent advancements in laccase engineering, employing strategies such as rational design, directed evolution, and heterologous expression are discussed. The review also explores the scope of "artificial intelligence and machine learning" in deducing the structure-function relationships, optimizing codon usage, predicting signal peptides, enhancing enzymatic performance, and developing host-specific genetic engineering techniques is also discussed for tailoring fungal laccases to meet the demands of industrial biocatalysis for improved activity and stability.

Laccase↗
PubMed2006

Initial clinical experience with a partly autonomous robotic surgical instrument server.

BACKGROUND: The authors believe it would be useful to have surgical robots capable of some degree of autonomous action in cooperation with the human members of a surgical team. They believe that a starting point for such development would be a system for delivering and retrieving instruments during a surgical procedure. METHODS: The described robot delivers instruments to the surgeon and retrieves the instruments when they are no longer being used. Voice recognition software takes in requests from the surgeon. A mechanical arm with a gripper is used to handle the instruments. Machine-vision cameras locate the instruments after the surgeon puts them down. Artificial intelligence software makes decisions about the best response to the surgeon's requests. RESULTS: A robot was successfully used in surgery for the first time June 16, 2005. The operation involved excision of a benign lipoma. The procedure lasted 31 min, during which time the robot performed 16 instrument deliveries and 13 instrument returns with no significant errors. The average time between verbal request and delivery of an instrument was 12.4 s. CONCLUSIONS: The described robot is capable of delivering instruments to a surgeon at command and can retrieve them independently using machine vision. This robot, termed a "surgical instrument server," represents a new class of information-processing machines that will relieve the operating room team of repetitive tasks and allow the members to focus more attention on the patient.

Adult↗
PubMed2000

Intracranial pressure processing with artificial neural networks: prediction of ICP trends.

It is well known that intracranial pressure (ICP) is influenced by an array of predictable and unpredictable factors, which gives rise to a signal heavily loaded with stochastic, i.e. random components. Hence, statistical modelling of this signal has proved to be of limited utility, in spite of the very sophisticated mathematical methods applied. In recent years, neural network algorithms (ANN), which are an alternative to statistical methods, have proved their effectiveness in the prediction of trends, as applied in a variety of medical and non-medical tasks. We therefore attempted to test the efficiency of neural models in the on-line prediction of ICP values, compare their effectiveness to statistically oriented algorithms and combine ANN methods with some newer signal processing algorithms, like wavelet decomposition. Prediction horizons of up to 5 minutes have been tested with various architectures of the neural predictor. For a 3 minute prediction horizon, a satisfactory accuracy of forecasting has been achieved with "plain" ANN, as expressed by the "average relative variance coefficient". This was measured by the ratio of the prediction error obtained, in relation to the error which would occur if a current value were taken as the forecasted one. The prediction quality with statistical autoregressive models has proved unsatisfactory, whilst the result obtained using the ANN model with the wavelet transform incorporated, performed significantly better than the ANN models alone. The prediction quality obtained with the ANN methodology seems to be satisfactory over a short time horizon, though no conclusion can be derived at this stage of the study, as to the clinical utility of this method. In particular, even with this methodology, it is not possible to forecast any sudden dehiscencies of the ICP signal with any practical reliability. From the point of view of modelling theory, such sharp deviations of the signal may be regarded as a "catastrophe". This implies the necessity for a different approach to the ICP signal analysis with the artificial intelligence methodology; one, that is more oriented towards the global properties of the signal.

Cerebral Hemorrhage↗
PubMed2006

A water quality monitoring network design methodology for the selection of critical sampling points: part II.

In order to resolve the spatial component of the design of a water quality monitoring network, a methodology has been developed to identify the critical sampling locations within a watershed. This methodology, called Critical Sampling Points (CSP), focuses on the contaminant total phosphorus (TP), and is applicable to small, predominantly agricultural-forested watersheds. The CSP methodology was translated into a model, called Water Quality Monitoring Station Analysis (WQMSA). It incorporates a geographic information system (GIS) for spatial analysis and data manipulation purposes, a hydrologic/water quality simulation model for estimating TP loads, and an artificial intelligence technology for improved input data representation. The model input data include a number of hydrologic, topographic, soils, vegetative, and land use factors. The model also includes an economic and logistics component. The validity of the CSP methodology was tested on a small experimental Pennsylvanian watershed, for which TP data from a number of single storm events were available for various sampling points within the watershed. A comparison of the ratios of observed to predicted TP loads between sampling points revealed that the model's results were promising.

Environmental Monitoring↗
PubMed2005

A Grid-based HIV expert system.

OBJECTIVES: This paper addresses Grid-based integration and access of distributed data from infectious disease patient databases, literature on in-vitro and in-vivo pharmaceutical data, mutation databases, clinical trials, simulations and medical expert knowledge. METHODS: Multivariate analyses combined with rule-based fuzzy logic are applied to the integrated data to provide ranking of patient-specific drugs. In addition, cellular automata-based simulations are used to predict the drug behaviour over time. Access to and integration of data is done through existing Internet servers and emerging Grid-based frameworks like Globus. Data presentation is done by standalone PC based software, Web-access and PDA roaming WAP access. The experiments were carried out on the DAS2, a Dutch Grid testbed. RESULTS: The output of the problem-solving environment (PSE) consists of a prediction of the drug sensitivity of the virus, generated by comparing the viral genotype to a relational database which contains a large number of phenotype-genotype pairs. CONCLUSIONS: Artificial Intelligence and Grid technology are effectively used to abstract knowledge from the data and provide the physicians with adaptive interactive advice on treatment applied to drug resistant HIV. An important aspect of our research is to use a variety of statistical and numerical methods to identify relationships between HIV genetic sequences and antiviral resistance to investigate consistency of results.

Biometry↗
PubMed2005

Database application of digital medical X-rays and labs: computerization, storage, retrieval, interpretation, and distribution.

Stenter lets the health care worker order an X-ray that is produced as a computer image rather than on flat film. The health care provider can be in any location with the correct equipment, and view the digital image. The dimensions of this discussion are extensive. The cost savings because of reduced media and storage cost is substantial. Health care quality can be improved because of the ability to obtain consultation via telemedicine and the enhanced ability to track medical problems over time via trends. The major downside is the limited cost imbursement system to pay for technology. Unfortunately, this may impact on the improved quality of care. In simple terms someone needs to pay for the technology and the quality of health care needs to be maintained or improved. The real cost to the health care systems needs to be correctly calculated and inappropriate charging kept to a minimum. Specific costs need to be kept in mind and the first is the cost for new staff or staff training. The number of health care providers that are able to read the X-ray can be enlarged remembering that only American Board Certified Radiologists are allowed to give the final recommendation. How do we view the cost of missing something? It could be argued that this risk will be reduced because of improved technology for obtaining the digital X-ray and improved enhancement software. One way to view this situation is to include technology, management, and organization. The cost and benefits occur through the interplay of all three dimensions. The development of digital imaging hardware and artificial intelligence software will demand change in the management and organization. The organization will require changes in its design to accommodate the technology as to support and resources. Management will evolve to include methods for control and monitoring this technology. Business processes and standard operating procedures will change to integrate the technology into the organization in the most effective and efficient manner.

Database Management Systems↗
PubMed2005

Web based Health Education, E-learning, for weight management.

Obesity is a major health problem across the United States and becoming a progressive world wide problem. An overweight person could access the weight management program and develop a personalized weight reduction plan. The customer enters specific data to personalize the program and in the future an artificial intelligence program can evaluate customer behavior and adjust accordingly. This is an on-line program with class room support, offered as back up when desired by the patient.

Education, Distance↗
PubMed2026

Collateral sensitivity-harnessing microbial vulnerabilities as a solution to antimicrobial resistance.

Bacteria exhibit an evolutionary trade-off through their development of collateral sensitivity (CS) which allows them to resist one antibiotic while becoming more vulnerable to another. This vulnerability offers a compelling therapeutic opportunity by selecting against resistant isolates. Laboratory evolution studies, genome sequencing, deep mutagenesis and use of artificial intelligence and machine learning can design the bespoke strategy against multi-drug-resistant bacteria. This review discusses about recent studies that are rationally designed to harness this evolutionary trade-off for the development of alternative antimicrobial strategies. The translational barriers to the clinical implementation of CS are addressed and evidence-based design principles for optimization of CS-guided therapy are discussed.

Bacteria↗
PubMed2026

The emerging impact of CRISPR and gene editing on global crop improvement.

The advent of CRISPR-based genome editing has revolutionized crop improvement, offering unprecedented precision and efficiency in modifying key agronomic traits. This review comprehensively examines the mechanisms, applications, and future potential of CRISPR technology in enhancing global crop production. CRISPR-Cas systems, originally identified as adaptive immune mechanisms in bacteria and archaea, have been repurposed for targeted genome editing in plants. The CRISPR-Cas9 system, in particular, has emerged as a powerful tool for introducing site-specific double-strand breaks, enabling precise genetic modifications. The three-stage process of adaptation, expression, and interference underlies the CRISPR mechanism, with guide RNAs directing Cas endonucleases to specific genomic loci. Advances in CRISPR technology have expanded its applications beyond gene knockouts, encompassing base editing, prime editing, and epigenome editing. These innovations have facilitated the development of crops with enhanced yield, stress tolerance, disease resistance, nutritional content, and post-harvest quality. However, challenges related to off-target effects, regulatory hurdles, ethical concerns, and public acceptance must be addressed to fully harness the potential of CRISPR in agriculture. Integration of CRISPR with other cutting-edge technologies, such as synthetic biology, artificial intelligence, and high-throughput phenotyping, holds immense promise for accelerating crop improvement efforts. As research continues to refine CRISPR tools and expand their applicability across diverse plant species, this transformative technology is poised to play a pivotal role in shaping a sustainable, resilient, and productive global food system for future generations.

Gene Editing↗
PubMed2025

Unlocking microbial potential: advances in omics and bioinformatics for aromatic hydrocarbon degradation.

Aromatic hydrocarbons (AHs) are persistent environmental pollutants with high toxicity. Bacterial degradation of AHs provides a sustainable and cost-effective approach for the remediation of sites contaminated with both mono- and polycyclic aromatic hydrocarbons. Aerobic degradation of AHs typically involves oxygenases-mediated hydroxylation followed by aromatic ring cleavage. In contrast, anaerobic degradation relies on diverse activation mechanisms that ultimately converge on the central intermediate benzoyl-CoA. Over the past decades, research on bacterial degradation of AHs has grown steadily, supported by advances in omics and bioinformatics. In this review, we summarize the current knowledge on the pathways, enzymes, and microbial diversity involved in AH degradation, highlighting how omics and bioinformatic approaches are advancing our understanding of this process. However, to improve our knowledge of microbial AHs catabolism, it is crucial to prioritize the characterization of novel enzymes and pathways, especially those mediating anaerobic and hybrid degradation strategies. Addressing this gap requires the development of specialized resources that incorporate a broader taxonomic diversity and an expanded inventory of anaerobic genes and enzymes supported by experimental evidence. Equally important is the integration of multi-omics technologies, artificial intelligence, and ecological modeling into unified analytical pipelines. These efforts will be key to fully unlocking microbial metabolic potential and guiding more effective bioremediation and monitoring strategies for AHs.

Biodegradation, Environmental↗
PubMed2006

SADE3: an effective system for automated detection of epileptiform events in long-term EEG based on context information.

Interictal spike detection is a time-consuming, low-efficiency task, but is important to epilepsy diagnosis. Automated systems reported to date usually have their practical efficacy compromised by elevated rates of false-positive detections per minute, which are caused mainly by the influence of artifacts (such as noise activity and ocular movements) and by the adoption of single or simple approaches. This work describes the development of a hybrid system for automatic detection of spikes in long-term electroencephalogram (EEG), named System for Automatic Detection of Epileptiform Events in EEG (SADE(3)), which uses wavelet transform, neural networks and artificial intelligence procedures to recognize epileptic and to reject non-epileptic activity. The system's pre-processing stage filters the EEG epochs with the Coiflet wavelet function, which showed the closest correlation to epileptogenic (EPG) activity, in opposition to some other wavelet functions that did not correlate with these events. In contrast to current attempts using continuous wavelet transform, we chose to work with fast wavelet transform to reduce processing time and data volume. Detail components at appropriate decomposition levels were used to accentuate spikes, sharp waves, high-frequency noise activity and ocular artifacts. These four detailed components were used to train four specialized neural networks, designed to detect and classify the EPG and non-EPG events. An expert module analyzes the networks' outputs, together with multichannel and context information and concludes the detection. The system was evaluated with 126,000 EEG epochs, obtained from seven different patients during long-term monitoring, under diverse behavior and mental states. More than 6,721 spikes and sharp waves were previously identified by three experienced human electroencephalographers. In these tests, the SADE(3) system simultaneously achieved 70.9% sensitivity, 99.9% specificity and a rate of 0.13 false-positives per minute, indicating its usefulness and low vulnerability to artifact influence. After tests, the SADE(3) system showed itself to be able to process bipolar cortical EEG records, from long-term monitoring, up to 32 channels, without any data preparation or event positioning. At the same time, SADE(3) revealed a high capacity to reject non-epileptic paroxysms, robustness in relation to a variety of spike morphologies, flexibility in adjustment of performance rates and the capacity to actually save time during EEG reading. Furthermore, it can be adapted to other applications for pattern recognition, with simple adjustments.

Artifacts↗
PubMed2004

Artificial neural networks for predictive modeling in prostate cancer.

Artificial neural networks (ANNs) represent a relatively new methodology for predictive modeling in medicine. ANNs, a form of artificial intelligence loosely based on the brain, have a demonstrated ability to learn complex and subtle relationships between variables in medical applications. In contrast with traditional statistical techniques, ANNs are capable of automatically resolving these relationships without the need for a priori assumptions about the nature of the interactions between variables. As with any technique, ANNs have limitations and potential drawbacks. This article provides an overview of the theoretical basis of ANNs, how they function, their strengths and limitations, and examples of how ANNs have been used to develop predictive models for the management of prostate cancer.

Diagnosis, Differential↗