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PubMed2002

The need to review the Spanish recommended dietary energy and nutrient intakes.

BACKGROUND: In the last decade, research in the field of nutrition has grown both in width and depth, and has incorporated the advances of other scientific fields, like artificial intelligence theories. Specifically in the case of recommended nutrient intakes, the fuzzy theory is an interesting contribution to the development of a new concept for the recommendations. The recommended intakes have passed from avoiding deficiency status to preventing several pathological aspects and reducing risk factors. OBJECTIVE: To establish whether the recommended dietary intakes for Spain (IREs) must be reviewed, taking into account the current scientific state of the art. METHODS: We have compared IREs, published in 1994, with the recently published 'Dietary Reference Intakes' (DRIs) for the American and Canadian population, the 'Referenzwerte für die Nährstoffzufuhr' (DACH) for the German, Swiss and Austrian population. In addition, the main guidelines of the EURODIET criteria (Nutrition and Diet for Healthy Lifestyles), which was developed for the population of the European Union Member States, were taken into account. As an example, comparisons were made for the elderly population group, establishing differences and similarities between the different scientific communities and the adequacy of Spanish recommended intakes for energy and nutrients. RESULTS: Recommended intakes for folate, calcium and vitamins C, D and E are lower in the IREs and probably should be increased. Recommended intakes for nutrients which have not previously been included in the IREs, such as vitamin K, selenium, copper, chromium, molybdenum, manganese, phosphorus, sodium, potassium, and water need to be developed. CONCLUSIONS: The current recommended intakes for the Spanish population need to be reviewed in order to encompass current scientific knowledge. Moreover, the opportunity to include non-nutrients (bioactive compounds) should be considered.

Adult↗
PubMed2004

Good wine may need to mature: a critique of accelerated higher specialist training. Evidence from cognitive neuroscience.

CONTEXT: Higher specialist training in the UK is to be further shortened in the absence of any valid educational evidence for the wisdom of this move. Some practitioners/teachers are becoming increasingly concerned at this. THESIS AND DISCUSSION: Whereas the optimum length of time for such training is as yet undetermined, there is much in medical practice that resonates with the thinking of recent authors, who recommend slow incubation and facilitated reflection on experience, which steeps the learner in a hidden curriculum of practice, and entrains intuitive, "slow mode" thinking. This engagement necessarily takes time. The author has surveyed some of the recent literature on problem solving, cognitive neuroscience, artificial intelligence and learning for practice, and discusses his conclusions. These are unsettling. CONCLUSION: Further truncation of the length of higher specialist training must be supported by robust educational evidence that supports this reduction. The author advises caution.

Education, Medical↗
PubMed2003

Prediction of stone disease by discriminant analysis and artificial neural networks in genetic polymorphisms: a new method.

OBJECTIVE: To use information from genetic polymorphisms and from patients (drinking/exercise habits) to identify their association with stone disease, the main analytical and predictive tools being discriminant analysis (DA) and artificial neural networks (ANNs). PATIENTS, SUBJECTS AND METHODS: Urinary stone disease is common in Taiwan; the formation of calcium oxalate stone is reportedly associated with genetic polymorphisms but there are many of these. Genotyping requires many individuals and markers because of the complexity of gene-gene and gene-environmental factor interactions. With the development of artificial intelligence, data-mining tools like ANNs can be used to derive more from patient data in predicting disease. Thus we compared 151 patients with calcium oxalate stones and 105 healthy controls for the presence of four genetic polymorphisms; cytochrome p450c17, E-cadherin, urokinase and vascular endothelial growth factor (VEGF). Information about environmental factors, e.g. water, milk and coffee consumption, and outdoor activities, was also collected. Stepwise DA and ANNs were used as classification methods to obtain an effective discriminant model. RESULTS: With only the genetic variables, DA successfully classified 64% of the participants, but when all related factors (gene and environmental factors) were considered simultaneously, stepwise DA was successful in classifying 74%. The results for DA were best when six variables (sex, VEGF, stone number, coffee, milk, outdoor activities), found by iterative selection, were used. The ANN successfully classified 89% of participants and was better than DA when considering all factors in the model. A sensitivity analysis of the input parameters for ANN was conducted after the ANN program was trained; the most important inputs affecting stone disease were genetic (VEGF), while the second and third were water and milk consumption. CONCLUSIONS: While data-mining tools such as DA and ANN both provide accurate results for assessing genetic markers of calcium stone disease, the ANN provides a better prediction than the DA, especially when considering all (genetic and environmental) related factors simultaneously. This model provides a new way to study stone disease in combination with genetic polymorphisms and environmental factors.

Adult↗
PubMed2026

Integrating Next-Generation Sequencing into von Willebrand Disease Diagnostics: Insights from the PCM-EVW-ES Multicenter Project.

Von Willebrand disease (VWD) is the most common inherited bleeding disorder, caused by quantitative or qualitative defects in von Willebrand factor (VWF). Diagnosis is challenging and requires integrating bleeding history, VWF antigen and activity measurements, FVIII assays, and specialized phenotyping. Genetic testing is increasingly recognized as a key component. Here, we review current concepts in VWD diagnostics and highlight the Spanish Clinical and Molecular Profile of von Willebrand Disease (PCM-EVW-ES) project as a model for genomics-enabled precision medicine. PCM-EVW-ES is a multicenter initiative involving 48 hospitals, centralized phenotypic testing, and next-generation sequencing of the VWF coding region, enabling definitive classification in 730 individuals with VWD to date. Harmonized recruitment criteria and standardized workflows improve subtype assignment, uncover complex genotypes, refine genotype-phenotype correlations, and facilitate the identification of asymptomatic carriers. The PCM-EVW-ES variant spectrum highlights recurrent disease-causing variants in Spain and underscores the value of coordinated national registries for variant curation. Building on these data, we propose a diagnostic algorithm in which bleeding assessment and first-line VWF/FVIII assays, combined with, early VWF molecular testing increases diagnostic accuracy and guides targeted second-line investigations to confirm and refine VWD subtype classification. We also outline persisting challenges, including the interpretation of variants of uncertain significance and patients without identifiable pathogenic VWF variants, and future directions integrating third-generation sequencing, expanded gene panels, functional studies, and artificial-intelligence-driven multiomic approaches. Together, these advances illustrate how robust multicenter studies can bridge the gap between complex diagnostics and clinical practice in VWD.

Humans↗
PubMed2026

Looking to the Future: How Will Personalised Medicine Impact Facial Plastic Surgery.

AIMS AND BACKGROUNDS: The objectives of this study are to examine the emerging role of personalized medicine in facial plastic surgery and to consider how biologically, anatomically, and psychologically tailored approaches may refine both aesthetic and reconstructive care. HISTORICAL ASPECTS: Facial plastic surgery has traditionally relied on anatomical principles, surgical expertise, and population-based evidence. Personalized medicine represents a shift toward more individualized care by incorporating patient-specific biological and phenotypic variation into clinical decision-making. ANATOMY: Facial plastic surgery is uniquely dependent on subtle anatomical variation, soft tissue characteristics, wound healing behavior, and age-related change. These factors differ considerably between individuals and have a direct impact on both surgical planning and outcomes. TECHNOLOGY: Advances in genomics, pharmacogenomics, artificial intelligence, tissue engineering, and three-dimensional modelling are expanding the scope of personalized care. These technologies may improve prediction of healing, treatment response, complication risk, and reconstructive requirements. PATIENT SELECTION: Personalized medicine may support more accurate patient selection by identifying those at increased risk of adverse scarring, variable response to injectables or pharmacotherapy, or differential reconstructive needs, thereby improving counselling and expectation management. TECHNIQUES: Potential applications include tailored incision planning, individualized facial rejuvenation strategies, personalized perioperative pharmacological regimens, and patient-specific reconstructive scaffolds, grafts, and implants. POSTOPERATIVE CARE: Postoperative management may also become more individualized through better prediction of inflammatory response, scar formation, analgesic requirements, and recovery trajectory, allowing more precise surveillance and adjunctive treatment. CURRENT AND FUTURE DEVELOPMENT: Although many applications remain investigational, continued progress in regenerative medicine, molecular profiling, and predictive analytics is likely to accelerate clinical translation. Ethical challenges relating to privacy, bias, and equitable access must, however, remain central. CONCLUSION AND CLINICAL RELEVANCE: Personalized medicine has the potential to enhance precision, safety, and patient-centered care in facial plastic surgery. Its future value will depend on thoughtful integration into practice as an adjunct to, rather than a replacement for, surgical judgement and aesthetic insight.

Journal Article↗
PubMed2006

Noninvasive tests for liver fibrosis.

The field of noninvasive markers for the assessment of hepatic fibrosis has seen tremendous growth in the last two decades. Surrogate markers are gradually being substituted for biomarkers that reflect the complex balance between synthesis and degradation of the extracellular matrix. Coupled with these promising tests are artificial intelligence networks devised by sophisticated statistical instruments that incorporate a battery of laboratory tests and biomarkers with imaging modalities. Recently, gene identification and protein analysis have shown promise in identifying selected patients with mild or advanced fibrosis. Despite such progress, an important limitation of these tests is the frequent inability to detect mild fibrosis although patients at the end of the histological spectrum (i.e., advanced fibrosis or cirrhosis) are readily identified. With time, it is anticipated a combination of serological and radiological tests together with genetic and proteomic investigations will provide accurate assessment of fibrosis in a cost-effective and timely manner.

Biomarkers↗
PubMed1978

The percepton of motion: Michotte revisited.

This paper extends the range of artificial-intelligence vision programs to the domain of moving objects, using the work of Michotte as a source of data for a computer program which purports to 'see' the action types he examined. Given the thesis that internal descriptions of a nonlinguistic type provide the basis for a constructive, knowledge-based theory of perception, the properties of a symbol-manipulating system capable of supporting this are explored as a challenge to the Michotte notion of "direct perception without interpretation".

Association↗
PubMed1993

Length illusion in fractional Müller-Lyer stimuli: an object-perception approach.

Length judgments were compared for Müller-Lyer stimuli and figures which had line junctions at only one end of the central shaft. A length illusion occurred for fractional figures, only slightly reduced in magnitude from the usual illusion, and the largest reduction occurred for fractional figures with fork junctions. These results are consistent with an hypothesis (drawn from artificial intelligence algorithms for interpreting line drawings) that isolated line junctions are treated as boundary junctions with constrained interpretations of convex and concave edges for the shafts of arrow and fork junctions, respectively. Information about relative position of edges may be used to constrain computation of metric properties and consequential differences in size scaling would be responsible for the illusion. Illusions can arise when information well suited for one kind of task (eg object recognition) is employed in tasks for which it is not well suited (eg size perception).

Adult↗
PubMed1973

Organization of memory.

A new learning algorithm is presented that may have applications in the theory of natural and artificial intelligence.

Brain↗
PubMed1992

Psychophysical support for a two-dimensional view interpolation theory of object recognition.

Does the human brain represent objects for recognition by storing a series of two-dimensional snapshots, or are the object models, in some sense, three-dimensional analogs of the objects they represent? One way to address this question is to explore the ability of the human visual system to generalize recognition from familiar to unfamiliar views of three-dimensional objects. Three recently proposed theories of object recognition--viewpoint normalization or alignment of three-dimensional models [Ullman, S. (1989) Cognition 32, 193-254], linear combination of two-dimensional views [Ullman, S. & Basri, R. (1990) Recognition by Linear Combinations of Models (Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge), A. I. Memo No. 1152], and view approximation [Poggio, T. & Edelman, S. (1990) Nature (London) 343, 263-266]--predict different patterns of generalization to unfamiliar views. We have exploited the conflicting predictions to test the three theories directly in a psychophysical experiment involving computer-generated three-dimensional objects. Our results suggest that the human visual system is better described as recognizing these objects by two-dimensional view interpolation than by alignment or other methods that rely on object-centered three-dimensional models.

Computer Graphics↗
PubMed1982

Cognitive and social aspects of sexual dysfunction: sexual scripts in sex therapy.

The formulation and treatment of sexual dysfunction are conceptualized within a framework of sexual scripts. This approach places particular emphasis on the cognitive and interpersonal dimensions of sexual interaction and provides greater continuity in our understanding of the development of sexual conduct in general. Clinical assessment of sexual scripts begins with a comparison of performative and cognitive scripts, which are then elaborated in terms of key script attributes such as complexity, rigidity, conventionality and satisfaction. Sex therapy is viewed as a process of script modification, in which particular attention is payed to the integration of covert and overt aspects of the script. Two case illustrations are provided. The discussion contrast the present use of script analysis with the way the term is used in transactional analysis and research in artificial intelligence.

Adult↗
PubMed2026

Precision Medicine in Transfusion-Dependent and Non-Transfusion-Dependent β-Thalassemia: Toward Personalized Diagnosis and Therapy.

β-thalassemia comprises a clinically heterogeneous group of disorders in which anemia severity, transfusion exposure, iron loading, and organ complications vary widely among individuals. This structured narrative review summarizes practical applications of precision medicine in transfusion-dependent thalassemia (TDT) and non-transfusion-dependent thalassemia (NTDT), with explicit attention to which strategies apply to each clinical category. Literature indexed in PubMed and Scopus from 2000 to 2025 was reviewed using terms related to thalassemia, precision medicine, magnetic resonance imaging (MRI), chelation tailoring, next-generation sequencing (NGS), fetal hemoglobin (HbF) modifiers, luspatercept, mitapivat, hepcidin, gene therapy, gene editing, and artificial intelligence (AI). Evidence was synthesized descriptively because interventions, outcomes, and populations were heterogeneous, and no pooled meta-analysis was performed. In TDT, precision care is centered on individualized transfusion planning, extended red-cell antigen matching, MRI-guided cardiac and hepatic iron monitoring, organ-directed chelation intensification, and selection of disease-modifying or curative approaches. In NTDT, precision care emphasizes accurate phenotype classification, MRI liver iron concentration, because serum ferritin may underestimate iron burden, selective chelation, surveillance for NTDT-specific complications, and individualized use of agents that improve anemia. Personalized chelation should include deferiprone, either alone or in combination, when cardiac iron is increased. Comprehensive molecular diagnosis should include HBB together with HBA1 and HBA2 assessment, while secondary and tertiary modifiers help explain phenotypic variability and complication risk. Hepcidin and growth differentiation factor 15 (GDF-15) are discussed as investigational biomarkers; transferrin saturation is not recommended for routine iron-overload assessment in thalassemia. AI currently has its strongest role in screening and diagnosis, whereas risk-stratification models remain exploratory. Equitable implementation requires standardized TDT/NTDT pathways, regional MRI and genomics access, longitudinal registries, and multidisciplinary interpretation.

Humans↗
PubMed2000

A new dose model for assessment of health risk due to contaminants in air.

The problem of making quantitative assessments of the risks associated with human exposure to toxic contaminants in the environment is a pressing one. This study demonstrates the capability of a new computational technique involving the use of fuzzy logic and neural networks to produce realistic risk assessments. The systematic analysis of human exposure involves the use of measurements and models, the results of which are sometimes used in regulatory decisions or in the drafting of legislation. Because of limited scientific understanding, however, interpretation of models often involves substantial uncertainty. Extensive measurement programs can be very expensive. The high complexity and inherent heterogeneity of exposure analysis is still a major challenge. The approach to this challenge tested here is to use a new model incorporating sophisticated artificial intelligence algorithms. Exposure assessment often requires that a number of factors be evaluated, including exposure concentrations, intake rates, exposure times, and frequencies. These factors are incorporated into a system that can "learn" the relevant relationships based on a known data set. The results can then be applied to new data sets and thus be applied widely without the need for extensive measurements. In this analysis, an example is developed for human health risk through inhalation exposure to benzene from vehicular emissions in the cities of Auckland and Christchurch, New Zealand. Risk factors considered were inhaled contaminant concentration, age, body weight, and activity patterns of humans. Three major variables affecting the inhaled contaminant concentration were emissions (mainly from motor vehicles), meteorology (wind speed, temperature, and atmospheric stability), and site factors (hilly, flat, etc.). The results are preliminary and used principally to demonstrate the technique, but they are very encouraging.

Age Factors↗
PubMed2001

QSAR modeling of large heterogeneous sets of molecules.

In aquatic toxicology, QSAR models are generally designed for chemicals presenting the same mode of toxic action. Their proper use provides good simulation results. Problems arise when the mechanism of toxicity of a chemical is not clearly identified. Indeed, in that case, the inappropriate application of a specific QSAR model can lead to a dramatic error in the toxicity estimation. With the advent of powerful computers and easy access to them, and the introduction of soft modeling and artificial intelligence in SAR and QSAR, radically different models, designed from large noncongeneric sets of chemicals have been proposed. Some of these new QSAR models are reviewed and their originality, advantages, and limitations are stressed.

Forecasting↗
PubMed2002

AI and SAR approaches for predicting chemical carcinogenicity: survey and status report.

A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR) approaches have been applied to tackling the general problem of predicting rodent chemical carcinogenicity. Given the diversity of chemical structures and mechanisms relative to this endpoint, the shared challenge of these approaches is to accurately delineate classes of active chemicals representing distinct biological and chemical mechanism domains, and within those classes determine the structural features and properties responsible for modulating activity. In the following discussion, we present a survey of AI and SAR approaches that have been applied to the prediction of rodent carcinogenicity, and discuss these in general terms and in the context of the results of two organized prediction exercises (PTE-1 and PTE-2) sponsored by the US National Cancer Institute/National Toxicology Program. Most models participating in these exercises were successful in identifying major structural-alerting classes of active carcinogens, but failed in modeling the more subtle modifiers to activity within those classes. In addition, methods that incorporated mechanism-based reasoning or biological data along with structural information outperformed models limited to structural information exclusively. Finally, a few recent carcinogenicity-modeling efforts are presented illustrating progress in tackling some aspects of the carcinogenicity prediction problem. The first example, a QSAR model for predicting carcinogenic potency of aromatic amines, illustrates that success is possible within well-represented classes of carcinogens. From the second example, a newly developed FDA/OTR MultiCASE model for predicting the carcinogenicity of pharmaceuticals, we conclude that the definitions of biological activity and nature of chemicals in the training set are important determinants of the predictive success and specificity/sensitivity characteristics of a derived model.

Animals↗
PubMed2025

Exploring precision medicine by utilizing individual genetic information for the management of Alzheimer's disease.

Alzheimer's Disease (AD) represents a formidable challenge in neurology, characterized by progressive neurodegeneration and cognitive decline. Traditional therapeutic approaches have failed to deliver significant outcomes, underscoring the need for innovative paradigms such as precision medicine. The review explores integrating genomic, biomarker-driven, and individualized therapeutic strategies to tackle AD. It examines the role of key genetic factors, including APOE and MTHFR polymorphisms, in influencing disease susceptibility and treatment responses. Advances in biomarker technologies, such as blood-based and imaging biomarkers, are highlighted for their potential in early diagnosis and patient stratification. Additionally, the review underscores the importance of tailoring interventions across different stages of AD, incorporating lifestyle modifications and emerging tools like artificial intelligence & recent patented technologies. Precision medicine offers a transformative pathway, aiming to deliver personalized, effective care that addresses the complex and multifactorial nature of AD. The paradigm shift promises improved clinical outcomes and enhanced patient quality of life.

Humans↗
PubMed2026

Genomic profiling as an option for ovarian cancer diagnostics.

INTRODUCTION: Ovarian cancer (OC) is a highly heterogeneous and lethal gynecological malignancy. Precision oncology has shifted the management paradigm to comprehensive molecular profiling. Genomic-based diagnostics are now a clinical necessity for accurate prognostic stratification and the rational selection of targeted therapeutics, such as PARP and immune checkpoint inhibitors. AREAS COVERED: This review evaluates current literature regarding the distinct genomic landscapes defining OC histotypes to underlined the role of molecular profiling in the diagnostic field of OC. We discuss the practical implementation, technical aspect, and clinical validity of the main molecular diagnostic platforms, focusing on tissue-based Comprehensive Genomic Profiling (CGP) and Homologous Recombination Deficiency (HRD). Furthermore, we explore emerging translational data on liquid biopsy (LBx) applications. EXPERT OPINION: While current tissue-based methodologies provide critical baseline data, the OC diagnostic paradigm must pivot from static testing to proactive and longitudinal tracking. Integrating advanced LBx approaches enables a real-time monitoring of dynamic parameters as minimal residual disease (MRD) and acquired resistance. Integrating these dynamic blood-based assays with multi-omic profiling and artificial intelligence (AI)-driven tools allows a full understanding of the complex tumor behavior.

Humans↗
PubMed2003

Enhanced prediction of lopinavir resistance from genotype by use of artificial neural networks.

Our objective was to accurately predict, from complex mutation patterns, human immunodeficiency virus type 1 resistance to the protease inhibitor lopinavir, by use of artificial intelligence. Two neural network models were constructed: 1 based on changes at 11 positions in the protease that were previously recognized as being significant for lopinavir resistance and another based on a newly derived set of 28 mutations that were identified by performing category prevalence analysis. Both models were trained, validated, and tested with 1322 clinical samples. A procedure of determining the optimal neural network parameters was proposed to speed up the training processes. The results suggested that the 28-mutation set was a more accurate predictor of lopinavir susceptibility (correlation coefficient, R2=0.88). We identified potentially significant new mutations associated with lopinavir resistance and demonstrated the utility of neural network models in predicting phenotypic susceptibility from complex genotypes.

Drug Resistance, Viral↗