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Results for “Intelligent agents”

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At least 109 records · Page 6Linked to original sources
PubMed1978

The high-risk cardiac patient undergoing general surgery.

The challenge of the high-risk cardiac patient undergoing general surgery will be met only when an aggressive approach is taken to avoid an unfavourable balance between the oxygen supply and demand of the myocardium. In the past this challenge has been accepted in the operating room but postoperative care has been less than adequate. The intelligent use of potent, effective pharmacologic agents and intensive monitoring of myocardial performance intra- and postoperatively have greatly reduced morbidity and mortality in patients with ischemic heart disease undergoing aortocoronary bypass procedures; they can achieve similar results in such patients who must undergo general surgery.

Anesthesia, General↗
PubMedDate not supplied

Review and update of neuromuscular blocking agents.

The patient arriving in the PACU or ICU following surgery requires vigilant and intelligent nursing care. The postanesthesia nurse or critical care nurse with an understanding of the neuromuscular blocking agents used in anesthesia care is best prepared to observe, evaluate, and care for this patient.

Critical Care↗
PubMed2026

Beyond antibiotics: artificial intelligence-enabled anti-infective ecosystems for next-generation precision therapeutics against antimicrobial resistance.

The rapid global expansion of antimicrobial resistance (AMR) threatens to undermine decades of progress in infectious disease management and highlights the limitations of conventional antibiotic-centered therapeutic strategies. Although emerging technologies-including antimicrobial peptides, bacteriophage therapy, CRISPR-based antimicrobials, microbiome therapeutics, anti-virulence approaches, nanotechnology-enabled drug delivery, and artificial intelligence (AI)-have individually demonstrated considerable promise, they are predominantly being developed as independent interventions rather than as coordinated components of an integrated therapeutic strategy. This Perspective proposes the Intelligent Anti-Infective Ecosystem (IAIE) as a conceptual systems-level framework that computationally integrates multimodal diagnostics, pathogen genomics, microbiome profiling, AI-assisted decision support, programmable precision therapeutics, ecological monitoring, and longitudinal clinical feedback within a continuously learning dynamically optimized workflow. Unlike existing paradigms that primarily optimize individual technologies or therapeutic decisions, IAIE emphasizes closed-loop coordination among complementary antimicrobial approaches to support precision-guided infection management while preserving microbiome integrity and mitigating resistance selection pressure. We further outline the core components, operational principles, translational challenges, and technology readiness of the major therapeutic platforms that could contribute to such an ecosystem, while distinguishing clinically established interventions from emerging experimental strategies. Importantly, IAIE should be interpreted as a prospective conceptual architecture rather than an existing clinical platform. Its proposed clinical value remains to be established through sequential computational, preclinical, and prospective clinical investigations using standardized microbiological, ecological, and patient-centered outcome measures. By framing antimicrobial innovation within an responsive systems perspective, IAIE provides a roadmap for future multidisciplinary research aimed at integrating artificial intelligence and systems microbiology to enable sustainable management of antimicrobial resistance.

Humans↗
PubMed2001

Neuropsychological changes after 30-day Ginkgo biloba administration in healthy participants.

Ginkgo biloba extract (EGb) from the world's oldest living tree has been reputed to ameliorate cognitive decline in the elderly and slow cognitive deterioration in patients with dementia of the Alzheimer's type. EGb remains as one of the most popular plant extracts to alleviate symptoms associated with a range of cognitive disorders such as Alzheimer's disease, vascular dementia and age-related amnesic conditions. EGb is known to contain a range of chemically active components that have antagonistic effects on platelet-activating factor, free-radical scavenging activity and direct effects on the cholinergic neurotransmitter system. Recently there has been much speculation, that EGb may act as a 'smart drug' or nootropic agent in the healthy young to improve intelligence. We conducted a 30-d randomized, double-blind, placebo-controlled clinical trial in which 61 participants were administered a battery of validated neuropsychological tests before and after treatment. Statistical analysis indicated significant improvements in speed of information processing working memory and executive processing attributable to the EGb.

Adult↗
PubMed1988

Long-term effects of MPA on human progeny: intellectual development.

Tests of verbal and spatial ability were done on 450 boys and 537 girls in their late teens of whom 73 and 97, respectively, had been exposed in utero to MPA. Exposed boys achieved higher raw scores than controls on verbal and spatial tests but the differences were explained by their more favorable demographic and social characteristics. Exposed girls did not differ from controls. Although, mothers of exposed boys reported that their offspring talked and walked later than controls, our results support the hypothesis that intrauterine exposure to MPA at contraceptive doses has no long-term effect on intellectual development.

Adolescent↗
PubMed2003

Component-based mediation services for the integration of medical applications.

Allowing exchange of information and cooperation among network-wide distributed and heterogeneous applications is a major need of current health-care information systems. The European project SynEx aims at developing an integration platform for both new and legacy applications on each partner's site. We developed, in this project, mediation services based on the generic and reusable software components that facilitate the construction of an integration platform and ease the communication and the meaningful transformation among distributed and heterogeneous applications. The main component of the mediation services is named Pilot, which serves as an intelligent broker. It uses a multi-agents service model allowing the integration platform to be multi-servers. It transforms a client request into a valid high level service on the platform. Each service is broken up into several elementary steps by the Pilot. For each step, the Pilot uses an agent to realize the operation configured by the step. At runtime, the Pilot synchronizes the execution of different steps. To ease the communication and the interaction with the heterogeneous systems, an agent can integrate a Mediator. The Mediators are the communication and interpretation tools within the mediation services. We have developed a generic model that can be specialized for creating specific mediators for the different use cases. The mediator model uses two interfaces to connect the mediator with two systems that need to communicate. Each interface deals with the three aspects through three managers (the Communication Manager, the Syntax Manager and the Semantic Manager). Some ready-to-use specializations are developed for some well defined cases which can reduce the development effort. Once a manager is specialized, it can be used in different combinations with other managers to resolve different problems. The meaningful transformation is ensured on a semantic level in each mediator through the Semantic Model component. This last component allows the mapping among different vocabularies used by different systems through a shared ontology which allows the mapping process to focus on the meaning of the transformed information. We have used XML in different components of the mediation services as the interchange format and the description format. This has enhanced the flexibility of the components. The component based approach allows the generic components to be reused in different contexts and also allows the mediations services to be open to integrate other available technologies thus largely reduce the development efforts.

Artificial Intelligence↗
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↗
PubMedDate not supplied

Agent computing themes in biologically inspired models of learning and development.

After evaluating general features and attributes of the agent notion, the overlap of features in candidate (attribute) cores, and several less central features, the paper addresses agent and related theory in neuroscience, observing how agent notions have penetrated portions of this field and how the field itself emphasizes and further develops some agent themes via, e.g. schema theory, neural net-artificial intelligence (AI) comparisons, and other research. In remaining sections, models for development of memory strategies in children are presented, illustrating cooperative and competitive neural modeling agents, an active role for a "human agent in the loop," and integrating broadly-based neural network (NN) modeling with other bio-inspired models.

Aging↗
PubMedDate not supplied

[Current knowledge on memory in the elderly].

In geriatric practice, much thought must be given to initial changes in intelligence and memory due to old age and often suddenly aggravated by dysmetabolic and circulatory disease. Awareness of the memory faculty in the elderly is vital in clinical practice for the purposes of pharmacological and psychological designed to reduce the intellectual decline of the patients. This deficiency in mental personality is an important limitation to rehabilitation which remains the aim of medical attention.

Age Factors↗
PubMed1995

[Contemporary strategies and methods of modeling antiparasitic drugs].

Contemporary methods of directed chemotherapy are based on multi-step procedures, which require co-ordinated activities of interdisciplinary teams of biochemists, pharmacologists, geneticists, crystallographers as well as computer scientists. Biochemists select the proper target, such as an enzyme, throughout screening of the biochemical influence of compounds-potential drugs on this target. For further research they use targets with very low inhibition constants (> 10(-6) M). Determination of the relation between therapeutic activity of the compound and modelling of its chemical structure constitutes an important part of the procedure. The most important part of the procedure is the recognition of the primary structure of the target. The two following pathways allow to do that: 1. isolation of DNA and gDNA or cDNA-started cloning of a gene responsible for production of the target protein and then its sequencing. 2. purification and crystallization of the target protein and further computer-aided processing of crystallographic data in order to determine the primary structure. Computational chemistry (C/C) methods are the basic part of the procedure of molecular modelling (M/M) of a target molecule and its interactions with a molecule of the future drug. Data obtained using a technology which engages the C/C and M/M methods not only allow to determine the aminoacid sequence of the target protein in question (e.g. a unique parasite enzyme); they also enable to further speculate on its secondary and tertiary structures. Such structure includes specified number of repeated motifs of alpha-helixes, beta-sheets and loops or turns. Particularly, the "barrel" structure is very common in numerous enzymes. Two following examples of research on target-antiparasitic drug interactions is presented. They are the interaction between phosphoglicerate kinase in Leishmania and drug suramin and malic enzyme of Trichinella and drug closantel. New promising targets for new anti-protozoan drugs (protozoa of Trypanosoma species) include e.g. microbody translocation signal in kinetosom proteins (SKL) or protein blocking the transport of proteins to glycosomes-metabolic centres in Trypanosoma (repetitive groups of QRLQ). Recently, scientists from Arris Pharmaceutical (San Francisco) have considered, employing new data, up to 100 to fully characterize the surface structure of a molecule, using the systems of artificial intelligence.

Anthelmintics↗
PubMed1984

Adverse pregnancy outcome: sensitive periods, types of adverse outcomes, and relationships with critical exposure periods.

A wide variety of agents has been demonstrated to be capable of affecting the fetus in utero. Certain generalizations can be made concerning these teratogens. Two of the most important of these principles are the specificity of the agent and the time during gestation of the exposure. Although noticeable adverse effects are structural malformations, there are numerous functional malformations, such as lower intelligence and poor reproductive outcome, that may follow exposure to these agents. There is some evidence that future behavior may be affected by in utero teratogen exposure; however, this field has been infrequently investigated and no firm conclusions can be drawn.

Abnormalities, Drug-Induced↗
PubMed1997

Retention of asthmatic patients in a longitudinal clinical trial.

BACKGROUND: Prevention of study patient attrition and assessment of its impact on outcome data are problems that receive little attention despite their obvious importance in asthma research. OBJECTIVE: The medical, demographic, and psychologic characteristics of asthmatic children and adults who dropped out of a yearlong medication trial were assessed to determine whether this group differed from those who completed the study, potentially introducing bias into the data set and interfering with completion of the study's objectives. METHODS: Profiles of 362 adult and pediatric asthmatic patient dropouts from the multicenter trial were contrasted with profiles of those who completed the study. Despite a 1-month prerandomization screening, 24% of patients failed to complete the trial for varied reasons, which largely included noncompliance and treatment dissatisfaction. RESULTS: Although attrition rates were equal among adults and children, dropout-completer differentiation was not. Adult completers did not differ from dropouts in any variables. However, pediatric dropouts were more likely than completers to be female (67% and 36%, p = 0.008) and to have more reactive airways (PD20, 2.29 +/- 1.32 and 5.2 +/- 1.23, p = 0.05), to have reduced scores on tests of intelligence (Full Scale IQ, 102.2 +/- 2.6 and 112.5 +/- 1.6, p = 0.002) and problem solving (Wisconsin Card Sorting Test Error Scores, 39.8 +/- 4.1 and 29.1 +/- 2.0, p = 0.01), and to have increased behavioral problems (Child Behavior Checklist Total Problem Score, 60.7 +/- 2.5 and 53.6 +/- 1.1, p = 0.003). CONCLUSION: These findings demonstrate the potential of patient attrition to bias outcome in clinical trials and underscore the necessity of: (1) preventing its occurrence, (2) correctly assessing its causes, and (3) determining its ultimate impact on study results. Strategies for each of these three tasks should be implemented at the study's initial planning stages.

Adolescent↗
PubMed1998

Discrimination between chronic pancreatitis and pancreatic adenocarcinoma using artificial intelligence-related algorithms based on image cytometry-generated variables.

The incidence of pancreatic adenocarcinomas (PA) is increased in the setting of chronic pancreatitis. Distinguishing chronic pancreatitis from pancreatic adenocarcinomas is often difficult, and is based on routine brush cytological specimens provided during endoscopic retrograde cholangiopancreatography (ERCP). Reactive epithelial changes in chronic pancreatitis may appear similar to those of a well-differentiated cancer. Brush cytology specimens were obtained during ERCP from 49 patients with diseases for which the differential diagnosis included chronic pancreatitis and/or pancreatic adenocarcinoma Image cytometry was performed involving the assessment of between 200-400 Feulgen-stained nuclei per case; for each case, 40 quantitative cytometric variables were generated. Data analysis was performed using artificial intelligence methods of data classification that produced decision trees and production rule systems. Different classification models were produced for a subset of 34 patients. The best models were identified by the use of a sampling technique (leave-one-out), and were tested on the remaining 15 patients. These models were based on 5 of the 40 variables associated with a significant discriminatory function. Pancreatic adenocarcinoma was diagnosed in the training data set of 34 patients during a leave-one-out process with an estimated sensitivity of 91% and specificity of 87%. Both sensitivity and specificity were 80% in the independent test set of 15 patients. We conclude that inflammatory and malignant pancreatic epithelia exhibit distinct morphological features that can be distinguished by decision tree-based classifiers employing image-cytometric numerical data.

Adenocarcinoma↗