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At least 703 records · Page 39Linked to original sources
PubMed2004

Effects of streptozotocin-induced diabetes in apolipoprotein AI deficient mice.

During the past decade a number of investigators have attempted to develop mouse models of diabetic macrovascular disease. Hyperglycemia might increase vascular damage because it increases oxidant stress. For this reason we studied animals that were deficient in HDL; HDL is widely believed to protect against oxidant stress. An inbred line of mice doubly deficient in LDL receptor and apoAI was made diabetic with streptozotocin (STZ); control mice had an average glucose of 7.2+/-2mmol/l and STZ-treated mice had an average glucose of 19.4+/-6.5mmol/l. The animals were fed a high cholesterol but low fat diet leading to plasma cholesterol levels of 9.4+/-1.6mmol/l in control animals and 10.1+/-1.8mmol/l in STZ-treated mice. The control and STZ-treated animals had similar plasma lipoprotein profiles. Atherosclerosis assessed at 23 weeks averaged 38154microm(2) in control and 32962microm(2) in STZ-treated mice. Therefore STZ-induced diabetes does not alter plasma lipoproteins or atherosclerosis in HDL deficient mice.

Animals↗
PubMed2005

Gender-specific effects of estrogen receptor alpha gene haplotype on high-density lipoprotein cholesterol response to atorvastatin: interaction with apolipoprotein AI gene polymorphism.

Statins can modestly raise the levels of HDL cholesterol and apolipoprotein A-I (APOA1). Recently, associations between polymorphisms in the estrogen receptor alpha (ESR1) and the HDL cholesterol response to hormone replacement therapy were reported. To test the hypothesis that common polymorphisms in ESR1 and APOA1 genes are associated with the response to statin therapy, two ESR1 (PvuII and XbaI) and two APOA1 (G-75A and +83) polymorphisms were examined in 338 hypercholesterolemic patients treated with atorvastatin 10mg. The ESR1 PvuII-XbaI+ haplotype was significantly, and independently, associated with a greater response of HDL raising in women (+13% versus +7%, p=0.010) but not in men (+9% versus +7%, p=0.248). Effects of the APOA1+83 variant allele on HDL cholesterol response also differed significantly by gender (p=0.012). The APOA1+83 variant allele was associated with higher basal LDL cholesterol levels in men as well, but not in women. Finally, significant interactions were observed between the ESR1 PvuII-XbaI+ haplotype and the APOA1+83 variant allele regarding both HDL (p=0.042) and LDL (p=0.031) cholesterol responses. In conclusion, the ESR1 haplotype was associated with a greater HDL-raising to atorvastatin in a gender-specific manner, and the interactions between ESR1 and APOA1 genotypes regarding HDL and LDL cholesterol response were also gender specific.

Aged↗
PubMed2006

Ac2-DPD, the bis-(O)-acetylated derivative of 4,5-dihydroxy-2,3-pentanedione (DPD) is a convenient stable precursor of bacterial quorum sensing autoinducer AI-2.

Ac2-DPD, the bis-(O)-acetylated derivative of 4,5-dihydroxy-2,3-pentanedione (DPD), was prepared both as a racemic mixture and in the optically active form found in naturally occurring DPD. It was shown to exhibit the same ability as DPD to induce bioluminescence in Vibrio harveyi and beta-galactosidase activity in Salmonella enterica Typhimurium, both gram-negative bacteria. Likewise, it was also shown to inhibit biofilm formation in gram-positive Bacillus cereus. The most likely hypothesis is that Ac2-DPD activity is due to the release of DPD by in situ hydrolysis of the ester groups. Importantly, by contrast with DPD, Ac2-DPD proved to be a stable compound which can be purified and stored.

Acetylation↗
PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗
PubMed2005

The bioconcentration factor of perfluorooctane sulfonate is significantly larger than that of perfluorooctanoate in wild turtles (Trachemys scripta elegans and Chinemys reevesii): an Ai river ecological study in Japan.

Turtles rank high in the river food chain, and are suitable for predicting the bioconcentrations of chemicals through the food chain. Trachemys scripta elegans (N=46) and Chinemys reevesii (N=51) were captured in a river in Japan, from September to October 2003 and April to June 2004. Surface water samples were collected simultaneously from the same sites at which the turtles were caught. Serum perfluorooctane sulfonate (PFOS) ranged from 2.4 to 486 microg/L, while water PFOS levels ranged from 2.9 to 37 ng/L. The geometric mean (GM) (geometric standard deviation, GSD) of the bioconcentration factor (BCF) of PFOS was 10,964 (2.5). In contrast, the perfluorooctanoate (PFOA) level in water ranged from 16.7-87,100 ng/L, and serum PFOA ranged from <0.2 to 870 microg/L. The GM (GSD) of the BCF of PFOA was 3.2 (7.9). Furthermore, the BCF of PFOA decreased as the PFOA level in the surface water increased. PFOS could be preferentially bioconcentrated in biota, and PFOA, slightly bioconcentrated.

Alkanesulfonic Acids↗
PubMed2006

Intelligent ship traffic monitoring for oil spill prevention: risk based decision support building on AIS.

The paper describes a model, which estimates the risk levels of individual crude oil tankers. The intended use of the model, which is ready for trial implementation at The Norwegian Coastal Administrations new Vardø VTS (Vessel Traffic Service) centre, is to facilitate the comparison of ships and to support a risk based decision on which ships to focus attention on. For a VTS operator, tasked with monitoring hundreds of ships, this is a valuable decision support tool. The model answers the question, "Which ships are likely to produce an oil spill accident, and how much is it likely to spill?".

Decision Support Techniques↗
PubMed2002

Fertility in postpartum dairy cows in winter or summer following estrus synchronization and fixed time AI after the induction of an LH surge with GnRH or hCG.

In this study, the fertility of postpartum dairy cows after a sequence of treatments with GnRH (Day 0), PGF2alpha (Day 7) and GnRH (Day 9) (GnRH group; n = 164) or hCG (Day 0), PGF2alpha (Day 7) and hCG (Day 9) (group hCG; n = 166) was investigated in summer and winter seasons. All cows were artificially inseminated without estrus detection, 16-18 h after the end of treatment. Control cows (CONT; n = 226) were not treated and were inseminated at natural estrus. The pregnancy rates at Day 90 (46% versus 33%; P < 0.05) and at Day 135 (76% versus 62%; P < 0.05) postpartum were significantly lower in CONT cows in summer compared to winter months but this effect was not observed in the two treated groups. The number of days from calving to conception was significantly lower in GnRH and hCG treatment groups compared to CONT cows in cold months (102 +/- 3.2, 106 +/- 4.2, 126 +/- 3.1, respectively; P < 0.001) and in hot months (112 +/- 3.2, 114 +/- 4.2, 139 +/- 3.1, respectively; P < 0.001). The concentration of insulin was significantly higher in winter (P < 0.001). There were no differences in average plasma concentration of glucose (P = 0.474), GH (P = 0.441) or IGF-I (P = 0.190). In conclusion, we have shown that veterinary supervision combined with a program of estrous synchronization and fixed time insemination can improve fertility of cows suffering heat stress.

Animals↗
PubMed2001

AI-based algorithms for protein surface comparisons.

Many current methods for protein analysis depend on the detection of similarity in either the primary sequence, or the overall tertiary structure (the Calpha atoms of the protein backbone). These common sequences or structures may imply similar functional characteristics or active properties. Active sites and ligand binding sites usually occur on or near the surface of the protein; so similarly shaped surface regions could imply similar functions. We investigate various methods for describing the shape properties of protein surfaces and for comparing them. Our current work uses algorithms from computer vision to describe the protein surfaces, and methods from graph theory to compare the surface regions. Early results indicate that we can successfully match a family of related ligand binding sites, and find their similarly shaped surface regions. This method of surface analysis could be extended to help identify unknown surface regions for possible ligand binding or active sites.

Algorithms↗
PubMed2002

Novel atom-type AI indices for QSPR studies of alcohols.

The novel vertex degree v(m) for heteroatom in molecular graph is derived on the basis of the valence connectivity delta(v) of Kier-Hall. The newly proposed atom-type Al indices and previously proposed Xu index, are further modified for compounds with heteroatoms by replacing the vertex-degree of heteroatom by the proposed v(m). The multiple linear regression using the modified Xu index and Al indices can provide high-quality QSPR models for the normal boiling points (BP), molar volumes (MV), molar refractions (MR), and molecular total surface areas (TSA) of alcohols with up to 17 non-hydrogen atoms. The results imply that these physical properties may be expressed as a linear combination of the individual indices related to molecular size and atom-types. For each of the four properties, the correlation coefficient r is greater than 0.996 and particularly the decrease in the standard error is within the range of 61 83% compared with the simple linear models based on the modified Xu index, and the standard errors are 3.814, 0.939, 0.187, and 3.348 for BP, MV, MR, and TSA, respectively. The final models correspond to a fit error of 2.33, 0.70, 0.53, and 0.95% for BP, MV, MR, and TSA, respectively. The more general leave-n-out method is used to do the cross-validation. The cross-validation demonstrates the outstanding predictive power of the final models. The contributions of individual indices are used to illustrate the role of the molecular size and individual groups in molecules. The results indicate that physical properties of alcohols are dominated by the molecular size. On the other hand, although the hydrogen-bonding interactions caused by the -OH group play an important role in determining the normal BPs, the branching seems to be a more important factor influencing the MVs, MRs, and TSAs than the hydrogen-bonding interaction. The contribution of individual atom type or group to properties is not a constant and depends on its structural environment in a molecule.

Journal Article↗
PubMed2004

Differences among breeds and manifestation of heterosis in AI boar sperm output.

A total of 271,547 records of semen collections were utilized to appraise sperm characteristics of 3319 boars belonging to eight breeds: Czech Large White (CLW), Czech Landrace (CLA), Prestice Black-Pied (PBP), Czech Meat Pig (CM), Hampshire (HA), Duroc (DC), Pietrain (PN), Large White (LW), and various crosses of these breeds. The data was collected over 8 years (1990-1997) from insemination stations for boars in the Czech Republic. The assessment of sperm output was based on semen volume, number of total spermatozoa and number of viable spermatozoa. A linear model was used for statistical analysis included fixed effects of breed or crossbred combinations, boar within breed or crossbred combinations, year-season, and linear and quadratic regression on age of boars at collection and on interval between collections. The average semen volume of boars ranged from 161 to 349 ml, number of total spermatozoa from 81x10(9) to 119x10(9) and number of viable spermatozoa from 60x10(9) to 86x10(9). The lowest values were detected in DC while the highest were observed in LW. In general, sperm output significantly differed across breeds and their crossbreeds. The highest heterosis effect for semen volume was 30.6% (HA x PN), for number of total spermatozoa 18.2% (HA x PN) and 10.4% for number of viable spermatozoa (CLA x DC). Sperm output varied with season, including high values in autumn and winter and low ones in spring and summer.

Animals↗
PubMed2000

AI planning and scheduling in the medical hospital environment.

Hospital management is a hard task due to the complexity of the organization, the costly infrastructure, the specialized services offered to different patients and the need for prompt reaction to emergencies. Artificial Intelligence planning and scheduling methods can offer substantial support to the management of hospitals, and help raising the standards of service. This editorial presents an overview of the achievements reported in therapy planning and hospital management together with a general roadmap of the published research in Artificial Intelligence planning and scheduling. Finally, a discussion for the future research and development in this area concludes the presentation.

Artificial Intelligence↗
PubMedDate not supplied

Report of research activities in fuzzy AI and medicine at USF CSE.

Several projects involving the use of fuzzy and neuro-fuzzy methods in medical applications, developed by members of the Department of Computer Science and Engineering, University of South Florida, Tampa, Florida, are briefly reviewed. The successful applications are emphasized.

Artificial Intelligence↗