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PubMed2025

Generative AI Models in Time-Varying Biomedical Data: Scoping Review.

BACKGROUND: Trajectory modeling is a long-standing challenge in the application of computational methods to health care. In the age of big data, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multimodal health data and long-term dependencies throughout medical histories. Recent advances in generative artificial intelligence (AI) have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions, with major impact in fields such as finance and environmental sciences, prompting researchers to apply these methods for disease modeling in health care. OBJECTIVE: While AI methods have proven powerful, their application in clinical practice remains limited due to their highly complex nature. The proliferation of AI algorithms also poses a significant challenge for nondevelopers to track and incorporate these advances into clinical research and application. In this paper, we introduce basic concepts in generative AI and discuss current algorithms and how they can be applied to health care for practitioners with little background in computer science. METHODS: We surveyed peer-reviewed papers on generative AI models with specific applications to time-series health data. Our search included single- and multimodal generative AI models that operated over structured and unstructured data, physiological waveforms, medical imaging, and multi-omics data. We introduce current generative AI methods, review their applications, and discuss their limitations and future directions in each data modality. RESULTS: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and reviewed 155 articles on generative AI applications to time-series health care data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand. CONCLUSIONS: We reviewed and critiqued existing applications of generative AI to time-series health data with the aim of bridging the gap between computational methods and clinical application. We also identified the shortcomings of existing approaches and highlighted recent advances in generative AI that represent promising directions for health care modeling.

Artificial Intelligence↗

PubMed2005

Clinical use of aromatase inhibitors (AI) in premenopausal women.

Aromatase inhibitors (AI) block the last enzymatic step of estrogen production, the aromatization of the A-cycle of aromatizable androgens and particularly, androstenedione (delta4) and testosterone (T). Molecules designed for interfering with aromatase activity have existed for many years. Yet the activity of products of the aminogluthetimide era was unspecific and these substances carried too many side effects for being used clinically. Newer third generation AIs, however, are highly specific and essentially devoid of side effects. These molecules have recently been approved for treating breast cancer in postmenopausal women either, in advanced forms or, as part of adjuvant therapy. In women whose ovaries are active, a temporary inhibition of E2 production will raise gonadotropins and in turn, stimulate follicular growth. In cancer patients, this property precludes the use of AIs in women whose ovaries are still active, unless gonadotropins are blocked. But in infertility patients, this property of AIs has been put to play for inducing ovulation. AIs have been used both in women who do not ovulate but whose hypothalamo-pituitary-gonadal (HPG) axis is active (oligo-anovulators of PCOD type) and those who ovulate regularly but in whom multiple ovulation is sought for treating unexplained infertility or as part of IVF. Like clomiphene citrate (CC), AIs are not usable in women whose gonadotropins are suppressed, as in the case of hypothalamic amenorrhea. The sum of data available on the use of AI for inducing ovulation remains however meager to this date and is mainly constituted of pilot and non-randomized trials. Yet mounting evidence tends to support AIs' advantages over CC for induction of ovulation. Hence, we think that the likelihood that these drugs will play a key role in induction of ovulation in the future is high. AIs appear particularly interesting for treating unexplained infertility because AI-FSH/hMG regimens are lighter than FSH-only regimens while retaining the high pregnancy rates of these latter treatments.

Aromatase Inhibitors↗

PubMed2006

AI-1 influences the kinase activity but not the phosphatase activity of LuxN of Vibrio harveyi.

The Gram-negative bacterium Vibrio harveyi produces and responds to three autoinducers, AI-1, AI-2, and CAI-1 to regulate cell density dependent gene expression by a process referred to as quorum sensing. The concentration of the autoinducers is sensed by three cognate hybrid sensor kinases, and information is channeled via the HPt protein LuxU to the response regulator LuxO. Here, a detailed biochemical study on the enzymatic activities of the membrane-integrated hybrid sensor kinase LuxN, the sensor for N-(d-3-hydroxybutanoyl)homoserine lactone (AI-1), is provided. LuxN was heterologously overproduced as the full-length protein in Escherichia coli. LuxN activities were characterized in vitro and are an autophosphorylation activity with an unusually high ATP turnover rate, stable LuxU phosphorylation, and a slow phosphatase activity with LuxU approximately P as substrate. The presence of AI-1 affected the kinase but not the phosphatase activity of LuxN. The influence of AI-1 on the LuxN--> LuxU signaling step was monitored, and in the presence of AI-1, the kinase activity of LuxN, and hence the amount of LuxU approximately P produced, were significantly reduced. Half-maximal inhibition of kinase activity by AI-1 occurred at 20 mum. Together, these results indicate that AI-1 directly interacts with LuxN to down-regulate its autokinase activity and suggest that the key regulatory step of the AI-1 quorum sensing system of Vibrio harveyi is AI-1-mediated repression of the LuxN kinase activity.

4-Butyrolactone↗

PubMed1998

Substitution of the carboxyl-terminal domain of apo AI with apo AII sequences restores the potential of HDL to reduce the progression of atherosclerosis in apo E knockout mice.

HDL metabolism and atherosclerosis were studied in apo E knockout (KO) mice overexpressing human apo AI, a des- (190-243)-apo AI carboxyl-terminal deletion mutant of human apo AI or an apo AI-(1-189)-apo AII-(12-77) chimera in which the carboxyl-terminal domain of apo AI was substituted with the pair of helices of apo AII. HDL cholesterol levels ranked: apo AI/apo E KO approximately apo AI-(1-189)-apo AII- (12-77)/apo E KO > > des-(190-243)-apo AI/apo E KO > apo E KO mice. Progression of atherosclerosis ranked: apo E KO > des-(190-243)-apo AI/apo E KO > > apo AI-(1-189)- apo AII-(12-77)/apo E KO approximately apo AI/apo E KO mice. Whereas the total capacity to induce cholesterol efflux from lipid-loaded THP-1 macrophages was higher for HDL of mice overexpressing human apo AI or the apo AI/apo AII chimera, the fractional cholesterol efflux rate, expressed in percent cholesterol efflux/microg apolipoprotein/h, for HDL of these mice was similar to that for HDL of mice overexpressing the deletion mutant and for HDL of apo E KO mice. This study demonstrates that the tertiary structure of apo AI, e.g., the number and organization of its helices, and not its amino sequence is essential for protection against atherosclerosis because it determines HDL cholesterol levels and not cholesterol efflux. Amino acid sequences of apo AII, which is considered to be less antiatherogenic, can be used to restore the structure of apo AI and thereby its antiatherogenicity.

Amino Acid Substitution↗

PubMed2026

The AI Revolution: Shaping the Present and Future of Pharmaceutical Research and Development.

The transformative role of artificial intelligence (AI) in the pharmaceutical industry is examined, with a focus on its significant contributions to drug discovery, development, and clinical trial processes. It highlights the inefficiencies and high costs associated with traditional drug development and explores how AI and machine learning (ML) can enhance these processes by analyzing extensive biological datasets. The historical context of AI in pharmaceutical development is examined, noting how advances in computational power and data accessibility have facilitated innovative methodologies, such as predictive analytics and natural language processing. Contemporary trends reveal the integration of AI technologies in drug design, repurposing, and patient response forecasting. This study also addresses the challenges of participant recruitment for clinical trials and proposes AI-driven solutions to optimize patient selection and data management. Furthermore, it discusses AI's role in tailored medicine, emphasizing its potential for advancing precision therapy through targeted drug development and personalized treatment strategies. The importance of digital tools, genomic data analysis, and AI-driven imaging technologies for customizing therapeutic approaches is underscored, along with the regulatory and ethical challenges posed by AI deployment in healthcare. This study illustrates the complexities of AI applications in the pharmaceutical sector, offering insights into both successful and unsuccessful initiatives. The findings suggest that the digitalization of the pharmaceutical industry and enhanced AI integration hold promise for developing safer and more effective therapeutic strategies, while also identifying obstacles to their widespread adoption and optimal functionality.

Artificial intelligence↗

PubMed2005

Use of intravaginal progesterone-releasing inserts in a synchronization protocol before timed AI and for synchronizing return to estrus in Holstein heifers.

Holstein heifers (n = 189) were submitted to a 42-d artificial insemination (AI) period in which they underwent AI after once-daily evaluation of rubbed tail chalk. At the onset of the AI period (d 0), heifers were assigned randomly to receive synchronization of ovulation and timed AI (TAI; d 0: 100 mug of GnRH; d 6: 25 mg of PGF(2alpha); d 8: 100 mug of GnRH + TAI) either without (GPG; n = 95), or with inclusion of a CIDR insert (CIDR; n = 94) from d 0 to 6. No CIDR heifers received AI before d 8 compared with 24% of GPG heifers, and pregnancy rate per AI (PR/AI) at 30 d after TAI did not differ between treatments. To synchronize return to estrus for heifers failing to conceive after TAI, heifers (n = 166) receiving TAI to first service were randomly assigned to receive no further treatment (control; n = 85) or receive a new CIDR insert between 14 and 20 d after TAI (Resynch; n = 81). No Resynch heifers received AI during CIDR treatment compared with 35% of control heifers, and the proportion of heifers receiving AI within 72 h after the day of CIDR removal was 78 vs. 50% for Resynch vs. control heifers, respectively. No treatment x inseminator interaction was detected at first or second AI; however, overall PR/AI was modest for heifers throughout the experiment due to poor performance of 2 of the 3 herd inseminators (14, 6, and 58% PR/AI, respectively). Inclusion of CIDR inserts suppressed estrus during the TAI protocol with no reduction in PR/ AI. Resynchronization of estrus using CIDR inserts resulted in tighter synchrony of estrus among nonpregnant heifers compared with untreated controls.

Administration, Intravaginal↗

PubMed1990

Variation at the apolipoprotein (apo) AI-CIII-AIV gene cluster and apo B gene loci is associated with lipoprotein and apolipoprotein levels in Italian children.

We have used RFLPs of the apolipoprotein (apo) B gene and apo AI-CIII-AIV gene cluster to estimate the genetic contribution of variation at these loci to the variability of plasmid lipid, lipoprotein, and apolipoprotein levels in 209 children from Sezze in central Italy. The sample was randomly divided into group I (107 children) and group II (102 children). Four site polymorphisms (PvuII, XbaI, MspI, and EcoRI) of the apo B gene and five site polymorphisms (XmnI, PstI, SstI, PvuII-CIII, and PvuII-AIV) of the apo AI-CIII-AIV gene cluster were examined in group I children. After adjustment for gender, age, and body-mass index, polymorphisms at both gene loci (PvuII-B, PvuII-CIII, and PvuII-AIV) were associated with significant effects on the levels of plasma apo AI, apo B, or high-density lipoprotein-cholesterol. RFLPs that showed significant effects in group I were genotyped in group II. All three polymorphisms were associated with similar effects on apolipoprotein levels, though for all RFLPs the magnitude of the effects was smaller in the group II children and only statistically significant for the effect of the PvuII-B genotype on apo AI levels. In the total sample of 209 children 7.4% of the sample variance in apo AI levels was explained by variation associated with the apo B PvuII-B RFLP. In addition, the PvuII-B RFLP was associated with significant effects on plasma apo B levels and explained 5.7% of the sample variance. The PvuII-CIII and PvuII-AIV polymorphisms were both associated with differences in apo AI levels, explaining 3.7%-5.7% of the sample variance. Taken together, the three PvuII polymorphisms explained 17.7% of the phenotypic variance in apo AI levels. There was significant evidence for an effect of nonlinearity of the PvuII-CIII genotypes on apo AI levels, with the individuals heterozygous for the polymorphism having the highest apo AI levels. No evidence of interaction between genotype and gender, age, and body-mass index was shown by covariance analysis. The molecular explanation of this effect is unclear. Our data show that variation at both the apo AI-CIII-AIV and apo B loci are associated with lipoprotein and apolipoprotein levels in this sample of Italian children.

Alleles↗

PubMed1997

Apolipoprotein AI isoforms in serum determined by isoelectric focusing and immunoblotting.

Quantitative evaluation of serum apolipoprotein AI (apo AI) isoforms through densitometric analysis following isoelectric focusing (IEF) is described. The apo AI isoforms were identified by immunoblotting. By combining these techniques, a qualitatively invariant pattern was observed in 54 serum samples, obtained from 27 men and 27 women. Peak 1 (proapo AI, pI 5.75) accounts for about 8% of the total densitometric area, peak 3 (apo AIzero, pI 5.59) for about 74%, peak 4 (apo AI-1, pI 5.42) for about 13%, and peak 5 (apo AI-2, pI 5.37) for about 5%. In some subjects, the proportion of proapo AI was increased. Variations of the ratio of the different apo AI isoforms may be due to modification of the fractional catabolic rate of this apolipoprotein. The procedure proposed in this study may be useful for the evaluation of quantitative abnormalities in apo AI isoforms involved in the development of coronary heart diseases (CHD).

Adult↗

PubMed1997

High density lipoprotein apolipoprotein AI kinetics in NIDDM: a stable isotope study.

High density lipoprotein (HDL) kinetics were studied by infusing [5,5,5-2H3]-leucine in five subjects with normal glucose tolerance and eight patients with non-insulin-dependent diabetes mellitus (NIDDM) with poor metabolic control (HbA1c = 8.16 +/- 1.93%) (mean +/- SD). HDL were modelled as a single compartment since no kinetic differences were observed between HDL2 and HDL3 subclasses. Plasma apolipoprotein AI (apo AI) concentration was significantly lower in NIDDM patients (96.1 +/- 12.1 vs 124.4 +/- 13.1 mg.dl-1, p < 0.01). HDL composition was altered in NIDDM, as an increase in HDL-triglyceride and a decrease in HDL-cholesterol, negatively correlated (r = 0.780, p < 0.01). The mean fractional catabolic rate (FCR) of apo AI-HDL was significantly higher (0.39 +/- 0.16 vs 0.21 +/- 0.06 d-1, p < 0.05) while the apo AI-HDL absolute production rate was not significantly greater (13.6 +/- 5.1 vs 12.0 +/- 4.2 mg.kg-1.d-1) in diabetic patients compared to normal subjects. There were significant correlations between apo AI-HDL FCR and plasma apo AI concentration (r = -0.580, p < 0.05), plasma triglycerides (r = 0.839, p < 0.0001) or HDL-triglyceride levels (r = 0.597, p < 0.05). No correlation was observed between apo AI-HDL FCR and HbA1c or HDL-cholesterol level. These data support the view that the decrease in plasma apo AI level in patients with NIDDM is due to an increase of apo AI-HDL FCR, which may itself be related to changes in HDL composition.

Adult↗

PubMed1985

Apolipoprotein AI and AII metabolism in patients with primary high-density lipoprotein deficiency associated with familial hypertriglyceridemia.

Plasma high-density lipoproteins (HDL) and their major proteins--apolipoprotein (apo) AI and apo AII--are subnormal in most patients with familial hypertriglyceridemia. However, the pathophysiology of low-plasma apo AI and apo AII is unclear. The kinetic parameters (turnover) of HDL apo AI and apo AII were studied in six lean patients with primary HDL deficiency associated with familial hypertriglyceridemia and five normolipidemic controls. Autologous 125I labeled HDL were injected intravenously (IV; 25 microCi) and blood samples drawn ten minutes after the injection and periodically thereafter for 12 days. Urine samples were collected daily and their radioactivity measured. Kinetic parameters were calculated from the area under the decay curve using three exponentials. Mean plasma apo AI and apo AII were significantly lower (P less than 0.001) in patients than normals (70.4 +/- 2.7 v 106.9 +/- 7.0; 24.2 +/- 1.6 v 39.2 +/- 0.9 mg/dL, respectively). The mean fractional catabolic rates (FCR) obtained from plasma 125I-HDL, apo AI, apo AII radioactivity decay curves and by Berson and Yalow's method (urine/plasma radioactivity ratios) were significantly greater (P less than 0.05) in patients than in controls (0.387 v 0.299; 0.391 v 0.309; 0.361 v 0.275; 0.272 v 0.207/d; respectively). The mean synthetic rates (SR) of apo AI and apo AII were significantly lower in patients than in controls (11.12 v 14.17 mg/kg body weight/d, P less than 0.05; 3.53 v 4.68 mg/kg body weight/d, P less than 0.05, respectively). In vitro lipolysis of triglyceride (TG) rich lipoproteins by bovine lipoprotein lipase, and measurement of hepatic TG lipase and lipoprotein lipase in postheparin plasma were similar in patients and controls, indicating no abnormality in these factors that are linked to HDL and TG catabolism. However, a significant positive correlation between hepatic TG lipase and the FCR of apo AI and apo AII was found. The data suggest that in this series of patients with HDL deficiency the low plasma HDL-cholesterol, apo AI, and apo AII levels resulted from decreased synthesis and an increased fractional catabolic rate of apo AI and apo AII, the major proteins of HDL.

Adult↗

PubMed2005

Pioglitazone increases the fractional catabolic and production rates of high-density lipoproteins apo AI in the New Zealand White Rabbit.

Pioglitazone is an agonist of the peroxisome proliferator-activated receptor gamma (PPARgamma) that raises HDL-cholesterol plasma in humans. Whether pioglitazone-mediated modifications in HDL-apolipoprotein AI (apo AI) turnover in vivo contribute to this effect has not been completely elucidated. Therefore, we performed kinetic studies of HDL-apo AI radiolabeled with 125I in male New Zealand White rabbits after 6 weeks of 0.6 (n = 8), 1.75 (n = 8), and 2.6 mg/kg/day (n = 7) pioglitazone and vehicle (n = 12) treatment. Fractional catabolic rate (FCR) of HDL-apo AI was significantly higher in 1.75 and 2.6 mg/kg pioglitazone-treated animals, as compared with control rabbits (0.057+/-0.014 and 0.049+/-0.01 versus 0.025+/-0.005 pools/h, respectively); these changes were associated to a similar increase in apo AI production rates (PR) (1.24+/-0.62 and 1.14+/-0.40 versus 0.53+/-0.17 mg/kg/h, p < 0.01). Consequently, apo AI plasma levels in pioglitazone-treated animals were similar to those of controls. The apo AI-FRC and -PR correlated with the relative proportion of the HDL3c subclass, as determined by polyacrylamide gradient electrophoresis. Our data demonstrate that pioglitazone markedly modifies apo AI kinetics and enhances the proportion of small HDL3c particles, despite the unchanged apo AI concentration. Whether or not the pioglitazone-induced structural changes of HDL contribute to the anti-atherosclerotic effects of the drug remains to be determined.

Animals↗

PubMed2004

Induction of the apolipoprotein AI promoter by Sp1 is repressed by saturated fatty acids.

Insulin induces transcription of the hepatic apolipoprotein AI (apo AI) gene by increasing Sp1 binding to the promoter. To determine the effect of fatty acids on this process, HepG2 cells cotransfected with the plasmid pAI.474.CAT containing the full-length apo AI promoter and the Sp1-expressing plasmid, pCMV-Sp1, were studied. Chloramphenicol acetyl transferase (CAT) activity (% acetylation) increased 1.98-fold in cells receiving the Sp1 expression construct relative to control cells (46.4% +/- 0.6% v 23.4% +/- 1.3%, P < .05). Treatment of cells with 3 saturated fatty acids, stearic, myristic, and palmitic acid, repressed the ability of exogenous Sp1 to induce apo AI reporter gene expression (15.2% +/- 1.7%, 22.5% +/- 0.3%, 22.9% +/- 0.1%, 23.5% +/- 0.8%, respectively, P < .05). Unsaturated fatty acids, oleic, linoleic, or linolenic acid had no effect on Sp1-mediated induction of the apo AI promoter. In the presence of the trans fatty acids, CAT activity in the Sp1-transfected cells was similar to control cells (16.7% +/- 3.3%, 19.3% +/- 0.5%, and 21.0% +/- 2.1% acetylation in cells exposed to elaidic acid, linolelaidic, or linolenelaidic acid, respectively). In cells treated with an equimolar mixture of oleic acid and stearic acid, apo AI promoter activity was suppressed in a manner similar to that observed in stearic acid-treated cells. Insulin (100 microU/mL) induced apo AI promoter activity 2.9-fold (22.4% +/- 1.7% v 7.8% +/- 2.4%, P < .05). However, in the presence of stearic acid, insulin was unable to induce apo AI promoter (6.3% +/- 1.6%). Stearic acid treatment did not alter Sp1-DNA binding as measured by gel shift analysis. Therefore, saturated fatty acids blunt Sp1 induction of apo AI promoter probably at a step beyond DNA binding.

Apolipoprotein A-I↗

PubMed1998

Efficacy of decreasing the dose of GnRH used in a protocol for synchronization of ovulation and timed AI in lactating dairy cows.

To determine the efficacy of reducing the dosage of GnRH used in a protocol for synchronization of ovulation and timed AI, primiparous and multiparous lactating Holstein cows (n = 237) were randomly assigned to 1 of 2 treatment groups. Ovulation was synchronized for cows in the first group using intramuscular injections of GnRH and PGF2 alpha as follows: Day 0, 100 micrograms GnRH; Day 7, 25 mg PGF2 alpha; Day 9, 100 micrograms GnRH. Ovulation was synchronized in the second group of cows using the same injection schedule and dosage of PGF2 alpha but only 50 micrograms GnRH per injection. All cows underwent a timed AI at 12 to 18 h after the second GnRH injection. The proportion of cows ovulating in response to the second GnRH injection (synchronization rate) and pregnancy status at 28 and 56 d post AI were determined using transrectal ultrasonography. The synchronization rate, double-ovulation rate, conception rate at 28 and 56 d post AI, and pregnancy loss from 28 to 56 d post AI did not differ statistically between treatment groups. For all cows, synchronization rate was 84.0%, and double-ovulation rate was 14.1%. Conception rates calculated using all cows receiving synchronization of ovulation were 41.1% at 28 d and 34.4% at 56 d post AI. Conception rates calculated for only synchronized cows were 47.6% at 28 d and 40.1% at 56 d post AI. For all cows, pregnancy loss from 28 to 56 d post AI was 13.5%, with an attrition rate of 0.5% per day. Estimated savings in hormone costs using 50 rather than 100 micrograms GnRH per injection for synchronizing ovulation were $6.40 per cow and $20.27 per pregnancy. Thus, decreasing the dosage of GnRH used for synchronization of ovulation and timed AI in lactating dairy cows reduces synchronization costs per cow and per pregnancy without compromising the efficacy of the synchronization protocol.

Animals↗

PubMed2026

Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences.

Artificial intelligence (AI) has seen transformative breakthroughs in the life sciences, expanding possibilities to interpret biological information at an unprecedented capacity. To maximize return on growing investments and accelerate progress, it is urgent to address long-standing research challenges arising from the rapid adoption of AI methods. We review the erosion of trust in AI outputs driven by poor reusability and reproducibility, and highlight their impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to support open and sustainable AI model development. In response, this Perspective introduces practical open and sustainable AI recommendations mapped to over 300 ecosystem components and provides guiding implementation pathways. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and reproducible AI. Built upon community consensus and aligned to existing efforts, these outputs will aid future policy development and structured pathways for guiding AI implementation.

Artificial Intelligence↗

PubMed2003

Lsr-mediated transport and processing of AI-2 in Salmonella typhimurium.

The LuxS-dependent autoinducer AI-2 is proposed to function in interspecies cell-cell communication in bacteria. In Salmonella typhimurium, AI-2 is produced and released during exponential growth and is subsequently imported into the bacteria via the Lsr (luxS regulated) ATP binding cassette (ABC) transporter. AI-2 induces transcription of the lsrACDBFGE operon, the first four genes of which encode the Lsr transport apparatus. In this report, we identify and characterize LsrK, a new protein that is required for the regulation of the lsr operon and the AI-2 uptake process. LsrK is a kinase that phosphorylates AI-2 upon entry into the cell. Our data indicate that phosphorylation of AI-2 results in its sequestration in the cytoplasm. We suggest that phospho-AI-2 is the inducer responsible for inactivation of LsrR, the repressor of the lsr operon. We also show that two previously uncharacterized members of the lsr operon, LsrF and LsrG, are necessary for the further processing of phospho-AI-2. Transport and processing of AI-2 could be required for removing the quorum-sensing signal, conveying the signal to an internal detector and/or scavenging boron.

ATP-Binding Cassette Transporters↗

PubMed2004

Parameters influencing AIS 1 neck injury outcome in frontal impacts.

In order to gain more knowledge of the neck injury scenario in frontal impacts, a statistical study of parameters influencing incidences of AIS 1 neck injuries was performed. The data set consisted of 616 occupants in Volvo cars. Information regarding the crash, the safety systems, occupant characteristics (including prior neck problems), behavior and sitting posture at the time of impact, and neck symptoms (including duration) was collected and analyzed. Occupant characteristics (mainly gender, weight, and age), kinematics (head impacts) and behavior at the time of impact were identified as the most prominent parameter areas with regard to AIS 1 neck injury outcome. Specifically, women had a significantly higher AIS 1 neck injury rate as compared to men, occupants under the age of 50 had a significantly higher AIS 1 neck injury rate as compared to those above 50 and occupants weighing less than 65 kg have a significantly higher AIS 1 neck injury rate than heavier occupants. Drivers stating that they impacted their head against a frontal interior structure had a significantly higher AIS 1 neck injury rate than those without head impact. Also, occupants who stated they had tensed their neck muscles at the time of impact, had a significantly higher AIS 1 neck injury rate as compared to occupants who did not. Occupant activities, such as tightly gripping the steering wheel or straightening their arms showed a significantly increased AIS 1 neck injury rate, indicating that occupant behavior at time of impact could be influential with respect to AIS 1 neck injury outcome. Also, occupants reporting prior neck problems had a higher rate of persistent symptoms (>1 year) but no difference with respect to passing symptoms (<3 months) as compared to those without prior neck problems. Additionally, there was no distinct pattern for the duration of neck symptoms.

Abbreviated Injury Scale↗

PubMed1991

Lipoproteins of human peripheral lymph. Apolipoprotein AI-containing lipoprotein with alpha-2 electrophoretic mobility.

Evidence from diverse sources has implicated a central role of apolipoprotein AI (apo AI), the most abundant protein of plasma high-density lipoproteins, in the transport of cholesterol from peripheral tissues to the liver (reverse cholesterol transport). Particles containing only apo AI appear to be more effective as cholesterol acceptors in tissue culture than do particles which also contain apo AII. The apo AI-containing lipoproteins of plasma have been extensively studied, but there is less information on those in tissue fluids, to which most peripheral cells are exposed. In the present study the heterogeneity of apo AI-containing particles in human peripheral lymph, collected from the dorsum of the foot, has been examined by starch block electrophoresis, exclusion chromatography and immunoelectrophoresis. The apo AI-containing particles of lymph were found to be more variable in both electrophoretic mobility and size than those of plasma from the same subjects. Of particular interest was a subpopulation which migrated on electrophoresis with the same mobility as alpha-2-macroglobulin. This fraction accounted for approximately 7% (range: 4-12%; n = 5) of lymph apo AI, contained no immunodetectable apo AII, and by exclusion chromatography was composed of particles the size of, or smaller than, albumin. Such physicochemical properties suggest that these alpha-2 migrating particles may function as the principal primary acceptors of cell cholesterol in the extracellular matrix of human peripheral tissues. By isoelectric focusing, lymph apo AI was found to contain a higher proportion of more negatively charged isoforms than the apo AI of plasma.

Adult↗

PubMed2005

Regulation of uptake and processing of the quorum-sensing autoinducer AI-2 in Escherichia coli.

AI-2 is a quorum-sensing signaling molecule proposed to be involved in interspecies communication. In Escherichia coli and Salmonella enterica serovar Typhimurium, extracellular AI-2 accumulates in exponential phase, but the amount decreases drastically upon entry into stationary phase. In S. enterica serovar Typhimurium, the reduction in activity is due to import and processing of AI-2 by the Lsr transporter. We show that the Lsr transporter is functional in E. coli, and screening for mutants defective in AI-2 internalization revealed lsrK and glpD. Unlike the wild type, lsrK and glpD mutants do not activate transcription of the lsr operon in response to AI-2. lsrK encodes the AI-2 kinase, and the lsrK mutant fails to activate lsr expression because it cannot produce phospho-AI-2, which is the lsr operon inducer. glpD encodes the glycerol-3-phosphate (G3P) dehydrogenase, which is involved in glycerol and G3P metabolism. G3P accumulates in the glpD mutant and represses lsr transcription by preventing cyclic AMP (cAMP)-catabolite activator protein (CAP)-dependent activation. Dihydroxyacetone phosphate (DHAP) also accumulates in the glpD mutant, and DHAP represses lsr transcription by a cAMP-CAP-independent mechanism involving LsrR, the lsr operon repressor. The requirement for cAMP-CAP in lsr activation explains why AI-2 persists in culture fluids of bacteria grown in media containing sugars that cause catabolite repression. These findings show that, depending on the prevailing growth conditions, the amount of time that the AI-2 signal is present and, in turn, the time that a given community of bacteria remains exposed to this signal can vary greatly.

Base Sequence↗