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Two types of asymmetric acetylcholinesterase in chick hindlimb muscle: developmental profiles, in vivo and in cell culture, and recovery after inactivation.

1. We have analyzed the behavior of two types of asymmetric molecular forms (A forms) of acetylcholinesterase (AChE) during development of chick hindlimb muscle, in vivo and in cell culture, and upon irreversible inactivation of peroneal muscle AChE with diisopropylfluorophosphate (DFP) in vivo. 2. In agreement with previous developmental studies on chick muscle, globular forms of AChE (G forms) are predominant in chick hindlimb at early embryonic ages, being gradually replaced by A forms as hatching (and, therefore, onset of locomotion) approaches. Of the two A-form types, AI appears and accumulates significantly earlier than AII, so that A/G and II/I ratios higher than 1 are attained only at about hatching time. 3. Cultures prepared from 11-day chick embryo hindlimb myoblasts express both types of A forms, with a combined activity of 27% of total AChE after 12 days in culture. AI forms appear again earlier and are much more abundant than type II asymmetric species through the life span of cultures. 4. All AChE activity in the peroneal muscle is irreversibly inactivated by injection of DFP in vivo. The recovery of A forms follows the same sequence described for normal development, with a delayed and slower recovery of AII forms as compared with AI. 5. Several hypotheses involving tail polypeptides or tissue target molecules, or posttranslational interconversion, are proposed to help explain the earlier appearance and accumulation of AI forms in chick muscle.

Acetylcholinesterase

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning

Microradiographic study of amelogenesis imperfecta.

A material of 22 primary and 4 permanent teeth from 22 children with amelogenesis imperfecta (AI) were examined by microradiographic techniques. The children were part of a patient material earlier examined in genetical and clinical studies. The results were compared with corresponding data two non-affected control groups and correlated with the available clinical and genetical data. Teeth were examined from seven of the eight different variants of AI seen in the clinical study. In most cases both hypoplasias and areas of hypomineralization were observed in the same tooth, indicating that both the secretory and the maturation phases of the amelogenesis are affected in AI. In teeth from children with the same clinical variant but different inheritance patterns, no specific finding could be related to a specific inheritance pattern. The findings in the one boy with AI as an X-linked trait were unique in this material. In all control teeth except one, no hypoplasisas or areas of hypomineralization were found in the enamel. In conclusion, the subclassification of AI into different forms can be questioned. Variations in clinical and histologic characteristics connected with the same inheritance pattern suggest that the genetic defect, in conjuction with a large biological variation, could explain the multiplicity in clinical expressivity that characterizes AI.

Amelogenesis Imperfecta

A Boveri perspective on cancer biomarker testing using artificial intelligence.

Artificial intelligence (AI) can predict genomic alterations from histology, yet its adoption is slowed by a lack of trust. We argue that deliberate morphology (i.e., a cognitive understanding of histological features supported by standardized annotations) creates a bidirectional feedback loop between clinical practice and model outputs.We translate these observations into an actionable hypothesis for clinical and computational teams: that by enhancing explainability, deliberate morphology could facilitate the responsible deployment of AI biomarkers in oncology.

Journal Article

Indirect evidence for a very fast recovery kinetics of chlorophyll-aII in spinach chloroplasts.

The 690 nm absorption change reflecting the turnover of the system-II-reaction center chlorophyll, Chl-aII (often referred to as P 680), has been investigated under different experimental conditions in spinach chloroplasts. A comparison was made with oxygen evolution and with absorption changes of Chl-aI measured at 703 nm, both indicating the number of electrons produced by system II. It was found: 1. The dependency on actinic flash intensity of the initial amplitudes of the measured 690 nm absorption change, deltaalpha0(Chl-aII) in Tris-washed chloroplasts is similar to that for the total amplutude of the 703 nm absorption change, deltaalpha0(Chl-aI) in normal chloroplasts, and can be described by an exponential function. On the other hand, deltaalphao(chl-aII) in normal chloroplasts exhibits a more complex biphasic dependency and much higher flash intensities are required for saturation. 3. Unver repetitive flash group excitation and in the presence of an ADRY(= acceleration of the deactivation reactions of the water-splitting enzyme system Y)-reagent the initial amplitude of the 690 nm absorption change oscillates in the same characteristic pattern as the oxygen evolution. 4. The initial amplitude of the 690 nm absorption change, deltaalpha0(Chl-aII), IN Tris-washed chloroplasts becomes significantly smaller (more than 50%) by the addition of system-II-electron donors (benzidine, p-phenylendiamine, tetraphenylboron), whereas the total amplitude of the 703 nm absorption change, detalalpha0)Chl-aI) increases 3-4-fold. In order to explain these results, the existance of a very fast reduction kinetics of Chl-aII+ is postulated, which is not detectable by our measuring equipment. The half time of this reaction is less than or equal to mus. Reaction centers with the very gast "undetected" Chl-aII+-reduction are photochemically transformed into slower one by double hit processes with a comparatively low quantum yield. Furthermore, it is inferred, that the dark recovery kinetics of Chl-aII is dependent on be charge accumulation state of the watersplitting enzyme system Y. This phenomenon is shown to explain also the oscillation pattern of delayed fluorescence. On the basis of the present results two alternative reaction schemes for the functional organization of the electron transport on the donor side of system II are discussed.

Chlorophyll

Replenishment of AI-doses with oestrogens in physiological amounts: effect on sow prolificacy in a field trial.

Basing on results about physiological functions of seminal oestrogens in the genital tract of sows, the effects of an oestrogen replenishment to AI-doses were investigated in a field trial. Each ejaculate was split into two halves, which were either diluted to normal AI-doses (controls, n = 353) or diluted and replenished with oestrogens in physiological amounts (n = 384). Insemination by qualified technicians led to an improvement of the pregnancy rate (82.8% vs. 77.1%; p less than 0.05) and the litter size (10.8% vs. 10.3%; p less than 0.05) in favour of the oestrogen replenishment. These results partly explain the known differences in prolificacy between natural mating and AI and thus provide a basis for improvement of pig AI.

Animals

Genetic determination of plasma apolipoprotein AI in a population-based sample.

Apolipoprotein AI (apo AI) is the major protein of high-density lipoprotein (HDL). Using radioimmunoassay, we measured plasma apo AI levels in 1,880 individuals in 283 pedigrees randomly selected from the population with respect to disease status and risk factors for coronary artery disease. Apo AI levels were first adjusted for date of assay (6.8% of apo AI variation) and then adjusted for variability in age and body mass index (an additional 6.6%, 20.4%, and 23.0% of apo AI variations for males, females not using exogenous hormones, and females using exogenous hormones, respectively). A mixture of two normal distributions fit the adjusted data better than did a single normal distribution. Genetic and environmental models that could explain the mixture of normal distributions were investigated using complex segregation analysis. Heterogeneous etiologies for individual differences in adjusted apo AI levels were suggested by the data in the 283 pedigrees. In a subset of 126 pedigrees, there is evidence for the major effect of a nontransmitted environmental factor that explains the mixture of distributions as well as polygenic loci that influence apo AI levels within each distribution. The environmental factor and polygenic loci account for 32% and 65% of the adjusted variation, respectively. In the other 157 pedigrees there is strong support for a single locus with a major effect that accounts for 27% of the adjusted variation. The effect of the polygenic loci is not different from zero in these 157 pedigrees. This is the first study to present evidence for the segregation of a single unmeasured locus with a major effect on levels of apo AI in a population-based sample of pedigrees.

Adolescent

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence

Genetic variation at the apolipoprotein gene loci contribute to response of plasma lipids to dietary change.

Dietary intervention studies (from a low polyunsaturated/saturated fatty acid ratio P/S diet to a high P/S diet), carried out on a group of healthy individuals from North Karelia, Eastern Finland between 1981-1984, provided evidence that there may be a genetic component contributing to variation in response to dietary change. We have resampled blood from 107 individuals involved in the original studies and used Restriction Fragment Length Polymorphisms (RFLPs) to study the genetic contribution of variation at a number of candidate gene loci to the response to dietary change. The genes investigated in this study were the apolipoprotein (apo) genes: apo B, apo AII, apo E (protein polymorphism), apo AI-CIII-AIV gene cluster, and the LDL-receptor gene. On the basal diet the major effect of genotype on lipid traits was due to variation at the apo E gene locus; this protein polymorphism explained 14.6% of the phenotypic variance in LDL cholesterol levels and 12.7% of the phenotypic variance in total cholesterol levels. When switched to low fat high P/S diet, these effects of variation at the apo E gene locus on the phenotypic variation of LDL and total cholesterol levels disappeared. The major effect on the response to dietary change, delta, was seen on the difference in apo AI levels mediated by variation at the apo B gene locus (MspI RFLP) explaining 6.3% of the phenotypic variance in apo AI change. For the RFLPs of the apo AI-CII-AIV gene cluster, small but not significant differences on delta were found. Our results indicate that within the limits of the candidate genes studied, the major effects in response to dietary change was on apo AI levels mediated through variation at the apo B gene locus.

Adult

AI-Driven Precision Medicine in Alzheimer's Disease: Drug Repurposing, Digital Therapeutics and Clinical Decision Support.

Alzheimer's Disease (AD) is a neurodegenerative disease that causes significant clinical, social, and economic burden worldwide. Despite improvements in understanding its multifaceted pathogenesis, current treatments are mostly symptomatic and ineffective across varied patient populations. To overcome these constraints, AI-driven precision medicine allows tailored risk assessment, treatment selection, and disease monitoring. This review covers AI's role in AD precision medicine, focusing on drug repurposing, digital therapies and clinical decision support systems. Machine and deep learning models are used to predict medication response, integrate heterogeneous data sources such as genomics, transcriptomics, neuroimaging and electronic health records, and uncover pharmacogenomic treatment success factors. The paper covers AIenabled precision pharmacology, including tailored dosing algorithms, adaptive therapeutic monitoring, and adverse drug reaction prediction. Bioinformatics-based target identification, network pharmacology, graphbased AI models, virtual screening, and real-world and clinical data validation are emphasized in AI-driven medication repurposing. AI-powered digital treatments like personalized cognitive training platforms, wearable- derived digital biomarkers, virtual and mixed reality interventions, adherence monitoring, and digital twins for therapy optimization have been discussed. AI-based clinical decision support systems are also thoroughly assessed for clinical value, accuracy, and explainability in disease subtyping, trajectory prediction, and risk stratification in preclinical and prodromal AD. Despite these promises, data heterogeneity, algorithmic bias, legal barriers, and privacy concerns exist. Federated learning enables safe multi-center collaboration and hybrid AI-human approaches, and it represents the future. AI's ability to alter AD care opens the door to precision medicine paradigms that use repurposed medications, digital tools and intelligent decision-making to improve patient outcomes.

Alzheimer’s disease

The HLA system and the clinical response to treatment with chlorpromazine.

A group of 33 schizophrenic patients were typed for HLA-SD antigens and their qualitative clinical responses to chlorpromazine therapy determined. A highly significant positive correlation was found between response to chlorpromazine and HLA-AI positive, while HLA-A2 positive subjects showed a significant negative correlation to chlorpromazine treatment. In a second group of 17 patients the clinical response to chlorpromazine were evaluated quantitatively, by WPRS, in HLA-AI positive and HLA-AI negative patients. There were no pre-treatment differences in the scores. After treatment the scores of positive patients were significantly lower, indicating that they responded to a greater degree. Since the frequency of HLA-AI in hebephrenic patients is higher than that in other schizophrenics this may explain our earlier finding that hebephrenics, as a group, respond better to chlorpromazine than do other schizophrenics.

Anxiety

The Impact of Chatbot Type and Normative Messaging on Chatbot Usage Intention Based on the Health Technology Acceptance Model: Randomized Controlled Trial.

BACKGROUND: Digital health tools, such as health chatbots, may improve access to scalable health support, but adoption remains inconsistent. Existing models do not fully integrate technology acceptance factors with health motivation factors relevant to digital health use. OBJECTIVE: This study proposed and tested the health technology acceptance model and examined whether normative message framing and chatbot type were associated with health motivation, technology acceptance, and intention to use a health chatbot. METHODS: In October 2025, we conducted a 4 &#xd7; 2 between-participants online experiment with 1000 US adults recruited from a nationally representative YouGov panel. Participants were randomized to 1 of 8 conditions varying norm message type (self-oriented, peer-oriented, expert-oriented, or family-oriented) and chatbot type (AI-powered or rule-based) in a cancer prevention and genetic risk information scenario. Outcomes included descriptive norms, injunctive norms, perceived susceptibility, perceived severity, perceived benefits, self-efficacy, perceived ease of use, trust, privacy concerns, and usage intention. Data were analyzed using a multivariate ANOVA with Bonferroni-adjusted post hoc tests and multiple linear regression. RESULTS: Peer-oriented and family-oriented messages produced higher usage intention than expert-oriented messages, and peer-oriented messages also increased descriptive norms, injunctive norms, self-efficacy, and trust. AI-powered chatbots were associated with higher usage intention (P=.02) and greater trust (P=.008) than rule-based chatbots. In regression analyses, the model explained 50.8% of the variance in usage intention. Usage intention was positively associated with descriptive norms (&#x3b2;=0.087; P=.003), injunctive norms (&#x3b2;=0.078; P=.009), perceived susceptibility (&#x3b2;=0.051; P=.03), perceived benefits (&#x3b2;=0.253; P<.001), and trust (&#x3b2;=0.33; P<.001), and negatively associated with perceived severity (&#x3b2;=-0.047; P=.049) and privacy concerns (&#x3b2;=-0.11; P<.001). Perceived ease of use and self-efficacy were not significant predictors. CONCLUSIONS: The health technology acceptance model was a useful framework for explaining the intention to use a health chatbot by combining technology acceptance and health motivation constructs. Both social design features and chatbot design features shaped adoption-related beliefs, with peer-oriented and family-oriented framing and AI-powered chatbots showing particular promise. Trust and privacy concerns remained central determinants of intended use.

Humans

Understanding the speech-understanding problems of the hearing impaired.

This article presents a tutorial overview of the speech-recognition difficulties of the hearing impaired. Much recent research indicates that the primary problem underlying the speech-recognition difficulties of the hearing impaired is the loss of hearing sensitivity and accompanying loudness recruitment. This tutorial demonstrates why loss of hearing sensitivity plays such an important role and why hearing aids fitted with contemporary fitting strategies provide only limited benefit in noise for many of these individuals. In addition, low-pass noise is often said to present the hearing-impaired listener with greater difficulties than broad-band noise. This observation is also explained quite simply by careful consideration of the loss of hearing sensitivity. Finally, two clinical articulation index (AI) calculation schemes, designed to quantify the effects of hearing loss on speech understanding, are reviewed and evaluated.

Audiometry, Speech

Polymorphisms in the apolipoprotein (apo) AI-CIII-AIV gene cluster: detection of genetic variation determining plasma apo AI, apo CIII and apo AIV concentrations.

We have examined the associations between levels of plasma apolipoprotein (apo) AI, apo CIII and apo AIV and genetic variation in the apo AI-CIII-AIV gene cluster in 162 boys and young men from Belgium aged from 7 to 23 years. Genotypes were determined for six restriction enzymes XmnI, PstI, SstI, PvuIIA-CIII, PvuIIB-AIV and XbaI, and for the G to A substitution at -75 bp in the 5' region of the apo AI gene. The polymorphism most strongly associated with apo AI levels was the G to A substitution (P = 0.025, R2 x 100 = 3.6%) confirming previous observations. The polymorphism most strongly associated with apo CIII levels was that of PvuIIA-CIII (P = 0.023, R2 x 100 = 2.9%) in the apo CIII gene. This novel association must be interpreted with caution until it has been confirmed in an independent sample. The polymorphism associated with the largest effect on apo AIV levels was that detected with XbaI in the apo AIV gene, but this association was not statistically significant. Previously reported associations between the SstI polymorphism and triglyceride levels, and between the PstI polymorphism and apo AI levels, were weakly detected in the present sample. Our results show that variation associated with some of the polymorphisms in the apo AI-CIII-AIV cluster makes a small, but statistically significant, contribution to the determination of apo AI and apo CIII levels in this sample of young men and boys. These observations may, in part, explain reported associations between polymorphisms in this gene cluster, differences in plasma lipid and lipoprotein levels, and prevalence of coronary artery disease.

Adolescent

Apolipoprotein E polymorphism and plasma lipid, lipoprotein, and apolipoprotein levels in Italian children.

We have investigated the effect of apolipoprotein (apo) E polymorphism on serum lipid, lipoprotein, and apolipoprotein levels in a sample of 195 children, aged 8-11 years, from Sezze, Central Italy. The relative frequencies of e2, e3, and e4 alleles were 0.062, 0.867, and 0.072, respectively. Variation at the apo E gene locus explained 5.1% of the sample variance in serum total cholesterol levels, 7.6% in low-density lipoprotein (LDL) cholesterol levels, 7.3% in apo B levels, and 14.1% in high-density lipoprotein-apo E (HDL-E) levels. The effect of the e2 allele was to lower levels of total cholesterol, LDL-cholesterol, and apo B and to raise levels of HDL-E, while the effect of the e4 allele was the opposite. Variation at the apo E gene locus was not associated with differences in serum triglyceride, HDL-cholesterol, or apo AI levels. The effects of common apo E polymorphisms and genetic variation associated with the PvuII RFLP of the apo B gene on serum apo B levels were additive, explaining 11.3% of the phenotypic variance in this sample. When the effect of apo E polymorphism on serum lipid traits was estimated in boys and girls separately, variation at the apo E gene locus explained 10.4, 13.3, 13.3, and 13.5% of the phenotypic variance in serum total cholesterol, LDL-cholesterol, apo B, and HDL-E levels, respectively, in boys, while in girls only the effect on HDL-E levels (19.3%) reached statistical significance. This study has demonstrated that genetic variations at the apo E locus contribute to the determination of serum lipid, lipoprotein, and apolipoprotein levels in youths and that the effects are gender specific.

Alleles

Spermatogenetic clones developing from repopulating stem cells surviving a high dose of an alkylating agent.

We investigated stem cell renewal and differentiation in 10- and 15-days-old spermatogonial clones developing in mouse seminiferous epithelium after an extremely large cell loss, inflicted by high doses of the alkylating agent Myleran. The spermatogonial clones arise from cells that resemble the Ais spermatogonia but have a larger nuclear diameter. In spite of their mitotic activity these 'repopulating stem cells' lie mainly isolated or in pairs. This explained by migration and differentiation. Migration appeared to occur at random in all directions along the basement membrane of the seminiferous tubule. After one or more divisions of the stem cells, a second type of cell appears, which is called the 'differentiating spermatogomium'. The time elapsing before this type of cell appears, depends on the dose of Myleran: the larger the dose the later differentiation starts. A relation could be demonstrated between the stage of the cycle of the seminiferous epithelium and the start of differentiation. Differentiating cells were found isolated or in groups of two, four, eight or sixteen cells. Hence we concluded that at least up to their fourth division differentiating cells divide synchronously without degenerations. Three types of division of repopulating stem cells were distinguished, producing (1) two repopulating stem cells, (2) one repopulating stem cell and one cell starting spermatogonial differentiation, or (3) two differentiating cells. Type 1 divisions were found most frequently.

Animals

Angiotensin II receptors labelled with 125I-[Sar1, Ile8]-AII in albino rabbit ocular tissues.

High affinity binding sites for the angiotensin II antagonist 125I-[Sar1,Ile8]-AII have been identified and characterized in membrane suspensions of ocular tissues of albino rabbits. Scatchard analysis of the binding indicated a single class of sites with Kd values of 186, 92, 152, 50, 102 pM for the iris + ciliary body, choroid, ciliary process, retina and cornea, respectively. The corresponding concentrations of binding sites were 22, 68, 35, 22 and 4 fmole/mg of protein. The order of potency for several AII analogs to compete with 125I-[Sar1,Ile8]-AII at its binding sites in iris + ciliary body membranes ([Sar1,Leu8]-AII = [Sar1,Ile8]-AII greater than AII = [Sar1, Ala8]-AII greater than AIII greater than AI) resembled the order of potency found for AII receptors in other tissues. The competition curves for this tissue using AII and AIII were best explained by the existence of two populations of binding sites. The addition of the guanine nucleotide, GppNHp, to the assay resulted in a 6.7-fold and 2.3-fold decrease in the respective affinities of AII and AIII for 125I-[Sar1,Ile8]-AII binding sites without a change in the slope of the competition curves. The GppNHp-induced effect was also observed in ciliary process membranes but not in retinal or choroidal membranes. These results indicate the presence of AII receptors regulated by a GTP-binding protein in both the ciliary process and the iris + ciliary body of the rabbit. They also suggest a difference in the guanine nucleotide regulation of AII receptors in different ocular tissues.

Angiotensin II

Lipids and lipoproteins in persons with Down's syndrome.

This study was designed to investigate whether the observed decreased prevalence of coronary artery disease in individuals with Down's syndrome may be explained by their serum lipid and lipoprotein profiles. Twenty-seven persons with Down's syndrome and 23 non-affected control individuals were enrolled in this study. Their fasting venous blood was analysed for total cholesterol, triglyceride, LDH cholesterol, HDL cholesterol, apo B and apo AI. The results revealed no significant differences between the study and control group with regard to total cholesterol, LDL cholesterol, apo B and the apo B:apo AI ratio. However, triglyceride levels were significantly increased, and serum HDL cholesterol, apo AI and HDL cholesterol:total cholesterol ratio were significantly decreased in patients with Down's syndrome when compared with the control group. The latter observations are all associated with an increased risk for coronary artery disease. Therefore, it is concluded that the decreased prevalence of coronary artery disease in individuals with Down's syndrome cannot be explained by the lipid and lipoprotein levels observed in this study population.

Adolescent