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PubMed1978

Opposing effects of estradiol and progesterone on oxytocin receptors in rabbit uterus.

Estradiol-17beta administration to young (10- to 12-week-old) rabbits to produce the "estrogen-dominated" uterus increased the uterine contractile response to both oxytocin and methacholine in vitro. In "progesterone-dominated" uteri, obtained from rabbits that received progesterone for 4 days after estrogen pretreatment, the contractile response to oxytocin in vitro was selectively abolished; the response to methacholine was unaffected. Parallel changes were observed in the concentration (but not affinity) of specific sites in uterine microsomal membranes that bind [(3)H]oxytocin with selectivity features expected for oxytocin receptors. Thus, estrogen-dominated uteri have an increased number of specific [(3)H]oxytocin binding sites per mg of membrane protein relative to untreated controls, whereas specific oxytocin binding sites are reduced to barely detectable levels in the progesterone-dominated uterus. Similar results are obtained when binding sites are measured in membranes from the myometrium of estrogen- or progesterone-dominated uteri. Short-term (24-hr) progesterone administration to estrogen-pretreated rabbits decreased, but did not abolish, specific [(3)H]oxytocin binding; the concentration of specific [(3)H]oxytocin binding sites was reduced without influence on the affinity of these sites. A sublethal dose of actinomycin D, administered over a 24-hr period to rabbits pretreated with estradiol for 4 days, likewise reduced specific oxytocin binding; additive effects were not observed when progesterone and actinomycin D were administered together. These results suggest that the regulatory effects of estrogens and progesterone upon the rabbit uterine contractile response to oxytocin are achieved, at least in part, by the opposing actions of these steroids in regulating the number of oxytocin receptors in smooth muscle cells. Estradiol increased the concentration of uterine oxytocin receptors; the maintenance of high receptor levels appears to depend upon the continuous de novo synthesis of oxytocin receptors. In contrast, progesterone, like actinomycin D, appears to act at the nuclear locus to repress synthesis of oxytocin receptors.

Animals↗

PubMed2025

Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method.

BACKGROUND: Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. This study utilized single-nucleotide polymorphisms (SNPs) as variables and employed ML-based genetic risk score (GRS) models to predict the occurrence of MetS, bringing it closer to clinical application. METHODS: Feature selection was performed using Least Absolute Shrinkage and Selection Operator. Six ML algorithms were employed to construct GRS models. A fivefold cross-validation was utilized to aid in the internal validation of models. The receiver operating characteristic (ROC) curve was used to select the better-performing GRS model. The SHapley Additive exPlanations (SHAP) was then applied to interpret the model. After extracting GRS, stratified analysis of BMI, age and gender was performed. Finally, these conventional risk factors and GRS were integrated through multivariate logistic regression to establish a combined model. RESULTS: A total of 17 SNPs were selected for analysis. Among the GRS models, the extreme gradient boosting (XGBoost) model demonstrated superior discriminative performance (AUC = 0.837). The XGBoost's optimal robustness was also validated through five-fold cross-validation (mean ROC-AUC = 0.706). The XGBoost-based SHAP algorithm not only elucidated the global effects of 17 SNPs across all samples, but also described the interaction between SNPs, providing a visual representation of how SNPs impact the prediction of MetS in an individual. There was a strong correlation between GRS and MetS risk, particularly observed among young individuals, males and overweight individuals. Furthermore, the model combining conventional risk factors and GRS exhibited excellent discriminative performance (AUC = 0.962) and outstanding robustness (mean ROC-AUC = 0.959). CONCLUSION: This study established a reliable XGBoost-based GRS model and a GRS prediction platform (https://metabolicsyndromeapps.shinyapps.io/geneticriskscore/) to assess individual genetic susceptibility to MetS. This model has high interpretability and can provide personalized reference for determining the necessity of primary prevention measures for MetS. Additionally, there may be interactions between traditional risk factors and GRS, and the integration of both in a comprehensive model is useful in the prediction of MetS occurrence.

Humans↗

PubMed2026

Stage-specific ROMO1 in rheumatoid arthritis: predictive immune insights into the MIF pathway and HLA-DR/IL2RA axis via integrated GWAS, transcriptomic, single-cell, and spatial profiling.

Emerging evidence links reactive oxygen species modulator 1 (ROMO1), a key mitochondrial ROS regulator, to rheumatoid arthritis (RA) pathogenesis. However, its exact mechanism remains elusive given the conflicting evidence about its specific function. We used a four-level integrative framework combining multi-omics data and literature‑supported mechanistic inference. At the genetic level, Mendelian randomization (MR) was performed to explore potential causal relationships between ROMO1, IL2RA, HLA-DR, MIF, and RA risk, followed by differential expression analysis and machine learning-based feature selection to identify key mROS genes. The temporal expression dynamics of ROMO1 were assessed in RA progression. At the cellular and tissue levels, we integrated single-cell RNA sequencing and spatial transcriptomics to map cell-type-specific expression and synovial localization of ROMO1-related immune cells and pathways. Finally, our multi-omics findings were contextualized with literature-supported mechanistic inference. (1) MR results were consistent with a potential protective effect of ROMO1 on RA (OR = 0.52) and its potential regulation of risk factors IL2RA (OR = 0.46) and HLA-DR (OR = 0.40). Conversely, IL2RA (OR = 1.42), HLA-DR (OR = 1.88), and MIF (OR = 1.17) were positively associated with RA risk. Additionally, ROMO1 was identified as a top candidate diagnostic predictor with stage-specific dynamics: downregulated in the early but upregulated in the late/remission stages. (2) Single-cell RNA sequencing showed ROMO1's cell-specific expression in CD14+ HLA-DR+ CD74+ monocytes and CD4+ IL2RA+ T cells. Cell communication analysis further suggested that these cells may participate in MIF pathway regulation. Spatial transcriptomics subsequently identified that ROMO1-related cells localized to synovial pathological regions, with MIF pathway changes correlated with RA progression. (3) Finally, literature-supported mechanistic inference suggests that ROMO1 may modulate mROS levels to promote anti-inflammatory M2 macrophage polarization, which could theoretically contribute to reduced systemic inflammation and the alleviation of multi-organ decline in RA. This integrated multi-omics investigation, supported by literature-based mechanistic inference, suggests ROMO1 as a stage-dependent biomarker candidate and potential immune regulator in RA.

Humans↗

PubMed2026

Estimating population structure using epigenome-wide methylation data.

Population stratification is one of the source of inflation in epigenome-wide association studies (EWAS) when not properly accounted for. To address this, we developed methylation population scores (MPSs) to predict genetic principal components (GPCs) using a feature selection approach. We used multi-ethnic DNA methylation data from Illumina EPIC arrays across five cohorts, including MESA (n = 929), CARDIA (n = 1123), JHS (n = 1365), ARIC (n = 2338), and HCHS/SOL (n = 1475), randomly splitting participants into training (85%) and test (15%) sets. Within each cohort, associations between GPCs and CpG sites were estimated using linear regression adjusting for age, sex, smoking and alcohol use, race/ethnicity, body mass index, and cell type proportions, followed by meta-analysis and selection of CpGs with FDR <0.05. We then applied a two-stage weighted least squares Lasso regression to construct MPSs, adjusting for the aforementioned covariates. In the test dataset, MPSs showed strong correlation with GPCs, with R² ranging from 0.27 (MPS7 vs. GPC7) to 0.98 (MPS1 vs. GPC1). Visualization demonstrated that MPSs recapitulated the pattern shown by GPCs in differentiating self-reported White, Black, and Hispanic/Latino groups and outperformed methylation-based principal components constructed using alternative published methods. Additionally, MPSs showed comparable performance to GPCs in reducing inflation in EWAS. Overall, MPSs uses supervised learning with covariate adjustment to capture genetic structure across diverse populations, and provide a reliable estimate of population structure in the data and can complement GPCs when genetic data are absent.

Humans↗

PubMed2026

CpGene: a web application for epigenetic signature identification from DNA methylation arrays.

MOTIVATION: DNA methylation (DNAme) is the best studied epigenetic mechanism that plays pivotal role in tissue differentiation and epigenetic disruption has been correlated to diverse disease types (e.g. cancer, metabolic disorders). While various DNAme array platforms have been discovered, data analysis remains a challenging task which often requires in-depth bioinformatic expertise. Here, we developed a user-friendly web-based application for data analysis and visualization that accommodates users ranging from early-career basic/translational researchers to experienced bioinformaticians. RESULTS: CpGene is a web application for analyzing DNA methylation array data. It supports Illumina 450K, EPIC, and EPICv2 methylation array platforms and processes .idat files with integrated preprocessing, normalization, and quality control. Biomarker discovery is available through either classic differential methylation point analysis or machine learning-based feature selection as well as gene enrichment analysis. Results are summarized with clear visualizations, to aid interpretation. By combining these functions in a unified interface, CpGene streamlines methylation analysis and helps identify CpG sites and genes with biological and clinical relevance. AVAILABILITY AND IMPLEMENTATION: CpGene is openly accessible as a web service through http://cpgene.duckdns.org:8001/ and it's source code is available on https://github.com/kostaslazaros/cpgenene.

DNA Methylation↗

PubMed2025

Model-based multifacet clustering with high-dimensional omics applications.

High-dimensional omics data often contain intricate and multifaceted information, resulting in the coexistence of multiple plausible sample partitions based on different subsets of selected features. Conventional clustering methods typically yield only one clustering solution, limiting their capacity to fully capture all facets of cluster structures in high-dimensional data. To address this challenge, we propose a model-based multifacet clustering (MFClust) method based on a mixture of Gaussian mixture models, where the former mixture achieves facet assignment for gene features and the latter mixture determines cluster assignment of samples. We demonstrate superior facet and cluster assignment accuracy of MFClust through simulation studies. The proposed method is applied to three transcriptomic applications from postmortem brain and lung disease studies. The result captures multifacet clustering structures associated with critical clinical variables and provides intriguing biological insights for further hypothesis generation and discovery.

Humans↗

PubMed1982

Dietary cadmium, zinc and copper: effects on chick lung morphology and elastin cross-linking.

Day-old White Leghorn cockerels were divided into seven dietary groups and fed one of the following diets: 1) a casein-based basal diet; 2) a casein-based diet supplemented with 10 mg/kg cadmium, 3) 100 mg/kg cadmium, 4) or 800 mg/kg zinc; 5) a casein-based diet pair-fed to the 100 mg/kg Cd group; 6) a spray-dried nonfat milk-based diet with no added copper, or 7) a spray-dried nonfat milk-based diet supplemented with 5 mg/kg copper. At termination (5 weeks), the birds were killed, and the effects of the diets on selected features of lung composition and morphology were assessed. Body weights were reduced in the 100 mg/kg Cd, pair-fed, and Cu-deficient groups when compared to their controls (casein-based or milk-based copper-supplemented diets). There were no differences in lung weights (expressed relative to metabolic body size) among the groups, although copper deficiency did result in a slight decrease in the dry to wet weight ratio of lung. Lung elastin content and the desmosine content in elastin were significantly lower in the Cu-deficient group and tended to be lower in the group fed 800 ppm Zn. Significant alterations (enlargement of the tertiary bronchial lumen) in morphology were also observed in lungs from both the 100 mg/kg Cd and Cu-deficient groups. Alteration in lung morphology observed in the 100 mg/kg Cd group could not be explained by changes in the elastin content of lung.

Animals↗

PubMed2026

Proteomic Immune Signatures of Severe HIV-Associated Tuberculosis in Sub-Saharan Africa: A Prospective, Multicenter Analysis From Uganda.

OBJECTIVES: Severe tuberculosis (TB) is a major cause of critical illness and death in people living with HIV (PLWH) worldwide. Despite this, the immunopathology of severe HIV-associated TB (HIV/TB) is poorly understood. We aimed to identify an immunopathologic signature of severe HIV/TB in sub-Saharan Africa. DESIGN AND SETTING: We analyzed proteomic data from two prospective observational cohorts of adults hospitalized with severe undifferentiated infection in Uganda: an urban discovery cohort (Entebbe, n = 241) and a rural validation cohort (Tororo, n = 253). PATIENTS: Adults (age ≥ 18 yr) hospitalized with severe febrile illness. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Across both cohorts, severe HIV/TB was common, affecting 18% of participants in the discovery cohort and 21% in the validation cohort. Overall mortality was significant (30-d mortality of 22% in the discovery cohort and 60-d mortality of 26% in the validation cohort). Participants were stratified into three HIV/TB phenotypes: HIV-negative without TB, PLWH without TB, and PLWH with microbiologically diagnosed TB. We applied ordinal random forest models in the discovery cohort as a supervised feature-selection approach to identify proteins associated with progressive HIV/TB phenotype. In both cohorts, PLWH with microbiologically diagnosed TB were at highest risk of critical illness and death (30-d mortality of 42% in the discovery cohort and 60-d mortality of 52% in the validation cohort). An eight-protein signature reliably distinguished this phenotype, reflecting mediators of macrophage/dendritic cell activation (lysosome-associated membrane glycoprotein 3), natural killer cell and T-cell stimulation and cytotoxicity (cluster of differentiation 70, class I-restricted T-cell-associated molecule), B-cell activation (immunoglobulin lambda constant 2), protease-mediated tissue injury (protease, serine 2 [trypsin-2]), dysregulated coagulation (serpin peptidase inhibitor, clade A [alpha-1 antitrypsin], member 5), extracellular matrix remodeling (epidermal growth factor-containing fibulin-like extracellular matrix protein 1), and growth hormone/insulin-like growth factor axis dysregulation (insulin-like growth factor binding protein 3). CONCLUSIONS: We identified an immunologic signature of severe HIV/TB defined by mediators of macrophage/dendritic cell and cytotoxic lymphocyte activation, extracellular matrix remodeling, and dysregulated coagulation. These findings offer new insight into HIV/TB pathobiology and highlight potential targets for host-directed therapies in this high-risk population.

Humans↗

PubMed2025

Estimating population structure using epigenome-wide methylation data.

INTRODUCTION: In epigenome-wide association analysis (EWAS), unaddressed population stratification often leads to inflation. We aimed to compute methylation population scores (MPSs) that predict genetic principal components (GPCs) using a feature selection and regression approach. METHODS: We used multi-ethnic methylation data (Illumina 450K/EPIC array) from unrelated MESA (n=929), CARDIA (n=1123), JHS (n=1365), ARIC (n=2338), and HCHS/SOL (n=1475) individuals, randomly assigning 85% of participants from each cohort to a training dataset and the remaining 15% to a test dataset. First, we estimated the associations of GPCs with each available CpG methylation site using linear regression within each cohort, adjusting for age, sex, smoking status, race/ethnic background (as a proxy for background information associated with lifestyle and other environmental exposures that may impact methylation), alcohol use status, body mass index, and cell type proportions. We meta-analyzed the associations across cohorts and selected CpG sites with association FDR-adjusted q-value <0.05. We next aggregated individuallevel data across the cohort-specific training datasets, and applied two-stage weighted least squares Lasso regression, with the GPCs as the outcomes and the selected CpG sites as penalized predictors, adjusting for the aforementioned covariates. The developed MPSs are the weighted sum of selected CpG sites from the Lasso. To evaluate the developed MPSs, we constructed them in the test dataset, and compared them with GPCs, and with MPSs constructed based on a previously-published paper. Comparison was based on correlation analysis and data visualization. We demonstrate the use of the MPSs in EWAS. RESULTS: In the test dataset, the MPSs were highly correlated with GPCs, with correlation decreasing, though not monotonically, for later components. Specifically, MPS1 and GPC1 had R2= 0.99, while MPS7 and GPC7 had R2=0.27 (the lowest observed correlation). In data visualization, MPSs had similar patterns as GPCs in differentiating self-reported White, Black, and Hispanic/Latino groups, while outperforming MPC constructed using alternative published methods. MPSs showed comparable performance to GPCs in reducing some of the inflation in EWAS. CONCLUSIONS: Methylation-based population scores provide a reliable estimate of population structure in the data and can complement GPCs when genetic data are absent. Unlike previous methods based on unsupervised methylation PCA, MPSs uses supervised learning with covariate adjustment to capture genetic structure across diverse populations. The weights for each GPCs derived in our study can be applied to generate MPSs in other studies.

Journal Article↗

PubMed2026

Knowledge-driven interpretable neural networks provide mechanistic insight.

Analyzing omics data in the context of pathway knowledge is critical for understanding the molecular mechanisms underlying pathological changes. However, current pathway analysis methods do not model the detailed mechanistic nature of biological interactions, limiting the understanding of pathway behavior to a relatively shallow level. To address this issue, we present a knowledge-driven machine learning framework that embeds features into pathway graphs and models reactions analytically, producing interpretable feature hierarchies and subnetworks in which functional associations are estimated to model biological interactions. The approach is agnostic to feature selection, enabling the use of full omics data sets without discarding weak signals. Applications to breast cancer microRNA-gene regulation data and COVID-19 metabolomic data highlight immune and metabolic pathways relevant to disease progression. This framework bridges predictive modeling with mechanistic interpretation and offers a foundation for integrative pathway analysis.

Humans↗

PubMed2026

Large-Scale Plasma Proteomics Identifies Early Molecular Deviations and Improves Risk Prediction for Heart Failure Among Individuals With Obesity.

AIMS: Heart failure (HF) is a major global public health challenge, with obesity being one of its key risk factors. Although several HF risk prediction models have been developed in the general population, few are specifically tailored to individuals with obesity. This underscores the urgent need for precise biomarkers to improve individual risk stratification and enable personalized prevention strategies. We aimed to develop and validate a plasma proteomics-based protein risk score (PRS) to predict incident HF among individuals with obesity. MATERIALS AND METHODS: We analysed 9831 participants with obesity (BMI ≥ 30 kg/m2) from the UK Biobank with baseline measurements of 2911 circulating proteins and up to 16 years of follow-up. Multivariable Cox regression identified proteins associated with incident HF after comprehensive covariate adjustment. A PRS was constructed using LASSO regression and evaluated in a held-out test set. Protein trajectories before HF onset were reconstructed using LOESS modelling. To enhance clinical feasibility, a minimal protein panel was identified using LightGBM with forward feature selection. RESULTS: A total of 727 participants developed HF during follow-up. Multivariable cox analyses identified 578 proteins significantly associated with HF. LASSO regression further selected 81 proteins to build the PRS, which showed a strong association with HF risk in both training (HR 3.57; 95% CI 3.19-4.00) and test cohorts (HR 2.45; 95% CI 2.20-2.74). Adding the PRS improved prediction beyond age and sex (ΔC = 0.091) and beyond the Pooled Cohort Equations to Prevent Heart Failure (PCP-HF) model (ΔC = 0.052), with consistent gains in NRI and IDI. Proteomic deviations were detectable up to 16 years before diagnosis. A four-protein panel (GDF15, NT-proBNP, TNFRSF10B, CTHRC1) achieved robust discrimination (AUC 0.789), outperforming NT-proBNP alone (AUC 0.695) and complementing the PCP-HF model (combined AUC 0.803). DISCUSSION: Large-scale plasma proteomics substantially improves HF risk prediction in individuals with obesity and reveals long-standing molecular alterations preceding clinical onset. A simplified four-protein panel maintains robust predictive accuracy and provides a practical approach for the early detection and targeted prevention of obesity-related HF.

Humans↗

PubMed2026

AI In Leukemia Diagnostics: Complementing the Pathologist's Role.

Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-generation sequencing, multi-omics analysis, and emerging computational frontiers such as quantum-inspired feature selection. This review outlines how contemporary AI tools can automate labor-intensive quantitation, flag diagnostically salient patterns, and standardize interpretation, while the pathologist or hematologist retains authority over validation, context-specific integration, and clinical decision-making. We present an illustrative "human-in-the-loop" workflow that embeds AI modules within current laboratory information systems, emphasizing points where expert oversight mitigates algorithmic bias and resolves discordant findings. We further map the validator-integrator role across morphology, flow cytometry, and genomic/multi-omic interpretation and provide practical training competencies and use cases for AI-assisted hematopathology. Beyond technical deployment, the article addresses the educational transformation required for sustainable adoption. Drawing on international competency frameworks, including the Digital Health Competencies in Medical Education Framework and recently proposed AI-specific Entrustable Professional Activities, we map core skills that future hematopathologists must master: data-science literacy, critical appraisal of AI outputs, and ethical governance. We highlight evaluated training models such as the Pathology Informatics Essentials for Residents curriculum, Stanford Artificial Intelligence in Machine and Imaging workshops, and College of American Pathologists bootcamps and propose integration strategies adaptable across resource settings. By pairing rigorous validation with targeted education, AI can elevate rather than eclipse the diagnostic role of the leukemia specialist, enabling more timely, reproducible, and personalized patient care.

Humans↗

PubMed1981

Spectral analysis of Doppler velocity patterns in normals and patients with carotid artery stenosis.

A computerized pattern recognition program was utilized to assess the predictive ability of various parameters obtained from the spectra of ultrasonic pulsed Doppler signals from the carotid arteries. The most accurate features selected by linear regression analysis were the natural log (ln) of the ratio of the mean velocity in the internal carotid artery compared to that in the common carotid artery, and the ln of the maximum velocity, the ln of the maximum frequency, and the square of the fractional broadening term, all of which were measured at peak systole in the internal carotid artery. Using the combination of the velocity ratio and the fractional broadening term, the average difference in the estimated percentage stenosis, as compared to that obtained by arteriography, was 12.8%.

Arteriosclerosis↗

PubMedDate not supplied

Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence.

A set of ultrasonograms of lesions from 200 patients between the ages of 14 and 93 years who underwent mammography followed by ultrasonographic examination and excisional biopsy has been studied with computer vision techniques to improve the ultrasonographic specificity of the diagnosis. Selected features representing the texture of the lesion were calculated and then classified by an artificial neural network. This network was biased toward correctly classifying all the malignant cases at the expense of some misclassification of the benign cases. The network diagnosed the malignant cases with 100% sensitivity and 40% specificity (compared with 0% specificity for the radiologists diagnosing the same set of cases in the breast imaging setting), and tests performed with a leave-one-out technique indicate that the network will generalize well to new cases. This suggests that methods based on neural network classification of texture features show promise for potentially decreasing the number of unnecessary biopsies by a significant amount in patients with sonographically identifiable lesions.

Adolescent↗

PubMed1994

Use of cephalexin-aztreonam-arabinose agar for selective isolation of Enterococcus faecium.

Cephalexin-aztreonam-arabinose agar (CAA), a new selective agar, was examined in comparison with nalidixic acid-colistin agar for the differentiation of Enterococcus faecium from other enterococci and the ability to isolate the organism from feces. Two hundred sixteen enterococcus isolates and a variety of gram-positive and gram-negative control strains were inoculated onto both media. All control strains of E. faecium were easily differentiated from Enterococcus faecalis and Enterococcus durans on the basis of arabinose fermentation on CAA. Differentiation of E. faecium from other enterococci or Streptococcus bovis was not possible on nalidixic acid-colistin agar. Increased isolation of E. faecium was demonstrated on CAA when both media were compared for the isolation of the organism from feces. CAA has been shown to possess excellent differential and selective features allowing the simple and effective isolation of E. faecium from heavily contaminated sites.

Agar↗

PubMed1990

Organizing principles for single joint movements. III. Speed-insensitive strategy as a default.

1. Human subjects made discrete elbow flexions in a horizontal plane over different distances, from a stationary initial position to a visually defined stationary target 9 degrees wide. We measured joint angle, acceleration, and electromyograms (EMGs) from two agonist and two antagonist muscles. 2. Subjects made movements over four different distances following one of four different instructions. The first instructed the subject simply to choose a comfortable speed. The other three explicitly emphasized either speed, accuracy, or maintenance of the "same" speed over different distances. These instructions produced a wide range of movement velocities. 3. The initial rises of the acceleration (and therefore of the inertial torque), as well as the initial slope of the agonist EMG, were all invariant over changes in the target distance for any single instruction but were all sensitive to the given instruction. 4. Our results demonstrate that the speed-insensitive strategy is a standard or default pattern for performing movements that may be carried out for different instructions over a wide range of speeds. A uniform intensity of excitation pulse is not a byproduct of moving at maximal speed. Submaximal intensities are associated with submaximal speeds and are a selected feature of the pattern of movement control.

Acceleration↗

PubMed1991

An autopsy study of the incidence of lacunes in relation to age, hypertension, and arteriosclerosis.

We investigated selected features of lacunes in 1,086 necropsy cases. Lacunes were found in brains from patients above the age of 40 years and were most common in brains from persons in their sixties but decreased in number in brains from older persons. The most common site of lacunes was the frontal lobe white matter, followed by the putamen, pons, parietal lobe white matter, thalamus, and caudate nucleus in descending order of frequency. By dividing the 1,086 cases into three groups according to blood pressure, we found more lacunes in the hypertensive and borderline hypertensive groups than in the normotensive group; the average number of lacunes per brain in each group was 3.61, 2.77, and 1.15, respectively. Diastolic hypertension was more closely related to the number of lacunes than was systolic hypertension. The extent of arteriolosclerosis of the medullary arteries in the frontal lobe white matter was measured and compared with the number of lacunes. There was a close correlation between lacunes and arterioloslerosis in all age groups.

Adult↗

PubMed1995

Alkaline phosphatase: placental and tissue-nonspecific isoenzymes hydrolyze phosphoethanolamine, inorganic pyrophosphate, and pyridoxal 5'-phosphate. Substrate accumulation in carriers of hypophosphatasia corrects during pregnancy.

Hypophosphatasia features selective deficiency of activity of the tissue-nonspecific (liver/bone/kidney) alkaline phosphatase (ALP) isoenzyme (TNSALP); placental and intestinal ALP isoenzyme (PALP and IALP, respectively) activity is not reduced. Three phosphocompounds (phosphoethanolamine [PEA], inorganic pyrophosphate [PPi], and pyridoxal 5'-phosphate [PLP]) accumulate endogenously and appear, therefore, to be natural substrates for TNSALP. Carriers for hypophosphatasia may have decreased serum ALP activity and elevated substrate levels. To test whether human PALP and TNSALP are physiologically active toward the same substrates, we studied PEA, PPi, and PLP levels during and after pregnancy in three women who are carriers for hypophosphatasia. Hypophosphatasemia corrected during the third trimester because of PALP in maternal blood. Blood or urine concentrations of PEA, PPi, and PLP diminished substantially during that time. After childbirth, maternal circulating levels of PALP decreased, and PEA, PPi, and PLP levels abruptly increased. In serum, unremarkable concentrations of IALP and low levels of TNSALP did not change during the study period. We conclude that PALP, like TNSALP, is physiologically active toward PEA, PPi, and PLP in humans. We speculate from molecular/crystallographic information, indicating significant similarity of structure of the substrate-binding site of ALPs throughout nature, that all ALP isoenzymes recognize these same three phosphocompound substrates.

Alkaline Phosphatase↗