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PubMed2025

Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function.

BACKGROUND: Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC. METHODS: Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. Then, a Nomogram was constructed and evaluated for predicting the prognosis of GC patients, based on univariate and multivariate Cox regression. Functional enrichment analysis was performed to initially investigate the potential biological mechanisms. Through Genomics of Drug Sensitivity in Cancer (GDSC) dataset, the estimated IC50 values of several chemotherapeutic drugs were calculated. Tumor-related transcription factors (TFs) were retrieved from the Cistrome Cancer database and utilized our model to screen these TFs, and weighted correlation network analysis (WGCNA) was performed to identify transcription factors strongly associated with immunotherapy in GC. Finally, 10 patients with advanced GC were enrolled from Sun Yat-sen University Cancer Center, including paired tumor tissues, paracancerous tissues and peritoneal metastases, for preparing sequencing library, in order to perform external validation. RESULTS: Lower ICI Score was correlated with improved prognosis in both the training and validation cohorts. First, lower mutant-allele tumor heterogeneity (MATH) was associated with lower ICI Score, and those GC patients with lower MATH and lower ICI Score had the best prognosis. Second, regardless of the T or N staging, the low ICI Score group had significantly higher overall survival (OS) compared to the high ICI Score group. For its mechanisms, consistently, for Camptothecin, Doxorubicin, Mitomycin, Docetaxel, Cisplatin, Vinblastine, Sorafenib and Paclitaxel, all of the IC50 values were significantly lower in the low ICI Score group compared to the high ICI Score group. As a result, based on univariate and multivariate Cox regression, ICI Score was considered to be an independent prognostic factor for GC. And our Nomogram showed good agreement between predicted and actual probabilities. Based on CIBERSORT deconvolution analysis, there was difference of immune cell composition found between high and low ICI Score groups, probably affecting the efficacy of immunotherapy. Then, MEF2C, a tumor-related transcription factor, was screened out by WGCNA analysis. Higher MEF2C expression is significantly correlated with a worse OS. Moreover, its higher expression is also negatively correlated with tumor mutation burden (TMB) and microsatellite instability (MSI), but positively correlated with several immunosuppressive molecules, indicating MEF2C may exert its influence on tumor development by upregulating immunosuppressive molecules. Finally, based on transcriptome sequencing data on 10 paired tumor tissues from Sun Yat-sen University Cancer Center, MEF2C expression was significantly lower in paracancerous tissues compared to tumor tissues and peritoneal metastases, and it was also lower in tumor tissues compared to peritoneal metastases, indicating a potential positive association between MEF2C expression and tumor invasiveness. CONCLUSIONS: Our prognostic model can effectively predict outcomes and facilitate stratification GC patients, offering valuable insights for clinical decision-making. The identified transcription factor MEF2C can serve as a biomarker for assessing the efficacy of immunotherapy for GC.

Humans↗

PubMed2025

Distinct immune-metabolic phenotypes underlie poor coronary collateral circulation.

BACKGROUND: Coronary collateral circulation (CCC) significantly impacts myocardial perfusion and clinical outcomes in coronary artery disease patients, yet the underlying molecular heterogeneity remains inadequately characterized. OBJECTIVE: To identify distinct molecular phenotypes in patients with poor CCC, validate these phenotypes using clinical parameters, and evaluate their prognostic implications. METHODS: This study enrolled 149 patients (80 with good CCC and 69 with poor CCC) for high-throughput proteomic profiling. Unsupervised consensus clustering identified molecular subtypes within poor CCC patients, followed by differential expression analysis and KEGG pathway enrichment. Boruta feature selection was implemented, and multiple machine learning algorithms were tested on clinical data, with XGBoost optimization (accuracy 80.0%, F1-score 80.31%) and SHAP value interpretation. External validation was performed using the MIMIC database. Kaplan-Meier analysis and Cox regression models assessed major adverse cardiovascular events (MACE). RESULTS: Two distinct phenotypes emerged among poor CCC patients: Cluster 1 (n = 39, Complement-Driven Vascular Remodeling [CDVR]) and Cluster 2 (n = 30, Immuno-Thrombotic Myocardial Dysfunction [ITMD]). An XGBoost model incorporating fasting glucose, eosinophil percentage, and HbA1c achieved excellent discrimination (AUC > 0.91). External validation confirmed the phenotype-specific clinical patterns. Notably, Cluster 2 demonstrated significantly higher MACE incidence compared to Cluster 1 (Log-rank p < 0.05), with KEGG analysis revealing significant upregulation of platelet activation, diabetic cardiomyopathy, and metabolic pathways in the ITMD phenotype. CONCLUSION: Poor CCC encompasses distinct immune-metabolic phenotypes that can be accurately classified using integrated proteomic-clinical modeling. This classification enables more precise risk stratification and may guide personalized therapeutic strategies for coronary artery disease patients with inadequate collateralization.

Humans↗

PubMed2025

Radiogenomic MRI biomarkers for noninvasive prediction of GPC3 expression and tumor microenvironment in hepatocellular carcinoma.

BACKGROUND: Glypican-3 (GPC3) is frequently overexpressed in hepatocellular carcinoma (HCC) and plays a key role in immune and metabolic remodeling of the tumor microenvironment. Reliable noninvasive biomarkers for predicting GPC3 status could improve patient stratification and support precision immunotherapy. METHODS: This multicenter retrospective study included 274 patients with pathologically confirmed hepatocellular carcinoma from three institutions, 34 external cases with MRI from The Cancer Imaging Archive, and 363 transcriptomic profiles from The Cancer Genome Atlas. Contrast-enhanced T1-weighted imaging and diffusion-weighted imaging were analyzed. Tumor and peritumoral regions were segmented manually and radiomic features extracted using PyRadiomics. Feature selection was performed with correlation filtering and least absolute shrinkage and selection operator regression. Machine learning classifiers including logistic regression, random forest, support vector machine, k-nearest neighbor, and decision tree were trained with 10-fold cross-validation and tested on independent external cohorts. A radiomics score was calculated for each patient. Radiogenomic analysis correlated radiomics scores with transcriptomic data using weighted gene co-expression network analysis. Hub genes and enriched pathways were identified, and immune infiltration and predicted immunotherapy response were assessed using computational methods. RESULTS: The random forest model using contrast-enhanced T1-weighted imaging achieved an area under the curve of 0.966 in training and 0.935 in internal validation. The integrated contrast-enhanced T1-weighted imaging plus diffusion-weighted imaging model reached an internal validation area under the curve of 0.979. In external testing, the best performance was obtained with a support vector machine model (area under the curve 0.756). Radiomics scores were significantly correlated with GPC3 expression (R = 0.78, p < 0.05). Transcriptomic analysis identified a 10-gene signature enriched in hypoxia and lipid metabolism pathways that stratified patients into prognostic subgroups (concordance index 0.720, hazard ratio 4.07, p < 0.0001). High-risk patients had greater immune infiltration and a lower predicted immune evasion score, suggesting a potential benefit from immunotherapy. CONCLUSIONS: MRI-based radiomics models can noninvasively predict GPC3 expression in hepatocellular carcinoma. Radiomics scores reflect underlying hypoxia and lipid metabolism pathways and stratify patients by prognosis and predicted immunotherapy response. These findings support radiogenomics as a translational approach to imaging-guided precision treatment in hepatocellular carcinoma.

Humans↗

PubMed2026

Machine learning-based clinical prediction model and multi-omics integration for assessing pancreatic cancer risk in new-onset diabetes.

BACKGROUND: Given that pancreatic cancer (PC) is typically diagnosed at an advanced stage but is often preceded by new-onset diabetes mellitus (NODM), providing a window for early detection, we sought to develop and validate an interpretable machine-learning model integrated with multi-omics profiling to identify early biomarkers of NODM-associated PC. METHODS: In a population-based cohort, individuals with NODM-associated PC and NODM without PC were identified and randomly divided (70:30) into training and validation sets after feature selection. Eight machine learning (ML) classifiers were compared using fivefold cross-validation, and model performance was evaluated in terms of discrimination, calibration, and decision curve–based clinical utility. We evaluated interpretability using the Shapley additive explanations (SHAP) analyses. Mechanistically, Olink proteomic profiling and metabolomics were analyzed through clinical classifications and model-defined risk strata. RESULTS: Categorical boosting achieved the best performance in the independent validation set (AUROC = 0.844). The NODM cohort was stratified into high- (n = 2,362) and low-risk (n = 5,030) groups, and internal validation together with SHAP analyses demonstrated consistent model performance and identified clinically interpretable predictors. Proteomic and metabolomic analyses under clinical and risk-based grouping identified 39 overlapping differentially expressed proteins and 145 overlapping metabolites with enriched across 11 shared KEGG pathways. Cross-platform validation highlighted PLTP, CRTAC1, and ITGAV as serum biomarkers with a strong potential for early NODM-PC detection. CONCLUSIONS: We developed an interpretable ML framework centered on NODM enables practical risk stratification for early PC detection by multi-omics and provides a pathway of ML-based triage followed by biomarker confirmation for earlier detection and diagnosis.

Humans↗

PubMed1988

Pharmacotherapy for aphasia.

Selected features of aphasia may reflect disruption of specific neurotransmitter systems. Pharmacotherapy focused on these aphasic symptoms may improve language performance following stroke. We attempted to restore speech fluency in a patient with long-standing transcortical motor aphasia by treating his symptoms of hesitancy and impaired initiation of speech with bromocriptine. During therapy his language performance improved substantially, due to reduced latency of response, decreased paraphasias, and increased naming ability. After cessation of drug therapy his language returned to baseline.

Aphasia↗

PubMed1979

Some applications of categorical data analysis to epidemiological studies.

Several examples of categorized data from epidemiological studies are analyzed to illustrate that more informative analysis than tests of independence can be performed by fitting models. All of the analyses fit into a unified conceptual framework that can be performed by weighted least squares. The methods presented show how to calculate point estimate of parameters, asymptotic variances, and asymptotically valid chi 2 tests. The examples presented are analysis of relative risks estimated from several 2 x 2 tables, analysis of selected features of life tables, construction of synthetic life tables from cross-sectional studies, and analysis of dose-response curves.

Actuarial Analysis↗

PubMed1988

Sampling irregularity perturbs visual reconstruction.

We explored the human observer's ability to detect and discriminate sine-wave and square-wave gratings that were sampled at intervals varying from 4.7 to 9.4 arcmin. To study the effect of sampling irregularity on visual performance, we varied the position of each line sample on the basis of a Gaussian probability distribution, the standard deviation of which varied from 0 (regular sampling) to 4.7 arcmin (highly irregular sampling). The results indicate that irregular sampling has no systematic effect on the observer's ability merely to detect the presence of a sine- or square-wave grating. In contrast, sampling irregularity strongly impairs the subject's ability to discriminate between these waveforms. A model based on the convolution of difference-of-Gaussians-type weighting profiles predicted that sampling irregularity should have little to no effect on the output of a channel tuned to the third harmonic of the square-wave grating. The findings thus suggest the existence of a sampling scheme in the visual system. This scheme is based on local feature-selective mechanisms, probably edge detectors, that are highly sensitive to the relative position of the sample points in the space domain.

Discrimination, Psychological↗

PubMed2025

AWGE-ESPCA: An edge sparse PCA model based on adaptive noise elimination regularization and weighted gene network for Hermetia illucens genomic data analysis.

Hermetia illucens is an important insect resource. Studies have shown that exploring the effects of Cu2+-stressed on the growth and development of the Hermetia illucens genome holds significant scientific importance. There are three major challenges in the current studies of Hermetia illucens genomic data analysis: firstly, the lack of available genomic data which limits researchers in Hermetia illucens genomic data analysis. Secondly, to the best of our knowledge, there are no Artificial Intelligence (AI) feature selection models designed specifically for Hermetia illucens genome. Unlike human genomic data, noise in Hermetia illucens data is a more serious problem. Third, how to choose those genes located in the pathway enrichment region. Existing models assume that each gene probe has the same priori weight. However, researchers usually pay more attention to gene probes which are in the pathway enrichment region. Based on the above challenges, we initially construct experiments and establish a new Cu2+-stressed Hermetia illucens growth genome dataset. Subsequently, we propose AWGE-ESPCA: an edge Sparse PCA model based on adaptive noise elimination regularization and weighted gene network. The AWGE-ESPCA model innovatively proposes an adaptive noise elimination regularization method, effectively addressing the noise challenge in Hermetia illucens genomic data. We also integrate the known gene-pathway quantitative information into the Sparse PCA(SPCA) framework as a priori knowledge, which allows the model to filter out the gene probes in pathway-rich regions as much as possible. Ultimately, this study conducts five independent experiments and compared four latest Sparse PCA models as well as representative supervised and unsupervised baseline models to validate the model performance. The experimental results demonstrate the superior pathway and gene selection capabilities of the AWGE-ESPCA model. Ablation experiments validate the role of the adaptive regularizer and network weighting module. To summarize, this paper presents an innovative unsupervised model for Hermetia illucens genome analysis, which can effectively help researchers identify potential biomarkers. In addition, we also provide a working AWGE - ESPCA model code in the address: https://github.com/yhyresearcher/AWGE_ESPCA.

Animals↗

PubMed2024

A framework for block-wise missing data in multi-omics.

High-throughput technologies have generated vast amounts of omic data. It is a consensus that the integration of diverse omics sources improves predictive models and biomarker discovery. However, managing multiple omics data poses challenges such as data heterogeneity, noise, high-dimensionality and missing data, especially in block-wise patterns. This study addresses the challenges of high dimensionality and block-wise missing data through a regularization and constrained-based approach. The methodology is implemented in the R package bwm for binary and continuous response variables, and applied to breast cancer and exposome multi-omics datasets, achieving strong performance even in scenarios with missing data present in all omics. In binary classification task, our proposed model achieves accuracy in the range of 86% to 92%, and F1 in the range of 68% to 79%. And, in regression task the correlation between true and predicted responses is in the range of 72% to 76%. However, there is a slight decline in performance metrics as the percentage of missing data increases. In scenarios where block-wise missing data affects multiple omics, the model performance actually surpasses that of scenarios where missing data is present in only one omics. One possible explanation for this might be that the other scenarios introduce a greater diversity of observation profiles, leading to a more robust model. Depending on the specific omics being studied, there is greater consistency in feature selection when comparing block-wise missing data scenarios.

Humans↗

PubMed2026

Benchmark of biomarker identification and prognostic modeling methods on diverse censored data.

The practices of identifying biomarkers and developing prognostic models using genomic data has become increasingly prevalent. Such data often features characteristics that make these practices difficult, namely high dimensionality, correlations between predictors, and sparsity. Many modern methods have been developed to address these problematic characteristics while performing feature selection and prognostic modeling, but a large-scale comparison of their performances in these tasks on diverse right-censored time to event data (aka survival time data) is much needed. We have compiled many existing methods, including some machine learning methods, several which have performed well in previous benchmarks, primarily for comparison in regards to variable selection capability, and secondarily for survival time prediction on many synthetic datasets with varying levels of sparsity, correlation between predictors, and signal strength of informative predictors. For illustration, we have also performed multiple analyses on a publicly available and widely used cancer cohort from The Cancer Genome Atlas using these methods. We evaluated the methods through extensive simulation studies in terms of the false discovery rate, F1-score, concordance index, Brier score, root mean square error, and computation time. Of the methods compared, CoxBoost and the Adaptive LASSO performed well in all metrics, and the LASSO and elastic net excelled when evaluating concordance index and F1-score. The Benjamini-Hoschberg and q-value procedures showed volatile performances in controlling the false discovery rate. Some methods' performances were greatly affected by differences in the data characteristics. With our extensive numerical study, we have identified the best performing methods for a plethora of data characteristics using informative metrics. This will help cancer researchers in choosing the best approach for their needs when working with genomic data.

Humans↗

PubMed2026

Discovery and validation of a multi-protein panel for predicting non-fatal major adverse cardiovascular events in diabetic kidney disease.

OBJECTIVE: To identify plasma protein biomarkers associated with incident non-fatal major adverse cardiovascular events (MACE) in diabetic kidney disease (DKD) patients. RESEARCH DESIGN AND METHODS: We analyzed 317 DKD patients from the UK Biobank. Plasma proteomics and clinical data (demographics, metabolism, renal function) were integrated. In an exploratory discovery phase, three sequential Cox regression models (crude, socio-demographic-adjusted, socio-demographic-metabolic adjusted) screened non-fatal MACE-associated proteins. To prevent information leakage, the cohort was then randomly split into training (70%) and testing (30%) sets; machine-learning feature selection, hyperparameter optimization, and final model development were performed exclusively within the training set. The associated proteins were input into the four-step machine-learning pipeline (LASSO-Cox, random survival forest, Boruta, XGBoost-Cox). Predictive performance was validated using Kaplan-Meier survival analyses, longitudinal trajectory modeling, and ROC benchmarking. An interactive web application was deployed for clinical implementation. RESULTS: Of 1,463 plasma proteins, 561 were associated with non-fatal MACE across Cox models, with 14 overlapping proteins. Nine core proteins (ANG, IL1R1, CXCL14, ESAM, PTGDS, HAVCR1, FGFR2, IGSF8, CCL3) were validated: ANG showed the strongest non-fatal MACE association (HR = 3.88, 95%CI 2.33-6.48, p<0.001), and all high-expression groups had elevated non-fatal MACE risk. GO/KEGG enrichment highlighted inflammatory-immune pathways like positive regulation of MAPK cascade, Cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway as key mechanisms. The model integrating proteins, demographic factors, and clinical variables achieved the highest predictive performance across non-fatal MACE (AUC = 0.768), myocardial infarction (MI) (0.808), and stroke (0.816) outcomes, with superior stability in cross-validation. CoxBoost + Elastic Net framework was selected as the optimal framework via benchmarking of 101 algorithms. The model demonstrated favorable calibration in high-risk patients and yielded positive net clinical benefit across decision thresholds of 5% to 45%. The web tool (https://jiangli2941.github.io/MACE-prediction-v2/) enables input of 28 variables, outputs non-fatal MACE risk status, risk probability, and highlights abnormal indicators. CONCLUSION: Plasma proteomics combined with machine learning identifies robust non-fatal MACE predictors in DKD.

Humans↗

PubMed2026

Integrated multi-omics profiling identifies aging-related molecular signatures and convergent interferon signaling in systemic lupus erythematosus.

BACKGROUND: Systemic lupus erythematosus (SLE) is characterized by chronic immune activation and molecular alterations that overlap with aging-related biological processes. However, how these alterations are organized across molecular layers and whether they converge on shared regulatory networks remain incompletely understood. METHODS: We performed an integrative multi-omics analysis combining in-house proteomic and phosphoproteomic data from 130 patients with SLE and 90 healthy controls (HCs) and publicly available transcriptomic datasets comprising 1,461 SLE patients. Proteins and phosphorylation sites were annotated using established aging-related gene resources. Differential protein abundance and phosphorylation changes were analyzed across disease-status and disease-activity comparisons. Nominal P-value thresholds were used for exploratory feature selection, whereas FDR-adjusted P values were used to assess robustness after multiple-testing correction. Kinase-substrate enrichment, transcription factor annotation, and cell-type-resolved transcriptomic comparison were used to explore potential regulatory programs. RESULTS: We identified 128 nominally altered proteins annotated to aging-related biological processes, including genomic instability, mitochondrial dysfunction, and epigenetic alterations. Phosphoproteomic analysis revealed 36 nominally altered phosphorylation sites, including previously unreported sites in IFI16 (S153, S780) and PKCδ (S507, S664). Clustering analysis demonstrated heterogeneous protein co-regulation patterns across disease states. Kinase activity inference suggested altered activity of TBK1 and IKKβ. TF analysis further highlighted STAT1, RELA, and PML as potential central nodes within the inferred regulatory network. Notably, these multi-omic alterations were not randomly distributed but showed convergence toward shared signaling pathways, particularly those related to interferon responses. CONCLUSIONS: This integrative multi-omics study identifies inflammatory and interferon-dominated molecular alterations in SLE PBMCs that overlap with aging-related biological processes and converge on shared regulatory networks. These findings provide a hypothesis-generating framework for investigating the intersection between chronic immune activation and aging-related molecular remodeling in SLE.

Humans↗

PubMed1992

Salience of stimulus and response features in choice-reaction tasks.

A pattern of differential reaction time (RT) benefits obtained in spatial-precuing tasks has been attributed to translation processes that operate on mental codes formed to represent the stimulus and response sets. According to the salient-features coding principle, the codes are based on the salient stimulus and response features, with RTs being fastest when the two sets of features correspond. Three experiments are reported in which the stimulus and response sets were manipulated using Gestalt grouping principles. In the first two experiments, stimuli and responses were grouped according to spatial proximity, whereas in the last experiment, they were grouped according to similarity. With both types of manipulations, the grouping of the stimulus set systematically affected the pattern of precuing benefits. Thus, in these experiments, the organization of the stimulus set was the primary determinant of the features selected for coding the stimulus and response sets in the translation process.

Adult↗

PubMed1984

Experimental infection with Rickettsia mooseri and antibody response of adult and newborn laboratory rats.

Quantitative studies of selected features of peripherally induced Rickettsia mooseri (= R. typhi) infection in Rattus norvegicus-derived white laboratory rats revealed a unique association between microbe and amplifying vertebrate host which appears to be especially conducive to maintenance of the enzootic cycle. Both adult and newborn (1-3 days old) rats were highly susceptible to percutaneous infection (ID50 = approximately 1 organism), but neither showed signs of disease or died even when inoculated with 10(4)-10(5) plaque-forming units. Gain in body weight of infected newborn rats was indistinguishable from that of uninfected newborn rats over the first 3 weeks of life. The course of the systemic infection, as measured by the rise and fall of R. mooseri titers in blood, brain and kidney and the serum antibody response, was almost identical in adult and newborn rats. Thus, despite their immaturity in certain immunological processes, newborn rats controlled postnatal R. mooseri infection about as well as did adult rats. The rickettsemic period of about 10 days corresponds to the period of infectivity of inoculated rats for fleas. Rickettsiae were not isolated from blood, brain or kidneys by methods employed for more than 4-5 weeks after infection. Serum antirickettsial antibodies persisted for at least 60 weeks postinfection, i.e., longer than the usual life span of rats in nature and, hence, are a valid measure of the cumulative experience of rat populations with R. mooseri infection.

Age Factors↗

PubMedDate not supplied

Overdose with chloral hydrate: a pharmacological and therapeutic review.

The purpose of this review is to highlight the toxicity of chloral hydrate and to review the management of overdoses with chloral hydrate. Three patients are presented in whom life-threatening cardiac arrhythmias dominated the clinical presentation. These arrhythmias were resistant to standard antiarrhythmic therapy. Also, we have reviewed selected features in eight patients who took overdoses of chloral hydrate who were admitted to an intensive care unit between 1981 and 1988. The pharmacology and toxicology of chloral hydrate are discussed with particular reference to the cardiac arrhythmias that are seen with overdosage. A proposed management scheme is detailed, including intravenously-administered propranolol as the preferred first-line antiarrhythmic agent. A case may be made for the discontinuation of the usage of chloral hydrate.

Adult↗

PubMed1976

Medical recommendations--followed or ignored? Factors influencing compliance in arthritis.

Physiatrists are frequently confronted by the failure of patients to follow medical advice, with resultant adverse effects on rehabilitation outcomes. This paper examines the compliance of arthritis patients with an exercise regimen. A number of factors are examined in this study in assessing elements related to patient compliance. Included are patient attributes and perceptions, professional home visits and form of instruction in the regimen and selected features of the patient's social environment. Age, activity status and marital status, use of medications, patient understanding of arthritis and professional home visits appear to have a bearing upon patient compliance. Careful attention to factors likely to influence patient compliance will prove helpful in efforts designed to enhance rehabilitative progress and, ultimately, improve the lives of patients.

Adult↗

PubMed1992

Ultrasonographic appearance of the uterus, placenta, fetus, and fetal membranes throughout accurately timed pregnancy in beagles.

Serial ultrasonographic examinations were performed on 8 Beagle bitches from 20 to 60 days pregnant to determine time of first detection, appearance, and sizes of selected features of pregnancy. Gestation was timed from the day of the preovulatory luteinizing hormone surge. Findings related to gestational age were consistent among bitches. Gestational ages at earliest detection of the following features were: chorionic cavity at day 20; placental layers in the uterine wall at day 22 to 24; zonary placenta at day 27 to 30; embryo and heartbeat at day 23 to 25; yolk sac membrane at day 25 to 28; allantoic membrane at day 27 to 31; choroid plexus of the brain at day 31 to 35; fetal movement at day 34 to 36; skeleton at day 33 to 39; bladder and stomach at day 35 to 39; kidney at day 39 to 47; and liver hypoechoic, compared with lung, at day 38 to 42. Extra-fetal structures were measurable from day 20 or 22 through day 48. Chorionic cavity diameter increased from 0.2 +/- 0.0 cm to 3.3 +/- 0.2 cm, outer uterine diameter increased from 0.8 +/- 0.03 cm to 4.8 +/- 0.2 cm, length of chorionic cavity or zonary placenta increased from 0.3 +/- 0.03 cm to 4.9 +/- 0.05 cm, uterine wall thickness increased from 0.3 +/- 0.03 cm to 0.8 +/- 0.01 cm, and placental thickness increased from 0.1 +/- 0.0 cm to 0.5 +/- 0.05 cm. Chorionic cavity diameter, outer uterine diameter, and placental length each increased at a linear rate through day 37, after which time, each had a marked plateau in growth. Of the extra-fetal structures, chorionic cavity diameter was the most accurate for estimation of gestational age. All of the fetal structures studied increased at an increasing (second order) rate. Crown-rump length increased from 0.3 +/- 0.05 cm on day 24 to 9.2 +/- 0.2 cm on day 48. Body diameter increased from 0.2 +/- 0.03 cm on day 24 to 4.6 +/- 0.15 cm on day 60. Head diameter increased from 0.8 +/- 0.05 cm on day 34 to 2.7 +/- 0.04 cm on day 60. Of the fetal structures, head diameter was the most accurate for estimation of gestational age.

Animals↗

PubMed1991

The immunobiology of heat shock proteins.

A cell's reaction to heat shock is one of the most conserved responses among evolutionary diverse species. Although selected features of the response may vary between organisms, the synthesis of heat shock proteins is universal. The induction of hsps is rapid and occurs immediately following exposure to only a few degrees above normal physiological temperature. The array of assigned functions as well as those hypothesized for heat shock proteins imply that hsps are designed to render cells capable of coping with a variety of physiological stresses, including heat and oxidative injury. Interestingly, the gene for the hsp70 is located between the complement and tumor necrosis factor genes within the class III region of the human major histocompatibility complex on the short arm of chromosome 6. Hsp70 proteins have been linked with a protective role during stress and as unfoldase(s) in protein translocation into the endoplasmic reticulum and mitochondria. Moreover, hsp analogs are often presented as antigens in bacterial or protozoal infections. The mapping of hsp70 in the MHC region may have significant implications with regard to immune mediated diseases. Moreover, the relationship of hsps to immunity and cancer represents the beginning of a major link of the role of hsps in human pathology. Finally, with its pleiotropic roles in essential cellular physiology and immune function, we predict that future experiments will define a relationship between hsps and allergy.

Animals↗