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PubMed1993

Post-reactivation cocaine administration facilitates later acquisition of an avoidance response in rats.

We previously demonstrated that cocaine administered immediately prior to a reactivation episode comprised of re-exposure to selected features of the original fear-conditioning session alters subsequent memory retrieval or reconsolidation. In the present study we determined that, similar to pre-reactivation administration, post-reactivation administration of cocaine also alters memory retrieval or reorganization, as measured by subsequent conditioned performance. The dose-response function for this effect of cocaine was U-shaped; maximal enhancement of subsequent avoidance performance was produced by a 7.5 mg/kg i.p. dose of cocaine. Because a dose of lidocaine equimolar to the effective cocaine dose was found not to alter subsequent conditioned performance, the effect of cocaine on memory processing most likely is not attributable to its local anesthetic properties.

Anesthetics, Local↗

PubMed1986

BMDP program for piecewise linear regression.

Piecewise linear regression has potentially broad applications in medical data analysis as well as other types of regression. Various kinds of algorithms have been proposed for finding optimum piecewise linear regressions. This paper presents a BMDP program for obtaining near optimum piecewise linear regression equations. An idea intrinsic to the method is that restricting parameter space to a discrete set makes the difficult problems become standard problems. Any software having the variable selection feature in the multiple linear regression can be used to apply the method.

Computers↗

PubMedDate not supplied

Beneficial effects of nicotine administered prior to a delayed matching-to-sample task in young and aged monkeys.

Our earlier studies have demonstrated that administration of low micrograms/kg doses of nicotine to young adult monkeys prior to a delayed matching-to-sample (DMTS) task resulted in a centrally mediated improvement in performance of the task, particularly when delay intervals which most greatly challenged the animal's capabilities were involved. The present study confirmed these findings using a completely computer driven and automated procedure. In addition, performance on the DMTS was observed to be enhanced when animals were again tested 24 h after the dose of nicotine. Further analysis of the data indicated that the majority of enhancement to nicotine could be accounted for by a greatly increased performance at the least preferred stimulus color. Position preference (left vs. right stimulus) was not a factor in nicotine-induced enhancement. Two aged monkeys (34 years old Macaca mulatta) were significantly more difficult to train in the DMTS task and their longest delay capabilities were significantly shorter than the young animals (Macaca fascicularis). Nevertheless, the aged animals were essentially similar in most respects in their responses to nicotine administration. These data are consistent with a role for central nicotinic systems in memory performance and with the ability of nicotine to produce enhancement of selective features of mnemonic strategy in young and old monkeys. Furthermore, it is possible that either model, the aged animal, or the young animal stressed to his mnemonic capability may provide a good model for learning and memory disorders in humans.

Aging↗

PubMed1986

Lung collagen and elastin after ozone exposure in vitamin B-6-deficient rats.

The effects of vitamin B-6 deficiency and ozone exposure on selected features of connective tissue metabolism in lung were investigated in groups of weanling male rats fed one of three diets: B-6-supplemented, fed ad lib; B-6-deficient, fed ad lib; or B-6-supplemented, restricted to the food intake of deficient rats for 5 weeks. Also, perinatal rat pups were studied that were nursed from dams fed one of the 3 diets from parturition to day 15 of lactation. During the final week of each experiment, half of the rats in each of the groups were exposed to 0.64 ppm of ozone (23.5 h per day). The collagen and elastin content, collagen synthesis rate, total protein synthesis rate, and lysyloxidase activity of lungs were measured. Perinatal pups rendered vitamin B-6-deficient were particularly sensitive to ozone exposure (65% died as compared to fewer than 5% of the ad lib or food-restricted controls). When L-proline incorporation into collagen and total protein was investigated using lung minces, food restriction and B-6-deficiency resulted in about one-half the incorporation normally observed. Total lung lysyl oxidase activity was also decreased in B-6-deficient and food-restricted rats compared to B-6-supplemented rats fed ad lib. Exposure to ozone resulted in increased lysyl oxidase activity and collagen synthesis in lungs from B-6-supplemented rats, but such responses were not observed in B-6-deficient or food-restricted (FR) rats exposed to ozone.

Aging↗

PubMedDate not supplied

Skin cancer recognition by computer vision.

Automatic detection of several features characteristic of basal cell epitheliomas is described. The features selected for this feasibility study are semitranslucency, telangiectasia, ulcer, crust, and tumor border. Image processing methods used in this study include frequency analysis of the Fourier transform of the image, the Sun-Wee texture analysis algorithm, and several other image analysis techniques suitable for skin photographs. This image analysis software is designed for use with AI/DERM, an expert system that models diagnosis of skin tumors by dermatologists.

Basal Cell Carcinoma↗

PubMed1996

Sleep classification in infants by decision tree-based neural networks.

This paper presents an AI-based approach to automatic sleep stage scoring. The system TBNN (Tree-Based Neural Network) uses a decision-tree generator to provide knowledge that defines the architecture of a backpropagation neural network, including feature selection and initialisation of the weights. The case study reports a successful application to the data from polygraphic all-night sleep of 8 babies aged 6 months. The teaching input was provided by a medical expert in accordance with the rules of Guilleminault and Souquet. The performance of TBNN is compared with 5 other methods and the results are discussed.

Artificial Intelligence↗

PubMed2026

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

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

Humans↗

PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n = 549) and a validation set (n = 236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60 mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60 mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗

PubMed2026

Integrated salivary proteomic and metabolomic analyses reveal molecular characterization and novel biomarker panels of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder characterized by chronic inflammation, oxidative stress, and metabolic dysregulation. The lack of convenient and easily-accessible non-invasive diagnostic approaches remains a major clinical challenge. This study applied an integrated saliva-based proteomic and untargeted metabolomic strategy to identify potential biomarkers for COPD classification. Comprehensive multi-omics analyses identified 225 differentially abundant proteins and 60 differentially abundant metabolites between patients with COPD and healthy controls, including 24 biologically relevant endogenous metabolites. Functional enrichment analyses revealed pronounced dysregulation of mitochondrial energy metabolism, redox homeostasis, lipid remodeling, and inflammatory-related pathways in COPD. By integrating salivary proteomic and metabolomic biomarkers, a stepwise feature selection combined with LASSO logistic regression was used to construct diagnostic models, yielding an optimized biomarker panel consisting of 11 proteins and 2 endogenous metabolites. This integrated model achieved excellent diagnostic performance, with an area under the ROC curve of 0.96. Collectively, these findings demonstrate that integrated salivary proteomic and metabolomic profiling provides a robust, non-invasive approach for COPD classification and offers a promising foundation for the development of biosensor-based diagnostic platforms and early disease detection. SIGNIFICANCE: Chronic obstructive pulmonary disease (COPD) remains a major global health burden. Current diagnostic approaches rely largely on spirometry and clinical assessment, which are limited in sensitivity for early-stage disease and unsuitable for large-scale screening. This study employs an integrated saliva-based proteomic and metabolomic strategy to identify non-invasive biomarkers for COPD classification. Our findings reveal coordinated dysregulation of mitochondrial energy metabolism, redox homeostasis, and lipid remodeling in COPD, highlighting the interconnected roles of metabolic reprogramming, oxidative stress, and inflammation in disease pathophysiology. Notably, a robust diagnostic panel comprising 11 proteins and 2 endogenous metabolites was established, achieving excellent classification performance (AUC of 0.96). To our knowledge, the integrated application of salivary proteomics and metabolomics for COPD diagnosis remains largely unexplored, underscoring the significance and translational potential of our findings.

Humans↗

PubMed2026

Multiomics Integration Identifies a Molecular Subtype of Intrahepatic Cholangiocarcinoma With Enhanced Benefit From Adjuvant Therapy.

Intrahepatic cholangiocarcinoma (iCCA) is a molecularly heterogeneous liver cancer with a poor prognosis. Improved stratification is needed to guide postoperative therapy. In this study, we applied integrative multiomics analysis to classify iCCA and identify biomarkers predictive of adjuvant treatment benefit. Using publicly available datasets (including whole exome sequencing, RNA sequencing, proteomics, and phosphoproteomics from FU-iCCA cohort and a transcriptomic cohort GSE244807), we defined 3 robust molecular subtypes of iCCA. These subtypes exhibited distinct genomic alterations, pathway activation, and immune microenvironments, with significant differences in overall survival (OS). Through protein-protein interaction network analysis and consensus feature selection using 10 clustering algorithms, we prioritized 8 marker genes distinguishing the subtypes. A Cox proportional-hazards model constructed from these markers stratified patients into high- and low-risk groups. High-risk iCCA, characterized by elevated expression of markers such as CLDN18, MUC1, and MUC5AC, had significantly worse OS in the absence of adjuvant therapy. Notably, in an independent validation of 174 patients with iCCA who underwent resection (single-center cohort), high expression of any of these 3 markers were associated with markedly prolonged OS in patients who received adjuvant chemotherapy or chemoembolization, compared with those who did not. In contrast, marker-negative patients showed no clear benefit from adjuvant therapy. In conclusion, our multiomics approach identified a high-risk, mucin-enriched subtype of iCCA. CLDN18, MUC1, and MUC5AC emerge as candidate predictive biomarkers for adjuvant chemotherapy benefit in iCCA, warranting prospective validation to improve personalized postoperative management.

Humans↗

PubMed2026

Unveiling the power of TIIC: A prognostic tool for esophageal adenocarcinoma.

BACKGROUND: Esophageal adenocarcinoma (EAC) remains a lethal malignancy with limited prognostic tools for guiding immunotherapy. Tumor-infiltrating immune cells (TIICs) play a critical role in EAC prognosis and treatment response. METHODS: We integrated single-cell RNA sequencing and bulk transcriptome data from TCGA and GEO databases. TIIC-specific RNAs were identified via tissue specificity index calculation combined with machine learning feature selection. Twenty machine learning algorithms were benchmarked to construct an optimal TIIC signature score (TIIC-Score) based on the comprehensive C-index. Immunotherapy response, genomic mutation, and copy number variation were analyzed. Summary-data-based Mendelian randomization (SMR) and two-sample Mendelian randomization (MR) were performed to explore genetic associations. Core prognostic TIIC-related genes were functionally validated in esophageal cancer cell lines through loss-of-function assays. RESULTS: The TIIC-Score demonstrated robust prognostic value for 1-, 2-, and 3-year overall survival across multiple cohorts, outperforming 22 published models. High TIIC-Score was associated with poor survival and increased chromosomal instability. Mutation profiling revealed high frequencies of TP53 (78.2%), TTN (48.7%), and SYNE1 (30.8%). MR analysis identified a significant association between gastro-oesophageal reflux and EAC risk at SNP rs8130507. Functionally, CCNI was upregulated in esophageal cancer cells, and its knockdown suppressed malignant phenotypes while promoting apoptosis, supporting its pro-tumorigenic role. CONCLUSION: The TIIC-Score provides a novel prognostic framework for EAC that effectively stratifies patient risk and may help identify individuals most likely to benefit from immunotherapy.

Esophageal adenocarcinoma↗

PubMed2026

Development and validation of a serum peptidomic signature for early detection of asymptomatic ovarian cancer: A multi-center prospective study.

Early detection of asymptomatic ovarian cancer (asym-OC) remains a critical challenge, the failure of which underlies its high mortality. Performing serum peptidomic profiling of 843 participants in the cohort SOCFCP, we distill 1,081 initial features into a 7-marker panel for asym-OC detection via a biology-informed machine-learning (ML)-based feature selection strategy. Three markers significantly revert toward non-OC levels after surgery. Integrating the panel with age, CA125, and HE4, we develop and externally validate (n = 159) a LightGBM model, ProMS+. For early-stage OC detection, ProMS+ shows a specificity of 92.6% at 95.0% sensitivity, outperforming CA125 (44.7%), HE4 (11.2%), and Risk of Ovarian Malignancy Algorithm (ROMA) (24.0%), with an area under the curve (AUC) of 0.993. In a simulated high-risk population (n = 100,000; OC prevalence = 1%), ProMS+ yields a high AUC (0.983) and a higher positive predictive value than CA125, HE4, and Age + CA125 + HE4 combined model (0.201 vs. 0.027, 0.090, and 0.064). ProMS+ offers a promising, non-invasive, and interpretable approach for the early detection of asym-OC.

Humans↗

PubMed2000

Proliferation in HHV-8-positive primary effusion lymphomas is associated with expression of HHV-8 cyclin but independent of p27(kip1).

Primary effusion lymphoma (PEL) develops in immunodeficient patients, selectively localizes to the serous body cavities, and harbors infection by human herpesvirus type-8 (HHV-8), also known as Kaposi's sarcoma-associated herpesvirus. HHV-8 encodes a viral (v)-cyclin homologous to cellular D-type cyclins, a class of positive cell-cycle regulators that are physiologically modulated by the p27(Kip1) cell cycle inhibitor. The aims of the present study were: 1) to establish the expression pattern of p27(Kip1) in PEL; and 2) to address the relationship between p27(Kip1) expression, proliferation index, and expression of cellular cyclin D1 and v-cyclin in PEL. Expression of p27(Kip1) was detected in all (n = 18) PEL samples analyzed by both immunocytochemistry and Western blot. All PELs displayed a high proliferation index as assessed by Ki-67 staining. Expression of cellular cyclin D1 was absent in all PELs tested, which conversely expressed (14 out of 14 samples) v-cyclin by immunocytochemistry and/or Western blot. In contrast to PELs, HHV-8-negative lymphomatous effusions secondary to a tissue-based lymphoma generally failed to express p27(Kip1). Overall, these data show that PELs consistently express p27(Kip1) protein despite the high proliferative rate of the lymphoma clone, suggesting that p27(Kip1) may be unable to drive cell-cycle arrest in PEL cells. The co-existence of p27(Kip1) expression and high proliferative index is a selective feature of PEL among lymphomas involving the serous body cavities, because lymphomatous effusions secondary to a tissue-based lymphoma generally display the inverse relationship between p27(Kip1) positivity and growth fraction observed in normal lymphoid tissues and in most other lymphomas. Expression of p27(Kip1) in PEL associates with expression of HHV-8 v-cyclin, but not of cellular cyclin D1. The fact that HHV-8 v-cyclin is resistant to p27(Kip1)-modulated inhibition, whereas cellular cyclin D1 is sensitive, may explain, at least in part, the co-existence of p27(Kip1) expression and high proliferative index observed in PEL.

Blotting, Western↗

PubMed2003

Bcl-2 promotes premature senescence induced by oncogenic Ras.

The expression of the apoptosis inhibitory protein, Bcl-2, is increased in naturally senescing human fibroblasts and upon induction of their senescence-like growth arrest by oxidative stress, implying its role in maintaining their extended viability. Oncogenic Ras(V12) protein induces signaling cascades that result in the premature senescence of primary fibroblast cells, which are insensitive to oncogene-dependent apoptosis. Here we show that constitutive expression of Bcl-2 accelerates selected features of the Ras-induced senescence program in primary human fibroblasts. Yet, Bcl-2 also inhibits fibroblast apoptosis induced by exogenous H(2)O(2), while both signals induce an increased endogenous Bcl-2 expression in these cells. Together, these data suggest a context-dependent phenotypic function of Bcl-2 in the regulation of overlapping cell fate specification programs, with potential implications for both physiology and multistep tumorigenesis.

Apoptosis↗

PubMed2003

Highly accurate classification of Watson-Crick basepairs on termini of single DNA molecules.

We introduce a computational method for classification of individual DNA molecules measured by an alpha-hemolysin channel detector. We show classification with better than 99% accuracy for DNA hairpin molecules that differ only in their terminal Watson-Crick basepairs. Signal classification was done in silico to establish performance metrics (i.e., where train and test data were of known type, via single-species data files). It was then performed in solution to assay real mixtures of DNA hairpins. Hidden Markov Models (HMMs) were used with Expectation/Maximization for denoising and for associating a feature vector with the ionic current blockade of the DNA molecule. Support Vector Machines (SVMs) were used as discriminators, and were the focus of off-line training. A multiclass SVM architecture was designed to place less discriminatory load on weaker discriminators, and novel SVM kernels were used to boost discrimination strength. The tuning on HMMs and SVMs enabled biophysical analysis of the captured molecule states and state transitions; structure revealed in the biophysical analysis was used for better feature selection.

Algorithms↗

PubMed1981

Diffusion coefficients of hemoglobin by intensity fluctuation spectroscopy: effects of varying pH and ionic strength.

Measurements of the mutual diffusion coefficients (D) of the liganded human hemoglobins (Hb) oxy-HbA and oxy-HbS were performed as a function of Hb concentration (CHb), pH, and ionic strength (tau) by intensity fluctuation spectroscopy (IFS). Average diffusion coefficients, (D), and normalized variances, ((D/(D) - 1)2), were recorded. Results are reported and select features are discussed quantitatively. (a) for tau = 0.15 M, the shape of the (d) vs. CHb curve is found to vary with pH. We developed a precise description of this effect in the form of an algebraic relationship between (D), CHb, and Z, the titration charge. (b) only slight differences between the (D) values of oxy-HbS and oxy-HbA are observed, at tau = 0.15 M, for CHb Less Than or Equal To 10 g%. These differences are explained by the theory of part a. (c) No evidence of aggregation is found in solutions of oxy-HbA or oxy-HbS, at tau = 0.15 M, for CHb Less Than or Equal To 10 g%. (d) Indications of aggregation appear in oxy-HbA solutions at very low concentrations of salt. An estimate is made of the extent of aggregation, and the average radius of a cluster is determined.

Diffusion↗

PubMed2004

Efficient and cost-effective experimental determination of kinetic constants and data: the success of a Bayesian systematic approach to drug transport, receptor binding, continuous culture and cell transport kinetics.

Details about the parameters of kinetic systems are crucial for progress in both medical and industrial research, including drug development, clinical diagnosis and biotechnology applications. Such details must be collected by a series of kinetic experiments and investigations. The correct design of the experiment is essential to collecting data suitable for analysis, modelling and deriving the correct information. We have developed a systematic and iterative Bayesian method and sets of rules for the design of enzyme kinetic experiments. Our method selects the optimum design to collect data suitable for accurate modelling and analysis and minimises the error in the parameters estimated. The rules select features of the design such as the substrate range and the number of measurements. We show here that this method can be directly applied to the study of other important kinetic systems, including drug transport, receptor binding, microbial culture and cell transport kinetics. It is possible to reduce the errors in the estimated parameters and, most importantly, increase the efficiency and cost-effectiveness by reducing the necessary amount of experiments and data points measured.

Bayes Theorem↗

PubMed1999

New ligand, N-(2-pyridylmethyl)aminoacetate, for use in the immobilised metal ion affinity chromatographic separation of proteins.

A new chelating compound has been developed for use in the immobilised metal ion affinity chromatographic separation of proteins. The tridentate ligand, sodium N-(2-pyridylmethyl)aminoacetate (carbpyr), 1, was prepared via a one-step synthesis from 2-picolylamine, 3 and then immobilised onto Sepharose CL-4B through the epoxide coupling procedure. The binding behaviour of the resulting IMAC sorbent, following chelation with Cu2+ ions to a density of 152 micromol Cu2+ ions/g gel was characterised by frontal analysis experiments using horse heart myoglobin (HMYO) at pH 7.0 and pH 9.0. From the derived isotherms, the adsorption capacity, q(m), for the binding of HMYO to immobilised Cu2+-N-(2-pyridylmethyl)aminoacetate (im-Cu2+-carbpyr)-Sepharose CL-4B at these pH values was found to be 1.92 and 1.91 micromol/g sorbent, respectively, whilst the dissociation constants K(D) were 0.0092 x 10(-6) M and 0.0062 x 10(-6) M at pH 7.0 and pH 9.0, respectively, indicating that the HMYO-im-Cu2+-N-(2-pyridylmethyl)aminoacetate complex was more stable under alkaline conditions, although the binding capacity in terms of micromol protein/g gel remained essentially unchanged. The selectivity features of the im-Cu2+-carbpyr-Sepharose CL-4B sorbent were further characterised in terms of the binding properties with several human serum proteins at pH 5.0, pH 7.0 and pH 9.0.

Adsorption↗