SEARCH · PubMed Health

Results for “Deep generative model”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5Linked to original sources

PubMed2026

Sequence optimization targeting mRNA stability enhances monoclonal antibody titers in CHO cells.

This study presents a DNA sequence optimization approach that integrates mRNA stability as a tunable design parameter to enhance monoclonal antibody expression in Chinese hamster ovary (CHO) cells. A comprehensive combinatorial library of synonymous coding-sequence variants of an IgG1 light chain was integrated as single copies at a defined genomic locus in CHO cells with identical regulatory elements. Steady-state mRNA abundance, quantified by deep sequencing of gDNA and mRNA, served as a proxy for mRNA stability. These data were used to train a machine learning model that predicts mRNA abundance from coding sequence using embeddings from a pre-trained nucleotide transformer. This abundance predictor, together with established translational metrics, was incorporated into a genetic algorithm for multi-objective codon optimization. As proof-of-concept, we optimized sequences encoding Trastuzumab to either maximize or minimize the abundance criterion and obtained benchmark sequences from two commercial providers. Using targeted integration, we generated CHO cell lines and measured protein titer and cell-specific productivity. Sequences optimized for high abundance significantly increased intracellular mRNA levels (+41%), protein titer (+59%), and cell-specific productivity (+85%) relative to low-abundance designs, while viable cell densities remained comparable. Compared to commercial benchmarks, high-abundance sequences achieved significantly higher titer (+70%) and cell-specific productivity (+98%). These findings establish mRNA stability as a practical and complementary design parameter for codon optimization in monoclonal antibody production, with potential applicability to other proteins and expression systems.

CHO↗

PubMed1990

Modelling coronary thrombosis from nonanticoagulated human blood in vitro.

The prevalence of ruptured atheromatous plaques underlying the adherent thrombus in the infarct-related coronary arteries, is well documented. In the thrombotic process associated with plaque rupture, hemodynamic forces and the interaction of platelets with exposed collagen fibers play the decisive roles. The shear-induced hemostasis from a nonanticoagulated human blood sample, perfused through polyethylene tubing, was used to simulate rheological changes in the coronary circulation due to plaque disruption. When the hemodynamic conditions of a plaque fissure were mimicked, the sequence of events corresponded to that in vivo: hemostasis (i.e., platelet plug formation in the wall) resulted in the formation of an occlusive thrombus in the lumen of the tubing. Further, the thrombus growth on a collagen fiber, mounted in the lumen of polyethylene tubing through which nonanticoagulated human blood was perfused, was used to mimick the exposure of thrombogenic elements during deep vessel wall injury and the formation of thrombus superimposed on plaque disruption. Morphology of both types of thrombi revealed large numbers of neutrophils and monocytes associated with the platelet mass. The mechanisms of thrombotic reactions were characterized by antagonists and monoclonal antibodies against the platelet activation pathways. Generation of thrombin at an early stage was shown to be the key event and determinant of the final outcome of both thrombotic reactions. It is suggested that the simultaneous measurements of shear-induced hemostasis, clotting, and platelet-collagen interaction from nonanticoagulated human blood provide close experimental approximations to the pathological process of acute coronary syndromes, namely thrombus formation at the site of a disrupted atherosclerotic plaque.

Adolescent↗

PubMed2026

Conditional Diffusion Model-Based Method for Annotation of Antibiotic Resistance Gene Properties.

The crisis of bacterial antibiotic resistance, which has led to a decline in the effectiveness of antibiotics originally used to combat bacterial infections, has emerged as an urgent challenge for public health. Antibiotic resistance genes (ARGs) are one of the key reasons for bacteria to develop resistance to antibiotics. Therefore, accurately identifying and annotating the critical properties of ARGs is of great importance for addressing the antibiotic resistance emergency. Although existing deep learning models demonstrate remarkable effectiveness in extracting local features from sequence data, they still face limitations in the capacity to further gain the enriched latent representations within the data. To address the critical challenge of extracting higher-quality representations from ARGs sequence data, we propose a novel ARGs properties annotation method based on the conditional diffusion model which is used to learn latent representations through domain-specific knowledge injection. Specifically, during the conditional information integration phase, we systematically incorporate ARGs' domain knowledge to guide the diffusion process in generating high-quality latent representations. To overcome information redundancy caused by direct concatenation of conditional information and intermediate features, we design a cross-attention mechanism that enables feature fusion between heterogeneous information sources, thereby enhancing further the quality of obtained representations. Experimental results on widely used data sets demonstrate the framework's effectiveness in achieving superior prediction performance compared to existing methods.

Anti-Bacterial Agents↗

PubMed1983

Capillary transport of H2 gas generated locally in renal tissue.

Previous measurements by microspheres have shown a higher blood flow in outer cortex and a lower blood flow in inner cortex than found by diffusible tracers. During vasodilation microspheres have indicated a disproportionate increase in deep cortical blood flow, whereas diffusible tracer distributions remained unchanged. These discrepancies could possibly be explained by a variable net inward transport of diffusible tracers in postglomerular vessels, the transport existing in control, but disappearing during vasodilation. To test this hypothesis H2 gas was produced electrolytically for 1 s at a platinum electrode in midcortex and the resulting gas concentration curve measured polarographically at two electrodes placed above and below the source. Analysis of a mathematical model showed that the ratio of the curve maxima at the two electrodes (Cmo/Cmi) would best reveal a radial net transport. Average Cmo/Cmi at 25 positions in 7 clamped dog kidneys was close to unity, but rose to 1.24 at control flow. During acetylcholine infusion Cmo/Cmi rose to 1.68. Local washout rates at the two electrodes increased equally. Calculations indicated a small outwardly directed net transport in control (3 X 10(-4) cm/s), becoming slightly reinforced during vasodilation (5 X 10(-4) cm/s). Thus the control transport direction is opposite to the hypothesis, and the change during vasodilation was estimated to be too small to explain the disparity between diffusible tracer uptake and microsphere distribution in control. H2 concentration maximum was obtained earlier under control flow than in the clamped kidney, indicating an increase in apparent D of the gas in tissue from 3 X 10(-5) cm2/s to 5 X 10(-5) cm2/s, probably due to mixing of H2 gas in the capillary net work.

Acetylcholine↗

PubMed2025

Efficient Detection and Characterization of Targets of Natural Selection Using Transfer Learning.

Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pretrained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.

Selection, Genetic↗

PubMed1988

Extraction of "deep" components from scalp EEG.

In an attempt to delineate the relative contribution of surface versus deep components in the EEG of patients with 3 per second generalized spike-wave discharges and clinical petit mal seizures, a mathematical method was devised which allows the splitting of the EEG into two major subsystems. It is based on the observation that broad electrical fields tend to represent activity at deeper structures while discrete narrow fields centered at one electrode position tend to be of more superficial origin. Since source derivation intentionally suppresses broad potential fields, a differentiation between superficial and deep activity can be achieved by subtracting the source density values from the corresponding electrode potential values. This will provide those aspects of the EEG which are contributed mainly by deep generators. The resultant data can then be subjected to eigenfunction analysis which yields few uncorrelated components. The percentage of contribution of each electrode to the total component thus derived can then be displayed as a topographic map. When this methodology was applied to ictal EEGs of three patients consistent results were obtained. The "deep" data yielded mainly two components with mutually perpendicular directions.

Cerebral Cortex↗

PubMed2025

Efficient detection and characterization of targets of natural selection using transfer learning.

Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pre-trained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.

Journal Article↗

PubMed1976

Vitamin-A-induced mucous metaplasia. An in vitro system for modulating tight and gap junction differentiation.

Stratified squamous epithelia from 14-day chick embryo shank skin contain rare tight-junctional strands and only small gap junctions. Exposure of this tissue to retinoic acid (vitamin-A) (20 U/ml) in organ culture, however, induces mucous metaplasia, accompanied by tight-junction formation and gap-junction growth; untreated specimens continue to keratinize. To investigate sequential stages of junctional assembly and growth, we examined thin sections and freeze-fracture replicas at daily intervals for 3 days. During the metaplastic process, tight junctions assemble in midepidermal and upper regions, beginning on day 1 and becoming maximal on day 3. Two tight-junctional patterns could be tentatively identified as contributing to the emergence of fully formed zonulae occludentes: (a) the formation of individual ridges along the margins of gap junctions; (b) de novo generation of continuous ramifying strands by fusion of short strand segments and linear particulate aggregates near cellular apices. Gap junction enlargement, already maximal at day 1, occurs primarily three to four cell layers deep. Growth appears to occur by annexation of islands of 20-40 8.5-nm particles into larger lattices of islands separated by particle-free aisles. Eventually, a single gap junction may occupy much of the exposed membrane face in freeze-fractured tissue, but during apical migration of the cells such junctions disappear. The vitamin- A chick-skin system is presented as a responsive model for the controlled study of junction assembly.

Animals↗

PubMed1991

Neuroblastoma grafts are noninvasively removed within mouse neocortex by selective laser activation of intracellular photolytic chromophore.

Studies of neural cell transplantation would be aided by the ability to damage or destroy, noninvasively and extremely selectively, grafted cells at defined times following their initial implantation. Mechanisms of graft integration and performance could be investigated, also providing insight into natural injury and repair mechanisms. At long wavelengths between 650 and 850 nm, laser energy can penetrate several millimeters of brain tissue without absorption or damage to the unpigmented tissue. However, targeted cells are selectively damaged by illumination at these long wavelengths if they contain latex nanospheres with incorporated cytolytic chromophores (e.g., chlorin e6). Light penetration allows many thousands of cells to be lesioned simultaneously, noninvasively, and deep within a surrounding matrix of other tissue. Such laser-activated damage has been termed laser photolysis (PL). We studied damage to C1300 neuroblastoma (NB) cells grafted into mouse neocortex in vivo by this process of PL. NB cells provided a simple and reproducible model of neural grafting, allowing direct histologic assessment of cellular growth and viability by distinct morphologic and mitotic criteria. Cells were cultured by standard methods, labeled in vitro by brief exposure to nanospheres containing chlorin e6, and grafted to sites within deep layers of mouse neocortex. Mice were exposed to transcranial, fractionated, unfocused pulses of 670-nm-wavelength energy totaling 90-120 J/cm2. We histologically assessed graft growth and cellular viability over a period from 2 d to 4 weeks, measured graft volumes quantitatively during the period of early rapid growth in controls (2 and 7 d), and generated 3-D reconstructions from serial sections to assist in visual analysis.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗

PubMedDate not supplied

[Changes in the sleep-wakefulness cycle in experimental Parkinson's syndrome].

It was demonstrated in rat experiments that in modelling the akinetico-rigid parkinsonian syndrome by injecting kainic acid into the caudate nuclei the phases of wakefulness and light slow-wave sleep decreased while the phase of deep slow-wave sleep increased. In the tremorogenic from of the parkinsonian syndrome induced by reserpine the phases of wakefulness and deep slow-wave sleep decreased in the rats while the phases of light slow-wave and paradoxical sleep increased. In destruction of the substantia nigra with kainic acid the phase of paradoxical sleep reduced. The results of the study are discussed from the standpoint of the generator mechanisms of the development of neuropathological syndromes and the mechanisms of the interrelations of parkinsonism and epilepsy.

Animals↗

PubMed1985

Infusion strategies to investigate the pharmacokinetics and pharmacodynamics of hypnotic drugs: etomidate as an example.

Etomidate was administered to six healthy volunteers by microprocessor controlled infusions, to generate three cycles of linearly increasing plasma levels, with an anticipated slope of 0.05 microgram ml-1 min-1. The infusions were stopped when a deep hypnotic state was obtained, as indicated by burst suppressions in the EEG. The infusions were restarted when the volunteers were fully orientated to person, place and time. The mean (+/-SD) doses of etomidate delivered by the first, second and third infusion were 165 +/- 30, 137 +/- 25 and 157 +/- 26 mg, respectively. Certain clinical signs were observed and related to the plasma concentrations of etomidate. Pharmacokinetic analysis was undertaken using an open two-compartment model. The therapeutic window was in a range of plasma concentrations between 0.3 and 1.0 microgram ml-1 of etomidate. Pharmacokinetic analysis gave a volume for the central compartment of 50 +/- 11 litre, an apparent volume of distribution of 252 +/- 51 litre and a total clearance of 1693 +/- 504 ml min-1. Microprocessor controlled infusions can serve as a powerful tool for research in clinical pharmacology. The achievement of linearly increasing plasma levels of etomidate allowed further pharmacokinetic and pharmacodynamic modelling concepts to be realized.

Adult↗

PubMed2026

Fast, accurate construction of multiple sequence alignments from protein language embeddings.

Multiple sequence alignment (MSA) is a foundational task in computational biology, underpinning protein structure prediction, evolutionary analysis, and domain annotation. Traditional MSA algorithms rely on pairwise amino acid substitution matrices derived from conserved protein families. While effective for aligning closely related sequences, these scoring schemes struggle in the low-identity "twilight zone." Here, we present a new approach for constructing MSAs leveraging amino acid embeddings generated by protein language models (PLMs), which capture rich evolutionary and contextual information from massive and diverse sequence datasets. We introduce a windowed reciprocal-weighted embedding similarity metric that is surprisingly effective in identifying corresponding amino acids across sequences. Building on this metric, we develop ARIES (Alignment via RecIprocal Embedding Similarity), an algorithm that constructs a PLM-generated template embedding and aligns each sequence to this template via dynamic time warping in order to build a global MSA. Across diverse benchmark datasets, ARIES achieves higher accuracies than existing state-of-the-art approaches, especially in low-identity regimes where traditional methods degrade, while scaling almost linearly with the number of sequences to be aligned. Together, these results provide the first large-scale demonstration of the power of PLMs for accurate and scalable MSA construction across protein families of varying sizes and levels of similarity, highlighting the potential of PLMs to transform comparative sequence analysis.

Deep Learning↗

PubMed2025

CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity.

Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, predicted protein 3D structures are readily available; however, methods for predicting binding affinity currently do not take full advantage of 3D protein information. Here, we present CASTER-DTA (Cross-Attention with Structural Target Equivariant Representations for Drug-Target Affinity), which uses an equivariant graph neural network to learn more robust protein representations alongside a standard graph neural network to learn molecular representations to predict drug-target affinity. We augment these representations by incorporating an attention-based mechanism between protein residues and drug atoms to improve interpretability. We show that CASTER-DTA represents a state-of-the-art improvement on multiple benchmarks for predicting drug-target affinity and that it generates novel insights for several related tasks. We then apply CASTER-DTA to create a large resource of the binding affinities of every FDA-approved drug against every protein in the human proteome and make these predictions freely available for download. We also make available a web server for researchers to apply a pretrained CASTER-DTA model for predicting binding affinities between arbitrary proteins and drugs.

deep learning↗

PubMedDate not supplied

Fluconazole therapy for experimental cryptococcosis and candidiasis in the rabbit.

Fluconazole is a second-generation azole compound with broad-spectrum antifungal activity. It has been examined in several animal models emphasizing important clinical sites of infection with common yeast pathogens. The drug has an excellent pharmacokinetic profile for central nervous system, renal, and ocular infections; at these sites fluconazole has been successful in the treatment of infections with Cryptococcus neoformans or Candida albicans. On the basis of the experience in animals, fluconazole should be critically evaluated in the treatment of human mycoses such as cryptococcosis of the central nervous system and renal/ocular candidiasis. This agent represents the new wave of interest in the increasingly troublesome problem of deep-seated fungal infections.

Animals↗

PubMed2025

Decoding the Functional Interactome of Non-Model Organisms with PHILHARMONIC.

Despite the widespread availability of genome sequencing pipelines, many genes remain part of the genome's "dark matter," where existing inference tools cannot even begin to guess the biological function of their proteins from sequence alone. This challenge is especially pronounced in organisms that are highly evolutionarily distant from well-studied models, where homology-based methods break down. Here, we describe PHILHARMONIC, a computational method that combines deep learning-based de novo protein interaction network inference with robust unsupervised spectral clustering and remote homology to illuminate functional organization in any non-model organism. From only a sequenced proteome, we show PHILHARMONIC predicts protein functions, functional communities, and higher-order network structure with high accuracy. We validate its performance using experimental gene expression and pathway data in D. melanogaster, and we demonstrate its broad utility by analyzing temperature sensing and stress response pathways in the reef-building coral P. damicornis and its algal symbiont C. goreaui. PHILHARMONIC provides a general-purpose engine for functional discovery and biological hypothesis generation in non-model organisms, enabling systems-level insights across the full diversity of life.

Journal Article↗

PubMed1990

Spatially-localized NMR spectroscopy employing an inhomogeneous surface-spoiling magnetic field gradient. 2. Surface coil experiments with multicompartment phantom and rat in vivo.

The use of inhomogeneous surface-spoiling magnetic field gradients for elimination of signal from surface lying regions of a sample was theoretically examined in the companion article (W. Chen and J.J.H. Ackerman, NMR Biomed. 3, 147-157 (1990)). Using the spoiling gradient coil design described therein, this article presents experimental verification of the feasibility of such an approach to enhanced spatial localization. Single coil mode 31P NMR surface coil interrogation of both a two compartment phantom and rat in vivo are shown to provide excellent suppression of surface lying regions with minimal degradation of signal from the deep lying region of interest. Both pulse-and-collect and spin echo sequences were highly efficient in concert with spoiling gradient periods of 0.5-2 ms and driving currents of 0.5-2 A. The use of a current-generated surface spoiling gradient offers a robust means to remove surface tissue signal contributions and can be implemented with a wide range of localizing pulse sequences and imaging protocols.

Animals↗

PubMed1975

[Formation of a generator of excitation in the gigantocellular nucleus of the medulla oblongata during disruption of inhibitory processes].

Neuronal activity in the gigantocellular nucleus after injection of tetanus toxin was studied on decerebrated cats. The toxin was used as a substance producing a deep and continuous suppression of inhibitory processes. The increase in the amplitude and rate of neuronal discharges, in the integral background and evoked activity as well as in the number of active neurons and that of neurons with burst activity was recorded in the "poisoned" nucleus. The enhanced activity in the investigated regions of the poisoned nucleus might be temporarily suppressed by a strong direct electrical shock and by glycin administrations to those regions. The obtained data indicate that a pool of neurons with disturbed inhibitory processes forms a generator of enhanced excitation. The mechanisms and characteristic features of the activity of such generators are discussed. The possibility of modelling neurological syndromes by production of similar generators in various parts of the central nervous system and their relation to the earlier described phenomenon of "dispatch station" are considered.

Animals↗

PubMed1992

Numerical phase algorithm for decompression computers and application.

Present generation decompression computers employ a simplified algorithm, limiting dissolved gas build-up in tissue and blood according to a method proposed by Haldane 80 years ago. Such a model works well for single dives, but is usually liberal and theoretically incomplete for multiple exposures within 24 hr spans. Using the critical phase hypothesis in a bubble model, we have extended the classical model of Haldane to multi-exposures. This model is discussed, and a decomputer algorithm described for multi-diving. The focus is permissible bubble excess, not just dissolved gas per se, with phase constraints affecting all tissues, fast and slow, and requiring a systematic lowering of repetitive tissue tensions. Deep repetitive and shallow multi-day exposures are impacted most by the procedure. Within nucleation theory deeper-than-first dives are also treated. A set of multi-diving fractions, xi, accounting for micronuclei excitation and regeneration, reduced bubble elimination in repetitive activity, and coupled effects on tissue tension, are proposed, with xi representing a set of multiplicative factors (less than one) applied to critical tissue tensions for multi-exposures. These factors affect repetitive activity over short time spans, deeper-than-previous and continuous multi-day activities, compared to standard computer software, and are easily encoded into existing decompression meters, potentially extending their range and flexibility over exposure regimes.

Algorithms↗