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At least 19 recordsLinked to original sources

PubMed2025

DigiNet: Optimizing personalized care for patients with stage IV non-small cell lung cancer (NSCLC) through a digitally connected provider network-analysis plan of a prospective multicenter cohort trial.

PURPOSE: The German sector-based healthcare system poses a major challenge to continuous patient monitoring and long-term follow-up, both essential for generating high-quality, longitudinal real-world data. The national Network for Genomic Medicine (nNGM) bridges the inpatient and outpatient care sectors to provide comprehensive molecular diagnostics and personalized treatment for non-small cell lung cancer (NSCLC) patients in Germany. Building on the established nNGM infrastructure, the DigiNet study aims to evaluate the impact of digitally integrated, personalized care on overall survival (OS) and the optimization of treatment pathways, compared to routine care. METHODS: DigiNet is a prospective, controlled, non-randomized multicenter cohort study including patients with stage IV NSCLC in two study regions (East and West) in Germany. The results of molecular diagnostics and clinical information, along with the entire treatment data are documented in a shared database. A board of lung cancer specialists monitors critical events. Patients digitally complete quality of life questionnaires, with results visualized for physicians. To assess the impact of this personalized digital care, a population-based control group will be identified by matching cohorts within the involved cancer registries. The primary endpoint is OS, and secondary endpoints comprise time on first-line treatment and hospitalization rates. Furthermore, a health economic and business economic evaluation will be conducted. Qualitative interviews with patients and physicians will be performed to assess barriers and facilitating factors for implementing the DigiNet intervention. ETHICS: The study protocol was reviewed and approved by the Ethics Committee of the University Hospital of Cologne (21-1521). TRIAL REGISTRATION: NCT05818449, registered retrospectively on December 12, 2022.

Humans↗

PubMed1976

International standard (C.C.I.T.T.) for transmitting biomedical analogue and digital data on the public telephone network.

Analogue transmission of biomedical signals over the public telephone network has advantages from the economic point of view over digitalized transmission. This paper deals with the special problems encountered with the transmission of biomedical signals. Furthermore, the new international transmission standard C.C.I.T.T. recommendation V. 16 is introduced. This standard has recently been adopted by the relevant study group and has been presented to the Plenary Assembly of the C.C.I.T.T. for final approval. This standard is compatible with the existing public telephone networks. The technical specifications of this standard allow the transmission of the three-channel ECG for diagnostic purposes, e.g., remote processing and computer-assisted evaluation, as well as the transmission of the one-channel ECG with acoustic coupling, e.g., in emergency cases and for pace maker monitoring.

Analog-Digital Conversion↗

PubMed1979

Spatial firing patterns of auditory neuron network modelling by computer simulation.

This communication examines, in digital computer simulated network, input signals and response patterns established at excitatory neurons' level i.e. the membrane potential of neuron soma. It is restricted to spatial patterns of the auditory neuron networks and time factor for nervous conduction and transmission is neglected compared with long maintained membrane potentials of neuron somas. The model analyzes the change in the spatial patterns of the membrane potential in the two dimensional networks of the auditory system. In order to evaluate the contribution of the various parameters, it is started that the simplest model has only one parameter, lateral inhibition. The other parameters are then added, one at a time, to successive models. The lateral inhibition is a necessary condition in the auditory nervous system if any sharpening of the response areas in the single neurons is to occur. A necessary condition for the validity of the model is that is should be applicable to the other senses such as vision and chemical patterns, taste. The threshold feature of auditory neurons aids in producing a sharpening in the neuron of the auditory relay nuclei. It does this clipping the spatial response patterns in one dimensional arrays of excitatory neurons. Recurrent inhibition seems a necessary condition in the sensory nervous system that any kinds of input signals are to be preserved over a wide range of stimulus intensity. In other words, this network has a wide dynamic range against any kinds of input signals. A simple self-recurrent negative feedback does not contribute to the sharpening, but more complex socalled averaged type does. A neuron network is capable of responding stably to stimuli with a wide range of intensity and with any kind of spatial patterns if there is a simple negative feedback mechanism. When there is no negative feedback, input signals soon disappear or saturate in the neuron network. Therefore, recurrent inhibition is the most important mechanism. Spontaneous activity appears to aid in the sharpening by providing a kind of contrast, that is by reducting the amount of activity in neurons adjacent to the excitatory area. Moreover, the effect of spontaneous activity in the model seems to make repples around the excitatory area and suggests that an introduction of activity at any stage of the networks, from whatever source for example reticulum formation and thalamus, might appreciably alter the response patterns at subsequent neuron network. This suggests that the mechanism of the consciousness that might be controlled by the thalamus and or reticular formation. These two dimensional neuron networks may be expanded to three dimensional neuron networks. The former might simulate the auditory nervous system while the latter might simulate the visual system.

Animals↗

PubMed2026

Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation.

Bioacoustics is increasingly shifting from a mostly descriptive pursuit to one that can anticipate ecological change. Recent innovations-from autonomous recording units and edge-computing sensors to speech-inspired feature extraction and machine-learning techniques like transfer learning, unsupervised discovery, and explainable AI-are transforming the study of animal communication. These advances let us work at scales previously difficult to imagine. Automated species recognition, individual identification, and even tracking cultural evolution over decades are now within reach. Entire ecosystem soundscapes can be mapped with unprecedented resolution. Looking ahead, global listening networks, adaptive acoustic indices, and live biodiversity dashboards seem increasingly realistic. We may soon build digital models that simulate communication networks under future scenarios. Closer integration with genomics, physiology, and robotics could link vocal traits to their genetic, physiological, and ecological drivers. Challenges remain, including data governance, acoustic privacy, and equitable access to the planet's sonic heritage. Bioacoustics may be on the way to becoming a predictive, integrative science - one particularly well suited to monitoring, interpreting, and helping safeguard life's communication systems in a rapidly changing world.

Animals↗

PubMed1977

Some quantitative results on Golgi impregnated axons in rat visual cortex using a computer assisted video digitizer.

Axonal fiber distributions of pyramidal cells in the visual cortex of the albino rat have been investigated using the rapid Golgi method and modern data collecting techniques. Three dimensional coordinate information was extracted from Golgi-impregnated axonal networks using a computer-assisted video digitizer. Computer programs used this data to generate various statistical distributions. In particular, angular distributions of the initial collateral segments and their endpoints were examined and found to reveal anisotropies. Inspection of the spatial distributions of the endpoints indicated a clustering at two distinct levels with respect to the pyramidal cell from which they originate. Dynamic graphic displays of the three dimensional data have been obtained and presented in the form of computer tracings of various orthogonal projections.

Animals↗

PubMed2026

Nested co-expression network analysis identifies compact gene clusters in a black box.

MOTIVATION: Digital analysis of biological systems requires methods capable of identifying both broad and nested gene modules reflecting complex biological processes. Existing transcriptomic methods often miss compact gene sets corresponding to subprocesses in specialized cell types, limiting insights into functional heterogeneity. RESULTS: We present Nested-WGCNA, a two-stage unsupervised network analysis algorithm designed to identify coarse-grained and fine-grained gene modules. Applied to bulk RNA-Seq data, Nested-WGCNA reveals stable modules reproducible across datasets. When validated against scRNA-Seq data, these modules correspond to both major and minor immune cell subtypes. Application to immunotherapy response datasets uncovers predictive and prognostic biomarkers, highlighting its utility in treatment stratification and biomarker discovery. AVAILABILITY: The NestedWGCNA source code and analysis pipeline are available on GitHub (https://github.com/ilyada/NestedWGCNA) and archived on Zenodo (https://doi.org/10.5281/zenodo.18959244).

Algorithms↗

PubMed2026

SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.

Bladder cancer survival outcomes exhibit significant heterogeneity, influenced by multifaceted factors. While digital pathology-based survival models leveraging artificial intelligence show promise, they often overlook complementary data sources. Conversely, imaging lacks cellular detail, and genomics/proteomics entail complexity and cost. To integrate multidimensional data for enhanced survival prediction, we propose SurvGRN, a multi-feature fusion framework. SurvGRN synergistically combines clinical variables, transcriptomics, and digital pathology slides using a gated residual network architecture. Pathological features are extracted via multiple instance learning, while clinical and transcriptomic data are processed as static inputs. These features are dynamically fused using a long short-term memory (LSTM) network for comprehensive survival risk assessment. Evaluated on 400 bladder cancer patients, SurvGRN significantly outperformed existing methods: improving the C-index by 12.6% over DeepMISL; 20.6% and 7.1% over graph-based models (DeepGraphConv and Patch-GCN); and 5.4% and 4.0% over attention-based approaches (Surformer and HVTSurv). Ablation studies confirmed the contributions of pathology features (extracted via ResNet-50 pre-trained on bladder tissue), clinical/transcriptomic data, and the LSTM fusion. SurvGRN also enabled significant stratification of patients into distinct risk cohorts. This work demonstrates that holistic integration of multi-source data through tailored fusion architectures substantially improves bladder cancer survival prediction.

bladder cancer↗

PubMed2026

Precision targeting of teacher burnout using network-informed ecological momentary interventions.

Teacher well-being affects classroom functioning and workforce stability, yet generic digital programs rarely use person-specific affect dynamics to select support. This cluster-randomised trial evaluated whether micro-interventions selected from high expected influence (EI) nodes in teachers' contemporaneous affect networks produced larger changes in burnout-related EI and everyday happiness than content-matched random allocation. The objectives were to estimate allocation effects on changes in estimated network summaries and happiness, evaluate network change as a statistical mediator, examine personality moderation, and benchmark simpler allocation rules. A two-arm cluster randomised platform trial was conducted in 84 public schools across four urban districts in H Province. After a 14 day baseline of ecological momentary assessment (EMA), person specific partial correlation networks were estimated for happiness, exhaustion, detachment, efficacy and rumination. An optimisation engine prioritised three brief micro-intervention types per teacher according to baseline EI, while the active control received the same library without network information. EMA continued for 8 weeks; Bayesian multilevel models, permutation-based mediation, and benchmarking analyses were applied. EI-based targeting produced larger reductions in the composite EI-change index than active control (mean difference 0.11, 95% credible interval 0.08 to 0.14) and higher week 7 EMA happiness (4.4 points on a 0 to 100 scale, 95% credible interval 2.7 to 6.0), with a positive arm by week slope difference of 0.62 points per week (95% credible interval 0.39 to 0.85). Model-based mediation estimates were consistent with approximately one half of the happiness difference being statistically associated with change in the composite EI-change index (average conditional mediation estimate 3.5 points, 95% credible interval 2.0 to 5.2). Benchmarking showed smaller gains under severity, threshold, or group-level centrality rules. Effects were stronger among teachers higher in conscientiousness. The findings indicate that integrating EMA, network modelling, and EI-driven optimisation yields measurable gains beyond content-matched exposure, providing a proof of concept for district-scale precision mental health that requires prospective implementation testing. Replication in additional regions, expanded node sets, and longer follow up are warranted to assess durability and generalisability.

Female↗

PubMed1979

Computer aided analysis of chromatin network and basophil color for differentiation of mononuclear peripheral blood cells.

Computer aided differentiation of plasmoblasts, Pfeiffer cells, immunoblasts, lymphocytes and centrocytes is achieved with the parameters of chromatin network arrangement and structure, and multispectral cytoplasm color. The digital methods involve: (a) segmenting the nuclear image into topographic sections and analyzing the optical density distribution from the chromatin in these sections; (b) determining the nuclear structure with a 7 x 7 median filter, gradient filter and contour following algorithms; and (c) clustering two-dimensional chromatic data from panoptically stained cellular components. The parameters reported here are a subset of those needed for the automated diagnosis of many hematologic diseases especially the leukemias.

Blood Cells↗

PubMed1978

Three-dimensional architecture of blood vessels of tendons demonstrated by corrosion casts.

Three-dimensional observation of the microcirculation of the tendon was readily and clearly demonstrated by preparing methyl methacrylate casts and observing them under the scanning electron microscope. In the muscles blood vessels made a network like a ladder surrounding every muscle fibre. The fibrous digital sheath had blood vessels made in a fine meshed cylinder. By microdissection of the vessels of the sheath the blood vessels of the vinculae and tendons were observed stereoscopically in relation to the peritendinous tissues. The casting method contributes to better understanding of vascular architecture of tendons.

Animals↗

PubMed2026

Artificial Intelligence for Colorectal Surgeons-Part II: Research Applications, Challenges in Adoption, and Practical Resources.

BACKGROUND: This is part II of a 2-part series examining artificial intelligence in colorectal surgery. Part I established foundational concepts and clinical applications. Implementation, however, requires understanding research methodologies, available resources, and the specific challenges currently limiting widespread adoption. These topics are the focus of part II. OBJECTIVE: To examine artificial intelligence's transformation of surgical research, provide practical implementation resources, address adoption challenges, and explore future directions in colorectal surgery. METHODS: Comprehensive literature review focusing on artificial intelligence research methodology, implementation barriers, educational resources, and emerging technologies relevant to colorectal surgeons. RESULTS: Artificial intelligence streamlines clinical trial design through predictive modeling and natural language processing, reducing enrollment challenges that contribute to failed or inadequate trial accrual. Machine learning enables heterogeneity analysis within clinical trials, identifying treatment-responsive subgroups. Foundation models unlock analysis of unstructured electronic health record data at scale. Professional societies and universities offer specialized artificial intelligence education programs, with open-access data sets facilitating research participation. However, implementation faces multifaceted challenges: technical infrastructure demands, with real-time processing requiring dedicated graphics processing unit clusters; regulatory frameworks struggling with continuously evolving algorithms; undefined liability distribution for artificial intelligence-assisted decisions; algorithmic bias risking health care disparities; and the "black box" problem limiting clinical trust. Economic barriers include substantial initial costs without clear reimbursement pathways. Future directions include multimodal artificial intelligence integrating imaging, genomics, and histopathology; cognitive robotic systems with real-time decision support; digital twin technology for patient-specific surgical simulation; and global surgical artificial intelligence networks enabling distributed learning across institutions. CONCLUSIONS: Although artificial intelligence offers transformative potential for colorectal surgery research and practice, successful implementation requires addressing technical, regulatory, ethical, and economic challenges. The surgeon's evolving role demands both traditional expertise and computational fluency. Future advances in multimodal integration, autonomous systems, and global collaboration will fundamentally reshape surgical practice but will require thoughtful implementation prioritizing patient benefit and clinical value.

Humans↗

PubMed1978

An inference upon the neural network finding binocular correspondence.

Previously, the authors proposed a model of neural network extracting binocular parallax (Hirai and Fukushima, 1975). It is a multilayered network whose final layers consist of neural elements corresponding to "binocular depth neurons" found in monkey's visual cortex. The binocular depth neuron is selectively sensitive to a binocular stimulus with a specific amount of binocular parallax and does not respond to a monocular one. As described in the last chapter of the previous article (Hirai and Fukushima, 1975), when a binocular pair of input patterns consist of, for example, many vertical bars placed very closely to each other, the binocular depth neurons might respond not only to correct binocular pairs, but also to incorrect ones. Our present study is concentrated upon how the visual system finds correct binocular pairs or binocular correspondence. It is assumed that some neural network is cascaded after the binocular depth neurons and finds out correct binocular correspondence by eliminating the incorrect binocular pairs. In this article a model of such neural network is proposed. The performance of the model has been simulated on a digital computer. The results of the computer simulation show that this model finds binocular correspondence satisfactorily. It has been demonstrated by the computer simulation that this model also explains the mechanism of the hysteresis in the binocular depth perception reported by Fender and Julesz (1967).

Computers↗

PubMed1976

Simple method for computer-aided analysis of echocardiograms.

The use of echocardiography in the diagnosis and assessment of heart disease is increasing as greater familiarity is obtained with this noninvasive procedure. Quantitative evaluation of echocardiographic studies has heretofore required time-consuming manipulation of mathematical formulas. A simple method utilizing a sonic digitizing tablet has been developed for computer-aided analysis of M-mode echocardiograms. This device can convert a point located manually with a digitizing pen into X and Y coordinates and with use of the standard telephone network can communicate on a time-shared basis with a DECSYSTEM-10 computer. A program has been written to compute and type the results of standard calculations involving mitral valve motion and left ventricular function. The information can also be stored on disk by the computer for future use. This simple, relatively inexpensive system is valuable because of the ease with which it permits usually laboriously obtained information to be extracted from the standard echocardiogram.

Diagnosis, Computer-Assisted↗

PubMed2026

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

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

Alzheimer’s disease↗

PubMed2025

A Digital Tool for Clinical Evidence-Driven Guideline Development by Studying Properties of Trial Eligible and Ineligible Populations: Development and Usability Study.

BACKGROUND: Clinical guideline development preferentially relies on evidence from randomized controlled trials (RCTs). RCTs are gold-standard methods to evaluate the efficacy of treatments with the highest internal validity but limited external validity, in the sense that their findings may not always be applicable to or generalizable to clinical populations or population characteristics. The external validity of RCTs for the clinical population is constrained by the lack of tailored epidemiological data analysis designed for this purpose due to data governance, consistency of disease or condition definitions, and reduplicated effort in analysis code. OBJECTIVE: This study aims to develop a digital tool that characterizes the overall population and differences between clinical trial eligible and ineligible populations from the clinical populations of a disease or condition regarding demography (eg, age, gender, ethnicity), comorbidity, coprescription, hospitalization, and mortality. Currently, the process is complex, onerous, and time-consuming, whereas a real-time tool may be used to rapidly inform a guideline developer's judgment about the applicability of evidence. METHODS: The National Institute for Health and Care Excellence-particularly the gout guideline development group-and the Scottish Intercollegiate Guidelines Network guideline developers were consulted to gather their requirements and evidential data needs when developing guidelines. An R Shiny (R Foundation for Statistical Computing) tool was designed and developed using electronic primary health care data linked with hospitalization and mortality data built upon an optimized data architecture. Disclosure control mechanisms were built into the tool to ensure data confidentiality. The tool was deployed within a Trusted Research Environment, allowing only trusted preapproved researchers to conduct analysis. RESULTS: The tool supports 128 chronic health conditions as index conditions and 161 conditions as comorbidities (33 in addition to the 128 index conditions). It enables 2 types of analyses via the graphic interface: overall population and stratified by user-defined eligibility criteria. The analyses produce an overview of statistical tables (eg, age, gender) of the index condition population and, within the overview groupings, produce details on, for example, electronic frailty index, comorbidities, and coprescriptions. The disclosure control mechanism is integral to the tool, limiting tabular counts to meet local governance needs. An exemplary result for gout as an index condition is presented to demonstrate the tool's functionality. Guideline developers from the National Institute for Health and Care Excellence and the Scottish Intercollegiate Guidelines Network provided positive feedback on the tool. CONCLUSIONS: The tool is a proof-of-concept, and the user feedback has demonstrated that this is a step toward computer-interpretable guideline development. Using the digital tool can potentially improve evidence-driven guideline development through the availability of real-world data in real time.

Humans↗

PubMedDate not supplied

Automated system for measurement of mechanics of breathing.

We describe a complete automated system for measurement of total respiratory resistance and compliance, and of pulmonary resistance and compliance in humans and anesthetized animals. The device for testing the chest-lung system consists of a sinusoidal pump with a stroke adjustable from 20 ml to 600 ml over a cycling frequency of 0.3 to 30 Hz. Pressure and flow or volume are inputted into an analog network for wave shaping, then to an arithmetic unit composed of sample-and-hold amplifiers, peak, minimum and zero detecting circuits, an analog division circuit, and a digital logic processor. The computer takes the peak-to-peak amplitude of one of two sinusoidal inputs at 0.3 to 10 Hz and the corresponding amplitude of the other input in the presence of a.c. noise and d.c. shifts, divides one into the other, and displays an answer on a digital voltmeter. In addition, the analog output is displayed on the cathode-ray tube of a storage oscilloscope. Plots of total resistance and pulmonary resistance are recorded as a function of lung volume in both humans inspiring voluntarily as well as anesthetized dogs inflated by positive pressure from the test apparatus. Total and pulmonary dynamic compliance, as a function of breathing frequency, can only be measured by the computer if a symmetrical waveform is presented to it. This cannot be achieved in spontaneously breathing subjects but is accomplished in apneic animals by producing sinusoidal oscillations from the test apparatus. Our on-line method for measurement of total respiratory resistance is now used in the Clinical Pulmonary Laboratory for experimental work, and we are in the process of obtaining values in normal subjects.

Adult↗

PubMed1978

[Anatomy of the hand arteries based on angiographic studies (author's transl)].

The authors report conclusions drawn from anatomical studies on ninety-two hand angiographies. The superficial and deep volar arcs were classified according to Coleman and Anson. The superficial arc was found to be incomplete in 66.3%, the deep arc in 9.8% of all cases. Variations in ramification of the digital arteries were divided in two groups, as they belonged to either the ulnar or the radial arterial system. In the view of the extreme variability of the arterial network, the authors warn against operations on the hand arteries without angiography.

Angiography↗

PubMed1979

Quantitative analysis of intermediary metabolism in Tetrahymena. Cells grown in proteose-peptone and resuspended in a defined nutrient-rich medium.

Tetrahymena pyriformis were grown to early-stationary phase and resuspended in a defined mixture containing glucose, fructose, ribose, glycerol, acetate, pyruvate, bicarbonate, glutamate, and hexanoate, with only one substrate labeled with 14C in any flask. Incorporation of label into CO2, glycogen, RNA, alanine, glutamate, glycine, lipid glycerol, and lipid fatty acids was measured 20, 40, and 60 min after the start of the incubation. To develop a model suitable for quantitative analysis of the data, it was necessary to join two preceding models, one for carbohydrate-metabolizing cells and one for acetate-metabolizing cells, eliminating the over-simplified sections of each. Equations were written and programmed for a digital computer to allow computation of the amount of label expected to be incorporated into any of the products measured for any given set of steady state flux values in the metabolic network. The model formed by simply joining the two preceding models did not yield satisfactory agreement with the complete data obtained in the present study, although each partial set of data could be fit well by the appropriate partial model. Analysis of the ways in which the model failed to yield good fits to the data indicated that another pool of P-enolpyruvate, of pyruvate, and of acetyl-CoA had to be added at the junction of the two models. The presence of such poolte into fatty acids as compared to the incorporation of label from glucose into fatty acids. A new model was therefore constructed which differed from the preceding model only in its structural organization at the level of P-enolpyruvate, pyruvate, and acetyl-CoA. The model is consistent with all known information on the compartmental structure of metabolism in Tetrahymena, on enzyme localization, and on the enzyme complement of this cell. Over 70 measurements of label incorporation into products were made at each time. These, plus a large number of "limit" measurements which constrain any possible solutions, were in sufficient excess of the 39 independent flux values to permit a stringent assessment of the model. A set of flux values was found which yielded a good fit to the data. These flux values therefore provide a quantitative description of metabolite flux in the intact cell during the slow adaptation to the nine-substrate mixture. The rates of utilization of glucose, fructose, glycerol, and ribose were in the ratio of about 10:1:0.33:0.16, i.e. fairly similar to the ratio observed with carbohydrate-metabolizing cells. Initial flux through phosphofructokinase is about 160 nmol/10(6) cells.h, increasing over 3-fold during tje jpir incubation. Initial flux through fructose-1,6-diphosphatase is about 110 nmol/10(6) cells.h and also increases almost 3-fold during the incubation. Thus net flux is glycolytic and increases 4-fold during the hour with a large amount of futile cycling at this step...

Acetates↗