Actual Glucose Concentration Research Articles

Toward Non-Invasive Estimation of Blood Glucose Concentration: A Comparative Performance

The present study comprises a comparison of the Mel Frequency Cepstral Coefficients (MFCC), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) as feature extraction methods using ten different regression algorithms (AdaBoost, Bayesian Ridge, Decision Tree, Elastic Net, k-NN, Linear Regression, MLP, Random Forest, Ridge Regression and Support Vector Regression) to quantify the blood glucose concentration. A total of 122 participants—healthy and diagnosed with type 2 diabetes—were invited to be part of this study. The entire set of participants was divided into two partitions: a training subset of 72 participants, which was intended for model selection, and a validation subset comprising the remaining 50 participants, to test the selected model. A 3D-printed chamber for providing a light-controlled environment and a low-cost microcontroller unit were used to acquire optical measurements. The MFCC, PCA and ICA were calculated by an open-hardware computing platform. The glucose levels estimated by the system were compared to actual glucose concentrations measured by venipuncture in a laboratory test, using the mean absolute error, the mean absolute percentage error and the Clarke error grid for this purpose. The best results were obtained for MCCF with AdaBoost and Random Forest (MAE = 11.6 for both).

An Electrochemical Nonenzymatic Microsensor Modified by Nickel Cobaltate Nanospheres for Glucose Sensing in Urine

Diabetes is a common chronic disease, so it is particularly important to develop a convenient, fast, and highly sensitive non-enzymatic glucose sensor. In this work, a high sensitivity, low cost sensor was prepared on the substrate of printed circuit board. The nickel cobaltate (NiCo <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ) spherical nanomaterials were synthesized by facile hydrothermal and calcining methods. The working electrode was modified by NiCo <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> nanospheres and the cation exchange resin in turn. Nafion cation exchange film can help to reduce the interference caused by surface-active compounds cyclic voltammetry- amperometric method was used to study the electrocatalytic performance of the sensor at room temperature. The sensor modified by NiCo <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> nanospheres showed good sensor performance for glucose, including excellent sensitivity (3449.14 μA mM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1 </sup> cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> ), wide linear range ( 1 μM to 100 mM), and low detection limit ( 0.376 μM). In addition, it has high selectivity, repeatability and stability. The sensor proposed in this work has been successfully applied to the detection of glucose in urine, and the results are consistent with the actual glucose concentration in urine.

Enzymatic hydrolysis optimization of sweet potato (Ipomoea batatas) peel using a statistical approach

In this work, two-step enzymatic hydrolysis of sweet potato peel was optimized. The effects of time, enzyme dose and temperature on glucose concentration were investigated. The Box-Behnken design was applied and a total of 17 experimental runs were generated for each step. For the liquefaction step, an ANOVA test showed the quadratic model obtained to be significant (p < 0.05). The statistical model predicted the maximum glucose concentration to be 126.66 g/L at a temperature of 56.4 ºC, α-amylase dose 1% (v/v) and time 60 min. A quadratic model was also obtained for the saccharification step and the model was also significant (p < 0.05). The statistical model for the second step predicted the maximum glucose concentration to be 178.39 g/L, established at the temperature of 45 ºC, glucoamylase dose 1% (v/v) and time 60 min. The optimized liquefaction and saccharification conditions were validated with the actual glucose concentrations of 126.03 and 176.89 g/L, respectively.

Simultaneous utilization of glucose and gluconate in Penicillium chrysogenum during overflow metabolism

The filamentous fungus Penicillium chrysogenum is one of the most important production organism for β-lactam antibiotics, especially penicillin. A specific feature of P. chrysogenum is the formation of gluconate as the primary overflow metabolite under non-limiting growth on glucose. Gluconate can be formed extracellularly by the enzyme glucose oxidase (GOD) that shows high activities under glucose excess conditions. Currently, it is assumed that under these conditions glucose is the preferred carbon substrate for P. chrysogenum and gluconate consumption first starts after glucose becomes limiting. Here, we specifically address this hypothesis by combining batch cultivation experiments on defined glucose media, time-dependent GOD activity measurements, and (13)C-tracer studies. Our data prove that both substrates are metabolized simultaneously independent from the actual glucose concentration and therefore suggest that no distinct mechanism of carbon catabolite repression exists for gluconate in P. chrysogenum. Moreover, gluconate consumption does not interfere with penicillin V production by repression of the penicillin genes. Finally, by following a model-driven approach the specific uptake rates for glucose and gluconate were quantified and found to be significantly higher for gluconate. In summary, our results show that P. chrysogenum metabolizes gluconate directly and at high rates making it an interesting alternative carbon source for production purposes.

Glitazones Exert Multiple Effects onβ-Cell Stimulus-Secretion Coupling

Earlier studies suggest that glitazones exert beneficial effects in patients with type 2 diabetes by directly affecting insulin secretion of β-cells, besides improving the effectiveness of insulin in peripheral tissues. The effects of glitazones on stimulus-secretion coupling (SSC) are poorly understood. We tested the influence of troglitazone and pioglitazone on different parameters of SSC, including insulin secretion (radioimmunoassay), cell membrane potential, various ion currents (patch-clamp), mitochondrial membrane potential (ΔΨ), and cytosolic Ca(2+) concentration (fluorescence). Troglitazone exerted stimulatory, inhibitory, or no effects on insulin secretion depending on the drug and glucose concentration. It depolarized the ΔΨ, thus lowering ATP production, which resulted in opening of ATP-dependent K(+) channels (K(ATP) channels) and reduced insulin secretion. However, it also exerted direct inhibitory effects on K(ATP) channels that can explain enhanced insulin secretion. Troglitazone also inhibited the currents through voltage-dependent Ca(2+) and K(+) channels. Pioglitazone was less effective than troglitazone on all parameters tested. The effects of both glitazones were markedly reduced in the presence of bovine serum albumin. Glitazones exert multiple actions on β-cell SSC that have to be considered as undesired side effects because the influence of these compounds on β-cells is not controllable. The final effect on insulin secretion depends on many parameters, including the actual glucose and drug concentration, protein binding of the drug, and the drug by itself. Troglitazone and pioglitazone differ in their influence on SSC. It can be assumed that the effects of pioglitazone on β-cells are negligible under in vivo conditions.

Contribution of an Intrinsic Lag of Continuous Glucose Monitoring Systems to Differences in Measured and Actual Glucose Concentrations Changing at Variable Rates in Vitro

Current continuous glucose monitoring (CGM) systems measure glucose levels in the interstitial fluid to estimate blood glucose concentration. A lag time has been observed between CGM system glucose readings and blood glucose levels when glucose levels are changing. Although this lag has been attributed to the time it takes glucose to equilibrate between blood and interstitial fluid compartments, it is unclear to what extent these inaccuracies reflect an intrinsic delay of the device itself. Four Guardian® REAL-Time CGM systems (CGMSs) (Medtronic Diabetes, Minimed, CA) and eight glucose sensors were tested in glucose solutions prepared in Krebs bicarbonate buffers at 37 °C. Glucose readings obtained from CGMSs were compared with actual glucose concentrations during controlled changes in glucose concentration performed at four rates (30, 90, and 220 mg/dl/hr(-1) and an instantaneous change of 110 mg/dl) using a linear gradient maker. Irrespective of the rate and direction of changes in glucose concentration, the readings obtained from CGMSs were significantly different from actual glucose levels. The faster the rise or fall in actual glucose concentration, the more pronounced the mismatch with CGMS glucose readings. Furthermore, the intrinsic lag times (8.3 to 40.1 min) were high enough to account for the lags reported in previous in vivo studies. The lag intrinsic of the CGMS may make a significant contribution to the mismatch between CGM system readings and blood glucose concentrations.

Glycemic index, postprandial glycemia, and the shape of the curve in healthy subjects: analysis of a database of more than 1000 foods

BackgroundThe glycemic index (GI) characterizes foods by using the incremental area under the glycemic response curve relative to a similar amount of oral glucose. Its ability to differentiate between curves of different shapes, the peak response, and other aspects of the glycemic response is debatable. ObjectiveThe objective was to explore the association between a food’s GI and the shape of the curve in healthy individuals. DesignA large database of 1126 foods tested by standardized GI methodology in 8–12 healthy subjects was analyzed systematically. Each food’s absolute and incremental blood glucose concentrations were compared at individual time points with the GI. The average curve was generated for low-GI (≤55), medium-GI (56–69), and high-GI (≥70) foods within major food categories. ResultsThe GI of individual foods was found to correlate strongly with the incremental and actual peak (Spearman’s correlations of r = 0.76 and r = 0.73, respectively), incremental and actual glucose concentration at 60 min (r = 0.70 and r = 0.66, respectively), and maximum amplitude of glucose excursion (r = 0.68) (all P < 0.001). In contrast, there was only a weak correlation between the food’s GI and the 120-min glucose concentration (incremental r = 0.20, P < 0.001; absolute r = 0.16, P < 0.001). Within food groups, the mean GI, 30- and 60-min glucose concentrations, and maximum amplitude of glucose excursion varied significantly for foods classified as having a low, medium, or high GI (P < 0.001). ConclusionsThe GI provides a good summary of postprandial glycemia. It predicts the peak (or near peak) response, the maximum glucose fluctuation, and other attributes of the response curve.

Prediction Capability of Optical Coherence Tomography for Blood Glucose Concentration Monitoring

Optical coherence tomography (OCT) has been shown to be a promising optical approach to noninvasive monitoring of blood glucose concentration because of its capability of probing optical properties at different depths in tissue with high resolution. This article investigates the capability of OCT to predict changes in blood glucose concentration. We varied blood glucose concentration in the physiological range in three sets of experiments. In the first set, we investigated large variations of blood glucose concentration ( approximately 400 mg/dl) and used 2100 OCT A scans for signal averaging. In the second set, we varied blood glucose concentration by approximately 200 mg/dl and used 8400 A scans for signal averaging. In the third set, we improved OCT blood glucose monitoring by increasing and controlling skin temperature under the OCT probe. In this set of experiments we increased the glucose concentration by approximately 300 mg/dl and used 4200 A scans for averaging. The predicted glucose concentrations in the first two sets were lower than actual glucose concentration by 10-20% (the mean shift), while the heating and temperature control in the third set of experiments reduced the mean shift down to 1.5%. Therefore, the mean shift was reduced substantially by tissue heating and temperature control. However, it did not depend on the number of A scans to be averaged. In contrast, the uncertainty in OCT prediction of glucose concentration (the standard deviation) did not depend on heating and temperature control, but was reduced substantially from 56 to 24 mg/dl by increasing the number of A scans from 2100 to 8400, respectively. These results suggest that the accuracy of OCT-based glucose monitoring is approaching that of standard invasive and minimally invasive techniques.

Continuous Noninvasive Glucose Monitoring Technology Based on “Occlusion Spectroscopy”

A truly noninvasive glucose-sensing device could revolutionalize diabetes treatment by leading to improved compliance with recommended glucose levels, thus reducing the long-term complications and cost of diabetes. Herein, we present the technology and evaluate the efficacy of a truly noninvasive device for continuous blood glucose monitoring, the NBM (OrSense Ltd.). In vitro analysis was used to validate the technology and algorithms. A clinical study was performed to quantify the in vivo performance of the NBM device. A total of 23 patients with type 1 (n = 12) and type 2 (n = 11) diabetes were enrolled in the clinical study and participated in 111 sessions. Accuracy was assessed by comparing NBM data with paired self-monitoring of blood glucose meter readings. In vitro experiments showed a strong correlation between calculated and actual glucose concentrations. The clinical trial produced a total of 1690 paired glucose values (NBM vs reference). In the paired data set, the reference glucose range was 40-496 mg/dl. No systematic bias was found at any of the glucose levels examined (70, 100, 150, and 200 mg/dl). The mean relative absolute difference was 17.2%, and a Clarke error grid analysis showed that 95.5% of the measurements fall within the clinically acceptable A&B regions (zone A, 69.7%; and zone B, 25.7%). This study indicates the potential use of OrSense's NBM device as a noninvasive sensor for continuous blood glucose evaluation. The device was safe and well tolerated.

On Real-Time Estimates of Blood Glucose Levels

In the comment from Trevino (1), three articles are cited as having “emphasized the need for real-time assessment of blood glucose levels.” Two of the cited articles (2,3) indeed relate to real-time assessment of blood glucose levels using new technology referred to as continuous glucose monitoring (CGM). The third article Trevino refers to, by Farmer et al. (4), relates to the use of traditional finger-stick blood analysis that is communicated to a nurse via telemedicine. We are engaged in the study of CGM, so further comments will be related only to continuous sensors (2,3) and not to telemedicine. Trevino further states that “[r]eal-time analysis is motivated by the belief that the random time series being analyzed manifests average properties that change with the passage of time. Results of such analyses are presented to patients as means (μ) ± SD.” In regard to our study (2), this statement is completely false. We do not rely on averaged properties of time series data. Patients received actual glucose concentration values transmitted every 5 min. Under no circumstances were patients presented with the mean and SD of the data in real time. Trevino further describes an equation for determining mean and SD and …

Temperature-Insensitive Measurement of Glucose Concentration Based on Near Infrared Spectroscopy and Partial Least Squares Analysis

Temperature-insensitive partial least squares analysis for glucose measurement is presented based on Near Infrared spectroscopy in the wavelength range from 1,250 to 1,800nm. Experiments are performed using aqueous glucose solutions. To create a temperature-insensitive prediction model, the glucose concentration and water temperature are varied independently. The results of the predicted glucose concentration agree well with the actual glucose concentration with an error of 4.4%.

Feed Forward/Feedback Control of Glucose Concentration During Cultivation of Escherichia Coli

A control system for the glucose concentration is presented, which enables to control the glucose concentration at a set point of 0.1 glL. Due to a drift of the measuring system the actual glucose concentration was a little bit higher, however lessthan 0.25 g/L. The measurement system based on flow injection analysis using a sample solution, which contains cells. As detector an oxygen electrode is employed to determine the oxygen concentration consumed by the cells as well as the oxygen concentration consumed by a mixture of cells and an enzyme solution (glucose oxidase). From the difference the glucose concentration in the culture broth can be determined. Applying an extended Kalman filter the glucose measurements are filtered and used for feed forward control combined with an additional feedback loop control for set point adjustment. The residual standard deviation of the on-line glucose measurements with respect to the set point was 0.043 g/L.

Diabetes Simulators: Ready for Prime Time?

55 D IABETES IS A DISEASE OF COMPROMISES. Irrefutable evidence demonstrates that good glycemic control significantly reduces the risk for developing many of the devastating longterm complications of diabetes. Conversely, limitations inherent in the current methods for providing insulin replacement make euglycemia a very dangerous proposition for most type 1 diabetics because of the risk of severe hypoglycemia. Normal beta cells within the pancreas modulate insulin secretion, responding in minutes to small changes in blood glucose concentrations. This tight feedback loop maintains blood glucose concentrations within a narrow range. Current clinical insulin regimes pale in comparison. Patients, and often their families, face a substantial challenge as they tread the narrow path between lowand high-glucose concentrations each day. Using a handful of glucose determinations each day, they must integrate diet and activity with a bewildering array of possible exogenous insulin preparations and doses to forge a reasonable level of glycemic control. Given this complexity, patients and providers alike seek a clear-cut set of rules to guide them to euglycemia. Given our increasing understanding of physiology and the massive computing capability on our desktops, an algorithm to predict glucose concentrations does not seem, at first blush, an unreasonable request. With some patient-specific information, shouldn’t we be able to accurately predict the glucose concentrations resulting from various dietary and insulin regimens? In the article elsewhere in this issue, Dr. E.D. Lehmann discusses one such glucose simulator, AIDA v4.0. This program is freely available via the Internet. A DOS program, it ran reasonably well in a Windows 95 environment. The user interface is a bit cumbersome. The program uses a physiology-based, single glucose compartment model to integrate the actions of carbohydrates and exogenous insulin. Glucose enters the compartment by intestinal absorption and liver gluconeogenesis. Glucose leaves the compartment via insulin-independent and insulin-dependent pathways. Hepatic and peripheral glucose utilization are modeled separately so that different insulin sensitivities may be assigned to each. Renal glucose losses occur when the renal threshold is exceeded. Insulin concentrations (from virtual subcutaneous injections) are derived using a two-compartment model. Counterregulatory hormones, exercise, stress, and circadian variations in insulin sensitivity are not included within this model. The technical guide that is included in the program contains a complete description of the equations involved in the model. The author has done some evaluation of the potential clinical utility of this model using patient specific parameters derived from actual historical glucose concentrations. Reasonably large differences were found between predicted and actual glucose concentrations.

Initial Evaluation of a 290-μm Diameter Subcutaneous Glucose Sensor: Glucose Monitoring with a Biocompatible, Flexible-Wire, Enzyme-Based Amperometric Microsensor in Diabetic and Nondiabetic Humans

Results of the initial clinical evaluation in 20 human subjects of a subcutaneously implanted microsensor-based amperometrically glycemia-monitoring system, carried out between April 1994 and June 1995, are reported. The system was based on the electrical connection (“wiring”) of the reaction centers of glucose oxidase to a gold electrode and on elimination of the chemicals that interfere with glucose monitoring through their horseradish peroxidase-catalyzed oxidation by internally generated hydrogen peroxide. The sensor was finer than a 29-gauge needle and had no leachable components. Because of its high selectivity for glucose, the sensor output was virtually nil at zero glucose level. This enables prompt “one-point” in vivo calibration of the sensor with a single blood glucose sample. Microsensors were subcutaneously implanted in ten nondiabetic and ten insulin-dependent diabetes mellitus (IDDM) volunteers. All subjects underwent standard meal tests and intravenous glucose-tolerance tests (IVGTT) in addition to hourly plasma glucose measurements. The sensor signals were continuously recorded, and the glucose concentration estimates were derived by calibrating the sensor using a single blood sample (one-point calibration). Regression analysis revealed that the sensor-estimated glucose concentrations were linearly related to the plasma glucose concentrations ( r 2 = 0.75) over a wide glucose concentration range (2–28 mmol/L) (sensor estimate = plasma * 0.96 + 0.26 mmol/L). The difference between the estimated and actual glucose concentration was −0.13 ± 0.23 mmol/L [mean ± 95% confidence interval (CI), n = 546], and 95% of the estimates fell in clinically acceptable zones of the Clarke error grid. The sensing delay time was 10.4 ± 2.3 min as measured by the IVGTT. The subjects reported no discomfort associated with wearing the sensors.

Microdialysis of glucose in subcutaneous adipose tissue up to 3 weeks in healthy volunteers.

To measure possible changes in dialysate glucose concentrations over time, to validate the diffusional model for glucose transport from tissue to the probe, and to evaluate the actual glucose concentration in adipose tissue. Glucose concentrations in the subcutaneous adipose tissue of five healthy subjects (age 25 +/- 2.7 years, BMI 23.2 +/- 2.3 kg/m2 [mean +/- SD]) were measured by the microdialysis technique and compared with blood glucose. We applied microdialysis probes with hollow fibers of various membrane length (10-35 mm), used eight perfusion flow rates (0.5-20 microl/min), and perfused four glucose solutions (0.0, 2.8, 8.3, 11.1 mmol/l). After implantation, a substantial decrease in glucose recovery to the lowest value of 26 +/- 10% of the final plateau value was noted during the first few hours (n = 4). Recovery increased and stabilized after 5-9 days at 84.0 +/- 7.4% of capillary blood glucose when a flow rate of 0.5 microl/min was applied. According to the zero net-flux method, the glucose concentration in equilibrium, Cequi, with the surrounding tissue can be obtained. This concentration also decreases; however, 1 h after recovery, Cequi increases again over 1 or 2 days to a stable value that is not significantly different from the measured capillary blood glucose (P < 0.05). Using various perfusion flow rates and probes (membrane length 10-35 mm), it is shown that diffusion is the rate-limiting process for glucose transport through tissue. Insertion of the microdialysis probes causes damage to the adipose cells and the vascular bed around the probe. Glucose recovery decreases because of a lower blood supply. In 5-9 days, glucose recovery increases; apparently, this time is needed to repair the microstructure of tissue around the probe. After stabilization of the recovery, no loss of probe permeability, which is due to biocompatibility problems, was seen. The change during the 2 days in equilibrium concentration is probably caused by an inflammation reaction that consumes glucose around the probe. The individual increase in recovery during the 1st days after probe insertion until a stable plateau value is reached (flow rate >0 microl/min) is complicated for short-term clinical glucose measurements in adipose tissue. After stabilization, the mean equilibrium concentration of all subjects was equal to the mean capillary blood glucose concentration. Therefore, we conclude that capillary blood glucose concentration probably is the driving force for diffusion through the capillary wall into the probe and is not some interstitial concentration.

Measurement of glucose consumption by hybridoma cells growing in hollow fiber cartridge bioreactors: use of blood glucose self-monitoring devices

Two different types of blood glucose self-monitoring device, designed for use by diabetics (Accu-Chek Advantage ® and Accu-Chek III ®), were evaluated for the purpose of monitoring glucose concentration in tissue culture media. The Accu-Chek Advantage ® meter was found to systematically overestimate the glucose concentration in a variety of commonly used tissue culture media by 50–90%, in comparison with their formulated glucose concentration. The Accu-Chek III ® meter reliably estimated glucose concentrations from 100 to 300 mg/dl and overestimated glucose concentrations above 300 mg/dl. The systematic overestimation of glucose concentration in tissue culture fluids by the Accu-Chek Advantage ® meter was further investigated. A standard curve was constructed and the meter reading was found to be linearly related to the actual glucose concentration and the best linear fit was given by the formula y=−43.504+1.9246 x, where y is the meter reading and x is the actual glucose concentration. Rearranging the equation to make x the subject gave the following algorithm x=( y+43.504)÷1.9246 which could be used to correct the `raw' meter reading. The mean corrected glucose concentrations deviated from the formulated glucose concentration by less than 3.5% in five media tested, indicating that this meter is more than adequate for monitoring glucose consumption by cells growing in hollow fiber cartridge bioreactors, when used in conjunction with this correction factor.

Evaluation of a rapid reagent strip test for blood glucose determination in diarrheic calves.

Sixty-three blood samples from 10 diarrheic calves were tested for glucose concentration by two methods. Plasma glucose concentration was measured by the conventional glucose-6-phosphate dehydrogenase method in the clinical laboratory, and the results compared to those obtained using a rapid reagent strip test for blood glucose concentration measurement. The rapid reagent strip test result could not be used to make an accurate prediction of the actual plasma glucose concentration as determined by the conventional method, due to the wide variability in actual plasma glucose concentrations corresponding to each rapid test result.

Digital modelling and robust control of the glucose homeostasis system

A digital controller for the regulation of glucose in the metabolic system was achieved through an open-loop scheme in which the continuous measurement of the actual glucose concentration is not required, assuming the model is sufficiently accurate and the controller is robust. The analysis of individual human subjects has shown great differences in their responses to glucose infusion. Consequently there were also distinct differences in the optimized controller parameters. In principle, such differences lead to significant differences in the closed-loop dynamics. A fourth-order digital model was obtained from the state differential equations which represent the system metabolism. The sample time was chosen sufficiently large to suit such a slow system. The zeros of the controller were selected to match the undesired complex poles of the metabolic system and the controller gain was determined using the root locus portrait. A set of well-behaved performances together with their corresponding internal state...

Effect of glucose and oxygen on β-lactam biosynthesis by Cephalosporium acremonium

The effects of glucose consumption rate ( q s) and oxygen limitation on the control of cephalosporin C (Ceph C) biosynthesis and the activities of deacetoxycephalosporin C synthetase/hydroxylase (DAOC-SH) and acetyl coenzyme A: deacetylcephalosporin C o-acetyltransferase (DAC-AT) were investigated in cultivations of the highly productive Cephalosporium acremonium strain TR87 under conditions similar to those used in industrial production. A carefully optimised time course of q s during the first part of fed batch cultivations was essential for maximal Ceph C production. The actual glucose concentration in the medium was of secondary importance. A decrease of q s between 20 and 35 h of cultivation was found to induce the early onset of antibiotic synthesis. By subsequently maintaining q s at a relatively low level using a controlled feed of glucose and a limiting amount of phosphate, maximal production rates were obtained. Oxygen starvation after the onset of Ceph C production led to a pronounced increase in penicillin N formation, a reduced Ceph C yield (−30%) and a strongly reduced activity of the two enzymes tested. In general, neither the time course nor the absolute levels of the two enzyme activities directly correlated with the actual production rates of Ceph C. This is the first time where an independent parameter ( q s) has been demonstrated to be responsible for triggering the synthesis of an antibiotic.

Enhanced Formation of β‐Glucosidase by Aspergillus niger VKMF‐2092 in Fed‐Batch Operation with Frequently Intermittent Glucose Addition

AbstractThe wild strain Aspergillus niger VKMF‐2092 forms β‐glucosidase on the basis of glucose as the sole carbon source. The formation of β‐glucosidase is initiated after the glucose concentration in the medium has reached a low level since the formation is subjected to catabolite repression. The β‐glucosidase is mainly cell‐bound and only released after a longer period. Under non‐repressed conditions the total formation of β‐glucosidase is not associated with growth.By supplying glucose in a fed‐batch‐technique, maintaining a low actual glucose concentration in the medium, the formation of β‐glucosidase is enhanced in comparison to simple batch fermentation. Using a fed‐batch‐technique with a frequently intermittent addition of glucose it is possible to increase the formation of β‐glucosidase with regard to both productivity and the activity related to the mycelium and the fermentation broth as well. The increase of productivity is about two to four times greater than at constant feed rate of the same overall amount of glucose.The reason for this increase will be discussed below. A method is presented which permits to investigate the influence of the substrate concentration and other parameters on the enzyme formation in short periods of one and the same fermentation run.