Diabetic Urine Samples Research Articles

Non-enzymatic glucose detection using nanocomposite decorated Cellulose-Based field effect transistor - a green electronics approach

Cellulose portrays suitable substrate material for biodegradable, cheaper, and sustainable electronic devices making it an alternative to traditional silicon/plastic materials. Additionally, field-effect transistors (FET) engross huge demand in biosensing applications owing to high amplification factors and work like transducers. Herein, fabrication, and the applicability of an eco-friendly, flexible, nanocomposite-decorated cellulose-based paper FET (PFET) chip, are demonstrated. Having cellulose as a dielectric as well as an active channel layer for depositing nanocomposite materials, and electrodes made of silver (Ag) ink, the as-fabricated PFET brings the concept of green electronics. The device exhibited good electrical and electronic behaviour having high electron and hole mobility (µe and µh) 3641 cm2V-1S-1 and 2436 cm2V-1S-1 in the absence and 8823 cm2V-1S-1 and 9116 cm2V-1S-1 respectively in the presence of glucose. Moreover, when tested for non-enzymatic glucose sensing under physiological pH conditions, a linear range from 0.1 mM to 5 mM with a low limit of detection (LOD) of 3 µM was observed. Finally, with satisfactory results, we proved the practical applicability of low-voltage PFET biosensors by measuring the glucose content in commercially available energy drinks and artificial urine samples. Hence, we developed a high-performance PFET chip utilising the circular economy concept with an optimised device structure and sensing techniques. We envisage that the device could enable real-time monitoring of glucose levels in commercially available drinks and diabetic urine samples.

Detection of insulin analogues and large peptides >2kDa in urine.

Insulin analogues and large bioactive peptides may be used by athletes to enhance performance and are banned by the World Anti-Doping Agency (WADA). In addition to insulin analogues, the large peptides include a structurally diverse set of peptides including analogues of growth hormone releasing hormone (GHRH), insulin-like growth factor-1 (IGF-1), and mechano-growth factor (MGF). Detection of this class of peptides is difficult due to their absorptive losses and presence at very low concentrations in urine. In this report, a high throughput method is described that allows sensitive detection of four classes of large peptides in one assay. Sample extraction is performed by ultrafiltration to concentrate the urine followed by solid phase extraction in a 96-well micro-elution plate. Peptides in the urine samples are detected on a triple quadrupole mass spectrometer coupled to standard flow liquid chromatography. The method was validated and evaluated for limit of detection, limit of identification, specificity, precision, carryover, recovery, matrix interference, and post-extraction stability. The limit of detection for insulin analogues is between 5 and 25 pg/ml and between 5 and 50 pg/ml for the other peptide classes. Specificity was good with no detection of interfering peaks in blank urine samples. Carryover from a high concentration sample was not observed and the post-extraction stability was between 77% and 107%. The method was able to detect insulin analogues in three diabetic urine samples. Increased screening for this class of peptides will improve detection and deterrence.

BoxCar increases the depth and reproducibility of diabetic urinary proteome analysis.

Mass spectrometry-based proteomics performs well in high throughput detection of urinary proteins. Nonetheless, protein identification depth and reproducibility remain the challenges in diabetic urinary proteome with high complexity and broad dynamic range, especially for low-abundant proteins. As a new data acquisition strategy, the BoxCar method was reported to benefit for low-abundant protein identification. Whether it is propitious to diabetic samples with high dynamic range proteomes has not been discussed yet. We aimed to apply BoxCar method to diabetic urine sample analysis, and to compare it with standard data dependent acquisition (DDA) method on protein identification in detail. We performed seven technical replicates analysis on two urine samples from healthy individuals and diabetic patients to evaluate protein detection of BoxCar and standard DDA methods on single sample. Further comparison of two methods was made on multiple diabetic urine samples. BoxCar could increase over 20% of identified proteins and performed better quantitative reproducibility than standard DDA method either in single or multiple diabetic urinary samples. BoxCar also improved the detection of low-abundant proteins. Functional enrichment analysis of normal albuminuria or microalbuminuria samples indicated that BoxCar acquired more diabetes-related biological information. The study demonstrates that BoxCar could enhance the depth and reproducibility in diabetic urinary proteome analysis, which provides reference for mass spectrometry approach selection in clinical urinary proteomic research.

Isolation and Characterization of Antibiotic-Resistant Bacteria from Urinary Tract Infection on Diabetic Mellitus Patients

Diabetes mellitus is a chronic disease-which occurs when the pancreas does not produce enough insulin, or when the body cannot effectively use the insulin it produces. This leads to an increased concentration of glucose in the blood (hyperglycemia). Diabetes mellitus is one of the most challenging health problems in India. The present study was investigated for isolation and characterization of antibiotic-resistant bacteria from urinary tract infection on diabetic mellitus patients. The 118 diabetic urine samples were collected and UTI bacteria using HiChrome UTI Agar was isolated. The diabetic UTI isolates were confirmed as Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, Staphylococcus saprophyticus, Proteus mirabilis and Klebsiella aerogenes based on morphological and biochemical characteristics. Our study identified that almost all the bacteria were highly sensitive to Cefoperazone/Sulbactam (75/30 mcg), Gentamicin (10 mcg), Meropenem (30 mcg), Piperacillin/Tazobactam (100/10 mcg) and Nitrofurantoin (100 mcg) antibiotics. Interestingly Klebsiella aerogenes alone was found to be more resistant to the entire antibiotics used in this study. The antibiotic resistant Klebsiella aerogenes is one of the biggest treats to human health, antibiotic resistance occurs naturally, but misuse of antibiotics in humans is accelerating the process.

A Study of Urinary Tract Infections in both Diabetic and Non diabetic Urine samples of IP and OP Clinical patients

A two months study was made on urinary tract infection in adults (male & female) diabetic and non-diabetic IP and OP patients. This study was basically done to identify the major causative micro-organisms (mainly bacteria) present in the urine samples collected from both catheterized and self-voided patients. During this study the following factors such as patient’s age, sex, mode of specimen collection, appearance of specimen, microscopic examination, colony count and blood sugar level were all taken into account. The study also includes the antibiotic sensitivity of various UTI bacteria. This study it is evident that E.coli is the most common clinically significant opportunistic bacteria. Klebsiella pnuemoniae was found as the second most predominant bacteria in UTI cases. This study is considered to be important because it gives us the clear picture of the common bacteria that cause UTI in diabetic/non-diabetic patients and gives us an idea about the antibiotics which are resistant and sensitive to the patients. Thus making the treatment and recovery of the patients easy for the physicians.

Prevalence Of Urinary Tract Infections In Diabetic Patients, Case-Control Study

There is evidence that patients with diabetes have an increased risk of asymptomatic bacteriuria and urinary tract infections (UTIs). UTI is the most common bacterial infection in diabetic patient.The aim of this study was to assess the prevalence of UTIs among diabetic patients and to identify the frequent of asymptomatic bacteriuria among diabetic subjects in compared with non-diabetic subject. The study population include 300 subjects .one-hundred and fifty patients with diabetes (84 female and 66 male) as a test group and 150 patients with non- diabetes mellitus (80 female and 70 male) as a control group. Between July 2012 – October2013.300 diabetic and non diabetic urine samples were collected All urine samples were processed in the lab. following standard laboratory protocol we collected patients’ personal history data..The prevalence of UTI was 50.7% in diabetic patients and 10% in non-diabetic subjects .there was significant difference in the effect of gender in both diabetic (62% female and 36%male had UTI) and non-diabetic (14% female and 7%male had UTI).there was significant increase asymptomatic bacteriuria in women within diabetic group (80% female and 62%male were asymptomatic)The frequency of UTI increase with diabetic by fourfold in comparison to that of non-diabetic subjects. the women had increased frequency of UTI more in diabetic patients than women in non-diabetic and most of them asymptomatic . The statistical significance of an association between two variables was assessed by Chi-square (X 2 ) test of independence.

Development of an optical system for monitoring urine substances -Preliminary study of concentration prediction using diabetic urine samples-

Development of an optical system for monitoring urine substances -Preliminary study of concentration prediction using diabetic urine samples-

Urinary exosomes and diabetic nephropathy: a proteomic approach

Urinary exosomes (UE) are nanovesicles released by every epithelial cell facing the urinary space and they are considered a promising source of molecular markers for renal dysfunction and structural injury. Exosomal proteomics has emerged as a powerful tool for understanding the molecular composition of exosomes and has potential to accelerate biomarker discovery. We employed this strategy in the study of diabetic nephropathy (DN) and the consequent end stage renal disease, which represent the dramatic evolution of diabetes, often leading the patients to dialysis or kidney transplantation. The identification of DN biomarkers is likely to help monitoring the disease onset and progression. A label free LC-MS/MS approach was applied to investigate the alteration of the proteome of urinary exosomes isolated from the Zucker diabetic fatty rats (ZDF), as a model of type 2 DN. We collected 24 hour urine samples from 7 ZDF and from 7 control rats at different ages (6, 12 and 20 weeks old) to monitor the development of DN. Exosomes were isolated by ultracentrifugation and their purity assessed by immunoblotting for known exosomal markers. Exosomal proteins from urine samples of 20 week old rats were pooled and analyzed by nLC-ESI-UHR-QToF-MS/MS after pre-filtration and tryptic digestion, leading to the identification and label free quantification of 286 proteins. Subcellular localization and molecular functions were assigned to each protein by UniprotKB, showing that the majority of identified proteins were membrane-associated or cytoplasmic and involved in transport, signalling and cellular adhesion, typical functions of exosomal proteins. We further validated label free mass spectrometry results by immunoblotting, as exemplified by: Xaa-Pro dipeptidase, Major Urinary Protein 1 and Neprilysin, which resulted increased, decreased and not different, respectively, in exosomes isolated from diabetic urine samples compared to controls, by both techniques. In conclusion we show the potential of exosome proteomics for DN biomarker discovery.

"Add to subtract": a simple method to remove complex background signals from the 1H nuclear magnetic resonance spectra of mixtures.

Because of its highly reproducible and quantitative nature and minimal requirements for sample preparation or separation, (1)H nuclear magnetic resonance (NMR) spectroscopy is widely used for profiling small-molecule metabolites in biofluids. However (1)H NMR spectra contain many overlapped peaks. In particular, blood serum/plasma and diabetic urine samples contain high concentrations of glucose, which produce strong peaks between 3.2 ppm and 4.0 ppm. Signals from most metabolites in this region are overwhelmed by the glucose background signals and become invisible. We propose a simple "Add to Subtract" background subtraction method and show that it can reduce the glucose signals by 98% to allow retrieval of the hidden information. This procedure includes adding a small drop of concentrated glucose solution to the sample in the NMR tube, mixing, waiting for an equilibration time, and acquisition of a second spectrum. The glucose-free spectra are then generated by spectral subtraction using Bruker Topspin software. Subsequent multivariate statistical analysis can then be used to identify biomarker candidate signals for distinguishing different types of biological samples. The principle of this approach is generally applicable for all quantitative spectral data and should find utility in a variety of NMR-based mixture analyses as well as in metabolite profiling.

Metabolomic studies of experimental diabetic urine samples by 1H NMR spectroscopy and LC/MS method

Metabolomic strategies based on nuclear magnetic resonance (NMR) and liquid chromatography coupled with mass spectrometry (LC/MS) have been developed to obtain metabolite profiles for urine samples excreted by male Goto–Kakizaki (G–K) and Wistar rats from 12–20 weeks of age. Multivariate statistical analysis was applied to the generated data sets. The efficiencies of two software packages for LC/MS data processing, MZmine and XCMS, were compared and gave similar results. The extracted data from both analytical methods were subjected to statistical analysis by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The performance of PLS-DA modeling was compared for both analytical methods and after different data normalization methods were used. The changes in metabolite profile included increased creatinine, glucose, and dimethylamine and decreased creatine, hippurate, formate, phenylalanine, allantoin, fumarate, citrate, acetate, amino acids, and some unidentified metabolites in the urine of G–K rats compared to Wistar rats. The obtained results gave evidence that 1H NMR procedures produce more information about the identity of metabolites. The multivariate analysis allowed the differentiation of the metabolic profiles related to animal age. In conclusion, the metabolomic studies of G–K rat urine samples provided further insights concerning experimental methodologies for data generation and processing, as well as possible markers for diabetes research.

Measurement of Microalbuminuria Using Protein Chip Electrophoresis

Microalbuminuria reflects the progression of nephropathy and cardiovascular disease in diabetic and hypertensive patients. Most commercially available tests currently used to measure microalbuminuria are immunoassays. We developed a microfluidics-based assay using the P200 protein chip (Caliper Life Sciences, Mountain View, CA, and Agilent Technologies, Santa Clara, CA) and 2100 Bioanalyzer (Agilent Technologies) to detect microalbuminuria. The method integrates and automates the electrophoretic separation and fluorescent detection of proteins from 14 to 200 kd. The assay was linear up to 750 mg/L and demonstrated good sensitivity with a lower detection limit of 7.5 mg/L. Intrachip and interchip coefficients of variation ranged from 0% to 4% and 4.9% to 13.5%, respectively. When albumin was measured by chip and immunoturbidimetry in diabetic urine samples, the chip consistently showed higher albumin concentrations. The discrepancy may be due to the chip's ability to detect immunounreactive albumin. Overall, this simple, cost-effective assay offers a sensitive and accurate measurement of microalbuminuria that can be easily implemented in a clinical laboratory.

Presence of immunounreactive albumin in the urine of diabetic patients

Recent studies have demonstrated that conventional immunochemical assays underestimate urinary albumin concentration because of the presence of immunounreactive albumin. It has been reported that intact urinary albumin in 24-hr diabetic urine samples could be detected as total concentration (immunoreactive+immunounreactive) by an HPLC method based on size exclusion chromatography. The aim of this study was to investigate urinary albumin concentration in diabetic spot urine samples by comparing the HPLC method with several other methods. The albumin concentrations on 80 diabetic spot urine specimens were measured by turbidimetric immunoassay (TIA), high performance liquid chromatography (HPLC), and a dipstick method. In addition, they were also analyzed by reducing sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS PAGE) and native polyacrylamide gel electrophoresis (Native PAGE). The albumin concentrations derived from diabetic spot urine samples measured by the HPLC method were higher than those of the other methods except for five of 80 samples. Furthermore, the albumin concentrations analyzed by Native PAGE were higher than SDS PAGE in 61 (76.2%) of 80 samples. This study suggests the need for evaluating diabetes not only by HPLC, but also by combining it with another method.

High prevalence of immuno-unreactive intact albumin in urine of diabetic patients

Background: Intact albumin in urine may exist in two forms, immunoreactive and immuno-unreactive. Previous estimates of albuminuria in diabetic urine have only detected immunoreactive forms. Methods: High performance liquid chromatography (HPLC) was used in this study to measure both forms of intact albumin (termed total intact albumin) to provide a more accurate measurement of albuminuria compared with radioimmunoassay (RIA) on 97 fresh urine samples from patients with diabetes. Eighty-six control urine samples from volunteers without diabetes were also tested. Results: There was no significant difference between the two methods for nondiabetic controls. For diabetic urine samples, 91.6% of samples showed a greater concentration of albumin measured by HPLC than RIA. For normoalbuminuric diabetic samples, HPLC gave a mean albumin excretion rate of 12.5 ± 4.4 μg/min (SD), whereas RIA gave a rate of 8.0 ± 6.7 μg/min (P = 0.004; N = 28). For microalbuminuric samples, there also was a statistically significant difference: HPLC albumin excretion rate, 82.0 ± 49.9 μg/min, and RIA, 49.0 ± 34.6 μg/min (P = 0.004; N = 30). Thirty-two urine samples were normoalbuminuric by RIA (albumin, 11.4 ± 3.9 μg/min), but in the microalbuminuric range as determined by HPLC (albumin, 38.5 ± 14.4 μg/min). For urine samples in the macroalbuminuric range, there was no statistically significant difference between HPLC and RIA. Immuno-unreactive albumin was confirmed as albumin, analyzed by two-dimensional electrophoresis and matrix-assisted laser desorption ionization mass spectrometry. Conclusion: These studies show that to determine microalbuminuria accurately, there is a need to assess urinary total intact albumin, rather than simply immunoreactive albumin. Am J Kidney Dis 41:336-342.

A long-term lifetime amperometric glucose sensor with a perfluorocarbon polymer coating

We have developed an amperometric glucose sensor whose electrodes are coated with a four-layered membrane: 3-aminopropyltriethoxysilane ( γ-APTES), Nafion®, glucose oxidase (GOX), and perfluorocarbon polymer (PFCP). Tests demonstrate the sensor's ability to accurately and successively determine glucose concentrations ranging from 2.8 to 167 mM, over a 66 day period with no increase in response time, while remaining imperviousness to the effects of interference species (2.8 mM ascorbic acid, 0.3 mM uric acid, 0.3 mM p-acetaminophen). Furthermore, tests on diabetic urine samples showed an excellent correlation coefficient of 0.985 ( y=1.04 x+4.73, n=30) between sensor results and those of Glucose-Dehydrogenase clinical laboratory analyses.

A micro-planar amperometric glucose sensor unsusceptible to interference species

The development of an amperometric glucose sensor in which a hydrogen peroxide detecting platinum electrode is coated with γ-APTES, acetylcellulose, Nafion®, glucose oxidase, and polydimethylsiloxane to prevent interference species is reported here. Tests of the sensor demonstrate its imperviousness to the effects of ascorbic acid (<11 mM), uric acid (<0.3 mM), and p-acetaminophen (<0.4 mM), as well as its ability to measure glucose concentrations ranging from 0.6 to 278 mM, and its capacity to reach steady-state response within 10 s. Furthermore, in tests on diabetic urine samples, the correlation coefficient between the glucose sensor and a clinical analyzer was good: 0.924 ( y=0.935 x+0.564, n=37).

Urinary ethanol and diabetes mellitus.

Ethanol concentrations were assayed in urine samples obtained from 10 newly presenting diabetic patients (aged 50-69 years) with glycosuria and symptoms suggesting genito-urinary candidiasis. No alcohol was detected initially with diabetic urine samples, but when stored at room temperature 8 out of 10 samples showed increasing levels of ethanol from day 3 to day 20. Peak levels were more than 10,000 mg/l in three samples and more than 1500 mg/l in six samples. Studies were also performed using normal urine and urine to which was added glucose and/or Candida. Samples were kept at either room temperature or 4 degrees C for up to 21 days and ethanol assays performed at regular intervals. Studies with non-diabetic urine showed a maximum ethanol level of 500 mg/l after 11 days only in samples containing both glucose and Candida and kept at room temperature.