30 May 2018
Cholesterol Checking Kit
Cardiovascular diseases cause about 17 million deaths every year. One of the main reasons for such conditions is high cholesterol levels in blood serum. While there are methods to detect cholesterol levels in blood serum, they require large samples and take a long time to process.
Recently, Gurpreet Kaur and team at the Delhi University reported developing an easier and better technology for a hand-held cholesterol detection unit. The team combined three fields – microfluidics, biosensors, and electrochemistry – to develop the technique.
They used polydimethylsiloxane, a biocompatible material, to fabricate a microchamber. Inside the chamber, a thin film of nickel oxide immobilises cholesterol oxidase – a bioreceptor for cholesterol molecules. Owing to good charge transfer characteristics, nickel oxide serves as working electrode and platinum as control electrode to facilitate the electrochemical process.
Each component is systematically stacked on a glass substrate. To avoid leakage, the microchamber is sandwiched between two plexiglass sheets. A laminar flow of a small sample of blood serum into the chamber is adequate to create contact between cholesterol oxidase and cholesterol. The lab-on-chip detection setup is now ready for testing and measurement.
The researchers used amperometry for quantifying cholesterol levels in the blood serum. At a particular applied electric potential, cholesterol is oxidised. This results in an oxidation current. The measured current is directly proportional to the concentration of cholesterol.
The test results match spectrophotometric data even for concentrations as low as 0.1 mM. “The sensitivity is much better than in existing techniques”, says Gurpreet Kaur, Ph D scholar, University of Delhi.
“This is proof-of-concept for a portable technique that provides faster, more accurate results in estimating cholesterol in blood serum in clinical settings” says Monika Tomar, Assistant Professor, Miranda House, Delhi.
“It can be a cost effective tool in health care monitoring”, says Vinay Gupta, University of Delhi.
The team hopes that industries will take up the technique to produce a point-of-care diagnostic tool.
Sens. Actuators B: Chem., 261: 460-466 (2018)
30 October 2017
Lysosomal Storage Disorders
Diagnosis with dried blood samples
Lysosomes, cell organelles that digest biomolecules, need several enzymes for effective functioning. Defects or malfunctioning of these enzymes lead to lysosomal storage diseases – a heterogeneous group of about fifty rare inherited metabolic disorders. Individually, these disorders are rare, but together, they may be as common as other metabolic disorders in several populations of the world.
In India, the incidence of lysosomal storage disorders has been only partially investigated. Given a large population and a high frequency of marriages between close relatives, the incidence of Lysosomal Storage Disorders in India is likely to be high. But what is the best method to assess the prevalence of the problem?
Recently, scientists from the NIMHANS, Bangalore, reported reference intervals – lower and higher limits – for lysosomal enzymes. And they claim that the enzymes can be easily estimated from dried blood spot samples.
The scientists used blood samples from about 3000 healthy Indians, of both genders between 2 days and 60 years. They used mass spectrometry – an analytical technique that ionises chemical species and sorts ions based on their mass-to-charge ratio – to determine five types of lysosomal enzymes.
The scientists observed significant differences between genders and various age groups in the range of reference intervals of enzymes. However, their standardised reference ranges can be used for the identification of prevalence of the lysosomal storage diseases, say the scientists. The easy sample collection and simple methodology to determine lysosomal enzymes make it a convenient technology to screen Indian populations and to assess the prevalence of the problem.
Clinical Biochemistry 50: 858-863 (2017)
24 October 2017
Diagnosis of Streptococcus
Detecting strep throat
Streptococcus pyogenes commonly infects the throat. Symptoms are mild fever and inflammation of the pharynx. If not treated, it can lead to rheumatic heart disease. Hence, rapid diagnosis is imperative to distinguish ‘strep throat’ from other throat infections.
Recently, teams of researchers from CSIR-IGIB and NCDC, New Delhi, came up with the design of a DNA chip sensor to detect S. pyogenes. They selected mga, a gene unique to that species, as a single stranded DNA probe to avoid ambiguity and to enhance the reliability of the outcome.
Initially, they took a carbon electrode chip embedded with gold nanoparticles as base. To attach the single stranded DNA probe to this chip, cysteine and dendrimer molecules were used as interlinking bridges. The thiol group of cysteine forms a strong bond with gold atoms and their free carboxyl group forms an amide bond with the dendrimer. Now, the amino group of the dendrimer could be attached to the single stranded DNA probe. And the chip was ready for testing.
They took and tested DNA from throat swab samples of 25 patients. The variation in the electrical signal was measured after the interaction of the chip with target DNA. Interestingly, five samples showed strong signals as DNA of S. pyogenes. But, the researchers did not detect any signals from samples with non-target bacteria and other DNA contaminants.
Besides its specificity, the chip only requires a small amount of target DNA for diagnosis. And, compared to other techniques, detection time was also reduced from days to hours.
With the help of this foolproof technology, we can overcome the limitations of existing detection techniques. To make this chip available for clinical use, entrepreneurs can now come forward to commercialize it and help save human lives.
In addition, this technique of using single stranded DNA to detect specific bacteria offers a strategy to develop such kits to diagnose infections by other pathogens.
Int J. Biol. Macromol 103: 355-359 (2017)
Parkinson’s Disease Diagnostics
Accuracy with Harr wavelet
Approximately seven to ten million people worldwide suffer from Parkinson’s, a neurodegenerative disease affecting the central nervous system. The symptoms – tremors and postural instability – are attributed to reduced dopamine levels in the brain. This reduction starts before the outward symptoms develop. The earlier it is detected, the more easy it is to tackle the progression of the disease.
The use of functional neuroimaging along with Daubechies2 wavelet analysis is quite popular for diagnosing Parkinson’s. However, the process is costly and time consuming. Researchers from the Indian Institute of Technology, Delhi, in collaboration with the Graphic Era University, Dehradun and the Quantum Business School, Roorkee, reported an alternative diagnostic technique this month.
They reasoned that any damage to the cerebral cortex area will affect walking pattern or gait variables. So the team measured gait variables such as swing interval, stance interval and stride interval. They used force sensitive resistors on the soles of feet of a large number of patients to collect data.
They analysed the data on gait cycle variables using Haar wavelet transform, instead of the more traditional Daubechies 2 wavelets, to diagnose the degree of Parkinsonism. They found that a classification accuracy of more than 90.32% was obtained using Haar wavelet for right swing and left stance intervals, taken independently. But when all the left leg gait parameters were taken together, there was 100% accuracy. Compared to the technique that uses Daubechies 2 wavelets, the Haar wavelet performed better.
The results demonstrate that the method suggested by the scientists can efficiently extract relevant features from gait parameters to classify Parkinson’s and healthy subjects. To translate the results to clinical settings will usually take time until entrepreneurs pick up the technique for developing commercially viable gait analysers.
Computer Methods and Programs in Biomedicine, 145: 135-145 (2017)
Pregnancy Related Breast Cancer
Genomic data provides clues
Within a decade and a half since the first draft of the human genome, the health care system has become more personalised. DNA based diagnostics and targeted therapy are equipping medical professionals to diagnose and treat patients more effectively.
Any divergence in DNA sequences and/or expression of the genes coded by those genes can lead to disease or predisposition to disease. Many genes associated with specific diseases have been identified. However, many other multigenic diseases, such as cancer, are yet to be fully understood.
Venkatesan and colleagues from the Pondicherry University came up with a new idea. They focussed on breast cancers which develop during pregnancy or within a year after delivery. Such pregnancy related breast cancer is the second leading cause of cancer deaths among women. This cancer is highly metastatic which makes treatment challenging. The medication increases the risk of foetus malformation. So it is important to be aware of the risks of developing such cancers.
The researchers mined the genomic and protein expression data available in various public domain databases. The researchers took a protein maturation method as data mining parameter and identified differentially matured proteins from a pregnancy associated breast cancer database. The team used two data mining algorithms – clustering algorithms and an association rule mining method. By correlating the selected proteins with their corresponding genes, they could identify four genes specifically associated with pregnancy related breast cancer.
The findings make it easy to detect women with a risk of developing pregnancy related breast cancers. The scientists are confident that drugs specifically targeting these genes will provide efficient control over the progression of breast cancer without harming foetal development.
A smart therapy to fight pregnancy related cancer is in its infancy!
Pathology and Oncology Research, 23 (3): 537-544 (2017)