Non-allopathic Indian medicines, referred to elsewhere in the world as complementary and alternative medicine have gathered increasing recognition in recent years with regard to both treatment options and health hazards. Ayurveda, Siddha, Unani and homeopathy are practiced in India as non-allopathic systems. These systems comprise a wide range of therapeutic approaches that include diet, herbs, metals, minerals, precious stones and their combinations as well as non-drug therapies. Ayurveda is the oldest system of medicine in the world and by far the most commonly practiced form of non-allopathic medicine in India, particularly in rural India, where 70% of the population lives. The difference between modern medicine and these systems stems from the fact that the knowledge base of many of the above systems, unlike Western medicine, is based on years of experience, observations, empiricism and intuition and has been handed down generations both through word of mouth and treatises. The focus on non-allopathic systems of medicine in India can be attributed to various causes including a need to revive a rich tradition, the dependency of 80% of the country's population on these drugs, their easy availability, increasing worldwide use of these medicines, the lack of focused concerted scientific research and the abuse of these systems by quacks. Elsewhere, the increasing use of herbal products worldwide and the growth of the herbal product industry has led to increasing concern regarding their safety. The challenges in these non-allopathic systems relate to the patient, physician, regulatory authorities, the abuse/misuse of these medicines, quality and purity issues. Safety monitoring is mandated by a changing ecological environment, the use of insecticides, new manufacturing techniques, an as yet unregulated pharmaceutical industry, the availability of combinations of herbs over the counter and not mentioned in ancient Ayurvedic texts, and the need to look at the active principles of these medicines as potential chemotherapeutic agents. The Indian traditional medicine industry has come a long way from the times when it was considered unnecessary to test these formulations prior to use, to the introduction of Good Manufacturing Practice guidelines for the industry. However, we still have a long way to go. The conflict between the traditional practitioners and the purists demanding evidence of safety and efficacy needs to be addressed. There is an urgent need for the practitioners of the allopathic and non-allopathic systems to work together to optimise the risk-benefit profile of these medicines.

Numerical data that are normally distributed can be analyzed with parametric tests, that is, tests which are based on the parameters that define a normal distribution curve. If the distribution is uncertain, the data can be plotted as a normal probability plot and visually inspected, or tested for normality using one of a number of goodness of fit tests, such as the Kolmogorov–Smirnov test. The widely used Student's t-test has three variants. The one-sample t-test is used to assess if a sample mean (as an estimate of the population mean) differs significantly from a given population mean. The means of two independent samples may be compared for a statistically significant difference by the unpaired or independent samples t-test. If the data sets are related in some way, their means may be compared by the paired or dependent samples t-test. The t-test should not be used to compare the means of more than two groups. Although it is possible to compare groups in pairs, when there are more than two groups, this will increase the probability of a Type I error. The one-way analysis of variance (ANOVA) is employed to compare the means of three or more independent data sets that are normally distributed. Multiple measurements from the same set of subjects cannot be treated as separate, unrelated data sets. Comparison of means in such a situation requires repeated measures ANOVA. It is to be noted that while a multiple group comparison test such as ANOVA can point to a significant difference, it does not identify exactly between which two groups the difference lies. To do this, multiple group comparison needs to be followed up by an appropriate post hoc test. An example is the Tukey's honestly significant difference test following ANOVA. If the assumptions for parametric tests are not met, there are nonparametric alternatives for comparing data sets. These include Mann–Whitney U-test as the nonparametric counterpart of the unpaired Student's t-test, Wilcoxon signed-rank test as the counterpart of the paired Student's t-test, Kruskal–Wallis test as the nonparametric equivalent of ANOVA and the Friedman's test as the counterpart of repeated measures ANOVA.

Deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) are simple linear polymers that have been the subject of considerable research in the last two decades and have now moved into the realm of being stand-alone therapeutic agents. Much of this has stemmed from the appreciation that they carry out myriad functions that go beyond mere storage of genetic information and protein synthesis. Therapy with nucleic acids either uses unmodified DNA or RNA or closely related compounds. From both a development and regulatory perspective, they fall somewhere between small molecules and biologics. Several of these compounds are in clinical development and many have received regulatory approval for human use. This review addresses therapeutic uses of DNA based on antisense oligonucleotides, DNA aptamers and gene therapy; and therapeutic uses of RNA including micro RNAs, short interfering RNAs, ribozymes, RNA decoys and circular RNAs. With their specificity, functional diversity and limited toxicity, therapeutic nucleic acids hold enormous promise. However, challenges that need to be addressed include targeted delivery, mass production at low cost, sustaining efficacy and minimizing off-target toxicity. Technological developments will hold the key to this and help accelerate drug approvals in the years to come. IntroductionDeoxyribonucleic acid (DNA) and ribonucleic acid (RNA) are simple linear polymers that consist of only four major subunits, yet these molecules carry out myriad functions both within the cell and in the laboratory [1]. Early assessment of nucleic acid function was rather narrow and restricted, but research in the past few decades has seen remarkable progress in developing nucleic acid-based therapeutics. The progress has covered diverse fields of research and a significant number of scientists and engineers are now involved in this area [2]. A series of pivotal discoveries in the last three decades has made this possible. First, a large body of work has obviously followed the decoding of the human genome that unlocked several molecular pathways that are important in disease. Secondly, several types of RNA with complex biological functions have been discovered in addition to messenger RNA (mRNA) and transfer RNA (tRNA) [1]. For example, two non-coding RNA types that were not considered essential but are now extremely relevant to therapeutics are the microRNA (miRNA) and the short interfering RNA (siRNA). Thirdly, the appreciation that RNAs can act as enzymes has led to the development of RNA analogues with useful or unusual properties. Of the analogues, the locked nucleic acids or LNAs have found therapeutic applications. In view of their polar nature, the cellular delivery of nucleic acids is poor relative to conventional low molecular weight drugs. The fourth major advance has been enhancing the bioavailability of nucleic acid-based drugs. None of these would have been possible without technological advances in DNA synthesis, including the de novo synthesis of increasingly longer DNA British Journal of Cli...

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient (r). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation (y = a + bx), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous.

This study was conducted to analyze alterations in the human serum proteome as a consequence of infection by malaria parasites Plasmodium falciparum and P. vivax to obtain mechanistic insights about disease pathogenesis, host immune response, and identification of potential protein markers. Serum samples from patients diagnosed with falciparum malaria (FM) (n = 20), vivax malaria (VM) (n = 17) and healthy controls (HC) (n = 20) were investigated using multiple proteomic techniques and results were validated by employing immunoassay-based approaches. Specificity of the identified malaria related serum markers was evaluated by means of analysis of leptospirosis as a febrile control (FC). Compared to HC, 30 and 31 differentially expressed and statistically significant (p<0.05) serum proteins were identified in FM and VM respectively, and almost half (46.2%) of these proteins were commonly modulated due to both of the plasmodial infections. 13 proteins were found to be differentially expressed in FM compared to VM. Functional pathway analysis involving the identified proteins revealed the modulation of different vital physiological pathways, including acute phase response signaling, chemokine and cytokine signaling, complement cascades and blood coagulation in malaria. A panel of identified proteins consists of six candidates; serum amyloid A, hemopexin, apolipoprotein E, haptoglobin, retinol-binding protein and apolipoprotein A-I was used to build statistical sample class prediction models. By employing PLS-DA and other classification methods the clinical phenotypic classes (FM, VM, FC and HC) were predicted with over 95% prediction accuracy. Individual performance of three classifier proteins; haptoglobin, apolipoprotein A-I and retinol-binding protein in diagnosis of malaria was analyzed using receiver operating characteristic (ROC) curves. The discrimination of FM, VM, FC and HC groups on the basis of differentially expressed serum proteins demonstrates the potential of this analytical approach for the detection of malaria as well as other human diseases.