Integration of Homeopathic System of Medicine into Main Health Stream of Pakistan

Editoria

Prospective Role of Eastern Medicine to Achieve the Goal of “Health for All” in Pakistan

The Eastern Medicine has a very strong backing and the research system that has been developed with science and technology in many respects and in its own way resembling with orthodox medicine. The concept of Eastern Medicine is growing on medieval and medical holistic approach, and health lifestyle woven together

Perceptual Quality Assessment of Digital Images Using Deep Features

Perceptual quality assessment is a tough task especially in the absence of reference information. No-reference image quality assessment is more challenging than full-reference or reduced reference methods, as the system has to model the different image distortions in the form of a quality score. Most of the approaches are based on handcrafted features which are based on natural scene statistics and are specific to some distortion types. These approaches provide high correlation with human opinion score for datasets containing specific distortions, but they fail to generalize well in scenarios were multiple distortions or real-time distortions are present in images. Deep learning algorithms, on the other hand, demonstrated their abilities to learn expert features with better discriminatory power for various classification and regression tasks. It is a big challenge to use those deep learning methods for image quality assessment as the image datasets with human opinion score are very small and cannot be used effectively to train a deep learning algorithm. We experimented with activations of different deep layers of thirteen pre-trained models and checked for their suitability for the task of no-reference quality assessment. Fine-tuning of these models on quality assessment datasets provided even better performance. A Gaussian process regression model is trained on these activations to perform the quality assessment and it provided state-of-the-art performance. Cross-dataset validation demonstrated its performance further and also provided further prospects of research in this direction

Phytochemical analysis, antimicrobial, antioxidant and enzyme inhibitory activities of ethanolic extract of Centaurea solstitialis L. and its different fractions

396-403Resistance to conventional antimicrobial regimes is one of the issues of concern in healthcare and it drives the need for development of new antimicrobial agents. Medicinal plants, as rich source of biochemical and bioactive compounds, serve as potential source for new drugs. Here, we evaluated the ethanolic extract of Centaurea solstitialis L. and its different fractions (n-hexane, choloroform and n-butanol soluble fraction) for antimicrobial, antioxidant and enzymes inhibitory activities. Antibacterial activity against two Gram-positive and three Gram-negative bacteria species were determined by using agar well diffusion and 96-wells microplate methods. Similarly, antifungal activity against two fungal strains was also evaluated by agar well diffusion method. Antioxidant activity analyzed by measuring the scavenging activity of DPPH radicals and acetylcholinesterase, butrylcholinesterase and chymotrypsin inhibitory activity was determined at 10 µg/mL and 1.0 mg/mL concentrations. Results revealed that the ethanolic extract of C. solstitialis and its different fractions possesses significant (P<0.05) antibacterial activity and effective against fungi Aspergillus niger and Macrophomina phaseolina. Significant (P<0.05) DPPH scavenging activity (88.52±0.23%) among all fractions was noted. n-Butanol fraction showed significant acetyl-cholinesterase (78.55±0.76%) and butrylcholinesterase inhibitory activity (78.1±0.41%) with IC50 values of 54.6±0.39 µg/mL and 211.9±0.15 µg/mL, respectively. Maximum chymotrypsin inhibition activity was shown by crude ethanolic extract (87.76±1.17) with IC50 value of 38.23±0.75 µg/mL. It is concluded that C. solstitialis extract and its fractions possess significant antimicrobial, antioxidant and enzyme inhibitory activity

Inference dynamics in transcriptional regulation

Computational systems biology is an emerging area of research that focuses on understanding the holistic view of complex biological systems with the help of statistical, mathematical and computational techniques. The regulation of gene expression in gene regulatory network is a fundamental task performed by all known forms of life. In this subsystem, modelling the behaviour of the components and their interactions can provide useful biological insights. Statistical approaches for understanding biological phenomena such as gene regulation are proving to be useful for understanding the biological processes that are otherwise not comprehensible due to multitude of information and experimental difficulties. A combination of both the experimental and computational biology can potentially lead to system level understanding of biological systems. This thesis focuses on the problem of inferring the dynamics of gene regulation from the observed output of gene expression. Understanding of the dynamics of regulatory proteins in regulating the gene expression is a fundamental task in elucidating the hidden regulatory mechanisms. For this task, an initial fixed structure of the network is obtained using experimental biology techniques. Given this network structure, the proposed inference algorithms make use of the expression data to predict the latent dynamics of transcription factor proteins. The thesis starts with an introductory chapter that familiarises the reader with the physical entities in biological systems; then we present the basic framework for inference in transcriptional regulation and highlight the main features of our approach. Then we introduce the methods and techniques that we use for inference in biological networks in chapter 2; it sets the foundation for the remaining chapters of the thesis. Chapter 3 describes four well-known methods for inference in transcriptional regulation with pros and cons of each method. Main contributions of the thesis are presented in the following three chapters. Chapter 4 describes a model for inference in transcriptional regulation using state space models. We extend this method to cope with the expression data obtained from multiple independent experiments where time dynamics are not present. We believe that the time has arrived to package methods like these into customised software packages tailored for biologists for analysing the expression data. So, we developed an open-sources, platform independent implementation of this method (TFInfer) that can process expression measurements with biological replicates to predict the activities of proteins and their influence on gene expression in gene regulatory network. The proteins in the regulatory network are known to interact with one another in regulating the expression of their downstream target genes. To take this into account, we propose a novel method to infer combinatorial effect of the proteins on gene expression using a variant of factorial hidden Markov model. We describe the inference mechanism in combinatorial factorial hidden model (cFHMM) using an efficient variational Bayesian expectation maximisation algorithm. We study the performance of the proposed model using simulated data analysis and identify its limitation in different noise conditions; then we use three real expression datasets to find the extent of combinatorial transcriptional regulation present in these datasets. This constitutes chapter 5 of the thesis. In chapter 6, we focus on problem of inferring the groups of proteins that are under the influence of same external signals and thus have similar effects on their downstream targets. Main objectives for this work are two fold: firstly, identifying the clusters of proteins with similar dynamics indicate their role is specific biological mechanisms and therefore potentially useful for novel biological insights; secondly, clustering naturally leads to better estimation of the transition rates of activity profiles of the regulatory proteins. The method we propose uses Dirichlet process mixtures to cluster the latent activity profiles of regulatory proteins that are modelled as latent Markov chain of a factorial hidden Markov model; we refer to this method as DPM-FHMM. We extensively test our methods using simulated and real datasets and show that our model shows better results for inference in transcriptional regulation compared to a standard factorial hidden Markov model. In the last chapter, we present conclusions about the work presented in this thesis and propose future directions for extending this work

KNOWLEDGE OF HEPATITIS B VIRUS, HEPATITIS C VIRUS AND HUMAN IMMUNODEFFICIENCY VIRUS (HIV) AMONG ATTENDANTS OF PATIENTS VISITING OUTPATIENT DEPARTMENTS.

Objective:- To assess the level of knowledge regarding hepatitis B, hepatitis C and HIV among attendants of patients visiting OPD of a tertiary care hospital. Study design: - Cross-sectional study. Setting: -Outpatient departments of Nishtar Medical University, Multan. Subjects and methods: - Three hundred and eight attendants of patients coming to the OPDs of Nishtar Hospital Multan were included and interviewed in this cross-sectional study. Non-probability convenient sampling technique was used. All the data was entered and analyzed by SPSS version 20. Results: - Three hundred and eight subjects were interviewed. The age of subjects varied from 15-70 years. The mean age was 37.06 years ±15.59 years Two hundred and twenty three (72.4 %) were familiar with hepatitis B, 196 (63.6%) with hepatitis C and 146 (47.4%) with HIV and 133 (43.2%) were familiar with all three viruses while 81 (26.5%) were unfamiliar with these viruses. Most of the subjects 93 (30.2%) knew about the transmission through injection by un-sterilized syringes, 90 (29.2%) were knowing that infected blood is important source of spread while 87 (28.2%) of the subjects were knowing that these are spread through infected razors. 84 (27.2%) were knowing unsafe sex as a mode of transmission. Sharing objects can be the source of spread was known to very less number of people, 9 (2.9%) were aware that these can spread through sharing infected tooth brushes, 7 (2.3%) with sharing infected “Miswaks” and only 3 (1%) were aware that these can spread through infected combs. Electronic media was the major source of knowledge 82 (26.6%), interpersonal communication 69 (22.4%) and newspapers in only 1 (0.3%). Conclusion: - knowledge about hepatitis B, hepatitis C and HIV is low in our study population. The results of the study have shown that many people still think that HIV is the only virus which is transmitted through sexual contacts and hepatitis B and C through contaminated blood. The knowledge about the modes of transmission is lower in the rural and un-educated community. General public has very little knowledge that infected combs, infected toothbrushes and infected “Miswaks” can also lead to transmission of hepatitis B and C. Electronic media particularly television &amp; radio and newspapers are the main source of knowledge and awareness for the urban population while in rural population it is not the case. Effective health awareness campaigns are needed to be started among rural population Keywords: - Hepatitis B, Hepatitis C, HIV

Impact of different moisture regimes and nitrogen rates on yield and yield attributes of maize (Zea mays L.)

Nitrogen and irrigation, both are essential to determine the yield and quality of maize (Zea mays L.). A field study was accomplished to determine the upshots of different levels of irrigation and varying nitrogen rates on yield, yield contributing attributes and radiation use efficiency (RUE) of maize hybrid on sandy clay loam soil. Different nitrogen rates and moisture regime treatments comprised of N0 = 0, N1 = 100 and N2 = 200kg N ha-1, I1 (25 mm water deficit), I2 (50 mm water deficit), I3 (three irrigations during vegetative development + one irrigation at tasseling stage) and I4 (three irrigations during vegetative development + one irrigation at tasseling stage + one irrigation at silking stage + one irrigation at grain filling stage), respectively. Results showed that maximum grain yield (7.04 t ha-1) was recorded when six irrigations were applied (three irrigations during vegetative development + one irrigation at tasseling stage + one irrigation at silking stage + one irrigation at grain filling stage) coupled with 200 kg N ha-1 (N2 × I4). The lowest grain yield (2.08 t ha-1) was obtained in response to 25 mm water deficits. Overall, N2 × I2 also gave a positive response in terms of yield attributes but highest plant height (160.80 cm), cob length (29.00 cm), number of grains per cob (308.33), 1000-grain weight (294.33 g) and biological yield (25.67 t ha-1) with maximum coefficient of correlation (R2) values (0.9399; 0.8851; 0.9161; 0.8743 and 0.9126), respectively, was attained with N2 × I4 treatment combinations. The superior (RUE) radiation use efficiency (5.33 g MJ-1) with higher R2 value (0.8821) was significantly affected by nitrogen rates and irrigation levels as obtained from N2 × I4 treatment. However, in all treatment combinations, N2 × I4 was superior by producing the highest maize grain yield.Keywords: Moisture regimes, nitrogen rates, deficit irrigation, Zea mays L., radiation use efficiency, maize yiel

COMPREHENSIVE REVIEW ON ETHANOBOTANICAL USES, PHYTOCHEMISTRY AND PHARMACOLOGICAL PROPERTIES OF MELIA AZEDARACH LINN.

Medicinal plants, since times immemorial, have been used in virtually all cultures for therapeutic purposes. The widespread use of herbal remedies and healthcare preparations obtained from commonly used traditional herbs and medicinal plants have been traced to the occurrence of natural products with medicinal properties. In the present review, an attempt has been made to collect the botanical, phytochemical, ethnomedicinal, pharmacological and toxicological information on Melia azedarach L. which is used traditionally as an anthelmintic, diuretic, emmenagouge, expectorant, vermifuge, used in piles, used as astringent, used in hysteria, leprosy, and in scrofula. Medicinally it has been shown to possess various pharmacological activities like antifungal, anti-malarial, antibacterial, hepatoprotective, anti-oxidant, anti-fertility, anthelmintic, antipyretic and cytotoxic activities. The available literature on the M. azedarach L. revealed that this plant contains many phytochemical constituents including alkaloids, terpenoids, saponins, glycosides, phenolic compounds, flavonoids and rutins. The aim of this article is to review those medicinal and pharmacological properties of M. azedarach which have been or still are being learned. The present review is therefore, an effort to give a detailed survey of the literature on its traditional, phytochemical and pharmacological properties

Phytochemical screening and antipyretic effects of hydro-methanol extract of Melia azedarach leaves in rabbits

The antipyretic effect of the hydro-methanol extract of Melia azedarach leaves was investigated using the yeast induced pyrexia method in rabbits. Paracetamol was used as a positive control and negative control group received distilled water. Rectal temperatures of all rabbits were recorded immediately before the administration of the extract or vehical or paracetamol and again at 1 h interval for 6 h using digital thermometer. The extract was also phytochemically screened for alkaloids, tannins, saponins, flavonoids, cardiac glycosides and phenols. At 500 mg/kg dose the extract showed significant (p<0.0001) reduction in yeast-induced elevated temperature as compared with that of standard drug paracetamol whereas the extract dose 250 mg/kg was less effective when compared with higher dose (p<0.05). Phytochemical screening showed the presence of alkaloids, tannins, flavonoids, saponins and phenols. This study showed that the hydro-methanol extract of M. azedarach leaves at a dose of 500 mg/kg possesses significant antipyretic effect against the yeast-induced elevated temperature