Knowledge and attitudes about health research amongst a group of Pakistani medical students

Background Health research training is an important part of medical education. This study was conducted to assess the level of knowledge and attitudes regarding health research in a group of Pakistani medical students at Aga Khan University, Karachi. Methods It was a cross-sectional pilot study conducted among a group of Pakistani medical students. Through stratified random sampling, a pre-tested, structured and validated questionnaire was administered to 220 medical students. Knowledge and attitudes were recorded on a scale (graduated in percentages). Results Mean scores of students were 49.0% on knowledge scale and 53.7% on attitude scale. Both knowledge and attitudes improved significantly with increasing years of study in medical college [Regression coefficient 4.10 (p-value; 0.019) and 6.67 (p-value; \u3c 0.001) for knowledge and attitudes, respectively]. Conclusion Medical students demonstrate moderate level of knowledge and attitude towards health research. Intensive training in this regard is associated with significant improvement in knowledge and attitudes of students towards health research

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result.</p> <p>Results</p> <p>In this paper, we describe the first systematic comparison of a wide range of normalisation methods, using two objectives that should be met by a good method. These objectives are minimisation of inter-spectra variance and maximisation of signal with respect to class separation. The former is assessed using an estimation of the coefficient of variation, the latter using the classification performance of three types of classifiers on real-world datasets representing two-class diagnostic problems. To obtain a maximally robust evaluation of a normalisation method, both objectives are evaluated over multiple datasets and multiple configurations of baseline correction and peak detection methods. Results are assessed for statistical significance and visualised to reveal the performance of each normalisation method, in particular with respect to using no normalisation. The normalisation methods described have been implemented in the freely available MASDA R-package.</p> <p>Conclusion</p> <p>In the general case, normalisation of mass spectra is beneficial to the quality of data. The majority of methods we compared performed significantly better than the case in which no normalisation was used. We have shown that normalisation methods that scale spectra by a factor based on the dispersion (e.g., standard deviation) of the data clearly outperform those where a factor based on the central location (e.g., mean) is used. Additional improvements in performance are obtained when these factors are estimated locally, using a sliding window within spectra, instead of globally, over full spectra. The underperforming category of methods using a globally estimated factor based on the central location of the data includes the method used by the majority of SELDI users.</p

Hydrophilic interaction liquid chromatography (HILIC) in proteomics

In proteomics, nanoflow multidimensional chromatography is now the gold standard for the separation of complex mixtures of peptides as generated by in-solution digestion of whole-cell lysates. Ideally, the different stationary phases used in multidimensional chromatography should provide orthogonal separation characteristics. For this reason, the combination of strong cation exchange chromatography (SCX) and reversed-phase (RP) chromatography is the most widely used combination for the separation of peptides. Here, we review the potential of hydrophilic interaction liquid chromatography (HILIC) as a separation tool in the multidimensional separation of peptides in proteomics applications. Recent work has revealed that HILIC may provide an excellent alternative to SCX, possessing several advantages in the area of separation power and targeted analysis of protein post-translational modifications

Individual characteristics and student's engagement in scientific research : a cross-sectional study

Background: In light of the increasing recognition of the importance of physician scientists, and given the association between undergraduate research experiences with future scientific activity, it is important to identify and understand variables related to undergraduate student’s decision to engage in scientific research activities. The present study assessed the influence of individual characteristics, including personality traits and socio-demographic characteristics, on voluntary engagement in scientific research of undergraduate medical students. Methods: For this study, all undergraduate students and alumni of the School of Health Sciences in Minho, Portugal were invited to participate in a survey about voluntary engagement in scientific research activities. Data were available on socio-demographic, personality and university admission variables, as part of an ongoing longitudinal study. A regression model was used to compare (1) engaged with (2) not engaged students. A classification and regression tree model was used to compare students engaged in (3) elective curricular research (4) and extra-curricular research. Results: A total of 466 students (88%) answered the survey. A complete set of data was available for 435 students (83%).Higher scores in admission grade point average and the personality dimensions of “openness to experience” and “conscientiousness” increased chances of engagement. Higher “extraversion” scores had the opposite effect. Male undergraduate students were two times more likely than females to engage in curricular elective scientific research and were also more likely to engage in extra-curricular research activities. Conclusions: This study demonstrated that student’s grade point average and individual characteristics, like gender, openness and consciousness have a unique and statistically significant contribution to student’s involvement in undergraduate scientific research activities.Fundação para a Ciência e a Tecnologia (FCT) - PTDC/ESC/65116/200

Functional sex differences in human primary auditory cortex

Background We used PET to study cortical activation during auditory stimulation and found sex differences in the human primary auditory cortex (PAC). Regional cerebral blood flow (rCBF) was measured in 10 male and 10 female volunteers while listening to sounds (music or white noise) and during a baseline (no auditory stimulation). Results and discussion We found a sex difference in activation of the left and right PAC when comparing music to noise. The PAC was more activated by music than by noise in both men and women. But this difference between the two stimuli was significantly higher in men than in women. To investigate whether this difference could be attributed to either music or noise, we compared both stimuli with the baseline and revealed that noise gave a significantly higher activation in the female PAC than in the male PAC. Moreover, the male group showed a deactivation in the right prefrontal cortex when comparing noise to the baseline, which was not present in the female group. Interestingly, the auditory and prefrontal regions are anatomically and functionally linked and the prefrontal cortex is known to be engaged in auditory tasks that involve sustained or selective auditory attention. Thus we hypothesize that differences in attention result in a different deactivation of the right prefrontal cortex, which in turn modulates the activation of the PAC and thus explains the sex differences found in the activation of the PAC. Conclusion Our results suggest that sex is an important factor in auditory brain studies

Transcriptional responses to glucose in Saccharomyces cerevisiae strains lacking a functional protein kinase A

Background The pattern of gene transcripts in the yeast Saccharomyces cerevisiae is strongly affected by the presence of glucose. An increased activity of protein kinase A (PKA), triggered by a rise in the intracellular concentration of cAMP, can account for many of the effects of glucose on transcription. In S. cerevisiae three genes, TPK1, TPK2, and TPK3, encode catalytic subunits of PKA. The lack of viability of tpk1 tpk2 tpk3 triple mutants may be suppressed by mutations such as yak1 or msn2/msn4. To investigate the requirement for PKA in glucose control of gene expression, we have compared the effects of glucose on global transcription in a wild-type strain and in two strains devoid of PKA activity, tpk1 tpk2 tpk3 yak1 and tpk1 tpk2 tpk3 msn2 msn4. Results We have identified different classes of genes that can be induced -or repressed- by glucose in the absence of PKA. Representative examples are genes required for glucose utilization and genes involved in the metabolism of other carbon sources, respectively. Among the genes responding to glucose in strains devoid of PKA some are also controlled by a redundant signalling pathway involving PKA activation, while others are not affected when PKA is activated through an increase in cAMP concentration. On the other hand, among genes that do not respond to glucose in the absence of PKA, some give a full response to increased cAMP levels, even in the absence of glucose, while others appear to require the cooperation of different signalling pathways. We show also that, for a number of genes controlled by glucose through a PKA-dependent pathway, the changes in mRNA levels are transient. We found that, in cells grown in gluconeogenic conditions, expression of a small number of genes, mainly connected with the response to stress, is reduced in the strains lacking PKA. Conclusions In S. cerevisiae, the transcriptional responses to glucose are triggered by a variety of pathways, alone or in combination, in which PKA is often involved. Redundant signalling pathways confer a greater robustness to the response to glucose, while cooperative pathways provide a greater flexibility.BT/BiotechnologyApplied Science

Are mesenchymal stromal cells immune cells?

Mesenchymal stromal cells (MSCs) are considered to be promising agents for the treatment of immunological disease. Although originally identified as precursor cells for mesenchymal lineages, in vitro studies have demonstrated that MSCs possess diverse immune regulatory capacities. Pre-clinical models have shown beneficial effects of MSCs in multiple immunological diseases and a number of phase 1/2 clinical trials carried out so far have reported signs of immune modulation after MSC infusion. These data indicate that MSCs play a central role in the immune response. This raises the academic question whether MSCs are immune cells or whether they are tissue precursor cells with immunoregulatory capacity. Correct understanding of the immunological properties and origin of MSCs will aid in the appropriate and safe use of the cells for clinical therapy. In this review the whole spectrum of immunological properties of MSCs is discussed with the aim of determining the position of MSCs in the immune system

Computational Methods for Protein Identification from Mass Spectrometry Data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology