Faculty Members and Students’ Opinion about Quality of Services Provided by the Central Library of Kerman University of Medical Sciences

Background & Objective : Libraries are among main parts of universities as libraries of high quality lead to improvement in education and research within universities and the society. Assessing quality of services in educational systems is important to improve quality of provided services. This study was conducted to assess the quality of services provided by the central library of Kerman University of Medical Sciences from the faculty members and students’ point of view . Methods : In this study, 200 faculty members and students of Kerman University of Medical Sciences were chosen using stratified random sampling. The data was collected using LibQUAL+TM standard questionnaire after its validity and reliability were confirmed Results : Our findings showed that the mean score of general satisfaction of the central library’s provided services was 6.13 out of 9. Among the three aspects of library service quality, information control was the most satisfactory factor (5.98) which was followed by efficacy of services (5.96) and the library’s atmosphere (5.89). Faculty members were more satisfied with the central library’s provided services in all aspects compared to students, although this difference was not significant. The most frequent referral to the central library and using references was once a month. Conclusion : Although the findings of this study were suggestive of faculty members and students’ relative satisfaction of provided services, improving quality of services necessitates better and more organized planning. Improvement of library service quality can lead to promoting faculty members and students’ scientific level in universities of medical sciences, medical knowledge, and medical education . Keywords: Service Quality, LibQUAL+TM survey, Faculty member, Student

Classification of Foetal Distress and Hypoxia Using Machine Learning

Foetal distress and hypoxia (oxygen deprivation) is considered a serious condition and one of the main factors for caesarean section in the obstetrics and gynaecology department. It is considered to be the third most common cause of death in new-born babies. Foetal distress occurs in about 1 in 20 pregnancies. Many foetuses that experience some sort of hypoxic effects can have series risks such as damage to the cells of the central nervous system that may lead to life-long disability (cerebral palsy) or even death. Continuous labour monitoring is essential to observe foetal wellbeing during labour. Many studies have used data from foetal surveillance by monitoring the foetal heart rate with a cardiotocography, which has succeeded traditional methods for foetal monitoring since 1960. Despite the indication of normal results, these results are not reassuring, and a small proportion of these foetuses are actually hypoxic. This study investigates the use of machine learning classifiers for classification of foetal hypoxic cases using a novel method, in which we are not only considering the classification performance only, but also investigating the worth of each participating parameter to the classification as seen by medical literature. The main parameters that are included in this study as indicators of metabolic acidosis are: pH level (which is a measure of the hydrogen ion concentration of blood to specify the acidity or alkalinity), as an indicator of respiratory acidosis; Base Deficit of extra-cellular fluid level and Base Excess (BE) (which is the measure of the total concentration of blood buffer base that indicates metabolic acidosis or compensated respiratory alkalosis). In addition to other parameters such as the PCO2 (partial pressure of carbon dioxide can reflect the hypoxic state of the foetus) and the Apgar scores (which shows the foetal physical activity within a specific time interval after birth). The provided data was an open-source partum clinical data obtained by Physionet, including both hypoxic cases and normal cases. Six well known machine learning classifier are used for the classification; each model was presented with a set of selected features derived from the clinical data. Classifier evaluation is performed using the receiver operating characteristic curve analysis, area under the curve plots, as well as confusion matrix. The simulation results indicate that machine learning classifiers provide good results in diagnosis of foetal hypoxia, in addition to acceptable results of different combinations of parameters to differentiate the cases

PERT: A Method for Expression Deconvolution of Human Blood Samples from Varied Microenvironmental and Developmental Conditions

The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity

World society of emergency surgery study group initiative on Timing of Acute Care Surgery classification (TACS).

Timing of surgical intervention is critical for outcomes of patients diagnosed with surgical emergencies. Facing the challenge of multiple patients requiring emergency surgery, or of limited resource availability, the acute care surgeon must triage patients according to their disease process and physiological state. Emergency operations from all surgical disciplines should be scheduled by an agreed time frame that is based on accumulated data of outcomes related to time elapsed from diagnosis to surgery. Although literature exists regarding the optimal timing of various surgical interventions, implementation of protocols for triage of surgical emergencies is lacking. For institutions of a repetitive triage mechanism, further discussion on optimal timing of surgery in diverse surgical emergencies should be encouraged. Standardizing timing of interventions in surgical emergencies will promote clinical investigation as well as a commitment by administrative authorities to proper operating theater provision for acute care surgery

Chronic p53-independent p21 expression causes genomic instability by deregulating replication licensing

The cyclin-dependent kinase inhibitor p21WAF1/CIP1 (p21) is a cell-cycle checkpoint effector and inducer of senescence, regulated by p53. Yet, evidence suggests that p21 could also be oncogenic, through a mechanism that has so far remained obscure. We report that a subset of atypical cancerous cells strongly expressing p21 showed proliferation features. This occurred predominantly in p53-mutant human cancers, suggesting p53-independent upregulation of p21 selectively in more aggressive tumour cells. Multifaceted phenotypic and genomic analyses of p21-inducible, p53-null, cancerous and near-normal cellular models showed that after an initial senescence-like phase, a subpopulation of p21-expressing proliferating cells emerged, featuring increased genomic instability, aggressiveness and chemoresistance. Mechanistically, sustained p21 accumulation inhibited mainly the CRL4–CDT2 ubiquitin ligase, leading to deregulated origin licensing and replication stress. Collectively, our data reveal the tumour-promoting ability of p21 through deregulation of DNA replication licensing machinery—an unorthodox role to be considered in cancer treatment, since p21 responds to various stimuli including some chemotherapy drugs

Systematic review of reduced therapy regimens for children with low risk febrile neutropenia

PURPOSE: Reduced intensity therapy for children with low-risk febrile neutropenia may provide benefits to both patients and the health service. We have explored the safety of these regimens and the effect of timing of discharge. METHODS: Multiple electronic databases, conference abstracts and reference lists were searched. Randomised controlled trials (RCT) and prospective observational cohorts examining the location of therapy and/or the route of administration of antibiotics in people younger than 18 years who developed low-risk febrile neutropenia following treatment for cancer were included. Meta-analysis using a random effects model was conducted. I (2) assessed statistical heterogeneity not due to chance. Registration: PROSPERO (CRD42014005817). RESULTS: Thirty-seven studies involving 3205 episodes of febrile neutropenia were included; 13 RCTs and 24 prospective observational cohorts. Four safety events (two deaths, two intensive care admissions) occurred. In the RCTs, the odds ratio for treatment failure (persistence, worsening or recurrence of fever/infecting organisms, antibiotic modification, new infections, re-admission, admission to critical care or death) with outpatient treatment was 0.98 (95% confidence interval (95%CI) 0.44-2.19, I (2) = 0 %) and with oral treatment was 1.05 (95%CI 0.74-1.48, I (2) = 0 %). The estimated risk of failure using outpatient therapy from all prospective data pooled was 11.2 % (95%CI 9.7-12.8 %, I (2) = 77.2 %) and using oral antibiotics was 10.5 % (95%CI 8.9-12.3 %, I (2) = 78.3 %). The risk of failure was higher when reduced intensity therapies were used immediately after assessment, with lower rates when these were introduced after 48 hours. CONCLUSIONS: Reduced intensity therapy for specified groups is safe with low rates of treatment failure. Services should consider how these can be acceptably implemented

Increasing consistency of disease biomarker prediction across datasets

Microarray studies with human subjects often have limited sample sizes which hampers the ability to detect reliable biomarkers associated with disease and motivates the need to aggregate data across studies. However, human gene expression measurements may be influenced by many non-random factors such as genetics, sample preparations, and tissue heterogeneity. These factors can contribute to a lack of agreement among related studies, limiting the utility of their aggregation. We show that it is feasible to carry out an automatic correction of individual datasets to reduce the effect of such 'latent variables' (without prior knowledge of the variables) in such a way that datasets addressing the same condition show better agreement once each is corrected. We build our approach on the method of surrogate variable analysis but we demonstrate that the original algorithm is unsuitable for the analysis of human tissue samples that are mixtures of different cell types. We propose a modification to SVA that is crucial to obtaining the improvement in agreement that we observe. We develop our method on a compendium of multiple sclerosis data and verify it on an independent compendium of Parkinson's disease datasets. In both cases, we show that our method is able to improve agreement across varying study designs, platforms, and tissues. This approach has the potential for wide applicability to any field where lack of inter-study agreement has been a concern. © 2014 Chikina, Sealfon

Modulation of the peripheral blood transcriptome by the ingestion of probiotic yoghurt and acidified milk in healthy, young men

The metabolic health benefits of fermented milks have already been investigated using clinical biomarkers but the development of transcriptomic analytics in blood offers an alternative approach that may help to sensitively characterise such effects. We aimed to assess the effects of probiotic yoghurt intake, compared to non-fermented, acidified milk intake, on clinical biomarkers and gene expression in peripheral blood. To this end, a randomised, crossover study was conducted in fourteen healthy, young men to test the two dairy products. For a subset of seven subjects, RNA sequencing was used to measure gene expression in blood collected during postprandial tests and after two weeks daily intake. We found that the postprandial response in insulin was different for probiotic yoghurt as compared to that of acidified milk. Moreover changes in several clinical biomarkers were associated with changes in the expression of genes representing six metabolic genesets. Assessment of the postprandial effects of each dairy product on gene expression by geneset enrichment analysis revealed significant, similar modulation of inflammatory and glycolytic genes after both probiotic yoghurt and acidified milk intake, although distinct kinetic characteristics of the modulation differentiated the dairy products. The aryl hydrocarbon receptor was a major contributor to the down-regulation of the inflammatory genesets and was also positively associated with changes in circulating insulin at 2h after yoghurt intake (p = 0.05). Daily intake of the dairy products showed little effect on the fasting blood transcriptome. Probiotic yoghurt and acidified milk appear to affect similar gene pathways during the postprandial phase but differences in the timing and the extent of this modulation may lead to different physiological consequences. The functional relevance of these differences in gene expression is supported by their associations with circulating biomarkers

Agent-Based Conceptual Model of Online Monitoring System, To Improve Pharmaceutical Distribution System

The purpose of paper is to present a conceptual model of the IOT-based online monitoring system to improve the distribution system‌‌ of pharmaceutical, using the agent-based modeling approach.First, by reviewing the research literature and interviewing industry experts, the basic concepts are extracted and using the grounded-theory method, the conceptual grounded-theory model is compiled; finally, using the obtained model and performing the agent-based modeling steps, the conceptual model of the agent-based is extracted.Based on the findings, data quality, information and communication technology infrastructure, automatic measurement and evaluation, and automatic action and evaluation are among the factors affecting the IoT-based online monitoring system. Also, the agents of the organization, customers, suppliers, governance and information technology infrastructure, interact with each other and with the environment. Based on the results, the IoT-based online monitoring system is an effective way to improve processes, and decision makers can make smarter decisions with this approach