Establishing Governing Equations for 3d Cell Culture in Perfusion Bioreactors

Culturing cells and regenerating tissues in vitro on 3D scaffolds involves several challenges, such as efficient nutrient transportation, uniform stress distribution, and the removal of wastes. Bioreactors not only allow reproducibility but also provide a controlled environment for production of tissues. The objective of this study was to establish fundamental governing equations for the design of tissue engineering bioreactors and scaffolds. The governing equations related to nutrient permeability, mechanical and structural properties of the scaffolds, as well as nutrient consumption kinetics were tested. Large scaffolds with a high aspect ratio were utilized so that the obtained experimental measurements have high signal-to-noise ratio. This allowed the validation of the governing equations used in the computational models with high fidelity. Three different scaffold preparation techniques, freeze drying, salt leaching, and electrospinning were used to fabricate scaffolds with different microarchitecture. Chitosan, gelatin, and polycaprolactone polymers were used to prepare scaffolds. Two types of bioreactor configurations, flow-through and axial-flow, were used in this study. Both were designed to hold same sized scaffolds, but differ in flow configuration, which made them suitable for evaluating and validating the equations. Bioreactors of appropriate flow configuration were constructed in-house for experimental analysis. Computational Fluid Dynamics (CFD) simulations were performed to predict pressure drop, shear stress, deformation, nutrient distribution profile and exit concentration at various operating conditions. Additionally, non-ideal distribution models such as segregation and dispersion were combined with residence time distribution to predict the exit concentration. The model predictions were validated using an experimental setup with metabolically active liver cells. The results show that the scaffold permeability can be calculated using scaffold pore characteristics and deformation could be predicted using simulation. The axial-flow bioreactor performed better than flow-through bioreactor with superior nutrient distribution, lower shear stress, and deformation. Comparison of the outlet oxygen concentrations between the simulation and experimental results showed good agreement with the dispersion model. However, outlet oxygen concentrations from segregation model were lower. These insights help monitor in vitro tissue regeneration, understand the effect of mechanical stimulus on 3D cell culture, and improve quality of the regenerated tissue.Chemical Engineerin

Email for communicating results of diagnostic medical investigations to patients

<p>Background: As medical care becomes more complex and the ability to test for conditions grows, pressure on healthcare providers to convey increasing volumes of test results to patients is driving investigation of alternative technological solutions for their delivery. This review addresses the use of email for communicating results of diagnostic medical investigations to patients.</p> <p>Objectives: To assess the effects of using email for communicating results of diagnostic medical investigations to patients, compared to SMS/ text messaging, telephone communication or usual care, on outcomes, including harms, for health professionals, patients and caregivers, and health services.</p> <p>Search methods: We searched: the Cochrane Consumers and Communication Review Group Specialised Register, Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library, Issue 1 2010), MEDLINE (OvidSP) (1950 to January 2010), EMBASE (OvidSP) (1980 to January 2010), PsycINFO (OvidSP) (1967 to January 2010), CINAHL (EbscoHOST) (1982 to February 2010), and ERIC (CSA) (1965 to January 2010). We searched grey literature: theses/dissertation repositories, trials registers and Google Scholar (searched July 2010). We used additional search methods: examining reference lists and contacting authors.</p> <p>Selection criteria: Randomised controlled trials, quasi-randomised trials, controlled before and after studies and interrupted time series studies of interventions using email for communicating results of any diagnostic medical investigations to patients, and taking the form of 1) unsecured email 2) secure email or 3) web messaging. All healthcare professionals, patients and caregivers in all settings were considered.</p> <p>Data collection and analysis: Two review authors independently assessed the titles and abstracts of retrieved citations. No studies were identified for inclusion. Consequently, no data collection or analysis was possible.</p> <p>Main results: No studies met the inclusion criteria, therefore there are no results to report on the use of email for communicating results of diagnostic medical investigations to patients.</p> <p>Authors' conclusions: In the absence of included studies, we can draw no conclusions on the effects of using email for communicating results of diagnostic medical investigations to patients, and thus no recommendations for practice can be stipulated. Further well-designed research should be conducted to inform practice and policy for communicating patient results via email, as this is a developing area.</p&gt

Email for clinical communication between healthcare professionals

Email is one of the most widely used methods of communication, but its use in healthcare is still uncommon. Where email communication has been utilised in health care, its purposes have included clinical communication between healthcare professionals, but the effects of using email in this way are not well known. We updated a 2012 review of the use of email for two-way clinical communication between healthcare professionals

The feasibility of genome-scale biological network inference using Graphics Processing Units

Abstract Systems research spanning fields from biology to finance involves the identification of models to represent the underpinnings of complex systems. Formal approaches for data-driven identification of network interactions include statistical inference-based approaches and methods to identify dynamical systems models that are capable of fitting multivariate data. Availability of large data sets and so-called ‘big data’ applications in biology present great opportunities as well as major challenges for systems identification/reverse engineering applications. For example, both inverse identification and forward simulations of genome-scale gene regulatory network models pose compute-intensive problems. This issue is addressed here by combining the processing power of Graphics Processing Units (GPUs) and a parallel reverse engineering algorithm for inference of regulatory networks. It is shown that, given an appropriate data set, information on genome-scale networks (systems of 1000 or more state variables) can be inferred using a reverse-engineering algorithm in a matter of days on a small-scale modern GPU cluster.https://deepblue.lib.umich.edu/bitstream/2027.42/136186/1/13015_2017_Article_100.pd

Evidence-based medicine among internal medicine residents in a community hospital program using smart phones

BACKGROUND: This study implemented and evaluated a point-of-care, wireless Internet access using smart phones for information retrieval during daily clinical rounds and academic activities of internal medicine residents in a community hospital. We did the project to assess the feasibility of using smart phones as an alternative to reach online medical resources because we were unable to find previous studies of this type. In addition, we wanted to learn what Web-based information resources internal medicine residents were using and whether providing bedside, real-time access to medical information would be perceived useful for patient care and academic activities. METHODS: We equipped the medical teams in the hospital wards with smart phones (mobile phone/PDA hybrid devices) to provide immediate access to evidence-based resources developed at the National Library of Medicine as well as to other medical Websites. The emphasis of this project was to measure the convenience and feasibility of real-time access to current medical literature using smart phones. RESULTS: The smart phones provided real-time mobile access to medical literature during daily rounds and clinical activities in the hospital. Physicians found these devices easy to use. A post-study survey showed that the information retrieved was perceived to be useful for patient care and academic activities. CONCLUSION: In community hospitals and ambulatory clinics without wireless networks where the majority of physicians work, real-time access to current medical literature may be achieved through smart phones. Immediate availability of reliable and updated information obtained from authoritative sources on the Web makes evidence-based practice in a community hospital a reality

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials

Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof-of-concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either "improvement," defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or "no improvement," defined as an inadequate treatment response (<20% reduction in total PANSS). A random forest algorithm performed best relative to other tree-based approaches in model ability to classify patients after 6 months of treatment. Although model ability to identify true positives, a measure of model sensitivity, was poor (<0.2), its specificity, true negative rate, was high (0.948). A second model, adapted from the first, was subsequently applied as a proof-of-concept for the ML approach to supplement trial enrollment by identifying patients not expected to improve based on their baseline diagnostic scores. In three virtual trials applying this screening approach, the percentage of patients predicted to improve ranged from 46% to 48%, consistently approximately double the CATIE response rate of 22%. These results show the promising application of ML to improve clinical trial efficiency and, as such, ML models merit further consideration and development

Evaluation of internet access and utilization by medical students in Lahore, Pakistan

<p>Abstract</p> <p>Background</p> <p>The internet is increasingly being used worldwide in imparting medical education and improving its delivery. It has become an important tool for healthcare professionals training but the data on its use by medical students in developing countries is lacking with no study on the subject from Pakistan. This study was, therefore, carried out with an aim to evaluate the pattern of internet access and utilization by medical students in Pakistan.</p> <p>Methods</p> <p>A structured pre-tested questionnaire was administered to a group of 750 medical students in clinical years studying at various public and private medical colleges in Lahore. The questions were related to patterns of internet access, purpose of use and self reported confidence in performing various internet related tasks, use of health related websites to supplement learning and the problems faced by students in using internet at the institution.</p> <p>Results</p> <p>A total of 532 medical students (70.9%) returned the questionnaire. The mean age of study participants was 21.04 years (SD 1.96 years). Majority of the respondents (84.0%) reported experience with internet use. About half of the students (42.1%) were using internet occasionally with 23.1%, 20.9% and 13.9% doing so frequently, regularly and rarely respectively. About two third of the students (61.0%) stated that they use internet for both academic and professional activities. Most of the participants preferred to use internet at home (70.5%). Self reported ability to search for required article from PubMed and PakMedinet was reported by only 34.0% of the entire sample. Students were moderately confident in performing various internet related tasks including downloading medical books from internet, searching internet for classification of diseases and downloading full text article. Health related websites were being accessed by 55.1% students to supplement their learning process. Lack of time, inadequate number of available computers and lack of support from staff were cited as the most common problems faced by students while accessing internet in the institution premises. There were significant differences among male and female students with respect to the place of internet use (p = 0.001) and the ability to search online databases for required articles (p = 0.014).</p> <p>Conclusions</p> <p>Majority of the medical students in this study had access to internet and were using it for both academic and personal reasons. Nevertheless, it was seen that there is under utilization of the potential of internet resources to augment learning. Increase in awareness, availability of requisite facilities and training in computing skills are required to enable better utilization of digital resources of digital resources by medical students.</p

Machine Learning in Drug Discovery and Development Part 1: A Primer

Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.Laboratorio de Investigación y Desarrollo de Bioactivo