Advances in current in vitro models on neurodegenerative diseases

Many neurodegenerative diseases are identified but their causes and cure are far from being well-known. The problem resides in the complexity of the neural tissue and its location which hinders its easy evaluation. Although necessary in the drug discovery process, in vivo animal models need to be reduced and show relevant differences with the human tissues that guide scientists to inquire about other possible options which lead to in vitro models being explored. From organoids to organ-on-a-chips, 3D models are considered the cutting-edge technology in cell culture. Cell choice is a big parameter to take into consideration when planning an in vitro model and cells capable of mimicking both healthy and diseased tissue, such as induced pluripotent stem cells (iPSC), are recognized as good candidates. Hence, we present a critical review of the latest models used to study neurodegenerative disease, how these models have evolved introducing microfluidics platforms, 3D cell cultures, and the use of induced pluripotent cells to better mimic the neural tissue environment in pathological conditions

Multiplex sorting of foodborne pathogens by on-chip free-flow magnetophoresis

This study reports multiplex sorting of Salmonella typhimurium and Escherichia coli 0157, from broth cultures and from pathogen-spiked skinned chicken breast enrichment broths by employing microfluidic free-flow magnetophoresis. Magnetic beads of different sizes and magnetite content, namely Dynabeads anti-salmonella and Hyglos-Streptavidin beads together with the corresponding pathogen-specific biotinylated recombinant phages, were utilised as affinity solid phases for the capture and concentration of viable S. typhimurium and E. coli 0157. Following optimisation, the protocol was used to demonstrate continuous magnetophoretic sorting of the two pathogen-bound magnetic bead populations from mixed cultures and from pathogen-spiked chicken pre-enrichment broths under the influence of a Halbach magnet array. For example, in the la tter case, a pure population of S. typhimurium-bound Dynabeads (72% recovery) was sorted from a 100 μL mixture containing E. coli 0157-bound Hyglos beads (67% recovery) within 1.2 min in the presence of 0.1% Tween 20. This proof-of-principle study demonstrates how more than one pathogen type can be simultaneously isolated/enriched from a single food pre-enrichment broth (e.g. Universal food enrichment broth)

Assessment of a Comparative Bayesian-Enhanced Population-Based Decision Model for COVID-19 Critical Care Prediction in the Dominican Republic Social Security Affiliates

Introduction: The novel coronavirus disease 2019 (COVID-19) has been a major health concern worldwide. This study aims to develop a Bayesian model to predict critical outcomes in patients with COVID-19. Methods: Sensitivity and specificity were obtained from previous meta-analysis studies. The complex vulnerability index (IVC-COV2 index for its abbreviation in Spanish) was used to set the pretest probability. Likelihood ratios were integrated into a Fagan nomogram for posttest probabilities, and IVC-COV2 + National Early Warning Score (NEWS) values and CURB-65 scores were generated. Absolute and relative diagnostic gains (RDGs) were calculated based on pretest and posttest differences. Results: The IVC-COV2 index was derived from a population of 1,055,746 individuals and was based on mortality in high-risk (71.97%), intermediate-risk (26.11%), and low-risk (1.91%) groups. The integration of models in which IVC-COV2 intermediate + NEWS ≥ 5 and CURB-65 \u3e 2 led to a number needed to (NNT) diagnose that was slightly improved in the CURB-65 model (2 vs. 3). A comparison of diagnostic gains revealed that neither the positive likelihood ratio (P = 0.62) nor the negative likelihood ratio (P = 0.95) differed significantly between the IVC-COV2 NEWS model and the CURB-65 model. Conclusion: According to the proposed mathematical model, the combination of the IVC-COV2 intermediate score and NEWS or CURB-65 score yields superior results and a greater predictive value for the severity of illness. To the best of our knowledge, this is the first population-based/mathematical model developed for use in COVID-19 critical care decision-making

On-chip acoustophoretic isolation of microflora including S. typhimurium from raw chicken, beef and blood samples

Pathogen analysis in food samples routinely involves lengthy growth-based pre-enrichment and selective enrichment of food matrices to increase the ratio of pathogen to background flora. Similarly, for blood culture analysis, pathogens must be isolated and enriched from large excess of blood cells to allow further analysis. Conventional techniques of centrifugation and filtration are cumbersome, suffer from low sample throughput, are not readily amenable to automation and carry a risk of damaging biological samples. We report on-chip acoustophoresis as a pre-analytical technique for the resolution of total microbial flora from food and blood samples. The resulting ‘clarified’ sample is expected to increase the performance of downstream systems for the specific detection of the pathogens. A microfluidic chip with three inlets, a central separation channel and three outlets was utilized. Samples were introduced through the side inlets, buffer through the central inlet. Upon ultrasound actuation, large debris particles (10–100 μm) from meat samples were continuously partitioned into the central buffer channel, leaving the “clarified” outer sample streams containing both, the pathogenic cells and the background flora (ca. 1 μm) to be collected over a 30 min operation cycle before further analysis. The system was successfully tested with Salmonella typhimurium-spiked (ca. 103 CFU mL⁻¹) samples of chicken and minced beef, demonstrating a high level of the pathogen recovery (60–90%). When applied to S. typhimurium contaminated blood samples (107 CFU mL⁻¹), acoustophoresis resulted in a high depletion of the red blood cells (99.8%) which partitioned in the buffer stream, whilst sufficient numbers of the viable S. typhimurium remained in the outer channels for further analysis. These results indicate that the technology may provide a generic approach for pre-analytical sample preparation prior to integrated and automated downstream detection of bacterial pathogens

Hierarchical Task Network Planning with Common-Sense Reasoning for Multiple-People Behaviour Analysis

Safety on public transport is a major concern for the relevant authorities. We address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities. Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone

Non-linear classifiers applied to EEG analysis for epilepsy seizure detection

This work presents a novel approach for automatic epilepsy seizure detection based on EEG analysis that exploits the underlying non-linear nature of EEG data. In this paper, two main contributions are presented and validated: the use of non-linear classifiers through the so-called kernel trick and the proposal of a Bag-of-Words model for extracting a non-linear feature representation of the input data in an unsupervised manner. The performance of the resulting system is validated with public datasets, previously processed to remove artifacts or external disturbances, but also with private datasets recorded under realistic and non-ideal operating conditions. The use of public datasets caters for comparison purposes whereas the private one shows the performance of the system under realistic circumstances of noise, artifacts, and signals of different amplitudes. Moreover, the proposed solution has been compared to state-of-the-art works not only for pre-processed and public datasets but also with the private datasets. The mean F1-measure shows a 10% improvement over the second-best ranked method including cross-dataset experiments. The obtained results prove the robustness of the proposed solution to more realistic and variable conditions. (C) 2017 Elsevier Ltd. All rights reserved

Randomized Clinical Trials of obesity treatments in Mexican population. Systematic Review and Meta-Analysis

Background: Mexicans and Mexican Americans share similar culture, genetic background, and predisposition for obesity and diabetes. Randomized clinical trials (RCT) assessing obesity treatments (ObT) are reliable to assess efficacy. To date, there is no systematic review to investigate ObT tested by RCT in Mexican adults. Methods: We conducted systematic searches in Pubmed, Scopus, and Web of Science to retrieve ObT RCT from 1990 to 2019. The ObT included alternative medicine, pharmacological, nutritional, behavioral, and surgical interventions. The analyzed RCT were at least three months of duration, and reported: BMI, weight, waist circumference, triglycerides, glucose and blood pressure. Results: We found 634 entries; after removal of duplicates and exclusions based on eligibility criteria, we analyzed 43 and 2 multinational-collaborative studies. Most of the national studies had small sample sizes, and did not have replications from other studies. The nutrition/behavioral interventions were difficult to blind, and most studies had medium to high risk of bias. Random effects meta-analysis of nutritional/behavioral interventions and medications showed effects on BMI, waist circumference, and blood pressure. Simple measures like plain water instead of sweet beverages decreased triglycerides and systolic blood pressure. Participants with obesity and hypertension had beneficial effects with antioxidants, and the treatment with insulin increased weight in those with T2D. Conclusions: The RCT’s in Mexico reported effects on metabolic components despite small sample sizes and lack of replication. In the future we should analyze ObT in population living on the U.S.-Mexico border; therefore, bi-national collaboration is desirable to disentangle cultural effects on ObT response

A Web Information System to Improve the Digital Library Service Quality

To asses the quality of the services provided by a digital library, traditional measures, such as the size of its collection, have usually been utilized. However, service quality also has to be evaluated by considering users’ expectations. In addition, as a digital library plays an important role in the educational progress of a society, it is very important not only to measure the quality of its services but also to improve them. In this contribution, we present a web information system which supports the staff of a digital library to carry out decisions with the aim of improving the services offered by it. To do so, this system provides some advice taking into account both objective criteria, related to quantitative data, and subjective criteria, related to users’ judgments