Development of a UK core dataset for geriatric medicine research: a position statement and results from a Delphi consensus process

BACKGROUND: There is lack of standardisation in assessment tools used in geriatric medicine research, which makes pooling of data and cross-study comparisons difficult. METHODS: We conducted a modified Delphi process to establish measures to be included within core and extended datasets for geriatric medicine research in the United Kingdom (UK). This included three complete questionnaire rounds, and one consensus meeting. Participants were selected from attendance at the NIHR Newcastle Biomedical Research Centre meeting, May 2019, and academic geriatric medicine e-mailing lists. Literature review was used to develop the initial questionnaire, with all responses then included in the second questionnaire. The third questionnaire used refined options from the second questionnaire with response ranking. RESULTS: Ninety-eight responses were obtained across all questionnaire rounds (Initial: 19, Second: 21, Third: 58) from experienced and early career researchers in geriatric medicine. The initial questionnaire included 18 questions with short text responses, including one question for responders to suggest additional items. Twenty-six questions were included in the second questionnaire, with 108 within category options. The third questionnaire included three ranking, seven final agreement, and four binary option questions. Results were discussed at the consensus meeting. In our position statement, the final consensus dataset includes six core domains: demographics (age, gender, ethnicity, socioeconomic status), specified morbidities, functional ability (Barthel and/or Nottingham Extended Activities of Daily Living), Clinical Frailty Scale (CFS), cognition, and patient-reported outcome measures (dependent on research question). We also propose how additional variables should be measured within an extended dataset. CONCLUSIONS: Our core and extended datasets represent current consensus opinion of academic geriatric medicine clinicians across the UK. We consider the development and further use of these datasets will strengthen collaboration between researchers and academic institutions

Hybrid Approach in Microscale Transport Phenomena: Application to Biodiesel Synthesis in Micro-reactors

A hybrid engineering approach to the study of transport phenomena, based on the synergy among computational, analytical, and experimental methodologies is reviewed. The focus of the chapter is on fundamental analysis and proof of concept developments in the use of nano- and micro-technologies for energy efficiency and heat and mass transfer enhancement applications. The hybrid approach described herein combines improved lumped-differential modeling, hybrid numericalanalytical solution methods, mixed symbolic-numerical computations, and advanced experimental techniques for micro-scale transport phenomena. An application dealing with micro-reactors for continuous synthesis of biodiesel is selected to demonstrate the instrumental role of the hybrid approach in achieving improved design and enhanced performance

A Combination of Receptor-Based Pharmacophore Modeling & QM Techniques for Identification of Human Chymase Inhibitors

Inhibition of chymase is likely to divulge therapeutic ways for the treatment of cardiovascular diseases, and fibrotic disorders. To find novel and potent chymase inhibitors and to provide a new idea for drug design, we used both ligand-based and structure-based methods to perform the virtual screening(VS) of commercially available databases. Different pharmacophore models generated from various crystal structures of enzyme may depict diverse inhibitor binding modes. Therefore, multiple pharmacophore-based approach is applied in this study. X-ray crystallographic data of chymase in complex with different inhibitors were used to generate four structure–based pharmacophore models. One ligand–based pharmacophore model was also developed from experimentally known inhibitors. After successful validation, all pharmacophore models were employed in database screening to retrieve hits with novel chemical scaffolds. Drug-like hit compounds were subjected to molecular docking using GOLD and AutoDock. Finally four structurally diverse compounds with high GOLD score and binding affinity for several crystal structures of chymase were selected as final hits. Identification of final hits by three different pharmacophore models necessitates the use of multiple pharmacophore-based approach in VS process. Quantum mechanical calculation is also conducted for analysis of electrostatic characteristics of compounds which illustrates their significant role in driving the inhibitor to adopt a suitable bioactive conformation oriented in the active site of enzyme. In general, this study is used as example to illustrate how multiple pharmacophore approach can be useful in identifying structurally diverse hits which may bind to all possible bioactive conformations available in the active site of enzyme. The strategy used in the current study could be appropriate to design drugs for other enzymes as well

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

EGFR Tyrosine Kinase Inhibitors Activate Autophagy as a Cytoprotective Response in Human Lung Cancer Cells

Epidermal growth factor receptor tyrosine kinase inhibitors gefitinib and erlotinib have been widely used in patients with non-small-cell lung cancer. Unfortunately, the efficacy of EGFR-TKIs is limited because of natural and acquired resistance. As a novel cytoprotective mechanism for tumor cell to survive under unfavorable conditions, autophagy has been proposed to play a role in drug resistance of tumor cells. Whether autophagy can be activated by gefitinib or erlotinib and thereby impair the sensitivity of targeted therapy to lung cancer cells remains unknown. Here, we first report that gefitinib or erlotinib can induce a high level of autophagy, which was accompanied by the inhibition of the PI3K/Akt/mTOR signaling pathway. Moreover, cytotoxicity induced by gefitinib or erlotinib was greatly enhanced after autophagy inhibition by the pharmacological inhibitor chloroquine (CQ) and siRNAs targeting ATG5 and ATG7, the most important components for the formation of autophagosome. Interestingly, EGFR-TKIs can still induce cell autophagy even after EGFR expression was reduced by EGFR specific siRNAs. In conclusion, we found that autophagy can be activated by EGFR-TKIs in lung cancer cells and inhibition of autophagy augmented the growth inhibitory effect of EGFR-TKIs. Autophagy inhibition thus represents a promising approach to improve the efficacy of EGFR-TKIs in the treatment of patients with advanced non-small-cell lung cancer

Withania somnifera Root Extract Enhances Chemotherapy through ‘Priming’

Withania somnifera extracts are known for their anti-cancerous, anti-inflammatory and antioxidative properties. One of their mechanisms of actions is to modulate mitochondrial function through increasing oxidative stress. Recently ‘priming’ has been suggested as a potential mechanism for enhancing cancer cell death. In this study we demonstrate that ‘priming’, in HT-29 colon cells, with W. somnifera root extract increased the potency of the chemotherapeutic agent cisplatin. We have also showed the W. somnifera root extract enhanced mitochondrial dysfunction and that the underlying mechanism of ‘priming’ was selectively through increased ROS. Moreover, we showed that this effect was not seen in non-cancerous cells

RNA Interference and Single Particle Tracking Analysis of Hepatitis C Virus Endocytosis

Hepatitis C virus (HCV) enters hepatocytes following a complex set of receptor interactions, culminating in internalization via clathrin-mediated endocytosis. However, aside from receptors, little is known about the cellular molecular requirements for infectious HCV entry. Therefore, we analyzed a siRNA library that targets 140 cellular membrane trafficking genes to identify host genes required for infectious HCV production and HCV pseudoparticle entry. This approach identified 16 host cofactors of HCV entry that function primarily in clathrin-mediated endocytosis, including components of the clathrin endocytosis machinery, actin polymerization, receptor internalization and sorting, and endosomal acidification. We next developed single particle tracking analysis of highly infectious fluorescent HCV particles to examine the co-trafficking of HCV virions with cellular cofactors of endocytosis. We observe multiple, sequential interactions of HCV virions with the actin cytoskeleton, including retraction along filopodia, actin nucleation during internalization, and migration of internalized particles along actin stress fibers. HCV co-localizes with clathrin and the ubiquitin ligase c-Cbl prior to internalization. Entering HCV particles are associated with the receptor molecules CD81 and the tight junction protein, claudin-1; however, HCV-claudin-1 interactions were not restricted to Huh-7.5 cell-cell junctions. Surprisingly, HCV internalization generally occurred outside of Huh-7.5 cell-cell junctions, which may reflect the poorly polarized nature of current HCV cell culture models. Following internalization, HCV particles transport with GFP-Rab5a positive endosomes, which is consistent with trafficking to the early endosome. This study presents technical advances for imaging HCV entry, in addition to identifying new host cofactors of HCV infection, some of which may be antiviral targets

Domestic violence and decision-making power of married women in Myanmar: analysis of a nationally representative sample

BACKGROUND: Women in Myanmar are not considered decision makers in the community and the physical and psychological effect of violence makes them more vulnerable. There is a strong negative reaction, usually violent, to any economic activity generated by women among poorer and middle-class families in Myanmar because a woman's income is not considered necessary for basic survival. OBJECTIVE: Explore the relationship between domestic violence on the decision-making power of married women in Myanmar. DESIGN: Cross-sectional. SETTING: National, both urban and rural areas of Myanmar. PATIENTS AND METHODS: Data from the Myanmar Demographic and Health Survey 2015-16 were used in this analysis. In that survey, married women aged between 15 to 49 years were selected for interview using a multistage cluster sampling technique. The dependent variables were domestic violence and the decision-making power of women. Independent variables were age of the respondents, educational level, place of residence, employment status, number of children younger than 5 years of age and wealth index. MAIN OUTCOME MEASURES: Domestic violence and decision-making power of women. SAMPLE SIZE: 7870 currently married women. RESULTS: About 50% respondents were 35 to 49 years of age and the mean (SD) age was 35 (8.4) years. Women's place of residence and employment status had a significant impact on decision-making power whereas age group and decision-making power of women had a relationship with domestic violence. CONCLUSION: Giving women decision making power will be indispensable for the achievement of sustainable development goals. Government and other stakeholders should emphasize this to eliminate violence against women. LIMITATIONS: Use of secondary data analysis of cross-sectional study design and cross-sectional studies are not suitable design to assess this causality. Secondly the self-reported data on violence may be subject to recall bias. CONFLICT OF INTEREST: None