DIGITIZATION OF EDUCATION AND ITS SOCIO-ECONOMIC IMPACTS WITH SPECIAL REFERENCE TO TRIVANDRUM

This study is makes a comparative analysis of the rural-urban divide in digital education and examines the benefits and challenges of e-learning. A total of 124 respondents consisting of students, teachers and parents residing in the rural and urban areas of Trivandrum were surveyed using a well-structured questionnaire administered in both English and Malayalam. The sample population was identified through stratified random sampling. The study finds that disruption of internet connectivity due to signal unavailability is a major challenge of e-learning. Also, majority of the stakeholders were unwilling to make a conscious shift to a digitised education mode. Mostly students favoured online education because of its ease of usage, convenience, remote accessibility and system response speed. But most of the parents and teachers preferred offline education

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Increasing frailty is associated with higher prevalence and reduced recognition of delirium in older hospitalised inpatients: results of a multi-centre study

Purpose: Delirium is a neuropsychiatric disorder delineated by an acute change in cognition, attention, and consciousness. It is common, particularly in older adults, but poorly recognised. Frailty is the accumulation of deficits conferring an increased risk of adverse outcomes. We set out to determine how severity of frailty, as measured using the CFS, affected delirium rates, and recognition in hospitalised older people in the United Kingdom. Methods: Adults over 65 years were included in an observational multi-centre audit across UK hospitals, two prospective rounds, and one retrospective note review. Clinical Frailty Scale (CFS), delirium status, and 30-day outcomes were recorded. Results: The overall prevalence of delirium was 16.3% (483). Patients with delirium were more frail than patients without delirium (median CFS 6 vs 4). The risk of delirium was greater with increasing frailty [OR 2.9 (1.8–4.6) in CFS 4 vs 1–3; OR 12.4 (6.2–24.5) in CFS 8 vs 1–3]. Higher CFS was associated with reduced recognition of delirium (OR of 0.7 (0.3–1.9) in CFS 4 compared to 0.2 (0.1–0.7) in CFS 8). These risks were both independent of age and dementia. Conclusion: We have demonstrated an incremental increase in risk of delirium with increasing frailty. This has important clinical implications, suggesting that frailty may provide a more nuanced measure of vulnerability to delirium and poor outcomes. However, the most frail patients are least likely to have their delirium diagnosed and there is a significant lack of research into the underlying pathophysiology of both of these common geriatric syndromes

BIOSYNTHESIS, CHARACTERIZATION, AND ANTI-INFLAMMATORY STUDY OF HEMIGRAPHIS COLORATA LOADED SILVER NANOPARTICLES

Objective: This study targeted at the synthesis, characterization, and evaluation of the anti-inflammatory effects of Hemigraphis colorata silver nanopartilces (HAgNPs). Methods: The HAgNPs were photosynthesized and characterized by UV-visible spectroscopy. The in vitro anti-inflammatory properties of HAgNPs were assessed by evaluating inhibition of protein denaturation and membrane stabilization ability assay. Results: HAgNPs exhibited an absorbance peak at 450 nm, characteristic for AgNPs, and their sizes ranged from 20 to 90 nm. The anti-inflammatory potential of HAgNPs when compared with H. colorata extract suggests that the AgNPs along with polyphenols and flavonoids from H. colorata can act as reducing or inhibiting agent on the release of acute inflammatory mediators. Conclusion: The biologically synthesized HAgNPs could be of enormous use in the medical field for their proficient anti-inflammatory activity, which can be utilized as novel therapeutic agent for prevention and cure of inflammation due to biocompatible nature. This work clearly demonstrates HAgNPs as a budding source for anti-inflammatory drugs

Microbiological assessment of pre-monsoon and post-monsoon open well water quality in Karumalloor panchayat, Ernakulam, Kerala

<p>Ground water is a source of potable water among the rural population in Kerala. This study was carried out among the rural population of Karumalloor panchayat in Paravoor Taluk, Ernakulam district. Bacteriological analysis of water from open wells was carried out in two consecutive years as part of department programme, once during post-monsoon and other during pre-monsoon season. Twenty three percentage of post-monsoon water samples tested were potable however only 5% of pre-monsoon samples were potable.</p

Performance evaluation of the IRIS XL-220 PET/CT system, a new camera dedicated to non-human primates

International audienceAbstract Background Non-human primates (NHP) are critical in biomedical research to better understand the pathophysiology of diseases and develop new therapies. Based on its translational and longitudinal abilities along with its non-invasiveness, PET/CT systems dedicated to non-human primates can play an important role for future discoveries in medical research. The aim of this study was to evaluate the performance of a new PET/CT system dedicated to NHP imaging, the IRIS XL-220 developed by Inviscan SAS. This was performed based on the National Electrical Manufacturers Association (NEMA) NU 4-2008 standard recommendations (NEMA) to characterize the spatial resolution, the scatter fraction, the sensitivity, the count rate, and the image quality of the system. Besides, the system was evaluated in real conditions with two NHP with 18 F-FDG and (-)-[ 18 F]FEOBV which targets the vesicular acetylcholine transporter, and one rat using 18 F-FDG. Results The full width at half maximum obtained with the 3D OSEM algorithm ranged between 0.89 and 2.11 mm in the field of view. Maximum sensitivity in the 400–620 keV and 250–750 keV energy windows were 2.37% (22 cps/kBq) and 2.81% (25 cps/kBq), respectively. The maximum noise equivalent count rate (NEC) for a rat phantom was 82 kcps at 75 MBq and 88 kcps at 75 MBq for energy window of 250–750 and 400–620 keV, respectively. For the monkey phantom, the maximum NEC was 18 kcps at 126 MBq and 19 kcps at 126 MBq for energy window of 250–750 and 400–620 keV, respectively. The IRIS XL provided an excellent quality of images in non-human primates and rats using 18 F-FDG. The images acquired using (-)-[ 18 F]FEOBV were consistent with those previously reported in non-human primates. Conclusions Taken together, these results showed that the IRIS XL-220 is a high-resolution system well suited for PET/CT imaging in non-human primates

A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer

Purpose Docetaxel has a demonstrated survival benefit for patients with metastatic castration-resistant prostate cancer (mCRPC); however, 10% to 20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and the management of risk factors for toxicity remains a challenge. Patients and Methods The comparator arms of four phase III clinical trials in first-line mCRPC were collected, annotated, and compiled, with a total of 2,070 patients. Early discontinuation was defined as treatment stoppage within 3 months as a result of adverse treatment effects; 10% of patients discontinued treatment. We designed an open-data, crowd-sourced DREAM Challenge for developing models with which to predict early discontinuation of docetaxel treatment. Clinical features for all four trials and outcomes for three of the four trials were made publicly available, with the outcomes of the fourth trial held back for unbiased model evaluation. Challenge participants from around the world trained models and submitted their predictions. Area under the precision-recall curve was the primary metric used for performance assessment. Results In total, 34 separate teams submitted predictions. Seven models with statistically similar area under precision-recall curves (Bayes factor 3) outperformed all other models. A postchallenge analysis of risk prediction using these seven models revealed three patient subgroups: high risk, low risk, or discordant risk. Early discontinuation events were two times higher in the high-risk subgroup compared with the low-risk subgroup. Simulation studies demonstrated that use of patient discontinuation prediction models could reduce patient enrollment in clinical trials without the loss of statistical power. Conclusion This work represents a successful collaboration between 34 international teams that leveraged open clinical trial data. Our results demonstrate that routinely collected clinical features can be used to identify patients with mCRPC who are likely to discontinue treatment because of adverse events and establishes a robust benchmark with implications for clinical trial design.This work is supported in part by the following: in-kind contribution from Sanofi US Services Inc., Project Data Sphere LLC, National Institutes of Health, National Library of Medicine (2T15-LM009451), Boettcher Foundation, Doctoral Programme in Mathematics and Computer Sciences at the University of Turku, European Union's Horizon 2020 research and innovation program, Academy of Finland, the European Research Council, Juvenile Diabetes Research Foundation, and Sigrid Juselius Foundationinfo:eu-repo/semantics/publishedVersio

A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration- resistant prostate cancer

Purpose Docetaxel has a demonstrated survival benefit for patients with metastatic castration-resistant prostate cancer (mCRPC); however, 10% to 20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and the management of risk factors for toxicity remains a challenge. Patients and Methods The comparator arms of four phase III clinical trials in first-linem CRPC were collected, annotated, and compiled, with a total of 2,070 patients. Early discontinuation was defined as treatment stoppage within 3 months as a result of adversetreatment effects; 10% of patients discontinued treatment. We designed an open-data, crowd-sourced DREAM Challenge for developing models with which to predict early discontinuation of docetaxel treatment. Clinical features for all four trials and outcomes for three of the four trials were made publicly available, with the outcomes of the fourth trial held back for unbiased model evaluation. Challenge participants from around the world trained models and submitted their predictions. Area under the precision-recall curve was the primary metric used for performance assessment. Results In total, 34 separate teams submitted predictions. Seven models with statistically similar area under precision-recall curves (Bayes factor≤3) outperformed all other models. Apostchallenge analysis of risk prediction using these seven models revealed three patient subgroups: high risk, low risk, or discordant risk. Early discontinuation events were two times higher in the high-risk subgroup compared with the low-risk subgroup. Simulation studies demonstrated that use of patient discontinuation prediction models could reduce patient enrollment in clinical trials without the loss of statistical power. Conclusion This work represents a successful collaboration between 34international teams that leveraged open clinical trial data. Our results demonstrate that routinely collected clinical features can be used to identify patients with mCRPC who are likely to discontinue treatment because of adverse events and establishes a robust benchmark with implications for clinical trial design.</p