26 research outputs found

    Perceived risk, preventive behavior and enabling environment among health workers during COVID-19 pandemic in Nepal: an Online Survey

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    Introduction: Perceived risk, preventive behavior and enabling environment play vital role to prevent COVID-19 transmission in health care settings. The study aimed to assess perceived risk, preventive behavior and enabling environment among healthcare workers of different cadre during COVID-19 pandemic in Nepal. Methods: A cross sectional online survey was conducted among 427 health workers from April 25 to June 10, 2020. A structured questionnaire was prepared in Google form. Perceived risk was measured using 10 items scale, value ranging from 10 to 50. Descriptive and inferential statistics were computed at 5% level of significance. Ethical approval was taken from Nepal Health Research Council. Results: Of total, 49.6% respondents were male; 38.4% were from government organizations and 48.0% were doctors. Mean perceived risk was 31.8, 32.8, 31.3 among doctors, nursing professionals and others respectively; and it did not have significant difference among them. However, significant differences were observed in different items of perceived risk across difference cadre of health workers. Most of the health workers reported practice of preventive behavior always or most of the time. Of total, 5.4% doctors and 6.9% other health workers reported they had sometimes access to soap and water. 11.7% doctors, 7.5% nursing professionals and 7.8% other health workers had sometimes access to hand sanitizer; 18.0% doctors, 10.4% nursing professionals and 12.1% other health workers had sometimes access to face mask. Conclusion: Perceived risk of COVID-19 was high, preventive behavior was satisfactory; but access to enabling environment was poor. Therefore, adequate attention should be given to ensure the availability of protective equipment at work place

    Regenerative callus induction and biochemical analysis of Stevia rebaudiana Bertoni

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    Stevia Leaves are the principal source of stevioside, which is estimated to be 100-300 times sweeter than table sugar. Stevioside has clinical significance as they are reported to maintain glucose levels in human blood. Owing to the difficulties in propagation of stevia through seeds and vegetative methods, callus culture has been an efficient alternative for generation of stevioside. The aim of this study is to develop an efficient and standardized protocol for maximum induction and multiplication of callus from a leaf. Callus culture was established from leaves in MS basal media fortified with various combinations (BAP, NAA, 2,4-D, KN, IBA) and concentrations of phytohormones. The best callusing (100%) was recorded in MS media supplemented with (2,4-D 1.0mg/l + NAA 1.0mg/l). The callus was harvested after 4 weeks and screened for the presence of various bioactive compounds. The qualitative results showed that the extracts of callus contained bioactive compounds like flavonoids, glycosides, phenol, tannins, sterols and saponins thereby making callus one of the sources for extraction of various secondary metabolites

    Phylogenomic analysis supports Mycobacterium tuberculosis transmission between humans and elephants

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    [Introduction]: Tuberculosis is an infectious disease caused by a group of acid-fast bacilli known as Mycobacterium tuberculosis complex (MTC), which has a major impact on humans. Transmission of MTC across the human-animal interface has been demonstrated by several studies. However, the reverse zoonotic transmission from humans to animals (zooanthroponosis) has often been neglected.[Methods]: In this study, we used Nanopore MinION and Illumina MiSeq approaches to sequence the whole genome of M. tuberculosis strains isolated from two deceased Asian elephants (Elephas maximus) and one human in Chitwan, Nepal. The evolutionary relationships and drug resistance capacity of these strains were assessed using the whole genome data generated by the stand-alone tool Tb-Profiler. Phylogenomic trees were also constructed using a non-synonymous SNP alignment of 2,596 bp, including 94 whole genome sequences representative of the previously described M. tuberculosis lineages from elephants worldwide (lineages 1 and 4) and from humans in Nepal (lineages 1, 2 and 3).[Results and discussion]: The new genomes achieved an average coverage of 99.6%, with an average depth of 55.67x. These M. tuberculosis strains belong to lineage 1 (elephant DG), lineage 2 (elephant PK) and lineage 4 (human), and none of them were found to have drug-resistant variants. The elephant-derived isolates were evolutionarily closely related to human-derived isolates previously described in Nepal, both in lineages 1 and 2, providing additional support for zooanthroponosis or bidirectional transmission between humans and elephants. The human-derived isolate clustered together with other published human isolates from Argentina, Russia and the United Kingdom in the lineage 4 clade. This complex multi-pathogen, multi-host system is challenging and highlights the need for a One Health approach to tuberculosis prevention and control at human-animal interface, particularly in regions where human tuberculosis is highly endemic.This work was supported by National Funds through FCT-Fundação para a Ciência e a Tecnologia in the scope of the project UIDP/50027/2020.Peer reviewe

    The perfect storm: Disruptions to institutional delivery care arising from the COVID-19 pandemic in Nepal

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    Background The COVID-19 pandemic has led to system-wide disruption of health services globally. We assessed the effect of the pandemic on the disruption of institutional delivery care in Nepal. Methods We conducted a prospective cohort study among 52356 women in nine hospitals to assess the disruption of institutional delivery care during the pandemic (comparing March to August in 2019 with the same months in 2020). We also conducted a nested follow up cohort study with 2022 women during the pandemic to assess their provision and experience of respectful care. We used linear regression models to assess the association between provision and experience of care with volume of hospital births and women’s residence in a COVID-19 hotspot area. Results The mean institutional births during the pandemic across the nine hospitals was 24563, an average decrease of 11.6% (P<0.0001) in comparison to the same time-period in 2019. The institutional birth in high-medium volume hospitals declined on average by 20.8% (P<0.0001) during the pandemic, whereas in low-volume hospital institutional birth increased on average by 7.9% (P=0.001). Maternity services halted for a mean of 4.3 days during the pandemic and there was a redeployment staff to COVID-19 dedicated care. Respectful provision of care was better in hospitals with low-volume birth (β=0.446, P<0.0001) in comparison to high-medium-volume hospitals. There was a positive association between women’s residence in a COVID-19 hotspot area and respectful experience of care (β=0.076, P=0.001). Conclusions The COVID-19 pandemic has had differential effects on maternity services with changes varying by the volume of births per hospital with smaller volume facilities doing better. More research is needed to investigate the effects of the pandemic on where women give birth and their provision and experience of respectful maternity care to inform a “building-back-better” approach in post-pandemic period

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

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    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

    Explainable AI in Credit Card Fraud Detection: Interpretable Models and Transparent Decision-making for Enhanced Trust and Compliance in the USA

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    Credit Card Fraud presents significant challenges across various domains, comprising, healthcare, insurance, finance, and e-commerce.&nbsp; The principal objective of this research was to examine the efficacy of Machine Learning techniques in detecting credit card fraud. Four key Machine Learning techniques were employed, notably, Support Vector Machine, Logistic Regression, Random Forest, and Artificial Neural Network. Subsequently, model performance was evaluated using Precision, Recall, Accuracy, and F-measure metrics. While all models demonstrated high accuracy rates (99%), this was largely due to the dataset's size, with 284,807 attributes and only 492 fraudulent transactions. Nevertheless, accuracy solely did not provide a comprehensive comparison metric. Support Vector Machine showed the highest recall (89.5), correctly identifying the most positive instances, highlighting its efficacy in detecting true positives. On the other hand, the Artificial Neural Network model exhibited the highest precision (79.4, indicating its capability to make accurate identifications, making it proficient in optimistic predictions

    Strategic Employee Performance Analysis in the USA: Deploying Machine Learning Algorithms Intelligently

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    Strategic employee performance assessment assists organizations in steering productivity, affirming employee satisfaction, and accomplishing strategic organizational goals. Machine learning algorithms provide several benefits over mainstream techniques in assessing employee performance. This research paper aimed to explore the deployment of machine learning algorithms in assessing employee performance. The prime objective of employee performance analysis is to assess an employee's achievement during a specific time frame. The dataset for this research revolved around the leadership team of a global retailer's specific store level in the USA, extending over 18 months. The dataset for this study was subjected to Python programming software for intensive and comprehensive data analysis as well as for visualization purposes. From the experiment design, it was evident that XG-Boost seems to be the best-performing model overall. In particular, it had the greatest AUC for both holdout and training data (0.86 and 0.88, respectively), and it has a relatively low runtime (16 minutes) and maximum memory utilization (12%). By contrast, Random Forest displayed an average AUC for training data (0.79) but a lesser AUC for holdout data (0.51), which indicates that it may be overfitting the training data; besides, it had a longer runtime than XG-Boost

    Ethical Considerations in AI-driven Dynamic Pricing in the USA: Balancing Profit Maximization with Consumer Fairness and Transparency

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    Organizations in the USA are progressively employing AI-driven dynamic pricing as a strategic intervention to flexibly modify their prices based on competition, market demand, and various other factors. This research paper focused on the ethical dimensions of AI-driven dynamic pricing and the crucial interplay between profitability and the establishment of unwavering consumer transparency and fairness. The recommended models for dynamic pricing solutions entailed ensemble learning methods, notably, XG-Boost, Light-GBM, Cat-Boost, and X-NGBoost models. Particularly, the proposed model consolidated the XG-Boost algorithm and the NG-Boost model, resulting in a novel methodology termed the X-NGBoost. To compare and contrast the performance of the proposed models, these algorithms were trained and subjected to the same dataset. The comparison between the models was mainly grounded on the root-mean-square error (RMSE) metric, which was quantified in meters. The results indicated that X-NGBoost had the lowest RMSE on both the testing and training sets, at 4.23 and 5.34 respectively. This indicated that X-NGBoost performed very well on both seen and unseen data. Therefore, from the outcomes it was deduced that, for the provided data set, the X-NGBoost model provided the accurate pricing solution
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