Laboratory biosafety measures in receiving, preparation and processing of pathology specimens in suspected and positive coronavirus infection

COVID-19, caused by SARS-CoV-2 virus which is declared as a pandemic by the WHO on March 2020 has made a huge difference in the practice and daily activities of the laboratory services. There are high chances of receiving potentially infectious samples to the laboratory for various tests. Authors propose a few biosafety measures in the preparation and processing of various pathology specimens received to the lab during this pandemic time in correlation with guidelines given by WHO. These safety measures aim at protecting and safe guarding the laboratory staff, trainees, and pathologists by minimizing the exposure to COVID-19

Multiple dimensions of excessive daytime sleepiness

Background: In this study we investigated subjective measures of sleepiness and related our findings to dimensions of affect, fatigue, emotion, mood and quality of life based on a hypothetical multidimensional model of sleepiness. Methods: Patients referred to a sleep clinic were assessed regarding their excessive daytime sleepiness (EDS), sleep complaints, routine and symptoms. Age, gender and body mass index (BMI), the Epworth Sleepiness Scale (ESS), the Stanford Sleepiness Scale (SSS), the Samn-Perelli fatigue Scale (SPS), the Global Vigor and Affect Scale (GVS and GAS, respectively), the Hospital Anxiety and Depression Scale (HADS-A and HADS-D, respectively), and the Positive and Negative Affect Schedule (PAS and NAS, respectively) scores were recorded. Results: Fifty patients [25 male, 45.2 (18.7) years] completed the questionnaires. The ESS scores were positively correlated with SSS, SPS, HADS-A, HADS-D and NAS scores and negatively with GVS and GAS scores (P<0.05). The SPS (P<0.001) and HADS-A scores (P=0.002) were independently associated with the ESS scores (R2=0.532, adjusted R2 =0.4794, P<0.001). Conclusions: A model of sleepiness that assesses dimensions of fatigue and anxiety could explain the symptom of subjective sleepiness better than the isolated use of the ESS

Design of optimal search engine using text summarization through artificial intelligence techniques

Natural language processing is the trending topic in the latest research areas, which allows the developers to create the human-computer interactions to come into existence. The natural language processing is an integration of artificial intelligence, computer science and computer linguistics. The research towards natural Language Processing is focused on creating innovations towards creating the devices or machines which operates basing on the single command of a human. It allows various Bot creations to innovate the instructions from the mobile devices to control the physical devices by allowing the speech-tagging. In our paper, we design a search engine which not only displays the data according to user query but also performs the detailed display of the content or topic user is interested for using the summarization concept. We find the designed search engine is having optimal response time for the user queries by analyzing with number of transactions as inputs. Also, the result findings in the performance analysis show that the text summarization method has been an efficient way for improving the response time in the search engine optimizations

An energy-efficient cluster head selection in wireless sensor network using grey wolf optimization algorithm

Clustering is considered as one of the most prominent solutions to preserve theenergy in the wireless sensor networks. However, for optimal clustering, anenergy efficient cluster head selection is quite important. Improper selectionofcluster heads(CHs) consumes high energy compared to other sensor nodesdue to the transmission of data packets between the cluster members and thesink node. Thereby, it reduces the network lifetime and performance of thenetwork. In order to overcome the issues, we propose a novelcluster headselection approach usinggrey wolf optimization algorithm(GWO) namelyGWO-CH which considers the residual energy, intra-cluster and sink distance.In addition to that, we formulated an objective function and weight parametersfor anefficient cluster head selection and cluster formation. The proposedalgorithm is tested in different wireless sensor network scenarios by varyingthe number of sensor nodes and cluster heads. The observed results conveythat the proposed algorithm outperforms in terms of achieving better networkperformance compare to other algorithms

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop

What predicts mental health literacy among school teachers?

Objectives: The present study aimed at assessing high school teachers’ mental health literacy (MHL) and predictors related to study outcomes.Design: Cross-sectional studyMethods: We employed 460 high school teachers who engaged with adolescents for at least six hours per week with a minimum of five years of teaching experience in southern India. Semi-structured questionnaires were used to assess their MHL. Descriptive analysis and backward logistic regression analysis were performed. A p-value &lt; 0.05 was set as significant.Results: Teachers’ MHL on depression was less than desirable; however, they identified 288 (62.6%) adolescents with mental health problems during their career, and 172(59.72%) were referred to mental health professionals. On logistic regression analysis, teachers’ educational status, their marital status, teaching a class with an average strength of 31-60 students per class, previous mental health training and having self-efficacy concerning seeking informationon mental health, perceived ability to spread awareness and to provide referrals were found to predict MHL among teachers.Conclusion: Sociodemographic factors including teachers’ educational status, average class strength and having had previous mental health training were predictors for MHL among high school teachers. Establishing training programs and referral networks may be key in early intervention among adolescents

Compliance with Guidelines for Treatment of Staphylococcus aureus Bacteremia is Associated with Decreased Mortality in Patients Hospitalized for Community-Acquired Pneumonia with Staphylococcus aureus Bacteremia

Introduction: Staphylococcus aureus bacteremia has a minimum treatment duration of two weeks, while S. aureus community-acquired pneumonia (CAP) treatment is at least five days. Treatment failure, persistent bacteremia, and recurrence are common among patients with community-acquired S. aureus bacteremia. There is conflicting information in the current Infectious Diseases Society of America (IDSA) guidelines for the treatment of S.aureus bacteremia patients with CAP. Therefore, the appropriate treatment duration and modality for S. aureus CAP with bacteremia is unclear. The objective of this study was to compare outcomes among patients with S. aureus CAP and bacteremia treated in compliance versus non-compliance with IDSA S. aureus bacteremia guidelines. Methods: This was a secondary data analysis of the Community-Acquired Pneumonia Organization (CAPO) study database. Logistic regression was used to compare outcomes. Results: A total of 117 patients with S. aureus CAP and bacteremia were included in the study. Compliance with S. aureus bacteremia guidelines was documented in 67 patients, and non-compliance was documented in 50 patients. Compliance with IDSA S. aureus bacteremia guidelines resulted in a decrease in odds of re-hospitalization of 30% after adjusting for confounding variables between the compliant and non-compliant groups (adjusted odds ratio (aOR) 0.70 [95% CI 0.29–1.70]; P=0.42). The 30-day mortality for the compliant group was 6% and for the non-compliant group was 10%; P=0.576. The 1-year mortality for the compliant group was 19% and for the non-compliant group was 44%; P=0.011. Conclusion: The present study demonstrated that when treated in compliance with IDSA guidelines for S. aureus bacteremia, there was decreased 1-year mortality for patients hospitalized for S. aureus CAP with bacteremia. In this case, the IDSA S. aureus bacteremia guidelines recommend treating uncomplicated S. aureus bacteremia with CAP for at least two weeks of antimicrobials and at least four weeks of antimicrobials for complicated S. aureus bacteremia with CAP

E-grocery challenges and remedies: Global market leaders perspective

The purpose of the study is to identify logistic elements germane to e-grocery businesses, and to reveal the challenges collateral with each logistic element. Further, it strives to create a better understanding of specific remedies that have been employed by top e-grocery retailers to overcome existing challenges while aligning identified challenges with Turban’s framework. Extensive semi-structured interviews were conducted with management staff in three of the top ten global online grocery retailers and another that was a market leader in a European country. The qualitative data collected was transcribed and coded using a non-hierarchical axial coding to identify emerging themes in content analysis. The results expose a range of challenges that could be compartmentalised into three broad categories, in harmony with the different stages of the order fulfilment process. Interestingly, the study found that most challenges were operational rather than tactical or strategic in nature. While the study expands existing knowledge, its revelation that most challenges lie in the management of roles and responsibilities domain is instructive. This makes it imperative for practitioners to focus on this specific area if meaningful improvement in e-grocery retailing performance is to be realised. This research offers a systematic understanding of supply and distribution challenges, including remedies utilised to ameliorate the effect of the challenges from the perspectives of the top companies in the industry. These remedies can be invaluable for existing and emerging e-grocers