Awareness of nursing assistants regarding COVID-19 epidemiology and preventions: a hospital based cross-sectional study

Background: COVID-19 is a disease caused by a beta coronavirus belonging to the larger family coronaviruses. It is very important that health care workers have adequate knowledge regarding COVID-19 and epidemiological characteristics for disease prevention. This study was meant to assess awareness of nursing assistants working in a tertiary care institution in Kerala regarding COVID-19 infection, modes of transmission, symptoms, isolation, and preventive measures instituted for self-protection and hospital sanitation. The objectives of this study was to assess the awareness including the knowledge attitude and practices of hospital staff about basic infection control practices and epidemiological characteristics.Methods: The study was conducted in medical college, Thiruvananthapuram. Study objective was to study awareness of nursing assistants working in the hospital about COVID-19 preventive measures and epidemiology. Participants selected by simple random sampling with sample size 100. Data was collected using semi structured questionnaire was entered into MS Excel and analysis done using appropriate statistical software. Total score calculated for each of the questions for each participant by summing up the responses for each choice in the question, individual domain and grand total scores calculated. Finally a grand total was calculated for each participant.Results: Out of total participants 77 (68.8%) scored between 35-49 (good), 36 (31.2%) (average) between 18 and 34, and 0 below 18 (poor).Conclusions: The results obtained indicate the nursing assistants had good knowledge about COVID-19 epidemiology which will prevent hospital infections of COVID-19.

Comparing Weighting Approaches in Scalogram Analysis in the Wa Municipality in the Upper West Region of Ghana

This study examined and compared the objectively-weighted, expert-based-weighted and stakeholder-based weighted Scalogram approaches based on their centrality indices and factors considered in assigning weights to the functions. A mixed-method approach, comprising both quantitative and qualitative techniques were employed to gather primary and secondary data for the study. All the three Scalograms with different weighting techniques were analyzed in Microsoft Excel, focusing on centrality and weighted centrality indices and simple linear regression models. The study discovered that the grand total centrality index of the objective Scalogram is 4,105.60, the expert-based Scalogram is 10,294.2 while the stakeholder-based one is 10,429.80. The co-efficients of determination for the three are 0.9892, 0.9757 and 0.9812 respectively, giving explanatory powers of 98.92%, 97.57% and 98.12% respectively. It is recommended that due to resource constraints, planners should rely more on the objective-based approach, followed by the stakeholder-based approach and then the expert-based approach, since the latter approach has rather reduced the explanatory power of population by increasing values of the centrality indices. Again, bottlenecks to the development of Area Council headquarters (intermediate settlements between Wa and lower-level settlements) should be tackled for efficient spatial distribution of functions. The contribution of this article to the spatial and development planning literature is its juxtaposition of the three techniques in Scalogram analysis.

Vagus Nerve Stimulation Therapy for the Treatment of Seizures in Refractory Postencephalitic Epilepsy: A Retrospective Study

BackgroundVagus nerve stimulation (VNS) has been demonstrated to be safe and effective for patients with refractory epilepsy, but there are few reports on the use of VNS for postencephalitic epilepsy (PEE). This retrospective study aimed to evaluate the efficacy of VNS for refractory PEE.MethodsWe retrospectively studied 20 patients with refractory PEE who underwent VNS between August 2017 and October 2019 in Chinese PLA General Hospital and Beijing Children’s Hospital. VNS efficacy was evaluated based on seizure reduction, effective rate (percentage of cases with seizure reduction ≥ 50%), McHugh classification, modified Early Childhood Epilepsy Severity Scale (E-Chess) score, and Grand Total EEG (GTE) score. The follow-up time points were 3, 6, and 12 months after VNS. Pre- and postoperative data were compared and analyzed.ResultsThe median [interquartile range (IQR)] seizure reduction rates at 3, 6, and 12 months after VNS were 23.72% (0, 55%), 46.61% (0, 79.04%), and 67.99% (0, 93.78%), respectively. The effective rates were 30% at 3 months, 45% at 6 months, and 70% at 12 months. E-chess scores before the operation and at 3, 6, and 12 months after the operation were 10 (10, 10.75), 9 (9, 10), 9 (9, 9.75), and 9 (8.25, 9) (P < 0.05), respectively. GTE scores before surgery and at 12 months after the operation were 11 (9, 13) and 9 (7, 11) (P < 0.05), respectively. The mean intensity of VNS current was 1.76 ± 0.39 (range: 1.0–2.5) mA. No intraoperative complications or severe post-operative adverse effects were reported.ConclusionsOur study shows that VNS can reduce the frequency and severity of seizure in patients with refractory PEE. VNS has a good application prospect in patients with refractory PEE.

Reconfiguring traditional EKG interpretation with artificial intelligence – a reliable, time-saving alternative?

Background Time and accuracy are key factors that may make or break an efficient triage and management in most medical premises, particularly so when expedited diagnosis saves lives - a not so uncommon scenario in the field of cardiology. By studying the different variables involved in cardiologist-EKG interactions that lead to the identification and management of different cardiovascular entities, we delved into the applications of Artificial Intelligence (AI) algorithms in order to improve upon the classic, but dated, EKG methodology. With this study, we pit our algorithm against cardiologists to perform a thorough analysis of the time invested to diagnose an EKG as Normal, as well as an assessment of the accuracy of said label. Purpose To present a faster and reliable AI-guided EKG interpretation methodology that outperforms cardiologists' capabilities in identifying Normal EKG records. Methods The International Telemedical System (ITMS) developed and tested an EKG assessing AI algorithm and incorporated it into the workflow of their Telemedicine Integrated Platform, a digital EKG reading program where cardiologists continuously report their findings remotely in real time. During the month of April 2019; 35 ITMS cardiologists reported a grand total of 61,441 EKG records, later subjecting them to the AI algorithm, implemented through the “One Click Report” process. Through this simple 2-step approach, the algorithm provides a suggestion of “Normal” or “Abnormal” to the cardiologist based on the patterns of the fiducial points included in said EKG reports. A comparison of the time of normal EKG diagnosis is made and the correlation between AI and cardiologists is assessed. Results On average, our AI algorithm discerned a normal EKG within 30.63s (95% CI 26.51s to 34.75s), in solid contrast with cardiologists' interpretations alone, which amounted to 83.54s (95% CI from 69.43s to 97.65s). This accounts for an overall saving of 52.91s (95% CI 42.45s to 63.83s) by implementing this innovative methodology in a cardiologist practice. In addition, this method correctly reported 23,213 Normal EKG records out of the total 25,013 AI output, reaching a 92.8% correlation between man and machine. The total average time saved in normal EKG readings with AI (23,213) would accrue an approximate of 20,470 minutes (ie, 42 8-hours work shifts worth of time dedicated to diagnosing a normal EKG). Conclusions The implementation of automated AI-driven technologies into daily EKG interpretation tasks poses an attractive time-saving alternative for faster and accurate results in a modern cardiology practice. By further expanding on the concept of an intelligent EKG characterization device, a more efficient and patient-centered clinical exercise will ensue. Funding Acknowledgement Type of funding source: None

Moving in sync – concordance betweena artificial intelligence and cardiologist on detecting normal electrocardiograms

Background Merging modern technologies with classic diagnostic tests often results in a sense of insecurity within the medical community, particularly so with potentially life-saving studies such as the electrocardiogram (EKG). In order to provide a greater sense of trust between Artificial Intelligence (AI) and cardiologists, we provide an AI-driven algorithm capable of accurately and reliably characterize an EKG as normal within a highly complex, cardiologist-reviewed EKG database and report the degree of concordance between this machine vs physician scenario. Purpose To provide a dependable and accurate AI algorithm that conducts EKG interpretation in a cardiologist-tier manner. Methods The International Telemedical System (ITMS) developed and tested an EKG assessing AI algorithm and incorporated it into the workflow of their Telemedicine Integrated Platform, a digital EKG reading program where cardiologists continuously report their findings remotely in real-time. During the month of April 2,019; 35 ITMS cardiologists reported a grand total of 61,441 EKG records, later submitting them to the AI algorithm implemented through the “One Click Report” process. Through this simple 2-step approach, the algorithm provides a suggestion of “Normal” or “Abnormal” to the cardiologist based on the patterns of the fiducial points included in said EKG reports. Confirmation of these suggestions by the cardiologists ensued. Results Overall, cardiologists confirmed 23,213 out of 25,013 AI outputs for “Normal” EKGs, demonstrating a concordance of 92.8% for Normal diagnosis. Conclusion Through this methodology, we provide an AI technology that can be reliably applied and trusted in EKG digital platforms to identify and suitably label a normal EKG. Further testing will accrue into a multi label algorithm compatible with abnormal cardiovascular entities, potentially precluding the role of the cardiologist for triaging, particularly in the prehospital setting. We anticipate that this approach will become a promising methodology in modern cardiology practice. Funding Acknowledgement Type of funding source: None

ANTI-TERMITE EFFECT OF ANADELPHIA AFZELIANA (RENDLE) STAPF EXTRACTS ON SELECTED WOOD SPECIES IN MAKURDI, BENUE STATE, NIGERIA

This study examined anti-termite effect of Anadelphia afzeliana extracts on Daniellia oliveri, Gmelina arborea and Terminalia ivorensis wood species in Makurdi, Nigeria. A. afzeliana was collected, dried under shade, pulverized and the phytochemical constituents of screened. Weight of 100 g was respectively dissolved in 200 mL of methanol and hot water and mixtures left to macerate within 24 hours and thereafter filtered to obtain extracts. Concentrations of 20 %, 30 % and 50 % were constituted from methanol and aqueous extracts.Test woods were weighed and impregnated in treatments for 72 hours, after which absorption and retention were calculated. Treated woods were laid out in a timber grave yard in a Completely Randomized Design with seven treatments and solignum as control. Grand total of 360 test wood were used. Percentage weight loss was calculated on test wood. Anthraquinones, balsams, flavonoids, phlobatannins, tannins, terpens, resins, phenols and saponins phytochemicals were present in A. afzeliana. Mean absorption of G. arborea, T. ivorensis and D. oliveri test wood were 93.04 -130.55 kg/m3, 100.54 - 142.04 kg/m3 and 96.94 - 175.01 kg/m3, respectively. The values were lowest in solignum and highest in 20 % A. afzeliana aqueous extract. Mean retention in G. arborea, T. ivorensis and D. oliveri were 9.29 - 62.51 kg/m3, 11.29 - 90.78 kg/m3 and 10.79 - 55.69 kg/m3; lowest in solignum and highest in 50 % A. afzeliana aqueous extract. Mean percentage weight loss was 13.81 - 30.65 % (G. arborea), 13.37 - 23.31 % (D. oliveri) and

Predicting Total Business Sales using Time Series Analysis

aEconomic activity undergoes 4 phases (expansion, peak, contraction, trough/recession) in which recession is a period of lowest activity and peak indicates the highest activity. Total Business sales is one of the key factors that influence the economic activity of a country. Total sales or gross sales is the grand total of all sales revenues a business generates from normal activities. The frequency of time series sales data can be monthly, quarterly, or annually. Prediction of business sales is highly important as it determines various factors in the market including Gross Domestic Product (GDP). The algorithms or models required for prediction of time series data are different from other machine learning models. Since sales is affected by time, a time series data should be stationary. Only when the data is stationarized, we can apply the algorithms on them. In this paper, monthly sales data is collected and predictions are done using moving average, simple exponential smoothing, Holt’s model, ARIMA, and SARIMAX. Root Mean Square(RMS) is the accuracy metric of time series models and lower RMS indicates higher accuracy. In this paper, a lower value of RMS is obtained for the SARIMAX model.

Complex Scenarios with Competing Factors - A Conception Paper Applied to the COVID-19 Case

A model to analyze complex systems facing threats with competing factors has been introduced. The Principle of Energy, in integral form, is used to conceive a theory in which competing factors dispute available resources to minimize undesirable outcomes. The general result indicates that the minimum response is obtained by a combination of the factors weighted by their corresponding criticalities. The theory has been applied to the case of the COVID-19 pandemic with two competing factors: Health and Economy. As result, to minimize the grand total number of deaths, the best recommendation is to balance the emphasis on both factors. The model can be generalized even further and may evolve from a qualitative to a quantitative status. In this evolution, it may allow for computational simulations and comparisons with field statistics for validation and forecasting. As such, this approach may become a useful tool for decision-making regarding resources allocations in order to reduce guessing in scenarios full of uncertainties.

Chronic subclinical mastitis reduces milk and components yield at the cow level

We evaluated the effects of chronic subclinical mastitis (CSM) caused by different types of pathogens on milk yield and milk components at the cow level. A total of 388 Holstein cows had milk yield measured and were milk sampled three times at intervals of two weeks for determination of SCC and milk composition, and microbiological culture was performed. Cows were considered healthy if all three samples of SCC were ≤200 000 cells/ml and were culture-negative at the third milk sampling. Cows with one result of SCC > 200 000 cells/ml were considered to suffer non-chronic subclinical mastitis whereas cows with at least 2 out of 3 results of SCC > 200 000 cells/ml had CSM. These latter cows were further sorted according to culture results into chronic negative-culture or chronic positive-culture. This resulted in four udder health statuses: healthy, non-chronic, chronicNC or chronicPC. The milk and components yields were evaluated according to the udder health status and by pathogen using a linear mixed effects model. A total of 134 out of 388 cows (34.5%) were chronicPC, 57 cows (14.7%) were chronicNC, 78 cows (20.1%) were non-chronic and 119 cows (30.7%) were considered healthy, which resulted in a grand total of 1164 cow records included in the statistical model. The healthy cows produced more milk than each of the other groups (+2.1 to +5.7 kg/cow/day) and produced higher milk component yields than the chronicPC cows. The healthy cows produced more milk than cows with chronicPC caused by minor (+5.2 kg/cow/day) and major pathogens (+7.1 kg/cow/day) and losses varied from 5.8 to 11.8 kg/cow/day depending on the pathogen causing chronicPC mastitis. Chronic positive-culture cows had a reduction of at least 24.5% of milk yield and 22.4% of total solids yield.

Future hydrology of Alpine rivers of Italy: the Interreg project GE.RI.KO Mera

<p>We present preliminary results in fulfilment of the IT-CH Interreg project “GE.RI.KO Mera”. The main aim of the project is to create a common strategy for the management of common water resources, in the transboundary Mera catchment, laid for ¼ in Switzerland, and for ¾ in Italy. Mera river sources in the Maloja mountains of Switzerland, crosses the Bregaglia valley, and reaches Valchiavenna of Italy, then receiving Liro river’s water, and then flows into the Novate-Mezzola lake, and in Como lake soon after.</p><p>This area is particularly important, for hydropower production, and large exploitation of water resources for fishing, and leisure in general.</p><p>Bregaglia valley carries large sediment load in the river, which affect aquatic species during floods, and lead to progressive filling of hydropower basins, and sediment accumulation along the river, with potential for increased flood risk, and often need for removal. GE.RI.KO project aims to jointly manage the transboundary waters of the Mera river to i) limit alteration of riverbed morphology and erosion, ii) avoid biodiversity loss, and iii) reduce flood risk along the river.</p><p>Here we report modelling of hydrology of this high altitude basin with Poli-Hydro model, and an analysis of future climatic conditions in the area of Valchiavenna for different Representative Concentration Pathways (RCP). We use several RCPs from IPCC’s AR5/6, and several GCMs, for a grand total of 21 climate scenarios (plus local downscaling) to force the Poli-Hydro model to depict future hydrological scenarios in the area.</p><p>We report main potential hydrological variations, and depict main challenges for water management in the Mera catchment under future scenarios, to be explored by the GE.RI.KO project.</p>