The evaluation of practical learning instruction management and student satisfaction with the "photocolorimetric methodology platform for measuring egg yolk color" during the COVID-19 pandemic situation

Online teaching management has been widely used during the COVID-19 pandemic situation and it has a direct impact on practical teaching management because students do not have access to equipment, chemicals, and tools. This study's purpose is to evaluate practical learning instruction management and student satisfaction with "photocolorimetric methodology platform for measuring egg yolk color" during the COVID-19 pandemic. This study compared the student satisfaction and effectiveness of a learning instruction platform for measuring egg yolk color using a laboratory machine and an online teaching management platform using photocolorimetric methodology. The results of this experiment revealed that the two platforms evaluated yolk colors L*, a*, and b* similarly (P > 0.05). Furthermore, the students were satisfied with the learning instruction with the photocolorimetric methodology platform for measuring egg yolk color at 4.76 points or the most level.

Special Issue: Cytometry and analytical methods in Covid-SARS-2 diagnostics and potential treatment. (Special Issue: Cytometry and analytical methods in Covid-SARS-2 diagnostics and potential treatment.)

This special issue contains 9 articles that discuss quantitative single-cell analysis, features original research reports and reviews of innovative scientific studies employing quantitative single-cell measurement, separation, manipulation, and modeling techniques, as well as original articles on mechanisms of molecular and cellular functions obtained by cytometry technique, particularly focusing on several research articles and reviews on exploring the use of flow cytometry techniques towards clinical opportunities in the detection and diagnostics of COVID-19.

Impacts of environmental policy uncertainty on urban environmental pollutant emissions in China: a study based on textual analysis

How to accelerate the clean use of fossil energy and promote the transformation and upgrading of energy structure is an important challenge commonly faced by countries around the world. In the post-Covid-19 era, the uncertainties faced by countries around the world are increasing and the frequency of policy adjustments in various countries is accelerating. The discharge of pollution by enterprises is significantly impacted by environmental regulatory policies. Under the carbon neutrality goal, the uncertainty of environmental policy caused by multiple political factors can directly influence the decisions made by businesses and residents, in turn, affect their confidence and expectations. However, researchers have given limited attention to measuring the environmental policy uncertainty index (EPUI). In this paper, we select 460 newspapers from the China National Knowledge Infrastructure (CNKI) newspaper database from 2001 to 2016 and use the text analysis method to directly construct China's national, provincial, and prefecture-level EPUI. The results show that China's EPUI has obvious stage characteristics and regional characteristics. By applying the Chinese city-level EPUI to the field of urban pollution reduction, we have obtained an important finding that when urban environmental policy uncertainty increases by 1%, urban industrial sulfur dioxide emission decreases by about 0.145%, and carbon dioxide emission decreases by about 0.053%. We believe that this is due to an increase in environmental policy uncertainty inhibiting the development and scaling of secondary industries.

Analytical performance of four rapid molecular testing for SARS-CoV-2

Objectives: Various reagents and equipment for testing SARS-CoV-2 infections have been developed, particularly rapid molecular tests based on polymerase chain reaction (PCR). Methods: We evaluated the analytical performance of four rapid molecular tests for SARS-CoV-2. We used 56 nasopharyngeal swabs from patients with confirmed SARS-CoV-2 infection;36 diagnosed as positive by the AmpdirectTM 2019-nCoV Detection Kit (Shimadzu assay) were considered as true-positive samples. Results: The sensitivity of CobasR Liat SARS-CoV-2 and Flu A/B (Cobas) was the highest among the four molecular test kits. The limit of detection was 1.49 x 10-2 copies/ micro L (95% confidence interval [CI]: 1.46x10-2-1.51 x 10-2 copies/ micro L) for Cobas;1.43 x 10-1 copies/ micro L (95% CI: 8.01x10-3-2.78 x 10-1 copies/ micro L) for XpertR Xpress SARS-CoV-2 test (Xpert);2.00 x 10-1 copies/ micro L (95% CI: 1.95x10-1-2.05 x 10-1 copies/ micro L) for FilmArray Respiratory Panel v2.1 (FilmArray);and 3.33 x 10 copies/ micro L (95% CI: 1.93 x 10-4.72x10 copies/ micro L) for Smart GeneR SARS-CoV-2 (Smart gene). Cobas also had a high sensitivity (100%) compared with Shimadzu assay. The sensitivities of Xpert, FilmArray, and Smart Gene were 97.2%, 97.2%, and 75.0%, respectively. The specificity of all tests was 100%. Conclusions: In conclusion, the four rapid SARS-CoV-2 molecular test kits have high specificity and sensitivity for detecting SARS-CoV-2. As they are easy to use, they could be a useful method for detecting SARS-CoV-2.

Elderberry Extracts: Characterization of the Polyphenolic Chemical Composition, Quality Consistency, Safety, Adulteration, and Attenuation of Oxidative Stress- and Inflammation-Induced Health Disorders.

Elderberry is highly reputed for its health-improving effects. Multiple pieces of evidence indicate that the consumption of berries is linked to enhancing human health and preventing or delaying the onset of chronic medical conditions. Compared with other fruit, elderberry is a very rich source of anthocyanins (approximately 80% of the polyphenol content). These polyphenols are the principals that essentially contribute to the high antioxidant and anti-inflammatory capacities and the health benefits of elderberry fruit extract. These health effects include attenuation of cardiovascular, neurodegenerative, and inflammatory disorders, as well as anti-diabetic, anticancer, antiviral, and immuno-stimulatory effects. Sales of elderberry supplements skyrocketed to $320 million over the year 2020, according to an American Botanical Council (ABC) report, which is attributable to the purported immune-enhancing effects of elderberry. In the current review, the chemical composition of the polyphenolic content of the European elderberry (Sambucus nigra) and the American elderberry (Sambucus canadensis), as well as the analytical techniques employed to analyze, characterize, and ascertain the chemical consistency will be addressed. Further, the factors that influence the consistency of the polyphenolic chemical composition, and hence, the consistency of the health benefits of elderberry extracts will be presented. Additionally, adulteration and safety as factors contributing to consistency will be covered. The role of elderberry in enhancing human health alone with the pharmacological basis, the cellular pathways, and the molecular mechanisms underlying the observed health benefits of elderberry fruit extracts will be also reviewed.

In celebration of providing the South and southern African sugar industry with more than 20 years of accredited quality analysis of mixed juice, molasses and sugar

The Sugar Milling Research Institute NPC (SMRI) has provided the South and southern African sugar industry with analytical services for more than 50 years. The need for an internationally-recognised quality assurance system to provide SMRI members and their customers with the necessary confidence in the results that they were receiving was recognised more than 25 years ago, and it culminated in the SMRI Analytical Services Division achieving ISO/IEC Guide 25:1990 accreditation in 1998. The SMRI analytical laboratory has since successfully progressed through the ISO/IEC 17025:1999 and ISO/IEC 17025:2005 iterations to the current ISO/IEC 17025:2017 standard. The SMRI has therefore had to continually expand the scope of its systems over the past 25 years, in order to match the updated requirements. The system is used for the analysis of weekly composite mixed juice and final molasses samples from all the SMRI South African member mills and some southern African member mills. Similarly, raw and white sugar analyses are conducted, with the reporting of results to the members' specifications. Critical to the accuracy of these results is the continual auditing and monitoring of the methods, equipment and chemical solutions that are used in the analysis methods. This is achieved by using Certified Reference Materials and control samples, as well as participating in internationally-recognised analytical proficiency schemes. Critical to the success of the laboratory in maintaining its accreditation is the competence of the laboratory staff who undergo continual training and assessments. This was demonstrated in 2020 and 2021, when they were able to continue providing the necessary services, despite the challenges faced during the Covid pandemic.

Fumbling out the effective pathways to apply the wildlife and forest analytic toolkit in the Mekong region: looking at data and analysis perspectives

The Wildlife and Forest Analytic Toolkit, introduced by the International Consortium on Combating Wildlife Crime (ICCWC), is designed to increase the effectiveness of measures combating wildlife and forest crimes (WAFCs). Greater Mekong Subregion (GMS) countries have applied this toolkit as one of their priority actions after recognizing concerns about the biodiversity system and conservational zone through several illegal wildlife trade (IWT) activities. Although the toolkit has realized its fundamental objectives to readjust legal frameworks, enhance enforcement involvement, and improve their judicial and prosecutorial operations, the last components of data and analysis have not yet been implemented. This leads to slow updates of both trends and patterns concerning WAFCs that raise questions about the real levels of exploitation in the region. Using gray literature with published materials, combined with the IWT's database in the CITES system, this study examines why the data and analysis component of the Toolkit created obstacles in the GMS countries. Findings point to there being at least four main challenges to implementing data and analysis as the toolkit has recommended in the region: (1) availability and reliability of data;(2) data collection;(3) data resources (internal vs. external level);and (4) analytic research and its related monitors. Some practical recommendations call for further discussions. Meanwhile, updated information and specific data relating to zoonotic disease transmission are timely, considering the coronavirus pandemic.

Psychometric properties of the Persian version of the COVID-19 phobia scale

Background and Objectives: It is predicted that the negative effects of the novel coronavirus disease 2019 (COVID-19) pandemic will continue. These negative effects are not limited to psychological problems. Serious physiological and economic problems have also been observed. It is important to develop and standardize appropriate tools to assess its different effects. This study aims to investigate the psychometric properties of the Persian version of the COVID-19 Phobia Scale (C19P-S) in Iranian samples. Methods: In this study, participants were people aged 18-60 years in Iran from March to May 2022, who were selected by a convenience sampling method. In order to evaluate the convergent and discriminant validity, the second version of the acceptance and action questionnaire, the brief version of the difficulty in emotion regulation scale, and the fear of COVID-19 scale were used. The factor structure of the questionnaire was examined by confirmatory factor analysis. Reliability was examined using Cronbach's alpha coefficient and the test-retest method. The data were analyzed in SPSS version 25 and LISREL version 8.8. Results: The results of confirmatory factor analysis confirmed the four-factor structure the Persian C19P-S. Cronbach's alpha coefficient for the whole scale was 0.90;for the subscales of psychological, psychological, economic and social factors, it was 0.87, 0.88, 0.89, and 0.91, respectively. In addition, the test-retest reliability with a four-week interval for the whole scale was 0.86;for the subscales of psychological, psychological, economic and social factors, it was 0.83, 0.79, 0.82, and 0.88, respectively. The correlation coefficients indicated the favorable convergent and discriminant validity of the Persian C19P-S (P < 0.001). Conclusion: The Persian C19P-S is a reliable and valid scale for measuring coronaphobia in Iranian samples.

Performance of the PanbioTM COVID-19 Ag rapid test in a health care setting in Ouagadougou, Burkina Faso

This study aimed to evaluate the performance of the PanbioTM Covid-19 Ag Rapid Test (Abbott) in a medical center in Ouagadougou. The PanbioTM COVID-19 Ag test was evaluated from January 26 to March 31, 2021 in symptomatic and asymptomatic patients in the medical Centre of Kossodo. A total of 268 individuals were tested by both SARS-CoV-2 RT-PCR, and antigen RDT. Of these 268 individuals, 52 were positive and 216 were negative for COVID-19 RT-PCR. The performance parameters of the test and its Kappa agreement with the RT-PCR were calculated according to the presence or absence of symptoms in the patients on one hand, and according to the time onset of symptoms on the other hand. The sensitivity of the PanbioTM COVID-19 Ag Rapid Test ranged from 29.63% (95% CI: 13.75 to 50.18) among COVID-19 asymptomatic patients, to 87.5% (95% CI: 52.91 to97.76) among symptomatic patients with symptom onset time of 1-5 days. Similarly, the PanbioTM COVID-19 Ag Rapid Test specificity was 97.3% (95% CI: 90.58 to 99.67) and 96.4% (95% CI: 91.81 to 98.82) in symptomatic and asymptomatic RT PCR negative patients. The PanbioTM COVID-19 Ag Rapid Test shows good performance in detecting COVID-19 cases in patients with a symptom onset time of less than seven (7) days. This performance is even better when the symptom onset is reduced to five (5) days. The results show that the antigen RDT is not suitable for COVID-19 detection among asymptomatic patients.

Analysis of the diet of obese elementary school students using various dietary intake survey methods

Purpose: Childhood obesity has become a social problem due to the social distancing necessitated by the coronavirus disease 2019 pandemic. This study aimed to identify the dietary problems of obese children through various dietary assessment methods and to confirm the usefulness of each method. Methods: The subjects were 88 students in the 4th to 6th grade of elementary school who participated in the nutrition camp organised by the Busan Metropolitan Office of Education, 2020. To evaluate dietary problems and assess diet quality, 24-hour meal records, monthly food intake frequency, and Dietary Screening Test (DST) data were analyzed. Results: Of the subjects, 15.7%, 30.3%, and 53.9% were normal weight, overweight, and obese, respectively. The average age was 11.77 +or- 0.77 years and the average body mass index was 23.96 +or- 3.01 kg/m2. It was observed from the 24-hour meal record method that the overweight and obese subject groups consumed fewer green vegetables (p < 0.001) and white vegetables (p < 0.01) than the normal weight group. In the monthly food intake frequency method, the consumption of ramen (p < 0.01), snacks (p < 0.05), and sausages (p < 0.05) were high in the obese group, and that of anchovies, broccoli, and sweet pumpkin was high in the normal group (p < 0.05). The comparative data from the DST revealed that the overweight and obese groups had less vegetable intake than the normal weight group (p < 0.01) and had higher intakes of dairy products, fast food, and sweet snacks (p < 0.05). Conclusion: The usefulness of each method in the dietary evaluation of obese children was confirmed. To address the problem of obesity, it is necessary to evaluate the dietary problem and approach it with a customized solution tailor-made for the individual subject.

43rd World Congress of Vine and Wine, Ensenada, Mexico, 31 October-4 November 2022

These proceedings contain over 100 articles discussing the latest developments and challenges facing the vine and wine industry, focusing on 4 major themes such as viticulture, oenology, economy and law, and safety and health. Specific topics discussed include the adaptation and optimization of viticultural resources, the characterization of different microclimate to improve grape quality, the validation of new oenological techniques, and the resilience of the wine sector after the COVID-19 pandemic. Other topics included the impact of climate change on viticulture, the market competitiveness of the wine sector, and public health aspects wine consumption.

Review: Recent Development in Food Adulteration Analytical Techniques

Food adulteration is an enduring concern to date, and its detection and food authentication are some of the major approaches which can eradicate adulteration. This article reviews modern analytical techniques which are currently used for optimization and identification of adulterants. The Analytical techniques reviewed are distributed in three sections- Computer vision, Spectral and spectral Imaging techniques, and Electrical Techniques. Computer vision is a very advanced technique, allowing the analysis of several parameters with accurate and precise results. The hazardous effect of adulteration and future challenges and impacts are briefly discussed. The Previous decade is considered to be a massive success for food adulterant detection as it involved a chemometrics study. The use of chemometrics with analytical techniques is briefly discussed. Chemometrics is the science of extracting information from the chemical system by data-driven means. The inevitability of food in perspective to the current COVID-19 pandemic is very strong and authenticated food is a prior requirement. Copyright © 2022 Society of Pharmaceutical Sciences and Research. All rights reserved.

The utilization of 'Jogja Pass' mobile application as an effort to prevent the spread of COVID-19 in Special Region of Yogyakarta

In order to respond to the increasing number of COVID-19 cases, the government created the Jogja Pass mobile application for screening and tracing as well as supporting the implementation of smart city in Special Region of Yogyakarta. This study aims to identify the utilization of the Jogja Pass, analyze the impact of the Jogja Pass in handling COVID-19, and identify obstacles in the utilization of the Jogja Pass for the application development purposes. This study employed quantitative descriptive and qualitative descriptive methods. The primary data were obtained from in-depth interviews with relevant institutions and through questionnaires to the public. The secondary data were obtained through recapitulation of application data. The results indicated that the users of the Jogja Pass are spread throughout the region of Yogyakarta and it is used at 113 public area points. The Jogja Pass has positive impact on the users and becomes one of the SOPs for health protocols in several public areas. The obstacles in the development of the Jogja Pass include limited human resources and funds, dishonesty of users in filling out the screening test, and the Jogja Pass has been taken down from the Android Play Store.

Perception of the COVID-19 effects in face-to-face and distance education in Peruvian university students

The pandemic caused many changes due to social distancing, one of them is in terms of virtual university classes. Therefore, the objective was to validate an instrument that measures the perception of the effects of COVID-19 on distance education in university students of health sciences and other careers in Peru. An instrumental and cross-sectional study was carried out. Thousands of distance learning students from health sciences and other careers participated during the first wave of the Coronavirus. The survey on the perception that it has of the repercussions caused by the health emergency on face-to-face and distance education was designed and validated;in addition, another scale was exploratory to measure the perception of the spread of the coronavirus. The analysis showed high reliability and good internal consistency, with adequate Cronbach's alpha values. For the Exploratory Factor Analysis (EFA), the Bartlett test and the Kaiser-Meyer-Olkin coefficient were used. All the items received a favorable evaluation by the experts (Aiken's V> 0.70);this with respect to relevance, representativeness and clarity. Three virtual average factors were identified, their use and presence, all saturation indices were greater than 0.50. The survey proved to be reliable and representative, this above all because it was evaluated in students from the different Peruvian departments;which can be used to evaluate this in diverse similar populations.

Indices of Borg dyspnea and perceived CRM in persistent COVID-19 patients with pulmonary hypertension

The SARS-CoV-2 that causes the COVID-19 disease, clinically ranges from mild flu-like episodes to other serious and even life-threatening, especially due to acute respiratory failure, regardless of the severity the acute course can be overcome, however, the persistence of symptoms or multi-organ symptom complex, called persistent COVID. Among these persistent symptoms is respiratory distress. A descriptive, observational and cross-sectional study was carried out in 29 people diagnosed with persistent COVID-19 plus comorbidity pulmonary hypertension, between 2019 and 2021. The Borg dyspnea scales were applied at rest and by stress test Walk test in 6;and the Medical Research Council dyspnea scale. IFB B categorizes between 0 and 2 and high R2 (0.7993) explains the perception of respiratory discomfort that appears with activity levels that do not normally cause this type of discomfort (daily activity), and can be weighted by the level of activity necessary for it to be triggered. Both scales have validity, reliability, specificity, sensitivity and predictive value for the use that has been established, easy to use and understand.

Techniques for analytical estimation of COVID-19 clinical candidate, niclosamide in pharmaceutical and biomedical samples.

Niclosamide is a well-known broad-spectrum antiparasitic drug used for human as well as veterinary tapeworm infections. Recently, it attracted attention as an antiviral agent for treating coronavirus disease 2019. It is administered orally in humans to treat tapeworm infections. Furthermore, it is a registered pesticide and molluscicide to control infections in the aquaculture industry. Its chronic environmental exposure has potential toxicities when such contaminated seafood is consumed. Therefore, monitoring its residual concentration in food products (seafood, water, water waste, etc.) and pharmaceuticals (active pharmaceutical ingredients, bulk drugs, and formulations) is imperative. The present review critically investigates the sophisticated techniques employed for analyzing niclosamide, its degradation products, and metabolites in various samples and matrices. The future scope for green analytical methods, green sample extraction and preparation is also deliberated.

Advances in spectral techniques for detection of pathogenic microorganisms

The highly contagious viral illness Coronavirus disease 2019, caused by severe acute respiratory syndrome coronavirus-2, has led to nearly 5 million deaths worldwide. The detection of highly infectious pathogens or novel pathogens causing emerging infectious diseases is highly challenging. Encouragingly, spectral detection-including laser-induced fluorescence spectroscopy, infrared absorption spectroscopy, Raman spectroscopy and their combinations-has been broadly used to detect pathogenic microorganisms on the basis of their physical and chemical characteristics. Surface-enhanced Raman spectroscopy with labels can detect organisms at a minimum concentration of 3 cells/mL. The changes in cells' biochemical reactions before and after polioviral infection can be detected by Fourier transform infrared spectroscopy. However, the sensitivity and specificity of different spectral detection categories differs, owing to their different detection principles. Flexible detection methods require interdisciplinary researchers familiar with both pathogen biology and instruments. This review summarizes the advances in spectral techniques used in detecting pathogenic microorganism.

Developing an intelligent system for diagnosis of COVID-19 based on artificial neural network

An outbreak of atypical pneumonia termed coronavirus disease 2019 (COVID-19) has spread worldwide since the beginning of 2020. It poses a significant threat to the global health and the economy. Physicians face ambiguity in their decision-making for COVID-19 diagnosis and treatment. In this respect, designing an intelligent system for early diagnosis of the disease is critical for mitigating virus spread and resource optimization. This study aimed to establish an artificial neural network (ANNs)-based clinical model to diagnose COVID-19. The retrospective dataset used in this study consisted of 400 COVID-19 case records (250 positives vs. 150 negatives) and 18 columns for the diagnostic features. The backpropagation technique was used to train a neural network. After designing multiple neural network configurations, the area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, and specificity values were calculated to measure the model performance. The two nested loops architecture of 9-10-15-2 (10 and 15 neurons used in layer one and layer two, respectively) with the ROC of 98.2%, sensitivity of 96.4%, specificity of 90.6%, and accuracy of 94% were introduced as the best configuration model for COVID-19 diagnosis. ANN is valuable as a decision-support tool for clinicians to improve the COVID-19 diagnosis. It is promising to implement the ANN model to improve the accuracy and speed of the COVID-19 diagnosis for timely screening, treatment, and careful monitoring. Further studies are warranted for verifying and improving the current ANN model.

A proposed hybrid algorithm for detecting COVID-19 patients

COVID-19, one of the most dangerous pandemics, is currently affecting humanity. COVID-19 is spreading rapidly due to its high reliability transmissibility. Patients who test positive more often have mild to severe symptoms such as a cough, fever, raw throat, and muscle aches. Diseased people experience severe symptoms in more severe cases. such as shortness of breath, which can lead to respiratory failure and death. Machine learning techniques for detection and classification are commonly used in current medical diagnoses. However, for treatment using neural networks based on improved Particle Swarm Optimization (PSO), known as PSONN, the accuracy and performance of current models must be improved. This hybridization implements Particle Swarm Optimization and a neural network to improve results while slowing convergence and improving efficiency. The purpose of this study is to contribute to resolving this issue by presenting the implementation and assessment of Machine Learning models. Using Neural Networks and Particle Swarm Optimization to help in the detection of COVID-19 in its early stages. To begin, we preprocessed data from a Brazilian dataset consisted primarily of early-stage symptoms. Following that, we implemented Neural Network and Particle Swarm Optimization algorithms. We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks algorithms and measured feature importance, and other, to justify the differences between classification models. Particle Swarm Optimization with Neura Network is being deployed to improve the efficiency of the detection method by more accurately predicting COVID-19 detection. Preprocessed datasets with important features are then fed into the testing and training phases as inputs. Particle Swarm Optimization was used for the training phase of a neural net to identify the best weights and biases. On training data, the highest rate of accuracy gained is 0.98.738 and on testing data, it is 98.689.

Online learning during the COVID-19 pandemic: development of digital module to improve analytical skill

The Covid-19 pandemic has caused learning activities that were originally carried out face-to-face in schools to be shifted to online learning. The implementation of online learning has several challenges, one of which is the development of student skills in the 21st century, especially analytical skills. Students' analytical skills are needed in learning physics. Students' analytical skills are currently still in the low category. To support the improvement of students' analytical skills, teaching material in the form of a digital module is applied. The purpose of this study was to examine the role of digital modules to improve students' analytical skills during the Covid-19 pandemic online learning period. The research was carried out using a qualitative descriptive method with 23 students as the subject of a high school in Karanganyar Regency, Indonesia. The study used a quantitative descriptive method with a test technique using 10 essay questions based on three indicators of analytical ability. Data were analyzed using N-gain and paired t-test. The results showed that the average value of analytical ability before using the digital module was 57.84, while the average value after using the digital module was 72.81. Based on the value of N gain, the increase in students' analytical skills on three indicators is included in the moderate category, namely > 0.3. The digital module has a significant effect on improving student analysis.