Non-targeted analysis of lipidic extracts by high-resolution mass spectrometry to characterise the chemical exposome: Comparison of four clean-up strategies applied to egg

Biota samples are used to monitor chemical stressors and their impact on the ecosystem and to describe dietary chemical exposure. These complex matrices require an extraction step followed by clean-up to avoid damaging sensitive analytical instruments based on chromatography coupled to mass spectrometry. While interest for non-targeted analysis (NTA) is increasing, there is no versatile or generic sample preparation for a wide range of contaminants suitable for a diversity of biotic matrices. Among the contaminants' variety, persistent contaminants are mostly hydrophobic (mid- to non-polar) and bio-magnify through the lipidic fraction. During their extraction, lipids are generally co-extracted, which may cause matrix effect during the analysis such as hindering the acquired signal. The aim of this study was to evaluate the efficacy of four clean-up methods to selectively remove lipids from extracts prior to NTA. We evaluated (i) gel permeation chromatography (GPC), (ii) Captiva EMR-lipid cartridge (EMR), (iii) sulphuric acid degradation (H2SO4) and (iv) polydimethyl siloxane (PDMS) for their efficiency to remove lipids from hen egg extracts. Gas and liquid chromatography coupled with high-resolution mass spectrometry fitted with either electron ionisation or electrospray ionisation sources operating in positive and negative modes were used to determine the performances of the clean-up methods. A set of 102 chemicals with a wide range of physico-chemical properties that covers the chemical space of mid- to non-polar contaminants, was used to assess and compare recoveries and matrix effects. Matrix effects, that could hinder the mass spectrometer signal, were lower for extracts cleaned-up with H2SO4 than for the ones cleaned-up with PDMS, EMR and GPC. The recoveries were satisfactory for both GPC and EMR while those determined for PDMS and H2SO4 were low due to poor partitioning and degradation/dissociation of the compounds, respectively. The choice of the clean-up methods, among those assessed, should be a compromise that takes into account the matrix under consideration, the levels and the physico-chemical properties of the contaminants.

Coronavirus disease 2019 in proportion to population: a historical analysis of Saudi Arabia

BACKGROUND

Saudi Arabia is one of the countries seriously affected by coronavirus disease 2019 (COVID-19) worldwide. With a few cases in early March, the daily spread of this disease increased to nearly 5000 at one point in time during the first wave to mid-June 2020. With committed efforts and public health interventions, it has been controlled to nearly 1000 by the end of August 2020 and less than 217 by November 28, 2020; thereafter, reporting declines and small increases. However, by December 2021, a third wave started, lasting for 2 months, during which the infection rate increased rapidly. By April 1, 2022, the number of infected persons in the country was 750,998, with 9047 deaths, 7131 active, and approximately 400 critical cases. This analysis of COVID-19 statistics of the Ministry of Health of Saudi Arabia (March 2020-April 2022) is carried out along with population data to extract patient proportions per 100,000 persons to illustrate the hypothesized social and community impact, which influences families and households.

RESULTS

The results showed a high rate of infection and mortality, but with recovery. These rates varied across localities and cities. A few cities with higher population densities are less affected by the spread of the epidemic. However, few localities and upcoming cities/townships were severely affected. These effects are explained as the percentage of the population affected, which exposes the impact on societies, families, and individual members. With concerted efforts, they are brought under control through recovery and adopting mitigation methods.

CONCLUSIONS

Localities could be classified into four categories based on the proportion of the infected population: rapidly increasing, moderately increasing, declining, and stabilizing. Moreover, differential proportions of the affected population have implications at social and familial levels. Analysis and understanding of these trends, considering the base population, are important for policy building and intervention strategies accounting for grassroots-level demographics, which might serve as a tool to enhance interventions at population and family levels. Strategies for awareness creation and compassionate care are essential to address the psychosocial impact of health emergencies, as proved by the Ministry of Health, Saudi Arabia.

Forecasting COVID-19 situation in Bangladesh

Forecasting the COVID-19 confirmed cases, deaths, and recoveries demands time to know the severity of the novel coronavirus. This research aims to predict all types of COVID-19 cases (verified people, deaths, and recoveries) from the deadliest 3rd wave data of the COVID-19 pandemic in Bangladesh. We used the official website of the Directorate General of Health Services as our data source. To identify and predict the upcoming trends of the COVID-19 situation of Bangladesh, we fit the Auto-Regressive Integrated Moving Average (ARIMA) model on the data from Mar. 01, 2021 to Jul. 31, 2021. The finding of the ARIMA model (forecast model) reveals that infected, deaths, and recoveries number will have experienced exponential growth in Bangladesh to October 2021. Our model reports that confirmed cases and deaths will escalate by four times, and the recoveries will improve by five times at a later point in October 2021 if the trend of the three scenarios of COVID-19 from March to July lasts. The prediction of the COVID-19 scenario for the next three months is very frightening in Bangladesh, so the strategic planner and field-level personnel need to search for suitable policies and strategies and adopt these for controlling the mass transmission of the virus.

Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models

Background

A viral disease due to a virus called SARS-Cov-2 spreads globally with a total of 34,627,141 infected people and 1,029,815 deaths. Algeria is an African country where 51,690, 1,741 and 36,282 are currently reported as infected, dead and recovered. A multivariate time series model has been used to model these variables and forecast their future scenarios for the next 20 days.

Results

The results show that there will be a minimum of 63 and a maximum of 147 new infections in the next 20 days with their corresponding 95% confidence intervals of − 89 to 214 and 108–186, respectively. Deaths’ forecast shows that there will be 8 and 12 minimum and maximum numbers of deaths in the upcoming 20 days with their 95% confidence intervals of 1–17 and 4–20, respectively. Minimum and maximum numbers of recovered cases will be 40 and 142 with their corresponding 95% confidence intervals of − 106 to 185 and 44–239, respectively. The total number of infections, fatalities and recoveries in the next 20 days will be 1850, 186 and 1680, respectively.

Conclusion

The results of this study suggest that the new infections are higher in number than recover cases, and therefore, the number of infected people may increase in future. This study can provide valuable information for policy makers including health and education departments.

Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan

COVID-19 emerged in Wuhan, China in December 2019 has now spread around the world causes damage to human life and economy. Pakistan is also severely effected by COVID-19 with 202,955 confirmed cases and total deaths of 4,118. Vector Autoregressive time series models was used to forecast new daily confirmed cases, deaths and recover cases for ten days. Our forecasted model results show maximum of 5,363/day new cases with 95% confidence interval of 3,013-8,385 on 3rd of July, 167/day deaths with 95% confidence interval of 112-233 and maximum recoveries 4,016/day with 95% confidence interval of 2,182-6,405 in the next 10 days. The findings of this research may help government and other agencies to reshape their strategies according to the forecasted situation. As the data generating process is identified in terms of time series models, then it can be updated with the arrival of new data and provide forecasted scenario in future.

Analysis on novel coronavirus (COVID-19) using machine learning methods

In this paper, we are working on a pandemic of novel coronavirus (COVID-19). COVID-19 is an infectious disease, it creates severe damage in the lungs. COVID-19 causes illness in humans and has killed many people in the entire world. However, this virus is reported as a pandemic by the World Health Organization (WHO) and all countries are trying to control and lockdown all places. The main objective of this work is to solve the five different tasks such as I) Predicting the spread of coronavirus across regions. II) Analyzing the growth rates and the types of mitigation across countries. III) Predicting how the epidemic will end. IV) Analyzing the transmission rate of the virus. V) Correlating the coronavirus and weather conditions. The advantage of doing these tasks to minimize the virus spread by various mitigation, how well the mitigations are working, how many cases have been prevented by this mitigations, an idea about the number of patients that will recover from the infection with old medication, understand how much time will it take to for this pandemic to end, we will be able to understand and analyze how fast or slow the virus is spreading among regions and the infected patient to reduce the spread based clear understanding of the correlation between the spread and weather conditions. In this paper, we propose a novel Support Vector Regression method to analysis five different tasks related to novel coronavirus. In this work, instead of simple regression line we use the supported vectors also to get better classification accuracy. Our approach is evaluated and compared with other well-known regression models on standard available datasets. The promising results demonstrate its superiority in both efficiency and accuracy.

Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan

In this paper, we have conducted analysis based on data obtained from National Institute of Health (NIH) - Islamabad and produced a forecast of COVID-19 confirmed cases as well as the number of deaths and recoveries in Pakistan using the Auto-Regressive Integrated Moving Average Model (ARIMA). The fitted forecasting models revealed high exponential growth in the number of confirmed cases, deaths and recoveries in Pakistan. Based on our model prediction the number of confirmed cases will be increased by 2.7 times, 95% prediction interval for the number of cases at the end of May 2020 = (5681 to 33079). There could be up to 500 deaths, 95% prediction interval = (168 to 885) and there could be eightfold increase in the number of recoveries, 95% prediction interval = (2391 to 16126). The forecasting results of COVID-19 are alarming for May in Pakistan. The health officials and government should adopt new strategies to control the pandemic from further spread until a proper treatment or vaccine is developed.

Analysing COVID-19 pandemic through cases, deaths, and recoveries

Background and aims

The novel Coronavirus disease (COVID-19) in Wuhan, China, became a pandemic after its outbreak in January 2020. Countries one after the other are witnessing peak effects of the disease, and they need to learn from the experience of others already affected or peaked countries. Thus, this paper aims to analyse the effect of the COVID-19 pandemic on different countries through COVID-19 cases, resulting in deaths and recoveries.

Methods

This study analyses quantitatively the lethal effects of the pandemic through the study of infections, deaths, and recoveries on the 13 most-affected COVID-19 countries as of 1 s t June. The daily change in cases, deaths, and recoveries for all the 13 countries were considered. Combined analysis for comparison and separate analysis for the detailed study were both taken for every country. All the graphs were made in RStudio using the R programming language, as it is best for statistical analysis.

Results

The casual and ignorant behaviour of people is a major reason for such a large scale spread of the coronavirus. The government of every country should be strict as well as considerate to all sections of people while making policies. There is no room for mistakes, as one wrong decision or one delayed decision can worsen the situation. However, some countries which were once the epicentre of this pandemic are now corona-free, proving that this global threat can be tackled and we should all keep our morale high.

Conclusions

The coronavirus disease is not any ordinary viral infection; it has become a pandemic as it has an impact on health, mortality, economy and social well being of the entire world. Qualitative and Quantitative analysis of the statistics related to COVID-19 in different countries is done based on their officials' data. The primary objective of this analysis is to learn about the relationships of various countries in containing the spread of COVID-19 and the various factors such as government policies, the cooperation of people, economy, and tourism.

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Analysis of COVID-19 pandemic on 12 most affected countries including China.

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Study of the performance curves of various countries in fighting this pandemic.

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Analyses the effectiveness of lockdown and to know the further implementations that can be done accordingly.

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Learning the right steps to contain the infection by not repeating the mistakes of other countries.

COVID-19: Clinical aspects and therapeutics responses

COVID-19 has created havoc in the world by causing thousands of demises in a short period of time. Up till now, several attempts have been made for potential therapeutics against SARS-COV2. In this retrospective, single-center study, we extracted data from 122 COVID-19, RT-PCR confirmed patients. who were treated with a new treatment strategy of lianhuaqingwen with Arbidol Hydrochloride. The patients were either asymptomatic or had mild symptoms for COVID-19 disease. Of 122 patients 21 (17.21%) patients developed severe conditions of COVID-19, while total 111 (90.9%) experienced mild symptoms such as fever in 93 (76.22%) patients, cough in 23 (20.17%) and muscle pain were observed in total 8 (7%) patients. Furthermore our newly applied drugs combination (Lianhuaqingwen and Arbidol Hydrochloride) showed therapeutic effects in 5-7 days in patients with mild symptoms with 98% recovery rate. These results indicate that COVID-19 patients with mild symptoms can be treated with Lianhuaqingwen and Arbidol Hydrochloride. However, extensive clinical investigations are required to confirm the effectiveness of these drugs.

Two important limitations relating to the spiking of environmental samples with contaminants of emerging concern: How close to the real analyte concentrations are the reported recovered values?

Occurrence and effects of contaminants of emerging concern pose a special challenge to environmental scientists. The investigation of these effects requires reliable, valid, and comparable analytical data. To this effect, two critical aspects are raised herein, concerning the limitations of the produced analytical data. The first relates to the inherent difficulty that exists in the analysis of environmental samples, which is related to the lack of knowledge (information), in many cases, of the form(s) of the contaminant in which is present in the sample. Thus, the produced analytical data can only refer to the amount of the free contaminant ignoring the amount in which it may be present in other forms; e.g., as in chelated and conjugated form. The other important aspect refers to the way with which the spiking procedure is generally performed to determine the recovery of the analytical method. Spiking environmental samples, in particular solid samples, with standard solution followed by immediate extraction, as is the common practice, can lead to an overestimation of the recovery. This is so, because no time is given to the system to establish possible equilibria between the solid matter-inorganic and/or organic-and the contaminant. Therefore, the spiking procedure need to be reconsidered by including a study of the extractable amount of the contaminant versus the time elapsed between spiking and the extraction of the sample. This study can become an element of the validation package of the method.

Determination of polycyclic aromatic hydrocarbons [PAHs] in processed meat products using gas chromatography - flame ionization detector

The concentrations of polycyclic aromatic hydrocarbons (PAHs) in smoked, grilled and boiled meats were determined using gas chromatography - flame ionization detector (GC-FID). PAHs in the processed meats were extracted in n-hexane after hydrolysis with methanolic KOH. Clean-up was achieved using solid phase extraction in neutral-Si/basic-Si/acidic-Si/neutral-Si frits. The fractions, benzo[k]fluoranthene (BkP), benzo[a]pyrene (BaP), indeno[123-cd]pyrene (IP) and benzo[ghi]perylene (BghiP) were separated and quantified using GC-FID. The method and instrument limits of detections were 0.1, 0.1, 0.2, 0.3μg/kg and 0.5, 0.5, 1.0, 1.5μg/kg, respectively, for BkP, BaP, IP and BghiP. The method's recovery and precision generally varied between 83.69% and 94.25% with relative standard deviation (RSD) of 3.18-15.60%; and 90.38-96.71% with relative standard deviation (RSD) of 1.82-12.87% respectively. The concentration of BkP, BaP, IP and BghiP in smoked, grilled and boiled meat samples were ranged 0.64-31.54μg/kg, 0.07-7.04μg/kg, 0.09-15.03, 0.51-46.67μg/kg and 0.01-5.11μg/kg, respectively.

Statistical evaluation of the influence of soil properties on recoveries and matrix effects during the analysis of pharmaceutical compounds and steroids by quick, easy, cheap, effective, rugged and safe extraction followed by liquid chromatography-tandem mass spectrometry

Numerous chemical products are dispersed in our environment. Many of them are recognized as harmful to humans and the ecosystem. Among these harmful substances are antibiotics and steroid hormones. Currently, very few data are available on the presence and fate of these substances in the environment, in particular for solid matrices, mainly due to a lack of analytical methodologies. Indeed, soil is a very complex matrix, and the nature and composition of the soil has a significant impact on the extraction efficiency and the sensitivity of the method. For this reason a statistical approach was performed to study the influence of soil parameters (clay, silt, sand and organic carbon percentages and cation exchange capacity (CEC)) on recoveries and matrix effects of various pharmaceuticals and steroids. Thus, an analysis of covariance (ANCOVA) was performed when several substances were analyzed simultaneously, whereas a Pearson correlation was used to study the compounds individually. To the best of our knowledge, this study is the first time such an experiment was performed. The results showed that clay and organic carbon percentages as well as the CEC have an impact on the recoveries of most of the target substances, the variables being anti-correlated. This result suggests that the compounds are trapped in soils with high levels of clay and organic carbon and a high CEC. For the matrix effects, it was shown that the organic carbon content has a significant effect on steroid hormones and penicillin G matrix effects (positive correlation). Finally, interaction effects (first order) were evaluated. This latter point corresponds to the crossed effects that occur between explanatory variables (soil parameters). Indeed, the value taken by an explanatory variable can have an influence on the effect that another explanatory variable has on a dependent variable. For instance, it was shown that some parameters (silt, sand) have an impact on the effect that clay content has on recoveries. Besides, CEC and silt affect the influence that organic carbon percentage has on matrix effect. This original approach provides a better understanding of the complex interactions that occur in soil and could be useful to understand and predict the performance of an analytical method.