Comprehensive profiling and semi-quantification of exogenous chemicals in human urine using HRMS-based strategies.

Chemicals infiltrate our daily experiences through multiple exposure pathways. Human biomonitoring (HBM) is routinely used to comprehensively understand these chemical interactions. Historically, HBM depended on targeted screening methods limited to a relatively small set of chemicals with triple quadrupole instruments typically. However, recent advances in high-resolution mass spectrometry (HRMS) have facilitated the use of broad-scope target, suspect, and non-target strategies, enhancing chemical exposome characterization within acceptable detection limits. Despite these advancements, establishing robust and efficient sample treatment protocols is still essential for trustworthy broad-range chemical analysis. This study sought to validate a methodology leveraging HRMS-based strategies for accurate profiling of exogenous chemicals and related metabolites in urine samples. We evaluated five extraction protocols, each encompassing various chemical classes, such as pharmaceuticals, plastic additives, personal care products, and pesticides, in terms of their extraction recoveries, linearity, matrix effect, sensitivity, and reproducibility. The most effective protocol was extensively validated and subsequently applied to 10 real human urine samples using wide-scope target analysis encompassing over 2000 chemicals. We successfully identified and semi-quantified a total of 36 chemicals using an ionization efficiency-based model, affirming the methodology's robust performance. Notably, our results dismissed the need for a deconjugation step, a typically labor-intensive and time-consuming process.

What is in the fish? Collaborative trial in suspect and non-target screening of organic micropollutants using LC- and GC-HRMS.

A collaborative trial involving 16 participants from nine European countries was conducted within the NORMAN network in efforts to harmonise suspect and non-target screening of environmental contaminants in whole fish samples of bream (Abramis brama). Participants were provided with freeze-dried, homogenised fish samples from a contaminated and a reference site, extracts (spiked and non-spiked) and reference sample preparation protocols for liquid chromatography (LC) and gas chromatography (GC) coupled to high resolution mass spectrometry (HRMS). Participants extracted fish samples using their in-house sample preparation method and/or the protocol provided. Participants correctly identified 9-69 % of spiked compounds using LC-HRMS and 20-60 % of spiked compounds using GC-HRMS. From the contaminated site, suspect screening with participants' own suspect lists led to putative identification of on average ∼145 and ∼20 unique features per participant using LC-HRMS and GC-HRMS, respectively, while non-target screening identified on average ∼42 and ∼56 unique features per participant using LC-HRMS and GC-HRMS, respectively. Within the same sub-group of sample preparation method, only a few features were identified by at least two participants in suspect screening (16 features using LC-HRMS, 0 features using GC-HRMS) and non-target screening (0 features using LC-HRMS, 2 features using GC-HRMS). The compounds identified had log octanol/water partition coefficient (KOW) values from -9.9 to 16 and mass-to-charge ratios (m/z) of 68 to 761 (LC-HRMS and GC-HRMS). A significant linear trend was found between log KOW and m/z for the GC-HRMS data. Overall, these findings indicate that differences in screening results are mainly due to the data analysis workflows used by different participants. Further work is needed to harmonise the results obtained when applying suspect and non-target screening approaches to environmental biota samples.

An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model.

Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R2 = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources.

Alzheimer's disease: using gene/protein network machine learning for molecule discovery in olive oil.

Alzheimer's disease (AD) poses a profound human, social, and economic burden. Previous studies suggest that extra virgin olive oil (EVOO) may be helpful in preventing cognitive decline. Here, we present a network machine learning method for identifying bioactive phytochemicals in EVOO with the highest potential to impact the protein network linked to the development and progression of the AD. A balanced classification accuracy of 70.3 ± 2.6% was achieved in fivefold cross-validation settings for predicting late-stage experimental drugs targeting AD from other clinically approved drugs. The calibrated machine learning algorithm was then used to predict the likelihood of existing drugs and known EVOO phytochemicals to be similar in action to the drugs impacting AD protein networks. These analyses identified the following ten EVOO phytochemicals with the highest likelihood of being active against AD: quercetin, genistein, luteolin, palmitoleate, stearic acid, apigenin, epicatechin, kaempferol, squalene, and daidzein (in the order from the highest to the lowest likelihood). This in silico study presents a framework that brings together artificial intelligence, analytical chemistry, and omics studies to identify unique therapeutic agents. It provides new insights into how EVOO constituents may help treat or prevent AD and potentially provide a basis for consideration in future clinical studies.

Wide-scope target and suspect screening of emerging contaminants in sewage sludge from Nigerian WWTPs by UPLC-qToF-MS.

There is currently a paucity of scientific data in Africa on the analysis and occurrence of emerging contaminants in sewage sludge. In this work, the occurrence of European Union (EU) Water Framework Directive priority substances and wide-range emerging contaminants were investigated and discussed comprehensively in the sewage sludge samples from three different wastewater treatment plants (WWTPs) in Lagos, Nigeria. The identification strategy was implemented by target and suspect screening in liquid chromatography-high resolution mass spectrometry. 250 compounds were identified in the sewage sludge samples from the investigated WWTPs. From 250 detected compounds, 182 compounds were quantified, and 78 compounds significantly show high environmental risk score (calculated from provisional no-effect concentrations values (PNEC) as well as their environmental quality data (EQs)). Most of contaminants detected at high amount belong to pharmaceuticals and are from hospital WWTP. While the highest concentration (72.4 mg kg-1) was measured for salicylic acid (a non-steroidal anti-inflammatory drug), antibiotics showed high concentrations up to 24.4 and 28.4 mg kg-1 for ciprofloxacin and ofloxacin, respectively. Three simple factors including frequency of exceedance, frequency of occurrence and extent of exceedance were used to aid prioritization of these substances in future monitoring campaigns. This work presents the first comprehensive and wide-scope screening of a large number of emerging contaminants in sewage sludge from Nigerian WWTPs.

Development of Liquid Chromatographic Retention Index Based on Cocamide Diethanolamine Homologous Series (C(n)-DEA).

There is a growing need for indexing and harmonizing retention time (tR) data in liquid chromatography derived under different conditions to aid in the identification of compounds in high resolution mass spectrometry (HRMS) based suspect and nontarget screening of environmental samples. In this study, a rigorously tested, inexpensive, and simple system-independent retention index (RI) approach is presented for liquid chromatography (LC), based on the cocamide diethanolamine homologous series (C(n = 0-23)-DEA). The validation of the CDEA based RI system was checked rigorously on eight different instrumentation and LC conditions. The RI values were modeled using molecular descriptor free technique based on structural barcoding and convolutional neural network deep learning. The effect of pH on the elution pattern of more than 402 emerging contaminants were studied under diverse LC settings. The uncertainty associated with the CDEA RI model and the pH effect were addressed and the first RI bank based on CDEA calibrants was developed. The proposed RI system was used to enhance identification confidence in suspect and nontarget screening while facilitating successful comparability of retention index data between various LC settings. The CDEA RI app can be accessed at https://github.com/raalizadeh/RIdea.

Assessment of contaminants of emerging concern in European apex predators and their prey by LC-QToF MS wide-scope target analysis.

Apex predators are good indicators of environmental pollution since they are relatively long-lived and their high trophic position and spatiotemporal exposure to chemicals provides insights into the persistent, bioaccumulative and toxic (PBT) properties of chemicals. Although monitoring data from apex predators can considerably support chemicals' management, there is a lack of pan-European studies, and longer-term monitoring of chemicals in organisms from higher trophic levels. The present study investigated the occurrence of contaminants of emerging concern (CECs) in 67 freshwater, marine and terrestrial apex predators and in freshwater and marine prey, gathered from four European countries. Generic sample preparation protocols for the extraction of CECs with a broad range of physicochemical properties and the purification of the extracts were used. The analysis was performed utilizing liquid (LC) chromatography coupled to high resolution mass spectrometry (HRMS), while the acquired chromatograms were screened for the presence of more than 2,200 CECs through wide-scope target analysis. In total, 145 CECs were determined in the apex predator and their prey samples belonging in different categories, such as pharmaceuticals, plant protection products, per- and polyfluoroalkyl substances, their metabolites and transformation products. Higher concentration levels were measured in predators compared to prey, suggesting that biomagnification of chemicals through the food chain occurs. The compounds were prioritized for further regulatory risk assessment based on their frequency of detection and their concentration levels. The majority of the prioritized CECs were lipophilic, although the presence of more polar contaminants should not be neglected. This indicates that holistic analytical approaches are required to fully characterize the chemical universe of biota samples. Therefore, the present survey is an attempt to systematically investigate the presence of thousands of chemicals at a European level, aiming to use these data for better chemicals management and contribute to EU Zero Pollution Ambition.

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry.

Background: The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide. Results: The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101). Conclusions: The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/). Supplementary Information: The online version contains supplementary material available at 10.1186/s12302-022-00680-6.

Target and suspect screening of 4777 per- and polyfluoroalkyl substances (PFAS) in river water, wastewater, groundwater and biota samples in the Danube River Basin.

Per- and polyfluoroalkyl substances (PFAS) are under regulatory scrutiny since some of them are persistent, bioaccumulative, and toxic. The occurrence of 4777 PFAS was investigated in the Danube River Basin (DRB; 11 countries) using target and suspect screening. Target screening involved investigation of PFAS with 56 commercially available reference standards. Suspect screening covered 4777 PFAS retrieved from the NORMAN Substance Database, including all individual PFAS lists submitted to the NORMAN Suspect List Exchange Database. Mass spectrometry fragmentation patterns and retention time index predictions of the studied PFAS were established for their screening by liquid chromatography - high resolution mass spectrometry using NORMAN Digital Sample Freezing Platform (DSFP). In total, 82 PFAS were detected in the studied 95 samples of river water, wastewater, groundwater, biota and sediments. Suspect screening detected 72 PFAS that were missed by target screening. Predicted no effect concentrations (PNECs) were derived for each PFAS via a quantitative structure-toxicity relationship (QSTR)-based approach and used for assessment of their environmental risk. Risk characterization revealed 18 PFAS of environmental concern in at least one matrix. The presence of PFAS in all studied environmental compartments across the DRB indicates a potentially large-scale migration of PFAS in Europe, which might require their further systematic regulatory monitoring.

First Novel Workflow for Semiquantification of Emerging Contaminants in Environmental Samples Analyzed by Gas Chromatography-Atmospheric Pressure Chemical Ionization-Quadrupole Time of Flight-Mass Spectrometry.

The ionization efficiency of emerging contaminants was modeled for the first time in gas chromatography-high-resolution mass spectrometry (GC-HRMS) which is coupled to an atmospheric pressure chemical ionization source (APCI). The recent chemical space has been expanded in environmental samples such as soil, indoor dust, and sediments thanks to recent use of high-resolution mass spectrometric techniques; however, many of these chemicals have remained unquantified. Chemical exposure in dust can pose potential risk to human health, and semiquantitative analysis is potentially of need to semiquantify these newly identified substances and assist with their risk assessment and environmental fate. In this study, a rigorously tested semiquantification workflow was proposed based on GC-APCI-HRMS ionization efficiency measurements of 78 emerging contaminants. The mechanism of ionization of compounds in the APCI source was discussed via a simple connectivity index and topological structure. The quantitative structure-property relationship (QSPR)-based model was also built to predict the APCI ionization efficiencies of unknowns and later use it for their quantification analyses. The proposed semiquantification method could be transferred into the household indoor dust sample matrix, and it could include the effect of recovery and matrix in the predictions of actual concentrations of analytes. A suspect compound, which falls inside the application domain of the tool, can be semiquantified by an online web application, free of access at http://trams.chem.uoa.gr/semiquantification/.

A novel workflow for semi-quantification of emerging contaminants in environmental samples analyzed by LC-HRMS.

There is an increasing need for developing a strategy to quantify the newly identified substances in environmental samples, where there are not always reference standards available. The semi-quantitative analysis can assist risk assessment of chemicals and their environmental fate. In this study, a rigorously tested and system-independent semi-quantification workflow is proposed based on ionization efficiency measurement of emerging contaminants analyzed in liquid chromatography-high-resolution mass spectrometry. The quantitative structure-property relationship (QSPR)-based model was built to predict the ionization efficiency of unknown compounds which can be later used for their semi-quantification. The proposed semi-quantification method was applied and tested in real environmental seawater samples. All semi-quantification-related calculations can be performed online and free of access at http://trams.chem.uoa.gr/semiquantification/ .

Liver metabolomics identifies bile acid profile changes at early stages of alcoholic liver disease in mice.

Alcohol consumption is a global healthcare problem with enormous social, economic, and clinical consequences. The liver sustains the earliest and the greatest degree of tissue injury due to chronic alcohol consumption and it has been estimated that alcoholic liver disease (ALD) accounts for almost 50% of all deaths from cirrhosis in the world. In this study, we used a modified Lieber-DeCarli (LD) diet to treat mice with alcohol and simulate chronic alcohol drinking. Using an untargeted metabolomics approach, our aim was to identify the various metabolites and pathways that are altered in the early stages of ALD. Histopathology showed minimal changes in the liver after 6 weeks of alcohol consumption. However, untargeted metabolomics analyses identified 304 metabolic features that were either up- or down-regulated in the livers of ethanol-consuming mice. Pathway analysis revealed significant alcohol-induced alterations, the most significant of which was in the FXR/RXR activation pathway. Targeted metabolomics focusing on bile acid biosynthesis showed elevated taurine-conjugated cholic acid compounds in ethanol-consuming mice. In summary, we showed that the changes in the liver metabolome manifest very early in the development of ALD, and when minimal changes in liver histopathology have occurred. Although alterations in biochemical pathways indicate a complex pathology in the very early stages of alcohol consumption, bile acid changes may serve as biomarkers of the early onset of ALD.

Assessment of BIV1-CovIran inactivated vaccine-elicited neutralizing antibody against the emerging SARS-CoV-2 variants of concern.

OBJECTIVES: The BIV1-CovIran vaccine is highly effective against COVID-19. The neutralizing potency of all SARS-CoV-2 vaccines seems to be decreased against variants of concern. We assessed the sensitivity of the Alpha (B.1.1.7), Beta (B.1.351), and Delta (B.1.617.2) variants to neutralizing antibodies (NAbs) present in sera from individuals who had received the BIV1-CovIran candidate vaccine compared with an original Wuhan-related strain. METHODS: The ability of vaccine serum to neutralize the variants was measured using the conventional virus neutralization test. The correlation of spike (S) protein antibody and anti-receptor binding domain with neutralizing activity was investigated. RESULTS: The current study demonstrated that 29 of 32 (90.6%; 95% CI: 75.0-98.0) of the vaccinees developed NAbs against a Wuhan-related strain. It is noteworthy that 28 (87.50%) and 24 of 32 (75%) of the recipients were able to produce NAbs against Alpha, Beta, and Delta variants, respectively. Serum virus-neutralizing titres for different SARS-CoV-2 strains were weakly correlated with anti-receptor binding domain antibodies (Spearman r = 36-42, p < 0.05), but not S-binding antibodies (p > 0.05). DISCUSSION: Although there was a reduction in neutralization titres against the Alpha, Beta, and Delta variants compared with the Wuhan strain, BIV1-CovIran still exhibited potent neutralizing activity against the SARS-CoV-2 variants of concern.

TrendProbe: Time profile analysis of emerging contaminants by LC-HRMS non-target screening and deep learning convolutional neural network.

Peak prioritization is one of the key steps in non-target screening of environmental samples to direct the identification efforts to relevant and important features. Occurrence of chemicals is sometimes a function of time and their presence in consecutive days (trend) reveals important aspects such as discharges from agricultural, industrial or domestic activities. This study presents a validated computational framework based on deep learning conventional neural network to classify trends of chemicals over 30 consecutive days of sampling in two sampling sites (upstream and downstream of a river). From trend analysis and factor analysis, the chemicals could be classified into periodic, spill, increasing, decreasing and false trend. The developed method was validated with list of 42 reference standards (target screening) and applied to samples. 25 compounds were selected by the deep learning and identified via non-target screening. Three classes of surfactants were identified for the first time in river water and two of them were never reported in the literature. Overall, 21 new homologous series of the newly identified surfactants were tentatively identified. The aquatic toxicity of the identified compounds was estimated by in silico tools and a few compounds along with their homologous series showed potential risk to aquatic environment.

Chemical characterisation of Pelargonium sidoides root based on LC-QToF-MS non-target screening strategies.

INTRODUCTION: Pelargonium sidoides is a member of the Geraniaceae family and it originates from the coastal regions of South Africa. In the last decades, Pelargonium sidoides root has been subjected to several surveys due to the assertion of its health benefits, such as the relief of symptoms of acute bronchitis, common cold and acute rhinosinusitis. Many studies have been conducted to reveal its naturally occurring bioactive chemicals, yet no wide-scope chemical characterisation strategies have been done using mass spectrometry. OBJECTIVE: This research aimed to comprehensively characterise the chemical profile of Pelargonium sidoides root via high-resolution mass spectrometry. METHODOLOGY: The Pelargonium sidoides root was extracted by a mixture of methanol: water in the proportion of 80:20. The extraction procedure included vortexing, shaking as well as the use of an ultrasound sonication bath under 40°C. After centrifugation, the supernatant was evaporated to dryness. The dry residue was reconstituted with a mixture of methanol/water (50:50, v/v), filtered and injected into an ultra-high-pressure liquid chromatography-quadruple time-of-flight mass spectrometer. RESULTS: Overall, 33 compounds were identified in the root using suspect and non-target screening. These compounds were originated from different classes of compounds such as amino acids, phenolic acids, α-hydroxy-acids, vitamins, polyphenols, flavonoids, coumarins, coumarins glucosides, coumarin sulphates and nucleotides. Quantitative results were provided for the identified compounds, where their reference standards were available. CONCLUSION: Some important compounds were elucidated, belonging to different classes of compounds such as antioxidants (coumarins and phenolic compounds), amino acids, nucleotides and vitamins revealing the importance of the bioactive content of this root.

SARS-CoV-2 wastewater surveillance data can predict hospitalizations and ICU admissions.

We measured SARS-CoV-2 RNA load in raw wastewater in Attica, Greece, by RT-qPCR for the environmental surveillance of COVID-19 for 6 months. The lag between RNA load and pandemic indicators (COVID-19 hospital and intensive care unit (ICU) admissions) was calculated using a grid search. Our results showed that RNA load in raw wastewater is a leading indicator of positive COVID-19 cases, new hospitalization and admission into ICUs by 5, 8 and 9 days, respectively. Modelling techniques based on distributed/fixed lag modelling, linear regression and artificial neural networks were utilized to build relationships between SARS-CoV-2 RNA load in wastewater and pandemic health indicators. SARS-CoV-2 mutation analysis in wastewater during the third pandemic wave revealed that the alpha-variant was dominant. Our results demonstrate that clinical and environmental surveillance data can be combined to create robust models to study the on-going COVID-19 infection dynamics and provide an early warning for increased hospital admissions.

MALDI-TOF-MS integrated workflow for food authenticity investigations: An untargeted protein-based approach for rapid detection of PDO feta cheese adulteration.

Advances in Matrix-assisted Laser Desorption/Ionization -Time-Of-Flight Mass Spectrometry (MALDI-TOF-MS) have led to its supremacy for complex assessment of food authenticity studies, like dairy products fraud, holding promise for the discovery of potential authenticity (bio)markers. In this study, an integrated untargeted protein-based workflow in combination with advanced chemometrics is presented, to address authenticity challenges in PDO feta cheese which is legally manufactured by the mixture of sheep/goat milk. Potential markers attributed to specific animal origin were found from protein profiles acquired for authentic feta and white cheeses (prepared from cow milk), belonging to 4 kDa-18.5 kDa mass area. Rapid detection of feta cheese adulteration from cow milk was also achieved down to 1% adulteration level. The discriminative models showed high predictive ability for feta cheese authenticity (Q2 = 0.920, RMSEE = 0.053) and its adulteration (Q2 = 0.835, RMSEE = 0.121), introducing a reliable approach in routine analysis. The methodology was successfully applied in detection of cow milk in sheep yoghurt.

Removal of drug losartan in environmental aquatic matrices by heat-activated persulfate: Kinetics, transformation products and synergistic effects.

In this study, the oxidative degradation of losartan (LOS), a widely administered medicine for high blood pressure by heat-activated persulfate was investigated. Increased temperature and persulfate concentration, as well as acidic conditions enhance the degradation efficiency of LOS, whose rate follows pseudo-first order kinetics. From the respective apparent rate constants in the range 40-60 °C, an apparent activation energy of 112.70 kJ/mol was computed. Radical scavenging tests demonstrated that both HO• and [Formula: see text] contribute towards LOS degradation. LOS degradation was suppressed in real water matrices including bottled water (BW) and secondary wastewater effluent (WW), while other experiments indicated that the presence of bicarbonates and humic acid negatively affected its oxidation. Instead, the addition of chloride ions at 250 mg/L resulted in a positive effect on LOS removal. The combination of heat-activated PS with low-frequency ultrasound exhibited a synergistic effect, with the ratio S being 2.29 in BW and 1.52 in WW. Five transformation products of LOS were identified through HRMS suspect and non-target screening approaches, among which two are reported for the first time. Using the in-house risk assessment program, ToxTrAMs was revealed that most of the identified TPs present higher toxicity than LOS against Daphnia magna.

Safety and potency of BIV1-CovIran inactivated vaccine candidate for SARS-CoV-2: A preclinical study.

The development of effective and safe COVID-19 vaccines is a major move forward in our global effort to control the SARS-CoV-2 pandemic. The aims of this study were (1) to develop an inactivated whole-virus SARS-CoV-2 candidate vaccine named BIV1-CovIran and (2) to determine the safety and potency of BIV1-CovIran inactivated vaccine candidate against SARS-CoV-2. Infectious virus was isolated from nasopharyngeal swab specimen and propagated in Vero cells with clear cytopathic effects in a biosafety level-3 facility using the World Health Organization's laboratory biosafety guidance related to COVID-19. After characterisation of viral seed stocks, the virus working seed was scaled-up in Vero cells. After chemical inactivation and purification, it was formulated with alum adjuvant. Finally, different animal species were used to determine the toxicity and immunogenicity of the vaccine candidate. The study showed the safety profile in studied animals including guinea pig, rabbit, mice and monkeys. Immunisation at two different doses (3 or 5 µg per dose) elicited a high level of SARS-CoV-2 specific and neutralising antibodies in mice, rabbits and nonhuman primates. Rhesus macaques were immunised with the two-dose schedule of 5 or 3 µg of the BIV1-CovIran vaccine and showed highly efficient protection against 104 TCID50 of SARS-CoV-2 intratracheal challenge compared with the control group. These results highlight the BIV1-CovIran vaccine as a potential candidate to induce a strong and potent immune response that may be a promising and feasible vaccine to protect against SARS-CoV-2 infection.

Change in the chemical content of untreated wastewater of Athens, Greece under COVID-19 pandemic.

COVID-19 pandemic spread rapidly worldwide with unanticipated effects on mental health, lifestyle, stability of economies and societies. Although many research groups have already reported SARS-CoV-2 surveillance in untreated wastewater, only few studies evaluated the implications of the pandemic on the use of chemicals by influent wastewater analysis. Wide-scope target and suspect screening were used to monitor the effects of the pandemic on the Greek population through wastewater-based epidemiology. Composite 24 h influent wastewater samples were collected from the wastewater treatment plant of Athens during the first lockdown and analyzed by liquid chromatography mass spectrometry. A wide range of compounds was investigated (11,286), including antipsychotic drugs, illicit drugs, tobacco compounds, food additives, pesticides, biocides, surfactants and industrial chemicals. Mass loads of chemical markers were estimated and compared with the data obtained under non-COVID-19 conditions (campaign 2019). The findings revealed increases in surfactants (+196%), biocides (+152%), cationic quaternary ammonium surfactants (used as surfactants and biocides) (+331%), whereas the most important decreases were estimated for tobacco (-33%) and industrial chemicals (-52%). The introduction of social-restriction measures by the government affected all aspects of life.