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This study introduces an innovative approach for quantifying isomeric pollutants utilizing an amperometric sensor. The determination of the isomers hydroquinone and catechol is based on the use of a glassy carbon electrode modified with Cu@PtPd/C nanoparticles (Cu@PtPd/C/GCE) in core-shell form, showing significant electrocatalytic activity in the oxidation of the later compounds. The determination was carried out at two different potentials: one at which where only hydroquinone is oxidized, and another in which where both hydroquinone and catechol are oxidized. Using these potentials, two calibration curves were built, one for the quantification of hydroquinone and the other for both isomers. Subsequently, the quantification of catechol was performed using a strategy based on the calculation of a difference using the information collected in the first step. The experiments using hydrogen peroxide as a redox probe demonstrate a clear synergistic effect in the catalytic reduction of hydrogen peroxide at -0.100 V, when Pt, Pd and Cu are incorporated into the core-shell nanostructure. The best performance was achieved with Cu@PtPd/C/GCE 1.00 mg mL-1. For the selected sensor, the analytical parameters are very competitive compared to similar devices reported in recent years for hydroquinone and catechol, with comparable linearity ranges of 0.010-0.200 mmol L-1 (hydroquinone) and 0.005-0.500 mmol L-1 (catechol), low limits of detection (LODs) of 14.0 nmol L-1 (S/N = 3.3) and 1.75 nmol L-1 (S/N = 3.3) for hydroquinone and catechol. The resulting sensor platform has been successfully applied for the quantification of hydroquinone and catechol in river and tap water and could be a promising candidate for environmental monitoring and drinking water safety.

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We searched for an extraction method that would allow a precise quantification of metal(loid)s in milligram-size samples using high-resolution graphite furnace atomic absorption spectrometry (HR-GFAAS). We digested biological (DORM-4, DOLT-5 and TORT-3) and sediment (MESS-4) certified reference materials (CRMs) using nitric acid in a drying oven, aqua regia in a drying oven, or nitric acid in a microwave. In addition, we digested MESS-4 using a mixture of nitric and hydrofluoric acids in a drying oven. We also evaluated the effect of sample size (100 and 200 mg) on the extraction efficiency. Nitric acid extraction in a drying oven yielded the greatest recovery rates for all metal(loid)s in all tested CRMs (80.0 %-100.0 %) compared with the other extraction methods tested (67.3 %-99.2 %). In most cases, the sample size did not have a significant effect on the extraction efficiency. Therefore, we conclude that nitric acid digestion in a drying oven is a reliable extraction method for milligram-size samples to quantify metal(loid)s with HR-GFAAS. This validated method could provide substantial benefits to environmental quality monitoring programs by significantly reducing the time and costs required for sample collection, storage, transport and preparation, as well as the amount of hazardous chemicals used during sample extraction and analysis. •Sample digestion with nitric acid in a drying oven yielded the greatest recovery rates of metal(loid)s from biological and sediment certified reference materials.•The recovery rates of metal(loid)s from biological and sediment certified reference materials using nitric acid digestion in a drying oven ranged from 73 % to 100 %.•Digestion with nitric acid in a drying oven is a simple and reliable method to extract small size environmental samples for metal(loid)s quantification by high-resolution graphite furnace atomic absorption spectrometry.

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Liquid chromatography-mass spectrometry (LC-MS) has emerged as a powerful analytical technique for analyzing complex biological samples. Among various chromatographic stationary phases, porous graphitic carbon (PGC) columns have attracted significant attention due to their unique properties-such as the ability to separate both polar and non-polar compounds and their stability through all pH ranges and to high temperatures-besides the compatibility with LC-MS. This review discusses the applicability of PGC for SPE and separation in LC-MS-based analyses of human biological samples, highlighting the diverse applications of PGC-LC-MS in analyzing endogenous metabolites, pharmaceuticals, and biomarkers, such as glycans, proteins, oligosaccharides, sugar phosphates, and nucleotides. Additionally, the fundamental principles underlying PGC column chemistry and its advantages, challenges, and advances in method development are explored. This comprehensive review aims to provide researchers and practitioners with a valuable resource for understanding the capabilities and limitations of PGC columns in LC-MS-based analysis of human biological samples, thereby facilitating advancements in analytical methodologies and biomedical research.

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Sample preparation in an analytical sequence increases the number of errors, is highly time-consuming, and involves the manipulation of hazardous reagents. Therefore, when an improvement in an analytical method is required, the sample preparation step needs to be optimised or redesigned. Moreover, this step can involve significant toxic reagents and a high volume of waste. In that regard, this study proposes a new procedure based on microwave-assisted wet digestion combining two green strategies: a miniaturised system (with a few microlitres of volume) and the only use of hydrogen peroxide. Three biological samples (human serum, urine, and plant in vitro material) were chosen due to their high potential for disease monitoring, toxicological studies, and biotechnology applications. Several trace elements (Ca, Cd, Co, Cu, Fe, Mg, Mn, Mo, Ni, Se, and Zn) were determined by inductively coupled plasma optical emission spectroscopy and inductively coupled plasma mass spectrometry. For human serum and urine, a certified reference material was used to check for accuracy; the recovery ranged from 72% (Cd, ICP-MS) to 105% (Mg, ICP OES) for serum, while for urine, they varied from 82% (Ni, ICP-MS) to 122% (Zn, ICP-MS). For the soybean callus sample (in vitro plant material), a comparison between the proposed method and the acid digestion method was conducted to evaluate the accuracy, and the results agreed. The detection limits were 0.001-60 µg L-1 (lowest for Cd), thus demonstrating a suitable sensitivity. Moreover, the decomposition efficiency was demonstrated by determining the residual carbon, and a low amount was found in the final product digested (below 0.8% w v-1). A green metric approach was calculated for the proposed method, and according to AGREEprep software, it was found to be around 0.4. Finally, the method was applied to urine samples collected in patients with COVID-19 and soybean callus cultivated with silver nanoparticles. This sample preparation method is a new acidless and miniaturised alternative for elemental analysis involving biological samples.

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BACKGROUND: Neonatal health assessment is crucial for detecting and intervening in various disorders. Traditional gene expression analysis methods often require invasive procedures during sample collection, which may not be feasible or ideal for preterm infants. In recent years, saliva has emerged as a promising noninvasive biofluid for assessing gene expression. Another trend that has been growing is the use of "omics" technologies such as transcriptomics in the analysis of gene expression. The costs for carrying out these analyses and the difficulty of analysis make the detection of candidate genes necessary. These genes act as biomarkers for the maturation stages of the oral feeding issue. METHODOLOGY: Salivary samples (n = 225) were prospectively collected from 45 preterm (<34 gestational age) infants from five predefined feeding stages and submitted to RT-qPCR. A better description of the targeted genes and results from RT-qPCR analyses were included. The six genes previously identified as predictive of feeding success were tested. The genes are AMPK, FOXP2, WNT3, NPHP4, NPY2R, and PLXNA1, along with two reference genes: GAPDH and 18S. RT-qPCR amplification enabled the analysis of the gene expression of AMPK, FOXP2, WNT3, NPHP4, NPY2R, and PLXNA1 in neonatal saliva. Expression results were correlated with the feeding status during sample collection. CONCLUSIONS: In summary, the genes AMPK, FOXP2, WNT3, NPHP4, NPY2R, and PLXNA1 play critical roles in regulating oral feeding and the development of premature infants. Understanding the influence of these genes can provide valuable insights for improving nutritional care and support the development of these vulnerable babies. Evidence suggests that saliva-based gene expression analysis in newborns holds great promise for early detection and monitoring of disease and understanding developmental processes. More research and standardization of protocols are needed to fully explore the potential of saliva as a noninvasive biomarker in neonatal care.

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AIMS: This study aimed to assess the use of cross-assembled phage (crAssphage) as an endogenous control employing a multivariate normalization analysis and its application as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data normalizer. METHODS AND RESULTS: A total of 188 twelve-hour composite raw sewage samples were obtained from eight wastewater treatment plants (WWTP) during a 1-year monitoring period. Employing the N1 and N2 target regions, SARS-CoV-2 RNA was detected in 94% (177) and 90% (170) of the samples, respectively, with a global median of 5 log10 genomic copies per liter (GC l-1). CrAssphage was detected in 100% of the samples, ranging from 8.29 to 10.43 log10 GC l-1, with a median of 9.46 ± 0.40 log10 GC l-1, presenting both spatial and temporal variabilities. CONCLUSIONS: Although SARS-CoV-2 data normalization employing crAssphage revealed a correlation with clinical cases occurring during the study period, crAssphage normalization by the flow per capita per day of each WWTP increased this correlation, corroborating the importance of normalizing wastewater surveillance data in disease trend monitoring.

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The dietary ecology of a species can provide information on habitat requirements, food resources, and trophic interactions, important to guide conservation efforts of wildlife populations in endangered habitats. In this study, we investigated the dietary ecology of bearded capuchin monkeys (Sapajus libidinosus) in Brasilia National Park, in the endangered Cerrado biome of central Brazil. To obtain diet composition and evaluate the role of these primates as seed dispersers of local tree species, fecal sample collections and feeding observations were performed for a 7-month period. To determine whether seeds germinated better after passing through a primate gut, we conducted germination trials with (i) pulped seeds from trees, (ii) depulped seeds from trees, (iii) seeds from feces planted with feces, and (iv) seeds from feces planted without feces. During experimental procedures, 7308 seeds from 8 families and 10 species were planted. We found that S. libidinosus spent more time feeding on fruits than on any other food item and the diet consisted of 33 plant species from 21 families. However, 20% of their diet consisted of anthropic food. Most seeds planted with feces germinated faster compared to seeds in other experimental treatments, suggesting that passing through the gut and being deposited with fecal material is advantageous. The bearded capuchins also defecated many medium- (5 species) and large-sized (2 species) seeds that may be inaccessible to smaller arboreal frugivores. The results obtained emphasize the important role of bearded capuchins as seed dispersers for the maintenance and conservation of the endangered Cerrado biome.

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Gestational diabetes mellitus (GDM) is a hyperglycemic state that is typically diagnosed by an oral glucose tolerance test (OGTT), which is unpleasant, time-consuming, has low reproducibility, and results are tardy. The machine learning (ML) predictive models that have been proposed to improve GDM diagnosis are usually based on instrumental methods that take hours to produce a result. Near-infrared (NIR) spectroscopy is a simple, fast, and low-cost analytical technique that has never been assessed for the prediction of GDM. This study aims to develop ML predictive models for GDM based on NIR spectroscopy, and to evaluate their potential as early detection or alternative screening tools according to their predictive power and duration of analysis. Serum samples from the first trimester (before GDM diagnosis) and the second trimester (at the time of GDM diagnosis) of pregnancy were analyzed by NIR spectroscopy. Four spectral ranges were considered, and 80 mathematical pretreatments were tested for each. NIR data-based models were built with single- and multi-block ML techniques. Every model was subjected to double cross-validation. The best models for first and second trimester achieved areas under the receiver operating characteristic curve of 0.5768 ± 0.0635 and 0.8836 ± 0.0259, respectively. This is the first study reporting NIR-spectroscopy-based methods for the prediction of GDM. The developed methods allow for prediction of GDM from 10 µL of serum in only 32 min. They are simple, fast, and have a great potential for application in clinical practice, especially as alternative screening tools to the OGTT for GDM diagnosis.

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Lactic Acid Bacteria (LAB) are predominantly probiotic microorganisms and the most are Generally Recognized As Safe (GRAS). LAB inhabit in the human gut ecosystem and are largely found in fermented foods and silage. In the last decades, LAB have also has been found in plant microbiota as a new class of microbes with probiotic activity to plants. For this reason, today the scientific interest in the study and isolation of LAB for agronomic application has increased. However, isolation protocols from complex samples such as plant tissues are scarce and inefficient. In this study, we developed a new protocol (CLI, Complex samples LAB Isolation) which yields purified LAB from plants. The sensitivity of CLI protocol was sufficient to isolate representative microorganisms of LAB genera (i.e. Leuconostoc, Lactococcus and Enterococcus). CLI protocol consists on five steps: i) sample preparation and pre-incubation in 1% sterile peptone at 30 °C for 24-48 h; ii) Sample homogenization in vortex by 10 min; iii) sample serial dilution in quarter-strength Ringer solution, iv) incubation in MRS agar plates with 0.2% of sorbic acid, with 1% of CaCO3, O2 < 15%, at pH 5.8 and 37 °C for 48 h.; v) Selection of single colonies with LAB morphology and CaCO3-solubilization halo. Our scientific contribution is that CLI protocol could be used for several complex samples and represents a useful method for further studies involving native LAB.

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Livestock predation induces global human-wildlife conflict, triggering the retaliatory killing of large carnivores. Although domestic dogs (Canis familiaris) contribute to livestock depredation, blame primarily falls on wild predators. Dogs can also transmit pathogens between wildlife, domestic animals, and humans. Therefore, the presence of free-ranging dogs can have negative consequences for biodiversity conservation, smallholder economy, food supply, and public health, four of the United Nations' Sustainable Developed Goals (SDGs) for 2030. In Ecuador, where livestock sustains rural households, retaliatory poaching threatens Andean bear (Tremarctos ornatus), jaguar (Panthera onca), and puma (Puma concolor) populations. However, the role of dogs in these incidents remains underexplored. The present study evaluates the possibility of reliable molecular identification of predatory species from DNA traces in bite wounds. Our results revealed the presence of dog saliva on four out of six livestock carcasses presumably attacked by wild predators. These findings highlight the importance of rectifying misinformation about large carnivores in Ecuador and the need to control dog populations. We recommend that local administrations incorporate DNA analysis into livestock predation events to examine how common the problem is, and to use the analysis to develop conflict mitigation strategies which are essential for the conservation of large carnivores.

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Yersinia ruckeri is the cause of hemorrhagic septicemia, known as enteric redmouth disease, in salmonid fish species. This bacterial pathogen can form biofilms on abiotic surfaces of aquaculture settings or even on the surfaces of the fish themselves, contributing to their persistence in the aquatic environment. Detection methods for this and other fish pathogens can be time-consuming and lack specificity and sensitivity, limiting timely monitoring, the treatment of microbial infections, and effective control of their transmission in aquaculture settings. Rapid and sensitive detection methods for nucleic acids can be crucial for an appropriate surveillance of bacterial pathogens, and the CRISPR/Cas-based assays have emerged as a good alternative since it has been proven to be a useful tool for the rapid, specific, and sensitive detection of viruses and some bacteria. In this study, we explored the capability of the CRISPR/Cas13a system (SHERLOCK) to specifically detect both DNA and RNA (gene transcripts) from planktonic and biofilm samples of the bacterial fish pathogen Y. ruckeri. The assay was designed to detect the gyrA gene and the small noncoding RNAs (sRNAs) MicA and RprA from planktonic cultures and biofilm samples prepared in marine broth. The specific crRNA designed for these gene targets included a 28 nt specific gene sequence, and a scaffold sequence necessary for Cas13-binding. For all the assays, the nucleic acids obtained from samples were previously subjected to isothermal amplification with the recombinase polymerase amplification (RPA) method and the subsequent T7 transcription of the RPA amplicons. Finally, the detection of nucleic acids of Y. ruckeri was by means of a reporter signal released by the Cas13a collateral RNA cleavage triggered upon target recognition, measured by fluorescence- or lateral-flow-based readouts. This CRISPR/Cas13a-based assay was able to specifically detect both DNA and sRNAs from the Y. ruckeri samples, and the sensitivity was comparable to that obtained with qPCR analysis, highlighting the potential applicability of this CRISPR/Cas13a-based assay for fish pathogen surveillance.

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This study was dedicated to developing analytical methods for determining macronutrients (Ca, K, and Mg) in soy leaf samples with and without petioles. The study's primary purpose was to present Laser-induced breakdown spectroscopy (LIBS) as a viable alternative for directly analyzing leaf samples using chemometric tools to interpret the data obtained. The instrumental condition chosen for LIBS was 70 mJ of laser pulse energy, 1.0 µs of delay time, and 100 µm of spot size, which was applied to 896 samples: 305 of soy without petioles and 591 of soy with petioles. The reference values of the analytes for the proposition of calibration models were obtained using inductively coupled plasma optical emission spectroscopy (ICP-OES) technique. Twelve normalization modes and two calibration strategies were tested to minimize signal variations and sample matrix microheterogeneity. The following were studied: multivariate calibration using partial least squares and univariate calibration using the area and height of several selected emission lines. The notable normalization mode for most models was the Euclidean norm. No analyte showed promising results for univariate calibrations. Micronutrients, P and S, were also tested, and no multivariate models presented satisfactory results. The models obtained for Ca, K, and Mg showed good results. The standard error of calibration ranged from 2.3 g/kg for Ca in soy leaves without petioles with two latent variables to 5.0 g/kg for K in soy leaves with petioles with two latent variables.

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Livestock plays a crucial role in ensuring food security and driving the global economy. However, viral infections can have far-reaching consequences beyond economic productivity, affecting the health of cattle, as well as posing risks to human health and other animals. Identifying viruses present in fecal samples, a primary route of pathogen transmission, is essential for developing effective prevention, control, and surveillance strategies. Viral metagenomic approaches offer a broader perspective and hold great potential for detecting previously unknown viruses or uncovering previously undescribed agents. Ubaté Province is Colombia's dairy capital and a key center for livestock production in the country. Therefore, the purpose of this study was to characterize viral communities in fecal samples from cattle in this region. A total of 42 samples were collected from three municipalities in Ubaté Province, located in central Colombia, using a convenient non-probabilistic sampling method. We utilized metagenomic sequencing with Oxford Nanopore Technologies (ONT), combined with diversity and phylogenetic analysis. The findings revealed a consistent and stable viral composition across the municipalities, primarily comprising members of the Picornaviridae family. At the species level, the most frequent viruses were Enterovirus E (EVE) and Bovine Astrovirus (BoAstV). Significantly, this study reported, for the first time in Colombia, the presence of viruses with veterinary importance occurring at notable frequencies: EVE (59%), Bovine Kobuvirus (BKV) (52%), and BoAstV (19%). Additionally, the study confirmed the existence of Circular replicase-encoding single-stranded (CRESS) Virus in animal feces. These sequences were phylogenetically grouped with samples obtained from Asia and Latin America, underscoring the importance of having adequate representation across the continent. The virome of bovine feces in Ubaté Province is characterized by the predominance of potentially pathogenic viruses such as BoAstV and EVE that have been reported with substantial frequency and quantities. Several of these viruses were identified in Colombia for the first time. This study showcases the utility of using metagenomic sequencing techniques in epidemiological surveillance. It also paves the way for further research on the influence of these agents on bovine health and their frecuency across the country.

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Lipid peroxidation occurs when substances, such as reactive oxygen species, attack lipids. Polyunsaturated fatty acids are the main targets. Several products are formed, including primary products such as lipid hydroperoxides and secondary products such as malondialdehyde (MDA), the most used lipid peroxidation biomarker. As MDA levels are elevated in several diseases, MDA is an essential indicator for assessing pathological states. The thiobarbituric acid reactive substances assay is the most widely used method for MDA determination. However, it lacks specificity. Capillary Electrophoresis (CE) is a separation technique that has been used to quantify MDA in biological samples. This technique has advantages such as the low amount of biological sample required, absence or low volume of organic solvent, short analysis time, separation of interferents, sample preparation step with only protein precipitation, and the possibility of direct detection of the MDA, without derivatization. To our knowledge, this review article is the first to show the CE background for analyzing MDA in biological samples. Therefore, given the potential of MDA in evaluating pathological states, this article demonstrates the potential of CE to become a reference method for MDA determination in clinical analysis laboratories, which will play a significant role in diagnosing and monitoring diseases.

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Escherichia coli is a key indicator of food hygiene, and its monitoring in meat samples points to the potential presence of antimicrobial-resistant strains capable of causing infections in humans, encompassing resistance profiles categorized as serious threats by the Centers for Disease Control and Prevention (CDC), such as Extended-Spectrum Beta-Lactamase (ESBL)-a problem with consequences for animal, human, and environmental health. The objective of the present work was to isolate and characterize ESBL-producing E. coli strains from poultry, pork, and beef meat samples, with a characterization of their virulence and antimicrobial resistance profiles. A total of 450 meat samples (150 chicken, 150 beef, and 150 pork) were obtained from supermarkets and subsequently cultured in medium supplemented with cefotaxime. The isolated colonies were characterized biochemically, followed by antibiogram testing using the disk diffusion technique. Further classification involved biofilm formation and the presence of antimicrobial resistance genes (blaCTX-M, AmpC-type, mcr-1, and fosA3), and virulence genes (eaeA, st, bfpA, lt, stx1, stx2, aggR, iss, ompT, hlyF, iutA, iroN, fyuA, cvaC, and hylA). Statistical analysis was performed via the likelihood-ratio test. In total, 168 strains were obtained, with 73% originating from chicken, 22% from pork, and 17% from beef samples. Notably, strains exhibited greater resistance to tetracycline (51%), ciprofloxacin (46%), and fosfomycin (38%), apart from ß-lactams. The detection of antimicrobial resistance in food-isolated strains is noteworthy, underscoring the significance of antimicrobial resistance as a global concern. More than 90% of the strains were biofilm producers, and strains carrying many ExPEC genes were more likely to be biofilm formers (OR 2.42), which increases the problem since the microorganisms have a greater chance of environment persistence and genetic exchange. Regarding molecular characterization, bovine samples showed a higher prevalence of blaCTX-M-1 (OR 6.52), while chicken strains were more likely to carry the fosA3 gene (OR 2.43, CI 1.17-5.05) and presented between 6 to 8 ExPEC genes (OR 2.5, CI 1.33-5.01) compared to other meat samples. Concerning diarrheagenic E. coli genes, two strains harbored eae. It is important to highlight these strains, as they exhibited both biofilm-forming capacities and multidrug resistance (MDR), potentially enabling colonization in diverse environments and causing infections. In conclusion, this study underscores the presence of ß-lactamase-producing E. coli strains, mainly in poultry samples, compared to beef and pork samples. Furthermore, all meat sample strains exhibited many virulence-associated extraintestinal genes, with some strains harboring diarrheagenic E. coli (DEC) genes.

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Bio-SELEX is a revolutionary method for the discovery of novel biomarkers within biological samples, offering profound insights into diagnosing both infectious and non-infectious diseases. This innovative strategy involves three crucial steps: Traditional SELEX, Pull Down, and mass spectrometry. Firstly, Traditional SELEX involves the systematic selection of specific nucleic acid sequences (aptamers) that bind to the target molecules of interest. These aptamers are generated through iterative rounds of selection, amplification, and enrichment, ultimately yielding highly selective ligands. Secondly, the Pull-Down phase employs these aptamers to capture and isolate the target biomarkers from complex biological samples. This step ensures the specificity of the selected aptamers in binding to their intended targets. Lastly, mass spectrometry is utilized to identify and quantify the captured biomarkers, providing precise information about their presence and concentration in the sample. These quantitative data are invaluable in disease diagnosis and monitoring. Bio-SELEX's significance lies in its ability to discover biomarkers for a wide range of diseases, spanning infectious and non-infectious conditions. This approach holds great promise for early disease detection, personalized medicine, and the development of targeted therapies. By harnessing the power of aptamers and mass spectrometry, Bio-SELEX advances our understanding of disease biology and opens new avenues for improved healthcare.

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Sewage surveillance provides useful epidemiologic and public health information on viral infections at the population level. We detected monkeypox virus DNA from sewage samples covering 85% of the population in Santiago Metropolitan Region Chile. We also isolated infective viruses from those samples. Wastewater surveillance could complement clinical surveillance for monkeypox virus.

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Obtaining sufficient and high-quality genomic DNA from sludge samples is a fundamental issue of feasibility and comparability in genomic studies of microbial diversity. Commercial kits for soil are often used for the extraction of gDNA from sludge samples due to the lack of specific kits. However, the evaluation of the performance of commercial kits for sludge DNA extraction is scarce and optimization of these methods to obtain a high quantity and quality of DNA is necessary, especially for downstream genomic sequencing. Sequential batch reactors (SBRs) loaded with lignocellulosic biomass are used for the synthesis of renewable resources such as levulinic acid (LA), adipic acid (AA), and polyhydroxyalkanoates (PHAs), and the biochemical synthesis of these compounds is conducted through the inoculation of microbes present in the residual activated sludge (AS) obtained from a municipal wastewater treatment plant. To characterize these microbes, the extraction of DNA from residual sewage sludge was conducted with three different commercial kits: Nucleospin® Soil from Macherey-Nagel, DNEasy® PowerSoil® from Qiagen, and E.Z.N.A.® Plant DNA Kit from Omega BIO-TEK. Nevertheless, to obtain the highest load and quality of DNA for next-generation sequencing (NGS) analysis, different pretreatments and different combinations of these pretreatments were used. The pretreatments considered were an ultrasonic bath and a temperature of 80 °C, together and separately with different incubation time periods of 30, 60, and 90 min. The results obtained suggest a significant improvement in the efficiency and quality of DNA extraction with the three commercial extraction kits when used together with the ultrasonic bath and 80 °C for 60 min. Here, we were able to prove that physical pretreatments are a viable alternative to chemical lysis for DNA extraction from complex samples such as sludge.

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The conversion of native forest into agricultural land, which is common in many parts of the world, poses important questions regarding soil degradation, demanding further efforts to better understand the effect of land use change on soil functions. With the advent of 3D computed tomography techniques and computing power, new methods are becoming available to address this question. In this direction, in the current work we implement a modification of the Fisher-Shannon method, borrowed from information theory, to quantify the complexity of twelve 3D CT soil samples from a sugarcane plantation and twelve samples from a nearby native Atlantic forest in northeastern Brazil. The distinction found between the samples from the sugar plantation and the Atlantic forest site is quite pronounced. The results at the level of 91.7% accuracy were obtained considering the complexity in the Fisher-Shannon plane. Atlantic forest samples are found to be generally more complex than those from the sugar plantation.

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Mycotoxins are a major source of contamination in cereals, posing risks to human health and causing significant economic losses to the industry. A comprehensive strategy for the analysis of 21 mycotoxins in Italian cereal grain samples (n = 200) was developed using a simple and quick sample preparation method combined with ultra-high-performance liquid chromatography coupled with quadrupole Orbitrap high-resolution mass spectrometry (UHPLC Q-Orbitrap HRMS). The proposed method showed some advantages, such as multi-mycotoxin analyses with simple sample preparation, fast determination, and high sensitivity. The analysis of the sample revealed the presence of 11 mycotoxins, with α-zearalenol being the most frequently detected, while deoxynivalenol exhibited the highest contamination level. Furthermore, co-occurrence was identified in 15.5% of the samples under analysis. Among these, 13% of the samples reported the simultaneous presence of two mycotoxins, while 2.5% showed the co-occurrence of three mycotoxins. Currently, there has been a renewed interest in guaranteeing the quality and safety of products intended for human consumption. This study holds significant value due to its ability to simultaneously detect multiple mycotoxins within a complex matrix. Furthermore, it provides findings regarding the occurrence and co-occurrence of emerging mycotoxins that currently lack regulation under the existing European Commission Regulation.

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