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Contamination of black pepper (Piper nigrum) with Salmonella is a frequent problem in retail and imported shipments. However, there is scarce information about the prevalence of the pathogen in the initial stages of black pepper production chain. This study sought to bridge this gap in research by determining the prevalence, as well as quantifying, and identifying the main Salmonella serovars present during black pepper primary production and processing. Black pepper (233) and environmental (175) samples were collected from farms (354) and processing plants (54) in Espirito Santo, Brazil. The pathogen was detected in soil (16.7 %), drying waste (20.4 %), fallen berries (3.7 %), threshed berries (14.3 %), and dried peppercorns (22.2 %) collected from farms. Salmonella was also detected in samples of raw material (11.1 %), export products (16.7 %), and processing waste (16.7 %) collected from processing plants. A total of 12 serotypes were identified, and Salmonella Javiana showed the highest prevalence (38.8 %). According to the results, contamination occurring in the post-harvest phase is not eliminated or reduced during processing. Therefore, the adoption of good agricultural and manufacturing practices, supported by hazard analysis and critical control points (HACCP), is crucial to mitigate this kind of contamination. These practices should be combined with decontamination treatments to ensure the safety of the final product., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples to identify anti-SARS-CoV-2 antibodies. The comprehensive database utilized Raman spectra from all 594 blood serum samples. Machine learning investigations were carried out using the Scikit-Learn library and were implemented in Python, and the characteristics of Raman spectra of positive and negative SARS-CoV-2 samples were extracted using the Uniform Manifold Approximation and Projection (UMAP) technique. The machine learning models used were k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Trees (DTs), logistic regression (LR), and Light Gradient Boosting Machine (LightGBM). The kNN model led to a sensitivity of 0.943, specificity of 0.9275, and accuracy of 0.9377. This study showed that combining Raman spectroscopy and a machine algorithm can be an effective diagnostic method. Furthermore, we highlighted the advantages and disadvantages of each algorithm, providing valuable information for future research.

Textile wastewater is commonly released into water bodies without appropriated treatment, resulting in environmental damages. Processes involving separation and adsorption using nanomagnetic supports have been considered a promising alternative to address this concern. However, challenges concerning the low stability of nanoparticles and the reproducibility of experiments make their large-scale application difficult. In this study, we evaluated the efficiency of methylene blue (MB) removal by Fe 3 O 4 nanoparticles coated with sodium dodecyl sulfate (MNP-SDS). The nanomaterial was characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman spectroscopy, and Fourier-transform infrared spectroscopy (FTIR). The adsorption process was optimized in two stages using factorial design. In the first stage the most influential variables (reaction time, temperature, agitation, pH, and dye concentration) were selected based on the existing literature and applied in a fractional factorial (2 (5-1) ). In the second stage, the main variables identified were used in a complete factorial (3 2 ). The highest removal percentage was obtained using 15 g L -1 of MNP-SDS, which led to a q exp of 1.5 mg g -1 . Isothermal analysis parameters and a negative Gibbs free energy indicate that the process was spontaneous, favorable, and that the data were best fitted to the Langmuir model., Competing Interests: The authors declare that they have no competing interests., (This journal is © The Royal Society of Chemistry.)

Aims: This paper aims to review the main sludge concentration methods used for SARS-CoV-2 detection in sewage sludge samples, discussing the main methods and sample volume related to increased viral load. In addition, we aim to evaluate the countries associated with increased positivity rates for SARS-CoV-2 in sludge samples., Methods: This systematic methodology was registered in PROSPERO and followed the PRISMA guidelines. The search was carried out in the SciELO, PubMed/MEDLINE, Lilacs, and Google Scholar databases in January-March 2022. Quantitative studies with conclusive results were included in this review. Concentration methods (polyethylene glycol (PEG), PEG + NaCl, gravity thickening, skimmed milk flocculation, ultrafiltration, filtration using charged filters, primary sedimentation, and anaerobic digestion), as well as detection methods (RTqPCR and reverse transcription droplet digital PCR assay) were evaluated in this review. The SPSS v23 software program was used for statistical analysis., Results: PEG (with or without NaCl addition) and gravity thickening were the most used sludge concentration methods to detect SARS-CoV-2. The main method associated with increased viral load (>2,02 × 10^4 copies/mL) was PEG + NaCl (p < 0.05, Mann-Whitney test). The average positivity rate for SARS-CoV-2 in sludge samples was 61 %, and a correlation was found between the sludge volume and the viral load (ro 0.559, p = 0.03, Spearman correlation)., Conclusion: The sludge volume may influence the SARS-CoV-2 load since the virus can adhere to solid particles in these samples. Other factors may be associated with SARS-CoV-2 load, including the methods used; especially PEG + NaCl may result in a high viral load detected in sludge, and may provide a suitable pH for SARS-CoV-2 recovery., Competing Interests: Declaration of competing interest The authors declare no conflict of interest, financial or otherwise., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The use of enzymes immobilized on nanomagnetic supports has produced surprising results in catalysis, mainly due to the increase in surface area and the potential for recovery and reuse. However, the meticulous control of the process and difficulties in reproducibility have made industrial-scale applications unfeasible. Furthermore, the role of conjugation strategies in the catalytic activity and recycling of catalysts is unclear. Therefore, the objective of this study was to compare the conjugation of enzymes on nanomagnetic supports through physical adsorption (naked) or covalent bonding with mercaptopropyltrimethoxysilane (MPTS) and aminopropyltriethoxysilane (APTS) ligands. The free lipase obtained from Rhizomucor miehei was used as a model enzyme. Total protein and enzyme activity were determined using spectrophotometry (UV-Vis) and the p-nitrophenyl palmitate (p-NPP) hydrolysis method. The results indicated that a more significant enzyme surface loading does not always mean better immobilization success. The physical adsorption binding strategy had higher surface loading and low catalytic activity. On the other hand, covalent coupling with free NH2 had an excellent catalytic activity with very low surface loading. Finally, we show that recyclability can be improved with conjugation mediated by disulfide bonds. The findings presented here are essential for developing nanoconjugates with high enzymatic activity, which can guarantee the success of several industrial applications., (© 2022. The Author(s).)

Lipases are triacylglycerol hydrolases that catalyze hydrolysis, esterification, interesterification, and transesterification reactions. These enzymes are targets of several industrial and biotech applications, such as catalysts, detergent production, food, biofuels, wastewater treatment, and others. Microbial enzymes are preferable for large scale production due to ease of production and extraction. Several studies have reported that lipases from filamentous fungi are predominantly extracellular and highly active. However, there are many factors that interfere with enzyme production (pH, temperature, medium composition, agitation, aeration, inducer type, and concentration, etc.), making control difficult and burdening the process. This work aimed to optimize the lipase production of four fungal isolates from oily residues (Penicillium sp., Aspergillus niger, Aspergillus sp., and Aspergillus sp.). The lipase-producing fungi isolates were morphologically characterized by optical and scanning electron microscopy. The optimal lipase production time curve was previously determined, and the response variable used was the amount of total protein in the medium after cultivation by submerged fermentation. A complete factorial design 3 2 was performed, evaluating the temperatures (28 °C, 32 °C, and 36 °C) and soybean oil inducer concentration (2%, 6%, and 10%). Each lipase-producing isolate reacted differently to the conditions tested, the Aspergillus sp. F18 reached maximum lipase production, compared to others, under conditions of 32 °C and 2% of oil with a yield of 11,007 (µg mL -1 ). Penicillium sp. F04 achieved better results at 36 °C and 6% oil, although for Aspergillus niger F16 was at 36 °C and 10% oil and Aspergillus sp. F21 at 32 °C and 2% oil. These results show that microorganisms isolated from oily residues derived from environmental sanitation can be a promising alternative for the large-scale production of lipases., (© 2021. The Author(s).)