Application of the partial least square regression method in determining the natural background of soil heavy metals: A case study in the Songhua River basin, China.

The "background" is an essential index for identifying anthropogenic inputs and potential ecological risks of soil heavy metals. However, the lithology of bedrock can cause significant spatial variation in the natural background of soil elements, posing considerable difficulties in estimating background values. In this study, an attempt was made to calculate the natural background through regression analysis of soil chemical composition, and reasonably evaluate the impact of lithology. A total of 1771 surface soil samples were collected from the Songhua River Basin, China, for chemical composition analysis, and the partial least square regression (PLSR) method was employed to establish the relationship between heavy metals (As, Hg, Cr, Cd, Pb, Cu, Zn, and Ni) and soil chemical composition/environmental parameters (SiO2, Al2O3, TFe2O3, MgO, CaO, K2O, Na2O, La, Y, Zr, V, Sc, Sr, Li and pH). The result shows that As, Cr, Pb, Cu, Zn, and Ni have significant linear relationships with soil chemical composition. Each of these six heavy metals obtained 1771 regression background values; some were higher than the uniform background value obtained from the boxplot, while others were lower. The regression background values recognized not only subtle anthropogenic inputs and potential ecological risks in low-background regions but also spurious contamination in high-background areas. All these indicate that the PLSR method can effectively improve the determination accuracy of the natural background of soil heavy metals. More attention should be paid to the serious anthropogenic inputs appearing in some places of the study area.

Role of the EM clustering method in determining the geochemical background of As and Cr in soils: a case study in the north of Changchun, China.

Determining the geochemical background for heavy metals is vital in soil management activities. Although many statistical methods for geochemical background determination have been proposed, the multi-population problem of geochemical data, primarily regional ones, derived mainly from mixing multiple populations belonging to various geological sources or processes, needs to be better addressed. In this study, the Expectation-Maximization (EM) algorithm was employed to separate multiple populations in a 1:250,000 scale regional geochemical data set of soils in a lithologically complex region in the north of Changchun, China. The data set included 3746 surface soil samples analyzed for SiO2, K2O, Al2O3, CaO, La, Rb, Y, Ti, Ce, V, Cr, and As. The potential high-risk areas of As and Cr were determined before and after the separation of multiple populations. The comparison results show that the EM clustering method can efficiently separate multiple populations and determine soil geochemical background more reasonably, thus eliminating false contamination that is easily misidentified and better revealing concealed contamination that is challenging to detect.

A Colorimetric Immunosensor Based on Hemin@MI Nanozyme Composites, with Peroxidase-like Activity for Point-of-care Testing of Pathogenic E. coli O157:H7.

Recently, nanozymes have become a topic of particular interest due to their high activity level, stability and biocompatibility. In this study, a visual, sensitive and selective point-of-care immunosensor was established to test the pathogen Escherichia coli O157:H7 (E. coli O157:H7). Hemin and magainin I (MI) hybrid nanocomposites (Hemin@MI) with peroxidase-mimicking activities were synthesized via a "one-pot" method, involving the simple mixing of an antimicrobial peptide (MI) against E. coli O157:H7 and hemin in a copper sulfate sodium phosphate saline buffer. Hemin@MI nanocomposites integrating target recognition and signal amplification were developed as signal probes for the point-of-care colorimetric detection of pathogenic E. coli O157:H7. Hemin@MI nanocomposites exhibit excellent peroxidase activity for the chromogenic reaction of ABTS, which allows for the visual point-of-care testing of E. coli O157:H7 in the range of 102 to 108 CFU/mL, with a limit of detection of 85 CFU/mL. These data suggest this immunosensor provides accessible and portable assessments of pathogenic E. coli O157:H7 in real samples.

A sensitive biosensor for determination of pathogenic bacteria using aldehyde dehydrogenase signaling system.

Aldehyde dehydrogenase (ALDH) was first developed as an enzymatic signaling system of a biosensor for sensitive point-of-care detection of pathogenic bacteria. ALDH and specific aptamers to Salmonella typhimurium (S. typhimurium), as organic components, were embedded in organic-inorganic nanocomposites as a biosensor signal label, integrating the functions of signal amplification and target recognition. The biosensing mechanism is based on the fact that ALDH can catalyze rapid oxidation of acetaldehyde into acetic acid, resulting in pH change with portable pH meter readout. The altered pH exhibited a linear relationship with the logarithm of S. typhimurium from 102 to 108 CFU/mL and detection limit of 46 CFU/mL. Thus, the proposed biosensor has potential application in the diagnosis of pathogenic bacteria.

Sandwich immunoassay based on antimicrobial peptide-mediated nanocomposite pair for determination of Escherichia coli O157:H7 using personal glucose meter as readout.

A sandwich immunoassay was developed for determination of E. coli O157:H7. This is based on an antimicrobial peptide-mediated nanocomposite pair and uses a personal glucose meter as signal readout. The antimicrobial peptides, magainins I, and cecropin P1 were employed as recognition molecules for the nanocomposite pair, respectively. With a one-step process, copper phosphate nanocomposites embedded by magainins I and Fe3O4 were used as "capturing" probes for bacterial magnetic isolation, and calcium phosphate nanocomplexes composed of cecropin P1 and invertase were used as signal tags. After magnetic separation, the invertase of the signal tags hydrolyzed sucrose to glucose, thereby converting E. coli O157:H7 levels to glucose levels. This latter can be quantified by a personal glucose meter. Under optimal conditions, the concentration of E. coli O157:H7 can be determined in a linear range of 10 to 107 CFU·mL-1 with a detection limit of 10 CFU·mL-1. The method was successfully applied to the determination of E. coli O157:H7 in milk samples. Graphical abstract Schematic representation of sandwich immunoassay for E. coli O157:H7. One-pot synthetic of Fe3O4-magainins I nanocomposites (MMP) were used for magnetic capture. Cecropin P1-invertase nanocomposites (PIP) were used as signal tags. A personal glucose meter was used as readout to determine the target.

Disposable syringe-based visual immunotest for pathogenic bacteria based on the catalase mimicking activity of platinum nanoparticle-concanavalin A hybrid nanoflowers.

Disposable syringes were used in a novel point-of-care visual test for detecting pathogenic bacteria (Escherichia coli O157:H7 and Salmonella typhimurium). Hybrid nanoflowers composed of platinum nanoparticles and concanavalin A (Pt-nanoflowers) were prepared through a one-pot reaction and were found to be viable catalase mimics. They catalyze the decomposition of hydrogen peroxide (H2O2) to generate O2. When used as labels in immunoassays, they integrate both the functions of biological recognition and signal amplification. The disposable syringe pressure readout was combined with Pt-nanoflower signal conversion and successfully applied to a visual bacteria detection scheme. Both Escherichia coli O157:H7 and Salmonella typhimurium can be quantified with detection limits of as low as 15 and 7 CFU·mL-1, respectively. Graphical abstract One-pot synthetic platinum nanoparticle (PtNP)-concanavalin A hybrid nanoflowers (Pt-nanoflowers), have been used as ideal signal labels for immunoassays and integrating both essential functions of biological recognition and signal amplification. Disposable syringes were used as a readout to detect pathogenic bacteria.

Hemin-incorporated nanoflowers as enzyme mimics for colorimetric detection of foodborne pathogenic bacteria.

Rapid, sensitive and point-of-care detection of foodborne pathogenic bacteria is essential for food safety. In this study, we found that hemin-concanavalin A hybrid nanoflowers (HCH nanoflowers), as solid mimic peroxidase, could catalyze oxidation of 2,2'-azino-bis (3-ethylbenzthiazoline-6-sulfonic acid) diammonium salt (ABTS) in the presence of H2O2 to a green-colored product. HCH nanoflowers, integrating the essential functions of both biological recognition and signal amplification, meet the requirements of signal labels for colorimetric immunoassay of bacteria. In view of the excellent peroxidase mimetic catalytic activity of HCH nanoflowers, a colorimetric biosensing platform was newly constructed and applied for sensitive detection of foodborne Escherichia coli O157:H7 (E. coli O157:H7). The corresponding detection limits was as low as 4.1 CFU/mL with wide linear ranges (101-106 CFU/mL).