Effect of CeO2, TiO2 and SiO2 nanoparticles on the growth and quality of model medicinal plant Salvia miltiorrhiza by acting on soil microenvironment.

In this study, a six-month pot experiment was conducted to explore the effects of nanoparticles (NPs), including CeO2, TiO2 and SiO2 NPs at 200 and 800 mg/kg, on the growth and quality of model medicinal plant Salvia miltiorrhiza. A control group was implemented without the application of NPs. Results showed that NPs had no significant effect on root biomass. Treatment with 200 mg/kg of SiO2 NPs significantly increased the total tanshinone content by 44.07 %, while 200 mg/kg of CeO2 NPs were conducive to a 22.34 % increase in salvianolic acid B content. Exposure to CeO2 NPs induced a substantial rise in the MDA content in leaves (176.25 % and 329.15 % under low and high concentration exposure, respectively), resulting in pronounced oxidative stress. However, TiO2 and SiO2 NPs did not evoke a robust response from the antioxidant system. Besides, high doses of CeO2 NP-amended soil led to reduced nitrogen, phosphorus and potassium contents. Furthermore, the NP amendment disturbed the carbon and nitrogen metabolism in the plant rhizosphere and reshaped the rhizosphere microbial community structure. The application of CeO2 and TiO2 NPs promoted the accumulation of metabolites with antioxidant functions, such as D-altrose, trehalose, arachidonic acid and ergosterol. NPs displayed a notable suppressive effect on pathogenic fungi (Fusarium and Gibberella) in the rhizosphere, while enriching beneficial taxa with disease resistance, heavy metal antagonism and plant growth promotion ability (Lysobacter, Streptomycetaceae, Bacillaceae and Hannaella). Correlation analysis indicated the involvement of rhizosphere microorganisms in plant adaptation to NP amendments. NPs regulate plant growth and quality by altering soil properties, rhizosphere microbial community structure, and influencing plant and rhizosphere microbe metabolism. These findings were beneficial to deepening the understanding of the mechanism by which NPs affect medicinal plants.

Acid-resistant chitosan/graphene oxide adsorbent for Cu2+ removal: The role of mixed cross-linking and amino-functionalized.

Copper ions in wastewater pose a significant threat to human and ecological safety. Therefore, preparing macroscopic adsorbents with reusable and high adsorption performance is paramount. This paper used graphene oxide as the adsorbent and chitosan as the thickener. Additionally, a silane coupling agent was employed to enhance the acid resistance of chitosan, and amino-modification of graphene oxide was performed. Macroscopic adsorbents with high adsorption capacity were fabricated using 3D printing technology. The results show that all five proportions of inks exhibit good printability. Dissolution experiments revealed that all materials maintained structural integrity after 180 days across pH values. Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Photoelectron Spectroscopy (XPS) confirmed the successful preparation of the materials. Adsorption experiments showed that the best performing material ratio was 8 wt% graphene oxide and 7 wt% chitosan. Adsorption kinetics and isothermal adsorption experiments demonstrated that the adsorption process occurred via monolayer chemisorption. The adsorption process was attributed to strong electrostatic forces, van der Waals forces, and nitrogen/oxygen-containing functional group coordination. Cycling experiments showed that the material retained good adsorption performance after 6 cycles, suggesting its potential for practical heavy metal treatment applications.

Low-Cycle Fatigue Behavior and the Combined Cyclic Hardening Material Model of Plate-Shaped Zn-22Al Alloy for Seismic Dampers.

This study investigates the potential of the plate-shaped Zn-22 wt.% Al (Zn-22Al) alloy as an innovative energy dissipation material for seismic damping devices, since plate-shaped material is more suitable to fabricate large-scale devices for building structures. The research begins with the synthesis of Zn-22Al alloy, given its unavailability in the commercial market. Monotonic tensile tests and low-cycle fatigue tests are performed to analyze material properties and fatigue performance of plate-shaped specimens. Monotonic tensile curves and cyclic stress-strain curves, along with SEM micrographs for microstructure and fracture surface analysis, are acquired. The combined cyclic hardening material model is calibrated to facilitate finite element analysis. Experimental results reveal exceptional ductility in Zn-22Al alloy, achieving a fracture strain of 200.37% (1.11 fracture strain). Fatigue life ranges from 1126 to 189 cycles with increasing strain amplitude (±0.8% to ±2.5%), surpassing mild steel by at least 6 times. The cyclic strain-life relationships align well with the Basquin-Coffin-Manson relationship. The combined kinematic/isotropic hardening model in ABAQUS accurately predicts the hysteretic behavior of the material, showcasing the promising potential of Zn-22Al alloy for seismic damping applications.

Fiber Synergy of Polyvinyl Alcohol and Steel Fibers on the Bond Behavior of a Hybrid Fiber-Reinforced Cementitious Composite.

Based on multi-scale characteristics inherent in the cracking process of cementitious composites, fibers with different geometric dimensions are simultaneously used to restrain the formation and development of cracks at different scales. Accordingly, hybrid fiber-reinforced cementitious composites (HyFRCCs) exhibit excellent bond behavior and deformation capacity in terms of tension and compression, accompanied by higher damage tolerance. Using these benefits of the mechanical properties of HyFRCCs, the structural performance of HyFRCC structures under complex loading conditions can be improved. To objectively evaluate the contributions of all fibers to the mechanical properties of HyFRCCs, steel macro-fibers, and polyvinyl alcohol (PVA) micro-fibers were used to design several reinforced cementitious composites. Four of the specimens were mono-fibrous cementitious composites, three specimens were cementitious composites reinforced with hybrid fibers, and one was a non-fibrous cementitious composite. The synergy effect of the steel and PVA fibers was analyzed using various fiber combinations. The results indicated a significant enhancement of the bonding properties of HyFRCCs through the incorporation of PVA and steel fibers. Specifically, the peak bond strength, peak slip displacement, and residual bond strength exhibited increments ranging from 31.0% to 41.7%, 60.6% to 118.4%, and 34.6% to 391.3%, respectively, in comparison to the reference test block. Notably, the combined presence of the PVA and steel fibers consistently demonstrated a positive confounding effect on the residual bond strength. However, negative confounding effects were observed in terms of the peak bond strength and peak slip displacement, particularly with 1.0% steel fiber content and 0.5% PVA fiber content.

Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia.

BACKGROUND: Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related retinal detachment (HMRD) is crucial for effective treatment and prevention of additional vision impairment. Consequently, our objective was to streamline and validate a machine-learning model based on clinical laboratory omics (clinlabomics) for the early detection of RD in HM patients. METHODS: We extracted clinlabomics data from the electronic health records for 24,440 HM and 5607 HMRD between 2015 and 2022. Lasso regression analysis assessed fifty-nine variables, excluding collinear variables (variance inflation factor > 10). Four models based on random forest, gradient boosting machine (GBM), generalized linear model, and Deep Learning Model were trained for HMRD diagnosis and employed for internal validation. An external test of the models was done. Three random data sets were further processed to validate the performance of the diagnostic model. The primary outcomes were the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) to diagnose HMRD. RESULTS: Nine variables were selected by all models. Given the AUC and AUCPR values across the different sets, the GBM model was chosen as the final diagnostic model. The GBM model had an AUC of 0.8550 (95%CI = 0.8322-0.8967) and an AUCPR of 0.5584 (95%CI = 0.5250-0.5879) in the training set. The AUC and AUCPR in the internal validation were 0.8405 (95%CI = 0.8060-0.8966) and 0.5355 (95%CI = 0.4988-0.5732). During the external test evaluation, it reached an AUC of 0.7579 (95%CI = 0.7340-0.7840) and an AUCPR of 0.5587 (95%CI = 0.5345-0.5880). A similar discriminative capacity was observed in the three random data sets. The GBM model was well-calibrated across all the sets. The GBM-RD model was implemented into a web application that provides risk prediction for HM individuals. CONCLUSION: GBM algorithms based on nine features successfully predicted the diagnosis of RD in patients with HM, which will help ophthalmologists to establish a preliminary diagnosis and to improve diagnostic accuracy in the clinic.

Response of antioxidant activity, active constituent and rhizosphere microorganisms of Salvia miltiorrhiza to combined application of microbial inoculant, microalgae and biochar under Cu stress.

Salvia miltiorrhiza, a widely used medicinal herb renowned for its properties in promoting blood circulation, removing blood stasis and alleviating pain, is currently facing quality degradation due to excessive heavy metal levels, posing a threat to medication safety. In order to investigate the effects of microbial inoculant, microalgae and biochar on the growth of Salvia miltiorrhiza under copper (Cu) stress, as well as its Cu absorption, antioxidant activity, active component contents and rhizosphere microbial community, a pot experiment was conducted. Salvia miltiorrhiza plants were cultivated in the soil containing 400 mg/kg of Cu for six months and treated with microbial inoculant, microalgae and biochar, either individually or in combination. Almost all soil amendment treatments led to an increase in root biomass. Notably, co-application of microbial inoculant and microalgae had the optimal effect with a 63.07 % increase compared to the group treated solely with Cu. Moreover, when microbial inoculant was applied alone or in combination with microalgae, the Cu content in plant roots was reduced by 19.29 % and 25.37 %, respectively, whereas other treatments failed to show a decreasing trend. Intriguingly, Cu stress increased the active component contents in plant roots, and they could also be enhanced beyond non-stress levels when microbial inoculant and microalgae were applied together or in combination with biochar. Analyses of plant antioxidant activity, soil properties and rhizosphere microorganisms indicated that these amendments may alleviate Cu stress by enhancing peroxidase activity, facilitating plant nutrient absorption, and enriching beneficial microorganisms capable of promoting plant growth and mitigating heavy metal-induced damage. This study suggests that the combined application of microbial inoculant and microalgae can reduce Cu levels in Salvia miltiorrhiza while enhancing its quality under Cu stress.

Identification of toxic Gelsemium elegans in processed food and honey based on real-time PCR analysis.

Gelsemium elegans (GE) is a widely distributed hypertoxic plant that has caused many food poisoning incidents. Its pollen can also be collected by bees to produce toxic honey, posing a great threat to the health and safety of consumers. However, for the complex matrices such as cooked food and honey, it is challenging to perform composition analysis. It is necessary to establish more effective strategies for investigating GE contamination. In this study, the real-time PCR (qPCR) analysis combined with DNA barcode matK was proposed for the identification and detection of GE. Fifteen honey samples along with twenty-eight individuals of GE and the common confusable objects Lonicera japonica, Ficus hirta, Stellera chamaejasme and Chelidonium majus were gathered. Additionally, the food mixtures treated with 20-min boiling and 30-min digestion were prepared. Specific primers were designed, and the detection capability and sensitivity of qPCR in honey and boiled and digested food matrices were tested. The results demonstrated that the matK sequence with sufficient mutation sites was an effective molecular marker for species differentiation. GE and the confusable species could be clearly classified by the fluorescence signal of qPCR assay with a high sensitivity of 0.001 ng/µl. In addition, this method was successfully employed for the detection of deeply processed food materials and honey containing GE plants which even accounted for only 0.1 %. The sequencing-free qPCR approach undoubtedly can serve as a robust support for the quality supervision of honey industry and the prevention and diagnosis of food poisoning.

DNA metabarcoding analysis of fungal community on surface of four root herbs.

Objective: Angelicae Sinensis Radix (ASR, Danggui in Chinese), Cistanches Herba (CH, Roucongrong in Chinese), Ginseng Radix et Rhizoma (PG, Renshen in Chinese), and Panacis Quinquefolii Radix (PQ, Xiyangshen in Chinese), widely used as medicine and dietary supplement around the world, are susceptible to fungal and mycotoxin contamination. In this study, we aim to analyze their fungal community by DNA metabarcoding. Methods: A total of 12 root samples were collected from three main production areas in China. The samples were divided into four groups based on herb species, including ASR, CH, PG, and PQ groups. The fungal community on the surface of four root groups was investigated through DNA metabarcoding via targeting the internal transcribed spacer 2 region (ITS2). Results: All the 12 samples were detected with fungal contamination. Rhizopus (13.04%-74.03%), Aspergillus (1.76%-23.92%), and Fusarium (0.26%-15.27%) were the predominant genera. Ten important fungi were identified at the species level, including two potential toxigenic fungi (Penicillium citrinum and P. oxalicum) and eight human pathogenic fungi (Alternaria infectoria, Candida sake, Hyphopichia burtonii, Malassezia globosa, M. restricta, Rhizopus arrhizus, Rhodotorula mucilaginosa, and Ochroconis tshawytschae). Fungal community in ASR and CH groups was significantly different from other groups, while fungal community in PG and PQ groups was relatively similar. Conclusion: DNA metabarcoding revealed the fungal community in four important root herbs. This study provided an important reference for preventing root herbs against fungal and mycotoxin contamination.

Research progress on remediation of organochlorine pesticide contamination in soil.

Deteriorated soil pollution has grown into a worldwide environmental concern over the years. Organochlorine pesticide (OCP) residues, featured with ubiquity, persistence and refractoriness, are one of the main pollution sources, causing soil degradation, fertility decline and nutritional imbalance, and severely impacting soil ecology. Furthermore, residual OCPs in soil may enter the human body along with food chain accumulation and pose a serious health threat. To date, many remediation technologies including physicochemical and biological ways for organochlorine pollution have been developed at home and abroad, but none of them is a panacea suitable for all occasions. Rational selection and scientific decision-making are grounded in in-depth knowledge of various restoration techniques. However, soil pollution treatment often encounters the interference of multiple factors (climate, soil properties, cost, restoration efficiency, etc.) in complex environments, and there is still a lack of systematic summary and comparative analysis of different soil OCP removal methods. Thus, to better guide the remediation of contaminated soil, this review summarized the most commonly used strategies for OCP removal, evaluated their merits and limitations and discussed the application scenarios of different methods. It will facilitate the development of efficient, inexpensive and environmentally friendly soil remediation strategies for sustainable agricultural and ecological development.