Combined toxicity of abamectin and carbendazim on enzymatic and transcriptional levels in the soil-earthworm microcosm.

To reveal the toxicological mechanisms of pesticide mixtures on soil organisms, this study concentrated on evaluating enzymatic activity and gene expression changes in the earthworm Eisenia fetida (Savigny 1826). Despite being frequently exposed to multiple pesticides, including the common combination of abamectin (ABA) and carbendazim (CAR), environmental organisms have primarily been studied for the effects of individual pesticides. Acute toxicity results exhibited that the combination of ABA and CAR caused a synergistic impact on E. fetida. The levels of MDA, ROS, T-SOD, and caspase3 demonstrated a significant increase across most individual and combined groups, indicating the induction of oxidative stress and cell death. Additionally, the expression of three genes (hsp70, gst, and crt) exhibited a significant decrease following exposure to individual pesticides and their combinations, pointing toward cellular damage and impaired detoxification function. In contrast, a noteworthy increase in ann expression was observed after exposure to both individual pesticides and their mixtures, suggesting the stimulation of reproductive capacity in E. fetida. The present findings contributed to a more comprehensive understanding of the potential toxicity mechanisms of the ABA and CAR mixture, specifically on oxidative stress, cell death, detoxification dysfunction, and reproductive capacity in earthworms. Collectively, these data offered valuable toxicological insights into the combined effects of pesticides on soil organisms, enhancing our understanding of the underlying risks associated with the coexistence of different pesticides in natural soil environments.

Joint toxic mechanism of clothianidin and prochloraz in the earthworm (Eisenia fetida).

Pesticides are typically present as combinations within soil ecosystems and have detrimental effects on untamed surroundings. However, the collective impacts and fundamental mechanisms of pesticides on soil living beings are currently inadequately assessed. In our current work, we evaluated the interactive consequences of clothianidin (CLO) and prochloraz (PRO) on earthworms (Eisenia fetida) using several toxicological tests, such as acute adverse effects, biocatalytic activity, and alterations in transcriptional activity. The findings revealed that CLO (with a 14-day LC50 value of 6.08 mg kg-1) exhibited greater toxicity compared to PRO (with a 14-day LC50 value of 79.41 mg kg-1). Moreover, the combinations of CLO and PRO had synergistic acute effects on E. fetida. Additionally, the activities of POD, CAT, and GST were significantly varied in most instances of single and mixed treatments when compared to the control. Surprisingly, the transcriptional levels of four genes (gst, sod, crt, and ann), related to oxidative load, metabolic detoxification systems, endoplasmic reticulum, and oxytocin neuropeptide, respectively, were also altered in response to single and mixture exposures, as compared to the control. Alterations in enzyme activity and gene transcriptional level could serve as early indicators for detecting co-exposure to pesticides. The findings of this research offered valuable holistic understanding regarding the toxicity of pesticide combinations on earthworms. Further research should be conducted to investigate the persistent effects of pesticide mixtures on terrestrial invertebrates in order to draw definitive conclusions about the associated risks.

Uncovering hidden dangers: The combined toxicity of abamectin and lambda-cyhalothrin on honey bees.

Studying the toxic effects of pesticides on bees has consistently been a prominent area of interest for researchers. Nonetheless, existing research has predominantly concentrated on individual toxicity assessments, leaving a gap in our understanding of mixed toxicity. This study delves into the individual and combined toxic effects of abamectin (ABA) and lambda-cyhalothrin (LCY) on honey bees (Apis mellifera) in laboratory settings. We discovered that ABA (96 h-LC50 value of 0.079 mg/L) exhibited greater acute toxicity to honey bees compared to LCY (96 h-LC50 value of 9.177 mg/L). Moreover, the mixture of ABA and LCY presented an acute antagonistic effect on honey bees. Additionally, our results indicated that exposure to LCY, at medium concentration, led to a reduction in the abundance of gut core bacterium Snodgrassella. However, an increase in the abundance of Bifidobacterium was noted when exposed to a medium concentration of LCY and its mixture with ABA. Transcriptomic analysis revealed significant regulation of certain genes in the medium concentration of all three treatments compared to the control group, primarily enriching in metabolism and immune-related pathways. Following chronic exposure to field-relevant concentrations of ABA, LCY, and their mixture, there were significant alterations in the activities of immunity-related enzyme polyphenol oxidase (PPO) and detoxification enzymes glutathione S-transferase (GST) and carboxylesterase (CarE). Additionally, the expression of four genes (abaecin, cyp9e2, cyp302a1, and GstD1) associated with immune and detoxification metabolism was significantly altered. These findings suggest a potential health risk posed by the insecticides ABA and LCY to honey bees. Despite exhibiting acute antagonistic effect, mixed exposure still induced damage to bees at all levels. This study advances our knowledge of the potential adverse effects of individual or combined exposure to these two pesticides on non-target pollinators and offers crucial guidance for the use of insecticides in agricultural production.

Metabolic perturbations in zebrafish (Danio rerio) larvae exposed to sulfentrazone and imidacloprid.

The intensive and widespread application of pesticides in agroecosystems can lead to the simultaneous exposure of non-target aquatic organisms to insecticides and herbicides. However, the underlying mechanisms through which aquatic organisms undergo metabolic reprogramming to withstand the combined effects of the insecticide imidacloprid (IMI) and herbicide sulfentrazone (SUL) remain poorly elucidated. This study employs metabolomics to investigate the effects of individual and combined exposures to IMI and SUL on zebrafish (Danio rerio), aiming to simulate complex environmental conditions. Metabolomics analysis revealed extensive metabolic reprogramming in larvae induced by the selected agrochemicals. Both individual and combined exposures disrupted nucleotide metabolism, inhibited glycolysis, and led to the accumulation of acetylcholine through the shared modulation of differential metabolites. Notably, individual exposure exhibited a unique mode of action. Larvae exposed to IMI alone showed mitochondrial dysfunction, potentially stemming from interference with the electron transport chain, while SUL-induced disruptions were associated with glycerophospholipid accumulation, marking it as a critical target. Additionally, calculations of the metabolic effect level index indicated antagonistic interactions between SUL and IMI mixtures at an overall metabolic level. The results obtained through investigating the lethal and sub-lethal effects also revealed that the simultaneous application of SUL and IMI may have the potential to diminish acute and developmental toxicity in zebrafish. This study underscores the significance of metabolomics as a valuable and effective strategy for deciphering the toxicity and interactions of agrochemical mixtures.

Relationship between 24 h blood pressure variability and mortality in acute myocardial infarction patients.

BACKGROUND: In recent years, the mortality of patients with AMI has not declined significantly. The relationship between blood pressure variability (BPV) and acute myocardial infarction (AMI) is unclear. We explored the relationship between 24-h BPV and mortality in patients with AMI. HYPOTHESIS: The mortality of patients with AMI is related to BPV. We hope to provide therapeutic ideas for reducing the risk of death in patients with AMI. METHODS: This is a retrospective cohort study. We extracted and analyzed data from the MIMIC-IV 2.0, which was established in 1999 under the auspices of the National Institutes of Health (America). The average real variability (ARV) was calculated for the first 24-h blood pressure measurement after patients with AMI were admitted to the intensive care unit (ICU). Patients were divided into four groups according to ARV quartiles. The outcomes were 30-day, 1-year, and 3-year all-cause mortalities. Data were analyzed using Cox regression, Kaplan-Meier curves, and restricted cubic spline (RCS) curves. RESULTS: We enrolled 1291 patients with AMI, including 475 female. The patients were divided into four groups according to the qualities of diastolic blood pressure (DBP)-ARV. There were significant differences in the 30-day, 1-year and 3-year mortality among the four groups (p = .02, p < .001, p < .001, respectively). After adjustment for confounding factors, systolic blood pressure (SBP)-ARV could not predict AMI patient mortality (p > .05), while the highest DBP-ARV was associated strongly with increased 30-day mortality (HR: 2.291, 95% CI 1.260-4.168), 1-year mortality (HR: 1.933, 95% CI 1.316-2.840) and 3-year mortality (HR: 1.743, 95% CI 1.235-2.461). Kaplan-Meier curves demonstrated that, regardless of SBP or DBP, the long-term survival probabilities of patients in the highest ARV group were significantly lower than that of those in other groups. RCS curves showed that the death risk of patients with AMI first decreased and then increased with the increase in ARV when DBP-ARV < 8.04. The 30-day death risk first increased and then decreased, and the 1-year and 3-year death risks increased and then stabilized with ARV increase when DBP-ARV > 8.04. CONCLUSION: This study showed that patients with AMI may have an increased risk of short- and long-term death if their DBP-ARV is higher or lower during the first 24-h in ICU.

Machine learning-based fracturing parameter optimization for horizontal wells in Panke field shale oil.

In the process of developing tight oil and gas reservoirs, multistage fractured horizontal wells (NFHWs) can greatly increase the production rate, and the optimal design of its fracturing parameters is also an important means to further increase the production rate. Accurate production prediction is essential for the formulation of effective development strategies and development plans before and during project execution. In this study, a novel workflow incorporating machine learning (ML) and particle swarm optimization algorithms (PSO) is proposed to predict the production rate of multi-stage fractured horizontal wells in tight reservoirs and optimize the fracturing parameters. The researchers conducted 10,000 numerical simulation experiments to build a complete training and validation dataset, based on which five machine learning production prediction models were developed. As input variables for yield prediction, eight key factors affecting yield were selected. The results of the study show that among the five models, the random forest (RF) model best establishes the mapping relationship between feature variables and yield. After verifying the validity of the Random Forest-based yield prediction model, the researchers combined it with the particle swarm optimization algorithm to determine the optimal combination of fracturing parameters under the condition of maximizing the net present value. A hybrid model, called ML-PSO, is proposed to overcome the limitations of current production forecasting studies, which are difficult to maximize economic returns and optimize the fracturing scheme based on operator preferences (e.g., target NPV). The designed workflow can not only accurately and efficiently predict the production of multi-stage fractured horizontal wells in real-time, but also be used as a parameter selection tool to optimize the fracture design. This study promotes data-driven decision-making for oil and gas development, and its tight reservoir production forecasts provide the basis for accurate forecasting models for the oil and gas industry.

Current-use pesticides monitoring and ecological risk assessment in vegetable soils at the provincial scale.

Pesticides represent one of the largest intentional inputs of potentially hazardous compounds into agricultural soils. However, as an important vegetable producing country, surveys on pesticide residues in soils of vegetable production areas are scarce in China. This study presented the occurrence, spatial distribution, correlation between vegetable types and pesticides, and ecological risk evaluation of 94 current-use pesticides in 184 soil samples from vegetable production areas of Zhejiang province (China). The ecological risks of pesticides to soil biota were evaluated with toxicity exposure ratios (TERs) and risk quotient (RQ). The pesticide concentrations varied largely from below the limit of quantification to 20703.06 µg/kg (chlorpyrifos). The situation of pesticide residues in Jiaxing is more serious than in other cities. Soils in the vegetable areas are highly diverse in pesticide combinations. Eisenia fetida suffered exposure risk from multiple pesticides. The risk posed by chlorpyrifos, which exhibited the highest RQs at all scenarios, was worrisome. Only a few pesticides accounted for the overall risk of a city, while the other pesticides make little or zero contribution. This work will guide the appropriate use of pesticides and manage soil ecological risks, achieving green agricultural production.