Resistance to the DMI fungicide mefentrifluconazole in Monilinia fructicola: risk assessment and resistance basis analysis.

BACKGROUND: Brown rot disease, caused by Monilinia fructicola, poses a significant challenge to peach production in China. The efficacy of mefentrifluconazole, a new triazole fungicide, in controlling brown rot in peaches has been remarkable. However, the resistance risk and mechanism associated with this fungicide remain unclear. This study was designed to assess the resistance risk of M. fructicola to mefentrifluconazole and reveal the potential resistance mechanism. RESULTS: The mean median effective concentration (EC50 ) of 101 M. fructicola isolates to mefentrifluconazole was 0.003 µg mL-1 , and the sensitivity exhibited a unimodal distribution. Seven mefentrifluconazole-resistant mutants were generated from three parental isolates in the laboratory through fungicide adaption. The biological characteristics of the resistant mutants revealed that three of them exhibited enhanced survival fitness compared to the parental isolates, whereas the remaining four mutants displayed reduced survival fitness. Mefentrifluconazole showed strong positive cross-resistance with fenbuconazole, whereas no cross-resistance was observed with pyrimethanil, procymidone or pydiflumetofen. No overexpression of MfCYP51 gene was detected in the resistant mutants. Multiple sequence alignment revealed that three resistant mutants (MXSB2-2, Mf12-1 and Mf12-2) had a point mutation (G461S) in MfCYP51 protein. Molecular docking techniques confirmed the contribution of this point mutation to mefentrifluconazole resistance. CONCLUSION: The risk of M. fructicola developing resistance to mefentrifluconazole is relatively low-to-medium and point mutation G461S in MfCYP51 could confer mefentrifluconazole resistance in M. fructicola. This study provided essential data for monitoring the emergence of resistance and developing resistance management strategies for mefentrifluconazole. © 2023 Society of Chemical Industry.

Acute pancreatitis: A review of diagnosis, severity prediction and prognosis assessment from imaging technology, scoring system and artificial intelligence.

Acute pancreatitis (AP) is a potentially life-threatening inflammatory disease of the pancreas, with clinical management determined by the severity of the disease. Diagnosis, severity prediction, and prognosis assessment of AP typically involve the use of imaging technologies, such as computed tomography, magnetic resonance imaging, and ultrasound, and scoring systems, including Ranson, Acute Physiology and Chronic Health Evaluation II, and Bedside Index for Severity in AP scores. Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity, while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications. Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild, moderate, or severe categories, guiding treatment decisions, such as intensive care unit admission, early enteral feeding, and antibiotic use. Despite the central role of imaging technologies and scoring systems in AP management, these methods have limitations in terms of accuracy, reproducibility, practicality and economics. Recent advancements of artificial intelligence (AI) provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data. AI algorithms can analyze large amounts of clinical and imaging data, identify scoring system patterns, and predict the clinical course of disease. AI-based models have shown promising results in predicting the severity and mortality of AP, but further validation and standardization are required before widespread clinical application. In addition, understanding the correlation between these three technologies will aid in developing new methods that can accurately, sensitively, and specifically be used in the diagnosis, severity prediction, and prognosis assessment of AP through complementary advantages.

Occurrence of emerging contaminants in pet hair and indoor air: integrative health risk assessment using multiple ToxCast endpoints.

Indoor exposome is a growing concern, including a mixture of legacy and emerging contaminants. Recent studies suggest that indoor pollutants may accumulate in pet hair, a part of indoor exposome, increasing health risks to pet owners; however, the source and hazards of pollutants associated with pet hair are largely unknown. Here, we found that hydrophobic pollutants often had higher indoor concentrations than hydrophilic ones, and polycyclic aromatic hydrocarbons (PAHs) were the most dominant fractions (61.1%) in indoor air exposome while polycyclic musks (PCMs) had the highest concentrations among all contaminant classes in indoor dust (1559 ± 1598 ng g-1 dw) and pet hair (2831 ± 2458 ng g-1 dw). The levels of hygiene-related contaminants (PCMs, current-use pesticides (CUPs), and antibiotics) were higher in pet hair than dust due to direct contact during applications. Health risk assessment using toxicity thresholds from high-throughput screening data showed that human health risks from the five classes of indoor contaminants (PAHs, PCMs, organophosphate esters, CUPs, and antibiotics) via inhalation, ingestion, and dermal contact were within acceptable limits, but the children may be exposed to a higher risk than the adults. The thresholds estimated from the ToxCast data using endpoint sensitivity distribution make the exposome risk assessment feasible in the absence of benchmarks, which is beneficial for including a mixture of emerging pollutants in risk assessment.

Biosafety assessment of Acinetobacter strains isolated from the Three Gorges Reservoir region in nematode Caenorhabditis elegans.

Acinetobacter has been frequently detected in backwater areas of the Three Gorges Reservoir (TGR) region. We here employed Caenorhabditis elegans to perform biosafety assessment of Acinetobacter strains isolated from backwater area in the TGR region. Among 21 isolates and 5 reference strains of Acinetobacter, exposure to Acinetobacter strains of AC1, AC15, AC18, AC21, A. baumannii ATCC 19606T, A. junii NH88-14, and A. lwoffii DSM 2403T resulted in significant decrease in locomotion behavior and reduction in lifespan of Caenorhabditis elegans. In nematodes, exposure to Acinetobacter strains of AC1, AC15, AC18, AC21, A. baumannii, A. junii and A. lwoffii also resulted in significant reactive oxygen species (ROS) production. Moreover, exposure to Acinetobacter isolates of AC1, AC15, AC18, and AC21 led to significant increase in expressions of both SOD-3::GFP and some antimicrobial genes (lys-1, spp-12, lys-7, dod-6, spp-1, dod-22, lys-8, and/or F55G11.4) in nematodes. The Acinetobacter isolates of AC1, AC15, AC18, and AC21 had different morphological, biochemical, phylogenetical, and virulence gene properties. Our results suggested that exposure risk of some Acinetobacter strains isolated from the TGR region exists for environmental organisms and human health. In addition, C. elegans is useful to assess biosafety of Acinetobacter isolates from the environment.

Comparability of thyroid-stimulating hormone immunoassays using fresh frozen human sera and external quality assessment data.

BACKGROUND: This study aimed to assess the comparability among assays using freshly frozen human sera and external quality assessment (EQA) data in China. METHODS: Twenty-nine serum samples and two commercial EQA materials, obtained from the National Center for Clinical Laboratories (NCCL), were analyzed in triplicate using eight routine TSH assays. The commutability of commercial EQA materials (NCCL materials) was evaluated in accordance with the CLSI EP30-A and IFCC bias analysis. Median values obtained for the NCCL EQA materials were used to determine the systematic and commutability-related biases among immunoassays through back-calculation. The comparability of TSH measurements from a panel of clinical samples and NCCL EQA data was determined on the basis of Passing-Bablok regression. Furthermore, human serum pools were used to perform commutable EQA. RESULTS: NCCL EQA materials displayed commutability among three or five of seven assay combinations according CLSI or IFCC approach, respectively. The mean of systematic bias ranged from -13.78% to 9.85% for the eight routine TSH assays. After correcting for systematic bias, averaged commutability-related biases ranged between -42.26% and 12.19%. After correction for systematic and commutability -related biases, the slopes indicating interassay relatedness ranged from 0.801 to 1.299 using individual human sera, from 0.735 to 1.254 using NCCL EQA data, and from 0.729 to 1.115 using pooled human serum EQA(the commutable EQA). CONCLUSIONS: The harmonization of TSH measurement is challenging; hence, systematic and commutability-related biases should be determined and corrected for accurate comparisons among assays when using human individual serum and the commercial EQA materials.

Development of a comprehensive assessment model for coral reef island carrying capacity(CORE-CC).

Coral reef islands provide precious living space and valuable ecological services for human beings, and its sustainability cannot be ignored under the pressure of human activities. Carrying capacity (CC) assessment has gradually become an important means to measure sustainability of islands. However, there is little comprehensive evaluation of the carrying capacity of coral reef islands, and traditional evaluation methods are difficult to express the social-ecological characteristics of coral reef islands. The present paper proposes a comprehensive assessment model for coral reef island carrying capacity (CORE-CC) which comprises dimensions of resources supply, environmental assimilative, ecosystem services, and socio-economic supporting. According to the characteristics of the coral reef islands, the core factors and indicators of each dimension are selected and the corresponding assessment index system of "pressure-support" is constructed. The assessment involves (1) identification of carrying dimensions and core factors, (2) pressure/support measurement and (3) assessment of carrying state. A case study is conducted in Zhaoshu Island of China, demonstrating the applicability of CORE-CC model and serving as a reference for adaptive management.

An evolving assessment model for environmental carrying capacity: A case study of coral reef islands.

Environmental carrying capacity (ECC) provides an insight into measuring sustainability of the vulnerable coral reef islands. However, an integrated assessment of ECC on the social-ecological system of reef islands is rarely existence. And conventional approaches miss addressing the difference of social development, which would lead to a misinterpretation of sustainable development of reef island system. This study develops an evolving model of RI-ECC which incorporates five specific development phases, and the assessment involves (1) identification and measurement of carrying components, (2) supply/demand surplus analysis of indicators and (3) ECC states determination. A case study is conducted in Zhaoshu Island of China, indicating the efficiency of RI-ECC model and serving as a reference for adaptive management.

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO2, and NO2, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

Commutability of possible external quality assessment materials for cardiac troponin measurement.

BACKGROUND: The measurement of cardiac troponin is crucial in the diagnosis of myocardial infarction. The performance of troponin measurement is most conveniently monitored by external quality assessment (EQA) programs. The commutability of EQA samples is often unknown and the effectiveness of EQA programs is limited. METHODS: Commutability of possible EQA materials was evaluated. Commercial control materials used in an EQA program, human serum pools prepared from patient samples, purified analyte preparations, swine sera from model animals and a set of patient samples were measured for cTnI with 4 assays including Abbott Architect, Beckman Access, Ortho Vitros and Siemens Centaur. The measurement results were logarithm-transformed, and the transformed data for patient samples were pairwise analyzed with Deming regression and 95% prediction intervals were calculated for each pair of assays. The commutability of the materials was evaluated by comparing the logarithmic results of the materials with the limits of the intervals. Matrix-related biases were estimated for noncommutable materials. The impact of matrix-related bias on EQA was analyzed and a possible correction for the bias was proposed. RESULTS: Human serum pools were commutable for all assays; purified analyte preparations were commutable for 2 of the 6 assay pairs; commercial control materials and swine sera were all noncommutable; swine sera showed no reactivity to Vitros assay. The matrix-related biases for noncommutable materials ranged from -83% to 944%. Matrix-related biases of the EQA materials caused major abnormal between-assay variations in the EQA program and correction of the biases normalized the variations. CONCLUSION: Commutability of materials has major impact on the effectiveness of EQA programs for cTnI measurement. Human serum pools prepared from patient samples are commutable and other materials are mostly noncommutable. EQA programs should include at least one human serum pool to allow proper interpretation of EQA results.

Toxicity assessment of mono-substituted benzenes and phenols using a Pseudomonas initial oxygen uptake assay.

A methodology is presented for assessing the toxicity of chemical substances through their inhibitory action toward the Pseudomonas initial oxygen uptake (PIOU) rate. The current studies reveal that the PIOU assay is rapid, cost-efficient, and easy to perform. The oxygen uptake rate was found to be associated with a putative benzoate transporter and highly dependent on benzoate concentration. The putative benzoate transporter has been shown to follow Michaelis-Menten kinetics. Most phenols were found to be noncompetitive inhibitors of the benzoate transporter. The inhibition constant (Ki) of these noncompetitive inhibitors can be related to the concentration causing 50% oxygen uptake inhibition in Pseudomonas putida. Modeling these data by using the response-surface approach leads to the development of a quantitative structure-activity relationship (QSAR) for the toxicity of phenols ((1/Ki) = -0.435 (+/-0.038) lowest-unoccupied-molecular orbital + 0.517 (+/-0.027)log K(OW) - 2.340 (+/-0.068), n = 49, r2 = 0.930, s = 0.107, r2adj = 0.926, F = 303.1). A comparison of QSAR models derived from the Ki data of the PIOU method and the toxicity data of 40-h Tetrahymena pyrifomis growth inhibition assay (Tetratox) indicated that there was a high correlation between the two approaches (r2 = 0.925).