Pesticides make indispensable contributions to agricultural productivity. However, the residues after their excessive use may be harmful to crop production, food safety and human health. Although the ...ability of plants (especially crops) to accumulate and metabolize pesticides has been intensively investigated, data describing the chemical and metabolic processes in plants are limited. Understanding how pesticides are metabolized is a key step toward developing cleaner crops with minimal pesticides in crops, creating new green pesticides (or safeners), and building up the engineered plants for environmental remediation. In this review, we describe the recently discovered mechanistic insights into pesticide metabolic pathways, and development of improved plant genotypes that break down pesticides more effectively. We highlight the identification of biological features and functions of major pesticide–metabolized enzymes such as laccases, glycosyltransferases, methyltransferases and ATP binding cassette (ABC) transporters, and discuss their chemical reactions involved in diverse pathways including the formation of pesticide S–conjugates. The recent findings for some signal molecules (phytohomormes) like salicylic acid, jasmonic acid and brassinosteroids involved in metabolism and detoxification of pesticides are summarized. In particular, the emerging research on the epigenetic mechanisms such DNA methylation and histone modification for pesticide metabolism is emphasized. The review would broaden our understanding of the regulatory networks of the pesticide metabolic pathways in higher plants.
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•Plants can metabolize pesticides through degradative enzymes and sequestration.•There are diverse reactions by which pesticides are metabolized.•Some signal molecules are involved in pesticide metabolism and detoxification.•Epigenetic mechanism is involved in metabolism of pesticides in plants.
In order to optimize the Chinese medical and health system and improve people’s health level, the SFA Malmquist model, the spatial econometric model, and the standard deviation ellipse method were ...used to measure the efficiency of medical and health services in China’s 31 provinces between 2010 and 2020. Study results indicated that the average efficiency value of the 31 provinces generally exceeded 0.8. Specifically, the average efficiency values in the eastern and central regions increased from 0.852 to 0.875 and from 0.858 to 0.88, respectively. In the western and northeastern regions, these values rose from 0.804 to 0.835 and from 0.827 to 0.854, respectively. From the perspective of spatial distribution, there were high-high and low-low clusters in most provinces with significant spatial dependence among them. This analysis reveals that medical and health services efficiency in China demonstrates a spatial pattern extending from northeast to southwest.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The suppression of types I and III interferon (IFN) responses by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) contributes to the pathogenesis of coronavirus disease 2019 (COVID‐19). ...The strategy used by SARS‐CoV‐2 to evade antiviral immunity needs further investigation. Here, we reported that SARS‐CoV‐2 ORF9b inhibited types I and III IFN production by targeting multiple molecules of innate antiviral signaling pathways. SARS‐CoV‐2 ORF9b impaired the induction of types I and III IFNs by Sendai virus and poly (I:C). SARS‐CoV‐2 ORF9b inhibited the activation of types I and III IFNs induced by the components of cytosolic dsRNA‐sensing pathways of RIG‐I/MDA5‐MAVS signaling, including RIG‐I, MDA‐5, MAVS, TBK1, and IKKε, rather than IRF3‐5D, which is the active form of IRF3. SARS‐CoV‐2 ORF9b also suppressed the induction of types I and III IFNs by TRIF and STING, which are the adaptor protein of the endosome RNA‐sensing pathway of TLR3‐TRIF signaling and the adaptor protein of the cytosolic DNA‐sensing pathway of cGAS–STING signaling, respectively. A mechanistic analysis revealed that the SARS‐CoV‐2 ORF9b protein interacted with RIG‐I, MDA‐5, MAVS, TRIF, STING, and TBK1 and impeded the phosphorylation and nuclear translocation of IRF3. In addition, SARS‐CoV‐2 ORF9b facilitated the replication of the vesicular stomatitis virus. Therefore, the results showed that SARS‐CoV‐2 ORF9b negatively regulates antiviral immunity and thus facilitates viral replication. This study contributes to our understanding of the molecular mechanism through which SARS‐CoV‐2 impairs antiviral immunity and provides an essential clue to the pathogenesis of COVID‐19.
Decomposing chemical interactions into bonds and other higher order interactions is a common practice to analyse chemical structures, and gave birth to many chemical concepts, despite the fact that ...the decomposition itself might be subjective in nature. Fragment molecular orbitals (FMOs) offer a more rigorous alternative to such intuition, but might be less interpretable due to extensive delocalisation in FMOs. Inspired by the Principal Component Analysis in statistics, we hereby present a novel framework, Principal Interacting Orbital (PIO) analysis, that can very quickly identify the “dominant interacting orbitals” that are semi‐localised and easily interpretable, while still maintaining mathematical rigor. Many chemical concepts that are often taken for granted, but could not be easily inferred from other computational techniques like FMO analysis, can now be visualised as PIOs. We have also illustrated, through various examples covering both organic and inorganic chemistry, how PIO analysis could help us pinpoint subtle features that might play determining roles in bonding and reactions.
Principal Interacting Orbitals: A novel framework has been developed, based on Principal Component Analysis (PCA), to identify the Principal Interacting Orbitals (PIOs) accounting for the bonding interaction between two chemical fragments. The novel framework has been employed to analyse structure, bonding, and reactivity for a number of examples covering various aspects of chemistry.
Inflammation and metabolic dysfunction are hallmarks of nonalcoholic steatohepatitis (NASH), which is one of the fastest‐growing liver diseases worldwide. Emerging evidence indicates that innate ...immune mechanisms are pivotal drivers of inflammation and other pathological manifestations observed in NASH, such as hepatosteatosis, insulin resistance (IR), and fibrosis. This robust innate immune reaction is intrinsic to the liver, which is an important immunological organ that contains a coordinated network of innate immune cells, including Kupffer cells (KCs), dendritic cells (DCs), and lymphocytes. Hepatocytes and liver sinusoidal endothelial cells (LSECs) are not formally innate immune cells, but they take on immune cell function when stressed. These cells can sense excess metabolites and bacterial products and translate those signals into immune responses and pathological hepatic changes during the development of NASH. In this review, we take a historical perspective in describing decades of research that aimed to identify the key molecular and cellular players in the innate immune system in the setting of NASH. Furthermore, we summarize the innate immune cells that are involved in the progression of NASH and illustrate how they sense disturbances in circulating metabolic factors by innate immune receptors and subsequently initiate the intercellular signaling cascades that lead to persistent inflammation and progression of hepatic complications.
Gold complexes have recently gained increasing attention in the design of new metal-based anticancer therapeutics. Gold(
iii
) complexes are generally reactive/unstable under physiological conditions
...via
intracellular redox reactions, and the intracellular Au
III
to Au
I
reduction reaction has recently been "traced" by the introduction of appropriate fluorescent ligands. Similar to most Au(
i
) complexes, Au(
iii
) complexes can inhibit the activities of thiol-containing enzymes, including thioredoxin reductase,
via
ligand exchange reactions to form Au-S(Se) bonds. Nonetheless, there are examples of physiologically stable Au(
iii
) and Au(
i
) complexes, such as Au(TPP)Cl (H
2
TPP = 5,10,15,20-tetraphenylporphyrin) and Au(dppe)
2
Cl (dppe = 1,2-bis(diphenylphosphanyl)ethane), which are known to display highly potent
in vitro
and
in vivo
anticancer activities. In this review, we summarize our current understanding of anticancer gold complexes, including their mechanisms of action and the approaches adopted to improve their anticancer efficiency. Some recent examples of gold anticancer chemotherapeutics are highlighted.
Anticancer gold complexes, including their mechanisms of action and the approaches adopted to improve the anticancer efficiency are described.
Parkinson's disease (PD) is a common, progressive, and currently incurable neurodegenerative movement disorder. The diagnosis of PD is challenging, especially in the differential diagnosis of ...parkinsonism and in early PD detection. Due to the advantages of machine learning such as learning complex data patterns and making inferences for individuals, machine-learning techniques have been increasingly applied to the diagnosis of PD, and have shown some promising results. Machine-learning-based imaging applications have made it possible to help differentiate parkinsonism and detect PD at early stages automatically in a number of neuroimaging studies. Comparative studies have shown that machine-learning-based SPECT image analysis applications in PD have outperformed conventional semi-quantitative analysis in detecting PD-associated dopaminergic degeneration, performed comparably well as experts' visual inspection, and helped improve PD diagnostic accuracy of radiologists. Using combined multi-modal (imaging and clinical) data in these applications may further enhance PD diagnosis and early detection. To integrate machine-learning-based diagnostic applications into clinical systems, further validation and optimization of these applications are needed to make them accurate and reliable. It is anticipated that machine-learning techniques will further help improve differential diagnosis of parkinsonism and early detection of PD, which may reduce the error rate of PD diagnosis and help detect PD at pre-motor stage to make it possible for early treatments (e.g., neuroprotective treatment) to slow down PD progression, prevent severe motor symptoms from emerging, and relieve patients from suffering.
Various quantum applications can be reduced to estimating expectation values, which are inevitably deviated by operational and environmental errors. Although errors can be tackled by quantum error ...correction, the overheads are far from being affordable for near-term technologies. To alleviate the detrimental effects of errors on the estimation of expectation values, quantum error mitigation techniques have been proposed, which require no additional qubit resources. Here we benchmark the performance of a quantum error mitigation technique based on probabilistic error cancellation in a trapped-ion system. Our results clearly show that effective gate fidelities exceed physical fidelities, i.e., we surpass the break-even point of eliminating gate errors, by programming quantum circuits. The error rates are effectively reduced from (1.10 ± 0.12) × 10
to (1.44 ± 5.28) × 10
and from (0.99 ± 0.06) × 10
to (0.96 ± 0.10) × 10
for single- and two-qubit gates, respectively. Our demonstration opens up the possibility of implementing high-fidelity computations on a near-term noisy quantum device.
In our previous studies, we reported that myeloid differentiation protein 1 (MD1) serves as a negative regulator in several cardiovascular diseases. However, the role of MD1 in heart failure with ...preserved ejection fraction (HFpEF) and the underlying mechanisms of its action remain unclear. Eight‐week‐old MD1‐knockout (MD1‐KO) and wild‐type (WT) mice served as models of HFpEF induced by uninephrectomy, continuous saline or d‐aldosterone infusion and a 1.0% sodium chloride treatment in drinking water for 4 weeks to investigate the effect of MD1 on HFpEF in vivo. H9C2 cells were treated with aldosterone to evaluate the role of MD1 KO in vitro. MD1 expression was down‐regulated in the HFpEF mice; HFpEF significantly increased the levels of intracellular reactive oxygen species (ROS) and promoted autophagy; and in the MD1‐KO mice, the HFpEF‐induced intracellular ROS and autophagy effects were significantly exacerbated. Moreover, MD1 loss activated the p38‐MAPK pathway both in vivo and in vitro. Aldosterone‐mediated cardiomyocyte autophagy was significantly inhibited in cells pre‐treated with the ROS scavenger N‐acetylcysteine (NAC) or p38 inhibitor SB203580. Furthermore, inhibition with the autophagy inhibitor 3‐methyladenine (3‐MA) offset the aggravating effect of aldosterone‐induced autophagy in the MD1‐KO mice and cells both in vivo and in vitro. Our results validate a critical role of MD1 in the pathogenesis of HFpEF. MD1 deletion exaggerates cardiomyocyte autophagy in HFpEF via the activation of the ROS‐mediated MAPK signalling pathway.
Nonalcoholic fatty liver disease (NAFLD) is rapidly becoming the most common liver disease worldwide. Individuals with NAFLD have a high frequency of developing progressive liver disease and ...metabolism‐related comorbidities, which result from of a lack of awareness and poor surveillance of the disease and a paucity of approved and effective therapies. Managing the complications of NAFLD has already begun to place a tremendous burden on health‐care systems. Although efforts to identify effective therapies are underway, the lack of validated preclinical NAFLD models that represent the biology and outcomes of human disease remains a major barrier. This review summarizes the characteristics and prevalence of the disease and the status of our understanding of its mechanisms and potential therapeutic targets.