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With the increasing resistance exhibited by undesirable bacteria to traditional antibiotics, the need to discover alternative (or, at least, supplementary) treatments to combat chemically resistant bacteria is becoming urgent. Quorum sensing (QS) refers to a novel bacterial communication system for monitoring cell density and regulation of a network of gene expression that is mediated by a group of signaling molecules called autoinducers (AIs). QS-regulated multicellular behaviors include biofilm formation, horizontal gene transfer, and antibiotic synthesis, which are demonstrating increasing pathogenicity to plants and aquacultural animals as well as contamination of wastewater treatment devices. To inhibit QS-regulated microbial behaviors, the strategy of quorum quenching (QQ) has been developed. Different quorum quenchers interfere with QS through different mechanisms, such as competitively inhibiting AI perception (e.g., by QS inhibitors) and AI degradation (e.g., by QQ enzymes). In this review, we first introduce different signaling molecules, including diffusible signal factor (DSF) and acyl homoserine lactones (AHLs) for Gram-negative bacteria, AIPs for Gram-positive bacteria, and AI-2 for interspecies communication, thus demonstrating the mode of action of the QS system. We next exemplify the QQ mechanisms of various quorum quenchers, such as chemical QS inhibitors, and the physical/enzymatic degradation of QS signals. We devote special attention to AHL-degrading enzymes, which are categorized in detail according to their diverse catalytic mechanisms and enzymatic properties. In the final part, the applications and advantages of quorum quenchers (especially QQ enzymes and bacteria) are summarized in the context of agricultural/aquacultural pathogen biocontrol, membrane bioreactors for wastewater treatment, and the attenuation of human pathogenic bacteria. Taken together, we present the state-of-the-art in research considering QS and QQ, providing theoretical evidence and support for wider application of this promising environmentally friendly biocontrol strategy.

The herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) has been widely used around the world in both agricultural and non-agricultural fields due to its high activity. However, the heavy use of 2,4-D has resulted in serious environmental contamination, posing a significant risk to non-target organisms, including human beings. This has raised substantial concerns regarding its impact. In addition to agricultural use, accidental spills of 2,4-D can pose serious threats to human health and the ecosystem, emphasizing the importance of prompt pollution remediation. A variety of technologies have been developed to remove 2,4-D residues from the environment, such as incineration, adsorption, ozonation, photodegradation, the photo-Fenton process, and microbial degradation. Compared with traditional physical and chemical remediation methods, microorganisms are the most effective way to remediate 2,4-D pollution because of their rich species, wide distribution, and diverse metabolic pathways. Numerous studies demonstrate that the degradation of 2,4-D in the environment is primarily driven by enzymatic processes carried out by soil microorganisms. To date, a number of bacterial and fungal strains associated with 2,4-D biodegradation have been isolated, such as Sphingomonas, Pseudomonas, Cupriavidus, Achromobacter, Ochrobactrum, Mortierella, and Umbelopsis. Moreover, several key enzymes and genes responsible for 2,4-D biodegradation are also being identified. However, further in-depth research based on multi-omics is needed to elaborate their role in the evolution of novel catabolic pathways and the microbial degradation of 2,4-D. Here, this review provides a comprehensive analysis of recent progress on elucidating the degradation mechanisms of the herbicide 2,4-D, including the microbial strains responsible for its degradation, the enzymes participating in its degradation, and the associated genetic components. Furthermore, it explores the complex biochemical pathways and molecular mechanisms involved in the biodegradation of 2,4-D. In addition, molecular docking techniques are employed to identify crucial amino acids within an alpha-ketoglutarate-dependent 2,4-D dioxygenase that interacts with 2,4-D, thereby offering valuable insights that can inform the development of effective strategies for the biological remediation of this herbicide.

Background Pertussis is a highly contagious respiratory disease. Even though vaccination has reduced the incidence, cases have resurfaced in certain regions due to immune escape and waning vaccine efficacy. Identifying high-risk patients to mitigate transmission and avert complications promptly is imperative. Nevertheless, the current diagnostic methods, including PCR and bacterial culture, are time-consuming and expensive. Some studies have attempted to develop risk prediction models based on multivariate data, but their performance can be improved. Therefore, this study aims to further optimize and expand the risk assessment tool to more efficiently identify high-risk individuals and compensate for the shortcomings of existing diagnostic methods. Objective The aim of this study was to develop a pertussis risk prediction model that is both efficient and has good generalization ability, applicable to different datasets. The model was constructed using machine learning techniques based on multicenter data and screened for key features. The performance and generalization ability of the model were evaluated by deploying it on an online platform. At the same time, this study aims to provide a rapid and accurate auxiliary diagnostic tool for clinical practice to help identify high-risk patients in a timely manner, optimize early intervention strategies, reduce the risk of complications and reduce transmission, thereby improving the efficiency of public health management. Methods First, data from 1085 suspected pertussis patients from 7 centers were collected, and ten key features were analyzed using the lasso regression and Boruta algorithm: PDW-MPV-RATIO, SII, white blood cells, platelet distribution width, mean platelet volume, lymphocytes, cough duration, vaccination, fever, and lytic lymphocytes.Eight models were then trained and validated to assess their performance and to confirm their generalization ability with external datasets based on these features. Finally, an online platform was constructed for clinicians to use the models in real time. Results The random forest model demonstrated excellent discrimination ability in the validation set, with an AUC of 0.98, and an AUC of 0.97 in the external validation set. Calibration curve and decision curve analysis showed that the model had high accuracy in predicting low-to-medium risk patients, which could help clinicians avoid unnecessary interventions, especially in resource-limited settings. The application of this model can help optimize the early identification and management of high-risk patients and improve clinical decision-making. Conclusion The pertussis prediction model devised in this study was validated using multicenter data, exhibited high prediction performance, and was successfully implemented online. Future research should broaden the data sources and incorporate dynamic data to enhance the model's accuracy and applicability.

Background Thaumatin-like proteins (TLPs) are crucial pathogenesis-related proteins that significantly contribute to plant defense rection. Fusarium oxysporum f. sp. cubense ( Foc ) causes Fusarium wilt of bananas, a serious threat to global production. However, the role of TLPs in disease resistance remains unclear. Results This study identified 49 TLP genes in banana, predominantly localized in the extracellular space, and distributed across 11 chromosomes. The ancestor–descendant relationship was explained, six genes remained remarkably conserved across species could represent the ancestral genes of the TLP gene family. Promoter regions, transcriptome and qRT-PCR analysis suggested that MaTLP16 might be involved in disease resistance. Furthermore, transcriptional silencing of MaTLP16 resulted in more severe leaf damage compared to the control, indicating that MaTLP16 is an important Foc resistance-related gene. Conclusion This study conducted a comprehensive genome-wide identification and systematic analysis of the TLP gene family in bananas. Our findings establish a foundation for further functional studies of MaTLP genes and highlight MaTLP16 as a strong candidate for use in breeding programs aimed at enhancing resistance to Musa diseases.

Glyphosate is widely used as an herbicide around the world. The extensive application of glyphosate, however, has serious adverse effects on living systems. Therefore, the elimination of residual glyphosate pollution has become an urgent issue worldwide. In the present study, a novel bacterial strain named F9D was identified as Bacillus albus , based on its physio-biochemical characteristics and 16S rDNA analysis. This strain can completely degrade glyphosate (400 mg/L) within 5 days. An effective, rapid, and stable detection method for glyphosate and aminomethylphosphonic acid (AMPA) was developed using ultra-performance liquid chromatography–tandem mass spectrometry technology (UPLC-MS/MS). The degradability of glyphosate by the degrading strain F9D was optimized, considering various conditions, as follows: initial pH (5–9), incubation temperature (20–40℃), glyphosate concentration (50–800 mg/L), and inoculation amount (1–5%). The strain also demonstrated strong degradation ability in soil and water–sediment systems: 78.1% glyphosate (400 mg/kg) and 83.2% glyphosate (200 mg/kg), respectively, degraded in soil and water–sediment systems within 5 days of incubation. Furthermore, the F9D strain is capable of degrading 50–800 mg/L of glyphosate and AMPA under various treatments. Hence, the notable ability of B. albus strain F9D to degrade glyphosate makes it a highly promising candidate for the removal of this emerging contaminant from the environment on a large scale.

The chemical oxygen demand (COD) removal mechanism and reaction kinetics were mainly studied in the treatment of oilfield oily sewage containing polymer by three-dimensional electrode reactor. The results proved that the residual active oxides O3, H2O2, •OH and active chlorine in the system of electrochemical reaction could be effectively detected, and the COD removal mechanism was co-oxidation of active oxides; Under these experimental conditions: the electrolysis current of 6 A, surface/volume ratio of 6/25(cm2·L−1), the reaction time of 50 min, the CODcr of treated sewage was no more than 50 mg·L−1; the removal reaction of COD conformed to apparent second-order reaction kinetic model, the correlation coefficient R2 was 0.9728, and the apparent reaction rate constant was k = 3.58 × 10−4 (L·min−1·mg−1·m−2). To reach the goal, the CODcr was no more than 50 mg·L−1 in treated sewage, and the theory minimum processing time was 45.73 min. The verification of experimental results was consistent with kinetic equations.

This study aimed to assess the feasibility of using magnetic resonance imaging (MRI)-based Delta radiomics characteristics extrapolated from the Ax LAVA + C series to identify intermediary- and high-risk factors in patients with cervical cancer undergoing surgery following neoadjuvant chemoradiotherapy. A total of 157 patients were divided into two groups: those without any intermediary- or high-risk factors and those with one intermediary-risk factor (negative group; n = 75). Those with any high-risk factor or more than one intermediary-risk factor (positive group; n = 82). Radiomics characteristics were extracted using Ax-LAVA + C MRI sequences. The data was divided into training (n = 126) and test (n = 31) sets in an 8:2 ratio. The training set data features were selected using the Mann–Whitney U test and the Least Absolute Shrinkage and Selection Operator (LASSO) test. The best radiomics features were then analyzed to build a preoperative predictive radiomics model for predicting intermediary- and high-risk factors in cervical cancer. Three models—the clinical model, the radiomics model, and the combined clinic and radiomics model—were developed in this study utilizing the random forest Algorithm. The receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity were used to assess the predictive efficacy and clinical benefits of each model. Three models were developed in this study to predict intermediary- and high-risk variables associated with postoperative pathology for patients who underwent surgery after receiving neoadjuvant radiation. In the training and test sets, the AUC values assessed using the clinical model, radiomics model, and combined clinical and radiomics models were 0.76 and 0.70, 0.88 and 0.86, and 0.91 and 0.89, respectively. The use of machine learning algorithms to analyze Delta Ax LAVA + C MRI radiomics features can aid in the prediction of intermediary- and high-risk factors in patients with cervical cancer receiving neoadjuvant therapy.

Tetramethrin is a common pyrethroid insecticide, but there is limited knowledge about its degradation kinetics and mechanisms. In this study, a novel fungal strain, Neocosmospora sp. AF3, was obtained from pesticide-contaminated fields and was shown to be highly effective for degrading tetramethrin and other widely used pyrethroids. The AF3 strain completely removed 10 mg/L of tetramethrin from mineral salt medium in 9 days. The first-order kinetic analysis indicated that the degradation rate constant of the AF3 strain on 50 mg/L tetramethrin was 0.2835 d −1 (per day), and the half-life was 2.45 days. A response surface model analysis showed that the optimal degradation conditions for the AF3 strain are a temperature of 33.37 ℃, pH of 7.97, and inoculation amount of 0.22 g/L dry weight. The Andrews nonlinear fitting results suggested that the optimal concentration of tetramethrin metabolized by the AF3 strain is 12.6073 mg/L, and the q max , K i , and K s values were 0.9919 d −1 , 20.1873 mg/L, and 7.8735 mg/L, respectively. The gas chromatography–mass spectrometry (GC–MS) analysis indicated that N -hydroxymethyl-3,4,5,6-tetrahydrophthalimide, chrysanthemic acid and tetrahydrophthalimide are the main intermediates involved in the metabolism of tetramethrin by the AF3 strain. Furthermore, this strain was shown to effectively degrade other pyrethroid pesticides including permethrin, beta-cypermethrin, chlorempenthrin, fenvalerate, d -cyphenothrin, bifenthrin, meperfluthrin, cyfluthrin, and deltamethrin within a short period, suggesting that Neocosmospora sp. AF3 can play an important role in the remediation of pyrethroid contamination. Taken together, these results shed a new light on uncovering the degradation mechanisms of tetramethrin and present useful agents for developing relevant pyrethroid bioremediation strategies.

Objective Diagnosing musculoskeletal infections in children is challenging. In recent years, with the advancement of ultrasound technology, high-resolution ultrasound has unique advantages for musculoskeletal children. The aim of this work is to summarize the ultrasonographic and clinical characteristics of children with pyogenic arthritis and osteomyelitis. This study provides a simpler and more effective diagnostic basis for clinical treatment. Methods Fifty children with osteomyelitis or arthritis were diagnosed via ultrasound, and the results of the ultrasound diagnosis were compared with those of magnetic resonance imaging and surgery. Clinical and ultrasound characteristics were also analyzed. Results Out of 50 patients, 46 were confirmed to have suppurative infection by surgical and microbiological examination. Among these 46 patients, 26 were diagnosed with osteomyelitis and 20 had arthritis. The manifestations of osteomyelitis were subperiosteal abscess (15 patients), bone destruction (17 patients), bone marrow abscess (9 patients), and adjacent joint abscess (13 patients). Osteomyelitis mostly affects the long bones of the limbs, femur and humerus (10 and 9 patients, respectively), followed by the ulna, radius, tibia and fibula (one patient each). The manifestations of arthritis were joint pus (20 patients) and joint capsule thickening (20 patients), and hip dislocation (8 patients). All the patients had arthritis involving the hip joint. Conclusion Subperiosteal abscess, bone destruction, and joint abscess with dislocation are ultrasonographic features of pyogenic osteoarthritis. The findings of this work can improve the early diagnosis and differentiation of pyogenic osteoarthritis and provide a reliable basis for treatment.

Vast quantities of synthetic pesticides have been widely applied in various fields to kill plant pathogens, resulting in increased pathogen resistance and decreased effectiveness of such chemicals. In addition, the increased presence of pesticide residues affects living organisms and the environment largely on a global scale. To mitigate the impact of crop diseases more sustainably on plant health and productivity, there is a need for more safe and more eco-friendly strategies as compared to chemical prevention. Quorum sensing (QS) is an intercellular communication mechanism in a bacterial population, through which bacteria adjust their population density and behavior upon sensing the levels of signaling molecules in the environment. As an alternative, quorum quenching (QQ) is a promising new strategy for disease control, which interferes with QS by blocking intercellular communication between pathogenic bacteria to suppress the expression of disease-causing genes. Black rot caused by Xanthomonas campestris pv. campestris ( Xcc ) is associated with the diffusible signal factor (DSF). As detailed in this study, a new QQ strain F25, identified as Burkholderia sp., displayed a superior ability to completely degrade 2 mM of DSF within 72 h. The main intermediate product in the biodegradation of DSF was identified as n-decanoic acid, based on gas chromatography-mass spectrometry (GC-MS). A metabolic pathway for DSF by strain F25 is proposed, based on the chemical structure of DSF and its intermediates, demonstrating the possible degradation of DSF via oxidation-reduction. The application of strain F25 and its crude enzyme as biocontrol agents significantly attenuated black rot caused by Xcc , and inhibited tissue maceration in the host plant Raphanus sativus L., without affecting the host plant. This suggests that agents produced from strain F25 and its crude enzyme have promising applications in controlling infectious diseases caused by DSF-dependent bacterial pathogens. These findings are expected to provide a new therapeutic strategy for controlling QS-mediated plant diseases.