Rapid determination of glyphosate and glufosinate in human blood by probe electrospray ionization tandem mass spectrometry.

In forensic science, glyphosate (GLYP) and glufosinate (GLUF), a class of non-selective broad-spectrum herbicides, have been frequently encountered in many fatal poisoning and suicide cases due to their widespread availability. Therefore, it is essential to develop an effective method for detecting these compounds. Some conventional methods, such as gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS), have been reported to detect these compounds. However, these methods are not ideal for their time-consuming and non-sensitive feature. Herein, probe electrospray ionization (PESI) tandem mass spectrometry (MS/MS), a fast and sensitive technique, was applied for the determination of GLYP and GLUF in human blood, which can obtain analytical results within 0.5 min without derivatization and chromatographic separation. After protein precipitation of blood samples, the supernatant was mixed with isopropanol and ultra-pure water (1:1 v/v). Then, 8 µL of the mixture was introduced into the plastic sample plate for PESI-MS/MS analysis. The limits of detection (LODs) of the method were 0.50 µg/mL and 0.25 µg/mL for two analytes, and the limits of quantitation (LOQs) were both 1.00 µg/mL, which are higher than the concentration of reported poisoning and fatal cases. In the linear range of 1-500 µg/mL, the regression coefficients (r2) for GLYP and GLUF were over 0.99. The matrix effects ranged from 94.8 % to 119.5 %, and the biases were below 4.3 %. The recoveries ranged between 84.8 % and 107.4 %, and the biases were below 7.6 %. Meanwhile, the method was effectively utilized to detect and quantify the blood, urine, and other samples. Consequently, the results suggest that PESI-MS/MS is a straightforward, fast, and sensitive method for detecting GLUF and GLYP in forensics. In the future, PESI-MS/MS will become an indispensable technique for polar substances in grassroots units of public security where rapid detection is essential.

The influence of environmental factors on the job burnout of physical education teachers in tertiary education.

This study takes environmental factors and individual factors as variables to explore the deep internal mechanism of the impact of a comprehensive environment on higher education physical education (PE) teachers' job burnout. Little research has been done on how environmental factors affect the internal mechanism of college and university PE teachers' job burnout through individual factors (e.g., professional pressure and teaching efficacy). In this study, the participants were 231 PE teachers from seven comprehensive universities, and four questionnaires were administered to measure the participants' job burnout, perceived overall environment, teaching efficacy, and occupational stress. Research has found that environmental factors have a significant negative impact on occupational stress, and occupational stress plays an important mediating role between environment and occupational burnout. Research has shown that differences in external environments lead to varying levels of personal stress among college physical education teachers, which in turn affects their level of occupational burnout. The study concludes that a good social, working, and living environment helps to reduce the work pressure on PE teachers, improves their sense of teaching efficacy, and inhibits the occurrence of teachers' job burnout.

Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study.

Purpose: Patients with advanced prostate cancer (PCa) often develop castration-resistant PCa (CRPC) with poor prognosis. Prognostic information obtained from multiparametric magnetic resonance imaging (mpMRI) and histopathology specimens can be effectively utilized through artificial intelligence (AI) techniques. The objective of this study is to construct an AI-based CRPC progress prediction model by integrating multimodal data. Methods and materials: Data from 399 patients diagnosed with PCa at three medical centers between January 2018 and January 2021 were collected retrospectively. We delineated regions of interest (ROIs) from 3 MRI sequences viz, T2WI, DWI, and ADC and utilized a cropping tool to extract the largest section of each ROI. We selected representative pathological hematoxylin and eosin (H&E) slides for deep-learning model training. A joint combined model nomogram was constructed. ROC curves and calibration curves were plotted to assess the predictive performance and goodness of fit of the model. We generated decision curve analysis (DCA) curves and Kaplan-Meier (KM) survival curves to evaluate the clinical net benefit of the model and its association with progression-free survival (PFS). Results: The AUC of the machine learning (ML) model was 0.755. The best deep learning (DL) model for radiomics and pathomics was the ResNet-50 model, with an AUC of 0.768 and 0.752, respectively. The nomogram graph showed that DL model contributed the most, and the AUC for the combined model was 0.86. The calibration curves and DCA indicate that the combined model had a good calibration ability and net clinical benefit. The KM curve indicated that the model integrating multimodal data can guide patient prognosis and management strategies. Conclusion: The integration of multimodal data effectively improves the prediction of risk for the progression of PCa to CRPC.

Extreme drought along the tropic of cancer (Yunnan section) and its impact on vegetation.

The frequent occurrence of extreme weather events is one of the future prospects of climate change, and how ecosystems respond to extreme drought is crucial for response to climate change. Taking the extreme drought event in the Tropic of Cancer (Yunnan section) during 2009-2010 as a case study, used the standardized precipitation evapotranspiration index to analyse the impact of extreme drought on enhanced vegetation index (EVI), leaf area index (LAI) and gross primary productivity (GPP), and to analyzed the post extreme drought vegetation recovery status. The results indicate the following: (1) Due to the cumulative effects of drought and vegetation phenology, vegetation growth in the months of March to May in 2010 was more severely affected. (2) Compared to EVI and LAI, GPP is more sensitive to drought and can accurately indicate areas where drought has impacted vegetation. (3) Following an extreme drought event, 70% of the vegetation can recover within 3 months, while 2.87-6.57% of the vegetation will remain unrecovered after 6 months. (4) Cropland and grassland show the strongest response, with longer recovery times, while woodland and shrubland exhibit weaker responses and shorter recovery times. This study provides a reference for the effects of extreme drought on vegetation.

Internet of things-based management versus standard management of home noninvasive ventilation in COPD patients with hypercapnic chronic respiratory failure: a multicentre randomized controlled non-inferiority trial.

Background: Effective monitoring and management are crucial during long-term home noninvasive positive pressure ventilation (NPPV) in patients with hypercapnic chronic obstructive pulmonary disease (COPD). This study investigated the benefit of Internet of Things (IOT)-based management of home NPPV. Methods: This multicenter, prospective, parallel-group, randomized controlled non-inferiority trial enrolled patients requiring long-term home NPPV for hypercapnic COPD. Patients were randomly assigned (1:1), via a computer-generated randomization sequence, to standard home management or IOT management based on telemonitoring of clinical and ventilator parameters over 12 months. The intervention was unblinded, but outcome assessment was blinded to management assignment. The primary outcome was the between-group comparison of the change in health-related quality of life, based on severe respiratory insufficiency questionnaire scores with a non-inferiority margin of -5. This study is registered with Chinese Clinical Trials Registry (No. ChiCTR1800019536). Findings: Overall, 148 patients (age: 72.7 ± 6.8 years; male: 85.8%; forced expiratory volume in 1 s: 0.7 ± 0.3 L; PaCO2: 66.4 ± 12.0 mmHg), recruited from 11 Chinese hospitals between January 24, 2019, and June 28, 2021, were randomly allocated to the intervention group (n = 73) or the control group (n = 75). At 12 months, the mean severe respiratory insufficiency questionnaire score was 56.5 in the intervention group and 50.0 in the control group (adjusted between-group difference: 6.26 [95% CI, 3.71-8.80]; P < 0.001), satisfying the hypothesis of non-inferiority. The 12-month risk of readmission was 34.3% in intervention group compared with 56.0% in the control group, adjusted hazard ratio of 0.56 (95% CI, 0.34-0.92; P = 0.023). No severe adverse events were reported. Interpretation: Among stable patients with hypercapnic COPD, using IOT-based management for home NPPV improved health-related quality of life and prolonged the time to readmission. Funding: Air Liquide Healthcare (Beijing) Co., Ltd.

odd skipped-related 2 as a novel mark for labeling the proximal convoluted tubule within the zebrafish kidney.

The proximal convoluted tubule (PCT) of the kidney is a crucial functional segment responsible for reabsorption, secretion, and the maintenance of electrolyte and water balance within the renal tubule. However, there is a lack of a well-defined endogenous transgenic line for studying PCT morphogenesis. By analyzing single-cell transcriptome data from the adult zebrafish kidney, we have identified the expression of odd-skipped-related 2 (osr2, which encodes an odd-skipped zinc-finger transcription factor) in the PCT. To gain insight into the role of osr2 in PCT morphogenesis, we have generated a transgenic zebrafish line Tg(osr2:EGFP), expressing enhanced green fluorescent protein (EGFP). The EGFP expression pattern closely mirrors that of endogenous Osr2, faithfully recapitulating its native expression profile. During kidney development, we can use EGFP to track PCT development, which is also preserved in adult zebrafish. Additionally, osr2:EGFP-labeled zebrafish PCT fragments displayed short lengths with infrequent overlap, rendering them conducive for nephrons counting. The generation of Tg(osr2:EGFP) transgenic line is accompanied by simultaneous disruption of osr2 activity. Importantly, our findings demonstrate that osr2 inactivation had no discernible impact on the development and regeneration of Tg(osr2:EGFP) zebrafish nephrons. Overall, the establishment of this transgenic zebrafish line offers a valuable tool for both genetic and chemical analysis of PCT.

First Report of the Rust Fungus Melampsora ferrinii on Salix babylonica in China and a New Spermogonial and Aecial Host, Corydalis bungeana.

The occurrence of rust fungi on Corydalis bungeana Turcz. and Salix babylonica L. were found in same area of Hebei Province, China from 2022 to 2023. The life cycle connection of these rust fungi was suspected because Peng et al. (2022) reported the life cycle of Melampsora ferrinii Toome & Aime by inoculations, producing spermogonia and aecia on Corydalis species, and uredinia on S. babylonica. The morphology of the uredinial and telial stages on S. babylonica collected in the field was identical with the description of M. ferrinii by Toome and Aime (2015), and its identity was confirmed by phylogenetic analyses using the method of Ji et al. (2020) (LSU-PP087777, ITS-PP091274; Similarity with M. ferrinii: LSU-100%, ITS-99.85%). To confirm the life cycle of this rust fungus, inoculations were conducted on C. bungeana with basidiospores obtained from the teliospores on fallen leaves of Salix babylonica. The fallen leaves producing basidiospores were cut into small pieces (ca. 5 mm2) and placed on healthy leaves of C. bungeana. The inoculated plants were kept in a moist plastic box in darkness at 15-20℃ for 2 days and then transferred to the floor near windows at about 15-20℃ for observations. Ten days after inoculations small yellow spots of spermogonia appeared on the upper surface of the leaves of C. bungeana. About 7 days later, pale yellow aecia with aeciospores were produced mainly on the under surface of the leaves and petioles. The morphology of rust fungus on C. bungeana collected from the fields and obtained by inoculations was identical with the description by Peng et al. (2022). Phylogenetic analyses also showed that a specimen on C. bungeana collected from the field (LSU-OR607838, ITS-OR612063) were included into the same clade of M. ferrinii (Similarity: LSU-100 %, ITS-99.85). Based on morphology, inoculations and DNA sequence analyses, the rust fungi on C. bungeana and S. babylonica are identified as different stages of life cycle of M. ferrinii. This rust fungus has been reported to produce spermogonia and aecia on C. acuminata Franch., C. edulis Maxim. and C. racemosa (Thunb.) Pers. in China (Peng et al. 2022), and uredinia and telia on S. babylonica in USA, Argentina and Iran (Toome and Aime 2015, Abbasi et al. 2024), and on Salix sp. in Chile (Zapata 2016). Therefore, C. bungeana is a new host for M. ferrinii, and its field occurrence on S. babylonica is reported for the first time in China although Peng et al. (2022) reported successful results in its inoculations to S. babylonica in China. This report contributes to the control of rust diseases caused by this species. Specimens used in this experiment were deposited in the Fungal Herbarium of the Jilin Agricultural University, Changchun, China (HMJAU) and sequences newly analyzed were deposited in GenBank.

[Development of an analytical system for dried blood spots for forensic toxicology: a case study of five common drugs and poisons].

Dried blood spot (DBS) technology is a simple and convenient method for collecting, transporting, and storing blood samples on filter paper, and has numerous applications in the clinical, research, and public health settings. This technique is gaining popularity in the field of forensic science because it facilitates the rapid analysis of prohibited drugs in blood samples and offers significant advantages in toxicology scenarios such as drinking-driving screening, drug abuse detection, and doping detection. However, the lack of a standardized system and the fact that its stability and reliability have not been thoroughly researched and demonstrated limit its application in judicial practice in China. DBS samples can be prepared, stored, and analyzed in various ways, all of which may significantly affect the results. In this study, we developed a method based on ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) that focuses on the preparation, pretreatment, analysis, and storage of DBS samples. A thorough investigation was conducted to examine the optimal preparation conditions, including the blood spot matrix, drying technique, and preprocessing parameters, such as the solvent and extraction method. Moreover, the analytical conditions, such as the mobile phase system and elution gradient, were established to facilitate the quantitative detection of methamphetamine, lidocaine, ketamine, fentanyl, and diazepam in both DBS and whole-blood samples. The impact of storage conditions, such as the temperature, humidity, and sealing, on the analytical results of the DBS and whole-blood samples was also examined. The results showed a strong linear relationship for lidocaine and fentanyl within the range of 0.5-100 ng/mL. Similarly, methamphetamine, ketamine, and diazepam exhibited good linearity within the range of 2-100 ng/mL. The coefficients of determination (r2) ranged from 0.9983 to 0.9997, and the limits of detection ranged from 0.2 to 0.5 ng/mL, indicating a high degree of correlation and sensitivity. Stability tests demonstrated that the five target substances remained stable in the DBS for 60 days, with the measured contents deviating from the nominal values by 15%. Moreover, the measurement results of the DBS samples were highly similar to those of the whole-blood samples, with mean percentage differences of 4.44%, 3.50%, 7.66%, 5.10%, and 5.25% for fentanyl, diazepam, ketamine, lidocaine, and methamphetamine, respectively. Throughout the 60-day storage period, the maintenance of temperatures of -20 and 4 ℃, as well as sealing and dry storage, was not necessary. Room temperature was the most practical storage environment for the DBS samples. The results for each target showed very small concentration differences between the whole-blood and DBS samples, indicating that the DBS samples were suitable for drug and poison analysis in blood. Furthermore, the DBSs exhibited high quantitative consistency with the whole-blood samples, rendering them suitable matrices for preserving blood samples. Because DBS samples are easy to handle and store, they can realize the lightweight preservation of blood samples and provide a novel solution for the analysis and preservation of blood samples in public security practice. We recommend conducting comprehensive validations before utilizing DBS for analysis, particularly in terms of quantification, to ensure the judicial reliability of the results.

Engineering functional materials through bacteria-assisted living grafting.

Functionalizing materials with biomacromolecules such as enzymes has broad applications in biotechnology and biomedicine. Here, we introduce a grafting method mediated by living cells to functionalize materials. We use polymeric scaffolds to trap engineered bacteria and micron-sized particles with chemical groups serving as active sites for grafting. The bacteria synthesize the desired protein for grafting and autonomously lyse to release it. The released functional moieties are locally grafted onto the active sites, generating the materials engineered by living grafting (MELGs). MELGs are resilient to perturbations because of both the bonding and the regeneration of functional domains synthesized by living cells. The programmability of the bacteria enables us to fabricate MELGs that can respond to external input, decompose a pollutant, reconstitute synthetic pathways for natural product synthesis, and purify mismatched DNA. Our work establishes a bacteria-assisted grafting strategy to functionalize materials with a broad range of biological activities in an integrated, flexible, and modular manner. A record of this paper's transparent peer review process is included in the supplemental information.

Morules and ß-catenin predict POLE mutation status in endometrial cancer: A pathway to more cost-effective diagnostic procedures.

OBJECTIVES: The characterization of DNA polymerase epsilon (POLE) mutations has transformed the classification of endometrial endometrioid carcinomas (EECs), highlighting the need for efficient identification methods. This study aims to examine the relationship between distinct morphologic features-namely, squamous morules and squamous differentiation (SD), as well as ß-catenin expression-and the POLE mutation status in endometrial cancer (EC). METHODS: Our study included 35 POLE-mutated (POLEmut) EC cases and 395 non-POLEmut EEC cases. RESULTS: Notably, we observed no presence of morules in POLEmut cases, while SD was identified in 20% of instances. Conversely, morules and SD were identified in 12.7% and 26.1% of non-POLEmut EC cases, respectively, with morules consistently linked to a POLE wild-type status. The nuclear ß-catenin expression is typically absent in tumors with wild-type POLE (wt-POLE) status. CONCLUSIONS: Our findings suggest that the presence of either morules or nuclear ß-catenin expression in EEC could practically rule out the presence of POLE mutations. These morphologic and immunohistochemical features can be used as preliminary screening tools for POLE mutations, offering significant savings in time and resources and potentially enhancing clinical decision-making and patient management strategies. However, further validation in larger, multi-institutional studies is required to fully understand the implications of these findings on clinical practice.

Discovering potential WRN inhibitors from natural product database through computational methods.

Microsatellite instability (MSI) is a relatively common feature associated with multiple cancers, and Werner syndrome (WRN) ATP-dependent helicase has been recognized as a novel target for treating MSI cancers, such as colorectal cancer. A small-molecule inhibitor targeting WRN would be a promising strategy for treating colorectal cancer with high MSI expression. In this study, we employed a computer-assisted drug discovery strategy to screen over 30,000 natural product molecules. By using a combination of docking, ligand efficiency, Molecular Mechanics/Generalized Born Surface Area (MM/GBSA), and thermodynamic integration (TI) calculations, we identified MOL008980, MOL010740, MOL011832, T4743, TN1166, and TNP-002173 as potential WRN inhibitors. Subsequent molecular dynamics simulation revealed that these screened natural products possessed better binding dynamic characteristics than ATP substrate and were capable of inhibiting the dynamic process of WRN, making them potential strong ATP competitive inhibitors. In conclusion, our computational approach revealed potential WRN inhibitors from a natural product database, providing a theoretical basis for future research.

Understanding learners' perceptions of ChatGPT: A thematic analysis of peer interviews among undergraduates and postgraduates in China.

ChatGPT, an artificial intelligence (AI)-driven language model engineered by OpenAI, has experienced a substantial upsurge in adoption within higher education due to its versatile applications and sophisticated capabilities. Although prevailing research on ChatGPT has predominantly concentrated on its technological aspects and pedagogical ramifications, a comprehensive understanding of students' perceptions and experiences regarding ChatGPT remains elusive. To address this gap, this study employed a peer interview methodology, conducting a thematic analysis of 106 first-year undergraduates and 81 first-year postgraduate students' perceptions from diverse disciplines at a comprehensive university in East China. The data analysis revealed that among the four factors examined-grade, age, gender, and major-grade emerged as the most influential determinant, followed by age and major. Postgraduate students demonstrated heightened awareness of the potential limitations of ChatGPT in addressing academic challenges and exhibited greater concern for security issues associated with its application. This research offers essential insights into students' perceptions and experiences with ChatGPT, emphasizing the importance of recognizing potential limitations and ethical concerns associated with ChatGPT usage. Additionally, the findings highlight ethical concerns, as students noted the importance of responsible data handling and academic integrity in ChatGPT usage, underscoring the need for ethical guidance in AI utilization. Moreover, further research is essential to optimize AI use in education, aiming to improve learning outcomes effectively.

Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer.

PURPOSE: Bone metastasis is a significant contributor to morbidity and mortality in advanced prostate cancer, and early diagnosis is challenging due to its insidious onset. The use of machine learning to obtain prognostic information from pathological images has been highlighted. However, there is a limited understanding of the potential of early prediction of bone metastasis through the feature combination method from various sources. This study presents a method of integrating multimodal data to enhance the feasibility of early diagnosis of bone metastasis in prostate cancer. METHODS AND MATERIALS: Overall, 211 patients diagnosed with prostate cancer (PCa) at Gansu Provincial Hospital between January 2017 and February 2023 were included in this study. The patients were randomized (8:2) into a training group (n = 169) and a validation group (n = 42). The region of interest (ROI) were segmented from the three magnetic resonance imaging (MRI) sequences (T2WI, DWI, and ADC), and pathological features were extracted from tissue sections (hematoxylin and eosin [H&E] staining, 10 × 20). A deep learning (DL) model using ResNet 50 was employed to extract deep transfer learning (DTL) features. The least absolute shrinkage and selection operator (LASSO) regression method was utilized for feature selection, feature construction, and reducing feature dimensions. Different machine learning classifiers were used to build predictive models. The performance of the models was evaluated using receiver operating characteristic curves. The net clinical benefit was assessed using decision curve analysis (DCA). The goodness of fit was evaluated using calibration curves. A joint model nomogram was eventually developed by combining clinically independent risk factors. RESULTS: The best prediction models based on DTL and pathomics features showed area under the curve (AUC) values of 0.89 (95% confidence interval [CI], 0.799-0.989) and 0.85 (95% CI, 0.714-0.989), respectively. The AUC for the best prediction model based on radiomics features and combining radiomics features, DTL features, and pathomics features were 0.86 (95% CI, 0.735-0.979) and 0.93 (95% CI, 0.854-1.000), respectively. Based on DCA and calibration curves, the model demonstrated good net clinical benefit and fit. CONCLUSION: Multimodal radiomics and pathomics serve as valuable predictors of the risk of bone metastases in patients with primary PCa.

Construction and validation of a clinical prediction model for deep vein thrombosis in patients with digestive system tumors based on a machine learning.

This study developed a deep vein thrombosis (DVT) risk prediction model based on multiple machine learning methods for patients with digestive system tumors undergoing surgical treatment. Data of 1048 patients with digestive system tumors admitted to Shanxi Provincial People's Hospital (College of Shanxi Medical University) from January 2020 to January 2023 were retrospectively analyzed, and 845 cases were screened according to the inclusion and exclusion criteria. The patients were divided into a training group (586 patients), and a validation group (259 patients), then feature selection was performed using six models, including Lasso regression, XGBoost, Random Forest, Decision Tree, Support Vector Machine, and Logistics. Predictive models were subsequently constructed from column-line plots, and the predictive validity of the models was assessed using receiver operating characteristic curves, precision-recall curves, and decision-curve analysis. In the model comparison, the XGBoost model showed the largest area under the curve (AUC) on the validation set (P < 0.05), demonstrating excellent predictive performance and generalization ability. We selected the common characteristic factors in the six models to further develop the column line plots to assess the DVT risk. The model performed well in clinical validation and effectively differentiated high-risk and low-risk patients. The differences in BMI, procedure time, and D-dimer were statistically significant between patients in the thrombus group and those in the non-thrombus group (P < 0.05). However, the AUC of the Xgboost model was found to be greater than that of the column chart model by the Delong test (P < 0.05). BMI, procedure time, and D-dimer are critical predictors of DVT risk in patients with digestive system tumors. Our model is an adequate assessment tool for DVT risk, which can help improve the prevention and treatment of DVT.

Inflammation-related pathology in the olfactory epithelium: its impact on the olfactory system in psychotic disorders.

Smell deficits and neurobiological changes in the olfactory bulb (OB) and olfactory epithelium (OE) have been observed in schizophrenia and related disorders. The OE is the most peripheral olfactory system located outside the cranium, and is connected with the brain via direct neuronal projections to the OB. Nevertheless, it is unknown whether and how a disturbance of the OE affects the OB in schizophrenia and related disorders. Addressing this gap would be the first step in studying the impact of OE pathology in the disease pathophysiology in the brain. In this cross-species study, we observed that chronic, local OE inflammation with a set of upregulated genes in an inducible olfactory inflammation (IOI) mouse model led to a volume reduction, layer structure changes, and alterations of neuron functionality in the OB. Furthermore, IOI model also displayed behavioral deficits relevant to negative symptoms (avolition) in parallel to smell deficits. In first episode psychosis (FEP) patients, we observed a significant alteration in immune/inflammation-related molecular signatures in olfactory neuronal cells (ONCs) enriched from biopsied OE and a significant reduction in the OB volume, compared with those of healthy controls (HC). The increased expression of immune/inflammation-related molecules in ONCs was significantly correlated to the OB volume reduction in FEP patients, but no correlation was found in HCs. Moreover, the increased expression of human orthologues of the IOI genes in ONCs was significantly correlated with the OB volume reduction in FEP, but not in HCs. Together, our study implies a potential mechanism of the OE-OB pathology in patients with psychotic disorders (schizophrenia and related disorders). We hope that this mechanism may have a cross-disease implication, including COVID-19-elicited mental conditions that include smell deficits.

Pathway Evolution Through a Bottlenecking-Debottlenecking Strategy and Machine Learning-Aided Flux Balancing.

The evolution of pathway enzymes enhances the biosynthesis of high-value chemicals, crucial for pharmaceutical, and agrochemical applications. However, unpredictable evolutionary landscapes of pathway genes often hinder successful evolution. Here, the presence of complex epistasis is identifued within the representative naringenin biosynthetic pathway enzymes, hampering straightforward directed evolution. Subsequently, a biofoundry-assisted strategy is developed for pathway bottlenecking and debottlenecking, enabling the parallel evolution of all pathway enzymes along a predictable evolutionary trajectory in six weeks. This study then utilizes a machine learning model, ProEnsemble, to further balance the pathway by optimizing the transcription of individual genes. The broad applicability of this strategy is demonstrated by constructing an Escherichia coli chassis with evolved and balanced pathway genes, resulting in 3.65 g L-1 naringenin. The optimized naringenin chassis also demonstrates enhanced production of other flavonoids. This approach can be readily adapted for any given number of enzymes in the specific metabolic pathway, paving the way for automated chassis construction in contemporary biofoundries.

Transcriptome analysis reveals the effect of propyl gallate on kiwifruit callus formation.

KEY MESSAGE: Exploring the potential action mechanisms of reactive oxygen species during the callus inducing, they can activate specific metabolic pathways in explants to regulate callus development. Reactive oxygen species (ROS) play an important role in the regulation of plant growth and development, but the mechanism of their action on plant callus formation remains to be elucidated. To address this question, kiwifruit was selected as the explant for callus induction, and the influence of ROS on callus formation was investigated by introducing propyl gallate (PG) as an antioxidant into the medium used for inducing callus. The results have unveiled that the inclusion of PG in the medium has disturbed the equilibrium of ROS during the formation of the kiwifruit callus. We selected the callus that was induced by the addition of 0.05 mmol/L PG to the MS medium. The callus exhibited a significant difference in the amount compared to the control medium without PG. The callus induced by the MS medium without PG was used as the control for comparison. KEGG enrichment indicated that PG exposure resulted in significant differences in gene expression in related pathways, such as phytohormone signaling and glutathione in kiwifruit callus. Weighted gene co-expression analysis indicated that the pertinent regulatory networks of both ROS and phytohormone signaling were critical for the establishment of callus in kiwifruit leaves. In addition, during the process of callus establishment, the ROS level of the explants was also closely related to the genes for transmembrane transport of substances, cell wall formation, and plant organ establishment. This investigation expands the theory of ROS-regulated callus formation and presents a new concept for the expeditious propagation of callus in kiwifruit.

Evaluation of nocturnal apnea and airflow limitation as indicators for cognitive dysfunction in patients with chronic obstructive pulmonary disease/obstructive sleep apnea hypopnea syndrome overlap syndrome.

OBJECTIVE: The aim of this study is to investigate how much intermittent hypoxemia and airflow limitation contribute to cognitive impairment in overlap syndrome (OS), which is the coexistence of two common diseases, obstructive sleep apnea hypopnea syndrome (OSAHS) and chronic obstructive pulmonary disease (COPD). METHODS: We conducted a cross-sectional study of patients with OSAHS, COPD or OS, compared with normal controls, to determine the association between sleep apnea/pulmonary function-related indicators and cognitive dysfunction in individuals with OSAHS, COPD or OS. RESULTS: A total of 157 participants were recruited. Both OSAHS and OS presented lower adjusted Montreal cognitive assessment (MoCA) scores compared with COPD group. In addition, the MoCA score was significantly lower in COPD group compared with control group. The incidence of cognitive impairment was 57.4% in OSAHS group, and 78% in OS group, which were significantly higher than COPD group (29%) and control group (8.8%). Furthermore, a broader range of cognitive domains were affected in OS group compared with OSAHS group. Elevated levels of oxygen desaturation index (ODI) and/or apnea hypopnea index (AHI) were positively correlated with increased Epworth sleeping scale (ESS) in OSAHS and OS. Forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1) and peak expiratory flow (PEF) were positively correlated with cognitive scores in OSAHS but not in OS. Serum level of hypoxia-inducible factor-1α (HIF-1α) was significantly higher in OS. Logistic regression identified ODI as an independent risk factor for cognitive impairment in OS, while severity of snoring and PEF were independent risk factors in OSAHS. DISCUSSION: This study revealed significant cognitive impairment in OS, OSAHS and COPD. Sleep-related indicators are warranted in OS patients for detection, differentiation and grading of cognitive impairment, whereas pulmonary functions are warranted in OSAHS patients for detection and early intervention of cognitive impairment.

Pseudomonas chlororaphis IRHB3 assemblies beneficial microbes and activates JA-mediated resistance to promote nutrient utilization and inhibit pathogen attack.

Introduction: The rhizosphere microbiome is critical to plant health and resistance. PGPR are well known as plant-beneficial bacteria and generally regulate nutrient utilization as well as plant responses to environmental stimuli. In our previous work, one typical PGPR strain, Pseudomonas chlororaphis IRHB3, isolated from the soybean rhizosphere, had positive impacts on soil-borne disease suppression and growth promotion in the greenhouse, but its biocontrol mechanism and application in the field are not unclear. Methods: In the current study, IRHB3 was introduced into field soil, and its effects on the local rhizosphere microbiome, disease resistance, and soybean growth were comprehensively analyzed through high-throughput sequencing and physiological and molecular methods. Results and discussion: We found that IRHB3 significantly increased the richness of the bacterial community but not the structure of the soybean rhizosphere. Functional bacteria related to phosphorus solubilization and nitrogen fixation, such as Geobacter, Geomonas, Candidatus Solibacter, Occallatibacter, and Candidatus Koribacter, were recruited in rich abundance by IRHB3 to the soybean rhizosphere as compared to those without IRHB3. In addition, the IRHB3 supplement obviously maintained the homeostasis of the rhizosphere microbiome that was disturbed by F. oxysporum, resulting in a lower disease index of root rot when compared with F. oxysporum. Furthermore, JA-mediated induced resistance was rapidly activated by IRHB3 following PDF1.2 and LOX2 expression, and meanwhile, a set of nodulation genes, GmENOD40b, GmNIN-2b, and GmRIC1, were also considerably induced by IRHB3 to improve nitrogen fixation ability and promote soybean yield, even when plants were infected by F. oxysporum. Thus, IRHB3 tends to synergistically interact with local rhizosphere microbes to promote host growth and induce host resistance in the field.

Applying ChatGPT to tackle the side effects of personal learning environments from learner and learning perspective: An interview of experts in higher education.

This paper investigates the capacity of ChatGPT, an advanced language model created by OpenAI, to mitigate the side effects encountered by learners in Personal Learning Environments (PLEs) within higher education. A series of semi-structured interviews were conducted with six professors and three Information and Communication Technology (ICT) experts. Employing thematic analysis, the interview data were assessed, revealing that the side effects stemming from the learner and learning perspectives could be primarily categorized into cognitive, non-cognitive, and metacognitive challenges. The findings of the thematic analysis indicate that, from a cognitive standpoint, ChatGPT can generate relevant and trustworthy information, furnish personalized learning resources, and facilitate interdisciplinary learning to fully actualize learners' potential. Moreover, ChatGPT can aid learners in cultivating non-cognitive skills, including motivation, perseverance, self-regulation, and self-efficacy, as well as metacognitive abilities such as self-determination, self-efficacy, and self-regulation, by providing tailored feedback, fostering creativity, and stimulating critical thinking activities. This study offers valuable insights for integrating artificial intelligence technologies to unleash the full potential of PLEs in higher education.