Response of bacterial and fungal communities in natural biofilms to bioavailable heavy metals in a mining-affected river.

Biofilms, known as "microbial skin" in rivers, respond to rapid and sensitive environmental changes. However, the ecological response mechanisms of bacterial and fungal communities in river biofilms toward heavy metal pollution (HMP) remains poorly understood. This study focused on the key driving factors of bacterial and fungal community diversity and composition and their ecological response mechanisms within periphytic biofilms of Asia's largest Pb-Zn mining area. The diversity, dominant bacterial taxa, and bacteria structure in biofilms were influenced by biologically available heavy metal (HM) fractions, with Ni-F3 (17.96 %) and Pb-F4 (16.27 %) as the main factors affecting the bacterial community structure. Fungal community structure and α-diversity were more susceptible to physicochemical parameters (pH and nutrient elements). Partial least squares path modeling revealed that environmental factors influencing bacterial and fungal communities in biofilms were ranked as water quality > metal fractions > total metals. Dispersal limitation was the most critical ecological process in bacterial (56.9 %) and fungal (73.4 %) assembly. The proportion of heterogeneous selection by bacteria (39.5 %) was higher than that of fungus (26.2 %), which increased with increasing HMP. Bacterial communities had a higher migration rate (0.48) and ecological drift proportion (3.6 %), making them more prone to escape environmental stress. Fungal communities exhibited more keystone species, larger niche width (23.24 ± 13.04 vs. 9.72 ± 5.48), higher organization level, and a more stable co-occurrence network than bacterial communities, which enabled them to better adapt to high environmental pollution levels. These findings expanded the understanding of the spatiotemporal dynamics of microbial communities within biofilms in HM-polluted watersheds and provided new insights into the ecological responses of microbial communities to HMP.

Farrerol alleviates insulin resistance and hepatic steatosis of metabolic associated fatty liver disease by targeting PTPN1.

Metabolic associated fatty liver disease (MAFLD) is the most common chronic liver disease worldwide, characterized by excess lipid deposition. Insulin resistance (IR) serves as a fundamental pathogenic factor in MAFLD. However, currently, there are no approved specific agents for its treatment. Farrerol, a novel compound with antioxidant and anti-inflammatory effects, has garnered significant attention in recent years due to its hepatoprotective properties. Despite this, the precise underlying mechanisms of action remain unclear. In this study, a network pharmacology approach predicted protein tyrosine phosphatase non-receptor type 1 (PTPN1) as a potential target for farrerol's action in the liver. Subsequently, the administration of farrerol improved insulin sensitivity and glucose tolerance in MAFLD mice. Furthermore, farrerol alleviated lipid accumulation by binding to PTPN1 and reducing the dephosphorylation of the insulin receptor (INSR) in HepG2 cells and MAFLD mice. Thus, the phosphoinositide 3-kinase/serine/threonine-protein kinases (PI3K/AKT) signalling pathway was active, leading to downstream protein reduction. Overall, the study demonstrates that farrerol alleviates insulin resistance and hepatic steatosis of MAFLD by targeting PTPN1.

Insights into the ameliorative effect of ZnONPs on arsenic toxicity in soybean mediated by hormonal regulation, transporter modulation, and stress responsive genes.

Arsenic (As) contamination of agricultural soils poses a serious threat to crop productivity and food safety. Zinc oxide nanoparticles (ZnONPs) have emerged as a potential amendment for mitigating the adverse effects of As stress in plants. Soybean crop is mostly grown on marginalized land and is known for high accumulation of As in roots than others tissue. Therefore, this study aimed to elucidate the underlying mechanisms of ZnONPs in ameliorating arsenic toxicity in soybean. Our results demonstrated that ZnOB significantly improved the growth performance of soybean plants exposed to arsenic. This improvement was accompanied by a decrease (55%) in As accumulation and an increase in photosynthetic efficiency. ZnOB also modulated hormonal balance, with a significant increase in auxin (149%), abscisic acid (118%), gibberellin (160%) and jasmonic acid content (92%) under As(V) stress assuring that ZnONPs may enhance root growth and development by regulating hormonal signaling. We then conducted a transcriptomic analysis to understand further the molecular mechanisms underlying the NPs-induced As(V) tolerance. This analysis identified genes differentially expressed in response to ZnONPs supplementation, including those involved in auxin, abscisic acid, gibberellin, and jasmonic acid biosynthesis and signaling pathways. Weighted gene co-expression network analysis identified 37 potential hub genes encoding stress responders, transporters, and signal transducers across six modules potentially facilitated the efflux of arsenic from cells, reducing its toxicity. Our study provides valuable insights into the molecular mechanisms associated with metalloid tolerance in soybean and offers new avenues for improving As tolerance in contaminated soils.

Early Drain Removal Versus Routine Drain Removal After Pancreaticoduodenectomy and/or Distal Pancreatectomy: A Meta-Analysis and Systematic Review.

BACKGROUND: Early drain removal (EDR) has been widely accepted, but not been routinely used in patients after pancreaticoduodenectomy (PD) and distal pancreatectomy (DP). This study aimed to evaluate the safety and benefits of EDR versus routine drain removal (RDR) after PD or DP. METHODS: A systematic search was conducted on medical search engines from January 1, 2008 to November 1, 2023, for articles that compared EDR versus RDR after PD or DP. The primary outcome was clinically relevant postoperative pancreatic fistula (CR-POPF). Further analysis of studies including patients with low-drain fluid amylase (low-DFA) on postoperative day 1 and defining EDR timing as within 3 days was also performed. RESULTS: Four randomized controlled trials (RCTs) and eleven non-RCTs with a total of 9465 patients were included in this analysis. For the primary outcome, the EDR group had a significantly lower rate of CR-POPF (OR 0.23; p < 0.001). For the secondary outcomes, a lower incidence was observed in delayed gastric emptying (OR 0.63, p = 0.02), Clavien-Dindo III-V complications (OR 0.48, p < 0.001), postoperative hemorrhage (OR 0.55, p = 0.02), reoperation (OR 0.57, p < 0.001), readmission (OR 0.70, p = 0.003) and length of stay (MD -2.04, p < 0.001) in EDR. Consistent outcomes were observed in the subgroup analysis of low-DFA patients and definite EDR timing, except for postoperative hemorrhage in EDR. CONCLUSION: EDR after PD or DP is beneficial and safe, reducing the incidence of CR-POPF and other postoperative complications. Further prospective studies and RCTs are required to validate this finding.

Differentiated management of ROS level in tumor and kidney to alleviate Cis-platinum induced acute kidney injury with improved efficacy.

Cisplatin (DDP) is a prevalent chemotherapeutic agent used in tumor therapy, yet DDP-induced acute kidney injury (AKI) severely limits its clinical application. Antioxidants as reactive oxygen species (ROS) scavengers can circumvent this adverse effect while leading to the decrease of efficacy to tumor. Herein, we report ultrasmall ruthenium nanoparticles (URNPs) as switchable ROS scavengers/generators to alleviate DDP-induced AKI and improve its therapeutic efficacy. In the physiological environment of the kidney, URNPs mimic multi-enzyme activities, such as superoxide dismutase and catalase, effectively protecting the renal cell and tissue by down-regulating the increased ROS level caused by DDP and alleviating AKI. Specifically, URNPs are oxidized by high levels of H2O2 in the tumor microenvironment (TME), resulting in the generation of oxygen vacancies and Ru3+/Ru4+ ions. This unique structure transformation endows URNPs to generate singlet oxygen (1O2) under laser irradiation and hydroxyl radicals (∙OH) through a Fenton-like reaction in tumor cell and tissue. The simultaneous generation of multifarious ROS effectively improves the efficacy of DDP in vitro and in vivo. This TME-responsive ROS scavenger/generator acts as an adjuvant therapeutic agent to minimize side effects and improve the efficacy of chemotherapy drugs, providing a new avenue to chemotherapy and facilitating clinical tumor therapy.

Time-varying metasurface driven broadband radar jamming and deceptions.

Conventional radar jamming and deception systems typically necessitate the custom design of complex circuits and algorithms to transmit an additional radio signal toward a detector. Consequently, they are often cumbersome, energy-intensive, and difficult to operate in broadband electromagnetic environment. With the ongoing trend of miniaturization of various devices and the improvement of radar system performance, traditional techniques no longer meet the requirements for broadband, seamless integration, and energy efficiency. Time-varying metasurfaces, capable of manipulating electromagnetic parameters in both temporal and spatial domains, have thus inspired many contemporary research studies to revisit established fields. In this paper, we introduce a time-varying metasurface driven radar jamming and deception system (TVM-RJD), which can perfectly overcome the aforementioned intrinsic challenges. Leveraging a programmable bias voltage, the TVM-RJD can alter the spectrum distribution of incident waves, thereby deceiving radar into making erroneous judgments about the target's location. Experimental outcomes affirm that the accuracy deviation of the TVM-RJD system is less than 0.368 meters, while achieving a remarkable frequency conversion efficiency of up to 96.67%. The TVM-RJD heralds the expansion into a wider application of electromagnetic spatiotemporal manipulation, paving the way for advancements in electromagnetic illusion, radar invisibility, etc.

Green synthesized lignin nanoparticles for the sustainable delivery of pyraclostrobin to control strawberry diseases caused by Botrytis cinerea.

Lignin, renowned for its renewable, biocompatible, and environmentally benign characteristics, holds immense potential as a sustainable feedstock for agrochemical formulations. In this study, raw dealkaline lignin (DAL) underwent a purification process involving two sequential solvent extractions. Subsequently, an enzyme-responsive nanodelivery system (Pyr@DAL-NPs), was fabricated through the solvent self-assembly method, with pyraclostrobin (Pyr) loaded into lignin nanoparticles. The Pyr@DAL-NPs shown an average particle size of 250.4 nm, demonstrating a remarkable loading capacity of up to 54.70 % and an encapsulation efficiency of 86.15 %. Notably, in the presence of cellulase and pectinase at a concentration of 2 mg/mL, the release of Pyr from the Pyr@DAL-NPs reached 92.66 % within 120 h. Furthermore, the photostability of Pyr@DAL-NPs was significantly improved, revealing a 2.92-fold enhancement compared to the commercially available fungicide suspension (Pyr SC). Bioassay results exhibited that the Pyr@DAL-NPs revealed superior fungicidal activity against Botrytis cinerea over Pyr SC, with an EC50 value of 0.951 mg/L. Additionally, biosafety assessments indicated that the Pyr@DAL-NPs effectively declined the acute toxicity of Pyr towards zebrafish and posed no negative effects on the healthy growth of strawberry plants. In conclusion, this study presents a viable and promising strategy for developing environmentally friendly controlled-release systems for pesticides, offering the unique properties of lignin.

Coating of maize seeds with acephate for precision agriculture: Safety assessment in earthworms, bees, and soil microorganisms.

Acephate is commonly used as a seed treatment (ST) in precision agriculture, but its impact on pollinators, earthworms, and soil microorganisms remains unclear. This study aimed to compare the fate of acephate seed dressing (SD) and seed coating (SC) treatments and assess potential risks to bees, earthworms, and soil microorganisms. Additionally, a follow-up study on maize seeds treated with acephate in a greenhouse was conducted to evaluate the maize growth process and the dissipation dynamics of the insecticide. The results indicated that acephate SC led to greater uptake and translocation in maize plants, resulting in lower residue levels in the soil. However, high concentrations of acephate metabolites in the soil had a negative impact on the body weight of earthworms, whereas acephate itself did not. The potential risk to bees from exposure to acephate ST was determined to be low, but dose-dependent effects were observed. Furthermore, acephate ST had no significant effect on soil bacterial community diversity and abundance compared to a control. This study provides valuable insights into the uptake and translocation of acephate SD and SC, and indicates that SC is safer than SD in terms of adverse effects on bees and nontarget soil organisms.

A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification.

Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew's correlation coefficient (MCC), and Cohen's kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models' interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen's kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images.

Deep learning in the radiologic diagnosis of osteoporosis: a literature review.

OBJECTIVE: Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep learning methods in the radiologic diagnosis of osteoporosis. METHODS: We conducted a two-step literature search using the PubMed and Web of Science databases. In this review, we focused on routine radiologic methods, such as X-ray, computed tomography, and magnetic resonance imaging, used to opportunistically screen for osteoporosis. RESULTS: A total of 40 studies were included in this review. These studies were divided into three categories: osteoporosis screening (n = 20), bone mineral density prediction (n = 13), and osteoporotic fracture risk prediction and detection (n = 7). CONCLUSIONS: Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.

Magnetic Field-Optimized Paramagnetic Nanoprobe for T2/T1 Switchable Histopathological-Level MRI.

Traditional magnetic resonance imaging (MRI) contrast agents (CAs) are a type of "always on" system that accelerates proton relaxation regardless of their enrichment region. This "always on" feature leads to a decrease in signal differences between lesions and normal tissues, hampering their applications in accurate and early diagnosis. Herein, we report a strategy to fabricate glutathione (GSH)-responsive one-dimensional (1-D) manganese oxide nanoparticles (MONPs) with improved T2 relaxivities and achieve effective T2/T1 switchable MRI imaging of tumors. Compared to traditional contrast agents with high saturation magnetization to enhance T2 relaxivities, 1-D MONPs with weak Ms effectively increase the inhomogeneity of the local magnetic field and exhibit obvious T2 contrast. The inhomogeneity of the local magnetic field of 1-D MONPs is highly dependent on their number of primary particles and surface roughness according to Landau-Lifshitz-Gilbert simulations and thus eventually determines their T2 relaxivities. Furthermore, the GSH responsiveness ensures 1-D MONPs with sensitive switching from the T2 to T1 mode in vitro and subcutaneous tumors to clearly delineate the boundary of glioma and metastasis margins, achieving precise histopathological-level MRI. This study provides a strategy to improve T2 relaxivity of magnetic nanoparticles and construct switchable MRI CAs, offering high tumor-to-normal tissue contrast signal for early and accurate diagnosis.

The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction.

Background: Lymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning (AutoML) algorithms for predicting LNM in Siewert type II T1 AEG. Methods: A total of 878 patients with Siewert type II T1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models. The patients from two hospitals in Suzhou were collected as the test set. We applied five machine learning algorithms to develop the LNM prediction models. The performance of predictive models was assessed using various metrics including accuracy, sensitivity, specificity, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. Results: Patients with LNM exhibited a higher proportion of male individuals, a poor degree of differentiation, and submucosal infiltration, with statistical differences. The deep learning (DL) model demonstrated relatively good accuracy (0.713) and sensitivity (0.868) among the five models. Moreover, the DL model achieved the highest AUC (0.781) and sensitivity (1.000) in the test set. Conclusion: The DL model showed good predictive performance among five AutoML models, indicating the advantage of AutoML in modeling LNM prediction in patients with Siewert type II T1 AEG.

Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays.

We aimed to develop and validate multimodal ICU patient prognosis models that combine clinical parameters data and chest X-ray (CXR) images. A total of 3798 subjects with clinical parameters and CXR images were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and an external hospital (the test set). The primary outcome was 30-day mortality after ICU admission. Automated machine learning (AutoML) and convolutional neural networks (CNNs) were used to construct single-modal models based on clinical parameters and CXR separately. An early fusion approach was used to integrate both modalities (clinical parameters and CXR) into a multimodal model named PrismICU. Compared to the single-modal models, i.e., the clinical parameter model (AUC = 0.80, F1-score = 0.43) and the CXR model (AUC = 0.76, F1-score = 0.45) and the scoring system APACHE II (AUC = 0.83, F1-score = 0.77), PrismICU (AUC = 0.95, F1 score = 0.95) showed improved performance in predicting the 30-day mortality in the validation set. In the test set, PrismICU (AUC = 0.82, F1-score = 0.61) was also better than the clinical parameters model (AUC = 0.72, F1-score = 0.50), CXR model (AUC = 0.71, F1-score = 0.36), and APACHE II (AUC = 0.62, F1-score = 0.50). PrismICU, which integrated clinical parameters data and CXR images, performed better than single-modal models and the existing scoring system. It supports the potential of multimodal models based on structured data and imaging in clinical management.

Transport and transformations of cadmium in water-biofilm-sediment phases as affected by hydrodynamic conditions.

Hydrodynamic conditions play a crucial role in governing the fate, transport, and risks of metal elements. However, the contribution of hydrodynamic conditions to the fate and transport of heavy metals among water, sediment, and biofilm phases is poorly understood. In our study, we conducted experiments in controlled hydrodynamic conditions using a total of 6 two-phase and 9 three-phase mesocosms consisting of water, biofilm, and sediment. We also measured Cd (cadmium) specification in different phases to assess how hydrodynamic forces control Cd bioavailability. We found that turbulent flow destroyed the surface morphology of the biofilm and significantly decreased the content of extracellular polymeric substances (p < 0.05). This led to a decrease in the biofilm's adsorption capacity for Cd, with the maximum adsorption capacity (0.124 mg/g) being one-tenth of that under static conditions (1.256 mg/g). The Cd chemical forms in the biofilm and sediment were significantly different, with the highest amount of Cd in the biofilm being acid-exchangeable, accounting for up to 95.1% of the total Cd content. Cd was more easily released in the biofilm due to its weak binding state, while Cd in the sediment existed in more stable chemical forms. Hydrodynamic conditions altered the migration behavior and distribution characteristics of Cd in the system by changing the adsorption capacity of the biofilm and sediment for Cd. Cd mobility increased in laminar flow but decreased in turbulent flow. These results enhance our understanding of the underlying mechanisms that control the mobility and bioavailability of metals in aquatic environments with varying hydrodynamic conditions.

Molecular mechanisms of pancreatic cancer liver metastasis: the role of PAK2.

Background: Pancreatic cancer remains an extremely malignant digestive tract tumor, posing a significant global public health burden. Patients with pancreatic cancer, once metastasis occurs, lose all hope of cure, and prognosis is extremely poor. It is important to investigate liver metastasis of Pancreatic cancer in depth, not just because it is the most common form of metastasis in pancreatic cancer, but also because it is crucial for treatment planning and prognosis assessment. This study aims to delve into the mechanisms of pancreatic cancer liver metastasis, with the goal of providing crucial scientific groundwork for the development of future treatment methods and drugs. Methods: We explored the mechanisms of pancreatic cancer liver metastasis using single-cell sequencing data (GSE155698 and GSE154778) and bulk data (GSE71729, GSE19279, TCGA-PAAD). Initially, Seurat package was employed for single-cell data processing to obtain expression matrices for primary pancreatic cancer lesions and liver metastatic lesions. Subsequently, high-dimensional weighted gene co-expression network analysis (hdWGCNA) was used to identify genes associated with liver metastasis. Machine learning algorithms and COX regression models were employed to further screen genes related to patient prognosis. Informed by both biological understanding and the outcomes of algorithms, we meticulously identified the ultimate set of liver metastasis-related gene (LRG). In the study of LRG genes, various databases were utilized to validate their association with pancreatic cancer liver metastasis. In order to analyze the effects of these agents on tumor microenvironment, we conducted an in-depth analysis, including changes in signaling pathways (GSVA), cell differentiation (pseudo-temporal analysis), cell communication networks (cell communication analysis), and downstream transcription factors (transcription factor activity prediction). Additionally, drug sensitivity analysis and metabolic analysis were performed to reveal the effects of LRG on gemcitabine resistance and metabolic pathways. Finally, functional experiments were conducted by silencing the expression of LRG in PANC-1 and Bx-PC-3 cells to validate its influence to proliferation and invasiveness on PANC-1 and Bx-PC-3 cells. Results: Through a series of algorithmic filters, we identified PAK2 as a key gene promoting pancreatic cancer liver metastasis. GSVA analysis elucidated the activation of the TGF-beta signaling pathway by PAK2 to promote the occurrence of liver metastasis. Pseudo-temporal analysis revealed a significant correlation between PAK2 expression and the lower differentiation status of pancreatic cancer cells. Cell communication analysis revealed that overexpression of PAK2 promotes communication between cancer cells and the tumor microenvironment. Transcription factor activity prediction displayed the transcription factor network regulated by PAK2. Drug sensitivity analysis and metabolic analysis revealed the impact of PAK2 on gemcitabine resistance and metabolic pathways. CCK8 experiments showed that silencing PAK2 led to a decrease in the proliferative capacity of pancreatic cancer cells and scratch experiments demonstrated that low expression of PAK2 decreased invasion capability in pancreatic cancer cells. Flow cytometry reveals that PAK2 significantly inhibited apoptosis in pancreatic cancer cell lines. Molecules related to the TGF-beta pathway decreased with the inhibition of PAK2, and there were corresponding significant changes in molecules associated with EMT. Conclusion: PAK2 facilitated the angiogenic potential of cancer cells and promotes the epithelial-mesenchymal transition process by activating the TGF-beta signaling pathway. Simultaneously, it decreased the differentiation level of cancer cells, consequently enhancing their malignancy. Additionally, PAK2 fostered communication between cancer cells and the tumor microenvironment, augments cancer cell chemoresistance, and modulates energy metabolism pathways. In summary, PAK2 emerged as a pivotal gene orchestrating pancreatic cancer liver metastasis. Intervening in the expression of PAK2 may offer a promising therapeutic strategy for preventing liver metastasis of pancreatic cancer and improving its prognosis.

Few-shot learning for the classification of intestinal tuberculosis and Crohn's disease on endoscopic images: A novel learn-to-learn framework.

Background and aim: Standard deep learning methods have been found inadequate in distinguishing between intestinal tuberculosis (ITB) and Crohn's disease (CD), a shortcoming largely attributed to the scarcity of available samples. In light of this limitation, our objective is to develop an innovative few-shot learning (FSL) system, specifically tailored for the efficient categorization and differential diagnosis of CD and ITB, using endoscopic image data with minimal sample requirements. Methods: A total of 122 white-light endoscopic images (99 CD images and 23 ITB images) were collected (one ileum image from each patient). A 2-way, 3-shot FSL model that integrated dual transfer learning and metric learning strategies was devised. Xception architecture was selected as the foundation and then underwent a dual transfer process utilizing oesophagitis images sourced from HyperKvasir. Subsequently, the eigenvectors derived from the Xception for each query image were converted into predictive scores, which were calculated using the Euclidean distances to six reference images from the support sets. Results: The FSL model, which leverages dual transfer learning, exhibited enhanced performance metrics (AUC 0.81) compared to a model relying on single transfer learning (AUC 0.56) across three evaluation rounds. Additionally, its performance surpassed that of a less experienced endoscopist (AUC 0.56) and even a more seasoned specialist (AUC 0.61). Conclusions: The FSL model we have developed demonstrates efficacy in distinguishing between CD and ITB using a limited dataset of endoscopic imagery. FSL holds value for enhancing the diagnostic capabilities of rare conditions.

Pectin-Coated Iron-Based Metal-Organic Framework Nanoparticles for Enhanced Foliar Adhesion and Targeted Delivery of Fungicides.

Conventional agrochemicals are underutilized due to their large particle sizes, poor foliar retention rates, and difficult translocation in plants, and the development of functional nanodelivery carriers with high adhesion to the plant body surface and efficient uptake and translocation in plants remains challenging. In this study, a nanodelivery system based on a pectin-encapsulated iron-based MOF (TF@Fe-MOF-PT NPs) was constructed to enhance the utilization of thifluzamide (TF) in rice plants by taking advantage of the pectin affinity for plant cell walls. The prepared TF@Fe-MOF-PT NPs exhibited an average particle size of 126.55 nm, a loading capacity of 27.41%, and excellent dual-stimulus responses to reactive oxygen species and pectinase. Foliar washing experiments showed that the TF@Fe-MOF-PT NPs were efficiently adhered to the surfaces of rice leaves and stems. Confocal laser scanning microscopy showed that fluorescently labeled TF@Fe-MOF-PT NPs were bidirectionally delivered through vascular bundles in rice plants. The in vitro bactericidal activity of the TF@Fe-MOF-PT NPs showed better inhibitory activity than that of a TF suspension (TF SC), with an EC50 of 0.021 mg/L. A greenhouse test showed that the TF@Fe-MOF-PT NPs were more effective than TF SC at 7 and 14 d, with control effects of 85.88 and 78.59%, respectively. It also reduced the inhibition of seed stem length and root length by TF SC and promoted seedling growth. These results demonstrated that TF@Fe-MOF-PT NPs can be used as a pesticide nanodelivery system for efficient delivery and intelligent release in plants and applied for sustainable control of pests and diseases.

Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning.

OBJECTIVE: Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL). METHODS: In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained Model α based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and Model ß was developed on the features after dimensionality reduction using principal component analysis. Third, Model γ was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score. RESULTS: A total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873-0.960) among all models [Model α: 0.709 (0.618-0.800); Model ß: 0.749 (0.675-0.824); Model γ: 0.687 (0.592-0.782); MCTSI: 0.778 (0.698-0.857); RANSON: 0.642 (0.559-0.725); BISAP: 0.751 (0.668-0.833); SABP: 0.710 (0.621-0.798)]. CONCLUSION: The proposed multimodal model outperformed any single-modality models and traditional scoring systems.

Deep learning to predict esophageal variceal bleeding based on endoscopic images.

OBJECTIVE: Esophageal varix (EV) bleeding is a particularly serious complications of cirrhosis. Prediction of EV bleeding requires extensive endoscopy experience; it remains unreliable and inefficient. This retrospective cohort study evaluated the feasibility of using deep learning (DL) to predict the 12-month risk of EV bleeding based on endoscopic images. METHODS: Six DL models were trained to perform binary classification of endoscopic images of EV bleeding. The models were subsequently validated using an external test dataset, then compared with classifications performed by two endoscopists. RESULTS: In the validation dataset, EfficientNet had the highest accuracy (0.910), followed by ConvMixer (0.898) and Xception (0.875). In the test dataset, EfficientNet maintained the highest accuracy (0.893), which was better than the endoscopists (0.800 and 0.763). Notably, one endoscopist displayed higher recall (0.905), compared with EfficientNet (0.870). When their predictions were assisted by artificial intelligence, the accuracies of the two endoscopists increased by 17.3% and 19.0%. Moreover, statistical agreement among the models was dependent on model architecture. CONCLUSIONS: This study demonstrated the feasibility of using DL to predict the 12-month risk of EV bleeding based on endoscopic images. The findings suggest that artificial intelligence-aided diagnosis will be a useful addition to cirrhosis management.

Green synthesis of a chlorfenapyr chitosan nanopesticide for maize root application: Reducing environmental pollution and risks to nontarget organisms.

Chlorfenapyr (CHL) is a pyrrole insecticide with a novel structure that is used to control resistant pests. However, its weak systemic activity limits its application to crop roots. Herein, a novel CHL formulation with improved effective utilization rates and suitability for root application is developed to avoid or reduce contamination caused by pesticide spraying. Accordingly, we prepared CHL@CS/CMCS nanoparticle (NP) suspensions with a particle size of approximately 100 nm using chitosan (CS) and carboxymethyl chitosan (CMCS). These suspensions exhibited better thermal stability, adhesion, permeability and systemic activity than a CHL suspension concentrate (CHL-SC). The nanoformulation deposition rate on maize leaves after spraying was 12.28 mg/kg, significantly higher than that of CHL-SC. The nanosuspension was effectively absorbed and transported by roots after irrigation and was suitable for root application. The efficacy was 89.46-92.36 % against Spodoptera frugiperda at 7 d, 7.5-17.5 times higher than that of CHL-SC. Furthermore, the CHL@CS/CMCS nanosuspension was safer for earthworms. These results suggest that chitosan-based nanoformulations improve the efficacy, utilization efficiency and active period of CHL control, providing a new approach for CHL application, reducing pollutant dispersal and the environmental impacts of pesticide application and facilitating sustainable agricultural production.