Steerable Interfacial Assembly of 1D Amyloid-Like Protein Nanocomposites for Enhanced Nanoherbicide Utilization.

Conventional herbicide formulations suffer from serious problems such as easy drift, run-off and scouring into the environment, which pose enormous threats to human health and environmental safety. Herein, an innovative strategy is proposed to prepare oil-in-water nanoemulsions with long-term stability, enhanced droplet deposition, and improved nanoherbicide adhesion via steerable interfacial assembly of 1D amyloid-like protein nanocomposites. Bovine serum albumin (BSA) undergoes rapid amyloid-like aggregation upon reduction of its disulfide bond. The resulting phase-transitioned BSA (PTB) oligomers instantly self-assemble on the surface of cellulose nanofibers (CNF) to form the 1D PTB/CNF nanocomposites, which greatly expands the parameter space for interfacial assembly of amyloid-like proteins. The PTB/CNF nanocomposites exhibit excellent interfacial activity, enabling spontaneous adsorption at the oil-water interface to stabilize nanoemulsion. The excess PTB/CNF nanocomposites would also self-assemble at the air-aqueous interface upon spraying, resulting in efficient droplet deposition on (super)hydrophobic leaves. The deposited nanoherbicides show excellent resistance to wind/rain corrosion due to the robust amyloid-mediated adhesion, with a retention rate of more than 80% after severe scouring. Consequently, herbicide applications can be reduced by at least 30% compared to commercial emulsifiable concentrates, showing greater herbicidal efficiency. This study provides novel insights and approaches to promote sustainable agricultural development.

Effectiveness of fentanyl buccal soluble film in cancer patients with inadequate breakthrough pain control.

BACKGROUND: Clinical evidence for the rapidity and effectiveness of fentanyl buccal soluble film (FBSF) in reducing pain intensity of breakthrough cancer pain (BTcP) remains inadequate. This study aimed to evaluate the efficacy of FBSF proportional to the around-the-clock (ATC) opioid regimens in rapidly relieving the intensity of BTcP episodes by determining the percentage of patients requiring further dose titration. METHODS: The study procedure included a dose-finding period followed by a 14-day observation period. Pain intensity was recorded with a Numeric Rating Scale (NRS) at onset and 5, 10, 15, and 30 min after FBSF self-administration. Meaningful pain relief was defined as the final NRS score ≤ 3. Satisfaction survey was conducted for each patient after treatment using the Global Satisfaction Scale. RESULTS: A total of 63 BTcP episodes occurred in 30 cancer patients. Only one patient required rescue medication at first BTcP episode and then achieved meaningful pain relief after titrating FBSF by 200 µg. Most BTcP episodes relieved within 10 min. Of 63 BTcP episodes, 30 (47.6%), 46 (73.0%), and 53 (84.1%) relieved within 5, 10, and 15 min after FBSF administration. Only grade 1/2 adverse events were reported, including somnolence, malaise, and dizziness. Of the 63 BTcP episodes, 82.6% were rated as excellent/good satisfaction with FBSF. CONCLUSION: FBSF can be administrated "on demand" by cancer patients at the onset of BTcP, providing rapid analgesia by achieving meaningful pain relief within 10 min. TRIAL REGISTRATION: This study was retrospectively registered 24 December, 2021 at Clinicaltrial.gov (NCT05209906): https://clinicaltrials.gov/study/NCT05209906 .

Automatic Localization of Soybean Seedlings Based on Crop Signaling and Multi-View Imaging.

Soybean is grown worldwide for its high protein and oil content. Weeds compete fiercely for resources, which affects soybean yields. Because of the progressive enhancement of weed resistance to herbicides and the quickly increasing cost of manual weeding, mechanical weed control is becoming the preferred method of weed control. Mechanical weed control finds it difficult to remove intra-row weeds due to the lack of rapid and precise weed/soybean detection and location technology. Rhodamine B (Rh-B) is a systemic crop compound that can be absorbed by soybeans which fluoresces under a specific excitation light. The purpose of this study is to combine systemic crop compounds and computer vision technology for the identification and localization of soybeans in the field. The fluorescence distribution properties of systemic crop compounds in soybeans and their effects on plant growth were explored. The fluorescence was mainly concentrated in soybean cotyledons treated with Rh-B. After a comparison of soybean seedlings treated with nine groups of rhodamine B solutions at different concentrations ranging from 0 to 1440 ppm, the soybeans treated with 180 ppm Rh-B for 24 h received the recommended dosage, resulting in significant fluorescence that did not affect crop growth. Increasing the Rh-B solutions reduced crop biomass, while prolonged treatment times reduced seed germination. The fluorescence produced lasted for 20 days, ensuring a stable signal in the early stages of growth. Additionally, a precise inter-row soybean plant location system based on a fluorescence imaging system with a 96.7% identification accuracy, determined on 300 datasets, was proposed. This article further confirms the potential of crop signaling technology to assist machines in achieving crop identification and localization in the field.

Computational and Experimental Comparison of Molecularly Imprinted Polymers Prepared by Different Functional Monomers-Quantitative Parameters Defined Based on Molecular Dynamics Simulation.

BACKGROUND: In recent years, the advancement of computational chemistry has offered new insights into the rational design of molecularly imprinted polymers (MIPs). From this aspect, our study tried to give quantitative parameters for evaluating imprinting efficiency and exploring the formation mechanism of MIPs by combining simulation and experiments. METHODS: The pre-polymerization system of sulfadimethoxine (SDM) was investigated using a combination of quantum chemical (QC) calculations and molecular dynamics (MD) simulations. MIPs were prepared on the surface of silica gel by a surface-initiated supplemental activator and reducing agent atom transfer radical polymerization (SI-SARA ATRP). RESULTS: The results of the QC calculations showed that carboxylic monomers exhibited higher bonding energies with template molecules than carboxylic ester monomers. MD simulations confirmed the hydrogen bonding sites predicted by QC calculations. Furthermore, it was observed that only two molecules of monomers could bind up to one molecule of SDM, even when the functional monomer ratio was up to 10. Two quantitative parameters, namely, the effective binding number (EBN) and the maximum hydrogen bond number (HBNMax), were defined. Higher values of EBN and HBNMax indicated a higher effective binding efficiency. Hydrogen bond occupancies and RDF analysis were performed to analyze the hydrogen bond formation between the template and the monomer from different perspectives. Furthermore, under the influence of the EBN and collision probability of the template and the monomers, the experimental results show that the optimal molar ratio of template to monomer is 1:3. CONCLUSIONS: The method of monomer screening presented in this study can be extended to future investigations of pre-polymerization systems involving different templates and monomers.

Discrimination of Deoxynivalenol Levels of Barley Kernels Using Hyperspectral Imaging in Tandem with Optimized Convolutional Neural Network.

Deoxynivalenol (DON) in raw and processed grain poses significant risks to human and animal health. In this study, the feasibility of classifying DON levels in different genetic lines of barley kernels was evaluated using hyperspectral imaging (HSI) (382-1030 nm) in tandem with an optimized convolutional neural network (CNN). Machine learning methods including logistic regression, support vector machine, stochastic gradient descent, K nearest neighbors, random forest, and CNN were respectively used to develop the classification models. Spectral preprocessing methods including wavelet transform and max-min normalization helped to enhance the performance of different models. A simplified CNN model showed better performance than other machine learning models. Competitive adaptive reweighted sampling (CARS) in combination with successive projections algorithm (SPA) was applied to select the best set of characteristic wavelengths. Based on seven wavelengths selected, the optimized CARS-SPA-CNN model distinguished barley grains with low levels of DON (<5 mg/kg) from those with higher levels (5 mg/kg < DON ≤ 14 mg/kg) with an accuracy of 89.41%. The lower levels of DON class I (0.19 mg/kg ≤ DON ≤ 1.25 mg/kg) and class II (1.25 mg/kg < DON ≤ 5 mg/kg) were successfully distinguished based on the optimized CNN model, yielding a precision of 89.81%. The results suggest that HSI in tandem with CNN has great potential for discrimination of DON levels of barley kernels.

Biodegradable, Flexible and Ultraviolet Blocking Nanocellulose Composite Film Incorporated with Lignin Nanoparticles.

The exploration of functional films using sustainable cellulose-based materials to replace plastics has been of much interest. In this work, two kinds of lignin nanoparticles (LNPs) were mixed with cellulose nanofibrils (CNFs) for the fabrication of composite films with biodegradable, flexible and ultraviolet blocking performances. LNPs isolated from p-toluenesulfonic acid hydrolysis was easily recondensed and deposited on the surface of composite film, resulting in a more uneven surface; however, the composite film consisting of CNFs and LNPs isolated from maleic acid hydrolysis exhibited a homogeneous surface. Compared to pure CNF film, the composite CNF/LNP films exhibited higher physical properties (tensile strength of 164 MPa and Young's modulus of 8.0 GPa), a higher maximal weight loss temperature of 310 °C, and a perfect UVB blocking performance of 95.2%. Meanwhile, the composite film had a lower environmental impact as it could be rapidly biodegraded in soil and manmade seawater. Overall, our results open new avenues for the utilization of lignin nanoparticles in biopolymer composites to produce functional and biodegradable film as a promising alternative to petrochemical plastics.

Fusobacterium nucleatum facilitates ulcerative colitis through activating IL-17F signaling to NF-κB via the upregulation of CARD3 expression.

Accumulating evidence links Fusobacterium nucleatum with ulcerative colitis (UC). The mechanism by which F. nucleatum promotes intestinal inflammation in UC remains poorly defined. Here, we first examined the abundance and impact of F. nucleatum on disease activity in UC tissues. Next, we isolated a strain of F. nucleatum from UC tissues and explored whether F. nucleatum aggravates the intestinal inflammatory response in vitro and in vivo. We also examined whether F. nucleatum infection involves the NF-κB or IL-17F signaling pathways. Our data showed that F. nucleatum was enriched in 51.78% of UC tissues and was correlated with the clinical course, clinical activity and refractory behavior of UC (p < 0.05). Furthermore, we demonstrated that F. nucleatum promoted intestinal epithelial damage and the expression of the inflammatory cytokines IL-1ß, Il-6, IL-17F and TNF-α. Mechanistically, F. nucleatum targeted caspase activation and recruitment domain 3 (CARD3) through NOD2 to activate the IL-17F/NF-κB pathway in vivo and in vitro. Thus, F. nucleatum orchestrates a molecular network involving CARD3 and IL-17F to control the UC process. Measuring and targeting F. nucleatum and its associated pathways will yield valuable insight into the prevention and treatment of UC. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Introduction of a conserved sequence into the 5'UTR of additional transcription down-regulation expression of foreign proteins of measles virus minigenome.

The noncoding sequences in the UTR between the Gene Start signal (GS) and the initial codon AUG from 25 measles virus genomes were compared. The conserved sequences 5'-U(NNNNN)5-8-3' and 5'-(CNNNNNC)-3' were found in the genome and the UTR of phosphoprotein (P) and Fusion (F) mRNA. These sequences were named the U-Unit and the C-Unit, respectively. These two sequences are different in the P and F mRNAs. To investigate whether these conserved sequences can be used to regulate expression of foreign proteins in additional transcription units (ATU), a series of minigenomes with various 5' UTRs were generated. From reporter gene assays and quantitation of gene copies by qPCR, we found that the introduction of the C-unit into the 5'UTR of mRNA can down-regulate the expression of foreign proteins in additional transcription units in vivo.

Exposure to 2,3,7,8-tetrachlorodibenzo-paradioxin (TCDD) hampers the host defense capability of a bivalve species, Tegillarca granosa.

Though increasing reports of deleterious impacts of dioxins and polychlorinated biphenyls (PCBs) on a variety of marine organisms have been described, their effects on the host defense capability of marine bivalve mollusks remain poorly understood. In the present study we used 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) as a representative of dioxins and PCBs to investigate its impacts on the host defense capability of the blood clam, Tegillarca granosa. After exposure of clams to a range (0, 0.01, 0.1, 1, and 10 µg/L) of TCDD for 96 h, the total count, cell type composition, and phagocytic rate of haemocytes were analyzed. In addition, alkaline phosphatase (ALP) activity, cell viability, and the extent of DNA damage of haemocytes were also investigated. Our results showed that exposure to relatively high TCDD concentrations led to significant reductions in the total count and phagocytic activity of haemocytes, which could be accounted by aggravated DNA damage and reduced cell viability. In addition, the percentage of red granulocyte was significantly decreased whereas that of basophil granulocyte was significantly increased upon high doses TCDD exposure (effective concentrations are 1 µg/L and 10 µg/L for red and basophil granulocytes, respectively). Moreover, clams exposed to TCDD had a significant higher activity of ALP, may also indicate an enhanced ability to eliminate pathogens through direct dephosphorylation process whereas a suppressed inflammatory response through indirect regulating of downstream molecular cascade reaction. These findings suggest that TCDD may hamper the host defense capability and therefore render bivalve mollusks more vulnerable to pathogen infections.

Waterborne Cd2+ weakens the immune responses of blood clam through impacting Ca2+ signaling and Ca2+ related apoptosis pathways.

Exposure to heavy metals such as Cadmium (Cd) may exert detrimental impacts on the immune responses of marine bivalve species. However, the immunotoxicity of Cd on blood clams remains unknown to date. Furthermore, though Cd2+ is known to compete with calcium (Ca2+) ions for their binding sites in cells and inhibit Ca2+ influx, whether Cd2+ weakens the immune responses of marine bivalves through inducing intracellular Ca2+ disorders still remains unclear. Therefore, the immunotoxicity of Cd2+ at different waterborne Ca2+ concentrations on blood clam, Tegillarca granosa, were investigated in the present study. Results obtained demonstrated that the total number, phagocytic activity, and red granulocytes ratio of the haemocytes were all significantly reduced after 10 days exposure of individuals to 25 µg/L Cd2+. However, when the waterborne Ca2+ concentrations were elevated by 10% and 20% (approximately 370 and 410 mg/L, respectively), mitigation effects on the immune responses of individuals were detected. In addition, though the expressions of genes from the Ca2+ signaling and Ca2+-related apoptosis pathways were significantly altered by Cd2+ exposure, the expression patterns of these genes were similar to that of the control when the waterborne Ca2+ concentrations were elevated, suggesting a relieving effect of waterborne Ca2+ on Cd2+ induced toxicity to haemocytes. The results obtained in the present study revealed that waterborne Cd2+ may hamper the immune responses of T. granosa through influencing Ca2+ signaling and Ca2+-related apoptosis pathways, which can be partially mitigated by elevating the waterborne Ca2+ concentrations.

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review.

Fourier transform infrared (FT-IR) and Raman and hyperspectral imaging (HSI) techniques have emerged as reliable analytical methods for effectively characterizing and quantifying quality attributes of different categories of powdery food products (such as milk powder, tea powder, cocoa powder, coffee powder, soybean flour, wheat flour, and chili powder). In addition to the ability for gaining rapid information about food chemical components (such as moisture, protein, and starch), and classifying food quality into different grades, such techniques have also been implemented to determine trace impurities in pure foods and other properties of particulate foods and ingredients with avoidance of extensive sample preparation. Developments of corresponding quality evaluation systems based on FT-IR, Raman, and HSI data that measure food quality parameters and ensure product authentication, would bring about technical and economic benefits to the food industry by enhancing consumer confidence in the quality of its products. Accordingly, a comprehensive review of the mushrooming spectroscopy-based FT-IR, Raman, and HSI literature is carried out in this article. The spectral data collected, the chemometric methods used, and the main findings of recent research studies on quality assessments of powdered materials are discussed and summarized. Providing a review in such a flourishing research field is relevant as a signpost for future study. The conclusion details the promise of how such noninvasive and powerful analytical techniques can be used for rapid and accurate determinations of powder quality attributes in both academical and industrial settings.

Multispectral Imaging for Plant Food Quality Analysis and Visualization.

The multispectral imaging technique is considered a reformation of hyperspectral imaging. It can be employed to noninvasively and rapidly evaluate food quality. Even though several imaging or sensor-based techniques have been conducted for the quality assessment of various food products, the rise of multispectral imaging has been more promising. This paper presents a comprehensive review of the use of the multispectral sensor in the quality assessment of plant foods (such as cereals, legumes, tubers, fruits, and vegetables). Different quality parameters (such as physicochemical and microbiological aspects) of plant-based foods that were determined and visualized by the combination of modeling methods and feature wavelength selection approaches are summarized. Based on the literature, the most frequently used wavelength selection methods are the successive projection algorithm (SPA) and the regression coefficient (RC). The most effective models developed for analyzing plant food products are the partial least squares regression (PLSR), least square support vector machine (LS-SVM), support vector machine (SVM), partial least squares discriminant analysis (PLSDA), and multiple linear regression (MLR). This article concludes with a discussion of challenges, potential uses, and future trends of this flourishing technique that is now also being applied to plant foods.

Non-Destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: A review.

Staple foods, including cereals, legumes, and root/tuber crops, dominate the daily diet of humans by providing valuable proteins, starch, oils, minerals, and vitamins. Quality evaluation of staple foods is primarily carried out on sensory (e.g. external defect, color), adulteration (e.g. species, origin), chemical (e.g. starch, proteins), mycotoxin (e.g. Fusarium toxin, aflatoxin), parasitic infection (e.g. weevil, beetle), and internal physiological (e.g. hollow heart, black heart) aspects. Conventional methods for the quality assessment of staple foods are always laborious, destructive, and time-consuming. Requirements for online monitoring of staple foods have been proposed to encourage the development of rapid, reagentless, and noninvasive techniques. Spectroscopic techniques, such as visible-infrared spectroscopy, Raman spectroscopy, nuclear magnetic resonance spectroscopy, and spectral imaging, have been introduced as promising analytical tools and applied for the quality evaluation of staple foods. This review summarizes the recent applications and progress of such spectroscopic techniques in determining various qualities of staple foods. Besides, challenges and future trends of these spectroscopic techniques are also presented.

Laboratory simulation reveals significant impacts of ocean acidification on microbial community composition and host-pathogen interactions between the blood clam and Vibrio harveyi.

It has been suggested that climate change may promote the outbreaks of diseases in the sea through altering the host susceptibility, the pathogen virulence, and the host-pathogen interaction. However, the impacts of ocean acidification (OA) on the pathogen components of bacterial community and the host-pathogen interaction of marine bivalves are still poorly understood. Therefore, 16S rRNA high-throughput sequencing and host-pathogen interaction analysis between blood clam (Tegillarca granosa) and Vibrio harveyi were conducted in the present study to gain a better understanding of the ecological impacts of ocean acidification. The results obtained revealed a significant impact of ocean acidification on the composition of microbial community at laboratory scale. Notably, the abundance of Vibrio, a major group of pathogens to many marine organisms, was significantly increased under ocean acidification condition. In addition, the survival rate and haemolytic activity of V. harveyi were significantly higher in the presence of haemolymph of OA treated T. granosa, indicating a compromised immunity of the clam and enhanced virulence of V. harveyi under future ocean acidification scenarios. Conclusively, the results obtained in this study suggest that future ocean acidification may increase the risk of Vibrio pathogen infection for marine bivalve species, such as blood clams.

Immunotoxicity of nanoparticle nTiO2 to a commercial marine bivalve species, Tegillarca granosa.

The increasing production and extensive application of nanoparticles (NPs) inevitably leads to increased release of NPs into the marine environment and therefore poses a potential threat to marine organisms, especially the sessile benthic bivalves. However, the impacts of NPs on the immunity of commercial and ecological important bivalve species, Tegillarca granosa, still remain unknown to date. In addition, the molecular mechanism of the immunotoxicity of NPs still remains unclear in marine invertebrates. Therefore, the immunotoxicity of nTiO2 exposure to T. granosa at environmental realistic concentrations was investigated in the present study. Results obtained showed that the total number, phagocytic activity, and red granulocytes ratio of the haemocytes were significantly reduced after 30 days nTiO2 exposures at the concentrations of 10 and 100 µg/L. Furthermore, the expressions of genes encoding Pattern Recognition Receptors (PPRs) and downstream immune-related molecules were significantly down-regulated by nTiO2 exposures, indicating a reduced sensitivity to pathogen challenges. In conclusion, evident immunotoxicity of nTiO2 to T. granosa at environmental realistic concentrations was detected by the present study. In addition, the gene expression analysis suggests that the PRRs (both TLRs and RIG1 investigated) may be the molecules for NPs recognition in marine invertebrates.

Benzo[a]pyrene exposure under future ocean acidification scenarios weakens the immune responses of blood clam, Tegillarca granosa.

Persistent organic pollutants (POPs) are known to converge into the ocean and accumulate in the sediment, posing great threats to marine organisms such as the sessile bottom burrowing bivalves. However, the immune toxicity of POPs, such as B[a]P, under future ocean acidification scenarios remains poorly understood to date. Therefore, in the present study, the impacts of B[a]P exposure on the immune responses of a bivalve species, Tegillarca granosa, under present and future ocean acidification scenarios were investigated. Results obtained revealed an increased immune toxicity of B[a]P under future ocean acidification scenarios in terms of reduced THC, altered haemocyte composition, and hampered phagocytosis, which may attribute to the synergetic effects of B[a]P and ocean acidification. In addition, the gene expressions of pathogen pattern recognition receptors (TLR1, TLR2, TLR4, TLR6), pathway mediators (TRAF6, TAK1, TAB2, IKKα and Myd88), and effectors (NF-ĸB) of the important immune related pathways were significantly down-regulated upon exposure to B[a]P under future ocean acidification scenarios. Results of the present study suggested an increased immune toxicity of B[a]P under future ocean acidification scenarios, which will significantly hamper the immune responses of T. granosa and subsequently render individuals more susceptible to pathogens challenges.

APHS-YOLO: A Lightweight Model for Real-Time Detection and Classification of Stropharia Rugoso-Annulata.

The classification of Stropharia rugoso-annulata is currently reliant on manual sorting, which may be subject to bias. To improve the sorting efficiency, automated sorting equipment could be used instead. However, sorting naked mushrooms in real time remains a challenging task due to the difficulty of accurately identifying, locating and sorting large quantities of them simultaneously. Models must be deployable on resource-limited devices, making it challenging to achieve both a high accuracy and speed. This paper proposes the APHS-YOLO (YOLOv8n integrated with AKConv, CSPPC and HSFPN modules) model, which is lightweight and efficient, for identifying Stropharia rugoso-annulata of different grades and seasons. This study includes a complete dataset of runners of different grades in spring and autumn. To enhance feature extraction and maintain the recognition accuracy, the new multi-module APHS-YOLO uses HSFPNs (High-Level Screening Feature Pyramid Networks) as a thin-neck structure. It combines an improved lightweight PConv (Partial Convolution)-based convolutional module, CSPPC (Integration of Cross-Stage Partial Networks and Partial Convolution), with the Arbitrary Kernel Convolution (AKConv) module. Additionally, to compensate for the accuracy loss due to lightweighting, APHS-YOLO employs a knowledge refinement technique during training. Compared to the original model, the optimized APHS-YOLO model uses 57.8% less memory and 62.5% fewer computational resources. It has an FPS (frames per second) of over 100 and even achieves 0.1% better accuracy metrics than the original model. These research results provide a valuable reference for the development of automatic sorting equipment for forest farmers.

Restoring Treg/Th17 cell balance in ulcerative colitis through HRas silencing and MAPK pathway inhibition.

This study investigates HRas-dependent mechanisms in the disruption of regulatory T (Treg) cells and T helper 17 (Th17) cells balance in ulcerative colitis (UC). Comprehensive RNA sequencing and bioinformatics analyses revealed elevated HRas and MAPK pathway-related protein expression in UC samples. Using a murine UC model induced by dextran sulfate sodium (DSS), HRas silencing was found to promote Treg cell differentiation and suppress Th17 cell production, effectively restoring balance. Inactivation of the MAPK pathway played a pivotal role in this rebalancing effect. In vivo experiments further confirmed that HRas silencing mitigated colon tissue damage in DSS-induced mice, emphasizing its potential as a therapeutic strategy for UC.

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To accurately estimate the age of individual tree and to achieve full-cycle sustainable management of natural Larix gmelinii forest in Great Xing'an Mountains of northeastern China, we constructed individual tree age prediction model using stepwise regression and random forest algorithms based on 44 fixed plots data and 280 stan-dard tree cores obtained from the Pangu Forest Farm. We analyzed the influence of stand structure, site conditions, and competition index on the accuracy of model prediction. The model was evaluated by the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results showed that the random forest model had the highest prediction accuracy when number of decision trees was 1500 and number of node con-tention variables was 8. The random forest model had better accuracy and prediction ability than the stepwise regression model, with R2, RMSE and MAE of 0.5882, 9.9259 a, 8.1155 a. Diameter at breast height was the most important factor affecting age prediction (83.8%), followed by tree height (34.4%), elevation (17.9%), and basal area per hectare (17.5%). The random forest algorithm exhibited better adaptability and modeling effect on constructing a predictive model for individual tree age. This research contributed to improving the accuracy of growth and harvest estimation for L. gmelinii, and could provide a reference for other scientific studies related to tree age estimation in forests.

The predictive value of serum tumor markers for EGFR mutation in non-small cell lung cancer patients with non-stage IA.

Objective: The predictive value of serum tumor markers (STMs) in assessing epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC), particularly those with non-stage IA, remains poorly understood. The objective of this study is to construct a predictive model comprising STMs and additional clinical characteristics, aiming to achieve precise prediction of EGFR mutations through noninvasive means. Materials and methods: We retrospectively collected 6711 NSCLC patients who underwent EGFR gene testing. Ultimately, 3221 stage IA patients and 1442 non-stage IA patients were analyzed to evaluate the potential predictive value of several clinical characteristics and STMs for EGFR mutations. Results: EGFR mutations were detected in 3866 patients (57.9 %) of all NSCLC patients. None of the STMs emerged as significant predictor for predicting EGFR mutations in stage IA patients. Patients with non-stage IA were divided into the study group (n = 1043) and validation group (n = 399). In the study group, univariate analysis revealed significant associations between EGFR mutations and the STMs (carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC), and cytokeratin-19 fragment (CYFRA21-1)). The nomogram incorporating CEA, CYFRA 21-1, pathology, gender, and smoking history for predicting EGFR mutations with non-stage IA was constructed using the results of multivariate analysis. The area under the curve (AUC = 0.780) and decision curve analysis demonstrated favorable predictive performance and clinical utility of nomogram. Additionally, the Random Forest model also demonstrated the highest average C-index of 0.793 among the eight machine learning algorithms, showcasing superior predictive efficiency. Conclusion: CYFRA21-1 and CEA have been identified as crucial factors for predicting EGFR mutations in non-stage IA NSCLC patients. The nomogram and 8 machine learning models that combined STMs with other clinical factors could effectively predict the probability of EGFR mutations.