Water toughening is generally required for the widely used manganese-system wear-resistant cast steels (0.9-1.4 % C, 5.5-14.0% Mn). The purpose of the treatment is to get rid of the needle-like carbides and carbide ne...Water toughening is generally required for the widely used manganese-system wear-resistant cast steels (0.9-1.4 % C, 5.5-14.0% Mn). The purpose of the treatment is to get rid of the needle-like carbides and carbide networks in the as-cast state,展开更多
In order to address the evolving emission characteristics of oxygenated volatile organic compounds(OVOCs),it is essential to develop adsorbent materials specifically designed for the efficient adsorption of OVOCs with...In order to address the evolving emission characteristics of oxygenated volatile organic compounds(OVOCs),it is essential to develop adsorbent materials specifically designed for the efficient adsorption of OVOCs with large kinetic diameters.In this study,we used co-pyrolysis to prepare a series of graded porous carbon materials with well-developed micropores by adjusting the doping ratios of root nodules and pretreated cellulose.The material with root nodule to cellulose mass ratio of 1:1(TCC-RN-1)exhibited the highest saturated adsorption capacity for butyl acetate(834 mg/g).This can be attributed to enhanced pore size distribution from nodule doping,which facilitates the development of a micropore-graded structure.Additionally,the nodules acted as auxiliary activating agents that enhanced the KOH micropore regulation effect during the activation stage,resulting in the highest micropore volume(0.863 cm^(3)/g).The doping of root nodules facilitated the formation of additional defects on the surface of the porous carbon material,leading to a more disordered arrangement that improved pollutant adsorption.Furthermore,TCC-RN-1 demonstrated good thermal stability in an air atmosphere,main-taining a butyl acetate adsorption capacity exceeding 95%after five adsorption-desorption cycles.This indicates its favorable potential for industrial applications.展开更多
Rhizobial inoculation in soybean is an effective strategy in sustainable agriculture to reduce chemical fertilizer application and to increase crop production.It not only provides nitrogen sources for host plants but ...Rhizobial inoculation in soybean is an effective strategy in sustainable agriculture to reduce chemical fertilizer application and to increase crop production.It not only provides nitrogen sources for host plants but also improves the rhizosphere soil environment.However,the inoculation efficiency of rhizobia remains to be improved.In this study,we investigated the nodulation efficiency of Bradyrhizobium and Sinorhizobium strains under different soil conditions and evaluated their impacts on the rhizocompartment bacterial community.We found that inoculation with Bradyrhizobium diazoefficiens UASD 110 increased the number of soybean nodules in acidic soil,while Sinorhizobium fredii CCBAU 45436 was more effective in alkaline soil.However,inoculation with neither strain significantly affected nodulation in neutral soil.Then,we demonstrated that UASD 110 was more competitive in nodulation than CCBAU 45436,which was related to its higher abundance in the rhizosphere.Furthermore,we showed that while single inoculation with UASD 110 or CCBAU 45436 failed to alter the bacterial diversity,these two strains differentially influenced the rhizosphere microbial composition.Finally,we identified the main rhizosphere microorganisms that were affected by these two strains.Our findings revealed that the nodulation capacity of rhizobia and their colonization of rhizosphere and nodules are soil-type dependent,yet their impact on the rhizobacterial community exhibited consistent patterns.These findings provide valuable insights into optimizing rhizobial inoculation strategies to enhance nitrogen fixation efficiency.展开更多
Currently,thyroid diseases are prevalent worldwide;therefore,it is necessary to develop techniques that help doctors improve their diagnostic skills for such diseases.In previous studies,2-dimensional convolutional ne...Currently,thyroid diseases are prevalent worldwide;therefore,it is necessary to develop techniques that help doctors improve their diagnostic skills for such diseases.In previous studies,2-dimensional convolutional neural network(2D CNN)techniques were employed to classify thyroid nodules as benign and malignant without detecting the presence of thyroid nodules in the obtained ultrasound images.To address this issue,we propose a 3-dimensional convolutional neural network(3D CNN)for thyroid nodule detection.The proposed CNN exploits the 3D information and spatial features contained in ultrasound images and generates distinctive features during its training using multiple samples,even for small nodules.In contrast,a 2D CNN only depends on spatial features.In this study,we used two datasets of 2210 ultrasound images obtained from the Sultan Abdul Aziz Shah Hospital in Malaysia,and a public open dataset,Digital Database Thyroid Image(DDTI).We created folders containing three images each,processed the images and extracted volumetric features suitable for the 3-dimensional convolutional neural network(3D CNN).The proposed model was assessed using four metrics:accuracy,recall,precision and F1 score.The results showed that the accuracy of the model in predicting the presence of thyroid nodules in ultrasound images was 96%.In conclusion,this study could help radiologists in hospitals and medical centres in classifying ultrasound images and detecting thyroid nodules.展开更多
Root nodule symbiosis(RNS)is a mutualistic association formed between nitrogen-fixing rhizobia or Frankia and host plants limited to four orders within Rosid I―Fabales,Fagales,Cucurbitales,and Rosales―which comprise...Root nodule symbiosis(RNS)is a mutualistic association formed between nitrogen-fixing rhizobia or Frankia and host plants limited to four orders within Rosid I―Fabales,Fagales,Cucurbitales,and Rosales―which comprise the so-called‘Nitrogen Fixing Nodulation Clade’(NFNC).The majority of nodulation studies have focused on Leguminosae,given their agricultural and environmental importance,as well as the widespread occurrence of nodulation among members of this family.Endowing cereal crops with nitrogen fixation,like Leguminosae,presents a strategy to reduce the detrimental effects of synthetic fertilizer overuse.Different hypotheses on the origin of RNS have been proposed;however,key genetic innovations underlying the evolution of RNS,even in Leguminosae,have been rarely reported.In this review,we begin by examining current knowledge of genetic innovations―including gene gain,gene loss,and the acquisition or loss of conserved noncoding sequences(CNS)in preexisting genes.We explore the available evidence supporting these genetic innovations underlying the evolution of RNS in Leguminosae and offer the phylogenomics approach that could be applied to uncover these genetic innovations.Finally,we conclude by proposing a model of genetic innovations underlying the evolution of RNS in Leguminosae and consider the potential implications for the development of nitrogen-fixing crops.展开更多
Flavonoids produced by legume roots act as signaling molecules that induce the expression of nod genes in symbiotic rhizobia.However,the role of flavonoids in root exudates under intercropping systems in promoting soy...Flavonoids produced by legume roots act as signaling molecules that induce the expression of nod genes in symbiotic rhizobia.However,the role of flavonoids in root exudates under intercropping systems in promoting soybean nodulation remains unclear.Two consecutive years of field experiments were conducted using maize–soybean strip intercropping with interspecific row spacings of 30 cm(MS30),45 cm(MS45),and 60 cm(MS60),along with sole cropping of soybean(SS)and maize(MM).Root interactions were manipulated using either no root barrier(NB)or a polyethylene plastic barrier(PB)to assess the relationship between flavonoids in root exudates and soybean nodulation.We found that root–root interaction between soybean and maize increased nodule number and fresh weight in intercropped soybean,with enhancement gradually increasing as interspecific distance widened.The proportion of nodules with diameters exceeding 0.4 cm was higher in intercropped soybean under NB compared to PB.Additionally,the expression of nodule-related genes-GmENOD40,Gm NIN2b,and Gm EXPB2-was up-regulated.Furthermore,compared to monocropping,isoflavone secretion by soybean roots decreased,whereas flavonoid and flavonol secretion by both maize and soybean roots increased under intercropping.The abundance of differentially secreted flavonoid metabolites in the rhizosphere of both species declined when root contact was prevented by the barrier.In soybean roots,the expression of Gm CHS8 and Gm IFS1 was up-regulated,while Gm ICHG was down-regulated under root interaction.Most flavonoid and flavonol compounds showed positive correlations with nodule diameter.Nodule number,fresh weight,and the proportion of nodules larger than 0.2 cm increased in diverse soybean genotypes treated with maize root exudates,which contributed to enhanced nitrogen fixation capacity.Therefore,maize–soybean strip intercropping,combined with optimal row spacing,enhances the positive effects of underground root interactions and improves nodulation and nitrogen fixation in intercropped soybean.展开更多
Objective:Non-diagnostic thyroid nodules(Bethesda I)account for 5%-20%of all thyroid nodules.Accurate differentiation of benign and malignant nodules can reduce unnecessary surgeries and repeat biopsies.Herein we eval...Objective:Non-diagnostic thyroid nodules(Bethesda I)account for 5%-20%of all thyroid nodules.Accurate differentiation of benign and malignant nodules can reduce unnecessary surgeries and repeat biopsies.Herein we evaluated the diagnostic efficacy of multigene testing in non-diagnostic thyroid nodules and developed a predictive model integrating molecular and clinical data.Methods:In this prospective cohort study,1,175 patients with thyroid nodules were evaluated for inclusion,of which 218 patients with Bethesda I nodules met our inclusion criteria.The primary outcome was diagnostic accuracy of molecular testing,and the secondary outcome was the performance of a predictive model integrating molecular and clinical data.Results:Final histopathology identified 165 benign and 53 malignant nodules.Molecular testing detected 10distinct point mutations and seven gene fusions.Among benign nodules,147 tested negative and 18 tested positive,whereas 44 malignant nodules tested positive and nine tested negative.In nodules with ultrasound grades 4-5 and fine-needle aspiration cytology(FNAC)results categorized as non-diagnostic,molecular testing achieved sensitivity of 83.00%,specificity of 89.00%,positive predictive value(PPV)of 71.00%,negative predictive value(NPV)of94.20%,and overall accuracy of 87.60%.The predictive model incorporated 18 clinical and 19 molecular features.Eleven non-zero predictors were selected via least absolute shrinkage and selection operator(LASSO),and the model achieved area under curve(AUC)of 0.95 in the training set and 0.96 in the testing set.Decision curve analysis indicated greater net benefit compared with conventional diagnostic approaches.Conclusions:Molecular testing significantly improved diagnostic accuracy for Bethesda I thyroid nodules.Integrating molecular and clinical data enabled the development of a robust predictive model,facilitating precise,individualized patient management and reducing the need for repeat FNAC and unnecessary surgeries.展开更多
BACKGROUND The liver is the most common site of digestive system tumor metastasis,but not all liver metastases can be traced back to the primary lesions.Although it is unusual,syphilis can impact the liver,manifesting...BACKGROUND The liver is the most common site of digestive system tumor metastasis,but not all liver metastases can be traced back to the primary lesions.Although it is unusual,syphilis can impact the liver,manifesting as syphilitic hepatitis with inflammatory nodules,which might be misdiagnosed as metastasis.CASE SUMMARY This case report involves a 46-year-old female who developed right upper abdominal pain and intermittent low fever that persisted for more than three months.No definitive diagnosis of a tumor had been made in the past decades,but signs of multiple liver metastases were recognized after a computed tomo-graphy scan without evidence of primary lesions.With positive serological tests for syphilis and a biopsy of the liver nodules,a diagnosis of hepatic syphilis was made and confirmed with follow-up nodule reduction after anti-syphilis therapy.CONCLUSION Clinicians must be aware of the possibility that syphilis can cause hepatic inflam-matory masses,especially when liver metastasis is suspected without evidence of primary lesions.A definitive diagnosis should be established in conjunction with a review of the patient’s medical history for accurate therapeutic intervention.展开更多
Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially...Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.展开更多
Objective:To explore the application value of artificial intelligence-assisted diagnostic systems in the computed tomography(CT)diagnosis of pulmonary nodules.Methods:A total of 80 patients with pulmonary nodules,trea...Objective:To explore the application value of artificial intelligence-assisted diagnostic systems in the computed tomography(CT)diagnosis of pulmonary nodules.Methods:A total of 80 patients with pulmonary nodules,treated from June 2023 to May 2024,were included.All patients underwent pathological examination and CT scans,with pathological results serving as the gold standard.The diagnostic performance of CT alone and CT combined with the artificial intelligence-assisted diagnostic system was analyzed,and differences in CT imaging features and evaluation results of benign and malignant pulmonary nodules were compared.Results:The sensitivity,specificity,and accuracy of CT combined with the artificial intelligence-assisted diagnostic system were significantly higher than those of CT alone(P<0.05).Moreover,the false-positive and false-negative rates were significantly lower for the combined approach compared to CT alone(P<0.05).Conclusion:The artificial intelligence-assisted diagnostic system effectively identifies malignant features in pulmonary nodules,providing valuable clinical reference data and enhancing diagnostic accuracy and efficiency.展开更多
Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to vari...Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.展开更多
Globally,liver cancer ranks as the sixth most frequent malignancy cancer.The importance of early detection is undeniable,as liver cancer is the fifth most common disease in men and the ninth most common cancer in wome...Globally,liver cancer ranks as the sixth most frequent malignancy cancer.The importance of early detection is undeniable,as liver cancer is the fifth most common disease in men and the ninth most common cancer in women.Recent advances in imaging,biomarker discovery,and genetic profiling have greatly enhanced the ability to diagnose liver cancer.Early identification is vital since liver cancer is often asymptomatic,making diagnosis difficult.Imaging techniques such as Magnetic Resonance Imaging(MRI),Computed Tomography(CT),and ultrasonography can be used to identify liver cancer once a sample of liver tissue is taken.In recent research,reliable detection of liver cancer with minimal computing computational complexity and time has remained a serious difficulty.This paper employs the DenseNet model to enhance the detection of liver nodules with tumors by segmenting them using UNet and VGG using Fastai(UVF)in CT images.Its dense interconnections distinguish the DenseNet between layers.These dense connections facilitate the propagation of gradients and the flow of information throughout the network,thereby enhancing the efficacy and performance of training.DenseNet’s architecture combines dense blocks,bottleneck layers,and transition layers,allowing it to achieve a compromise between expressiveness and computing efficiency.Finally,the 3D liver nodular models were created using a raycasting volume rendering approach.Compared to other state-of-the-art deep neural networks,it is suitable for clinical applications to assist doctors in diagnosing liver cancer.The proposed approach was tested on a 3Dircadb dataset.According to experiments,UVF segmentation on the 3Dircadb dataset is 97.9%accurate.According to the study,the DenseNet and UVF segment liver cancer better than prior methods.The system proposes automated 3D liver cancer tumor visualization.展开更多
BACKGROUND Several trace minerals have been shown to be associated with thyroid cancer.Democratic Republic of Congo(DRC)is deemed the most mineral-rich country globally.Data on the characteristics of thyroid nodules i...BACKGROUND Several trace minerals have been shown to be associated with thyroid cancer.Democratic Republic of Congo(DRC)is deemed the most mineral-rich country globally.Data on the characteristics of thyroid nodules in various mineral-rich regions of the DRC is scarce.AIM To analyze the differential spectrum of thyroid nodules based on locoregional variance in mineral density.METHODS We conducted a cross-sectional study on 529 patients with thyroid nodules residing in Katanga,South Kivu and Kinshasa between 2005 and 2019.Of these three provinces,Katanga and South Kivu have the highest mineral density with the DRC.RESULTS Mean patient age was 44.2 years±14.6 years with a female predominance,with a female to male ratio of 5.4.The 66.5%of patients had a family history of thyroid disease.Total 74 patients had simple nodules,and the remaining 455 patients had multiple nodules.The 87.7%of patients were euthyroid.The nodules exhibited varying characteristics namely hypoechogenicity(84.5%),solid echostructure(72.2%),macronodular appearance(59.8%),calcifications(14.4%)and associated lymphadenopathy(15.5%).The 22.3%of the nodules were malignant.Factors independently associated with malignancy were older age(≥60 years)[adjusted odds ratio(aOR)=2.81],Katanga province(aOR=8.19),solid echostructure(aOR=7.69),hypoechogenicity(aOR=14.19),macronodular appearance(aOR=9.13),calcifications(aOR=2.6)and presence of lymphadenopathy(aOR=6.94).CONCLUSION Thyroid nodules emanating from the mineral-laden province of Katanga were more likely to be malignant.Early and accurate risk-stratification of patients with thyroid nodules residing in high-risk areas could be instrumental in optimizing survival in these patients.展开更多
To explore the geochemical characteristics and genesis of the elements in ferromanganese nodules from the Northwest Pacific,this study analyses the mineral composition,elemental content,occurrence phase and genetic me...To explore the geochemical characteristics and genesis of the elements in ferromanganese nodules from the Northwest Pacific,this study analyses the mineral composition,elemental content,occurrence phase and genetic mechanisms of samples by X-ray diffraction(XRD),inductively coupled plasma-optical emission spectrometry(ICP-OES),inductively coupled plasma-mass spectrometry(ICP-MS)and phase analysis methods.The results show that ferromanganese nodules are mainly hydrogenetic,and Mn/Fe content ratio ranges from 0.95 to 2.05.The major minerals are vernadite(δ-MnO_(2))and amorphous ferric oxyhydroxide(FeOOH),and the secondary minerals include todorokite,birnessite,quartz and plagioclase.Ferromanganese nodules contain high contents of Co(0.24%-0.42%),Cu(0.23%-0.73%),Ni(0.33%-0.86%)and rare earth elements(REEs,1192-1990μg/g),which have positive Ce and negative Y anomalies but no Eu anomaly.A cluster analysis suggests that the elements in ferromanganese nodules can be divided into three groups:hydrogenetic components,including Fe,Ti,Zr,P,Pb,Co,Ba,Sr,V and REEs;diagenetic components,including Mn,Ni,Mg,Zn and Cu;and detrital components,including Al,Na,K and Ca.According to chemical leaching,ferromanganese nodules can be divided into four phases:Na,Ca,Mg and Sr are mainly enriched in the carbonate phase;Mn,Co,Ni and Ba are mainly enriched in the Mn-oxide phase;Fe,P,Ti,Cu,Pb,V,Zn,Zr and REEs are mainly enriched in the Fe-oxide phase;and Al and K are mainly enriched in the residual phase.A combination of the two different methods reveal selective enrichment of metal elements from seawater by ferromanganese nodules,featuring multisource mineralization.Moreover,through ion exchange and adsorption,approximately 71.2%of REEs are enriched in the Fe-oxide phase,15.4%in the Mn-oxide phase and 12.4%in the residual phase,while REE contents in the carbonate phase are relatively low.In addition,under the oxic conditions of seawater,the oxidation of soluble Ce^(3+)to insoluble CeO_(2)together with Fe-Mn minerals results in Ce enrichment in ferromanganese nodules.This study provides a reference for the metallogenesis of ferromanganese nodules from the Northwest Pacific.展开更多
Objective: To assess the predictors of successful inactivation of benign thyroid nodules using radiofrequency ablation (RFA) and the hormonal responses thereafter. Methods: A retrospective study conducted at Zhongnan ...Objective: To assess the predictors of successful inactivation of benign thyroid nodules using radiofrequency ablation (RFA) and the hormonal responses thereafter. Methods: A retrospective study conducted at Zhongnan Hospital of Wuhan University (January 2022 to January 2024) analysed thyroid tumor characteristics using B-mode ultrasound, colour Doppler imaging, and CEUS post-RFA. Thyroid hormone levels were also assessed before RFA and at 1, 3, and 6 months after the procedure. Results: The study involved 72 patients with benign thyroid nodules, comprising 13 males and 59 females, with a mean age of 45.8 ± 12.1 years. Complete inactivation was achieved in 70.8% of nodules, while 29.2% showed partial inactivation. Nodules with complete inactivation exhibited more calcification (p = 0.040), whereas those with partial inactivation demonstrated higher vascularity (p Conclusion: In conclusion, this study found that therapeutic RFA effectively achieves high rates of complete inactivation in benign thyroid nodules, with the degree of inactivation mainly influenced by nodule vascularity and calcifications.展开更多
Peanuts are important oilseed legume crops that are susceptible to contamination by Aspergillus flavus in soil,leading to serious economic losses.Previously,our research team developed the Aspergillus-Rihizobia coupli...Peanuts are important oilseed legume crops that are susceptible to contamination by Aspergillus flavus in soil,leading to serious economic losses.Previously,our research team developed the Aspergillus-Rihizobia coupling(ARC)microbial inoculants and found it can reduce A.flavus abundance in the soil and promote efficient nodulation in peanuts.However,the impact of ARC microbial inoculants on different resistant varieties of A.flavus remains unclear.In this study,we screened peanut varieties that were resistant and susceptible to A.flavus and evaluated their nodulation ability and growth performance after ARC microbial inoculants treatment in the field.The results demonstrated that the nodule number and nitrogenase activity of both varieties significantly increased after ARC microbial inoculants treatment,with the highly susceptible variety AH24 showing a greater increase.For photosynthetic parameters,both varieties also increased after ARC microbial inoculants treatment,but the increase was greater in the moderately resistant variety AH1 than in the highly susceptible variety AH24.Finally,we found that the yield and yield-related traits of the moderately resistant variety AH1 were better than those of the highly susceptible variety AH24.After ARC microbial inoculants treatment,the yield traits of both peanut varieties still increased significantly,but the degree of increase of the moderately resistant variety AH1 was smaller than that of the highly susceptible variety AH24.In addition,the abundance of A.flavus in the rhizosphere soil of the two varieties significantly decreased after ARC microbial inoculants treatment,with no significant difference between the varieties.These results indicated that ARC microbial inoculants exert differential effects on the nodulation and growth of different resistant peanut varieties and have a better effect on highly susceptible varieties.These results provide a solid theoretical basis for the efficient use of ARC microbial inoculants in the field of peanuts in the future.展开更多
Objective:This study aimed to construct a model that predicts invasive lung cancer using longitudinal radiological features from multiple low-dose computed tomography(LDCT)scans,thereby addressing overdiagnosis in lun...Objective:This study aimed to construct a model that predicts invasive lung cancer using longitudinal radiological features from multiple low-dose computed tomography(LDCT)scans,thereby addressing overdiagnosis in lung cancer screening.Methods:In this retrospective study,628 patients with pulmonary nodules who underwent three LDCT scans followed by surgical resection were categorized into invasive carcinoma(n=155)and non-invasive nodule(n=473)groups on the basis of pathological diagnosis.This derivation aimed to identify risk factors and construct a multivariate logistic model.The predictive performance was externally validated in two independent cohorts(retrospectively designed,n=252;prospectively designed,n=269).The discrimination and calibration of the model were evaluated using area under the curve(AUC),and calibration plots.Decision curve analysis(DCA)was further performed to evaluate the net benefit in practical clinical scenarios.Results:The model,termed multiple CTs-invasive lung cancer(MCT-ILC),incorporated eleven factors encompassing nodule features at baseline and feature variability during follow-up.The standard deviation of diameter variability(SD_(diameter))was the most reliable predictor,with an odds ratio[95% confidence interval(95%CI)of 7.35(5.32-10.16)(P<0.001)].AUCs with 95% CIs for the MCT-ILC model were 0.912(0.864-0.960)and 0.906(0.833-0.979)in the two testing cohorts and were superior to those for the model containing only features at baseline(PD_(elong)=0.002 and 0.021,respectively).For calibration,the Brier scores of the MCT-ILC model were0.091(95% CI:0.064-0.118) and 0.078(95% CI:0.055-0.101)in the two test sets.The decision curve image showed that the MCT-ILC model was the only model that maintained positive net benefits across the entire threshold range.Furthermore,the MCT-ILC model score could classify more than 90% of patients with invasive nodules into the high-risk group.Conclusions:The MCT-ILC model could assess pulmonary nodule invasiveness,potentially mitigating overdiagnosis in lung cancer screening.展开更多
Thyroid nodules are common,with a prevalence of approximately 70%on thyroid ultrasonography;approximately 5%of these nodules are malignant.Distingui-shing malignant and benign thyroid nodules is critical for clinical ...Thyroid nodules are common,with a prevalence of approximately 70%on thyroid ultrasonography;approximately 5%of these nodules are malignant.Distingui-shing malignant and benign thyroid nodules is critical for clinical management.Clinicians can judiciously select patients for fine-needle aspiration,understand the cytology results and subsequent follow-up,and determine surveillance stra-tegies for non-operated nodules.The challenge in selecting thyroid nodules for fine-needle aspiration is to avoid the diagnosis of small,clinically insignificant cancers without missing more severe diseases.The molecular characteristics of thyroid nodules are critical for their diagnosis and treatment.However,iden-tifying these characteristics is costly and challenging because of unexpected technical difficulties.An imaging association model based on molecular features will bridge the essential link between molecular characteristics and the computed tomography radiomics,then improve diagnostic efficiency,reducing invasive examinations.展开更多
Accurate classification of pulmonary nodules is critical for early diagnosis of lung cancer. However, non-invasive and accurate diagnosis of benign and malignant pulmonary nodules faces great challenges. In this study...Accurate classification of pulmonary nodules is critical for early diagnosis of lung cancer. However, non-invasive and accurate diagnosis of benign and malignant pulmonary nodules faces great challenges. In this study, we develop a nano zero-valent iron(nZVI)-assisted laser desorption/ionization mass spectrometry(LDI MS) platform, which enables ultra-high-throughput acquisition of abundant metabolic fingerprint information of serum in negative ion mode. We further recruit a large-scale multicenter prospective cohort and collect 1099 serum samples from participants with benign and malignant nodules. The accurate machine learning models are built and validated based on n ZVI-assisted LDI MS metabolomics to achieve efficient classification of benign and malignant nodules. Using our established stacking ensemble learning model, the AUC of the ROC curve for benign and malignant lung nodule classification can be as high as 0.9, and the sensitivity can reach 85.5%, which is significantly better than existing clinical models. This work provides an integrated workflow from detection technology to diagnostic models for biomarkerbased pulmonary nodule diagnosis, which would be widely used in rapid and large-scale screening of pulmonary nodules.展开更多
Objective This study aimed to develop a few-shot learning model for lung nodule detection in CT images by leveraging visual open-set object detection.Methods The Lung Nodule Analysis 2016(LUNA16)public dataset was use...Objective This study aimed to develop a few-shot learning model for lung nodule detection in CT images by leveraging visual open-set object detection.Methods The Lung Nodule Analysis 2016(LUNA16)public dataset was used for validation.It was split into training and testing sets in an 8:2 ratio.Classical You Only Look Once(YOLO)models of three sizes(n,m,x)were trained on the training set.Transfer learning experiments were then conducted using the mainstream open-set object detection models derived from Detection Transformer(DETR)with Improved DeNoising AnchOr Boxes(DINO),i.e.,Grounding DINO and Open-Vocabulary DINO(OV-DINO),as well as our proposed few-shot learning model,across a range of different shot sizes.Finally,all trained models were compared on the test set.Results After training on LUNA16,the precision,recall,and mean average precision(mAP)of the different-sized YOLO models showed no significant differences,with peak values of 82.8%,73.1%,and 77.4%,respectively.OV-DINO’s recall was significantly higher than YOLO’s,but it did not show clear advantages in precision or mAP.Using only one-fifth of the training samples and one-tenth of the training epochs,our proposed model outperformed both YOLO and OV-DINO,achieving improvements of 6.6%,9.3%,and 6.9%in precision,recall,and mAP,respectively,with final values of 89.4%,96.2%,and 87.7%.Conclusion The proposed few-shot learning model demonstrates stronger scene transfer capabilities,requiring fewer samples and training epochs,and can effectively improve the accuracy of lung nodule detection.展开更多
文摘Water toughening is generally required for the widely used manganese-system wear-resistant cast steels (0.9-1.4 % C, 5.5-14.0% Mn). The purpose of the treatment is to get rid of the needle-like carbides and carbide networks in the as-cast state,
基金supported by the National Natural Science Foundation of China(No.52370112).
文摘In order to address the evolving emission characteristics of oxygenated volatile organic compounds(OVOCs),it is essential to develop adsorbent materials specifically designed for the efficient adsorption of OVOCs with large kinetic diameters.In this study,we used co-pyrolysis to prepare a series of graded porous carbon materials with well-developed micropores by adjusting the doping ratios of root nodules and pretreated cellulose.The material with root nodule to cellulose mass ratio of 1:1(TCC-RN-1)exhibited the highest saturated adsorption capacity for butyl acetate(834 mg/g).This can be attributed to enhanced pore size distribution from nodule doping,which facilitates the development of a micropore-graded structure.Additionally,the nodules acted as auxiliary activating agents that enhanced the KOH micropore regulation effect during the activation stage,resulting in the highest micropore volume(0.863 cm^(3)/g).The doping of root nodules facilitated the formation of additional defects on the surface of the porous carbon material,leading to a more disordered arrangement that improved pollutant adsorption.Furthermore,TCC-RN-1 demonstrated good thermal stability in an air atmosphere,main-taining a butyl acetate adsorption capacity exceeding 95%after five adsorption-desorption cycles.This indicates its favorable potential for industrial applications.
基金supported by the Agricultural Science and Technology Innovation Program(CAAS-ZDRW202416)the Science and Technology Major Projects of Hubei Province(2023BBA002)the National Natural Science Foundation of China grants(32441047,32441046 and 32471627)。
文摘Rhizobial inoculation in soybean is an effective strategy in sustainable agriculture to reduce chemical fertilizer application and to increase crop production.It not only provides nitrogen sources for host plants but also improves the rhizosphere soil environment.However,the inoculation efficiency of rhizobia remains to be improved.In this study,we investigated the nodulation efficiency of Bradyrhizobium and Sinorhizobium strains under different soil conditions and evaluated their impacts on the rhizocompartment bacterial community.We found that inoculation with Bradyrhizobium diazoefficiens UASD 110 increased the number of soybean nodules in acidic soil,while Sinorhizobium fredii CCBAU 45436 was more effective in alkaline soil.However,inoculation with neither strain significantly affected nodulation in neutral soil.Then,we demonstrated that UASD 110 was more competitive in nodulation than CCBAU 45436,which was related to its higher abundance in the rhizosphere.Furthermore,we showed that while single inoculation with UASD 110 or CCBAU 45436 failed to alter the bacterial diversity,these two strains differentially influenced the rhizosphere microbial composition.Finally,we identified the main rhizosphere microorganisms that were affected by these two strains.Our findings revealed that the nodulation capacity of rhizobia and their colonization of rhizosphere and nodules are soil-type dependent,yet their impact on the rhizobacterial community exhibited consistent patterns.These findings provide valuable insights into optimizing rhizobial inoculation strategies to enhance nitrogen fixation efficiency.
基金supported by the Ministry of Higher Education under the Fundamentals Research Grant Scheme(FRGS/1/2024/ICT02/UPM/02/5).
文摘Currently,thyroid diseases are prevalent worldwide;therefore,it is necessary to develop techniques that help doctors improve their diagnostic skills for such diseases.In previous studies,2-dimensional convolutional neural network(2D CNN)techniques were employed to classify thyroid nodules as benign and malignant without detecting the presence of thyroid nodules in the obtained ultrasound images.To address this issue,we propose a 3-dimensional convolutional neural network(3D CNN)for thyroid nodule detection.The proposed CNN exploits the 3D information and spatial features contained in ultrasound images and generates distinctive features during its training using multiple samples,even for small nodules.In contrast,a 2D CNN only depends on spatial features.In this study,we used two datasets of 2210 ultrasound images obtained from the Sultan Abdul Aziz Shah Hospital in Malaysia,and a public open dataset,Digital Database Thyroid Image(DDTI).We created folders containing three images each,processed the images and extracted volumetric features suitable for the 3-dimensional convolutional neural network(3D CNN).The proposed model was assessed using four metrics:accuracy,recall,precision and F1 score.The results showed that the accuracy of the model in predicting the presence of thyroid nodules in ultrasound images was 96%.In conclusion,this study could help radiologists in hospitals and medical centres in classifying ultrasound images and detecting thyroid nodules.
基金supported by the National Natural Science Foundation of China(32300512)and the Xplorer Prize.
文摘Root nodule symbiosis(RNS)is a mutualistic association formed between nitrogen-fixing rhizobia or Frankia and host plants limited to four orders within Rosid I―Fabales,Fagales,Cucurbitales,and Rosales―which comprise the so-called‘Nitrogen Fixing Nodulation Clade’(NFNC).The majority of nodulation studies have focused on Leguminosae,given their agricultural and environmental importance,as well as the widespread occurrence of nodulation among members of this family.Endowing cereal crops with nitrogen fixation,like Leguminosae,presents a strategy to reduce the detrimental effects of synthetic fertilizer overuse.Different hypotheses on the origin of RNS have been proposed;however,key genetic innovations underlying the evolution of RNS,even in Leguminosae,have been rarely reported.In this review,we begin by examining current knowledge of genetic innovations―including gene gain,gene loss,and the acquisition or loss of conserved noncoding sequences(CNS)in preexisting genes.We explore the available evidence supporting these genetic innovations underlying the evolution of RNS in Leguminosae and offer the phylogenomics approach that could be applied to uncover these genetic innovations.Finally,we conclude by proposing a model of genetic innovations underlying the evolution of RNS in Leguminosae and consider the potential implications for the development of nitrogen-fixing crops.
基金funded by the National Key Research and Development Program of China(2021YFF1000500)the National Natural Science Foundation of China(32372231)(3187101212)the earmarked fund for China Agriculture Research System(CARS-04-PS21)。
文摘Flavonoids produced by legume roots act as signaling molecules that induce the expression of nod genes in symbiotic rhizobia.However,the role of flavonoids in root exudates under intercropping systems in promoting soybean nodulation remains unclear.Two consecutive years of field experiments were conducted using maize–soybean strip intercropping with interspecific row spacings of 30 cm(MS30),45 cm(MS45),and 60 cm(MS60),along with sole cropping of soybean(SS)and maize(MM).Root interactions were manipulated using either no root barrier(NB)or a polyethylene plastic barrier(PB)to assess the relationship between flavonoids in root exudates and soybean nodulation.We found that root–root interaction between soybean and maize increased nodule number and fresh weight in intercropped soybean,with enhancement gradually increasing as interspecific distance widened.The proportion of nodules with diameters exceeding 0.4 cm was higher in intercropped soybean under NB compared to PB.Additionally,the expression of nodule-related genes-GmENOD40,Gm NIN2b,and Gm EXPB2-was up-regulated.Furthermore,compared to monocropping,isoflavone secretion by soybean roots decreased,whereas flavonoid and flavonol secretion by both maize and soybean roots increased under intercropping.The abundance of differentially secreted flavonoid metabolites in the rhizosphere of both species declined when root contact was prevented by the barrier.In soybean roots,the expression of Gm CHS8 and Gm IFS1 was up-regulated,while Gm ICHG was down-regulated under root interaction.Most flavonoid and flavonol compounds showed positive correlations with nodule diameter.Nodule number,fresh weight,and the proportion of nodules larger than 0.2 cm increased in diverse soybean genotypes treated with maize root exudates,which contributed to enhanced nitrogen fixation capacity.Therefore,maize–soybean strip intercropping,combined with optimal row spacing,enhances the positive effects of underground root interactions and improves nodulation and nitrogen fixation in intercropped soybean.
基金supported by Military Key Clinical Speciality(No.51561Z23612)Chongqing Talents Project(No.cstc2022ycjh-bgzxm0091)。
文摘Objective:Non-diagnostic thyroid nodules(Bethesda I)account for 5%-20%of all thyroid nodules.Accurate differentiation of benign and malignant nodules can reduce unnecessary surgeries and repeat biopsies.Herein we evaluated the diagnostic efficacy of multigene testing in non-diagnostic thyroid nodules and developed a predictive model integrating molecular and clinical data.Methods:In this prospective cohort study,1,175 patients with thyroid nodules were evaluated for inclusion,of which 218 patients with Bethesda I nodules met our inclusion criteria.The primary outcome was diagnostic accuracy of molecular testing,and the secondary outcome was the performance of a predictive model integrating molecular and clinical data.Results:Final histopathology identified 165 benign and 53 malignant nodules.Molecular testing detected 10distinct point mutations and seven gene fusions.Among benign nodules,147 tested negative and 18 tested positive,whereas 44 malignant nodules tested positive and nine tested negative.In nodules with ultrasound grades 4-5 and fine-needle aspiration cytology(FNAC)results categorized as non-diagnostic,molecular testing achieved sensitivity of 83.00%,specificity of 89.00%,positive predictive value(PPV)of 71.00%,negative predictive value(NPV)of94.20%,and overall accuracy of 87.60%.The predictive model incorporated 18 clinical and 19 molecular features.Eleven non-zero predictors were selected via least absolute shrinkage and selection operator(LASSO),and the model achieved area under curve(AUC)of 0.95 in the training set and 0.96 in the testing set.Decision curve analysis indicated greater net benefit compared with conventional diagnostic approaches.Conclusions:Molecular testing significantly improved diagnostic accuracy for Bethesda I thyroid nodules.Integrating molecular and clinical data enabled the development of a robust predictive model,facilitating precise,individualized patient management and reducing the need for repeat FNAC and unnecessary surgeries.
文摘BACKGROUND The liver is the most common site of digestive system tumor metastasis,but not all liver metastases can be traced back to the primary lesions.Although it is unusual,syphilis can impact the liver,manifesting as syphilitic hepatitis with inflammatory nodules,which might be misdiagnosed as metastasis.CASE SUMMARY This case report involves a 46-year-old female who developed right upper abdominal pain and intermittent low fever that persisted for more than three months.No definitive diagnosis of a tumor had been made in the past decades,but signs of multiple liver metastases were recognized after a computed tomo-graphy scan without evidence of primary lesions.With positive serological tests for syphilis and a biopsy of the liver nodules,a diagnosis of hepatic syphilis was made and confirmed with follow-up nodule reduction after anti-syphilis therapy.CONCLUSION Clinicians must be aware of the possibility that syphilis can cause hepatic inflam-matory masses,especially when liver metastasis is suspected without evidence of primary lesions.A definitive diagnosis should be established in conjunction with a review of the patient’s medical history for accurate therapeutic intervention.
文摘Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.
基金supported by Chengdu University of Traditional Chinese Medicine“Xinglin Scholars”Subject Talent Scientific Research Enhancement Plan(No.YYZX2022056).
文摘Objective:To explore the application value of artificial intelligence-assisted diagnostic systems in the computed tomography(CT)diagnosis of pulmonary nodules.Methods:A total of 80 patients with pulmonary nodules,treated from June 2023 to May 2024,were included.All patients underwent pathological examination and CT scans,with pathological results serving as the gold standard.The diagnostic performance of CT alone and CT combined with the artificial intelligence-assisted diagnostic system was analyzed,and differences in CT imaging features and evaluation results of benign and malignant pulmonary nodules were compared.Results:The sensitivity,specificity,and accuracy of CT combined with the artificial intelligence-assisted diagnostic system were significantly higher than those of CT alone(P<0.05).Moreover,the false-positive and false-negative rates were significantly lower for the combined approach compared to CT alone(P<0.05).Conclusion:The artificial intelligence-assisted diagnostic system effectively identifies malignant features in pulmonary nodules,providing valuable clinical reference data and enhancing diagnostic accuracy and efficiency.
文摘Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
文摘Globally,liver cancer ranks as the sixth most frequent malignancy cancer.The importance of early detection is undeniable,as liver cancer is the fifth most common disease in men and the ninth most common cancer in women.Recent advances in imaging,biomarker discovery,and genetic profiling have greatly enhanced the ability to diagnose liver cancer.Early identification is vital since liver cancer is often asymptomatic,making diagnosis difficult.Imaging techniques such as Magnetic Resonance Imaging(MRI),Computed Tomography(CT),and ultrasonography can be used to identify liver cancer once a sample of liver tissue is taken.In recent research,reliable detection of liver cancer with minimal computing computational complexity and time has remained a serious difficulty.This paper employs the DenseNet model to enhance the detection of liver nodules with tumors by segmenting them using UNet and VGG using Fastai(UVF)in CT images.Its dense interconnections distinguish the DenseNet between layers.These dense connections facilitate the propagation of gradients and the flow of information throughout the network,thereby enhancing the efficacy and performance of training.DenseNet’s architecture combines dense blocks,bottleneck layers,and transition layers,allowing it to achieve a compromise between expressiveness and computing efficiency.Finally,the 3D liver nodular models were created using a raycasting volume rendering approach.Compared to other state-of-the-art deep neural networks,it is suitable for clinical applications to assist doctors in diagnosing liver cancer.The proposed approach was tested on a 3Dircadb dataset.According to experiments,UVF segmentation on the 3Dircadb dataset is 97.9%accurate.According to the study,the DenseNet and UVF segment liver cancer better than prior methods.The system proposes automated 3D liver cancer tumor visualization.
文摘BACKGROUND Several trace minerals have been shown to be associated with thyroid cancer.Democratic Republic of Congo(DRC)is deemed the most mineral-rich country globally.Data on the characteristics of thyroid nodules in various mineral-rich regions of the DRC is scarce.AIM To analyze the differential spectrum of thyroid nodules based on locoregional variance in mineral density.METHODS We conducted a cross-sectional study on 529 patients with thyroid nodules residing in Katanga,South Kivu and Kinshasa between 2005 and 2019.Of these three provinces,Katanga and South Kivu have the highest mineral density with the DRC.RESULTS Mean patient age was 44.2 years±14.6 years with a female predominance,with a female to male ratio of 5.4.The 66.5%of patients had a family history of thyroid disease.Total 74 patients had simple nodules,and the remaining 455 patients had multiple nodules.The 87.7%of patients were euthyroid.The nodules exhibited varying characteristics namely hypoechogenicity(84.5%),solid echostructure(72.2%),macronodular appearance(59.8%),calcifications(14.4%)and associated lymphadenopathy(15.5%).The 22.3%of the nodules were malignant.Factors independently associated with malignancy were older age(≥60 years)[adjusted odds ratio(aOR)=2.81],Katanga province(aOR=8.19),solid echostructure(aOR=7.69),hypoechogenicity(aOR=14.19),macronodular appearance(aOR=9.13),calcifications(aOR=2.6)and presence of lymphadenopathy(aOR=6.94).CONCLUSION Thyroid nodules emanating from the mineral-laden province of Katanga were more likely to be malignant.Early and accurate risk-stratification of patients with thyroid nodules residing in high-risk areas could be instrumental in optimizing survival in these patients.
基金The Fund of Laoshan Laboratory under contract No.LSKJ202203602the National key R&D Program of China under contract No.2022YFC2803600the Taishan Scholarship from Shandong Province.
文摘To explore the geochemical characteristics and genesis of the elements in ferromanganese nodules from the Northwest Pacific,this study analyses the mineral composition,elemental content,occurrence phase and genetic mechanisms of samples by X-ray diffraction(XRD),inductively coupled plasma-optical emission spectrometry(ICP-OES),inductively coupled plasma-mass spectrometry(ICP-MS)and phase analysis methods.The results show that ferromanganese nodules are mainly hydrogenetic,and Mn/Fe content ratio ranges from 0.95 to 2.05.The major minerals are vernadite(δ-MnO_(2))and amorphous ferric oxyhydroxide(FeOOH),and the secondary minerals include todorokite,birnessite,quartz and plagioclase.Ferromanganese nodules contain high contents of Co(0.24%-0.42%),Cu(0.23%-0.73%),Ni(0.33%-0.86%)and rare earth elements(REEs,1192-1990μg/g),which have positive Ce and negative Y anomalies but no Eu anomaly.A cluster analysis suggests that the elements in ferromanganese nodules can be divided into three groups:hydrogenetic components,including Fe,Ti,Zr,P,Pb,Co,Ba,Sr,V and REEs;diagenetic components,including Mn,Ni,Mg,Zn and Cu;and detrital components,including Al,Na,K and Ca.According to chemical leaching,ferromanganese nodules can be divided into four phases:Na,Ca,Mg and Sr are mainly enriched in the carbonate phase;Mn,Co,Ni and Ba are mainly enriched in the Mn-oxide phase;Fe,P,Ti,Cu,Pb,V,Zn,Zr and REEs are mainly enriched in the Fe-oxide phase;and Al and K are mainly enriched in the residual phase.A combination of the two different methods reveal selective enrichment of metal elements from seawater by ferromanganese nodules,featuring multisource mineralization.Moreover,through ion exchange and adsorption,approximately 71.2%of REEs are enriched in the Fe-oxide phase,15.4%in the Mn-oxide phase and 12.4%in the residual phase,while REE contents in the carbonate phase are relatively low.In addition,under the oxic conditions of seawater,the oxidation of soluble Ce^(3+)to insoluble CeO_(2)together with Fe-Mn minerals results in Ce enrichment in ferromanganese nodules.This study provides a reference for the metallogenesis of ferromanganese nodules from the Northwest Pacific.
文摘Objective: To assess the predictors of successful inactivation of benign thyroid nodules using radiofrequency ablation (RFA) and the hormonal responses thereafter. Methods: A retrospective study conducted at Zhongnan Hospital of Wuhan University (January 2022 to January 2024) analysed thyroid tumor characteristics using B-mode ultrasound, colour Doppler imaging, and CEUS post-RFA. Thyroid hormone levels were also assessed before RFA and at 1, 3, and 6 months after the procedure. Results: The study involved 72 patients with benign thyroid nodules, comprising 13 males and 59 females, with a mean age of 45.8 ± 12.1 years. Complete inactivation was achieved in 70.8% of nodules, while 29.2% showed partial inactivation. Nodules with complete inactivation exhibited more calcification (p = 0.040), whereas those with partial inactivation demonstrated higher vascularity (p Conclusion: In conclusion, this study found that therapeutic RFA effectively achieves high rates of complete inactivation in benign thyroid nodules, with the degree of inactivation mainly influenced by nodule vascularity and calcifications.
基金supported by the Agricultural Science and Technology Innovation Program(CAAS-ZDRW202416)the Foundation of Hubei Hongshan Laboratory(2021hszd015)+1 种基金the Science and Technology Major Projects of Hubei Province(2023BBA002)the Knowledge Innovation Program of Wuhan-Basi Research(2023020201010126)。
文摘Peanuts are important oilseed legume crops that are susceptible to contamination by Aspergillus flavus in soil,leading to serious economic losses.Previously,our research team developed the Aspergillus-Rihizobia coupling(ARC)microbial inoculants and found it can reduce A.flavus abundance in the soil and promote efficient nodulation in peanuts.However,the impact of ARC microbial inoculants on different resistant varieties of A.flavus remains unclear.In this study,we screened peanut varieties that were resistant and susceptible to A.flavus and evaluated their nodulation ability and growth performance after ARC microbial inoculants treatment in the field.The results demonstrated that the nodule number and nitrogenase activity of both varieties significantly increased after ARC microbial inoculants treatment,with the highly susceptible variety AH24 showing a greater increase.For photosynthetic parameters,both varieties also increased after ARC microbial inoculants treatment,but the increase was greater in the moderately resistant variety AH1 than in the highly susceptible variety AH24.Finally,we found that the yield and yield-related traits of the moderately resistant variety AH1 were better than those of the highly susceptible variety AH24.After ARC microbial inoculants treatment,the yield traits of both peanut varieties still increased significantly,but the degree of increase of the moderately resistant variety AH1 was smaller than that of the highly susceptible variety AH24.In addition,the abundance of A.flavus in the rhizosphere soil of the two varieties significantly decreased after ARC microbial inoculants treatment,with no significant difference between the varieties.These results indicated that ARC microbial inoculants exert differential effects on the nodulation and growth of different resistant peanut varieties and have a better effect on highly susceptible varieties.These results provide a solid theoretical basis for the efficient use of ARC microbial inoculants in the field of peanuts in the future.
基金funded by grants from Project supported by the Funds for Noncommunicable Chronic Diseases-National Science and Technology Major Project(No.2024ZD0520000,2024ZD0520003)Noncommunicable Chronic Diseases-National Science and Technology Major Project(No.2024ZD0524400,2024ZD0524403)+2 种基金National Natural Science Foundation of China(No.82388102)Jiangsu Medical Association Medical Research Project of Health Management,SYH-32099-0119(No.2024023)the Specialized Diseases Clinical Research Fund of Jiangsu Province Hospital(No.DL202411)。
文摘Objective:This study aimed to construct a model that predicts invasive lung cancer using longitudinal radiological features from multiple low-dose computed tomography(LDCT)scans,thereby addressing overdiagnosis in lung cancer screening.Methods:In this retrospective study,628 patients with pulmonary nodules who underwent three LDCT scans followed by surgical resection were categorized into invasive carcinoma(n=155)and non-invasive nodule(n=473)groups on the basis of pathological diagnosis.This derivation aimed to identify risk factors and construct a multivariate logistic model.The predictive performance was externally validated in two independent cohorts(retrospectively designed,n=252;prospectively designed,n=269).The discrimination and calibration of the model were evaluated using area under the curve(AUC),and calibration plots.Decision curve analysis(DCA)was further performed to evaluate the net benefit in practical clinical scenarios.Results:The model,termed multiple CTs-invasive lung cancer(MCT-ILC),incorporated eleven factors encompassing nodule features at baseline and feature variability during follow-up.The standard deviation of diameter variability(SD_(diameter))was the most reliable predictor,with an odds ratio[95% confidence interval(95%CI)of 7.35(5.32-10.16)(P<0.001)].AUCs with 95% CIs for the MCT-ILC model were 0.912(0.864-0.960)and 0.906(0.833-0.979)in the two testing cohorts and were superior to those for the model containing only features at baseline(PD_(elong)=0.002 and 0.021,respectively).For calibration,the Brier scores of the MCT-ILC model were0.091(95% CI:0.064-0.118) and 0.078(95% CI:0.055-0.101)in the two test sets.The decision curve image showed that the MCT-ILC model was the only model that maintained positive net benefits across the entire threshold range.Furthermore,the MCT-ILC model score could classify more than 90% of patients with invasive nodules into the high-risk group.Conclusions:The MCT-ILC model could assess pulmonary nodule invasiveness,potentially mitigating overdiagnosis in lung cancer screening.
文摘Thyroid nodules are common,with a prevalence of approximately 70%on thyroid ultrasonography;approximately 5%of these nodules are malignant.Distingui-shing malignant and benign thyroid nodules is critical for clinical management.Clinicians can judiciously select patients for fine-needle aspiration,understand the cytology results and subsequent follow-up,and determine surveillance stra-tegies for non-operated nodules.The challenge in selecting thyroid nodules for fine-needle aspiration is to avoid the diagnosis of small,clinically insignificant cancers without missing more severe diseases.The molecular characteristics of thyroid nodules are critical for their diagnosis and treatment.However,iden-tifying these characteristics is costly and challenging because of unexpected technical difficulties.An imaging association model based on molecular features will bridge the essential link between molecular characteristics and the computed tomography radiomics,then improve diagnostic efficiency,reducing invasive examinations.
基金financially supported by the Fundamental Research Funds for the Central Universities (No. WHU 2042024kf0009)National Key Research and Development Program of China (No. 2021YFC2700700)the National Natural Science Foundation of China (Nos. 22074111, 22004093)。
文摘Accurate classification of pulmonary nodules is critical for early diagnosis of lung cancer. However, non-invasive and accurate diagnosis of benign and malignant pulmonary nodules faces great challenges. In this study, we develop a nano zero-valent iron(nZVI)-assisted laser desorption/ionization mass spectrometry(LDI MS) platform, which enables ultra-high-throughput acquisition of abundant metabolic fingerprint information of serum in negative ion mode. We further recruit a large-scale multicenter prospective cohort and collect 1099 serum samples from participants with benign and malignant nodules. The accurate machine learning models are built and validated based on n ZVI-assisted LDI MS metabolomics to achieve efficient classification of benign and malignant nodules. Using our established stacking ensemble learning model, the AUC of the ROC curve for benign and malignant lung nodule classification can be as high as 0.9, and the sensitivity can reach 85.5%, which is significantly better than existing clinical models. This work provides an integrated workflow from detection technology to diagnostic models for biomarkerbased pulmonary nodule diagnosis, which would be widely used in rapid and large-scale screening of pulmonary nodules.
基金supported by the Natural Science Foundation of Beijing Municipality(No.7222320)the Capital Health Research and Development Special Fund(No.2022–2–6081)+1 种基金the Scientific Research Fund of Aerospace Center Hospital(No.YN202301)Aerospace Medical Health Science and Technology Research Projects(No.2021YK09).
文摘Objective This study aimed to develop a few-shot learning model for lung nodule detection in CT images by leveraging visual open-set object detection.Methods The Lung Nodule Analysis 2016(LUNA16)public dataset was used for validation.It was split into training and testing sets in an 8:2 ratio.Classical You Only Look Once(YOLO)models of three sizes(n,m,x)were trained on the training set.Transfer learning experiments were then conducted using the mainstream open-set object detection models derived from Detection Transformer(DETR)with Improved DeNoising AnchOr Boxes(DINO),i.e.,Grounding DINO and Open-Vocabulary DINO(OV-DINO),as well as our proposed few-shot learning model,across a range of different shot sizes.Finally,all trained models were compared on the test set.Results After training on LUNA16,the precision,recall,and mean average precision(mAP)of the different-sized YOLO models showed no significant differences,with peak values of 82.8%,73.1%,and 77.4%,respectively.OV-DINO’s recall was significantly higher than YOLO’s,but it did not show clear advantages in precision or mAP.Using only one-fifth of the training samples and one-tenth of the training epochs,our proposed model outperformed both YOLO and OV-DINO,achieving improvements of 6.6%,9.3%,and 6.9%in precision,recall,and mAP,respectively,with final values of 89.4%,96.2%,and 87.7%.Conclusion The proposed few-shot learning model demonstrates stronger scene transfer capabilities,requiring fewer samples and training epochs,and can effectively improve the accuracy of lung nodule detection.
