Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of...Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of complex diseases,with some even achieving clinical translation.Changes in the overall size,shape,boundary,and other morphological features of organoids provide a noninvasive method for assessing organoid drug sensitivity.However,the precise segmentation of organoids in bright-field microscopy images is made difficult by the complexity of the organoid morphology and interference,including overlapping organoids,bubbles,dust particles,and cell fragments.This paper introduces the precision organoid segmentation technique(POST),which is a deep-learning algorithm for segmenting challenging organoids under simple bright-field imaging conditions.Unlike existing methods,POST accurately segments each organoid and eliminates various artifacts encountered during organoid culturing and imaging.Furthermore,it is sensitive to and aligns with measurements of organoid activity in drug sensitivity experiments.POST is expected to be a valuable tool for drug screening using organoids owing to its capability of automatically and rapidly eliminating interfering substances and thereby streamlining the organoid analysis and drug screening process.展开更多
Cerebral small vessel disease encompasses a group of neurological disorders characterized by injury to small blood vessels,often leading to stroke and dementia.Due to its diverse etiologies and complex pathological me...Cerebral small vessel disease encompasses a group of neurological disorders characterized by injury to small blood vessels,often leading to stroke and dementia.Due to its diverse etiologies and complex pathological mechanisms,preventing and treating cerebral small vessel vasculopathy is challenging.Recent studies have shown that the glymphatic system plays a crucial role in interstitial solute clearance and the maintenance of brain homeostasis.Increasing evidence also suggests that dysfunction in glymphatic clearance is a key factor in the progression of cerebral small vessel disease.This review begins with a comprehensive introduction to the structure,function,and driving factors of the glymphatic system,highlighting its essential role in brain waste clearance.Afterwards,cerebral small vessel disease was reviewed from the perspective of the glymphatic system,after which the mechanisms underlying their correlation were summarized.Glymphatic dysfunction may lead to the accumulation of metabolic waste in the brain,thereby exacerbating the pathological processes associated with cerebral small vessel disease.The review also discussed the direct evidence of glymphatic dysfunction in patients and animal models exhibiting two subtypes of cerebral small vessel disease:arteriolosclerosis-related cerebral small vessel disease and amyloid-related cerebral small vessel disease.Diffusion tensor image analysis along the perivascular space is an important non-invasive tool for assessing the clearance function of the glymphatic system.However,the effectiveness of its parameters needs to be enhanced.Among various nervous system diseases,including cerebral small vessel disease,glymphatic failure may be a common final pathway toward dementia.Overall,this review summarizes prevention and treatment strategies that target glymphatic drainage and will offer valuable insight for developing novel treatments for cerebral small vessel disease.展开更多
Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and...Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.展开更多
The heterogeneity and invasiveness of cancer cells pose serious challenges in cancer diagnosis and treatment.Advancements and innovations in metal-based nanomedicines provide novel avenues for addressing these challen...The heterogeneity and invasiveness of cancer cells pose serious challenges in cancer diagnosis and treatment.Advancements and innovations in metal-based nanomedicines provide novel avenues for addressing these challenges.Metal-based nanomedicines possess unique physicochemical properties that enable their interaction with living organisms,thereby inducing complex biological responses.These nanomaterials have been extensively used to enhance the contrast and sensitivity of cancer imaging and to amplify the distinction between cancerous and healthy tissues.Moreover,these nanomaterials can effectively combat a wide spectrum of cancers through various methods,including drug delivery,radiotherapy,photothermal therapy(PTT),photodynamic therapy(PDT),sonodynamic therapy(SDT),biocatalytic therapy,ion interference therapy(IIT),and immunotherapy.Currently,there is still a need for a comprehensive summary on the metal-based nanomaterials for cancer diagnosis and treatment.Herein,we present a systematic and complete overview of action mechanisms and the applications of metal-based nanomaterials in cancer theranostics.A summary of common strategies for synthesizing and modifying metal-based nanomedicines is presented,and their biosafety is analyzed.Then,the latest developments in their applications for cancer imaging and anticancer treatment are provided.Finally,the key technical challenges and reasonable perspectives of metal-based nanomedicines for cancer theranostics in clinical applications are discussed.展开更多
In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,eff...In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,efficiently combining the advantages of both images while overcoming their shortcomings is necessary.To handle this challenge,we developed an end-to-end IRI andVI fusionmethod based on frequency decomposition and enhancement.By applying concepts from frequency domain analysis,we used the layering mechanism to better capture the salient thermal targets from the IRIs and the rich textural information from the VIs,respectively,significantly boosting the image fusion quality and effectiveness.In addition,the backbone network combined Restormer Blocks and Dense Blocks;Restormer blocks utilize global attention to extract shallow features.Meanwhile,Dense Blocks ensure the integration between shallow and deep features,thereby avoiding the loss of shallow attributes.Extensive experiments on TNO and MSRS datasets demonstrated that the suggested method achieved state-of-the-art(SOTA)performance in various metrics:Entropy(EN),Mutual Information(MI),Standard Deviation(SD),The Structural Similarity Index Measure(SSIM),Fusion quality(Qabf),MI of the pixel(FMI_(pixel)),and modified Visual Information Fidelity(VIF_(m)).展开更多
Microscopy imaging is fundamental in analyzing bacterial morphology and dynamics,offering critical insights into bacterial physiology and pathogenicity.Image segmentation techniques enable quantitative analysis of bac...Microscopy imaging is fundamental in analyzing bacterial morphology and dynamics,offering critical insights into bacterial physiology and pathogenicity.Image segmentation techniques enable quantitative analysis of bacterial structures,facilitating precise measurement of morphological variations and population behaviors at single-cell resolution.This paper reviews advancements in bacterial image segmentation,emphasizing the shift from traditional thresholding and watershed methods to deep learning-driven approaches.Convolutional neural networks(CNNs),U-Net architectures,and three-dimensional(3D)frameworks excel at segmenting dense biofilms and resolving antibiotic-induced morphological changes.These methods combine automated feature extraction with physics-informed postprocessing.Despite progress,challenges persist in computational efficiency,cross-species generalizability,and integration with multimodal experimental workflows.Future progress will depend on improving model robustness across species and imaging modalities,integrating multimodal data for phenotype-function mapping,and developing standard pipelines that link computational tools with clinical diagnostics.These innovations will expand microbial phenotyping beyond structural analysis,enabling deeper insights into bacterial physiology and ecological interactions.展开更多
Background:Sarcomatoid carcinoma of the ureter(SCU)is a highly aggressive and relatively uncommon malignant tumor of the urinary tract.Its frequency is quite low,and its prognosis is very bad when compared to other ca...Background:Sarcomatoid carcinoma of the ureter(SCU)is a highly aggressive and relatively uncommon malignant tumor of the urinary tract.Its frequency is quite low,and its prognosis is very bad when compared to other cancers of the urinary system.SCU clinical reports are still hard to come by.MRI and PEI/CT imaging of ureteral sarcomatoid cancer is presented in this case to promote diagnostic awareness and comprehension of the imaging characteristics of this uncommon illness.Method:The patient had ureteral sarcomatoid cancer,which was verified by pathological investigation after ureteroscopic biopsy.The patient’s clinical information,imaging results,surgical outcomes,and pathological findings were gathered.A retrospective study was carried out in combinationwith pertinent national and international literature.Results:An 84-year-old female patient was admitted for“left flank discomfort lasting over one month.”MRI revealed an irregular soft tissue mass in the middle-lower segment of the left ureter.T2-weighted imaging showed an unevenly slightly hyperintense signal.Diffusion-weighted imaging demonstrated restricted diffusion.Contrastenhanced imaging exhibited heterogeneous enhancement.PET/CT demonstrated significantly increased fluorodeoxyglucose metabolism in the mass with secondary left upper urinary tract obstruction.Concurrent findings included a solitary metastatic lesion in hepatic segment S6 and multiple lymph node metastases along the left common iliac and external iliac arteries.Preoperative diagnosis suggested a malignant tumor of the ureter.The patient underwent left nephroureteroscopy with biopsy,and the postoperative pathological diagnosis was ureteral sarcomatoid carcinoma.Conclusion:Ureteral sarcomatoid carcinoma is a rare,highly malignant,and aggressive tumor with nonspecific imaging features,typically presenting as an invasively growing mass.Diagnosis relies on postoperative pathology and immunohistochemical examination.MRI and PET/CT scans are valuable for preoperative localization and characterization,tumor staging,treatment planning,and postoperative follow-up.The prognosis is extremely negative.The main treatment option is radical surgery,although constant monitoring is necessary since early recurrence and metastases are frequent after surgery.展开更多
The level of glutathione(GSH)is significantly associated with numerous pathological processes,thus,real-time detection of the GSH level is of significance for early diagnosis of GSH-related diseases.Herein,we develope...The level of glutathione(GSH)is significantly associated with numerous pathological processes,thus,real-time detection of the GSH level is of significance for early diagnosis of GSH-related diseases.Herein,we developed in vivo second near-infrared(NIR-II)window fluorescence(FL)and ratiometric photoacoustic(RPA)dual-modality imaging of GSH using a GSH-activatable probe(LET-14).LET-14 was synthesized based on a rhodamine hybrid xanthene skeleton with a FL shielding 2,4-dinitrobenzene sulfonyl group that can be specifically cleaved by GSH,thus resulting in a markedly bathochromic-shift absorption,a 6.5-fold increase in NIR-II FL intensity(FL920)and a 13-fold increase in RPA signal(PA880/PA705)in vitro.Intriguingly,LET-14 exhibits good selectivity and sensitivity for NIR-II FL and RPA dual-modality imaging of GSH in 4T1 tumor-bearing mouse model.Our findings develop an in vivo detection tool of GSH,which has great potential in the field of cancer diagnosis.展开更多
Schlieren imaging is a widely used technique to visualize the structure of supersonic flow field,which is usually dominated by shock waves.Precise identification of shock waves in schlieren image provides critical ins...Schlieren imaging is a widely used technique to visualize the structure of supersonic flow field,which is usually dominated by shock waves.Precise identification of shock waves in schlieren image provides critical insights for flow diagnostics,especially for supersonic inlet whose performance is highly associated with that of the whole flight.However,conventional shock wave identification methods have limited accuracy in segmenting the shock wave.To overcome the limitation,we proposed an automated shock wave identification method(SW-Segment)that can attain high resolution and automatic shock wave segmentation by integrating correlation-based feature extraction with graph search.We demonstrated the efficacy of SW-Segment via the identification of shock waves in simulatively and experimentally obtained schlieren image.The results proved that SW-Segment showed a shock wave identification accuracy of 95.24%in the numerical schlieren image and an accuracy of 88.33%in the experimental image,clearly demonstrating its reliability.SW-Segment holds broad applicability for shock wave detection in diverse schlieren imaging scenarios,offering robust data support for flow field analysis and supersonic flight design.展开更多
This study aims to develop a novel,cost-effective method for fabricating silicone vascular phantoms(SVPs)using"chewy candy"as a dissolvable core material.The study explores the feasibility of using chewy can...This study aims to develop a novel,cost-effective method for fabricating silicone vascular phantoms(SVPs)using"chewy candy"as a dissolvable core material.The study explores the feasibility of using chewy candy to create detailed and intricate vascular models for clinical applications.The chewy candy,an amorphous material,was manually extruded to form vascular models of varying diameters.These models were embedded in a silicone mixture,which was then cured.The chewy candy was subsequently dissolved,leaving behind hollow silicone vascular channels.The SVPs were evaluated for their morphological accuracy and functionality through laser speckle contrast imaging.The SVPs successfully replicated vascular channels with consistent diameters,demonstrating minimal variation across different regions.Functional evaluation using laser speckle contrast imaging revealed distinct flow dynamics in Y-shaped and H-shaped SVPs,highlighting the potential for these phantoms to simulate realistic fluid dynamics in vascular systems.This study presents a simple,time-saving,and innovative approach to fabricating complex 3D SVPs using chewy candy.This method offers a viable alternative to traditional fabrication techniques,with potential applications in various biomedical fields.展开更多
Visual diagnosis of skin cancer is challenging due to subtle inter-class similarities,variations in skin texture,the presence of hair,and inconsistent illumination.Deep learning models have shown promise in assisting ...Visual diagnosis of skin cancer is challenging due to subtle inter-class similarities,variations in skin texture,the presence of hair,and inconsistent illumination.Deep learning models have shown promise in assisting early detection,yet their performance is often limited by the severe class imbalance present in dermoscopic datasets.This paper proposes CANNSkin,a skin cancer classification framework that integrates a convolutional autoencoder with latent-space oversampling to address this imbalance.The autoencoder is trained to reconstruct lesion images,and its latent embeddings are used as features for classification.To enhance minority-class representation,the Synthetic Minority Oversampling Technique(SMOTE)is applied directly to the latent vectors before classifier training.The encoder and classifier are first trained independently and later fine-tuned end-to-end.On the HAM10000 dataset,CANNSkin achieves an accuracy of 93.01%,a macro-F1 of 88.54%,and an ROC–AUC of 98.44%,demonstrating strong robustness across ten test subsets.Evaluation on the more complex ISIC 2019 dataset further confirms the model’s effectiveness,where CANNSkin achieves 94.27%accuracy,93.95%precision,94.09%recall,and 99.02%F1-score,supported by high reconstruction fidelity(PSNR 35.03 dB,SSIM 0.86).These results demonstrate the effectiveness of our proposed latent-space balancing and fine-tuned representation learning as a new benchmark method for robust and accurate skin cancer classification across heterogeneous datasets.展开更多
Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing,this paper proposes a YOLO-LKSDS automatic driving d...Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing,this paper proposes a YOLO-LKSDS automatic driving detection model.Firstly,the Contrast-Limited Adaptive Histogram Equalisation(CLAHE)image enhancement algorithm is improved to increase the image contrast and enhance the detailed features of the target;then,on the basis of the YOLOv5 model,the Kmeans++clustering algorithm is introduced to obtain a suitable anchor frame,and SPPELAN spatial pyramid pooling is improved to enhance the accuracy and robustness of the model for multi-scale target detection.Finally,an improved SEAM(Separated and Enhancement Attention Module)attention mechanism is combined with the DIOU-NMS algorithm to optimize the model’s performance when dealing with occlusion and dense scenes.Compared with the original model,the improved YOLO-LKSDS model achieves a 13.3%improvement in accuracy,a 1.7%improvement in mAP,and 240,000 fewer parameters on the BDD100K dataset.In order to validate the generalization of the improved algorithm,we selected the KITTI dataset for experimentation,which shows that YOLOv5’s accuracy improves by 21.1%,recall by 36.6%,and mAP50 by 29.5%,respectively,on the KITTI dataset.The deployment of this paper’s algorithm is verified by an edge computing platform,where the average speed of detection reaches 24.4 FPS while power consumption remains below 9 W,demonstrating high real-time capability and energy efficiency.展开更多
Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows rais...Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.展开更多
Background:Brain volume measurement serves as a critical approach for assessing brain health status.Considering the close biological connection between the eyes and brain,this study aims to investigate the feasibility...Background:Brain volume measurement serves as a critical approach for assessing brain health status.Considering the close biological connection between the eyes and brain,this study aims to investigate the feasibility of estimating brain volume through retinal fundus imaging integrated with clinical metadata,and to offer a cost-effective approach for assessing brain health.Methods:Based on clinical information,retinal fundus images,and neuroimaging data derived from a multicenter,population-based cohort study,the Kai Luan Study,we proposed a cross-modal correlation representation(CMCR)network to elucidate the intricate co-degenerative relationships between the eyes and brain for 755 subjects.Specifically,individual clinical information,which has been followed up for as long as 12 years,was encoded as a prompt to enhance the accuracy of brain volume estimation.Independent internal validation and external validation were performed to assess the robustness of the proposed model.Root mean square error(RMSE),peak signal-tonoise ratio(PSNR),and structural similarity index measure(SSIM)metrics were employed to quantitatively evaluate the quality of synthetic brain images derived from retinal imaging data.Results:The proposed framework yielded average RMSE,PSNR,and SSIM values of 98.23,35.78 d B,and 0.64,respectively,which significantly outperformed 5 other methods:multi-channel Variational Autoencoder(mcVAE),Pixelto-Pixel(Pixel2pixel),transformer-based U-Net(Trans UNet),multi-scale transformer network(MT-Net),and residual vision transformer(ResViT).The two-(2D)and three-dimensional(3D)visualization results showed that the shape and texture of the synthetic brain images generated by the proposed method most closely resembled those of actual brain images.Thus,the CMCR framework accurately captured the latent structural correlations between the fundus and the brain.The average difference between predicted and actual brain volumes was 61.36 cm~3,with a relative error of 4.54%.When all of the clinical information(including age and sex,daily habits,cardiovascular factors,metabolic factors,and inflammatory factors)was encoded,the difference was decreased to 53.89 cm~3,with a relative error of 3.98%.Based on the synthesized brain magnetic resonance images from retinal fundus images,the volumes of brain tissues could be estimated with high accuracy.Conclusion:This study provides an innovative,accurate,and cost-effective approach to characterize brain health status through readily accessible retinal fundus images.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
[Objectives]To analyze the clinical symptoms and epidemiological characteristics of 188 hospitalized pertussis cases in Jingzhou City.[Methods]Clinical data from 188 patients diagnosed with pertussis and admitted to t...[Objectives]To analyze the clinical symptoms and epidemiological characteristics of 188 hospitalized pertussis cases in Jingzhou City.[Methods]Clinical data from 188 patients diagnosed with pertussis and admitted to two tertiary hospitals in Jingzhou City between March and August 2024 were collected.Patients were randomly divided into three groups:<3-year-old,3—17-year-old,and≥18-year-old.A retrospective analysis was performed on their clinical features(including laboratory findings,disease course,and imaging characteristics)and epidemiological characteristics.[Results]In the<3-year-old group,28 cases(36.4%)were unvaccinated and 22 cases(28.6%)had received only one dose of the pertussis vaccine.In the 3—17-year-old group,91 cases(94.8%)had received four doses.Vaccination history was unknown for the≥18-year-old adult group.The<3-year-old group exhibited significantly higher incidences of cough with wheezing/dyspnea,paroxysmal spasmodic cough,cough with cyanosis or facial flushing,wheezes,and moist rales in the lungs compared to both the 3—17-year-old and≥18-year-old groups.Post-tussive vomiting was less frequent in the<3-year-old group than in the 3—17-year-old group but more frequent than in the≥18-year-old group;these differences were statistically significant(P<0.05).The≥18-year-old group had significantly lower incidences of cough with wheezing/dyspnea,paroxysmal spasmodic cough,cough with cyanosis or facial flushing,wheezes,and moist rales in the lungs compared to both the<3-year-old and 3—17-year-old groups(P<0.05).The proportion of cases with pneumonia and increased lung markings was higher in the<3-year-old group than in the 3—17-year-old group but lower than in the≥18-year-old group,showing statistically significant differences(P<0.05).The proportion of cases with pulmonary nodules and fibrotic foci was lower in the<3-year-old group than in both the 3—17-year-old and≥18-year-old groups,and these differences were also statistically significant(P<0.05).The proportion of pneumonia cases in the 3—17-year-old group was lower than in both the<3-year-old and≥18-year-old groups.The proportion of cases with increased lung markings was lower than in the<3-year-old group but higher than in the≥18-year-old group;these differences were statistically significant(P<0.05).The proportion of cases with pulmonary nodules and fibrotic foci in the 3—17-year-old group was higher than in the<3-year-old group but lower than in the≥18-year-old group,with statistically significant differences(P<0.05).The proportion of cases with pulmonary nodules and fibrotic foci was higher in the≥18-year-old group than in both the<3-year-old and 3—17-year-old groups,and these differences were also statistically significant(P<0.05).[Conclusions]Analysis of the clinical symptoms and epidemiological characteristics of 188 hospitalized pertussis cases in Jingzhou City contributes to enhancing the prevention and control of pertussis within the city.展开更多
The photoacoustic imaging of lipid is intrinsically constrained by the feeble nature of endogenous lipid signals,posing a persistent sensitivity challenge that demands innovative solutions.Although adopting high-effic...The photoacoustic imaging of lipid is intrinsically constrained by the feeble nature of endogenous lipid signals,posing a persistent sensitivity challenge that demands innovative solutions.Although adopting high-efficiency excitation and detection elements may improve the imaging sensitivity to a certain extent,the application of the elements is inevitably subject to various limitations in practical applications,particularly during in vivo imaging and endoscopic imaging.In this study,we propose a multi-combinatorial approach to enhance the sensitivity of lipid photoacoustic imaging.The approach involves wavelet transform processing of one-dimensional A-line signals,gradient-based denoising of two-dimensional B-scan images,and finally,threedimensional spatial weighted averaging of the data processed by the previous two steps.This method not only significantly improves the signal-to-noise ratio(SNR)in distinguished feature regions of the image by around 10 dB,but also efficiently extracts weak signals with no distinct features in the original image.After processing with this method,the images acquired under single scanning were compared with those obtained under multiple scanning.The results showed highly consistent image features,with the structural similarity index increasing from 0.2 to 0.8,confirming the accuracy and reliability of the multi-combinatorial approach.展开更多
基金supported by the National Key R&D Program of China(No.2022YFC2504403)the National Natural Science Foundation of China(No.62172202)+1 种基金the Experiment Project of China Manned Space Program(No.HYZHXM01019)the Fundamental Research Funds for the Central Universities from Southeast University(No.3207032101C3)。
文摘Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of complex diseases,with some even achieving clinical translation.Changes in the overall size,shape,boundary,and other morphological features of organoids provide a noninvasive method for assessing organoid drug sensitivity.However,the precise segmentation of organoids in bright-field microscopy images is made difficult by the complexity of the organoid morphology and interference,including overlapping organoids,bubbles,dust particles,and cell fragments.This paper introduces the precision organoid segmentation technique(POST),which is a deep-learning algorithm for segmenting challenging organoids under simple bright-field imaging conditions.Unlike existing methods,POST accurately segments each organoid and eliminates various artifacts encountered during organoid culturing and imaging.Furthermore,it is sensitive to and aligns with measurements of organoid activity in drug sensitivity experiments.POST is expected to be a valuable tool for drug screening using organoids owing to its capability of automatically and rapidly eliminating interfering substances and thereby streamlining the organoid analysis and drug screening process.
基金supported by the National Natural Science Foundation of China,No.82274304(to YH)the Major Clinical Study Projects of Shanghai Shenkang Hospital Development Center,No.SHDC2020CR2046B(to YH)Shanghai Municipal Health Commission Talent Plan,No.2022LJ010(to YH).
文摘Cerebral small vessel disease encompasses a group of neurological disorders characterized by injury to small blood vessels,often leading to stroke and dementia.Due to its diverse etiologies and complex pathological mechanisms,preventing and treating cerebral small vessel vasculopathy is challenging.Recent studies have shown that the glymphatic system plays a crucial role in interstitial solute clearance and the maintenance of brain homeostasis.Increasing evidence also suggests that dysfunction in glymphatic clearance is a key factor in the progression of cerebral small vessel disease.This review begins with a comprehensive introduction to the structure,function,and driving factors of the glymphatic system,highlighting its essential role in brain waste clearance.Afterwards,cerebral small vessel disease was reviewed from the perspective of the glymphatic system,after which the mechanisms underlying their correlation were summarized.Glymphatic dysfunction may lead to the accumulation of metabolic waste in the brain,thereby exacerbating the pathological processes associated with cerebral small vessel disease.The review also discussed the direct evidence of glymphatic dysfunction in patients and animal models exhibiting two subtypes of cerebral small vessel disease:arteriolosclerosis-related cerebral small vessel disease and amyloid-related cerebral small vessel disease.Diffusion tensor image analysis along the perivascular space is an important non-invasive tool for assessing the clearance function of the glymphatic system.However,the effectiveness of its parameters needs to be enhanced.Among various nervous system diseases,including cerebral small vessel disease,glymphatic failure may be a common final pathway toward dementia.Overall,this review summarizes prevention and treatment strategies that target glymphatic drainage and will offer valuable insight for developing novel treatments for cerebral small vessel disease.
文摘Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.
基金supported by the National Natural Science Foundation of China(82071981)the Program of Youth Science and Technology Innovation and Entrepreneurship Outstanding Talents(Team)of Jilin Province,China(20230508063RC)+3 种基金the Excellent Youth Training Foundation of Jilin University,China(419080520665)the Innovation and Entrepreneurship Talent Funding Program of Jilin Province,Chinathe Health Special Project of the Finance Department of Jilin Province,Chinathe Graduate Innovation Fund of Jilin University,China(2025CX297)。
文摘The heterogeneity and invasiveness of cancer cells pose serious challenges in cancer diagnosis and treatment.Advancements and innovations in metal-based nanomedicines provide novel avenues for addressing these challenges.Metal-based nanomedicines possess unique physicochemical properties that enable their interaction with living organisms,thereby inducing complex biological responses.These nanomaterials have been extensively used to enhance the contrast and sensitivity of cancer imaging and to amplify the distinction between cancerous and healthy tissues.Moreover,these nanomaterials can effectively combat a wide spectrum of cancers through various methods,including drug delivery,radiotherapy,photothermal therapy(PTT),photodynamic therapy(PDT),sonodynamic therapy(SDT),biocatalytic therapy,ion interference therapy(IIT),and immunotherapy.Currently,there is still a need for a comprehensive summary on the metal-based nanomaterials for cancer diagnosis and treatment.Herein,we present a systematic and complete overview of action mechanisms and the applications of metal-based nanomaterials in cancer theranostics.A summary of common strategies for synthesizing and modifying metal-based nanomedicines is presented,and their biosafety is analyzed.Then,the latest developments in their applications for cancer imaging and anticancer treatment are provided.Finally,the key technical challenges and reasonable perspectives of metal-based nanomedicines for cancer theranostics in clinical applications are discussed.
基金funded by Anhui Province University Key Science and Technology Project(2024AH053415)Anhui Province University Major Science and Technology Project(2024AH040229)+3 种基金Talent Research Initiation Fund Project of Tongling University(2024tlxyrc019)Tongling University School-Level Scientific Research Project(2024tlxyptZD07)TheUniversity Synergy Innovation Programof Anhui Province(GXXT-2023-050)Tongling City Science and Technology Major Special Project(Unveiling and Commanding Model)(200401JB004).
文摘In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,efficiently combining the advantages of both images while overcoming their shortcomings is necessary.To handle this challenge,we developed an end-to-end IRI andVI fusionmethod based on frequency decomposition and enhancement.By applying concepts from frequency domain analysis,we used the layering mechanism to better capture the salient thermal targets from the IRIs and the rich textural information from the VIs,respectively,significantly boosting the image fusion quality and effectiveness.In addition,the backbone network combined Restormer Blocks and Dense Blocks;Restormer blocks utilize global attention to extract shallow features.Meanwhile,Dense Blocks ensure the integration between shallow and deep features,thereby avoiding the loss of shallow attributes.Extensive experiments on TNO and MSRS datasets demonstrated that the suggested method achieved state-of-the-art(SOTA)performance in various metrics:Entropy(EN),Mutual Information(MI),Standard Deviation(SD),The Structural Similarity Index Measure(SSIM),Fusion quality(Qabf),MI of the pixel(FMI_(pixel)),and modified Visual Information Fidelity(VIF_(m)).
基金financially supported by the Open Project Program of Wuhan National Laboratory for Optoelectronics(No.2022WNLOKF009)the National Natural Science Foundation of China(No.62475216)+2 种基金the Key Research and Development Program of Shaanxi(No.2024GH-ZDXM-37)the Fujian Provincial Natural Science Foundation of China(No.2024J01060)the Startup Program of XMU,and the Fundamental Research Funds for the Central Universities.
文摘Microscopy imaging is fundamental in analyzing bacterial morphology and dynamics,offering critical insights into bacterial physiology and pathogenicity.Image segmentation techniques enable quantitative analysis of bacterial structures,facilitating precise measurement of morphological variations and population behaviors at single-cell resolution.This paper reviews advancements in bacterial image segmentation,emphasizing the shift from traditional thresholding and watershed methods to deep learning-driven approaches.Convolutional neural networks(CNNs),U-Net architectures,and three-dimensional(3D)frameworks excel at segmenting dense biofilms and resolving antibiotic-induced morphological changes.These methods combine automated feature extraction with physics-informed postprocessing.Despite progress,challenges persist in computational efficiency,cross-species generalizability,and integration with multimodal experimental workflows.Future progress will depend on improving model robustness across species and imaging modalities,integrating multimodal data for phenotype-function mapping,and developing standard pipelines that link computational tools with clinical diagnostics.These innovations will expand microbial phenotyping beyond structural analysis,enabling deeper insights into bacterial physiology and ecological interactions.
文摘Background:Sarcomatoid carcinoma of the ureter(SCU)is a highly aggressive and relatively uncommon malignant tumor of the urinary tract.Its frequency is quite low,and its prognosis is very bad when compared to other cancers of the urinary system.SCU clinical reports are still hard to come by.MRI and PEI/CT imaging of ureteral sarcomatoid cancer is presented in this case to promote diagnostic awareness and comprehension of the imaging characteristics of this uncommon illness.Method:The patient had ureteral sarcomatoid cancer,which was verified by pathological investigation after ureteroscopic biopsy.The patient’s clinical information,imaging results,surgical outcomes,and pathological findings were gathered.A retrospective study was carried out in combinationwith pertinent national and international literature.Results:An 84-year-old female patient was admitted for“left flank discomfort lasting over one month.”MRI revealed an irregular soft tissue mass in the middle-lower segment of the left ureter.T2-weighted imaging showed an unevenly slightly hyperintense signal.Diffusion-weighted imaging demonstrated restricted diffusion.Contrastenhanced imaging exhibited heterogeneous enhancement.PET/CT demonstrated significantly increased fluorodeoxyglucose metabolism in the mass with secondary left upper urinary tract obstruction.Concurrent findings included a solitary metastatic lesion in hepatic segment S6 and multiple lymph node metastases along the left common iliac and external iliac arteries.Preoperative diagnosis suggested a malignant tumor of the ureter.The patient underwent left nephroureteroscopy with biopsy,and the postoperative pathological diagnosis was ureteral sarcomatoid carcinoma.Conclusion:Ureteral sarcomatoid carcinoma is a rare,highly malignant,and aggressive tumor with nonspecific imaging features,typically presenting as an invasively growing mass.Diagnosis relies on postoperative pathology and immunohistochemical examination.MRI and PET/CT scans are valuable for preoperative localization and characterization,tumor staging,treatment planning,and postoperative follow-up.The prognosis is extremely negative.The main treatment option is radical surgery,although constant monitoring is necessary since early recurrence and metastases are frequent after surgery.
基金supported by the National Natural Science Foundation of China(Nos.82372116,U23A2097)Guangdong Basic and Applied Basic Research Foundation(No.2022A1515010620)+2 种基金Shenzhen Medical Research Fund(Nos.B2302047,A2302047)Shenzhen Science and Technology Program(No.JCYJ20220818095806014)Research Team Cultivation Program of Shenzhen University(No.2023QNT019).
文摘The level of glutathione(GSH)is significantly associated with numerous pathological processes,thus,real-time detection of the GSH level is of significance for early diagnosis of GSH-related diseases.Herein,we developed in vivo second near-infrared(NIR-II)window fluorescence(FL)and ratiometric photoacoustic(RPA)dual-modality imaging of GSH using a GSH-activatable probe(LET-14).LET-14 was synthesized based on a rhodamine hybrid xanthene skeleton with a FL shielding 2,4-dinitrobenzene sulfonyl group that can be specifically cleaved by GSH,thus resulting in a markedly bathochromic-shift absorption,a 6.5-fold increase in NIR-II FL intensity(FL920)and a 13-fold increase in RPA signal(PA880/PA705)in vitro.Intriguingly,LET-14 exhibits good selectivity and sensitivity for NIR-II FL and RPA dual-modality imaging of GSH in 4T1 tumor-bearing mouse model.Our findings develop an in vivo detection tool of GSH,which has great potential in the field of cancer diagnosis.
基金supported by the National Natural Science Foundation of China(Grant Nos.12402336,U20A2070,12025202)the Natural Science Foundation of Jiangsu Province(Grant No.BK20230876)+2 种基金the National High-Level Talent Project(Grant No.YQR23069)the Key Laboratory of Intake and Exhaust Technology,Ministry of Education(Grant No.CEPE2024015)the Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics)(Grant No.MCAS-I-0325K01)。
文摘Schlieren imaging is a widely used technique to visualize the structure of supersonic flow field,which is usually dominated by shock waves.Precise identification of shock waves in schlieren image provides critical insights for flow diagnostics,especially for supersonic inlet whose performance is highly associated with that of the whole flight.However,conventional shock wave identification methods have limited accuracy in segmenting the shock wave.To overcome the limitation,we proposed an automated shock wave identification method(SW-Segment)that can attain high resolution and automatic shock wave segmentation by integrating correlation-based feature extraction with graph search.We demonstrated the efficacy of SW-Segment via the identification of shock waves in simulatively and experimentally obtained schlieren image.The results proved that SW-Segment showed a shock wave identification accuracy of 95.24%in the numerical schlieren image and an accuracy of 88.33%in the experimental image,clearly demonstrating its reliability.SW-Segment holds broad applicability for shock wave detection in diverse schlieren imaging scenarios,offering robust data support for flow field analysis and supersonic flight design.
基金supported by the Regional Innovation System&Education(RISE)program through the Gangwon RISE Center,funded by the Ministry of Education(MOE)and the Gangwon State(G.S.),Republic of Korea(2025-RISE-10-006).
文摘This study aims to develop a novel,cost-effective method for fabricating silicone vascular phantoms(SVPs)using"chewy candy"as a dissolvable core material.The study explores the feasibility of using chewy candy to create detailed and intricate vascular models for clinical applications.The chewy candy,an amorphous material,was manually extruded to form vascular models of varying diameters.These models were embedded in a silicone mixture,which was then cured.The chewy candy was subsequently dissolved,leaving behind hollow silicone vascular channels.The SVPs were evaluated for their morphological accuracy and functionality through laser speckle contrast imaging.The SVPs successfully replicated vascular channels with consistent diameters,demonstrating minimal variation across different regions.Functional evaluation using laser speckle contrast imaging revealed distinct flow dynamics in Y-shaped and H-shaped SVPs,highlighting the potential for these phantoms to simulate realistic fluid dynamics in vascular systems.This study presents a simple,time-saving,and innovative approach to fabricating complex 3D SVPs using chewy candy.This method offers a viable alternative to traditional fabrication techniques,with potential applications in various biomedical fields.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Visual diagnosis of skin cancer is challenging due to subtle inter-class similarities,variations in skin texture,the presence of hair,and inconsistent illumination.Deep learning models have shown promise in assisting early detection,yet their performance is often limited by the severe class imbalance present in dermoscopic datasets.This paper proposes CANNSkin,a skin cancer classification framework that integrates a convolutional autoencoder with latent-space oversampling to address this imbalance.The autoencoder is trained to reconstruct lesion images,and its latent embeddings are used as features for classification.To enhance minority-class representation,the Synthetic Minority Oversampling Technique(SMOTE)is applied directly to the latent vectors before classifier training.The encoder and classifier are first trained independently and later fine-tuned end-to-end.On the HAM10000 dataset,CANNSkin achieves an accuracy of 93.01%,a macro-F1 of 88.54%,and an ROC–AUC of 98.44%,demonstrating strong robustness across ten test subsets.Evaluation on the more complex ISIC 2019 dataset further confirms the model’s effectiveness,where CANNSkin achieves 94.27%accuracy,93.95%precision,94.09%recall,and 99.02%F1-score,supported by high reconstruction fidelity(PSNR 35.03 dB,SSIM 0.86).These results demonstrate the effectiveness of our proposed latent-space balancing and fine-tuned representation learning as a new benchmark method for robust and accurate skin cancer classification across heterogeneous datasets.
基金supported by the Key R&D Program of Shaanxi Province(No.2025CYYBXM-078).
文摘Aiming at the scale adaptation of automatic driving target detection algorithms in low illumination environments and the shortcomings in target occlusion processing,this paper proposes a YOLO-LKSDS automatic driving detection model.Firstly,the Contrast-Limited Adaptive Histogram Equalisation(CLAHE)image enhancement algorithm is improved to increase the image contrast and enhance the detailed features of the target;then,on the basis of the YOLOv5 model,the Kmeans++clustering algorithm is introduced to obtain a suitable anchor frame,and SPPELAN spatial pyramid pooling is improved to enhance the accuracy and robustness of the model for multi-scale target detection.Finally,an improved SEAM(Separated and Enhancement Attention Module)attention mechanism is combined with the DIOU-NMS algorithm to optimize the model’s performance when dealing with occlusion and dense scenes.Compared with the original model,the improved YOLO-LKSDS model achieves a 13.3%improvement in accuracy,a 1.7%improvement in mAP,and 240,000 fewer parameters on the BDD100K dataset.In order to validate the generalization of the improved algorithm,we selected the KITTI dataset for experimentation,which shows that YOLOv5’s accuracy improves by 21.1%,recall by 36.6%,and mAP50 by 29.5%,respectively,on the KITTI dataset.The deployment of this paper’s algorithm is verified by an edge computing platform,where the average speed of detection reaches 24.4 FPS while power consumption remains below 9 W,demonstrating high real-time capability and energy efficiency.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.
基金supported by the National Natural Science Foundation of China(62522119 and 62372358)the Beijing Natural Science Foundation(7242267)+2 种基金the Beijing Scholars Program([2015]160)the Natural Science Basic Research Program of Shaanxi(2023-JC-QN-0719)the Guangdong Basic and Applied Basic Research Foundation(2022A1515110453)。
文摘Background:Brain volume measurement serves as a critical approach for assessing brain health status.Considering the close biological connection between the eyes and brain,this study aims to investigate the feasibility of estimating brain volume through retinal fundus imaging integrated with clinical metadata,and to offer a cost-effective approach for assessing brain health.Methods:Based on clinical information,retinal fundus images,and neuroimaging data derived from a multicenter,population-based cohort study,the Kai Luan Study,we proposed a cross-modal correlation representation(CMCR)network to elucidate the intricate co-degenerative relationships between the eyes and brain for 755 subjects.Specifically,individual clinical information,which has been followed up for as long as 12 years,was encoded as a prompt to enhance the accuracy of brain volume estimation.Independent internal validation and external validation were performed to assess the robustness of the proposed model.Root mean square error(RMSE),peak signal-tonoise ratio(PSNR),and structural similarity index measure(SSIM)metrics were employed to quantitatively evaluate the quality of synthetic brain images derived from retinal imaging data.Results:The proposed framework yielded average RMSE,PSNR,and SSIM values of 98.23,35.78 d B,and 0.64,respectively,which significantly outperformed 5 other methods:multi-channel Variational Autoencoder(mcVAE),Pixelto-Pixel(Pixel2pixel),transformer-based U-Net(Trans UNet),multi-scale transformer network(MT-Net),and residual vision transformer(ResViT).The two-(2D)and three-dimensional(3D)visualization results showed that the shape and texture of the synthetic brain images generated by the proposed method most closely resembled those of actual brain images.Thus,the CMCR framework accurately captured the latent structural correlations between the fundus and the brain.The average difference between predicted and actual brain volumes was 61.36 cm~3,with a relative error of 4.54%.When all of the clinical information(including age and sex,daily habits,cardiovascular factors,metabolic factors,and inflammatory factors)was encoded,the difference was decreased to 53.89 cm~3,with a relative error of 3.98%.Based on the synthesized brain magnetic resonance images from retinal fundus images,the volumes of brain tissues could be estimated with high accuracy.Conclusion:This study provides an innovative,accurate,and cost-effective approach to characterize brain health status through readily accessible retinal fundus images.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
文摘[Objectives]To analyze the clinical symptoms and epidemiological characteristics of 188 hospitalized pertussis cases in Jingzhou City.[Methods]Clinical data from 188 patients diagnosed with pertussis and admitted to two tertiary hospitals in Jingzhou City between March and August 2024 were collected.Patients were randomly divided into three groups:<3-year-old,3—17-year-old,and≥18-year-old.A retrospective analysis was performed on their clinical features(including laboratory findings,disease course,and imaging characteristics)and epidemiological characteristics.[Results]In the<3-year-old group,28 cases(36.4%)were unvaccinated and 22 cases(28.6%)had received only one dose of the pertussis vaccine.In the 3—17-year-old group,91 cases(94.8%)had received four doses.Vaccination history was unknown for the≥18-year-old adult group.The<3-year-old group exhibited significantly higher incidences of cough with wheezing/dyspnea,paroxysmal spasmodic cough,cough with cyanosis or facial flushing,wheezes,and moist rales in the lungs compared to both the 3—17-year-old and≥18-year-old groups.Post-tussive vomiting was less frequent in the<3-year-old group than in the 3—17-year-old group but more frequent than in the≥18-year-old group;these differences were statistically significant(P<0.05).The≥18-year-old group had significantly lower incidences of cough with wheezing/dyspnea,paroxysmal spasmodic cough,cough with cyanosis or facial flushing,wheezes,and moist rales in the lungs compared to both the<3-year-old and 3—17-year-old groups(P<0.05).The proportion of cases with pneumonia and increased lung markings was higher in the<3-year-old group than in the 3—17-year-old group but lower than in the≥18-year-old group,showing statistically significant differences(P<0.05).The proportion of cases with pulmonary nodules and fibrotic foci was lower in the<3-year-old group than in both the 3—17-year-old and≥18-year-old groups,and these differences were also statistically significant(P<0.05).The proportion of pneumonia cases in the 3—17-year-old group was lower than in both the<3-year-old and≥18-year-old groups.The proportion of cases with increased lung markings was lower than in the<3-year-old group but higher than in the≥18-year-old group;these differences were statistically significant(P<0.05).The proportion of cases with pulmonary nodules and fibrotic foci in the 3—17-year-old group was higher than in the<3-year-old group but lower than in the≥18-year-old group,with statistically significant differences(P<0.05).The proportion of cases with pulmonary nodules and fibrotic foci was higher in the≥18-year-old group than in both the<3-year-old and 3—17-year-old groups,and these differences were also statistically significant(P<0.05).[Conclusions]Analysis of the clinical symptoms and epidemiological characteristics of 188 hospitalized pertussis cases in Jingzhou City contributes to enhancing the prevention and control of pertussis within the city.
基金supported by the National Key Research and Development Program of China(2022YFC2402400)the National Natural Science Foundation of China(82027803,62275062)+7 种基金the Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology(2020B121201010)the Shenzhen Science and Technology Innovation Committee under Grant(JCYJ20220818101417039)the Shenzhen Key Laboratory for Molecular lmaging(ZDSY20130401165820357)the Shenzhen Medical Research Fund(D2404002)the Project of Shandong Innovation and Startup Community of High-end Medical Apparatus and Instruments(2023-SGTTXM-002 and 2024-SGTTXM-005)the Shandong Province Technology Innovation Guidance Plan(Central Leading Local Science and Technology Development Fund)(YDZX2023115)the Taishan Scholar Special Funding Project of Shandong Provinceand the Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai(ZL202402).
文摘The photoacoustic imaging of lipid is intrinsically constrained by the feeble nature of endogenous lipid signals,posing a persistent sensitivity challenge that demands innovative solutions.Although adopting high-efficiency excitation and detection elements may improve the imaging sensitivity to a certain extent,the application of the elements is inevitably subject to various limitations in practical applications,particularly during in vivo imaging and endoscopic imaging.In this study,we propose a multi-combinatorial approach to enhance the sensitivity of lipid photoacoustic imaging.The approach involves wavelet transform processing of one-dimensional A-line signals,gradient-based denoising of two-dimensional B-scan images,and finally,threedimensional spatial weighted averaging of the data processed by the previous two steps.This method not only significantly improves the signal-to-noise ratio(SNR)in distinguished feature regions of the image by around 10 dB,but also efficiently extracts weak signals with no distinct features in the original image.After processing with this method,the images acquired under single scanning were compared with those obtained under multiple scanning.The results showed highly consistent image features,with the structural similarity index increasing from 0.2 to 0.8,confirming the accuracy and reliability of the multi-combinatorial approach.
