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BACKGROUND: Accurately redirecting reconstructed Positron emission tomography (PET) images into short-axis (SA) images shows great significance for subsequent clinical diagnosis. We developed a system for automatic redirection and quantitative analysis of myocardial PET images. METHODS: A total of 128 patients were enrolled for 18 F-FDG PET/CT myocardial metabolic images (MMIs), including 3 image classifications: without defects, with defects, and excess uptake. The automatic reorientation system includes five modules: regional division, myocardial segmentation, ellipsoid fitting, image rotation and quantitative analysis. First, the left ventricular geometry-based canny edge detection (LVG-CED) was developed and compared with the other 5 common region segmentation algorithms, the optimized partitioning was determined based on partition success rate. Then, 9 myocardial segmentation methods and 4 ellipsoid fitting methods were combined to derive 36 cross combinations for diagnostic performance in terms of Pearson correlation coefficient (PCC), Kendall correlation coefficient (KCC), Spearman correlation coefficient (SCC), and determination coefficient. Finally, the deflection angles were computed by ellipsoid fitting and the SA images were derived by affine transformation. Furthermore, the polar maps were used for quantitative analysis of SA images, and the redirection effects of 3 different image classifications were analyzed using correlation coefficients. RESULTS: On the dataset, LVG-CED outperformed other methods in the regional division module with a 100% success rate. In 36 cross combinations, PSO-FCM and LLS-SVD performed the best in terms of correlation coefficient. The linear results indicate that our algorithm (LVG-CED, PSO-FCM, and LLS-SVD) has good consistency with the reference manual method. In quantitative analysis, the similarities between our method and the reference manual method were higher than 96% at 17 segments. Moreover, our method demonstrated excellent performance in all 3 image classifications. CONCLUSION: Our algorithm system could realize accurate automatic reorientation and quantitative analysis of PET MMIs, which is also effective for images suffering from interference.
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The reduction of aromatic compounds constitutes a fundamental and ongoing area of investigation. The selective reduction of polycyclic aromatic compounds to give either fully or partially reduced products remains a challenge, especially in applications to complex molecules at scale. Herein, we present a selective electrochemical hydrogenation of polycyclic arenes conducted under mild conditions. A noteworthy achievement of this approach is the ability to finely control both the complete and partial reduction of specific aromatic rings within polycyclic arenes by judiciously varying the reaction solvents. Mechanistic investigations elucidate the pivotal role played by in situ proton generation and interface regulation in governing reaction selectivity. The reductive electrochemical conditions show a very high level of functional-group tolerance. Furthermore, this methodology represents an easily scalable reduction (demonstrated by the reduction of 1â kg scale starting material) using electrochemical flow chemistry to give key intermediates for the synthesis of specific drugs.
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Polyoxymethylene (POM) is a commonly used engineering thermoplastic, but its recycling by conventional means, i.e., mechanical recycling, is not practiced to any meaningful extent, due to technical limitations. Instead, waste POM is typically incinerated or disposed in landfills, where it becomes a persistent environmental pollutant. An attractive alternative to mechanical recycling is upcycling, namely, the conversion of waste POM into value-added chemicals, but this has received very little attention. Herein, we report the upcycling of POM into useful chemicals through three different reactions, all of which are efficiently catalyzed by a single pincer complex of earth-abundant manganese. One method involves hydrogenation of POM into methanol using H2 gas as the only reagent, whereas another method converts POM into methanol and CO2 through a one-pot process comprising acidolysis followed by Mn-catalyzed disproportionation. The third method utilizes POM as a reagent for the methylation of ketones and amines.
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Poly(3,4-ethylenedioxythiophene): poly(styrene sulfonate) (PEDOT: PSS) is a promising material for organic thermoelectric (TE) applications. However, it is challenging to achieve PEDOT: PSS composites with stretchable, self-healable, and high TE performance. Furthermore, some existing self-healing TE materials employ toxic reagents, posing risks to human health and the environment. In this study, a novel intrinsically self-healable and wearable composite is developed by incorporating environmentally friendly, highly biocompatible, and biodegradable materials of polyvinyl alcohol (PVA) and citric acid (CA) into PEDOT: PSS. This results in the formation of double hydrogen bonding networks among CA, PVA, and PEDOT: PSS, inducing microstructure alignment and leading to simultaneous enhancements in both TE performance and stretchability. The resulting composites exhibit a high electrical conductivity and power factor of 259.3 ± 11.7 S·cm-1, 6.9 ± 0.4 µW·m-1·K-2, along with a tensile strain up to 68%. Furthermore, the composites display impressive self-healing ability, with 84% recovery in electrical conductivity and an 85% recovery in tensile strain. Additionally, the temperature and strain sensors based on the PEDOT: PSS/PVA/CA are prepared, which exhibit high resolution suitable for human-machine interaction and wearable devices. This work provides a reliable and robust solution for the development of environmentally friendly, self-healing and wearable TE thermoelectrics.
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To ensure the safe operation and dispatching control of a low-voltage distributed photovoltaic (PV) power distribution network (PDN), the load forecasting problem of the PDN is studied in this study. Based on deep learning technology, this paper proposes a robot-assisted load forecasting method for low-voltage distributed photovoltaic power distribution networks using enhanced long short-term memory (LSTM). This method employs the frequency domain decomposition (FDD) to obtain boundary points and incorporates a dense layer following the LSTM layer to better extract data features. The LSTM is used to predict low-frequency and high-frequency components separately, enabling the model to precisely capture the voltage variation patterns across different frequency components, thereby achieving high-precision voltage prediction. By verifying the historical operation data set of a low-voltage distributed PV-PDN in Guangdong Province, experimental results demonstrate that the proposed "FDD+LSTM" model outperforms both recurrent neural network and support vector machine models in terms of prediction accuracy on both time scales of 1 h and 4 h. Precisely forecast the voltage in different seasons and time scales, which has a certain value in promoting the development of the PDN and related technology industry chain.
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BACKGROUND: Low-dose ungated CT is commonly used for total-body PET attenuation and scatter correction (ASC). However, CT-based ASC (CT-ASC) is limited by radiation dose risks of CT examinations, propagation of CT-based artifacts and potential mismatches between PET and CT. We demonstrate the feasibility of direct ASC for multi-tracer total-body PET in the image domain. METHODS: Clinical uEXPLORER total-body PET/CT datasets of [18F]FDG (N = 52), [18F]FAPI (N = 46) and [68Ga]FAPI (N = 60) were retrospectively enrolled in this study. We developed an improved 3D conditional generative adversarial network (cGAN) to directly estimate attenuation and scatter-corrected PET images from non-attenuation and scatter-corrected (NASC) PET images. The feasibility of the proposed 3D cGAN-based ASC was validated using four training strategies: (1) Paired 3D NASC and CT-ASC PET images from three tracers were pooled into one centralized server (CZ-ASC). (2) Paired 3D NASC and CT-ASC PET images from each tracer were individually used (DL-ASC). (3) Paired NASC and CT-ASC PET images from one tracer ([18F]FDG) were used to train the networks, while the other two tracers were used for testing without fine-tuning (NFT-ASC). (4) The pre-trained networks of (3) were fine-tuned with two other tracers individually (FT-ASC). We trained all networks in fivefold cross-validation. The performance of all ASC methods was evaluated by qualitative and quantitative metrics using CT-ASC as the reference. RESULTS: CZ-ASC, DL-ASC and FT-ASC showed comparable visual quality with CT-ASC for all tracers. CZ-ASC and DL-ASC resulted in a normalized mean absolute error (NMAE) of 8.51 ± 7.32% versus 7.36 ± 6.77% (p < 0.05), outperforming NASC (p < 0.0001) in [18F]FDG dataset. CZ-ASC, FT-ASC and DL-ASC led to NMAE of 6.44 ± 7.02%, 6.55 ± 5.89%, and 7.25 ± 6.33% in [18F]FAPI dataset, and NMAE of 5.53 ± 3.99%, 5.60 ± 4.02%, and 5.68 ± 4.12% in [68Ga]FAPI dataset, respectively. CZ-ASC, FT-ASC and DL-ASC were superior to NASC (p < 0.0001) and NFT-ASC (p < 0.0001) in terms of NMAE results. CONCLUSIONS: CZ-ASC, DL-ASC and FT-ASC demonstrated the feasibility of providing accurate and robust ASC for multi-tracer total-body PET, thereby reducing the radiation hazards to patients from redundant CT examinations. CZ-ASC and FT-ASC could outperform DL-ASC for cross-tracer total-body PET AC.
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Objectives: The aims of this study were to explore the incidence characteristics and trend prediction of lymphoma from 2005 to 2035, and to provide data basis for the prevention and control of lymphoma in China. Method: The data on lymphoma incidence in China from 2005 to 2017 were obtained from the Chinese Cancer Registry Annual Report. The Joinpoint regression model was used to calculate annual percentage change (APC) and average annual percentage change (AAPC) to reflect time trends. Age-period-cohort models were conducted to estimate age, period, and cohort effects on the lymphoma incidence. A Bayesian age-period-cohort model was used to predict lymphoma incidence trends from 2018 to 2035. Results: From 2005 to 2017, the incidence of lymphoma was 6.26/100,000, and the age-standardized incidence rate (ASIR) was 4.11/100,000, with an AAPC of 1.4% [95% confidence interval (CI): 0.3%, 2.5%]. The ASIR was higher in men and urban areas than in women and rural areas, respectively. The age effect showed that the incidence risk of lymphoma increased with age. In the period effect, the incidence risk of lymphoma in rural areas decreased first and then increased with 2010 as the cutoff point. The overall risk of lymphoma incidence was higher in the cohort before the 1970-1974 birth cohort than in the cohort after. From 2018 to 2035, the lymphoma incidence in men, women, and urban areas will show an upward trend. Conclusion: From 2005 to 2017, the incidence of lymphoma showed an increasing trend, and was different in regions, genders, and age groups in China. It will show an upward trend from 2018 to 2035. These results are helpful for the formulation and adjustment of lymphoma prevention, control, and management strategies, and have important reference significance for the treatment of lymphoma in China.
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BACKGROUND: Gastropods of the genus Biomphalaria (Family Planorbidae) are exploited as vectors by Schistosoma mansoni, the most common causative agent of human intestinal schistosomiasis. Using improved genomic resources, overviews of how Biomphalaria responds to S. mansoni and other metazoan parasites can provide unique insights into the reproductive, immune, and other systems of invertebrate hosts, and their responses to parasite challenges. RESULTS: Using Illumina-based RNA-Seq, we compared the responses of iM line B. glabrata at 2, 8, and 40 days post-infection (dpi) to single infections with S. mansoni, Echinostoma paraensei (both digenetic trematodes) or Daubaylia potomaca (a nematode parasite of planorbid snails). Responses were compared to unexposed time-matched control snails. We observed: (1) each parasite provoked a distinctive response with a predominance of down-regulated snail genes at all time points following exposure to either trematode, and of up-regulated genes at 8 and especially 40dpi following nematode exposure; (2) At 2 and 8dpi with either trematode, several snail genes associated with gametogenesis (particularly spermatogenesis) were down-regulated. Regarding the phenomenon of trematode-mediated parasitic castration in molluscs, we define for the first time a complement of host genes that are targeted, as early as 2dpi when trematode larvae are still small; (3) Differential gene expression of snails with trematode infection at 40dpi, when snails were shedding cercariae, was unexpectedly modest and revealed down-regulation of genes involved in the production of egg mass proteins and peptide processing; and (4) surprisingly, D. potomaca provoked up-regulation at 40dpi of many of the reproduction-related snail genes noted to be down-regulated at 2 and 8dpi following trematode infection. Happening at a time when B. glabrata began to succumb to D. potomaca, we hypothesize this response represents an unexpected form of fecundity compensation. We also document expression patterns for other Biomphalaria gene families, including fibrinogen domain-containing proteins (FReDs), C-type lectins, G-protein coupled receptors, biomphalysins, and protease and protease inhibitors. CONCLUSIONS: Our study is relevant in identifying several genes involved in reproduction that are targeted by parasites in the vector snail B. glabrata and that might be amenable to manipulation to minimize their ability to serve as vectors of schistosomes.
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Biomphalaria , Schistosoma mansoni , Transcriptoma , Animais , Biomphalaria/parasitologia , Biomphalaria/genética , Schistosoma mansoni/genética , Schistosoma mansoni/fisiologia , Interações Hospedeiro-Parasita/genética , Trematódeos/fisiologia , Trematódeos/genética , Vetores de Doenças , Perfilação da Expressão GênicaRESUMO
Amyotrophic lateral sclerosis (ALS) is a debilitating motor neurological disorder for which there is still no cure. The disease seriously jeopardizes the health and lifespan of adult populations. The authors extensively retrieved the current literature about clinical and experimental ALS treatments. Based on them, this review primarily focused on summarizing the current potential clinical usage and trialing therapeutics of ALS. Currently, the clinical ALS treatments have focused primarily on relieving symptoms to improve the quality of life yet. There are a number of therapeutic approaches such as medicine, gene therapy, neuron protectants, combination therapy and stem cells. Among them, Stem cells including embryonic stem cells, mesenchymal stem cells, neural stem cells, and many other types of stem cells have been used in ALS treatment, and although the short-term efficacy is good, it is worth exploring whether this improved efficacy leads to prolonged patient survival. In addition, the supportive treatments also exert an important effect on improving the quality of life and prolong the survival of ALS patients in absence of effectively care for stopping or reversing the progression of ALS.
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Single-atom catalysts, characterized by transition metal-(N/O)4 units on nanocarbon (M-(N/O)4-C), have emerged as efficient performers in water electrolysis. However, there are few guiding principles for accurately controlling the ligand fields of single atoms to further stimulate the catalyst activities. Herein, using the Ni-(N/O)4-C unit as a model, we develop a further modification of the P anion on the outer shells to modulate the morphology of the ligand. The catalyst thus prepared possesses high activity and excellent long-term durability, surpassing commercial Pt/C, RuO2, and currently reported single-atom catalysts. Notably, mechanistic studies demonstrated that the pseudocapacitive feature of multiscale anion-hybrid nanocarbon is considerable at accumulating enough positive charge [Q], contributing to the high oxygen evolution reaction (OER) order (ß) through the rate formula. DFT calculations also indicate that the catalytic activity is decided by the suitable barrier energy of the intermediates due to charge accumulation. This work reveals the activity origin of single atoms on multihybrid nanocarbon, providing a clear experiential formula for designing the electronic configuration of single-atom catalysts to boost electrocatalytic performance.
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OBJECTIVES: We sought to develop a novel deep learning (DL) workflow to interpret single-photon emission computed tomography (SPECT) wall motion. BACKGROUND: Wall motion assessment with SPECT is limited by image temporal and spatial resolution. Visual interpretation of wall motion can be subjective and prone to error. Artificial intelligence (AI) may improve accuracy of wall motion assessment. METHODS: A total of 1038 patients undergoing rest electrocardiogram (ECG)-gated SPECT and echocardiography were included. Using echocardiography as truth, a DL-model (DL-model 1) was trained to predict the probability of abnormal wall motion. Of the 1038 patients, 317 were used to train a DL-model (DL-model 2) to assess regional wall motion. A 10-fold cross-validation was adopted. Diagnostic performance of DL was compared with human readers and quantitative parameters. RESULTS: The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) of DL model (AUC: .82 [95% CI: .79-.85]; ACC: .88) were higher than human (AUC: .77 [95% CI: .73-.81]; ACC: .82; P < .001) and quantitative parameter (AUC: .74 [95% CI: .66-.81]; ACC: .78; P < .05). The net reclassification index (NRI) was 7.7%. The AUC and accuracy of DL model for per-segment and per-vessel territory diagnosis were also higher than human reader. The DL model generated results within 30 seconds with operable guided user interface (GUI) and therefore could provide preliminary interpretation. CONCLUSIONS: DL can be used to improve interpretation of rest SPECT wall motion as compared with current human readers and quantitative parameter diagnosis.
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Aprendizado Profundo , Tomografia Computadorizada de Emissão de Fóton Único , Humanos , Masculino , Feminino , Pessoa de Meia-Idade , Idoso , Tomografia Computadorizada de Emissão de Fóton Único/métodos , Ecocardiografia/métodos , Curva ROC , Reprodutibilidade dos Testes , Eletrocardiografia , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/fisiopatologiaRESUMO
With the fast development of large-scale Photovoltaic (PV) plants, the automatic PV fault identification and positioning have become an important task for the PV intelligent systems, aiming to guarantee the safety, reliability, and productivity of large-scale PV plants. In this paper, we propose a residual learning-based robotic (UAV) image analysis model for low-voltage distributed PV fault identification and positioning. In our target scenario, the unmanned aerial vehicles (UAVs) are deployed to acquire moving images of low-voltage distributed PV power plants. To get desired robustness and accuracy of PV image detection, we integrate residual learning with attention mechanism into the UAV image analysis model based on you only look once v4 (YOLOv4) network. Then, we design the sophisticated multi-scale spatial pyramid fusion and use it to optimize the YOLOv4 network for the nuanced task of fault localization within PV arrays, where the Complete-IOU loss is incorporated in the predictive modeling phase, significantly enhancing the accuracy and efficiency of fault detection. A series of experimental comparisons in terms of the accuracy of fault positioning are conducted, and the experimental results verify the feasibility and effectiveness of the proposed model in dealing with the safety and reliability maintenance of low-voltage distributed PV systems.
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BACKGROUND: Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability. PURPOSE: To introduce a temporal-regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome. STUDY TYPE: Retrospective. POPULATION: 194 OA progressors (mean age, 62 ± 9 years) and 406 controls (mean age, 61 ± 9 years) from the OA Initiative were randomly divided into training (80%) and testing (20%) cohorts. FIELD STRENGTH/SEQUENCE: Sagittal 2D IW-TSE-FS (IW) and 3D-DESS-WE (DESS) at 3T. ASSESSMENT: Anatomical subregions of cartilage, subchondral bone, meniscus, and the infrapatellar fat pad at baseline, 12-month, and 24-month were automatically segmented and served as inputs to form compartment-based graphs for a TRGCN model, which containing both regional and temporal information. The performance of models based on (i) clinical variables alone, (ii) radiologist score alone, (iii) combined features (containing i and ii), (iv) composite TRGCN (combining TRGCN, i and ii), (v) radiomics features, (vi) convolutional neural network based on Densenet-169 were compared. STATISTICAL TESTS: DeLong test was performed to compare the areas under the ROC curve (AUC) of all models. Additionally, interpretability analysis was done to evaluate the contributions of individual regions. A P value <0.05 was considered significant. RESULTS: The composite TRGCN outperformed all other models with AUCs of 0.841 (DESS) and 0.856 (IW) in the testing cohort (all P < 0.05). Interpretability analysis highlighted cartilage's importance over other structures (42%-45%), tibiofemoral joint's (TFJ) dominance over patellofemoral joint (PFJ) (58%-67% vs. 12%-37%), and importance scores changes in compartments over time (TFJ vs. PFJ: baseline: 44% vs. 43%, 12-month: 52% vs. 39%, 24-month: 31% vs. 48%). DATA CONCLUSION: The composite TRGCN, capturing temporal and regional information, demonstrated superior discriminative ability compared with other methods, providing interpretable insights for identifying knee OA progression. TECHNICAL EFFICACY: Stage 2.
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Objective. To go beyond the deficiencies of the three conventional multimodal fusion strategies (i.e. input-, feature- and output-level fusion), we propose a bidirectional attention-aware fluid pyramid feature integrated fusion network (BAF-Net) with cross-modal interactions for multimodal medical image diagnosis and prognosis.Approach. BAF-Net is composed of two identical branches to preserve the unimodal features and one bidirectional attention-aware distillation stream to progressively assimilate cross-modal complements and to learn supplementary features in both bottom-up and top-down processes. Fluid pyramid connections were adopted to integrate the hierarchical features at different levels of the network, and channel-wise attention modules were exploited to mitigate cross-modal cross-level incompatibility. Furthermore, depth-wise separable convolution was introduced to fuse the cross-modal cross-level features to alleviate the increase in parameters to a great extent. The generalization abilities of BAF-Net were evaluated in terms of two clinical tasks: (1) an in-house PET-CT dataset with 174 patients for differentiation between lung cancer and pulmonary tuberculosis. (2) A public multicenter PET-CT head and neck cancer dataset with 800 patients from nine centers for overall survival prediction.Main results. On the LC-PTB dataset, improved performance was found in BAF-Net (AUC = 0.7342) compared with input-level fusion model (AUC = 0.6825;p< 0.05), feature-level fusion model (AUC = 0.6968;p= 0.0547), output-level fusion model (AUC = 0.7011;p< 0.05). On the H&N cancer dataset, BAF-Net (C-index = 0.7241) outperformed the input-, feature-, and output-level fusion model, with 2.95%, 3.77%, and 1.52% increments of C-index (p= 0.3336, 0.0479 and 0.2911, respectively). The ablation experiments demonstrated the effectiveness of all the designed modules regarding all the evaluated metrics in both datasets.Significance. Extensive experiments on two datasets demonstrated better performance and robustness of BAF-Net than three conventional fusion strategies and PET or CT unimodal network in terms of diagnosis and prognosis.
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Processamento de Imagem Assistida por Computador , Humanos , Prognóstico , Processamento de Imagem Assistida por Computador/métodos , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada , Neoplasias Pulmonares/diagnóstico por imagem , Imagem Multimodal , Neoplasias de Cabeça e Pescoço/diagnóstico por imagemRESUMO
BACKGROUND: The issue of using deep learning to obtain accurate gross tumor volume (GTV) and metastatic lymph nodes (MLN) segmentation for nasopharyngeal carcinoma (NPC) on heterogeneous magnetic resonance imaging (MRI) images with limited labeling remains unsolved. METHOD: We collected 918 patients with MRI images from three hospitals to develop and validate models and proposed a semi-supervised framework for the fine delineation of multi-center NPC boundaries by integrating uncertainty-based implicit neural representations named SIMN. The framework utilizes the deep mutual learning approach with CNN and Transformer, incorporating dynamic thresholds. Additionally, domain adaptive algorithms are employed to enhance the performance. RESULTS: SIMN predictions have a high overlap ratio with the ground truth. Under the 20 % labeled cases, for the internal test cohorts, the average DSC in GTV and MLN are 0.7981 and 0.7804, respectively; for external test cohort Wu Zhou Red Cross Hospital, the average DSC in GTV and MLN are 0.7217 and 0.7581, respectively; for external test cohorts First People Hospital of Foshan, the average DSC in GTV and MLN are 0.7004 and 0.7692, respectively. No significant differences are found in DSC, HD95, ASD, and Recall for patients with different clinical categories. Moreover, SIMN outperformed existing classical semi-supervised methods. CONCLUSIONS: SIMN showed a highly accurate GTV and MLN segmentation for NPC on multi-center MRI images under Semi-Supervised Learning (SSL), which can easily transfer to other centers without fine-tuning. It suggests that it has the potential to act as a generalized delineation solution for heterogeneous MRI images with limited labels in clinical deployment.
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Imageamento por Ressonância Magnética , Carcinoma Nasofaríngeo , Neoplasias Nasofaríngeas , Humanos , Imageamento por Ressonância Magnética/métodos , Carcinoma Nasofaríngeo/diagnóstico por imagem , Neoplasias Nasofaríngeas/diagnóstico por imagem , Masculino , Feminino , Pessoa de Meia-Idade , Adulto , Aprendizado Profundo , Algoritmos , Interpretação de Imagem Assistida por Computador/métodos , Redes Neurais de ComputaçãoRESUMO
Real-time telemedicine detection can solve the problem of the shortage of public medical resources caused by the coming aging society. However, the development of such an integrated monitoring system is hampered by the need for high-performance sensors and the strict-requirement of long-distance signal transmission and reproduction. Here, a bionic crack-spring fiber sensor (CSFS) inspired by spider leg and cirrus whiskers for stretchable and weavable electronics is reported. Trans-scale conductive percolation networks of multilayer graphene around the surface of outer spring-like Polyethylene terephthalate (PET) fibers and printing Ag enable a high sensitivity of 28475.6 and broad sensing range over 250%. The electromechanical changes in different stretching stages are simulated by Comsol to explain the response mechanism. The CSFS is incorporated into the fabric and realized the human-machine interactions (HMIs) for robot control. Furthermore, the 5G Narrowband Internet of Things (NB-IoT) system is developed for human healthcare data collection, transmission, and reproduction together with the integration of the CSFS, illustrating the huge potential of the approach in human-machine communication interfaces and intelligent telemedicine rehabilitation and diagnosis monitoring.
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OBJECTIVE: The preoperative prediction of the overall survival (OS) status of patients with head and neck cancer (HNC) is significant value for their individualized treatment and prognosis. This study aims to evaluate the impact of adding 3D deep learning features to radiomics models for predicting 5-year OS status. METHODS: Two hundred twenty cases from The Cancer Imaging Archive public dataset were included in this study; 2212 radiomics features and 304 deep features were extracted from each case. The features were selected by univariate analysis and the least absolute shrinkage and selection operator, and then grouped into a radiomics model containing Positron Emission Tomography /Computed Tomography (PET/CT) radiomics features score, a deep model containing deep features score, and a combined model containing PET/CT radiomics features score +3D deep features score. TumorStage model was also constructed using initial patient tumor node metastasis stage to compare the performance of the combined model. A nomogram was constructed to analyze the influence of deep features on the performance of the model. The 10-fold cross-validation of the average area under the receiver operating characteristic curve and calibration curve were used to evaluate performance, and Shapley Additive exPlanations (SHAP) was developed for interpretation. RESULTS: The TumorStage model, radiomics model, deep model, and the combined model achieved areas under the receiver operating characteristic curve of 0.604, 0.851, 0.840, and 0.895 on the train set and 0.571, 0.849, 0.832, and 0.900 on the test set. The combined model showed better performance of predicting the 5-year OS status of HNC patients than the radiomics model and deep model. The combined model was shown to provide a favorable fit in calibration curves and be clinically useful in decision curve analysis. SHAP summary plot and SHAP The SHAP summary plot and SHAP force plot visually interpreted the influence of deep features and radiomics features on the model results. CONCLUSIONS: In predicting 5-year OS status in patients with HNC, 3D deep features could provide richer features for combined model, which showed outperformance compared with the radiomics model and deep model.
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Aprendizado Profundo , Neoplasias de Cabeça e Pescoço , Nomogramas , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada , Humanos , Neoplasias de Cabeça e Pescoço/diagnóstico por imagem , Masculino , Feminino , Pessoa de Meia-Idade , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada/métodos , Prognóstico , Idoso , Imageamento Tridimensional/métodos , Adulto , Estudos Retrospectivos , RadiômicaRESUMO
Schistosomiasis, urogenital and intestinal, afflicts 251 million people worldwide with approximately two-thirds of the patients suffering from the urogenital form of the disease. Freshwater snails of the genus Bulinus (Gastropoda: Planorbidae) serve as obligate intermediate hosts for Schistosoma haematobium, the etiologic agent of human urogenital schistosomiasis. These snails also act as vectors for the transmission of schistosomiasis in livestock and wildlife. Despite their crucial role in human and veterinary medicine, our basic understanding at the molecular level of the entire Bulinus genus, which comprises 37 recognized species, is very limited. In this study, we employed Illumina-based RNA sequencing (RNAseq) to profile the genome-wide transcriptome of Bulinus globosus, one of the most important intermediate hosts for S. haematobium in Africa. A total of 179,221 transcripts (N50 = 1,235) were assembled and the benchmarking universal single-copy orthologs (BUSCO) was estimated to be 97.7%. The analysis revealed a substantial number of transcripts encoding evolutionarily conserved immune-related proteins, particularly C-type lectin (CLECT) domain-containing proteins (n = 316), Toll/Interleukin 1-receptor (TIR)-containing proteins (n = 75), and fibrinogen related domain-containing molecules (FReD) (n = 165). Notably, none of the FReDs are fibrinogen-related proteins (FREPs) (immunoglobulin superfamily (IgSF) + fibrinogen (FBG)). This RNAseq-based transcriptional profile provides new insights into immune capabilities of Bulinus snails, helps provide a framework to explain the complex patterns of compatibility between snails and schistosomes, and improves our overall understanding of comparative immunology.
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Bulinus , Esquistossomose Urinária , Humanos , Animais , Bulinus/genética , Schistosoma haematobium/genética , Água Doce , FibrinogênioRESUMO
Objective: To comprehensively capture intra-tumor heterogeneity in head and neck cancer (HNC) and maximize the use of valid information collected in the clinical field, we propose a novel multi-modal image-text fusion strategy aimed at improving prognosis. Method: We have developed a tailored diagnostic algorithm for HNC, leveraging a deep learning-based model that integrates both image and clinical text information. For the image fusion part, we used the cross-attention mechanism to fuse the image information between PET and CT, and for the fusion of text and image, we used the Q-former architecture to fuse the text and image information. We also improved the traditional prognostic model by introducing time as a variable in the construction of the model, and finally obtained the corresponding prognostic results. Result: We assessed the efficacy of our methodology through the compilation of a multicenter dataset, achieving commendable outcomes in multicenter validations. Notably, our results for metastasis-free survival (MFS), recurrence-free survival (RFS), overall survival (OS), and progression-free survival (PFS) were as follows: 0.796, 0.626, 0.641, and 0.691. Our results demonstrate a notable superiority over the utilization of CT and PET independently, and exceed the result derived without the clinical textual information. Conclusions: Our model not only validates the effectiveness of multi-modal fusion in aiding diagnosis, but also provides insights for optimizing survival analysis. The study underscores the potential of our approach in enhancing prognosis and contributing to the advancement of personalized medicine in HNC.
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BACKGROUND: Control and elimination of schistosomiasis is an arduous task, with current strategies proving inadequate to break transmission. Exploration of genetic approaches to interrupt Schistosoma mansoni transmission, the causative agent for human intestinal schistosomiasis in sub-Saharan Africa and South America, has led to genomic research of the snail vector hosts of the genus Biomphalaria. Few complete genomic resources exist, with African Biomphalaria species being particularly underrepresented despite this being where the majority of S. mansoni infections occur. Here we generate and annotate the first genome assembly of Biomphalaria sudanica sensu lato, a species responsible for S. mansoni transmission in lake and marsh habitats of the African Rift Valley. Supported by whole-genome diversity data among five inbred lines, we describe orthologs of immune-relevant gene regions in the South American vector B. glabrata and present a bioinformatic pipeline to identify candidate novel pathogen recognition receptors (PRRs). RESULTS: De novo genome and transcriptome assembly of inbred B. sudanica originating from the shoreline of Lake Victoria (Kisumu, Kenya) resulted in a haploid genome size of ~ 944.2 Mb (6,728 fragments, N50 = 1.067 Mb), comprising 23,598 genes (BUSCO = 93.6% complete). The B. sudanica genome contains orthologues to all described immune genes/regions tied to protection against S. mansoni in B. glabrata, including the polymorphic transmembrane clusters (PTC1 and PTC2), RADres, and other loci. The B. sudanica PTC2 candidate immune genomic region contained many PRR-like genes across a much wider genomic region than has been shown in B. glabrata, as well as a large inversion between species. High levels of intra-species nucleotide diversity were seen in PTC2, as well as in regions linked to PTC1 and RADres orthologues. Immune related and putative PRR gene families were significantly over-represented in the sub-set of B. sudanica genes determined as hyperdiverse, including high extracellular diversity in transmembrane genes, which could be under pathogen-mediated balancing selection. However, no overall expansion in immunity related genes was seen in African compared to South American lineages. CONCLUSIONS: The B. sudanica genome and analyses presented here will facilitate future research in vector immune defense mechanisms against pathogens. This genomic/transcriptomic resource provides necessary data for the future development of molecular snail vector control/surveillance tools, facilitating schistosome transmission interruption mechanisms in Africa.