Uncommon presentation of gastric mucosa-associated lymphoid tissue lymphoma in a 13-year-old girl: acute vomiting of blood as the initial symptom.

Introduction and importance: Extranodal marginal zone lymphoma (EMZL lymphoma), also known as mucosa-associated lymphoid tissue (MALT) lymphoma, is a rare B-cell lymphoma that rarely affects children. The involvement of infectious agents, especially H. pylori, has been observed in the formation and progression of MALT lymphoma in the stomach. Hematemesis as the primary clinical manifestation is uncommon, highlighting the need for case studies with this presentation. This article uses SCARE2023 criteria as a framework to sort out a case report in order. Case presentation: A 13-year-old female patient was admitted in August 2022 with an episode of hematemesis. She had a prior diagnosis of anaemia and was found positive for H. pylori. Despite treatment, she developed symptoms of chronic non-atrophic gastritis and had recurring episodes of hematemesis. Physical and diagnostic examinations revealed B-cell lymphoma localized in the gastric antrum. The primary diagnosis was extranodal MALT lymphoma with unique plasma cell differentiation. Clinical discussion: The presentation of gastric MALT lymphoma can be variable, with definitive diagnosis often achieved via endoscopic biopsy. H. pylori plays a significant role in the onset and progression of this lymphoma, emphasizing the importance of its eradication for treatment. Effective outcomes can be achieved through anti-H. pylori treatment, although it is essential for clinicians to ensure its complete eradication post-treatment. Conclusion: Paediatric presentation of gastric MALT lymphoma, especially with hematemesis as the primary symptom, is rare and can be easily misdiagnosed. Compared to adults, children generally exhibit a better prognosis with effective H. pylori treatment. It is vital for medical professionals to recognize the differences in presentation between children and adults to ensure accurate diagnosis and treatment.

Efficacy of polysaccharide iron complex in IDA rats: A comparative study with iron protein succinylate and ferrous succinate.

Iron deficiency anemia (IDA) is the most common nutrient-related health problem in the world. There is still a lack of comprehensive comparative study on the efficacies of commonly used iron supplements such as polysaccharide iron complex (PIC), iron protein succinylate (IPS) and ferrous succinate (FS) for IDA. In this study, we compared the PIC, IPS and FS efficacies in IDA rats via intragastric administration. The results showed that the three iron supplements had similar efficacies. PIC/IPS/FS at a dose of 15 mg Fe/kg/d for 10 d increased the hematological and serum biochemical parameters to 2.15/2.12/2.18 (Hb), 1.71/1.67/1.69 (RBC), 2.10/2.11/2.12 (HCT), 1.26/1.22/1.22 (MCV), all 1.34 (MCH), 1.15/1.15/1.14 (MCHC), 1.94/1.82/1.91 (SF), 9.75/9.67/9.53 (SI), and 23.30/22.68/21.64 (TS) times, and reduced TIBC to 0.42/0.43/0.44 times, compared to untreated IDA rats. PIC performed slightly better than IPS and FS in restoring MCV level. Meanwhile, the heart, spleen and kidney coefficients reduced to 67%/74%/65% (heart), all 59% (spleen) and 87%/88%/88% (kidney), and the liver coefficient increased to 116%/115%/116%, compared to untreated IDA rats. The liver iron content was found to be more affected by IDA than the spleen iron content. PIC/IPS/FS at 15 mg Fe/kg/d increased organ iron contents to 4.20/3.97/4.03 times (liver) and 1.36/1.24/1.41 times (spleen) within 10 d compared to untreated IDA rats, and PIC-H and FS were slightly better than IPS in restoring spleen iron content. The results of this study can provide useful data information for the comparison of three iron supplements, PIC, IPS and FS.

Normalizing Flow-Based Distribution Estimation of Pharmacokinetic Parameters in Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

OBJECTIVE: The pharmacokinetic (PK) parameters estimated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide valuable information for clinical research and diagnosis. However, these estimated PK parameters suffer from many sources of variability. Thus, the estimation of the posterior distributions of these PK parameters could provide a way to simultaneously quantify the values and uncertainties of the PK parameters. Our objective is to develop an efficient and flexible method to more closely approximate and estimate the underlying posterior distributions of the PK parameters. METHODS: The normalizing flow model-based parameters distribution estimation neural network (FPDEN) is proposed to adaptively learn and estimate the posterior distributions of the PK parameters. The maximum likelihood estimation (MLE) loss is directly constructed based on the parameter distributions learned by the normalizing flow model, rather than pre-defined distributions. RESULTS: Experimental analysis shows that the proposed method can improve parameter estimation accuracy. Moreover, the uncertainty derived from the parameter distribution constitutes an effective indicator to exclude unreliable parametric results. A successful demonstration is the improved classification performance of the glioma World Health Organization (WHO) grading task, specifically in terms of distinguishing between low and high grades, as well as between Grade III and Grade IV. CONCLUSION: The FPDEN method offers improved accuracy for estimation of PK parameters and boosts the performance of the glioma grading task. SIGNIFICANCE: By enhancing the precision and reliability of DCE-MRI, the proposed method promotes its further applications in clinical practice.

Fe(II)/LXQ-10 bifunctional resin materials for boosting synergistic adsorption/oxidation of benzene in industrial waste gas.

A series of adsorption/oxidation bifunctional material with different Fe(II) loading amounts was prepared by using ultrahigh crosslinking adsorption resin (LXQ-10) as a carrier and FeCl2 as an impregnating solution. The bifunctional material was characterized by BET, SEM, XRD, XPS, and EPR. The effects of Fe loading, reaction temperature, and space velocity on benzene adsorption efficiency were investigated using self-made experimental equipment to explore the optimal reaction condition. The adsorption results were fitted and analyzed by using four typical models: the quasi-first-order kinetic model, the quasi-second-order kinetic model, Elovich's kinetic model, and the Weber and Morris kinetic model. The quasi-first-order kinetic model had the highest R2 value (0.998) and the best applicability. The fitting effect of the Freundlich equation (R2 = 0.997) was better than that of the Langmuir equation (R2 = 0.919). Furthermore, the effects of Fe loading, H2O2 concentration, benzene inlet concentration, and temperature on the catalytic oxidation efficiency of benzene were studied. The catalytic oxidation efficiency of 3-Fe(II)/LXQ-10 was maintained at about 95% at a temperature of 303 K and an H2O2 concentration of 150 mmol/L. Compared with the adsorption efficiency, the catalytic oxidation efficiency of bifunctional resin materials in a heterogeneous Fenton system was remarkably improved and had excellent stability. A possible migration and transformation path during benzene removal was proposed according to the results of the analysis of GC-MS intermediates. This study provided a novel process for the adsorption and oxidative degradation of VOCs.

Synthesizing MR Image Contrast Enhancement Using 3D High-Resolution ConvNets.

OBJECTIVE: Gadolinium-based contrast agents (GBCAs) have been widely used to better visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium deposition within the brain and body has raised safety concerns about the use of GBCAs. Therefore, the development of novel approaches that can decrease or even eliminate GBCA exposure while providing similar contrast information would be of significant use clinically. METHODS: In this work, we present a deep learning based approach for contrast-enhanced T1 synthesis on brain tumor patients. A 3D high-resolution fully convolutional network (FCN), which maintains high resolution information through processing and aggregates multi-scale information in parallel, is designed to map pre-contrast MRI sequences to contrast-enhanced MRI sequences. Specifically, three pre-contrast MRI sequences, T1, T2 and apparent diffusion coefficient map (ADC), are utilized as inputs and the post-contrast T1 sequences are utilized as target output. To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors. RESULTS: Extensive quantitative and visual assessments are performed, with our proposed model achieving a PSNR of 28.24 dB in the brain and 21.2 dB in tumor regions. CONCLUSION AND SIGNIFICANCE: Our results suggest the potential of substituting GBCAs with synthetic contrast images generated via deep learning.

Development and validation of a meta-learning-based multi-modal deep learning algorithm for detection of peritoneal metastasis.

PURPOSE: The existing medical imaging tools have a detection accuracy of 97% for peritoneal metastasis(PM) bigger than 0.5 cm, but only 29% for that smaller than 0.5 cm, the early detection of PM is still a difficult problem. This study is aiming at constructing a deep convolution neural network classifier based on meta-learning to predict PM. METHOD: Peritoneal metastases are delineated on enhanced CT. The model is trained based on meta-learning, and features are extracted using multi-modal deep Convolutional Neural Network(CNN) with enhanced CT to classify PM. Besides, we evaluate the performance on the test dataset, and compare it with other PM prediction algorithm. RESULTS: The training datasets are consisted of 9574 images from 43 patients with PM and 67 patients without PM. The testing datasets are consisted of 1834 images from 21 testing patients. To increase the accuracy of the prediction, we combine the multi-modal inputs of plain scan phase, portal venous phase and arterial phase to build a meta-learning-based multi-modal PM predictor. The classifier shows an accuracy of 87.5% with Area Under Curve(AUC) of 0.877, sensitivity of 73.4%, specificity of 95.2% on the testing datasets. The performance is superior to routine PM classify based on logistic regression (AUC: 0.795), a deep learning method named ResNet3D (AUC: 0.827), and a domain generalization (DG) method named MADDG (AUC: 0.834). CONCLUSIONS: we proposed a novel training strategy based on meta-learning to improve the model's robustness to "unseen" samples. The experiments shows that our meta-learning-based multi-modal PM predicting classifier obtain more competitive results in synchronous PM prediction compared to existing algorithms and the model's improvements of generalization ability even with limited data.

Self-assembled Cupric Oxide Nanoclusters for Highly efficient chemodynamic therapy.

Chemodynamic therapy (CDT) based on Fenton and Fenton-like reactions induces cancer cell killing via in situ catalyzing H2 O2 and generating highly oxidative hydroxyl radicals (⋅OH) in tumor sites. Their application is not limited by tumor grown depth or hypoxic microenvironment. However, the reaction efficiency is still hampered due to the structure of catalytic agents and the requirement for low pH environment. Here, we design a porous CuO nanocluster (CuO NC) through self-assembly of oleylamine stabilized CuO NPs (OAm-CuO NPs), and functionalize it with folic acid (CuO NC-FA) for specific tumor cell targeting. It can catalyze H2 O2 with high efficiency in nearly neutral environment. Besides, the porous structure of CuO NC also helps the diffusion of H2 O2 to the interior of nanocluster to further improve Fenton-like reaction efficiency. The convenient synthesis of CuO NC-FA with good Fenton-like reaction efficiency at neutral environment demonstrates good chemodynamic therapy effect.

2D probabilistic undersampling pattern optimization for MR image reconstruction.

With 3D magnetic resonance imaging (MRI), a tradeoff exists between higher image quality and shorter scan time. One way to solve this problem is to reconstruct high-quality MRI images from undersampled k-space. There have been many recent studies exploring effective k-space undersampling patterns and designing MRI reconstruction methods from undersampled k-space, which are two necessary steps. Most studies separately considered these two steps, although in theory, their performance is dependent on each other. In this study, we propose a joint optimization model, trained end-to-end, to simultaneously optimize the undersampling pattern in the Fourier domain and the reconstruction model in the image domain. A 2D probabilistic undersampling layer was designed to optimize the undersampling pattern and probability distribution in a differentiable manner. A 2D inverse Fourier transform layer was implemented to connect the Fourier domain and the image domain during the forward and back propagation. Finally, we discovered an optimized relationship between the probability distribution of the undersampling pattern and its corresponding sampling rate. Further testing was performed using 3D T1-weighted MR images of the brain from the MICCAI 2013 Grand Challenge on Multi-Atlas Labeling dataset and locally acquired brain 3D T1-weighted MR images of healthy volunteers and contrast-enhanced 3D T1-weighted MR images of high-grade glioma patients. The results showed that the recovered MR images using our 2D probabilistic undersampling pattern (with or without the reconstruction network) significantly outperformed those using the existing start-of-the-art undersampling strategies for both qualitative and quantitative comparison, suggesting the advantages and some extent of the generalization of our proposed method.

Ensemble learning accurately predicts the potential benefits of thrombolytic therapy in acute ischemic stroke.

BACKGROUND: Finding methods to accurately predict the final infarct volumes for acute ischemic stroke patients with full or no recanalization would significantly help to evaluate the potential benefits of thrombolytic therapy. We proposed such a method by constructing a model of ensemble deep learning and machine learning using diffusion-weighted imaging (DWI) only. METHODS: The proposed prediction model (named AUNet) combines an adaptive linear ensemble model (ALEM) of machine learning and a deep U-Net network with an accelerated non-local module (U-NL-Net) to learn voxel-wise and spatial features, respectively. Of 40 patients with acute ischemic stroke who received thrombolytic therapy, 17 were fully recanalized, 14 were not recanalized, and nine were partially recanalized. The AUNet was separately trained for full recanalization conditions (AUNetR) and no recanalization (AUNetN) as the best and worst outcomes of thrombolysis, respectively. RESULTS: AUNet performed significantly better in predicting the final infarct volumes in both the recanalization and non-recanalization conditions [area under the receiver operating characteristic curve (AUC) =0.898±0.022, recanalization; AUC =0.875±0.036, non-recanalization: Matthew's correlation coefficient (MCC) =0.863±0.033, recanalization; MCC =0.851±0.025, non-recanalization] than the fixed-thresholding method (AUC =0.776±0.021, P<0.0001, recanalization; AUC =0.692±0.023, P<0.0001, non-recanalization: MCC =0.742±0.035, recanalization; MCC =0.671±0.024, non-recanalization), the logistic regression method (AUC =0.797±0.023, P<0.003, recanalization; AUC =0.751±0.030, P<0.003, non-recanalization: MCC =0.762±0.035, recanalization; MCC =0.730±0.031, non-recanalization), and a recently developed convolutional neural network (AUC =0.814±0.013, P<0.003, recanalization; AUC =0.781±0.027, P<0.003, non-recanalization: MCC =792±0.022, recanalization; MCC =0.758±0.016, non-recanalization). The potential benefit of thrombolysis calculated from AUNetR and AUNetN showed large individual differences (from 12.81% to 239.73%). CONCLUSIONS: AUNet improved predictive accuracy over current state-of-the-art methods. More importantly, the accurate prediction of infarct volumes under different recanalization conditions may provide benefitial information for physicians in selecting suitable patients for thrombolytic therapy.

Conjugated Polymer-Ferrocence Nanoparticle as an NIR-II Light Powered Nanoamplifier to Enhance Chemodynamic Therapy.

Chemodynamic therapy (CDT) is a promising therapeutic modality with transition metal ions and endogenous H2O2 as reagents, but its efficiency is impaired by low endogenous H2O2 levels and nonregeneration of metal ions. Most intracellular H2O2 supplement strategies use oxidases and are intensively dependent on oxygen participation. The hypoxia microenvironments of solid tumors weaken their performance. Here, we develop a near-infrared II light powered nanoamplifier to improve the local oxygen level and to enhance CDT. The nanoamplifier CPNP-Fc/Pt consists of ferrocene (Fc)- and cisplatin prodrug (Pt(IV))-modified conjugated polymer nanoparticles (CPNPs). CPNP has a donor-acceptor structure and demonstrates a good photothermal effect under 1064 nm light irradiation, which accelerates blood flow and efficiently elevates the local oxygen content. In response to intracellular glutathione, Pt(II) is released from CPNP-Fc/Pt and triggers enzymatic cascade reactions with nicotinamide adenine dinucleotide phosphate oxidase (NADPH oxidase) and superoxide dismutase to convert oxygen into H2O2. The enhanced oxygen level results in efficient intracellular H2O2 supply. Fc is reacted with H2O2 and converted to Fc+ via the Fenton reaction, with the generation of hydroxyl radicals for CDT. Unlike free metal ions, the Fe(III) in Fc+ is reduced to Fe(II) by intracellular NAD(P)H, which achieves the regeneration of Fc. The sufficient intracellular H2O2 supply and efficient Fc regeneration effectively enhance the Fenton reaction and demonstrate good in vivo CDT results with tumor growth suppression. This design offers a promising strategy to enhance CDT efficiency in the hypoxia microenvironment of solid tumors.

Convolutional neural network for accelerating the computation of the extended Tofts model in dynamic contrast-enhanced magnetic resonance imaging.

Quantitative physiological parameters can be obtained from nonlinear pharmacokinetic models, such as the extended Tofts (eTofts) model, applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). However, the computation of such nonlinear models is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for accelerating the computation of fitting eTofts model without sacrificing agreement with conventional nonlinear-least-square (NLLS) fitting. This was a retrospective study, which included 13 patients with brain glioma for training (75%) and validation (25%), and 11 patients (three glioma, four brain metastases, and four lymphoma) for testing. CAIPIRINHA-Dixon-TWIST DCE-MRI and double flip angle T1 map acquired at 3 T were used. A CNN with both local pathway and global pathway modules was designed to estimate the eTofts model parameters, the volume transfer constant (Ktrans ), blood volume fraction (vp ), and volume fraction of extracellular extravascular space (ve ), from DCE-MRI data of tumor and normal-appearing voxels. The CNN was trained on mixed dataset consisting of synthetic and patient data. The CNN result and computation speed were compared with NLLS fitting. The robustness to noise variations and generalization to brain metastases and lymphoma data were also evaluated. Statistical tests used were Student's t test on mean absolute error, concordance correlation coefficient (CCC), and normalized root mean squared error. Including global pathway modules in the CNN and training the network with mixed data significantly (p < 0.05) improved the CNN performance. Compared with NLLS fitting, CNN yields an average CCC greater than 0.986 for Ktrans , greater than 0.965 for vp , and greater than 0.948 for ve . The CNN accelerated computation speed approximately 2000 times compared to NLLS, showed robustness to noise (signal-to-noise ratio >34.42 dB), and had no significant (p > 0.21) difference applied to brain metastases and lymphoma data. In conclusion, the proposed CNN to estimate eTofts parameters showed comparable result as NLLS fitting while significantly reducing the computation time. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1.