Unsupervised MRI motion artifact disentanglement: introducing MAUDGAN.

OBJECTIVE: This study developed an unsupervised motion artifact reduction method for MRI images of patients with brain tumors. The proposed novel design uses multi-parametric multicenter contrast-enhanced T1W (ceT1W) and T2-FLAIR MRI images. Approach: The proposed framework included two generators, two discriminators, and two feature extractor networks. A 3-fold cross-validation was used to train and fine-tune the hyperparameters of the proposed model using 230 brain MRI images with tumors, which were then tested on 148 patients' in-vivo datasets. An ablation was performed to evaluate the model's compartments. Our model was compared with Pix2pix and CycleGAN. Six evaluation metrics were reported, including normalized mean squared error (NMSE), structural similarity index (SSIM), multi-scale-SSIM (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multi-scale gradient magnitude similarity deviation (MS-GMSD). Artifact reduction and consistency of tumor regions, image contrast, and sharpness were evaluated by three evaluators using Likert scales and compared with ANOVA and Tukey's HSD tests. Main results: On average, our method outperforms comparative models to remove heavy motion artifacts with the lowest NMSE (18.34±5.07%) and MS-GMSD (0.07±0.03) for heavy motion artifact level. Additionally, our method creates motion-free images with the highest SSIM (0.93±0.04), PSNR (30.63±4.96), and VIF (0.45±0.05) values, along with comparable MS-SSIM (0.96±0.31). Similarly, our method outperformed comparative models in removing in-vivo motion artifacts for different distortion levels except for MS- SSIM and VIF, which have comparable performance with CycleGAN. Moreover, our method had a consistent performance for different artifact levels. For the heavy level of motion artifacts, our method got the highest Likert scores of 2.82±0.52, 1.88±0.71, and 1.02±0.14 (p-values<<0.0001) for our method, CycleGAN, and Pix2pix respectively. Similar trends were also found for other motion artifact levels. Significance: Our proposed unsupervised method was demonstrated to reduce motion artifacts from the ceT1W brain images under a multi-parametric framework.

Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review.

Magnetic resonance imaging (MRI) has revolutionized medical imaging, providing a non-invasive and highly detailed look into the human body. However, the long acquisition times of MRI present challenges, causing patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, researchers are exploring various techniques to reduce acquisition time and improve the overall efficiency of MRI. One such technique is compressed sensing (CS), which reduces data acquisition by leveraging image sparsity in transformed spaces. In recent years, deep learning (DL) has been integrated with CS-MRI, leading to a new framework that has seen remarkable growth. DL-based CS-MRI approaches are proving to be highly effective in accelerating MR imaging without compromising image quality. This review comprehensively examines DL-based CS-MRI techniques, focusing on their role in increasing MR imaging speed. We provide a detailed analysis of each category of DL-based CS-MRI including end-to-end, unroll optimization, self-supervised, and federated learning. Our systematic review highlights significant contributions and underscores the exciting potential of DL in CS-MRI. Additionally, our systematic review efficiently summarizes key results and trends in DL-based CS-MRI including quantitative metrics, the dataset used, acceleration factors, and the progress of and research interest in DL techniques over time. Finally, we discuss potential future directions and the importance of DL-based CS-MRI in the advancement of medical imaging. To facilitate further research in this area, we provide a GitHub repository that includes up-to-date DL-based CS-MRI publications and publicly available datasets - https://github.com/mosaf/Awesome-DL-based-CS-MRI.

Diffeomorphic Transformer-based Abdomen MRI-CT Deformable Image Registration.

This paper aims to create a deep learning framework that can estimate the deformation vector field (DVF) for directly registering abdominal MRI-CT images. The proposed method assumed a diffeomorphic deformation. By using topology-preserved deformation features extracted from the probabilistic diffeomorphic registration model, abdominal motion can be accurately obtained and utilized for DVF estimation. The model integrated Swin transformers, which have demonstrated superior performance in motion tracking, into the convolutional neural network (CNN) for deformation feature extraction. The model was optimized using a cross-modality image similarity loss and a surface matching loss. To compute the image loss, a modality-independent neighborhood descriptor (MIND) was used between the deformed MRI and CT images. The surface matching loss was determined by measuring the distance between the warped coordinates of the surfaces of contoured structures on the MRI and CT images. The deformed MRI image was assessed against the CT image using the target registration error (TRE), Dice similarity coefficient (DSC), and mean surface distance (MSD) between the deformed contours of the MRI image and manual contours of the CT image. When compared to only rigid registration, DIR with the proposed method resulted in an increase of the mean DSC values of the liver and portal vein from 0.850 and 0.628 to 0.903 and 0.763, a decrease of the mean MSD of the liver from 7.216 mm to 3.232 mm, and a decrease of the TRE from 26.238 mm to 8.492 mm. The proposed deformable image registration method based on a diffeomorphic transformer provides an effective and efficient way to generate an accurate DVF from an MRI-CT image pair of the abdomen. It could be utilized in the current treatment planning workflow for liver radiotherapy.

Neuronal hypofunction and network dysfunction in a mouse model at an early stage of tauopathy.

We previously reported altered neuronal Ca 2+ dynamics in the motor cortex of 12-month-old JNPL3 tauopathy mice during quiet wakefulness or forced running, with a tau antibody treatment significantly restoring the neuronal Ca 2+ activity profile and decreasing pathological tau in these mice 1 . Whether neuronal functional deficits occur at an early stage of tauopathy and if tau antibody treatment is effective in younger tauopathy mice needed further investigation. In addition, neuronal network activity and neuronal firing patterns have not been well studied in behaving tauopathy models. In this study, we first performed in vivo two-photon Ca 2+ imaging in JNPL3 mice in their early stage of tauopathy at 6 months of age, compared to 12 month old mice and age-matched wild-type controls to evaluate neuronal functional deficits. At the animal level, frequency of neuronal Ca 2+ transients decreased only in 6 month old tauopathy mice compared to controls, and only when animals were running on a treadmill. The amplitude of neuronal transients decreased in tauopathy mice compared to controls under resting and running conditions in both age groups. Total neuronal activity decreased only in 6 month old tauopathy mice compared to controls under resting and running conditions. Within either tauopathy or wild-type group, only total activity decreased in older wild-type animals. The tauopathy mice at different ages did not differ in neuronal Ca 2+ transient frequency, amplitude or total activity. In summary, neuronal function did significantly attenuate at an early age in tauopathy mice compared to controls but interestingly did not deteriorate between 6 and 12 months of age. A more detailed populational analysis of the pattern of Ca 2+ activity at the neuronal level in the 6 month old cohort confirmed neuronal hypoactivity in layer 2/3 of primary motor cortex, compared to wild-type controls, when animals were either resting or running on a treadmill. Despite reduced activity, neuronal Ca 2+ profiles exhibited enhanced synchrony and dysregulated responses to running stimulus. Further ex vivo electrophysiological recordings revealed reduction of spontaneous excitatory synaptic transmission onto and in pyramidal neurons and enhanced excitability of inhibitory neurons in motor cortex, which were likely responsible for altered neuronal network activity in this region. Lastly, tau antibody treatment reduced pathological tau and gliosis partially restored the neuronal Ca 2+ activity deficits but failed to rescue altered network changes. Taken together, substantial neuronal and network dysfunction occurred in the early stage of tauopathy that was partially alleviated with acute tau antibody treatment, which highlights the importance of functional assessment when evaluating the therapeutic potential of tau antibodies. Highlights: Layer 2/3 motor cortical neurons exhibited hypofunction in awake and behaving mice at the early stage of tauopathy.Altered neuronal network activity disrupted local circuitry engagement in tauopathy mice during treadmill running.Layer 2/3 motor cortical neurons in tauopathy mice exhibited enhanced neuronal excitability and altered excitatory synaptic transmissions.Acute tau antibody treatment reduced pathological tau and gliosis, and partially restored neuronal hypofunction profiles but not network dysfunction.

Research and Analysis of Molecules such as CD28, CD45RA, CD45RO, CD38, HLA-DR, and CD57 on T Cells in Multiple Myeloma.

BACKGROUND: For many years it has been postulated that the immune system controls the progress of multiple myeloma (MM). However, the phenotypes of T cells in MM remain to be elucidated. In this study, we compared the phenotypes of T cells, which were obtained from the peripheral blood, in MM patients with those in healthy donors (HD). The expression of CCR7, CD57, CD28, HLA-DR, CD38, CD45RA, and CD45RO were assessed on T cells from MM patients and HDs using multicolor flow cytometry (MFC). METHODS: For this study, 17 newly diagnosed MM patients were selected, and 20 healthy people were selected as a control group. MFC was used to detect the markers on T cells. RESULTS: We detected significant increases in the expression levels of HLA-DR, CD38, and CD57on CD8+ T cells, significant decreases in the expression levels of CD28 and CD45RA on CD8+ T cells, and a decrease of CD4+ effec-tor T cells in MM patients, compared to the HD group. CONCLUSIONS: Our study shows that the accumulation of peripheral CD8+CD57+T cells, CD8+CD38high T cells, and CD8+HLA-DR+CD38high T cells is reflective of an ongoing antitumor T cell response and a progressive immune dysfunction in MM. During chemotherapy, the recovery of immune function can be monitored by detecting the proportion of activated molecules of T lymphocytes.

Cardiac MRI segmentation using shifted-window multilayer perceptron mixer networks.

PURPOSE: In this work, we proposed a deep-learning segmentation algorithm for cardiac magnetic resonance imaging (MRI) to aid in contouring of the left ventricle (LV), right ventricle (RV), and Myocardium (Myo). Methods: We proposed a shifted window multilayer perceptron (Swin-MLP) mixer network which is built upon a 3D U-shaped symmetric encoder-decoder structure. We evaluated our proposed network using public data from 100 individuals. The network performance was quantitatively evaluated using 3D volume similarity between the ground truth contours and the predictions using Dice score coefficient, sensitivity, and precision as well as 2D surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMSD). We benchmarked the performance against two other current leading edge networks known as Dynamic UNet and Swin-UNetr on the same public dataset. Results: The proposed network achieved the following volume similarity metrics when averaged over three cardiac segments: Dice = 0.952±0.017, precision = 0.948±0.016, sensitivity = 0.956±0.022. The average surface similarities were HD = 1.521±0.121 mm, MSD = 0.266±0.075 mm, and RMSD = 0.668±0.288 mm. The network shows statistically significant improvement in comparison to the Dynamic UNet and Swin-UNetr algorithms for most volumetric and surface metrics with p-value less than 0.05. Overall, the proposed Swin-MLP mixer network demonstrates better or comparable performance than competing methods. Conclusions: The proposed Swin-MLP mixer network demonstrates more accurate segmentation performance compared to current leading edge methods. This robust method demonstrates the potential to streamline clinical workflows for multiple applications.

Customizable Click Biochemistry Strategy for the Design and Preparation of Glucagon-like Peptide-1 Conjugates and Coagonists.

The development of oligomeric glucagon-like peptide-1 (GLP-1) and GLP-1-containing coagonists holds promise for enhancing the therapeutic potential of the GLP-1-based drugs for treating type 2 diabetes mellitus (T2DM). Here, we report a facile, efficient, and customizable strategy based on genetically encoded SpyCatcher-SpyTag chemistry and an inducible, cleavable self-aggregating tag (icSAT) scheme. icSAT-tagged SpyTag-fused GLP-1 and the dimeric or trimeric SpyCatcher scaffold were designed for dimeric or trimeric GLP-1, while icSAT-tagged SpyCatcher-fused GLP-1 and the icSAT-tagged SpyTag-fused GIP were designed for dual GLP-1/GIP (glucose-dependent insulinotropic polypeptide) receptor agonist. These SpyCatcher- and SpyTag-fused protein pairs were spontaneously ligated directly from the cell lysates. The subsequent icSAT scheme, coupled with a two-step standard column purification, resulted in target proteins with authentic N-termini, with yields ranging from 35 to 65 mg/L and purities exceeding 99%. In vitro assays revealed 3.0- to 4.1-fold increased activities for dimeric and trimeric GLP-1 compared to mono-GLP-1. The dual GLP-1/GIP receptor agonist exhibited balanced activity toward the GLP-1 receptor or the GIP receptor. All the proteins exhibited 1.8- to 3.0-fold prolonged half-lives in human serum compared to mono-GLP-1 or GIP. This study provides a generally applicable click biochemistry strategy for developing oligomeric or dual peptide/protein-based drug candidates.

Overexpressed Trophoblast Glycoprotein Contributes to Preeclampsia Development by Inducing Abnormal Trophoblast Migration and Invasion Toward the Uterine Spiral Artery.

BACKGROUND: Preeclampsia is a significant pregnancy disorder with an unknown cause, mainly attributed to impaired spiral arterial remodeling. METHODS: Using RNA sequencing, we identified key genes in placental tissues from healthy individuals and preeclampsia patients. Placenta and plasma samples from pregnant women were collected to detect the expression of TPBG (trophoblast glycoprotein). Pregnant rats were injected with TPBG-carrying adenovirus to detect preeclamptic features. HTR-8/SVneo cells transfected with a TPBG overexpression lentiviral vector were used in cell function experiments. The downstream molecular mechanisms of TPBG were explored using RNA sequencing and single-cell RNA sequencing data. TPBG expression was knocked down in the lipopolysaccharide-induced preeclampsia-like rat model to rescue the preeclampsia features. We also assessed TPBG's potential as an early preeclampsia predictor using clinical plasma samples. RESULTS: TPBG emerged as a crucial differentially expressed gene, expressed specifically in syncytiotrophoblasts and extravillous trophoblasts. Subsequently, we established a rat model with preeclampsia-like phenotypes by intravenously injecting TPBG-expressing adenoviruses, observing impaired spiral arterial remodeling, thus indicating a causal correlation between TPBG overexpression and preeclampsia. Studies with HTR-8/SVneo cells, chorionic villous explants, and transwell assays showed TPBG overexpression disrupts trophoblast/extravillous trophoblast migration/invasion and chemotaxis. Notably, TPBG knockdown alleviated the lipopolysaccharide-induced preeclampsia-like rat model. We enhanced preeclampsia risk prediction in early gestation by combining TPBG expression with established clinical predictors. CONCLUSIONS: These findings are the first to show that TPBG overexpression contributes to preeclampsia development by affecting uterine spiral artery remodeling. We propose TPBG levels in maternal blood as a predictor of preeclampsia risk. The proposed mechanism by which TPBG overexpression contributes to the occurrence of preeclampsia via its disruptive effect on trophoblast and extravillous trophoblast migration/invasion on uterine spiral artery remodeling, thereby increasing the risk of preeclampsia.

SAH is a major metabolic sensor mediating worsening metabolic crosstalk in metabolic syndrome.

In this study, we observed worsening metabolic crosstalk in mouse models with concomitant metabolic disorders such as hyperhomocysteinemia (HHcy), hyperlipidemia, and hyperglycemia and in human coronary artery disease by analyzing metabolic profiles. We found that HHcy worsening is most sensitive to other metabolic disorders. To identify metabolic genes and metabolites responsible for the worsening metabolic crosstalk, we examined mRNA levels of 324 metabolic genes in Hcy, glucose-related and lipid metabolic systems. We examined Hcy-metabolites (Hcy, SAH and SAM) by LS-ESI-MS/MS in 6 organs (heart, liver, brain, lung, spleen, and kidney) from C57BL/6J mice. Through linear regression analysis of Hcy-metabolites and metabolic gene mRNA levels, we discovered that SAH-responsive genes were responsible for most metabolic changes and all metabolic crosstalk mediated by Serine, Taurine, and G3P. SAH-responsive genes worsen glucose metabolism and cause upper glycolysis activation and lower glycolysis suppression, indicative of the accumulation of glucose/glycogen and G3P, Serine synthesis inhibition, and ATP depletion. Insufficient Serine due to negative correlation of PHGDH with SAH concentration may inhibit the folate cycle and transsulfurarion pathway and consequential reduced antioxidant power, including glutathione, taurine, NADPH, and NAD+. Additionally, we identified SAH-activated pathological TG loop as the consequence of increased fatty acid (FA) uptake, FA ß-oxidation and Ac-CoA production along with lysosomal damage. We concluded that HHcy is most responsive to other metabolic changes in concomitant metabolic disorders and mediates worsening metabolic crosstalk mainly via SAH-responsive genes, that organ-specific Hcy metabolism determines organ-specific worsening metabolic reprogramming, and that SAH, acetyl-CoA, Serine and Taurine are critical metabolites mediating worsening metabolic crosstalk, redox disturbance, hypomethylation and hyperacetylation linking worsening metabolic reprogramming in metabolic syndrome.

Enhancing the stability of a novel D-allulose 3-epimerase from Ruminococcus sp. CAG55 by interface interaction engineering and terminally attached a self-assembling peptide.

D-allulose, a highly desirable sugar substitute, is primarily produced using the D-allulose 3-epimerase (DAE). However, the availability of usable DAE enzymes is limited. In this study, we discovered and engineered a novel DAE Rum55, derived from a human gut bacterium Ruminococcus sp. CAG55. The activity of Rum55 was strictly dependent on the presence of Co2+, and it exhibited an equilibrium conversion rate of 30.6 % and a half-life of 4.5 h at 50 °C. To enhance its performance, we engineered the interface interaction of Rum55 to stabilize its tetramer structure, and the best variant E268R was then attached with a self-assembling peptide to form active enzyme aggregates as carrier-free immobilization. The half-life of the best variant E268R-EKL16 at 50 °C was dramatically increased 30-fold to 135.3 h, and it maintained 90 % of its activity after 13 consecutive reaction cycles. Additionally, we identified that metal ions played a key role in stabilizing the tetramer structure of Rum55, and the dependence on metal ions for E268R-EKL16 was significantly reduced. This study provides a useful route for improving the thermostability of DAEs, opening up new possibilities for the industrial production of D-allulose.

Take CT, get PET free: AI-powered breakthrough in lung cancer diagnosis and prognosis.

PET scans provide additional clinical value but are costly and not universally accessible. Salehjahromi et al.1 developed an AI-based pipeline to synthesize PET images from diagnostic CT scans, demonstrating its potential clinical utility across various clinical tasks for lung cancer.

Legumain-Triggered Macrocyclization of Radiofluorinated Tracer for Enhanced PET Imaging.

Enhancing the accumulation and retention of small-molecule probes in tumors is an important way to achieve accurate cancer diagnosis and therapy. Enzyme-stimulated macrocyclization of small molecules possesses great potential for enhanced positron emission tomography (PET) imaging of tumors. Herein, we reported an 18F-labeled radiotracer [18F]AlF-RSM for legumain detection in vivo. The tracer was prepared by a one-step aluminum-fluoride-restrained complexing agent ([18F]AlF-RESCA) method with high radiochemical yield (RCY) (88.35 ± 3.93%) and radiochemical purity (RCP) (>95%). More notably, the tracer can be transformed into a hydrophobic macrocyclic molecule under the joint action of legumain and reductant. Simultaneously, the tracer could target legumain-positive tumors and enhance accumulation and retention in tumors, resulting in the amplification of PET imaging signals. The enhancement of radioactivity enables PET imaging of legumain activity with high specificity. We envision that, by combining this highly efficient 18F-labeled strategy with our intramolecular macrocyclization reaction, a range of radiofluorinated tracers can be designed for tumor PET imaging and early cancer diagnosis in the future.

DNA-functionalized cryogel based colorimetric biosensor for sensitive on-site detection of aflatoxin B1 in food samples.

Hydrogel biosensors present numerous advantages in food safety analysis owing to their remarkable biocompatibility, cargo-loading capabilities and optical properties. However, the current drawbacks (slow target responsiveness and poor mechanical strength) restricted their further utilization at on-site detection of targets. To address these challenges, a DNA-functionalized cryogel with hierarchical pore structures is constructed to improve the reaction rate and the robustness of hydrogel biosensor. During cryogel preparation, ice crystals serve as templates, shaping interconnected hierarchical microporous structures to enhance mass transfer for faster responses. Meanwhile, in the non-freezing zone, concentrated monomers create a dense cross-linked network, strengthening cryogel matrix strength. Accordingly, a colorimetric biosensor based on DNA cryogel has been developed as a proof of concept for rapid detection of aflatoxin B1 (AFB1) in food samples, and an excellent analytical performance was obtained under the optimized conditions with a low detection limit (1 nM), broad detection range (5-100 nM), satisfactory accuracy and precision (recoveries, 81.2-112.6 %; CV, 2.75-5.53 %). Furthermore, by integrating with a smartphone sensing platform, a portable device was created for rapid on-site measurement of target within 45 min, which provided some insight for hydrogel biosensors design.

Achieving robust synthetic tolerance in industrial E. coli through negative auto-regulation of a DsrA-Hfq module.

In industrial fermentation processes, microorganisms often encounter acid stress, which significantly impact their productivity. This study focused on the acid-resistant module composed of small RNA (sRNA) DsrA and the sRNA chaperone Hfq. Our previous study had shown that this module improved the cell growth of Escherichia coli MG1655 at low pH, but failed to obtain this desired phenotype in industrial strains. Here, we performed a quantitative analysis of DsrA-Hfq module to determine the optimal expression mode. We then assessed the potential of the CymR-based negative auto-regulation (NAR) circuit for industrial application, under different media, strains and pH levels. Growth assay at pH 4.5 revealed that NAR-05D04H circuit was the best acid-resistant circuit to improve the cell growth of E. coli MG1655. This circuit was robust and worked well in the industrial lysine-producing strain E. coli SCEcL3 at a starting pH of 6.8 and without pH control, resulting in a 250 % increase in lysine titer and comparable biomass in shaking flask fermentation compared to the parent strain. This study showed the practical application of NAR circuit in regulating DsrA-Hfq module, effectively and robustly improving the acid tolerance of industrial strains, which provides a new approach for breeding industrial strains with tolerance phenotype.

Automatic Detection of Scalp High-Frequency Oscillations Based on Deep Learning.

Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have difficulty with accurately eliminating artifacts and thus do not provide sufficient reliability to meet clinical needs. Therefore, we propose a high-performance sHFOs detector based on a deep learning algorithm. An initial detection module was designed to extract candidate high-frequency oscillations. Then, one-dimensional (1D) and two-dimensional (2D) deep learning models were designed, respectively. Finally, the weighted voting method is used to combine the outputs of the two model. In experiments, the precision, recall, specificity and F1-score were 83.44%, 83.60%, 96.61% and 83.42%, respectively, on average and the kappa coefficient was 80.02%. In addition, the proposed detector showed a stable performance on multi-centre datasets. Our sHFOs detector demonstrated high robustness and generalisation ability, which indicates its potential applicability as a clinical assistance tool. The proposed sHFOs detector achieves an accurate and robust method via deep learning algorithm.

High-resolution 3T to 7T ADC map synthesis with a hybrid CNN-transformer model.

BACKGROUND: 7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts. PURPOSE: To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multimodal 3T MRI. METHODS: The Vision CNN-Transformer (VCT), composed of both Vision Transformer (ViT) blocks and convolutional layers, is proposed to produce high-resolution synthetic 7T ADC maps from 3T ADC maps and 3T T1-weighted (T1w) MRI. ViT blocks enabled global image context while convolutional layers efficiently captured fine detail. The VCT model was validated on the publicly available Human Connectome Project Young Adult dataset, comprising 3T T1w, 3T DWI, and 7T DWI brain scans. The Diffusion Imaging in Python library was used to compute ADC maps from the DWI scans. A total of 171 patient cases were randomly divided into 130 training cases, 20 validation cases, and 21 test cases. The synthetic ADC maps were evaluated by comparing their similarity to the ground truth volumes with the following metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). In addition, RESULTS: The results are as follows: PSNR: 27.0 ± 0.9 dB, SSIM: 0.945 ± 0.010, and MSE: 2.0E-3 ± 0.4E-3. Both qualitative and quantitative results demonstrate that VCT performs favorably against other state-of-the-art methods. We have introduced various efficiency improvements, including the implementation of flash attention and training on 176×208 resolution images. These enhancements have resulted in the reduction of parameters and training time per epoch by 50% in comparison to ResViT. Specifically, the training time per epoch has been shortened from 7.67 min to 3.86 min. CONCLUSION: We propose a novel method to predict high-resolution 7T ADC maps from low-resolution 3T ADC maps and T1w MRI. Our predicted images demonstrate better spatial resolution and contrast compared to 3T MRI and prediction results made by ResViT and pix2pix. These high-quality synthetic 7T MR images could be beneficial for disease diagnosis and intervention, producing higher resolution and conformal contours, and as an intermediate step in generating synthetic CT for radiation therapy, especially when 7T MRI scanners are unavailable.

Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI).

Background: Magnetic resonance imaging (MRI) is a primary non-invasive imaging modality for tumor segmentation, leveraging its exceptional soft tissue contrast and high resolution. Current segmentation methods typically focus on structural MRI, such as T1-weighted post-contrast-enhanced or fluid-attenuated inversion recovery (FLAIR) sequences. However, these methods overlook the blood perfusion and hemodynamic properties of tumors, readily derived from dynamic susceptibility contrast (DSC) enhanced MRI. This study introduces a novel hybrid method combining density-based analysis of hemodynamic properties in time-dependent perfusion imaging with deep learning spatial segmentation techniques to enhance tumor segmentation. Methods: First, a U-Net convolutional neural network (CNN) is employed on structural images to delineate a region of interest (ROI). Subsequently, Hierarchical Density-Based Scans (HDBScan) are employed within the ROI to augment segmentation by exploring intratumoral hemodynamic heterogeneity through the investigation of tumor time course profiles unveiled in DSC MRI. Results: The approach was tested and evaluated using a cohort of 513 patients from the open-source University of Pennsylvania glioblastoma database (UPENN-GBM) dataset, achieving a 74.83% Intersection over Union (IoU) score when compared to structural-only segmentation. The algorithm also exhibited increased precision and localized predictions of heightened segmentation boundary complexity, resulting in a 146.92% increase in contour complexity (ICC) compared to the reference standard provided by the UPENN-GBM dataset. Importantly, segmenting tumors with the developed new approach uncovered a negative correlation of the tumor volume with the scores in the Karnofsky Performance Scale (KPS) clinically used for assessing the functional status of patients (-0.309), which is not observed with the prevailing segmentation standard. Conclusions: This work demonstrated that including hemodynamic properties of tissues from DSC MRI can improve existing structural or morphological feature-based tumor segmentation techniques with additional information on tumor biology and physiology. This approach can also be applied to other clinical indications that use perfusion MRI for diagnosis or treatment monitoring.

Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.

PURPOSE: Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. METHODS: We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), multi-scale structure similarity index (SSIM), normalized cross-correlation (NCC), and clinical evaluation including Human Ranking Score (HRS) and Standardized Uptake Value (SUV) Error analysis shows its superiority in synthesizing full-dose PET images from low-dose inputs. RESULTS: In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278 ± 0.122%, PSNR of 33.783 ± 0.824 dB, SSIM of 0.964 ± 0.009, NCC of 0.968 ± 0.011, HRS of 4.543, and SUV Error of 0.255 ± 0.318%, with an average generation time of 62 s per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12× faster. Similarly, in the quarter-dose to full-dose image experiments, PET-CM delivered competitive outcomes, achieving an NMAE of 0.973 ± 0.066%, PSNR of 36.172 ± 0.801 dB, SSIM of 0.984 ± 0.004, NCC of 0.990 ± 0.005, HRS of 4.428, and SUV Error of 0.151 ± 0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario. CONCLUSIONS: We propose PET-CM, the first efficient diffusion-model-based method, for estimating full-dose PET images from low-dose images. PET-CM provides comparable quality to the state-of-the-art diffusion model with higher efficiency. By utilizing this approach, it becomes possible to maintain high-quality PET images suitable for clinical use while mitigating the risks associated with radiation. The code is availble at https://github.com/shaoyanpan/Full-dose-Whole-body-PET-Synthesis-from-Low-dose-PET-Using-Consistency-Model.

Thienopyrimidine-derived multifunctional fluorescence sensor for the detection of Cu2+, Fe3+, and PPi in different solvents.

Hydrazine substituted thienopyrimidine, a new fluorophore, was used to synthesize a novel Schiff base R1 as a chemosensor via the condensation with p-formyltriphenylamine, and the structure was confirmed using nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) analysis. When treated with Cu2+ in dimethylsulfoxide (DMSO)/H2O buffer, R1 showed a phenomenon of fluorescence quenching, which was reversible with the action of ethylenediaminetetraacetic acid (EDTA). When treated with Fe3+ in dimethylformamide (DMF)/H2O buffer, R1 exhibited the same phenomenon, but fluorescence was recovered with inorganic pyrophosphate (PPi) quantitatively. The complexation ratios for R1-Cu2+ and R1-Fe3+ were both 1:2, which were manifested by MS titrations and corresponding Job's plots. The limits of detection of R1 for Cu2+ and Fe3+ were 3.11 × 10-8 and 1.24 × 10-7 M, respectively. The sensing mechanism of R1 toward Cu2+ and Fe3+ was confirmed using density functional theory calculations and electrostatic potential analysis. Test strips of R1 were fabricated successfully for on-site detection of Cu2+ and Fe3+. In addition, R1 was applied to recognize Cu2+ and Fe3+ in actual water samples with satisfactory recovery.

The Transcription Factor Zfp335 Promotes Differentiation and Survival of Effector Th1 Cells by Directly Regulating Lmna Expression.

Ag-specific effector CD4+ T cells play a crucial role in defending against exogenous pathogens. However, the mechanisms governing the differentiation and function of IFN-γ-producing effector CD4+ Th1 cells in immune responses remain largely unknown. In this study, we elucidated the pivotal role of zinc finger protein 335 (Zfp335) in regulating effector Th1 cell differentiation and survival during acute bacterial infection. Mice with Zfp335 knockout in OT-II cells exhibited impaired Ag-specific CD4+ T cell expansion accompanied by a significant reduction in resistance to Listeria infection. Furthermore, Zfp335 deficiency restricted the effector CD4+ Th1 cell population and compromised their survival upon Listeria challenge. The expression of T-bet and IFN-γ was accordingly decreased in Zfp335-deficient Th1 cells. Mechanistically, Zfp335 directly bound to the promoter region of the Lmna gene and regulated its expression. Overexpression of Lmna was able to rescue the survival and function of Zfp335-deficient effector Th1 cells. Therefore, our study provides novel insights into the mechanisms governing effector Th1 cell differentiation and survival during acute infection.