Structurally diverse stilbenes from Gnetum parvifolium and their anti-neuroinflammatory activities

Phytochemical investigation on the aerial parts of Gnetum parvifolium led to the isolation of 15 new and eight known structurally diverse stilbenes. The isolated compounds comprised (E)- or (Z)-stilbene (1-6, 15-20), dihydrostilbene (21), phenylbenzofuran (7, 8, 22), benzylated stilbene (9-11), benzylated stilbene dimer (12), and nitrogen-containing stilbene (13a, 13b, 14) types. The structures of the new compounds (1-12, 13a, 13b, 14) were established through spectroscopic analyses and experimental and calculated ECD data. Compound 12 is the first stilbene dimer connected through a benzyl group. In the anti-neuroinflammatory activity assay, compounds 4, 5, 9-11, 13b, and 16-21 displayed significant inhibitory effects against LPS-induced NO release in BV-2 microglial cells, with IC50 values of 0.35-16.1 μM. Compound 10 had the most potent activity (IC50 = 0.35 μM), and the further research indicated that it could decrease the mRNA levels of iNOS, IL-1β, IL-6, and TNF-α in a dose-dependent manner.

Ability of dynamic gadoxetic acid-enhanced magnetic resonance imaging combined with water-specific T1 mapping to reflect inflammation in a rat model of early-stage nonalcoholic steatohepatitis

Background

Gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA) has shown potential in reflecting the hepatic function alterations in nonalcoholic steatohepatitis (NASH). The purpose of this study was to evaluate whether Gd-EOB-DTPA combined with water-specific T1 (wT1) mapping can be used to detect liver inflammation in the early-stage of NASH in rats.

Methods

In this study, 54 rats with methionine- and choline-deficient (MCD) diet-induced NASH and 10 normal control rats were examined. A multiecho variable flip angle gradient echo (VFA-GRE) sequence was performed and repeated 40 times after the injection of Gd-EOB-DTPA. The wT1 of the liver and the reduction rate of wT1 (rrT1) were calculated. All rats were histologically evaluated and grouped according to the NASH Clinical Research Network scoring system. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect the expression of Gd-EOB-DTPA transport genes. Analysis of variance and least significant difference tests were used for multiple comparisons of quantitative results between all groups. Multiple regression analysis was applied to identify variables associated with precontrast wT1 (wT1pre), and receiver operating characteristic (ROC) analysis was performed to assess the diagnostic performance.

Results

The rats were grouped according to inflammatory stage (G0 =4, G1 =15, G2 =12, G3 =23) and fibrosis stage (F0 =26, F1 =19, F2 =9). After the infusion of Gd-EOB-DTPA, the rrT1 showed significant differences between the control and NASH groups (P<0.05) but no difference between the different inflammation and fibrosis groups at any time points. The areas under curve (AUCs) of rrT1 at 10, 20, and 30 minutes were only 0.53, 0.58, and 0.61, respectively, for differentiating between low inflammation grade (G0 + G1) and high inflammation grade (G2 + G3). The MRI findings were verified by qRT-PCR examination, in which the Gd-EOB-DTPA transporter expressions showed no significant differences between any inflammation groups.

Conclusions

The wT1 mapping quantitative method combined with Gd-EOB-DTPA was not capable of discerning the inflammation grade in a rat model of early-stage NASH.

JAK/STAT in leukemia: a clinical update

Over the past three decades, considerable efforts have been expended on understanding the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway in leukemia, following the identification of the JAK2V617F mutation in myeloproliferative neoplasms (MPNs). The aim of this review is to summarize the latest progress in our understanding of the involvement of the JAK/STAT signaling pathway in the development of leukemia. We also attempt to provide insights into the current use of JAK/STAT inhibitors in leukemia therapy and explore pertinent clinical trials in this field.

The effect of FOXP3 genetic polymorphisms on correlations with hepatitis B virus-hepatocellular carcinoma: A case-control study

Background

Previous studies have reported that transcription factor forkhead box protein 3 (FOXP3) polymorphisms are correlated with the progress of some cancers, but the relationships between the FOXP3 polymorphisms and hepatocellular carcinoma (HCC) risk remain unclear.

Method

Genotypes were detected in156 hepatitis B virus (HBV)-HCC patients, 109 HBV-liver cirrhosis (LC) patients, 125 chronic hepatitis B (CHB) patients, and 188 healthy controls. The FOXP3 rs3761547 and rs3761548 polymorphisms were genotyped by polymerase chain reaction (PCR) combined with restriction fragment length polymorphism, and the rs2232365 polymorphism was genotyped using PCR with sequence-specific primers.

Results

We did not obtain any significant results with the FOXP3 rs3761547, rs3761548, and rs2232365 polymorphisms in groups of patients compared to healthy controls (all p > 0.05), no matter the overall group or subgroup.

Conclusions

Our findings suggest that the FOXP3 polymorphisms at rs3761547, rs3761548, and rs2232365 were not related to HBV-HCC risk in the Chinese population.

Rapid capture and quantification of food-borne spores based on the double-enhanced Fe3O4@PEI@Ag@PEI core-shell structure SERS sensor

To realize rapid capture and quantification of food-borne spores and prevent their potential harm, Fe3O4@PEI@Ag@PEI core-shell structure nanoparticles were combined with flower-like AgNPs for double enhancement and efficient capture of spores. The developed sensor showed excellent reproducibility and SERS enhancement factor (AEF) is 4.6 × 104. Orthogonal partial least-squares discrimination analysis and linear discriminant analysis accurately identified the three spores (Bacillus subtilis, Bacillus cereus, and Clostridium perfringens), and the qualitative identification accuracy of linear discriminant analysis was 100 %. Efficient enrichment of B. subtilis spores was realized within 5 min, with a detection limit of 3 cfu/mL. Spiked tests revealed that this sensor was effective in detecting spores in milk, orange juice, and water samples, with recovery ratio of 95.2-103.9 % and relative standard deviation of 3.1-7.7 %. Thus, the developed sensor was accurate and reliable, and could achieve rapid identification and quantitative detection of food-borne spores.

An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images

Objective.Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, the high-density distribution of nuclei, and cell adhesion.Approach.In this paper, we present an interactive nuclei segmentation framework that increases the precision of nuclei segmentation. Our framework incorporates expert monitoring to gather as much prior information as possible and accurately segment complex nucleus images through limited pathologist interaction, where only a small portion of the nucleus locations in each image are labeled. The initial contour is determined by the Voronoi diagram generated from the labeled points, which is then input into an optimized weighted convex difference model to regularize partition boundaries in an image. Specifically, we provide theoretical proof of the mathematical model, stating that the objective function monotonically decreases. Furthermore, we explore a postprocessing stage that incorporates histograms, which are simple and easy to handle and prevent arbitrariness and subjectivity in individual choices.Main results.To evaluate our approach, we conduct experiments on both a cervical cancer dataset and a nasopharyngeal cancer dataset. The experimental results demonstrate that our approach achieves competitive performance compared to other methods.Significance.The Voronoi diagram in the paper serves as prior information for the active contour, providing positional information for individual cells. Moreover, the active contour model achieves precise segmentation results while offering mathematical interpretability.

Abscisic acid promotes selenium absorption, metabolism and toxicity via stress-related phytohormones regulation in Cyphomandra betacea Sendt. (Solanum betaceum Cav.)

High levels of selenium (Se) uptakes negatively affect plant growth. In this study, the possible molecular mechanism for the effects of abscisic acid (ABA) on Se absorption, metabolism and toxicity in Cyphomandra betacea Sendt. (Solanum betaceum Cav.) young plants were investigated. Se+ABA treatment promoted significant Se absorption in C. betacea while impeding plant growth as compared to Se treatment. The expression levels of sulfate/phosphate transporter protein genes indicated that Se+ABA triggered more S/Se absorption and transportation into chloroplast. Furthermore, Se+ABA promoted higher metabolisms of inorganic sulfur (S)/Se and organic S/Se. The organic Se might be in several forms (SeCysth, SeCys and SeMet) in Se+ABA treatment, whereas SeCysth was the major organic form in Se treatment. More reactive oxygen species production was suggested in Se+ABA treatment from a series of genes involved in antioxidant enzymes and molecules, including superoxide dismutase, peroxiredoxin, glutathione sulfur-transferase and glutathione. Se+ABA further improved the expression levels of genes involved in biosynthesis and signaling transduction genes involved in stress-related phytohormones (jasmonic acid and salicylic acid). Combining with the data in ABA treatment, we hypothesized a model that ABA might first affect the biosynthesis and signaling transduction pathways of stress-related phytohormones, and subsequently altered the metabolic processes responding to Se stress.

Selective purification and rapid quantitative detection of spores using a "stepped" magnetic flow device

A study on the inactivation and germination mechanism of spores is very important in the application of spores, as such high-purity spores are the basis of related research. However, spores and vegetative cells of bacteria often coexist, and it is difficult to separate them. In this study, a magnetic flow device for the purification of spores in the culture medium system was developed based on a "stepped" structure with a magnetic force that could absorb vegetative cells with magnetic nanoparticles. The operation process was as follows: first, vancomycin functionalized nanoparticles were used to prepare Van-Fe3O4 NPs, which were then combined with vegetative cells to form a magnetic conjugate. Subsequently, the magnetic conjugate (vegetative cells) flowed through the "stepped" magnetic flow device and was adsorbed. Meanwhile, the spores moved through the channel and were collected. The achieved purity of the collected spores was more than 95%. Further, the number of the obtained spores was quickly quantified using Raman spectroscopy. The entire purification and quantitative process can be completed within 30 min and the limit of detection was 5 CFU mL-1. This study showed outstanding spore purification ability and provided a new method for purification and rapid quantitative detection of spores.

Transferring U-Net between low-dose CT denoising tasks: a validation study with varied spatial resolutions

Background

Recently, deep learning techniques have been widely used in low-dose computed tomography (LDCT) imaging applications for quickly generating high quality computed tomography (CT) images at lower radiation dose levels. The purpose of this study is to validate the reproducibility of the denoising performance of a given network that has been trained in advance across varied LDCT image datasets that are acquired from different imaging systems with different spatial resolutions.

Methods

Specifically, LDCT images with comparable noise levels but having different spatial resolutions were prepared to train the U-Net. The number of CT images used for the network training, validation and test was 2,400, 300 and 300, respectively. Afterwards, self- and cross-validations among six selected spatial resolutions (62.5, 125, 250, 375, 500, 625 µm) were studied and compared side by side. The residual variance, peak signal to noise ratio (PSNR), normalized root mean square error (NRMSE) and structural similarity (SSIM) were measured and compared. In addition, network retraining on a small number of image set was performed to fine tune the performance of transfer learning among LDCT tasks with varied spatial resolutions.

Results

Results demonstrated that the U-Net trained upon LDCT images having a certain spatial resolution can effectively reduce the noise of the other LDCT images having different spatial resolutions. Regardless, results showed that image artifacts would be generated during the above cross validations. For instance, noticeable residual artifacts were presented at the margin and central areas of the object as the resolution inconsistency increased. The retraining results showed that the artifacts caused by the resolution mismatch can be greatly reduced by utilizing about only 20% of the original training data size. This quantitative improvement led to a reduction in the NRMSE from 0.1898 to 0.1263 and an increase in the SSIM from 0.7558 to 0.8036.

Conclusions

In conclusion, artifacts would be generated when transferring the U-Net to a LDCT denoising task with different spatial resolution. To maintain the denoising performance, it is recommended to retrain the U-Net with a small amount of datasets having the same target spatial resolution.

Reducing pediatric total-body PET/CT imaging scan time with multimodal artificial intelligence technology

OBJECTIVES

This study aims to decrease the scan time and enhance image quality in pediatric total-body PET imaging by utilizing multimodal artificial intelligence techniques.

METHODS

A total of 270 pediatric patients who underwent total-body PET/CT scans with a uEXPLORER at the Sun Yat-sen University Cancer Center were retrospectively enrolled. 18F-fluorodeoxyglucose (18F-FDG) was administered at a dose of 3.7 MBq/kg with an acquisition time of 600 s. Short-term scan PET images (acquired within 6, 15, 30, 60 and 150 s) were obtained by truncating the list-mode data. A three-dimensional (3D) neural network was developed with a residual network as the basic structure, fusing low-dose CT images as prior information, which were fed to the network at different scales. The short-term PET images and low-dose CT images were processed by the multimodal 3D network to generate full-length, high-dose PET images. The nonlocal means method and the same 3D network without the fused CT information were used as reference methods. The performance of the network model was evaluated by quantitative and qualitative analyses.

RESULTS

Multimodal artificial intelligence techniques can significantly improve PET image quality. When fused with prior CT information, the anatomical information of the images was enhanced, and 60 s of scan data produced images of quality comparable to that of the full-time data.

CONCLUSION

Multimodal artificial intelligence techniques can effectively improve the quality of pediatric total-body PET/CT images acquired using ultrashort scan times. This has the potential to decrease the use of sedation, enhance guardian confidence, and reduce the probability of motion artifacts.

The role of dynamic, static, and delayed total-body PET imaging in the detection and differential diagnosis of oncological lesions

OBJECTIVES

Commercialized total-body PET scanners can provide high-quality images due to its ultra-high sensitivity. We compared the dynamic, regular static, and delayed 18F-fluorodeoxyglucose (FDG) scans to detect lesions in oncologic patients on a total-body PET/CT scanner.

MATERIALS & METHODS

In all, 45 patients were scanned continuously for the first 60 min, followed by a delayed acquisition. FDG metabolic rate was calculated from dynamic data using full compartmental modeling, whereas regular static and delayed SUV images were obtained approximately 60- and 145-min post-injection, respectively. The retention index was computed from static and delayed measures for all lesions. Pearson's correlation and Kruskal-Wallis tests were used to compare parameters.

RESULTS

The number of lesions was largely identical between the three protocols, except MRFDG and delayed images on total-body PET only detected 4 and 2 more lesions, respectively (85 total). FDG metabolic rate (MRFDG) image-derived contrast-to-noise ratio and target-to-background ratio were significantly higher than those from static standardized uptake value (SUV) images (P < 0.01), but this is not the case for the delayed images (P > 0.05). Dynamic protocol did not significantly differentiate between benign and malignant lesions just like regular SUV, delayed SUV, and retention index.

CONCLUSION

The potential quantitative advantages of dynamic imaging may not improve lesion detection and differential diagnosis significantly on a total-body PET/CT scanner. The same conclusion applied to delayed imaging. This suggested the added benefits of complex imaging protocols must be weighed against the complex implementation in the future.

CLINICAL RELEVANCE

Total-body PET/CT was known to significantly improve the PET image quality due to its ultra-high sensitivity. However, whether the dynamic and delay imaging on total-body scanner could show additional clinical benefits is largely unknown. Head-to-head comparison between two protocols is relevant to oncological management.

A slow-release fertilizer containing cyhalofop-butyl reduces N2O emissions by slowly releasing nitrogen and down-regulating the relative abundance of nirK

To simplify the process of the application of fertilizers and herbicides for farmers, a slow-release fertilizer containing cyhalofop-butyl (SFC) was developed to prolong the combined effect of the herbicide-fertilizer and achieve a synergistic effect on weeding and reducing N2O emissions. A greenhouse pot experiment was conducted using five treatments: CK (no fertilizer), CF (compound fertilizers), FC (fertilizers combined with cyhalofop-butyl), FF (film-coated compound fertilizers), and SFC (a slow-release fertilizer containing cyhalofop-butyl). The findings indicated that SFC exhibited the lowest N2O emissions, the highest paddy yield, and the highest nitrogen utilization rate among all the treatments. When compared to CF, the nitrogen release was notably delayed, leading to a significant reduction in cumulative N2O emissions under FF and SFC. When compared to CF, N2O emissions under FC were significantly decreased, suggesting that cyhalofop-butyl exerted a reduction role in N2O emissions. The SFC-treated nirK abundance was significantly lower than FF and FC, suggesting that the cyhalofop-butyl of SFC interacted with film of SFC inhibited the denitrification process in the paddy soil. Thus, the SFC reduced N2O emissions by slowing nitrogen release and down-regulating the relative abundance of nirK.

Human breastmilk-derived Bifidobacterium longum subsp. infantis CCFM1269 regulates bone formation by the GH/IGF axis through PI3K/AKT pathway

Bifidobacterium longum subsp. infantis is a prevalent member of the gut microbiota of breastfed infants. In this study, the effects of human breastmilk-derived B.longum subsp. infantis CCFM1269 on bone formation in developing BALB/c mice were investigated. Newborn female and male mice were assigned to control group (administered saline), CCFM11269 group (administered B. longum subsp. infantis CCFM1269, 1 × 109 CFU/mouse/day) and I5TI group (administered B. longum subsp. infantis I5TI, 1 × 109 CFU/mouse/day) from 1-week-old to 3-, 4- and 5-week old. B. longum subsp. infantis I5TI served as a negative control in this study. The results demonstrated that B. longum subsp. infantis CCFM1269 promoted bone formation in growing mice by modulating the composition of the gut microbiota and metabolites. The expression of genes and proteins in the PI3K/AKT pathway was stimulated by B. longum subsp. infantis CCFM1269 through the GH/IGF-1 axis in growing mice. This finding suggests B. longum subsp. infantis CCFM1269 may be useful for modulating bone metabolism during growth.

Fast and robust pulsed chemical exchange saturation transfer (CEST) MRI using a quasi-steady-state (QUASS) algorithm at 3 T

Chemical exchange saturation transfer (CEST) has emerged as a powerful technique to image dilute labile protons. However, its measurement depends on the RF saturation duration (Tsat) and relaxation delay (Trec). Although the recently developed quasi-steady-state (QUASS) solution can reconstruct equilibrium CEST effects under continuous-wave RF saturation, it does not apply to pulsed-CEST MRI on clinical scanners with restricted hardware or specific absorption rate limits. This study proposed a QUASS algorithm for pulsed-CEST MRI and evaluated its performance in muscle CEST measurement. An approximated expression of a steady-state pulsed-CEST signal was incorporated in the off-resonance spin-lock model, from which the QUASS pulsed-CEST effect was derived. Numerical simulation, creatine phantom, and healthy volunteer scans were conducted at 3 T. The CEST effect was quantified with asymmetry analysis in the simulation and phantom experiments. CEST effects of creatine, amide proton transfer, phosphocreatine, and combined magnetization transfer and nuclear Overhauser effects were isolated from a multi-pool Lorentzian model in muscles. Apparent and QUASS CEST measurements were compared under different Tsat/Trec and duty cycles. Paired Student's t-test was employed with P < 0.05 as statistically significant. The simulation, phantom, and human studies showed the strong impact of Tsat/Trec on apparent CEST measurements, which were significantly smaller than the corresponding QUASS CEST measures, especially under short Tsat/Trec times. In comparison, the QUASS algorithm mitigates such impact and enables accurate CEST measurements under short Tsat/Trec times. In conclusion, the QUASS algorithm can accelerate robust pulsed-CEST MRI, promising the efficient detection and evaluation of muscle diseases in clinical settings.

Deep Generalized Learning Model for PET Image Reconstruction

Low-count positron emission tomography (PET) imaging is challenging because of the ill-posedness of this inverse problem. Previous studies have demonstrated that deep learning (DL) holds promise for achieving improved low-count PET image quality. However, almost all data-driven DL methods suffer from fine structure degradation and blurring effects after denoising. Incorporating DL into the traditional iterative optimization model can effectively improve its image quality and recover fine structures, but little research has considered the full relaxation of the model, resulting in the performance of this hybrid model not being sufficiently exploited. In this paper, we propose a learning framework that deeply integrates DL and an alternating direction of multipliers method (ADMM)-based iterative optimization model. The innovative feature of this method is that we break the inherent forms of the fidelity operators and use neural networks to process them. The regularization term is deeply generalized. The proposed method is evaluated on simulated data and real data. Both the qualitative and quantitative results show that our proposed neural network method can outperform partial operator expansion-based neural network methods, neural network denoising methods and traditional methods.

Three new phenolic glycosides and a new lignan glycoside from Gaultheria leucocarpa var. yunnanensis

Three new phenolic glycosides (1-3) and a new lignan glycoside (4), together with five known compounds (5-9) were isolated from the ethanol extract of the aerial part of Gaultheria leucocarpa var. yunnanensis (Franch.) T.Z.Hsu & R.C.Fang. Their structures were determined on the basis of spectroscopic techniques, experimental and calculated ECD spectra, acid hydrolysis, and enzymatic hydrolysis experiments. All the isolates were evaluated for their anti-inflammatory and antioxidant activities. Compounds 7 and 8 exhibited inhibitory effects against the LPS-induced production of NO with IC50 of 63.71 and 10.66 μM, respectively, compared to L-NMMA having an IC50 of 6.95 μM. Besides, compound 7 also represented significant DPPH radical scavenging activity with EC50 of 18.75 μM, comparable with vitamin C (EC50 = 15.77 μM).

Performance evaluation of quantitative material decomposition in slow kVp switching dual-energy CT

BACKGROUND

Slow kVp switching technique is an important approach to realize dual-energy CT (DECT) imaging, but its performance has not been thoroughly investigated yet.

OBJECTIVE

This study aims at comparing and evaluating the DECT imaging performance of different slow kVp switching protocols, and thus helps determining the optimal system settings.

METHODS

To investigate the impact of energy separation, two different beam filtration schemes are compared: the stationary beam filtration and dynamic beam filtration. Moreover, uniform tube voltage modulation and weighted tube voltage modulation are compared along with various modulation frequencies. A model-based direct decomposition algorithm is employed to generate the water and iodine material bases. Both numerical and physical experiments are conducted to verify the slow kVp switching DECT imaging performance.

RESULTS

Numerical and experimental results demonstrate that the material decomposition is less sensitive to beam filtration, voltage modulation type and modulation frequency. As a result, robust material-specific quantitative decomposition can be achieved in slow kVp switching DECT imaging.

CONCLUSIONS

Quantitative DECT imaging can be implemented with slow kVp switching under a variety of system settings.

Development and performance of SIAT bPET: a high-resolution and high-sensitivity MR-compatible brain PET scanner using dual-ended readout detectors

PURPOSE

Positron emission tomography/magnetic resonance imaging (PET/MRI) is a powerful tool for brain imaging, but the spatial resolution of the PET scanners currently used for brain imaging can be further improved to enhance the quantitative accuracy of brain PET imaging. The purpose of this study is to develop an MR-compatible brain PET scanner that can simultaneously achieve a uniform high spatial resolution and high sensitivity by using dual-ended readout depth encoding detectors.

METHODS

The MR-compatible brain PET scanner, named SIAT bPET, consists of 224 dual-ended readout detectors. Each detector contains a 26 × 26 lutetium yttrium oxyorthosilicate (LYSO) crystal array of 1.4 × 1.4 × 20 mm3 crystal size read out by two 10 × 10 silicon photomultiplier (SiPM) arrays from both ends. The scanner has a detector ring diameter of 376.8 mm and an axial field of view (FOV) of 329 mm. The performance of the scanner including spatial resolution, sensitivity, count rate, scatter fraction, and image quality was measured. Imaging studies of phantoms and the brain of a volunteer were performed. The mutual interferences of the PET insert and the uMR790 3 T MRI scanner were measured, and simultaneous PET/MRI imaging of the brain of a volunteer was performed.

RESULTS

A spatial resolution of better than 1.5 mm with an average of 1.2 mm within the whole FOV was obtained. A sensitivity of 11.0% was achieved at the center FOV for an energy window of 350-750 keV. Except for the dedicated RF coil, which caused a ~ 30% reduction of the sensitivity of the PET scanner, the MRI sequences running had a negligible effect on the performance of the PET scanner. The reduction of the SNR and homogeneity of the MRI images was less than 2% as the PET scanner was inserted to the MRI scanner and powered-on. High quality PET and MRI images of a human brain were obtained from simultaneous PET/MRI scans.

CONCLUSION

The SIAT bPET scanner achieved a spatial resolution and sensitivity better than all MR-compatible brain PET scanners developed up to date. It can be used either as a standalone brain PET scanner or a PET insert placed inside a commercial whole-body MRI scanner to perform simultaneous PET/MRI imaging.

Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions

OBJECTIVES

To propose a novel model-free data-driven approach based on the voxel-wise mapping of DCE-MRI time-intensity-curve (TIC) profiles for quantifying and visualizing hemodynamic heterogeneity and to validate its potential clinical applications.

MATERIALS AND METHODS

From December 2018 to July 2022, 259 patients with 325 pathologically confirmed breast lesions who underwent breast DCE-MRI were retrospectively enrolled. Based on the manually segmented breast lesions, the TIC of each voxel within the 3D whole lesion was classified into 19 subtypes based on wash-in rate (nonenhanced, slow, medium, and fast), wash-out enhancement (persistent, plateau, and decline), and wash-out stability (steady and unsteady), and the composition ratio of these 19 subtypes for each lesion was calculated as a new feature set (type-19). The three-type TIC classification, semiquantitative parameters, and type-19 features were used to build machine learning models for identifying lesion malignancy and classifying histologic grades, proliferation status, and molecular subtypes.

RESULTS

The type-19 feature-based model significantly outperformed models based on the three-type TIC method and semiquantitative parameters both in distinguishing lesion malignancy (respectively; AUC = 0.875 vs. 0.831, p = 0.01 and 0.875vs. 0.804, p = 0.03), predicting tumor proliferation status (AUC = 0.890 vs. 0.548, p = 0.006 and 0.890 vs. 0.596, p = 0.020), but not in predicting histologic grades (p = 0.820 and 0.970).

CONCLUSION

In addition to conventional methods, the proposed computational approach provides a novel, model-free, data-driven approach to quantify and visualize hemodynamic heterogeneity.

CLINICAL RELEVANCE STATEMENT

Voxel-wise intra-lesion mapping of TIC profiles allows for visualization of hemodynamic heterogeneity and its composition ratio for differentiation of malignant and benign breast lesions.

KEY POINTS

• Voxel-wise TIC profiles were mapped, and their composition ratio was compared between various breast lesions. • The model based on the composition ratio of voxel-wise TIC profiles significantly outperformed the three-type TIC classification model and the semiquantitative parameters model in lesion malignancy differentiation and tumor proliferation status prediction in breast lesions. • This novel, data-driven approach allows the intuitive visualization and quantification of the hemodynamic heterogeneity of breast lesions.

Super-resolution dual-layer CBCT imaging with model-guided deep learning

Objective.This study aims at investigating a novel super resolution CBCT imaging approach with a dual-layer flat panel detector (DL-FPD).Approach.With DL-FPD, the low-energy and high-energy projections acquired from the top and bottom detector layers contain over-sampled spatial information, from which super-resolution CT images can be reconstructed. A simple mathematical model is proposed to explain the signal formation procedure in DL-FPD, and a dedicated recurrent neural network, named suRi-Net, is developed based upon the above imaging model to nonlinearly retrieve the high-resolution dual-energy information. Physical benchtop experiments are conducted to validate the performance of this newly developed super-resolution CBCT imaging method.Main Results.The results demonstrate that the proposed suRi-Net can accurately retrieve high spatial resolution information from the low-energy and high-energy projections of low spatial resolution. Quantitatively, the spatial resolution of the reconstructed CBCT images from the top and bottom detector layers is increased by about 45% and 54%, respectively.Significance.In the future, suRi-Net will provide a new approach to perform high spatial resolution dual-energy imaging in DL-FPD-based CBCT systems.

PEARL: Cascaded Self-supervised Cross-fusion Learning For Parallel MRI Acceleration

Supervised deep learning (SDL) methodology holds promise for accelerated magnetic resonance imaging (AMRI) but is hampered by the reliance on extensive training data. Some self-supervised frameworks, such as deep image prior (DIP), have emerged, eliminating the explicit training procedure but often struggling to remove noise and artifacts under significant degradation. This work introduces a novel self-supervised accelerated parallel MRI approach called PEARL, leveraging a multiple-stream joint deep decoder with two cross-fusion schemes to accurately reconstruct one or more target images from compressively sampled k-space. Each stream comprises cascaded cross-fusion sub-block networks (SBNs) that sequentially perform combined upsampling, 2D convolution, joint attention, ReLU activation and batch normalization (BN). Among them, combined upsampling and joint attention facilitate mutual learning between multiple-stream networks by integrating multi-parameter priors in both additive and multiplicative manners. Long-range unified skip connections within SBNs ensure effective information propagation between distant cross-fusion layers. Additionally, incorporating dual-normalized edge-orientation similarity regularization into the training loss enhances detail reconstruction and prevents overfitting. Experimental results consistently demonstrate that PEARL outperforms the existing state-of-the-art (SOTA) self-supervised AMRI technologies in various MRI cases. Notably, 5-fold  ∼ 6-fold accelerated acquisition yields a 1 %  ∼  2 % improvement in SSIM ROI and a 3 %  ∼  6 % improvement in PSNR ROI, along with a significant 15 %  ∼  20 % reduction in RLNE ROI.

Dynamic Changes of Phenolic Composition, Antioxidant Capacity, and Gene Expression in 'Snow White' Loquat (Eriobotrya japonica Lindl.) Fruit throughout Development and Ripening

The newly released 'Snow White' (SW), a white-fleshed loquat (Eriobotrya japonica Lindl.) cultivar, holds promise for commercial production. However, the specifics of the phenolic composition in white-fleshed loquats, along with the antioxidant substances and their regulatory mechanisms, are not yet fully understood. In this study, we examined the dynamic changes in the phenolic compounds, enzyme activities, antioxidant capacity, and gene expression patterns of SW during the key stages of fruit development and ripening. A total of 18 phenolic compounds were identified in SW, with chlorogenic acid, neochlorogenic acid, and coniferyl alcohol being the most predominant. SW demonstrated a stronger antioxidant capacity in the early stages of development, largely due to total phenolics and flavonoids. Neochlorogenic acid may be the most significant antioxidant contributor in loquat. A decline in enzyme activities corresponded with fruit softening. Different genes within a multigene family played distinct roles in the synthesis of phenolics. C4H1, 4CL2, 4CL9, HCT, CCoAOMT5, F5H, COMT1, CAD6, and POD42 were implicated in the regulation of neochlorogenic acid synthesis and accumulation. Consequently, these findings enhance our understanding of phenolic metabolism and offer fresh perspectives on the development of germplasm resources for white-fleshed loquats.

CMID: Crossmodal Image Denoising via Pixel-Wise Deep Reinforcement Learning

Removing noise from acquired images is a crucial step in various image processing and computer vision tasks. However, the existing methods primarily focus on removing specific noise and ignore the ability to work across modalities, resulting in limited generalization performance. Inspired by the iterative procedure of image processing used by professionals, we propose a pixel-wise crossmodal image-denoising method based on deep reinforcement learning to effectively handle noise across modalities. We proposed a similarity reward to help teach an optimal action sequence to model the step-wise nature of the human processing process explicitly. In addition, We designed an action set capable of handling multiple types of noise to construct the action space, thereby achieving successful crossmodal denoising. Extensive experiments against state-of-the-art methods on publicly available RGB, infrared, and terahertz datasets demonstrate the superiority of our method in crossmodal image denoising.

Effect of Molecular Weight on the Structural and Emulsifying Characteristics of Bovine Bone Protein Hydrolysate

The emulsifying capacity of bovine bone protein extracted using high-pressure hot water (HBBP) has been determined to be good. Nevertheless, given that HBBP is a blend of peptides with a broad range of molecular weights, the distinction in emulsifying capacity between polypeptide components with high and low molecular weights is unclear. Therefore, in this study, HBBP was separated into three molecular weight components of 10-30 kDa (HBBP 1), 5-10 kDa (HBBP 2), and <5 kDa (HBBP 3) via ultrafiltration, and the differences in their structures and emulsifying properties were investigated. The polypeptide with the highest molecular weight displayed the lowest endogenous fluorescence intensity, the least solubility in an aqueous solution, and the highest surface hydrophobicity index. Analysis using laser confocal Raman spectroscopy showed that with an increase in polypeptide molecular weight, the α-helix and β-sheet contents in the secondary structure of the polypeptide molecule increased significantly. Particle size, rheological characteristics, and laser confocal microscopy were used to characterize the emulsion made from peptides of various molecular weights. High-molecular-weight peptides were able to provide a more robust spatial repulsion and thicker interfacial coating in the emulsion, which would make the emulsion more stable. The above results showed that the high-molecular-weight polypeptide in HBBP effectively improved the emulsion stability when forming an emulsion. This study increased the rate at which bovine bone was utilized and provided a theoretical foundation for the use of bovine bone protein as an emulsifier in the food sector.

Energy resolving dark-field imaging with dual phase grating interferometer

X-ray dark-filed imaging is a powerful approach to quantify the dimension of micro-structures of the object. Often, a series of dark-filed signals have to be measured under various correlation lengths. For instance, this is often achieved by adjusting the sample positions by multiple times in Talbot-Lau interferometer. Moreover, such multiple measurements can also be collected via adjustments of the inter-space between the phase gratings in dual phase grating interferometer. In this study, the energy resolving capability of the dual phase grating interferometer is explored with the aim to accelerate the data acquisition speed of dark-filed imaging. To do so, both theoretical analyses and numerical simulations are investigated. Specifically, the responses of the dual phase grating interferometer at varied X-ray beam energies are studied. Compared with the mechanical position translation approach, the combination of such energy resolving capability helps to greatly shorten the total dark-field imaging time in dual phase grating interferometer.

Burden of type 1 and type 2 diabetes and high fasting plasma glucose in Europe, 1990-2019: a comprehensive analysis from the global burden of disease study 2019

Introduction

With population aging rampant globally, Europe faces unique challenges and achievements in chronic disease prevention. Despite this, comprehensive studies examining the diabetes burden remain absent. We investigated the burden of type 1 and type 2 diabetes, alongside high fasting plasma glucose (HFPG), in Europe from 1990-2019, to provide evidence for global diabetes strategies.

Methods

Disease burden estimates due to type 1 and type 2 diabetes and HFPG were extracted from the GBD 2019 across Eastern, Central, and Western Europe. We analyzed trends from 1990 to 2019 by Joinpoint regression, examined correlations between diabetes burden and Socio-demographic indices (SDI), healthcare access quality (HAQ), and prevalence using linear regression models. The Population Attributable Fraction (PAF) was used to described diabetes risks.

Results

In Europe, diabetes accounted for 596 age-standardized disability-adjusted life years (DALYs) per 100,000 people in 2019, lower than globally. The disease burden from type 1 and type 2 diabetes was markedly higher in males and escalated with increasing age. Most DALYs were due to type 2 diabetes, showing regional inconsistency, highest in Central Europe. From 1990-2019, age-standardized DALYs attributable to type 2 diabetes rose faster in Eastern and Central Europe, slower in Western Europe. HFPG led to 2794 crude DALYs per 100,000 people in 2019. Type 1 and type 2 diabetes burdens correlated positively with diabetes prevalence and negatively with SDI and HAQ. High BMI (PAF 60.1%) and dietary risks (PAF 34.6%) were significant risk factors.

Conclusion

Europe's diabetes burden was lower than the global average, but substantial from type 2 diabetes, reflecting regional heterogeneity. Altered DALYs composition suggested increased YLDs. Addressing the heavy burden of high fasting plasma glucose and the increasing burden of both types diabetes necessitate region-specific interventions to reduce type 2 diabetes risk, improve healthcare systems, and offer cost-effective care.

A future for digital public goods for monitoring SDG indicators

Digital public goods (DPGs), if implemented with effective policies, can facilitate the realization of the United Nations Sustainable Development Goals (SDGs). However, there are ongoing deliberations on how to define DPGs and assure that society can extract the maximum benefit from the growing number of digital resources. The International Research Center of Big Data for Sustainable Development Goals (CBAS) sees DPGs as an important mechanism to facilitate information-driven policy and decision-making processes for the SDGs. This article presents the results of a CBAS survey of 51 respondents from around the world spanning multiple scientific fields, who shared their expert opinions on DPGs and their thoughts about challenges related to their practical implementation in supporting the SDGs. Based on the survey results, the paper presents core principles in a proposed strategy, including establishment of international standards, adherence to open science and open data principles, and scalability in monitoring SDG indicators. A community-driven strategy to develop DPGs is proposed to accelerate DPG production in service of the SDGs while adhering to the core principles identified in the survey.

Methylation of AcGST1 Is Associated with Anthocyanin Accumulation in the Kiwifruit Outer Pericarp

Most red-fleshed kiwifruit cultivars, such as Hongyang, only accumulate anthocyanins in the inner pericarp; the trait of full red flesh becomes the goal pursued by breeders. In this study, we identified a mutant "H-16" showing a red color in both the inner and outer pericarps, and the underlying mechanism was explored. Through transcriptome analysis, a key differentially expressed gene AcGST1 was screened out, which was positively correlated with anthocyanin accumulation in the outer pericarp. The result of McrBC-PCR and bisulfite sequencing revealed that the SG3 region (-292 to -597 bp) of AcGST1 promoter in "H-16" had a significantly lower CHH cytosine methylation level than that in Hongyang, accompanied by low expression of methyltransferase genes (MET1 and CMT2) and high expression of demethylase genes (ROS1 and DML1). Transient calli transformation confirmed that demethylase gene DML1 can activate transcription of AcGST1 to enhance its expression. Overexpression of AcGST1 enhanced the anthocyanin accumulation in the fruit flesh and leaves of the transgenic lines. These results suggested that a decrease in the methylation level of the AcGST1 promoter may contribute to accumulation of anthocyanin in the outer pericarp of "H-16".

Acceleration of high-quality Raman imaging via a locality enhanced transformer network

Raman imaging (RI) is an outstanding technique that enables molecular-level medical diagnostics and therapy assessment by providing characteristic fingerprint and morphological information about molecules. However, obtaining high-quality Raman images generally requires a long acquisition time, up to hours, which is prohibitive for RI applications of timely cytopathology and histopathology analyses. To address this issue, image super-resolution (SR) based on deep learning, including convolutional neural networks and transformers, has been widely recognized as an effective solution to reduce the time required for achieving high-quality RI. In this study, a locality enhanced transformer network (LETNet) is proposed to perform Raman image SR. Specifically, the general architecture of the transformer is adopted with the replacement of self-attention by convolution to generate high-fidelity and detailed SR images. Additionally, the convolution in the LETNet is further optimized by utilizing depth-wise convolution to improve the computational efficiency of the model. Experiments on hyperspectral Raman images of breast cancer cells and Raman images of a few channels of brain tumor tissues demonstrate that the LETNet achieves superior 2×, 4×, and 8× SR with fewer parameters compared with other SR methods. Consequently, high-quality Raman images can be obtained with a significant reduction in time, ranging from 4 to 64 times. Overall, the proposed method provides a novel, efficient, and reliable solution to expedite high-quality RI and promote its application in real-time diagnosis and therapy.

Accelerated submillimeter wave-encoded magnetic resonance imaging via deep untrained neural network

BACKGROUND

Wave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto-calibration signals (ACS) signal acquisition and is time-consuming, while the latter requires a large amount of training data.

PURPOSE

To tackle the above issues, an untrained neural network (UNN) model incorporating wave-encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS- and training data-free.

METHODS

Generally, the proposed method can provide powerful missing data interpolation capability using the wave physical encoding framework and designed UNN to characterize the MR image (k-space data) priors. Specifically, the MRI reconstruction combining physical wave encoding and elaborate UNN is modeled as a generalized minimization problem. The designation of UNN is driven by the coil sensitivity maps (CSM) smoothness and k-space linear predictability. And then, the iterative paradigm to recover the full k-space signal is determined by the projected gradient descent, and the complex computation is unrolled to the network with optimized parameters by the optimizer. Simulated wave encoding and in vivo experiments are exploited to demonstrate the feasibility of the proposed method. The best quantitative metrics RMSE/SSIM/PSNR of 0.0413, 0.9514, and 37.4862 gave competitive results in all experiments with at least six-fold acceleration, respectively.

RESULTS

In vivo experiments of human brains and knees showed that the proposed method can achieve comparable reconstruction quality and even has superiority relative to the comparison, especially at a high resolution of 0.67 mm and fewer ACS. In addition, the proposed method has a higher computational efficiency achieving a computation time of 9.6 s/per slice.

CONCLUSIONS

The model proposed in this work addresses two limitations of MRI reconstruction in the wave encoding framework. The first is to eliminate the need for ACS signal acquisition to perform the time-consuming calibration process and to avoid errors such as motion during the acquisition procedure. Furthermore, the proposed method has clinical application friendly without the need to prepare large training datasets, which is difficult in the clinical. All results of the proposed method demonstrate more confidence in both quantitative and qualitative metrics. In addition, the proposed method can achieve higher computational efficiency.

Lignan constituents with α-amylase and α-glucosidase inhibitory activities from the fruits of Viburnum urceolatum

Eleven previously undescribed lignan constituents, including five 8-O-4' type neolignans, viburnurcosides A-E (1-5), three benzofuran type neolignans, viburnurcosides F-H (6-8), and three tetrahydrofuran type lignans, viburnurcosides I-K (9-11), were isolated from the fruits of Viburnum urceolatum. The structures of all isolates were elucidated by an extensive analysis of the NMR and HRESIMS data. The absolute configurations of these compounds were determined by quantum-chemical electronic circular dichroism calculation and comparison. The sugar units of viburnurcosides A-K were identified by acid hydrolysis and HPLC analysis of the chiral derivatives of monosaccharides. The in vitro enzyme inhibition assay exhibited that viburnurcoside J (10) had the most potent inhibitory activity against α-amylase and α-glucosidase with the IC50 values of 19.75 and 9.14 μM, respectively, which were stronger than those of the positive control acarbose (37.31 and 26.75 μM, respectively). The potential binding modes of viburnurcoside J (10) with α-amylase and α-glucosidase were also analyzed by molecular modeling.

The role of SLC12A family of cation-chloride cotransporters and drug discovery methodologies

The solute carrier family 12 (SLC12) of cation-chloride cotransporters (CCCs) comprises potassium chloride cotransporters (KCCs, e.g. KCC1, KCC2, KCC3, and KCC4)-mediated Cl- extrusion, and sodium potassium chloride cotransporters (N[K]CCs, NKCC1, NKCC2, and NCC)-mediated Cl- loading. The CCCs play vital roles in cell volume regulation and ion homeostasis. Gain-of-function or loss-of-function of these ion transporters can cause diseases in many tissues. In recent years, there have been considerable advances in our understanding of CCCs' control mechanisms in cell volume regulations, with many techniques developed in studying the functions and activities of CCCs. Classic approaches to directly measure CCC activity involve assays that measure the transport of potassium substitutes through the CCCs. These techniques include the ammonium pulse technique, radioactive or nonradioactive rubidium ion uptake-assay, and thallium ion-uptake assay. CCCs' activity can also be indirectly observed by measuring γ-aminobutyric acid (GABA) activity with patch-clamp electrophysiology and intracellular chloride concentration with sensitive microelectrodes, radiotracer 36Cl-, and fluorescent dyes. Other techniques include directly looking at kinase regulatory sites phosphorylation, flame photometry, 22Na+ uptake assay, structural biology, molecular modeling, and high-throughput drug screening. This review summarizes the role of CCCs in genetic disorders and cell volume regulation, current methods applied in studying CCCs biology, and compounds developed that directly or indirectly target the CCCs for disease treatments.

Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning

PURPOSE

The axial field of view (AFOV) of a positron emission tomography (PET) scanner greatly affects the quality of PET images. Although a total-body PET scanner (uEXPLORER) with a large AFOV is more sensitive, it is more expensive and difficult to widely use. Therefore, we attempt to utilize high-quality images generated by uEXPLORER to optimize the quality of images from short-axis PET scanners through deep learning technology while controlling costs.

METHODS

The experiments were conducted using PET images of three anatomical locations (brain, lung, and abdomen) from 335 patients. To simulate PET images from different axes, two protocols were used to obtain PET image pairs (each patient was scanned once). For low-quality PET (LQ-PET) images with a 320-mm AFOV, we applied a 300-mm FOV for brain reconstruction and a 500-mm FOV for lung and abdomen reconstruction. For high-quality PET (HQ-PET) images, we applied a 1940-mm AFOV during the reconstruction process. A 3D Unet was utilized to learn the mapping relationship between LQ-PET and HQ-PET images. In addition, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed to evaluate the model performance. Furthermore, two nuclear medicine doctors evaluated the image quality based on clinical readings.

RESULTS

The generated PET images of the brain, lung, and abdomen were quantitatively and qualitatively compatible with the HQ-PET images. In particular, our method achieved PSNR values of 35.41 ± 5.45 dB (p < 0.05), 33.77 ± 6.18 dB (p < 0.05), and 38.58 ± 7.28 dB (p < 0.05) for the three beds. The overall mean SSIM was greater than 0.94 for all patients who underwent testing. Moreover, the total subjective quality levels of the generated PET images for three beds were 3.74 ± 0.74, 3.69 ± 0.81, and 3.42 ± 0.99 (the highest possible score was 5, and the minimum score was 1) from two experienced nuclear medicine experts. Additionally, we evaluated the distribution of quantitative standard uptake values (SUV) in the region of interest (ROI). Both the SUV distribution and the peaks of the profile show that our results are consistent with the HQ-PET images, proving the superiority of our approach.

CONCLUSION

The findings demonstrate the potential of the proposed technique for improving the image quality of a PET scanner with a 320 mm or even shorter AFOV. Furthermore, this study explored the potential of utilizing uEXPLORER to achieve improved short-axis PET image quality at a limited economic cost, and computer-aided diagnosis systems that are related can help patients and radiologists.

Universal generative modeling in dual domains for dynamic MRI

Dynamic magnetic resonance image reconstruction from incomplete k-space data has generated great research interest due to its ability to reduce scan time. Nevertheless, the reconstruction problem remains a thorny issue due to its ill posed nature. Recently, diffusion models, especially score-based generative models, have demonstrated great potential in terms of algorithmic robustness and flexibility of utilization. Moreover, a unified framework through the variance exploding stochastic differential equation is proposed to enable new sampling methods and further extend the capabilities of score-based generative models. Therefore, by taking advantage of the unified framework, we propose a k-space and image dual-domain collaborative universal generative model (DD-UGM), which combines the score-based prior with a low-rank regularization penalty to reconstruct highly under-sampled measurements. More precisely, we extract prior components from both image and k-space domains via a universal generative model and adaptively handle these prior components for faster processing while maintaining good generation quality. Experimental comparisons demonstrate the noise reduction and detail preservation abilities of the proposed method. Moreover, DD-UGM can reconstruct data of different frames by only training a single frame image, which reflects the flexibility of the proposed model.

Equilibrated Zeroth-Order Unrolled Deep Network for Parallel MR Imaging

In recent times, model-driven deep learning has evolved an iterative algorithm into a cascade network by replacing the regularizer's first-order information, such as the (sub)gradient or proximal operator, with a network module. This approach offers greater explainability and predictability compared to typical data-driven networks. However, in theory, there is no assurance that a functional regularizer exists whose first-order information matches the substituted network module. This implies that the unrolled network output may not align with the regularization models. Furthermore, there are few established theories that guarantee global convergence and robustness (regularity) of unrolled networks under practical assumptions. To address this gap, we propose a safeguarded methodology for network unrolling. Specifically, for parallel MR imaging, we unroll a zeroth-order algorithm, where the network module serves as a regularizer itself, allowing the network output to be covered by a regularization model. Additionally, inspired by deep equilibrium models, we conduct the unrolled network before backpropagation to converge to a fixed point and then demonstrate that it can tightly approximate the actual MR image. We also prove that the proposed network is robust against noisy interferences if the measurement data contain noise. Finally, numerical experiments indicate that the proposed network consistently outperforms state-of-the-art MRI reconstruction methods, including traditional regularization and unrolled deep learning techniques.

Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex-Related Epilepsy Using Deep Learning

BACKGROUND AND PURPOSE

Tuberous sclerosis complex disease is a rare, multisystem genetic disease, but appropriate drug treatment allows many pediatric patients to have positive outcomes. The purpose of this study was to predict the effectiveness of antiseizure medication treatment in children with tuberous sclerosis complex-related epilepsy.

MATERIALS AND METHODS

We conducted a retrospective study involving 300 children with tuberous sclerosis complex-related epilepsy. The study included the analysis of clinical data and T2WI and FLAIR images. The clinical data consisted of sex, age of onset, age at imaging, infantile spasms, and antiseizure medication numbers. To forecast antiseizure medication treatment, we developed a multitechnique deep learning method called WAE-Net. This method used multicontrast MR imaging and clinical data. The T2WI and FLAIR images were combined as FLAIR3 to enhance the contrast between tuberous sclerosis complex lesions and normal brain tissues. We trained a clinical data-based model using a fully connected network with the above-mentioned variables. After that, a weighted-average ensemble network built from the ResNet3D architecture was created as the final model.

RESULTS

The experiments had shown that age of onset, age at imaging, infantile spasms, and antiseizure medication numbers were significantly different between the 2 drug-treatment outcomes (P < .05). The hybrid technique of FLAIR3 could accurately localize tuberous sclerosis complex lesions, and the proposed method achieved the best performance (area under the curve = 0.908 and accuracy of 0.847) in the testing cohort among the compared methods.

CONCLUSIONS

The proposed method could predict antiseizure medication treatment of children with rare tuberous sclerosis complex-related epilepsy and could be a strong baseline for future studies.

Imaging-proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer

BACKGROUND

Optimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, the biologic meaning of these latent features is poorly understood, preventing the clinical use of such noninvasive imaging markers. The study aimed to develop a deep learning signature (DLS) from pretreatment magnetic resonance imaging (MRI) for predicting responses to neoadjuvant chemotherapy in patients with breast cancer and to further investigate the biologic meaning of the DLS by identifying its underlying pathways using paired MRI and proteomic sequencing data.

METHODS

MRI-based DLS was constructed (radiogenomic training dataset, n = 105) and validated (radiogenomic validation dataset, n = 26) for the prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy. Proteomic sequencing revealed biological functions facilitating pCR (n = 139). Their associations with DLS were uncovered by radiogenomic analysis.

RESULTS

The DLS achieved a prediction accuracy of 0.923 with an AUC of 0.958, higher than the performance of the model trained by transfer learning. Cellular membrane formation, endocytosis, insulin-like growth factor binding, protein localization to membranes, and cytoskeleton-dependent trafficking were differentially regulated in patients showing pCR. Oncogenic signaling pathways, features correlated with human phenotypes, and features correlated with general biological processes were significantly correlated with DLS in both training and validation dataset (p.adj < 0.05).

CONCLUSIONS

Our study offers a biologically interpretable DLS for the prediction of pCR to neoadjuvant chemotherapy in patients with breast cancer, which may guide personalized medication.

Robust RGB-T Tracking via Graph Attention-Based Bilinear Pooling

RGB-T tracker possesses strong capability of fusing two different yet complementary target observations, thus providing a promising solution to fulfill all-weather tracking in intelligent transportation systems. Existing convolutional neural network (CNN)-based RGB-T tracking methods often consider the multisource-oriented deep feature fusion from global viewpoint, but fail to yield satisfactory performance when the target pair only contains partially useful information. To solve this problem, we propose a four-stream oriented Siamese network (FS-Siamese) for RGB-T tracking. The key innovation of our network structure lies in that we formulate multidomain multilayer feature map fusion as a multiple graph learning problem, based on which we develop a graph attention-based bilinear pooling module to explore the partial feature interaction between the RGB and the thermal targets. This can effectively avoid uninformed image blocks disturbing feature embedding fusion. To enhance the efficiency of the proposed Siamese network structure, we propose to adopt meta-learning to incorporate category information in the updating of bilinear pooling results, which can online enforce the exemplar and current target appearance obtaining similar sematic representation. Extensive experiments on grayscale-thermal object tracking (GTOT) and RGBT234 datasets demonstrate that the proposed method outperforms the state-of-the-art methods for the task of RGB-T tracking.

Construction of Graphene@Ag-MLF composite structure SERS platform and its differentiating performance for different foodborne bacterial spores

A new surface-enhanced Raman spectroscopy (SERS) biosensor of Graphene@Ag-MLF composite structure has been fabricated by loading AgNPs on graphene films. The response of the biosensor is  based on plasmonic sensing. The results showed that the enhancement factor of three different spores reached 107 based on the Graphene@Ag-MLF substrate. In addition, the SERS performance was stable, with good reproducibility (RSD<3%). Multivariate statistical analysis and chemometrics were used to distinguish different spores. The accumulated variance contribution rate was up to 96.35% for the top three PCs, while HCA results revealed that the spectra were differentiated completely. Based on optimal principal components, chemometrics of KNN and LS-SVM were applied to construct a model for rapid qualitative identification of different spores, of which the prediction set and training set of LS-SVM achieved 100%. Finally, based on the Graphene@Ag-MLF substrate, the LOD of three different spores was lower than 102 CFU/mL. Hence, this novel Graphene@Ag-MLF SERS substrate sensor was rapid, sensitive, and stable in detecting spores, providing strong technical support for the application of SERS technology in food safety.

Dynamic landscapes and the influence of human activities in the Yellow River Delta wetland region

The Yellow River Delta (YRD) wetland is one of the largest and youngest wetland ecosystems in the world. It plays an important role in regulating climate and maintaining ecological balance in the region. This study analyzes the spatiotemporal changes in land use, wetland migration, and landscape pattern from 2013 to 2022 using Landsat-8 and Sentinel-1 data in YRD. Then wetland landscape changes and the impact of human activities are determined by analyzing correlation between landscape and socio-economic indicators including nighttime light centroid, total light intensity, cultivated land area and centroid, building area and centroid, economic and population. The results show that the total wetland area increased 1426 km2 during this decade. However, the wetland landscape pattern tended to be fragmented from 2013 to 2022, with wetlands of different types interlacing and connectivity decreasing, and distribution becoming more concentrated. Different types of human activities had influences on different aspects of wetland landscape, with the expansion of cultivated land mainly compressing the core area of wetlands from the edge, the expansion of buildings mainly disrupting wetland connectivity, and socio-economic indicators such as total light intensity and the centroid mainly causing wetland fragmentation. The results show the changes of the YRD wetland and provide an explanation of how human activities effect the change of its landscape, which provides available data to achieve sustainable development goals 6.6 and may give an access to measure the change of wetland using human-activity data, which could help to adject behaviors to protect wetlands.

Melatonin Promotes Iron Reactivation and Reutilization in Peach Plants under Iron Deficiency

The yellowing of leaves due to iron deficiency is a prevalent issue in peach production. Although the capacity of exogenous melatonin (MT) to promote iron uptake in peach plants has been demonstrated, its underlying mechanism remains ambiguous. This investigation was carried out to further study the effects of exogenous MT on the iron absorption and transport mechanisms of peach (Prunus persica) plants under iron-deficient conditions through transcriptome sequencing. Under both iron-deficient and iron-supplied conditions, MT increased the content of photosynthetic pigments in peach leaves and decreased the concentrations of pectin, hemicellulose, cell wall iron, pectin iron, and hemicellulose iron in peach plants to a certain extent. These effects stemmed from the inhibitory effect of MT on the polygalacturonase (PG), cellulase (Cx), phenylalanine ammonia-lyase (PAL), and cinnamoyl-coenzyme A reductase (CCR) activities, as well as the promotional effect of MT on the cinnamic acid-4-hydroxylase (C4H) activity, facilitating the reactivation of cell wall component iron. Additionally, MT increased the ferric-chelate reductase (FCR) activity and the contents of total and active iron in various organs of peach plants under iron-deficient and iron-supplied conditions. Transcriptome analysis revealed that the differentially expressed genes (DEGs) linked to iron metabolism in MT-treated peach plants were primarily enriched in the aminoacyl-tRNA biosynthesis pathway under iron-deficient conditions. Furthermore, MT influenced the expression levels of these DEGs, regulating cell wall metabolism, lignin metabolism, and iron translocation within peach plants. Overall, the application of exogenous MT promotes the reactivation and reutilization of iron in peach plants.

Correction of a homoplasmic mitochondrial tRNA mutation in patient-derived iPSCs via a mitochondrial base editor

Pathogenic mutations in mitochondrial DNA cause severe and often lethal multi-system symptoms in primary mitochondrial defects. However, effective therapies for these defects are still lacking. Strategies such as employing mitochondrially targeted restriction enzymes or programmable nucleases to shift the ratio of heteroplasmic mutations and allotopic expression of mitochondrial protein-coding genes have limitations in treating mitochondrial homoplasmic mutations, especially in non-coding genes. Here, we conduct a proof of concept study applying a screened DdCBE pair to correct the homoplasmic m.A4300G mutation in induced pluripotent stem cells derived from a patient with hypertrophic cardiomyopathy. We achieve efficient G4300A correction with limited off-target editing, and successfully restore mitochondrial function in corrected induced pluripotent stem cell clones. Our study demonstrates the feasibility of using DdCBE to treat primary mitochondrial defects caused by homoplasmic pathogenic mitochondrial DNA mutations.

A radio-pathologic integrated model for prediction of lymph node metastasis stage in patients with gastric cancer

BACKGROUND

Accurate prediction of lymph node metastasis stage (LNMs) facilitates precision therapy for gastric cancer. We aimed to develop and validate a deep learning-based radio-pathologic model to predict the LNM stage in patients with gastric cancer by integrating CT images and histopathological whole-slide images (WSIs).

METHODS

A total of 252 patients were enrolled and randomly divided into a training set (n = 202) and a testing set (n = 50). Both pretreatment contrast-enhanced abdominal CT and WSI of biopsy specimens were collected for each patient. The deep radiologic and pathologic features were extracted from CT and WSI using ResNet-50 and Vision Transformer (ViT) network, respectively. By fusing both radiologic and pathologic features, a radio-pathologic integrated model was constructed to predict the five LNM stages. For comparison, four single-modality models using CT images or WSIs were also constructed, respectively. All models were trained on the training set and validated on the testing set.

RESULTS

The radio-pathologic integrated mode achieved an overall accuracy of 84.0% and a kappa coefficient of 0.795 on the testing set. The areas under the curves (AUCs) of the integrated model in predicting the five LNM stages were 0.978 (95% Confidence Interval (CI 0.917-1.000), 0.946 (95% CI 0.867-1.000), 0.890 (95% CI 0.718-1.000), 0.971 (95% CI 0.920-1.000), and 0.982 (95% CI 0.911-1.000), respectively. Moreover, the integrated model achieved an AUC of 0.978 (95% CI 0.912-1.000) in predicting the binary status of nodal metastasis.

CONCLUSION

Our study suggests that radio-pathologic integrated model that combined both macroscale radiologic image and microscale pathologic image can better predict lymph node metastasis stage in patients with gastric cancer.

One-Shot Generative Prior in Hankel-k-Space for Parallel Imaging Reconstruction

Magnetic resonance imaging serves as an essential tool for clinical diagnosis. However, it suffers from a long acquisition time. The utilization of deep learning, especially the deep generative models, offers aggressive acceleration and better reconstruction in magnetic resonance imaging. Nevertheless, learning the data distribution as prior knowledge and reconstructing the image from limited data remains challenging. In this work, we propose a novel Hankel-k-space generative model (HKGM), which can generate samples from a training set of as little as one k-space. At the prior learning stage, we first construct a large Hankel matrix from k-space data, then extract multiple structured k-space patches from the Hankel matrix to capture the internal distribution among different patches. Extracting patches from a Hankel matrix enables the generative model to be learned from the redundant and low-rank data space. At the iterative reconstruction stage, the desired solution obeys the learned prior knowledge. The intermediate reconstruction solution is updated by taking it as the input of the generative model. The updated result is then alternatively operated by imposing low-rank penalty on its Hankel matrix and data consistency constraint on the measurement data. Experimental results confirmed that the internal statistics of patches within single k-space data carry enough information for learning a powerful generative model and providing state-of-the-art reconstruction.

Reducing the ambiguity of field inhomogeneity and chemical shift effect for fat-water separation by field factor

PURPOSE

To reduce the ambiguity between chemical shift and field inhomogeneity with flexible TE combinations by introducing a variable (field factor).

THEORY AND METHODS

The ambiguity between chemical shift and field inhomogeneity can be eliminated directly from the multiple in-phase images acquired at different TEs; however, it is only applicable to few echo combinations. In this study, we accommodated such an implementation in flexible TE combinations by introducing a new variable (field factor). The effects of the chemical shift were removed from the field inhomogeneity in the candidate solutions, thus reducing the ambiguity problem. To validate this concept, multi-echo MRI data acquired from various anatomies with different imaging parameters were tested. The derived fat and water images were compared with those of the state-of-the-art fat-water separation algorithms.

RESULTS

Robust fat-water separation was achieved with the accurate solution of field inhomogeneity, and no apparent fat-water swap was observed. In addition to the good performance, the proposed method is applicable to various fat-water separation applications, including different sequence types and flexible TE choices.

CONCLUSION

We propose an algorithm to reduce the ambiguity of chemical shift and field inhomogeneity and achieved robust fat-water separation in various applications.

Preclinical Development of Brain Permeable ERβ Agonist for the Treatment of Glioblastoma

Glioblastoma (GBM) is the most prevalent and aggressive type of adult brain tumors with low 5-year overall survival rates. Epidemiologic data suggest that estrogen may decrease brain tumor growth, and estrogen receptor beta (ERβ) has been demonstrated to exert antitumor functions in GBM. The lack of potent, selective, and brain permeable ERβ agonist to promote its antitumor action is limiting the therapeutic promise of ERβ. In this study, we discovered that Indanone and tetralone-keto or hydroxyl oximes are a new class of ERβ agonists. Because of its high activity in ERβ reporter assays, specific binding to ERβ in polar screen assays, and potent growth inhibitory activity in GBM cells, CIDD-0149897 was discovered as a possible hit by screening a library of compounds. CIDD-0149897 is more selective for ERβ than ERα (40-fold). Treatment with CIDD-0149897 markedly reduced GBM cell viability with an IC50 of ∼7 to 15 μmol/L, while having little to no effect on ERβ-KO cells and normal human astrocytes. Further, CIDD-0149897 treatment enhanced expression of known ERβ target genes and promoted apoptosis in established and patient-derived GSC models. Pharmacokinetic studies confirmed that CIDD-0149897 has systemic exposure, and good bioavailability in the brain. Mice tolerated daily intraperitoneal treatment of CIDD-0149897 (50 mg/kg) with a 7-day repeat dosage with no toxicity. In addition, CIDD-0149897 treatment significantly decreased tumor growth in U251 xenograft model and extended the survival of orthotopic GBM tumor-bearing mice. Collectively, these findings pointed to CIDD-0149897 as a new class of ERβ agonist, offering patients with GBM a potential means of improving survival.

Amphoteric composite of ZrP and N-doped porous carbon: Synthesis, characterization, and potential use for cycloaddition of CO2

Composites of amorphous ZrP and N-doped carbon were prepared in a one-step pyrolysis process instead of general post-loading technique. Owing to their mesoporous structure (6-10 nm) and Zr content (up to 41 wt%), the amphoteric materials have potential use in the cycloaddition of CO2 to epoxides, which is an acid‒base tandem process including the ring opening of epoxides and the addition of CO2. Substantial work has been done on how starting materials impact the structure and performance of composite materials. The coordination between metal and melamine has been confirmed, and it can be implanted in the melamine-polymer initiation of formation of porous metal-carbon materials. The composite catalysts exhibit amphoteric properties, present broad-spectrum adsorption, and finally produce carbonates via cycloaddition of CO2 to epoxides. It is remarkable that the multiple characteristics of porous solids are stabilized, and no significant loss of catalytic performance is observed after four cycles.

Tobacco roots increasing diameter and secondary lateral density in response to drought stress

Exploring the responses of root morphology and its physiological mechanisms under drought stress is significant for further improving water and nutrient absorption in roots. Here, we simulated drought through hydroponics combined with PEG treatments in tobacco to characterize the changes in tobacco root architecture. Our results showed the total root length, first lateral root number, and first lateral root length were significantly reduced upon increasing drought severity, but the average root diameter and secondary lateral root density increased under certain drought conditions. The change of auxin content in roots under drought stress was correlated with the root diameter and second lateral root density responses. Exogenous addition of the auxin analog (NAA) and the auxin transport inhibitor (NPA), as well as DR5:GUS staining experiments further demonstrated that auxin participated in this physiological process. Meanwhile, brassinolide (BR) exhibited a similar trend. Exogenous addition of BR (EBR) and the BR synthesis inhibitor BRZ experiments demonstrated that BR may participate upstream of auxin under drought stress. PEG treatment significantly up-regulated NtBRI1 at 9-24 h, and promoted the up-regulation of NtBSK2 and NtBSK3 at 48 h and 24 h, respectively, these genes may contribute to the change in root morphology under drought stress. This study shows that auxin and BR are involved in the changes in root morphology in tobacco exposed to drought stress. The elucidation of the molecular mechanism at play thus represents a future target for breeding drought-tolerant tobacco varieties.