Mild Iron-Catalyzed Oxidative Cross-Coupling of Quinoxalinones with Indoles.

Utilizing iron chloride as a Lewis acid catalyst, we developed a straightforward and mild oxidative cross-coupling reaction between quinoxalinones and indoles, yielding a series of versatile 3-(indol-3-yl)quinoxalin-2-one derivatives. This approach allows for the incorporation of a wide array of functional groups into the final products, demonstrating its synthetic versatility. Notably, the method was successfully scaled up to gram-scale reactions while maintaining high yields. Our mechanistic investigation indicates that iron chloride serves as a catalyst to facilitate the formation of key intermediates which subsequently undergo oxidation to afford the desired products. The merits of this protocol include its cost effectiveness, operational simplicity, and the ease of product isolation via filtration.

MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study.

Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.

Clinical significance of immune-related antigen CD58 in gliomas and analysis of its potential core related gene clusters.

Background: The clinical significance of immune-related antigen CD58 in gliomas remains uncertain. The aim of this study was to examine the clinical importance and possible core related genes of CD58 in gliomas. Methods: Pan-cancer analysis was to observe the association between CD58 and different tumors, glioma RNA sequencing data and clinical sample analyses were used to observe the relationship between CD58 and glioma, shRNA interference models were to observe the impact of CD58 on glioma cell function, and four glioma datasets and two online analysis platforms were used to explore the core related genes affecting the correlation between CD58 and glioma. Results: High CD58 expression was associated with worse prognosis in various tumors and higher malignancy in glioma. Down regulation of CD58 expression was linked to decreased proliferation, increased apoptosis, and reduced metastasis in glioma cells. The pathways involved in CD58-related effects were enriched for immune cell adhesion and immune factor activation, and the core genes were CASP1, CCL2, IL18, MYD88, PTPRC, and TLR2. The signature of CD58 and its core-related genes showed superior predictive power for glioma prognosis. Conclusion: High CD58 expression is correlated with more malignant glioma types, and also an independent risk factor for mortality in glioma. CD58 and its core-related genes may serve as novel biomarkers for diagnosing and treating glioma.

The Metabolism of Coenzyme A and Its Derivatives Plays a Crucial Role in Diseases.

Coenzyme A (CoA) functions as a crucial carrier of acyl groups within cells, playing a fundamental role in regulating acyl transfer reactions and participating in cellular metabolic processes. As the principal substrate and cofactor engaged in diverse metabolic reactions, CoA and its derivatives exert central influence over various physiological processes, primarily modulating lipid and ketone metabolism, as well as protein modification. This paper presents a comprehensive review of the molecular mechanisms by which CoA influences the onset and progression of cancer, cardiovascular disease (CVD), neurodegenerative disorders, and other illnesses. The main focal points include the following. (1) In cancer, enzymes such as acetyl-CoA synthetase 2, ATP citrate lyase, and acetyl-CoA carboxylase regulate lipid synthesis and energy metabolism by modulating acetyl-CoA levels. (2) In CVD, the effects of enzymes such as stearoyl-CoA desaturase-1, 3-hydroxy-3-methylglutaryl-CoA (HMGC) synthase 2, and HMGC reductase on the formation and advancement of these diseases are elucidated by their regulation of CoA metabolism across multiple organs. (3) In neurodegenerative disorders, the significance of CoA in maintaining cholesterol homeostasis in the brain and its implications on the development of such disorders are thoroughly discussed. The metabolic processes involving CoA and its derivatives span all physiological aspects within cells, playing a critical role in the onset and progression of various diseases. Elucidating the role of CoA in these conditions yields important insights that can serve as valuable references and guidance for disease diagnosis, treatment, and drug development.

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.

Concurrent brain structural and functional alterations in the thalamus of adult survivors of childhood brain tumors: a multimodal MRI study.

Adult survivors of childhood brain tumors often present with cognitive deficits that affect their quality of life. Studying brain structure and function in brain tumor survivors can help understand the underlying mechanisms of their cognitive deficits to improve long-term prognosis of these patients. This study analyzed voxel-based morphometry (VBM) derived from T1-weighted MRI and the amplitude of low-frequency fluctuation (ALFF) from resting-state functional magnetic resonance imaging (rs-fMRI) to examine the structural and functional alterations in 35 brain tumor survivors using 35 matching healthy individuals as controls. Compared with healthy controls, brain tumor survivors had decreased gray matter volumes (GMV) in the thalamus and increased GMV in the superior frontal gyrus. Functionally, brain tumor survivors had lower ALFF values in the inferior temporal gyrus and medial prefrontal area and higher ALFF values in the thalamus. Importantly, we found concurrent but negatively correlated structural and functional alterations in the thalamus based on observed significant differences in GMV and ALFF values. These findings on concurrent brain structural and functional alterations provide new insights towards a better understanding of the cognitive deficits in brain tumor survivors.

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.

In Situ Engineering Cancer Mask to Immobilize Tumor Cells and Block Metastasis.

This work reports a new concept of cancer mask in situ to alter the specific biological functions of cancer cells. Metastatic cancer cells are highly invasive in part due to the presence of the glycan matrix in the cell membrane. Using a rational designed bio-orthogonal reaction, the cancer cell surface is reconstructed in situ by incorporating endogenous polysialic acids in the glycan matrix on the cell membrane to form a mesh-like network, called cancer mask. The network of the glycan matrix can not only immobilize cancer cells but also effectively block the stimulation of metastasis promoters to tumor cells and inhibit the formation of epithelial to mesenchymal transition (EMT), causing metastatic cancer cells incarceration. The results demonstrate a new strategy to control and even eliminate the cancer metastasis that is a major cause of treatment failure and poor patient outcome.

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Further evidence is needed to explore the impact of high-altitude environments on the neurologic function of neonates. Non-invasive techniques such as cerebral near-infrared spectroscopy and amplitude-integrated electroencephalography can provide data on cerebral oxygenation and brain electrical activity. This study will conduct multiple cerebral near-infrared spectroscopy and amplitude-integrated electroencephalography monitoring sessions at various time points within the first 3 days postpartum for healthy full-term neonates at different altitudes. The obtained data on cerebral oxygenation and brain electrical activity will be compared between different altitudes, and corresponding reference ranges will be established. The study involves 6 participating centers in the Chinese High Altitude Neonatal Medicine Alliance, with altitude gradients divided into 4 categories: 800 m, 1 900 m, 2 400 m, and 3 500 m, with an anticipated sample size of 170 neonates per altitude gradient. This multicenter prospective cohort study aims to provide evidence supporting the impact of high-altitude environments on early brain function and metabolism in neonates.

Development of a CT-based radiomics-clinical model to diagnose acute pancreatitis on nonobvious findings on CT in children with pancreaticobiliary maljunction.

OBJECTIVES: Since neither abdominal pain nor pancreatic enzyme elevation is specific for acute pancreatitis (AP), the diagnosis of AP in patients with pancreaticobiliary maljunction (PBM) may be challenging when the pancreas appears normal or nonobvious on CT. This study aimed to develop a quantitative radiomics-based nomogram of pancreatic CT for identifying AP in children with PBM who have nonobvious findings on CT. METHODS: PBM patients with a diagnosis of AP evaluated at the Children's Hospital of Soochow University from June 2015 to October 2022 were retrospectively reviewed. The radiological features and clinical factors associated with AP were evaluated. Based on the selected variables, multivariate logistic regression was used to construct clinical, radiomics, and combined models. RESULTS: Two clinical parameters and 6 radiomics characteristics were chosen based on their significant association with AP, as demonstrated in the training (area under curve [AUC]: 0.767, 0.892) and validation (AUC: 0.757, 0.836) datasets. The radiomics-clinical nomogram demonstrated superior performance in both the training (AUC, 0.938) and validation (AUC, 0.864) datasets, exhibiting satisfactory calibration (P > .05). CONCLUSIONS: Our radiomics-based nomogram is an accurate, noninvasive diagnostic technique that can identify AP in children with PBM even when CT presentation is not obvious. ADVANCES IN KNOWLEDGE: This study extracted imaging features of nonobvious pancreatitis. Then it developed and evaluated a combined model with these features.

Trial of labor versus elective cesarean delivery for patients with two prior cesarean sections: a systematic review and meta-analysis.

OBJECTIVE: Cesarean section (CS) rates have been on the rise globally, leading to an increasing number of women facing the decision between a Trial of Labor after two Cesarean Sections (TOLAC-2) or opting for an Elective Repeat Cesarean Section (ERCS). This study evaluates and compares safety outcomes of TOLAC and ERCS in women with a history of two previous CS deliveries. METHODS: PubMed, MEDLINE, EMbase, and Cochrane Central Register of Controlled Trials (CENTRAL) databases were searched for studies published until 30 June 2023. Eligible studies were included based on predetermined criteria, and a random-effects model was employed to pool data for maternal and neonatal outcomes. RESULTS: Thirteen studies with a combined sample size of 101,011 women who had two prior CS were included. TOLAC-2 was associated with significantly higher maternal mortality (odds ratio (OR)=1.50, 95% confidence interval (CI)= 1.25-1.81) and higher chance of uterine rupture (OR = 7.15, 95% CI = 3.44-14.87) compared to ERCS. However, no correlation was found for other maternal outcomes, including blood transfusion, hysterectomy, or post-partum hemorrhage. Furthermore, neonatal outcomes, such as Apgar scores, NICU admissions, and neonatal mortality, were comparable in the TOLAC-2 and ERCS groups. CONCLUSION: Our findings suggest an increased risk of uterine rupture and maternal mortality with TOLAC-2, emphasizing the need for personalized risk assessment and shared decision-making by healthcare professionals. Additional studies are needed to refine our understanding of these outcomes in the context of TOLAC-2.

Tumor integrin targeted theranostic iron oxide nanoparticles for delivery of caffeic acid phenethyl ester: preparation, characterization, and anti-myeloma activities.

Multiple myeloma (MM) is characterized by the accumulation of malignant plasma cells preferentially in the bone marrow. Currently, emerging chemotherapy drugs with improved biosafety profiles, such as immunomodulatory agents and protease inhibitors, have been used in clinics to treat MM in both initial therapy or maintenance therapy post autologous hematopoietic stem cell transplantation (ASCT). We previously discovered that caffeic acid phenethyl ester (CAPE), a water-insoluble natural compound, inhibited the growth of MM cells by inducing oxidative stress. As part of our continuous effort to pursue a less toxic yet more effective therapeutic approach for MM, the objective of this study is to investigate the potential of CAPE for in vivo applications by using magnetic resonance imaging (MRI)-capable superparamagnetic iron oxide nanoparticles (IONP) as carriers. Cyclo (Arg-Gly-Asp-D-Phe-Cys) (RGD) is conjugated to IONP (RGD-IONP/CAPE) to target the overexpressed αvß3 integrin on MM cells for receptor-mediated internalization and intracellular delivery of CAPE. A stable loading of CAPE on IONP can be achieved with a loading efficiency of 48.7% ± 3.3% (wt%). The drug-release studies indicate RGD-IONP/CAPE is stable at physiological (pH 7.4) and basic pH (pH 9.5) and subject to release of CAPE at acidic pH (pH 5.5) mimicking the tumor and lysosomal condition. RGD-IONP/CAPE causes cytotoxicity specific to human MM RPMI8226, U266, and NCI-H929 cells, but not to normal peripheral blood mononuclear cells (PBMCs), with IC50s of 7.97 ± 1.39, 16.75 ± 1.62, and 24.38 ± 1.71 µM after 72-h treatment, respectively. Apoptosis assays indicate RGD-IONP/CAPE induces apoptosis of RPMI8226 cells through a caspase-9 mediated intrinsic pathway, the same as applying CAPE alone. The apoptogenic effect of RGD-IONP/CAPE was also confirmed on the RPMI8226 cells co-cultured with human bone marrow stromal cells HS-5 in a Transwell model to mimic the MM microenvironment in the bone marrow. In conclusion, we demonstrate that water-insoluble CAPE can be loaded to RGD-IONP to greatly improve the biocompatibility and significantly inhibit the growth of MM cells in vitro through the induction of apoptosis. This study paves the way for investigating the MRI-trackable delivery of CAPE for MM treatment in animal models in the future.

TRIM34 suppresses non-small-cell lung carcinoma via inducing mTORC1-dependent glucose utilization and promoting cellular death.

Non-small-cell lung carcinoma (NSCLC) is a type of pernicious tumor, which owns high morbidity and mortality. TRIM34 has a stimulative role in cell apoptosis and a suppressive role in inflammation. However, no studies were focused on the regulatory impacts of TRIM34 in NSCLC. This study aimed to examine the underlying regulatory effects of TRIM34 in NSCLC. TRIM34 exhibited lower expression in NSCLC. TRIM34 facilitated mitochondrial damage and apoptosis in NSCLC. TRIM34 induced the increased activity of mTORC1 and accelerated glycolysis in NSCLC. Enhanced mitochondrial damage induced by TRIM34 overexpression was reversed after rapamycin (mTORC1 inhibitor) treatment in NSCLC. The strengthened cell apoptosis stimulated by TRIM34 overexpression was rescued after rapamycin treatment. TRIM34 activated mTORC1 to suppress NSCLC progression in vivo. TRIM34 suppressed NSCLC via inducing mTORC1-dependent glucose utilization and promoting cellular death. The results suggest that TRIM34 can be a useful therapeutic biomarker for NSCLC patients.

Risk factors of neuropathic pain in multiple sclerosis: a retrospective case-cohort study.

Background: Pain is a common symptom in multiple sclerosis (MS), especially neuropathic pain, which has a significant impact on patients' mental and physical health and quality of life. However, risk factors that related to neuropathic pain, still remain unclear. Objective: The study aimed to explore the risk factors of neuropathic pain among MS patients. Materials and methods: This retrospective study examined the consecutive patients diagnosed with MS in the Department of Neurology of Guangdong Provincial Hospital of Chinese Medicine between August 2011 and October 2022. Neuropathic pain was defined as "pain arising as a direct consequence of a lesion or disease affecting the somatosensory system". Demographic and clinical features were obtained from the electronic system of the hospital. Results: Our cohort revealed that the prevalence of patients with neuropathic pain in MS was 34.1%. The results indicated that the longer the spinal lesions, the greater the neuropathic pain risks (2-4: OR, 13.3(2.1-82), >5: OR, 15.2(2.7-86.8), p for tread: 0.037). Meanwhile, multivariate regression analysis showed that cervical and thoracic lesions (OR 4.276, 95% CI 1.366-13.382, P = 0.013), upper thoracic lesions (T1-T6) (OR 3.047, 95% CI 1.018-9.124, P = 0.046) were positively correlated with neuropathic pain, while basal ganglia lesions (OR 0.188, 95% CI 0.044-0.809, P = 0.025) were negatively correlated with neuropathic pain among MS patients. Conclusion: Extended spinal lesions (≥3 spinal lesions), cervical and thoracic lesions, upper thoracic lesions were independent risk factors of neuropathic pain among MS patients. Furthermore, our study found that the longer the spinal lesions, the greater the neuropathic pain risks.

Preparation of Low-Molecular-Weight Polyacrylamide as the Delayed Crosslinking Plugging Agent for Drilling Fluid.

Deep wells and ultra-deep wells often encounter cracks, karst caves, and other developed strata, which can lead to leakage during drilling. Conventional bridge slurry plugging technology is prone to leaking due to the poor plugging effect of the plugging agent. The gel plugging agent possesses characteristics of flexible plugging and adaptive matching of formation leakage channels. It can fill cracks or caves and enhance the pressure-bearing capacity of the formation. A controllable crosslinking plugging agent based on low-molecular-weight polyacrylamide was studied. Polyacrylamide with different molecular weights is synthesized from acrylamide and an initiator. A crosslinking time-controllable polymer is synthesized from low-molecular-weight polyacrylamide by adding crosslinking agent and retarder. The low-molecular-weight polyacrylamide plugging agent has low viscosity before gelation and good fluidity in the wellbore. After being configured on the ground, it is transported by pipeline and sent underground to reach the thickening condition. The gel solution rapidly solidifies, and its strength improves after high-temperature crosslinking. The synthesis conditions of the polymer were as follows: a monomer concentration of 9%, initiator 3.5%, synthesis temperature of 65 °C, and hydrogen peroxide initiator. The optimal formula of the gel plugging agent is as follows: a polymer concentration of 6%, a crosslinking agent concentration of 1%, and a retarder concentration of 8%. The generated polymer molecular structure contains amide groups. This crosslinking time-controllable plugging agent based on low-molecular-weight polyacrylamide has stable rheology, and its temperature resistance can reach 150 °C. At 150 °C, the gelation time can be controlled by adjusting the concentration of retarder, and the longest can reach 4 h. The plugging efficiency of the gel plugging agent is more than 95%. With the increase in seam width, the pressure of the gel plugging agent gradually decreases.

MRI-based prostate cancer classification using 3D efficient capsule network.

BACKGROUND: Prostate cancer (PCa) is the most common cancer in men and the second leading cause of male cancer-related death. Gleason score (GS) is the primary driver of PCa risk-stratification and medical decision-making, but can only be assessed at present via biopsy under anesthesia. Magnetic resonance imaging (MRI) is a promising non-invasive method to further characterize PCa, providing additional anatomical and functional information. Meanwhile, the diagnostic power of MRI is limited by qualitative or, at best, semi-quantitative interpretation criteria, leading to inter-reader variability. PURPOSES: Computer-aided diagnosis employing quantitative MRI analysis has yielded promising results in non-invasive prediction of GS. However, convolutional neural networks (CNNs) do not implicitly impose a frame of reference to the objects. Thus, CNNs do not encode the positional information properly, limiting method robustness against simple image variations such as flipping, scaling, or rotation. Capsule network (CapsNet) has been proposed to address this limitation and achieves promising results in this domain. In this study, we develop a 3D Efficient CapsNet to stratify GS-derived PCa risk using T2-weighted (T2W) MRI images. METHODS: In our method, we used 3D CNN modules to extract spatial features and primary capsule layers to encode vector features. We then propose to integrate fully-connected capsule layers (FC Caps) to create a deeper hierarchy for PCa grading prediction. FC Caps comprises a secondary capsule layer which routes active primary capsules and a final capsule layer which outputs PCa risk. To account for data imbalance, we propose a novel dynamic weighted margin loss. We evaluate our method on a public PCa T2W MRI dataset from the Cancer Imaging Archive containing data from 976 patients. RESULTS: Two groups of experiments were performed: (1) we first identified high-risk disease by classifying low + medium risk versus high risk; (2) we then stratified disease in one-versus-one fashion: low versus high risk, medium versus high risk, and low versus medium risk. Five-fold cross validation was performed. Our model achieved an area under receiver operating characteristic curve (AUC) of 0.83 and 0.64 F1-score for low versus high grade, 0.79 AUC and 0.75 F1-score for low + medium versus high grade, 0.75 AUC and 0.69 F1-score for medium versus high grade and 0.59 AUC and 0.57 F1-score for low versus medium grade. Our method outperformed state-of-the-art radiomics-based classification and deep learning methods with the highest metrics for each experiment. Our divide-and-conquer strategy achieved weighted Cohen's Kappa score of 0.41, suggesting moderate agreement with ground truth PCa risks. CONCLUSIONS: In this study, we proposed a novel 3D Efficient CapsNet for PCa risk stratification and demonstrated its feasibility. This developed tool provided a non-invasive approach to assess PCa risk from T2W MR images, which might have potential to personalize the treatment of PCa and reduce the number of unnecessary biopsies.

Advances in polysaccharides of natural source of anti-diabetes effect and mechanism.

PURPOSE: Diabetes is a chronic disease in metabolic disorder, and the pathology is characterized by insulin resistance and insulin secretion disorder in blood. In current, many studies have revealed that polysaccharides extracted from natural sources with significant anti-diabetic effects. Natural polysaccharides can ameliorate diabetes through different action mechanisms. All these polysaccharides are expected to have an important role in the clinic. METHODS: Existing polysaccharides for the treatment of diabetes are reviewed, and the mechanism of polysaccharides in the treatment of diabetes and its structural characteristics are described in detail. RESULTS: This article introduced the natural polysaccharide through different mechanisms of action in the treatment of diabetes, including oxidative stress, apoptosis, inflammatory response and regulation of intestinal bacteria. Natural polysaccharides can treat of diabetes by regulating signaling pathways is also a research hotspot. In addition, the structural characteristics of polysaccharides were explored. There are some structure-activity relationships between natural polysaccharides and the treatment of diabetes.

Synergy of Pd2+/S2--Doped TiO2 Supported on 2-Methylimidazolium-Functionalized Polypyrrole/Graphene Oxide for Enhanced Nitrogen Electrooxidation.

The electrosynthesis of nitrate catalyzed by electrochemical nitrogen oxidation reaction (NOR) is considered as an alternative and sustainable approach to the conventional industrial manufacture, but optimizing the electrocatalytic NOR performance and fabricating the efficient NOR electrocatalysts at the design level still encounter great challenges. Herein, unique Pd2+- and S2--doped TiO2 (Pb/S-TiO2) nanoparticles are successfully in situ grown on the surface of 2-methylimidazolium-functionalized polypyrrole/graphene oxide (2-MeIm/PPy/GO), which present the typical hierarchical micro-nanostructures, resulting in the excellent electrocatalytic NOR performance with the highest NO3 - yield of 72.69 µg h-1 mg-1 act. and the maximum Faraday efficiency of 8.92% at 2.04 V (vs reversible hydrogen electrode) due to the synergistic effect of each component. Due to the doping effect, the appropriate oxygen evolution reaction (OER) activity is achieved by Ti-site, where OER principally occurs, providing *O during the non-electrochemical step of NOR, while the electrocatalytic NOR process as the electrochemical conversion of inert N2 to active *NO intermediates mainly occurs at the Pd-site. Especially, the sulfate radicals in situ formed on Pb/S-TiO2@2-MeIm/PPy/GO further promote nitrogen adsorption and decrease the reaction energy barrier, resulting in the acceleration of NOR. It provides theoretical and practical experience for the design and preparation of NOR electrocatalysts.

High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling.

Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion diagnosis, prognosis, and delineation. However, gradient power and hardware limitations prohibit recording thin slices or sub-1 mm resolution. Furthermore, long scan time is not clinically acceptable. Conventional high-resolution images generated using statistical or analytical methods include the limitation of capturing complex, high-dimensional image data with intricate patterns and structures. This study aims to harness cutting-edge diffusion probabilistic deep learning techniques to create a framework for generating high-resolution MRI from low-resolution counterparts, improving the uncertainty of denoising diffusion probabilistic models (DDPM).Approach. DDPM includes two processes. The forward process employs a Markov chain to systematically introduce Gaussian noise to low-resolution MRI images. In the reverse process, a U-Net model is trained to denoise the forward process images and produce high-resolution images conditioned on the features of their low-resolution counterparts. The proposed framework was demonstrated using T2-weighted MRI images from institutional prostate patients and brain patients collected in the Brain Tumor Segmentation Challenge 2020 (BraTS2020).Main results. For the prostate dataset, the bicubic interpolation model (Bicubic), conditional generative-adversarial network (CGAN), and our proposed DDPM framework improved the noise quality measure from low-resolution images by 4.4%, 5.7%, and 12.8%, respectively. Our method enhanced the signal-to-noise ratios by 11.7%, surpassing Bicubic (9.8%) and CGAN (8.1%). In the BraTS2020 dataset, the proposed framework and Bicubic enhanced peak signal-to-noise ratio from resolution-degraded images by 9.1% and 5.8%. The multi-scale structural similarity indexes were 0.970 ± 0.019, 0.968 ± 0.022, and 0.967 ± 0.023 for the proposed method, CGAN, and Bicubic, respectively.Significance. This study explores a deep learning-based diffusion probabilistic framework for improving MR image resolution. Such a framework can be used to improve clinical workflow by obtaining high-resolution images without penalty of the long scan time. Future investigation will likely focus on prospectively testing the efficacy of this framework with different clinical indications.