Soluble expression of hMYDGF was improved by strain engineering and optimizations of fermentation strategies in Escherichia coli.

Myeloid-derived growth factor (MYDGF) is a cytokine that exhibits a variety of biological functions. This study focused on utilizing BL21(DE3) strain engineering and fermentation strategies to achieve high-level expression of soluble human MYDGF (hMYDGF) in Escherichia coli. Initially, the E. coli expressing strain BL21(DE3) was engineered by deleting the IpxM gene and inserting the GROEL/S and Trigger factor genes. The engineered E. coli strain BL21(TG)/pT-MYDGF accumulated 3557.3 ± 185.6 µg/g and 45.7 ± 6.7 mg/L of soluble hMYDGF in shake flask fermentation, representing a 15.6-fold increase compared to the control strain BL21(DE3)/pT-MYDGF. Furthermore, the yield of hMYDGF was significantly enhanced by optimizing the fermentation conditions. Under optimized conditions, the 5L bioreactor yielded up to 2665.8 ± 164.3 µg/g and 407.6 ± 42.9 mg/L of soluble hMYDGF. The results indicate that the implementation of these optimization strategies could enhance the ratio and yield of soluble proteins expressed by E.coli, thereby meeting the demands of industrial production. This study employed sophisticated strategies to lay a solid foundation for the industrial application of hMYDGF.

Predicting cachexia in hepatocellular carcinoma patients: a nomogram based on MRI features and body composition.

BACKGROUND: Approximately half of all patients with hepatocellular carcinoma (HCC) develop cachexia during the course of the disease. It is important to be able to predict which patients will develop cachexia at an early stage. PURPOSE: To develop and validate a nomogram based on the magnetic resonance imaging (MRI) features of HCC and body composition for potentially predicting cachexia in patients with HCC. MATERIAL AND METHODS: A retrospective two-center study recruited the pretreatment clinical and MRI data of 411 patients with HCC undergoing abdominal MRI. The data were divided into three cohorts for development, internal validation, and external validation. Patients were followed up for six months after the MRI scan to record each patient's weight to diagnose cachexia. Logistic regression analyses were performed to identify independent variables associated with cachexia in the development cohort used to build the nomogram. RESULTS: The multivariable analysis suggested that the MRI parameters of tumor size > 5 cm (P = 0.001), intratumoral artery (P = 0.004), skeletal muscle index (P < 0.001), and subcutaneous fat area (P = 0.004) were independent predictors of cachexia in patients with HCC. The nomogram derived from these parameters in predicting cachexia reached an area under receiver operating characteristic curve of 0.819, 0.783, and 0.814 in the development, and internal and external validation cohorts, respectively. CONCLUSION: The proposed multivariable nomogram suggested good performance in predicting the risk of cachexia in HCC patients.

Tropheryma whipplei pneumonia revealed by Metagenomic next-generation sequencing: Report of two cases.

Tropheryma whipplei is the causative agent of Whipple's disease, which is a rare multiorgan systemic disease. We report two cases of Tropheryma whipplei infection, all routine tests were negative and it was finally detected by mNGS. This may help clinicians increase awareness of the diagnosis and treatment of acute severe pneumonia and interstitial pneumonia caused by Tropheryma whipplei.

Development and Validation of a Biparametric MRI Deep Learning Radiomics Model with Clinical Characteristics for Predicting Perineural Invasion in Patients with Prostate Cancer.

RATIONALE AND OBJECTIVES: Perineural invasion (PNI) is an important prognostic biomarker for prostate cancer (PCa). This study aimed to develop and validate a predictive model integrating biparametric MRI-based deep learning radiomics and clinical characteristics for the non-invasive prediction of PNI in patients with PCa. MATERIALS AND METHODS: In this prospective study, 557 PCa patients who underwent preoperative MRI and radical prostatectomy were recruited and randomly divided into the training and the validation cohorts at a ratio of 7:3. Clinical model for predicting PNI was constructed by univariate and multivariate regression analyses on various clinical indicators, followed by logistic regression. Radiomics and deep learning methods were used to develop different MRI-based radiomics and deep learning models. Subsequently, the clinical, radiomics, and deep learning signatures were combined to develop the integrated deep learning-radiomics-clinical model (DLRC). The performance of the models was assessed by plotting the receiver operating characteristic (ROC) curves and precision-recall (PR) curves, as well as calculating the area under the ROC and PR curves (ROC-AUC and PR-AUC). The calibration curve and decision curve were used to evaluate the model's goodness of fit and clinical benefit. RESULTS: The DLRC model demonstrated the highest performance in both the training and the validation cohorts, with ROC-AUCs of 0.914 and 0.848, respectively, and PR-AUCs of 0.948 and 0.926, respectively. The DLRC model showed good calibration and clinical benefit in both cohorts. CONCLUSION: The DLRC model, which integrated clinical, radiomics, and deep learning signatures, can serve as a robust tool for predicting PNI in patients with PCa, thus aiding in developing effective treatment strategies.

Enhancing zinc biofortification and mitigating cadmium toxicity in soil-earthworm-spinach systems using different zinc sources.

Cadmium (Cd) pollution poses significant threats to soil organisms and human health by contaminating the food chain. This study aimed to assess the impact of various concentrations (50, 250, and 500 mg·kg-1) of zinc oxide nanoparticles (ZnO NPs), bulk ZnO, and ZnSO4 on morphological changes and toxic effects of Cd in the presence of earthworms and spinach. The results showed that Zn application markedly improved spinach growth parameters (such as fresh weight, plant height, root length, and root-specific surface area) and root morphology while significantly reducing Cd concentration and Cd bioconcentration factors (BCF-Cd) in spinach and earthworms, with ZnO NPs exhibiting the most pronounced effects. Earthworm, spinach root, and shoot Cd concentration decreased by 82.3 %, 77.0 %, and 75.6 %, respectively, compared to CK. Sequential-step extraction (BCR) analysis revealed a shift in soil Cd from stable to available forms, consistent with the available Cd (DTPA-Cd) results. All Zn treatments significantly reduced Cd accumulation, alleviated Cd-induced stress, and promoted spinach growth, with ZnO NPs demonstrating the highest Cd reduction and Zn bioaugmentation efficiencies compared to bulk ZnO and ZnSO4 at equivalent concentrations. Therefore, ZnO NPs offer a safer and more effective option for agricultural production and soil heavy metal pollution management than other Zn fertilizers.

Environmental efficacy of polyethylene microplastics: Enhancing the solidification of CuO nanoparticles and reducing the physiological toxicity to peanuts.

Microplastics and metal-based nanoparticles (NPs) are environmental pollutants that have attracted significant attention. However, there have been relatively few studies on the combined pollution of these substances in the soil-plant system. To investigate the environmental impact and interaction mechanisms of these two pollutants, a pot experiment was conducted to examine the effects of soil exposure on peanut growth. The experiment results revealed that polyethylene (PE) had a minimal effect on peanut growth, while CuO NPs significantly inhibited peanut growth. Peanut biomass decreased by over 50 % in all Cu treatments. The presence of PE significantly impacted the dissolution and absorption of CuO NPs. When 0.5 % PE was present, the dissolution and transformation of CuO NPs were limited, resulting in a total Cu concentration of 458 mg/kg. Conversely, when 5 % PE was present, the dissolution and transformation of CuO NPs were promoted, leading to a DTPA-Cu concentration of 141 mg/kg, the highest level observed. The distribution of trace elements in peanut stems also responded to the differences in Cu concentration. Both pollutants significantly disrupted soil bacteria, with CuO NPs having a more pronounced effect than PE. Throughout the entire growth cycle of peanuts, no chemical adsorption occurred between PE and CuO NPs, and CuO NPs had no significant impact on the aging rate of PE. In summary, this study provides insights into the environmental impact and transport mechanisms of composite pollution involving microplastics and metal-based nanoparticles in the soil-peanut system.

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.

Development of a Dual-Factor Activatable Covalent Targeted Photoacoustic Imaging Probe for Tumor Imaging.

Photoacoustic imaging (PAI) is an emerging modality in biomedical imaging with superior imaging depth and specificity. However, PAI still has significant limitations, such as the background noise from endogenous chromophores. To overcome these limitations, we developed a covalent activity-based PAI probe, NOx-JS013, targeting NCEH1. NCEH1, a highly expressed and activated serine hydrolase in aggressive cancers, has the potential to be employed for the diagnosis of cancers. We show that NOx-JS013 labels active NCEH1 in live cells with high selectivity relative to other serine hydrolases. NOx-JS013 also presents its efficacy as a hypoxia-responsive imaging probe in live cells. Finally, NOx-JS013 successfully visualizes aggressive prostate cancer tumors in mouse models of PC3, while being negligibly detected in tumors of non-aggressive LNCaP mouse models. These findings show that NOx-JS013 has the potential to be used to develop precision PAI reagents for detecting metastatic progression in various cancers.

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.

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.

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.

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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.

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.

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.

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.

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.

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.

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.

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.