A new terpestacin-type sesterterpenoid with tricyclic 3/15/5 scaffold from an endophytic fungus sp. J019.

ABSTACTChemical investigation of an endophytic fungus, sp. J019, isolated from the plant species R. Br.

PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification.

Deep learning (DL) models are effective in leveraging latent representations from MR data, emerging as state-of-the-art solutions for accelerated MRI reconstruction. However, challenges arise due to the inherent uncertainties associated with undersampling in k-space, coupled with the over- or under-parameterized and opaque nature of DL models. Addressing uncertainty has thus become a critical issue in DL MRI reconstruction.

Fe-based nanozyme with photothermal activity prepared from polymerization-induced self-assembly assays boosts the recovery of bacteria-infected wounds.

Nowadays, the overuse of antibiotics has escalated bacterial infections into an increasingly severe global health threat. Developing non-antibiotic treatments has emerged as a promising strategy for treating bacterial infections. Notably, nanozyme-based composite materials have garnered growing interest.

Unbiased and reproducible liver MRI-PDFF estimation using a scan protocol-informed deep learning method.

Objective: To estimate proton density fat fraction (PDFF) from chemical shift encoded (CSE) MR images using a deep learning (DL)-based method that is precise and robust to different MR scanners and acquisition echo times (TEs).

Methods: Variable echo times neural network (VET-Net) is a two-stage framework that first estimates nonlinear variables of the CSE-MR signal model, to posteriorly estimate water/fat signal components using the least-squares method. VET-Net incorporates a vector with TEs as an auxiliary input, therefore enabling PDFF calculation with any TE setting.

In Vivo Delivery Processes and Development Strategies of Lipid Nanoparticles.

Lipid nanoparticles (LNPs) represent an advanced and highly efficient delivery system for RNA molecules, demonstrating exceptional biocompatibility and remarkable delivery efficiency. This is evidenced by the clinical authorization of three LNP formulations: Patisiran, BNT162b2, and mRNA-1273. To further maximize the efficacy of RNA-based therapy, it is imperative to develop more potent LNP delivery systems that can effectively protect inherently unstable and negatively charged RNA molecules from degradation by nucleases, while facilitating their cellular uptake into target cells.

Multilevel Fine-Grained Features-Based General Framework for Object Detection.

This article proposes a practical and generalizable object detector, termed feature extraction-fusion-prediction network (FEFP-Net) for real-world application scenarios. The existing object detection methods have recently achieved excellent performance, however they still face three major challenges for real-world applications, i.e.

NUP85 alleviates lipid metabolism and inflammation by regulating PI3K/AKT signaling pathway in nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) is one of the common causes of chronic liver disease in the world. The problem of NAFLD had become increasingly prominent. However, its pathogenesis is still indistinct.

Platelet RNA sequencing reveals profile of caffeic acid affecting hemostasis in mice.

Background: Caffeic acid (CA) is a naturally occurring phenolic compound with diverse pharmacologic properties. CA plays a crucial role in hemostasis by increasing platelet count. However, the mechanism by which CA regulates platelets to promote hemostasis remains unclear.

Carnosine regulation of intracellular pH homeostasis promotes lysosome-dependent tumor immunoevasion.

Tumor cells and surrounding immune cells undergo metabolic reprogramming, leading to an acidic tumor microenvironment. However, it is unclear how tumor cells adapt to this acidic stress during tumor progression. Here we show that carnosine, a mobile buffering metabolite that accumulates under hypoxia in tumor cells, regulates intracellular pH homeostasis and drives lysosome-dependent tumor immune evasion.

McSTRA: A multi-branch cascaded swin transformer for point spread function-guided robust MRI reconstruction.

Deep learning MRI reconstruction methods are often based on Convolutional neural network (CNN) models; however, they are limited in capturing global correlations among image features due to the intrinsic locality of the convolution operation. Conversely, the recent vision transformer models (ViT) are capable of capturing global correlations by applying self-attention operations on image patches. Nevertheless, the existing transformer models for MRI reconstruction rarely leverage the physics of MRI.

Deep learning based synthesis of MRI, CT and PET: Review and analysis.

Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). However, translating between two image modalities presents difficulties due to the complex and non-linear domain mappings.

Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images.

Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans.

A Dynamic Weights-Based Wavelet Attention Neural Network for Defect Detection.

Automatic defect detection plays an important role in industrial production. Deep learning-based defect detection methods have achieved promising results. However, there are still two challenges in the current defect detection methods: 1) high-precision detection of weak defects is limited and 2) it is difficult for current defect detection methods to achieve satisfactory results dealing with strong background noise.

Co-delivery of a tumor microenvironment-responsive disulfiram prodrug and CuO nanoparticles for efficient cancer treatment.

Disulfiram (DSF) has been used as a hangover drug for more than seven decades and was found to have potential in cancer treatment, especially mediated by copper. However, the uncoordinated delivery of disulfiram with copper and the instability of disulfiram limit its further applications. Herein, we synthesize a DSF prodrug using a simple strategy that could be activated in a specific tumor microenvironment.

Xanthatin induce DDP-resistance lung cancer cells apoptosis through regulation of GLUT1 mediated ROS accumulation.

Chemoresistance to cisplatin (DDP) therapy is a major obstacle that needs to be overcome in treating lung cancer patients. Xanthatin has been reported to exhibit an antitumor effect on various cancers, but the function of xanthatin in DDP-resistance lung cancer remains unclear. The study aimed to explore the effect and mechanisms of xanthatin on proliferation, apoptosis, and migration in DDP-resistance lung cancer cells.

Iron-siRNA Nanohybrids for Enhanced Chemodynamic Therapy via Ferritin Heavy Chain Downregulation.

Ferrous iron (Fe ) has more potent hydroxyl radical (⋅OH)-generating ability than other Fenton-type metal ions, making Fe-based nanomaterials attractive for chemodynamic therapy (CDT). However, because Fe can be converted by ferritin heavy chain (FHC) to nontoxic ferric form and then sequestered in ferritin, therapeutic outcomes of Fe-mediated CDT agents are still far from satisfactory. Here we report the synthesis of siRNA-embedded Fe nanoparticles (Fe -siRNA NPs) for self-reinforcing CDT via FHC downregulation.

Secreted protease PRSS35 suppresses hepatocellular carcinoma by disabling CXCL2-mediated neutrophil extracellular traps.

Hepatocytes function largely through the secretion of proteins that regulate cell proliferation, metabolism, and intercellular communications. During the progression of hepatocellular carcinoma (HCC), the hepatocyte secretome changes dynamically as both a consequence and a causative factor in tumorigenesis, although the full scope of secreted protein function in this process remains unclear. Here, we show that the secreted pseudo serine protease PRSS35 functions as a tumor suppressor in HCC.

Recent advances of Phosphodiesterase 4B in cancer.

Introduction: Phosphodiesterase 4B (PDE4B) is a crucial enzyme in the phosphodiesterases (PDEs), acting as a regulator of cyclic adenosine monophosphate (cAMP). It is involved in cancer process through PDE4B/cAMP signaling pathway. Cancer occurs and develops with the regulation of PDE4B in the body, suggesting that PDE4B is a promising therapeutic target.

Research progress on the role of cholesterol in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is often diagnosed at advanced stages with no effective treatment options. Mechanistically, it is a complex biological process. Recently, the main cause of its incidence is changing from viral to non-viral.

Influence of , polymorphisms and metabolism rate on tacrolimus exposure in renal post-transplant recipients.

To investigate the influence of and polymorphisms on tarcolimus metabolism and renal function for renal transplantation recipients at a stable period. and polymorphisms, together with other clinical factors, were collected for 149 renal transplantation patients at postoperative stable period. Statistics analysis was performed to explore key factors affecting tarcolimus metabolism.

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements.