A Multimodal Point Cloud-Based Method for Tumor Localization in Robotic Ultrasound-Guided Radiotherapy.

Objectives: Part of the tumor localization methods in radiotherapy have poor real-time performance and may generate additional radiation. We propose a multimodal point cloud-based method for tumor localization in robotic ultrasound-guided radiotherapy, which only irradiates computed tomography (CT) during radiotherapy planning to avoid additional radiation. Methods: The tumor position was determined using the CT point cloud, and the red green blue depth (RGBD) point cloud was used to determine body surface scanning location corresponding to the tumor location. The relationship between the CT point cloud and RGBD point cloud was established through multi-modal point cloud registration. The point cloud was then used for robot tumor localization through coordinate transformation between camera and robot. Results: The maximum mean absolute error of the tumor location in the X, Y, and Z directions of the robot coordinate system were 0.781, 1.334, and 1.490 mm, respectively. The average point-to-point translation mean absolute error between the actual and predicted positions of the localization points was 1.847 mm. The maximum error in the random positioning experiment was 1.77 mm. Conclusion: The proposed method is radiation free and has real-time performance, with tumor localization accuracy that meets the requirements of radiotherapy. The proposed method, which potentially reduces the risks associated with radiation exposure while ensuring efficient and accurate tumor localization, represents a promising advancement in the field of radiotherapy.

Metal implant segmentation in CT images based on diffusion model.

BACKGROUND: Computed tomography (CT) is widely in clinics and is affected by metal implants. Metal segmentation is crucial for metal artifact correction, and the common threshold method often fails to accurately segment metals. PURPOSE: This study aims to segment metal implants in CT images using a diffusion model and further validate it with clinical artifact images and phantom images of known size. METHODS: A retrospective study was conducted on 100 patients who received radiation therapy without metal artifacts, and simulated artifact data were generated using publicly available mask data. The study utilized 11,280 slices for training and verification, and 2,820 slices for testing. Metal mask segmentation was performed using DiffSeg, a diffusion model incorporating conditional dynamic coding and a global frequency parser (GFParser). Conditional dynamic coding fuses the current segmentation mask and prior images at multiple scales, while GFParser helps eliminate high-frequency noise in the mask. Clinical artifact images and phantom images are also used for model validation. RESULTS: Compared with the ground truth, the accuracy of DiffSeg for metal segmentation of simulated data was 97.89% and that of DSC was 95.45%. The mask shape obtained by threshold segmentation covered the ground truth and DSCs were 82.92% and 84.19% for threshold segmentation based on 2500 HU and 3000 HU. Evaluation metrics and visualization results show that DiffSeg performs better than other classical deep learning networks, especially for clinical CT, artifact data, and phantom data. CONCLUSION: DiffSeg efficiently and robustly segments metal masks in artifact data with conditional dynamic coding and GFParser. Future work will involve embedding the metal segmentation model in metal artifact reduction to improve the reduction effect.

CMAF-Net: a cross-modal attention fusion-based deep neural network for incomplete multi-modal brain tumor segmentation.

Background: The information between multimodal magnetic resonance imaging (MRI) is complementary. Combining multiple modalities for brain tumor image segmentation can improve segmentation accuracy, which has great significance for disease diagnosis and treatment. However, different degrees of missing modality data often occur in clinical practice, which may lead to serious performance degradation or even failure of brain tumor segmentation methods relying on full-modality sequences to complete the segmentation task. To solve the above problems, this study aimed to design a new deep learning network for incomplete multimodal brain tumor segmentation. Methods: We propose a novel cross-modal attention fusion-based deep neural network (CMAF-Net) for incomplete multimodal brain tumor segmentation, which is based on a three-dimensional (3D) U-Net architecture with encoding and decoding structure, a 3D Swin block, and a cross-modal attention fusion (CMAF) block. A convolutional encoder is initially used to extract the specific features from different modalities, and an effective 3D Swin block is constructed to model the long-range dependencies to obtain richer information for brain tumor segmentation. Then, a cross-attention based CMAF module is proposed that can deal with different missing modality situations by fusing features between different modalities to learn the shared representations of the tumor regions. Finally, the fused latent representation is decoded to obtain the final segmentation result. Additionally, channel attention module (CAM) and spatial attention module (SAM) are incorporated into the network to further improve the robustness of the model; the CAM to help focus on important feature channels, and the SAM to learn the importance of different spatial regions. Results: Evaluation experiments on the widely-used BraTS 2018 and BraTS 2020 datasets demonstrated the effectiveness of the proposed CMAF-Net which achieved average Dice scores of 87.9%, 81.8%, and 64.3%, as well as Hausdorff distances of 4.21, 5.35, and 4.02 for whole tumor, tumor core, and enhancing tumor on the BraTS 2020 dataset, respectively, outperforming several state-of-the-art segmentation methods in missing modalities situations. Conclusions: The experimental results show that the proposed CMAF-Net can achieve accurate brain tumor segmentation in the case of missing modalities with promising application potential.

Humidity-Responsive Amorphous Calcium-Magnesium Pyrophosphate/Cassava Starch Scaffold for Enhanced Neurovascular Bone Regeneration.

Developing a neurovascular bone repair scaffold with an appropriate mechanical strength remains a challenge. Calcium phosphate (CaP) is similar to human bone, but its scaffolds are inherently brittle and inactive, which require recombination with active ions and polymers for bioactivity and suitable strength. This work discussed the synthesis of amorphous magnesium-calcium pyrophosphate (AMCP) and the subsequent development of a humidity-responsive AMCP/cassava starch (CS) scaffold. The scaffold demonstrated enhanced mechanical properties by strengthening the intermolecular hydrogen bonds and ionic bonds between AMCP and CS during the gelatinization and freeze-thawing processes. The release of active ions was rapid initially and stabilized into a long-term stable release after 3 days, which is well-matched with new bone growth. The release of pyrophosphate ions endowed the scaffold with antibacterial properties. At the cellular level, the released active ions simultaneously promoted the proliferation and mineralization of osteoblasts, the proliferation and migration of endothelial cells, and the proliferation of Schwann cells. At the animal level, the scaffold was demonstrated to promote vascular growth and peripheral nerve regeneration in a rat skull defect experiment, ultimately resulting in the significant and rapid repair of bone defects. The construction of the AMCP/CS scaffold offers practical suggestions and references for neurovascular bone repair.

Disinfection-residual bacteria (DRB) after chlorine dioxide treatment: Microbial community structure, regrowth potential, and secretion characteristics.

This study investigates the effects of chlorine dioxide (ClO2) disinfection on the community structure, regrowth potential, and metabolic product secretion of disinfection-residual bacteria (DRB) in secondary effluent (SE), denitrification filter effluent (DFE), and ultrafiltration effluent (UE). Results show that ClO2 effectively reduces bacteria in SE and UE, achieving log removal values exceeding 3 at 1 mg/L within 30 min. A salient positive correlation (R2 > 0.95) exists between changes in total fluorescence intensity and disinfection efficacy. Post-treatment, Acinetobacter abundance increased in SE, while Pseudomonas decreased in DFE and UE. At lower ClO2 concentrations, Staphylococcus, Mycobacterium, Aeromonas, and Lactobacillus increased in DFE, but decreased at higher concentrations. After storage, bacterial counts in disinfected samples exceeded those in the control group, surpassing 105 CFU/mL. Despite an initial decline, species richness and evenness partially recovered but remained lower than control levels. Culturing DRB for 72 h showed elevated extracellular polymeric substances (EPS) secretion, quantified as total organic carbon (TOC), ranging from 5 to 27 mg/L, with significantly higher EPS in the disinfection group. Parallel factor analysis with self-organizing maps (PARAFAC-SOM) effectively differentiated water sample types and EPS fluorescent substances, underscoring the potential of three-dimensional fluorescence as an indirect measure of ClO2 disinfection efficacy.

DiffRecon: Diffusion-based CT reconstruction with cross-modal deformable fusion for DR-guided non-coplanar radiotherapy.

In non-coplanar radiotherapy, DR is commonly used for image guiding which needs to fuse intraoperative DR with preoperative CT. But this fusion task performs poorly, suffering from unaligned and dimensional differences between DR and CT. CT reconstruction estimated from DR could facilitate this challenge. Thus, We propose a unified generation and registration framework, named DiffRecon, for intraoperative CT reconstruction based on DR using the diffusion model. Specifically, we use the generation model for synthesizing intraoperative CTs to eliminate dimensional differences and the registration model for aligning synthetic CTs to improve reconstruction. To ensure clinical usability, CT is not only estimated from DR but the preoperative CT is also introduced as prior. We design a dual-encoder to learn prior knowledge and spatial deformation among pre- and intra-operative CT pairs and DR parallelly for 2D/3D feature deformable conversion. To calibrate the cross-modal fusion, we insert cross-attention modules to enhance the 2D/3D feature interaction between dual encoders. DiffRecon has been evaluated by both image quality metrics and dosimetric indicators. The high image synthesis metrics are with RMSE of 0.02±0.01, PSNR of 44.92±3.26, and SSIM of 0.994±0.003. The mean gamma passing rates between rCT and sCT for 1%/1 mm, 2%/2 mm and 3%/3 mm acceptance criteria are 95.2%, 99.4% and 99.9% respectively. The proposed DiffRecon can reconstruct CT accurately from a single DR projection with excellent image generation quality and dosimetric accuracy. These demonstrate that the method can be applied in non-coplanar adaptive radiotherapy workflows.

Self-assembling gelatin based delivery of multienzyme activity nanozyme and photosensitizer for ROS storm based cancer therapy.

Nanozymes with multienzyme activity for reactive oxygen species (ROS) generation and intracellular redox imbalance are attractive strategy for cancer therapy. However, it is severely limited by low biocompatibility and catalytic efficiency, hypoxic and high levels of GSH in the tumor microenvironment. To address these issues, a copper doping carbon nanozyme (CC) with multienzyme activity was designed and integrated with photosensitizer Ce6 and gelatin to fabricate ROS amplifier (CCC). Gelatin endowed CCC with good biocompatibility, low hemolysis, and enzyme responsive degradation. CCC with high CAT-like, POD-like, OXD-like, and GSHox-like activities can induce the intracellular ROS storm formation to eliminate the cancer cells. The OXD-like activity and PDT performance mediated 1O2 generation was markedly potentiated by the CAT-like activity of CCC via catalyzing high expression of H2O2 to generate O2. At the same time, a large amount of ·OH were produced through POD-like activity of CCC and GSH was depleted by the GSHox-like activities of CCC, resulting in a destructive ROS storm formation and cellular redox homeostasis disruption. Both in vivo and in vitro experiments showed that CCC displayed satisfactory anti-tumor activity and biocompatibility. Our work provides a novel strategy for the development of nanozyne enhanced photodynamic therapy of cancer.

MR-CT image fusion method of intracranial tumors based on Res2Net.

BACKGROUND: Information complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation. PURPOSE: The purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method. METHODS: This paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image. RESULTS: Qualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics. CONCLUSION: The proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

Amifostine loaded lipid-calcium carbonate nanoparticles as an oral drug delivery system for radiation protection.

Amifostine (AMF) as the first-line radiation protection drug, usually suffered from low compliance and short half-life upon clinical applications. The development of oral drug delivery system (DDS) for AMF is a promising solution. However, the inherent shortages of AMF present significant challenges in the design of suitable oral DDS. Here in this study, we utilized the ability of calcium ions to bind with AMF and prepared AMF loaded calcium carbonate (CC) core, CC/AMF, using phase transferred coprecipitation method. We further modified the CC/AMF using phospholipids to prepare AMF loaded lipid-calcium carbonate (LCC) hybrid nanoparticles (LCC/AMF) via a thin-film dispersion method. LCC/AMF combines the oral advantages of lipid nanoparticles with the drug-loading capabilities of CC, which was shown as uniform nano-sized formulation with decent stability in aqueous solution. With favorable intestinal transport and absorption effects, it effectively enhances the in vivo radiation protection efficacy of AMF through oral administration. More importantly, we further investigated the cellular accumulation profile and intracellular transport mechanism of LCC/AMF using MDCK and Caco-2 cell lines as models. This research not only alters the current administration method of AMF to enhance its convenience and compliance, but also provides insights and guidance for the development of more suitable oral DDS for AMF in the future.

Highly Stable Amorphous (Pyro)phosphate Aggregates: Pyrophosphate as a Carrier for Bioactive Ions and Drugs in Bone Repair Applications.

Pyrophosphate is widely used as an iron supplement because of its excellent complexation and hydrolysis ability; however, there are few reports on the use of pyrophosphate in active ionophores for bone repair. In this research, we proposed a simple and efficient ultrasonic method to prepare magnesium-calcium (pyro)phosphate aggregates (AMCPs). Due to strong hydration, AMCPs maintain a stable amorphous form even at high temperatures (400 °C). By changing the molar ratio of calcium and magnesium ions, the content of calcium and magnesium ions can be customized. AMCPs had surface negativity and complexing ability that realized the controlled release of ions (Ca2+, Mg2+, and P) and drugs (such as doxorubicin) over a long period. Pyrophosphate gave it an excellent bacteriostatic effect. Increasingly released Mg2+ exhibited improved bioactivity though the content of Ca2+ decreased. While Mg2+ content was regulated to 15 wt %, it performed significantly enhanced stimulation on the proliferation, attachment, and differentiation (ALP activity, calcium nodules, and the related gene expression of osteogenesis) of mouse embryo osteoblast precursor cells (MC3T3-E1). Furthermore, the high content of Mg2+ also effectively promoted the proliferation, attachment, and migration of human umbilical vein endothelial cells (HUVECs) and the expression of angiogenic genes. In conclusion, pyrophosphate was an excellent carrier for bioactive ions, and the AMCPs we prepared had a variety of active functions for multiscenario bone repair applications.

Recent advances in microfluidic-based spectroscopic approaches for pathogen detection.

Rapid identification of pathogens with higher sensitivity and specificity plays a significant role in maintaining public health, environmental monitoring, controlling food quality, and clinical diagnostics. Different methods have been widely used in food testing laboratories, quality control departments in food companies, hospitals, and clinical settings to identify pathogens. Some limitations in current pathogens detection methods are time-consuming, expensive, and laborious sample preparation, making it unsuitable for rapid detection. Microfluidics has emerged as a promising technology for biosensing applications due to its ability to precisely manipulate small volumes of fluids. Microfluidics platforms combined with spectroscopic techniques are capable of developing miniaturized devices that can detect and quantify pathogenic samples. The review focuses on the advancements in microfluidic devices integrated with spectroscopic methods for detecting bacterial microbes over the past five years. The review is based on several spectroscopic techniques, including fluorescence detection, surface-enhanced Raman scattering, and dynamic light scattering methods coupled with microfluidic platforms. The key detection principles of different approaches were discussed and summarized. Finally, the future possible directions and challenges in microfluidic-based spectroscopy for isolating and detecting pathogens using the latest innovations were also discussed.

Tumor Cell Membrane-Encapsulated MLA Solid Lipid Nanoparticles for Targeted Diagnosis and Radiosensitization Therapy of Cutaneous Squamous Cell Carcinoma.

Squamous cell carcinoma (SCC) is a common nonmelanoma skin cancer. Radiotherapy plays an integral role in treating SCC due to its characteristics, such as diminished intercellular adhesion, heightened cell migration and invasion capabilities, and immune evasion. These problems lead to inaccurate tumor boundary positioning and radiotherapy tolerance in SCC treatment. Thus, accurate localization and enhanced radiotherapy sensitivity are imperative for effective SCC treatment. To address the existing limitations in SCC therapy, we developed monoglyceride solid lipid nanoparticles (MG SLNs) and enveloped them with the A431 cell membrane (A431 CM) to create A431@MG. The characterization results showed that A431@MG was spherical. Furthermore, A431@MG had specific targeting for A431 cells. In A431 tumor-bearing mice, A431@MG demonstrated prolonged accumulation within tumors, ensuring precise boundary localization of SCC. We further advanced the approach by preparing MG SLNs encapsulating 5-aminolevulinic acid methyl ester (MLA) and desferrioxamine (DFO) with an A431 CM coating to yield A431@MG-MLA/DFO. Several studies have revealed that DFO effectively reduced iron content, impeding protoporphyrin IX (PpIX) biotransformation and promoting PpIX accumulation. Simultaneously, MLA was metabolized into PpIX upon cellular entry. During radiotherapy, the heightened PpIX levels enhanced reactive oxygen species (ROS) generation, inducing DNA and mitochondrial damage and leading to cell apoptosis. In A431 tumor-bearing mice, the A431@MG-MLA/DFO group exhibited notable radiotherapy sensitization, displaying superior tumor growth inhibition. Combining A431@MG-MLA/DFO with radiotherapy significantly improved anticancer efficacy, highlighting its potential to serve as an integrated diagnostic and therapeutic strategy for SCC.

Polyvinylpyrrolidone Assisted One-Pot Synthesis of Size-Tunable Cocktail Nanodrug for Multifunctional Combat of Cancer.

Background: The in vivo barriers and multidrug resistance (MDR) are well recognized as great challenges for the fulfillment of antitumor effects of current drugs, which calls for the development of novel therapeutic agents and innovative drug delivery strategies. Nanodrug (ND) combining multiple drugs with distinct modes of action holes the potential to circumvent these challenges, while the introduction of photothermal therapy (PTT) can give further significantly enhanced efficacy in cancer therapy. However, facile preparation of ND which contains dual drugs and photothermal capability with effective cancer treatment ability has rarely been reported. Methods: In this study, we selected curcumin (Cur) and doxorubicin (Dox) as two model drugs for the creation of a cocktail ND (Cur-Dox ND). We utilized polyvinylpyrrolidone (PVP) as a stabilizer and regulator to prepare Cur-Dox ND in a straightforward one-pot method. Results: The size of the resulting Cur-Dox ND can be easily adjusted by tuning the charged ratios. It was noted that both loaded drugs in Cur-Dox ND can realize their functions in the same target cell. Especially, the P-glycoprotein inhibition effect of Cur can synergistically cooperate with Dox, leading to enhanced inhibition of 4T1 cancer cells. Furthermore, Cur-Dox ND exhibited pH-responsive dissociation of loaded drugs and a robust photothermal translation capacity to realize multifunctional combat of cancer for photothermal enhanced anticancer performance. We further demonstrated that this effect can also be realized in 3D multicellular model, which possibly attributed to its superior drug penetration as well as photothermal-enhanced cellular uptake and drug release. Conclusion: In summary, Cur-Dox ND might be a promising ND for better cancer therapy.

Advances in Composite Biofilm Biomimetic Nanodrug Delivery Systems for Cancer Treatment.

Single biofilm biomimetic nanodrug delivery systems based on single cell membranes, such as erythrocytes and cancer cells, have immune evasion ability, good biocompatibility, prolonged blood circulation, and high tumor targeting. Because of the different characteristics and functions of each single cell membrane, more researchers are using various hybrid cell membranes according to their specific needs. This review focuses on several different types of biomimetic nanodrug-delivery systems based on composite biofilms and looks forward to the challenges and possible development directions of biomimetic nanodrug-delivery systems based on composite biofilms to provide reference and ideas for future research.

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer.

OBJECTIVES: Accurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically. METHODS: A total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively. RESULTS: The proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89-9.33% and 14.75-16.67%, respectively, while 95HD and ASD decreased by 33.16-67.81% and 36.05-75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective. CONCLUSIONS: Using the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.

[Research on Position Verification of Multi-Leaf Collimator (MLC) and Dose Verification Based on Electronic Portal Imaging Device].

Objective: A quality control (QC) system based on the electronic portal imaging device (EPID) system was used to realize the Multi-Leaf Collimator (MLC) position verification and dose verification functions on Primus and VenusX accelerators. Methods: The MLC positions were calculated by the maximum gradient method of gray values to evaluate the deviation. The dose of images acquired by EPID were reconstructed using the algorithm combining dose calibration and dose calculation. The dose data obtained by EPID and two-dimensional matrix (MapCheck/PTW) were compared with the dose calculated by Pinnacle/TiGRT TPS for γ passing rate analysis. Results: The position error of VenusX MLC was less than 1 mm. The position error of Primus MLC was significantly reduced after being recalibrated under the instructions of EPID. For the dose reconstructed by EPID, the average γ passing rates of Primus were 98.86% and 91.39% under the criteria of 3%/3 mm, 10% threshold and 2%/2 mm, 10% threshold, respectively. The average γ passing rates of VenusX were 98.49% and 91.11%, respectively. Conclusion: The EPID-based accelerator quality control system can improve the efficiency of accelerator quality control and reduce the workload of physicists.

Identification of m6A/m5C-related lncRNA signature for prediction of prognosis and immunotherapy efficacy in esophageal squamous cell carcinoma.

N6-methyladenosine (m6A) and 5-methylcytosine (m5C) RNA modifications have garnered significant attention in the field of epigenetic research due to their close association with human cancers. This study we focus on elucidating the expression patterns of m6A/m5C-related long non-coding RNAs (lncRNAs) in esophageal squamous cell carcinoma (ESCC) and assessing their prognostic significance and therapeutic potential. Transcriptomic profiles of ESCC were derived from public resources. m6A/m5C-related lncRNAs were obtained from TCGA using Spearman's correlations analysis. The m6A/m5C-lncRNAs prognostic signature was selected to construct a RiskScore model for survival prediction, and their correlation with the immune microenvironment and immunotherapy response was analyzed. A total of 606 m6A/m5C-lncRNAs were screened, and ESCC cases in the TCGA cohort were stratified into three clusters, which showed significantly distinct in various clinical features and immune landscapes. A RiskScore model comprising ten m6A/m5C-lncRNAs prognostic signature were constructed and displayed good independent prediction ability in validation datasets. Patients in the low-RiskScore group had a better prognosis, a higher abundance of immune cells (CD4 + T cell, CD4 + naive T cell, class-switched memory B cell, and Treg), and enhanced expression of most immune checkpoint genes. Importantly, patients with low-RiskScore were more cline benefit from immune checkpoint inhibitor treatment (P < 0.05). Our findings underscore the potential of RiskScore system comprising ten m6A/m5C-related lncRNAs as effective biomarkers for predicting survival outcomes, characterizing the immune landscape, and assessing response to immunotherapy in ESCC.

Prognosis Prediction of Diffuse Large B-Cell Lymphoma in 18F-FDG PET Images Based on Multi-Deep-Learning Models.

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in 18F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0.770, 95% CI: 0.705-0.834, with feature adding fusion method and C-index: 0.764, 95% CI: 0.695-0.832, with feature concatenate fusion method) and OS prediction (C-index: 0.770 (0.692-0.848) and 0.771 (0.694-0.849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.

Nanofabrications of Erythrocyte Membrane-Coated Telmisartan Delivery System Effective for Radiosensitivity of Tumor Cells in Mice Model.

Background: Radiation stimulates the secretion of tumor stroma and induces resistance, recurrence, and metastasis of stromal-vascular tumors during radiotherapy. The proliferation and activation of tumor-associated fibroblasts (TAFs) are important reasons for the production of tumor stroma. Telmisartan (Tel) can inhibit the proliferation and activation of TAFs (resting TAFs), which may promote radiosensitization. However, Tel has a poor water solubility. Methods: In this study, self-assembled telmisartan nanoparticles (Tel NPs) were prepared by aqueous solvent diffusion method to solve the insoluble problem of Tel and achieve high drug loading of Tel. Then, erythrocyte membrane (ECM) obtained by hypotonic lysis was coated on the surface of Tel NPs (ECM/Tel) for the achievement of in vivo long circulation and tumor targeting. Immunofluorescence staining, western blot and other biological techniques were used to investigate the effect of ECM/Tel on TAFs activation inhibition (resting effect) and mechanisms involved. The multicellular spheroids (MCSs) model and mouse breast cancer cells (4T1) were constructed to investigate the effect of ECM/Tel on reducing stroma secretion, alleviating hypoxia, and the corresponding promoting radiosensitization effect in vitro. A mouse orthotopic 4T1 breast cancer model was constructed to investigate the radiosensitizing effect of ECM/Tel on inhibiting breast cancer growth and lung metastasis of breast cancer. Results: ECM/Tel showed good physiological stability and tumor-targeting ability. ECM/Tel could rest TAFs and reduce stroma secretion, alleviate hypoxia, and enhance penetration in tumor microenvironment. In addition, ECM/Tel arrested the cell cycle of 4T1 cells to the radiosensitive G2/M phase. In mouse orthotopic 4T1 breast cancer model, ECM/Tel played a superior role in radiosensitization and significantly inhibited lung metastasis of breast cancer. Conclusion: ECM/Tel showed synergistical radiosensitization effect on both the tumor microenvironment and tumor cells, which is a promising radiosensitizer in the radiotherapy of stroma-vascular tumors.

NSUN2 promotes lung adenocarcinoma progression through stabilizing PIK3R2 mRNA in an m5C-dependent manner.

It is well known that 5-methylcytosine (m5C) is involved in variety of crucial biological processes in cancers. However, its biological roles in lung adenocarcinoma (LAUD) remain to be determined. The LUAD samples were used to assess the clinical value of NOP2/Sun RNA Methyltransferase 2 (NSUN2). Dot blot was used to determine global m5C levels. ChIP and dual-luciferase assays were performed to investigate the MYC-associated zinc finger protein (MAZ)-binding sites in NSUN2 promoter. RNA-seq was used to explore the downstream molecular mechanisms of NSUN2. Dual luciferase reporter assay, m5C-RIP-qPCR, and mRNA stability assay were conducted to explore the effect of NSUN2-depletion on target genes. Cell viability, transwell, and xenograft mouse model were designed to demonstrate the characteristic of NSUN2 in promoting LUAD progression. The m5C methyltransferase NSUN2 was highly expressed and caused elevated m5C methylation in LUAD samples. Mechanistically, MAZ positively regulated the transcription of NSUN2 and was related to poor survival of LUAD patients. Silencing NSUN2 decreased the global m5C levels, suppressed proliferation, migration and invasion, and inhibited activation of PI3K-AKT signaling in A549 and SPAC-1 cells. Phosphoinositide-3-Kinase Regulatory Subunit 2 (PIK3R2) was upregulated by NSUN2-mediated m5C methylation by enhancing its mRNA stabilization and activated the phosphorylation of the PI3K-AKT signaling. The present study explored the underlying mechanism and biological function of NSUN2-meditated m5C RNA methylation in LUAD. NSUN2 was discovered to facilitate the malignancy progression of LUAD through regulating m5C modifications to stabilize PIK3R2 activating the PI3K-AKT signaling, suggesting that NSUN2 could be a novel biomarker and promising therapeutic target for LUAD patients.