lncTIMP3 promotes osteogenic differentiation of bone marrow mesenchymal stem cells via miR-214/Smad4 axis to relieve postmenopausal osteoporosis.

BACKGROUND: Promoting the balance between bone formation and bone resorption is the main therapeutic goal for postmenopausal osteoporosis (PMOP), and bone marrow mesenchymal stem cells (BMSCs) osteogenic differentiation plays an important regulatory role in this process. Recently, several long non-coding RNAs (lncRNAs) have been reported to play an important regulatory role in the occurrence and development of OP and participates in a variety of physiological and pathological processes. However, the role of lncRNA tissue inhibitor of metalloproteinases 3 (lncTIMP3) remains to be investigated. METHODS: The characteristics of BMSCs isolated from the PMOP rat model were verified by flow cytometry assay, alkaline phosphatase (ALP), alizarin red and Oil Red O staining assays. Micro-CT and HE staining assays were performed to examine histological changes of the vertebral trabeculae of the rats. RT-qPCR and western blotting assays were carried out to measure the RNA and protein expression levels. The subcellular location of lncTIMP3 was analyzed by FISH assay. The targeting relationships were verified by luciferase reporter assay and RNA pull-down assay. RESULTS: The trabecular spacing was increased in the PMOP rats, while ALP activity and the expression levels of Runx2, Col1a1 and Ocn were all markedly decreased. Among the RNA sequencing results of the clinical samples, lncTIMP3 was the most downregulated differentially expressed lncRNA, also its level was significantly reduced in the OVX rats. Knockdown of lncTIMP3 inhibited osteogenesis of BMSCs, whereas overexpression of lncTIMP3 exhibited the reverse results. Subsequently, lncTIMP3 was confirmed to be located in the cytoplasm of BMSCs, implying its potential as a competing endogenous RNA for miRNAs. Finally, the negative targeting correlations of miR-214 between lncTIMP3 and Smad4 were elucidated in vitro. CONCLUSION: lncTIMP3 may delay the progress of PMOP by promoting the activity of BMSC, the level of osteogenic differentiation marker gene and the formation of calcium nodules by acting on the miR-214/Smad4 axis. This finding may offer valuable insights into the possible management of PMOP.

A Tumor Environment-Activated Photosensitized Biomimetic Nanoplatform for Precise Photodynamic Immunotherapy of Colon Cancer.

Aggressive nature of colon cancer and current imprecise therapeutic scenarios simulate the development of precise and effective treatment strategies. To achieve this, a tumor environment-activated photosensitized biomimetic nanoplatform (PEG2000-SiNcTI-Ph/CpG-ZIF-8@CM) is fabricated by encapsulating metal-organic framework loaded with developed photosensitizer PEG2000-SiNcTI-Ph and immunoadjuvant CpG oligodeoxynucleotide within fusion cell membrane expressing programmed death protein 1 (PD-1) and cluster of differentiation 47 (CD47). By stumbling across, systematic evaluation, and deciphering with quantum chemical calculations, a unique attribute of tumor environment (low pH plus high concentrations of adenosine 5'-triphosphate (ATP))-activated photodynamic effect sensitized by long-wavelength photons is validated for PEG2000-SiNcTI-Ph/CpG-ZIF-8@CM, advancing the precision of cancer therapy. Moreover, PEG2000-SiNcTI-Ph/CpG-ZIF-8@CM evades immune surveillance to target CT26 colon tumors in mice mediated by CD47/signal regulatory proteins α (SIRPα) interaction and PD-1/programmed death ligand 1 (PD-L1) interaction, respectively. Tumor environment-activated photodynamic therapy realized by PEG2000-SiNcTI-Ph/CpG-ZIF-8@CM induces immunogenic cell death (ICD) to elicit anti-tumor immune response, which is empowered by enhanced dendritic cells (DC) uptake of CpG and PD-L1 blockade contributed by the nanoplatform. The photodynamic immunotherapy efficiently combats primary and distant CT26 tumors, and additionally generates immune memory to inhibit tumor recurrence and metastasis. The nanoplatform developed here provides insights for the development of precise cancer therapeutic strategies.

Unlocking the NIR-II AIEgen for High Brightness through Intramolecular Electrostatic Locking.

Fluorescent imaging and biosensing in the near-infrared-II (NIR-II) window holds great promise for non-invasive, radiation-free, and rapid-response clinical diagnosis. However, it's still challenging to develop bright NIR-II fluorophores. In this study, we report a new strategy to enhance the brightness of NIR-II aggregation-induced emission (AIE) fluorophores through intramolecular electrostatic locking. By introducing sulfur atoms into the side chains of the thiophene bridge in TSEH molecule, the molecular motion of the conjugated backbone can be locked through intramolecular interactions between the sulfur and nitrogen atoms. This leads to enhanced NIR-II fluorescent emission of TSEH in both solution and aggregation states. Notably, the encapsulated nanoparticles (NPs) of TSEH show enhanced brightness, which is 2.6-fold higher than TEH NPs with alkyl side chains. The in vivo experiments reveal the feasibility of TSEH NPs in vascular and tumor imaging with a high signal-to-background ratio and precise resection for tiny tumors. In addition, polystyrene nanospheres encapsulated with TSEH are utilized for antigen detection in lateral flow assays, showing a signal-to-noise ratio 1.9-fold higher than the TEH counterpart in detecting low-concentration antigens. This work highlights the potential for developing bright NIR-II fluorophores through intramolecular electrostatic locking and their potential applications in clinical diagnosis and biomedical research.

Experimental validation of the accuracy of robotic-assisted radioactive seed implantation for tumor treatment.

An experimental validation of a robotic system for radioactive iodine-125 seed implantation (RISI) in tumor treatment was conducted using customized phantom models and animal models simulating liver and lung lesions. The robotic system, consisting of planning, navigation, and implantation modules, was employed to implant dummy radioactive seeds into the models. Fiducial markers were used for target localization. In phantom experiments across 40 cases, the mean errors between planned and actual seed positions were 0.98 ± 1.05 mm, 1.14 ± 0.62 mm, and 0.90 ± 1.05 mm in the x, y, and z directions, respectively. The x, y, and z directions correspond to the left-right, anterior-posterior, and superior-inferior anatomical planes. Silicone phantoms exhibiting significantly smaller x-axis errors compared to liver and lung phantoms (p < 0.05). Template assistance significantly reduced errors in all axes (p < 0.05). No significant dosimetric deviations were observed in parameters such as D90, V100, and V150 between plans and post-implant doses (p > 0.05). In animal experiments across 23 liver and lung cases, the mean implantation errors were 1.28 ± 0.77 mm, 1.66 ± 0.69 mm, and 1.86 ± 0.93 mm in the x, y, and z directions, slightly higher than in phantoms (p < 0.05), with no significant differences between liver and lung models. The dosimetric results closely matched planned values, confirming the accuracy of the robotic system for RISI, offering new possibilities in clinical tumor treatment.

Structure-specific nucleases in genome dynamics and strategies for targeting cancers.

Nucleases are a super family of enzymes that hydrolyze phosphodiester bonds present in genomes. They widely vary in substrates, causing differentiation in cleavage patterns and having a diversified role in maintaining genetic material. Through cellular evolution of prokaryotic to eukaryotic, nucleases become structure-specific in recognizing its own or foreign genomic DNA/RNA configurations as its substrates, including flaps, bubbles, and Holliday junctions. These special structural configurations are commonly found as intermediates in processes like DNA replication, repair, and recombination. The structure-specific nature and diversified functions make them essential to maintaining genome integrity and evolution in normal and cancer cells. In this article, we review their roles in various pathways, including Okazaki fragment maturation during DNA replication, end resection in homology-directed recombination repair of DNA double-strand breaks, DNA excision repair and apoptosis DNA fragmentation in response to exogeneous DNA damage, and HIV life cycle. As the nucleases serve as key points for the DNA dynamics, cellular apoptosis, and cancer cell survival pathways, we discuss the efforts in the field in developing the therapeutic regimens, taking advantage of recently available knowledge of their diversified structures and functions.

Effects of aspirin on colon cancer using quantitative proteomic analysis.

Background: Colon cancer is one of the most prevalent digestive cancers worldwide. Results of epidemiological, experimental, and clinical studies suggest that aspirin inhibits the development of colon cancer. This study aimed to systematically elucidate the molecular mechanisms by which aspirin prevents colon carcinogenesis. Methods: We determined the global protein expression profiles of colorectal cancer and aspirin-treated cells using quantitative proteomic analysis. We analyzed the proteomic results using bioinformatics (including differential proteins, protein annotation, Kyoto Encyclopedia of Genes and Genomes [KEGG] pathways, and protein-protein interaction [PPI] network). The viability of the colon cancer cell line and HT29 â€‹cells treated with aspirin was determined using the cell counting kit-8 assay. The differentially expressed proteins, such as p53 and cyclin-dependent kinase 1 (CDK1), were quantified using real-time polymerase chain reaction (PCR) and Western blotting. We measured cell cycle distribution and apoptosis in HT29 â€‹cells exposed to aspirin using fluorescence-activated cell sorting (FACS). Results: We found that 552 proteins were significantly dysregulated, of which 208 and 334 were upregulated and downregulated, respectively, in colon cancer cells exposed to 10 â€‹mmol/L of aspirin (95% confidence interval [CI]: -1.269 to -0.106, P â€‹< â€‹0.05). Further gene enrichment analysis revealed that cell cycle-related proteins, such as p53 and CDK1, were significantly differentially expressed. Proteomic analysis showed that after 24 â€‹h of aspirin exposure, the level of p53 increased by 2.52-fold and CDK1 was downregulated to half that of the controls in HT29 â€‹cells (95% CI: -0.619 to -0.364, P â€‹< â€‹0.05). Real-time PCR and Western blotting results showed that p53 was upregulated (95%CI: -3.088 to -1.912, P â€‹< â€‹0.001) and CDK1 was significantly downregulated after aspirin exposure in colon cancer cells (95% CI: 0.576 to 1.045, P â€‹< â€‹0.05). We observed that aspirin promoted G1/S cell cycle arrest in HT29 â€‹cells. We confirmed that aspirin induces apoptosis in human HT29 colon cancer cells in a concentration-dependent manner. Conclusions: These results indicate that aspirin induces G1 arrest and apoptosis in colorectal cancer cells via the p53-CDK1 pathway. Aspirin may be a promising drug candidate for colon cancer prevention.

Deep learning-based vessel extraction in 3D confocal microscope images of cleared human glioma tissues.

Comprehensive visualization and accurate extraction of tumor vasculature are essential to study the nature of glioma. Nowadays, tissue clearing technology enables 3D visualization of human glioma vasculature at micron resolution, but current vessel extraction schemes cannot well cope with the extraction of complex tumor vessels with high disruption and irregularity under realistic conditions. Here, we developed a framework, FineVess, based on deep learning to automatically extract glioma vessels in confocal microscope images of cleared human tumor tissues. In the framework, a customized deep learning network, named 3D ResCBAM nnU-Net, was designed to segment the vessels, and a novel pipeline based on preprocessing and post-processing was developed to refine the segmentation results automatically. On the basis of its application to a practical dataset, we showed that the FineVess enabled extraction of variable and incomplete vessels with high accuracy in challenging 3D images, better than other traditional and state-of-the-art schemes. For the extracted vessels, we calculated vascular morphological features including fractal dimension and vascular wall integrity of different tumor grades, and verified the vascular heterogeneity through quantitative analysis.

Donor-Acceptor Modulating of Ionic AIE Photosensitizers for Enhanced ROS Generation and NIR-II Emission.

Photosensitizers (PSs) with aggregation-induced emission (AIE) characteristics are competitive candidates for bioimaging and therapeutic applications. However, their short emission wavelength and nonspecific organelle targeting hinder their therapeutic effectiveness. Herein, a donor-acceptor modulation approach is reported to construct a series of ionic AIE photosensitizers with enhanced photodynamic therapy (PDT) outcomes and fluorescent emission in the second near-infrared (NIR-II) window. By employing dithieno[3,2-b:2',3'-d]pyrrole (DTP) and indolium (In) as the strong donor and acceptor, respectively, the compound DTP-In exhibits a substantial redshift in absorption and fluorescent emission reach to NIR-II region. The reduced energy gap between singlet and triplet states in DTP-In also increases the reactive oxygen species (ROS) generation rate. Further, DTP-In can self-assemble in aqueous solutions, forming positively charged nanoaggregates, which are superior to conventional encapsulated nanoparticles in cellular uptake and mitochondrial targeting. Consequently, DTP-In aggregates show efficient photodynamic ablation of 4T1 cancer cells and outstanding tumor theranostic in vivo under 660 nm laser irradiation. This work highlights the potential of molecular engineering of donor-acceptor AIE PSs with multiple functionalities, thereby facilitating the development of more effective strategies for cancer therapy.

Feasibility of the application of deep learning-reconstructed ultra-fast respiratory-triggered T2-weighted imaging at 3 T in liver imaging.

OBJECTIVE: The evaluate the feasibility of a novel deep learning-reconstructed ultra-fast respiratory-triggered T2WI sequence (DL-RT-T2WI) In liver imaging, compared with respiratory-triggered Arms-T2WI (Arms-RT-T2WI) and respiratory-triggered FSE-T2WI (FSE-RT-T2WI) sequences. METHODS: 71 patients with liver lesions underwent 3-T MRI and were prospectively enrolled. Two readers independently analyzed images acquired with DL-RT-T2WI, Arms-RT-T2WI, and FSE-RT-T2WI. The qualitative evaluation indicators, including overall image quality (OIQ), sharpness, noise, artifacts, lesion detectability (LC), lesion characterization (LD), cardiacmotion-related signal loss (CSL), and diagnostic confidence (DC), were evaluated in two readers, and further statistically compared using paired Wilcoxon rank-sum test among three sequences. RESULTS: 176 lesions were detected in DL-RT-T2W and Arms-RT-T2WI, and 175 were detected in FSE-RT-T2WI. The acquisition time of DL-RT-T2WI was improved by 4.8-7.9 folds compared to the other two sequences. The OIQ was scored highest for DL-RT-T2WI (R1, 4.61 ± 0.52 and R2, 4.62 ± 0.49), was significantly superior to Arms-RT-T2WI (R1, 4.30 ± 0.66 and R2, 4.34 ± 0.69) and FSE-RT-T2WI (R1, 3.65 ± 1.08 and R2, 3.75 ± 1.01). Artifacts and sharpness scored highest for DL-RT-T2WI, followed by Arms-RT-T2WI, and were lowest for FSE-RT-T2WI in both two readers. Noise and CSL for DL-RT-T2WI scored similar to Arms-RT-T2WI (P > 0.05) and were significantly superior to FSE-RT-T2WI (P < 0.001). Both LD and LC for DL-RT-T2WI were significantly superior to Arms-RT-T2WI and FSE-RT-T2WI in two readers (P < 0.001). DC for DL-RT-T2WI scored best, significantly superior to Arms-RT-T2WI (P < 0.010) and FSE-RT-T2WI (P < 0.001). CONCLUSIONS: The novel ultra-fast DL-RT-T2WI is feasible for liver imaging and lesion characterization and diagnosis, not only offers a significant improvement in acquisition time but also outperforms Arms-RT-T2WI and FSE-RT-T2WI concerning image quality and DC.

The Value of Dual-Energy Computed Tomography-Based Radiomics in the Evaluation of Interstitial Fibers of Clear Cell Renal Carcinoma.

OBJECTIVE: We investigated the potential of dual-energy computed tomography (DECT) radiomics in assessing cancer-associated fibroblasts in clear cell renal carcinoma (ccRCC). METHODS: A retrospective analysis was conducted on 132 patients with ccRCC. The arterial and venous phase iodine-based material decomposition images (IMDIs), virtual non-contrast images, 70 keV, 100 keV, and 150 keV virtual monoenergetic images, and mixed energy images (MEIs) were obtained from the DECT datasets. On the Radcloud platform, radiomics feature extraction, feature selection, and model establishment were performed. Seven radiomics models were established using the support vector machine. The predictive performance was evaluated by utilizing receiver operating characteristic and the area under the curve (AUC) was calculated. Nomograms were constructed. RESULTS: The combined model demonstrated high efficiency in evaluating pseudocapsule thickness with AUC, specificity, and sensitivity of 0.833, 0.870, and 0.750, respectively in the validation set, surpassing those of other models. The precision, F1-score, and Youden index were also higher for the combined model. For evaluating the number of collagen fibers, the combined model exhibited the highest AUC (0.741) among all models, with a specificity of 0.830 and a sensitivity of 0.330. The AUC in the 150 kv model and IMDI model were slightly lower than those in the combined model (0.728 and 0.710, respectively), with corresponding sensitivity and specificity of 0.560/0.780 and 0.670/0.830. The nomogram exhibited that Rad-score had good prediction efficiency. CONCLUSION: DECT radiomics features have significant value in evaluating the interstitial fibers of ccRCC. The combined model of IMDI + MEI exhibits superior performance in assessing the thickness of the pseudocapsule, while the combined, 150 keV, and IMDI models demonstrate higher efficacy in evaluating collagen fiber number. Radiomics, combined with imaging features and clinical features, has excellent predictive performance. These findings offer crucial support for the clinical diagnosis, treatment, and prognosis of ccRCC and provide valuable insights into the application of DECT.

PARVB promotes malignant melanoma progression and is enhanced by hypoxic conditions.

Beta-Parvin (PARVB) is an actin-binding protein with functionality in extracellular matrix binding. Recent studies suggest its potential as a biomarker for various cancers, given its role in governing several malignancies. Yet, its involvement and modulatory mechanisms in malignant melanoma remain under-explored.  In this research, we undertook a comprehensive pan-cancer analysis centered on PARVB. We probed its aberrant expression and prognostic implications, and assessed correlations between PARVB expression and immunocyte infiltration. This expression was subsequently corroborated using clinical samples. Both in vitro and in vivo, we discerned the functional ramifications of PARVB on melanoma. Furthermore, we scrutinized how HIF-1α/2α modulates PARVB and initiated a preliminary investigation into potential downstream pathways influenced by PARVB. Our results illuminate that elevated PARVB expression manifests across various tumors and significantly influences the prognosis of multiple cancers, emphasizing its peculiar expression and prognostic relevance in melanoma. Augmented PARVB levels were inversely proportional to immunocyte penetration in melanoma. Silencing PARVB curtailed cellular proliferation, migration, and invasion in vitro and decelerated tumor expansion in vivo. Notably, hypoxic conditions, triggering HIF-1α/2α activation, appear to elevate PARVB expression by anchoring to the hypoxia-specific responsive element within the PARVB promoter. Enhanced PARVB levels seem intertwined with the activation of cellular proliferation circuits and the damping of inflammatory trajectories. Collectively, these revelations posit PARVB as a potential prognostic indicator and therapeutic linchpin for malignant melanoma.

Fusobacterium nucleatum in tumors: from tumorigenesis to tumor metastasis and tumor resistance.

Fusobacterium nucleatum, an anaerobic Gram-negative bacterium primarily residing in the oral cavity, has garnered significant attention for its emerging role in cancer progression and prognosis. While extensive research has revealed mechanistic links between Fusobacterium nucleatum and colorectal cancer, a comprehensive review spanning its presence and metastatic implications in cancers beyond colorectal origin is conspicuously absent. This paper broadens our perspective from colorectal cancer to various malignancies associated with Fusobacterium nucleatum, including oral, pancreatic, esophageal, breast, and gastric cancers. Our central focus is to unravel the mechanisms governing Fusobacterium nucleatum colonization, initiation, and promotion of metastasis across diverse cancer types. Additionally, we explore Fusobacterium nucleatum's adverse impacts on cancer therapies, particularly within the domains of immunotherapy and chemotherapy. Furthermore, this paper underscores the clinical research significance of Fusobacterium nucleatum as a potential tumor biomarker and therapeutic target, offering a novel outlook on its applicability in cancer detection and prognostic assessment.

Automatic planning for head and neck seed implant brachytherapy based on deep convolutional neural network dose engine.

BACKGROUND: Seed implant brachytherapy (SIBT) is an effective treatment modality for head and neck (H&N) cancers; however, current clinical planning requires manual setting of needle paths and utilizes inaccurate dose calculation algorithms. PURPOSE: This study aims to develop an accurate and efficient deep convolutional neural network dose engine (DCNN-DE) and an automatic SIBT planning method for H&N SIBT. METHODS: A cohort of 25 H&N patients who received SIBT was utilized to develop and validate the methods. The DCNN-DE was developed based on 3D-unet model. It takes single seed dose distribution from a modified TG-43 method, the CT image and a novel inter-seed shadow map (ISSM) as inputs, and predicts the dose map of accuracy close to the one from Monte Carlo simulations (MCS). The ISSM was proposed to better handle inter-seed attenuation. The accuracy and efficacy of the DCNN-DE were validated by comparing with other methods taking MCS dose as reference. For SIBT planning, a novel strategy inspired by clinical practice was proposed to automatically generate parallel or non-parallel potential needle paths that avoid puncturing bone and critical organs. A heuristic-based optimization method was developed to optimize the seed positions to meet clinical prescription requirements. The proposed planning method was validated by re-planning the 25 cases and comparing with clinical plans. RESULTS: The absolute percentage error in the TG-43 calculation for CTV V100 and D90 was reduced from 5.4% and 13.2% to 0.4% and 1.1% with DCNN-DE, an accuracy improvement of 93% and 92%, respectively. The proposed planning method could automatically obtain a plan in 2.5 ± 1.5 min. The generated plans were judged clinically acceptable with dose distribution comparable with those of the clinical plans. CONCLUSIONS: The proposed method can generate clinically acceptable plans quickly with high accuracy in dose evaluation, and thus has a high potential for clinical use in SIBT.

Surgical Strategy for Recurrent Giant Cell Tumor in the Thoracolumbar Spine.

OBJECTIVE: Recurrent giant cell tumor (RGCT) of the spine represents a clinical challenge for surgeons, and the treatment strategy remains controversial. This study aims to describe the long-term follow-up outcomes and compare the efficacy of en bloc spondylectomy versus piecemeal spondylectomy in treating RGCT of the thoracolumbar spine. METHODS: A total of 32 patients with RGCT of the thoracolumbar spine treated from June 2012 to June 2019 were retrospectively reviewed. A total of 15 patients received total en bloc spondylectomy (TES) with wide or marginal margin while 17 patients received total piecemeal spondylectomy (TPS) with intralesional margin. Postoperative Eastern Cooperative Oncology Group Performance Score (ECOG-PS), Frankel classification and recurrence-free survival (RFS) were evaluated after surgery. Survival curves were estimated by the Kaplan-Meier method and differences were analyzed with the log-rank test. Multivariate analysis was performed with Cox regression to identify the independent prognostic factors affecting RFS. RESULTS: During a median follow-up of 41.9 ± 17.5 months, all patients with compromised neurologic functions exhibit significant improvement, with the mean ECOG-PS decreasing from 1.5 ± 1.3 to 0.13 ± 0.3 (p < 0.05). Among the 17 patients treated with TPS, eight patients developed local recurrence after a median time of 15.9 ± 6.4 months and four patients died from progressive disease. On the other hand, local recurrence were well managed with TES, since only one out of 15 patients experienced local relapse and all patients are alive with satisfied function at the latest follow-up. The median RFS for patients receiving TES and TPS are 75.0 months (95% CI: 67.5-82.5 m) and 38.3 months (95% CI: 27.3-49.3 m) respectively (p = 0.008). Multivariate analysis shows that the Ki67 index (p = 0.016), resection mode (p = 0.022), and denosumab (p = 0.039) are independent risk factors affecting RFS. CONCLUSIONS: TES with wide/marginal margin should be offered to patients with RGCT whenever feasible, given its long-term benefits in local control and symptom alleviation. Additionally, patients with lower Ki67 index and application of denosumab tend to have a better prognosis.

Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules.

PURPOSE: To develop a comprehensive multi-classification model that combines radiomics and clinic-radiological features to accurately predict the invasiveness and differentiation of pulmonary adenocarcinoma nodules. METHODS: A retrospective analysis was conducted on a cohort comprising 500 patients diagnosed with lung adenocarcinoma between January 2020 and December 2022. The dataset included preoperative CT images and histological reports of adenocarcinoma in situ (AIS, n = 97), minimally invasive adenocarcinoma (MIA, n = 139), and invasive adenocarcinoma (IAC, n = 264) with well-differentiated (WIAC, n = 99), moderately differentiated (MIAC, n = 84), and poorly differentiated IAC (PIAC, n = 81). The patients were classified into two groups (IAC and non-IAC) for binary classification and further divided into three and five groups for multi-classification. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm to identify the most informative radiomics and clinic-radiological features. Eight machine learning (ML) models were developed using these features, and their performance was evaluated using accuracy (ACC) and the area under the receiver-operating characteristic curve (AUC). RESULTS: The combined model, utilizing the support vector machine (SVM) algorithm, demonstrated improved performance in the testing cohort, achieving an AUC of 0.942 and an ACC of 0.894 for the two-classification task. For the three- and five-classification tasks, the combined model employing the one versus one strategy of SVM (SVM-OVO) outperformed other models, with ACC values of 0.767 and 0.607, respectively. The AUC values for histological subtypes ranged from 0.787 to 0.929 in the testing cohort, while the Macro-AUC and Micro-AUC of the multi-classification models ranged from 0.858 to 0.896. CONCLUSIONS: A multi-classification radiomics model combined with clinic-radiological features, using the SVM-OVO algorithm, holds promise for accurately predicting the histological characteristics of pulmonary adenocarcinoma nodules, which contributes to personalized treatment strategies for patients with lung adenocarcinoma.

Influence of minimum segment width on intensity-modulated radiotherapy plan for left-sided breast cancer after breast conserving surgery.

To research the influence of the minimum segment width (MSW) on intensity-modulated radiotherapy (IMRT) plan for left breast cancer after breast conserving surgery and provide a reference for plan optimization. Ten patients with left-sided early-stage breast cancer after breast-conserving surgery (BCS) were selected for postoperative radiotherapy. The Monaco 5.11 treatment planning system (TPS) was used, and the optimization parameters of the plan were fixed, while MSW were set to 0.5, 1, 1.5, and 2 cm, and four 5 field-IMRT plans were designed for every patient. The plan quality and execution efficiency of each plan were compared, including planning target volume (PTV) dose, organs at risk (OAR) dose, segments number (SN), monitor units (MU), delivery time (DT), and dose verification results. With the increase of the MSW, the dose indexes of the OAR were similar (P > .05). The D2%, D98%, and D50% of the PTV gradually deviated from the prescription dose, and the uniformity gradually deteriorated (P < .05). The gamma passing rates (GPR) of dose verification and the dose deviation of the PTV were similar (P > .05). The SN, MU, and DT gradually decreased (P < .05), but the amplitude change caused by each increase of 0.5 cm gradually decreased, the decrease of 0.5 to 1 cm was the largest, reaching 16.41%, 21.25%, and 0.35 minutes, respectively, while the 3 values of 1.5 and 2 cm were close. When using Monaco 5.11 TPS to design IMRT plan for left breast cancer after BCS, 1.5 cm MSW could ensure better dose coverage and uniformity of the PTV, as little as possible exposure dose to the OAR, and high dose verification accuracy. The SN, MU, and DT were significantly reduced, and the treatment efficiency was improved.

STM2457 Inhibits the Invasion and Metastasis of Pancreatic Cancer by Down-Regulating BRAF-Activated Noncoding RNA N6-Methyladenosine Modification.

Pancreatic cancer is a malignant tumor of the digestive system that is highly malignant, difficult to treat, and confers a poor prognosis for patients. BRAF-activated noncoding RNA (BANCR) has been proven to play an important role in the invasion and metastasis of pancreatic cancer. In this study, we focused on BANCR as a potential therapeutic target for human pancreatic cancer. The BANCR level in pancreatic cancer tissues and cells is affected by m6A methylation. Based on this, the aim of our study was to investigate the effect of a highly potent and selective first-in-class catalytic inhibitor of METTL3 (STM2457) on BANCR m6A methylation and its malignant biological behaviors in pancreatic cancer. The relationship between BANCR expression and BANCR m6A modification was detected with RT-qPCR and MeRIP-PCR. The expression of methyltransferase-like 3 (METTL3), the key enzyme involved in m6A methylation, in pancreatic cancer tissues was detected using a Western blot. STM2457 was used in vitro to investigate its resistance to the proliferation, invasion, and metastasis of pancreatic cancer cells. BANCR was overexpressed in pancreatic cancer tissues and cells, which was associated with poor clinical outcomes and validated in pancreatic cancer cell lines. m6A modification was highly enriched within BANCR and enhanced its expression. Remarkably, STM2457 inhibited the proliferation, invasion, and metastasis of pancreatic cancer cells by down-regulating BANCR m6A modifications. This study demonstrates the promise of BANCR as a new diagnostic and therapeutic target for pancreatic cancer and reveals the therapeutic effect that STM2457 exerts on pancreatic cancer by down-regulating BANCR m6A modifications.

Brain tumor image segmentation based on improved FPN.

PURPOSE: Automatic segmentation of brain tumors by deep learning algorithm is one of the research hotspots in the field of medical image segmentation. An improved FPN network for brain tumor segmentation is proposed to improve the segmentation effect of brain tumor. MATERIALS AND METHODS: Aiming at the problem that the traditional full convolutional neural network (FCN) has weak processing ability, which leads to the loss of details in tumor segmentation, this paper proposes a brain tumor image segmentation method based on the improved feature pyramid networks (FPN) convolutional neural network. In order to improve the segmentation effect of brain tumors, we improved the model, introduced the FPN structure into the U-Net structure, captured the context multi-scale information by using the different scale information in the U-Net model and the multi receptive field high-level features in the FPN convolutional neural network, and improved the adaptability of the model to different scale features. RESULTS: Performance evaluation indicators show that the proposed improved FPN model has 99.1% accuracy, 92% DICE rating and 86% Jaccard index. The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. CONCLUSIONS: The experimental results show that this method can effectively segment brain tumor regions and has certain generalization, and the segmentation effect is better than other networks. It has positive significance for clinical diagnosis of brain tumors.

Therapeutic Effects of HLA-G5 Overexpressing hAMSCs on aGVHD After Allo-HSCT: Involving in the Gut Microbiota at the Intestinal Barrier.

Background: Acute graft-versus-host disease (aGVHD) initiated by intestinal barrier dysfunction and gut microbiota dysbiosis, remains one of the main obstacles for patients undergoing allogenic hematopoietic stem cell transplantation (allo-HSCT) to achieve good prognosis. Studies have suggested that mesenchymal stem cells (MSCs) can suppress immune responses and reduce inflammation, and human leukocyte antigen-G5 (HLA-G5) plays an important role in the immunomodulatory effects of MSCs, but very little is known about the potential mechanisms in aGVHD. Thus, we explored the effect of HLA-G5 on the immunosuppressive properties of human amnion MSCs (hAMSCs) and demonstrated its mechanism related to the gut microbiota at the intestinal barrier in aGVHD. Methods: Patients undergoing allo-HSCT were enrolled to detect the levels of plasma-soluble HLA-G (sHLA-G) and regulatory T cells (Tregs). Humanized aGVHD mouse models were established and treated with hAMSCs or HLA-G5 overexpressing hAMSCs (ov-HLA-G5-hAMSCs) to explore the mechanism of HLA-G5 mediated immunosuppressive properties of hAMSCs and the effect of ov-HLA-G5-hAMSCs on the gut microbiota at the intestinal barrier in aGVHD. Results: The plasma levels of sHLA-G on day +30 after allo-HSCT in aGVHD patients were lower than those in patients without aGVHD, and the sHLA-G levels were positively correlated with Tregs percentages. ov-HLA-G5-hAMSCs had the potential to inhibit the expansion of CD3+CD4+ T and CD3+CD8+ T cells and promote Tregs differentiation, suppress proinflammatory cytokine secretion but promote anti-inflammatory cytokines release. Besides, ov-HLA-G5-hAMSCs also could reverse the intestinal barrier dysfunction and gut microbiota dysbiosis in aGVHD. Conclusion: We demonstrated that HLA-G might work with Tregs to create a regulatory network together to reduce the occurrence of aGVHD. HLA-G5 mediated hAMSCs to exert higher immunosuppressive properties in vivo and reverse the immune imbalance caused by T lymphocytes and cytokines. Furthermore, HLA-G5 overexpressing hAMSCs could restore gut microbiota and intestinal barriers, thereby ameliorating aGVHD.

Farnesoid X receptor activation inhibits pancreatic carcinogenesis.

Farnesoid X receptor (FXR), a member of the nuclear receptor superfamily that controls bile acid (BA) homeostasis, has also been proposed as a tumor suppressor for breast and liver cancer. However, its role in pancreatic ductal adenocarcinoma (PDAC) tumorigenesis remains controversial. We recently found that FXR attenuates acinar cell autophagy in chronic pancreatitis resulting in reduced autophagy and promotion of pancreatic carcinogenesis. Feeding Kras-p48-Cre (KC) mice with the BA chenodeoxycholic acid (CDCA), an FXR agonist, attenuated pancreatic intraepithelial neoplasia (PanIN) progression, reduced cell proliferation, neoplastic cells and autophagic activity, and increased acinar cells, elevated pro-inflammatory cytokines and chemokines, with a compensatory increase in the anti-inflammatory response. Surprisingly, FXR-deficient KC mice did not show any response to CDCA, suggesting that CDCA attenuates PanIN progression and decelerate tumorigenesis in KC mice through activating pancreatic FXR. FXR is activated in pancreatic cancer cell lines in response to CDCA in vitro. FXR levels were highly increased in adjuvant and neoadjuvant PDAC tissue compared to healthy pancreatic tissue, indicating that FXR is expressed and potentially activated in human PDAC. These results suggest that BA exposure activates inflammation and suppresses autophagy in KC mice, resulting in reduced PanIN lesion progression. These data suggest that activation of pancreatic FXR has a protective role by reducing the growth of pre-cancerous PDAC lesions in response to CDCA and possibly other FXR agonists.