Stiff extracellular matrix drives the differentiation of mesenchymal stem cells toward osteogenesis by the multiscale 3D genome reorganization.

Extracellular matrix (ECM) stiffness is a major driver of stem cell fate. However, the involvement of the three-dimensional (3D) genomic reorganization in response to ECM stiffness remains unclear. Here, we generated comprehensive 3D chromatin landscapes of mesenchymal stem cells (MSCs) exposed to various ECM stiffness. We found that there were more long-range chromatin interactions, but less compartment A in MSCs cultured on stiff ECM than those cultured on soft ECM. However, the switch from compartment B in MSCs cultured on soft ECM to compartment A in MSCs cultured on stiff ECM included genes encoding proteins primarily enriched in cytoskeleton organization. At the topologically associating domains (TADs) level, stiff ECM tends to have merged TADs on soft ECM. These merged TADs on stiff ECM include upregulated genes encoding proteins enriched in osteogenesis, such as SP1, ETS1, and DCHS1, which were validated by quantitative real-time polymerase chain reaction and found to be consistent with the increase of alkaline phosphatase staining. Knockdown of SP1 or ETS1 led to the downregulation of osteogenic marker genes, including COL1A1, RUNX2, ALP, and OCN in MSCs cultured on stiff ECM. Our study provides an important insight into the stiff ECM-mediated promotion of MSC differentiation towards osteogenesis, emphasizing the influence of mechanical cues on the reorganization of 3D genome architecture and stem cell fate.

A patient-specific auto-planning method for MRI-guided adaptive radiotherapy in prostate cancer.

BACKGROUND AND PURPOSE: Fast and automated generation of treatment plans is desirable for magnetic resonance imaging (MRI)-guided adaptive radiotherapy (MRIgART). This study proposed a novel patient-specific auto-planning method and validated its feasibility in improving the existing online planning workflow. MATERIALS AND METHODS: Data from 40 patients with prostate cancer were collected retrospectively. A patient-specific auto-planning method was proposed to generate adaptive treatment plans. First, a population dose-prediction model (M0) was trained using data from previous patients. Second, a patient-specific model (Mps) was created for each new patient by fine-tuning M0 with the patient's data. Finally, an auto plan was optimized using the parameters derived from the predicted dose distribution by Mps. The auto plans were compared with manual plans in terms of plan quality, efficiency, dosimetric verification, and clinical evaluation. RESULTS: The auto plans improved target coverage, reduced irradiation to the rectum, and provided comparable protection to other organs-at-risk. Target coverage for the planning target volume (+0.61 %, P = 0.023) and clinical target volume 4000 (+1.60 %, P < 0.001) increased. V2900cGy (-1.06 %, P = 0.004) and V1810cGy (-2.49 %, P < 0.001) to the rectal wall and V1810cGy (-2.82 %, P = 0.012) to the rectum were significantly reduced. The auto plans required less planning time (-3.92 min, P = 0.001), monitor units (-46.48, P = 0.003), and delivery time (-0.26 min, P = 0.004), and their gamma pass rates (3 %/2 mm) were higher (+0.47 %, P = 0.014). CONCLUSION: The proposed patient-specific auto-planning method demonstrated a robust level of automation and was able to generate high-quality treatment plans in less time for MRIgART in prostate cancer.

Gamma-aminobutyric acid-mediated neuro-immune interactions in glioblastoma: Implications for prognosis and immunotherapy response.

AIMS: This study aimed to investigate the role of gamma-aminobutyric acid (GABA) in the glioblastoma (GBM) tumor immune microenvironment (TIME) and its impact on prognosis and response to immunotherapy. MAIN METHODS: This study employed single-cell RNA sequencing (scRNA-seq) to delineate the TIME of GBM, utilized non-negative matrix factorization (NMF) for GABA-associated cell clustering, and performed pseudotime analysis for cellular trajectories. Additionally, we integrated immunohistochemistry (IHC), immunofluorescence (IF), and protein-protein interaction (PPI) analysis to explore the regulatory mechanisms within the tumor microenvironment. KEY FINDINGS: The study identified distinct GABA-associated immune cell subtypes, particularly macrophages and T-cells, with unique gene expression and developmental trajectories. The development of the GABA-associated scoring model (GABAAS), introduced novel prognostic indicators, enhancing our ability to predict patient outcomes. This study also suggests that GABA-related genes, including NDRG2 and TIMP1, play a crucial role in immune modulation, with potential implications for immunotherapy responsiveness. SIGNIFICANCE: The findings underscore the potential of targeting GABA-related genes (NDRG2 and TIMP1) and M2 macrophage to reshape the glioblastoma immune landscape, offering a new frontier in personalized neuro-immunotherapy. This approach holds promise to counter individual tumor immunosuppressive mechanisms, enhancing patient outcomes.

Genetic Regulatory Perturbation of Gene Expression Impacted by Genomic Introgression in Fiber Development of Allotetraploid Cotton.

Interspecific genomic introgression is an important evolutionary process with respect to the generation of novel phenotypic diversity and adaptation. A key question is how gene flow perturbs gene expression networks and regulatory interactions. Here, an introgression population of two species of allopolyploid cotton (Gossypium) to delineate the regulatory perturbations of gene expression regarding fiber development accompanying fiber quality change is utilized. De novo assembly of the recipient parent (G. hirsutum Emian22) genome allowed the identification of genomic variation and introgression segments (ISs) in 323 introgression lines (ILs) from the donor parent (G. barbadense 3-79). It documented gene expression dynamics by sequencing 1,284 transcriptomes of developing fibers and characterized genetic regulatory perturbations mediated by genomic introgression using a multi-locus model. Introgression of individual homoeologous genes exhibiting extreme low or high expression bias can lead to a parallel expression bias in their non-introgressed duplicates, implying a shared yet divergent regulatory fate of duplicated genes following allopolyploidy. Additionally, the IL N182 with improved fiber quality is characterized, and the candidate gene GhFLAP1 related to fiber length is validated. This study outlines a framework for understanding introgression-mediated regulatory perturbations in polyploids, and provides insights for targeted breeding of superior upland cotton fiber.

Dose rate correction for the novel 2D diode array MapCHECK 3.

PURPOSE: To investigate the dose rate dependence of MapCHECK3 and its influence on measurement accuracy, as well as the effect of dose rate correction. MATERIALS AND METHODS: The average and instantaneous dose rate dependence of MapCHECK2 and MapCHECK3 were studied. The accuracy of measurements was investigated where the dose rate differed significantly between dose calibration of the MapCHECK and the measurement. Measurements investigated include: the central axis dose for different fields at different depths, off-axis doses outside the field, and off-axis doses along the wedge direction. Measurements using an ion chamber were taken as the reference. Exponential functions were fit to account for average and instantaneous dose rate dependence for MapCHECK3 and used for dose rate correction. The effect of the dose rate correction was studied by comparing the differences between the measurements for MapCHECK (with and without the correction) and the reference. RESULTS: The maximum dose rate dependence of MapCHECK3 is greater than 2.5%. If the dose calibration factor derived from a 10 × 10 cm2 open field at 10 cm depth was used for measurements, the average differences in central diode dose were 0.8% ± 1.0% and 1.0% ± 0.8% for the studied field sizes and measurement depths, respectively. The introduction of wedge would not only induce -1.8% ± 1.3% difference in central diode dose, but also overestimate the effective wedge angle. After the instantaneous dose rate correction, above differences can be changed to 1.9% ± 8.1%, 0.2% ± 0.1%, and 0.0% ± 0.9%. The pass rate can be improved from 98.4% to 98.8%, 98.3%-100.0%, and 96.3%-100.0%, respectively. CONCLUSION: Compared with MapCHECK2 (SunPoint1 diodes), the more pronounced dose rate dependence of MapCHECK3 (SunPoint2 diodes) should be carefully considered. To ensure highly accurate measurement, it is suggested to perform the dose calibration at the same condition where measurement will be performed. Otherwise, the dose rate correction should be applied.

Improvement of accumulated dose distribution in combined cervical cancer radiotherapy with deep learning-based dose prediction.

Purpose: Difficulties remain in dose optimization and evaluation of cervical cancer radiotherapy that combines external beam radiotherapy (EBRT) and brachytherapy (BT). This study estimates and improves the accumulated dose distribution of EBRT and BT with deep learning-based dose prediction. Materials and methods: A total of 30 patients treated with combined cervical cancer radiotherapy were enrolled in this study. The dose distributions of EBRT and BT plans were accumulated using commercial deformable image registration. A ResNet-101-based deep learning model was trained to predict pixel-wise dose distributions. To test the role of the predicted accumulated dose in clinic, each EBRT plan was designed using conventional method and then redesigned referencing the predicted accumulated dose distribution. Bladder and rectum dosimetric parameters and normal tissue complication probability (NTCP) values were calculated and compared between the conventional and redesigned accumulated doses. Results: The redesigned accumulated doses showed a decrease in mean values of V50, V60, and D2cc for the bladder (-3.02%, -1.71%, and -1.19 Gy, respectively) and rectum (-4.82%, -1.97%, and -4.13 Gy, respectively). The mean NTCP values for the bladder and rectum were also decreased by 0.02‰ and 0.98%, respectively. All values had statistically significant differences (p < 0.01), except for the bladder D2cc (p = 0.112). Conclusion: This study realized accumulated dose prediction for combined cervical cancer radiotherapy without knowing the BT dose. The predicted dose served as a reference for EBRT treatment planning, leading to a superior accumulated dose distribution and lower NTCP values.

Analysis of PD-L1 promoter methylation combined with immunogenic context in pancreatic ductal adenocarcinoma.

Despite the successful application of programmed cell death ligand 1 (PD-L1)-blocking strategies in some types of cancers and well-established prognostic indicators in pancreatic ductal adenocarcinoma (PDAC), the biological and clinical implications of the methylation status of PD-L1/PD-L2 in PDAC remain largely unknown. Therefore, this study aimed to explore the biological role of PD-L1/PD-L2 methylation and its association with clinicopathological features, clinical outcomes, and the immune microenvironment by analyzing the data on PD-L1/PD-L2 methylation and mRNA expression in PDAC cohorts obtained from the Cancer Genome Atlas and International Cancer Genome Consortium. The correlation between PD-L1 promoter methylation and PD-L1 expression and survival was further validated in an independent validation cohort (Peking Union Medical College Hospital [PUMCH] cohort) using pyrosequencing and immunohistochemistry. These results demonstrated that hypomethylation of the PD-L1 promoter was strongly associated with upregulated PD-L1 expression and shorter overall survival in PDAC. Multivariate Cox regression analyses revealed that the PD-L1 promoter methylation was an independent prognostic factor. PD-L1 promoter hypomethylation and high expression were related to aggressive clinical phenotypes. Moreover, both PD-L1 and PD-L2 methylation correlated with immune cell infiltration and the expression of immune checkpoint genes. PD-L1 promoter methylation status was further validated as an independent prognostic biomarker in patients with PDAC using the PUMCH cohort. The prognostic significance of PD-L1 promoter methylation was more discriminative in tumors with perineural/lymphovascular invasion and distant metastasis than in those without perineural/lymphovascular invasion and distant metastasis. In summary, the methylation status of the PD-L1 promoter is a promising biomarker for survival outcomes, immune infiltration, and the potential immune benefits of immunotherapy in PDAC.

Functionalizing Sgc8-Paclitaxel Conjugates with F-Base Modifications: Targeted Drug Delivery with Optimized Cardiac Safety.

Recent advancements in cancer treatment have improved patient prognoses, but chemotherapy induced cardiotoxicity remains a prevalent concern. This study explores the potential of F-base-modified aptamers for targeted drug delivery, focusing on their impact on cardiotoxicity. From the phosphoramidite, F-base-functionalized Sgc8-F23 was prepared in an automated and programmable way, which was further reacted with paclitaxel (PTX) to give the F-base- modified aptamer Sgc8-paclitaxel conjugates (Sgc8-F23-PTX) efficiently. The conjugate exhibited prolonged circulation time and enhanced efficacy as a precision anticancer drug delivery system. Echocardiographic assessments revealed no exacerbation of cardiac dysfunction after myocardial infarction (MI) and no pathological changes or increased apoptosis in non-infarcted cardiac regions. Autophagy pathway analysis showed no discernible differences in Sgc8-F23-PTX-treated cardiomyocytes compared with controls, in contrast to the increased autophagy with nanoparticle albumin-bound-paclitaxel (Nab-PTX). Similarly, apoptosis analysis showed no significant differences. Moreover, Sgc8-F23-PTX exhibited no inhibitory effect on hERG, hNav1.5, or hCav1.2 channels. These findings suggest the safety and efficacy of F-base-modified Sgc8 aptamers for targeted drug delivery with potential clinical applications. Further research is warranted for clinical translation and exploration of other drug carriers.

Mechanical memory based on chromatin and metabolism remodeling promotes proliferation and smooth muscle differentiation in mesenchymal stem cells.

Stem cells respond and remember mechanical cues from the microenvironment, which modulates their therapeutic effects. Chromatin organization and energy metabolism regulate the stem cell fate induced by mechanical cues. However, the mechanism of mechanical memory is still unclear. This study aimed to investigate the effects of mechanical amplitude, frequency, duration, and stretch cycle on mechanical memory in mesenchymal stem cells. It showed that the amplitude was the dominant parameter to the persistence of cell alignment. F-actin, paxillin, and nuclear deformation are more prone to be remolded than cell alignment. Stretching induces transcriptional memory, resulting in greater transcription upon subsequent reloading. Cell metabolism displays mechanical memory with sustained mitochondrial fusion and increased ATP production. The mechanical memory of chromatin condensation is mediated by histone H3 lysine 27 trimethylation, leading to much higher smooth muscle differentiation efficiency. Interestingly, mechanical memory can be transmitted based on direct cell-cell interaction, and stretched cells can remodel the metabolic homeostasis of static cells. Our results provide insight into the underlying mechanism of mechanical memory and its potential benefits for stem cell therapy.

Unlocking the Potential of Pterostilbene: A Pharmaceutical Element for Aptamer-Based Nanomedicine.

Natural compounds like pterostilbene (PTE) have gained recognition for their various biological activities and potential health benefits. However, challenges related to bioavailability and limited clinical efficacy have prompted efforts to strengthen their therapeutic potential. To meet these challenges, we herein rationally designed and successfully synthesized a pharmaceutical phosphoramidite that allows for the programmable incorporation of PTE into oligonucleotides. The resultant aptamer-PTE conjugate can selectively bind to cancer cells, leading to a specific internalization and drug release. Moreover, compared with free PTE, the conjugate exhibits superior cytotoxicity in cancer cells. Specifically, in a zebrafish xenograft model, the nanomedicine effectively inhibits tumor growth and neovascularization, highlighting its potential for targeted antitumor therapy. This approach presents a promising avenue for harnessing the therapeutic potential of natural compounds via a nanomedicine solution.

Early Predicting Osteogenic Differentiation of Mesenchymal Stem Cells Based on Deep Learning Within One Day.

Osteogenic differentiation of mesenchymal stem cells (MSCs) is proposed to be critical for bone tissue engineering and regenerative medicine. However, the current approach for evaluating osteogenic differentiation mainly involves immunohistochemical staining of specific markers which often can be detected at day 5-7 of osteogenic inducing. Deep learning (DL) is a significant technology for realizing artificial intelligence (AI). Computer vision, a branch of AI, has been proved to achieve high-precision image recognition using convolutional neural networks (CNNs). Our goal was to train CNNs to quantitatively measure the osteogenic differentiation of MSCs. To this end, bright-field images of MSCs during early osteogenic differentiation (day 0, 1, 3, 5, and 7) were captured using a simple optical phase contrast microscope to train CNNs. The results showed that the CNNs could be trained to recognize undifferentiated cells and differentiating cells with an accuracy of 0.961 on the independent test set. In addition, we found that CNNs successfully distinguished differentiated cells at a very early stage (only 1 day). Further analysis showed that overall morphological features of MSCs were the main basis for the CNN classification. In conclusion, MSCs differentiation detection can be achieved early and accurately through simple bright-field images and DL networks, which may also provide a potential and novel method for the field of cell detection in the near future.

Synthesis and Selective Anticancer Activity Evaluation of 2-phenylacrylonitrile Derivatives as Tubulin Inhibitors.

OBJECTIVE: This study aimed at synthesizing 13 series of novel derivatives with 2-phenylacrylonitrile, evaluating antitumor activity both in vivo and in vitro, and obtaining novel tubulin inhibitors. METHOD: The 13 series of 2-phenylacrylonitrile derivatives were synthesized by Knoevenagel condensation and the anti-proliferative activities were determined by MTT assay. The cell cycle and apoptosis were analyzed by flow cytometer. Quantitative cell migration was performed using 24-well Boyden chambers. The proteins were detected by western blotting. in vitro kinetics of microtubule assembly was measured using ELISA kit for Human ß-tubulin (TUBB). Molecular docking was done by Discovery Studio (DS) 2017 Client online tool. RESULTS: Among the derivatives, compound 1g2a possessed strong inhibitory activity against HCT116 (IC50 = 5.9 nM) and BEL-7402 (IC50 = 7.8 nM) cells. Compound 1g2a exhibited better selective antiproliferative activities and specificities than all the positive control drugs, including taxol. Compound 1g2a inhibited proliferation of HCT116 and BEL-7402 cells by arresting them in the G2/M phase of the cell cycle, inhibited the migration of HCT116 and BEL-7402 cells and the formation of cell colonies. Compound 1g2a showed excellent tubulin polymerization inhibitory activity on HCT116 and BEL-7402 cells. The results of molecular docking analyses showed that 1g2a may inhibit tubulin to exert anticancer effects. CONCLUSION: Compound 1g2a shows outstanding antitumor activity both in vivo and in vitro and has the potential to be further developed into a highly effective antitumor agent with little toxicity to normal tissues.

Semi-supervised breast cancer pathology image segmentation based on fine-grained classification guidance.

Breast cancer pathological image segmentation (BCPIS) holds significant value in assisting physicians with quantifying tumor regions and providing treatment guidance. However, achieving fine-grained semantic segmentation remains a major challenge for this technology. The complex and diverse morphologies of breast cancer tissue structures result in high costs for manual annotation, thereby limiting the sample size and annotation quality of the dataset. These practical issues have a significant impact on the segmentation performance. To overcome these challenges, this study proposes a semi-supervised learning model based on classification-guided segmentation. The model first utilizes a multi-scale convolutional network to extract rich semantic information and then employs a multi-expert cross-layer joint learning strategy, integrating a small number of labeled samples to iteratively provide the model with class-generated multi-cue pseudo-labels and real labels. Given the complexity of the breast cancer samples and the limited sample quantity, an innovative approach of augmenting additional unlabeled data was adopted to overcome this limitation. Experimental results demonstrate that, although the proposed model falls slightly behind supervised segmentation models, it still exhibits significant progress and innovation. The semi-supervised model in this study achieves outstanding performance, with an IoU (Intersection over Union) value of 71.53%. Compared to other semi-supervised methods, the model developed in this study demonstrates a performance advantage of approximately 3%. Furthermore, the research findings indicate a significant correlation between the classification and segmentation tasks in breast cancer pathological images, and the guidance of a multi-expert system can significantly enhance the fine-grained effects of semi-supervised semantic segmentation.

Th1/Th2 cytokine profile in patients with acute and chronic calculus cholecystitis.

Relatively little is known about the relationship between Th1/Th2 cytokines and calculus cholecystitis (CC). The purpose of this study was to investigate the correlation between serum Th1 and Th2 cytokine expression and CC, including both acute and chronic cases. In total, 102 patients with chronic calculous cholecystitis (CCC), 64 patients with acute calculous cholecystitis (ACC), and 55 healthy controls (HCs) were recruited for the study. Serum concentration of Th1 (IL-2, TNF-α, IFN-γ) and Th2 cytokines (IL-4, IL-6, IL-10) was measured at admission and on the fifth day after cholecystectomy using flow cytometry. In addition, the ratio of IL-6/IL-10 was calculated. Correlation of the corresponding factors was then analysed, and univariate and multivariate Cox regression analyses were performed to identify independent markers of ACC severity. Compared to HCs, CCC patients exhibited significantly elevated expression levels of IL-6 and IL-10, while ACC patients demonstrated higher expression of IL-2, TNF-α, and IL-6/ IL-10 in addition to IL-6, and IL-10. In ACC patients, there was a strong positive correlation between IL-6 and IL-10 concentration, the expression of IL-2 was observed to positively correlate with serum ALT and AST concentration, and TNF-α expression positively correlated with the duration of hospitalization. Moreover, patients with moderate-to-severe ACC presented with higher expression of IL-10 compared to those with mild ACC. Cox regression analysis confirmed that IL-10 and IL-6 were independent factors for the severity of ACC. Following surgery, the levels of IL-6 and IL-6/IL-10 significantly decreased but did not fully return to baseline levels in ACC patients. Our study reveals atypical Th1/Th2 cytokine expression profiles in patients with acute and chronic CC, and further highlights the significant potential of these cytokines, particularly IL-6 and IL-10, in assessing the severity and progression of CC.

Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma.

BACKGROUND: Although magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis studies based on deep learning have significantly progressed, the similarity between synthetic CT (sCT) and real CT (rCT) has only been evaluated in image quality metrics (IQMs). To evaluate the similarity between synthetic CT (sCT) and real CT (rCT) comprehensively, we comprehensively evaluated IQMs and radiomic features for the first time. METHODS: This study enrolled 127 patients with nasopharyngeal carcinoma who underwent CT and MRI scans. Supervised-learning (Unet) and unsupervised-learning (CycleGAN) methods were applied to build MRI-to-CT synthesis models. The regions of interest (ROIs) included nasopharynx gross tumor volume (GTVnx), brainstem, parotid glands, and temporal lobes. The peak signal-to-noise ratio (PSNR), mean absolute error (MAE), root mean square error (RMSE), and structural similarity (SSIM) were used to evaluate image quality. Additionally, 837 radiomic features were extracted for each ROI, and the correlation was evaluated using the concordance correlation coefficient (CCC). RESULTS: The MAE, RMSE, SSIM, and PSNR of the body were 91.99, 187.12, 0.97, and 51.15 for Unet and 108.30, 211.63, 0.96, and 49.84 for CycleGAN. For the metrics, Unet was superior to CycleGAN (P < 0.05). For the radiomic features, the percentage of four levels (i.e., excellent, good, moderate, and poor, respectively) were as follows: GTVnx, 8.5%, 14.6%, 26.5%, and 50.4% for Unet and 12.3%, 25%, 38.4%, and 24.4% for CycleGAN; other ROIs, 5.44% ± 3.27%, 5.56% ± 2.92%, 21.38% ± 6.91%, and 67.58% ± 8.96% for Unet and 5.16% ± 1.69%, 3.5% ± 1.52%, 12.68% ± 7.51%, and 78.62% ± 8.57% for CycleGAN. CONCLUSIONS: Unet-sCT was superior to CycleGAN-sCT for the IQMs. However, neither exhibited absolute superiority in radiomic features, and both were far less similar to rCT. Therefore, further work is required to improve the radiomic similarity for MRI-to-CT synthesis. TRIAL REGISTRATION: This study was a retrospective study, so it was free from registration.

Identification of lactate regulation pattern on tumor immune infiltration, therapy response, and DNA methylation in diffuse large B-cell lymphoma.

Background: Lactate, produced through glycolytic metabolism in the tumor microenvironment (TME), is implicated in tumorigenesis and progression in diverse cancers. However, the impact of lactate on the remodeling of the TME in diffuse large B-cell lymphoma (DLBCL) and its implications for therapy options remain unclear. Method: A lactate-related (LAR) scoring model was constructed in DLBCL patients using bioinformatic methods. CIBERSORT, XCELL, and ssGSEA algorithms were used to determine the correlation between LAR score and immune cell infiltration. Tumor Immune Dysfunction and Exclusion (TIDE), rituximab, cyclophosphamide, adriamycin, vincristine, and prednisone (R-CHOP) cohorts, and Genomics of Drug Sensitivity in Cancer (GDSC) were utilized to predict the therapeutic response of DLBCL patients. The impact of the hub gene STAT4 on tumor biological behavior and DNA methylation was experimentally validated or accessed by the TSIDE database. Results: The LAR scoring model was developed based on 20 prognosis-related lactate genes, which enabled the division of DLBCL patients into high- and low-risk groups based on the median LAR score. Patients with high-risk DLBCL exhibited significantly worse survival outcomes in both the training cohorts (GSE181063) and the validation cohorts (GSE10846, GSE32918, and GSE69053), as indicated by statistically significant differences (all P<0.05) and area under the curve (AUC) values exceeding 0.6. Immune analyses revealed that low-risk DLBCL patients had higher levels of immune cell infiltration and antitumor immune activation compared to high-risk DLBCL patients. Furthermore, DLBCL patients with high LAR scores were associated with a lower TIDE value and poor therapeutic efficacy of the R-CHOP regimen. GDSC analysis identified 18 drugs that exhibited significant response sensitivity in low-risk DLBCL patients. Moreover, in vitro experiments demonstrated that overexpression of the lactate key gene STAT4 could suppress proliferation and migration, induce cell cycle arrest, and promote cell apoptosis in DLBCL cells. Transcriptional expression and methylation of the STAT4 gene were found to be associated with immunomodulators and chemokines. Conclusion: The lactate-based gene signature effectively predicts the prognosis and regulates TME in DLBCL. Our study underscores the role of lactate gene, STAT4, as an important tumor suppressor in DLBCL. Modulating STAT4 could be a promising strategy for DLBCL in clinical practice.

Visualization Analysis of Research Trends and Hotspots in Inspiratory Muscle Training.

BACKGROUND Inspiratory muscle training (IMT) aims to train inspiratory muscles based mainly on the diaphragm by applying a load resistance during the inspiratory process. Many papers related to IMT have been published in various journals; however, no articles objectively and directly present the development trends and research hotspots of IMT. Therefore, this study used CiteSpace to visually analyze recent IMT-related publications to provide valuable information for future IMT-related studies. MATERIAL AND METHODS CiteSpace was applied to analyze the IMT-related publications by countries, institutions, journals, authors, references, and keywords. RESULTS We included 504 papers. The number of IMT-related publications trended upward between 2009 and 2022. Leuven had the highest number of publications by an institution. The American Journal of Respiratory and Critical Care Medicine was the most frequently co-cited journal. Half of the top 10 references cited were from Journal Citation Reports (JCR) Q1 and half were about the application of IMT in chronic obstructive pulmonary disorder. Gosselink was the author with the highest number of publications and Aldrich was the author with the highest co-citation frequency. The preponderance of studies on the surgical population and postoperative pulmonary complications reflects potential application of IMT in enhanced recovery after surgery. CONCLUSIONS This study provides scholars with important information related to IMT research. It analyzes IMT research trends and status, which can help researchers identify primary topics in the field and find ways to explore new research directions to promote the application of IMT in clinical practice and the cooperation of IMT-related disciplines.

Efficient segmentation using domain adaptation for MRI-guided and CBCT-guided online adaptive radiotherapy.

BACKGROUND: Delineation of regions of interest (ROIs) is important for adaptive radiotherapy (ART) but it is also time consuming and labor intensive. AIM: This study aims to develop efficient segmentation methods for magnetic resonance imaging-guided ART (MRIgART) and cone-beam computed tomography-guided ART (CBCTgART). MATERIALS AND METHODS: MRIgART and CBCTgART studies enrolled 242 prostate cancer patients and 530 nasopharyngeal carcinoma patients, respectively. A public dataset of CBCT from 35 pancreatic cancer patients was adopted to test the framework. We designed two domain adaption methods to learn and adapt the features from planning computed tomography (pCT) to MRI or CBCT modalities. The pCT was transformed to synthetic MRI (sMRI) for MRIgART, while CBCT was transformed to synthetic CT (sCT) for CBCTgART. Generalized segmentation models were trained with large popular data in which the inputs were sMRI for MRIgART and pCT for CBCTgART. Finally, the personalized models for each patient were established by fine-tuning the generalized model with the contours on pCT of that patient. The proposed method was compared with deformable image registration (DIR), a regular deep learning (DL) model trained on the same modality (DL-regular), and a generalized model in our framework (DL-generalized). RESULTS: The proposed method achieved better or comparable performance. For MRIgART of the prostate cancer patients, the mean dice similarity coefficient (DSC) of four ROIs was 87.2%, 83.75%, 85.36%, and 92.20% for the DIR, DL-regular, DL-generalized, and proposed method, respectively. For CBCTgART of the nasopharyngeal carcinoma patients, the mean DSC of two target volumes were 90.81% and 91.18%, 75.17% and 58.30%, for the DIR, DL-regular, DL-generalized, and the proposed method, respectively. For CBCTgART of the pancreatic cancer patients, the mean DSC of two ROIs were 61.94% and 61.44%, 63.94% and 81.56%, for the DIR, DL-regular, DL-generalized, and the proposed method, respectively. CONCLUSION: The proposed method utilizing personalized modeling improved the segmentation accuracy of ART.

Improving accelerated 3D imaging in MRI-guided radiotherapy for prostate cancer using a deep learning method.

PURPOSE: This study was to improve image quality for high-speed MR imaging using a deep learning method for online adaptive radiotherapy in prostate cancer. We then evaluated its benefits on image registration. METHODS: Sixty pairs of 1.5 T MR images acquired with an MR-linac were enrolled. The data included low-speed, high-quality (LSHQ), and high-speed low-quality (HSLQ) MR images. We proposed a CycleGAN, which is based on the data augmentation technique, to learn the mapping between the HSLQ and LSHQ images and then generate synthetic LSHQ (synLSHQ) images from the HSLQ images. Five-fold cross-validation was employed to test the CycleGAN model. The normalized mean absolute error (nMAE), peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), and edge keeping index (EKI) were calculated to determine image quality. The Jacobian determinant value (JDV), Dice similarity coefficient (DSC), and mean distance to agreement (MDA) were used to analyze deformable registration. RESULTS: Compared with the LSHQ, the proposed synLSHQ achieved comparable image quality and reduced imaging time by ~ 66%. Compared with the HSLQ, the synLSHQ had better image quality with improvement of 57%, 3.4%, 26.9%, and 3.6% for nMAE, SSIM, PSNR, and EKI, respectively. Furthermore, the synLSHQ enhanced registration accuracy with a superior mean JDV (6%) and preferable DSC and MDA values compared with HSLQ. CONCLUSION: The proposed method can generate high-quality images from high-speed scanning sequences. As a result, it shows potential to shorten the scan time while ensuring the accuracy of radiotherapy.

The RNA-binding proteins hnRNP H and F regulate splicing of a MYC-dependent HRAS exon in prostate cancer cells.

The MYC proto-oncogene contributes to the pathogenesis of more than half of human cancers. Malignant transformation by MYC transcriptionally up-regulates the core pre-mRNA splicing machinery and causes misregulation of alternative splicing. However, our understanding of how splicing changes are directed by MYC is limited. We performed a signaling pathway-guided splicing analysis to identify MYC-dependent splicing events. These included an HRAS cassette exon repressed by MYC across multiple tumor types. To molecularly dissect the regulation of this HRAS exon, we used antisense oligonucleotide tiling to identify splicing enhancers and silencers in its flanking introns. RNA-binding motif prediction indicated multiple binding sites for hnRNP H and hnRNP F within these cis-regulatory elements. Using siRNA knockdown and cDNA expression, we found that both hnRNP H and F activate the HRAS cassette exon. Mutagenesis and targeted RNA immunoprecipitation implicate two downstream G-rich elements in this splicing activation. Analyses of ENCODE RNA-seq datasets confirmed hnRNP H regulation of HRAS splicing. Analyses of RNA-seq datasets across multiple cancers showed a negative correlation of HNRNPH gene expression with MYC hallmark enrichment, consistent with the effect of hnRNP H on HRAS splicing. Interestingly, HNRNPF expression showed a positive correlation with MYC hallmarks and thus was not consistent with the observed effects of hnRNP F. Loss of hnRNP H/F altered cell cycle progression and induced apoptosis in the PC3 prostate cancer cell line. Collectively, our results reveal mechanisms for MYC-dependent regulation of splicing and point to possible therapeutic targets in prostate cancers.