Organ-Specific Gene Expression Control Using DNA Origami-Based Nanodevices.

Nanodevices that function in specific organs or cells are one of the ultimate goals of synthetic biology. The recent progress in DNA nanotechnology such as DNA origami has allowed us to construct nanodevices to deliver a payload (e.g., drug) to the tumor. However, delivery to specific organs remains difficult due to the fragility of the DNA nanostructure and the low targeting capability of the DNA nanostructure. Here, we constructed tough DNA origami that allowed us to encapsulate the DNA origami into lipid-based nanoparticles (LNPs) under harsh conditions (low pH), harnessing organ-specific delivery of the gene of interest (GOI). We found that DNA origami-encapsulated LNPs can increase the functionality of payload GOIs (mRNA and siRNA) inside mouse organs through the contribution from different LNP structures revealed by cryogenic electron microscope (Cryo-EM). These data should be the basis for future organ-specific gene expression control using DNA origami nanodevices.

Heart failure promotes multimorbidity through innate immune memory.

Patients with heart failure (HF) often experience repeated acute decompensation and develop comorbidities such as chronic kidney disease and frailty syndrome. Although this suggests pathological interaction among comorbidities, the mechanisms linking them are poorly understood. Here, we identified alterations in hematopoietic stem cells (HSCs) as a critical driver of recurrent HF and associated comorbidities. Bone marrow transplantation from HF-experienced mice resulted in spontaneous cardiac dysfunction and fibrosis in recipient mice, as well as increased vulnerability to kidney and skeletal muscle insults. HF enhanced the capacity of HSCs to generate proinflammatory macrophages. In HF mice, global chromatin accessibility analysis and single-cell RNA-seq showed that transforming growth factor-ß (TGF-ß) signaling was suppressed in HSCs, which corresponded with repressed sympathetic nervous activity in bone marrow. Transplantation of bone marrow from mice in which TGF-ß signaling was inhibited similarly exacerbated cardiac dysfunction. Collectively, these results suggest that cardiac stress modulates the epigenome of HSCs, which in turn alters their capacity to generate cardiac macrophage subpopulations. This change in HSCs may be a common driver of repeated HF events and comorbidity by serving as a key carrier of "stress memory."

Dendritic cell therapy for neurospoagioma: Immunomodulation mediated by tumor vaccine.

Neurospagioma, arising from different glial cells such as astrocytes, oligodendrocytes, and ependymal cells, stands as the prevalent intracranial tumor within the central nervous system. Among its variants, glioblastoma (GBM) represents the most aggressive form, characterized by a notably high occurrence rate and a discouragingly low survival prognosis. The formidable challenge posed by glioblastoma underscores its critical importance as a life-threatening ailment. Currently, clinical approaches often involve surgical excision along with a combination of radiotherapy and chemotherapy. However, these treatments frequently result in a notable recurrence rate, accompanied by substantial adverse effects that significantly compromise the overall prognosis. Hence, there is a crucial need to investigate novel and dependable treatment strategies. Dendritic cells (DCs), being specialized antigen-presenting cells (APCs), hold a significant position in both innate and adaptive immune responses. Presently, DC vaccines have gained widespread application in the treatment of various tumors, including neurospoagioma. In this review, we summarize the immunomodulatory effects and related mechanisms of DC vaccines in neurospoagioma as well as the progress of clinical trials to propose possible challenges of DC vaccines and new development directions.

Round Window Niche Veil is Visible on High-Resolution Computed Tomography and a Predictor of Local Drug Efficacy to Inner Ear.

OBJECTIVES: To determine the morphologies and effect of the round window niche veil (RWNV) on local drug delivery efficacy and develop diagnostic criteria on high-resolution computed tomography (HRCT). METHODS: Patients diagnosed with otosclerosis, bilateral profound sensorineural hearing loss or vestibular schwannoma were enrolled from 2019 to 2022, receiving temporal bone HRCT scanning, and anatomic variations of RWMV were summarized intraoperative. For patients with vestibular schwannoma, 1 mL of dexamethasone solution (4 mg/mL) was administered via facial recess during operation, and samples of perilymph were collected to analyze. The diagnostic criteria of RWNV on HRCT were developed and verified. RESULTS: A total of 85 patients were enrolled. RWNV was observed in 54 cases intraoperatively with an incidence of 63.5% (95% CI, 52.9%-73.0%). The median perilymph concentrations were 4.86-fold higher in the group without RWNV than with RWNV (p < 0.0001). RWNV could be visualized on HRCT with a window width of 3500-4500 HU and a window level of 300-500 HU. The characteristic features were as follows: (1) a thin soft tissue shadow could be seen at the entrance of the round window niche (RWN); (2) it was visible in at least 2 consecutive layers along the upper margin of RWN from top to bottom; (3) it was discontinuous with the adjacent bone margin. The sensitivity and specificity of the diagnostic criteria were 77.8% and 93.6%, respectively. CONCLUSION: RWNV could reduce local dexamethasone diffusion efficacy to the inner ear, which could be diagnosed on HRCT and used as a predictor of local drug delivery efficacy to the inner ear. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:1396-1402, 2024.

Pretreatment information-aided automatic segmentation for online magnetic resonance imaging-guided prostate radiotherapy.

BACKGROUND: It is necessary to contour regions of interest (ROIs) for online magnetic resonance imaging (MRI)-guided adaptive radiotherapy (MRIgART). These updated contours are used for online replanning to obtain maximum dosimetric benefits. Contouring can be accomplished using deformable image registration (DIR) and deep learning (DL)-based autosegmentation methods. However, these methods may require considerable manual editing and thus prolong treatment time. PURPOSE: The present study aimed to improve autosegmentation performance by integrating patients' pretreatment information in a DL-based segmentation algorithm. It is expected to improve the efficiency of current MRIgART process. METHODS: Forty patients with prostate cancer were enrolled retrospectively. The online adaptive MR images, patient-specific planning computed tomography (CT), and contours in CT were used for segmentation. The deformable registration of planning CT and MR images was performed first to obtain a deformable CT and corresponding contours. A novel DL network, which can integrate such patient-specific information (deformable CT and corresponding contours) into the segmentation task of MR images was designed. We performed a four-fold cross-validation for the DL models. The proposed method was compared with DIR and DL methods on segmentation of prostate cancer. The ROIs included the clinical target volume (CTV), bladder, rectum, left femur head, and right femur head. Dosimetric parameters of automatically generated ROIs were evaluated using a clinical treatment planning system. RESULTS: The proposed method enhanced the segmentation accuracy of conventional procedures. Its mean value of the dice similarity coefficient (93.5%) over the five ROIs was higher than both DIR (87.5%) and DL (87.2%). The number of patients (n = 40) that required major editing using DIR, DL, and our method were 12, 18, and 7 (CTV); 17, 4, and 1 (bladder); 8, 11, and 5 (rectum); 2, 4, and 1 (left femur head); and 3, 7, and 1 (right femur head), respectively. The Spearman rank correlation coefficient of dosimetry parameters between the proposed method and ground truth was 0.972 ± 0.040, higher than that of DIR (0.897 ± 0.098) and DL (0.871 ± 0.134). CONCLUSION: This study proposed a novel method that integrates patient-specific pretreatment information into DL-based segmentation algorithm. It outperformed baseline methods, thereby improving the efficiency and segmentation accuracy in adaptive radiotherapy.

A novel loss function to reproduce texture features for deep learning-based MRI-to-CT synthesis.

BACKGROUND: Studies on computed tomography (CT) synthesis based on magnetic resonance imaging (MRI) have mainly focused on pixel-wise consistency, but the texture features of regions of interest (ROIs) have not received appropriate attention. PURPOSE: This study aimed to propose a novel loss function to reproduce texture features of ROIs and pixel-wise consistency for deep learning-based MRI-to-CT synthesis. The method was expected to assist the multi-modality studies for radiomics. METHODS: The study retrospectively enrolled 127 patients with nasopharyngeal carcinoma. CT and MRI images were collected for each patient, and then rigidly registered as pre-procession. We proposed a gray-level co-occurrence matrix (GLCM)-based loss function to improve the reproducibility of texture features. This novel loss function could be embedded into the present deep learning-based framework for image synthesis. In this study, a typical image synthesis model was selected as the baseline, which contained a Unet trained mean square error (MSE) loss function. We embedded the proposed loss function and designed experiments to supervise different ROIs to prove its effectiveness. The concordance correlation coefficient (CCC) of the GLCM feature was employed to evaluate the reproducibility of GLCM features, which are typical texture features. Besides, we used a publicly available dataset of brain tumors to verify our loss function. RESULTS: Compared with the baseline, the proposed method improved the pixel-wise image quality metrics (MAE: 107.5 to 106.8 HU; SSIM: 0.9728 to 0.9730). CCC values of the GLCM features in GTVnx were significantly improved from 0.78 ± 0.12 to 0.82 ± 0.11 (p < 0.05 for paired t-test). Generally, > 90% (22/24) of the GLCM-based features were improved compared with the baseline, where the Informational Measure of Correlation feature was improved the most (CCC: 0.74 to 0.83). For the public dataset, the loss function also shows its effectiveness. With our proposed loss function added, the ability to reproduce texture features was improved in the ROIs. CONCLUSIONS: The proposed method reproduced texture features for MRI-to-CT synthesis, which would benefit radiomics studies based on image multi-modality synthesis.

Histone lactylation-derived LINC01127 promotes the self-renewal of glioblastoma stem cells via the cis-regulating the MAP4K4 to activate JNK pathway.

Gliomas are the most prevalent and aggressive brain tumors, exhibiting high proliferation, abnormal glycolysis, and poor prognosis. LncRNAs act as regulatory molecules and play crucial roles in the malignant behaviors of GBM cells, including cell self-renewal. However, the regulatory mechanisms involved are largely unknown. In this study, we performed bioinformatics analysis to explore NF-κB pathway-related lncRNAs. ECAR and qRT-PCR were used to measure the relationship between glycolytic activity and lncRNA expression. Assays such as RIP-PCR and ChIP-PCR were employed to reveal the regulatory mechanisms of the lncRNA. Neurosphere formation and limiting dilution assays were performed to evaluate the self-renewal capacity of GBM cells. In our study, we identified an NF-κB pathway-related lncRNA named LINC01127 in GBM, which was found to be associated with poor progression of GBM. Functionally, the NF-κB pathway promoted warburg effect, which, in turn, induced the lactylation of H3 histone and increased the expression of LINC01127. Consequently, this enhancement of LINC01127 expression led to the promotion of self-renewal in GBM cells. Furthermore, LINC01127 regulated MAP4K4 expression in cis by directly guiding POLR2A to the MAP4K4 promoter regions, thereby leading to JNK pathway activation, and ultimately modulating the self-renewal of GBM cells. Moreover, the activated JNK pathway promoted the phosphorylation of IκBα. Overall, targeting LINC01127-mediated axis impeded orthotopic tumor growth in GBM xenografts. Taken together these results revealed that warburg effect-induced histone lactylation drives NF-κB-related LINC01127 expression, thereby promoting the self-renewal of GBM cells through the MAP4K4/JNK/NF-κB axis, and providing substantial evidence that LINC01127 might provide a novel therapeutic strategy for GBM patients.

Celastrol suppresses human pancreatic cancer via m6A-YTHDF3-mediated downregulation of Claspin and Bcl-2.

BACKGROUND: Celastrol has been revealed to exhibit anticancer pharmacological activity, however, the molecular mechanisms of celastrol involved in pancreatic cancer remain to be further elucidated. The present study was to illustrate whether celastrol suppresses pancreatic cancer through modulating RNA m6A modification. METHODS: Effect of celastrol treatment on the malignant phenotypes of pancreatic cancer cells was evaluated by CCK-8 assay, EdU assay, colony formation assay, flow cytometry analysis and subcutaneous xenograft experiments. RNA sequencing (RNA-seq) analysis was carried out to analyze the genes differentially expressed in celastrol-treated pancreatic cancer cells. RT-qPCR, Western blotting and immunohistochemistry were employed to evaluate the expression of the indicated genes. RNA dot blot and quantification of total RNA m6A modification assays, MeRIP-qPCR assay, RIP-qPCR assay, RNA stability and protein stability assays were applied to evaluate the regulatory mechanism of celastrol treatment in pancreatic cancer cells. RESULTS: We demonstrated that celastrol suppressed cell proliferation and induced cell cycle arrest and apoptosis of pancreatic cancer cells in vitro, and decreased tumor growth in vivo. Specifically, Bcl-2, Claspin, METTL3 and YTHDF3 were identified as the potential targets of celastrol treatment in pancreatic cancer cells. Moreover, our results indicated that celastrol treatment downregulated METTL3 and decreased m6A levels of Claspin and Bcl-2 mRNA, leading to the degradation of Claspin and Bcl-2 mRNA in pancreatic cancer cells. Furthermore, we revealed that celastrol treatment downregulated Claspin and Bcl-2, at least in part, in an m6A-YTHDF3-mediated manner in pancreatic cancer cells. CONCLUSION: Our study highlighted a novel mechanism underlying celastrol-induced cellular proliferation inhibition and apoptosis in pancreatic cancer cells via m6A-YTHDF3-mediated downregulation of Claspin and Bcl-2.

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.

Performance of DNA methylation analysis in the detection of high-grade cervical intraepithelial neoplasia or worse (CIN3+): a cross-sectional study.

It is commonly accepted that host genes show high methylation in cervical intraepithelial neoplasia 3 (CIN3) or worse (CIN3+). However, study quality varies, as does the clinical performance of markers in different populations. We aimed to validate candidate gene DNA methylation with standardized testing methods in the same batch of samples. We first compared the performance of 16 DNA methylation markers for detecting CIN3+ in the 82-sample training set, including 24 subjects with ≤ CIN1, 10 subjects with CIN2, 23 subjects with CIN3, and 25 subjects with cervical cancer (CC). Then five methylation markers were selected and subsequently validated among an independent set of 74 subjects, including 47 subjects with ≤ CIN1, 13 subjects with CIN2, 6 subjects with CIN3, and 8 subjects with CC. The results in the validation set revealed that methylation analysis of the SOX1 (SOX1m) showed a superior level of clinical performance (AUC = 0.879; sensitivity = 85.7%; specificity = 90.0%). SOX1m had better accuracy than cytology, with a reduced referral rate (23.0% vs. 31.4%) and a lower number of overtreatment (5 vs. 13) cases among high-risk human papillomavirus (hrHPV)-positive women. Importantly, among hrHPV-positive and SOX1m-negative women, only 1 CIN3 patient was at risk for follow-up after 1 year, whereas 1 CIN3 patient and 1 CC patient were at risk among hrHPV-positive and cytology-negative women. In this investigation, we screened 16 reported methylation markers to provide a basis for future studies related to potential precancerous lesion/cancer methylation markers in the Chinese population. The study also revealed that SOX1m has optimal CIN3+ detection performance, suggesting that it may be a promising biomarker for detecting CIN3+ in the Chinese population.

LINC01806 Promotes Breast Cancer Growth and Metastasis via Sponging miR-1286 to Disinhibit ZEB1 Expression.

Breast cancer (BC) is the most abundant and aggressive cancer that impacts millions of women with poorly understood mechanisms. Here, we aimed to investigate the function of LINC01806 in BC development. Human BC tissues and nearby normal specimens were taken from diagnosed BC patients. The expression levels of LINC01806, miR-1286, ZEB1, and EMT-related markers were evaluated by qRT-PCR and western blotting. FISH was used to visualize the subcellular localization of LINC01806. The viability, proliferation, migration and invasion capacities of BC cells were assessed by MTT, colony formation, and transwell assays. Interactions among LINC01806, miR-1286 and ZEB1 were validated by dual luciferase assay. The unpaired Student t-test (for two groups) or one-way ANOVA following with Tukey post-hoc test (for more than three groups) was employed for statistical analysis. LINC01806 level was elevated in BC tissues. Knockdown of LINC01806 suppressed EMT process and BC cell proliferation, migration, and invasion. LINC01806 co-localized and directly bound with miR-1286 in the cytoplasm. MiR-1286 inhibitor blocked the effects of LINC01806 knockdown on BC cell EMT, proliferation and migration. MiR-1286 targeted ZEB1 and overexpression of ZEB1 blocked the regulatory functions of miR-1286 mimics in BC. LINC01806 facilitates EMT and accelerates BC cell proliferation, migration, and invasion via acting as miR-1286 sponge to disinhibit ZEB1 expression.

Using integrated analysis from multicentre studies to identify RNA methylation-related lncRNA risk stratification systems for glioma.

BACKGROUND: N6-methyladenosine (m6A), 5-methylcytosine (m5C) and N1-methyladenosine (m1A) are the main RNA methylation modifications involved in the progression of cancer. However, it is still unclear whether RNA methylation-related long noncoding RNAs (lncRNAs) affect the prognosis of glioma. METHODS: We summarized 32 m6A/m5C/m1A-related genes and downloaded RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify differentially expressed (DE-) RNA methylation-related lncRNAs in order to construct a prognostic signature of glioma and in order to determine their correlation with immune function, immune therapy and drug sensitivity. In vitro and in vivo assays were performed to elucidate the effects of RNA methylation-related lncRNAs on glioma. RESULTS: A total of ten RNA methylation-related lncRNAs were used to construct a survival and prognosis signature, which had good independent prediction ability for patients. It was found that the high-risk group had worse overall survival (OS) than the low-risk group in all cohorts. In addition, the risk group informed the immune function, immunotherapy response and drug sensitivity of patients with glioma in different subgroups. Knockdown of RP11-98I9.4 and RP11-752G15.8 induced a more invasive phenotype, accelerated cell growth and apparent resistance to temozolomide (TMZ) both in vitro and in vivo. We observed significantly elevated global RNA m5C and m6A levels in glioma cells. CONCLUSION: Our study determined the prognostic implication of RNA methylation-related lncRNAs in gliomas, established an RNA methylation-related lncRNA prognostic model, and elucidated that RP11-98I9.4 and RP11-752G15.8 could suppress glioma proliferation, migration and TMZ resistance. In the future, these RNA methylation-related lncRNAs may become a new choice for immunotherapy of glioma.

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.

Deep learning framework to improve the quality of cone-beam computed tomography for radiotherapy scenarios.

BACKGROUND: The application of cone-beam computed tomography (CBCT) in image-guided radiotherapy and adaptive radiotherapy remains limited due to its poor image quality. PURPOSE: In this study, we aim to develop a deep learning framework to generate high-quality CBCT images for therapeutic applications. METHODS: The synthetic CT (sCT) generation from the CBCT was proposed using a transformer-based network with a hybrid loss function. The network was trained and validated using the data from 176 patients to produce a general model that can be extensively applied to enhance CBCT images. After the first therapy, each patient can receive paired CBCT/planning CT (pCT) scans, and the obtained data were used to fine-tune the general model for further improvement. For subsequent treatment, a patient-specific, personalized model was made available. In total, 34 patients were examined for general model testing, and another six patients who underwent rescanned pCT scan were used for personalized model training and testing. RESULTS: The general model decreased the mean absolute error (MAE) from 135 HU to 59 HU as compared to the CBCT. The hybrid loss function demonstrated superior performance in CT number correction and noise/artifacts reduction. The proposed transformer-based network also showed superior power in CT number correction compared to the classical convolutional neural network. The personalized model showed improvement based on the general model in some details, and the MAE was reduced from 59 HU (for the general model) to 57 HU (p < 0.05 Wilcoxon signed-rank test). CONCLUSION: We established a deep learning framework based on transformer for clinical needs. The deep learning model demonstrated potential for continuous improvement with the help of a suggested personalized training strategy compatible with the clinical workflow.

Laparoscopic Photoacoustic Imaging System Based on Side-Illumination Diffusing Fibers.

OBJECTIVE: To develop a flexible miniaturized photoacoustic (PA) imaging probe for detecting anatomical structures during laparoscopic surgery. The proposed probe aimed to facilitate intraoperative detection of blood vessels and nerve bundles embedded in tissue not directly visible to the operating physician to preserve these delicate and vital structures. METHODS: We modified a commercially available ultrasound laparoscopic probe by incorporating custom-fabricated side-illumination diffusing fibers that illuminate the probe's field of view. The probe geometry, including the position and orientation of the fibers and the emission angle, was determined using computational models of light propagation in the simulation and subsequently validated through experimental studies. RESULTS: In wire phantom studies within an optical scattering medium, the probe achieved an imaging resolution of 0.43 ±0.09 mm and a signal-to-noise ratio of 31.2±1.84 dB. We also conducted an ex vivo study using a rat model, demonstrating the successful detection of blood vessels and nerves. CONCLUSION: Our results indicate the viability of a side-illumination diffusing fiber PA imaging system for guidance during laparoscopic surgery. SIGNIFICANCE: The potential clinical translation of this technology could enhance the preservation of critical vascular structures and nerves, thereby minimizing post-operative complications.

A projection-domain correction method in CBCT reconstruction for head and neck radiotherapy using cycle-GAN and nonlocal means filter.

BACKGROUND: In radiation treatments for head and neck tumors, cone-beam computed tomography (CBCT) is employed for patient positioning and dose calculation of adaptive radiotherapy. However, the quality of CBCT is degraded by the scatter and noise, majorly impacting the accuracy of patient positioning and dose calculation. PURPOSE: To improve the quality of CBCT for patients with head and neck cancer, a projection-domain CBCT correction method was proposed using a cycle-consistent generative adversarial network (cycle-GAN) and a nonlocal means filter (NLMF) based on a reference digitally reconstructed radiograph (DRR). METHODS: A cycle-GAN was initially trained to learn mapping from CBCT projections to a DRR using the data obtained from 30 patients. For each patient, 671 CBCT projections were measured for CBCT reconstruction. Moreover, 360 Digital Reconstructed Radiographs (DRR) were computed from each patient's planning computed tomography (CT), whose projection angles ranged from 0° to 359° with an interval of 1°. By applying the trained generator of the cycle-GAN to the unseen CBCT projection, a synthetic DRR with considerably less scatter was obtained. However, annular artifacts were observed in the CBCT reconstructed with synthetic DRR. To address this issue, a NLMF based on reference DRR was used to further correct the synthetic DRR, which corrected the synthetic DRR using the calculated DRR as a reference image. Finally, the CBCT with no annular artifact and little noise was reconstructed with the corrected synthetic DRR. The proposed method was tested using the data of six patients. The corrected synthetic DRR and CBCT were compared with the corresponding real DRR and CT images. The structural preservation ability of the proposed method was evaluated using the Dice coefficients of the automatically extracted nasal cavity. Moreover, the image quality of CBCT corrected with the proposed method was objectively assessed with an five-point human scoring system and compared with CT, original CBCT and CBCT corrected with other strategies. RESULTS: The mean absolute value (MAE) of the relative error between the corrected synthetic and real DRR was <8%. The MAE between the corrected CBCT and corresponding CT was <30 HU. Moreover, the Dice coefficient of nasal cavity between the corrected CBCT image and the original image exceeded 98.8 for all the patients. Last but not least, the objective assessment of image quality showed the proposed method had an average score of 4.2 in overall image quality, which was higher than that of the original CBCT, CBCT reconstructed with synthetic DRR, and CBCT reconstructed with projections filtered with NLMF only. CONCLUSIONS: The proposed method can considerably improve the CBCT image quality with little anatomical distortion, improving the accuracy of radiotherapy for head and neck patients.

Biomimetic Boron Nitride Nanoparticles for Targeted Drug Delivery and Enhanced Antitumor Activity.

Boron nitride nanomaterials are being increasingly recognized as vehicles for cancer drug delivery that increase drug loading and control drug release because of their excellent physicochemical properties and biocompatibility. However, these nanoparticles are often cleared rapidly by the immune system and have poor tumor targeting effects. As a result, biomimetic nanotechnology has emerged to address these challenges in recent times. Cell-derived biomimetic carriers have the characteristics of good biocompatibility, long circulation time, and strong targeting ability. Here, we report a biomimetic nanoplatform (CM@BN/DOX) prepared by encapsulating boron nitride nanoparticles (BN) and doxorubicin (DOX) together using cancer cell membrane (CCM) for targeted drug delivery and tumor therapy. The CM@BN/DOX nanoparticles (NPs) were able to target cancer cells of the same type on its own initiative through homologous targeting of cancer cell membranes. This led to a remarkable increase in cellular uptake. In vitro simulation of an acidic tumor microenvironment could effectively promote drug release from CM@BN/DOX. Furthermore, the CM@BN/DOX complex exhibited an excellent inhibitory effect against homotypic cancer cells. These findings suggest that CM@BN/DOX are promising in targeted drug delivery and potentially personalized therapy against their homologous tumor.

Development and validation of a pyroptosis-related genes signature for risk stratification in gliomas.

Background: Glioma is a highly heterogeneous disease, causing the prognostic prediction a challenge. Pyroptosis, a programmed cell death mediated by gasdermin (GSDM), is characterized by cell swelling and the release of inflammatory factors. Pyroptosis occurs in several types of tumor cells, including gliomas. However, the value of pyroptosis-related genes (PRGs) in the prognosis of glioma remains to be further clarified. Methods: In this study, mRNA expression profiles and clinical data of glioma patients were acquired from TCGA and CGGA databases, and one hundred and eighteen PRGs were obtained from the Molecular Signatures Database and GeneCards. Then, consensus clustering analysis was performed to cluster glioma patients. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to establish a polygenic signature. Functional verification of the pyroptosis-related gene GSDMD was achieved by gene knockdown and western blotting. Moreover, the immune infiltration status between two different risk groups were analyzed through the "gsva" R package. Results: Our results demonstrated that the majority of PRGs (82.2%) were differentially expressed between lower-grade gliomas (LGG) and glioblastoma (GBM) in the TCGA cohort. In univariate Cox regression analysis, eighty-three PRGs were shown to be associated with overall survival (OS). A five-gene signature was constructed to divide patients into two risk groups. Compared with patients in the low-risk group, patients in the high-risk group had obviously shorter OS (p < 0.001). Also, we found that the high-risk group showed a higher infiltrating score of immune cells and immune-related functions. Risk score was an independent predictor of OS (HR > 1, p < 0.001). Furthermore, knockdown of GSDMD decreased the expression of IL-1ß and cleaved caspase-1. Conclusion: Our study constructed a new PRGs signature, which can be used to predict the prognosis of glioma patients. Targeting pyroptosis might serve as a potential therapeutic strategy for glioma.