Deep learning-based 3D dose prediction boosts radiotherapy planning efficiency and consistency, yet most models rely solely on anatomical data and assume homogeneous beam configurations, impairing their robustness in esophageal cancer intensity-modulated radiotherapy (IMRT) with heterogeneous beam arrangements. This study explored whether explicit beam geometry modeling enhances voxel-level dose prediction accuracy, robustness in rare beam configurations, and clinical workflow efficiency. A retrospective analysis was performed on 751 esophageal cancer IMRT patients. Two models with the identical AS-NeSt backbone were constructed: an anatomy-only dose prediction model (ADP) and an anatomy-and-angle-based model (AADP) integrating ray-tracing-derived beam geometry representations (normalized beam coverage and overlap maps) accessible in pre-planning. Model performance was assessed on an independent test set (n = 100), a rare-beam configuration cohort (n = 33) and a clinical validation cohort (n = 42), against clinical plans as the reference baseline. Evaluation was based on dosimetric metrics and isodose spatial similarity, along with an analysis of its robustness on unseen beam configurations and impact in a crossover clinical workflow study. Compared with ADP, AADP significantly reduced prediction errors for most targets and organs at risk, cutting average dosimetric error from 2.88

BACKGROUND:Celastrol (CEL) has demonstrated promising anti-cancer properties, yet its specific mechanisms against melanoma remain insufficient. This study investigated the CEL's anti-tumor effects and determined its potential mechanisms in the regulation of MHC-I expression in melanoma. In addition, we also tested its efficacy in sensitizing immune checkpoint inhibitors (ICIs) to melanoma. METHODS:CEL's anti-tumor activity was evaluated in B16F10 melanoma-bearing C57BL/6 mice across five groups (control, CEL 0.5 mg/kg, CEL 1 mg/kg, CEL 2 mg/kg, and ICIs), the tumor volume, histopathology, and body weight were assessed. Mechanistic insights were obtained through network pharmacology and RNA sequencing in B16F10 cells. Differential gene and pathway analysis were validated using qRT-PCR, Western blotting, and flow cytometry. CD8+T cell activation and cytotoxicity were analyzed in co-culture with CEL-pretreated B16F10 cells using flow cytometry and ELISA. CEL's interaction with potential targets was determined by molecular docking, surface plasmon resonance (SPR), and siRNA. The synergistic effect of CEL combined with ICIs was confirmed in B16F10-bearing C57BL/6 mice, and tumor-infiltrating T cells were assessed by flow cytometry across four groups (control, CEL, ICIs, CEL+ICIs). RESULTS:CEL exhibited a significant anti-tumor effect in B16F10 melanoma-bearing mice. Mechanistically, CEL-pretreated B16F10 cells notably enhanced CD8+T cell activation and promoted IFNγ and TNFα secretion, leading to B16F10 cell death. CEL upregulated MHC-I expression through activation of the JAK/STAT1 pathway in B16F10 cells. The binding assay revealed that CEL interacted with SHP2, with an affinity of 37.93 μM. When SHP2 was silenced in B16F10 cells by siRNA, CEL failed to induce MHC-I upregulation. Moreover, CEL combined with ICIs produced superior antitumor efficacy compared to ICIs alone, which was accompanied by increased CD8+T cell infiltration in melanoma. CONCLUSION:CEL enhanced CD8+T cell immunity by upregulating MHC-I expression in melanoma cells, these effects were at least partially through targeting SHP2 and activating JAK/STAT1 pathway. CEL might be a novel sensitizer for ICIs in melanoma.

The VenusX and Edge accelerators are two commercially available systems used for lung stereotactic body radiation therapy (SBRT) with Intensity-Modulated Radiation Therapy (IMRT) techniques. A retrospective analysis was conducted on 40 lung cancer patients treated with the Edge accelerator. Treatment plans using both the Edge (PlanEdge) and VenusX (PlanVX) accelerators were generated and evaluated using various dosimetric metrics, employing statistical analyses to identify significant differences. For planned target volume (PTV), Plan VX outperformed PlanEdge across the entire cohort, achieving a higher D2 dose (76.84 vs 75.56 Gy, p = 0.005), and showing significant improvements in conformity index (0.84 vs 0.80), homogeneity index (0.50 vs 0.47), and gradient index (5.12 vs 5.62), all with p < 0.001. Plan VX also recorded a lower D2 cm (27.27 vs 27.87 Gy, p = 0.004). Subgroup analyses revealed significant enhancements in conformity index (CI), homogeneity index (HI), and gradient index (GI) for both single and multi-target lesion patients, with multi-target also seeing a notably lower D2 cm. regarding organs at risk (OARs), Plan VX significantly reduced lung mean dose (Dmean) (3.85 to 3.60 Gy), V5 (16.88

This study aims to construct and train the WingsNet model, which leverages the parameters recorded in log files to rapidly and accurately predict the patient-specific three-dimensional (3D) dose distribution for IMRT quality assurance (QA). We conducted a retrospective analysis of data from 286 lung cancer patients treated with a prescription of 60 Gy in 30 fractions, with 242 cases used for model training and 44 for testing. Log files containing information such as multi-leaf collimator (MLC) positions, monitor units (MU), and gantry angles were collected from Varian treatment accelerators. Pylinac software was employed to extract mechanical parameters from the log files, generating 2D fluence maps, which were then converted into 3D volumes using a ray-tracing algorithm. CT images, RT structures, and 3D volumes were resampled to a uniform dimension of 128*128*128 to serve as input for the WingsNet model, with the 3D dose distribution calculated by the treatment planning system (TPS) serving as output. The model training utilized L1 loss and mean squared error (MSE) as evaluation metrics. The results of this study demonstrate that the WingsNet model can effectively predict the 3D dose distribution of IMRT plans based on the parameters recorded in the log files. Evaluation through metrics such as mean absolute error (MAE), root mean square error (RMSE), and dose-volume histogram (DVH) indices reveals that the model performs well in most areas, with some errors observed in the planning target volume (PTV) region and at high dose levels, yet it retains potential for clinical use. Visually, the isodose line distributions are consistent. The Dice coefficients between the predicted and reference dose distributions at varying isodose line levels indicate a decreasing trend as the dose level increases. The WingsNet model developed in this study successfully predicts the patient-specific 3D dose distribution for QA by parsing the parameters recorded in log files. This model shows promise for use in 3D dose distribution verification for IMRT, providing an efficient and reliable tool for the verification of 3D dose distributions in patient-specific QA.

BACKGROUND:Lutetium-177 DOTA-TATE peptide receptor radionuclide therapy (PRRT) is an established and effective treatment modality for patients with metastatic neuroendocrine tumors (NETs). PURPOSE:This study aims to predict patient-absorbed doses from [177Lu]Lu-DOTA-TATE PRRT in the liver, kidney and lesion by utilizing patient-specific absorbed doses from pre-therapeutic [68Ga]Ga-DOTA-TATE PET/CT. METHODS:Before the treatment of cycle 1, 11 patients with NETs underwent PET/CT scans at 0.5, 1.0, 2.0 and 4.0 h after the injection of [68Ga]Ga-DOTA-TATE. Patients then received [177Lu]Lu-DOTA-TATE PRRT and underwent SPECT/CT scans at 4, 24, 96, and 168 h post-administration. The segmentations and dosimetry were performed by using a professional software. The linear regression model used the absorbed doses from [68Ga]Ga-DOTA-TATE alone as the predictor variable. The multiple linear regression model used the absorbed doses from [68Ga]Ga-DOTA-TATE and the relevant clinical biomarkers as the predictor variables. RESULTS:The mean absorbed doses from [177Lu]Lu-DOTA-TATE PRRT in kidney and liver were 4.1 and 2.1 Gy, respectively. In comparison, the mean absorbed doses from [68Ga]Ga-DOTA-TATE were significantly lower: 18.0 mGy and 11.0 mGy, respectively. For lesions, the maximum absorbed dose from [68Ga]Ga-DOTA-TATE ranged from 24.1 to 170.4 mGy, while the maximum absorbed dose from [177Lu]Lu-DOTA-TATE PRRT was significantly higher, ranging from 9.6 to 77.9 Gy. The linear regression model yielded moderate R-squared values of 0.50, 0.59, and 0.36 for kidney, liver and lesion, respectively. The performance of multiple linear regression model was better, with R-squared values increasing to 0.81, 0.77, and 0.84. CONCLUSION:Absorbed doses from [177Lu]Lu-DOTA-TATE PRRT can be accurately predicted. Moreover, our models are formalized into simple equations.

Objective To improve the PET image quality by a deep progressive learning (DPL) reconstruction algorithm and evaluate the DPL performance in lesion quantification. Methods We reconstructed PET images from 48 oncological patients using ordered subset expectation maximization (OSEM) and deep progressive learning (DPL) methods. The patients were enrolled into three overlapped studies: 11 patients for image quality assessment (study 1), 34 patients for sub-centimeter lesion quantification (study 2), and 28 patients for imaging of overweight or obese individuals (study 3). In study 1, we evaluated the image quality visually based on four criteria: overall score, image sharpness, image noise, and diagnostic confidence. We also measured the image quality quantitatively using the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), contrast-to-background ratio (CBR), and contrast-to-noise ratio (CNR). To evaluate the performance of the DPL algorithm in quantifying lesions, we compared the maximum standardized uptake values (SUV max ), SBR, CBR, SNR and CNR of 63 sub-centimeter lesions in study 2 and 44 lesions in study 3. Results DPL produced better PET image quality than OSEM did based on the visual evaluation methods when the acquisition time was 0.5, 1.0 and 1.5 min/bed. However, no discernible differences were found between the two methods when the acquisition time was 2.0, 2.5 and 3.0 min/bed. Quantitative results showed that DPL had significantly higher values of SBR, CBR, SNR, and CNR than OSEM did for each acquisition time. For sub-centimeter lesion quantification, the SUV max , SBR, CBR, SNR, and CNR of DPL were significantly enhanced, compared with OSEM. Similarly, for lesion quantification in overweight and obese patients, DPL significantly increased these parameters compared with OSEM. Conclusion The DPL algorithm dramatically enhanced the quality of PET images and enabled more accurate quantification of sub-centimeters lesions in patients and lesions in overweight or obese patients. This is particularly beneficial for overweight or obese patients who usually have lower image quality due to the increased attenuation.

Unprecedented progress in immune checkpoint blockade (ICB) therapy has been made in cancer treatment. However, the response to ICB therapy is limited to a small subset of patients. The development of ICB sensitizers to improve cancer immunotherapy outcomes is urgently needed. Berberine (BBR), a well-known phytochemical compound isolated from many kinds of medicinal plants such as Berberis aristata, Coptis chinensis, and Phellondendron chinense Schneid, has shown the ability to inhibit the proliferation, invasion and metastasis of cancer cells. In this study, we investigated whether BBR can enhance the therapeutic benefit of ICB for melanoma, and explored the underlying mechanisms involved. The results showed that BBR could sensitize ICB to inhibit tumor growth and increased the survival rate of mice. Moreover, BBR stimulated intracellular ROS production partially by inhibiting NQO1 activity, which induced immunogenic cell death (ICD) in melanoma, elevated the levels of damage-associated molecular patterns (DAMPs), and subsequently activated DC cells and CD8 + T cells in vitro and in vivo. In conclusion, BBR is a novel ICD inducer. BBR could enhance the therapeutic benefit of ICB for melanoma. These effects were partially mediated through the inhibition of NQO1 and ROS activation.

Purpose: This study aims to assess the dosimetry and treatment efficiency of TaiChiB-based Stereotactic Body Radiotherapy (SBRT) plans applying to treat two-lung lesions with one overlapping organs at risk. Methods: For four retrospective patients diagnosed with two-lung lesions each patient, four treatment plans were designed including Plan Edge, TaiChiB linac-based, RGS-based, and a linac-RGS hybrid (Plan TCLinac, Plan TCRGS, and Plan TCHybrid). Dosimetric metrics and beam-on time were employed to evaluate and compare the TaiChiB-based plans against Plan Edge. Results: For Conformity Index (CI), Plan TCRGS outperformed all other plans with an average CI of 1.06, as opposed to Plan Edge′s 1.33. Similarly, for R50 %, Plan TCRGS was superior with an average R50 % of 3.79, better than Plan Edge′s 4.28. In terms of D2 cm, Plan TCRGS also led with an average of 48.48%, compared to Plan Edge′s 56.25%. For organ at risk (OAR) sparing, Plan TCRGS often displayed the lowest dosimetric values, notably for the spinal cord (Dmax 5.92 Gy) and lungs (D1500cc 1.00 Gy, D1000cc 2.61 Gy, V10 Gy 15.14%). However, its high Dmax values for the heart and great vessels sometimes exceeded safety thresholds. Plan TCHybrid presented a balanced approach, showing doses comparable to or better than Plan Edge without crossing safety limits. In terms of beam-on time, Plan TCLinac emerged as the most efficient treatment option in three out of four cases, followed closely by Plan Edge in one case. Plan TCRGS, despite its dosimetric advantages, was the least efficient, recording notably longer beam-on times, with a peak at 33.28 minutes in Case 2. Conclusion: For patients with two-lung lesions treated by SBRT whose one lesion overlaps with OARs, the Plan TCHybrid delivered by TaiChiB digital radiotherapy system can be recommended as a clinical option.

A novel plasma mirror is proposed for realizing all-optical Compton scattering, and its performance is compared with that of planar and concave plasma mirrors. Compared to a planar mirror, a concave mirror augments the radiation energy, but it decreases the collimation of the emitted photon beam. With the aid of the increased pulse length of the reflected laser, our proposed plasma mirror boosts the radiation energy and simultaneously improving the collimation of the emitted radiation. The pulse length and radius of the reflected laser can be controlled by adjusting the parameters of the proposed plasma mirror. The dependences of the pulse length and radius on the mirror parameters have been demonstrated. The impact of non-ideal conditions encountered in real experiments on the proposed mechanism has been discussed, which precisely demonstrates the robustness of the proposed mechanism. Additionally, the required gas density for a wakefield accelerator is derived to achieve optimal scattering under the given plasma mirror configurations.

Polarized electron beam production via laser wakefield acceleration in pre-polarized plasma is investigated by particle-in-cell simulations. The evolution of the electron beam polarization is studied based on the Thomas-Bargmann-Michel-Telegdi equation for the transverse and longitudinal self-injection, and the depolarization process is found to be influenced by the injection schemes. In the case of transverse self-injection as found typically in the bubble regime, the spin precession of the accelerated electrons is mainly influenced by the wakefield. However, in the case of longitudinal injection in the quasi-one-dimensional regime (for example, F. Y. Li \emph{et al}., Phys. Rev. Lett. 110, 135002 (2013)), the direction of electron spin oscillates in the laser filed. Since the electrons move around the laser axis, the net influence of the laser field is nearly zero and the contribution of the wakefield can be ignored. Finally, an ultra-short electron beam with polarization of $99\%$ can be obtained using longitudinal self-injection.

OBJECTIVE: This study aims to explore the feasibility of DenseNet in the establishment of a three-dimensional (3D) gamma prediction model of IMRT based on the actual parameters recorded in the log files during delivery. METHODS: A total of 55 IMRT plans (including 367 fields) were randomly selected. The gamma analysis was performed using gamma criteria of 3% /3 mm (Dose Difference/Distance to Agreement), 3% /2 mm, 2% /3 mm, and 2% /2 mm with a 10% dose threshold. In addition, the log files that recorded the gantry angle, monitor units (MU), multi-leaf collimator (MLC), and jaws position during delivery were collected. These log files were then converted to MU-weighted fluence maps as the input of DenseNet, gamma passing rates (GPRs) under four different gamma criteria as the output, and mean square errors (MSEs) as the loss function of this model. RESULTS: Under different gamma criteria, the accuracy of a 3D GPR prediction model decreased with the implementation of stricter gamma criteria. In the test set, the mean absolute error (MAE) of the prediction model under the gamma criteria of 3% /3 mm, 2% /3 mm, 3% /2 mm, and 2% /2 mm was 1.41, 1.44, 3.29, and 3.54, respectively; the root mean square error (RMSE) was 1.91, 1.85, 4.27, and 4.40, respectively; the Sr was 0.487, 0.554, 0.573, and 0.506, respectively. There was a correlation between predicted and measured GPRs (P < 0.01). Additionally, there was no significant difference in the accuracy between the validation set and the test set. The accuracy in the high GPR group was high, and the MAE in the high GPR group was smaller than that in the low GPR group under four different gamma criteria. CONCLUSIONS: In this study, a 3D GPR prediction model of patient-specific QA using DenseNet was established based on log files. As an auxiliary tool for 3D dose verification in IMRT, this model is expected to improve the accuracy and efficiency of dose validation.

BACKGROUND: The error magnitude is closely related to patient-specific dosimetry and plays an important role in evaluating the delivery of the radiotherapy plan in QA. No previous study has investigated the feasibility of deep learning to predict error magnitude. OBJECTIVE: The purpose of this study was to predict the error magnitude of different delivery error types in radiotherapy based on ResNet. METHODS: A total of 34 chest cancer plans (172 fields) of intensity-modulated radiation therapy (IMRT) from Eclipse were selected, of which 30 plans (151 fields) were used for model training and validation, and 4 plans including 21 fields were used for external testing. The collimator misalignment (COLL), monitor unit variation (MU), random multi-leaf collimator shift (MLCR), and systematic MLC shift (MLCS) were introduced. These dose distributions of portal dose predictions for the original plans were defined as the reference dose distribution (RDD), while those for the error-introduced plans were defined as the error-introduced dose distribution (EDD). Different inputs were used in the ResNet for predicting the error magnitude. RESULTS: In the test set, the accuracy of error type prediction based on the dose difference, gamma distribution, and RDD+ EDD was 98.36%, 98.91%, and 100%, respectively; the root mean squared error (RMSE) was 1.45-1.54, 0.58-0.90, 0.32-0.36, and 0.15-0.24; the mean absolute error (MAE) was 1.06-1.18, 0.32-0.78, 0.25-0.27, and 0.11-0.18, respectively, for COLL, MU, MLCR and MLCS. CONCLUSIONS: In this study, error magnitude prediction models with dose difference, gamma distribution, and RDD+ EDD are established based on ResNet. The accurate prediction of the error magnitude under different error types can provide reference for error analysis in patient-specific QA.

Purpose: This study investigated the role of Planning Target Volume (PTV) in determining the suitability of manual and automatic Stereotactic Body Radiation Therapy (SBRT) planning for lung cancer patients. Methods: We retrospectively created manual and automatic lung SBRT plans for ninety-eight patients using the Pinnacle 16.2 Treatment Planning System (TPS). The superior plan, whether manual or automatic, was selected for each patient through a combined index. Receiver Operating Characteristic (ROC) analysis was used to assess the predictive potential of the PTV volume in determining the superior plan and to establish a cutoff value. Patients were then stratified into two groups based on this value, and dosimetric variances were evaluated. Results: The ROC analysis highlighted the PTV volume's proficiency in predicting the superior choice between manual and automatic lung SBRT plans (Area Under Curve (AUC): 0.918, p = 0.005). The delineated cutoff value was set at 22.675 cc. For PTV volumes below this threshold, automatic plans surpassed manual plans in the Conformity Index (CI), Gradient Index (GI), and lung doses. Conversely, for PTV volumes exceeding 22.675 cc, manual plans exhibited improved results in the Heterogeneity Index (HI), GI, and dosimetric metric of heart and lung. Conclusion: The PTV volume is a significant determinant in guiding the optimal selection between manual and automatic lung SBRT plans with the Pinnacle TPS. Automatic plans are recommended for patients with PTV volumes below 22.675 cc, and manual plans for those above this threshold.

BACKGROUND:Patients with non-small cell lung cancer (NSCLC) are susceptible to coronavirus disease-2019 (COVID-19), but current treatments are limited. Icariside II (IS), a flavonoid compound derived from the plant epimedin, showed anti-cancer,anti-inflammation and immunoregulation effects. The present study aimed to evaluate the possible effect and underlying mechanisms of IS on NSCLC patients with COVID-19 (NSCLC/COVID-19). METHODS:NSCLC/COVID-19 targets were defined as the common targets of NSCLC (collected from The Cancer Genome Atlas database) and COVID-19 targets (collected from disease database of Genecards, OMIM, and NCBI). The correlations of NSCLC/COVID-19 targets and survival rates in patients with NSCLC were analyzed using the survival R package. Prognostic analyses were performed using univariate and multivariate Cox proportional hazards regression models. Furthermore, the targets in IS treatment of NSCLC/COVID-19 were defined as the overlapping targets of IS (predicted from drug database of TMSCP, HERBs, SwissTarget Prediction) and NSCLC/COVID-19 targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of these treatment targets were performed aiming to understand the biological process, cellular component, molecular function and signaling pathway. The hub targets were analyzed by a protein-protein interaction network and the binding capacity with IS was characterized by molecular docking. RESULTS:The hub targets for IS in the treatment of NSCLC/COVID-19 includes F2, SELE, MMP1, MMP2, AGTR1 and AGTR2, and the molecular docking results showed that the above target proteins had a good binding degree to IS. Network pharmacology showed that IS might affect the leucocytes migration, inflammation response and active oxygen species metabolic process, as well as regulate the interleukin-17, tumor necrosus factor and hypoxia-inducible factor-1 signaling pathway in NSCLC/COVID-19. CONCLUSIONS:IS may enhance the therapeutic efficacy of current clinical anti-inflammatory and anti-cancer therapy to benefit patients with NSCLC combined with COVID-19.

This study aimed to improve the image quality and CT Hounsfield unit accuracy of daily cone-beam computed tomography (CBCT) using registration generative adversarial networks (RegGAN) and apply synthetic CT (sCT) images to dose calculations in radiotherapy. The CBCT/planning CT images of 150 esophageal cancer patients undergoing radiotherapy were used for training (120 patients) and testing (30 patients). An unsupervised deep-learning method, the 2.5D RegGAN model with an adaptively trained registration network, was proposed, through which sCT images were generated. The quality of deep-learning-generated sCT images was quantitatively compared to the reference deformed CT (dCT) image using mean absolute error (MAE), root mean square error (RMSE) of Hounsfield units (HU), and peak signal-to-noise ratio (PSNR). The dose calculation accuracy was further evaluated for esophageal cancer radiotherapy plans, and the same plans were calculated on dCT, CBCT, and sCT images. The quality of sCT images produced by RegGAN was significantly improved compared to the original CBCT images. ReGAN achieved image quality in the testing patients with MAE sCT vs. CBCT: 43.7 ± 4.8 vs. 80.1 ± 9.1; RMSE sCT vs. CBCT: 67.2 ± 12.4 vs. 124.2 ± 21.8; and PSNR sCT vs. CBCT: 27.9 ± 5.6 vs. 21.3 ± 4.2. The sCT images generated by the RegGAN model showed superior accuracy on dose calculation, with higher gamma passing rates (93.3 ± 4.4, 90.4 ± 5.2, and 84.3 ± 6.6) compared to original CBCT images (89.6 ± 5.7, 85.7 ± 6.9, and 72.5 ± 12.5) under the criteria of 3 mm/3

PURPOSE:Implementing artificial intelligence technologies allows for the accurate prediction of radiation therapy dose distributions, enhancing treatment planning efficiency. However, esophageal cancers present unique challenges because of tumor complexity and diverse prescription types. Additionally, limited data availability hampers the effectiveness of existing artificial intelligence models. This study developed a deep learning model, trained on a diverse data set of esophageal cancer prescriptions, to improve dose prediction accuracy. METHODS AND MATERIALS:We retrospectively collected data from 530 patients with esophageal cancer, including single-target and simultaneous integrated boost prescriptions, for model building. The proposed Asymmetric ResNeSt (AS-NeSt) model features novel 3-dimensional (3D) ResNeSt blocks and an asymmetrical architecture. We constructed a loss function targeting global and local doses and validated the model's performance against existing alternatives. Model-assisted experiments were used to validate its clinical benefits. RESULTS:The AS-NeSt model maintained an absolute prediction error below 5% for each dosimetric metric. The average Dice similarity coefficient for isodose volumes was 0.93. The model achieved an average relative prediction error of 2.02%, statistically lower than Hierarchically Densely Connected U-net (4.17%), DoseNet (2.35%), and Densely Connected Network (3.65%). It also demonstrated significantly fewer parameters and shorter prediction times. Clinically, the AS-NeSt model raised physicians' ability to accurately preassess appropriate treatment methods before planning from 95.24% to 100%, reduced planning time by over 61% for junior dosimetrists and 52% for senior dosimetrists, and decreased both inter- and intra-dosimetrist discrepancies by more than 50%. CONCLUSIONS:The AS-NeSt model, developed with innovative 3D ResNeSt blocks and an asymmetrical encoder-decoder structure, has been validated using clinical esophageal cancer patient data. It accurately predicts 3D dose distributions for various prescriptions, including simultaneous integrated boost, showing potential to improve the management of esophageal cancer treatment in a clinical setting.

ETHNOPHARMACOLOGICAL RELEVANCE:Chronic obstructive pulmonary disease (COPD) is a major global health concern characterized by pulmonary inflammation and airway remodeling. Traditional Chinese medicine, such as Modified Jiawei Bushen Yiqi Formula (MBYF), has been used as a complementary therapy for COPD in China. AIM OF THE STUDY:To investigate the therapeutic potential of MBYF in a rat model of COPD induced by cigarette smoke (CS) exposure and explore the underlying mechanism. MATERIALS AND METHODS:The COPD rat model was established through 24 weeks of CS exposure, with MBYF administration starting in the 9th week. Pulmonary function, histological analysis, inflammatory cell count and molecular assays were employed to assess the effects of MBYF on airway remodeling, pulmonary inflammation, neutrophils chemotaxis and the IL17 signaling pathway. RESULTS:MBYF treatment effectively delayed airway remodeling, as evidenced by improved pulmonary function parameters. Histological examination and bronchoalveolar lavage fluid analysis revealed that MBYF mitigated CS-induced pulmonary inflammation by reducing inflammatory cell infiltration. Pharmacological network analysis suggested that MBYF may act through the IL17 signaling pathway to regulate inflammatory responses. RNA-sequencing and molecular assays indicated that MBYF inhibited neutrophils chemotaxis through downregulating the CXCL1/CXCL5/CXCL8-CXCR2 axis, and suppressed IL17A, IL17F and its downstream cytokines, including IL6, TNFα, IL1β, and COX2. Furthermore, MBYF inhibited the activation of NF-κB and MAPKs in the IL17 signaling pathway. CONCLUSION:MBYF exhibits potential as an adjunct or alternative treatment for COPD, effectively mitigating CS-induced pulmonary inflammation and airway remodeling through the inhibition of neutrophil chemotaxis and IL17 signaling pathway.

Background: Immune checkpoint blockade agents, such as anti-PD-1 antibodies, show promising antitumor ef-ficacy but only a limited response in patients with non-small cell lung cancer (NSCLC). Icariside II (IS), a metabolite of Herba Epimedii, is a COX-2 and EGFR inhibitor that can enhance the anti-PD-1 effect. This study aimed to evaluate the antitumor effect of IS in combination with anti-PD-1 and explore the underlying mechanism.Methods: Tumor growth was assessed in Lewis Lung Cancer (LLC) tumor-bearing mice in seven groups (control, IS 20 mg/kg, IS 40 mg/kg, anti-PD-1, IS 20 mg/kg+anti-PD-1, IS 40 mg/kg+anti-PD-1, ERK inhibitor+anti-PD-1). Tumor-infiltrating immune cells were measured by flow cytometry. The mechanisms were explored by tumor RNA-seq and validated in LLC cells through molecular biological experiments using qRT-PCR, ELISA, and western blotting.Results: Animal experiments showed that IS in combination with anti-PD-1 further inhibited tumor growth and remarkably reduced the infiltration of myeloid-derived suppressor cells (MDSCs) into the tumor compared with anti-PD-1 monotherapy. RNA-seq and in vitro experiments showed that IS suppressed the chemotactic migration of MDSCs by downregulating the expression of CXC chemokine ligands 2 (CXCL2) and CXCL3. Moreover, IS promoted reactive oxygen species (ROS) generation and inhibited the activation of SRC/ERK/STAT3 in LLC cells, which are upstream signaling pathways of these chemokines.Conclusion: IS potentiates the anti-PD-1 anti-tumor effect by reducing chemotactic infiltration of the myeloid-derived suppressor cell into the tumor microenvironment, via ROS-mediated inactivation of SRC/ERK/STAT3 signaling pathways.

Ethnopharmacological relevance: Modified Bushen Yiqi formula (MBYF) has shown efficacy as an herbal combination therapy with anti-PD-1 for lung cancer patients. However, the underlying mechanisms of its antitumor effects in lung cancer remain unclear.Aim of the study: This study aims to observe the antitumor effect of MBYF and explore its synergistic mechanism in combination with anti-PD-1 based on the tumor immune microenvironment. Materials and methods: The antitumor effect of MBYF was assessed in Lewis Lung Cancer (LLC)-bearing mice by evaluating tumor volume, weight, and histology in five groups (model control, MBYF 8.125 g/kg, MBYF 16.25 g/ kg, MBYF 32.50 g/kg, anti-PD-1). Mechanisms were analyzed using pharmacology network and tumor RNAsequencing. Tumor-infiltrating immune cells were measured by flow cytometry and immunohistochemistry. Targets and pathways were validated through qRT-PCR, immuno-histochemistry, and western blotting. The synergistic effect of MBYF in combination with anti-PD-1 was validated in three groups (model control, anti-PD1, anti-PD-1+MBYF 16.25 g/kg).Results: MBYF inhibited tumor growth and proliferation and demonstrated safety for the heart, liver, and kidney. Mechanistically, MBYF downregulated tumor proliferation by suppressing the expression of CCND1, CTNNB1, EGFR, and the PI3K-AKT/STAT3/ERK pathway. Furthermore, MBYF may upregulated the antitumor immunity (CD4+T cells, active CD8+ T cells, and NK cells) by reducing the infiltration of M2-TAMs and PMN-MDSCs. MBYF may inhibit the recruitment of M2-TAMs by downregulating the CCR5-CCLs axis and PMN-MDSCs by the CXCR2-CXCLs axis. In vivo study confirmed that MBYF enhanced the antitumor effect of anti-PD-1 therapy.Conclusion: Modified Bushen Yiqi formula enhances antitumor immunity in the treatment of lung cancer by reducing the chemotactic recruitment of M2-TAMs and PMN-MDSCs, suggesting its potential as an adjunct therapy to enhance anti-PD-1 responses and improve treatment outcomes. Further research and clinical studies are needed to validate and expand upon these promising findings.

Abstract Background Inhaled glucocorticoid corticosteroid (ICS), long-acting β2-adrenoceptor agonist (LABA), and other drugs have limited therapeutic effects on COPD with significant individual differences. Traditional Chinese medicine (TCM)-modified Bushen Yiqi formula (MBYF) demonstrates advantages in COPD management in China. This study aims to evaluate the efficacy and safety of MBYF as an add-on to budesonide/formoterol in COPD patients and confirm the related genes affecting the therapeutic effect in the treatment of COPD. Methods In this multicentre, randomised, double-blind, placebo-controlled, parallel-group study, eligible patients with COPD will randomly receive a 360-day placebo or MBYF as an adjuvant to budesonide/formoterol in a 1:1 ratio and be followed up with every 2 months. The primary outcomes will be the frequency, times, and severity of acute exacerbation of COPD (AECOPD), COPD assessment test (CAT) score, and pulmonary function tests (PFTs). The secondary outcomes will include the modified Medical Research Council (mMRC) dyspnoea scale, 6-min walking test (6MWT), BODE index, quantitative scores of syndromes classified in TCM, inflammation indices, and hypothalamic-pituitary-adrenaline (HPA) axis function. We will also test the genotype to determine the relationship between drugs and efficacy. All the data will be recorded in case report forms (CRFs) and analysed by SPSS V.20.0. Discussion A randomized clinical trial design to evaluate the efficacy and safety of MBYF in COPD is described. The results will provide evidence for the combination therapy of modern medicine and TCM medicine, and individual therapy for COPD.Trial registration. Trial registration ID: ChiCTR1900026124 , Prospective registration.