Background GTPase, IMAP family genes (GIMAPFGs) are extensively used for diagnosis and treatment in cancer, their prognostic significance and therapeutic potential in hepatocellular carcinoma (HCC) remain insufficiently explored.Methods GIMAPFGs associated with HCC prognosis were identified using Cox regression analysis. a prognostic model was constructed through multivariate Cox regression, the model was validated on an independent dataset. followed by an analysis of its biological function. We analyzed variations in tumor immune cell infiltration according to the prognostic signature. Meanwhile, single-cell analysis demonstrated the expression of GIMAPFGs in HCC.Results A prognostic risk model was developed using 2 GIMAPFGs (GIMAP1 and GIMAP8) and its prognostic significance was confirmed with an independent external HCC dataset, establishing it as an independent risk factor for HCC patients. This characteristic is also associated with the immune microenvironment of HCC.Conclusions This novel GIMAPFGs signature provides a valuable tool for patient stratification and offer a new therapeutic approach for HCC.

Rationale and Objectives: Predicting neoadjuvant chemotherapy (NACT) efficacy is vital for advanced nasopharyngeal carcinoma (LA-NPC) management. Existing models have limited generalizability. The combination of MRI-based deep learning features (DLF) and Vision Transformer (ViT) for this purpose remains unexplored. This study therefore aims to evaluate the value of multi-sequence MRI-based DLF combined with ViT for predicting NACT efficacy in LA-NPC. Materials and Methods: This study retrospectively enrolled 266 LA-NPC patients receiving standard NACT, categorized by RECIST 1.1 into CR and non-CR groups, and split into training and testing sets (3:1). Traditional radiomics and 2D/2.5D/3D deep learning models were built and compared. Select the optimal architecture as the feature extractor, features were reduced via PCA and input into ViT. Results: The XGBoost model on T2-FS sequences performed best among traditional radiomics models, with a validation AUC of 0.760. For deep learning models, performance improved with model complexity: 2D models were least effective (AUC: 0.502-0.653), followed by 2.5D (best AUC: 0.713), while 3D models were optimal (best AUC: 0.755). Ultimately, we integrated the deep learning features extracted from the two optimal single models (2.5D-ResNet50 and 3D-DenseNet121) and input them into the ViT architecture for global context modeling. This fused model achieved superior performance, with a validation AUC of 0.926, accuracy of 0.903, and F1-score of 0.927, significantly outperforming all previous models (all P < 0.05). Conclusion: The integrated model combining multi-sequence MRI DLF with ViT significantly enhances predictive performance for NACT efficacy in LA-NPC.

Background:Multidrug-resistant tuberculosis (MDR-TB) poses an escalating public health challenge that complicates diagnosis and treatment. Early detection is crucial for improving the outcomes. This study aimed to evaluate the diagnostic performance of whole-lung computed tomography (CT) radiomics features combined with clinical characteristics in distinguishing MDR-TB from drug-sensitive tuberculosis (DS-TB). Methods:This retrospective study included 750 patients with MDR-TB and DS-TB from two hospitals. Clinical data and non-contrast CT images were obtained. The radiomic features were extracted using PyRadiomics. A three-step feature selection process, including t-tests/U-tests, Pearson correlation, and the least absolute shrinkage and selection operator (LASSO), was employed to identify the optimal features. Diagnostic models based on clinical and radiomic features were constructed using LightGBM and multilayer perceptron (MLP) algorithms, respectively. A combined model integrated both types of features. Model performance was assessed using area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. Results:Diabetes mellitus and tuberculosis (TB) retreatment were identified as independent risk factors for MDR-TB. The clinical model achieved AUC values of 0.742, 0.738, and 0.725 for training, internal validation, and external validation sets, respectively. Seven radiomics features were selected, with the radiomics model achieving AUC values of 0.724, 0.720, and 0.703. The combined model outperformed the individual models, with AUC values of 0.816, 0.795, and 0.835, and superior sensitivity and specificity. Conclusions:Integrating whole-lung CT radiomics with clinical features significantly enhances the diagnostic accuracy of MDR-TB. The combined model outperforms individual models, underscoring the potential of radiomic-clinical data integration. This approach could expand MDR-TB screening coverage without additional economic burden, thereby facilitating prevention and control.

This study aims to develop an integrated model combining habitat-based radiomics and clinical data to predict lymph node metastasis in patients with clinical N0 peripheral lung adenocarcinomas measuring ≤ 3 cm in diameter. We retrospectively analyzed 1132 patients with lung adenocarcinoma from two centers who underwent surgical resection with lymph node dissection and had preoperative computed tomography (CT) scans showing peripheral nodules ≤ 3 cm. Multivariable logistic regression was employed to identify independent risk factors for the clinical model. Radiomics and habitat models were constructed by extracting and analyzing radiomic features and habitat regions from contrast-enhanced CT images. Subsequently, a combined model was developed by integrating habitat-based radiomic features with clinical characteristics. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The habitat model exhibited promising predictive performance for lymph node metastasis, outperforming other standalone models with AUCs of 0.962, 0.865, and 0.853 in the training, validation, and external test cohorts, respectively. The combined model demonstrated superior discriminative ability, achieving the highest AUCs of 0.983, 0.950, and 0.877 for the training, validation, and external test cohorts, respectively. The integration of habitat-based radiomic features with clinical data offers a non-invasive approach to assess the risk of lymph node metastasis, potentially supporting clinicians in optimizing patient management decisions.

Nasopharyngeal carcinoma（NPC） is a distinct type of head and neck cancer closely associated with Epstein-Barr virus（EBV） infection and exhibits significant geographic variations in its incidence. Despite recent advancements in radiotherapy techniques and precision medicine for NPC, the overall survival rate remains unsatisfactory due to tumor metastasis, recurrence, and drug resistance. Single-cell RNA sequencing（scRNA-seq） is an emerging technology that allows for the analysis of gene expression at single-cell resolution, providing a clearer understanding of tumor cell subpopulations, the evolutionary trajectory of tumor cells, and the functional roles and interactions of cells within the tumor microenvironment. This provides new ideas for the development of precision medicine in NPC. Here, we review the applications of scRNA-seq in exploring the mechanisms of NPC pathogenesis, tumor heterogeneity, the tumor microenvironment, drug resistance, and therapeutic response.

A robust predictive biomarker is critical for identifying patients with NSCLC who may benefit from immunotherapy. This study developed a CT-based habitat model using 590 advanced NSCLC cases. The model was constructed in contrast-enhanced CT images and validated on an independent cohort with non-contrast CT. Tumor volumes were segmented into three subregions via K-means clustering. Radiomic features were extracted from each habitat and used to build predictive models with six machine learning classifiers. The ExtraTrees-based habitat model demonstrated superior predictive performance in the test cohort (AUC = 0.814). Compared to traditional radiomics, 3D deep learning, clinical, and PD-L1 expression models, the habitat model maintained strong predictive advantages, enabling efficient prediction of immunotherapy benefit and aiding in the identification of suitable patients for personalized.

PD-1/PD-L1 immunotherapy represents the primary treatment for advanced NSCLC patients; however, response rates to this therapy vary among individuals. This dual-center study aimed to integrate habitat radiomics and multi-instance deep learning to predict durable clinical benefits from immunotherapy. We retrospectively collected 590 NSCLC patients from two medical centers who received PD-1/PD-L1 inhibitor immunotherapy. Patients from the GMU center were divided into a training cohort (n = 375) and an internal validation cohort (n = 161) for habitat analysis and multi-instance deep learning model development. Patients from the YJ center formed an external testing cohort (n = 54) for model validation. We implemented a DenseNet121-based architecture extracting radiomics features from triplanar (axial/coronal/sagittal) tumor sequences to construct a 2.5D deep-learning dataset. Then, we fuse 2.5D features through multi-instance learning. Additionally, we use K-means clustering to divide the tumor VOI into three subregions to extract radiological features for building a Habitat model. Finally, we use the Extra-Trees classifier to construct MIL, Habitat, and Combined models, the Combined model integrating age factors into the analysis. The primary endpoint was durable clinical benefit. Finally, a separate PD-L1 expression dataset was used to compare the predictive performance of imaging models against PD-L1 status (positive/negative) and expression levels (high/low) to identify the optimal model for predicting immunotherapy clinical benefit. The Combined model combining Habitat, MIL, and patient age demonstrated robust DCB prediction with AUCs of 0.906(95

Dual-atom nanozymes (DAzymes) is widely studied in biomedical fields owing to well-defined coordination environments and electronic structures, with catalytic activity can be enhanced by modulating the active sites. However, most of the synthesis methods are complex and costly. Herein, spatially axial O coordinated Cu/Mn DAzymes with Cu-N4O/MnO5 motifs (Cu/Mn NC DAzymes) were developed by a hydrothermal method using dopamine as chelating agent for near-infrared-II (NIR-II) reinforced tumor catalytic therapy. The atomically dispersed Cu and Mn active sites confer trienzyme performance of catalase-like, oxidase-like, and peroxidase-like activities. In addition, Cu/Mn NC DAzymes possess a low band gap of 1.15 eV, allowing them to be excited by a 1064 nm laser and generate reactive oxygen species. Furthermore, the DAzymes exhibit strong light absorption in the NIR-II region, showing favorable photothermal conversion performance (eta = 36.1%). The trienzymatic property combined with 1064 nm laser phototherapy yields an efficient anticancer synergy. Tumor triggers the Cu/Mn NC DAzymes degradation, ensuring in vivo biosafety of materials, and the released Mn2+ serving for magnetic resonance imaging. The degraded Cu2+ induces multiple modes of cell death, thereby improving therapeutic outcomes. The work provides innovative insight to develop asymmetric dual active sites on dopamine-derived carbon supports for tumor therapy.

Background:Pulmonary nodules categorized as Lung Imaging Reporting and Data System (Lung-RADS) 3 and 4A constitute a substantial proportion of radiologically indeterminate lesions, and current imaging criteria demonstrate limited accuracy for distinguishing benign from malignant nodules. To develop and validate an integrated predictive model combining radiomics and two-and-a-half (2.5D) deep transfer learning (DTL) for differentiating benign from malignant pulmonary nodules classified as Lung-RADS categories 3 and 4A on computed tomography (CT). Methods:This retrospective study included 298 patients with Lung-RADS 3 and 4A nodules from three centers. The cohort from Center 1 (n=247) was divided into training (n=172) and test (n=75) sets, while patients from Centers 2 and 3 (n=51) formed an independent validation set. We constructed three models: a radiomics (Rad) model, a DTL model, and an integrated deep transfer radiomics (DTR) model. Model performance was evaluated using the area under the receiver operating characteristic curve, and clinical utility was assessed using decision curve analysis. Results:The DTR model demonstrated superior performance in the training [area under the curve (AUC): 0.975, 95% confidence interval (CI): 0.9529-0.9981], testing (AUC: 0.851, 95% CI: 0.7328-0.9683), and external validation cohorts (AUC: 0.727, 95% CI: 0.5876-0.8663), outperforming both the Rad model (training: AUC: 0.743; testing: AUC: 0.642; validation: AUC: 0.613) and the DTL model (training: AUC: 0.843; testing: AUC: 0.757; validation: AUC: 0.701). The DTR model exhibited high specificity (0.895) and positive predictive value (0.956) in the test cohort. SHapley Additive exPlanations (SHAP) analysis revealed that the DTR model effectively leveraged complementary features from both Rad and DTL. Conclusions:The integration of Rad and 2.5D DTL significantly improves the diagnostic accuracy for differentiating between benign and malignant Lung-RADS 3 and 4A nodules. This approach provides a robust decision-support tool that could potentially reduce unnecessary interventions for benign nodules while facilitating earlier detection and treatment of malignant lesions.

BACKGROUND:The optimal surgical strategy for lymph node dissection in lung adenocarcinoma remains controversial. Accurate predicting occult lymph node metastasis (OLNM) in patients with clinical T1 lung adenocarcinoma is essential for optimizing treatment decisions and improving patient outcomes. This study analyzes the relationship between anaplastic lymphoma kinase (ALK) status, clinicopathological characteristics, computed tomography (CT) features, and OLNM in patients with clinical T1 lung adenocarcinoma. METHODS:A retrospective analysis was conducted on data from patients with clinical T1 lung adenocarcinoma who showed no lymph node metastasis on preoperative CT and underwent surgical resection with lymph node dissection at two centers from January 2016 to December 2023. Univariate and multivariate logistic regression analyses were performed to identify factors associated with OLNM. RESULTS:Among 1138 patients with clinical T1 lung adenocarcinoma, 167 (14.6%) were found to have OLNM, including 55 (4.8%) with pathological N1 status and 112 (9.8%) with pathological N2 status. Multivariate logistic regression analysis identified lobulation, spiculation, solid density, lymphovascular invasion, spread through air spaces (STAS), micropapillary pattern, solid pattern, and carcinoembryonic antigen (CEA) levels as independent positive predictors of OLNM. Furthermore, lobulation, lymphovascular invasion, STAS, micropapillary pattern, solid pattern, CEA levels, and ALK were independent positive predictors of occult N2 lymph node metastasis. The lepidic pattern, however, was identified as an independent negative predictor for OLNM and occult N2 lymph node metastasis. CONCLUSION:The identified predictors may assist clinicians in evaluating the risk of OLNM in patients with clinical T1 lung adenocarcinoma, potentially guiding more targeted intervention strategies.

BACKGROUND:Occult lymph node metastasis (OLNM) refers to lymph node involvement that remains undetectable by conventional imaging techniques, posing a significant challenge in the accurate staging of lung adenocarcinoma. This study aims to investigate the potential of combining 2.5D deep learning radiomics with clinical data to predict OLNM in lung adenocarcinoma. METHODS:Retrospective contrast-enhanced CT images were collected from 1,099 patients diagnosed with lung adenocarcinoma across two centers. Multivariable analysis was performed to identify independent clinical risk factors for constructing clinical signatures. Radiomics features were extracted from the enhanced CT images to develop radiomics signatures. A 2.5D deep learning approach was used to extract deep learning features from the images, which were then aggregated using multi-instance learning (MIL) to construct MIL signatures. Deep learning radiomics (DLRad) signatures were developed by integrating the deep learning features with radiomic features. These were subsequently combined with clinical features to form the combined signatures. The performance of the resulting signatures was evaluated using the area under the curve (AUC). RESULTS:The clinical model achieved AUCs of 0.903, 0.866, and 0.785 in the training, validation, and external test cohorts The radiomics model yielded AUCs of 0.865, 0.892, and 0.796 in the training, validation, and external test cohorts. The MIL model demonstrated AUCs of 0.903, 0.900, and 0.852 in the training, validation, and external test cohorts, respectively. The DLRad model showed AUCs of 0.910, 0.908, and 0.875 in the training, validation, and external test cohorts. Notably, the combined model consistently outperformed all other models, achieving AUCs of 0.940, 0.923, and 0.898 in the training, validation, and external test cohorts. CONCLUSION:The integration of 2.5D deep learning radiomics with clinical data demonstrates strong capability for OLNM in lung adenocarcinoma, potentially aiding clinicians in developing more personalized treatment strategies.

PURPOSE:This study assesses the reliability of deep learning models based on planar whole-body bone scintigraphy for diagnosing Skull base invasion (SBI) in nasopharyngeal carcinoma (NPC) patients. METHODS:In this multicenter study, a deep learning model was developed using data from one center with a 7:3 allocation to training and internal test sets, to diagnose SBI in patients newly diagnosed with NPC using planar whole-body bone scintigraphy. Patients were diagnosed based on a composite reference standard incorporating radiologic and follow-up data. Ten different convolutional neural network (CNN) models were applied to both whole-image and partial-image input modes to determine the optimal model for each analysis. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration, decision curve analysis (DCA), and compared with expert assessments by two nuclear medicine physicians. RESULTS:The best-performing model using partial-body input achieved AUCs of 0.80 (95% CI: 0.73, 0.86) in the internal test set, 0.84 (95% CI: 0.77, 0.91) in the external cohort, and 0.78 (95% CI: 0.73, 0.83) in the treatment test cohort. Calibration curves and DCA confirmed the models' excellent discrimination, calibration, and potential clinical utility across internal and external datasets. The AUCs of both nuclear medicine physicians were lower than those of the best-performing deep learning model in external test set (AUC: 0.75 vs. 0.77 vs. 0.84). CONCLUSION:Deep learning models utilizing partial-body input from planar whole-body bone scintigraphy demonstrate high discriminatory power for diagnosing SBI in NPC patients, surpassing experienced nuclear medicine physicians.

Introduction: The tumor microenvironment and multidrug resistance of tumor cells seriously impair the activity of the nanozymes.Methods: Herein, a polyethylene glycol (PEG)-modified vanadium-doped molybdenum disulfide (V-MoS2@PEG) nanozymes were constructed to enhance anti-tumor activity through multi-enzymatic catalysis and photothermal effect with simultaneous reactive oxygen species replenishment and glutathione depletion.Results and discussion: V-MoS2@PEG nanosheets exerted peroxidase activity by causing molybdenum ion (Mo4+) to react with hydrogen peroxide to form toxic hydroxyl radicals (·OH). Meanwhile, the V-doping can deplete glutathione avoiding ·OH consumption. In addition, the high heat generated by V-MoS2@PEG nanozymes under near-infrared laser irradiation brought about a desirable local temperature gradient, which produced an enhanced catalytic effect by promoting band bending. Furthermore, the photothermally inspired polarized charge increased the permeability of the tumor cell membrane and promoted further aggregation of the nanozymes, which realized the combination of photothermal therapy with multi-enzymatic catalysis, solved the problem of multi-enzyme catalysis, and improved the anti-tumor efficiency.

Erastin can induce ferroptosis in tumor cells as an effective small molecule inhibitor. However, its application is hampered by a lack of water solubility. This study investigated the effects of superparamagnetic iron oxide (SPIO)–erastin–polyethylene glycol (PEG) nanoparticles prepared by loading SPIO–PEG nanoparticles with erastin on ferroptosis. SPIO–erastin–PEG nanoparticles exhibited square and spherical shapes with good dispersibility. The zeta potential and hydrodynamic size of SPIO–erastin–PEG were measured as (−37.68 ± 2.706) mV and (45.75 ± 18.88) nm, respectively. On T2-weighted imaging, the nanosystem showed significant contrast enhancement compared to no-enhancement magnetic resonance imaging (MRI). SPIO–erastin–PEG induced ferroptosis by increasing reactive oxygen species and iron content and promoting the accumulation of lipid peroxides and the degradation of glutathione peroxidase 4. Pharmacokinetic experiments revealed a half-life of 1.25 ± 0.05 h for the SPIO–erastin–PEG solution in circulation. Moreover, significant antitumorigenic effects of SPIO–erastin–PEG have been demonstrated in 5–8F cells and mouse-bearing tumors. These results indicated that the synthesized SPIO–erastin–PEG nanoplatform could induce ferroptosis effects in vitro and in vivo while exhibiting favorable physical characteristics. This approach may provide a new strategy for theranostic nanoplatform for nasopharyngeal cancer.

Tuberculosis (TB) remains one of the major infectious diseases in the world with a high incidence rate. Drug-resistant tuberculosis (DR-TB) is a key and difficult challenge in the prevention and treatment of TB. Early, rapid, and accurate diagnosis of DR-TB is essential for selecting appropriate and personalized treatment and is an important means of reducing disease transmission and mortality. In recent years, imaging diagnosis of DR-TB has developed rapidly, but there is a lack of consistent understanding. To this end, the Infectious Disease Imaging Group, Infectious Disease Branch, Chinese Research Hospital Association; Infectious Diseases Group of Chinese Medical Association of Radiology; Digital Health Committee of China Association for the Promotion of Science and Technology Industrialization, and other organizations, formed a group of TB experts across China. The conglomerate then considered the Chinese and international diagnosis and treatment status of DR-TB, China's clinical practice, and evidence-based medicine on the methodological requirements of guidelines and standards. After repeated discussion, the expert consensus of imaging diagnosis of DR-PB was proposed. This consensus includes clinical diagnosis and classification of DR-TB, selection of etiology and imaging examination [mainly X-ray and computed tomography (CT)], imaging manifestations, diagnosis, and differential diagnosis. This expert consensus is expected to improve the understanding of the imaging changes of DR-TB, as a starting point for timely detection of suspected DR-TB patients, and can effectively improve the efficiency of clinical diagnosis and achieve the purpose of early diagnosis and treatment of DR-TB.

The issue of multi-drug-resistant tuberculosis (MDR-TB) presents a substantial challenge to global public health. Regrettably, the diagnosis of drug-resistant tuberculosis (DR-TB) frequently necessitates an extended period or more extensive laboratory resources. The swift identification of MDR-TB poses a particularly challenging endeavor. To identify the biomarkers indicative of multi-drug resistance, we conducted a screening of the GSE147689 dataset for differentially expressed genes (DEGs) and subsequently conducted a gene enrichment analysis. Our analysis identified a total of 117 DEGs, concentrated in pathways related to the immune response. Three machine learning methods, namely random forest, decision tree, and support vector machine recursive feature elimination (SVM-RFE), were implemented to identify the top 10 genes according to their feature importance scores. A4GALT and S1PR1, which were identified as common genes among the three methods, were selected as potential molecular markers for distinguishing between MDR-TB and drug-susceptible tuberculosis (DS-TB). These markers were subsequently validated using the GSE147690 dataset. The findings suggested that A4GALT exhibited area under the curve (AUC) values of 0.8571 and 0.7121 in the training and test datasets, respectively, for distinguishing between MDR-TB and DS-TB. S1PR1 demonstrated AUC values of 0.8163 and 0.5404 in the training and test datasets, respectively. When A4GALT and S1PR1 were combined, the AUC values in the training and test datasets were 0.881 and 0.7551, respectively. The relationship between hub genes and 28 immune cells infiltrating MDR-TB was investigated using single sample gene enrichment analysis (ssGSEA). The findings indicated that MDR-TB samples exhibited a higher proportion of type 1 T helper cells and a lower proportion of activated dendritic cells in contrast to DS-TB samples. A negative correlation was observed between A4GALT and type 1 T helper cells, whereas a positive correlation was found with activated dendritic cells. S1PR1 exhibited a positive correlation with type 1 T helper cells and a negative correlation with activated dendritic cells. Furthermore, our study utilized connectivity map analysis to identify nine potential medications, including verapamil, for treating MDR-TB. In conclusion, our research identified two molecular indicators for the differentiation between MDR-TB and DS-TB and identified a total of nine potential medications for MDR-TB.

Biodegradable silicate nanoconstructs have aroused tremendous interest in cancer therapeutics due to their variable framework composition and versatile functions. Nevertheless, low intratumoral retention still limits their practical application. In this study, oxygen vacancy (OV)-enriched bimetallic silicate nanozymes with Fe-Ca dual active sites via modification of oxidized sodium alginate and gallic acid (GA) loading (OFeCaSA-V@GA) were developed for targeted aggregation-potentiated therapy. The band gap of silica markedly decreased from 2.76 to 1.81 eV by codoping of Fe3+ and Ca2+, enabling its excitation by a 650 nm laser to generate reactive oxygen species. The OV that occurred in the hydrothermal synthetic stage of OFeCaSA-V@GA can anchor the metal ions to form an atomic phase, offering a massive fabrication method of single-atom nanozymes. Density functional theory results reveal that the Ca sites can promote the adsorption of H2O2, and Fe sites can accelerate the dissociation of H2O2, thereby realizing a synergetic catalytic effect. More importantly, the targeted delivery of metal ions can induce a morphological transformation at tumor sites, leading to high retention (the highest retention rate is 36.3%) of theranostic components in tumor cells. Thus, this finding may offer an ingenious protocol for designing and engineering highly efficient and long-retention nanodrugs.

Background: The anti-Programmed Death-Ligand 1 (termed aPD-L1) immune checkpoint blockade therapy has emerged as a promising treatment approach for various advanced solid tumors. However, the effect of aPD-L1 inhibitors limited by the tumor microenvironment makes most patients exhibit immunotherapy resistance. Methods: We conjugated the Sialyl Lewis X with a polyethylene glycol-coated ultrasmall superparamagnetic iron oxide (USPIOPEG) to form UPS nanoparticles (USPIO-PEG-SLex, termed UPS). The physicochemical properties of UPS were tested and characterized. Transmission electron microscopy and ICP-OES were used to observe the cellular uptake and targeting ability of UPS. Flow cytometry, mitochondrial membrane potential staining, live-dead staining and scratch assay were used to verify the in vitro photothermal effect of UPS, and the stimulation of UPS on immune-related pathways at the gene level was analyzed by sequencing. Biological safety analysis and pharmacokinetic analysis of UPS were performed. Finally, the amplification effect of UPS-mediated photothermal therapy on aPD-L1-mediated immunotherapy and the corresponding mechanism were studied. Results: In vitro experiments showed that UPS had strong photothermal therapy ability and was able to stimulate 5 immune-related pathways. In vivo, when the PTT assisted aPD-L1 treatment, it exhibited a significant increase in CD4+ T cell infiltration by 14.46-fold and CD8+ T cell infiltration by 14.79-fold, along with elevated secretion of tumor necrosis factor-alpha and interferon-gamma, comparing with alone aPD-L1. This PTT assisted aPD-L1 therapy achieved a significant inhibition of both primary tumors and distant tumors compared to the alone aPD-L1, demonstrating a significant difference. Conclusion: The nanotheranostic agent UPS has been introduced into immunotherapy, which has effectively broadened its application in biomedicine. This photothermal therapeutic approach of the UPS nanotheranostic agent enhancing the efficacy of aPD-L1 immune checkpoint blockade therapy, can be instructive to address the challenges associated with immunotherapy resistance, thereby offering potential for clinical translation.

Tracheobronchial tuberculosis (TBTB), defined as a tuberculosis occurring at mucosa, submucosa, smooth muscle, cartilage, and/or adventitia of trachea or bronchi [1], is one type of pulmonary tuberculosis. About 10–40% of pulmonary tuberculosis patients are complicated with tracheobronchial tuberculosis, and its prevalence keeps rising little by little with the gradual application of bronchoscopy during recent years [2]. The absence of specific early clinical manifestations in tracheobronchial tuberculosis results in misdiagnosis and missed diagnosis in many patients. The diagnosis of TBTB relies on bronchoscopy, which is capable of detecting promptly lesions in tracheobronchial lumen, such as intimal congestion, purulent secretion, swelling, membranous matter and granuloma, and the most characteristic microscopic finding is white or milky caseous substances covering diffusely bronchial mucosa [3]. TBTB can be categorized into six types based on findings under tracheoscopy: inflammatory infiltration (type I), ulcerative necrosis (type II), granulation proliferation (type III), cicatricial stenosis (type IV), softening of tracheobronchial wall (type V), and lymph node fistula (type VI) [1]. Bronchoscopic brush biopsy, having increased significantly the positiveness rate of acid fast bacilli in bronchial tuberculosis patients, provides a basis for pathogenic diagnosis. In addition, imaging examination is the first option for examining bronchial tuberculosis, and CT scans when used to examine bronchial tuberculosis may define not only the location of bronchial lesions, but also the situation of bronchial lumen, such as obstruction or stenosis and atelectasis. Bronchial stenosis, interruption, and deformation shown by chest CT scans are evidences supporting the diagnosis of bronchial tuberculosis. Main modalities for treating tracheobronchial tuberculosis are anti-tuberculosis medications, and the cure rate in treated patients is about 75% with an amelioration rate of about 24% [4].

磁共振成像(magnetic resonance imaging,MRI)作为乳腺癌主要的影像学检查手段在其肿瘤诊治过程中发挥重要作用,近年来人工智能(artificial intelligence,AI)技术在医学影像领域发展迅速,在AI技术协助下MRI对乳腺癌的诊断效能进一步提高,在病灶检出、分割、诊断、病理及分子分型、病情预测、疗效评估等临床需求方面均有显著进展,本文就AI在乳腺癌MRI方面的应用研究进展进行综述.