Immune checkpoint inhibitors and anti-vascular endothelial growth factor antibody/tyrosine kinase inhibitors with or without transarterial chemoembolization as first-line treatment for advanced hepatocellular carcinoma (CHANCE2201): a target trial emulation study.

Background: The role of transarterial chemoembolization (TACE) in the treatment of advanced hepatocellular carcinoma (HCC) is unconfirmed. This study aimed to assess the efficacy and safety of immune checkpoint inhibitors (ICIs) plus anti-vascular endothelial growth factor (anti-VEGF) antibody/tyrosine kinase inhibitors (TKIs) with or without TACE as first-line treatment for advanced HCC. Methods: This nationwide, multicenter, retrospective cohort study included advanced HCC patients receiving either TACE with ICIs plus anti-VEGF antibody/TKIs (TACE-ICI-VEGF) or only ICIs plus anti-VEGF antibody/TKIs (ICI-VEGF) from January 2018 to December 2022. The study design followed the target trial emulation framework with stabilized inverse probability of treatment weighting (sIPTW) to minimize biases. The primary outcome was overall survival (OS). Secondary outcomes included progression-free survival (PFS), objective response rate (ORR), and safety. The study is registered with ClinicalTrials.gov, NCT05332821. Findings: Among 1244 patients included in the analysis, 802 (64.5%) patients received TACE-ICI-VEGF treatment, and 442 (35.5%) patients received ICI-VEGF treatment. The median follow-up time was 21.1 months and 20.6 months, respectively. Post-application of sIPTW, baseline characteristics were well-balanced between the two groups. TACE-ICI-VEGF group exhibited a significantly improved median OS (22.6 months [95% CI: 21.2-23.9] vs 15.9 months [14.9-17.8]; P < 0.0001; adjusted hazard ratio [aHR] 0.63 [95% CI: 0.53-0.75]). Median PFS was also longer in TACE-ICI-VEGF group (9.9 months [9.1-10.6] vs 7.4 months [6.7-8.5]; P < 0.0001; aHR 0.74 [0.65-0.85]) per Response Evaluation Criteria in Solid Tumours (RECIST) version 1.1. A higher ORR was observed in TACE-ICI-VEGF group, by either RECIST v1.1 or modified RECIST (41.2% vs 22.9%, P < 0.0001; 47.3% vs 29.7%, P < 0.0001). Grade ≥3 adverse events occurred in 178 patients (22.2%) in TACE-ICI-VEGF group and 80 patients (18.1%) in ICI-VEGF group. Interpretation: This multicenter study supports the use of TACE combined with ICIs and anti-VEGF antibody/TKIs as first-line treatment for advanced HCC, demonstrating an acceptable safety profile. Funding: National Natural Science Foundation of China, National Key Research and Development Program of China, Jiangsu Provincial Medical Innovation Center, Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, and Nanjing Life Health Science and Technology Project.

Bronchial arterial chemoembolization with Drug-Eluting beads plus sequential chemotherapy for the treatment of stage III and IV lung squamous cell carcinoma.

PURPOSE: This retrospective study aimed to investigate the effectiveness and safety of bronchial arterial chemoembolization with drug-eluting beads (DEB-BACE) plus chemotherapy versus chemotherapy alone in patients with stage III and IV lung squamous cell carcinoma (LSCC) who are not appropriate candidates for radiochemotherapy. MATERIALS AND METHODS: In this retrospective analysis, we screened all adult patients undergoing either DEB-BACE plus chemotherapy or chemotherapy alone for stage III or IV LCSS at authors' center from January 2018 to August 2021. Each 21-day chemotherapy cycle consisted of intravenous injection of gemcitabine (1.0 g/m2) on days 1 and 8 and cisplatin 75 (mg/m2) on day 1. The planned cycles were 4. DEB-BACE consisted of microcatheter infusion of CalliSpheres beads carrying cisplatin (75 mg/m2) and gemcitabine (1.0 g/m2), at 3 weeks prior to chemotherapy. The primary outcome was overall survival (OS). The secondary outcomes included progression-free survival (PFS), pulmonary response, and adverse events (AEs). RESULTS: The final analysis included 95 patients in the chemotherapy group and 41 patients in the combination treatment group. The median OS was 14 months (95 % CI 11.0-17.0) in the chemotherapy group and 19 months (95 % CI 18.0-24.0) in the combination group (P = 0.015). In multivariate Cox regression analysis, DEB-BACE plus chemotherapy was associated with lower risk of death versus chemotherapy only (HR 0.16, 95 % CI 0.05-0.52; log rank test P = 0.003). The median PFS was 6 months (95 % CI 4.0-7.0) in the chemotherapy group and 8 months (95 % CI 6.0-8.0) in the combination group (P = 0.015). The pulmonary objective response rate (ORR) and disease control rate (DCR) were 48.4 % and 62.1 % in chemotherapy group versus 82.9 % and 90.2 % in combination group (P < 0.001 and = 0.001, respectively). AEs occurred in 133 patients (97.8 %). The rate of bone marrow suppression was 48.4 % (46/95) in the chemotherapy group versus 7.3 % (3/41) in the combination group (P < 0.001). CONCLUSION: Compared with chemotherapy alone, DEB-BACE plus chemotherapy was associated with longer survival outcomes and lower rate of bone marrow suppression.

Acidic/hypoxia dual-alleviated nanoregulators for enhanced treatment of tumor chemo-immunotherapy.

Chemotherapy plays a crucial role in triple-negative breast cancer (TNBC) treatment as it not only directly kills cancer cells but also induces immunogenic cell death. However, the chemotherapeutic efficacy was strongly restricted by the acidic and hypoxic tumor environment. Herein, we have successfully formulated PLGA-based nanoparticles concurrently loaded with doxorubicin (DOX), hemoglobin (Hb) and CaCO3 by a CaCO3-assisted emulsion method, aiming at the effective treatment of TNBC. We found that the obtained nanomedicine (DHCaNPs) exhibited effective drug encapsulation and pH-responsive drug release behavior. Moreover, DHCaNPs demonstrated robust capabilities in neutralizing protons and oxygen transport. Consequently, DHCaNPs could not only serve as oxygen nanoshuttles to attenuate tumor hypoxia but also neutralize the acidic tumor microenvironment (TME) by depleting lactic acid, thereby effectively overcoming the resistance to chemotherapy. Furthermore, DHCaNPs demonstrated a notable ability to enhance antitumor immune responses by increasing the frequency of tumor-infiltrating effector lymphocytes and reducing the frequency of various immune-suppressive cells, therefore exhibiting a superior efficacy in suppressing tumor growth and metastasis when combined with anti-PD-L1 (αPD-L1) immunotherapy. In summary, this study highlights that DHCaNPs could effectively attenuate the acidic and hypoxic TME, offering a promising strategy to figure out an enhanced chemo-immunotherapy to benefit TNBC patients.

Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.

BACKGROUND: This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI). METHODS: A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility. RESULTS: Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model. CONCLUSIONS: The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.

Computed tomography perfusion deficit volume predicts the functional outcome of endovascular therapy for basilar artery occlusion.

OBJECTIVES: To investigate the relationship between baseline computed tomography perfusion deficit volumes and functional outcomes in patients with basilar artery occlusion (BAO) undergoing endovascular therapy. METHODS: This was a single-center study in which the data of 64 patients with BAO who underwent endovascular therapy were retrospectively analyzed. All the patients underwent multi-model computed tomography on admission. The posterior-circulation Acute Stroke Prognosis Early Computed Tomography Score was applied to assess the ischemic changes. Perfusion deficit volumes were obtained using Syngo.via software. The primary outcome of the analysis was a good functional outcome (90-day modified Rankin Scale score ≤ 3). Logistic regression and receiver operating characteristic curves were used to explore predictors of functional outcome. RESULTS: A total of 64 patients (median age, 68 years; 72 % male) were recruited, of whom 26 (41 %) patients achieved good functional outcomes, while 38 (59 %) had poor functional outcomes. Tmax > 10 s, Tmax > 6 s, and rCBF < 30 % volume were independent predictors of good functional outcomes (odds ratio range, 1.0-1.2; 95 % confidence interval [CI], 1.0-1.4]) and performed well in the receiver operating characteristic curve analyses, exhibiting positive prognostic value; the areas under the curve values were 0.85 (95 % CI, 0.75-0.94), 0.81 (95 % CI, 0.70-0.90), and 0.78 (95 % CI, 0.67-0.89). CONCLUSION: Computed tomography perfusion deficit volume represents a valuable tool in predicting high risk of disability and mortality in patients with BAO after endovascular treatment.

Multiple Immunomodulatory Strategies Based on Targeted Regulation of Proprotein Convertase Subtilisin/Kexin Type 9 and Immune Homeostasis against Hepatocellular Carcinoma.

Immunotherapy is the most promising systemic therapy for hepatocellular carcinoma. However, the outcome remains poor. Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a role in altering cell-surface protein levels, potentially undermining the efficacy of immunotherapy against tumors. This highlights its potential as a target for antitumor therapy. Herein, CaCO3-based nanoparticles coencapsulated with DOX, an immunogenic cell death (ICD) inducer, and evolocumab was developed to enhanced the efficacy of immunotherapy. The obtained DOX/evolocumab-loaded CaCO3 nanoparticle (named DECP) exhibits a good capacity of acid neutralization and causes ICD of cancer cells. In addition, DECP is able to evaluate the cell-surface level of MHC-I, a biomarker that correlates positively with patients' overall survival. Upon intravenous injection, DECP accumulates within the tumor site, leading to growth inhibition of hepa1-6 bearing subcutaneous tumors. Specifically, DECP treatment causes augmented ratios of matured dendritic cells, tumor-infiltrating CD8+ T cells and natural killing cells, while concurrently depleting Foxp3+ regulatory T cells. Peritumoral delivery of DECP enhances the immune response of distant tumors and exhibits antitumor effects when combined with intravenous αPD-L1 therapy in a bilateral tumor model. This study presents CaCO3-based nanoparticles with multiple immunomodulatory strategies against hepatocellular carcinoma by targeting PCSK9 inhibition and modulating immune homeostasis in the unfavorable TME.

Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer (BC) patients who underwent total mastectomy (TM) and had 1-2 positive sentinel lymph nodes (SLNs). MATERIALS AND METHODS: In total, 494 patients were retrospectively enrolled from two hospitals, and were divided into the training (n = 286), internal validation (n = 122), and external validation (n = 86) cohorts. Features were extracted from DCE-MRI images for each patient and screened. Six ML classifies were trained and the best classifier was evaluated to calculate radiomics (Rad)-scores. A combined model was developed based on Rad-scores and clinical risk factors, then the calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS: 14 radiomics features were ultimately selected. The random forest (RF) classifier showed the best performance, with the highest average area under the curve (AUC) of 0.833 in the validation cohorts. The combined model incorporating RF-based Rad-scores, tumor size, lymphovascular invasion, and proportion of positive SLNs resulted in the best discrimination ability, with AUCs of 0.903, 0.890, and 0.836 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the combined model significantly improved the classification accuracy and clinical benefit for NSLN metastasis prediction. CONCLUSION: A RF-based combined model using DCE-MRI images exhibited a promising performance for predicting NSLN metastasis in Chinese BC patients who underwent TM and had 1-2 positive SLNs, thereby aiding in individualized clinical treatment decisions.

Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics.

OBJECTIVES: Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer. METHODS: A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves. RESULTS: Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively. CONCLUSIONS: The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients. ADVANCES IN KNOWLEDGE: This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.

Development and Validation of a Dual-Energy CT-Based Model for Predicting the Number of Central Lymph Node Metastases in Clinically Node-Negative Papillary Thyroid Carcinoma.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a dual-energy CT (DECT)-based model for preoperative prediction of the number of central lymph node metastases (CLNMs) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC) patients. MATERIALS AND METHODS: Between January 2016 and January 2021, 490 patients who underwent lobectomy or thyroidectomy, CLN dissection, and preoperative DECT examinations were enrolled and randomly allocated into the training (N = 345) and validation cohorts (N = 145). The patients' clinical characteristics and quantitative DECT parameters obtained on primary tumors were collected. Independent predictors of> 5 CLNMs were identified and integrated to construct a DECT-based prediction model, for which the area under the curve (AUC), calibration, and clinical usefulness were assessed. Risk group stratification was performed to distinguish patients with different recurrence risks. RESULTS: More than 5 CLNMs were found in 75 (15.3%) cN0 PTC patients. Age, tumor size, normalized iodine concentration (NIC), normalized effective atomic number (nZeff) and the slope of the spectral Hounsfield unit curve (λHu) in the arterial phase were independently associated with> 5 CLNMs. The DECT-based nomogram that incorporated predictors demonstrated favorable performance in both cohorts (AUC: 0.842 and 0.848) and significantly outperformed the clinical model (AUC: 0.688 and 0.694). The nomogram showed good calibration and added clinical benefit for predicting> 5 CLNMs. The KaplanMeier curves for recurrence-free survival showed that the high- and low-risk groups stratified by the nomogram were significantly different. CONCLUSION: The nomogram based on DECT parameters and clinical factors could facilitate preoperative prediction of the number of CLNMs in cN0 PTC patients.

The value of computed tomography perfusion deficit volumes in acute isolated brainstem infarction.

Purpose: Diagnosis of acute isolated brainstem infarction is challenging owing to non-specific, variable symptoms, and the effectiveness of non-contrast computed tomography (NCCT) is poor owing to limited spatial resolution and artifacts. Computed tomography perfusion (CTP) imaging parameters are significantly associated with functional outcomes in posterior circulation acute ischemic stroke; however, the role of CTP in isolated brainstem infarction remains unclear. We aimed to determine the value of CTP imaging parameters in predicting functional outcomes for affected patients. Methods: In total, 116 consecutive patients with isolated pontine/midbrain hypoperfusion who underwent CTP and follow-up by magnetic resonance imaging (MRI) between January 2018 and March 2022, were retrospectively analyzed. Perfusion deficit volumes on all maps, and the final infarction volume (FIV) on MRI were quantified. "Good" functional outcome was defined as a 90-day modified Rankin Scale score of 0 and 1. Statistical analysis included uni- and multivariate regression analyses, binary logistic regressions, and receiver operating characteristics (ROC) analyses. Results: In total, 113 patients had confirmed isolated pontine/midbrain infarction on follow-up MRI. Onset-to-scan time, visibility of ischemic lesions on NCCT, the baseline National Institutes of Health Stroke Scale (NIHSS) score, and perfusion deficit volumes on all CTP maps were significantly associated with FIV (p < 0.05). In a multivariate linear regression model, adjusted for age, sex, NIHSS score, onset-to-scan time, and visibility of NCCT, perfusion deficit volumes remained significantly associated with FIV. In binary logistic regression analyses, perfusion deficit volumes on all CTP maps remained independent predictors of a good functional outcome. In ROC analyses, the cerebral blood flow deficit volume showed a slightly higher discriminatory value with the largest area under the curve being 0.683 [(95% CI, 0.587-0.780), p = 0.001]. Conclusion: Perfusion deficit volumes of CTP imaging could reflect the FIV and contain prognostic information on functional outcomes in patients with acute isolated brainstem infarction.

Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study.

Background: For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images. Methods: In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n = 519), internal validation set (n = 129), external test set 1 (n = 296), and external test set 2 (n = 44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740. Findings: For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0 cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model. Interpretation: The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively. Funding: National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.

macJNet: weakly-supervised multimodal image deformable registration using joint learning framework and multi-sampling cascaded MIND.

Deformable multimodal image registration plays a key role in medical image analysis. It remains a challenge to find accurate dense correspondences between multimodal images due to the significant intensity distortion and the large deformation. macJNet is proposed to align the multimodal medical images, which is a weakly-supervised multimodal image deformable registration method using a joint learning framework and multi-sampling cascaded modality independent neighborhood descriptor (macMIND). The joint learning framework consists of a multimodal image registration network and two segmentation networks. The proposed macMIND is a modality-independent image structure descriptor to provide dense correspondence for registration, which incorporates multi-orientation and multi-scale sampling patterns to build self-similarity context. It greatly enhances the representation ability of cross-modal features in the registration network. The semi-supervised segmentation networks generate anatomical labels to provide semantics correspondence for registration, and the registration network helps to improve the performance of multimodal image segmentation by providing the consistency of anatomical labels. 3D CT-MR liver image dataset with 118 samples is built for evaluation, and comprehensive experiments have been conducted to demonstrate that macJNet achieves superior performance over state-of-the-art multi-modality medical image registration methods.

Mesenchymal stem cell-derived extracellular vesicles in skin wound healing: roles, opportunities and challenges.

Skin wounds are characterized by injury to the skin due to trauma, tearing, cuts, or contusions. As such injuries are common to all human groups, they may at times represent a serious socioeconomic burden. Currently, increasing numbers of studies have focused on the role of mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs) in skin wound repair. As a cell-free therapy, MSC-derived EVs have shown significant application potential in the field of wound repair as a more stable and safer option than conventional cell therapy. Treatment based on MSC-derived EVs can significantly promote the repair of damaged substructures, including the regeneration of vessels, nerves, and hair follicles. In addition, MSC-derived EVs can inhibit scar formation by affecting angiogenesis-related and antifibrotic pathways in promoting macrophage polarization, wound angiogenesis, cell proliferation, and cell migration, and by inhibiting excessive extracellular matrix production. Additionally, these structures can serve as a scaffold for components used in wound repair, and they can be developed into bioengineered EVs to support trauma repair. Through the formulation of standardized culture, isolation, purification, and drug delivery strategies, exploration of the detailed mechanism of EVs will allow them to be used as clinical treatments for wound repair. In conclusion, MSC-derived EVs-based therapies have important application prospects in wound repair. Here we provide a comprehensive overview of their current status, application potential, and associated drawbacks.

Biomaterials evolution: from inert to instructive.

The field of biomaterials has experienced substantial evolution in recent years, driven by advancements in materials science and engineering. This has led to an expansion of the biomaterials definition to include biocompatibility, bioactivity, bioderived materials, and biological tissues. Consequently, the intended performance of biomaterials has shifted from a passive role wherein a biomaterial is merely accepted by the body to an active role wherein a biomaterial instructs its biological environment. In the future, the integration of bioinspired designs and dynamic behavior into fabrication technologies will revolutionize the field of biomaterials. This perspective presents the recent advances in the evolution of biomaterials in fabrication technologies and provides a brief insight into smart biomaterials.

Positive Chemotaxis of CREKA-Modified Ceria@Polydopamine Biomimetic Nanoswimmers for Enhanced Penetration and Chemo-photothermal Tumor Therapy.

Tumor interstitial pressure represents the greatest barrier against drug diffusion into the depth of the tumor. Biometric nanomotors highlight the possibility of enhanced deep penetration and improve cellular uptake. However, control of their directionality remains difficult to achieve. Herein, we report cysteine-arginine-glutamic acid-lysine-alanine (CREKA)-modified ceria@polydopamine nanobowls as tumor microenvironment-fueled nanoscale motors for positive chemotaxis into the tumor depth or toward tumor cells. Upon laser irradiation, this nanoswimmer rapidly depletes the tumor microenvironment-specific hydrogen peroxide (H2O2) in the nanobowl, contributing to a self-generated gradient and subsequently propulsion (9.5 µm/s at 46 °C). Moreover, the asymmetrical modification of CREKA on nanobowls could automatically reconfigure the motion direction toward tumor depth or tumor cells in response to receptor-ligand interaction, leading to a deep penetration (70 µm in multicellular spheroids) and enhanced antitumor effects over conventional nanomedicine-induced chemo-photothermal therapy (tumor growth inhibition rate: 84.2% versus 56.9%). Thus, controlling the direction of nanomotors holds considerable potential for improved antitumor responses, especially in solid tumors with high tumor interstitial pressure.

A photo-activable nano-agonist for the two-signal model of T cell in vivo activation.

The two-signal model of T cell activation has helped shape our understanding of the adaptive immune response for over four decades. According to the model, activation of T cells requires a stimulus through the T cell receptor/CD3 complex (signal 1) and a costimulatory signal 2. Stimulation of activatory signals via T cell agonists has thus emerged. However, for a robust T cell activation, it necessitates not only the presence of both signal 1 and signal 2, but also a high signaling strength. Herein, we report a photo-activable nano-agonist for the two-signal model of T cell in vivo activation. A UV-crosslinkable polymer is coated onto upconversion nanoparticles with satisfactory NIR-to-UV light conversion efficiency. Then dual signal molecules, i.e., signal 1 and signal 2, are conjugated to the polymer end to yield the photo-activable T cell nano-agonist. In melanoma and breast cancer models, photo-activable nano-agonist could bind onto corresponding activatory receptors on the surface of T cells, but has limited activity without the application of NIR light (absence of photo-crosslinking of receptors and consequently a poor signaling strength). While when the NIR light is switched on locally, T cells in tumor are remarkably activated and kill tumor cells effectively. Moreover, we do not observe any detectable toxicities related to the photo-activable nano-agonist. We believe with two activatory signals being simultaneously strengthened by local photo-switched crosslinking, T cells realize a robust and selective activation in tumor and, consequently contribute to an enhanced and safe tumor immunotherapy.

Predictive Models for Colon Adenocarcinoma Diagnosis, Prognosis, and Immune Microenvironment Based on 2 Hypoxia-Related Genes: KDM3A and ENO3.

Background: Hypoxia is known to play a critical role in tumor occurrence, progression, prognosis, and therapy resistance. However, few studies have investigated hypoxia markers for diagnosing and predicting prognosis in colon adenocarcinoma (COAD). This study aims to identify a hypoxia genes-based biomarker for predicting COAD patients' prognosis and response to immunotherapy on an individual basis. Methods: Hypoxia-related genes were extracted from the Molecular Signatures Database. Gene expression, clinical data, and mutation data of COAD were collected retrospectively from the Cancer Genome Atlas, the Gene Expression Omnibus, and the International Cancer Genome Consortium databases. Univariate and multivariate cox regression, and the least absolute shrinkage and selection operator method were used to select the genes most associated with the prognosis of COAD patients. Kaplan-Meier survival analysis, receiver operating characteristic curves, calibration curves, and decision curve analyses were performed to validate the efficacy of the signature in predicting the prognosis of COAD patients. EdU incorporation assays, cell survival assays, western blot assays, and trans-well invasion assays were performed to further confirm the function of the screened genes in tumorigenesis. Results: ENO3 and KDM3A were identified as key genes for constructing prognostic and diagnostic signatures, which were found to be independent risk factors for predicting the prognosis and diagnosis of COAD patients. Using these signatures, COAD patients could be stratified into high-risk and low-risk groups, with the latter exhibiting better overall survival outcomes. Moreover, the high-risk group displayed elevated levels of immune checkpoint genes and tumor mutation burden, indicating that these patients may benefit from immune checkpoint inhibitor therapy. Conclusion: The signature developed in this study demonstrates excellent efficacy in prognosticating the outcomes of COAD patients. Moreover, it can serve as a valuable tool for clinicians to identify COAD patients who are suitable for ICI therapy.

Recombinant supercharged polypeptides for safe and efficient heparin neutralization.

Heparin is a widely used anticoagulant agent in the clinic. After application, its anticoagulant effect must be reversed to prevent potential side effects. Protamine sulfate (PS) is the only clinically licensed antidote that has been used for this purpose in the last 80 years, which, however, provokes severe adverse effects, such as systemic hypotension and even death. Herein, we demonstrate the potential of supercharged polypeptides as a promising alternative for protamine sulfate. A series of supercharged polypeptides with multiple positive charges was recombinantly produced, and the heparin-neutralizing performance of the polypeptides was evaluated in comparison with PS. It was found that increasing the number of charges significantly enhanced the ability to neutralize heparin and resist the screening effect induced by salt. In particular, the polypeptide bearing 72 charges (K72) exhibited an excellent heparin-neutralizing behavior that was comparable to that of PS. Further in vivo studies revealed that the heparin-triggered bleeding was almost completely alleviated by K72 while a negligible toxic effect was observed. Therefore, such recombinant supercharged polypeptides might replace protamine sulfate as heparin-reversal agents.