Preoperative discrimination between follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) remains challenging, as imaging and cytological approaches often show limited efficacy. Even fine-needle aspiration (FNA) biopsy and intraoperative frozen sections frequently fail to provide conclusive results. Thus, follicular thyroid neoplasms (FNs) typically necessitate complete surgical excision for definitive diagnosis, leading to unnecessary thyroidectomies for benign conditions or delayed treatment for malignancies. To address this gap, we developed FTC-Net, a vision-language foundation model, to preoperatively classify FNs using ultrasound images. In a multicenter retrospective study of 2421 patients (6477 images) from 14 institutions, FTC-Net was trained on 1462 patients and validated in two independent cohorts (n = 578 and n = 381). FTC-Net achieved AUCs of 0.836 and 0.841 in external validation, outperforming benchmark deep learning models and established TI-RADS systems. It also substantially reduced both total FNA rates and unnecessary FNA rates compared to ACR TI-RADS and C-TI-RADS. FTC-Net has the potential to serve as a non-invasive and advanced tool for the preoperative diagnosis of FNs, thereby improving clinical decision-making and reducing unnecessary procedures.

Rationale and Objectives The interstitial-lymphatic network (ILN) is critical in cancer development, metastasis, and treatment. This study aimed to investigate characteristics of ILN in different types of breast tumor using Super-Resolution Ultrasound (SRUS). Materials and Methods This prospective study was conducted from January 2024 to March 2024 in a tertiary referral hospital. A total of 192 patients hospitalized for breast lesions (including 54 benign and 138 malignant tumors) were enrolled. SRUS was performed by injecting contrast agents into tumor interstitial space. Parameters derived from SRUS images were compared in different breast tumors using chi-square, t-tests and non-parametric tests as appropriate. Results Compared with benign tumor, malignant lesions exhibited significantly lower vessel density, flow velocity, and vessel ratio (p<0.001) and more often exhibited peripheral, heterogeneous ILN patterns with loss of branch-like networks (p < 0.001). Structurally, the complexity level and the maximum curvature values of ILN in breast cancers were significantly lower than benign breast tumors (p < 0.05). The difference of ILN diameters and the mean distance in the long axis of tumors also showed significance in two groups (p < 0.05). In Non-Luminal breast cancer, the ILN density were higher than in Luminal breast cancer (p ≤ 0.01 for all). Moreover, the distance standard deviation of ILN was significantly smaller than that of without sentinel lymph node metastases (p < 0.05). Conclusion SRUS effectively visualized the ILN in breast tumors at micron-scale resolution, revealing distinct morphological and hydrodynamic patterns across different types of breast tumors.

PURPOSE:To evaluate the longitudinal evolution of transabdominal bowel ultrasound parameters during ustekinumab therapy in patients with postoperative recurrence of Crohn's disease (CD), and to investigate the association between ultrasound changes and clinical and biochemical outcomes. METHODS:This retrospective longitudinal study included 65 patients with endoscopically confirmed postoperative recurrence of CD who received ustekinumab therapy between April 2020 and December 2024. Patients were classified into improvement and non-improvement groups according to endoscopic outcomes at week 24. Serial transabdominal bowel ultrasound examinations were performed at baseline and at weeks 8, 16, and 24, including assessment of bowel wall thickness, Limberg vascularity grade, bowel wall stratification, perienteric fat edema, and luminal stenosis. Longitudinal changes in ultrasound parameters were analyzed using mixed-effects models and generalized estimating equations, as appropriate for each outcome type. Associations between bowel wall thickness changes and clinical/biochemical outcomes were evaluated using Spearman correlation analysis. RESULTS:Distinct temporal response patterns were observed among bowel ultrasound parameters during ustekinumab therapy. In the improvement group, bowel wall thickness and Limberg grade improved as early as week 8, bowel wall stratification improved at week 16, and perienteric fat edema improved at week 24, whereas no significant longitudinal changes were observed in the non-improvement group.Linear mixed-effects model analysis demonstrated a significant time × group interaction for bowel wall thickness (P < 0.001). Generalized estimating equation analyses demonstrated significant time × group interactions for Limberg grade, bowel wall stratification, and perienteric fat edema (all P < 0.001), while luminal stenosis remained relatively stable throughout follow-up (all P > 0.05).At week 24, patients in the improvement group showed significantly lower CRP levels (P < 0.001) and HBI scores (P = 0.006) than those in the non-improvement group. Furthermore, reductions in bowel wall thickness were significantly correlated with reductions in CRP levels (rs = 0.678, 95 % CI: 0.503-0.806, P < 0.001) and HBI scores (rs = 0.566, 95 % CI: 0.359-0.722, P < 0.001). CONCLUSION:Transabdominal bowel ultrasound may dynamically reflect transmural inflammatory changes during ustekinumab therapy in patients with postoperative recurrence of CD. Different ultrasound parameters demonstrated distinct temporal recovery patterns, with bowel wall thickness and vascularity showing the earliest improvement. The association between bowel wall thickness changes and CRP/HBI further supports the potential clinical utility of bowel ultrasound as a noninvasive longitudinal monitoring tool in this high-risk postoperative population.

Zero-valent iron (Fe0) is widely applied for reductive dehalogenation but is limited by inefficient electron utilization and rapid corrosion in aqueous environments. Herein, 3D printing was employed to engineer the structural and interfacial properties of Fe0 for enhanced reductive transformation of florfenicol (FLO) under anoxic conditions. Compared with pristine Fe0 powders, the 3D-printed Fe0 (3DP-Fe0) exhibited a hierarchical porous architecture, lattice expansion, and enhanced hydrophobicity, which collectively regulated Fe0 corrosion behavior and interfacial electron transfer. These structural and interfacial modifications improved electron utilization efficiency toward FLO dehalogenation while suppressing non-productive hydrogen evolution. Mechanistic investigations revealed that atomic hydrogen was the dominant reactive species responsible for sequential FLO dechlorination. Benefiting from regulated corrosion and preserved Fe0 reactivity, 3DP-Fe0 maintained high FLO removal efficiency during repeated cycles and prolonged anoxic aging, accompanied by substantially reduced Fe0 consumption and Fe leaching. Transformation products generated through sequential dechlorination exhibited markedly decreased antibacterial activity, indicating effective toxicity reduction during FLO degradation. Furthermore, 3DP-Fe0 retained robust performance in complex water matrices and enabled efficient removal of other recalcitrant pharmaceuticals, demonstrating its broad applicability. Overall, this study highlights 3D printing as an effective strategy to enhance Fe0 reactivity and stability for the efficient reductive treatment of emerging contaminants.

This study investigated the association between the atherogenic index of plasma (AIP) and stroke risk across blood pressure (BP) strata, with exploratory assessments of the bidirectional mediating roles of body mass index (BMI) and AIP. We included 8,695 participants from the China Health and Retirement Longitudinal Study (CHARLS, 2011-2020). AIP was calculated as log₁₀(TG/HDL-C). Time-dependent Cox models were used to estimate the hazard ratios (HRs). Restricted cubic spline (RCS) and mediation analyses were performed. Higher AIP was independently associated with increased stroke risk (HR: 1.60, 95% CI 1.22‑2.09).This association was observed in both the high-normal BP (HR: 2.08, 95% CI 1.21‑3.59) and hypertension strata (HR: 1.47, 95% CI 1.03‑2.11), with no significant interaction by BP strata (P = 0.660). RCS analysis showed a dose-response relationship between baseline AIP levels and stroke risk across all participants, high-normal BP, and hypertension strata. Exploratory mediation analysis suggested that BMI mediated 20.59% of AIP's effect on stroke, while AIP mediated 20.52% of BMI's effect. AIP is an independent risk factor for stroke, with no significant effect modification by BP strata. Exploratory mediation analyses suggest that integrating AIP screening with BMI management could be beneficial for stroke prevention, a finding that requires prospective validation.

Pseudogene-derived long non-coding RNAs (lncRNAs) contribute to carcinogenesis. However, the role of the pseudogene ANXA2P1 in gastric cancer (GC) growth and glucose metabolism remains unknown. Analysis of microarray and RNA sequencing (RNA-seq) reveals that ANXA2P1 is increased upon glucose starvation in GC cells and displays elevated expression in GC. Moreover, ANXA2P1 overexpression promotes proliferation and metastasis by enhancing aerobic glycolysis in GC. Mechanistically, ANXA2P1 binds to the RNA-binding protein hnRNP F and promotes proximal polyadenylation site usage of HK2, thereby generating a short 3 ' UTR isoform with enhanced stability. Consequently, elevated HK2 expression accelerates GC proliferation and metabolic reprogramming. Interestingly, HK2 exerts a non-metabolic role by serving as a co-activator of transcription factor c-Myc to collaboratively drive ANXA2P1 expression. Clinically, ANXA2P1, hnRNP F, HK2, and c-Myc were augmented in specimens from GC patients compared to matched normal gastric mucosa. This study illustrates that ANXA2P1 is considered an oncogene, and the ANXA2P1-hnRNP F-HK2/c-Myc positive feedback loop may act as a potential therapeutic target for GC.

Myeloid sarcoma (MS) involving the breast is rare. We reviewed a case of a 21-year-old female with palpable masses in the bilateral breasts without medullary acute myeloid leukemia. Ultrasound revealed irregular, indistinct, and complex hypoechoic masses with internal blood flow and hyperechoic halos in the bilateral breasts. A biopsy was subsequently recommended, and MS was confirmed. Further cytogenetic evaluation of breast biopsy specimens demonstrated positive t(16;16)(p13.1;q22) translocation. The patient was admitted for chemotherapy. Subsequent follow-up breast ultrasound following chemotherapy revealed a notable treatment response. This report aims to delineate the ultrasound characteristics of breast MS and the utilization of ultrasound in the evaluation of early response to chemotherapy.

Because of their distinct physical and chemical characteristics, soft materials with low modulus, great deformability, and large compliance have progressively emerged as a significant area of study. However, the responsiveness of traditional soft materials is inherently limited by passive diffusion and equilibrium thermodynamics. Thus, by incorporating stimuli-responsive artificial materials into motile bacterial species with anticancer properties, a soft biohybrid bacterial system has been created, converting soft materials from static scaffolding into adaptable, living systems. Specifically, the surface of Escherichia coli DH5α is modified by liposomes co-loaded with glucose oxidase and Tirapazamine (TPZ). The soft biohybrid bacterial system uses anaerobic targeting to deliver anticancer drugs and catalyzes glucose in the tumor microenvironment, consuming local oxygen in the process. The enhanced hypoxic microenvironment fully activates TPZ, significantly boosting its cytotoxic effect on tumor cells. Moreover, the soft biohybrid bacterial system further induces strong immunogenic cell death, which enhances the therapeutic effect while minimizing side effects on normal tissues. This innovative strategy offers a promising solution to overcome the therapeutic challenges associated with traditional soft materials.

The efficacy of immunotherapy in triple-negative breast cancer (TNBC) is primarily challenged by its inherent low immunogenicity, which is further undermined by the immunosuppressive activity of tumor-associated polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs). The hypoxic microenvironment renders PMN-MDSCs susceptible to ferroptosis and exacerbates immunosuppression. This study aimed to design a multifunctional nanoplatform comprising Cu/Mn bimetallic metal-organic frameworks decorated with gold (Au) nanozymes (CMAP MOFs) that simultaneously enhances tumor immunogenicity and alleviates immunosuppression. The characteristics of the nanosystem were evaluated using transmission electron microscopy, X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, and ultraviolet-visible spectrophotometry. In vitro experiments employed Western blotting, immunofluorescence, and flow cytometry to investigate the mechanisms of enhanced immunogenicity and alleviated immunosuppression in 4T1 cells. In vivo experiments evaluated the therapeutic efficacy of the nanosystem in BALB/c mice bearing implanted 4T1 tumors. Within tumor cells, the CMAP MOFs undergo GSH-responsive disintegration. The released Cu/Mn ions catalyze Fenton-like reaction to induce tumor cell ferroptosis and mitochondrial DNA damage, activating cGAS-STING pathway, and facilitating CD8+ T cells recruitment. Simultaneously, released Au nanozymes catalyze H2O2 conversion to O2, alleviating hypoxia and preventing PMN-MDSCs ferroptosis and associated immunosuppression. While the immunogenicity-enhancing CMP MOFs group increased CD8⁺ T cell infiltration from 21

IntroductionThe study aims to evaluate the performance of an interpretable machine learning model in predicting preoperative axillary lymph node metastasis using primary breast cancer and lymph node features derived from contrast-enhanced mammography (CEM) and ultrasound (US) breast imaging reporting and data systems (BI-RADS).MethodsThis retrospective study included patients diagnosed with primary breast cancer. Two experienced radiologists extracted the BI-RADS features from the largest cross-section of the lesions and axillary lymph nodes based on CEM and US images, creating three datasets. Each dataset will train six base models to predict axillary lymph nodes, with pathological results serving as the gold standard. The top three models were used to train the five ensemble models. Additionally, SHapley Additive exPlanations (SHAP) was used to interpret the optimal model. The receiver-operating characteristic curve (ROC) and AUC were used to evaluate model performance.ResultsThis study involved 292 female patients, of whom 99 had axillary lymph node metastasis and 193 did not. The combination of CEM and ultrasound BI-RADS demonstrated the best performance in predicting axillary lymph node metastasis. Among these, the LightGBM achieved the highest AUC (0.762) and specificity (86.67%, while the ensemble model using RF as the meta-model had an AUC (0.754) and specificity (83.33%. The most important variables identified by SHAP were the long diameters of the lymph nodes in the CEM recombined image, along with their complete morphology in the low-energy image.ConclusionThe machine learning model using CEM and US BI-RADS features accurately predicted axillary lymph node metastasis before surgery, thereby serving as a valuable tool for clinical decision-making in patients with breast cancer.

Background This study aimed to investigate factors associated with central lymph node metastasis (CLNM) in clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC) with tumor contact with the thyroid capsule. Additionally, we aimed to develop a predictive model using clinical data to enhance diagnostic and treatment strategies for this patient population. Methods This retrospective study reviewed 713 PTC (cN0) patients with tumor contact with the thyroid capsules from two institutions. Variables analyzed included sex, age, tumor characteristics (position, location, size, composition, echogenicity, aspect ratio, margin, and echogenic foci), capsular features (protrusion and interruption), distance from the tumor to the trachea, angle between the tumor and the trachea, and tumor contact with the tracheoesophageal groove (TEG). This group of patients was categorized into training and validation cohorts in a 7:3 ratio. A nomogram was developed using univariate and multivariate logistic regression analyses of the training cohort. Receiver operating characteristic curves were used to evaluate the diagnostic performance of the models. Internal validation was performed using a validation cohort. The Hosmer-Lemeshow test and decision curve analysis (DCA) were employed to assess the calibration and clinical utility of the model. Results Four variables associated with PTC were identified using multivariate logistic regression analysis and were used to establish a nomogram. The predictive model showed an area under the receiver operating characteristic curve (AUC) of 0.775 (95% confidence interval [CI] 0.733–0.818), and in internal validation, the AUC was 0.730 (95% CI 0.659–0.801). The calibration curve confirmed a good model fit, and the Hosmer-Lemeshow test indicated a high level of agreement between the predicted and observed values ( p = 0.813). DCA revealed that applying the nomogram to predict the risk of CLNM would benefit patients with PTC (cN0) whose tumors were in contact with the capsule when the threshold probability ranged from 15–74%. Conclusion Four independent predictors of CLNM were identified: irregular or lobulated margins, echogenic foci, capsular interruption, and contact with the TEG. A nomogram model was established based on these four predictors, which could serve as a basis for central cervical lymph node dissection in patients with PTC (cN0).

Osteoarthritis (OA) is a multifactorial joint disorder characterized by articular cartilage degradation and progressive synovial inflammation. The dense and avascular nature of cartilage hinders the delivery efficiency of nanocarriers to their target cells, resulting in limited therapeutic efficacy in clinical trials. Here, we report the design of a Piezo-MnO2 motor for in situ reestablishment of the articular microenvironment by effectively degrading the local excess hydrogen peroxide in the OA microenvironment into oxygen. The generated oxygen ameliorates hypoxic conditions and acts as a propellant for nanomotor actuation, thereby enabling the motor to penetrate deeply into cartilage and synovium. Under mechanical stress induced by ultrasonic vibration, Piezo-MnO2 motors efficiently produced electrical signals via piezoelectric effect. This initiates an influx of extracellular calcium ions, which further upregulates the expression of transforming growth factors and drives cartilage repair. Recognized for its anti-inflammatory and antioxidant properties, the hydrogen produced by ultrasonic piezoelectric effect of the motors significantly diminishes the level of pro-inflammatory cytokines. The developed strategy facilitates rapid in situ cartilage regeneration and articular microenvironment modulation, offering a transformative alternative to conventional OA interventions.

Background:The subjective assessment of Breast Imaging Reporting and Data System (BI-RADS) 4A lesions leads to a high number of unnecessary biopsies, highlighting the need for more objective and accurate diagnostic tools. This study aimed to construct and validate a novel multimodal framework that integrates deep learning (DL), radiomics, and clinical and breast ultrasound (US) features to distinguish between benign and malignant breast lesions classified as BI-RADS 4A. Methods:A total of 935 patients with pathologically confirmed BI-RADS 4A lesions were retrospectively enrolled and randomly divided into a training cohort (n=654) and an internal validation cohort (n=281). An additional 488 patients were enrolled as the external validation cohort. DL and radiomics models were developed with the light gradient boosting machine (LightGBM) algorithm, a machine learning technique known for its efficiency in handling high-dimensional data. Univariate and multivariate logistic regression (LR) analyses identified significant clinical and US features. After handcrafted radiomics features, DL features, and clinical/breast US features were combined, dimensionality reduction and feature selection were performed. The integrated model was developed via LightGBM, and the SHapley Additive Explanations (SHAP) approach was applied to rank feature contributions. Results:The integrated model achieved robust discrimination between benign and malignant BI-RADS 4A lesions, with an area under the curve (AUC) of 0.938, 0.870, and 0.861 for the training cohort, internal validation cohort, and external validation cohort, respectively. Furthermore, in the external validation cohort, the integrated model significantly outperformed the handcrafted radiomics model (AUC =0.719; P<0.001) and the DL model (AUC =0.763; P=0.011). Decision curve analysis confirmed that the integrated model was the most clinically useful across the three cohorts. Conclusions:The integrated model demonstrated high accuracy in differentiating between benign and malignant BI-RADS 4A lesions, exhibiting the ability to significantly reduce unnecessary biopsies while maintaining diagnostic accuracy.

Background:Polycystic ovary syndrome (PCOS) has a significant impact on endocrine metabolism, reproductive function, and mental health in women of reproductive age. The accuracy of ultrasound in identifying polycystic ovarian morphology remains variable. Aims:To develop a deep learning model capable of rapidly and accurately identifying PCOS using ovarian ultrasound images. Study Design:Prospective diagnostic accuracy study. Methods:This prospective study included data from 1,751 women with suspected PCOS with clinical and ultrasound information collected and archived. Patients from center 1 were randomly divided into a training set and an internal validation set in a 7:3 ratio, while patients from center 2 served as the external validation set. Using the YOLOv11 deep learning framework, an automated recognition model for ovarian ultrasound images in PCOS cases was constructed, and its diagnostic performance was evaluated. Results:Ultrasound images from 933 patients (781 from center 1 and 152 from center 2) were analyzed. The mean average precision of the YOLOv11 model in detecting the target ovary was 95.7%, 97.6%, and 97.8% for the training, internal validation, and external validation sets, respectively. For diagnostic classification, the model achieved an F1 score of 95.0% in the training set and 96.9% in both validation sets. The area under the curve values were 0.953, 0.973, and 0.967 for the training, internal validation, and external validation sets respectively. The model also demonstrated significantly faster evaluation of a single ovary compared to clinicians (doctor, 5.0 seconds; model, 0.1 seconds; p < 0.01). Conclusion:The YOLOv11-based automatic recognition model for PCOS ovarian ultrasound images exhibits strong target detection and diagnostic performance. This approach can streamline the follicle counting process in conventional ultrasound and enhance the efficiency and generalizability of ultrasound-based PCOS assessment.

The immunosuppressive tumor microenvironment (ITME) and inherent radioresistance of tumor cells limit the effectiveness of radioimmunotherapy and exacerbate immune evasion. To address these challenges, PEGylated Azacitidine-loaded and Mn2+-doped calcium carbonate nanoparticles (A@MCP NPs) are synthesized as multifunctional nanoagent to enhance radioimmunotherapy outcomes. Upon acidic TME, the release of Ca2+ and Mn2+ from A@MCP NPs co-triggers intracellular reactive oxygen species (ROS) generation via Ca2+ overload and Fenton-like reactions, inducing cytochrome C release and caspase-3 activation. Concurrently, released Azacitidine inhibits DNA methylation, upregulating GSDME expression in irradiated tumor cells, which synergistically amplifies caspase-3/GSDME-induced pyroptosis. The resulting pyroptotic cell damage, coupled with radiotherapy (RT)-induced DNA, activates Mn2+-sensitized cGAS-STING pathways, amplifying immune responses. Collectively, A@MCP, as a nano radiosensitizer, together with RT, co-activates pyroptosis and cGAS-STING to further amplify anti-tumor immune response, overcome ITME-mediated resistance and offer significant potential for improved cancer radioimmunotherapy.

Background:Conventional diagnostic tools, including ultrasound, fine-needle aspiration cytology, and intraoperative frozen section pathology, may fail to reliably distinguish between benign and malignant follicular-patterned thyroid neoplasms (FNs), leading to unnecessary or inadequate surgical interventions. We aimed to develop and validate a deep learning (DL) system for the preoperative diagnosis of FNs using routine ultrasound images, with the goal of improving diagnostic accuracy and reducing unnecessary procedures. Methods:In this multicenter, retrospective study, we included 3817 patients (2877 [75.4%] female) with a definitive diagnosis of FNs from 11 centers across China. All patients underwent preoperative ultrasound examinations. The dataset comprised 9393 ultrasound images, including thyroid follicular adenoma (n = 1787, 4317 images), follicular carcinoma (n = 446, 1593 images), and follicular variant of papillary thyroid carcinoma (n = 1584, 3483 images) collected between 2012 and 2025. A state-of-the-art OverLoCK (Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic Kernels) model was developed on a dataset comprising 2728 patients (6625 images) and validated on an internal cohort (n = 683, 1905 images) and an external cohort (n = 406, 863 images). Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Model calibration was evaluated using calibration curves, while clinical usefulness was assessed through decision curve analysis (DCA). Findings:The OverLoCK model exhibited excellent performance in both the internal and external validation sets. In the internal validation cohort, the OverLoCK model achieved an AUC of 0.937 (95% confidence interval [CI]: 0.919-0.954), with accuracy of 90.9% (95% CI: 87.7-92.0), sensitivity of 93.9% (95% CI: 91.5-95.6), specificity of 84.8% (95% CI: 82.6-86.0), PPV of 92.7% (95% CI: 90.7-93.8), NPV of 87.2% (95% CI: 86.0-91.0), and F1 score of 0.911 (95% CI: 0.887-0.932). In the external validation cohort, the model yielded an AUC of 0.853 (95% CI: 0.832-0.876), accuracy of 82.8% (95% CI: 81.7-84.4), sensitivity of 84.5% (95% CI: 82.5-86.2), specificity of 81.1% (95% CI: 79.2-84.5), PPV of 80.4% (95% CI: 79.0-84.0), NPV of 85.1% (95% CI: 83.2-87.7), and F1 score of 0.839 (95% CI: 0.802-0.877). The DL model demonstrates good agreement between the predicted and actual probabilities of malignancy. DCA confirmed that the model was clinically useful. Interpretation:Our study demonstrates that a DL-based system can provide a noninvasive, accurate, and reliable tool for the preoperative diagnosis of FNs. By improving diagnostic precision, this approach has the potential to optimize clinical decision-making and reduce the burden of overtreatment in patients with FNs. Further prospective studies are warranted to validate these findings in real-world clinical settings. Funding:This work was supported by the National Key Research and Development Program of China (2023YFF1204600), the National Natural Science Foundation of China (82227802 and 82302190), the Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University (No. JNU1AF-CFTP-2022-a01201), the Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038, 2025A04J7006), the Outstanding Young Talents of Guangdong Special Support Program (Health Commission of Guangdong Province) (0720240213), and the Science and Technology Youth Talent Nurturing Program of Jinan University (21623209).

Triple-negative breast cancer (TNBC) shows limited therapeutic efficacy due to the absence of effective targeted treatment options, with its immunosuppressive tumor microenvironment further diminishing the effectiveness of immunotherapy. Intracellular Ca²⁺ homeostasis plays a critical role in cancer progression, yet the efficacy of traditional inorganic nanoparticles in inducing calcicoptosis through Ca²⁺ release is constrained by poor water solubility, stability, and delivery efficiency. To address these challenges, we developed novel nanoparticles (NPs) by coordinating Ca²⁺ with the natural bioactive compound glycyrrhizic acid to create a multifunctional carrier (GC NPs), which was further loaded with the therapeutic natural compound curcumin (CUR) to form GC@CUR NPs. Through synergistic effects of Ca²⁺ overload and CUR-mediated regulation, these NPs significantly promote Ca²⁺ accumulation in mitochondria, triggering mitochondrial dysfunction and efficiently inducing calcicoptosis. Additionally, low-intensity pulsed ultrasound (LIPUS)-mediated targeted release markedly improved antitumor effects. Importantly, this strategy effectively induces immunogenic cell death (ICD) in TNBC, thereby significantly boosting antitumor immune responses. This study provides an innovative and efficient strategy for integrating chemotherapy and immunotherapy to transform TNBC tumors into immunoresponsive ones, offering new possibilities for comprehensive TNBC treatment.

The emergence of precision cancer treatment has triggered a paradigm shift in the field of oncology, facilitating the implementation of more effective and personalized therapeutic approaches that enhance patient outcomes. The pH of the tumor microenvironment (TME) plays a pivotal role in both the initiation and progression of cancer, thus emerging as a promising focal point for precision cancer treatment. By specifically targeting the acidic conditions inherent to the tumor microenvironment, innovative therapeutic interventions have been proposed, exhibiting significant potential in augmenting treatment efficacy and ameliorating patient prognosis. The concept of ultra-pH-sensitive (UPS) nanoplatform was proposed several years ago, demonstrating exceptional pH sensitivity and an adjustable pH transition point. Subsequently, diverse UPS nanoplatforms have been actively explored for biomedical applications, enabling the loading of fluorophores, therapeutic drugs, and photosensitizers. This review aims to elucidate the design strategy and response mechanism of the UPS nanoplatform, with a specific emphasis on its applications in surgical therapy, immunotherapy, drug delivery, photodynamic therapy, and photothermal therapy. The potential and challenges of translating in the clinic on UPS nanoplatforms are finally explored. Thanks to its responsive and easily modifiable nature, the integration of multiple functional units within a UPS nanoplatform holds great promise for future advancements in tumor precision theranositcs.

Parathyroid ultrasound is widely used in clinical practice and plays a crucial role in the diagnosis and treatment of parathyroid diseases. Nevertheless, ultrasound physicians frequently encounter a number of challenges and doubts in their professional practice. For this reason, Superficial Organs and Peripheral Vessels Committee of Chinese Association of Ultrasound in Medicine and Engineering has formulated the expert consensus on certain common clinical problems of parathyroid ultrasound based on the current research progress and clinical experience, in order to guide the clinical practice. This consensus describes in detail the diagnostic and interventional common problems of parathyroid ultrasound and provides in-depth discussion on related contents.