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Herein, MoS2‐embedded human serum albumin hollow nanocapsules (denoted MoS2/HSA) are successfully prepared via a hard‐core‐assisted layer‐by‐layer coating approach. The hollow MoS2/HSA nanocapsules possess an uniform size (280 nm), a larger hollow cavity, a low Young's modulus (222 ± 20 MPa), excellent photothermal conversion ability, and a high drug loading capacity for doxorubicin (27 wt%). The flexible hollow MoS2/HSA nanocapsules exhibit significantly enhanced cellular uptake efficiency against breast cancer cells duo to their flexible property and targeting ability of HSA. In vivo experiments show that the flexible MoS2/HSA could reduce phagocytosis and effectively improve blood persistence and tumor accumulation. In vitro and in vivo experiments have demonstrated that the flexible MoS2/HSA based nanoplatforms effectively ablate breast cancer cells via synergistic photothermal and chemical therapy. Flexible MoS2‐embedded human serum albumin hollow nanocapsules (denoted MoS2/HSA) with a longer blood circulation time and higher tumor accumulation are successfully prepared. After loading with the anticancer drug doxorubicin, the MoS2/HSA‐based nanoplatforms can effectively ablate breast cancer cells via synergistic photothermal and chemotherapy. [ABSTRACT FROM AUTHOR]

Background: Dissection of the lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLNs) in papillary thyroid cancer (PTC) remains controversial., Objective: This study aimed to determine the capability of ultrasonography (US)-based radiomics for presurgical prediction of metastasis in LN-prRLNs in PTC., Methods: Patients were retrospectively enrolled and pathologically confirmed as LN-prRLN metastasis with PTC after surgery. Radiomic analysis based on preoperative US images with manual segmentation of targets was used to develop a radiomics model. US features described in ACR TI-RADS were collected to construct a clinical model. The Radiomics model, a combined model integrating radiomics and clinical model, was also developed for the presurgical prediction of metastasis in LN-prRLNs., Results: A total of 570 patients, including 488 patients with non-LN-prRLN metastasis and 82 with LN-prRLN metastasis, were assessed. The 15 topperforming features finally remained significant for constructing the radiomics model. The combined model showed that US measured tumor size (OR: 1.036, P = 0.044), US suspected lateral lymph node metastasis (OR: 2.247, P = 0.009), multifocality (OR: 1.920, P = 0.021), Delphian lymph node metastasis (DLNM) (OR: 2.300, P = 0.039), VIa compartment metastasis (OR: 5.357, P = 0.000), the radiomics score (OR: 1.003, P = 0.001) were significant risk factors for predicting LN-prRLN metastasis. The combined model achieved a higher AUC of 0.849 than that of the clinical model (AUC: 0.759) and radiomics model (AUC: 0.826)., Conclusion: The US-based radiomics combined model can more effectively predict LN-prRLN metastasis in PTCs patients preoperatively. This approach had the potential to assist surgeons indecision-making regarding LN-prRLN dissection., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Herein, we report, for the first time, a unique stiffness-transformable manganese oxide hybridized mesoporous organosilica nanoplatform (MMON) for enhancing tumor therapeutic efficacy. The prepared MMONs had a quasi-spherical morphology and were completely transformed into soft bowl-like nanocapsules in the simulated tumor microenvironment through the breakage of Mn-O bonds, which decreased their Young's modulus from 165.7 to 84.5 MPa. Due to their unique stiffness transformation properties, the MMONs had reduced macrophage internalization, improved tumor cell uptake, and enhanced penetration of multicellular spheroids. In addition, in vivo experiments showed that the MMONs displayed a 3.79- and 2.90-fold decrease in non-specific liver distribution and a 2.87- and 1.83-fold increase in tumor accumulation compared to their soft and stiff counterparts, respectively. Furthermore, chlorin e6 (Ce6) modified MMONs had significantly improved photodynamic therapeutic effect., (© 2022 Wiley-VCH GmbH.)

Background: Delphian lymph node (DLN) has been considered to be a gate that predicts widespread lymph node involvement, higher recurrence and mortality rates of head and neck cancer., Objective: This study aimed to establish a preoperative ultrasonography integrated machine learning prediction model to predict Delphian lymph node metastasis (DLNM) in patients with diagnosed papillary thyroid carcinoma (PTC)., Methods: Ultrasonographic and clinicopathologic variables of PTC patients from 2014 to 2021 were retrospectively analyzed. The risk factors associated with DLNM were identified and validated through a developed random forest (RF) algorithm model based on machine learning and a logistic regression (LR) model., Results: A total of 316 patients with 402 thyroid lesions were enrolled for the training dataset and 280 patients with 341 lesions for the validation dataset, with 170 (28.52%) patients developed DLNM. The elastography score of ultrasonography, central lymph node metastasis, lateral lymph node metastasis, and serum calcitonin were predictive factors for DLNM in both models. The RF model has better predictive performance in the training dataset and validation dataset (AUC: 0.957 vs. 0.890) than that in the LR model (AUC: 0.908 vs. 0.833)., Conclusion: The preoperative ultrasonography integrated RF model constructed in this study could accurately predict DLNM in PTC patients, which may provide clinicians with more personalized clinical decision-making recommendations preoperatively. Machine learning technology has the potential to improve the development of DLNM prediction models in PTC patients., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)