octreotide

Streamlining Radioembolization without mapping angiography for Cholangiocarcinoma or Metastatic Liver Tumors ≤7 cm Using Yttrium-90 Resin Microspheres: Protocol for a Multicenter Prospective Registry Study (ISTAR-03).

Kim Hyo-Cheol HC, Lee In Joon IJ, Hyun Dong Ho DH, Kim Gyoung Min GM et al.

NIR-II photothermal-driven pulsatile PTH release for osteoporotic bone repair.

Wu Yiqing Y, Yao Yongkang Y, He Zirui Z, Xie Fangru F et al.

Integrating light-controlled intelligent materials into three-dimensional (3D) printing technology holds great promise for bone tissue engineering, enabling spatiotemporal control over in situ drug dosing. However, this integration faces a critical molecular design challenge, as incorporated photoresponsive molecules must simultaneously fulfill the dual demands of high ultraviolet (UV)-stability during UV-mediated photopolymerization, and strong long-wavelength absorption for deep-tissue activation. Here, we report a wavelength-selective quinolinium dye (QC7CN) as photoresponsive 'dye ink' for addressing this dilemma. This dye features high UV transparency that ensures stability during the printing process, while possessing robust second near infrared (NIR-II) absorbance for deep-seated photothermal responsiveness. Leveraging these properties, we engineer an NIR-II light-controlled 3D-printed scaffold, composed of methacryloylated gelatin and calcium phosphate oligomers as a printing matrix, thermosensitive phase-transformable microspheres as a parathyroid hormone (PTH) drug carrier and QC7CN as a photothermal trigger. Upon NIR-II laser irradiation, QC7CN induces localized mild hyperthermia to drive the gel-sol phase transition of microspheres, thereby manipulating pulsatile PTH release for deep-tissue osteoporotic bone repair. As such, our study provides a full demonstration in molecular design and additive manufacturing to push the limits of intelligent materials in precision medicine.

Establishment and characterization of a mouse model with chronic pneumonia infected by Pseudomonas aeruginosa.

Ai Lingli L, Li Lu L, Hu Lingfei L, Zhao Jin J et al.

Pseudomonas aeruginosa is a clinically significant opportunistic pathogen causing both acute and chronic pneumonia infections. However, constructing an animal model for chronic infection remains challenging, hindering research advancement in this field. This study employed microfluidic technology to prepare alginate-encapsulated P. aeruginosa microspheres and established a chronic pneumonia model in C57BL/6J mice following aerosolized intratracheal (i.t.) delivery of the encapsulated bacteria. Comprehensive evaluation included bacterial load measurement, histopathological analysis by hematoxylin and eosin (H&E) staining, inflammatory cytokine detection by enzyme-linked immunosorbent assay (ELISA), immune cell profiling (flow cytometry), and transcriptomic analysis by RNA sequencing (RNA-seq). We successfully established a mouse model of P. aeruginosa-induced chronic pneumonia with persistent bacterial colonization (~2,000 colony-forming units (CFU)/g at day 14), consistent histopathological features across independent experiments, and sustained elevation of inflammatory cytokines. At 14 days post-challenge, mice maintained high bacterial loads (2,000 CFU/g) with obvious lung histopathological damage. The model exhibited gradual body weight decrease, elevated inflammatory cytokines in bronchoalveolar lavage fluid (BALF) and serum, and increased neutrophils, macrophages, and NK cells in lung tissue. RNA-seq revealed upregulation of inflammation and pathogen resistance genes, with downregulation of metabolic process genes. This approach combines microfluidic-generated alginate microspheres with non-surgical aerosolized i.t. delivery, providing a reproducible platform for investigating chronic infection mechanisms in P. aeruginosa pneumonia research.

Predicting Response to Somatostatin Analog Therapy in GEP-NET Liver Metastases: A Machine Learning Approach with ⁶⁸Ga-DOTATATE and ¹⁸F-FDG PET/CT.

Xu Junhao J, Yu Miao M, Wang Jingrong J, Huang Nanlin N et al.

Predicting response to somatostatin analogs (SSAs) in gastroenteropancreatic neuroendocrine tumors (GEP-NETs) with liver metastases (LMs) remains challenging. This study aimed to develop and validate an interpretable machine learning (ML) model based on dual-tracer (68Ga-DOTATATE and 18F-FDG) PET/CT parameters to predict octreotide response and identify key imaging predictors using SHAP analysis. 32 GEP-NET patients with LMs who received first-line octreotide therapy were retrospectively enrolled. Quantitative PET/CT metrics, dual-tracer ratios, and lesion size were extracted from 202 hepatic lesions. Lesions were classified as disease-control (SD/PR) or disease-progression (PD) using size-change thresholds derived from RECIST 1.1. Patients were divided at the patient level into training and validation cohorts. LASSO regression was used for feature selection, followed by the construction of multiple ML models. Leave-one-patient-out cross-validation (LOPOCV) was performed to further assess model robustness, and SHAP analysis was applied for model interpretability. ⁶⁸Ga-DOTATATE TLR, rTLR, and primary size differed significantly between disease-control and disease-progression groups. LASSO identified six key predictive features. The XGBoost model showed the best performance in the patient-level validation cohort, with an AUC of 0.825 and an accuracy of 0.742. In LOPOCV, XGBoost achieved an AUC of 0.856 and an accuracy of 0.787. SHAP analysis revealed that ⁶⁸Ga-DOTATATE TLR, primary size, and ⁶⁸Ga-DOTATATE SUVmean were the three most important predictors. An interpretable ML model integrating dual-tracer PET/CT parameters can effectively predict octreotide response in GEP-NETs with LMs. This approach may provide a noninvasive tool for treatment-response stratification and individualized management.