The chemical synthesis of random poly(proline-co-glycine) (PPrG), a collagen-inspired polypeptide with promising biomedical applications, was challenging and underexplored due to the poor solubility of long glycine (Gly) and proline (Pro) segments forming β-sheets and all-cis right-handed type I (PPI) helices, respectively. Owing to the new developments in the ring-opening polymerization (ROP) of amino acid N-thiocarboxyanhydrides (NTAs), we herein reported a well-controlled statistical copolymerization of Pro-NTA with Gly-NTA in benzonitrile (PhCN) catalyzed by carboxylic acids. In the optimized polymerization conditions, premature precipitations were effectively suppressed, and statistical copolymers of PPrG were synthesized with predictable molecular weights (2.4 ~ 25.6 kg/mol), narrow dispersity, designable Gly compositions (0 ~ 47 mol%) and composition drifting structure with reactivity ratios (rGly = 1.42, rPro = 0.108). PPrGs exhibited random coil structure in aqueous solution and formed gels at elevated concentrations ( 14.6 wt%). In addition, a third α-amino acid monomer including leucine-NTA (Leu-NTA), alanine-NTA (Ala-NTA) and phenylalanine-NTA (Phe-NTA) was able to be incorporated into backbones randomly without modification of the polymerization conditions. This contribution provided a versatile platform to synthesize collagen-inspired polypeptides with tunable structures, expanding the scope of advanced biomaterials

PurposeTo propose a model based on computed tomography (CT) subregional radiomics to predict the preoperative mitotic index of 2-5 cm Gastrointestinal Stromal Tumors (GISTs) of the stomach.Materials and methodsThis retrospective study enrolled a total of 368 patients with GISTs from two institutions: Center 1 comprised 239 patients (122 M, 117 F; mean age 61.66 ± 10.86 years), and Center 2 comprised 129 patients (51 M, 78 F; mean age 60.28 ± 9.72 years). Radiomics features were extracted from the entire tumor. Concurrently, k-means clustering was applied to imaging features to define three distinct tumor subregions, from which radiomics features were subsequently extracted. The Recursive Feature Addition method was used to identify features correlated with the mitotic index in patients with 2-5 cm gastric GISTs. Using the selected features from each subregion and the whole tumor, logistic regression (LR) was employed to construct subregion-based radiomics models and conventional whole-tumor-based radiomics models, respectively.ResultsBetter performance was observed for unenhanced CT subregions 1, 2, and 3 compared with the conventional radiomics model. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the model for subregion 3 in the training set were 0.98, 0.97, 0.98, and 0.90, respectively. In the validation and external test sets, the AUC values were 0.874 and 0.804, respectively. The conventional whole-tumor radiomics model based on venous phase CT demonstrated superior performance compared to all subregion-based models, achieving an AUC of 0.956 in the training set, with accuracy, sensitivity, and specificity of 0.94, 0.97, and 0.83, respectively. In the validation and external test sets, it attained AUC values of 0.892 and 0.805, respectively.ConclusionSubregional CT radiomics may be used to predict the mitotic index of patients with 2-5 cm gastric GIST before surgery. In particular, subregional radiomics models based on unenhanced CT showed excellent predictive performance.

RATIONALE AND OBJECTIVE:The study aimed to develop and validate a multimodal radiomics model that integrates radiologist-informed feature augmentation leveraging expert-selected suspicious lymph nodes (LNs) based on ESGAR criteria to improve the accuracy of preoperative lymph node metastasis (LNM) prediction in patients with rectal cancer (RC). MATERIALS AND METHODS:This retrospective study included 563 eligible patients with RC. From each patient's high-resolution T2-weighted imaging (HRT2WI) and diffusion-weighted imaging (DWI) sequences, we extracted radiomic features from three distinct regions: the primary tumor, the entire mesorectal nodal region, and suspicious mesorectal nodes identified by radiologists. Clinical factors associated with LNM were identified through univariate and multivariate logistic regression analyses to establish a clinical prediction model. Finally, we constructed an integrated predictive model by combining these clinical factors with multimodal radiomic features, followed by a comprehensive comparison and evaluation of the predictive performance across all developed models. RESULTS:The integrated model, incorporating radiomic features derived from DWI sequences of the entire mesorectal nodal region and radiologist-annotated suspicious LNs, along with clinical factors, achieved optimal performance in predicting LNM. It yielded an area under the curve of 0.87 (95% confidence interval [CI]: 0.83-0.90) in the internal validation cohort and 0.83 (95% CI: 0.78-0.89) in the external validation cohort. CONCLUSION:Our findings show that the multimodal radiomics model integrating radiologists' prior knowledge offers potential for improving preoperative LNM assessment in RC, particularly in internal validation, and may provide supportive information for personalized treatment strategies in clinical practice. However, the incremental benefit of the radiologist-informed component was not consistently demonstrated in external validation, and further multi-center prospective studies are warranted.

It is promising but challenging to prepare poly(amino acid)s that directly bond to alcohols, especially saccharides. Herein, we report a novel method for the controlled ring-opening polymerization (ROP) of sarcosine N-carboxyanhydride (Sar-NCA) quantitatively initiated by hydroxyl groups using lutetium triflate (Lu(OTf)3) as catalyst. Lu(OTf)3 is devised to slow down the propagation by reducing the nucleophilicity of the amino group on the propagating chain end, thus realizing quantitative initiation efficiency (IE) of alcohol. Polysarcosine (PSar) samples with controlled molecular weights (Mn = 2.2-12.7 kg/mol) and low dispersities (Đ = 1.11-1.15) are obtained and full IE of the hydroxyl group is realized. Kinetic studies reveal that the propagation rate of Sar-NCA is significantly decreased in the presence of Lu(OTf)3. The addition of Lu(OTf)3 converts the characteristic of alcohol-initiated ROP of Sar-NCA from "slow initiation and fast propagation" to "fast initiation and slow propagation" which is essential of the polymerization control. Density functional theory (DFT) calculations provide mechanistic insights that Lu(OTf)3 is prone to coordinate with the propagating chain end species of secondary amino and carbamate groups in the form of five- or eight-membered rings and retards the propagation. PSar products bearing glucose or mannose ester end groups, analogs of glycoproteins, are successfully synthesized by applying this protocol. The obtained mannose-functionalized PSar shows significantly accelerated ingestion by cancer cells.

A trade-off relationship between enhancing controllability and increasing reaction rate is always a challenge, as achieving better controlled polymerization typically requires reducing the reaction rate. Synchronous raises of polymerization rate and controllability require specially designed catalysts. In the contribution, living and controlled polymerizations of N-phenoxycarbonyl 3,4-dihydroxy-l-phenylalanine (DOPA-NPC) with increased rate are carried out using an organic "acid-and-base" catalytic system, producing poly(3,4-dihydroxy-l-phenylalanine) (PDOPA) with controlled molecular weights (6.3-16.1 kg/mol) and narrow distributions (& Dstrok; < 1.15). Kinetic studies demonstrate that benzoic acid effectively inhibits the side reaction to form 3,6-bis(3,4-dihydroxybenzyl)piperazine-2,5-dione (DDP) while N,N-diisopropylethylamine dramatically accelerates monomer consumption. PDOPA exhibits reversible chelation of ferric ions in proper pH ranges.

The aim of the present study was to investigate whether a multimodal radiomics model powered by machine learning could accurately predict the occurrence of metachronous liver metastasis (MLM) in patients with colorectal cancer (CRC). A total of 157 patients diagnosed with CRC between 2010 and 2020 were retrospectively included in the present study; of these patients, 67 patients developed liver metastases within 2 years of treatment, while the remaining patients (n=90) did not. Radiomics features were extracted from annotated MR images of the tumor and portal venous phase CT images of the liver in each patient. Subsequently, machine learning-based radiomics models were developed and integrated with the clinical features for MLM prediction, employing Least Absolute Shrinkage and Selection Operator and Random Forest algorithms. The performance of the models were evaluated using the receiver operating characteristic curve analysis, while the clinical utility was measured using the decision curve analysis. A total of 922 and 1,082 radiomics features were extracted from the MR and CT images of each patient, respectively, which quantified the intensity, shape, orientation and texture of the tumor and liver. The mean area under the curve (AUC) values for the prediction of MLM were 0.80, 0.68 and 0.82 for the CT, MRI and merged models, respectively. For the clinical and clinical-merged models, the AUC values were 0.62 and 0.75, respectively. There was no significant difference between the CT model and the merged model (P>0.05). In conclusion, the preliminary results of the present study demonstrated the utility of machine learning-based radiomics models in the prediction of MLM in patients with CRC. However, further research is warranted to explore the potential of multimodal fusion models, due to the minimal improvement observed in diagnostic performance.

Background:Magnetic resonance (MR) diffusion-derived 'vessel density' (DDVD) is calculated according to: DDVDb0b2 = Sb0/ROIarea0 - Sb2/ROIarea2, where Sb0 and Sb2 refer to the tissue signal when b-value is 0 or 2 s/mm2. Sb2 and ROIarea2 can also be approximated by other low b-values diffusion-weighted imaging (DWI). This study investigates the influence of the second motion probing gradient b-value and T2 on DDVD calculations of the liver, spleen, and liver simple cyst. Literature analysis shows the liver and spleen have very similar amounts of perfusion. At 3T, liver and spleen have a T2 of around 42 and 60 ms respectively, while cyst has a very long T2. Methods:Twenty-eight subjects had 1.5T DWI data with b-values of 0, 1, 2, 15, 20, 30 s/mm2. Twenty-one subjects had 3.0T DWI data with b-values of 0, 2, 4, 7, 10, 15, 20, 30 s/mm2. DDVDb0b1, DDVDb0b2, DDVDb0b4, DDVDb0b7, DDVDb0b10, DDVDb0b15, DDVDb0b20, and DDVDb0b30 were calculated from b=0 and b=1 images, b=0 and b=2 images, b=0 and b=4 images, b=0 and b=7 images, b=0 and b=10 images, b=0 and b=15 images, b=0 and b=20 images, b=0 and b=30 s/mm2 images, respectively. For liver simple cyst, two cysts totaling six slices scanned at 1.5T were available for DDVD measurement. Results:At 1.5T, when the second b-value was 1 s/mm2, DDVDspleen value was slightly higher than DDVDliver value; when the second b-value was 2 s/mm2, DDVDliver value was slightly higher than DDVDspleen value. After that, the absolute difference between DDVDliver value and DDVDspleen value became increasingly larger, with DDVDliver value being consistently higher. DDVDcyst showed values close to 0 when the second b-value was 1 s/mm2. When the second b-value was 20 or 30 s/mm2, DDVDcyst value was higher than DDVDliver value. The absolute DDVD values measured higher at 3.0T than at 1.5T. However, the ratio of DDVDspleen to DDVDliver did not apparently differ between 1.5T and 3.0T. From the second b-value being 2 s/mm2 onward, an increasingly larger second b-value was associated with a trend of slow decreasing of the ratio of DDVDspleen to DDVDliver. Conclusions:When a very low second b-value is applied, the liver and spleen measure similar perfusions by DDVD, and cysts measure DDVD close to zero. When a higher second b-value is applied, relative to the liver, the DDVD of spleen is suppressed while the DDVD of cyst is artificially promoted, which we consider are related to the T2 relaxation times of the liver, spleen, and cyst.

Purpose Osteosarcoma (OS) is a prevalent primary malignant bone tumor that predominantly affects children, adolescents, and young adults. Proline-rich protein 11 (PRR11) is wellknown for its role in regulating cell cycle progression and promoting tumorigenesis. Nevertheless, the precise molecular mechanisms underlying PRR11-driven tumorigenesis in OS have yet to be elucidated. In the present study, we aimed to elucidate the role of PRR11 in OS and its underlying molecular mechanisms. Methods Genotype‒tissueexpression(GTEx) and The Cancer Genome Atlas (TCGA) data were analyzed for PRR11 expression (normal vs OS) and survival differences (low vs high expression). Immunohistochemistry(IHC) and western blotting(WB) were performed to examine the expression distribution of the PRR11 protein in OS tissues and cell lines. Three types of lentiviral vectors were used to establish stable 143B cell lines: (1) miRNA-based shRNA vectors, (2) Lenti-CRISPR-Cas9 vectors, and (3) overexpression vectors. RNA-seq analysis of the miRNA-based shRNAs. WB was used to elucidate the mechanisms by whichPRR11 affects DNA damage, DNA repair, the cell cycle, and the Hippo signaling pathway. Moreover, functional assays included colony formation, wound healing, and transwell assays in vitro and subcutaneous inoculation in vivo . Results This studyidentified PRR11 as a pivotal regulator that promotes OS cell migration, invasion, and proliferation in vitro and promotes OS subcutaneous inoculation. RNA-seq analysis revealed that PRR11 silencing regulates several signaling pathways, including the cell cycle, the DNA damage response, and DNA repair;subsequently, the detection of DNA damage/repair markers and cell cycle-related proteins further confirmed alterations in these signaling pathways. Subsequent flow cytometry experiments revealed that PRR11 could prolong the G0/G1 phase and shorten the G2/M phase. Conclusions PRR11 functions as an oncogene in OS, where the PRR11-Hippo axis drivestumor progression through a DNA damage-cell cycle coupling mechanism.

Objective: To establish a classification system which differentiates cystic intraductal papillary neoplasm of the bile duct (cystic IPNB) from hepatic mucinous cystic tumors (MCN) based on their radiological difference. Methods: A total of 75 patients pathologically diagnosed as MCN and IPNB in two major hospitals from 2015 to 2024 were enrolled. Radiological features were recorded and compared between these two tumors. Variables with significant differences were included in multivariate logistic regression (LR) analysis. A decision model was built and simplified based on importance ranking of variables. K-nearest-neighbor (KNN) model was introduced to learn distribution of individuals in main dimensions based on multiple correspondence analysis (MCA) and predicted diagnosis. The diagnostic efficacy of the classification system and the KNN model was compared. Results: Significant differences existed in Dmax-IVC angle, septation, mural nodule, upstream and downstream biliary dilatation, communication with bile duct between MCN and cystic IPNB. Downstream biliary dilatation and communication with bile duct were highly specific for IPNB (specificity, 97.9 % and 100 %, respectively), which could independently diagnose IPNB. Among four significant indicators in LR analysis, upstream biliary dilatation and Dmax-IVC angle were used for a simplified decision model to attain good applicability. The KNN model based on MCA data achieved highest accuracy (0.910) when K = 11. Overall, the classification system achieved an AUC of 0.882 (0.95CI: 0.797-0.966), compared with 0.911 (0.95CI: 0.818-1.000) in the KNN model, which demonstrated no significant difference (p = 0.655) in differential performance. Conclusion: The classification system combining four important indicators had equivalent performance to KNN model in discrimination, which was simple and applicable for clinical practice, and also accessible on unenhanced examinations.

Ca2+ overload is one of the most widely causes of inducing apoptosis, pyroptosis, immunogenic cell death, autophagy, paraptosis, necroptosis, and calcification of tumor cells, and has become the most valuable therapeutic strategy in the field of cancer treatment. Nevertheless, several challenges remain in translating Ca2+ overload-mediated therapeutic strategies into clinical applications, such as the precise control of Ca2+ dynamics, specificity of Ca2+ homeostasis dysregulation, as well as comprehensive mechanisms of Ca2+ regulation. Given this, we comprehensively reviewed the Ca2+-driven intracellular signaling pathways and the application of Ca2+-based biomaterials (such as CaCO3-, CaP-, CaO2-, CaSi-, CaF2-, and CaH2-) in mediating cancer diagnosis, treatment, and immunotherapy. Meanwhile, the latest researches on Ca2+ overload-mediated therapeutic strategies, as well as those combined with multiple-model therapies in mediating cancer immunotherapy are further highlighted. More importantly, the critical challenges and the future prospects of the Ca2+ overload-mediated therapeutic strategies are also discussed. By consolidating recent findings and identifying future research directions, this review aimed to advance the field of oncology therapy and contribute to the development of more effective and targeted treatment modalities.

Medical image segmentation is very important for the diagnosis of related diseases. To reduce the labeling work of related medical images, numerous models based on U-Net have been proposed to achieve automatic segmentation of target regions. However, most of these models are only trained in one coordinate system, ignoring the joint effects of different spatial coordinate systems. In addition, most of the encoding modules in these models do not pay attention to multi-scale spatial information. Our proposed solution for the aforementioned challenges involves using U-Net model with joint spatial domains and multi-scale encoding module, which enables us to segment rectal image better. The model includes a self-designed multi-layer dilated convolution encoding module named AIR (Atrous Inception Residual Block), to achieve a multi-scale content fusion. Besides this, it utilizes the center point and polar coordinates to realize attention mechanism and rotation invariance. Furthermore, retraining the output of polar coordinate network with Cartesian coordinate system realizes the translation invariance of segmentation. Compared with the commonly used medical segmentation models, the dice coefficient of our model is improved by about 2

Effective and accurate molecular imaging methods are particularly desirable for specifically non-invasive biological diagnosis. However, highly sensitive and smart probes on a single platform for contrast imaging remain challenging. Simultaneous detecting multiple physiological parameters immensely decreases misdiagnosis. In this contribution, we report the first difluoroboron beta-diketone-based dual-modal nanosensor for luminescence bioimaging with synergistic response of hypoxia and pH combined with magnetic resonance imaging (MRI). Acidic pH associated with hypoxia-stimulated luminescence enhancement makes the nanosensor more suitable for early diagnosis of tumors. Furthermore, the highly stable Gd3+ coordinated nanoparticles efficiently enhance relaxivity and have excellent biocompatibility. The smart dual-modal system is promising for further applications in bioimaging and theragnostic integrative biomedical techniques.

Rectal cancer (RC) ranks among the most common malignant tumors worldwide, with ~30% of patients presenting at a locally advanced stage at the time of diagnosis. The standard treatment for locally advanced RC (LARC) combines neoadjuvant chemoradiotherapy (nCRT) with total mesorectal excision. While this treatment paradigm has been effective in reducing the local recurrence rate, its efficacy in enhancing overall survival and disease-free survival is still limited. Consequently, the identification of adverse prognostic factors in patients with LARC is crucial for improving long-term survival outcomes. Traditional imaging methods offer limited predictive power for early diagnosis, treatment efficacy assessment and prognosis in LARC. Recent advancements in MRI-based radiomics and deep learning (DL), leveraging high-dimensional feature extraction and nonlinear modeling, have markedly enhanced prognostic predictive performance. Single-sequence MRI radiomics models derived from pre-nCRT imaging demonstrate areas under the curve (AUC) of 0.79-0.87 for predicting local recurrence and distant metastasis. Multiparametric radiomic models further improve predictive accuracy, achieving AUCs of 0.81-0.83. Delta radiomics, which captures temporal-spatial heterogeneity evolution in tumors during therapy, elevates AUC performance to 0.77-0.89. Notably, DL-based models exhibit superior and more stable predictive capabilities, with concordance indices (C-indices) ranging from 0.82 to 0.94. This paper reviews recent progress in MRI-based radiomics and DL for predicting the prognosis of patients with LARC subjected to nCRT.

To construct and validate a multi-phase contrast-enhanced computed tomography delta-radiomics signature for preoperatively predicting lymphovascular invasion (LVI) and perineural invasion (PNI) in patients with rectal cancer (RC). This study retrospectively enrolled 519 patients with RC between January 2017 and December 2022, with patients assigned to the training (n = 363) or validation (n = 156) sets. Radiomic features were extracted from routine scanning (A0), the arterial phase (A1), and the venous phase (A2). Delta-1 and Delta-2 radiomic signatures were derived by subtracting radiomic features acquired from A0 images from those of A2 and A1, respectively. Subsequently, Delta-3 and Delta-4 radiomic features were obtained by performing image subtraction between the A0 images and A2 and A1 images, then extracting the radiomic features from the resulting residual images. A delta-radiomics model was constructed using the Least Absolute Shrinkage and Selection Operator method. Model performance was evaluated using receiver operating characteristic, calibration, and decision curves. Delta-1-Delta-4 models exhibited moderate predictive performance for LVI and PNI in patients with RC, with area under the curve (AUC) values of 0.73, 0.73, 0.67, and 0.68, respectively. The combined model (C-Delta-12) showed the best predictive performance (AUC, 0.81; accuracy, 0.76; sensitivity, 0.86; specificity, 0.65). Calibration curves confirmed high goodness of fit, and decision curve analysis confirmed the clinical value. Integrating delta-radiomics signature and clinical predictors into a radiomics prediction model enables accurate and non-invasive risk assessments of PNI and LVI in RC. Stratifying patients based on their PNI and LVI status may facilitate more individualised treatment.

X-ray computed tomography (CT) offers high spatial resolution and deep tissue penetration, but its low sensitivity limits early disease diagnosis. In contrast, fluorescence imaging (FI) provides high sensitivity, suffering from poor spatial resolution and limited tissue penetration. Combining CT and FI creates a complementary imaging method. Herein, we report bismuth-loaded poly(α-amino acid) nanoparticles (abbreviated as Bi NPs including Bi@POS, Bi@POS-FITC, and Bi@POS-ICG) for CT and fluorescence bimodal imaging. Bi NPs exhibit superior X-ray attenuation and fluorescence emission properties by integrating bismuth complexes with fluorescent dyes, including fluorescein isothiocyanate (FITC) and indocyanine green (ICG). The catechol moieties in poly(α-amino acid)s not only chelate Bi3+ but also exhibit reactive oxygen and nitrogen species (RONS) scavenging activity. In vitro and in vivo experiments demonstrate that Bi NPs have superior bimodal imaging performance, significantly enhanced CT contrast, and prolonged fluorescence signals, suggesting their great potential as bimodal contrast agents for diagnosis applications.

Rectal cancer is a major cause of cancer-related mortality, requiring accurate diagnosis via MRI scans. However, detecting rectal cancer in MRI scans is challenging due to image complexity and the need for precise localization. While transformer-based object detection has excelled in natural images, applying these models to medical data is hindered by limited medical imaging resources. To address this, we propose the Spatially Prioritized Detection Transformer (SP DETR), which incorporates a Spatially Prioritized (SP) Decoder to constrain anchor boxes to regions of interest (ROI) based on anatomical maps, focusing the model on areas most likely to contain cancer. Additionally, the SP cross-attention mechanism refines the learning of anchor box offsets. To improve small cancer detection, we introduce the Global Context-Guided Feature Fusion Module (GCGFF), leveraging a transformer encoder for global context and a Globally-Guided Semantic Fusion Block (GGSF) to enhance high-level semantic features. Experimental results show that our model significantly improves detection accuracy, especially for small rectal cancers, demonstrating the effectiveness of integrating anatomical priors with transformer-based models for clinical applications.

Colorectal cancer (CRC) is a leading global malignancy with a poor prognosis in advanced stages. Early and accurate diagnosis remains challenging due to the overlapping of clinical manifestations between early-stage CRC and inflammatory bowel diseases. Although dynamic contrast-enhanced MRI (DCE-MRI) is a critical imaging modality for the diagnosis of CRC and colorectal cancer liver metastasis (CRLM), conventional gadolinium-based contrast agents (GBCAs) have the limitations of rapid clearance and potential toxicity risks. In this study, we report a gadolinium-free T1-weighted nanocontrast agent based on Fe(III)-coordinated poly(α-amino acid)s (Fe@POS) nanomicelles. Fe@POS nanomicelles exhibit a high longitudinal relaxivity (r1 = 5.56 mM−1s−1) and prolonged blood circulation time with selective CRC tumor accumulation via enhanced permeability and retention (EPR) effect. In vivo MRI studies revealed long-period MRI of CRC. In CRLM lesions, normal hepatic tissue demonstrates greater Fe@POS uptake compared to tumor tissue, which enables clear delineation of tumor margins on MRI. Histological and biochemical analysis confirmed the biocompatibility of Fe@POS nanomicelles, with no acute toxicity observed, highlighting their potential as alternatives to GBCAs for clinical diagnostic applications.