Influence of Endogenous Derivatives on the Chemical Sensing Performance of Carbonized Polymer Dots.

Endogenous derivatives, inevitably introduced during carbonized polymer dots (CPDs) synthesis, are considered to significantly influence their sensing performance. In this study, comparative studies on CPDs prepared via hydrothermal methods (CPDs-HO) and airflow-assisted melt polymerization (CPDs-AMP) reveal that CPDs-HO, containing abundant derivatives, exhibit superior stability and robustness in Fe sensing in both solution and cells compared to derivative-deficient CPDs-AMP. Mechanistic investigations highlight the crucial role of endogenous derivatives in enhancing the intrinsic stability of CPDs polymer frameworks, challenging conventional perceptions and providing valuable insights for designing robust CPDs-based sensors for practical applications.

Single cobalt atoms with unconventional dynamic coordination mechanism for selective ammonia sensor.

Developing gas sensors that can simultaneously achieve high sensitivity and selectivity for the detection of a single-type gas remains a significant challenge. Herein we demonstrate cobalt (Co) single atoms with an unconventional dynamically changing coordination structure that could be used as NH-sensing material with superior sensitivity and selectivity. According to the steric effect of 2-methylimidazole (2MI) molecules and carbonyl groups on graphene, the Co single atom is evolved into a bidentate coordinated structure (Co-2MI-G).

Constructing phylogenetic trees for microbiome data analysis: A mini-review.

As next-generation sequencing technologies advance rapidly and the cost of metagenomic sequencing continues to decrease, researchers now face an unprecedented volume of microbiome data. This surge has stimulated the development of scalable microbiome data analysis methods and necessitated the incorporation of phylogenetic information into microbiome analysis for improved accuracy. Tools for constructing phylogenetic trees from 16S rRNA sequencing data are well-established, as the highly conserved regions of the 16S gene are limited, simplifying the identification of marker genes.

Target Fitting Method for Spherical Point Clouds Based on Projection Filtering and K-Means Clustered Voxelization.

Industrial computed tomography (CT) is widely used in the measurement field owing to its advantages such as non-contact and high precision. To obtain accurate size parameters, fitting parameters can be obtained rapidly by processing volume data in the form of point clouds. However, due to factors such as artifacts in the CT reconstruction process, many abnormal interference points exist in the point clouds obtained after segmentation.

The Synthesis of Functionalized Carbonized Polymer Dots via Reversible Assembly of Oligomers for Anti-Counterfeiting, Catalysis, and Gas storage.

Carbonized polymer dots (CPDs) have shown exceptional potential across a wide range of applications. However, their practical utilization is significantly greatly impeded by the lack of precise control over their structures and functionalities. Consequently, the development of controlled synthesis strategies for CPDs with well-defined structures and tailored functionalities remains a critical challenge in the field.

survivalContour: visualizing predicted survival via colored contour plots.

Summary: Advances in survival analysis have facilitated unprecedented flexibility in data modeling, yet there remains a lack of tools for illustrating the influence of continuous covariates on predicted survival outcomes. We propose the utilization of a colored contour plot to depict the predicted survival probabilities over time. Our approach is capable of supporting conventional models, including the Cox and Fine-Gray models.

Expanding the Reach of Personalized Medicine in Cancer Care: Current Progress and Future Directions of .

ASCO Editorial Fellows share insights into publication trends and future directions for JCO Precision Oncology.

Uncertainty-aware prototypical learning for anomaly detection in medical images.

Anomalous object detection (AOD) in medical images aims to recognize the anomalous lesions, and is crucial for early clinical diagnosis of various cancers. However, it is a difficult task because of two reasons: (1) the diversity of the anomalous lesions and (2) the ambiguity of the boundary between anomalous lesions and their normal surroundings. Unlike existing single-modality AOD models based on deterministic mapping, we constructed a probabilistic and deterministic AOD model.

Dynamic radiomics based on contrast-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma.

Objective: To exploit the improved prediction performance based on dynamic contrast-enhanced (DCE) MRI by using dynamic radiomics for microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Methods: We retrospectively included 175 and 75 HCC patients who underwent preoperative DCE-MRI from September 2019 to August 2022 in institution 1 (development cohort) and institution 2 (validation cohort), respectively. Static radiomics features were extracted from the mask, arterial, portal venous, and equilibrium phase images and used to construct dynamic features.

Green, pH-Sensitive, Highly Stretchable, and Hydrogen Bond-Dominated Ionogel for Wound Healing Activity.

Traditional hydrogel dressings generally have poor mechanical properties and stability when subjected to external stress due to the undesirable chain entanglement structure of their single valence bond compositions. Therefore, it is particularly important to develop a type of gel dressing with good mechanical strength, stability, and environment-friendly monitoring. In this work, a transparent, pH-sensitive, highly stretchable, and biocompatible anthocyanidin ionogel dressing was prepared, realizing green and accurate detection.

Development and testing of a polygenic risk score for breast cancer aggressiveness.

Aggressive breast cancers portend a poor prognosis, but current polygenic risk scores (PRSs) for breast cancer do not reliably predict aggressive cancers. Aggressiveness can be effectively recapitulated using tumor gene expression profiling. Thus, we sought to develop a PRS for the risk of recurrence score weighted on proliferation (ROR-P), an established prognostic signature.

Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer.

There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome profiles. We propose a novel unsupervised clustering approach in the Bayesian framework that innovates over existing model-based clustering approaches, such as the Dirichlet multinomial mixture model, in three key respects: we incorporate feature selection, learn the appropriate number of clusters from the data, and integrate information on the tree structure relating the observed features.

Development of a hepatocellular carcinoma imaging database and structured imaging reports based on PACS, HIS, and repository.

Purpose: To establish a hepatocellular carcinoma imaging database and structured imaging reports based on PACS, HIS, and repository.

Methods: This study was approved by the Institutional Review Board. The steps of establishing the database are as follows: 1) According to the standards required for the intelligent diagnosis of HCC, it was attempted to design the corresponding functional modules after analyzing the requirements; 2) Based on client/server (C/S) mode, 3-tier architecture model was adopted.

Diet-derived metabolites and mucus link the gut microbiome to fever after cytotoxic cancer treatment.

Not all patients with cancer and severe neutropenia develop fever, and the fecal microbiome may play a role. In a single-center study of patients undergoing hematopoietic cell transplant ( = 119), the fecal microbiome was characterized at onset of severe neutropenia. A total of 63 patients (53%) developed a subsequent fever, and their fecal microbiome displayed increased relative abundances of , a species of mucin-degrading bacteria ( = 0.

Transfer efficiency and impact on disease phenotype of differing methods of gut microbiota transfer.

To test causal relationships between complex gut microbiota (GM) and host outcomes, researchers frequently transfer GM between donor and recipient mice via embryo transfer (ET) rederivation, cross-fostering (CF), and co-housing. In this study, we assess the influence of the transfer method and the differences in baseline donor and recipient microbiota richness, on transfer efficiency. Additionally, recipient mice were subjected to DSS-induced chronic colitis to determine whether disease severity was affected by GM transfer efficiency or features within the GM.

Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: a retrospective multicenter study.

Objectives: To establish and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI), and to predict microsatellite instability (MSI) status in rectal cancer patients.

Methods: A total of 199 patients with pathologically confirmed rectal cancer were included. The MSI status was confirmed by immunohistochemistry (IHC) staining.

Reduced housing density improves statistical power of murine gut microbiota studies.

The gut microbiome of humans and animals is critical to host health. Mice are used to investigate the microbiome and its influences; however, the predictive value of such studies is hindered by cage effects due to coprophagy. Our objectives were to evaluate the influence of cage density on the statistical power to detect treatment-dependent effects of a selective pressure on microbiome composition.

Performance determinants of unsupervised clustering methods for microbiome data.

Background: In microbiome data analysis, unsupervised clustering is often used to identify naturally occurring clusters, which can then be assessed for associations with characteristics of interest. In this work, we systematically compared beta diversity and clustering methods commonly used in microbiome analyses. We applied these to four published datasets where highly distinct microbiome profiles could be seen between sample groups, as well a clinical dataset with less clear separation between groups.