Applications of Gold Nanoparticles in Cancer Imaging and Treatment

Cancer is one of the leading causes of death worldwide. In the last two decades, the development of nanotechnology has facilitated our ability to design new nanoparticles for the diagnosis and treatment of cancer. In this chapter, we reviewed the applications of gold nanoparticles as contrast agents for cancer imaging, including optical imaging, photoacoustic imaging, and X-ray–based imaging. We also reviewed their applications as delivery carriers for small molecule drugs, therapeutic genes, vaccines, and adjuvants and as therapeutic agents by themselves in cancer treatment, including photothermal therapy, photodynamic therapy, and radiation therapy

Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancement

Although extensive research has been conducted on 3D point cloud segmentation, effectively adapting generic models to novel categories remains a formidable challenge. This paper proposes a novel approach to improve point cloud few-shot segmentation (PC-FSS) models. Unlike existing PC-FSS methods that directly utilize categorical information from support prototypes to recognize novel classes in query samples, our method identifies two critical aspects that substantially enhance model performance by reducing contextual gaps between support prototypes and query features. Specifically, we (1) adapt support background prototypes to match query context while removing extraneous cues that may obscure foreground and background in query samples, and (2) holistically rectify support prototypes under the guidance of query features to emulate the latter having no semantic gap to the query targets. Our proposed designs are agnostic to the feature extractor, rendering them readily applicable to any prototype-based methods. The experimental results on S3DIS and ScanNet demonstrate notable practical benefits, as our approach achieves significant improvements while still maintaining high efficiency. The code for our approach is available at https://github.com/AaronNZH/Boosting-Few-shot-3D-Point-Cloud-Segmentation-via-Query-Guided-EnhancementComment: Accepted to ACM MM 202

The fault lies on the other side: altered brain functional connectivity in psychiatric disorders is mainly caused by counterpart regions in the opposite hemisphere

Many psychiatric disorders are associated with abnormal resting-state functional connectivity between pairs of brain regions, although it remains unclear whether the fault resides within the pair of regions themselves or other regions connected to them. Identifying the source of dysfunction is crucial for understanding the etiology of different disorders. Using pathway- and network-based techniques to analyze resting-state functional magnetic imaging data from a large population of patients with attention-deficit-hyperactivity-disorder (239 patients, 251 controls), major depression (69 patients, 67 controls) and schizophrenia (169 patients, 162 controls), we show for the first time that only network-based cross-correlation identifies significant functional-connectivity changes in all three disorders which survive correction. This demonstrates that the primary source of dysfunction resides not in the regional pairs themselves but in their external connections. Combining pathway and network-based functional-connectivity analysis we established that in all three disorders, th

Reliability-based cleaning of noisy training labels with inductive conformal prediction in multi-modal biomedical data mining

Accurately labeling biomedical data presents a challenge. Traditional semi-supervised learning methods often under-utilize available unlabeled data. To address this, we propose a novel reliability-based training data cleaning method employing inductive conformal prediction (ICP). This method capitalizes on a small set of accurately labeled training data and leverages ICP-calculated reliability metrics to rectify mislabeled data and outliers within vast quantities of noisy training data. The efficacy of the method is validated across three classification tasks within distinct modalities: filtering drug-induced-liver-injury (DILI) literature with title and abstract, predicting ICU admission of COVID-19 patients through CT radiomics and electronic health records, and subtyping breast cancer using RNA-sequencing data. Varying levels of noise to the training labels were introduced through label permutation. Results show significant enhancements in classification performance: accuracy enhancement in 86 out of 96 DILI experiments (up to 11.4%), AUROC and AUPRC enhancements in all 48 COVID-19 experiments (up to 23.8% and 69.8%), and accuracy and macro-average F1 score improvements in 47 out of 48 RNA-sequencing experiments (up to 74.6% and 89.0%). Our method offers the potential to substantially boost classification performance in multi-modal biomedical machine learning tasks. Importantly, it accomplishes this without necessitating an excessive volume of meticulously curated training data