OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System

Improving the retrieval relevance on noisy datasets is an emerging need for the curation of a large-scale clean dataset in the medical domain. While existing methods can be applied for class-wise retrieval (aka. inter-class), they cannot distinguish the granularity of likeness within the same class (aka. intra-class). The problem is exacerbated on medical external datasets, where noisy samples of the same class are treated equally during training. Our goal is to identify both intra/inter-class similarities for fine-grained retrieval. To achieve this, we propose an Outlier-Sensitive Content-based rAdiologhy Retrieval System (OSCARS), consisting of two steps. First, we train an outlier detector on a clean internal dataset in an unsupervised manner. Then we use the trained detector to generate the anomaly scores on the external dataset, whose distribution will be used to bin intra-class variations. Second, we propose a quadruplet (a, p, nintra, ninter) sampling strategy, where intra-class negatives nintra are sampled from bins of the same class other than the bin anchor a belongs to, while niner are randomly sampled from inter-classes. We suggest a weighted metric learning objective to balance the intra and inter-class feature learning. We experimented on two representative public radiography datasets. Experiments show the effectiveness of our approach

C1ql4 regulates breast cancer cell stemness and epithelial-mesenchymal transition through PI3K/AKT/NF-κB signaling pathway

BackgroundThe stemness characteristic of breast cancer (BC) is a crucial factor underlying cancer recurrence and metastasis after operative therapy and chemoradiotherapy. Understanding the potential mechanism of breast cancer stem cells (BCSCs) may ameliorate the prognosis of patients.MethodsWe collected clinical specimens of BC patients for staining and statistical analysis to verify the expression status and clinical significance of complement C1q-like 4 (C1ql4). Western blot and qRT-PCR were employed to detect the expression of molecules. Flow cytometry was used to examine cell cycle, cell apoptosis and the portion of BCSCs. Wound healing and Transwell assays were used to detect cell metastasis. The effect of C1ql4 on breast cancer progression in vivo was examined in a nude mouse tumor bearing model.ResultsOur clinical analysis showed that C1ql4 was highly expressed in BC tissues and cell lines, and the high expression of C1ql4 was significantly corelated with the malignancy of BC patients. Moreover, we also found that C1ql4 was overexpressed in BCSCs. C1ql4 knockdown suppressed the BCSC and EMT properties, promoted cell cycle progression, enhanced BC cell apoptosis, and inhibited cell migration and invasion, whereas the C1ql4 overexpression exhibited the opposite effects. Mechanistically, C1ql4 promoted the activation and nuclear location of NF-κB and the expression of downstream factors TNF-α and IL-1β. Moreover, inhibition of PI3K/AKT signaling suppressed the C1ql4-induced stemness and EMT.ConclusionsOur findings suggest that C1ql4 promotes the BC cell stemness and EMT via modulating the PI3K/AKT/NF-κB signaling, and provides a promising target for BC treatment

Multi-Label Medical Image Retrieval Via Learning Multi-Class Similarity

Introduction: Multi-label image retrieval is a challenging problem in the medical area. First, compared to natural images, labels in the medical domain exhibit higher class-imbalance and much nuanced variations. Second, pair-based sampling for positives and negatives during similarity optimization are ambiguous in the multi-label setting, as samples with the same set of labels are limited. Methods: To address the aforementioned challenges, we propose a proxy-based multi-class similarity (PMS) framework, which compares and contrasts samples by comparing their similarities with the discovered proxies. In this way, samples of different sets of label attributes can be utilized and compared indirectly, without the need for complicated sampling. PMS learns a class-wise feature decomposition and maintains a memory bank for positive features from each class. The memory bank keeps track of the latest features, used to compute the class proxies. We compare samples based on their similarity distributions against the proxies, which provide a more stable mean against noise. Results: We benchmark over 10 popular metric learning baselines on two public chest X-ray datasets and experiments show consistent stability of our approach under both exact and non-exact match settings. Conclusions: We proposed a methodology for multi-label medical image retrieval and design a proxy-based multi-class similarity metric, which compares and contrasts samples based on their similarity distributions with respect to the class proxies. With no perquisites, the metrics can be applied to various multi-label medical image applications. The implementation code repository will be publicly available after acceptance

Diagnostic Yields of Trio-WES Accompanied by CNVseq for Rare Neurodevelopmental Disorders

ObjectiveThis study is to investigate the diagnostic yield of the combination of trio whole exome sequencing (Trio-WES) and copy number variation sequencing (CNVseq) for rare neurodevelopmental disorders (NDDs).MethodsClinical data from consecutive pediatric patients who were diagnosed with rare NDDs that were suspected to be monogenic disorders, who were admitted to our hospital from April 2017 to March 2019, and who underwent next generation sequencing (NGS) were extracted from the medical records. Patients for whom Trio-WES and CNVseq data were available were enrolled in this study. Sanger sequencing was applied for the validation of the variants identified by Trio-WES. Sequence alignment and structural modeling were conducted for analyzing the possibility of the variants in the onset of the NDDs.ResultsIn total, 54 patients were enrolled in this study, with the median age of 15 (8–26) months. A total of 242 phenotypic abnormalities belonging to 20 different systems were identified in the cohort. Twenty-four patients were diagnosed by Trio-WES, eight patients were diagnosed by CNVseq, and one case was identified by both WES and CNVseq. Compared with Trio-WES, the diagnosis rate of Trio-WES accompanied by CNVseq was significantly higher (P = 0.016). Trio-WES identified 36 variants in 26 different genes, among which 27 variants were novel. CNVseq detected four duplications and eight deletions, ranging from 310 kb to 23.27 Mb. Our case examples demonstrated the high heterogeneity of NDDs and showed the challenges of rare NDDs for physicians.ConclusionThe significantly higher diagnosis rate of Trio-WES accompanied by CNVseq makes this strategy a potential alternative to the most widely used approaches for pediatric children with rare and undiagnosed NDDs