Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans

Gastric cancer is the third leading cause of cancer-related mortality worldwide, but no guideline-recommended screening test exists. Existing methods can be invasive, expensive, and lack sensitivity to identify early-stage gastric cancer. In this study, we explore the feasibility of using a deep learning approach on non-contrast CT scans for gastric cancer detection. We propose a novel cluster-induced Mask Transformer that jointly segments the tumor and classifies abnormality in a multi-task manner. Our model incorporates learnable clusters that encode the texture and shape prototypes of gastric cancer, utilizing self- and cross-attention to interact with convolutional features. In our experiments, the proposed method achieves a sensitivity of 85.0% and specificity of 92.6% for detecting gastric tumors on a hold-out test set consisting of 100 patients with cancer and 148 normal. In comparison, two radiologists have an average sensitivity of 73.5% and specificity of 84.3%. We also obtain a specificity of 97.7% on an external test set with 903 normal cases. Our approach performs comparably to established state-of-the-art gastric cancer screening tools like blood testing and endoscopy, while also being more sensitive in detecting early-stage cancer. This demonstrates the potential of our approach as a novel, non-invasive, low-cost, and accurate method for opportunistic gastric cancer screening.Comment: MICCAI 202

CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans

Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases. This might severely limit AI's clinical adoption. A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan. In this paper, we construct a Unified Tumor Transformer (CancerUniT) model to jointly detect tumor existence & location and diagnose tumor characteristics for eight major cancers in CT scans. CancerUniT is a query-based Mask Transformer model with the output of multi-tumor prediction. We decouple the object queries into organ queries, tumor detection queries and tumor diagnosis queries, and further establish hierarchical relationships among the three groups. This clinically-inspired architecture effectively assists inter- and intra-organ representation learning of tumors and facilitates the resolution of these complex, anatomically related multi-organ cancer image reading tasks. CancerUniT is trained end-to-end using a curated large-scale CT images of 10,042 patients including eight major types of cancers and occurring non-cancer tumors (all are pathology-confirmed with 3D tumor masks annotated by radiologists). On the test set of 631 patients, CancerUniT has demonstrated strong performance under a set of clinically relevant evaluation metrics, substantially outperforming both multi-disease methods and an assembly of eight single-organ expert models in tumor detection, segmentation, and diagnosis. This moves one step closer towards a universal high performance cancer screening tool.Comment: ICCV 2023 Camera Ready Versio

Molecular prevalence and subtyping of Cryptosporidium hominis among captive long-tailed macaques (Macaca fascicularis) and rhesus macaques (Macaca mulatta) from Hainan Island, southern China

Abstract Background Cryptosporidium is an important zoonotic parasite that is commonly found in non-human primates (NHPs). Consequently, there is the potential for transmission of this pathogen from NHPs to humans. However, molecular characterization of the isolates of Cryptosporidium from NHPs remains relatively poor. The aim of the present work was to (i) determine the prevalence; and (ii) perform a genetic characterization of the Cryptosporidium isolated from captive Macaca fascicularis and M. mulatta on Hainan Island in southern China. Methods A total of 223 fresh fecal samples were collected from captive M. fascicularis (n = 193) and M. mulatta (n = 30). The fecal specimens were examined for the presence of Cryptosporidium spp. by polymerase chain reaction (PCR) and sequencing of the partial small subunit (SSU) rRNA gene. The Cryptosporidium-positive specimens were subtyped by analyzing the 60-kDa glycoprotein (gp60) gene sequence. Results Cryptosporidium spp. were detected in 5.7% (11/193) of M. fascicularis. All of the 11 Cryptosporidium isolates were identified as C. hominis. Subtyping of nine of these isolates identified four unique gp60 subtypes of C. hominis. These included IaA20R3a (n = 1), IoA17a (n = 1), IoA17b (n = 1), and IiA17 (n = 6). Notably, subtypes IaA20R3a, IoA17a, and IoA17b were novel subtypes which have not been reported previously. Conclusions To our knowledge, this is the first reported detection of Cryptosporidium in captive M. fascicularis from Hainan Island. The molecular characteristics and subtypes of the isolates here provide novel insights into the genotypic variation in C. hominis

Dual-functional gum arabic binder for silicon anodes in lithium ion batteries

Si has attracted enormous research and manufacturing attention as an anode material for lithium ion batteries (LIBs) because of its high specific capacity. The lack of a low cost and effective mechanism to prevent the pulverization of Si electrodes during the lithiation/ delithiation process has been a major barrier in the mass production of Si anodes. Naturally abundant gum arabic (GA), composed of polysaccharides and glycoproteins, is applied as a dualfunction binder to address this dilemma. Firstly, the hydroxyl groups of the polysaccharide in GA are crucial in ensuring strong binding to Si. Secondly, similar to the function of fiber in fiberreinforced concrete (FRC), the long chain glycoproteins provide further mechanical tolerance to dramatic volume expansion by Si nanoparticles. The resultant Si anodes present an outstanding capacity of ca. 2000 mAh/g at a 1 C rate and 1000 mAh/g at 2 C rate, respectively, throughout 500 cycles. Excellent long-term stability is demonstrated by the maintenance of 1000 mAh/g specific capacity at 1 C rate for over 1000 cycles. This low cost, naturally abundant and environmentally benign polymer is a promising binder for LIBs in the future

Highly Active PdNi/RGO/Polyoxometalate Nanocomposite Electrocatalyst for Alcohol Oxidation

A PdNi/RGO/polyoxometalate nanocomposite has been successfully synthesized by a simple wet-chemical method. Characterizations such as transmission electron microscopy, energy dispersive X-ray spectroscopy, X-ray diffraction analysis, and X-ray photoelectron spectroscopy are employed to verify the morphology, structure, and elemental composition of the as-prepared nanocomposite. Inspired by the fast-developing fuel cells, the electrochemical catalytic performance of the nanocomposite toward methanol and ethanol oxidation in alkaline media is further tested. Notably, the nanocomposite exhibits excellent catalytic activity and long-term stability toward alcohol electrooxidation compared with the PdNi/RGO and commercial Pd/C catalyst. Furthermore, the electrochemical results reveal that the prepared nanocomposite is attractive as a promising electrocatalyst for direct alcohol fuel cells, in which the phosphotungstic acid plays a crucial role in enhancing the electrocatalytic activities of the catalyst