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Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986–0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening. A deep learning model provides high accuracy in detecting pancreatic lesions in multicenter data, outperforming radiology specialists.

Nasopharyngeal carcinoma (NPC) diagnosis and routine follow-up for recurrence typically rely on contrast-enhanced MRI. This study introduces a deep learning model for diagnosing NPC using only non-contrast MRI, reducing the need for gadolinium-based contrast agents (GBCA). This approach helps avoid safety concerns related to GBCA deposition, while also shortening scan times and reducing costs. In this study, we propose an innovative deep learning model for NPC diagnosis using only non-contrast MRI, reducing the need for gadolinium-based contrast agents (GBCA). This approach not only mitigates potential safety concerns associated with residual GBCA deposition but also reduces scan time and examination costs. The study consisted of three phases. Firstly, a novel knowledge distilled modality fusion model is developed using a cohort of 854 cases and tested its performance on an internal set (257 cases, AUC = 0.95) and an independent external set (277 cases, AUC = 0.86). Secondly, the proposed method was compared with: (1) Non-contrast MRI without model improvement (Baseline 1) and (2) current virtual-contrast enhancement-based NPC diagnosis using three state-of-the-art methods (Baselines 2-4). The proposed model consistently outperformed Baselines 1 on both internal dataset (AUC: 0.95 vs. 0.93) and external test set (AUC: 0.86 vs. 0.82). Additionally, it surpassed Baselines 2-4, achieving performance gains of 6.7%, 69.8%, and 28.6% in AUC, over three state-of-the-art methods. Thirdly, the effectiveness of this model was evaluated through a fully crossed multi-reader, multi-case study involving 13 readers from 6 hospitals. The results showed that with AI assistance, readers could diagnose NPC using only non-contrast MRI, achieving results that were not inferior to contrast-enhanced imaging (AUC: 0.90 vs. 0.93, p<0.01). In conclusion, this study demonstrated the model’s potential as a safe, cost-effective, and GBCA-free option for NPC diagnosis in clinical practice.

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 .

Accurate forecasts of fog and visibility are very important to air and high way traffic, and are still a big challenge. A 1D fog model (PAFOG) is coupled to MM5 by obtaining the initial and boundary conditions (IC/BC) and some other necessary input parameters from MM5. Thus, PAFOG can be run for any area of interest. On the other hand, MM5 itself can be used to simulate fog events over a large domain. This paper presents evaluations of the fog predictability of these two systems for December of 2006 and December of 2007, with nine regional fog events observed in a field experiment, as well as over a large domain in eastern China. Among the simulations of the nine fog events by the two systems, two cases were investigated in detail. Daily results of ground level meteorology were validated against the routine observations at the CMA observational network. Daily fog occurrences for the two study periods was validated in Nanjing. General performance of the two models for the nine fog cases are presented by comparing with routine and field observational data. The results of MM5 and PAFOG for two typical fog cases are verified in detail against field observations. The verifications demonstrated that all methods tended to overestimate fog occurrence, especially for near-fog cases. In terms of TS/ETS, the LWC-only threshold with MM5 showed the best performance, while PAFOG showed the worst. MM5 performed better for advection–radiation fog than for radiation fog, and PAFOG could be an alternative tool for forecasting radiation fogs. PAFOG did show advantages over MM5 on the fog dissipation time. The performance of PAFOG highly depended on the quality of MM5 output. The sensitive runs of PAFOG with different IC/BC showed the capability of using MM5 output to run the 1D model and the high sensitivity of PAFOG on cloud cover. Future works should intensify the study of how to improve the quality of input data (e.g. cloud cover, advection, large scale subsidence) for the 1D model, particularly how to eliminate near-fog case in fog forecasting.

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. 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. 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. 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.

Mass concentrations of Total Suspended Particles (TSP) and size-segregated particles were obtained from July 2001 to June 2002 in Qingdao to characterize the seasonal variations of atmospheric aerosols and to show the impact of dust events on the air quality in Qingdao. Data on size-segregated aerosols show that 73.74% of the TSP mass concentration is contributed by particles with diameters less than 11µm. Particles with diameters less than 1.1µm have a higher concentration during the winter. In spring, larger particles tend to have higher mass concentrations. Bimodal particle size distributions have been observed, with maxima around 4.7-7µm and 0.43-0.65µm in the winter season, and 7-11µm and 0.65-1.lµm in the autumn season. Measurements made during the dust events in March 2002 show high concentrations of particles in the size range 2.1-7µm.[PUBLICATION ABSTRACT]

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.

Real-world medical image segmentation has tremendous long-tailed complexity of objects, among which tail conditions correlate with relatively rare diseases and are clinically significant. A trustworthy medical AI algorithm should demonstrate its effectiveness on tail conditions to avoid clinically dangerous damage in these out-of-distribution (OOD) cases. In this paper, we adopt the concept of object queries in Mask Transformers to formulate semantic segmentation as a soft cluster assignment. The queries fit the feature-level cluster centers of inliers during training. Therefore, when performing inference on a medical image in real-world scenarios, the similarity between pixels and the queries detects and localizes OOD regions. We term this OOD localization as MaxQuery. Furthermore, the foregrounds of real-world medical images, whether OOD objects or inliers, are lesions. The difference between them is less than that between the foreground and background, possibly misleading the object queries to focus redundantly on the background. Thus, we propose a query-distribution (QD) loss to enforce clear boundaries between segmentation targets and other regions at the query level, improving the inlier segmentation and OOD indication. Our proposed framework is tested on two real-world segmentation tasks, i.e., segmentation of pancreatic and liver tumors, outperforming previous state-of-the-art algorithms by an average of 7.39% on AUROC, 14.69% on AUPR, and 13.79% on FPR95 for OOD localization. On the other hand, our framework improves the performance of inlier segmentation by an average of 5.27% DSC when compared with the leading baseline nnUNet.

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 (UniT) model to detect (tumor existence and location) and diagnose (tumor characteristics) eight major cancer-prevalent organs in CT scans. UniT is a query-based Mask Transformer model with the output of multi-organ and multi-tumor semantic segmentation. We decouple the object queries into organ queries, detection queries and 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. UniT 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, UniT has demonstrated strong performance under a set of clinically relevant evaluation metrics, substantially outperforming both multi-organ segmentation methods and an assembly of eight single-organ expert models in tumor detection, segmentation, and diagnosis. Such a unified multi-cancer image reading model (UniT) can significantly reduce the number of false positives produced by combined multi-system models. This moves one step closer towards a universal high-performance cancer screening tool.

The genetic basis of colorectal cancer (CRC) and its clinical associations remain poorly understood due to limited samples or targeted genes in current studies. Here, we perform ultradeep whole-exome sequencing on 1015 patients with CRC as part of the ChangKang Project. We identify 46 high-confident significantly mutated genes, 8 of which mutate in 14.9% of patients: LYST , DAPK1 , CR2 , KIF16B , NPIPB15 , SYTL2 , ZNF91 , and KIAA0586 . With an unsupervised clustering algorithm, we propose a subtyping strategy that classisfies CRC patients into four genomic subtypes with distinct clinical characteristics, including hypermutated, chromosome instability with high risk, chromosome instability with low risk, and genome stability. Analysis of immunogenicity uncover the association of immunogenicity reduction with genomic subtypes and poor prognosis in CRC. Moreover, we find that mitochondrial DNA copy number is an independent factor for predicting the survival outcome of CRCs. Overall, our results provide CRC-related molecular features for clinical practice and a valuable resource for translational research. The ChangKang (Heathy Bowel) project was established to collect molecular and clinical information of a thousand Chinese colorectal cancer patients. Here, the authors present the genomic landscape of the ChangKang cohort and find a subgroup of patients defined by abnormal mitochondrial copy numbers.