Publications by authors named "Bowen Xin"

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily.

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SARS-COV-2 infection-induced excessive or uncontrolled cytokine storm may cause injury of host tissue or even death. However, the mechanism by which SARS-COV-2 causes the cytokine storm is unknown. Here, we demonstrated that SARS-COV-2 protein NSP9 promoted cytokine production by interacting with and activating TANK-binding kinase-1 (TBK1).

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Cyclic GMP-AMP synthase (cGAS) recognizes Y-form cDNA of human immunodeficiency virus type 1 (HIV-1) and initiates antiviral immune response through cGAS-stimulator of interferon genes (STING)-TBK1-IRF3-type I interferon (IFN-I) signalingcascade. Here, we report that the HIV-1 p6 protein suppresses HIV-1-stimulated expression of IFN-I and promotes immune evasion. Mechanistically, the glutamylated p6 at residue Glu6 inhibits the interaction between STING and tripartite motif protein 32 (TRIM32) or autocrine motility factor receptor (AMFR).

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Article Synopsis
  • - The study developed and validated two machine learning models that use F-FDG PET/CT radiomic features to predict HER2 expression and patient prognosis in gastric cancer (GC).
  • - Involving a total of 90 GC patients, the models utilized 2,100 radiomic features, with successful performance metrics showing a sensitivity of up to 85% and specificity of 80% for HER2 prediction.
  • - The findings suggest that F-FDG PET/CT radiomics analysis offers a quantitative and reliable way to predict HER2 status and prognosis, potentially aiding in personalized treatment for GC patients.
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Background: Microvascular invasion (MVI) is a critical risk factor for early recurrence of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The aim of this study was to explore the contribution of F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) radiomic features for the preoperative prediction of HCC and ICC classification and MVI.

Methods: In this retrospective study, 127 (HCC: ICC =76:51) patients with suspected MVI accompanied by either HCC or ICC were included (In HCC group, MVI positive: negative =46:30 in ICC group, MVI positive: negative =31:20).

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Milk protein is one of the major food allergens. As an effective processing method, fermentation may reduce the potential allergenicity of allergens. This study aimed to evaluate the therapeutic potential of co-fermented milk protein using Lactobacillus helveticus KLDS 1.

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Antibiotic-associated diarrhea (AAD) is a common side effect during antibiotic treatment. In this study, we evaluated the regulatory effect of subsp XLTG11 on mouse diarrhea caused by antibiotic-induced intestinal flora disturbance. Then, two strains of subsp XLTG11 and subsp BB-12 were administered to AAD mice.

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Background: To develop and validate a survival model with clinico-biological features and F- FDG PET/CT radiomic features via machine learning, and for predicting the prognosis from the primary tumor of colorectal cancer.

Methods: A total of 196 pathologically confirmed patients with colorectal cancer (stage I to stage IV) were included. Preoperative clinical factors, serum tumor markers, and PET/CT radiomic features were included for the recurrence-free survival analysis.

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Quantitatively analysing the spatial patterns of multifocal lesions on clinical MRI is an important step towards a better understanding of the disease and for precision medicine, which is yet to be properly explored by feature engineering and deep learning methods. Network science addresses this issue by explicitly modeling the inter-lesion topology. However, the construction of the informative graph with optimal edge sparsity and quantification of community graph structures are the current challenges in network science.

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Objectives: The accurate assessment of lymph node metastases (LNMs) and the preoperative nodal (N) stage are critical for the precise treatment of patients with gastric cancer (GC). The diagnostic performance, however, of current imaging procedures used for this assessment is sub-optimal. Our aim was to investigate the value of preoperative F-FDG PET/CT radiomic features to predict LNMs and the N stage.

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Background: Misdiagnosis of multiple sclerosis (MS) and neuromyelitis optica (NMO) may delay the treatment, resulting in poor prognosis. However, the precise identification of these two diseases is still challenging in clinical practice. We aimed to evaluate the value of quantitative radiomic features extracted from the brain white matter lesions for differential diagnosis of MS and NMO.

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Objectives: Microsatellite instability (MSI) status is an important hallmark for prognosis prediction and treatment recommendation of colorectal cancer (CRC). To address issues due to the invasiveness of clinical preoperative evaluation of microsatellite status, we investigated the value of preoperative F-FDG PET/CT radiomics with machine learning for predicting the microsatellite status of colorectal cancer patients.

Methods: A total of 173 patients that underwent F-FDG PET/CT scans before operations were retrospectively analyzed in this study.

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Background: Myopic maculopathy (MM) is the most serious and irreversible complication of pathologic myopia, which is a major cause of visual impairment and blindness. Clinic proposed limited number of factors related to MM. To explore additional features strongly related with MM from optic disc region, we employ a machine learning based radiomics analysis method, which could explore and quantify more hidden or imperceptible MM-related features to the naked eyes and contribute to a more comprehensive understanding of MM and therefore may assist to distinguish the high-risk population in an early stage.

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: Functional magnetic resonance imaging (fMRI) has been widely used to assess neural activity changes in gray matter (GM) in patients with multiple sclerosis (MS); however, brain function alterations in white matter (WM) relatively remain under-explored. : This work aims to identify the functional connectivity in both the WM and the GM of patients with MS using fMRI and the correlations between these functional changes and cumulative disability as well as the lesion ratio. : For this retrospective study, 37 patients with clinically definite MS and 43 age-matched healthy controls were included between 2010 and 2014.

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F-FDG PET / CT is used clinically for the detection of extramedullary lesions in patients with relapsed acute leukemia (AL). However, the visual analysis of F-FDG diffuse bone marrow uptake in detecting bone marrow involvement (BMI) in routine clinical practice is still challenging. This study aims to improve the diagnostic performance of F-FDG PET/CT in detecting BMI for patients with suspected relapsed AL.

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Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning models.

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Objectives: To determine the integrative value of clinical, hematological, and computed tomography (CT) radiomic features in survival prediction for locally advanced non-small cell lung cancer (LA-NSCLC) patients.

Methods: Radiomic features and clinical and hematological features of 118 LA-NSCLC cases were firstly extracted and analyzed in this study. Then, stable and prognostic radiomic features were automatically selected using the consensus clustering method with either Cox proportional hazard (CPH) model or random survival forest (RSF) analysis.

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