Publications by authors named "Kuan Lv"

Glioblastoma (GBM) is highly invasive and lethal. The failure to cure GBM highlights the necessity of developing more effective targeted therapeutic strategies. KIF15 is a motor protein to be involved in cell mitosis promotion, cell structure assembly and cell signal transduction.

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Objectives: To develop a multi-omics prognostic model integrating transcriptomics and radiomics for predicting overall survival in patients with glioblastoma multiforme (GBM), and investigate the biological pathways of radiomics patterns.

Materials And Methods: Transcription profiles of GBM patients and normal controls were used to obtain differentially expressed mRNAs and long non-coding RNAs (lncRNAs). Radiomics features were extracted from magnetic resonance imaging (MRI).

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As a kind of proteolytic enzyme extracted from , lumbrokinase has been used as an antithrombotic drug clinically. Nevertheless, its potential in anti-cancer, especially in anti-non-small cell lung cancer (NSCLC), as a single form of treatment or in combination with other therapies, is still poorly understood. In this study, we explored the anti-tumor role and the responsive molecular mechanisms of lumbrokinase in suppressing tumor angiogenesis and chemoresistance development in NSCLC and its clinical potential in combination with bevacizumab and chemotherapeutics.

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  • Accurate segmentation of gastric tumors from CT scans is crucial for effective diagnosis and treatment of gastric cancer, but it faces challenges like varying resolution and complex tumor characteristics.
  • The study introduces a new segmentation method called Hierarchical Class-Aware Domain Adaptive Network (HCA-DAN), which combines a 3D neural network and a Transformer to effectively extract features from 3D CT images while addressing cross-center data variations.
  • Results show that HCA-DAN outperforms other segmentation models, achieving higher mean dice similarity coefficients in both in-center and cross-center tests, indicating promising performance in accurately identifying gastric tumors.
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To investigate potential correlations between the susceptibility values of certain brain regions and the severity of disease or neurodevelopmental status in children with autism spectrum disorder (ASD), 18 ASD children and 15 healthy controls (HCs) were recruited. The neurodevelopmental status was assessed by the Gesell Developmental Schedules (GDS) and the severity of the disease was evaluated by the Autism Behavior Checklist (ABC). Eleven brain regions were selected as regions of interest and the susceptibility values were measured by quantitative susceptibility mapping.

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Hemifacial spasm (HFS) is a syndrome characterized by involuntary contractions of the facial muscles innervated by the ipsilateral facial nerve. Currently, microvascular decompression (MVD) is an effective treatment for HFS. Diffusion weighted imaging (DWI) is a non-invasive advanced magnetic resonance technique that allows us to reconstruct white matter (WM) virtually based on water diffusion direction.

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Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients.

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Background: Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. This study aimed to construct immune-related long non-coding RNAs (lncRNAs) signature and radiomics signature to probe the prognosis and immune infiltration of GBM patients.

Methods: We downloaded GBM RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database, and MRI data were obtained from The Cancer Imaging Archive (TCIA).

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Article Synopsis
  • The study aimed to evaluate how effective radiomics-based machine learning models are at predicting survival in glioblastoma multiforme (GBM) patients.
  • A total of 131 patients were analyzed using a traditional Cox model and four machine learning models, with their performance assessed through the C-index.
  • Results showed that the DeepSurv model outperformed others, achieving the highest C-index scores, indicating that deep learning can more accurately predict survival compared to traditional methods.
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Accurate segmentation of gastric tumors from computed tomography (CT) images provides useful image information for guiding the diagnosis and treatment of gastric cancer. Researchers typically collect datasets from multiple medical centers to increase sample size and representation, but this raises the issue of data heterogeneity. To this end, we propose a new cross-center 3D tumor segmentation method named unsupervised scale-aware and boundary-aware domain adaptive network (USBDAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale features from the CT images with anisotropic resolution, and a scale-aware and boundary-aware domain alignment (SaBaDA) module for adaptively aligning multi-scale features between two domains and enhancing tumor boundary drawing based on location-related information drawn from each sample across all domains.

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Objective: The objective of this study was to evaluate the whole-brain pattern of oxygen extraction fraction (OEF), cerebral blood flow (CBF), and cerebral metabolic rate of oxygen consumption (CMRO) perturbation in Alzheimer's disease (AD) and investigate the relationship between regional cerebral oxygen metabolism and global cognition.

Methods: Twenty-six AD patients and 25 age-matched healthy controls (HC) were prospectively recruited in this study. Mini-Mental State Examination (MMSE) was used to evaluate cognitive status.

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Background: The most common cause of lower motor neuron facial palsy is Bell's palsy (BP). BP results in partial or complete inability to automatically move the facial muscles on the affected side and, in some cases, to close the eyelids, which can cause permanent eye damage. This study investigated changes in brain function and connectivity abnormalities in patients with BP.

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Introduction: Meige syndrome (MS) is an adult-onset segmental dystonia disease, mainly manifested as blepharospasm and involuntary movement caused by dystonic dysfunction of the oromandibular muscles. The changes of brain activity, perfusion and neurovascular coupling in patients with Meige syndrome are hitherto unknown.

Methods: Twenty-five MS patients and thirty age- and sex-matched healthy controls (HC) were prospectively recruited in this study.

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Article Synopsis
  • * It was found that MED27 expression is elevated in breast cancer, especially in triple-negative cases, and is associated with increased cancer aggressiveness, while knocking it down impairs cancer cell metastasis and stem cell characteristics.
  • * The study identified KLF4 as a downstream target of MED27, with MED27 knockdown promoting cell death and decreasing tumorsphere formation, suggesting that targeting the MED27/KLF4 signaling pathway could be a new therapeutic strategy for breast cancer.
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Introduction: Pseudocontinuous Arterial Spin Labeling (pCASL) perfusion imaging allows non-invasive quantification of regional cerebral blood flow (CBF) as part of a multimodal magnetic resonance imaging (MRI) protocol. This study aimed to compare regional CBF in autism spectrum disorders (ASD) individuals with their age-matched typically developing (TD) children using pCASL perfusion imaging.

Materials And Methods: This cross-sectional study enrolled 17 individuals with ASD and 13 TD children.

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  • Spinocerebellar degeneration (SCD) includes various disorders like spinocerebellar ataxia, characterized by progressive cerebellar ataxia and structural brain changes, which this study investigates through cerebral blood flow (CBF) patterns using arterial spin labeling (ASL).
  • The study involved 30 SCD patients and 30 healthy controls, assessing disease severity with scales like SARA and ICARS, and evaluating psychological status through anxiety, depression, and sleep scales.
  • Results showed decreased CBF in specific brain regions of SCD patients, with negative correlations found between CBF values and both disease severity and psychological issues, indicating a relationship between blood flow, ataxia severity, and mental health challenges.
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Background: In Alzheimer's disease (AD), cerebral iron accumulation colocalizes with the pathological proteins amyloid-β (Aβ) and tau. Furthermore, tau-induced cortical thinning is associated with cognitive decline. In this study, quantitative susceptibility mapping (QSM) was used to investigate the whole-brain distribution pattern of cortical iron deposition and its relationships with cognition and cortical thickness in AD.

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As the largest subunit of the nuclear remodeling factor complex, Bromodomain PHD Finger Transcription Factor (BPTF) has been reported to be involved in tumorigenesis and development in several cancers. However, to date, its functions and related molecular mechanisms in colorectal cancer (CRC) are still poorly defined and deserve to be revealed. In this study, we uncovered that, under the expression regulation of c-Myc, BPTF promoted CRC progression by targeting Cdc25A.

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Preoperative prediction of lymph node (LN) metastasis based on computed tomography (CT) scans is an important task in gastric cancer, but few machine learning-based techniques have been proposed. While multi-center datasets increase sample size and representation ability, they suffer from inter-center heterogeneity. To tackle the above issue, we propose a novel multi-source domain adaptation framework for this diagnosis task, which not only considers domain-invariant and domain-specific features, but also achieves the imbalanced knowledge transfer and class-aware feature alignment across domains.

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Objective: This study analyzed the differences in the cerebral blood flow (CBF) between unilateral Sudden Sensorineural Hearing Loss (SSNHL) patients and healthy controls (HCs). We also investigated CBF differences in auditory-related areas in patients with left- and right-sided SSNHL (lSSNHL and rSSNHL) and HCs. We further explore the correlation between unilateral SSNHL characteristics and changes in the CBF.

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Hemifacial spasm (HFS) is a kind of motor disorder, and the striatum plays a significant role in motor function. The purpose of this study was to explore the alterations of the cortical-striatal network in HFS using resting-state functional magnetic resonance imaging (fMRI). The fMRI data of 30 adult patients with primary unilateral HFS (15 left-side and 15 right-side) and 30 healthy controls were collected.

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Article Synopsis
  • The study aimed to assess the effectiveness of F-FDG PET/CT and MRI in detecting lymph-node metastasis in early-stage cervical cancer through a review of 87 patients.
  • Results showed that PET/CT outperformed MRI significantly in terms of sensitivity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy for both node-based and patient-based metastases.
  • The findings conclude that PET/CT is a more reliable imaging method than MRI for identifying lymphatic metastases in early-stage cervical cancer patients.
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