Publications by authors named "Linbo Wang"

Purpose Of Review: Genomic and transcriptomic sequencing technologies have revolutionized our ability to characterize prostate cancer at the molecular level. The underlying premise of next-generation sequencing technologies and their current and evolving applications in prostate cancer management are provided in the review.

Recent Findings: Improved methodologies are allowing timely sequencing of the coding regions or both the coding and noncoding regions of the genome to help identify potential mutations and structural variations in the prostate cancer genome, some of which are currently also targetable therapeutically.

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While increasing peripheral mechanisms related to chronic pain, the plasma proteomics profile associated with it and its prognosis remains elusive. This study utilizes 2923 plasma proteins and chronic pain of 51 644 participants from UK Biobank and finds 474 proteins linked to chronic pain in six sites: head, neck or shoulder, back, stomach or abdominal, hip, and knee, with 11 proteins sharing across pain sites. The identified proteins are largely enriched in immune and metabolic pathways and highly expressed in tissues like lungs and small intestines.

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Breast cancer is the most common cancer among women, with differences in clinical features due to its distinct molecular subtypes. Current studies have demonstrated that epigenetic modifications play a crucial role in regulating the progression of breast cancer. Among these mechanisms, DNA demethylation and its reverse process have been studied extensively for their roles in activating or silencing cancer related gene expression.

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The early pathophysiology of Parkinson's disease (PD) is poorly understood. We analyzed 2,920 Olink-measured plasma proteins in 51,804 UK Biobank participants, identifying 859 incident PD cases after 14.45 years.

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Background: The impact of concurrent proton pump inhibitors (PPIs) use on the prognosis of patients with breast cancer undergoing cyclin-dependent kinase inhibitors (CDKIs) treatment is currently uncertain. Considerable divergence exists regarding the clinical studies. In this study, we aim to perform a comprehensive analysis to evaluate the influence of concomitant PPI use on the effectiveness and adverse effects of CDKIs in patients with breast cancer.

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Racial/ethnic differences are associated with the symptoms and conditions of post-acute sequelae SARS-CoV-2 infection (PASC) in adults. These differences may exist among children and warrant further exploration. We conducted a retrospective cohort study with difference-in-differences analyzes to assess these differences in children and adolescents under the age of 21.

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Triple-negative breast cancer (TNBC) is a subtype of breast cancer known for its high aggressiveness and poor prognosis. Conventional treatment of TNBC is challenging due to its heterogeneity and lack of clear targets. Recent advancements in immunotherapy have shown promise in treating TNBC, with immune checkpoint therapy playing a significant role in comprehensive treatment plans.

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Triple-negative breast cancer (TNBC) represents the most aggressive subtype of breast cancer, lacking effective targeted therapies and presenting with a poor prognosis. In this study, we utilized the epigenomic landscape, TCGA database, and clinical samples to uncover the pivotal role of HJURP in TNBC. Our investigation revealed a strong correlation between elevated HJURP expression and unfavorable prognosis, metastatic progression, and late-stage of breast cancer.

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Background: The impact of pre-infection vaccination on the risk of long COVID remains unclear in the pediatric population. We aim to assess the effectiveness of BNT162b2 on long COVID risks with various strains of the SARS-CoV-2 virus in children and adolescents, using comparative effectiveness methods. We further explore if such pre-infection vaccination can mitigate the risk of long COVID beyond its established protective benefits against SARS-CoV-2 infection using causal mediation analysis.

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Pathophysiological evolutions in early-stage Alzheimer's disease (AD) are not well understood. We used data of 2923 Olink plasma proteins from 51,296 non-demented middle-aged adults. During a follow-up of 15 years, 689 incident AD cases occurred.

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Proteomic alterations preceding the onset of depression offer valuable insights into its development and potential interventions. Leveraging data from 46,165 UK Biobank participants and 2920 plasma proteins profiled at baseline, we conducted a longitudinal analysis with a median follow-up of 14.5 years to explore the relationship between plasma proteins and incident depression.

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Article Synopsis
  • This study focused on using machine learning to predict prognosis in breast cancer patients by analyzing electronic medical records from 6,477 individuals, identifying 15 key clinical features related to survival.
  • Eight different algorithms, including XGBoost, were tested, with XGBoost being the most effective, achieving an AUC of 0.813 and outperforming existing prognostic models.
  • The findings suggest that incorporating machine learning into clinical practice can enhance decision-making for personalized treatment strategies in breast cancer care.
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Breast reconstruction is essential for improving the appearance of patients after cancer surgery. Traditional breast prostheses are not appropriate for those undergoing partial resections and cannot detect and treat locoregional recurrence. Personalized shape prostheses that can smartly sense tumor relapse and deliver therapeutics are needed.

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  • Sorafenib is a common treatment for advanced hepatocellular carcinoma (HCC), but its effectiveness is often reduced due to drug resistance.
  • A study integrating posttranslational modification analysis and CRISPR screening discovered that ubiquitination of CCT3 plays a key role in this resistance.
  • Targeting the CCT3/ACTN4/TFRC pathway may improve treatment outcomes by enhancing ferroptosis and overcoming resistance to Sorafenib in HCC patients.
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Parkinson's disease (PD) exhibits heterogeneity in terms of symptoms and prognosis, likely due to diverse neuroanatomical alterations. This study employs a contrastive deep learning approach to analyze Magnetic Resonance Imaging (MRI) data from 932 PD patients and 366 controls, aiming to disentangle PD-specific neuroanatomical alterations. The results reveal that these neuroanatomical alterations in PD are correlated with individual differences in dopamine transporter binding deficit, neurodegeneration biomarkers, and clinical severity and progression.

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Article Synopsis
  • A recent study identified potential new biomarkers for Alzheimer's disease (AD) through an extensive analysis of over 6,300 cerebrospinal fluid proteins from the ADNI database, highlighting YWHAG as a leading candidate for diagnosis.
  • The study demonstrated that combinations of proteins (four or five) significantly improved diagnostic accuracy for AD, showing exceptional performance in distinguishing between AD and non-AD cases.
  • Findings suggest these biomarkers could predict clinical progression to AD dementia and are linked to cognitive decline, with implications for future clinical trials targeting various disease mechanisms.
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Background And Objectives: Identification of individuals at high risk of developing Parkinson disease (PD) several years before diagnosis is crucial for developing treatments to prevent or delay neurodegeneration. This study aimed to develop predictive models for PD risk that combine plasma proteins and easily accessible clinical-demographic variables.

Methods: Using data from the UK Biobank (UKB), which recruited participants across the United Kingdom, we conducted a longitudinal study to identify predictors for incident PD.

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Background: Dementia has a long prodromal stage with various pathophysiological manifestations; however, the progression of pre-diagnostic changes remains unclear. We aimed to determine the evolutional trajectories of multiple-domain clinical assessments and health conditions up to 15 years before the diagnosis of dementia.

Methods: Data was extracted from the UK-Biobank, a longitudinal cohort that recruited over 500,000 participants from March 2006 to October 2010.

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Article Synopsis
  • Parkinson's disease (PD) patients experience progressive gray matter volume loss, and the study investigates different patterns of this atrophy among patients.* -
  • A total of 107 PD patients were analyzed through MRI scans, leading to the identification of two distinct subtypes based on their rates of brain atrophy: Subtype 1 with moderate atrophy and Subtype 2 with more rapid deterioration.* -
  • Subtype 2 not only showed faster brain atrophy but also correlated with worsened symptoms in non-motor and motor functions, memory, and depression, suggesting the need for tailored treatment approaches.*
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Background: Whether there is hypothalamic degeneration in Parkinson's disease (PD) and its association with clinical symptoms and pathophysiological changes remains controversial.

Objectives: We aimed to quantify microstructural changes in hypothalamus using a novel deep learning-based tool in patients with PD and those with probable rapid-eye-movement sleep behavior disorder (pRBD). We further assessed whether these microstructural changes associated with clinical symptoms and free thyroxine (FT4) levels.

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  • This study analyzes lymph node metastasis in breast cancer patients who did not undergo preoperative treatments, revealing a 25.57% occurrence in a large sample.
  • Multiple factors such as younger age, African-American ethnicity, tumor location, types of carcinoma, HER2 positivity, and tumor size were found to significantly associate with increased risk of lymph node metastasis.
  • The research highlighted the development of predictive nomograms to assist in understanding and predicting the incidence of lymph node involvement based on identified risk factors.
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Developing a single-domain assay to identify individuals at high risk of future events is a priority for multi-disease and mortality prevention. By training a neural network, we developed a disease/mortality-specific proteomic risk score (ProRS) based on 1461 Olink plasma proteins measured in 52,006 UK Biobank participants. This integrative score markedly stratified the risk for 45 common conditions, including infectious, hematological, endocrine, psychiatric, neurological, sensory, circulatory, respiratory, digestive, cutaneous, musculoskeletal, and genitourinary diseases, cancers, and mortality.

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