Publications by authors named "Zhongzhi Xu"

Background: The global population of adults aged 60 and above surpassed 1 billion in 2020, constituting 13.5% of the global populace. Projections indicate a rise to 2.

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Background: Due to their accessibility and anonymity, web-based counseling services are expanding at an unprecedented rate. One of the most prominent challenges such services face is repeated users, who represent a small fraction of total users but consume significant resources by continually returning to the system and reiterating the same narrative and issues. A deeper understanding of repeated users and tailoring interventions may help improve service efficiency and effectiveness.

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Objective: Fatal road accidents are statistically rare, posing challenges for accurate estimation through the classic logit model (LM). This study seeks to validate the efficacy of a rare events logistic model (RELM) in enhancing the precision of fatal crash estimations.

Methods: Both LM and RELM were employed to examine the relationship between pertinent risk factors and the incidence of fatal crashes.

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Background: Cardiovascular disease (CVD) and cancer are the first and second causes of death in over 130 countries across the world. They are also among the top three causes in almost 180 countries worldwide. Cardiovascular complications are often noticed in cancer patients, with nearly 20% exhibiting cardiovascular comorbidities.

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Article Synopsis
  • Occupational burnout is a psychological syndrome that can cause serious mental and physical health issues, but individuals often hide their feelings about it due to potential bias from superiors.* -
  • The study analyzed data from 5,794 participants, using various statistical tests to explore the connections between occupational burnout and factors like demographics, work-related situations, health status, and lifestyle.* -
  • Significant associations with burnout were found in demographics (age, education), work (type of position), health conditions (self-rated health, chronic diseases), and lifestyle behaviors (diet, exercise habits).
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The discovery and repurposing of drugs require a deep understanding of the mechanism of drug action (MODA). Existing computational methods mainly model MODA with the protein-protein interaction (PPI) network. However, the molecular interactions of drugs in the human body are far beyond PPIs.

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Patients with acute coronary syndrome (ACS) are at high risk of heart failure (HF). Early prediction and management of HF among ACS patients are essential to provide timely and cost-effective care. The aim of this study is to train and evaluate a machine learning model to predict the acute onset of HF subsequent to ACS.

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Background And Aims: Understanding the age-related trend of risk in high blood pressure (BP) is important for preventing heart failure and cardiovascular diseases. But such a trend is still underexplored. This study aims to (a) depict the relationship of BP patterns with age, and (b) understand the trend of high BP prevalence over time in different age groups.

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Background: More so than face-to-face counseling, users of online text-based services might drop out from a session before establishing a clear closure or expressing the intention to leave. Such may be indicative of heightened risk or dissatisfaction with the service or counselor. However, there is no systematic way to identify this understudied phenomenon.

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Men who have sex with men (MSM) make up the majority of new human immunodeficiency virus (HIV) diagnoses among young people in China. Understanding HIV transmission dynamics among the MSM population is, therefore, crucial for the control and prevention of HIV infections, especially for some newly reported genotypes of HIV. This study presents a metapopulation model considering the impact of pre-exposure prophylaxis (PrEP) to investigate the geographical spread of a hypothetically new genotype of HIV among MSM in Guangdong, China.

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Objective: To develop an end-to-end deep learning framework based on a protein-protein interaction (PPI) network to make synergistic anticancer drug combination predictions.

Materials And Methods: We propose a deep learning framework named Graph Convolutional Network for Drug Synergy (GraphSynergy). GraphSynergy adapts a spatial-based Graph Convolutional Network component to encode the high-order topological relationships in the PPI network of protein modules targeted by a pair of drugs, as well as the protein modules associated with a specific cancer cell line.

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Prognostic prediction is the task of estimating a patient's risk of disease development based on various predictors. Such prediction is important for healthcare practitioners and patients because it reduces preventable harm and costs. As such, a prognostic prediction model is preferred if: 1) it exhibits encouraging performance and 2) it can generate intelligible rules, which enable experts to understand the logic of the model's decision process.

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Background: Cumulative evidence has shown that the non-invasive modality of coronary computed tomography angiography (CCTA) has evolved as an alternative to invasive coronary angiography, which can be used to quantify plaque burden and stenosis and identify vulnerable plaque, assisting in diagnosis, prognosis and treatment. With the increasing elderly population, many patients scheduled for non-cardiovascular surgery may have concomitant coronary artery disease (CAD). The aim of this study was to investigate the usefulness of preoperative CCTA to rule out or detect significant CAD in this cohort of patients and the impact of CCTA results to clinical decision-making.

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Background: Given concerns about adverse outcomes for older people taking antidepressants in the literature, we investigated whether taking antidepressants elevates the risk of dementia.

Objective: This study aims to investigate the putative association of antidepressants with the risk of dementia.

Methods: We conducted a population-based self-controlled case series analysis of older people with dementia and taking antidepressants, using territory-wide medical records of 194,507 older patients collected by the Hospital Authority of Hong Kong, to investigate the association between antidepressant treatment and the risk of developing dementia in older people.

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Background: Self-harm is preventable if the risk can be identified early. The co-occurrence of multiple diseases is related to self-harm risk. This study develops a comorbidity network-based deep learning framework to improve the prediction of individual self-harm.

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Background And Aims: Risk stratification in acute myocardial infarction (AMI) is important for guiding clinical management. Current risk scores are mostly derived from clinical trials with stringent patient selection. We aimed to establish and evaluate a composite scoring system to improve short-term mortality classification after index episodes of AMI, independent of electrocardiography (ECG) pattern, in a large real-world cohort.

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Introduction: Depressive disorder is one of the major public health problems among the elderly. An effective depression risk prediction model can provide insights on the disease progression and potentially inform timely targeted interventions. Therefore, research on predicting the onset of depressive disorder for elderly adults considering the sequential progression patterns is critically needed.

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A rare carotane-type sesquiterpenoid, forkienin A (1), a new eudesmane-type sesquiterpenoid, forkienin B (2), and a new natural eudesmane-type sesquiterpenoid, forkienin C (3), were isolated from the twigs and leaves of Fokienia hodginsii, along with eight known sesquiterpenoids. The structures of the new compounds were elucidated on the basis of their spectroscopic analysis, including 1D and 2D NMR methods. All compounds were evaluated for cytotoxicity against HL-60, SMMC-7721, A-549, MCF-7, and SW480 cell lines.

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