cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25442/hku.30017380.v1
title: Supported data for "Investigating the Pathological Mechanisms for the Spread of α-Synuclein in Experimental Parkinson’s Disease"

description: Parkinson’s disease (PD) is the second most common neurodegenerative disease worldwide. Loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and Lewy bodies in neurons are two major pathological hallmarks of the PD. The spread of a-synuclein to other brain regions has been speculated to induce neuronal death and loss of some brain functions. However, it is still a major question in PD research field. While free radicals play an important role in the pathogenesis of PD, it is unclear whether this factor also makes impact on the spreading of a-synuclein to other brain regions. This study aims to investigate whether free radicals affect the spread of a-synuclein in experimental models of PD. This data set includes data presented in the result chapters.
DOI: 10.25442/hku.30019501
title: <i>Supporting data for “STING/IFN-I Axis Links to Tissue Resident CD4+ T cell Pathogenecity in Primary Sjögren's Disease.</i><i>”</i>

description: The dataset comprises a multi-faceted collection of experimental data derived from murine salivary gland studies, with a central focus on elucidating the impacts of STING activation on glandular morphology, protein localization, and gene expression profiles. It includes high-resolution fluorescence images acquired by laser scanning confocal microscopy, offering detailed visualizations of tissue microstructure and biomarker distribution. Comprehensive immunophenotyping data obtained via flow cytometry—using both the BD FACSymphony™ A5 SE for high-parameter spectral analysis and the BD LSRFortessa™ for conventional polychromatic approaches—are provided in .fcs format. These were processed with FlowJo™ software to quantify immune cell subsets, assess functional markers, and evaluate treatment-related changes in cellular profiles. The dataset also contains qPCR results from whole salivary gland tissues and purified primary epithelial cells, highlighting expression changes in genes associated with immune responses, hypoxia, apoptosis, and epithelial integrity. Additional components comprise transcription factor activity profiles generated with commercial assay kits, multiplexed protein and cytokine expression data from antibody-based microarrays, and bioinformatically processed high-throughput sequencing data. Together, these integrated data resources support a systems-level investigation into salivary gland immunobiology and STING pathway-mediated effects.
DOI: 10.25442/hku.30019501.v1
title: <i>Supporting data for “STING/IFN-I Axis Links to Tissue Resident CD4+ T cell Pathogenecity in Primary Sjögren's Disease.</i><i>”</i>

description: The dataset comprises a multi-faceted collection of experimental data derived from murine salivary gland studies, with a central focus on elucidating the impacts of STING activation on glandular morphology, protein localization, and gene expression profiles. It includes high-resolution fluorescence images acquired by laser scanning confocal microscopy, offering detailed visualizations of tissue microstructure and biomarker distribution. Comprehensive immunophenotyping data obtained via flow cytometry—using both the BD FACSymphony™ A5 SE for high-parameter spectral analysis and the BD LSRFortessa™ for conventional polychromatic approaches—are provided in .fcs format. These were processed with FlowJo™ software to quantify immune cell subsets, assess functional markers, and evaluate treatment-related changes in cellular profiles. The dataset also contains qPCR results from whole salivary gland tissues and purified primary epithelial cells, highlighting expression changes in genes associated with immune responses, hypoxia, apoptosis, and epithelial integrity. Additional components comprise transcription factor activity profiles generated with commercial assay kits, multiplexed protein and cytokine expression data from antibody-based microarrays, and bioinformatically processed high-throughput sequencing data. Together, these integrated data resources support a systems-level investigation into salivary gland immunobiology and STING pathway-mediated effects.
DOI: 10.25442/hku.30022420
title: Supporting data for “Characterization of the function of PLA2G7 in chronic metabolic disorders”

description: The uploaded research data included all the supporting data for the thesis titled "Characterization of the function of PLA2G7 in chronic metabolic disorders". The supporting data for Chapter 3 includes the original lipidomic data and clinical data for analyzing the differences in lipid species between incident-T2D and non-T2D participants, as well as the analyzed results. The supporting data for Chapter 4 includes the expression data (qPCR and protein level measured by ELISA) as well as the two clinical cohort used for analyzing the correlation between my target and clinical parameters. The supporting data for Chapter 5 includes the in vivo study phenotypical data, including monitor data for body weight, body composition, glucose, insulin, lipid content, etc. The tissue section pictures were also included in the dataset. Through these data, this research identified several lipid species as T2D predictors using lipidomics, then through in vitro and in vivo studies we unraveled the effects of the identified target on chronic metabolic diseases.
DOI: 10.25442/hku.30022420.v1
title: Supporting data for “Characterization of the function of PLA2G7 in chronic metabolic disorders”

description: The uploaded research data included all the supporting data for the thesis titled "Characterization of the function of PLA2G7 in chronic metabolic disorders". The supporting data for Chapter 3 includes the original lipidomic data and clinical data for analyzing the differences in lipid species between incident-T2D and non-T2D participants, as well as the analyzed results. The supporting data for Chapter 4 includes the expression data (qPCR and protein level measured by ELISA) as well as the two clinical cohort used for analyzing the correlation between my target and clinical parameters. The supporting data for Chapter 5 includes the in vivo study phenotypical data, including monitor data for body weight, body composition, glucose, insulin, lipid content, etc. The tissue section pictures were also included in the dataset. Through these data, this research identified several lipid species as T2D predictors using lipidomics, then through in vitro and in vivo studies we unraveled the effects of the identified target on chronic metabolic diseases.
DOI: 10.25442/hku.30024028
title: Supporting data for" <b>Diatom frustules in trash fish and their association with the esophageal cancer epidemic in </b><b>Eastern China</b><b>"</b>

description: Esophageal squamous cell carcinoma (ESCC) is the predominant histological type of esophageal cancer in China, accounting for over half of all global cases. Despite extensive research and prevention efforts since the 1970s, its persistently high incidence and distinct regional clustering remain poorly understood. Dietary factors, particularly sharp-shaped silica fragments, have been implicated in esophageal physical damage, yet the potential role of diatom frustules—naturally occurring silica structures with sharp morphologies—remains underexplored. In certain ESCC epicenters in eastern China, pennate diatoms with sharp morphologies dominate freshwater ecosystems. Common dietary components in these areas include small fish species known as trash fish, which may carry these sharp diatoms fragments into the human diet via filter feeding. This thesis explores the role of diatom frustules in ESCC etiology by characterizing their presence in the food chain, developing methods for their detection, and assessing population-level exposure.
DOI: 10.25442/hku.30024028.v1
title: Supporting data for" <b>Diatom frustules in trash fish and their association with the esophageal cancer epidemic in </b><b>Eastern China</b><b>"</b>

description: Esophageal squamous cell carcinoma (ESCC) is the predominant histological type of esophageal cancer in China, accounting for over half of all global cases. Despite extensive research and prevention efforts since the 1970s, its persistently high incidence and distinct regional clustering remain poorly understood. Dietary factors, particularly sharp-shaped silica fragments, have been implicated in esophageal physical damage, yet the potential role of diatom frustules—naturally occurring silica structures with sharp morphologies—remains underexplored. In certain ESCC epicenters in eastern China, pennate diatoms with sharp morphologies dominate freshwater ecosystems. Common dietary components in these areas include small fish species known as trash fish, which may carry these sharp diatoms fragments into the human diet via filter feeding. This thesis explores the role of diatom frustules in ESCC etiology by characterizing their presence in the food chain, developing methods for their detection, and assessing population-level exposure.
DOI: 10.25442/hku.30028177
title: Supporting data for “Hepatitis B virus integration in hepatocellular carcinoma: from detection and mechanistic insights to clinical translation”

description: Hepatitis B virus (HBV) infection is a significant risk factor for hepatocellular carcinoma (HCC), primarily through viral integration into the host genome and other mechanisms, which can lead to genomic instability and tumorigenesis. This study aimed to deepen the understanding of HBV integration in HCC from event detection, landscape characterization and mechanistic determination to translational application. To enhance HBV integration detection and support our subsequent HBV-associated HCC investigation, AVID (Accurate Viral Integration Detector) algorithm was developed with improved sensitivity and accuracy for viral integration detection. It outperformed other existing tools and was applicable beyond HCC, with demonstrated performance in various oncovirus-associated cancers. Utilizing AVID, we further investigated the mechanistic implications of HBV integration in HCC, revealing notable enrichment and co-involvement of HBV integrations at telomeres and centromeres, which may contribute to genomic instability and poor clinical outcomes. Notably, since <i>TERT </i>is the most frequently integrated gene and its specific role in telomere maintenance and tumorigenesis, we pinpointed our investigation on <i>TERT </i>events and identified the orientation and relative distance of HBV integration concurrently modulate TERT transcription activation. These above findings highlight both gene-independent and gene-dependent mechanisms by which HBV integration promotes HCC progression. Given the critical role of clonal expansion in various oncogenic processes, we further focused on the clonal enrichment of HBV integration, revealing such enrichment may vary across different geographical regions and patients’ viral infection history. A clonal disparity score was designed to quantify the diverse clonal enrichment and served as a potential prognostic indicator for patients. Recognizing the significant role of clonal HBV integration in HCC, we finally explored its potential as a blood-based biomarker through circulating cell-free DNA (cfDNA). Clonal disparity score was further applied on cfDNA data and alteration profile including HBV integration and HBV fragment size was employed to construct a machine learning model for early HCC detection, highlighting the potential of using HBV integration in guiding clinical management strategies for HCC.
DOI: 10.25442/hku.30028177.v1
title: Supporting data for “Hepatitis B virus integration in hepatocellular carcinoma: from detection and mechanistic insights to clinical translation”

description: Hepatitis B virus (HBV) infection is a significant risk factor for hepatocellular carcinoma (HCC), primarily through viral integration into the host genome and other mechanisms, which can lead to genomic instability and tumorigenesis. This study aimed to deepen the understanding of HBV integration in HCC from event detection, landscape characterization and mechanistic determination to translational application. To enhance HBV integration detection and support our subsequent HBV-associated HCC investigation, AVID (Accurate Viral Integration Detector) algorithm was developed with improved sensitivity and accuracy for viral integration detection. It outperformed other existing tools and was applicable beyond HCC, with demonstrated performance in various oncovirus-associated cancers. Utilizing AVID, we further investigated the mechanistic implications of HBV integration in HCC, revealing notable enrichment and co-involvement of HBV integrations at telomeres and centromeres, which may contribute to genomic instability and poor clinical outcomes. Notably, since <i>TERT </i>is the most frequently integrated gene and its specific role in telomere maintenance and tumorigenesis, we pinpointed our investigation on <i>TERT </i>events and identified the orientation and relative distance of HBV integration concurrently modulate TERT transcription activation. These above findings highlight both gene-independent and gene-dependent mechanisms by which HBV integration promotes HCC progression. Given the critical role of clonal expansion in various oncogenic processes, we further focused on the clonal enrichment of HBV integration, revealing such enrichment may vary across different geographical regions and patients’ viral infection history. A clonal disparity score was designed to quantify the diverse clonal enrichment and served as a potential prognostic indicator for patients. Recognizing the significant role of clonal HBV integration in HCC, we finally explored its potential as a blood-based biomarker through circulating cell-free DNA (cfDNA). Clonal disparity score was further applied on cfDNA data and alteration profile including HBV integration and HBV fragment size was employed to construct a machine learning model for early HCC detection, highlighting the potential of using HBV integration in guiding clinical management strategies for HCC.
DOI: 10.25442/hku.30028318
title: supporting data for PHD thesis entitled " Arousal Regulation and Neurofeedback Treatment for ADHD Children"

description: This is the supporting data for my PHD thesis entitled " Arousal Regulation and Neurofeedback Treatment for ADHD Children"The data are extracted data supporting the meta analysis of EEG Neurofeedback for Children with ADHD - A Systematic Review and Network Meta-Analysis<br>This meta-analysis pools randomized controlled trials of EEG-based neurofeedback for pediatric ADHD, with primary endpoints of core symptoms (parent- and teacher-rated inattention, hyperactivity/impulsivity, and combined scores) and secondary neurocognitive measures (e.g., attention composites, commission errors, reaction-time variability, flexibility, fine motor) at post-treatment and follow-up. Analyses use standardized mean differences (Hedges g) under random-effects models, stratified by comparator type (medicine, active, sham, passive) and, where applicable, contrasted across protocol families (customised algorithm, SCP, SMR, TBR).The supporting dataset contains the <b>raw arm-level descriptive statistics</b> required to compute effect sizes: per study, outcome, and timepoint it lists group means, standard deviations, and sample sizes for neurofeedback and control arms, along with rater, comparator category, protocol type, and outcome direction coding (so higher values consistently reflect the intended construct). When studies report change scores, the file includes the reported change mean/SD; when only pre/post values are available, both are provided to enable derivation. Study identifiers and basic design notes are included to map each record to the corresponding comparison in the meta-analysis.<br>'Descriptive statistics' is the descriptive data of the included papers'p_rating_pair' is the effect size comparison of parent-rated outcomes comparing between groups't_rating_pair' is the effect size comparison of teacher-rated outcomes comparing between groups'cognitive_assessment' is the effect size comparison of cognitive assessments comparing between groups
DOI: 10.25442/hku.30028318.v1
title: supporting data for PHD thesis entitled " Arousal Regulation and Neurofeedback Treatment for ADHD Children"

description: This is the supporting data for my PHD thesis entitled " Arousal Regulation and Neurofeedback Treatment for ADHD Children"The data are extracted data supporting the meta analysis of EEG Neurofeedback for Children with ADHD - A Systematic Review and Network Meta-Analysis<br>This meta-analysis pools randomized controlled trials of EEG-based neurofeedback for pediatric ADHD, with primary endpoints of core symptoms (parent- and teacher-rated inattention, hyperactivity/impulsivity, and combined scores) and secondary neurocognitive measures (e.g., attention composites, commission errors, reaction-time variability, flexibility, fine motor) at post-treatment and follow-up. Analyses use standardized mean differences (Hedges g) under random-effects models, stratified by comparator type (medicine, active, sham, passive) and, where applicable, contrasted across protocol families (customised algorithm, SCP, SMR, TBR).The supporting dataset contains the <b>raw arm-level descriptive statistics</b> required to compute effect sizes: per study, outcome, and timepoint it lists group means, standard deviations, and sample sizes for neurofeedback and control arms, along with rater, comparator category, protocol type, and outcome direction coding (so higher values consistently reflect the intended construct). When studies report change scores, the file includes the reported change mean/SD; when only pre/post values are available, both are provided to enable derivation. Study identifiers and basic design notes are included to map each record to the corresponding comparison in the meta-analysis.<br>'Descriptive statistics' is the descriptive data of the included papers'p_rating_pair' is the effect size comparison of parent-rated outcomes comparing between groups't_rating_pair' is the effect size comparison of teacher-rated outcomes comparing between groups'cognitive_assessment' is the effect size comparison of cognitive assessments comparing between groups
DOI: 10.25442/hku.30038956
title: Supporting data for " Antibody Responses to COVID-19 Infection and Vaccination"

description: In light of the COVID-19 pandemic, this study developed and optimized serological assays to assess humoral immunity following SARS-CoV-2 infection and vaccination. Optimized platforms—including IgG ELISA (OD450 was shown in the dataset), surrogate virus neutralization tests (sVNT) (% inhibition was shown in the dataset), and live virus PRNT—enabled quantification of binding and neutralizing antibodies (PRNT90 and PRNT50 were shown in the dataset). Novel ELISAs targeting ORF8 and N-CTD distinguished infection-induced from vaccine-elicited responses. These assays were applied to large-scale sero-epidemiology studies to estimate infection attack rates and vaccine protection. These data were shown in sheets " convalescent cohorts","sVNT prepandemic", "ORF8 Optimization" ,"CTD Optimization".Longitudinal cohort analysis revealed that antibody magnitude and durability correlated with disease severity. Mechanistic modeling using an antibody-secreting cell framework quantified antibody decay dynamics. Comparative vaccine studies showed BNT162b2 elicited higher neutralizing titers than CoronaVac, with heterologous boosting enhancing breadth against Omicron variants. However, neutralization titers did not fully predict protection levels. (Data in sheet " 2 doses", "Low responders", "Cobovax extended""Neutralization against emerging Omicron subvariants and bivalent booster performance highlighted ongoing antigenic drift. This work advances serological surveillance tools and informs vaccine strategy optimization in the evolving COVID-19 landscape. "Data in sheet " BA.1", "BA.2" and "BA45".
DOI: 10.25442/hku.30038956.v1
title: Supporting data for " Antibody Responses to COVID-19 Infection and Vaccination"

description: In light of the COVID-19 pandemic, this study developed and optimized serological assays to assess humoral immunity following SARS-CoV-2 infection and vaccination. Optimized platforms—including IgG ELISA (OD450 was shown in the dataset), surrogate virus neutralization tests (sVNT) (% inhibition was shown in the dataset), and live virus PRNT—enabled quantification of binding and neutralizing antibodies (PRNT90 and PRNT50 were shown in the dataset). Novel ELISAs targeting ORF8 and N-CTD distinguished infection-induced from vaccine-elicited responses. These assays were applied to large-scale sero-epidemiology studies to estimate infection attack rates and vaccine protection. These data were shown in sheets " convalescent cohorts","sVNT prepandemic", "ORF8 Optimization" ,"CTD Optimization".Longitudinal cohort analysis revealed that antibody magnitude and durability correlated with disease severity. Mechanistic modeling using an antibody-secreting cell framework quantified antibody decay dynamics. Comparative vaccine studies showed BNT162b2 elicited higher neutralizing titers than CoronaVac, with heterologous boosting enhancing breadth against Omicron variants. However, neutralization titers did not fully predict protection levels. (Data in sheet " 2 doses", "Low responders", "Cobovax extended""Neutralization against emerging Omicron subvariants and bivalent booster performance highlighted ongoing antigenic drift. This work advances serological surveillance tools and informs vaccine strategy optimization in the evolving COVID-19 landscape. "Data in sheet " BA.1", "BA.2" and "BA45".
DOI: 10.25442/hku.30073687
title: Supporting data for "A low-frequency and/or low-volume exercise approach to address the perceived barriers to exercise in adults with central obesity and adults with chronic illness comorbid with depression"

description: This dataset includes data from two randomized controlled trials (RCTs), one secondary analysis, and one meta-analysis.Chapter 2 presents a RCT that examined the effects of a duration-matched once-weekly and thrice-weekly high-intensity interval training (HIIT) regimen on total body fat mass in adults with central obesity. Participants were randomly allocated in a 1:1:1 ratio to once-weekly HIIT (n = 105), thrice-weekly HIIT (n = 105), and health education control (n = 105). The dataset of this chapter consists of data on 19 variables (including outcomes on body composition, anthropometric measurements, glucose and cholesterol markers, and cardiorespiratory fitness) for 315 participants at 3 assessment timepoints (baseline, week 16, and week 32).Chapter 3 also presents a RCT that investigated the effects of one year of once-weekly, single-bout Norwegian 4 × 4-minute HIIT (25 minutes of exercise per week) on liver fat and adiposity. Participants were randomly allocated in a 1:1 ratio to HIIT (n = 80) or a health education control (n = 80). The dataset of this chapter consists of data on 23 variables (including outcomes on liver fat, body composition, anthropometric measurements, glucose, cholesterol and liver enzyme markers, and cardiorespiratory fitness) for 160 participants at 3 assessment timepoints (baseline, month 12, and month 24).Chapter 4 is a secondary analysis of the RCT in Chapter 2, in which the effects of once-weekly and thrice-weekly walking/jogging HIIT on lean mass were examined. The dataset of this chapter consists of data on lean mass outcomes for the 315 participants at the 3 assessment timepoints (baseline, week 16, and week 32).Chapter 5 presents a meta-analysis of 36 RCTs addressing the feasibility of aerobic exercise in managing the severity of depressive symptoms in adults with chronic illness comorbid with depression. The dataset of this chapter consists of summary statistics on the primary and secondary outcomes, and the trial, participant, comparator, and intervention characteristics of the individual RCTs included.