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Biomedical subjects

Hui Xie

Publications and source records attributed to Hui Xie.

3 recordsLinked to original sources

Proteomic Immune Signatures of Severe HIV-Associated Tuberculosis in Sub-Saharan Africa: A Prospective, Multicenter Analysis From Uganda.

OBJECTIVES: Severe tuberculosis (TB) is a major cause of critical illness and death in people living with HIV (PLWH) worldwide. Despite this, the immunopathology of severe HIV-associated TB (HIV/TB) is poorly understood. We aimed to identify an immunopathologic signature of severe HIV/TB in sub-Saharan Africa. DESIGN AND SETTING: We analyzed proteomic data from two prospective observational cohorts of adults hospitalized with severe undifferentiated infection in Uganda: an urban discovery cohort (Entebbe, n = 241) and a rural validation cohort (Tororo, n = 253). PATIENTS: Adults (age ≥ 18 yr) hospitalized with severe febrile illness. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Across both cohorts, severe HIV/TB was common, affecting 18% of participants in the discovery cohort and 21% in the validation cohort. Overall mortality was significant (30-d mortality of 22% in the discovery cohort and 60-d mortality of 26% in the validation cohort). Participants were stratified into three HIV/TB phenotypes: HIV-negative without TB, PLWH without TB, and PLWH with microbiologically diagnosed TB. We applied ordinal random forest models in the discovery cohort as a supervised feature-selection approach to identify proteins associated with progressive HIV/TB phenotype. In both cohorts, PLWH with microbiologically diagnosed TB were at highest risk of critical illness and death (30-d mortality of 42% in the discovery cohort and 60-d mortality of 52% in the validation cohort). An eight-protein signature reliably distinguished this phenotype, reflecting mediators of macrophage/dendritic cell activation (lysosome-associated membrane glycoprotein 3), natural killer cell and T-cell stimulation and cytotoxicity (cluster of differentiation 70, class I-restricted T-cell-associated molecule), B-cell activation (immunoglobulin lambda constant 2), protease-mediated tissue injury (protease, serine 2 [trypsin-2]), dysregulated coagulation (serpin peptidase inhibitor, clade A [alpha-1 antitrypsin], member 5), extracellular matrix remodeling (epidermal growth factor-containing fibulin-like extracellular matrix protein 1), and growth hormone/insulin-like growth factor axis dysregulation (insulin-like growth factor binding protein 3). CONCLUSIONS: We identified an immunologic signature of severe HIV/TB defined by mediators of macrophage/dendritic cell and cytotoxic lymphocyte activation, extracellular matrix remodeling, and dysregulated coagulation. These findings offer new insight into HIV/TB pathobiology and highlight potential targets for host-directed therapies in this high-risk population.

Humans

Effectiveness of passive vs. assistive robotic gait training on functional recovery and neuroplasticity post-stroke: A randomized controlled trial.

OBJECTIVE: This study seeks to compare the impacts of various robotic gait training (RAGT) modes on lower limb motor function recovery in stroke patients while exploring the corresponding neural mechanisms. DESIGN: A single-blind, randomized controlled trial. SETTING: Inpatient Rehabilitation Facility. PARTICIPANTS: Forty-eight patients aged 18-80 who had experienced their first unilateral subacute stroke accompanied by walking impairments were included. INTERVENTIONS: Participants were randomly assigned to: (1) assistive mode training, (2) passive mode training, or (3) control group receiving only traditional rehabilitation. Clinical and neurological outcomes were assessed at pre-intervention (T0), and post-2-week intervention (T1). MAIN OUTCOME MEASURES: Outcomes were evaluated using the Fugl-Meyer Assessment for Lower Extremity, Berg Balance Scale, Modified Barthel Index, the Functional Ambulatory Category, and functional near-infrared spectroscopy. RESULTS: Among the 48 patients recruited, significant time effects were observed across all groups in FMA-LE scores (p&#x202f;<&#x202f;0.001). Notable improvements were detected in the conventional group (MD = 2.69, p&#xff1c;0.01) and the passive group (MD = 3.67, p&#x202f;<&#x202f;0.001), with the assistive mode also demonstrating a significant effect (MD = 1.79, p&#x202f;<&#x202f;0.05). BBS scores improved across all groups; however, no significant differences were noted between the groups (p&#x202f;=&#x202f;0.11). Similarly, MBI scores showed a significant time effect (p&#x202f;<&#x202f;0.001), without notable group differences (p&#x202f;=&#x202f;0.29). CONCLUSION: All training modalities effectively enhanced motor function, balance, and daily living skills in stroke patients. Distinct cortical activation and connectivity patterns were observed between training modalities, which may reflect different neuroplastic mechanisms. These preliminary neural differences may help inform personalized rehabilitation strategies, although no clinical superiority of one mode over another can be concluded from the present data.

Humans

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.

BACKGROUND: Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. METHOD: RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). RESULTS: Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient&#x2009;=&#x2009;-&#x2009;1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10&#xa0;years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. CONCLUSION: In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

Adenocarcinoma of Lung