Shuqi Qiu

Shuqi Qiu


Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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7 October 2022 in Journal of Asthma and Allergy
Verified
Journal Article
Open access
PDF file
PLPeng Liu
Peng Liu
THTianyong Hu
Tianyong Hu
CKChenglin Kang
Chenglin Kang
JLJiangqi Liu
Jiangqi Liu
JZJin Zhang
Jin Zhang
SQShuqi Qiu
Shuqi Qiu
Allergic rhinitis (AR) impairs the quality of life of patients and reduces the efficiency of social work, it is an increasingly serious public medical and economic problem in the world. Conventional anti-allergic drugs for the treatment of allergic rhinitis (AR) can cause certain side effects, which limit the quality of life of patients. Therefore, it makes sense to look for other forms of treatment. Several studies in recent years have shown that probiotics have shown anti-allergic effects in various mouse and human studies. For example, the application of certain probiotic strains can effectively relieve the typical nasal and ocular symptoms of allergic rhinitis in children and adults, thereby improving the quality of life and work efficiency. At the same time, previous studies in humans and mice have found that probiotics can produce multiple effects, such as reduction of Th2 cell inflammatory factors and/or increase of Th1 cell inflammatory factors, changes in allergy-related immunoglobulins and cell migration, regulate Th1/Th2 balance or restore intestinal microbiota disturbance. For patients with limited activity or allergic rhinitis with more attacks and longer attack duration, oral probiotics have positive effects. The efficacy of probiotics in the prevention and treatment of allergic rhinitis is remarkable, but its specific mechanism needs further study. This review summarizes the research progress of probiotics in the treatment of allergic rhinitis in recent years.

17 November 2022 in International Immunopharmacology
Verified
Journal Article
Closed access
PLPeng Liu
Peng Liu
CKChenglin Kang
Chenglin Kang
JZJin Zhang
Jin Zhang
YLYue Liu
Yue Liu
JLJiangqi Liu
Jiangqi Liu
SQShuqi Qiu
Shuqi Qiu

5 March 2025 in BMJ Open
Verified
Journal Article
Open access
PDF file
XWXijia Wang
Xijia Wang
PZPeng Zhang
Peng Zhang
HLHuifei Lu
Huifei Lu
DLDandan Luo
Dandan Luo
DYDunhui Yang
Dunhui Yang
XZXianhai Zeng
Xianhai Zeng
Objective This study aimed to systematically evaluate published predictive models for dental caries in children and adolescents. Design A systematic review and meta-analysis of observational studies. Data sources Comprehensive searches were conducted in PubMed, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, Embase, China National Knowledge Infrastructure, Wanfang Database, China Science and Technology Journal Database (VIP) and SinoMed for relevant studies published up to 18 January 2024. The search focused on caries prediction models in children and adolescents. Eligibility criteria Eligible studies included observational research (cohort, case–control and cross-sectional designs) that developed risk prediction models for dental caries in children and adolescents aged ≤18 years. Each model was required to include a minimum of two predictors. Studies were excluded if they were not available in English or Chinese, primarily focused on oral microbiome modelling, or lacked essential details regarding study design, model construction or statistical analyses. Results A total of 11 studies were included in the review. All models demonstrated a high risk of bias, primarily due to inappropriate statistical methods and unclear applicability resulting from insufficiently detailed presentations of the models. Logistic regression, random forests and support vector machines were the most commonly employed methods. Frequently used predictors included fluoride toothpaste use and brushing frequency. Reported area under the curve (AUC) values ranged from 0.57 to 0.91. A combined predictive model incorporating six caries predictors achieved an AUC of 0.79 (95% CI: 0.73 to 0.84). Conclusions Simplified predictive models for childhood caries showed moderate discriminatory performance but exhibited a high risk of bias, as assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Future research should adhere to PROBAST guidelines to minimise bias risk, focus on enhancing model quality, employ rigorous study designs and prioritise external validation to ensure reliable and generalisable clinical predictions. PROSPERO registration number CRD42024523284.