Biomedical subjects

Ying Cai

Publications and source records attributed to Ying Cai.

3 recordsLinked to original sources

PubMed2026

The asymmetry of working memory training transfer: A systematic review and meta-analysis.

Working memory (WM) training is widely used to enhance cognitive performance; however, its transfer to untrained tasks remains controversial. Traditional theories emphasize task similarity as the primary determinant of training transfer, but they cannot fully explain emerging evidence of asymmetric transfer across tasks. Two directional transfer hypotheses are proposed here to explain this asymmetry: the resource-based transfer advantage hypothesis predicts stronger transfer from more to less cognitively demanding tasks, whereas the ability-based transfer advantage hypothesis predicts stronger transfer from tasks engaging broader task-general abilities to tasks engaging task-specific narrower abilities. The contrast between span and updating paradigms provides an informative framework for distinguishing these accounts, because updating tasks are generally more cognitively demanding, whereas span tasks involve broader abilities. Accordingly, we conducted a three-level meta-analysis of 55 studies (208 effect sizes; N = 3,492). The results showed that updating training transferred reliably to span tasks (g = 0.176, p < .001), whereas span training did not reliably transfer to updating tasks (g = 0.048, p = .453), supporting the resource-based account. This advantage of updating training also extended to non-WM outcomes and was more pronounced at lower training doses, in non-adult samples, and with verbal stimuli. Together, these findings extend WM transfer theory beyond task similarity by highlighting the importance of cognitive demand and offer guidance for WM training design.

Humans↗

PubMed2025

Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration.

OBJECTIVE: Diagnosis of hepatocellular carcinoma (HCC) remains challenging for clinicians. Machine learning approaches and big data analyses are viable strategies for identifying HCC diagnostic markers. MATERIALS AND METHODS: In this study, we downloaded mRNA expression profiles of HCC from the GEO database and used random forest and machine learning algorithms, such as least absolute shrinkage and selection operator, to screen for reliable diagnostic genes. Disease Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis enrichment analyses were performed to explore differential gene functions and disease pathways. CIBERSORT was performed to calculate the immune cell infiltration of HCC and the correlation between diagnostic genes and immune cells. Cell experiments were performed to evaluate the function of R-spondin 3 (RSPO3) in HCC cells. Immunohistochemical staining was used to evaluate the protein expression of CD138, CD206 and iNOS. RESULTS: The results indicated that extracellular matrix protein 1 (ECM1), Niemann-Pick C1-Like 1 (NPC1L1) and RSPO3 were down-regulated in HCC compared with the normal group (p < 0.05), which was validated in clinical tissue samples. Moreover, ECM1, NPC1L1 and RSPO3 had high diagnostic values (AUC > 0.75) for HCC in both training and test groups. Immuno-infiltration analysis revealed that ECM1 and RSPO3 were highly positively correlated with neutrophil and macrophage M2 levels, whereas they were negatively correlated with Tregs. RSPO3-si affected cell proliferation and apoptosis in HCC. Furthermore, RSPO3 exhibited a positive correlation with tumour progression, the proportion of plasma cells and M2 macrophages in mice, while showing a negative association with M1 macrophages. CONCLUSION: The present study identified ECM1, NPC1L1 and RSPO3 as new diagnostic biomarkers for HCC based on normal and diseased samples from HCC, meanwhile the pro-oncogenic function of RSPO3 and its regulation on immune infiltration have been confirmed.

Carcinoma, Hepatocellular↗

PubMed2024

Effect of Metagenomic Next-Generation Sequencing on Clinical Outcomes of Patients With Severe Community-Acquired Pneumonia in the ICU: A Multicenter, Randomized Controlled Trial.

BACKGROUND: Metagenomic next-generation sequencing (mNGS) was previously established as a method that can increase the pathogen identification rate in patients with severe community-acquired pneumonia (SCAP). RESEARCH QUESTION: What is the impact on clinical outcomes of mNGS of BAL fluid (BALF) in patients with SCAP in the ICU? STUDY DESIGN AND METHODS: A multicenter randomized controlled open-label clinical trial was conducted in 10 ICUs. Patients were randomized in a 1:1 ratio to undergo BALF assessment with conventional microbiological tests (CMTs) only (ie, the CMT group) or BALF assessment with both mNGS and CMTs (ie, the mNGS group). The primary outcome was the time to clinical improvement, defined as the time from randomization to either an improvement of two points on a six-category ordinal scale or discharge from the ICU, whichever occurred first. RESULTS: A total of 349 patients were randomized to treatment between January 1, 2021, and November 18, 2022; 170 were assigned to the CMT group and 179 to the mNGS group. In the intention-to-treat analysis, the time to clinical improvement was better in the mNGS group than in the CMT group (10 days vs 13 days; difference, -2.0 days; 95% CI, -3.0 to 0.0 days). Similar results were obtained in the per-protocol analysis. The proportion of patients with clinical improvement within 14 days was significantly higher in the mNGS group (62.0%) than in the CMT group (46.5%). There was no significant difference in other secondary outcomes. INTERPRETATION: We found that compared with the use of CMTs alone, mNGS combined with CMTs reduced the time to clinical improvement for patients with SCAP. CLINICAL TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR; www.chictr.org.cn/index.html; ChiCTR2000037894.

Humans↗