Biomedical subjects

Ping Liu

Publications and source records attributed to Ping Liu.

4 recordsLinked to original sources

PubMed2026

Comparative effects of 12-week resistance training on unstable and stable surfaces on muscle stiffness, muscle co-activation, and balance in older patients with knee osteoarthritis.

OBJECTIVE: This randomized trial compared the effects of unstable resistance training (URT), involving resistance exercises on unstable surfaces, and stable resistance training (SRT), performed on stable surfaces, on muscle stiffness, co-activation, and balance in older adults with knee osteoarthritis (KOA). We hypothesized that URT would yield greater improvements by enhancing neuromuscular adaptability. METHODS: Fifty patients with KOA were randomly assigned to the URT group (n = 25) or the SRT group (n = 25). After attrition, 46 participants (URT: n = 23; SRT: n = 23) completed the intervention and were included in the final analysis. Both groups completed a 12-week supervised lower-limb resistance training program (3 sessions/week) consisting of 10 exercises performed under either unstable or stable support conditions. RESULTS: After 12 weeks of intervention, both groups showed significant reductions in pain intensity (p < 0.001). However, compared with the SRT group, the URT group demonstrated significantly greater reductions in quadriceps stiffness (p < 0.05), selected hamstring stiffness outcomes (p < 0.05), and quadriceps-hamstring co-activation (p < 0.001), alongside superior improvements in both dynamic balance and static balance (all p < 0.05). CONCLUSION: While both training modalities are effective for pain relief, URT elicited greater improvements in balance-related performance and neuromuscular-mechanical outcomes than SRT in older adults with KOA. These findings suggest that incorporating unstable support conditions into resistance training may provide additional rehabilitation benefits for this population.

Humans↗

PubMed2025

A high-quality chromosome-scale genome assembly of Xingan mandarin (Citrus reticulata 'Xingan'), a primitive Mandarin type.

Mandarin (Citrus reticulata) is broadly recognized as one of the foremost citrus crops globally. Our study identified the Xingan mandarin (Citrus reticulata 'Xingan') as a primitive type found near Maoer Mountain. This report provides a high-resolution, chromosome-scale genome assembly for the Xingan mandarin. The total size of the genome assembly is an impressive 325.12 Mb, including contig N50 and scaffold N50 values of 29.32 Mb and 29.62 Mb, respectively. Notably, we successfully anchored approximately 93.08% of the assembled sequences onto nine pseudochromosomes. Our predictions identified 30,581 protein-coding genes, 166 miRNAs, 415 tRNAs, 728 rRNAs, 325 snRNAs, and 659 snoRNAs. We were able to predict the functions of 27,242 genes, constituting 89.08% of the total protein-coding genes. A notable finding of our study was the high degree of genome synteny between the Xingan mandarin and the Mangshan mandarin (Citrus reticulata 'Mangshan'), reinforcing their genetic similarity. The acquisition of the chromosome-level genome for the Xingan Mandarin represents a significant milestone, laying an indispensable foundation for rigorous molecular investigations of this species. Moreover, it is poised to invigorate advanced research in comparative genomics within the Citrus genus.

Citrus↗

PubMed2025

Development and validation of a deep learning model based on cascade mask regional convolutional neural network to noninvasively and accurately identify human round spermatids.

INTRODUCTION: The difficulty of identifying human round spermatids (hRSs) has impeded applications of the human round spermatid injection (ROSI) technique. RSs can be accurately screened through flow cytometric analysis utilizing the Hoechst fluorescence profile reflecting DNA, but this method is not suitable for isolating hRSs due to the toxicity associated with Hoechst staining. OBJECTIVE: To evaluate the capacity of a deep learning model grounded in a cascade mask region-based convolutional neural network (R-CNN) for the noninvasive and accurate identification of hRSs. METHODS: In this study, we presented the development and validation of a deep learning model for identifying hRSs through the analysis of 3457 optical light microscope images of sorted hRSs obtained via flow cytometric analysis. The model's accuracy and specificity were evaluated by calculating the mean average precision (mAP). Furthermore, a double-blind experiment was conducted to access the reliability of the proposed model in accurately identifying hRSs. It detected the expression of protamine (PRM1) and/or peanut lectin (PNA), which are established markers for RSs. RESULTS: Our deep learning-based model demonstrated a high precision, achieving a mAP of over 0.80 for isolating hRSs in test datasets. The expression of PRM1 and/or PNA was observed in all cells noninvasively selected by our AI model during an independent double-blind test. This phenomenon confirmed the accuracy and effectiveness of the proposed model. The model's capability for noninvasive and accurate isolation of hRSs among spermatogenic cells highlighted its robustness and generalizability for clinical applications. CONCLUSION: The deep learning AI model based on a cascade R-CNN has the ability to accurately identify hRSs among spermatogenic cells. The application of this noninvasive method, which requires no additional procedures in clinical practice, is able to facilitate the widespread implementation of ROSI technique. Therefore, it can provide patients with spermatogenic arrest the opportunity to become biological fathers.

Humans↗

PubMed2023

Photoaffinity labeling coupled with proteomics identify PDI-ADAM17 module is targeted by (-)-vinigrol to induce TNFR1 shedding and ameliorate rheumatoid arthritis in mice.

Various biological agents have been developed to target tumor necrosis factor alpha (TNF-α) and its receptor TNFR1 for the rheumatoid arthritis (RA) treatment, whereas small molecules modulating such cytokine receptors are rarely reported in comparison to the biologicals. Here, by revealing the mechanism of action of vinigrol, a diterpenoid natural product, we show that inhibition of the protein disulfide isomerase (PDI, PDIA1) by small molecules activates A disintegrin and metalloprotease 17 (ADAM17) and then leads to the TNFR1 shedding on mouse and human cell membranes. This small-molecule-induced receptor shedding not only effectively blocks the inflammatory response caused by TNF-α in cells, but also reduces the arthritic score and joint damage in the collagen-induced arthritis mouse model. Our study indicates that targeting the PDI-ADAM17 signaling module to regulate the shedding of cytokine receptors by the chemical approach constitutes a promising strategy for alleviating RA.

Mice↗