Collagen-based materials, renowned for their biocompatibility and minimal immunogenicity, serve as exemplary substrates in a myriad of biomedical applications. Collagen-based micro/nanogels, in particular, are valued for their increased surface area, tunable degradation rates, and ability to facilitate targeted drug delivery, making them instrumental in advanced therapeutics and tissue engineering endeavors. Although extensive reviews on micro/nanogels exist, they tend to cover a wide range of biomaterials and lack a specific focus on collagen-based materials. The current review offers an in-depth look into the manufacturing technologies, drug release mechanisms, and biomedical applications of collagen-based micro/nanogels to address this gap. First, we provide an overview of the synthetic strategies that allow the precise control of the size, shape, and mechanical strength of these collagen-based micro/nanogels by controlling the degree of cross-linking of the materials. These properties are crucial for their performance in biomedical applications. We then highlight the environmental responsiveness of these collagen-based micro/nanogels, particularly their sensitivity to enzymes and pH, which enables controlled drug release under various pathological conditions. The discussion then expands to include their applications in cancer therapy, antimicrobial treatments, bone tissue repair, and imaging diagnosis, emphasizing their versatility and potential in these critical areas. The challenges and future perspectives of collagen-based micro/nanogels in the field are discussed at the end of the review, with an emphasis on the translation to clinical practice. This comprehensive review serves as a valuable resource for researchers, clinicians, and scientists alike, providing insights into the current state and future directions of collagen-based micro/nanogel research and development.
Collagen/chemistry* ; Drug Delivery Systems/methods* ; Humans ; Tissue Engineering/methods* ; Animals ; Biocompatible Materials/chemistry*

PURPOSE: To assess the relationship between dislocation and functional outcomes in supination-external rotation (SER) ankle fractures. METHODS: A retrospective case series study was performed on patients with ankle fractures treated surgically at a large trauma center from January 2015 to December 2021. The inclusion criteria were young and middle-aged patients of 18 - 65 years with SER ankle fractures that can be classified by Lauge-Hansen classification and underwent surgery at our trauma center. Exclusion criteria were serious life-threatening diseases, open fractures, fractures delayed for more than 3 weeks, fracture sites ≥ 2, etc. Then patients were divided into dislocation and no-dislocation groups. Patient demographics, injury characteristics, surgery-related outcomes, and postoperative functional outcomes were collected and analyzed. The functional outcomes of SER ankle fractures were assessed postoperatively at 1-year face-to-face follow-up using the foot and ankle outcome score (FAOS) and American Orthopedic Foot and Ankle Society ankle hindfoot score and by 2 experienced orthopedic physicians. Relevant data were analyzed using SPSS version 22.0 by Chi-square or t-test. RESULTS: During the study period, there were 371 ankle fractures. Among them, 190 (51.2%) were SER patterns with 69 (36.3%) combined with dislocations. Compared with the no-dislocation group, the dislocation group showed no statistically significant differences in gender, age composition, fracture type, diabetes, or smoking history, preoperative waiting time, operation time, and length of hospital stay (all p > 0.05), but a significantly higher Lauge-Hansen injury grade (p < 0.001) and syndesmotic screw fixation rate (p = 0.033). Moreover, the functional recovery was poorer, revealing a significantly lower FAOS in the sport/rec scale (p < 0.001). Subgroup analysis showed that among SER IV ankle fracture patients, FAOS was much lower in pain (p = 0.042) and sport/rec scales (p < 0.001) for those with dislocations. American Orthopedic Foot and Ankle Society ankle hindfoot score revealed no significant difference between dislocation and no-dislocation patients. CONCLUSION Dislocation in SER ankle fractures suggests more severe injury and negatively affects functional recovery, mainly manifested as more pain and poorer motor function, especially in SER IV ankle cases.
Humans ; Ankle Fractures/physiopathology* ; Male ; Female ; Retrospective Studies ; Adult ; Middle Aged ; Supination ; Aged ; Young Adult ; Rotation ; Joint Dislocations/surgery* ; Fracture Fixation, Internal/methods* ; Adolescent ; Recovery of Function ; Treatment Outcome

OBJECTIVE: To explore the potential effects and mechanisms of Liang-Ge-San (LGS) for the treatment of acute respiratory distress syndrome (ARDS) through network pharmacology analysis and to verify LGS activity through biological experiments. METHODS: The key ingredients of LGS and related targets were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. ARDS-related targets were selected from GeneCards and DisGeNET databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using the Metascape Database. Molecular docking analysis was used to confirm the binding affinity of the core compounds with key therapeutic targets. Finally, the effects of LGS on key signaling pathways and biological processes were determined by in vitro and in vivo experiments. RESULTS: A total of LGS-related targets and 496 ARDS-related targets were obtained from the databases. Network pharmacological analysis suggested that LGS could treat ARDS based on the following information: LGS ingredients luteolin, wogonin, and baicalein may be potential candidate agents. Mitogen-activated protein kinase 14 (MAPK14), recombinant V-Rel reticuloendotheliosis viral oncogene homolog A (RELA), and tumor necrosis factor alpha (TNF-α) may be potential therapeutic targets. Reactive oxygen species metabolic process and the apoptotic signaling pathway were the main biological processes. The p38MAPK/NF-κ B signaling pathway might be the key signaling pathway activated by LGS against ARDS. Moreover, molecular docking demonstrated that luteolin, wogonin, and baicalein had a good binding affinity with MAPK14, RELA, and TNF α. In vitro experiments, LGS inhibited the expression and entry of p38 and p65 into the nucleation in human bronchial epithelial cells (HBE) cells induced by LPS, inhibited the inflammatory response and oxidative stress response, and inhibited HBE cell apoptosis (P<0.05 or P<0.01). In vivo experiments, LGS improved lung injury caused by ligation and puncture, reduced inflammatory responses, and inhibited the activation of p38MAPK and p65 (P<0.05 or P<0.01). CONCLUSION LGS could reduce reactive oxygen species and inflammatory cytokine production by inhibiting p38MAPK/NF-κ B signaling pathway, thus reducing apoptosis and attenuating ARDS.
Drugs, Chinese Herbal/pharmacology* ; Respiratory Distress Syndrome/enzymology* ; p38 Mitogen-Activated Protein Kinases/metabolism* ; NF-kappa B/metabolism* ; Animals ; Signal Transduction/drug effects* ; Molecular Docking Simulation ; Humans ; Male ; Network Pharmacology ; Apoptosis/drug effects* ; Mice

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Recent studies have indicated that the expression of ubiquitin-specific protease 51 (USP51), a novel deubiquitinating enzyme (DUB) that mediates protein degradation as part of the ubiquitin‒proteasome system (UPS), is associated with tumor progression and therapeutic resistance in multiple malignancies. However, the underlying mechanisms and signaling networks involved in USP51-mediated regulation of malignant phenotypes remain largely unknown. The present study provides evidence of USP51's functions as the prominent DUB in chemoresistant triple-negative breast cancer (TNBC) cells. At the molecular level, ectopic expression of USP51 stabilized the 78 kDa Glucose-Regulated Protein (GRP78) protein through deubiquitination, thereby increasing its expression and localization on the cell surface. Furthermore, the upregulation of cell surface GRP78 increased the activity of ATP binding cassette subfamily B member 1 (ABCB1), the main efflux pump of doxorubicin (DOX), ultimately decreasing its accumulation in TNBC cells and promoting the development of drug resistance both in vitro and in vivo. Clinically, we found significant correlations among USP51, GRP78, and ABCB1 expression in TNBC patients with chemoresistance. Elevated USP51, GRP78, and ABCB1 levels were also strongly associated with a poor patient prognosis. Importantly, we revealed an alternative intervention for specific pharmacological targeting of USP51 for TNBC cell chemosensitization. In conclusion, these findings collectively indicate that the USP51/GRP78/ABCB1 network is a key contributor to the malignant progression and chemotherapeutic resistance of TNBC cells, underscoring the pivotal role of USP51 as a novel therapeutic target for cancer management.

Evoked Potentials ; Social Isolation ; Attention ; Humans

Objective Based on a novel type of cell death induced by disulfide stress,known as disulfidptosis,this study explores the role of long non-coding RNA(LncRNA)in gastric cancer and establishes a prognosis model re-lated to disulfidptosis,providing a new method for assessing the prognosis of gastric cancer treatment.Methods Transcriptomic data from gastric cancer and normal tissue samples were obtained from the public database TCGA,and disulfidptosis-related LncRNAs were selected through Pearson analysis and LASSO-Cox regression analysis.A relevant prognostic model for gastric cancer was constructed based on the above LncRNAs and validated by function-al enrichment analysis,tumour microenvironment and immune cell infiltration analysis,drug sensitivity analysis and quantitative reverse transcription PCR(RT-qPCR).Results In this study,400 disulfide death-associated LncR-NAs were identified and five of them were screened to construct a prognostic model for assessing the prognosis of gastric cancer patients.The models showed in validation that the survival of the high-risk score group was shorter than that of the low-risk score group(P＜0.05).In addition,the predictive ability of the prognostic model(AUC=0.725)was better than that based only on basic characteristics such as age and gender.The expression levels of disulfide death-associated LncRNAs differed between normal and gastric cancer tissues(P＜0.001).Conclusion The disulfidptosis-related LncRNA prognosis model developed in this study can effectively assess the prognosis of gastric cancer patients and the tumor microenvironment,providing potential targets and a theoretical basis for new immunotherapeutic strategies for gastric cancer.

Deep vein thrombosis(DVT)is a common peripheral vascular disease in clinical practice.The lack of precise and efficient early diagnostic techniques renders it susceptible to being overlooked or misdiagnosed,and therefore,identifying trustworthy biomarkers is a major issue that has to be resolved.In this study,the endogenous metabolites in the urine of DVT rats were screened by metabolomics technology based on gas chromatograph-mass spectrometry(GC-MS)and the characteristic metabolites were identified by multiple feature selection algorithms and multivariate statistical analysis,for the development of a machine learning-based diagnostic model for DVT.The urine samples in metabolic cage in the thrombus development phase(between 48 and 72 h)of rats were collected,which was used as the models for inferior vena cava ligation.The metabolic profiles of the control group and DVT were obtained using the GC-MS method.A total of 176 kinds of endogenous metabolites were identified in rat urine through comparison with the FiehnLib database,26 kinds of differential metabolites associated with DVT were screened through a combination of the Mann-Whitney U test and orthogonal partial least squares discriminant analysis(OPLS-DA),and 13 kinds of significant metabolites strongly correlated with DVT were further evaluated in conjunction with various machine learning feature selection techniques.For DVT diagnosis,machine learning models such as Gaussian Naive Bayes(GNB),support vector machine(SVM),logistic regression(LR),and linear discriminant analysis(LDA)were developed.The diagnostic model constructed using 13 kinds of key metabolites demonstrated excellent accuracy and stability,and surpassed the predictive performance of the models utilizing 176 kinds of metabolites and 26 kinds of differential metabolites,as evidenced by examination and comparison of each model's efficacy.The study showed that the integration of multiple feature selection algorithms for analyzing metabolite information in DVT rat urine was capable of effectively identifying reliable potential markers of DVT.Furthermore,the developed machine learning model offered a novel technical approach for the automated diagnosis of DVT.