p21 (encoded by the CDKN1A gene) is a critical cell cycle regulatory protein endowed with versatile biological functions. In various sex hormone-related cancers, p21 exhibits a paradoxical dual role, capable of both inhibiting tumorigenesis and promoting cancer progression, exerting dual, often opposing, effects on cellular fate that are dictated by the specific context. The clinical targeting of p21 remains elusive, largely due to its functionally pleiotropic and context-dependent nature within intricate regulatory networks. During the initial, hormone-dependent phase of cancers like breast and prostate cancer, p21 expression and activity are largely governed by the transcriptional programs of estrogen or androgen receptor signaling. This hormonal regulation contributes to the control of tumor cell proliferation and underpins the initial efficacy of endocrine therapies. In contrast, as these diseases advance to late stages or evolve into non-hormone-dependent subtypes—exemplified by castration-resistant prostate cancer (CRPC) and specific forms of triple-negative breast cancer (TNBC)—these conventional hormonal control mechanisms often become dysfunctional or are entirely bypassed. This fundamental transition creates a critical therapeutic void, highlighting the urgent need to identify and exploit alternative molecular pathways to effectively target p21’s function. Promising strategies may include the precise modulation of its upstream transcriptional regulators, downstream effector proteins, or the intersecting parallel signaling networks that critically influence its activity. This review provides a systematic synthesis of the intricate and interconnected mechanisms that underpin the dual effects of p21 in sex hormone-related tumors. These mechanisms are categorized into three core, interrelated functional domains. (1) cell cycle regulation: p21 executes its canonical tumor-suppressive role by binding to and inhibiting cyclin-dependent kinases (CDKs) and by directly interacting with proliferating cell nuclear antigen (PCNA), thereby inducing cell cycle arrest, predominantly at the G1/S checkpoint; (2) apoptosis modulation: p21 exerts a highly context-dependent influence on programmed cell death, functioning either as a pro-apoptotic agent under severe genotoxic stress or as a pro-survival factor by inhibiting apoptosis through interactions with proteins like Bcl-2; (3) hormonal and signaling crosstalk: p21 is an integral node within broader cellular networks, engaging in direct physical interactions with hormone receptors(e.g., AR, ER) and participating in complex feedback loops with key oncogenic pathways, including PI3K/AKT, MAPK/ERK, and p53. Critically, the role of p21 is not static but highly dynamic. It can undergo a functional switch from tumor-suppressive to tumor-promoting in response to therapeutic pressures, metabolic alterations, or evolving tumor microenvironment cues. These adaptive shifts are frequently implicated in the development of therapy resistance and disease recurrence, particularly in advanced, hormone-resistant cancers. By synthesizing these insights, this review aims to establish a coherent theoretical framework to guide the future development of novel therapeutic strategies that target the p21 pathway. It underscores the necessity of moving beyond a simplistic, binary view of p21 and emphasizes the forthcoming challenges, such as the discovery of reliable biomarkers to predict its functional state and the rational design of context-specific pharmacological modulators to selectively harness its therapeutic potential.

OBJECTIVE To investigate the effect of pharmaceutical services guided by root cause analysis （RCA） in a problem-oriented manner combined with knowledge-attitude-practice （KAP） theory on reducing the incidence of protocol violations of investigational medicinal products in pediatric clinical trials. METHODS A total of 617 participants from 69 drug clinical trial projects conducted in our hospital from January 2016 to December 2020 were selected as the control group， and 868 participants from 72 drug clinical trial projects from January 2022 to December 2025 as the observation group. RCA was performed on the protocol violations of investigational medicinal product in the control group to identify the types and underlying causes. The control group received routine pharmaceutical services for drug clinical trials， while the observation group was provided with precision pharmaceutical services from the three dimensions of knowledge， attitude and practice on the basis of routine pharmaceutical services， according to the root causes identified by RCA. The occurrence of investigational medicinal products protocol violations was compared between the two groups. RESULTS The total incidence of protocol violations of investigational medicinal products， as well as the incidences of minor and major protocol violations， were all significantly lower in the observation group than in the control group （ P ＜0.001）. The main types of protocol violations in both groups included missed/under-/over-dosing of medications， non-adherence to administration time， failure to adjust dosage as required， and combined medication/vaccination in violation of the protocol. Regarding the responsible subjects of protocol violations， the incidences of protocol violations attributed to participants and their guardians as well as investigators and accidental factors were significantly lower in the observation group than in the control group （ P ＜0.001， P ＜0.001， P ＝0.025）. However， there were no statistically significant differences in the incidences of protocol violations caused by sponsor-related reasons between the two groups （ P ＞0.05）. CONCLUSIONS Pharmaceutical services led by pharmacists， based on problem-oriented RCA and combined with KAP theory， can effectively reduce the protocol violations of investigational medicinal products rate in pediatric clinical trials， thereby safeguarding the safety and rights of study participants.

BACKGROUND:Exosomes derived from mesenchymal stem cells play pivotal roles in cell communication and epigenetic regulation due to their low immunogenicity and targeted delivery effects,and have been clinically applied in the treatment of various diseases.OBJECTIVE:To review the isolation,purification,identification methods,and application progress of mesenchymal stem cell-derived exosomes,and to facilitate the development of large-scale preparation techniques and clinical translation of mesenchymal stem cell-derived exosomes.METHODS:The Chinese search terms"exosome,mesenchymal stem cells,isolation,purification,characterization,clinical application"and the English search terms"exosome,extracellular vesicles,mesenchymal stem cells,isolation,characterization,application"were used to search the literature published before September 2024 in CNKI,PubMed,and Web of Science databases.Articles with poor relevance to the topic,outdated,or duplicated content were excluded,and finally,109 articles were included for review.RESULTS AND CONCLUSION:(1)This paper reviews recent methods for isolating and purifying exosomes,comparing the characteristics of ultracentrifugation,ultrafiltration,size-exclusion chromatography,polymer precipitation,immunoaffinity,microfluidic methods,and other novel approaches based on their underlying principles.(2)Methods for identifying exosomes can be categorized into physical and biochemical analyses,characterizing exosomes based on their shape,size,and characteristic proteins.(3)Mesenchymal stem cell-derived exosomes have broad applications in multiple fields such as medical aesthetics,wound repair,and cancer treatment,due to their immune-regulatory properties and ability to cross biological barriers.(4)The clinical translation of exosomes faces challenges due to their complex structure,lack of universal isolation techniques,and poor stability,making it difficult to achieve in a short period of time.

OBJECTIVE:Postoperative recurrence is a common complication of percutaneous endoscopic lumbar discectomy for lumbar disc herniation,which can significantly increase the risk of reoperation.A well-performing risk prediction model can help identify high-risk groups early and prevent postoperative recurrence.This study systematically evaluated the risk prediction model for postoperative recurrence after percutaneous endoscopic lumbar discectomy to provide a reference for surgical decision-making.METHODS:The PubMed,Embase,Web of Science,CNKI,WanFang Data,VIP,and CBM were electronically searched to collect studies on the recurrence risk prediction models after percutaneous endoscopic lumbar discectomy from inception to July 1,2024.Two reviewers independently screened the literature and extracted data.The models' risk of bias,applicability,and report quality were assessed using prediction model risk of bias assessment tool(PROBAST)and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis(TRIPOD)tools,respectively.Meta-analysis of postoperative recurrence rate of percutaneous endoscopic lumbar discectomy and related predictors was performed using Revman 5.4 software.RESULTS:(1)A total of 15 studies were included,all of which were retrospective studies,including 24 models for predicting the risk of recurrence after percutaneous endoscopic lumbar discectomy.(2)The PROBAST evaluation results indicated that all 15 studies exhibited a high risk of bias.Regarding applicability,two studies demonstrated a low risk,while 13 presented a high risk.(3)Regarding the TRIPOD reporting quality,the overall quality across the 15 studies was low.The primary reasons for this low compliance included the failure to report blinding,a lack of explanation for the sample size calculation method,lack of detailed description of missing data processing methods,and lack of information such as introduction to the model used.(4)Furthermore,the area under the receiver operating characteristic curve for the model ranged from 0.684 to 0.972,with the number of potential predictor variables varying from 15 to 28.(5)The results of meta-analysis showed that the postoperative recurrence rate of lumbar disc herniation patients treated with percutaneous endoscopic lumbar discectomy was 12％(95％CI=9.0％-15.0％),Modic changes(OR=6.72,95％CI=3.90-11.59),body mass index(OR=1.28,95％CI=1.10-1.49),work intensity(OR=3.22,95％CI=1.85-5.59),age(OR=2.28,95％CI=1.50-3.48),and smoking history(OR=2.65,95％CI=1.75-4.00)were independent influencing factors for postoperative recurrence of percutaneous endoscopic lumbar discectomy(all P＜0.05).CONCLUSION:The overall predictive performance of the recurrence risk prediction models after percutaneous endoscopic lumbar discectomy is satisfactory;however,the model exhibits a high overall risk of bias and applicability,coupled with low reporting quality.Additionally,there is a lack of prospective research and external validation.Future,risk prediction models should consider factors such as Modic changes,body mass index,work intensity,age,and smoking history as potential predictors.

OBJECTIVE:Postoperative recurrence is a common complication of percutaneous endoscopic lumbar discectomy for lumbar disc herniation,which can significantly increase the risk of reoperation.A well-performing risk prediction model can help identify high-risk groups early and prevent postoperative recurrence.This study systematically evaluated the risk prediction model for postoperative recurrence after percutaneous endoscopic lumbar discectomy to provide a reference for surgical decision-making.METHODS:The PubMed,Embase,Web of Science,CNKI,WanFang Data,VIP,and CBM were electronically searched to collect studies on the recurrence risk prediction models after percutaneous endoscopic lumbar discectomy from inception to July 1,2024.Two reviewers independently screened the literature and extracted data.The models' risk of bias,applicability,and report quality were assessed using prediction model risk of bias assessment tool(PROBAST)and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis(TRIPOD)tools,respectively.Meta-analysis of postoperative recurrence rate of percutaneous endoscopic lumbar discectomy and related predictors was performed using Revman 5.4 software.RESULTS:(1)A total of 15 studies were included,all of which were retrospective studies,including 24 models for predicting the risk of recurrence after percutaneous endoscopic lumbar discectomy.(2)The PROBAST evaluation results indicated that all 15 studies exhibited a high risk of bias.Regarding applicability,two studies demonstrated a low risk,while 13 presented a high risk.(3)Regarding the TRIPOD reporting quality,the overall quality across the 15 studies was low.The primary reasons for this low compliance included the failure to report blinding,a lack of explanation for the sample size calculation method,lack of detailed description of missing data processing methods,and lack of information such as introduction to the model used.(4)Furthermore,the area under the receiver operating characteristic curve for the model ranged from 0.684 to 0.972,with the number of potential predictor variables varying from 15 to 28.(5)The results of meta-analysis showed that the postoperative recurrence rate of lumbar disc herniation patients treated with percutaneous endoscopic lumbar discectomy was 12％(95％CI=9.0％-15.0％),Modic changes(OR=6.72,95％CI=3.90-11.59),body mass index(OR=1.28,95％CI=1.10-1.49),work intensity(OR=3.22,95％CI=1.85-5.59),age(OR=2.28,95％CI=1.50-3.48),and smoking history(OR=2.65,95％CI=1.75-4.00)were independent influencing factors for postoperative recurrence of percutaneous endoscopic lumbar discectomy(all P＜0.05).CONCLUSION:The overall predictive performance of the recurrence risk prediction models after percutaneous endoscopic lumbar discectomy is satisfactory;however,the model exhibits a high overall risk of bias and applicability,coupled with low reporting quality.Additionally,there is a lack of prospective research and external validation.Future,risk prediction models should consider factors such as Modic changes,body mass index,work intensity,age,and smoking history as potential predictors.

BACKGROUND:Exosomes derived from mesenchymal stem cells play pivotal roles in cell communication and epigenetic regulation due to their low immunogenicity and targeted delivery effects,and have been clinically applied in the treatment of various diseases.OBJECTIVE:To review the isolation,purification,identification methods,and application progress of mesenchymal stem cell-derived exosomes,and to facilitate the development of large-scale preparation techniques and clinical translation of mesenchymal stem cell-derived exosomes.METHODS:The Chinese search terms"exosome,mesenchymal stem cells,isolation,purification,characterization,clinical application"and the English search terms"exosome,extracellular vesicles,mesenchymal stem cells,isolation,characterization,application"were used to search the literature published before September 2024 in CNKI,PubMed,and Web of Science databases.Articles with poor relevance to the topic,outdated,or duplicated content were excluded,and finally,109 articles were included for review.RESULTS AND CONCLUSION:(1)This paper reviews recent methods for isolating and purifying exosomes,comparing the characteristics of ultracentrifugation,ultrafiltration,size-exclusion chromatography,polymer precipitation,immunoaffinity,microfluidic methods,and other novel approaches based on their underlying principles.(2)Methods for identifying exosomes can be categorized into physical and biochemical analyses,characterizing exosomes based on their shape,size,and characteristic proteins.(3)Mesenchymal stem cell-derived exosomes have broad applications in multiple fields such as medical aesthetics,wound repair,and cancer treatment,due to their immune-regulatory properties and ability to cross biological barriers.(4)The clinical translation of exosomes faces challenges due to their complex structure,lack of universal isolation techniques,and poor stability,making it difficult to achieve in a short period of time.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Objective To investigate the clinical value of tumor-stroma ratio (TSR) in combination with KRAS, BRAF, NRAS, and microsatellite status for prognostic assessment of patients with colorectal cancer. Methods A total of 51 colorectal cancer cases meeting the inclusion and exclusion criteria were enrolled in this study. TSR levels were evaluated through optical microscopy. The KRAS/NRAS/BRAF mutation profiles and microsatellite status were determined in accordance with genetic testing results. Clinical data, pathological characteristics, and survival outcomes were systematically recorded. Results Among the 51 patients with colorectal cancer, 19 (37.3%) were categorized into the low stromal group and 32 (62.7%) into the high stromal group. Statistically significant differences were observed between the two groups in drug resistance, M stage, TNM stage, neural invasion, and microsatellite status (P<0.05). Compared with patients exhibiting high TSR, those with low TSR demonstrated significantly increased recurrence rates (5 vs. 21 cases, P=0.007), shortened disease-free survival (34.21 vs. 14.34 months, P=0.001), and reduced overall survival (38.79 vs. 23.09 months, P=0.021). Multivariate Cox regression analysis identified N stage, M stage, TNM stage, neural invasion, lymphovascular invasion, and TSR as independent risk factors for disease-free survival. N stage, M stage, neural invasion, lymphovascular invasion, and TSR emerged as independent prognostic factors for overall survival (P<0.05). Although the combined models of TSR with KRAS, NRAS, BRAF, and microsatellite status, respectively, demonstrated overall statistical significance (P<0.05), none of the dummy variables in these models reached individually statistical significance (P>0.05), and therefore cannot be considered independent prognostic factors. Conclusion TSR serves as an independent predictor of poor prognosis in advanced colorectal cancer, with patients exhibiting low TSR demonstrating a significantly higher risk of recurrence and metastasis than those with high TSR. For patients with colon cancer undergoing first-line palliative chemotherapy after postoperative recurrence, histopathological assessment of TSR in primary tumor sites holds prognostic value and may serve as a relevant factor for evaluating treatment resistance in clinical management.

Objective To investigate the effects and underlying mechanisms of a spray prepared from exosomes derived from M2 macrophages induced by interleukin-4 （IL-4） and tantalum particles （Ta） on the healing of pressure ulcers. Methods Bone marrow-derived macrophages were polarized into M2 macrophages using IL-4 or Ta, and exosomes （Exo-IL-4/Exo-Ta） were extracted. The regulatory effects of Exo-IL-4/Exo-Ta on M1 macrophage phenotypes and fibroblast matrix secretion were evaluated in vitro. Proteomic analysis was conducted to explore the biological processes and regulatory networks associated with Exo-Ta. A rat pressure ulcer model was used to assess the effects of Exo-IL-4/Exo-Ta spray on wound healing rate, inflammatory cell infiltration, and collagen deposition. Results In vitro, Exo-IL-4/Exo-Ta induced the polarization of M1 macrophages to M2 macrophages, reduced the secretion of pro-inflammatory factors, and promoted the expression of anti-inflammatory substances. Additionally, Exo-IL-4/Exo-Ta enhanced the production of collagen and fibronectin in fibroblasts. Proteomic analysis revealed that Exo-Ta primarily participated in biological processes such as energy metabolism and macromolecule biosynthesis. In vivo, Exo-IL-4/Exo-Ta spray accelerated wound healing, reduced inflammatory infiltration, and improved tissue remodeling in the rat pressure ulcer model. Conclusion Exosome sprays derived from M2 macrophages could accelerate pressure ulcer healing by modulating inflammation and promoting tissue regeneration, which demonstrated excellent clinical application potential.

ObjectiveTo investigate the alleviating effect of calcium cyanamide （CaCN₂） soil fumigation on replanting problems of Rehmannia glutinosa. MethodsNewly soil （NP） was used as the control group， while three treatment groups were established： replanted soil （RP）， newly soil treated with CaCN₂ （120 g·m²， tillage depth 25 cm） （NPCC）， and replanted soil treated with CaCN₂ （RPCC）. R. glutinosa was cultivated in all groups. At harvest， the tuber agronomic traits （number of enlarged roots， maximum root diameter， fresh weight， dry weight） were measured. The content of catalpol and rehmannioside D was quantified by ultra-high-performance liquid chromatography （UPLC） to evaluate medicinal quality. Rhizosphere soil available nutrients and enzyme activities were analyzed by assay kits. The community structure and composition of fungi and bacteria in rhizosphere soil were assessed via internal transcribed spacer 2 （ITS2） sequencing and 16S rDNA sequencing， respectively. ResultsCompared with NP， the RP group showed obviously reduced in tuber agronomic traits and quality indicators （P0.05）. However， the RPCC group showed significant improvement in agronomic traits and a notable increase in rehmannioside D content compared to RP （P0.05）. The contents of available phosphorus and potassium in RPCC and NP groups were obviously lower than those in RP （P0.05）. The polyphenol oxidase soil （S-PPO） activity in RP was obviously lower than in NP （P0.05）， while sucrose soil （S-SC）， acid phosphatase soil （S-ACP）， and S-PPO activities in RPCC were obviously higher than in RP （P0.05）. Microbial richness and diversity in RP were obviously higher than in NP （P0.05）， whereas no significant differences were observed between the RPCC and NP. The relative abundances of fungal genera Nectria， Myrothecium， Tomentella， and bacterial genus Skermanella were obviousl lower in RPCC and NP than in RP （P0.05）. Correlation analysis that S-ACP activity was positively correlated with the content of rehmannioside D （P0.05）. Fungal genera Engyodontium and Alternaria， and bacterial genera Pir4 lineage， Pirellula， Methyloversatilis， Brevundimonas， Ralstonia， and Acidibacter were obviously positively correlated with tuber dry weight （P0.05）. Conversely， fungal genera Pseudaleuria， Nectria， Haematonectria， Ceratobasidium， and bacterial genera Streptomyces， Skermanella， RB41， Gemmatimonas， and Bacillus were obviously negatively correlated with dry weight （P0.05）. The fungal genus Alternaria and bacterial genera Brevundimonas， Ralstonia， Acidibacter， and Dongia showed positive correlations with medicinal quality of R.glutinosa tuber， while fungal genera Pseudaleuria， Nectria， Stachybotrys， Fusarium， Gibberella， Ceratobasidium， and bacterial genera Sphingomonas， Skermanella， RB41， Gemmatimonas， and Bacillus were obviously negatively correlated （P0.05）. ConclusionCaCN₂ soil fumigation can significantly improve enzyme activities in replanted Rehmannia rhizosphere soil， enhance the utilization of available nutrients， reshape microbial community structure of replanted R.glutinosa at the family and genus level， and notably improve tuber agronomic traits and medicinal quality. This study provides a novel approach to alleviating replanting problems and offers insights for the integrated development of standardized cultivation techniques， including soil disinfection， nutrient-targeted regulation， and microbial inoculant application.