BACKGROUND:Gradient artificial bone repair scaffolds can mimic unique anatomical features in musculoskeletal tissues,showing great potential for repairing injured musculoskeletal tissues. OBJECTIVE:To review the latest research advances in gradient artificial bone repair scaffolds for tissue engineering in the musculoskeletal system and describe their advantages and fabrication strategies. METHODS:The first author of the article searched the Web of Science and PubMed databases for articles published from 2000 to 2023 with search terms"gradient,bone regeneration,scaffold".Finally,76 papers were analyzed and summarized after the screening. RESULTS AND CONCLUSION:(1)As an important means of efficient and high-quality repair of skeletal system tissues,gradient artificial bone repair scaffolds are currently designed bionically for the natural gradient characteristics of bone tissue,bone-cartilage,and tendon-bone tissue.These scaffolds can mimic the extracellular matrix of native tissues to a certain extent in terms of structure and composition,thus promoting cell adhesion,migration,proliferation,differentiation,and regenerative recovery of damaged tissues to their native state.(2)Advanced manufacturing technology provides more possibilities for gradient artificial bone repair scaffold preparation:Gradient electrospun fiber scaffolds constructed by spatially differentiated fiber arrangement and loading of biologically active substances have been developed;gradient 3D printed scaffolds fabricated by layered stacking,graded porosity,and bio-3D printing technology;gradient hydrogel scaffolds fabricated by in-situ layered injections,simple layer-by-layer stacking,and freeze-drying method;and in addition,there are also scaffolds made by other modalities or multi-method coupling.These scaffolds have demonstrated good biocompatibility in vitro experiments,were able to accelerate tissue regeneration in small animal tests,and were observed to have significantly improved histological structure.(3)The currently developed gradient artificial bone repair scaffolds have problems such as mismatch of gradient scales,unclear material-tissue interactions,and side effects caused by degradation products,which need to be further optimized by combining the strengths of related disciplines and clinical needs in the future.

ObjectiveTo investigate the mechanisms of mitochondrial dynamics and metabolic reprogramming in the treatment of diabetic nephropathy （DN） by Shenxiao Tongluo prescription via the peroxisome proliferator-activated receptor γ coactivator-1α （PGC-1α）/sirtuin-3 （SIRT3）/hypoxia-inducible factor-1α （HIF-1α） signaling pathway. MethodsSixty-five SD rats were randomized into a sham group （10 rats） and a modeling group （55 rats）， and the modeling rats underwent left nephrectomy and intraperitoneal injection of streptozotocin （35 mg·kg^-1） to prepare a DN model. After successful modeling， the rats were randomized into model， empagliflozin （10 mg·kg^-1）， and low-， medium-， and high-dose （7.656， 15.312， 30.624 g·kg^-1， respectively） Shenxiao Tongluo prescription groups. The urine microalbumin （UmAlb）， blood urea nitrogen （BUN）， and serum creatinine （SCr） levels of rats in each group were assessed after continuous gavage for 8 weeks. The corresponding kits were used to measure the levels of lactate， superoxide dismutase （SOD）， and malondialdehyde （MDA） in the kidney tissue. Hematoxylin-eosin staining， Masson staining， and periodic acid-Schiff staining were performed to observe the pathological changes in the kidney tissue. Transmission electron microscopy was employed to observe mitochondrial morphology. Immunohistochemistry was employed to determine the expression levels of dynamin-related protein 1 （DRP1） and pyruvate kinase M2 （PKM2） in the kidney tissue. Western blot was adopted to assess the protein levels of PGC-1α， SIRT3， HIF-1α， dynamin-related protein 1 （Drp1）， optic atrophy 1 （OPA1）， hexokinase 2 （HK2）， and pyruvate kinase M2 （PKM2） in the kidney tissue. ResultsCompared with the sham group， the model group showed elevated levels of UmAlb， BUN， SCr， lactate， and MDA， decreased SOD level （P<0.05）， glomerular hypertrophy， thickening of the mesangial basement membrane， vacuolar degeneration of renal tubular epithelial cells， and infiltration of renal interstitial inflammatory cells， oval mitochondria with disordered， blurred or disappearing cristae， down-regulated protein levels of PGC-1α， SIRT3， and OPA1， and up-regulated protein levels of HIF-1α， DRP1， HK2， and PKM2 （P<0.05）. Compared with the model group， the treatment in all the groups increased the body weight， lowered the levels of GLU， UmAlb， BUN， and MDA， raised the level of SOD， alleviated the pathological damage in the kidney tissue and mitochondrial damage， up-regulated the expression of PGC-1α， SIRT3， and OPA1， and down-regulated the expression of HIF-1α， DRP1， and PKM2 （P<0.05）. Empagliflozin and Shenxiao Tongluo prescription at medium and high doses lowered the levels of SCr and lactate and down-regulated the expression of HK2 （P<0.05）， which had no statistical significance in the low-dose Shenxiao Tongluo prescription group. ConclusionShenxiao Tongluo prescription may regulate mitochondrial dynamics and metabolic reprogramming by activating the PGC-1α/SIRT3/HIF-1α pathway， thereby alleviating oxidative damage in the kidney tissue and delaying the progression of DN.

ObjectiveTo investigate the mechanisms of mitochondrial dynamics and metabolic reprogramming in the treatment of diabetic nephropathy （DN） by Shenxiao Tongluo prescription via the peroxisome proliferator-activated receptor γ coactivator-1α （PGC-1α）/sirtuin-3 （SIRT3）/hypoxia-inducible factor-1α （HIF-1α） signaling pathway. MethodsSixty-five SD rats were randomized into a sham group （10 rats） and a modeling group （55 rats）， and the modeling rats underwent left nephrectomy and intraperitoneal injection of streptozotocin （35 mg·kg^-1） to prepare a DN model. After successful modeling， the rats were randomized into model， empagliflozin （10 mg·kg^-1）， and low-， medium-， and high-dose （7.656， 15.312， 30.624 g·kg^-1， respectively） Shenxiao Tongluo prescription groups. The urine microalbumin （UmAlb）， blood urea nitrogen （BUN）， and serum creatinine （SCr） levels of rats in each group were assessed after continuous gavage for 8 weeks. The corresponding kits were used to measure the levels of lactate， superoxide dismutase （SOD）， and malondialdehyde （MDA） in the kidney tissue. Hematoxylin-eosin staining， Masson staining， and periodic acid-Schiff staining were performed to observe the pathological changes in the kidney tissue. Transmission electron microscopy was employed to observe mitochondrial morphology. Immunohistochemistry was employed to determine the expression levels of dynamin-related protein 1 （DRP1） and pyruvate kinase M2 （PKM2） in the kidney tissue. Western blot was adopted to assess the protein levels of PGC-1α， SIRT3， HIF-1α， dynamin-related protein 1 （Drp1）， optic atrophy 1 （OPA1）， hexokinase 2 （HK2）， and pyruvate kinase M2 （PKM2） in the kidney tissue. ResultsCompared with the sham group， the model group showed elevated levels of UmAlb， BUN， SCr， lactate， and MDA， decreased SOD level （P<0.05）， glomerular hypertrophy， thickening of the mesangial basement membrane， vacuolar degeneration of renal tubular epithelial cells， and infiltration of renal interstitial inflammatory cells， oval mitochondria with disordered， blurred or disappearing cristae， down-regulated protein levels of PGC-1α， SIRT3， and OPA1， and up-regulated protein levels of HIF-1α， DRP1， HK2， and PKM2 （P<0.05）. Compared with the model group， the treatment in all the groups increased the body weight， lowered the levels of GLU， UmAlb， BUN， and MDA， raised the level of SOD， alleviated the pathological damage in the kidney tissue and mitochondrial damage， up-regulated the expression of PGC-1α， SIRT3， and OPA1， and down-regulated the expression of HIF-1α， DRP1， and PKM2 （P<0.05）. Empagliflozin and Shenxiao Tongluo prescription at medium and high doses lowered the levels of SCr and lactate and down-regulated the expression of HK2 （P<0.05）， which had no statistical significance in the low-dose Shenxiao Tongluo prescription group. ConclusionShenxiao Tongluo prescription may regulate mitochondrial dynamics and metabolic reprogramming by activating the PGC-1α/SIRT3/HIF-1α pathway， thereby alleviating oxidative damage in the kidney tissue and delaying the progression of DN.

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

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 evaluate the effects of fasting and high-fat diet on the pharmacokinetics of rabeprazole sodium enteric-coated tablets in healthy Chinese subjects.Methods A single-center,randomized,open,two-agent,two-sequence,four-cycle,fully repeated crossover,single-dose trial design was used in this study,healthy subjects were assigned to receive single dose of rabeprazole sodium enteric-coated tablets 0.1 g in either fasting or high-fat diet state,and blood samples were taken at different time points,respectively.The concentrations of rabeprazole sodium enteric-coated in plasma were determined by liquid chromatography-tandem mass spectrometry(LC-MS/MS),the model method of the non-compartmental was used to calculate the pharmacokinetic parameters by Phoenix WinNonlin 8.2.Results The main pharmacokinetic parameters of rabeprazole sodium enteric-coated tablets in fasting state and high-fat diet state were as follows:Cmax were(339.63±156.47)and(318.86±132.13)ng·mL-1;t1/2 were(2.34±0.68)and(3.60±2.40)h;AUC0_t were(556.62±251.65)and(528.50±201.78)ng·mL-1·h;AUC0-∞ were(563.39±255.69)and(535.15±203.24)ng·mL-1·h;tmax were 3.65 and 6.99 h.After high-fat diet,the Cmax and AUC of rapeprazole sodium after high-fat and high-calorie diet decreased,Cmax decreased by 6.12％,AUC0-t decreased by 5.05％,AUC0-∞ decreased by 5.01％,andtmaxwas delayed by about 3.34 h.Cmax,AUC0-t and AUC0-∞ 90％confidence interval were 73.13％-115.10％,83.22％-112.28％and 83.40％-112.13％,respectively.Neither was between 85.00％-125.00％.Conclusion High-fat diet affects the absorption rate and degree of rabeprazole sodium enteric-coated,so it is suitable to be administered on an empty stomach.

Objective To explore the relationship between IgA nephropathy(IgAN)and intestinal flora and mucosal immune response disorder based on the theory of"intestinal-kidney axis",and to summarize and enumerate the measures of Chinese and Western medicine in the treatment of IgAN by regulating intestinal flora,mucosal immune response and improving intestinal microecology.Methods Through literature search,the changes of intestinal flora between healthy people and IgAN patients and their effects on intestinal mucosal immunity were compared and analyzed,and then the relationship between different intestinal flora and IgAN was summarized.Results This paper summarizes that different floras including Firmicutes,Escherichia coli,Bifidobacterium and Enterococcus faecalis are closely related to the occurrence and development of IgAN.Conclusions Intestinal flora imbalance can negatively regulate IgAN,regulate intestinal flora and improve intestinal microecological environment,which may become a new target for the treatment of IgAN.