OBJECTIVE: To explore the anti-photoaging properties of salvianolic acid B (Sal B). METHODS: The optimal photoaging model of human immortalized keratinocytes (HaCaT cells) were constructed by expose to ultraviolet B (UVB) radiation. The cells were divided into control, model and different concentrations of Sal B groups. Cell viability was measured via cell counting kit-8. Subsequently, the levels of oxidative stress, including reactive oxygen species (ROS), hydroxyproline (Hyp), catalase (CAT), and glutathione peroxidase (GSH-Px) were detected using the relevant kits. Silent information regulator 1 (SIRT1) protein level was detected using Western blot. The binding pattern of Sal B and SIRT1 was determined via molecular docking. RESULTS: Sal B significantly increased the viability of UVB-irradiated HaCaT cells (P<0.05 or P<0.01). Sal B effectively scavenged the accumulation of ROS induced by UVB (P<0.05 or P<0.01). In addition, Sal B modulated oxidative stress by increasing the intracellular concentrations of Hyp and CAT and the activity of GSH-Px (P<0.05 or P<0.01). The Western blot results revealed a substantial increase in SIRT1 protein levels following Sal B administration (P<0.05). Moreover, Sal B exhibited good binding affinity toward SIRT1, with a docking energy of -7.5 kCal/mol. CONCLUSION Sal B could improve the repair of photodamaged cells by alleviating cellular oxidative stress and regulating the expression of SIRT1 protein.
Humans ; Sirtuin 1/metabolism* ; Ultraviolet Rays ; Oxidative Stress/radiation effects* ; Keratinocytes/metabolism* ; Molecular Docking Simulation ; Benzofurans/pharmacology* ; Skin Aging/radiation effects* ; Reactive Oxygen Species/metabolism* ; Cell Survival/radiation effects* ; HaCaT Cells ; Hydroxyproline/metabolism* ; Glutathione Peroxidase/metabolism* ; Catalase/metabolism* ; Depsides

OBJECTIVES: Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia. METHODS: Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases. RESULTS: A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. CONCLUSIONS AEBP1 and COL8A2 have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.
Sarcopenia/genetics* ; Osteoarthritis/genetics* ; Computational Biology/methods* ; Humans ; Protein Interaction Maps/genetics* ; Animals ; Mice ; Gene Expression Profiling ; Gene Ontology ; Transcriptome ; Male ; Signal Transduction/genetics* ; Gene Regulatory Networks

OBJECTIVES: Osteoarthritis (OA) is one of the most common chronic degenerative diseases, with chondrocyte apoptosis and extracellular matrix (ECM) degradation as the major pathological changes. The mechanical stimulation can attenuate chondrocyte apoptosis and promote ECM synthesis, but the underlying molecular mechanisms remain unclear. This study aims to investigate the role of primary cilia (PC) in mediating the effects of mechanical stimulation on OA progression. METHODS: In vivo, conditional knockout mice lacking intraflagellar transport 88 (IFT88^flox/flox IFT88 knockout; i.e., primary cilia-deficient mice) were generated, with wild-type mice as controls. OA models were established via anterior cruciate ligament transection combined with destabilization of the medial meniscus, followed by treadmill exercise intervention. OA progression was evaluated by hematoxylin-eosin staining, safranin O-fast green staining, and immunohistochemistry; apoptosis was assessed by TUNEL staining; and limb function by rotarod testing. In vitro, primary articular chondrocytes were isolated from mice and transfected with lentiviral vectors to suppress IFT88 expression, thereby constructing a primary cilia-deficient cell model. Interleukin-1β (IL-1β) was used to induce an inflammatory environment, while cyclic tensile strain (CTS) was applied via a cell stretcher to mimic mechanical loading on chondrocytes. Immunofluorescence and Western blotting were used to detect the protein expression levels of type II collagen α1 chain (COL2A1), primary cilia, IFT88, and caspase-12; reverse transcription polymerase chain reaction was performed to assess COL2A1 mRNA levels; and flow cytometry was used to evaluate apoptosis. RESULTS: In vivo, treadmill exercise significantly reduced Osteoarthritis Research Society International (OARSI) scores and apoptotic cell rates, and improved balance ability in wild-type OA mice, whereas IFT88-deficient OA mice showed no significant improvement. In vitro, CTS inhibited IL-1β-induced ECM degradation and apoptosis in primary chondrocytes; however, this protective effect was abolished in cells with suppressed primary cilia expression. CONCLUSIONS Mechanical stimulation delays OA progression by mediating signal transduction through primary cilia, thereby inhibiting cartilage degeneration and chondrocyte apoptosis.
Animals ; Chondrocytes/cytology* ; Apoptosis/physiology* ; Mice ; Cilia/metabolism* ; Osteoarthritis/pathology* ; Extracellular Matrix/metabolism* ; Mice, Knockout ; Disease Progression ; Interleukin-1beta ; Male ; Cells, Cultured

Patients suffering from nerve injury often experience exacerbated pain responses and complain of memory deficits. The dorsal hippocampus (dHPC), a well-defined region responsible for learning and memory, displays maladaptive plasticity upon injury, which is assumed to underlie pain hypersensitivity and cognitive deficits. However, much attention has thus far been paid to intracellular mechanisms of plasticity rather than extracellular alterations that might trigger and facilitate intracellular changes. Emerging evidence has shown that nerve injury alters the microarchitecture of the extracellular matrix (ECM) and decreases ECM rigidity in the dHPC. Despite this, it remains elusive which element of the ECM in the dHPC is affected and how it contributes to neuropathic pain and comorbid cognitive deficits. Laminin, a key element of the ECM, consists of α-, β-, and γ-chains and has been implicated in several pathophysiological processes. Here, we showed that peripheral nerve injury downregulates laminin β1 (LAMB1) in the dHPC. Silencing of hippocampal LAMB1 exacerbates pain sensitivity and induces cognitive dysfunction. Further mechanistic analysis revealed that loss of hippocampal LAMB1 causes dysregulated Src/NR2A signaling cascades via interaction with integrin β1, leading to decreased Ca²⁺ levels in pyramidal neurons, which in turn orchestrates structural and functional plasticity and eventually results in exaggerated pain responses and cognitive deficits. In this study, we shed new light on the functional capability of hippocampal ECM LAMB1 in the modulation of neuropathic pain and comorbid cognitive deficits, and reveal a mechanism that conveys extracellular alterations to intracellular plasticity. Moreover, we identified hippocampal LAMB1/integrin β1 signaling as a potential therapeutic target for the treatment of neuropathic pain and related memory loss.
Animals ; Laminin/genetics* ; Hippocampus/metabolism* ; Neuralgia/metabolism* ; Cognitive Dysfunction/etiology* ; Male ; Peripheral Nerve Injuries/metabolism* ; Extracellular Matrix/metabolism* ; Integrin beta1/metabolism* ; Pyramidal Cells/metabolism* ; Signal Transduction

Objective To develop a deep learning method for small sample multi-omics data using attention mechanism and Meta-learning for the establishment of autism diagnosis model.Methods MLAN(Meta-learning based attentive network)consisting of the omics feature pre-reduction module,the multi-omics data fusion and feature learning module,and the parameter optimization module was designed.Firstly,differential expression analysis was performed on high-dimensional multi-omics data to preliminarily screen out unimportant features.Secondly,a multi-channel attention mechanism was used to learn the importances of every set of omics data and to realize data fusion,and a two-layer fully connected network was constructed to further extract latent features and realize the diagnosis task.Finally,the Meta-learning algorithm Reptile was used to optimize the initial parameters of the above model to obtain the optimal parameters.A total of 58 children's saliva samples were collected,including 21 children diagnosed with autism,12 children with social disorders,and 25 healthy controls,and the protein and metabolomics data were detected by mass spectrometry.All data were randomly divided into training set and test set by 4∶1,and the training set was divided into training data and validation data in the same way for model training and validation.The test set was used for the final evaluation of the model effect.Five baseline models and three ablated models were constructed and evaluated along with MLAN based on metrics including multi-classification accuracy,F1-macro and F1-weighted scores.Results The constructed multi-classification autism diagnosis model MLAN achieved multi-classification accuracy,F1-macro and F1-weighted scores of 0.850±0.066,0.817±0.103 and 0.834±0.087.The values of all three indicators were better than those of baseline models and the ablated models.Conclusion The proposed MLAN can effectively deal with heterogeneous multi-omics data with small samples and achieve good results,which is expected to provide assistance for the clinical diagnosis of autism.

Objective:To investigate the biomechanical correlation between ankle instability and osteochondral lesions of the talus (OLT) under loading conditionsMethods:A healthy 29-year-old male volunteer was selected for the study. A 64-slice spiral CT scan of the right lower limb was performed to construct a detailed finite element model of the ankle joint, including ligaments and cartilage. Three injury models were created: models of distal tibiofibular syndesmosis injury, lateral collateral ligament injury, and a combined injury of the distal tibiofibular syndesmosis and lateral collateral ligament. Differences in stress distribution on the tibiotalar joint surface, talus stress, and talus displacement were analyzed through anterior drawer test, inversion stress test, and external rotation stress test.Results:In the anterior drawer test, as the forward traction force increased (40, 60, 80, 100, 120, 140, and 150 N), all the injury models showed a progressive increase in tibiotalar joint surface stress, talus stress, and talus displacement. The combined injury model showed the highest tibiotalar joint surface stress (32.6 MPa), while the lateral collateral ligament injury model demonstrated the highest talus stress (56.5 MPa). Talus displacement increased significantly with traction, reaching the maximum (4.88 mm) in the combined injury model under 150 N. In the inversion stress test, stress on the tibiotalar joint surface in the lateral collateral ligament injury model was concentrated on the posterior-lateral and posterior-medial regions, whereas in the combined injury model, stress on the tibiotalar joint surface was predominantly concentrated in the posterior-medial region. Talus stress was localized to the talus neck and body in all the models, with the combined injury model showing the largest talus displacement (8.46 mm). In the external rotation stress test, stress on the tibiotalar joint surface was mainly distributed in the posterior-medial, posterior-lateral, and anterior-lateral regions in all the models. Talus stress was concentrated at the talus neck and body. The combined injury model exhibited the greatest talus displacement (12.50 mm).Conclusion:Ankle instability, particularly from combined injuries of the distal tibiofibular syndesmosis and lateral collateral ligament, significantly increases the stress concentration and talus displacement under loading conditions, thus elevating the risk of OLT.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective:Based on the background of high-quality development,we analyze the operational efficiency of traditional Chinese medicine(TCM)hospitals in China's Yangtze River Economic Belt in 2021 and explore the impact of external environmental factors on operational efficiency,so as to provide a reference for promoting the high-quality development of TCM hospitals in the Yangtze River Economic Belt.Methods:The three-stage DEA model was used to analyze the operational efficiency of TCM hospitals in 11 provinces and cities in the Yangtze River Economic Zone in China in 2021.Results:After three-stage DEA analysis,the values of comprehensive efficiency,pure technical efficiency and scale efficiency of TCM hospitals in China's Yangtze River Economic Belt are 0.976,0.986 and 0.990,respectively.5 provinces and cities,Shanghai,Jiangsu,Hunan,Chongqing and Guizhou,are efficient before and after the adjustment,and the comprehensive efficiency of Zhejiang,Anhui,Hubei,Jiangxi,Sichuan and Yunnan have increased compared with that before the adjustment.Ranking of the average value of the comprehensive efficiency of TCM hospitals operation in the three major city clusters of the Yangtze River Economic Belt after adjustment:Chengdu-Chongqing city cluster(0.998)>city cluster in the Yangtze River Delta(0.964)>city cluster in the middle reaches of the Yangtze River(0.962).Conclusion:The operational efficiency of TCM hospitals in the Yangtze River Economic Zone has been underestimated,and the comprehensive efficiency is mainly affected by scale efficiency;there are differences in the operational efficiency of TCM hospitals in the three major urban agglomerations,and balanced development is needed between regions;the operational efficiency of TCM hospitals is affected by the external environment,and it is necessary to improve the external environment;it is necessary to strengthen the construction of digital and informatization of TCM,and to pay attention to the role of talents in TCM,so as to promote the high-quality development of TCM hospitals.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

ObjectiveTo establish a model that can quickly identify the aroma components in different parts of Nardostachys jatamansi， so as to provide a quality control basis for the market circulation and clinical use of N. jatamansi. MethodsHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） combined with Smart aroma database and National Institute of Standards and Technology（NIST） database were used to characterize the aroma components in different parts of N. jatamansi， and the aroma components were quantified according to relative response factor（RRF） and three internal standards， and the markers of aroma differences in different parts of N. jatamansi were identified by orthogonal partial least squares-discriminant analysis（OPLS-DA） and cluster thermal analysis based on variable importance in the projection（VIP） value >1 and P<0.01. The odor data of different parts of N. jatamansi were collected by Heracles Ⅱ Neo ultra-fast gas phase electronic nose， and the correlation between compound types of aroma components collected by the ultra-fast gas phase electronic nose and the detection results of HS-SPME-GC-MS was investigated by drawing odor fingerprints and odor response radargrams. Chromatographic peak information with distinguishing ability≥0.700 and peak area≥200 was selected as sensor data， and the rapid identification model of different parts of N. jatamansi was established by principal component analysis（PCA）， discriminant factor alysis（DFA）， soft independent modeling of class analogies（SIMCA） and statistical quality control analysis（SQCA）. ResultsThe HS-SPME-GC-MS results showed that there were 28 common components in the underground and aboveground parts of N. jatamansi， of which 22 could be quantified and 12 significantly different components were screened out. Among these 12 components， the contents of five components（ethyl isovalerate， 2-pentylfuran， benzyl alcohol， nonanal and glacial acetic acid，） in the aboveground part of N. jatamansi were significantly higher than those in the underground part（P<0.01）， the contents of β-ionone， patchouli alcohol， α-caryophyllene， linalyl butyrate， valencene， 1，8-cineole and p-cymene in the underground part of N. jatamansi were significantly higher than those in the aboveground part（P<0.01）. Heracles Ⅱ Neo electronic nose results showed that the PCA discrimination index of the underground and aboveground parts of N. jatamansi was 82， and the contribution rates of the principal component factors were 99.94% and 99.89% when 2 and 3 principal components were extracted， respectively. The contribution rate of the discriminant factor 1 of the DFA model constructed on the basis of PCA was 100%， the validation score of the SIMCA model for discrimination of the two parts was 99， and SQCA could clearly distinguish different parts of N. jatamansi. ConclusionHS-SPME-GC-MS can clarify the differential markers of underground and aboveground parts of N. jatamansi. The four analytical models provided by Heracles Ⅱ Neo electronic nose（PCA， DFA， SIMCA and SQCA） can realize the rapid identification of different parts of N. jatamansi. Combining the two results， it is speculated that terpenes and carboxylic acids may be the main factors contributing to the difference in aroma between the underground and aboveground parts of N. jatamansi.